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Uncover interesting and unusual findings in the microbiology laboratory by browsing case studies, shared by your clinical and public health microbiology colleagues. Cases can be used as a teaching tool or to further your individual knowledge of the field.

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Cases in Medical Microbiology and Infectious Diseases, Fourth Edition

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Lori Dockstader Racsa, Cases in Medical Microbiology and Infectious Diseases, Fourth Edition, Laboratory Medicine , Volume 46, Issue 1, Winter 2015, Page e18, https://doi.org/10.1309/LMHKL8HWSPZK1UE3

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The Fourth Edition of Cases in Medical Microbiology and Infectious Diseases is an updated case series that examines a broad range of clinical infectious diseases that will likely be encountered throughout a clinician’s career in medicine. It spans the knowledge base from the beginners’ level, including a glossary of basic medical terminology, through information widely held by those with more experience in general medical practice.

The case-based book has sections, divided by systems, varying from urogenital tract to central nervous systems. There is a final section focused on more advanced cases, including tropical medicine and zoonotic diseases. Each section starts with an overview about the disease processes and contains a table of the most common pathogens pertinent to the organ system, both of which are a useful study tool for students studying for board examinations.

Each case has multiple questions that ask readers to make a diagnosis based on clinical information; most include illustrated aspects of microscopic and microbiologic findings. The questions delve into different aspects of the disease and give a great overview of which microbiology laboratory tests may be applied to each situation. The book offers up-to-date information on molecular diagnostics that are now being used in most microbiology laboratories, replacing former standard culture techniques. The book also offers pharmacologic information on treatment for each disease, including susceptibility patterns and genes responsible for resistance patterns. In addition to diagnostics and treatment, most cases discuss the epidemiology of the given disease and give examples of outbreaks, when pertinent.

Cases in Medical Microbiology and Infectious Diseases is a great book for medical students and residents rotating through the infectious disease service. It offers a broad overview of the clinical aspect of each disease and diagnostics and treatment. The number of cases makes this book slightly longer than most medical students may want to read; however, the quality of information makes the text well worth the length. This book is an asset to medical technologists who want to learn more about the clinical disease process of organisms they are identifying, especially medical technology students. However, the book is not a bench top resource for the next steps in diagnostics in the laboratory. An overview of biochemicals and procedures used to identify organisms are presented—for instance, optochin, which is used for identifying Streptococcus pneumonia e. However, the zone of inhibition is undefined. Additional information with flowcharts and next steps in the decision making process would have made this book a bit more useful for technologists.

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J Microbiol Biol Educ
v.16(2); 2015 Dec

Updated Cases for Medical Microbiology

Review of: Cases in Medical Microbiology and Infectious Diseases , 4 th ed.; Peter H. Gilligan, Daniel S. Shapiro, and Melissa B. Miller; (2014). ASM Press, Washington, DC. 589 pages.

The fourth edition of Cases in Medical Microbiology and Infectious Diseases is a well-organized compendium of real-life case studies focused on the practical applications of medical microbiology. Primarily written for medical students to help them study for board exams and infectious disease rotations, this volume may also be useful for instructors of upper-division undergraduate microbiology courses.

The fourth edition contains timely updates reflecting the emergence of many new infectious diseases and an increase in multi-drug-resistant infections. In addition, this new edition encompasses information about the latest molecular diagnostic tools such as MALDI-TOF, transcription-mediated amplification (TMA), nucleic acid sequence based amplification (NASBA), and strand displacement amplification (SDA) assays and incorporates these into the cases. There is an entire chapter devoted to the explanation of these different diagnostic approaches. Each color-coded chapter of the book corresponds to a particular organ system and begins with an introduction to the incidence and transmission of diseases associated with that system. Important pathogens for each system are noted in a table at the beginning of each chapter, with information about the general characteristics of the pathogen, its reservoir, and disease manifestations. The main focus of each chapter is the pathogens’ characteristics, their associated diseases, clinical manifestations, and an appropriate course of treatment.

The purpose of the case studies is to foster critical thinking, problem-solving ability, and real-life diagnostic skills for future healthcare professionals. Each case is concisely summarized with the relevant presentation of symptoms, history, and clinical findings. Color photos and micrographs of clinical findings accompany each case to give the reader a “hands-on” feel for the experience of diagnosing a patient’s illness. Each case scenario description is followed by five to eight questions that prompt the reader to work through a diagnosis of the case. Notably, these questions are targeted toward areas of common confusion that could lead to misdiagnoses. Over half of the 74 cases presented in the fourth edition are new and those that appeared in the third edition have been updated. The fourth edition includes an additional “Advanced Cases” chapter providing exposure to many emerging infectious diseases and special cases of multidrug- resistant infections. An extensive “Case Discussion” is provided at the end of each scenario, with thorough, clearly-written explanations for each question. An up-to-date list of five to eight primary reference articles is also given at the end of each case. The last 50 pages of the book consist of a useful glossary of medical terms and an index that can be used to quickly find information on a particular topic.

Overall, the fourth edition provides a wealth of current resources for both students and instructors and can be used in a variety of ways in upper division microbiology courses (e.g., incorporating cases into lecture material, in-class group work, homework assignments).

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Case Studies: Microbiology

All microbiology case studies.

A Bioinformatic Investigation of a Mysterious Meningoencephalitis

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Lablogatory

A blog for medical laboratory professionals

Microbiology Case Study: A 15 Year Old Male with Endocarditis

Case History

A 15 year old male with a past medical history significant for Tetralogy of Fallot (congenital heart defect), multiple valve replacements, chronic kidney disease, and prior Bartonella endocarditis. He presented with a “flu-like” illness including muscle aches, fevers, fatigue, and night sweats. His symptoms slowly dissipated after about three days. However, he had labs drawn including multiple blood culture sets which were all positive for growth.

Laboratory Findings

Gram stain showed gram positive bacilli and culture plates grew two morphologies of slow growing gray, granular and opaque colonies.This organism was identified by MALDI-TOF as Corynebacterium pseudodiphtheriticum.

The genus Corynebacterium comprises a collection of irregular-formed, rod-shaped or coccoid bacteria that are non-motile, catalase -positive, and non- spore -forming.

Corynebacterium pseudodiphtheriticum (previously designated as Corynebacterium hofmannii ) is a nonlipophilic, nonfermentive, urease- and nitrate-positive Corynebacterium species. 1 C. pseudodiphtheriticum is part of the usual oropharyngeal bacterial flora, including the nares and throat. It appears to play a role in preventing colonization of oropharyngeal epithelia by pathogenic bacteria.

Most commonly, C. pseduodiptheriticum is a pathogen of the respiratory tract with cases of nosocomial and community-acquired pneumonia, bronchitis, tracheitis, pharyngitis, and rhinosinusitis. Endocarditis is the second most common infection site, although very rare. Cases of urinary tract and wound infections have also been reported.

Treatment is usually with penicillin alone or in combination with aminoglycosides. Antibiotic susceptibility profiling of C. pseudodiphtheriticum isolates showed that resistance to oxacillin, erythromycin, clindamycin, and macrolides are common. 1

Burkovski A. Corynebacterium pseudodiphtheriticum : Putative probiotic, opportunistic infector, emerging pathogen. Virulence . 2015;6(7):673–674. doi:10.1080/21505594.2015.1067747

-Nicole Mendelson, MD is a 1 st year Anatomic and Clinical Pathology resident at the University of Vermont Medical Center.

-Christi Wojewoda, MD, is the Director of Clinical Microbiology at the University of Vermont Medical Center and an Associate Professor at the University of Vermont .

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13: Putting it all Together—Case Studies in Microbiology

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Joan Petersen & Susan McLaughlin
Queensborough Community College

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In this lab, a series of stations will be set up around the room, each of which will present a microbiology case study. Some background information will be given to you, along with organisms on slides, growth media, etc. This is your opportunity to put together everything that you have learned about microbiology this semester. You will be expected to integrate the various pieces of information that are provided to answer some questions about the situation and come up with a diagnosis. The case studies will not be graded, and you will have an opportunity to work together to solve these problems. All of these activities will be good practice for your lab practical—try to enjoy the process!

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COVID-19 Response Case Studies

Aligned with our mission to advance the understanding and impact of microbiology, the Society reached out to our community of microbiologists to share their experiences in responding to SARS-CoV-2. We aim to showcase the perspective of scientists during the COVID-19 pandemic and the variety of roles adopted to mitigate the global crisis.

Understanding how antiviral antibodies can activate natural killer cells to improve viral control

This is the first case study in our series that comes from a group of Project Investigators (PIs). Professor Ian Humphreys, Professor Eddie Wang, Professor Richard Stanton and Professor Alan Parker are from the Viral Immunology Research Group at Cardiff University, UK. Collectively, they describe how they responded to the COVID-19 pandemic, the challenges they faced and how basic microbiology and immunology are critical components of any pandemic response.

Water-Based Epidemiology – finding a needle in a very dirty haystack

This case study comes from a research group led by David Graham, a Professor of Ecosystems Engineering at Newcastle University, UK. The account explains how the group developed an array of methods for quantifying viral load in sewage, creating the new science of water-based epidemiology, and how they employed their knowledge across various projects.

The epidemiology of COVID-19 in Ugandan settlements and neighbouring communities

This case study is written by Professor Richard Birtles, Chair of Biomedicine at the University of Salford, UK. Richard recounts his experience of establishing COVID-19 diagnostics facilities in Uganda, creating capacity for in situ SARS-CoV-2 whole genome sequencing, and how he used these new resources to clarify virus transmission routes.

Locking down labs and setting up COVID-19 testing facilities

This case study is written by Andrew Martin, a laboratory technician from the University of Salford, UK. He gives his perspective of how lockdown unfolded and how he used his time away from the lab to volunteer at the Lighthouse COVID-19 Testing labs at Alderley Park, near Manchester, UK.

Basic microbiology knowledge is crucial to the understanding of new emerging diseases

This is the second case study written by Dr Chloe James, a Lecturer in Medical Microbiology at The University of Salford, UK. In this case study, she focuses on the media engagement aspect of her response to the pandemic, including the challenges of commenting on highly politicised and emotive issues.

Searching for SARS-CoV-2 in animals

This case study was submitted by Dr Sharon Brookes, who is currently the Lead Scientist for Animal and Zoonotic Viral Diseases at Animal and Plant Health Agency (APHA). She discusses the projects that the APHA have been are involved with since the start of the pandemic, exploring SARS-CoV-2 in animals and its ability to transmit to, from and between people and animals, the development of new testing techniques and how these experiences aided the development of the APHA coronavirus team.

Using baker’s yeast to develop reference viral antigens of SAR-CoV-2

This case study is written by Professor Ed Louis of the University of Leicester, UK and Chief Scientific Officer of Phenotypeca Ltd. Ed’s account highlights the how collaboration between academia and industry can aid innovation, the challenges of establishing new working practices during lockdown and the joy of getting his hands dirty at the lab bench after many years.

Rapid development of an international radiographers online training resource

This case study is written by Dr Chloe James, a Lecturer of Medical Microbiology at the University of Salford. She recounts her experience of contributing to the University of Salford-led effort to develop new e-learning resources for radiographers caring for patients with COVID-19. She highlights the difficulties of working on this fast-paced, international effort involving over 40 people whilst working remotely.

Evaluating the utility of SARS-CoV-2 serological and rapid antigen lateral flow devices

This case study was written by Dr Suzy Pickering, a Research Fellow at King’s College London. Suzy volunteered to help at a local COVID-19 testing facility to increase testing capacity. She describes the highs and the lows of this experience, including times where she felt she was “swimming against the tide”.

From PhD student to COVID-19 testing scientist

This case study was written by Megan Taggart, a PhD candidate from Ulster University. She explains how, as a first year student, she suspended her PhD to become an RNA extraction scientist at her local COVID-19 testing labs. Megan details her time there and how it aided her development.

Saliva versus nasopharyngeal swab specimens for the detection of SARS-CoV-2

This case study comes to you from Dr Anne Wyllie, a Research Scientist at the Yale School of Public Health. She gives us a unique perspective of the pandemic from our neighbours over the water in the US. Her case study highlights the bottlenecks that were present in mass scale testing at the start of the pandemic and how she contributed to overcoming these problems.

The interface between microbiology and civil engineering

This case study is courtesy of Dr Lena Ciric, a Senior Lecturer in the Department of Civil, Environmental and Geomatic Engineering at University College London. She discusses her work on research projects during the pandemic, including a collaboration with Transport for London (TfL), and the associated challenges.

The challenge of balancing an academic career with clinical practice

This case study is written by Dr Suzy Moody, a Lecturer of Eukaryotic Microbiology at Kingston University London who’s research specialises in bioremediation of plastics. However, before she embarked on a PhD she was an Intensive Care Unit (ICU) nurse. Here, she tells us about juggling her academic responsibilities with returning to the wards to treat COVID-positive patients.

Understanding the determinants of resistance to type I interferons and IFITMs in HIV-1 envelope and SARS-CoV-2 spike

This case study is written by Helena Winstone, a PhD Candidate at King’s College London. She gives a research student’s perspective of how a project can be quickly derailed by worldwide events and can pivot to explore new avenues, as well as the frustrations involved with studying a readily-mutating virus and the associated mental health effects.

Exploring how the cell surface SARS-CoV-2 nucleocapsid protein modulates innate and adaptive immunity

This case study was written by Alberto D. López-Muñoz, a postdoctoral researcher. His account of the pandemic focuses on using immune modulation strategies to study SARS-CoV-2; volunteering for a Phase III clinical trial of the COVID-19 vaccine; the difficulties of travel restrictions and being featured in the “Postdoc Profile” series by the National Institute of Health.

The COVID-19 Genomics UK Consortium and genomic surveillance

This case study comes from Andrew Page, Head of Bioinformatics at the Quadram Institute in Norwich. He shares with us his experience of helping to establish one of the founding sequencing centres for the COVID-19 Genomics UK (COG-UK) Consortium, and how his work there aided the government and impacted on the wider society

A mid-career virologist’s pandemic!

This case study is written by Rachael Tarlinton, an Associate Professor in Veterinary Virology at the University of Nottingham. Her account of the pandemic expresses the challenges of teaching, supervising and researching throughout lockdowns; how the pressure of this affected many researchers’ mental health; and the media engagement duties of scientists.

Reverse genetics to characterise SARS-CoV-2

This case study is from Dr Maia Kavanagh Williamson, a postdoctoral research associate in Andrew Davidson’s lab at the University of Bristol. As the COVID-19 pandemic emerged, her research pivoted from RNA viruses HIV and Dengue, to using reverse genetics approaches to characterise basic properties of SARS-CoV-2. Here she details her experience of working throughout the pandemic.

The study of the airborne longevity of SARS-CoV-2

This case study is written by Henry Oswin, a PhD Candidate from Professor Jonathan Reid’s research group at the University of Bristol. He discusses how his project to develop and optimise an instrument to study the airborne longevity of E. coli rapidly adapted to study SARS-CoV-2. He details the technical challenges, as well as his own personal hurdles, that lead to significant progress in understanding the airborne longevity of the virus.

The challenges of being a virologist during the COVID-19 pandemic

This case study is from Dr Elisabetta Groppelli, a Lecturer in Global Health at St George’s University of London, who led the establishment of SARS-CoV-2 research at the Institute for Infection and Immunity. Elisabetta discusses the challenges of starting a new position and establishing a research group during a pandemic, her involvement with public engagement and the personal challenges of living through the first pandemic of our lifetime.

Stepping into public outreach during the COVID-19 pandemic

This case study was written by Dr Cheryl Walter, a Lecturer of Microbiology at the University of Hull. Cheryl discusses her involvement with public outreach, education, consultancy and the development of a SARS-CoV-2 point-of-care testing device. This work enabled her to use her virology skills and knowledge to adapt to the ongoing situation.

Screening antiviral drugs against the envelope (E) protein of SARS-CoV-2

This case study was written by Dr Gemma Swinscoe who transitioned from being a PhD student to a Postdoctoral Research Assistant in mid-2020. The study focusses on Gemma’s research investigating the envelope (E) protein from SARS-CoV-2, the challenges that she faced during the first 18 months of the pandemic and her thoughts on the influence that the pandemic has had on microbiology.

Using genomic surveillance to identify waves of SARS-CoV-2 infections in Zimbabwe

This case study was written by Professor Rob Kingsley, Group Leader at the Quadram Institute. The study focusses on Rob’s genomic surveillance work in Zimbabwe through a partnership with the National Microbiology Reference Laboratory in Harare.

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Clinical Case Study

You are a pathologist working in the lab at Daigger Memorial Hospital (located in central Oregon). The emergency room is sending you cultures collected from patients. Your job is to determine:

The pathogenic bacteria causing the disease
What disease is being presented
What is the best treatment given the patient’s unique symptoms and history

Review the patient assessment form provided. Consider the type of sample taken and determine the relevant primary and secondary symptoms. Generally, samples taken in a clinical setting contain multiple organisms. In this case study, the sample will contain one contaminant organism and one causative agent.

Note: Watch for allergies. These may affect the appropriate treatment you will prescribe for the patient.

For some diseases, an irregularity in pulse or respiration, for example, may be indicative of the seriousness of the illness or may even be a direct indicator of the disease presented. Therefore, it is important that you understand what is considered “Within Normal Limits” for all assessment areas on the patient assessment form. Familiarize yourself with the acceptable normal ranges for pulse, respiration, oxygen saturation, blood pressure, and temperature for the age group your patient falls within.

Clinical Case Study Flowchart »

Patient Assessment Form »

Normal Flora According to Body System

Adapted from: Strohl, W.A. et.al. Lippincott's Illustrated Reviews: Microbiology. Lippincott Williams & Wilkins. Baltimore, MD. 2001 and Forbes, B.A. et.al. Bailey and Scott's Diagnostic Microbiology. Eleventh Edition. Mosby. St. Louis, MO. 2002

Pathogenic Organisms According to Body System

Adapted from: Gilligan, P.H. et.al. Cases in Medical Microbiology and Infectious Diseases. 2nd ed. American Society for Microbiology, Washington, D.C. 1997. Murray, Patrick R. Editor. Manual of Clinical Microbiology. 8th ed. ASM Press. Washington, D.C. 2003. Strohl, W.A. et.al. Lippincott's Illustrated Reviews: Microbiology. Lippincott Williams & Wilkins. Baltimore, MD. 2001

ORIGINAL RESEARCH article

Explainable artificial intelligence and microbiome data for food geographical origin: the mozzarella di bufala campana pdo case of study.

$\r\nMichele Magarelli$

1 Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
2 Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
3 Dipartimento di Agraria, Università degli Studi di Napoli Federico II, Naples, Italy
4 Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Bari, Italy

Identifying the origin of a food product holds paramount importance in ensuring food safety, quality, and authenticity. Knowing where a food item comes from provides crucial information about its production methods, handling practices, and potential exposure to contaminants. Machine learning techniques play a pivotal role in this process by enabling the analysis of complex data sets to uncover patterns and associations that can reveal the geographical source of a food item. This study aims to investigate the potential use of explainable artificial intelligence for identifying the food origin. The case of study of Mozzarella di Bufala Campana PDO has been considered by examining the composition of the microbiota in each samples. Three different supervised machine learning algorithms have been compared and the best classifier model is represented by Random Forest with an Area Under the Curve (AUC) value of 0.93 and the top accuracy of 0.87. Machine learning models effectively classify origin, offering innovative ways to authenticate regional products and support local economies. Further research can explore microbiota analysis and extend applicability to diverse food products and contexts for enhanced accuracy and broader impact.

1 Introduction

With the burgeoning demand for high-quality, region-specific products, the need to ensure the origin and treceability of food products plays a pivotal role in ensuring authenticity, quality, and safety in the global food supply chain ( Gallo et al., 2021 ). The concepts of food traceability and origin are closely interlinked and hold pivotal significance in ensuring food safety and transparency throughout the production process but also supports local economies and encourages sustainable agricultural practices. They are integral in guaranteeing that foods are safe, genuine, and adhere to quality standards. Traceability refers to the ability to follow the journey of a product along the entire supply chain, encompassing detailed information about its production, processing, packaging, distribution, and sale ( del Rio-Lavín et al., 2023 ). On the other hand, the origin of food products indicates the specific location where they were cultivated, manufactured, or processed. Understanding the origin of a food item is essential for various reasons, including ensuring its safety, quality, and sustainability. Presently, determining the origin of a food product relies on diverse methods and tools. Collaboration among producers, distributors, and other stakeholders in the supply chain is crucial to ensuring transparency and accuracy in disclosing the origin of food products ( Corallo et al., 2020 ). Some food products may acquire origin certifications, such as the Protected Designation of Origin (PDO) in Europe or other regional certifications, which verify that the product originates from a specific geographical area and complies with designated standards ( Badia-Melis et al., 2015 ). Analyzing the intricate ecosystem of microorganisms inhabiting food, known as the food microbiota, can be a useful tool for understanding the safety, quality, and characteristics of food products of foods. This diverse microbial community, comprising bacteria, fungi, and viruses, is influenced by various factors such as geographical location, production methods, and processing techniques. A fundamental aspect of harnessing the food microbiota for product origin lies in its dynamic composition, which reflects the unique environmental conditions and production practices of each food item. By scrutinizing the microbiota composition of food samples, distinctive microbial signatures indicative of their origin or production environment can be discerned. Recent advancements in molecular biology and sequencing technologies have revolutionized our ability to characterize the food microbiota with unprecedented precision and speed. High-throughput sequencing methods, including next-generation sequencing, facilitate rapid and accurate identification of microbial species present in food samples ( Reuter et al., 2015 ). Comparative analysis of microbiota profiles among different food samples enables the identification of subtle variations that serve as valuable markers for product origin. Specific microbial strains or community structures may be linked to particular regions or production facilities, offering distinctive identifiers for food products. Moreover, the food microbiota serves as a sentinel for monitoring food quality and safety along the supply chain ( Guidone et al., 2016 ). Alterations in microbial composition or abundance can signal potential contamination or spoilage incidents, enabling prompt interventions to mitigate risks and uphold food safety standards. In addition to conventional laboratory techniques, emerging methodologies such as metagenomics and metatranscriptomics provide comprehensive insights into the food microbiota. These cutting-edge approaches enable holistic analysis of all microbial genetic material within a sample, facilitating deeper understanding of microbial dynamics and functions ( Cao et al., 2021 ). The use of machine learning in food classification and origin represents a significant step forward in ensuring the safety and authenticity of food products. Firstly, machine learning enables the development of predictive models that can differentiate between different types of foods based on specific characteristics. By leveraging machine learning algorithms, it becomes possible to process vast amounts of data, including information on production practices, environmental factors, and biochemical compositions, to accurately predict the origin of a food product. For example, using data from chemical, sensory, or genetic analyses, models can be trained to recognize the presence of contaminants or identify the geographical origin of a food. Furthermore, the application of machine learning to food classification offers numerous opportunities to enhance food safety, ensure product authenticity, and optimize the identification of food origin. The integration of machine learning and microbiota offers an innovative approach to understanding the complexity of interactions between the microbiome and food. By analyzing microbiome data using machine learning algorithms, it is possible to identify patterns and associations that can be valuable for enabling the develop preventive strategies to reduce risks and improve the nutritional quality of foods. The application of machine learning techniques in the field of food microbiota presents multiple opportunities to analyze large amounts of microbiological data, identify patterns and associations between microbial composition and food characteristics, predict food quality and safety, to understanding microbial dynamics and search for solutions to promote health ( Bellantuono et al., 2023 ; Papoutsoglou et al., 2023 ). Through data analysis and the development of predictive models, crucial challenges in the food industry can be addressed, promoting greater transparency and trust among consumers. Explainable Artificial Intelligence (XAI) algorithms are useful to make artificial intelligence (AI) models understandable and interpretable to humans, because many machine learning and AI models often operate as “black boxes,” making it difficult to understand how and why they produce certain predictions or decisions. The goal of XAI is to provide explanations and insights into the operation of AI models, enabling users to understand the reasons behind their predictions or decisions. This is particularly important in contexts where transparency, accountability, and trust in AI are crucial. In Explainable Artificial Intelligence (XAI), trustworthiness plays a role in ensuring the reliability and transparency of AI models. It refers to the degree of confidence and faith users have in the explanations provided by the model regarding its predictions and decision-making processes. XAI techniques may include SHapley Additive exPlanations (SHAP) analysis that seek to translate the internal workings of AI models into understandable human explanations ( Novielli et al., 2024 ). This research delves into the crucial realm of preserving and authenticating the geographical origin of Mozzarella di Bufala Campana PDO, specifically focusing on the provinces of Salerno and Caserta. The characteristic that will be used for data analysis is the abundance of bacteria present in the microbiota of the samples. This information will be crucial for identifying any patterns or correlations between bacterial composition and the geographical origin of Mozzarella di Bufala PDO. By utilizing data analysis techniques such as machine learning ( Monaco et al., 2021 ; Papoutsoglou et al., 2023 ), it will be possible to create predictive models capable of accurately classifying the geographical origin of each sample based on microbiota information. This approach will provide a trustworthy assessment of the mozzarella's origins, thereby contributing to food quality and safety.

2 Materials

The data utilized in this study, decripted in Table 1 stems from the microbiological analysis of the microbiome of 65 samples of Mozzarella di Bufala PDO originating from 30 dairies in the province of Salerno and 35 dairies in the province of Caserta. These samples underwent thorough examination in the laboratories of the Microbiology Division within the Department of Agricultural Sciences at the University of Naples Federico II. All dairies were located within the PDO area produced traditional Mozzarella di Bufala according to the PDO regulation. Total DNA was extracted using the Qiagen Power Soil Pro kit. Metagenomic libraries were prepared using the Nextera XT Index Kit (Illumina, San Diego, California, United States), then whole metagenome sequencing was performed on an Illumina NovaSeq platform, leading to 2 × 150 bp, paired-end reads. Reads were quality-checked and filtered through Prinseq-lite v. 0.20.4, using parameters “-trim_qual_right 5” and “-min_len 60.” An average of 25 M of paired-end reads were obtained (2 × 150 bp) for each sample. Raw reads were pre-processed and filtered as previously described ( De Filippis et al., 2021 ). Briefly, contamination from host reads was removed using the Human Sequence Removal pipeline developed within the Human Microbiome Project by using the Best Match Tagger (BMtagger) mapping reads against the Bubalus bubalis (Mediterranean breed) genome (accession number: GCA003121395.1). Then, non-host reads were quality-filtered using PRINSEQ v. 0.20.4 ( Schmieder and Edwards, 2011 ). Bases having a Phred score < 15 were trimmed and those < 75 bp were discarded. High-quality reads were further processed to obtain microbiome taxonomic profiles using MetaPhlAn v. 4.0 ( Blanco-Míguez et al., 2023 ).

Table 1 . Description of samples and input variables.

Our analysis encompasses a diverse set of samples, reflecting the regional diversity of Mozzarella di Bufala PDO production across different dairies in the provinces of Salerno and Caserta. The 65 samples provide a robust dataset for investigating variations in microbial composition, offering valuable insights into the distinctive qualities of Mozzarella di Bufala PDO from different geographic origins. The species abundance data unveils the relative prevalence of microbial species, offering insights into the intricate microbiome of Mozzarella di Bufala PDO. This information is organized in a tabular format, where each row corresponds to a specific sample, and each column represents a distinct microbial species. To enhance our understanding of the origin of each Mozzarella di Bufala PDO sample, we include details about the respective cheese dairy, specifying both the dairy name and its geographic origin. Each sample presents 139 output variables, each representing the abundance of a specific bacterium. In the context of your analysis on the microbiome of Mozzarella di Bufala PDO, these output variables likely reflect the proportions or relative quantities of different types of bacteria present in each sample. The type of bacteria and their relative abundance in each sample could have significant implications for the quality and sensory characteristics of the product. Since many samples have abundance values equal to zero, indicating the absence of the bacteria, a preprocessing step was performed. In this pre-processing step, columns with more than 70% zero values were removed, reducing the total number of columns to 23. In order to conduct a robust analysis, the initial dataset has been strategically partitioned into a validation dataset and a test dataset to. This partitioning is designed to ensure a representative and unbiased evaluation of the models developed during the study ( Ibrahimi et al., 2023 ). The validation dataset consists of 22 samples from the province of Salerno and 33 samples from the province of Caserta. This division allows for the exploration of regional variations within the microbiome of Mozzarella di Bufala PDO, considering the distinctive characteristics of these geographical locations. The validation set was then used to assess three different classifiers through a five-fold cross-validation repeated 20 times ( Schaffer, 1993 ), and the performance of the best classifier (Random Forest, RF) was analyzed. Following that, the trained model was tested on the test dataset, and its performance was evaluated on this separate set of samples.

The independent test dataset, on the other hand, comprises eight samples from Salerno and two samples from Caserta. Notably, these 10 test samples are collected on the same day from the same dairy as the samples present in the validation set. By adopting this partitioning strategy, we aim to develop a model that not only captures the nuances of the training dataset but also demonstrates robust predictive abilities when faced with previously unseen samples.

The main steps of our analysis are outlined in the flowcharts in Figure 1 . It provides a comprehensive overview of the model's performance during both the training and validation phases, as well as in the subsequent testing phase, allowing for an overall evaluation of its predictive capabilities.

Figure 1 . The flowchart outlines the steps of the conducted analysis. The validation set was used to assess three different classifiers through a five-fold repeated 20 times cross-validation, and the performance of the best classifier (Random Forest, RF) was analyzed. Following that, the trained model was tested on the test dataset, and its performance was evaluated on this separate set of samples.

3.1 Machine learning based classification

To assess the classification of these samples, three distinct supervised machine learning methods were employed: Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). The identification of the optimal classifier was based on both accuracy and Area Under the Curve (AUC).

3.1.1 Random forest classifier

The Random Forest Classifier represents a sophisticated ensemble learning algorithm within the realm of machine learning ( Chaudhary et al., 2016 ). Envisioned as a confluence of decision trees, it operates on the principle of aggregating predictions from diverse models to augment stability and overall performance. The ensemble is constituted by an assembly of decision trees, each meticulously trained on a distinct subset of the training dataset through the lens of bootstrap sampling a method characterized by its sampling with replacement. The algorithm's efficacy is derived from the varied nature of decision trees. This diversity, arising from the differential subsets of data upon which each tree is trained, mitigates the risk of overfitting, fostering a robust model. In the predictive phase, each decision tree contributes its prediction, and the final class is determined through a majoritarian consensus. This collective decision-making process amplifies the model's resilience and generalization capabilities ( Breiman, 2001 ).

3.1.2 EXtreme gradient boosting classifier

EXtreme Gradient Boosting (XGBoost) is a widely-used machine learning algorithm for regression and classification problems renowned for its prowess in diverse applications, particularly excelling in the realm of structured or tabular data and supervised learning scenarios ( Shwartz-Ziv and Armon, 2022 ). XGBoost has been extensively used in data science and machine learning competitions due to its ability to achieve excellent performance on a wide range of problems and datasets. It's also known for its flexibility and ability to handle large amounts of data. Positioned within the domain of ensemble learning, XGBoost elevates traditional gradient boosting algorithms to new heights. XGBoost typically builds an ensemble of decision trees, where each tree contributes to the final prediction. The combination of multiple trees enhances the model's predictive capabilities. XGBoost supports built-in cross-validation, enabling robust model evaluation and parameter tuning for optimal performance. XGBoost has an high predictive accuracy. By constructing an ensemble of models, each correcting the errors of the others, it can provide more accurate predictions compared to many other algorithms. It also incorporates regularization techniques that help manage the issue of overfitting, keeping the model general and adaptable to new data ( Chen and Guestrin, 2016 ).

3.1.3 Multi-layer perceptron classifier

The Multi-Layer Perceptron (MLP) stands as a sophisticated architecture within the domain of artificial neural networks, prominently featured in the landscape of machine learning. It is distinguished by its layered composition, comprising an input layer, one or more hidden layers, and an output layer. Each layer encompasses interconnected nodes, or artificial neurons, where the transmission of information follows a feedforward trajectory, progressing from the input layer through the hidden layers and culminating in the output layer. In a Multi-Layer Perceptron (MLP), input nodes constitute the initial layer of the neural network and serve as the units through which data is introduced into the system. Each input node represents a specific feature or variable from the dataset intended for model training. The hidden layers are intermediary layers between the input and output layers, responsible for capturing and learning complex patterns and representations within the input data. These layers contribute to the model's ability to discern intricate relationships that may not be immediately apparent in the raw features. Output nodes constitute the final layer of the neural network and are responsible for producing the model's predictions or outcomes. The configuration and characteristics of the output layer depend on the nature of the task, whether it involves classification, regression, or other specific objectives ( Ruck et al., 1990 ).

3.2 Evaluation metrics

Evaluation metrics are crucial tools for assessing the performance and effectiveness of machine learning models ( Ferrer, 2022 ). These metrics provide quantitative measures that help quantify how well a model is performing on a given task. The choice of evaluation metrics depends on the nature of the problem (classification, regression, etc.) and the specific goals of the analysis. Here are some commonly used evaluation metrics:

• Accuracy:

The proportion of correctly classified instances among the total instances

• Sensitivity:

The fraction of true positive predictions out of all actual positive instances

• Specificity:

Specificity is the proportion of actual negatives correctly identified by the model out of the total number of actual negatives.

• Precision:

The fraction of true positive predictions out of all positive predictions

• Area Under the ROC Curve (AUC-ROC):

The Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) are assessment tools employed to gauge the effectiveness of a binary classification model. The ROC curve presents a graphical depiction of how sensitivity (true positives) and specificity (true negatives) change across various classification thresholds. Essentially, it illustrates the balance between accurately identifying positive and negative instances by the model. The AUC quantifies the overall performance of the model by measuring the area under the ROC curve: a value closer to 1 signifies superior model performance, while a value around 0.5 suggests random classification. In summary, these metrics are vital for evaluating and contrasting the classification ability of binary models ( Ozenne et al., 2015 ).

3.3 Explainable artificial intelligence methods

Explainable Artificial Intelligence (XAI) is a crucial aspect in the development of AI systems, focused on making artificial intelligence (AI) models understandable and interpretable to humans. A specific method employed for XAI is the SHapley Additive exPlanations (SHAP) ( Arrieta et al., 2020 ). SHAP values are used to evaluate the impact of individual features on the model's performance, particularly on a validation set. Mathematically, the SHAP value for a specific feature ( j ) is calculated based on the inclusion or exclusion of that feature from the model as:

where Φ j ( x ) represents the SHAP value of feature j for the prediction of the model f given the input x , S is the set of all features, F ⊆ S −{ j } represents all possible subsets of features excluding feature j , | F | ! ( | S | - | F | - 1 ) ! | S | ! is a weight parameter that multiplies all of the permutations of S! by the potential permutations of the remaining class that doesn't belong to S, while f x ( F ∪ j ) and f x ( F ) denote respectively the model's prediction when feature j is added to the subset F and when it is absent ( Lundberg and Lee, 2017 ). We also averaged the ten realizations of SHAP values in order to obtain a single representative SHAP vector.

The SHAP value measures how much including feature j changes the model's prediction compared to the prediction without feature j, averaged over all possible combinations of features. Positive SHAP values indicate that the feature contributes positively to the prediction, while negative values indicate a negative contribution. The SHAP values provide a quantitative measure of the contribution of each feature to the model's output, enabling a more interpretable understanding of how individual features influence the algorithm's decision-making process. This transparency is crucial for building trust in AI systems and facilitating their use in various real-world applications where interpretability is essential ( Janzing et al., 2020 ). This approach contributes to the trustworthiness and applicability of our findings, enhancing the overall validity of the study's outcomes in the context of Mozzarella di Bufala PDO from Salerno and Caserta.

This study aims to investigate the potential use of explainable artificial intelligence for identifying the food origin. The case of study of Mozzarella di Bufala Campana PDO has been considered by examining the composition of the microbiota in 65 samples.

This study involved evaluating the effectiveness of three supervised machine learning algorithms, namely XGBoost, Random Forest, and a complex Multi-Layer Perceptron network. The analysis revealed that the Random Forest classifier outperformed the others, demonstrating the highest Area Under the Curve (AUC) value of 0.93 ± 0.10 and the top accuracy score of 0.87 ± 0.11. Table 2 provides a comprehensive comparison of the three models based on their AUC and accuracy scores.

Table 2 . Comparison between evaluation metrics of XGBoost (XGB), Random Forest (RF), and Multi-Layer Perceptron (MLP) classifiers.

4.1 Machine learning analysis

The results are illustrated in the confusion matrix in Table 3 , obtained following a five-fold repeated 20 times cross-validation procedure on the validation set. This methodology allows us to assess the effectiveness of our algorithm in a robust and reliable manner. In Figure 2 it is possible to observe the boxplot displaying the trend evaluation metrics, including accuracy ( Equation 1 ), specificity ( Equation 3 ), sensitivity ( Equation 2 ) and precision ( Equation 4 ), obtained through a five-fold repeated cross-validation scheme.

Table 3 . Confusion matrix depicts predicted values against actual values.

Figure 2 . Boxplot of the distributions of evaluation metrics (accuracy, specificity, sensitivity and precision) following five-fold cross-validation repeated 20 times.

The confusion matrix highlights the algorithm's ability to correctly classify observations based on the geographical origin of the samples, divided between the Salerno and Caserta areas. We observe that the algorithm achieved an accuracy of 87.87% in correctly identifying samples from the Salerno area and 86.36% for those from the Caserta area. These results indicate a good capability of our machine learning model in distinguishing the geographical origin of Mozzarella di Bufala Campana PDO based on the microbiota structure. The accuracy in both cases is quite high, suggesting that the model generalizes well to new data and could be used as a supportive tool in determining the geographical origin of unknown samples.

The Receiver Operating Characteristic curve in the Figure 3 defines AUC score, measuring the area under this curve, is 0.93 ± 0.10 and it suggests a high accuracy in classifying samples based on their geographical origin, affirming the robustness of the model's performance.

Figure 3 . ROC curve depicts the classification model's ability to vary the trade-off between sensitivity (True Positive Rate) and specificity (1 – False Positive Rate).

After conducting cross-validation, the outcomes were then utilized to compute feature importance employing SHapley Additive exPlanations (SHAP), as expressed in Equation (5) . The SHAP ranking plot is a graph that displays the importance of features in machine learning models using SHAP and features are arranged along the y-axis based on their importance, with the most important features at the top and the least important ones at the bottom. Each colored point represents a single data instance, and the horizontal position of the point indicates the value of the shap for that specific instance. The color of the point indicates the value of the feature: higher values are represented in warm colors (red), while lower values are represented in cool colors (blue). Through a SHAP analysis, the 20 most important feature were identified, deriving from the analysis of the microbiota 65 samples. In the SHAP plot in Figure 4 it is evident how certain features, such as Lactococcus lactis and Moraxella osloensis , contribute significantly to the model's prediction. The feature Lactobacillus helveticus is important for the model's interpretability, as the colored points are well distinguished, and red points indicate that high values of that bacterium have influenced Salerno class, and vice versa. This suggests that these elements play a crucial role in the geographical discrimination of the samples.

Figure 4 . The SHapley Additive exPlanations (SHAP) summary plot provides an overview of the importance of features in contributing to model predictions. In this type of plot, each point represents a data instance, and the horizontal position of the point indicates how much the effect of a specific feature contributes to the change in prediction compared to the model's average prediction. The color of the point represents the value of the feature, with darker colors indicating higher values.

The results of the Shap analysis highlight the fact that two Phyla are most represented (Firmicutes and Proteobacteria). The taxonomy of each sample of SHAP analysis is descripted in Table 4 . Lactobacillaceae is represented by five bacteria, Moraxella family is represented by four bacteria, while Lactococcaceae family is represented by three bacteria. Starting from the taxonomic group of the genus, it can be seen that there is a significant diversity of microbes, even if the Lactococcus genus and Lacotbacillus genus is represented three times each other.

Table 4 . Classification of the first 20 bacteria deriving from the Shap analysis.

A possible application of the classification model is to execute it on the previously selected test dataset. In testing the model, a dataset consisting of 10 samples from the same study was utilized, including two from Caserta and eight from Salerno. These samples were previously excluded during the model training phase. The confusion matrix of the test, depicted in the figure, provides a detailed overview of the model's performance on this specific test dataset. It is particularly noteworthy that all samples from Caserta were correctly classified by the model. On the other hand, only one sample from Salerno was misclassified. This result suggests a significant accuracy in the model's ability to discriminate between the two production locations, with a particularly high success rate for samples from Caserta. The confusion matrix in Table 5 offers a detailed assessment of the model's performance on the specific test dataset.

Table 5 . Confusion matrix depicts predicted values against actual values.

5 Discussion

Mozzarella di Bufala Campana PDO is a designation that certifies the mozzarella is produced in the Campania region, Italy, and follows traditional production methods and established quality standards to preserve its authenticity and excellence. The PDO protects the product name from imitations and assures buyers that they are purchasing a genuine product produced according to the traditional specifications of the designated area. Recognizing the correct origin is crucial to preserving the diversity and excellence of local productions. Protection against imitations and counterfeits, guaranteed by the PDO, helps maintain the product's reputation and preserves its cultural history. Ultimately, correctly identifying the origin of PDO mozzarella not only ensures product quality but also contributes to preserving the cultural and gastronomic heritage associated with this unique Italian specialty.

Indeed, the integration of machine learning (ML) and explainable artificial intelligence (XAI) techniques holds significant value in various contexts, including the analysis of biological data such as microbiota and metabolomics. Machine learning facilitates the creation of accurate predictive models based on microbiological data, aiding in the authentication and protection of PDO products like Mozzarella di Bufala Campana. XAI techniques ensure transparency and interpretability, reinforcing trust among consumers, regulators, and industry stakeholders. This combination not only enhances the certification of food origin but also strengthens the preservation of cultural and gastronomic heritage associated with traditional foods. Overall, microbiota analysis plays a vital role in ensuring the authenticity, quality, and safety of food products like Mozzarella di Bufala Campana PDO. In this study, each sample exhibits a relative abundance of various microbial species, which are not present in all samples. The most prevalent genera are Pseudomonas, Lactobacillus, Streptococcus , and Acinetobacter . The cheese-making process of Mozzarella di Bufala Campana is a combination of high-quality ingredients and specific procedures, with particular attention to the crucial role played by natural whey containing thermophilic lactic bacteria. The presence of thermophilic lactic bacteria is interesting because they survive at high temperatures during the processing, thus contributing to the uniqueness of Mozzarella di Bufala Campana ( Levante et al., 2023 ). The ecological complexity of these thermophilic lactic bacteria is an aspect that can be studied in detail to better understand the fermentation process and the production of this traditional cheese. Research conducted has shown that, despite ecological complexity, only certain thermophilic lactic acid bacteria (LAB), namely Streptococcus thermophilus, Lactobacillus delbrueckii , and Lactobacillus helveticus , are the main players in the curd fermentation. This is one of the peculiarities that helps preserve the unique characteristics of the cheese and protects local producers from imitations and counterfeits. It also assures buyers that they are purchasing an authentic and high-quality product, respecting the long history and reputation of Mozzarella di Bufala Campana as a traditional and artisanal product ( Pisano et al., 2016 ).

6 Conclusion

This paper is an example of how an XAI analysis can be applied with trustworthiness in the context of discriminating the geographical origin of PDO Mozzarella di Bufala Campana based on microbiota bacterial abundance. This validates the approach employed in our study and confirms that certain bacteria can be considered reliable indicators of geographical origin. The predictive models developed using machine learning techniques have proven to be effective in classifying the geographical origin of mozzarella samples. These results provides strong support for food traceability, enabling consumers to make informed choices and ensuring that products are authentic and safe. The results obtained have significant implications for the food industry as they offer an innovative and reliable method to authenticate and protect high-quality regional products. This can contribute to strengthening consumer confidence in food products and supporting local economies through the promotion of sustainable agricultural practices. Further research could delve deeper into microbiota analysis and assess the effectiveness of other analytical techniques in improving the accuracy of predictions regarding the geographical origin of food products. Machine learning facilitates the creation of robust predictive models capable of accurately identifying the origin of food products based on microbiological data. Furthermore, XAI techniques provide transparency and interpretability, enabling stakeholders to understand how these models arrive at their conclusions. This combination not only ensures the trustworthiness of predictions but also fosters trust among consumers, regulators, and industry professionals. Moving forward, further research could delve deeper into microbiota analysis and explore the effectiveness of additional analytical techniques in enhancing the accuracy of predictions regarding the geographical origin of food products. Additionally, investigating the application of these approaches in diverse contexts and food products would expand the scope and applicability of our findings, driving continual advancements in food traceability and quality assurance practices.

Data availability statement

The data presented in the study are deposited in the Sequence Read Archive (SRA) database of the NCBI, accession numbers PRJNA1084214 and PRJNA997821.

Author contributions

MM: Writing – review & editing, Writing – original draft, Software, Methodology, Investigation, Formal analysis. PN: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Conceptualization. FD: Writing – review & editing, Validation, Investigation, Data curation. RM: Writing – review & editing, Data curation. PD: Writing – review & editing, Validation. DD: Writing – review & editing, Validation. RB: Writing – review & editing, Validation. ST: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. METROFOOD-IT project has received funding from the European Union—NextGenerationEU, PNRR—Mission 4 “Education and Research” Component 2: from research to business, Investment 3.1: Fund for the realization of an integrated system of research and innovation infrastructures - IR0000033 (D.M. Prot. n.120 del 21/06/2022).

Acknowledgments

Authors would like to thank the resources made available by ReCaS, a project funded by the MIUR (Italian Ministry for Education, University and Re- 270 search) in the “PON Ricerca e Competitivit'a 2007–2013-Azione I-Interventi di rafforzamento strutturale” PONa3 00052, Avviso 254/Ric, University of Bari.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher's note

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Keywords: explainable artificial intelligence, machine learning, microbiome, food origin, PDO

Citation: Magarelli M, Novielli P, De Filippis F, Magliulo R, Di Bitonto P, Diacono D, Bellotti R and Tangaro S (2024) Explainable artificial intelligence and microbiome data for food geographical origin: the Mozzarella di Bufala Campana PDO Case of Study. Front. Microbiol. 15:1393243. doi: 10.3389/fmicb.2024.1393243

Received: 28 February 2024; Accepted: 13 May 2024; Published: 03 June 2024.

Reviewed by:

Copyright © 2024 Magarelli, Novielli, De Filippis, Magliulo, Di Bitonto, Diacono, Bellotti and Tangaro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sabina Tangaro, sabina.tangaro@uniba.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Actinomycetes are a natural resource for sustainable pest control and safeguarding agriculture

Published: 19 May 2024
Volume 206 , article number 268 , ( 2024 )

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Mohamed Khaled Diab ORCID: orcid.org/0000-0001-7879-1357 1 ,
Hala Mohamed Mead ORCID: orcid.org/0000-0002-5625-7281 1 ,
Mohamad M. Ahmad Khedr ORCID: orcid.org/0000-0001-8549-1336 2 ,
Abdelghafar Mohamed Abu-Elsaoud ORCID: orcid.org/0000-0002-6269-3418 3 &
Sahar Ahmed El-Shatoury ORCID: orcid.org/0000-0002-6093-5145 3

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Actinomycetes, a diverse group of bacteria with filamentous growth characteristics, have long captivated researchers and biochemists for their prolific production of secondary metabolites. Among the myriad roles played by actinomycete secondary metabolites, their historical significance in the field of biocontrol stands out prominently. The fascinating journey begins with the discovery of antibiotics, where renowned compounds like streptomycin, tetracycline, and erythromycin revolutionized medicine and agriculture. The history of biocontrol traces its roots back to the early twentieth century, when scientists recognized the potential of naturally occurring agents to combat pests and diseases. The emergence of synthetic pesticides in the mid-twentieth century temporarily overshadowed interest in biocontrol. However, with growing environmental concerns and the realization of the negative ecological impacts of chemical pesticides, the pendulum swung back towards exploring sustainable alternatives. Beyond their historical role as antibiotics, actinomycete-produced secondary metabolites encompass a rich repertoire with biopesticide potential. The classification of these compounds based on chemical structure and mode of action is highlighted, demonstrating their versatility against both plant pathogens and insect pests. Additionally, this review provides in-depth insights into how endophytic actinomycete strains play a pivotal role in biocontrol strategies. Case studies elucidate their effectiveness in inhibiting Spodoptera spp. and nematodes through the production of bioactive compounds. By unraveling the multifunctional roles of endophytic actinomycetes, this review contributes compelling narrative knowledge to the field of sustainable agriculture, emphasizing the potential of these microbial allies in crafting effective, environmentally friendly biocontrol strategies for combating agricultural pests.

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Role of Secondary Metabolites of Actinomycetes in Crop Protection

Recent advancement in the development of biopesticides by actinomycetes for the control of insect pests.

Exploring the potential of endophytes and their metabolites for bio-control activity

Data availability.

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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This work was partially supported by Suez Canal University (Rally Project Fund, 1/2020). Author SE had received research and financial support from Suez Canal University (Rally Project Fund, 1/2020).

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Review Article
Published: 23 May 2024

Molecular insights into the Ebola virus life cycle

Bianca S. Bodmer ORCID: orcid.org/0000-0001-9695-9168 1 ,
Thomas Hoenen ORCID: orcid.org/0000-0002-5829-6305 1 &
Lisa Wendt ORCID: orcid.org/0000-0002-0390-5820 1

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Ebola virus and other orthoebolaviruses cause severe haemorrhagic fevers in humans, with very high case fatality rates. Their non-segmented single-stranded RNA genome encodes only seven structural proteins and a small number of non-structural proteins to facilitate the virus life cycle. The basics of this life cycle are well established, but recent advances have substantially increased our understanding of its molecular details, including the viral and host factors involved. Here we provide a comprehensive overview of our current knowledge of the molecular details of the orthoebolavirus life cycle, with a special focus on proviral host factors. We discuss the multistep entry process, viral RNA synthesis in specialized phase-separated intracellular compartments called inclusion bodies, the expression of viral proteins and ultimately the assembly of new virus particles and their release at the cell surface. In doing so, we integrate recent studies into the increasingly detailed model that has developed for these fundamental aspects of orthoebolavirus biology.

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The continued threat of emerging flaviviruses

Viral genomics in Ebola virus research

Resurgence of Ebola virus in 2021 in Guinea suggests a new paradigm for outbreaks

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Acknowledgements

We thank A. Groseth (FLI) for helpful discussions and corrections to the paper. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grants 389002253 and 452208680.

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Bodmer, B.S., Hoenen, T. & Wendt, L. Molecular insights into the Ebola virus life cycle. Nat Microbiol (2024). https://doi.org/10.1038/s41564-024-01703-z

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Review on Mucormycosis: Pathogenesis, Epidemiology, Microbiology and Diagnosis

Affiliation.

1 Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, Uttar Pradesh, India.
PMID: 37608614
DOI: 10.2174/1871526523666230822154407

Mucormycosis is a serious and invasive fungal infection caused by Mucorales fungi. This review article provides a concise overview of the pathogenesis, epidemiology, microbiology, and diagnosis of mucormycosis. The introduction section highlights the key microbiological properties of the pathogen and delves into the underlying mechanisms of mucormycosis pathogenesis, including the invasion and proliferation of the fungus within the host. The description of the disease section focuses on the epidemiology of mucormycosis, including its incidence, risk factors, and geographical distribution. It also explores the specific context of mucormycosis infection about COVID-19 and diabetes mellitus, highlighting the increased susceptibility observed in individuals with these conditions. A case study illustrates the clinical manifestations and challenges associated with mucormycosis, emphasizing the importance of early detection. Additionally, the review discusses the diagnosis of mucormycosis, emphasizing the significance of clinical assessment, radiological imaging, and microbiological tests for accurate and timely detection of the infection. Regarding treatment, the article covers the various therapeutic approaches, including antifungal therapy, surgical interventions, and management of underlying predisposing conditions. The limitations and challenges associated with treatment options are also addressed. This review aims to provide a comprehensive understanding of mucormycosis, equipping healthcare professionals with valuable insights into its pathogenesis, epidemiology, microbiology, and diagnostic strategies. By enhancing knowledge and awareness of this fungal infection, this review can improve patient outcomes through early diagnosis and appropriate management.

Keywords: Black fungus; India; co-infection; coronavirus; diabetes mellitus; mucorales; oxygen therapy..

Publication types

Antifungal Agents / therapeutic use
Diabetes Mellitus*
Mucormycosis* / diagnosis
Mucormycosis* / epidemiology
Mucormycosis* / therapy
Risk Factors
Antifungal Agents

CBC must strengthen its case for our – and its own – survival

We still need $3,300 to make our budget. Will you chip in?

Sometimes, the CBC is exactly the public broadcaster we need it to be in an emergency.

Witness the first year of the COVID pandemic, when CBC hosts and reporters quickly repurposed their kitchen tables and provided us on a daily basis with the best information available on how to keep each other safe.

Get daily news from Canada's National Observer

More recently, when wildfires were wreaking havoc in my home province of B.C. last summer, CBC radio provided remarkable emergency broadcasting, in some cases making heroic efforts to keep people informed of the latest breaking developments, with front-line reporting that was urgent, relevant and sometimes very moving.

When our public broadcaster shows us its best face in times such as these, we’re reminded why having a media organization that can prioritize clear, factual and compassionate communication over profit is so important to a healthy democracy. And it makes political calls to “defund the CBC” ring especially hollow and tone deaf.

But in the face of the most profound crisis we confront — the climate emergency — we have yet to see the CBC be that public broadcaster. For any of us who follow the daily climate news and science on specialized news sites (such as Canada’s National Observer ), the disconnect between what’s coming and the coverage in Canada’s mainstream media is deeply disconcerting.

CBC must strengthen its case for our – and its own – survival. @SethDKlein writes for @nationalobserver #climatechange #climatecrisis #climateemergency

Failure to connect the dots

One of the dynamics that stymies necessary and bold climate action is that, while a majority of Canadians are very worried about the climate crisis, the level of basic climate literacy in Canada is abysmal. Much of the Canadian public does not understand what actually causes climate change. When surveyed, barely over half of Canadians correctly identify the main source of global warming as the burning of fossil fuels . Hence the seemingly contradictory polling results in support of pipelines and expanded oil and gas development, even as people rank tackling climate change as a high priority. Nor do most people have clarity about what the necessary solutions are, or that these solutions exist. That needs to be urgently remedied.

As fellow columnist Chris Hatch has written : “Voters remain unclear about the real-world steps needed and the rationale behind government policies. Six in 10 Canadians either think we can expand fossil fuels and reach net zero, or aren’t sure. Heat pumps remain mysterious. Electrification and batteries, dubious. About half of Canadians aren’t sure whether solar panels emit more greenhouse gases than they end up saving.” Yikes.

In the face of such low levels of public understanding, we desperately need the CBC to lean into its role as public educator.

A year ago, the Climate Emergency Unit (CEU, with which I work) teamed up with Simon Fraser University’s Community-Engaged Research Initiative (SFU CERi) to investigate the CBC’s climate reporting. Led by Doug Hamilton-Evans and Tara Mahoney, the project saw 30 volunteers across the country spend two weeks between April 24 and May 4 carefully monitor the weekday programming of The National, The Current, The World at Six and eight CBC morning radio shows, using a questionnaire to track the climate reporting they heard. A report summarizing the findings – Quiet Alarm: A Review of the CBC’s Climate Reporting – is available here .

The report’s core finding: while the CBC’s coverage of the climate crisis has improved in recent years and is periodically superb, on the whole it remains “sporadic, inconsistent and often absent in daily programming.” CBC is doing a better job of connecting extreme weather events to climate change (in my observation, even more so since the survey period in Spring 2023). But it is decidedly failing to connect the next dot, namely, linking climate disruption to its primary cause — fossil fuels (with notable exceptions, such as this report , or this excellent Fifth Estate piece ). And the CBC is rarely providing solutions, much less providing its audience with a sense of agency about how we can help bring such solutions about.

As Quiet Alarm notes, the CBC has committed to strengthening its climate reporting and employs some journalists with a keen understanding of the crisis. We have seen the introduction of some new programs such as CBC Radio’s What On Earth , the appointment of a new international climate reporter (Susan Ormiston) and the launch of a new CBC Climate Dashboard that tracks current conditions and extreme weather events across Canada in real time and compares them to historical trends. These are all welcome developments. But Quiet Alarm suggests that the CBC has yet to meet the standards it has set for itself or climate journalism best practices (developed by international collaborations such as Covering Climate Now ) within its flagship national news and local current affairs programs.

Some of the report’s specific findings:

Only 48 per cent of the broadcasts monitored had a single climate item and of those, half were stories about extreme weather events (specifically wildfires and flooding).
Of the stories about extreme weather events, 78 per cent failed to mention that climate change makes such events more frequent and severe.
Only nine per cent of all climate items mentioned the burning of fossil fuels as the primary cause of climate change.
Only 22 per cent of the climate items clearly discussed solutions to climate change, while only six per cent of those items clearly communicated that there are things the audience could do to address the climate crisis.

The findings varied across regions and programs. Some CBC shows are doing better than others at covering climate. Among the national shows, for example, The Current does notably better than The National . And among local radio morning shows, CBC Vancouver and Victoria provide some very good regular climate programming (such as The Early Edition ’s excellent bi-weekly climate panel with Dr. Melissa Lem and Janelle Lapointe out of CBC Vancouver), while the Alberta morning shows do particularly poorly.

Among the report’s key recommendations:

1. Make the connection between the causes of the climate crisis (the burning of fossil fuels), its consequences (extreme weather events) and the communities impacted here and around the world. It would boost climate literacy if, whenever reporting on extreme weather, CBC journalists were to employ language like “climate-induced extreme weather, caused mainly by the burning of fossil fuels.” Those few simple words could go a long way to enhancing public understanding of the connections between oil and gas and the crisis we confront.

2. Develop a daily climate emergency report for flagship local and national news and current affairs shows. Ideally, a national climate emergency unit within the CBC could feed daily content to the flagship shows.

Is there enough news for a daily climate report? As anyone who subscribes to Canada’s National Observer knows well, absolutely! There is a plethora of climate news every day, stories both harrowing and hopeful across Canada and around the world, many of them breathtakingly urgent. But most of them are not reaching the CBC audience.

Along with the release of the Quiet Alarm report, the CEU and SFU CERi teamed up with science journalist Ziya Tong to model three sample episodes of what a daily climate emergency broadcast could sound like, which are available here .

As noted, CBC is producing some great climate programming. Too much of it, however, can only be found on specific shows ( The Nature of Things, What on Earth, Planet Wonder ) or when you search for it under the climate banner on the CBC news webpage, where already interested and climate-curious people proactively seek it out. The key is that daily climate reporting needs to be embedded in the CBC’s flagship shows — The National , The World at Six and local radio shows in particular.

If our CBC morning radio shows across Canada can have hourly sports and business reports, then surely the CBC can make room for a daily morning climate emergency report, telling us how this battle of our lives is unfolding at home and abroad. A report that provides a balanced mix of the bad news and the hopeful. A report that provides insight, but also a sense of agency — telling us how we can all play our part, not in a trite way, but in real and transformative ways. Let us hear much more about what other countries are doing to confront the climate crisis — many of which are acting with more determination and focus than Canada — so that our sense of what is possible may expand.

The CBC during the Second World War

As readers of this column will know, I think a lot about emergency lessons from the Second World War. Those learnings have much to offer those in the news business.

In a speech to a conference jointly hosted by The Nation and the Columbia Journalism Review in 2019 entitled “ What if we covered the climate emergency like we did World War II? ”, famed PBS journalists Bill Moyers said to an audience of mostly journalists, “Many of us have recognized that our coverage of global warming has fallen short.” He recalled how, as a child, he used to listen to the radio coverage of the war with his parents, delivered by the renowned journalist Edward R. Murrow and his CBS colleagues. Moyers recounts an amazing story about how, on the eve of the war, CBS headquarters in New York felt there had been “too much bad news” from Europe and directed Murrow and his European-based colleagues to produce a song-and-dance feature to lighten things up (what was “hot” in Europe’s capitals). Murrow, who was the CBS “man in London”, called his counterpart in Berlin to relay the instructions, and then told his colleague, “But we’re not going to do that.” Murrow and his colleagues ignored that absurd directive, and instead they were on scene to cover the Nazi invasion of Poland.

For the next two years, the reporting of Murrow and his CBS team, covering London during the Blitz and the rest of what was unfolding in Europe, dramatically shifted U.S. public opinion in favour of joining the war in support of the Allies (their compelling coverage is credited with helping to bring about a 20 percentage point shift — from majority opposition to majority support — before the bombing of Pearl Harbor). Those reporters were, says Moyers, “On the right side. At the right time. In the right way — reporting on the biggest story of all, the fight for life itself.”

And we remember Murrow not as a biased advocate or propagandist, but as among the greatest journalists of the 20th century — we have prestigious journalism awards in his name. In that 2019 speech, Moyers implored an audience of mainly young and soon-to-be journalists, “Can we get this story right? Can we tell it whole? Can we connect the dots and inspire people with the possibility of change? What’s journalism for? Really, in the war, what was journalism for, except to awaken the world to the catastrophe looming ahead of it?”

Here in Canada, in a fortuitous bit of timing, the Canadian Broadcasting Corporation was established three years before the Second World War. The CBC, then just a radio service, offered daily coverage of the war, with reporting from Europe led by senior foreign correspondent Matthew Halton. And in an era when radio and entertainment options were limited, pretty much everyone shared in the nightly experience of hearing those reports.

Depending on one’s age, the name of Canadian-born actor Lorne Greene conjures up different memories. If you are of my parents’ generation, you likely think of the old TV western Bonanza , in which Greene played Pa Cartwright. If you are a child of the 1970s like me, you may well think of the original (and very cheesy) Battlestar Galactica series, in which Greene played Commander Adama. But before Greene went off to become a Hollywood star, he was a CBC news reader and served in that role right through the war. He was widely and affectionately known then as “The Voice of Doom,” as the Canadian evening news began each night with the latest update from the war, delivered by Greene’s fabulous deep and dramatic voice.

The CBC played a vital role in mobilizing the Canadian public; making the threat real, so Canadians understood what was unfolding as a clear and present danger.

“Surely,” I have been asked, “you are not advocating that the news media become the kind of propaganda outlets we saw in the war.” No, I am not. We want our news to be factual and science-based. But, in the face of a humanitarian emergency and with the fate of civilization as we know it in the balance, I think we rightly want our media to pick a goddamn side. The side of science and of human survival. And if the CBC hadn’t done that in the war, Canadians would have been rightly appalled.

Just as Lorne Greene and Matthew Halton brought Canadians up to speed on the war effort every night during the Second World War — and as CBC did during the first year of the pandemic — today’s flagship CBC news shows like The National and The World at Six can and should do so again.

Rallying to the defense of the CBC

Given a political context in which Conservative leader Pierre Poilievre is leading mass rallies in enthusiastic chants to “Defund the CBC,” maybe the CBC should stop playing duck-and-cover and bring on the fight — motivate us to rally to its defense. If the CBC wants young people in particular to find a compelling new reason to care about the public broadcaster’s future, look no further!

As CNO’s Max Fawcett correctly wrote a few months ago, “ the CBC needs to stand and fight ... Rather than hoping for the best, the CBC’s leadership needs to prepare for the worst. That means battle-testing their own assumptions and blind spots and bracing for a political environment where their own existence will be called into question. It means presenting a coherent case for its contributions to Canadian life that acknowledges the rapidly shifting landscape and adjusts the corporation’s aims accordingly. And it means gathering as many allies as possible in order to mount a vigorous defense.” (Although Max may disagree with my specific recommendations on that score.)

The CBC’s own research and audience feedback describes “a hunger for constructive solutions” to the climate crisis and indicates that Canadians want to better understand what can be done.

A 2022 survey by Leger found “most Canadians (80%) indicated that they need more information on climate change.”

True, most young people are getting their news from social media, but the CBC alone is our public broadcaster, with a mandate to inform and enlighten. And its compelling reports and investigations can seed content for social media.

Were the CBC to introduce a daily climate emergency report, it may worry about push-back from some quarters (just as it appears very sensitive to push-back with respect to its Gaza reporting ). The CBC, like all mainstream media, has a deep-seated nervousness of appearing to engage in advocacy, even when that results in “two-side-ism” to a fault. But again, there is no virtue in “neutrality” when confronting a civilizational threat.

Much like the CBC’s dispassionate coverage of the horror and humanitarian emergency that is being inflicted upon Gaza, the broadcaster’s overly passive and inconsistent climate reporting is incongruous with the scale and scope of the catastrophe that looms.

The ubiquity of climate news and the language employed should align with the gravity of what we confront.

As all good journalists can agree, words matter. In 2019, in a welcome move, the British newspaper The Guardian announced it was updating its style guide and would no longer be using the terms “climate change” or “global warming,” swapping that language for more compelling, urgent and scientifically accurate terminology, such as “climate crisis,” “climate emergency” or “climate breakdown.” The CBC has not followed suit.

“We want to ensure that we are being scientifically precise, while also communicating clearly with readers on this very important issue,” said Guardian editor-in-chief Katherine Viner. “The phrase ‘climate change,’ for example, sounds rather passive and gentle when what scientists are talking about is a catastrophe for humanity.”

Indeed. But is that a message CBC listeners would take from the coverage they currently hear, or are they being provided with something decidedly more subdued? The tenor and irregularity of climate reporting from our public broadcaster is, at present, at odds with what climate scientists are telling us.

The frequency and tone with which we see and hear about these matters carries huge weight.

This is the fight of our lives, after all. And it is the duty of our public broadcaster, in an emergency, to make that clear.

And just maybe, if we see and hear the CBC acting in our collective defense, we will reciprocate in turn.

@SethDKlein

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Klein: "Hence the seemingly

Klein: "Hence the seemingly contradictory polling results in support of pipelines and expanded oil and gas development, even as people rank tackling climate change as a high priority."

Give some credit to Canada's petro-progressive leaders. Starting right at the top. PM Trudeau: "Buying the Trans Mountain pipeline wasn't about hoping to turn a profit for the government. It was about making sure that Alberta crude was not landlocked and was not prisoner to one single customer in the United States. "I took a lot of grief across the country for buying a pipeline. But I knew that if we want to be able to pay for the innovation, the transformation of our economy to be greener, to be cleaner, we need to get the best possible price for our oil products now, and that means getting out across the Pacific. That meant twinning the Trans Mountain pipeline. "That's why we bought the pipeline, because it was good for Alberta and it's good for the country." (21-Feb-24)

When the IPCC issued its latest report, then-Environment Minister "Wilkinson reaffirmed Canada's commitment to phasing out fossil fuels and achieving net zero carbon emissions by 2050, but said achieving that target will require money generated by fossil fuels." "Ottawa says it needs revenue generated by the Trans Mountain pipeline to fight climate change" (CBC, 9-Aug-21)

"[Liberal MP] Qualtrough says the pipeline is a 'transitionary tool' that will help fund Canada's shift to a more climate-friendly economy."

Then-Natural Resources Minister Jim Carr: "Our plan is to use this time of transition to Canada's advantage by building the infrastructure to get our resources to global markets and using the revenues to invest in clean forms of energy." (2018)

Up until the last minute before Pres. Biden cancelled Keystone XL, the Trudeau govt was still advertising that Canada's climate plan had room for new export pipelines transporting oilsands bitumen. Kirsten Hillman, Canada's ambassador to the U.S.: "Keystone XL fits within Canada's climate plan" (National Observer, 2021)

The Liberals have been hammering on that duplicitous message for years. Echoed by petro-progressive provincial NDP politicians in Alberta and B.C. No wonder progressives believe we can have our cake and eat it too. Maybe if Mark Jaccard's "climate-sincere" Liberals devoted half as much effort to promoting carbon "tax" rebates as they do to flogging pipelines, they would not be twenty points behind in the polls.

Klein: "But in the face of

Klein: "But in the face of the most profound crisis we confront — the climate emergency — we have yet to see the CBC be that public broadcaster."

The CBC is also failing in the face of the most profound humanitarian crisis we continue to enable: genocide in Gaza. As Klein alludes to later in his column. The spineless Mother Corp. is still silencing Palestinian voices and stifling stories on Palestinian loss and suffering. Still refusing to call a spade a spade. Still manipulating coverage to avoid offending the Zionist lobby. And still coming down hard on journalists seeking to give a voice to Palestinian suffering.

"CBC has whitewashed Israel's crimes in Gaza. I saw it firsthand" (The Breach, May 16 2024) https://breachmedia.ca/cbc-whitewashed-israels-crimes-gaza-firsthand/

The CBC's longstanding bias/cowardice on this file makes it hard to defend. Do we rally behind our public broadcaster and strive to improve it — or toss it on the trash heap?

I totally agree. I am so

I totally agree. I am so disappointed with CBC's reporting or lack of reporting on the Climate Crisis. I also feel annoyed that they have resorted to non- news or "fluffy" news stories The National: too many interviews with pop stars, too much sports coverage and some generally petty topics. It seems that the CBC is afraid to have more authentic coverage on Climate Change because they might offend the oil and gas companies. Is this what is going on?

Canadians need to know what is on the horizon in terms of Climate Change. If they knew the truth and understood the causes, maybe more people would be galvanized to do something about it.

And it is true that the CBC is capable of excellent informative news coverage, such as exhibited during the Pandemic.

I shudder at Poilievre's calls to defund the CBC.

This article is right on, the

This article is right on, the CBC seems to do more harm than good on climate. When the government announced EV targets, the CBC went on a bender to discredit EV's. The coverage was slanted to all the things that people didn't like about EV's, but failed to mention any of the benefits. I have no problem with giving people facts but at least cover it properly. In addition they have business friendly panelists who continually mock climate programs as unnecessary and bad for the economy. These panelists are never confronted about their position and allowed to spew partial truths or just flat out misinformation.

the relationship between

the relationship between deforestation (degradation) and climate change is not discussed. Also, the true GHG emissions from the the chain of production in the nuclear industry is deliberately understated where we are told ad nauseum it's clean and even green! Government and the media are captured by industry and greenwashing is the message. Great the National Observer is speaking out.

Something else CBC could do

Something else CBC could do is drive home the connection, to listeners/viewers, between voters saying that they 'get' climate change, then see-saw vote between 2 main culprit parties (one which essentially is still in climate change denial, and the other which makes half-assed measures to 'be on board'); who say they 'get' climate change then drive trucks and SUVs down the highway at more than 120 km per hour while squawking about the cost of gasoline; and who say they 'get' climate change yet squawk when they're told that heat pumps 'cost more money upfront', as if to say "someone else should pay for it if they want me to buy one". I could go on and on.

CBC could strive to get viewers/listeners to think about changing their attitudes on being part of the solution, not part of the problem; to pay attention to the party climate policies and candidate statements in the real media and on social media; and to think about giving a 'new kid' party on the block a reasonable length of time for a chance to govern to implement the changes we need to make. And if that new kid doesn't do it, choose another 'new kid'. What we've been doing so far in voting is failing us.

Give it a try CBC.

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The use of microbiology case studies were modified to maintain their value as tools that result in critical thinking and knowledge retention while providing a more realistic context for preparing future health care professionals. Consequently, the text has real life, personally-oriented microbiology cases appropriate for those in nursing ...
Case Studies in Microbiology : A Personal Approach
The use of microbiology case studies were modified to maintain their value as tools that result in critical thinking and knowledge retention while providing a more realistic context for preparing future health care professionals. Consequently, the text has real life, personally-oriented microbiology cases appropriate for those in nursing ...
Case Studies in Microbiology: A Personal Approach
The use of microbiology case studies were modified to maintain their value as tools that result in critical thinking and knowledge retention while providing a more realistic context for preparing future health care professionals. Consequently, the text has real life, personally-oriented microbiology cases appropriate for those in nursing ...
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One of the dynamics that stymies necessary and bold climate action is that, while a majority of Canadians are very worried about the climate crisis, the level of basic climate literacy in Canada is abysmal. Much of the Canadian public does not understand what actually causes climate change. When surveyed, barely over half of Canadians correctly identify the main source of global warming as the ...

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Cases in Medical Microbiology and Infectious Diseases, Fourth Edition

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Case Studies: Microbiology

Microbiology Case Study: A 15 Year Old Male with Endocarditis

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13: Putting it all Together—Case Studies in Microbiology

COVID-19 Response Case Studies

Understanding how antiviral antibodies can activate natural killer cells to improve viral control

Water-Based Epidemiology – finding a needle in a very dirty haystack

The epidemiology of COVID-19 in Ugandan settlements and neighbouring communities

Locking down labs and setting up COVID-19 testing facilities

Basic microbiology knowledge is crucial to the understanding of new emerging diseases

Searching for SARS-CoV-2 in animals

Using baker’s yeast to develop reference viral antigens of SAR-CoV-2

Rapid development of an international radiographers online training resource

Evaluating the utility of SARS-CoV-2 serological and rapid antigen lateral flow devices

From PhD student to COVID-19 testing scientist

Saliva versus nasopharyngeal swab specimens for the detection of SARS-CoV-2

The interface between microbiology and civil engineering

The challenge of balancing an academic career with clinical practice

Understanding the determinants of resistance to type I interferons and IFITMs in HIV-1 envelope and SARS-CoV-2 spike

Exploring how the cell surface SARS-CoV-2 nucleocapsid protein modulates innate and adaptive immunity

The COVID-19 Genomics UK Consortium and genomic surveillance

A mid-career virologist’s pandemic!

Reverse genetics to characterise SARS-CoV-2

The study of the airborne longevity of SARS-CoV-2

The challenges of being a virologist during the COVID-19 pandemic

Stepping into public outreach during the COVID-19 pandemic

Screening antiviral drugs against the envelope (E) protein of SARS-CoV-2

Using genomic surveillance to identify waves of SARS-CoV-2 infections in Zimbabwe

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Clinical Case Study

Normal Flora According to Body System

Pathogenic Organisms According to Body System

ORIGINAL RESEARCH article

1 Introduction

2 Materials

3.1 Machine learning based classification

3.1.1 Random forest classifier

3.1.2 EXtreme gradient boosting classifier

3.1.3 Multi-layer perceptron classifier

3.2 Evaluation metrics

3.3 Explainable artificial intelligence methods

4.1 Machine learning analysis

5 Discussion

6 Conclusion

Data availability statement

Author contributions

Acknowledgments

Conflict of interest

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Actinomycetes are a natural resource for sustainable pest control and safeguarding agriculture

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Role of Secondary Metabolites of Actinomycetes in Crop Protection

Exploring the potential of endophytes and their metabolites for bio-control activity

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