Antimicrobial resistance poses one of the gravest threats to modern medicine, undermining decades of progress in treating infectious diseases. Hospitals, where vulnerable patients congregate and antibiotics are frequently administered, are particularly fertile grounds for the emergence and spread of resistant microbes. Asymptomatic carriers—individuals harboring resistant organisms without showing symptoms—represent a silent but significant vector in this dissemination. Identifying these carriers has proven elusive due to the limitations of routine microbiological screening and the complex dynamics within healthcare settings.

The multidisciplinary team employed an integrative framework combining whole-genome sequencing (WGS) of bacterial isolates with detailed microbiological profiling and comprehensive data on patient movements within hospital wards. This triangulated strategy enables the reconstruction of transmission networks with unprecedented precision. Genomic data reveal the relatedness of microbial strains, microbiology provides context on resistance mechanisms, and patient mobility elucidates potential contact pathways facilitating spread. Together, these elements forge a comprehensive portrait of ARO dissemination.

A central highlight of the study is the use of advanced bioinformatics algorithms to infer asymptomatic carriage events. Traditional detection relies heavily on symptomatic testing, often missing carriers who are undiagnosed yet contagious. By integrating patient movement trajectories with high-resolution genomic data, the researchers could infer probable transmission nodes where asymptomatic carriers likely contributed. This represents a paradigm shift, moving from reactive to proactive infection control by targeting hidden reservoirs of resistance.

The research focuses specifically on common hospital-acquired pathogens known for their resistance, including carbapenem-resistant Enterobacterales (CRE) and methicillin-resistant Staphylococcus aureus (MRSA). These organisms are notorious for causing outbreaks that complicate patient outcomes and inflate healthcare costs. The study’s methodology allowed for tracing the microevolution of these pathogens in situ, capturing single nucleotide variations that mark transmission events. Such granularity empowers infection control teams to deploy targeted interventions with surgical precision.

Patient mobility data synthesis emerged as a cornerstone of the approach. Modern hospitals generate massive amounts of electronic health record (EHR) data detailing admissions, transfers, and room assignments. By harnessing this rich data trove, the team mapped contact networks within wards and units that traditional epidemiology overlooks. This dynamic mapping could pinpoint critical times and locations where ARO transmission risk peaks, opening new avenues for intervention tailored to hospital logistics.

Moreover, the researchers emphasized the synergy between microbiological testing and genomic insights. Standard culture methods provide phenotype-level information about resistance but fall short in resolving transmission pathways. Genomic sequencing bridges this gap by offering a molecular fingerprint of isolates, which, when combined with phenotype data, clarifies clonal expansions and horizontal gene transfer events. The integration of both datasets elevates outbreak investigations from descriptive to mechanistic understanding.

Implementing this integrative framework in real-world hospital settings proved feasible and yields immediate public health benefits. Beyond identifying asymptomatic carriers, it informed changes in infection prevention protocols, such as adjustments in patient cohorting, environmental cleaning schedules, and targeted screening expansions. Early adoption in pilot hospitals showed significant reductions in secondary cases, underscoring the system’s potential as a frontline defense against AMR propagation.

The study also addressed challenges inherent to data privacy and ethical considerations. Patient movement and genomic data are sensitive, requiring stringent safeguards to protect confidentiality. The authors advocate for robust de-identification protocols and transparent ethical oversight as essential components of deploying such systems at scale. Additionally, they call for interdisciplinary collaboration involving clinicians, microbiologists, data scientists, and hospital administrators to translate analytic findings into actionable hospital policies.

In the broader context of global health, this research exemplifies precision epidemiology—a field that leverages advanced technologies to tailor interventions at the individual and community levels. As antimicrobial resistance accelerates worldwide, scalable tools that detect and disrupt transmission hold enormous promise. Integrating pathogen genomics with behavioral data like patient movements represents a promising frontier, fostering predictive and preventive medicine within healthcare ecosystems.

The implications extend beyond hospitals, as the framework could adapt to other institutional settings such as long-term care facilities and nursing homes, where asymptomatic carriage also fuels spread. Adapting methodologies to resource-limited settings may pose challenges, but the scalable nature of genomic sequencing and digital health records points toward broad applicability. Collaborative global initiatives could harness this approach to build real-time AMR surveillance networks, transforming how societies respond to microbial threats.

Further research is anticipated to refine computational models, incorporating machine learning techniques to enhance predictive accuracy and automate flagging of high-risk carriers and zones. Integrating environmental sampling, such as from surfaces and medical devices, might yield a more comprehensive ecosystem view of resistance dynamics. The study offers a clarion call for sustained investment in antimicrobial resistance research, emphasizing innovation at the intersection of biology, informatics, and healthcare delivery.

This pioneering work by Pei and colleagues sets a new standard for infection control surveillance, transforming invisibility into insight. By illuminating the hidden carriers and pathways of antimicrobial resistance within hospitals, it equips healthcare providers with the knowledge needed to outmaneuver one of medicine’s most tenacious adversaries. As hospitals worldwide grapple with the escalating burden of resistant infections, this integrative genomic and mobility-driven approach offers a beacon of hope, signaling a future where silent spreaders are unmasked and stopped before outbreaks ignite.

The study’s success hinges on the confluence of multiple technological advancements—next-generation sequencing platforms, comprehensive electronic health record systems, and sophisticated computational pipelines—that together enable real-time, actionable insights. This infrastructure, while currently concentrated in high-resource settings, is rapidly becoming more accessible, setting the stage for wider adoption. The potential public health impact is profound, transforming how hospitals monitor, respond to, and ultimately prevent the spread of antimicrobial resistance.

As the fight against AMR intensifies, harnessing multifaceted data streams to infer otherwise invisible transmission events marks a watershed moment. It reflects a shift from passive detection to anticipatory control, empowering hospitals to stay one step ahead in the ongoing microbial arms race. The integration of genomics, microbiology, and patient mobility data heralds a new era of precision infection control—one poised to save lives, protect healthcare resources, and safeguard the efficacy of lifesaving antibiotics for generations to come.

Subject of Research: Inferring asymptomatic carriers of antimicrobial-resistant organisms in hospital settings through integrated genomic, microbiological, and patient mobility data.

Article Title: Inferring asymptomatic carriers of antimicrobial-resistant organisms in hospitals using genomic, microbiological and patient mobility data.

Article References:
Pei, S., Seeram, D., Blumberg, S. et al. Inferring asymptomatic carriers of antimicrobial-resistant organisms in hospitals using genomic, microbiological and patient mobility data. Nat Commun 16, 10140 (2025). https://doi.org/10.1038/s41467-025-65241-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41467-025-65241-w

Electronic health records have long been viewed as a treasure trove of data containing rich patient histories, diagnostics, medications, lab results, and clinical notes. However, their sheer volume and heterogeneity have posed significant barriers to extracting meaningful clinical insights. Traditional approaches to processing EHRs often fall short due to the nonlinear, multifaceted correlations underlying patient health trajectories. The creators of InfEHR recognized the necessity for a sophisticated computational framework capable of modeling these complexities. Their solution capitalizes on emerging developments in geometric deep learning, a subset of machine learning designed to operate on data structured as graphs, manifolds, or other non-Euclidean domains.

The core innovation behind InfEHR lies in its capacity to represent EHR data as geometric entities embedded within high-dimensional spaces, enabling the capture of nuanced relationships that conventional vector-based models overlook. In this framework, each patient’s clinical data is conceptualized as a manifold—a mathematical space that locally resembles Euclidean space but can exhibit intricate global structure—and the algorithm explores changes in this manifold to identify latent phenotypic patterns. This geometric interpretation enables the model to discern complex hierarchies and temporal dynamics inherent in disease progression, fostering a more holistic understanding of patient conditions.

Importantly, the InfEHR approach transcends simple classification tasks. It provides a resolution of clinical phenotypes, differentiating subtle variations within disease entities that frequently manifest overlapping symptoms or comorbidities. This capability is critical in areas like autoimmune diseases, neurodegenerative disorders, and multifactorial chronic conditions, where patients may present heterogeneous clinical signatures that defy binary categorization. By parsing these latent subphenotypes wrapped within noisy and irregular EHR data, the model aids clinicians and researchers in defining patient subsets with shared pathophysiological traits, enhancing targeted therapeutic decision-making.

The researchers validated InfEHR on diverse, real-world datasets encompassing millions of patient records from multiple healthcare systems, demonstrating the model’s robustness and scalability. Their experimental results highlighted superior performance in phenotype resolution compared to existing state-of-the-art machine learning methods, including classical deep learning architectures and ensemble models. Not only did InfEHR improve diagnostic accuracy, but it also unveiled previously unrecognized disease trajectories, underscoring the untapped potential of geometric representations in clinical data science.

One of the most captivating aspects of InfEHR is its dynamic interpretation of time-series data embedded in EHRs. Clinical phenomena evolve non-linearly, with patient states shifting according to multifactorial influences like treatment interventions, environmental exposures, and genetic predispositions. The geometric deep learning model integrates temporal information to model patient health evolution as trajectories along complex manifolds, offering a synthesized view that better captures disease onset, remission, and relapse patterns. This temporal manifold learning marks a conceptual advancement in medical AI, bridging the gap between static snapshot analyses and true longitudinal understanding.

The implementation of InfEHR comprises several sophisticated components, including graph neural networks designed to encode heterogeneous clinical entities and their interactions, geometric convolutional filters to extract meaningful features on non-Euclidean domains, and manifold regularization techniques to enforce smoothness constraints for interpretability. By skillfully orchestrating these elements, the framework preserves the structural integrity of the data while enhancing signal extraction in the presence of noise and missingness—a perennial challenge in EHR analytics.

Moreover, InfEHR exhibits impressive versatility across clinical contexts, functioning effectively in domains ranging from oncology to cardiology. Its ability to adaptively learn latent phenotypic embeddings tailored to distinct disease domains speaks to its generalizability and broad applicability. Such wide-ranging utility holds promise for accelerating research in complex disorders where phenotype definitions are currently ambiguous or evolving, potentially catalyzing new discoveries and improved predictive biomarkers.

The development process behind InfEHR was remarkably collaborative, involving computational scientists, clinicians, and biostatisticians who co-designed the algorithms while ensuring clinical relevance and rigor. This synergy between domain experts helped navigate the challenges of aligning computational outputs with biomedical interpretability, an essential criterion for translational impact. The research team also emphasized transparency, providing accessible code bases and documentation to encourage reproducibility and adoption across medical research institutions.

Ethical considerations associated with applying AI to sensitive health data were integral to the InfEHR project. The team implemented privacy-preserving protocols and rigorous data governance frameworks to maintain patient confidentiality throughout model training and deployment. Additionally, efforts were made to mitigate biases inherent in health records, such as those arising from demographic imbalances or socioeconomic factors, by incorporating fairness-enhancing techniques within the learning process.

Looking forward, the potential implications of InfEHR extend far beyond academic inquiry. The technology could empower healthcare providers with actionable insights during clinical workflows, enabling more precise patient stratification and risk prediction in real time. Integrating InfEHR into electronic health systems may enhance early detection capabilities, optimize resource allocation, and facilitate personalized interventions that improve patient outcomes while reducing costs.

The advent of InfEHR aligns seamlessly with broader aspirations to leverage artificial intelligence for healthcare’s grand challenges. Its fusion of advanced geometric learning with complex clinical data heralds a new paradigm in phenotype resolution that surpasses traditional methodologies. As healthcare systems worldwide increasingly digitize and generate vast troves of information, the ability to decode this data’s latent structures will be paramount to unlocking new frontiers in disease understanding and treatment.

While the research remains cutting-edge, future extensions of InfEHR may incorporate multimodal data sources beyond EHRs, such as genomics, imaging, and wearable sensor readings, to construct even richer patient representations. Combining these diverse modalities within a unified geometric learning framework could offer unparalleled insight into multifactorial diseases and personalized health trajectories. Such integrative models would further push the boundaries of precision medicine into revolutionary territories.

In summary, InfEHR marks a significant milestone in medical AI innovations, demonstrating how deep geometric learning techniques can surmount longstanding barriers in electronic health record analysis. By elevating clinical phenotype resolution to a new level of detail and accuracy, this approach reshapes the way diseases are characterized and managed, holding tremendous promise for the future of personalized healthcare. The research exemplifies the transformative impact of interdisciplinary collaboration in applying state-of-the-art AI tools to solve pressing biomedical challenges.

The publication of this work in a high-profile, peer-reviewed journal underscores its scientific rigor and importance, inviting the broader community to explore, validate, and extend the findings. As interest in AI-enabled clinical applications continues to surge, InfEHR stands out as a pioneering exemplar of how sophisticated mathematical frameworks can unlock hidden value within the complex tapestry of healthcare data, ultimately delivering meaningful benefits to patients and practitioners alike.

The vision articulated by the creators of InfEHR is one where technology and medicine converge more deeply, enabling earlier, more accurate diagnoses and personalized, effective treatments. This vision harnesses the power of geometry—not only as a mathematical abstraction but as a practical tool in disentangling the intricate web of clinical phenotypes encoded in patient records. As the healthcare industry embraces this cutting-edge approach, it takes a decisive step towards realizing a future of truly data-driven, precision medicine.

Subject of Research: Clinical phenotype resolution through advanced deep geometric learning applied to electronic health records (EHRs).

Article Title: InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.

Article References:
Kauffman, J., Holmes, E., Vaid, A. et al. InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records. Nat Commun 16, 8475 (2025). https://doi.org/10.1038/s41467-025-63366-6

Image Credits: AI Generated

The genesis of this innovation lies in the remarkable natural features of gastric mucus, which protects the stomach lining by forming a viscous, adherent barrier impervious to the corrosive gastric acids. By mimicking the molecular architecture and functional properties of natural mucus, Wang’s team tailored a synthetic hydrogel capable of adhering robustly to gastrointestinal tissues while resisting acid-mediated degradation far beyond what current clinical protectants achieve. Published in the esteemed journal Cell Reports Physical Science, their findings demonstrate that UMIH not only withstands the acidic milieu of the stomach but actively promotes tissue regeneration in animal models, surpassing existing mucosal protectants such as aluminum phosphate gel (APG).

UMIH’s exceptional performance is attributable to its unique molecular composition, which integrates three critical components engineered to optimize stability and adhesion within the gastrointestinal tract. Central to its design is the protein ELR-IK24, a polypeptide construct specifically engineered to bind protons under low pH conditions. This protonation capability effectively buffers the local environment, mitigating acidity at the hydrogel-tissue interface and preserving polymer integrity. The inclusion of tannic acid, a polyphenol known for its adhesive qualities, enhances hydrogel adherence by facilitating hydrogen bonding and covalent interactions with tissue surfaces. Moreover, hexamethylene diisocyanate (HDI), a crosslinking agent, stabilizes the hydrogel’s polymer network, maintaining mechanical strength over prolonged acidic exposure.

Laboratory tests underscore the superiority of UMIH in replicating and even surpassing mucus’s protective roles. When exposed to simulated gastric acid conditions with a pH of approximately 2, UMIH’s adhesive strength was quantified to be fifteen times greater than APG, the current clinical standard. Notably, while APG samples completely degraded within three days under identical conditions, UMIH retained half of its structural integrity even after a week, signifying a quantum leap in durability. Equally important was the demonstration of UMIH’s biocompatibility: it elicited no cytotoxic effects on cultured gastrointestinal epithelial cells, reinforcing its safety profile for potential clinical use.

Beyond its mechanical and adhesive robustness, UMIH exhibits promising antimicrobial activity, inhibiting the proliferation of pathogenic bacteria such as Escherichia coli and Staphylococcus aureus. This dual function—protection and antimicrobial defense—could be pivotal in preventing wound infections and accelerating healing processes in compromised gastrointestinal tissues. The pathogen inhibition likely derives from the synergistic action of tannic acid and the hydrogel’s physical barrier properties, which together deter bacterial colonization and biofilm formation.

The translational relevance of these properties was rigorously tested in vivo using rat and pig models of esophageal injury, reflecting clinically relevant scenarios such as ulcers or post-surgical wounds. UMIH was applied endoscopically to injured mucosal surfaces, where it demonstrated excellent adherence even amidst the dynamic and moist gastrointestinal environment. Treated animals exhibited significantly accelerated wound closure rates, reduced inflammation markers, and enhanced neovascularization—the growth of new blood vessels critical for tissue regeneration. Such multifaceted therapeutic benefits indicate that UMIH not only acts as a passive physical barrier but also actively modulates the tissue microenvironment to favor healing.

Dr. Bei Li of Sichuan University, a coauthor on the study, emphasizes UMIH’s clinical promise: “Its versatility allows for application in diverse gastrointestinal pathologies, including gastroesophageal reflux disease and gastric ulcers, while also lending itself to minimally invasive delivery techniques.” The hydrogel’s robust adhesion profiles ensure it remains localized at the target injury site, enhancing therapeutic efficiency and reducing the need for repeated applications. This feature is particularly advantageous in complex clinical cases where maintaining material placement can be challenging.

From a manufacturing standpoint, scalability and safety are crucial for any biomaterial poised for clinical adoption. UMIH ticks both boxes, as highlighted by coauthor Feng Lou, who underscores the cost-effectiveness and established safety profiles of its constituent components. The straightforward synthesis and potential for mass production set the stage for expedited trials and eventual commercialization. Importantly, UMIH’s modular chemistry allows for future enhancements, such as integrating drug delivery systems or embedding flexible, implantable electronics to create ‘smart’ gastrointestinal devices capable of real-time monitoring and therapeutic modulation.

The researchers are now focusing on optimizing UMIH’s formulations and initiating preclinical safety assessments to prepare for eventual human clinical trials. These trials will be critical for validating long-term safety, efficacy, and functional benefits in diverse patient populations. Given the high incidence of gastrointestinal disorders worldwide and the limitations of current therapeutic materials, UMIH offers a highly attractive candidate to fill significant medical gaps.

In summary, ultrastable mucus-inspired hydrogel (UMIH) represents a transformative advance in biomaterials engineering, with potential clinical applications ranging from ulcer treatment to post-surgical wound care within acidic environments. Its unique multi-component design mimics natural protective mucus while enhancing acid resistance, adhesion, and antimicrobial defense. Animal models confirm its efficacy in promoting rapid, durable healing, and its proven biocompatibility and manufacturability bode well for clinical translation. As research progresses, UMIH may well become a new standard for treating and protecting the gastrointestinal tract, improving patient outcomes for millions worldwide.

Subject of Research: Not applicable

Article Title: Mucus-inspired hydrogels with protonation-driven adhesion for extreme acidic conditions

News Publication Date: 4-Sep-2025

Web References:
https://www.cell.com/cell-reports-physical-science/home

References:
Yang et al., “Mucus-inspired hydrogels with protonation-driven adhesion for extreme acidic conditions,” Cell Reports Physical Science, DOI: 10.1016/j.xcrp.2025.102772

Image Credits: Not specified

Keywords

Hydrogels, Mucus, Gastrointestinal tract, Wound healing

In a groundbreaking study spearheaded by the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS), researchers analyzed comprehensive molecular datasets derived from more than four thousand patients suffering from 45 distinct diseases. They employed a cutting-edge computational method that integrates gene expression profiles to unravel the biological foundations of these disease pairings. This study represents the largest multidisciplinary effort to date focusing on deciphering the molecular explanations behind clinically observed disease interactions. Remarkably, the findings reveal that nearly two-thirds, or 64%, of known epidemiological disease links can be attributed to similarities in gene expression patterns.

At the heart of this investigation was RNA sequencing technology, a powerful tool that enables scientists to read the active genetic instructions within each patient’s cells. Through this method, the team was able to map positive interactions where the presence of one condition increases the risk of another. For instance, conditions like asthma have been noted to precede Parkinson’s disease in certain populations, suggesting a molecular predisposition facilitating this cascade. Conversely, negative interactions were also uncovered, illustrating instances where having one disease appears to protect a patient from another. Notably, the inverse relationship between cancer and neurodegenerative disorders such as Huntington’s disease was molecularly characterized, providing new insights into these protective phenomena.

Beatriz Urda, the lead researcher at BSC, highlighted this revelation: “We have known for years that patients with Huntington’s disease have a surprisingly lower incidence of solid tumors, like lung or breast cancer, than the general population. Our study sheds light on this by demonstrating that the biological pathways active in Huntington’s disease often run counter to those promoting cancer development. This opens up promising avenues for investigating molecular mechanisms that could be leveraged therapeutically.” This molecular antagonism suggests a delicate balance in cellular regulation that might be exploited to design novel treatments or diagnostic tools.

A striking conclusion from the research is the central role of the immune system as a nexus for many of these disease interactions. Altered immune pathways were detected in an astonishing 95% of the diseases analyzed, indicating that immune dysregulation is a common thread weaving together diverse pathological states. This discovery accentuates the need to focus on the immune network when studying co-morbidities and supports a systemic rather than disease-centric perspective on medicine. By pinpointing shared immune modifications, new diagnostic markers and therapeutic targets can be identified to better manage complex patient profiles.

The study further delved into lesser-known or newly proposed disease pairings. For example, an intriguing molecular association between Down syndrome and lupus was identified, hinting at possible shared biological pathways. Such findings have significant clinical implications, as recognizing these links could enhance diagnostic accuracy and inspire the development of therapeutic strategies aimed at multiple interrelated conditions, potentially improving patient outcomes through a more holistic approach.

Innovation in this research was also achieved through patient stratification based on molecular profiles rather than solely clinical diagnosis. By grouping patients with similar gene expression footprints, the team uncovered disease associations that remain invisible when patients are viewed as uniform groups. This stratification uncovered that within breast cancer cohorts, some subgroups manifest molecular connections with neurological disorders like autism or bipolar disorder, while others show protective interactions against autoimmune diseases such as multiple sclerosis. This molecular classification elucidates why patients ostensibly diagnosed with the same disease may experience dramatically different clinical courses.

Urda emphasized, “Our ability to detect associations appearing only in select patient subpopulations provides a powerful framework for personalizing medicine. Understanding these intra-disease differences not only explains varied clinical trajectories but also points to potentially underdiagnosed disease links. By revealing the molecular scaffolding behind these relationships, we can better anticipate and manage patient-specific risks.” This granular approach marks a shift towards precision medicine, with treatments and prognoses tailored to molecularly defined patient groups.

The methodology’s sensitivity extends to rare diseases, a category often hampered by insufficient clinical data due to the low prevalence of cases. Despite these challenges, the computational approach employed demonstrated comparable effectiveness in detecting molecular interactions for rare disorders. According to Alfonso Valencia, ICREA professor and director of the Life Sciences Department at BSC, this capacity paves the way for demystifying understudied and minority diseases, which could lead to the discovery of unique molecular mechanisms and novel therapeutic avenues often overlooked in traditional research paradigms.

The implications of this research transcend academic insight by offering tangible benefits for clinical practice. Integrating genomic and clinical data under a systemic integrative framework enables clinicians to predict the trajectory of diseases more accurately and to tailor interventions proactively. This predictive capability is not only crucial for managing existing conditions but also for anticipating the emergence of secondary diseases, thus fostering a preventive, rather than reactive, model of healthcare. Such innovation is especially timely as healthcare moves towards more personalized and precise treatment regimens.

To empower both researchers and clinicians in exploring these complex disease networks, the BSC team has launched a publicly accessible web resource. This interactive platform enables detailed exploration of both positive and negative disease interactions and their underlying molecular mechanisms. By facilitating this open-access model, the scientific community and healthcare professionals can leverage these insights to accelerate research, validate findings, and inform patient care strategies across diverse medical fields.

This milestone study eloquently demonstrates that diseases are far from isolated anomalies; they are interconnected within a vast molecular ecosystem. Understanding diseases through this interconnected lens allows researchers to move beyond surface-level clinical observations towards unraveling the root molecular architectures shaping human health. The synergy of high-throughput sequencing, computational modeling, and patient stratification heralds a transformative era in biomedical sciences, where disease co-occurrence is not a perplexing coincidence but a decipherable molecular narrative.

As this research unfolds, it promises to catalyze novel approaches in diagnostics and therapeutics, simultaneously enhancing scientific knowledge and clinical acumen. The future of medicine lies in acknowledging the interconnectedness of human diseases and harnessing this network to design more effective, personalized interventions. With robust computational tools and comprehensive molecular datasets, the path toward this vision is more attainable than ever before.

Subject of Research: People
Article Title: Patient stratification reveals the molecular basis of disease co-occurrences
News Publication Date: 29-Aug-2025
References: B. Urda-García, J. Sánchez-Valle, R. Lepore, & A. Valencia, Patient stratification reveals the molecular basis of disease co-occurrences, Proc. Natl. Acad. Sci. U.S.A. 122 (35) e2421060122, https://doi.org/10.1073/pnas.2421060122
Keywords: Diseases and disorders, Immune system, RNA sequencing, Computer modeling, Personalized medicine