Background Meteorological factors are among the key extrinsic triggers for the onset and exacerbation of cardiovascular and cerebrovascular diseases (CVD). Against the backdrop of sustained global warming, elucidating the impact of ambient temperature and atmospheric pressure on CVD, especially on pre-hospital CVD emergent events, has become imperative for evidence-based prevention and emergency preparedness. Objective To quantify the temporal trends of daily mean temperature and atmospheric pressure and their associations with pre-hospital CVD emergent events in Yantai, and to explore effect modification by demographic subgroups and geographic areas, thereby providing an empirical basis for the rational allocation of emergency medical resources. Methods Pre-hospital CVD emergency data from January 1, 2016 to December 31, 2022 were selected from the Yantai 120 Emergency Medical Command System. Synchronous meteorological factors and environmental pollutant data were obtained from the websites of the National Oceanic and Atmospheric Administration and the National Centers for Environmental Information of the United States. Time-series analysis combined with distributed lag non-linear model was used to analyze the association between daily temperature, atmospheric pressure, and pre-hospital CVD emergencies. Average annual percentage changes (AAPC) were calculated using Joinpoint (version 5.2.0.0) to reflect temporal trends. Spearman correlation analysis was employed to screen variables with low collinearity for inclusion in the multi-pollutant adjusted models. Results From 2016 to 2022, a total of 268275 pre-hospital CVD emergency cases were recorded in Yantai City, showing an increasing annual trend (AAPC=7.16%, P=0.002). Emergency volume, daily temperature, atmospheric pressure, and PM₁₀ displayed marked seasonal patterns, with summer bottoms and winter peaks for emergencies, daily temperature higher in summer and lower in winter, and the inverse for atmospheric pressure. Correlation analysis showed that both daily temperature and atmospheric pressure were positively correlated with O₃, with correlation coefficients of 0.745 and 0.723, respectively; negatively correlated with PM_2.5, with correlation coefficients of −0.246 and −0.235, respectively; and negatively correlated with PM₁₀, with correlation coefficients of −0.241 and −0.229, respectively (P < 0.05). A U-shaped correlation was revealed between daily temperature and pre-hospital CVD emergency risk. Using 24 °C as the reference, risk was elevated at both low and high daily temperatures, with the strongest cold effect at –11 ℃ (RR=1.53, 95%CI: 1.35, 1.73) and strongest heat effect at 30 ℃ (RR=1.06, 95%CI: 1.01, 1.11); cold effects were prolonged (lag0–14d) compared to heat effects. Atmospheric pressure exhibited a positive association with emergencies; using a median of 1012.2 hPa as the reference, the maximum risk occurred at 1037 hPa (RR=1.25, 95%CI: 1.03, 1.52), with a 0–5 d lag. In the subgroup analysis, the impact of low temperature was more pronounced among females (RR_{−9 ℃}=1.66), people over 60 years (RR_{−9 ℃}=1.91), and rural residents (RR_{−9 ℃}=1.76). High atmospheric pressure significantly increased the risk of pre-hospital CVD emergency among females (RR_{1035 hPa}=1.54), people under 18 years (RR_{1035 hPa}=3.62), and rural residents (RR_{1035 hPa}=1.46). The sensitivity analysis results indicated that, after excluding the impact of pandemic and the degrees of freedom for time variations, the effects of daily average temperature and atmospheric pressure on pre-hospital CVD emergency volume were consistent with the primary analysis findings. Conclusion Both non-optimal temperature and atmospheric pressure significantly increase the pre-hospital CVD emergency volume; therefore, emergency medical services should develop targeted response protocols, optimize resource allocation, and intensify public health education to mitigate the pre-hospital burden attributable to temperature and pressure fluctuations.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide. Despite therapeutic advancements over recent decades, the prognosis for patients with metastatic CRC (mCRC) remains poor. Approximately 2%-4% of mCRC cases exhibit human epidermal growth factor receptor 2 (HER2) amplification or overexpression, defining a distinct molecular subtype. This HER2-positive status is strongly associated with primary resistance to anti-epidermal growth factor receptor (EGFR) therapies, which are the standard of care for patients with RAS wild-type tumors. Beyond its well-established role in breast and gastric cancers, HER2 has emerged as a pivotal biomarker and actionable therapeutic target in mCRC. However, selecting appropriate treatment strategies remains challenging due to patient heterogeneity and diverse molecular subtypes. This review systematically summarizes the molecular biology, diagnostic strategies, and advances in targeted therapies for HER2-positive mCRC. On the diagnostic front, we discuss the applications of immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), next-generation sequencing (NGS), and circulating tumor DNA (ctDNA) detection technologies. We highlight discrepancies in diagnostic criteria across key clinical trials—such as HERACLES, DESTINY, and MOUNTAINEER—underscoring the urgent need for standardized, CRC-specific definitions to ensure consistent patient selection and comparability of efficacy data across studies. Although NGS enables comprehensive genomic profiling, its cost-effectiveness relative to traditional methods must be carefully considered. Therapeutically, we summarize clinical trial data for HER2-directed agents, including tyrosine kinase inhibitors (TKIs) such as tucatinib and lapatinib, monoclonal antibodies like trastuzumab, bispecific antibodies, and antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan. We review dual-targeting strategies and note recent FDA approvals that represent significant milestones in second-line treatment. Additionally, we explore the potential of combining immune checkpoint inhibitors with HER2-targeted therapies to enhance antitumor immunity through mechanisms including antibody-dependent cellular cytotoxicity (ADCC) and modulation of the tumor microenvironment. ADCs enable precise delivery of cytotoxic payloads, reducing off-target toxicity while effectively inhibiting oncogenic pathways. A substantial portion of this review is dedicated to dissecting the molecular mechanisms underlying primary and acquired resistance to HER2-targeted therapies—persistent challenges that limit clinical benefit. These mechanisms include reactivation of downstream signaling pathways such as PI3K/AKT/mTOR and MAPK, concurrent mutations in genes like KRAS or BRAF, and alterations in HER2 expression that compromise treatment efficacy. For instance, specific HER2 mutations (e.g., L755S) can reduce drug binding affinity, while ctDNA monitoring facilitates early detection of emerging resistance clones during disease progression, thereby enabling timely therapeutic adjustments. Tumor heterogeneity and dynamic interactions with the microenvironment further complicate resistance patterns observed in clinical practice. HER2-targeted therapy represents a new frontier in precision oncology for mCRC, offering renewed hope for improving patient outcomes. Realizing this potential will require continued optimization of diagnostic algorithms and treatment workflows. Future efforts must focus on overcoming resistance, validating liquid biopsy approaches for dynamic monitoring, and establishing unified clinical guidelines. HER2 has become an essential biomarker for stratifying mCRC patients beyond traditional RAS and BRAF status, underscoring the shift from empiric treatment to biomarker-driven precision medicine. International, multidisciplinary collaboration will be critical to validate emerging biomarkers and refine treatment algorithms globally.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective: To assess the clinical application value of a prediction model for red blood cell (RBC) demand in surgical procedures. Methods: Demographic data, laboratory parameters, anesthesia and transfusion records, and model prediction data were retrospectively collected from surgical patients at the First Medical Center of Chinese PLA General Hospital between 2018 and 2024. Statistical analysis was performed using the Chi-square test, t-test, and Mann-Kendall trend test. Results: From 2018 to 2024, the predictive model for RBC demand in surgical procedures was used to evaluate a total of 112 293 surgeries. During this period, the model call rate (77.49%-98.91%, P<0.05), compliance rate (56.81%-84.92%, P<0.05), and prediction accuracy rate (66.82%-94.17%, P<0.05) all showed significant upward trends. The total blood usage across the hospital (13645.4-7723.5 units, P<0.05) and the average blood usage per surgery (0.21-0.1 units, P<0.05) exhibited overall downward trends. Postoperative average hemoglobin levels in the non-compliance group (112.1-105.3 g/L in the non-compliance group vs 106.9-92.7 g/L in the compliance group, P<0.05) and the intraoperative excessive transfusion rate (5.06%-6.05% in the non-compliance group vs 0.09%-0.04% in the compliance group, P<0.05) were significantly higher in the non-compliance group compared to the compliance group. Conclusion: The predictive model for RBC demand in surgical procedures has played a positive role in conserving blood resources, optimizing blood resource allocation, and reducing intraoperative risks.

OBJECTIVE To ensure the safety of patients’ drug use and control the risk of medical insurance expenditure by upgrading the pre-prescription review system to conduct pre-review on prescriptions from internet hospitals and external prescriptions， as well as to review the payment methods of drugs （including in-hospital and external drug dispensing）. METHODS The data interfaces of prescriptions from internet hospitals and external prescriptions were integrated to achieve real-time rational drug use intervention. Additionally， an intelligent review project for payment method was added to precisely intervene in the medical insurance payment methods of drugs. The effect of the system upgrade was evaluated by comparing the qualification rates of prescriptions from internet hospitals and external prescriptions and the suspected amounts of drug violations from January to April 2025 （before the system upgrade） and May to August 2025 （after the system upgrade）. RESULTS After the upgrade of the pre-prescription review system， the qualification rates of prescriptions from internet hospitals and external prescriptions increased by 3.5% [95% confidence interval （CI）＝0.3%-6.7%， P ＝0.037 ] ； the suspected amounts of drug violations decreased to 52.9% of the pre-upgrade level （95%CI＝31.6%-88.5%， P ＝0.026）， and the average monthly sequential decrease was 29.5% （95%CI＝12.2%-43.4%， P ＝0.012）. Moreover， the addition of the intelligent review project for payment methods promoted the management of off-label drug use in our hospital. After the upgrade， a total of 79 filling valid applications for off-label drug use were received and archived. CONCLUSIONS The upgrade of the pre-prescription review system effectively improves the review qualification rates of prescriptions from internet hospitals and external prescriptions and the accuracy of medical insurance payment for drugs， and strengthens the supervision of off-label drug use， achieving dual guarantees of clinical rationality and medical insurance compliance.

Driven by the growing practical need to accelerate drug development and the continuous innovation of trial design in recent years， the number of protocol amendments during clinical trials have gradually increased， and the changed contents have become more flexible and complex， which significantly heightens the difficulty of ethical review on amendments. Against this backdrop， it is of great importance to fully leverage the role and responsibilities of ethics committees， effectively control clinical trial risks， and ensure subject safety. This paper analyzed development trends of protocol amendments in recent years， sorted out requirements for protocol amendments in Chinese regulations and guiding principles， and examined difficulties of amendment ethical review in practical work. Based on these， targeted strategies and recommendations were proposed， namely， strengthening the integration with scientific review， enhancing the formal review， adjusting the scope of review according to approval notifications， and adopting appropriate review methods， with a view to providing insights and references for the management of the amendment ethical review in drug clinical trials.

Objective This study used The Cancer Genome Atlas (TCGA) database to investigate the methylation levels of PCDHGB7 in lung adenocarcinoma tissues and analyze its association with clinical pathological features of lung adenocarcinoma, and its prognostic predictive value. Methods Methylation data of PCDHGB7 from 473 lung adenocarcinoma tissues and 32 normal tissues in the TCGA database were derived and analyzed to assess between-group variance and determine the correlation of methylation levels with clinical pathological features and overall survival. Results PCDHGB7 exhibited significantly higher methylation levels in lung adenocarcinoma tissues than in normal tissues (P<0.001), demonstrating a strong discriminative ability for lung adenocarcinoma (AUC=0.926, P<0.001). The methylation level was strongly associated with gender (P=0.047) and T stage (P=0.013). The critical prognostic sites of PCDHGB7 were identified through univariate Cox regression analysis. We found significant differences in overall survival between patient groups with low or high methylation levels (P=0.0099). Conclusion PCDHGB7 shows significantly elevated methylation in lung adenocarcinoma, which correlates with tumor malignancy and possesses certain prognostic predictive potential.