Zhongheng Zhang

Background and Objective: Older adults bear a disproportionate burden of sepsis, yet chronological age does not capture the biological heterogeneity that shapes immune dysfunction and recovery. This structured narrative review examines how immunosenescence and inflammaging influence immune reserve and resilience in older adults with sepsis. It also evaluates how these concepts may inform longitudinal phenotyping and precision therapeutic research. Methods: PubMed and the Web of Science Core Collection were searched on 27 July 2026 for literature published from 1 January 2001. Reference lists and citing articles were also examined. Direct studies in older adults were prioritized, while general adult, healthy-aging, animal and ex vivo evidence was retained when needed for mechanistic or translational context. Key Content and Findings: Age-associated hematopoietic remodeling, reduced adaptive diversity, impaired neutrophil targeting, defective antigen presentation, lymphocyte loss, metabolic inflexibility, and endothelial injury reshape septic immunity. Rather than following a fixed transition from hyperinflammation to immune suppression, older adults may exhibit heterogeneous recovery patterns shaped by pre-infection reserve, inflammatory burden, and resilience. Persistent failure to recover may be associated with secondary infection, chronic critical illness, functional decline, and loss of healthspan. Biomarkers, omics signatures, and candidate immune endotypes are most informative when tied to a defined clinical purpose, serial sampling and staged validation. Most immunomodulatory and geroscience interventions remain investigational and require phenotype-enriched trials with explicit timing, reversibility and safety criteria. Conclusions: Immunosenescence in sepsis reflects a dynamic interaction between baseline reserve, inflammatory burden, acute perturbation and recovery capacity. Progress toward precision therapy will require geriatric-aware longitudinal cohorts, harmonized functional assays, validated trajectories and biomarker-enriched trials that measure secondary infection, cognition, function, independence and survival.

Introduction: Sepsis is a heterogeneous syndrome characterized by dysregulated host responses and diverse patterns of organ dysfunction. This study aimed to characterize serum extracellular vesicle (EV)-enriched proteomic heterogeneity in sepsis and assess whether proteomic subtypes identified among Sepsis-only patients showed molecular similarity to established organ dysfunction phenotypes. Methods: In this single-center observational study, serum EV-enriched preparations were analyzed from 250 patients with sepsis and 50 healthy controls enrolled between June 2021 and December 2022. ARDS and AKI status was determined at blood sampling, and patients with neither condition were classified as Sepsis-only. After proteomic quality control, 231 patients and 48 controls were included. Non-negative matrix factorization was used to identify proteomic subtypes among Sepsis-only patients. Centroid projection and clinically adjusted sensitivity analyses evaluated cross-sectional molecular similarity between these subtypes and established S-AKI, S-ARDS, and S-ARDS&AKI phenotypes. An exploratory elastic-net protein panel was derived, and the downstream supervised workflow was evaluated using repeated nested cross-validation conditional on the original subtype labels. Results: A total of 4,754 protein groups were quantified across 279 samples. Three proteomic subtypes were identified among 96 Sepsis-only patients. S1 showed less pronounced inflammatory features, whereas S2 was characterized by cellular immune and metabolic-stress pathways and S3 by humoral immune, complement/coagulation, hemostatic, and acute-phase processes. These molecular patterns were not fully captured by conventional severity scores. The organ dysfunction groups showed partial and reference-dependent molecular similarity to S3 after clinical adjustment. A hypothesis-generating 15-protein panel captured features shared by S3 and established organ dysfunction relative to S2. Conclusions: Serum EV-enriched proteomics revealed distinct host-response subtypes within the Sepsis-only group and contemporaneous molecular features shared with established organ dysfunction. These findings advance the molecular characterization of sepsis heterogeneity and provide a focused framework for future longitudinal and external validation.

1. 1.1. Chronic kidney disease (CKD) has emerged as a global public health challenge, with its disease spectrum in China increasingly aligning with that of developed countries, which carries signif...

Objective: Clinical practice guidelines (CPGs) support evidence-informed decision-making, but the randomized controlled trials (RCTs) cited as supporting evidence may have restricted eligibility criteria and limited external validity for clinically complex populations. This study aimed to describe exclusion criteria and underrepresented populations in RCTs cited by adult critical care CPGs published from October 2015 to October 2020. Design: Systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data sources: PubMed and Scopus electronic databases were searched from October 9, 2015, to October 9, 2020. Eligibility criteria for selecting studies: A CPG was included if it fulfilled one of the following criteria: (1) the target population was adult critically ill patients; (2) critical care experts were involved in its development; or (3) all authors agreed that it was relevant to critical care medicine (CCM). CPGs were excluded if (1) the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework was not used to develop the recommendations; (2) they were editorials, comments, meta-analyses, and reviews, duplicates, protocols, or irrelevant to CCM; (3) they were original studies that investigated the implementation of the CPGs; or (4) the topics specifically addressed critically ill child patients. Data extraction and analysis: Two independent reviewers extracted CPG characteristics, recommendations, RCT citations, and RCT exclusion criteria. Guideline quality was evaluated using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool. Descriptive statistics were used for analysis. Results: Thirty-three CPGs publications were included. The overall average score for each AGREE II domain was >70%. These CPGs contained 1,134 recommendations and cited 1,231 RCT references. Only 8.7% of RCT citations had no exclusion criteria. The most frequent exclusion-related categories were age <18 years (37.9%), pregnancy (29.5%), moribund status (19.2%), surgical history (14.8%), renal impairment (14.2%), liver dysfunction (12.6%), hemodynamic instability (10.2%), traumatic brain injury (8.4%), allergy (8.2%), and coagulopathy (7.8%). Because the included guidelines primarily targeted adult patients, age <18 years should be interpreted as age-defined non-representation rather than inappropriate exclusion. After excluding this age-defined category, pregnancy, moribund status, surgical history, renal impairment, and liver dysfunction were the most frequent underrepresented categories. Conclusion: Among adult critical care CPGs published between 2015 and 2020, the RCT evidence cited in guideline recommendations frequently did not include several clinically important populations. These findings highlight the need for clinicians to consider the external validity of the underlying evidence when applying guideline recommendations to underrepresented or clinically complex patients.

(https://www.frontiersin.org/research-topics/70036/advances-in-combatingantimicrobial-resistance-focus-on-diagnosis-therapy-and-prevention)The Escalating Threat of Antimicrobial Resistance: Global and ICU Epidemiology Antimicrobial resistance (AMR) has emerged as one of the most pressing public health threats of the 21 st century. The World Health Organization (WHO) has projected that without effective interventions, AMR could claim 10 million lives annually by 2050, surpassing cancer as a leading cause of death and imposing a cumulative economic burden of $100 trillion globally (1). The clinical landscape of AMR is particularly dire in intensive care units (ICUs), where the convergence of critically ill patients, extensive use of invasive devices, and high selective pressure from broad-spectrum antibiotics creates an ideal environment for the emergence and dissemination of multidrug-resistant organisms (MDROs) and carbapenemresistant organisms (CROs) (2,3).Infections caused by carbapenem-resistant Enterobacteriaceae (CRE), carbapenemresistant Acinetobacter baumannii (CRAB), carbapenem-resistant Pseudomonas aeruginosa (CRPA), and vancomycin-resistant Enterococci (VRE) are associated with devastating clinical outcomes (4,5). A systematic analysis from the GBD 2021 Antimicrobial Resistance Collaborators revealed that bacterial AMR directly caused 1.14 million deaths in 2021 and was associated with 4.71 million deaths globally (1). In ICU settings, patients with CRO infections experience prolonged mechanical ventilation, extended ICU and hospital stay (often exceeding 20-30 days), exorbitant healthcare costs (2-3 times higher than susceptible infections), and mortality rates ranging from 40% to over 60% for CRE and CRAB infections (6)(7)(8).The health economic burden is also staggering. A recent multicenter study across 10 countries estimated that the excess cost per CRO infection ranges from $20,000 to $120,000, driven by the need for combined use of broad-spectrum antibiotics, prolonged use of extracorporeal life support devices, enhanced infection control measures, and prolonged rehabilitation (9). Moreover, the erosion of last-line antibiotics has forced clinicians to rely on older, more toxic agents such as colistin and tigecycline, further compromising patient outcomes. This Research Topic, Advances in Combating Antimicrobial Resistance: Focus on Diagnosis, Therapy, and Prevention, brings together 19 articles from dozens of submissions that address this multifaceted crisis across three interdependent pillars. The contributions herein collectively illuminate a transition from reactive, empiric management to proactive, precisionbased countermeasures.A fundamental obstacle in AMR management has been the reliance on traditional culturebased methods, which require 48-72 hours for pathogen identification and an additional 24-48 hours for antimicrobial susceptibility testing (AST). This delay forces clinicians to initiate empiric broad-spectrum therapy, inadvertently driving further resistance. In response, rapid diagnostic tests (RDTs) have revolutionized the field, enabling same-day pathogen identification and resistance gene detection directly from clinical specimens. Meanwhile, the process of blood culture is constantly being improved to shorten turn around time (TAT) (10,11).Several articles in this Research Topic showcase diagnostic innovations. Yu and colleagues developed a deep learning algorithm based on endoscopic features for rapid Helicobacter pylori infection status assessment, reducing reliance on multiple biopsies and culture confirmation. Beyond molecular panels, metagenomic next-generation sequencing (mNGS) and CRISPR-based assays represent the next frontier. mNGS enables unbiased detection of virtually almost all pathogens in a single test, proving invaluable for culture-negative infections, unexpected or fastidious pathogens, and polymicrobial communities (12,13). CRISPR-based diagnostics offer rapid, low-cost, point-of-care detection with high specificity. The application of these advanced diagnostics is moving us from educated guesswork to precise pathogen identification, forming the bedrock of modern antimicrobial stewardship. After nearly a decade of clinical application, mNGS has demonstrated its advantages over traditional detection methods in the rapid and accurate identification of pathogens. In the future, it will be further localized and efforts will be made to build multi-disciplinary teams (MDT) for the treatment of infections. Together, they will work to accurately interpret relevant reports, contributing to improved patient outcomes.Parallel to rapid phenotypic profiling, understanding the spatial architecture of polymicrobial infections is critical, as standard diagnostic cultures often disrupt complex microbial communities. In this context, ribosomal fluorescence in situ hybridization (FISH) serves as an indispensable tool for the direct, culture-independent visualization of bacterial biofilms within host tissues. As pioneered by Swidsinski and colleagues, the deployment of sequence-specific fluorescent oligonucleotide probes enables the identification of structured, pathogen-specific consortia that otherwise evade conventional mono-infectious diagnostic paradigms (14). By combining strict taxonomic identification with exact morphological localization, FISH-based diagnostic frameworks bypass the limitations of routine microbiology, offering crucial insights into biofilm-mediated resistance and guiding targeted, localized therapeutic interventions.Confronted with pan-resistant bacteria, replenishing the therapeutic pipeline is an urgent clinical necessity. This Research Topic presents real-world evidence on both established and emerging agents, as well as non-antibiotic strategies.β -lactam/ β -lactamase inhibitor (BLBLI) combinations, including ceftazidime-avibactam, ceftolozane-tazobactam, meropenem-vaborbactam, imipenem-relebactam and...

While biomedical artificial intelligence (AI) shows great clinical potential, existing systems relying on general-domain large language models (LLMs) often lack sufficient medical data to grasp complex healthcare concepts. Furthermore, recent Large Language and Vision Assistant (LLaVA)-based medical multimodal large language models (MLLMs) still struggle to effectively capture the intricate alignments between medical images and text. Therefore, we introduce Doctor Sun, an MLLM specialized in medicine, developed to encode, integrate, and interpret diverse biomedical data modalities such as text and images. In particular, Doctor Sun integrates a pre-trained vision encoder with a medical LLM and conducts two-stage training on various medical datasets, focusing on feature alignment and instruction tuning. Moreover, we release SunMed-VL, a wide-range bilingual medical multimodal dataset, along with all associated models, code, and resources, to freely support the advancement of biomedical multimodal research.

Background: Artificial intelligence (AI) is undergoing an era of transformative advancement, particularly through the emergence of Transformer-based large language models (LLMs). While these systems demonstrate strong reasoning and generalization capabilities, their clinical applicability, particularly in emergency and critical care decision-making, remains underexplored.. In time-sensitive settings, diagnostic reasoning must align rigorously with evidence-based standards and ensure the relevance of timing to clinical decisions. Objective: This study aims to provide a preliminary evaluation of the decision-support performance of the DeepSeek model in acute medical scenarios. We systematically evaluate its diagnostic reasoning, temporal consistency of recommendations, and adherence to evidence-based critical care protocols using standardized case-based assessments. Methods: Twenty-nine representative clinical cases were extracted from the Merck Manual of Diagnosis and Therapy, a widely used medical reference providing standardized case descriptions. The model’s outputs were evaluated across four decision-making dimensions: differential diagnosis, diagnostic testing, final diagnosis, and management planning. Human raters scored each response for accuracy, and multivariable linear regression was applied to assess associations between performance and case parameters (age, gender, and Rapid Emergency Medicine Score [REMS]). Results: DeepSeek achieved an overall mean accuracy of 82.9% (95% CI: 80.2–85.6%) across all cases. Accuracy peaked in final diagnosis (97.7%), but declined in differential diagnosis (73.0%). Model performance showed no significant variation across demographic or severity strata. Conclusions: DeepSeek shows promising performance in structured case-based diagnostic tasks, particularly in confirmatory diagnostic reasoning. However, its early-stage reasoning and handling of ambiguous cases require enhancement. Future studies using larger and more diverse clinical datasets are needed to further evaluate the model’s robustness and potential clinical applicability.