Yinghe Xu

Background: To assess the global burden and trends of childhood sepsis, and to evaluate whether clarifying these aspects can provide a basis for formulating targeted prevention and treatment strategies for the condition. Methods: Using the Global Burden of Disease (GBD) 2021 database, we analyzed sepsis in 0–14-year-olds, covering 62 pathogens (21 drug-resistant), 84 “pathogen–drug” combinations, 12 infection syndromes, and indicators (deaths, disability-adjusted life years [DALYs], age-standardized rates). Antimicrobial resistance (AMR) was defined by two counterfactual scenarios; an autoregressive integrated moving average (ARIMA) model forecast AMR-related sepsis to 2050. Results: From 1990 to 2021, global sepsis-related deaths among children declined from 6.43 million to 2.24 million, and DALYs dropped from 578 million to 203 million. The estimated annual percentage change (EAPC) in age-standardized mortality rate (ASMR) and DALY rate (ASDR) was –3.35 and –3.33, respectively. AMR-related sepsis burden also decreased, with notable declines in both AMR-associated and AMR-attributable deaths and DALYs. In 2021, the highest burden remained in Sub-Saharan Africa, while the lowest was in High-income regions. Leading pathogens causing child sepsis deaths included Streptococcus pneumoniae, Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. Bloodstream infections, lower respiratory infections, and diarrhea were the most common infection syndromes leading to sepsis-related deaths. Conclusion: Global childhood sepsis burden dropped, but high-burden regions (e.g., Sub-Saharan Africa, South Asia) need focus. Future efforts should optimize healthcare, strengthen AMR control, promote vaccination, and improve sanitation.

Background: Climate change significantly impacts health, particularly for individuals aged 65 and older, increasing heat-related illnesses and mortality. Understanding this burden is vital for public health planning. Methods: This study analyzes data from the Global Burden of Disease (GBD) Study 2021, focusing on deaths, disability-adjusted life years (DALYs), and age-standardized ratios related to high-temperature exposure in individuals aged 65+ across 204 nations and territories. It examines trends from 1990 to 2021, assesses health inequities using measures like the Inequality Slope Index and Concentration Index, and projects future trends using a Bayesian Age-Period-Cohort (BAPC) model. Results: In 2021, there were 247,098 heat-related deaths globally among those aged 65+, with an age-standardized mortality rate (ASMR) increase from 26.3 to 27.1 per 100,000 (annual average percentage change (AAPC): 0.22). Aggregate DALYs reached 3,986,215, with the age-standardized DALYs rate (ASDR) rising from 419.6 to 526.7 per 100,000. South Asia and East Asia experienced higher burdens, while Oceania and Western Australia had lower rates. Lower-middle Socio-demographic Index (SDI) regions faced greater burdens, with absolute inequality increasing and relative inequality slightly declining. Non-communicable diseases (NCDs), such as ischemic heart disease and stroke, accounted for a significant proportion of heat-related deaths and DALYs, with upward trends. Projections indicate continued increases in deaths and DALYs by 2050. Conclusions: This study highlights the growing global burden of high-temperature exposure on older adults, emphasizing regional disparities and the dominance of NCDs. These findings underscore the need for targeted public health strategies to address climate-related health risks in vulnerable populations.

Objective: To investigate whether the neutrophil-to-albumin ratio (NPAR) predicts adverse outcomes in acute large vessel occlusion (LVO) stroke. Methods: This cohort study enrolled 893 LVO stroke patients (2021–2023). Associations between NPAR and outcomes were assessed using logistic regression, restricted cubic spline, and ROC analysis. Results: Higher NPAR correlated with elevated NIHSS, inflammatory markers, coagulation parameters, and dysphagia, and decreased hemoglobin, lymphocytes, and GCS. NPAR was an independent predictor for 90-day poor functional outcome (OR = 1.10, 95%CI: 1.07–1.14), pneumonia (OR = 1.14, 95%CI: 1.10–1.18), and intracerebral hemorrhage (ICH) (OR = 1.06, 95%CI:1.03–1.10). After multivariable adjustment, associations remained robust (aOR: 1.06–1.14). Linear dose–response relationships were observed. NPAR showed modest predictive performance (AUC: 0.60–0.65). Adding NPAR significantly improved pneumonia prediction over AIS-APS (AUC: 0.684 → 0.709, p = 0.0049), enhanced ICH sensitivity (56.6% → 64.6%), but minimally improved 90-day outcome prediction. Conclusion: Elevated NPAR independently predicts pneumonia, ICH, and poor functional recovery in LVO stroke, serving as a potential early risk stratification tool. However, these findings should be interpreted in light of the study’s limitations, including its single-center retrospective design, single time-point NPAR measurement, and predominantly Han Chinese population, which may limit generalizability.

Background: Streptococcus species are predominant ​commensal residents of the respiratory tract​ in healthy individuals and contribute to immune and metabolic regulation. However, the association between streptococcal colonization and clinical outcomes in patients with severe pneumonia remains undercharacterized. This study aimed to explore the clinical characteristics and the impact of streptococcal colonization on the prognosis of critically ill patients with pneumonia. Method: We conducted a multicenter, retrospective, observational cohort study of critically ill pneumonia patients admitted to 12 intensive care units (ICUs) between January 2019 and December 2023 who underwent metagenomic next-generation sequencing (mNGS). Patients were stratified into Streptococcus -colonized and non-colonized groups based on bronchoalveolar lavage fluid (BALF) mNGS results, conventional microbiological testing (CMT), and clinical assessments. Propensity score matching (PSM) was utilized to minimize baseline confounding variables. Using nearest-neighbor matching at a 1:2 ratio, baseline characteristics were balanced between groups post-matching. The primary endpoint was 28-day all-cause mortality. Results: A total of 1,897 patients were enrolled in this study. Among them, 21 patients under 18 years of age, 139 patients lost to follow-up within 28 days, and 4 patients with confirmed streptococcal infection were excluded. Finally, 1,733 patients met the inclusion criteria. The cohort had a mean age of 65 years, with the majority being males (1,213/1,733, 70%). Among these, 148 (8.5%) were classified as Streptococcus -colonized, and 1,585 (91.5%) were Streptococcus -colonization-negative. No significant difference in 28-day all-cause mortality was observed between the colonized and non-colonized groups (35.81% vs. 38.51%, p=0.578). Patients with Streptococcus colonization had a significantly shorter median length of stay (LOS) (17 days, interquartile range [IQR] 11–30) than those without colonization (22 days, IQR 12–33; P = 0.044). Similarly, their median intensive care unit (ICU) LOS (11 days, IQR 7–16) was also significantly shorter than that of non-colonized patients (14 days, IQR 8–25; P = 0.003). Multivariable Cox regression analysis further demonstrated that Streptococcus colonization was not an independent risk factor for 28-day mortality (HR = 1.10, 95% CI: 0.79–1.51, p=0.579). Conclusion: Our findings suggest a potential role for Streptococcus colonization in improving clinical outcomes in severe pneumonia. The presence or absence of Streptococcus colonization may influence short-term prognostic benefits in critically ill pneumonia patients. Further research is needed to clarify the clinical significance and potential mechanisms of Streptococcus colonization.

Extracorporeal membrane oxygenation (ECMO) is a critical life-support intervention for patients with severe cardiopulmonary failure. However, its use is associated with a substantially increased risk of nosocomial infections, with reported incidence rates ranging from 8.8% to 64.0%. These infections—particularly ventilator-associated pneumonia and bloodstream infections—are linked to heightened morbidity, prolonged intensive care and hospital stays, and elevated mortality. This review aims to systematically compile Chinese and English literature published between 2018 and 2025, clarify the unique pathophysiological mechanisms of ECMO-related infections, analyze the limitations and breakthroughs of existing prediction models, and explore the potential role of machine learning in developing personalized early warning systems. Additionally, it seeks to establish a clinical decision-making framework for precise prevention and control. We conclude that improving ECMO infection control requires establishing standardized, clinically applicable diagnostic criteria, conducting a multicenter prospective validation study, and developing transparent, AI-enhanced predictive tools to enable real-time infection monitoring and improved patient prognosis during ECMO support.

Objective: It is well recognized that high heterogeneity represents a key driver of the elevated mortality in severe community-acquired pneumonia (sCAP). Precise subtype classification is therefore critical for both treatment strategy formulation and prognostic evaluation in this patient population. This study aimed to develop a predictive model for novel clinical subtypes of sCAP, leveraging microbiome profiles identified via metagenomic next-generation sequencing (mNGS). Methods: This retrospective multicenter cohort study enrolled adult patients with sCAP who underwent clinical mNGS testing of bronchoalveolar lavage fluid in intensive care units (ICUs) across 17 medical centers in China. Based on mNGS-identified microbiome characteristics, unsupervised machine learning (UML) was employed for clustering analysis of sCAP patients. LASSO regression and random forest (RF) algorithms were applied to screen and identify predictors of novel sCAP subtypes. A predictive model for the new clinical subtypes was constructed according to the screening results, with a nomogram generated. The discriminative ability, calibration, and clinical utility of the model were evaluated using ROC curves, calibration curves, and decision curve analysis, respectively. Results: A total of 1,051 sCAP patients were included in the final analysis. The 28-day all-cause mortality rate was 45% (473/1,051). UML clustering identified two distinct sCAP subtypes: the 28-day mortality rate was 42.19% (343/813) in subtype 1 and 54.62% (130/238) in subtype 2. Incorporating clinical and microbial features, a predictive model for the novel sCAP subtypes was developed using the following predictors: immunosuppression (OR = 37,411.46, P < 0.001), connective tissue disease (CTD) (OR = 12,144.60, P = 0.004), hematological malignancy (HM) (OR = 107,768.13, P < 0.001), chronic kidney disease (CKD) (OR = 49.71, P < 0.001), cytomegalovirus (CMV) (OR = 0.00, P < 0.001), Epstein-Barr virus (EBV) (OR = 131.97, P < 0.001), Pneumocystis (OR = 47,949.56, P < 0.001), and Klebsiella (OR = 0.02, P = 0.003). The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.992. Calibration curves showed good agreement between predicted and observed outcomes. Decision curve analysis confirmed high clinical utility for predicting novel sCAP subtypes. Conclusion: This study identified novel clinical subtypes of sCAP based on mNGS-derived microbiome characteristics. This approach exhibits superior performance in identifying high-risk sCAP patients, facilitating precise subtyping.

Xenin, a 25-amino acid peptide hormone predominantly secreted by intestinal K cells, demonstrates evolutionary conservation with neuropeptides such as xenopsin and neurotensin. Functionally, it engages neurotensin receptor 1 (NTSR1) to regulate appetite via hypothalamic signaling pathways and modulates glucose homeostasis through synergistic interactions with incretin hormones. Preclinical studies highlight its dual role in suppressing appetite and enhancing pancreatic β-cell survival, while a single pilot human study suggests xenin-25 may delay gastric emptying and attenuate postprandial glucose excursions; however, these data await independent confirmation. Native xenin, however, is constrained by rapid proteolytic degradation and limited bioavailability. Advances in peptide engineering, including C-terminal truncation, site-directed amino acid substitution, and lipidation, have generated analogues that exhibit prolonged metabolic activity in rodent models, with plasma half-life extended from minutes to hours. In murine models of metabolic dysfunction, these derivatives enhance insulin secretion, improve glycaemic profiles and restore incretin responsiveness. Furthermore, multi-agonist peptides combining xenin with other gastrointestinal hormones show synergistic potential in preclinical studies, concurrently augmenting insulin secretion and reducing energy intake, though their clinical relevance remains to be validated in human trials. Despite promising preclinical outcomes, challenges persist in translating xenin-based therapies to clinical practice, including incomplete mechanistic insights into receptor cross-talk and species-specific variations in gastrointestinal responses. This review uniquely integrates the preclinical landscape of xenin biology, peptide-engineering principles, and emerging multi-agonist design, identifying knowledge gaps critical for future translation. We conclude that xenin-based therapeutics are a promising yet early-stage strategy whose efficacy and safety in human metabolic diseases remain to be established through rigorous pharmacokinetic profiling and phased clinical trials.