As of late January 2026, the World Health Organization (WHO) reported a small-scale Nipah virus (NiV) outbreak in West Bengal, India. Despite that NiV was primarily transmitted through animal sources, we assessed the transmission risks of NiV at human community level based on a total of 23 studies identified from literature of historical outbreaks that investigated human-to-human transmission of laboratory-confirmed NiV in Bangladesh and India. The data included transmission events containing 33 primary NiV cases and 152 secondary cases. We constructed a modelling framework for superspreading risk assessment, and found that 92.0% (95% CrI: 87.0, 95.4) of the NiV cases could not generate secondary cases via human-to-human transmission. With 5 seed cases imported to a community, there was a 1.1% (95% CrI: 0.3, 2.3) chance of observing an outbreak exceeding 50 NiV cases. Our findings may inform public health preparedness at community level and cross-border travel ban.

In November 2025, Marburg virus caused the first outbreak of Marburg virus disease (MVD) in Ethiopia. By December 15, a total of 14 laboratory-confirmed cases were reported, including 9 deaths, corresponding to a case fatality ratio of 64.3% among confirmed cases. In the absence of licenced vaccines or antivirals, non-pharmaceutical interventions (NPIs) were implemented to control transmission. We developed a stochastic epidemic model incorporating a stochastic exponential growth process with reporting adjustment to assess the effectiveness of NPIs. The reporting ratio was modelled using a beta distribution, and transmission parameters were estimated via Markov chain Monte Carlo with particle filtering for likelihood calculation. Using pre-NPI data (November 12-23), we estimated an exponential growth rate of 0.012 (95% credible interval [CrI]: 0.008, 0.014) per day, corresponding to an initial reproduction number of 1.13 (95% CrI: 1.09, 1.16). We projected 64 cases (95% CrI: 19, 189) and 41 deaths (95% CrI: 12, 122) during post-NPI period (November 24 to December 15) without interventions. Compared with 4 observed cases, NPIs were associated with an estimated effectiveness of 93.7% (95% CrI: 78.9, 97.9). These findings indicated moderate transmissibility of MVD, and a potential transmission risk reduction following the implementation of control measures, underscoring the importance of timely intervention and sustained surveillance regarding local MVD activities.

In this paper, we are concerned with the global stability of disease-free steady state for a reaction-advection-diffusion SI epidemic model with heterogeneous diffusion and different advection when basic reproduction number R0 = 1. Furthermore, we also establish the criteria for the global stability of exponential positive steady state.

Purpose This study aimed to develop and validate a machine learning model that integrates radiomic features of epicardial adipose tissue (EAT) from pre-procedural CT angiography with clinical variables to predict atrial fibrillation (AF) recurrence after pulmonary vein isolation (PVI).Materials and methods This retrospective study initially included 1,551 AF patients who underwent PVI. After data integrity screening and 1:1 propensity score matching (PSM) to balance confounding factors, the final analysis cohort consisted of 302 patients (151 with recurrence and 151 without recurrence). EAT was segmented from preoperative CT angiography images using a SwinUNETR model, which was pre-trained via transfer learning on manually annotated images. Following segmentation, radiomic features were extracted. Subsequently, six machine learning models were developed and evaluated.Results The SwinUNETR segmentation model achieved a dice similarity coefficient of 0.87. For AF recurrence prediction, the fusion model demonstrated superior and robust performance in internal validation. The random forest-based fusion model achieved the highest area under the curve (AUC) of 0.81 (95% CI: 0.59-0.87). Key predictive features included NT-proBNP and texture heterogeneity features from EAT, which align with known pathophysiological mechanisms involving systemic inflammation, metabolic dysregulation, and local atrial adipose tissue remodeling.Conclusion A fusion model incorporating EAT radiomics and clinical variables effectively predicts AF recurrence after PVI, with ensemble methods showing optimal performance. This study provides a multiscale, interpretable computational tool for individualized postoperative risk stratification, highlighting the complementary role of EAT imaging biomarkers to systemic clinical factors.

Diabetic kidney disease (DKD) is one of the leading causes of kidney failure (KF). Emerging lipid markers such as remnant cholesterol (RC) and apolipoprotein B (ApoB) have been associated with kidney disease progression. However, no study has comprehensively evaluated the associations of these two markers—analyzed both continuously and jointly—with kidney function decline and progression to KF among patients with established DKD. In this retrospective cohort study, we included 472 hospitalized patients with DKD. RC and ApoB were primarily analyzed as continuous variables, with effects expressed per 1‑standard deviation (SD) increment. For descriptive purposes, patients were also categorized into four joint groups according to baseline medians (RC: 0.76 mmol/L; ApoB: 0.91 g/L). Mixed‑effect regression models were used to estimate the eGFR slope, and Cox proportional hazards models were used to assess KF risk. During a median follow‑up of 26.4 months (among 435 patients after excluding those with follow‑up < 6 months), 59 patients (13.6

Introduction:In China, the increase in high-risk pregnancies along with rising maternal age and complications has underscored the need for the development of maternal and newborn risk management programmes. The Chinese National Maternal and Newborn Safety Action Plan (CNMNSAP) was initiated in 2017. Given that neonatal mortality is a key indicator of healthcare quality, we evaluate the real-world effects of CNMNSAP against neonatal mortality among pregnant women with high-risk conditions. Methods:In this retrospective, matched, population-based cohort study, we collected information on all pregnant women with clinically diagnosed conditions from electronic medical records in Chengdu, China, between July 2014 and December 2019. Individual-level data, covering all healthcare services and testing records in public hospitals, were obtained and categorised into two groups based on the timing of CNMNSAP implementation (pre-CNMNSAP vs post-CNMNSAP). After 1:1 propensity score matching, we calculated the annual percentage change (APC) of neonatal mortality within 7 days post-delivery and compared outcomes between two groups of pregnant women with conditions. We then employed multivariate log-binomial regression models to examine the association between the CNMNSAP implementation and temporal changes in neonatal mortality. Results:During the 5-year study period, a total of 241 343 women with high-risk conditions delivered prior to CNMNSAP and 163 367 after its implementation. After 1:1 propensity score matching, 299 190 mothers were included for analysis. We estimated that the APC changed from 10.0% (95% CI -0.4% to 21.5%) prior to the maternal risk management programme to -28.5% (95% CI -44.2% to -8.4%) after its implementation, with an attributed risk reduction of 1.29 neonatal deaths per 1000 deliveries annually. In subgroup analysis, we found a significant reduction in neonatal mortality after policy implementation among mothers aged 18-34 years, those with a normal body mass index and those having a history of abortion. Conclusions:The CNMNSAP was found to be associated with a significant annual reduction in early neonatal mortality risk among pregnant women with high-risk conditions in Chengdu, China. The maternal risk management programme effectively improved outcomes for high-risk pregnancies, highlighting the importance of maternal risk classification and management throughout pregnancy.

Background:Although CD4 recovery has been widely studied, its long-term temporal dynamics and phase-specific characteristics in the modern ART era remain incompletely characterized. Methods:This retrospective cohort study included adults with HIV in Nanjing, China, who initiated ART between 2010 and 2019 and maintained viral suppression. The optimal CD4 recovery trajectory model was identified using piecewise linear mixed-effects models with exhaustive grid search. Cumulative probability curves estimated probabilities of CD4 count recovery to ≥500 and ≥350 cells/μL. Results:2611 individuals contributing 22 970 person-years and 37 959 observations were analyzed. The best-fitting model identified a 4-phase CD4 recovery trajectory with breakpoints at 0.5, 2.5, and 6 years, characterized by the fastest increase during 0-0.5 years (265.4 cells/μL/year), progressive slowing during 0.5-2.5 and 2.5-6 years, and modest growth beyond 6 years (8.6 cells/μL/year). Cumulative probabilities of reaching both thresholds rose steadily but decelerated markedly after 6 years. When stratified by baseline CD4 counts at ART initiation, compared with the 350-499 subgroup, the <200 subgroup showed slower early CD4 count gains (≤49: -57.9; 50-199: -40.3 cells/μL/year) during 0-0.5 years, but accelerated increases during 0.5-2.5 years (≤49: +28.3; 50-199: +10.2 cells/μL/year) that persisted after 6 years (≤49: +4.7; 50-199: +5.3 cells/μL/year). Cumulative probabilities of reaching both thresholds in the <200 subgroup increased continuously throughout follow-up, whereas those with higher baseline levels plateaued at 6 years. Conclusions:CD4 recovery under sustained viral suppression followed a phase-specific trajectory. Individuals with advanced immunosuppression showed delayed but sustained CD4 recovery. These findings may help understand when CD4 recovery approaches its maximal potential.

BACKGROUND:Little is known about the immune response induced by the SARS-CoV-2 Omicron BA.5 variants or the inhaled adenovirus vector vaccine. In this study, we investigated the short-term profile of antireceptor-binding-domain IgG antibody responses elicited by either the virus or the vaccine. METHODS:A cohort of healthcare workers who were infected with Omicron BA.5 or who received the inhaled adenovirus vector vaccine was identified between August and December 2022. Blood samples were collected twice to detect IgG antibodies against the receptor-binding domain of the SARS-CoV-2 spike protein. Baseline characteristics were obtained through questionnaires. Multivariate linear mixed-effect models were applied to assess the changes in IgG antibody levels between the two laboratory test time points, adjusting for baseline covariates. RESULTS:Among 1146 identified healthcare workers, a total of 419 healthcare workers with known infection status who provided informed consent were eligible for inclusion in the study. The study participants were mostly female (71.8%), had received three doses of intramuscularly injected inactivated vaccine before follow-up (93.8%), and had no comorbidities (94.3%). We estimated that the IgG level increased by 5.1% per week over the 2 months following Omicron BA.5 infection. No significant change in IgG antibody levels in the short term was observed within 1 month after receiving the inhaled adenovirus vector vaccine. CONCLUSION:These findings suggested that the inhaled adenovirus vector vaccine may provide modest protection against Omicron BA.5 infection in the healthcare worker population.

In this article, we investigate the dynamical behavior and asymptotic profiles for a host-pathogen epidemic model, where the different advection rates and degenerated heterogeneous diffusions are adopted and the total population is variable. First, the scalar equation of susceptible with diffusion d(S)(& sdot;) and advection rate qS is investigated. The existence, global stability and prior estimations of positive steady state are established, and then the asymptotic properties of positive steady state are discussed as d(S)(& sdot;) and qS approach to zero or infinity, respectively. Next, the well-posedness of solutions for the model, including the global existence, nonnegativity and ultimate boundedness of solutions, and the existence of global attractor are established. Following, the display expression of the basic reproduction number R-0 is calculated by means of the variational method. The local reproduction number R(& sdot;), the special forms R (0), R- 0, R- 0 of R-0 and the relationships between these reproduction numbers are presented. Then, the global dynamics of solutions for the model in terms with R0 are established by using the comparison principle, properties of principle eigenvalue and the persistence theory of dynamical systems. That is, when R-0 < 1, the disease-free steady state is globally asymptotic stable, otherwise when R-0 > 1, the disease is uniformly persistent. Furthermore, it is proved that R-0 is monotonically decreasing with respect to advection rate q(I )of infected individuals. The asymptotic profiles of R-0 in relation to the heterogeneous diffusion rates dS(& sdot;), dI (& sdot;) and advection rates q(S), q(I) approaching zero or infinity are discussed in detail by means of the display expression of R0 and the corresponding principal eigenvalue and weight eigenvalue problems, including the eighteen limit cases of d(S)(& sdot;), d(I) (& sdot;), q(S) and q(I) , and involving single limits and double limits. Finally, some open questions are proposed for the model, left us to further explore. Compared with the existing results for the constant diffusion rates and common advection rate, our model is more general and more complicated, the results established in this paper are richer and more meaningful

Objectives Construct a seasonal dynamic model that captures the transmission characteristics of brucellosis in Gansu, Guangdong and Sichuan provinces of China, in order to fit and predict the epidemic trends of new human brucellosis, and further formulate scientific and targeted prevention and control strategies. Methods Based on the number of new human brucellosis cases reported by the Centers for Disease Control and Prevention in Gansu, Guangdong and Sichuan provinces from 2021 to 2024, a seasonal SEIV dynamic model of brucellosis transmission between sheep/cattle and humans was constructed, with parameters estimated by using the nonlinear least squares method and the Markov Chain Monte Carlo method. The model fitted the epidemic trends of new human brucellosis and estimated the basic reproduction number R 0 . Through parameter sensitivity analysis, effective prevention and control measures can be proposed. Results The established seasonal SEIV dynamic model can well fit the number of new human brucellosis cases and cumulative new human brucellosis cases in Gansu, Guangdong and Sichuan provinces, respectively. The calculated mean absolute percentage error ( MAPE ) values were approximately 20% and 6%, respectively, indicating a good agreement between the fitted and actual values. It is projected that Gansu and Sichuan provinces will reach peak values of 146,310 and 157,903 cases in July and May 2038, respectively, followed by a gradual declines toward a stable state. The epidemic in Guangdong province will reach a relatively stable, sustained prevalence by 2038. The estimated basic reproduction number R 0 for brucellosis transmission in Gansu, Guangdong and Sichuan provinces were 2.2510 (95%CI: 2.2160 - 2.2859), 2.7937 (95%CI: 2.7592 - 2.8283) and 2.9499 (95%CI: 2.9007 - 2.9992), respectively. These findings indicate that brucellosis will continue to spread under the current prevention and control measures. Finally, sensitivity analyses of the number of new human brucellosis cases and R 0 were conducted based on specific parameters, it is demonstrated that increasing the culling rate of infected sheep/cattle, raising the vaccination rate of susceptible sheep/cattle, and reducing the immune loss rate of vaccinated sheep/cattle can effectively suppress the spread of brucellosis. Conclusion The constructed seasonal SEIV dynamic model can accurately simulates the epidemic trends of new human brucellosis cases in Gansu, Guangdong and Sichuan provinces, quantitatively proposes effective prevention and control measures, and lay a theoretical foundation for combating the spread of brucellosis.

BackgroundNortheastern China is a recognized hotspot for tick-borne viral diseases. However, there remains a lack of methods capable of simultaneously detecting these emerging and re-emerging tick-borne viruses.MethodsPrimer and probe sets were designed using Beacon Designer 8.0, targeting conserved regions of viral genomes from Alongshan virus (ALSV), Tick-borne encephalitis virus (TBEV), Severe fever with thrombocytopenia syndrome virus (SFTSV), Beiji nairovirus (BJNV), Yezo virus (YEZV), and Songling virus (SGLV). Recombinant plasmids were constructed to assess assay sensitivity, while virus-positive cDNA from tick samples was used to verify specificity. The performance of these assays was further validated using field-collected tick samples, with results compared against established reference methods serving as gold standards.ResultsSpecific primer and probe sets were designed with amplicon lengths ranging from 83 to 199 bp. Optimization of the reaction components yielded final primer volumes of 0.2–0.5 μL and probe volumes of 0.4–1.0 μL (from 10 μM working stocks). Sensitivity analysis demonstrated a limit of detection (LOD) as low as 10 copies/μL for all six viruses, while specificity testing confirmed no cross-reactivity among the targets. In validation trials, the established TaqMan RT-qPCR assays showed complete concordance with reference methods for ALSV (5/15) and TBEV (4/14). Notably, the TaqMan assays identified additional positive samples for SFTSV (n=1), BJNV (n=3), and YEZV (n=1) that were missed by the reference assays, particularly in samples with low viral loads. Conversely, two samples that tested positive for SGLV using SYBR Green RT-qPCR yielded negative results with the TaqMan RT-qPCR assay.ConclusionWe have successfully developed a suite of highly sensitive and specific single-plex TaqMan RT-qPCR assays for the rapid detection of six key tick-borne viruses in northeastern China. These assays facilitate efficient viral surveillance in tick populations and provide a diagnostic tool with significant potential for clinical applications.

Background: Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia worldwide. Catheter ablation is the firstline therapy for symptomatic/refractory AF, yet post-procedural recurrence remains extremely common, driving a high rate of repeat ablation procedures. Repeat ablation is associated with elevated medical costs, incremental procedural risks, and impaired quality of life and clinical outcomes in affected patients. Existing clinical risk scores for predicting repeat AF ablation have limited discriminative ability, poor interpretability, and suboptimal clinical utility. This study aimed to develop and validate an explainable machine learning model, using routine clinical and echocardiographic features, to predict the risk of requiring repeat catheter ablation for AF. Methods: A retrospective cohort of 1073 patients undergoing AF ablation from 2012 to 2023 was analyzed, with data split into training (70%) and testing (30%) sets. Feature selection was performed using LASSO regression and the Boruta algorithm, followed by the construction of eight machine learning models. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1 score, balanced accuracy, Brier score, and clinical utility via decision curve analysis. Interpretability was enhanced using Shapley Additive Explanations (SHAP). Results: Among 1073 patients undergoing AF ablation, 352 (32.8%) required a second procedure. LASSO regression combined with the Boruta algorithm identified nine predictive features: NT-proBNP, age, globulin (GLO), direct bilirubin (DBIL), left ventricular ejection fraction (LVEF), cystatin C (Cys-C), smoking history, creatine kinase (CK), and urea. Among the eight models evaluated, XGBoost demonstrated the best overall performance, achieving an AUC of 0.811 (95% CI: 0.762-0.859) in the testing cohort, with a sensitivity of 0.748, specificity of 0.726, and Brier score of 0.1682. It also outperformed alternative models in terms of F1 score and clinical net benefit. SHAP analysis confirmed NT-proBNP and age as the most influential predictors, alongside non-linear contributions from the remaining variables. Conclusion: The XGBoost model may provide a useful and interpretable tool for predicting repeat AF ablation, providing clinical insights to guide patient management and optimize procedural outcomes.

Introduction Unintended pregnancy and sexually transmitted infections (STIs) are major public health issues in developing countries. While long-acting reversible contraception (LARC) effectively prevents unintended pregnancy, there is limited evidence from large multinational studies on its association with condom use and STI-related outcomes. This study aimed to investigate the association between LARC use, condom use and STIs among women in developing countries.Methods This serial cross-sectional study extracted data from Demographic and Health Surveys (DHS), a series of nationally representative household surveys conducted in developing countries. The analysis included women aged 15 to 49 years, with data on contraceptive methods and demographics. Generalised linear mixed effect models (GLMMs) were used to estimate adjusted prevalence ratios (aPRs) for condom use and self-reported STI-related outcomes, comparing LARC users with both non-LARC users and oral contraceptive users. Subgroup analyses were conducted at the individual level and at the country level.Results Data from 2 171 884 women across 31 countries were analysed. Overall, the prevalence of self-reported STI-related outcomes was 7.4%, 3.9% of participants used LARC, and 7.5% of participants reported consistent condom use. LARC users were significantly less likely to use condoms compared with non-LARC users (aPR=0.40, 95% CI 0.30 to 0.53) and compared with oral contraceptive users (aPR=0.61, 95% CI 0.48 to 0.78). LARC use was associated with a higher prevalence of STI-related outcomes compared with non-LARC users (aPR=1.19, 95% CI 1.10 to 1.28) and compared with oral contraceptives users (aPR=1.14, 95% CI 1.05 to 1.24). Associations were stronger in low-Human Development Index (HDI) countries, especially among younger women (15–19 years), but were not significant in high-HDI countries. Country-level heterogeneity was observed.Conclusions LARC use is associated with reduced condom use and higher self-reported STI prevalence, particularly among younger women and in lower HDI developing countries. These findings support integrating STI prevention into LARC services and promoting dual-method use to prevent both unintended pregnancies and STIs.

BACKGROUND:The tolerance of inactivated SARS-CoV-2 vaccines in people living with HIV (PLWH) remains unclear. We aimed to evaluate the tolerance of inactivated SARS-CoV-2 vaccines in PLWH. METHODS:This retrospective cohort study recruited 3327 PLWH for questionnaires and laboratory testing. Subjects were screened to ensure they were receiving antiretroviral therapy for PLWH without SARS-CoV-2 infection. Poisson regression analyses were conducted to assess the association between vaccination and HIV viral rebound, estimating absolute risk difference and relative risk (RR). RESULTS:A total of 724 PLWH without SARS-CoV-2 infection participated in this study. No significant increase in HIV viral rebound risk was observed after vaccination in the 1/2-dose, 3-dose, and 4-dose groups compared to the 0-dose group. The RRs for the 1/2-dose, 3-dose, and 4-dose groups were 1.22 (95% confidence interval [CI]: 0.55, 2.72), 0.90 (95% CI: 0.48, 1.69), and 1.01 (95% CI: 0.35, 2.89), respectively. Similar results were observed across subgroups. Post-vaccination adverse reactions were minimal, occurring in 2.16% of cases, mostly fatigue and muscle soreness. CONCLUSION:Our study suggests that inactivated SARS-CoV-2 vaccines do not adversely affect the risk of HIV viral rebound and were well-tolerated in PLWH.

Alpine ecosystems on the Tibetan Plateau are characterized by different soil hydrothermal conditions and vegetation composition across the elevation gradient, and contribute differently to the net landscape methane (CH4) budget. However, the spatiotemporal variation of CH4 fluxes from alpine ecosystems remains poorly understood, underpinning the uncertainty of upscaling the regional and global CH4 budgets. Here, we investigated the spatial and temporal patterns and environmental controls of CH4 fluxes over two years across a Tibetan alpine landscape spanning different elevations (spanning 3200-3500 m above sea level) and major ecosystem types (including alpine meadow, steppe, forest and wetland). On the annual scale, all alpine upland (meadow, steppe and forest) ecosystems consistently functioned as soil CH4 sinks, ranging between 1.12 and 2.49 kg C ha(-1) yr(-1), whereas alpine wetlands emitted 17.2-34.3 kg C ha(-1) yr(-1) to the atmosphere. Non-growing season CH4 fluxes accounted for 29-46 % of the annual budgets, underscoring its significant contribution that was often neglected in previous studies. Our study also demonstrated that for individual alpine upland and wetland ecosystems, soil water content and soil temperature were the main factors regulating the seasonal patterns of CH4 fluxes. While across all alpine ecosystems, soil water content outweighed temperature as the primary control on the landscape patterns of CH4 fluxes and higher wetland CH4 emissions were associated with increased soil inorganic N availability. Despite their small area contribution to the landscape, alpine wetlands emitted disproportionate amounts of CH4, weakening the landscape CH4 sink. The resulting net landscape CH4 balance was a weak sink of 0.72 kg C ha(-1) yr(-1). Overall, the multiple parameters and insights gained from our study provide valuable information for better predicting the role of alpine ecosystem CH4 carbon-climate feedbacks in high-altitude regions.

This study developed and validated a machine learning (ML) model to predict in-hospital cardiac mortality in 18,727 atrial fibrillation (AF) patients using electronic medical record data. Four ML algorithms—random forest, extreme gradient boosting (XGBoost), deep neural network, and logistic regression—were applied to 79 clinical variables, including demographics, vital signs, comorbidities, lifestyle factors, and laboratory parameters. The XGBoost model achieved the best performance, with an area under the curve of 0.964 ± 0.014 in the training set and 0.932 ± 0.057 in the validation set, alongside precision, accuracy, and recall of 0.909 ± 0.021, 0.910 ± 0.021, and 0.897 ± 0.038, respectively. Shapley Additive Explanations identified key predictors such as thyroid function indices (e.g., total triiodothyronine, total thyroxine), procalcitonin, N-terminal pro-brain natriuretic peptide, and international normalized ratio. This interpretable model holds promise for improving early risk stratification and individualized care in AF patients. Prospective, multi-center validation is needed to confirm its generalizability.

BACKGROUND:To develop a nomogram for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with postoperative early-stage (pT1-2N0M0) tongue squamous cell carcinoma (TSCC), and to explore the association between postoperative radiotherapy (PORT) and patient survival. METHODS:Data from 7,637 patients with pT1-2N0M0 TSCC who underwent surgery between 2000 and 2021 were extracted from the SEER database. Patients were randomly divided into a training cohort and a validation cohort in a 2:1 ratio. Prognostic factors were identified via Kaplan-Meier analysis and Cox regression, and a nomogram was constructed. To minimize confounding, propensity score matching (PSM) was used to compare outcomes between patients who received PORT and those who did not. Subgroup and interaction analyses were performed to assess potential effect modifiers. RESULTS:Of the 7,637 patients included, 1,336 (17.5%) received PORT. Multivariate Cox analysis identified age, race, marital status, grade, tumor size, lymph node (LN) removed status, and PORT as independent prognostic factors for OS and CSS. The nomogram demonstrated strong predictive performance based on time-dependent ROC curves, concordance indices, calibration plots, and decision curve analyses in both training and validation cohorts. After PSM, PORT remained associated with worse OS and CSS. Subgroup analyses revealed that the association between PORT and poorer OS was most evident in younger patients, married individuals, T1 stage patients, those with smaller tumors, and those without LN removal, with racial disparities also observed. For CSS, this association was more pronounced in married individuals, well-differentiated patients, T1 stage patients, those with smaller tumors, and those without LN removal. CONCLUSION:The SEER-based nomogram provides survival predictions for postoperative pT1-2N0M0 TSCC patients. Although PORT was associated with worse survival in several subgroups, findings should be cautiously interpreted given the observational design and absence of key clinical variables (PNI, LVI, surgical margins). Prospective studies incorporating comprehensive clinicopathological data are warranted to confirm associations and guide individualized PORT decisions in early-stage TSCC patients.

This study centers on Urumqi, utilizing whole-genome sequencing and comparative genomics, we explored virulence factor mutations in different Mycobacterium tuberculosis lineages and their impact on tuberculosis patient prognosis. We utilized routine national drug resistance surveillance data from Urumqi, gathering demographic, epidemiological, and clinical data of patients with tuberculosis between 1 January 2017 to 31 December 2021. Whole-genome sequencing was employed, followed by bioinformatics analysis using various methods and statistical models. A total of 457 patients with tuberculosis were analyzed. Through whole-genome sequencing and bioinformatics analysis, we categorized these strains into three lineages: Lineage2 (347), Lineage3 (37), and Lineage4 (73), identifying 71 virulence factor mutations. The mutation rates of virulence factors in M. tuberculosis exhibited polarization. Significant differences in virulence factor mutation rates were observed among different M. tuberculosis lineages (all p values  < 0.05). Additionally, mutations in espE, fadE29, and mbtI genes among Lineage2 patients were considered as risk factors influencing treatment outcomes (all p values < 0.05), with odds ratios of 13.6200 (1.7285-107.3201), 7.1262 (1.3294-38.1997), and 14.8340 (1.1577-190.0784), respectively. Varied virulence factor mutations and virulence factor-related gene mutations exist across different M. tuberculosis lineages. Mutations in the espE, fadE29, and mbtI genes are risk factors that significantly affect the treatment outcome of Lineage2 patients. This finding serves as a reference for investigating the future evolutionary direction, transmissibility, drug resistance, and pathogenicity of M. tuberculosis virulence factors in regions with diverse lineages and frequent population movements.

The circulating enteroviruses (EVs) serotypes in hand, foot and mouth disease (HFMD) inpatients remained unclear. This study aimed to investigate the serotype-specific associations between clinical characteristics and severity of HFMD inpatients. The study utilised a prospective, hospital-based cohort design and a tiered diagnostic algorithm incorporating real-time RT-PCR and nested RT-PCR for serotyping. Clinical data were prospectively collected throughout hospitalization. Clinical severity was measured using diagnoses of central nervous system (CNS) complications and three other outcomes. A total of 1768 inpatients were enrolled consecutively between February 2017 and February 2018. The proportions of CNS complications varied by serotype (p < 0.001), with the highest for EV-A71 (40%), followed by CV-A4 (17%), CV-A2 (13%), CV-A10 (10%), CV-A6 (7%), and CV-A16 (4%). Children with CV-A2 and CV-A4 were less likely to have rashes on hands, feet, or buttocks and more likely to develop high fever, while those with EV-A71 had fewer mouth lesions. Of 230 lab-confirmed HFMD inpatients with CNS complications, EV-A71 accounted for 45% while CV-A6, CV-A16, CV-A4, CV-A10 and CV-A2 accounted for 35%. The logistic regression analysis revealed that non-CNS-specific symptoms such as cold limbs and vomiting, and clinical testing indicators including blood globulin, platelet, serum chloride and neutrophil counts, were associated with CNS complications. Non-EV-A71 EVs can also cause severe diseases, but those with EV-A71 infection are more likely to suffer CNS complications and other severe manifestations. The study highlighted the emergence of enterovirus serotypes, suggesting the need for future research on virus changes and associated disease burden.