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Understanding Clinical Trials

Clinical research: what is it.

Your doctor may have said that you are eligible for a clinical trial, or you may have seen an ad for a clinical research study. What is clinical research, and is it right for you?

Clinical research is the comprehensive study of the safety and effectiveness of the most promising advances in patient care. Clinical research is different than laboratory research. It involves people who volunteer to help us better understand medicine and health. Lab research generally does not involve people — although it helps us learn which new ideas may help people.

Every drug, device, tool, diagnostic test, technique and technology used in medicine today was once tested in volunteers who took part in clinical research studies.

At Johns Hopkins Medicine, we believe that clinical research is key to improve care for people in our community and around the world. Once you understand more about clinical research, you may appreciate why it’s important to participate — for yourself and the community.

What Are the Types of Clinical Research?

There are two main kinds of clinical research:

Observational Studies

Observational studies are studies that aim to identify and analyze patterns in medical data or in biological samples, such as tissue or blood provided by study participants.

Clinical Trials

Clinical trials, which are also called interventional studies, test the safety and effectiveness of medical interventions — such as medications, procedures and tools — in living people.

Clinical research studies need people of every age, health status, race, gender, ethnicity and cultural background to participate. This will increase the chances that scientists and clinicians will develop treatments and procedures that are likely to be safe and work well in all people. Potential volunteers are carefully screened to ensure that they meet all of the requirements for any study before they begin. Most of the reasons people are not included in studies is because of concerns about safety.

Both healthy people and those with diagnosed medical conditions can take part in clinical research. Participation is always completely voluntary, and participants can leave a study at any time for any reason.

“The only way medical advancements can be made is if people volunteer to participate in clinical research. The research participant is just as necessary as the researcher in this partnership to advance health care.” Liz Martinez, Johns Hopkins Medicine Research Participant Advocate

Types of Research Studies

Within the two main kinds of clinical research, there are many types of studies. They vary based on the study goals, participants and other factors.

Biospecimen studies

Healthy volunteer studies.

Clinical trials study the safety and effectiveness of interventions and procedures on people’s health. Interventions may include medications, radiation, foods or behaviors, such as exercise. Usually, the treatments in clinical trials are studied in a laboratory and sometimes in animals before they are studied in humans. The goal of clinical trials is to find new and better ways of preventing, diagnosing and treating disease. They are used to test:

Drugs or medicines

New types of surgery

Medical devices

New ways of using current treatments

New ways of changing health behaviors

New ways to improve quality of life for sick patients

 Goals of Clinical Trials

Because every clinical trial is designed to answer one or more medical questions, different trials have different goals. Those goals include:

Treatment trials

Prevention trials, screening trials, phases of a clinical trial.

In general, a new drug needs to go through a series of four types of clinical trials. This helps researchers show that the medication is safe and effective. As a study moves through each phase, researchers learn more about a medication, including its risks and benefits.

Is the medication safe and what is the right dose?   Phase one trials involve small numbers of participants, often normal volunteers.

Does the new medication work and what are the side effects?   Phase two trials test the treatment or procedure on a larger number of participants. These participants usually have the condition or disease that the treatment is intended to remedy.

Is the new medication more effective than existing treatments?  Phase three trials have even more people enrolled. Some may get a placebo (a substance that has no medical effect) or an already approved treatment, so that the new medication can be compared to that treatment.

Is the new medication effective and safe over the long term?   Phase four happens after the treatment or procedure has been approved. Information about patients who are receiving the treatment is gathered and studied to see if any new information is seen when given to a large number of patients.

“Johns Hopkins has a comprehensive system overseeing research that is audited by the FDA and the Association for Accreditation of Human Research Protection Programs to make certain all research participants voluntarily agreed to join a study and their safety was maximized.” Gail Daumit, M.D., M.H.S., Vice Dean for Clinical Investigation, Johns Hopkins University School of Medicine

Is It Safe to Participate in Clinical Research?

There are several steps in place to protect volunteers who take part in clinical research studies. Clinical Research is regulated by the federal government. In addition, the institutional review board (IRB) and Human Subjects Research Protection Program at each study location have many safeguards built in to each study to protect the safety and privacy of participants.

Clinical researchers are required by law to follow the safety rules outlined by each study's protocol. A protocol is a detailed plan of what researchers will do in during the study.

In the U.S., every study site's IRB — which is made up of both medical experts and members of the general public — must approve all clinical research. IRB members also review plans for all clinical studies. And, they make sure that research participants are protected from as much risk as possible.

Earning Your Trust

This was not always the case. Many people of color are wary of joining clinical research because of previous poor treatment of underrepresented minorities throughout the U.S. This includes medical research performed on enslaved people without their consent, or not giving treatment to Black men who participated in the Tuskegee Study of Untreated Syphilis in the Negro Male. Since the 1970s, numerous regulations have been in place to protect the rights of study participants.

Many clinical research studies are also supervised by a data and safety monitoring committee. This is a group made up of experts in the area being studied. These biomedical professionals regularly monitor clinical studies as they progress. If they discover or suspect any problems with a study, they immediately stop the trial. In addition, Johns Hopkins Medicine’s Research Participant Advocacy Group focuses on improving the experience of people who participate in clinical research.

Clinical research participants with concerns about anything related to the study they are taking part in should contact Johns Hopkins Medicine’s IRB or our Research Participant Advocacy Group .

Learn More About Clinical Research at Johns Hopkins Medicine

For information about clinical trial opportunities at Johns Hopkins Medicine, visit our trials site.

Video Clinical Research for a Healthier Tomorrow: A Family Shares Their Story

Clinical Research for a Healthier Tomorrow: A Family Shares Their Story

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Tufts School of Medicine researchers enjoy open, flexible, modern laboratory spaces in the 165,000 square foot Jaharis Center. Located at 150 Harrison Avenue on our Health Science Campus, this facility is home to over fifty different laboratories focused on basic and translational approaches to important disease problems.

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Clinical Research vs Lab Research: An In-depth Analysis

Clinical research , a cornerstone in advancing patient care, involves human subjects to test the safety and effectiveness of new treatments , ranging from drugs to diagnostic tools. Unlike clinical research , laboratory research focuses on the foundational science behind medicine without direct human involvement , contributing significantly to medical lab science.

The contrast between clinical research vs lab research highlights the diverse approaches in the scientific pursuit of better healthcare, where every medical advancement once relied on volunteer participation in clinical studies 1 . Bridging these two fields promises to accelerate the translation of lab discoveries into practical medical applications, underscoring the importance of collaboration in future developments in medical lab science 1 2 .

The Evolution of Clinical Research

The evolution of clinical research traces its origins back to ancient times, with the world's first recorded clinical tria l found in the "Book of Daniel" where a dietary intervention was observed to improve health after 10 days. This historical milestone was followed by significant advancements including Avicenna's rules for drug testing in his ‘ Canon of Medicine’ and Ambroise Pare's accidental trial in 1537 , which introduced a novel therapy for wounded soldiers. The modern era of clinical trials was marked by James Lind's controlled trial on scurvy in 1747 , laying the foundational principles for contemporary clinical research methodologies. The progression from these early experiments to the structured, ethical, and scientifically rigorous trials of today highlights the dynamic nature of clinical research. This evolution was further shaped by the introduction of the placebo in the early 1800s and the establishment of ethical frameworks , starting with the Hippocratic Oath and later formalized by the Nuremberg Code in 1947 . The development of clinical research has been instrumental in advancing medical science, with each phase of clinical trials meticulously designed to ensure the safety and efficacy of new treatments for the benefit of patient care.

Key Components of Laboratory Research

Clinical Research Facility Sciences, pivotal in the realm of medical lab science, leverage laboratory data and services extensively for disease diagnosis, monitoring, and treatment 2 4 . These sciences are underpinned by professionals who, after obtaining a Bachelor's degree in fields such as clinical research facility science or biomedical sciences from NAACLS-accredited programs, perform crucial laboratory tests, analyze specimens, and furnish healthcare providers with critical insights into the results' significance and validity 2 . Notably, these activities are conducted in laboratory settings without involving human subjects, emphasizing the distinction between clinical and laboratory research 2 .

The infrastructure of laboratories is meticulously designed to support the complex and sensitive nature of laboratory tests and analyses. This includes sturdy tables and ample counter space for heavy equipment, overhead and adjustable shelving for efficient space utilization, and cabinets and drawers for organized storage. Additionally, the deployment of fume hoods, customized for specific research needs, is essential for the safe handling of chemicals. Compliance with safety regulations and proper storage of flammable items underscore the operational standards necessary for high-quality testing and analysis in medical breakthroughs 6 .

The scientific process in laboratory research unfolds through several key steps: hypothesis formulation, experiment design, data collection, data analysis, and report writing. This structured approach begins with formulating a tentative explanation for a phenomenon, followed by planning and conducting experiments using appropriate methods and tools. The subsequent collection and analysis of data facilitate testing the hypothesis, culminating in the documentation of the entire process and findings in a formal report or paper 7 . This systematic methodology underscores the rigorous and methodical nature of laboratory research, contributing significantly to advancements in medical lab science.

Bridging the Gap: Collaboration between Clinical and Laboratory Research

Bridging the gap between clinical and laboratory research involves fostering collaborative environments that leverage the strengths of both fields to advance medical science. Medical scientific studies bifurcate into clinical laboratory scientists, who interpret critical data for healthcare professionals, and clinical researchers, who lay the groundwork for medical education and understanding 4 . This collaboration is pivotal for both building the future of medicine and administering its current benefits 4 . Enhanced operational efficiency is achieved through cross-departmental synergy, reducing redundancies in resource and personnel utilization, and fostering faster adoption of best practices and innovations across the lab 8 . These collaborations are exemplified by real-world success stories from renowned institutions like Mayo Clinic and Stanford Health Care, which have demonstrated the profound impact of integrated efforts on medical advancements 8 .

Key strategies for effective collaboration include regular meetings to address challenges, the integration of digital communication platforms with lab databases for swift sharing of results, and the establishment of clear guidelines for consistency in sample collection and result dissemination 8 . Unified objectives ensure that despite methodological differences, the end goals of improving patient care and advancing medical knowledge remain aligned 8 . Furthermore, the adoption of cloud-based data systems and AI technologies not only facilitates seamless data sharing but also automates routine tasks, thereby enhancing productivity and enabling the discovery of new insights 9 .

Challenges such as competition, ethics reviews, insufficient research funds, and the recruitment of project managers underscore the complexities of collaborative efforts 9 . However, the benefits, including improved reputation, publication quality, knowledge transfer, and acceleration of the research process, often outweigh the costs and risks associated with collaboration 9 . Collaborative relationships in Translational Medical Research (TMR) among clinicians highlight a strong willingness to collaborate, with preferences varying across different stages of research and between preferring independent and interdependent relationships 9 . This willingness to collaborate is crucial for bridging the gap between clinical and laboratory research, ultimately leading to groundbreaking advancements in medical science.

Future Trends in Clinical and Laboratory Research

The future of clinical and laboratory research is poised for transformative changes, driven by technological advancements and evolving healthcare needs. Notably:

Greater Efficiency through Automation : The integration of automation in research processes promises to streamline workflows, reducing manual labor and enhancing precision 13 .

Collaboration and Capacity Sharing : Partnerships between research institutions will facilitate shared resources and expertise, optimizing research outputs 13 .

Remote Sample Support and Diagnostic Data Interoperability : These advancements will enable more inclusive research and improved patient care by allowing data to flow seamlessly between different healthcare systems 13 .

Artificial Intelligence and Machine Learning : AI and machine learning are set to revolutionize both clinical and laboratory research by providing advanced data analysis, predictive modeling, and personalized medicine approaches 13 14 .

Staffing Solutions and Digital Workflows : Addressing staffing shortages through innovative solutions, alongside the adoption of digital workflows, will be crucial for maintaining research momentum 14 .

New Diagnostic Technologies : The development of novel diagnostic methods and technologies, including next-generation sequencing and biomarker-based screenings, will enhance disease diagnosis and treatment 14 .

Regulatory Changes and Patient-Centric Approaches : Increased FDA oversight of laboratory-developed tests and a shift towards patient-centric research models will ensure safer and more effective healthcare solutions 14 16 .

Precision Medicine and Big Data Analytics : The focus on precision medicine, supported by real-world evidence and big data analytics, will tailor treatments to individual patient needs, improving outcomes 15 .

Decentralized Clinical Trials and Digital Health Technologies : The rise of decentralized trials and digital health tools, including remote monitoring, will make research more accessible and patient-friendly 15 .

Innovation in Testing and Consumer Health : Laboratories will explore new frontiers in diagnostics, such as multi-drug-of-abuse testing and T-cell testing, while also responding to consumer health trends with at-home testing services 14 18 .

These trends underscore a dynamic shift towards more efficient, patient-centered, and technologically advanced clinical and laboratory research, setting the stage for groundbreaking discoveries and innovations in healthcare 13 14 15 16 18 .

Through this detailed exploration, we have seen the distinct yet intertwined roles that clinical and laboratory research play in the advancement of medical science and patient care. By comparing their methodologies, evolution, and collaborative potential, it becomes clear that both domains are crucial for fostering innovations that can bridge the gap between theoretical knowledge and practical healthcare solutions. The synergy between clinical and laboratory research, as highlighted by various examples and future trend predictions, establishes an essential framework for the continual improvement of medical practices and patient outcomes.

As we look toward the future, the significance of embracing technological advancements, enhancing collaboration, and adopting patient-centric approaches cannot be overstressed. These elements are pivotal in navigating the challenges and leveraging the opportunities within clinical and laboratory research landscapes. The potential impacts of such advancements on the field of medicine and on societal health as a whole are immense, underscoring the imperative for ongoing research, dialogue, and innovation in bridging the gap between the laboratory bench and the patient's bedside.

What distinguishes clinical research from laboratory research? Clinical research involves studies that include human participants, aiming to understand health and illness and answer medical questions. Laboratory research, on the other hand, takes place in environments such as chemistry or biology labs, typically at colleges or medical schools, and does not involve human subjects. Instead, it focuses on experiments conducted on non-human samples or models.

How does a clinical laboratory differ from a research laboratory? Clinical laboratories are specialized facilities where laboratory information and services are utilized to diagnose, monitor, and treat diseases. Research laboratories, in contrast, are settings where scientific investigation is conducted to study illness and health in humans to answer medical and behavioral questions.

In what ways do clinical research and scientific research differ? Clinical research is a branch of medical research that directly applies knowledge to improve patient care, often through the study of human subjects. Scientific research, including basic science research, aims to understand the mechanisms of diseases and biological processes, which may not have immediate applications in patient care.

Can you outline the various types of medical research analysis? Medical research can be categorized into three primary types based on the study's nature: basic (experimental) research, clinical research, and epidemiological research. Clinical and epidemiological research can be further divided into interventional studies, which actively involve treating or intervening in the study subjects, and noninterventional studies, which observe outcomes without intervention.

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Nih clinical research trials and you, finding a clinical trial, around the nation and worldwide.

NIH conducts clinical research trials for many diseases and conditions, including cancer , Alzheimer’s disease , allergy and infectious diseases , and neurological disorders . To search for other diseases and conditions, you can visit ClinicalTrials.gov.

ClinicalTrials.gov [ How to Use Search ] This is a searchable registry and results database of federally and privately supported clinical trials conducted in the United States and around the world. ClinicalTrials.gov gives you information about a trial's purpose, who may participate, locations, and phone numbers for more details. This information should be used in conjunction with advice from health care professionals.

Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read the disclaimer on ClinicalTrials.gov for details.

Before participating in a study, talk to your health care provider and learn about the risks and potential benefits.

At the NIH Clinical Center in Bethesda, Maryland

Search NIH Clinical Research Studies The NIH maintains an online database of clinical research studies taking place at its Clinical Center, which is located on the NIH campus in Bethesda, Maryland. Studies are conducted by most of the institutes and centers across the NIH. The Clinical Center hosts a wide range of studies from rare diseases to chronic health conditions, as well as studies for healthy volunteers. Visitors can search by diagnosis, sign, symptom or other key words.

Join a National Registry of Research Volunteers

ResearchMatch This is an NIH-funded initiative to connect 1) people who are trying to find research studies, and 2) researchers seeking people to participate in their studies. It is a free, secure registry to make it easier for the public to volunteer and to become involved in clinical research studies that contribute to improved health in the future.

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Faculty Research Labs

In Dell Medical School’s research environment — which spans the campuses of Dell Med, the Dell Pediatric Research Institute, The University of Texas at Austin and other locations in the city — faculty using similar techniques and equipment work near each other and share expertise from diverse fields.

This fosters transdisciplinary discourse and operational efficiencies that bolster the school’s research output. Researchers and lab members benefit from opportunities to gather with colleagues in related areas of expertise to connect on their work. The result of this model? A strong team, focused on transforming health.

Areas of Research

Explore a listing of research lab websites managed by Dell Med-affiliated investigators. Faculty lab sites provide details on active work, opportunities to collaborate, openings and people working within them.

Not all research faculty members have lab sites or are represented on this page. To learn more about research taking place at Dell Med, view the latest  news and discoveries , or explore individual departments, centers and institutes .

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RTP-RESEARCH FELLOW - The Medical Microrobots Lab

• Scottsdale, AZ

Not ready to apply? Join our talent community

The Medical Microrobots Lab at the Mayo Clinic Arizona Campus is seeking a highly motivated Postdoctoral Fellow to join our innovative team of scientists, engineers, and medical doctors. 

We are passionate about tiny, soft-bodied robots and unleashing their disruptive potential, pushing the boundaries of targeted therapies and minimally invasive procedures. Our lab is uniquely positioned to develop translational microrobots, using human tissues, small and large animal disease models as primary testbeds. 

This position is initially offered for 1 year. The funding is available for up to 3 years after satisfactorily meeting the academic performance expectations. 

Key Responsibilities:

• Develop and test micro/milli/soft robotic medical devices
• Contribute to developing testbeds for microrobot validation using in vitro phantoms, and animal models (rats and rabbits)
• Create and refine research ideas, projects and study protocols
• Participate in weekly research meetings, journal clubs and mentoring activities
• Report to and visit our research partners located across Rochester and Jacksonville Campuses 
• Collaborate with institutional bodies to ensure compliance with safety regulations and efficient use of research labs and shared spaces
• Represent our lab by presenting high-impact research at the leading international conferences.

Training and Career Development: Dr. Hakan Ceylan (PI) is committed to supporting the career development of the fellow. Individual career development plans will be tailored to include training in advanced career skills, such as 

• Project management
• Grant writing
• Team leadership
• Manuscript preparation and peer review
• Networking skills toward academic and professional independence

A Research Fellow position in Life Sciences (LS) will require knowledge of either clinical-based research or laboratory-based research often obtained from a postdoctoral program. A Research Fellow at Mayo Clinic is a temporary position intended to provide training and education in research. Individuals will train in the research program of a Mayo Clinic principal investigator. Qualified individuals will demonstrate the potential for research as evidenced by their training and peer-reviewed publications and should become competitive for national research grants. Proof of English proficiency is required for J-1 Short-Term Scholars, Research Scholars, Professors, Specialists, and Student Interns sponsored by Mayo Clinic.

Excellent communication and teamwork skills.

Ph.D. in a relevant field, such as materials science, biomaterials, drug delivery

Strong background with a demonstrable track record in at least one of these skills: in vitro pharmacokinetic testing, primary cell culture, controlled drug release systems

Experience with rat, rabbit, or pig animal models is a big advantage. Otherwise, willingness to learn and perform experiments with animals.

“Can-do” attitude, a strong willingness to build up a career in soft robots, microrobots, medical device development

Demonstrated ability to conduct high-impact research and present findings at conferences.

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COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

Clinical Research Coordinator

• Columbia University Medical Center
• Opening on: Jun 15 2024
• Job Type: Officer of Administration
• Bargaining Unit:
• Regular/Temporary: Regular
• End Date if Temporary:
• Hours Per Week: 35
• Standard Work Schedule:
• Salary Range: $70,000- $80,000

Position Summary

Reporting to the Principal Investigator of the Ultrasound Elasticity Imaging laboratory, the Clinical Research Coordinator will act as a cardiovascular sonographer and coordinator of patient-related research. As part of the sonography responsibilities, the candidate will work with our team of researchers to acquire high-quality transthoracic cardiac and vascular images in human subjects and large animal studies. Responsibilities also include participation in clinical study monitoring, data maintenance, and recruitment and consenting of subjects. The ideal candidate will possess excellent scanning abilities, strong anatomical knowledge. Organization, time management, and the ability to prioritize the collection of sonographic data for a variety of research projects is required, as well as strong interpersonal skills to recruit and consent research subjects. The candidate will have diverse responsibilities in the laboratory imaging and electronic health record (EHR) human subjects data collection, data organization, quality assessment and reporting, computer-based database management (Epic, Excel, etc.). Additional duties will relate to operations of the laboratory including ordering and management of equipment and supplies, assist with IRB protocol submissions, website updates and assistance with preparation of publications and grant applications. The Clinical Research Coordinator will work closely with the PI, as well as with other faculty, staff members and trainees within the department.

Responsibilities

• Perform 2D and 3D sonography on research human subjects and large animals (canine/swine);
• Assist the PI in the preparation of clinical study and large animal study planning;
• HS recruitment, pre-consent, and informed consent prior to scanning;
• Safely handle study information and informed consent forms;
• Maintains and organizes imaging and EHR human subjects data collection;
• Performs quality assessment and reporting for the laboratory;
• Conducts computer-based database management (Epic, Excel, etc.);
• Assists the PI with the preparation of publications and grant applications.
• Assists with the preparation of institutional review board (IRB) forms, material transfer agreements (MTAs), data use agreements (DUA) and maintenance of study documentation;
• Works closely with PI to coordinate clinical trials associated with MRI and/or other imaging;
• Maintains and organizes study documentation and records. Compiles data and assists in consolidating /analyzing data for presentation;
• Collaborates with investigators and the research team on the development of new research projects, databases and protocols or new department /service initiatives;
• Serves as the contact person for all investigators and research coordinators within radiology and from other departments who are interested in utilizing radiology imaging data for clinical research purposes. This includes proactively addressing any potential issues, facilitating database enhancements, and overseeing/managing special requests;
• Performs additional duties related to the operations of the laboratory including ordering and management of equipment and supplies, and website updates.
• Perform other duties as assigned.

Minimum Qualifications

• Bachelor’s degree or equivalent in education, training, and experience.

Preferred Qualifications

• Graduate of an accredited diagnostic medical ultrasound program in Sonography
• Registered Diagnostic Cardiac Sonographer (RDCS) from ARDMS or
• Registered Cardiac Sonographer (RCS) from CCI

Other Requirements

• Comfort working with computer-based applications is required.
• Ability to take initiative and work independently is required.
• Experience with ultrasound and related imaging modalities
• Excellent communication, interpersonal, and organizational skills are a must.
• Working knowledge of MS Office (Word, Excel) and database is needed.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

Commitment to Diversity 

Columbia university is dedicated to increasing diversity in its workforce, its student body, and its educational programs. achieving continued academic excellence and creating a vibrant university community require nothing less. in fulfilling its mission to advance diversity at the university, columbia seeks to hire, retain, and promote exceptionally talented individuals from diverse backgrounds.  , share this job.

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• Clin Infect Dis

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)–Specific T Cells and Antibodies in Coronavirus Disease 2019 (COVID-19) Protection: A Prospective Study  

Ivan a molodtsov.

Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia

Evgenii Kegeles

Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia

Alexander N Mitin

National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia

Olga Mityaeva

Oksana e musatova.

Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia

Anna E Panova

National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia

Mikhail V Pashenkov

Iuliia o peshkova.

National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Almaqdad Alsalloum

Walaa asaad, anna s budikhina, alexander s deryabin, inna v dolzhikova.

Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, Moscow, Russia

Ioanna N Filimonova

Alexandra n gracheva, oxana i ivanova.

A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia

Anastasia Kizilova

Viktoria v komogorova, anastasia komova.

Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, Russia

Natalia I Kompantseva

Ekaterina kucheryavykh.

Government of Moscow, Moscow, Russia

Denis А Lagutkin

Yakov a lomakin, alexandra v maleeva, elena v maryukhnich, afraa mohammad, vladimir v murugin, nina e murugina, anna navoikova, margarita f nikonova, leyla a ovchinnikova, yana panarina, natalia v pinegina, daria m potashnikova, elizaveta v romanova, aleena a saidova, anastasia g samoilova, yana serdyuk, naina t shakirova, nina i sharova, saveliy a sheetikov, anastasia f shemetova, liudmila v shevkova, alexander v shpektor, anna trufanova, anna v tvorogova, valeria m ukrainskaya, anatoliy s vinokurov, daria a vorobyeva, ksenia v zornikova, grigory a efimov, musa r khaitov.

Pirogov Russian National Research Medical University, Moscow, Russia

Ilya A Kofiadi

Alexey a komissarov, denis y logunov, nelli b naigovzina, yury p rubtsov, irina a vasilyeva, pavel volchkov, elena vasilieva, associated data.

In a prospective study involving 5340 individuals, humoral and cellular responses revealed magnitude-dependent protection from COVID-19. Antibodies alone significantly decreased infection rates; isolated cellular response provided an intermediate level of protection. The lowest COVID-19 incidence was in the double-positive group.

During the ongoing coronavirus disease 2019 (COVID-19) pandemic, many individuals were infected with and have cleared the virus, developing virus-specific antibodies and effector/memory T cells. An important unanswered question is what levels of T-cell and antibody responses are sufficient to protect from the infection.

In 5340 Moscow residents, we evaluated anti–severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin M (IgM)/immunoglobulin G (IgG) titers and frequencies of the T cells specific to the membrane, nucleocapsid, and spike proteins of SARS-CoV-2, using interferon gamma (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) assay. Additionally, we evaluated the fractions of virus-specific CD4 + and CD8 + T cells using intracellular staining of IFN-γ and interleukin 2 followed by flow cytometry. We analyzed the COVID-19 rates as a function of the assessed antibody and T-cell responses, using the Kaplan–Meier estimator method, for up to 300 days postinclusion.

We showed that T-cell and antibody responses are closely interconnected and are commonly induced concurrently. Magnitudes of both responses inversely correlated with infection probability. Individuals positive for both responses demonstrated the highest levels of protectivity against the SARS-CoV-2 infection. A comparable level of protection was found in individuals with antibody response only, whereas the T-cell response by itself granted only intermediate protection.

Conclusions

We found that the contribution of the virus-specific antibodies to protection against SARS-CoV-2 infection is more pronounced than that of the T cells. The data on the virus-specific IgG titers may be instructive for making decisions in personalized healthcare and public anti–COVID-19 policies.

Clinical Trials Registration.   {"type":"clinical-trial","attrs":{"text":"NCT04898140","term_id":"NCT04898140"}} NCT04898140 .

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as a causative agent of a new coronavirus disease 2019 (COVID-19). Individuals who have cleared the virus or who have been vaccinated develop an adaptive immune response including virus-specific T cells and antibodies [ 1–3 ], which have been shown to protect from reinfection [ 4–8 ]. However, the antibody and T-cell response levels vary considerably from person to person and substantially decrease over time [ 9 , 10 ]. These facts raise an important question: What levels of T-cell response and immunoglobulin G (IgG) titers are sufficient to protect from the infection? The definitive answer requires a population-level study of the immune response to SARS-CoV-2 followed by the tracing of infection rates.

Here, we report on a prospective study based on evaluation of the virus-specific immunoglobulin levels and virus-specific T cells in a cohort of 5340 Moscow residents. Specifically, we evaluated the anti-SARS-CoV-2 immunoglobulin M (IgM)/IgG titers and the frequencies of the T cells specific to membrane (M), nucleocapsid (N), and spike (S) proteins of SARS-CoV-2, using interferon gamma (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) assay. Furthermore, we assessed the fractions of the virus-specific IFN-γ– and interleukin 2 (IL-2)–producing CD4 + and CD8 + T cells using flow cytometry. Finally, we monitored the participants for up to 300 days and analyzed the postinclusion COVID-19 rates as a function of the antibody and T-cell response levels.

This study was approved by the Moscow City Ethics Committee and performed according to the Helsinki Declaration. All participants provided written informed consent. The study was registered at ClinicalTrials.gov (identifier: {"type":"clinical-trial","attrs":{"text":"NCT04898140","term_id":"NCT04898140"}} NCT04898140 ). Individuals enrolled in the study were Moscow residents >18 years old who voluntarily visited Moscow city clinics for routine testing for COVID-19 antibodies and agreed to participate. No specific inclusion or exclusion criteria were applied. The Moscow State COVID-19 registry was used to extract information about participants’ vaccination status and previous polymerase chain reaction (PCR)–confirmed COVID-19.

Peripheral blood was collected into two 8-mL Vacutainer Cell Preparation Tube tubes with sodium citrate (BD). Peripheral blood mononuclear cells (PBMCs) were isolated according to the manufacturer’s protocol within 2 hours after venipuncture (for details, see Supplementary Material 1 ). For serum isolation, peripheral blood was collected into S-Monovette 7.5-mL Z tubes (Sarstedt, Germany).

SARS-CoV-2–specific antibodies were evaluated using an automated CL-series chemiluminescent immunoassay analyzer with compatible reagent kits (Mindray, China). The assay detects an integrated pool of antibodies specific to full-length N protein, as well as receptor-binding-domain fragment of the S protein (see Supplementary material ). According to the manufacturer, the assay units can be converted into the World Health Organization standard binding antibody units/mL by dividing by 1.32 (for details, see Supplementary Material 1 ). Virus-neutralizing activity of plasma was analyzed with a microneutralization assay using a SARS-CoV-2 strain (hCoV-19/Russia/Moscow_PMVL-1/2020) in a 96-well plate and a 50% tissue culture infective dose of 100 as described in [ 6 ], with plasma dilutions of 10, 20, 40, 80, 160, 320, 640, and 1280 times.

Flow cytometry was performed on freshly isolated PBMCs stimulated with a mixture of SARS-CoV-2 PepTivator S, S1, S+, N, and M peptide pools (1 μg/mL each, Miltenyi Biotec, Germany). After 14–16 hours of stimulation, cells were stained with a panel of antibodies against surface markers and cytokines and then analyzed with flow cytometry ( Supplementary Figure 1 ). Data were analyzed using FlowJo software (BD Biosciences) (for details, see Supplementary Material 1 ). IFN-γ ELISpot assay was performed on freshly isolated PBMCs using the IFN-γ Single-Color ELISPOT kit (CTL). For each donor, 5 wells were tested without replicates: a negative control well without stimulation, a positive control well nonspecifically stimulated with 10 µg/mL phytohemagglutinin, and 3 experimental wells stimulated with PepTivator peptide pools covering the M, N, or S protein of SARS-CoV-2. Spots were visualized and counted using an automated spot counter CTL ImmunoSpot Analyzer and ImmunoSpot software (CTL) (for details, see Supplementary Material 1 ). It should be noted that there were no replicates in our ELISpot protocol; undoubtedly, it represents a limitation of the study. However, we believe that the large number of samples analyzed allowed us to mitigate the variability of the method.

Statistical analysis was performed with the Python3 programming language with numpy , scipy , pandas , and lifelines packages (for details, see Supplementary Material 1 ). Serology positivity thresholds were set according to the assay manufacturer’s instructions at 10 AU/mL for IgG and 1 cutoff index for IgM, respectively. For IFN-γ ELISpot and flow cytometry assays, positivity criteria were developed individually (see Supplementary Material 2 ).

Cohort Characteristics

In total, 5340 individuals from the Moscow general population were included in the study. All of them were tested for SARS-CoV-2–specific IgG/IgM titers, while virus-specific T cells in peripheral blood were estimated for 156 participants using ELISpot, for 1640 participants using flow cytometry, and for 1629 participants using both assays ( Figure 1 ​ A A ). The difference in these numbers resulted from cases in which the peripheral blood amount was not enough to perform both T-cell assays, or from which samples were excluded from the analysis because of failed controls. Cohort recruitment lasted from October to December 2020; the age and sex distribution of the participants is presented in Figure 1 ​ B B . Accordingly, 854 participants (17%) with previously PCR-confirmed COVID-19 infection were included, 81 (2%) COVID-19 cases were diagnosed at the time of inclusion, and 496 (10%) COVID-19 cases were registered postinclusion ( Figure 1 ​ C C ). The postinclusion observation continued until the end of August 2021; the distribution of all COVID-19 cases in time is presented in Figure 1 ​ D D . The cohort recruitment took place before the onset of the public vaccination program in Moscow. However, among enrolled participants, there were 175 individuals who had participated in the Sputnik V vaccine clinical trial and thus had received either vaccine or placebo.

Study overview and experimental cohort description. ( A ), Schematic study design. We tested volunteers for severe acute respiratory syndrome coronavirus 2–specific antibodies (blue circle) and virus-specific T cells using interferon-γ enzyme-linked immunosorbent spot (ELISpot) assay (pink circle) and flow cytometry with intracellular staining (green circle) (Figure was created using Biorender.com). ( B ), Age and sex distribution of volunteers included in the study. ( C ), Coronavirus disease 2019 (COVID-19) status of volunteers included in the study according to the Moscow State COVID-19 registry provided by the Moscow Department of Healthcare. ( D ), COVID-19 cases among study participants per week from April 2020 to August 2021.

Correlation Between Antibody and T-Cell Responses

At the time of inclusion, 1382 (26%) individuals were positive for SARS-CoV-2–specific IgM and 2455 (46%) for IgG ( Figure 2 ​ A A and ​ and2 2 ​ B B and Supplementary Figure 2 ). By analyzing a subgroup of 854 participants with confirmed previous COVID-19, we found that IgM titers considerably decreased 60 days post–disease onset ( Figure 2 ​ C C ), whereas IgG titers stayed relatively high and unaltered up to 270 days post–disease onset ( Figure 2 ​ D D ). Among 180 randomly selected individuals, we detected a strong correlation between the virus-neutralizing activity (VNA) of plasma and integrated IgG titers, as well as S and N protein–specific antibodies ( Supplementary Material 3 and Supplementary Figure 3 ).

Evaluation of coronavirus disease 2019 (COVID-19)–specific antibody immunity. ( A ), Percentages of patients positive for virus-specific immunoglobulin M (IgM) and immunoglobulin G (IgG). ( B ), Venn diagram showing the number of participants positive for severe acute respiratory syndrome coronavirus 2–specific IgG (green), IgM (red), and both antibody types (orange). ( C ) and ( D ), Time dependence of the IgM and IgG levels among a subgroup of 854 nonvaccinated participants who had previous polymerase chain reaction–confirmed coronavirus disease 2019 (COVID-19). Each dot represents a single patient. Time is counted from the date of disease onset according to the Moscow State COVID-19 registry to the day of inclusion in the study. Time interval presented in each boxplot is 30 days. Abbreviation: COI, cutoff index.

We analyzed the frequencies of the T cells specific to the M, N, and S proteins of SARS-CoV-2 in peripheral blood, using the IFN-γ ELISpot assay; we also analyzed the frequencies of IL-2– and IFN-γ–producing virus-specific CD4 + and CD8 + T cells, with flow cytometry. For this purpose, we used a stimulation protocol described elsewhere [ 2 , 11 , 12 ]. Both ELISpot and flow cytometry assays showed that approximately half of the individuals analyzed had a T-cell response against SARS-CoV-2 antigens, which was consistent with the level of the specific antibodies in the cohort ( Figure 3 ​ A A and ​ and3 3 ​ B B ). Overall, 1145 (64.1%) individuals had SARS-CoV-2–specific T-cell responses to at least 1 of the SARS-CoV-2 proteins (M, N, or S), including 692 (38.8%) with T-cell responses to all 3 proteins ( Figure 3 ​ C C ). Flow cytometry revealed that 2217 (67.8%) participants had SARS-CoV-2–specific CD4 + T cells expressing IL-2, IFN-γ, or both cytokines, with 1095 (33.5%) participants having all 3 cell populations ( Figure 3 ​ D D ). All of the metrics of T-cell immunity appeared to be relatively stable up to 270 days after disease onset ( Figure 3 ​ E E and ​ and3 3 ​ F F and Supplementary Figure 4 ).

Evaluation of coronavirus disease 2019 (COVID-19)–specific T-cell immunity. Freshly isolated peripheral blood mononuclear cells (PBMCs) were stimulated with peptide pools covering severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins, and cytokine responses were assessed with enzyme-linked immunosorbent spot (ELISpot) assay or flow cytometry. The percentages of patients exceeding the positivity threshold for M, N, and S proteins in the ELISpot assay ( A ) or exceeding the percentage of cells expressing both interleukin 2 (IL-2) and interferon gamma (IFN-γ), or either of these cytokines, in the flow cytometry assay ( B ) are shown. Venn diagrams showing relation in positivity between different SARS-CoV-2 proteins in the ELISpot assay ( C ) or between expression of different cytokines in response to activation with SARS-CoV-2 proteins in the flow cytometry assay ( D ). The time dependence of the spot-forming units (SFU) per 10 6 PBMC for S protein in the ELISpot assay is shown in ( E ) and that of the fraction of CD4 + T cells expressing IL-2 out of total CD4 + cells in the flow cytometry assay is shown in ( F ). Each dot represents a single participant. Time is counted from the date of disease onset according to the Moscow State COVID-19 registry to the day of inclusion in the study, and thus serology testing. Time interval presented in each boxplot is 30 days. The dashed line represents a positivity threshold for ELISpot. For flow cytometry, the positivity threshold was variable (see Supplementary Material 2 ).

We observed a strong correlation between the frequencies of SARS-CoV-2–specific T cells detected with ELISpot and those detected with flow cytometry; also, a strong correlation between IgG titers and T-cell frequencies was determined ( Supplementary Figure 5 ). This correlation was found in the cases of M, N, and S protein–specific T cells, as well as for different populations of CD4 + T cells.

Protectivity of Different Immune Responses Against SARS-CoV-2 Infection

To evaluate the effects of antibody and T-cell responses on susceptibility to SARS-CoV-2 infection, we analyzed the postinclusion COVID-19 rates as functions of the assessed parameters. To avoid possible bias, we excluded from the analysis 175 individuals who had participated in the Sputnik V clinical trial and 81 individuals who were already infected at the moment of blood collection. Vaccinated participants were withdrawn from the study on the day of vaccination. Since we have subjects who were excluded from the dataset during observation and have >2 groups in all comparisons, we employed the nonparametric Kaplan–Meier estimator method for initial exploration and the Cox proportional hazards (CPH) model for further quantitative assessment of observed effects. Accordingly, among the 3989 participants who were eligible for the postinclusion observation, 420 postinclusion COVID-19 cases were registered. For each of the immune parameters, participants were divided by the quantiles depending on the levels of their responses, and corresponding Kaplan-Meier curves for each quantile were analyzed and CPH models were built ( Supplementary Material 4 ).

For all the immune response metrics, we found an inverse correlation with the SARS-CoV-2 infection rates. Thus, at the end of the observation individuals with IgG titers <0.29 AU/mL (quantile [Q] 1) were characterized by a 22% chance of becoming infected ( Figure 4 ​ A A ). For individuals in Q2 and Q3 (IgG titers 0.29–0.97 and 0.97–8.23 AU/mL, respectively), age-adjusted log-hazard ratios (HRs) compared with Q1 were significantly below zero: −0.3 (95% confidence interval [CI], −.5 to −.03) and −0.33 (95% CI, −.6 to −.1), respectively. Individuals representing Q4 and Q5 (IgG titers: 8.23–66.5 AU/mL and >66.5 AU/mL, respectively) had the lowest infection chances: log(HRs), −1.5 (95% CI, −1.9 to −1.2) and −2.4 (95% CI, −3 to −2), respectively. We found that Q4 and Q5, which demonstrated the highest protection, were at the same time characterized by the highest VNA ( Supplementary Figure 3 B ). Surprisingly, Q3, with infection chances in the intermediate range, also had VNA significantly higher than Q1, demonstrating an absence of protectivity. There was no difference in VNA between Q1 and Q2.

Evaluation of the effects of antibody and T-cell immune responses on coronavirus disease 2019 (COVID-19) infection rates. The patients were split into 5 nearly equal groups by quantiles of immunoglobulin G (IgG) levels ( A , top) or by S protein–specific spot-forming units estimated from enzyme-linked immunosorbent spot (ELISpot) assay ( B , top) from quartile (Q) 1 to Q5. Additionally, participants were split into 4 groups ( C , top): positive only by antibodies (A + T − ), positive only by S protein–specific T cells estimated from ELISpot (A − T + ), double-positive (A + T + ), and double-negative (A − T − ). Corresponding Kaplan–Meier curves were generated for each group, and COVID-19 rates were analyzed. A – C (bottom), Age-adjusted Cox proportional hazard models were fitted (with age measured in decades for ease of representation) and hazard ratios in comparison with either Q1 or the A − T − group were calculated together with the model concordance index ( c -index). ✢, decades were used as units for age measurements. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. Abbreviations: CI, confidence interval; HR, hazard ratio; IgG, immunoglobulin G; PBMC, peripheral blood mononuclear cells; Q, quartile; SFU, spot-forming units.

An almost binary relationship was observed between infection chances and the frequencies of virus-specific T cells identified by ELISpot ( Figure 4 ​ B B and Supplementary Figure 6 A–C ). For all of the SARS-CoV-2 proteins analyzed, individuals in Q4 and Q5 were characterized by the highest levels of protection against the infection, whereas Q1–Q3 were similar and demonstrated no considerable protection. For example, the maximal protection was achieved when the number of S protein–specific spot-forming units per 10 6 PBMCs exceeded 67. In contrast to ELISpot, the results of the T-cell response analysis using flow cytometry revealed a gradual relationship between the frequencies of T cells producing IFN-γ, IL-2, or both cytokines, and infection chances ( Supplementary Figure 6 D–H ). However, when CD4 + T cells expressing different cytokines were combined, the relationship with infection chances transformed into a binary one.

A similar strategy was employed to separate the effects of cellular and antibody responses on protection against SARS-CoV-2 infection. The participants were split into 4 groups: positive only by antibody response (A + T − ), positive only by any metric of the T-cell response (A − T + ), double-positive (A + T + ), and double-negative (A – T – ), according to the previously estimated positivity criteria ( Supplementary Material 2 ). Such analysis was performed for all metrics of the T-cell response, except for CD8 + T cells since it was impossible to develop a reliable positivity criterion. The group size depended on the T-cell immune response metric used; for example, for S protein–specific T cells ( Figure 4 ​ C C ), the numbers of participants at the start of observation were 446, 113, 147, and 609 for A + T + , A + T − , A − T + , and A − T − , respectively. For all of the T-cell response metrics, the A − T − group had the highest infection chances, while the strongest protection was observed for A + T + and A + T − groups, the latter two groups being statistically indistinguishable ( Figure 4 ​ C C and Supplementary Figure 7 ). For example, for S protein–specific T cells, age-adjusted log-HRs for all other groups compared with the A − T − group were significantly above zero: for A − T + : log(HR), −0.7 (95% CI, −1.3 to −.1); for A + T − : log(HR), −1.8 (95% CI, −2.8 to −.8); and for A + T + : log(HR), −2.6 (95% CI, −3.3 to −1.9). For all studied metrics, the A − T + group demonstrated an intermediate protection that was significantly higher than in the A − T − group, but was lower than in the A + T + and A + T − groups. In particular, the protection provided by the T cells in the absence of antibodies was observed when the response was estimated from the numbers of N and S protein–specific T cells with ELISpot ( Figure 4 ​ C C and Supplementary Figure 7 A–C ). The trend for increased protection was observed for the CD4 + T cells producing IFN-γ, IL-2, both cytokines, and, especially, these populations combined ( Supplementary Figure 7 D–G ). It is noteworthy that individuals single-positive for N- and S protein–specific T cells, as well as for virus-specific CD4 + T cells, were characterized by higher IgG levels than individuals of the A – T – group, although the antibody levels were below the positivity cutoff value of 10 AU/mL ( Supplementary Figure 8 ).

With the progression of the COVID-19 epidemic, a growing number of individuals develop immune responses against SARS-CoV-2. Prospective studies in humans [ 13–15 ] and studies using primate models with SARS-CoV-2 rechallenge [ 16–18 ] have demonstrated that an acquired post–COVID-19 immune response provides protection from reinfection. The goal of our study was to evaluate what metrics of the antibody and T-cell immune responses against SARS-CoV-2 correlate with protection against infection in humans in the context of the COVID-19 epidemic in Moscow between October 2020 and August 2021.

As expected, we found a strong correlation between frequencies of SARS-CoV-2–specific T cells evaluated with ELISpot and with flow cytometry, since these methods detect cytokine expression in activated T cells [ 19 ]. IgG titers strongly correlated with the frequencies of SARS-CoV-2–specific T cells, confirming that antibody and cellular responses are closely interconnected and induced concurrently. This correlation existed even at IgG values below the seropositivity cutoff.

From April 2021 in Russia, the B.1.1 lineage of SARS-CoV-2 predominated [ 20 , 21 ], while from April to August the vast majority of SARS-CoV-2 variants detected belonged to the B.1.617 (Delta and derivatives) lineage [ 22 ]. However, we found that IgG titers and parameters of the T-cell response negatively correlated with infection probabilities regardless of the predominant virus variant. T-cell response was characterized by a binary relationship between response level and infection probabilities, as measured with ELISpot. This means that for all individuals with a frequency of SARS-CoV-2–specific T cells surpassing a particular threshold, protection against SARS-CoV-2 infection was the same. A different pattern was observed for IgG titers. We identified 3 groups of individuals characterized by different infection chances. Individuals with very low IgG titers were characterized by the highest infection chances, while high titers were associated with the lowest infection chances. Meanwhile, infection chances for individuals with intermediate IgG titers were also intermediate, notwithstanding the fact that these titers were below the seropositivity cutoff. Moreover, we found significant VNA among these individuals. Given the strong correlation between antibody and T-cell responses found in the study, the protection observed in these individuals might be T-cell dependent. We surmise that this group may consist of individuals who developed a COVID-19–specific response after previous asymptomatic infection [ 23 , 24 ], or after infection with either cross-reactive “common cold” coronaviruses [ 11 ] or other pathogens [ 25 , 26 ]. The low-level humoral response could nevertheless be indicative of successful formation of memory B cells, as it is known that SARS-CoV-2 induces the formation of durable B-cell memory [ 27–29 ].

Depending on estimated T-cell and antibody responses, we split the participants into 4 groups and analyzed the protection against the SARS-CoV-2 infection. Two groups were characterized by the highest protection: individuals positive for both types of responses and those with antibody response only. Apparently, these groups contain individuals with previous COVID-19 that had not been confirmed by PCR for some reasons. It is noteworthy that even though the reinfection rates in these groups were very small, few cases were still detected. Individuals with T-cell response alone demonstrated intermediate protection levels that nevertheless were higher than levels in individuals without either type of immunity. Statistically significant protection was observed for N and S protein–specific T-cell responses. Individuals single-positive for these cellular response metrics had higher IgG titers than individuals without either type of immunity, although the titers were below the positivity cutoff. Taken together, our results demonstrated that antibodies better correlated with protection against the SARS-CoV-2 infection, indicating that IgG evaluation is a more precise method for prediction of infection chances than virus-specific T cells. However, the most important role of T cells might be not in protection from the infection but rather in viral clearance and managing disease severity [ 30–35 ]. Moreover, rhesus macaque models [ 17 , 36 ] and recent human studies [ 37 , 38 ] have supported that T-cell protection becomes important as neutralizing antibodies decline.

Our study has several limitations. The cohort analyzed is likely to be nonrepresentative and includes only individuals who have visited outpatient clinics for COVID-19 antibody tests and who agreed to participate in the study. Some cases of COVID-19 infections, especially asymptomatic, were inevitably missed as they were not reported to the Moscow State COVID-19 registry, though we do not expect any nonrandom distribution of unreported cases between different groups. Additionally, our study was focused on the systemic immune responses detected in peripheral blood, while local concentrations of antibodies and tissue-resident T cells in the mucosa and respiratory system may differ from blood levels; this issue deserves thorough investigation.

In summary, our data suggest that serological testing is advantageous for the prediction of protection against SARS-CoV-2 infection. Our data on the specific IgG titers may be instructive for making decisions in personalized healthcare and for development of public anti–COVID-19 policies.

Supplementary Data

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Author contributions. G. A. E., I. A. K., A. A. K., M. R. K., D. Y. L., N. B. N., Y. P. R., A. V. S., I. A. V., P. V., and E. V. designed and coordinated the study. I. A. M. performed the statistical analysis and, together with E. K., A. N. M., O. M., O. E. M., A. E. P., M. V. P., and I. O. P., performed initial data interpretation. A. A., W. A., A. S. B., A. S. D., I. V. D., I. N. F., A. N. G., O. I. I., A. K., V. V. K., A. K., N. I. K., D. A. L., Y. A. L., A. V. M., E. V. M., A. M., V. V. M., N. E. M., A. N., M. F. N., L. A. O., N. V. P., D. M. P., E. V. R., A. A. S., N. S., A. G. S., Y. S., N. T. S., N. I. S., S. A. S., A. F. S., L. S., A. T., A. V. T., V. M. U., A. S. V., D. A. V., and K. V. Z. performed the experiments. E. K. and Y. P. provided the information from the Moscow State COVID-19 registry. Every author contributed to the initial draft of the manuscript and agreed on submission for publication. All authors reviewed the manuscript and approved the final version.

Acknowledgments. The authors thank Dr Leonid Margolis (Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland) and Professor Michael M. Lederman (Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, Ohio) for their valuable comments and suggestions during experimental design, discussion of the results, and manuscript preparation; and Dr Barry Alpher for assistance in editing and improving the English style of the manuscript. The authors also thank the Moscow Department of Healthcare for help in organization of the study.

Financial support. This study was supported by Moscow Department of Healthcare (DC number DZM-30-3, payments made to institution). T-cell response analysis using ELISpot was supported in part by the Ministry of Science and Higher Education of the Russian Federation (agreement number 075-15-2020-899, payments made to institution). T-cell response analysis using flow cytometry was supported in part by an A. I. Yevdokimov Moscow State University of Medicine and Dentistry grant (agreement number KNP-06/21, purchase and supply of study materials).

Supplementary Material

Ciac278_supplementary_data, contributor information.

Ivan A Molodtsov, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia.

Evgenii Kegeles, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Alexander N Mitin, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Olga Mityaeva, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Oksana E Musatova, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Anna E Panova, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Mikhail V Pashenkov, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Iuliia O Peshkova, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Almaqdad Alsalloum, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Walaa Asaad, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Anna S Budikhina, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Alexander S Deryabin, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Inna V Dolzhikova, Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, Moscow, Russia.

Ioanna N Filimonova, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Alexandra N Gracheva, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Oxana I Ivanova, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Anastasia Kizilova, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Viktoria V Komogorova, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Anastasia Komova, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia. Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, Russia.

Natalia I Kompantseva, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Ekaterina Kucheryavykh, Government of Moscow, Moscow, Russia.

Denis А Lagutkin, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Yakov A Lomakin, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Alexandra V Maleeva, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Elena V Maryukhnich, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Afraa Mohammad, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Vladimir V Murugin, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Nina E Murugina, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Anna Navoikova, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Margarita F Nikonova, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Leyla A Ovchinnikova, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Yana Panarina, Government of Moscow, Moscow, Russia.

Natalia V Pinegina, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Daria M Potashnikova, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Elizaveta V Romanova, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia.

Aleena A Saidova, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia.

Nawar Sakr, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Anastasia G Samoilova, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Yana Serdyuk, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Naina T Shakirova, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Nina I Sharova, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia.

Saveliy A Sheetikov, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Anastasia F Shemetova, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Liudmila V Shevkova, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia. Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, Russia.

Alexander V Shpektor, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Anna Trufanova, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.

Anna V Tvorogova, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia.

Valeria M Ukrainskaya, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Anatoliy S Vinokurov, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Daria A Vorobyeva, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Ksenia V Zornikova, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Grigory A Efimov, National Medical Research Center of Hematology, Moscow, Russian Federation (Russia)

Musa R Khaitov, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia. Pirogov Russian National Research Medical University, Moscow, Russia.

Ilya A Kofiadi, National Research Center–Institute of Immunology Federal Medical-Biological Agency of Russia, Moscow, Russia. Pirogov Russian National Research Medical University, Moscow, Russia.

Alexey A Komissarov, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Denis Y Logunov, Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, Moscow, Russia.

Nelli B Naigovzina, A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Yury P Rubtsov, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Irina A Vasilyeva, National Medical Research Center for Phthisiopulmonology and Infectious Diseases of the Ministry of Health of the Russian Federation, Moscow, Russia.

Pavel Volchkov, Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia. Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, Russia.

Elena Vasilieva, Clinical City Hospital named after I. V. Davydovsky, Moscow Department of Healthcare, Moscow, Russia. A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia.

Renovations approved to improve safety and efficiency of classrooms, research laboratories

The Indiana University Board of Trustees approved one architectural design and several facilities projects during its June meeting in Bloomington, including:

• The architectural designs for the IU Indianapolis Science Laboratory expansion and renovation project.
• Renovations to the IU School of Medicine’s VanNuys Medical Science Building Laboratory on the Indianapolis campus.
• Renovation and rehabilitation to chemistry teaching labs on the IU Bloomington campus.
• Replacement, upgrades and expansion of cooling capacity and distribution on the IU Bloomington campus.
• The 2025 Repair and Rehabilitation Plan for all IU campuses.
• 2025 Regional Campus Deferred Maintenance projects at five regional campuses.

IU Indianapolis Science Laboratory Building

The architectural designs for the IU Indianapolis Science Laboratory Building will introduce a cutting-edge, multidisciplinary research facility spanning 52,000 square feet, strategically positioned at the intersection of North Blackford and West New York streets in the IU Indianapolis Science and Technology Corridor.

The architecture of the new building is designed to complement the existing Science and Engineering Laboratory Building at IU Indianapolis and address the pressing need for enhanced research infrastructure to accommodate the campus’s growth.

The trustees approved this $60 million expansion and renovation project at their June 15, 2023, meeting and the project was also approved for state funding during the 2023 Indiana General Assembly as part of IU’s 2023-25 Capital Appropriation Request.

IU School of Medicine VanNuys Medical Science Building Laboratory

This project will renovate academic spaces across areas of the first, second and third floors of the North Wing of the IU School of Medicine’s VanNuys Medical Science Building at IU Indianapolis to convert existing office and classroom space into new research laboratories.

The anticipated opening of IU’s new Medical Education and Research Building provides opportunities to renovate existing office and classroom space in the VanNuys building to meet IU’s research laboratory goals. These new units in the VanNuys building will allow the university to explore how common research equipment can be efficiently leveraged by placing researchers with similar interests or approaches in close proximity.

In addition to academic space, renovations will include interior structural, mechanical and infrastructure updates and installation of new laboratory utilities, HVAC, plumbing, fire protection, power, data and finishes as required for new laboratory layouts. In all, the project will cover a total area of 28,099 square feet.

Chemistry teaching labs at IU Bloomington

This project will renovate eight teaching laboratories and related support areas in the Chemistry Building on the IU Bloomington campus, as well as replace four air handling units that serve these laboratories and related areas in the building.

Upgrades to more than 31,000 square feet — including more than 19,000 square feet of academic space — will be made in support of IU Bloomington Provost Rahul Shrivastav’s Project Inspire, a multiyear effort to systematically improve and update instructional spaces on the Bloomington campus.

The chemistry lab project will reconfigure and modernize 1980s-era teaching laboratories to improve safety, sightlines and accessibility, enabling flexible and collaborative instruction. Nearly 100 new fume hoods — which make use of high-efficiency, low-flow technology for significant energy savings, as well as improved laboratory safety — will be installed as well. The project will replace the building’s central heating, ventilation and air conditioning systems.

Work will be completed in two phases, allowing for uninterrupted instructional delivery during the construction process.

Cooling capacity and distribution at IU Bloomington

Phase one of a two-phase project will replace two aging chillers at the Main Chilled Water Plant and add one additional chiller in support of growing energy management and utility distribution needs on the Bloomington campus.

Since 2015, more than 2.1 million gross square feet of space has been added to the central chilled water system. This increase, especially during periods of extreme or prolonged periods of heat, has significantly increased cooling demand. New higher-capacity replacement chillers will operate more efficiently while largely making use of existing plant infrastructure.

In addition to the new chillers, this project will connect the standalone Union Street plant to the Forest Quad plant, adding Union Street to the central system and expanding reliability and diversity of cooling capacity campus-wide. The new chiller equipment as well as the physical connection of the Union Street plant will add a net increase of more than 3,200 tons of additional capacity — a 16% increase — to IU Bloomington’s central chilled water loop.

2025 Repair and Rehabilitation Plan

Funded by state appropriation and student fees, the 2025 Repair and Rehabilitation Plan includes repairs or replacement of roofing; windows; elevators; electrical, fire protection, mechanical and plumbing systems; steam, utilities, and electricity distribution systems; and classroom and site improvements at IU Bloomington, IU Indianapolis and all regional campuses.

2025 Regional Campus Deferred Maintenance

The Regional Campus Deferred Maintenance projects impact 14 buildings on five campuses and will provide safe, effective and efficient learning and work environments for students, faculty and staff through repairs and renovations of facilities and infrastructure.

Renovations will include replacing or updating building heating systems, mechanical systems and controls, and will continue to address work begun in Phases I-V of the Regional Campus Deferred Maintenance requests. This project was previously funded by the Indiana General Assembly in the 2023-2025 State Budget and was part of Indiana University’s 2023-25 Capital Appropriation Request.

Marah Yankey

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