The computational workflow developed in this study enables the identification and prioritization of candidate HLA-DRB5*01:01-restricted CD4+ T-cell epitopes ...

RNA therapeutics manufacturing is undergoing a transformative shift from batch-based to continuous processing systems, powered by artificial intelligence and advanced automation. The industry is moving beyond achieving scale to focus on reducing costs, improving accessibility, and enabling personalized therapies. Manufacturers are investing in continuous processing workflows that integrate synthesis, purification, and formulation into streamlined operations, alongside next-generation RNA platforms such as self-amplifying RNA and circular RNA. These integrated technologies create more stable production kinetics, reduce manufacturing inefficiencies, and enable real-time quality monitoring. This evolution represents a broader industry trend toward adaptive, data-driven, and decentralized biomanufacturing ecosystems that promise significant improvements in production efficiency and therapeutic accessibility.

It is accompanied by post-exertional symptom exacerbation and non-restorative sleep. Fatigue is often disabling and reduces daily activity by more than 50 ...

1.In Vitro Transcription (IVT): Reaction system comprises ... mRNA Purification: Captures full-length target mRNA and eliminates dsRNA contaminants.

This research article examines the efficacy of second-generation COVID-19 vaccines administered during pregnancy. The study evaluates immunological responses and protective outcomes for both pregnant individuals and their newborns following vaccination with updated vaccine formulations. The findings contribute to understanding vaccine safety and effectiveness in this vulnerable population, providing evidence to inform clinical guidelines for prenatal immunization strategies. The research addresses critical gaps in maternal-fetal health data, supporting informed decision-making for healthcare providers and expectant mothers regarding COVID-19 vaccination during gestation.

In vitro transcription (IVT) has emerged as the critical manufacturing platform for mRNA-based therapeutics, spanning vaccines, immunotherapies, and gene editing applications. However, conventional lab-scale synthesis faces significant technical hurdles including residual double-stranded RNA, endotoxin contamination, compromised RNA integrity, and inconsistent batch performance. These impurities trigger undesired immune responses and impair translational efficacy. This article addresses standardized IVT workflows, quality control specifications, and analytical validation systems essential for robust preclinical research. It examines key contaminants—immunostimulatory dsRNA, endotoxin residues, and structural defects—and demonstrates how optimized platforms with integrated purification strategies and systematic quality monitoring support reproducible, high-performance mRNA production.

A major study determines that autoimmunity is causing major symptoms in long COVID ultimately laying the groundwork for precision-guided treatments.

This article examines the autoimmune mechanisms underlying chronic pain in long COVID, a multifactorial condition affecting a significant subset of individuals with persistent post-acute sequelae following SARS-CoV-2 infection. The authors highlight that long COVID presents as a clinically heterogeneous syndrome characterized by diverse symptoms including fatigue, dyspnea, cognitive impairment, and chronic pain, with notable variability across patients. The research emphasizes marked sex differences, with women disproportionately affected and more susceptible to multisystem and pain-related phenotypes. Despite growing clinical recognition, the underlying pathomechanisms remain incompletely understood, with current evidence implicating complex interactions between persistent immune activation, viral persistence, neuroinflammation, and autoimmunity. This mechanistic uncertainty has significantly hindered the development of reliable biomarkers and phenotype-specific diagnostics, underscoring the need for further investigation into autoimmune-mediated pathways in long COVID management.

Through the study of viral genomes, scientists can identify mutations that might influence transmissibility, immune escape, and the effectiveness of vaccines.

This research examines how aging and inflammatory processes impair the development of SARS-CoV-2-specific immune responses. The study investigates the mechanisms by which advancing age and chronic inflammation compromise the body's ability to generate effective adaptive immunity against the coronavirus. Understanding these immunological limitations in older populations is critical for improving vaccine efficacy and therapeutic strategies in elderly individuals who face elevated disease severity risks. The findings have significant implications for public health interventions targeting vulnerable demographics and inform vaccine design considerations for aging populations.

This systematic review examines neurological manifestations of post-viral syndromes, particularly Long COVID, across adult and pediatric populations. Analyzing 23 studies published between January 2020 and May 2025, the research identifies persistent fatigue, cognitive dysfunction, headaches, sleep disturbances, dysautonomia, and neuropathic symptoms as predominant neurological features. The study reveals a multifactorial pathophysiology involving immune dysregulation, neuroinflammation, endothelial dysfunction, mitochondrial impairment, and autonomic nervous system disruption. Pediatric cases demonstrate similar symptom profiles with notable cognitive and behavioral impacts. These findings underscore neurological dysfunction as a fundamental component of Long COVID, establishing the complexity of underlying mechanisms and informing future therapeutic strategies for this emerging clinical condition.

Machine learning is revolutionizing protein dynamics characterization by overcoming limitations of traditional structural biology techniques. While X-ray crystallography and cryo-electron microscopy provide valuable static snapshots, they fail to capture the conformational fluctuations essential to biological function. Computational methods like molecular dynamics offer dynamic insights but are computationally expensive and face sampling limitations. Machine learning addresses these challenges by refining force fields, optimizing adaptive sampling strategies, and automating cryo-EM and NMR workflows. Additionally, deep learning and protein language models enhance structure prediction capabilities. By integrating physical principles with data-driven approaches, machine learning bridges the gap between static structural representations and dynamic functional ensembles, significantly advancing structural biology and drug discovery applications.

This research article examines Long COVID through a network disorder framework, proposing mechanism-anchored approaches to understanding the condition's complex pathophysiology. Rather than treating Long COVID as a single disease entity, the authors analyze it as a multisystem disorder involving interconnected biological networks. The study integrates evidence from immunological, neurological, and vascular mechanisms to explain persistent symptoms following acute COVID-19 infection. By anchoring explanations in specific biological mechanisms, the research provides a comprehensive model for understanding disease heterogeneity among Long COVID patients. This network-based perspective offers important insights for developing targeted therapeutic interventions and improving clinical management strategies for this emerging chronic condition.

# Summary Recent research has identified a potential autoimmune mechanism underlying Long COVID symptoms, a condition affecting numerous SARS-CoV-2 survivors. Patients experience persistent manifestations including debilitating fatigue, cognitive dysfunction, cardiac palpitations, and musculoskeletal pain that can persist for extended periods following acute infection. This study advances understanding of Long COVID's pathophysiology by pointing toward autoimmune dysfunction as a key driver of these prolonged symptoms. Such findings have significant implications for developing targeted therapeutic interventions and improving patient outcomes for the millions experiencing Long COVID complications.

This study examines clinical characteristics and antibody responses among pregnant women infected with Omicron variants in China from December 2022 to April 2023. Researchers investigated 223 pregnant women with at least six weeks gestation and 31 healthy controls to evaluate humoral immune responses following natural SARS-CoV-2 infection, particularly in those previously vaccinated with inactivated vaccines. The research addresses critical knowledge gaps regarding how immune imprinting from inactivated vaccines may influence cross-neutralization against emerging Omicron subvariants. This cross-sectional investigation provides important insights into pregnancy-specific immune dynamics during COVID-19 infection and vaccine responses, contributing valuable data for understanding maternal and fetal health implications during the pandemic.

HLA-C is a major histocompatibility complex class I gene in humans, identified as Gene ID 3107 in the National Center for Biotechnology Information database. This gene encodes a protein involved in the immune system's ability to present antigens to T cells, playing a crucial role in immune recognition and response. Located within the human genome, HLA-C is essential for transplant compatibility assessment, disease susceptibility studies, and understanding immune-mediated conditions. The NCBI database provides comprehensive genomic data, including sequencing information, expression profiles, and mapped phenotypes associated with HLA-C variants, supporting clinical and research applications in immunology and precision medicine.

This study presents an innovative computational framework for optimizing therapeutic mRNA sequences by combining deep learning with evolutionary algorithms. Researchers employ a pretrained CodonTransformer to generate initial mRNA candidates, followed by a genetic algorithm that iteratively refines sequences through codon-aware modifications. The optimization targets multiple critical parameters: translation efficiency metrics including codon adaptation index and tRNA adaptation index, structural stability through mRNA secondary structure analysis, and reduced immunogenic potential by minimizing problematic motif frequencies. Results demonstrate substantial improvements across successive generations, achieving enhanced codon adaptation and tRNA matching while maintaining stable secondary structures and low immunogenicity. This integrated approach offers a promising alternative to existing mRNA design methodologies for developing safer and more effective therapeutic vaccines and treatments.

Artificial intelligence-directed computational protein design has revolutionized therapeutic discovery, particularly for vaccines and antibody-based treatments against infectious diseases. The COVID-19 pandemic demonstrated both the transformative potential and significant limitations of these technologies. AI-driven approaches successfully accelerated viral characterization, vaccine development, and broadly neutralizing antibody design. However, critical challenges emerged, including data bias, model interpretability issues, experimental validation bottlenecks, and regulatory framework integration gaps. The pandemic revealed a crucial disconnect between computational promise and translational readiness, necessitating closer collaboration between computational design, laboratory experimentation, and clinical evaluation. Additionally, rapid AI innovation has outpaced established regulatory pathways, highlighting the need for enhanced oversight mechanisms before these technologies can serve as reliable pandemic preparedness cornerstones.

This review examines the application of machine learning and artificial intelligence in predicting immunogenicity risks associated with biologic therapies during preclinical development. Anti-drug antibodies resulting from unwanted immune responses pose significant challenges to clinical development and patient outcomes. Currently, predicting immunogenicity before clinical trials remains impossible due to immune system complexity and multiple contributing factors. However, advances in computational methods offer promising opportunities for improved prediction accuracy. Industry experts and academics convened at an EMBL-EBI workshop to assess AI and ML contributions to immunogenicity prediction, review current industry practices, and identify future opportunities for deploying these transformative technologies in therapeutic development.

People vaccinated against COVID in Germany in 2023 experienced reduced hospitalization rates, fewer long-COVID diagnoses, and lower all-cause mortality than those who weren't vaccinated, according to a new study published in Eurosurveillance. These clinical benefits translated to significant reductions in health care costs and lost productivity due to sick leave.