Sheng Wei

Background: In previously vaccinated older adults, SARS-CoV-2 breakthrough infection induces hybrid immunity, but SARS-CoV-2-specific IgG antibodies may still decline rapidly. Longitudinal serum proteomic correlates of rapid IgG waning in older adults remain insufficiently characterized. Methods: In a nested case-control study within the Yichang COVID-19 Antibody Longitudinal Survey (YC-CALS) cohort, 67 previously vaccinated elderly participants with monotonic SARS-CoV-2-specific IgG decline after breakthrough infection were included: slow decliners ( n = 33) and rapid decliners ( n = 34). None received additional COVID-19 vaccination during serological follow-up. Serum proteomics was performed on March and October 2023 samples. Linear mixed-effects models, adjusted for age, sex, BMI, and comorbidities, were used to identify candidate dynamic and endpoint proteins. Baseline proteomic profiles and clinical characteristics were then used for exploratory Lasso-based feature selection and internally cross-validated classification modelling. Results: Rapid decliners were older and showed distinct longitudinal proteomic trajectories. Thirteen candidate dynamic proteins, including IGHA1, AZGP1, SERPINA1, and C9, showed significant group-by-time interaction signals and were enriched in complement activation, coagulation cascades, humoral immunity, and serine protease inhibitor activity. Baseline modelling identified 14 Lasso-selected protein features. In exploratory internal cross-validation, the neural network model showed the highest discrimination among the algorithms evaluated (AUC = 0.872), and SHAP analysis highlighted C1QB, GRN, AZGP1, and CFHR4 as major contributors. Conclusions: Longitudinal serum proteomics suggests that rapid hybrid immunity-associated IgG waning after breakthrough infection in elderly adults is associated with complement-coagulation activity and sustained innate immune activation. Baseline proteomic signatures may provide hypothesis-generating candidate biomarkers for future externally validated risk-stratification studies. Clinical trial number: Clinical trial number: not applicable.

Background: Unstructured electronic health records (EHRs) hinder the monitoring of intestinal infections. Large language models (LLMs) enable automated symptom extraction. However, their clinical validation is limited by a lack of systematic multimodel comparisons, unclear prompting strategies, and the privacy risks of cloud-based models (eg, data leakage and cross-border data transfer). Objective: This study aimed to systematically evaluate the performance of locally deployed open-source LLMs across 4 model families in extracting intestinal symptoms from unstructured EHR chief complaints under different prompting strategies. Methods: From a citywide health care information platform in Wuhan, China, we randomly selected 1000 chief complaints from outpatient records of intestinal clinics, infectious disease departments, pediatrics, and fever clinics. Six symptoms related to intestinal infectious diseases—diarrhea/bloody/mucoid stools, vomiting, abdominal pain, fever, nausea, and rash—were manually annotated as a gold-standard dataset. Twelve locally deployed open-source LLMs across 4 families, namely, Gemma3 (1b, 4b, 12b), Qwen3 (1.7b, 8b, 14b), DeepSeek-R1 (1.5b, 7b, 14b), and Llama (Llama2-Chinese 7b, 13b; Llama3.1 8b), were evaluated on the symptom extraction task using the gold-standard dataset. Three prompting strategies (no-role, zero-shot, and few-shot) were tested. Performance metrics included accuracy, precision, recall, F ₁ -score, specificity, balanced accuracy, and inference time. Statistical comparisons used Friedman tests for global differences, followed by Wilcoxon signed-rank and Mann-Whitney U tests with Bonferroni and false discovery rate corrections for pairwise comparisons. Results: Among the 4 families, Qwen3 models showed higher F ₁ -scores and balanced accuracy, with Qwen3-1.7b achieving a macroaveraged F ₁ -score of 0.85 under zero-shot prompting and Qwen3-8b reaching 0.89 under no-role prompting, while Gemma3 demonstrated robust performance at small to medium scales. Symptom-wise, models agreed more on frequent symptoms such as diarrhea and fever, whereas greater variability was observed for rarer symptoms like rash and nausea. The effect of prompting strategy varied across models, with no single strategy consistently outperforming the others. Although some pairwise differences reached statistical significance ( P <.05), the absolute gains in F ₁ -score were small. Conclusions: This study provides a systematic comparison of several open-source LLMs on a structured intestinal symptom extraction task. Among the LLM families, Qwen3 models offer a favorable balance between accuracy and efficiency, making them suitable for resource-constrained scenarios.

Background: Public health surveillance systems are critical for decision-making and have been advanced by monitoring infectious diseases. Objective: This study aims to assess the effectiveness and timeliness of multiple surveillance systems in tracking COVID-19 cases in the postpandemic era. Methods: Data of COVID-19–reported cases in a southern city of China were collected from the National Notifiable Disease Reporting Information System over a 1-year period, following the easing of the COVID-19 pandemic restrictions (from April 1, 2023, to June 30, 2024) as the operational benchmark. A total of 4 surveillance systems (hospital, wastewater, meteorological, and internet search engine) were integrated into a daily time series. Spearman correlation and 60-day moving window analyses with 7-day lags were used to assess associations. Distributed lag nonlinear models captured nonlinear meteorological effects. Time-series regression models assessed lead effects (0‐7 d) of each surveillance indicator, with and without meteorological adjustment. Results: Among 4 surveillance systems, 16 variables correlated significantly with reported cases. The nucleic acid amplification test (NAAT) positivity rate showed the strongest correlation, with a coefficient of 0.834 (95% CI 0.803‐0.860). Wastewater surveillance system demonstrated a moderate correlation, with the correlation coefficient of 0.776 (95% CI 0.737‐0.810) for the N gene positivity rate and 0.698 (95% CI 0.648‐0.743) for the N gene concentration. Moving-window analyses confirmed a stable correlation between NAAT positivity and reported cases (median 0.534, IQR 0.394‐0.724; 58% of windows ρ>0.5), while wastewater indicators exhibited greater temporal fluctuation, with the N gene concentration (median 0.585, IQR 0.214‐0.766; 60.8% of windows ρ>0.5) exceeding the N gene positivity rate (median 0.530, IQR 0.222‐0.742; 53.5% of windows ρ>0.5). Time-series analysis identified same-day associations (lag 0) for both NAAT positivity ( β =.819, 95% CI 0.768‐0.870) and wastewater signals (maximum effect: β =1.023, 95% CI 0.931‐1.115). Meteorological factors significantly modified the effect of internet surveillance indicators ( P <.05), particularly temperature and absolute humidity. Conclusions: An integrated, multichannel surveillance strategy of leveraging wastewater, clinical, and digital streams with meteorological contextualization can strengthen early warning and situational awareness for respiratory pathogen threats.

Following the adjustment of the dynamic zero-COVID-19 strategy, China has witnessed a rapid increase in COVID-19 cases. However, the relationship between diverse vaccination statuses and the spectrum of symptom presentation, disease severity, and long-term post-COVID conditions remains to be fully understood. This study aimed to assess the impact of vaccination on the incidence of SARS-CoV-2 infections, the manifestation of acute symptoms, and the occurrence of long-term symptoms post-infection within the context of China's revised pandemic prevention and control measures. This study investigated 6,572 participants from the Yichang COVID-19 Antibody Longitudinal Survey (YC-CALS) in March 2023, focusing on demographic and health-related information, including vaccination history, infection status, and symptomatology. Univariate and multivariate analysis methods were used to determine the correlations between vaccination status and COVID-19 outcomes. Among the surveyed participants, 85.91% reported positive for SARS-CoV-2. Of those who were positive, 96.79% experienced at least one common symptom, with fever (79.34%), cough (62.56%), and fatigue (60.59%) being the most reported symptoms. Despite the high incidence of symptoms, only 2.05% of those who tested positive needed hospitalization. Importantly, symptoms such as palpitations, chest pain, and shortness of breath persisted beyond 90 days in certain individuals. Booster shots significantly reduced the risk of specific symptoms including dyspnea, eye soreness and fatigue, and gastrointestinal discomfort (P < 0.05). Vaccination beyond 12 months significantly increased symptoms such as decreased sense of smell, palpitations, and chest distress (P < 0.05). Additionally, the study found a positive correlation between the timing of vaccination and the development of long COVID symptoms, with individuals vaccinated more than 12 months prior exhibiting a higher likelihood of enduring symptoms such as fatigue (OR = 1.218, P = 0.023) and headache dizziness (OR = 1.244, P = 0.038) for over one month. The findings highlight the critical importance of vaccination in controlling the spread of COVID-19 and reducing symptoms and disease prognosis, providing important insights for future vaccine policy.