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.

Zoledronic acid (ZA), a nitrogen-containing bisphosphonate with established clinical utility in osteoporosis management, exhibits emerging antitumor potential in estrogen receptor-positive breast cancer. However, the molecular mechanisms underlying its nonapoptotic anticancer effects remain poorly characterized. This study revealed that ZA induced ferroptosis in ER+ breast cancer cells through dual suppression of cystine-glutamate antiporter SLC7A11 and glutathione peroxidase 4 (GPX4), key repressors of ferroptosis. Pharmacological inhibition of ferroptosis using Ferrostatin-1 significantly attenuated ZA-induced cytotoxicity, while combinatorial treatment with the GPX4 inhibitor RSL3 synergistically enhanced lipid peroxidation and cell death. Mechanistically, ZA activated the Hippo-YAP signaling pathway, promoting YAP phosphorylation, proteasomal degradation, and cytoplasmic retention, thereby silencing SLC7A11 and GPX4. We established a novel metabolic vulnerability in hormone-responsive malignancies.These findings position ZA as a bifunctional ferroptosis inducer in ER+ breast cancer, offering a promising strategy to overcome endocrine resistance.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

Autologous blood patch pleurodesis (ABPP) was first proposed in 1987. Now it is mainly used to treat intractable pneumothorax and persistent air leakage after pneumonectomy, and also used to treat pneumothorax in children and other rare secondary pneumothorax. Persistent air leakage and pneumothorax of various causes are essentially alveolar pleural fistula. It can usually be treated by closed thoracic drainage, continuous negative pressure suction and surgery. Pleurodesis is a safe and effective alternative to surgery for patients who have failed conventional conservative treatment and can not receive operations. Compared with other pleurodesis adhesives, autologous blood (ABPP) is safer and more effective, and it is simple, painless, cheap and easy to be accepted by patients. But in the domestic and foreign researches in recent years, many details of ABPP treatment have not been standardized. For further research and popularization of ABPP, this article reviews the detailed regulations, efficacy and safety of this technology.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To explore the evaluation and relationship of hepatitis B virus(HBV)surface antigen(HBsAg)in clinical laboratory in different detection systems,further scientifically and reasonably to explain the test results for serving clinical practices.Methods:During the period from June 2021 to July 2022,100 425 specimens of patients with screened,suspected and confirmed HBV infections were collected from the clinical departments(mainly infectious hepatology)of Nanyang Central Hospital.Detection methodology included quantitative(electrochemiluminescence and chemiluminescence),qualitative(gold standard),semi-quantita-tive(ELISA),and highly sensitive HBV-DNA(RT-PCR)methods,and then analyzed the strengths and weaknesses and closeness be-tween each methodology.The relationship between the two Roche HBsAg detection systems was analyzed by correlation analysis.The HBsAg efficacy analysis was validated using Cut/Off value setting and detection limit,which in turn analyzed the distribution of false-positive and false-negative reporting models.Results:Detection results for low-and medium-concentration HBsAg showed a correla-tion between the electrochemical luminescence semi-quantitative method and ELISA method.ELISA method still had advantages in terms of sensitivity and specificity when detecting HBsAg,and there were no significant differences compared to domestic and interna-tional HBsAg quantitative detection systems.Performance validation conducted in accordance with the CNAS-GL038 document showed that the minimum detection limit for HBsAg calculated using the ELISA method in this laboratory was 0.1 U/ml.When the ROC curve Cut/Off value was set to 0.105,the area under the curve was the largest(AUC=0.986).Based on Roche's semi-quantitative electroche-miluminescence detection and patient medical history,in the common reporting model for hepatitis B five-item detection using ELISA method,HBsAg false positives occurred most frequently when HBsAg was positive alone,and HBsAg occurred most frequently in the false positive range when the OD value was less than 0.5.In ELISA method for detecting HBsAg,as the OD value increased from 0.01 to 0.10,the number of false-negative results also increased.Roche Elecsys HBsAg Ⅱ Quant Ⅱ and Elecsys HBsAg Ⅱ testing systems exhibited good linearity under certain conditions,with a ratio of approximately 1/0.18.In Elecsys HBsAg Ⅱ Quant Ⅱ detection sys-tem,the test results of HBsAg samples diluted 400 times were highly consistent with the original test results with a coefficient of deter-mination R2=0.993 8.Conclusion:There was a certain relationship between various detection systems of HBsAg at a suitable concen-tration.The detection of HBsAg by ELISA can meet the needs of clinical detection.

Objective:To explore the construction and efficacy of cranial magnetic resonance imaging(MRI)classification model based on attention mechanism in type 2 diabetes patients with cognitive impairment.Methods:The case data of 100 patients with type 2 diabetes who were treated in the General Hospital of North China Petroleum Administration Bureau from June 2022 to January 2024 were retrospectively selected.A total of 100 MRI images of cranial FLAIR_LongTR sequence with cognitive impairment(32 cases)and those without cognitive impairment(68 cases)were respectively collected.The images of the above two kinds of samples were horizontally and vertically translated to expand to 1000 samples,respectively.The samples were randomly divided into training samples(n=700)and test samples(n=300)as the ratio of 7:3 according to affine transformation data augmentation method.Then,the attention mechanism model was established to test the images with full scan of the test samples.The ability of the attention mechanism system in screening cognitive impairment was analyzed according to the method of setting threshold value.The 100 MRI images of cranial FLAIR_LongTR sequence of patients in our hospital from January 2024.From January to May 2024 were used as a verification set to verify the diagnostic value of attention mechanism.Results:With the increasing of iteration times,the sample loss of training and verification of attention mechanism model gradually decreased and tended toward stability,and the accuracy of training set and verification set gradually increased and tended toward stability.In the attention mechanism model,the average loss rate of training samples was 10.024%,and that of test samples was 15.247%.In the attention mechanism model,the average accuracy of training samples was 99.078%,and the average accuracy of test samples was 99.753%.Receiver operating characteristic(ROC)curves showed that the area under curve(AUC)of attention mechanism model was 0.998,which can better diagnose cognitive impairment of patients with type 2 diabetes than the resNET model(AUC=0.656)(Z=3.437,P<0.001).Conclusion:The constructed cranial MRI classification model by using attention mechanism has favorable diagnostic value for cognitive impairment in patients with type 2 diabetes.

Objective:Understanding of the psychological experiences of young and middle-aged male patients with schizophrenia regarding protective restraints.Methods:This study was qualitative study. Using purposive sampling, middle-aged and young male schizophrenia patients who had undergone protective restraints in the Affiliated Brain Hospital, Guangzhou Medical University were selected as participants from September to December 2022. Data were collected through semi-structured interviews and analyzed using Colaizzi 7-step analysis method.Results:A total of 12 young and middle-aged male schizophrenic patients were interviewed, and four themes were summarized, including patients' attitudes toward protective restraints, patients' emotional experiences, patients' physical experiences, and patients' needs during protective restraints.Conclusions:Protective restraints provide patients with safety in the acute phase, as well as a variety of positive and negative psychological experiences. Psychiatric nurses should grasp the patient's condition, dynamically assess the risk level, adopt appropriate restraint devices, improve restraint care based on the patient's needs, and actively seek alternative measures to restrain simultaneously.

Objective:To explore the construction and efficacy of cranial magnetic resonance imaging(MRI)classification model based on attention mechanism in type 2 diabetes patients with cognitive impairment.Methods:The case data of 100 patients with type 2 diabetes who were treated in the General Hospital of North China Petroleum Administration Bureau from June 2022 to January 2024 were retrospectively selected.A total of 100 MRI images of cranial FLAIR_LongTR sequence with cognitive impairment(32 cases)and those without cognitive impairment(68 cases)were respectively collected.The images of the above two kinds of samples were horizontally and vertically translated to expand to 1000 samples,respectively.The samples were randomly divided into training samples(n=700)and test samples(n=300)as the ratio of 7:3 according to affine transformation data augmentation method.Then,the attention mechanism model was established to test the images with full scan of the test samples.The ability of the attention mechanism system in screening cognitive impairment was analyzed according to the method of setting threshold value.The 100 MRI images of cranial FLAIR_LongTR sequence of patients in our hospital from January 2024.From January to May 2024 were used as a verification set to verify the diagnostic value of attention mechanism.Results:With the increasing of iteration times,the sample loss of training and verification of attention mechanism model gradually decreased and tended toward stability,and the accuracy of training set and verification set gradually increased and tended toward stability.In the attention mechanism model,the average loss rate of training samples was 10.024%,and that of test samples was 15.247%.In the attention mechanism model,the average accuracy of training samples was 99.078%,and the average accuracy of test samples was 99.753%.Receiver operating characteristic(ROC)curves showed that the area under curve(AUC)of attention mechanism model was 0.998,which can better diagnose cognitive impairment of patients with type 2 diabetes than the resNET model(AUC=0.656)(Z=3.437,P<0.001).Conclusion:The constructed cranial MRI classification model by using attention mechanism has favorable diagnostic value for cognitive impairment in patients with type 2 diabetes.

Objective:To develop a quality control model for anal sphincter ultrasound images and validate its diagnostic performance across multiple centers.Methods:A retrospective analysis was conducted on anal sphincter ultrasound images from seven medical centers in China between May 2019 and June 2022. A total of 7 040 images from 3 116 patients were included and divided into a training set（4 912 images）and a validation set（2 128 images）. The images were classified as standard or non-standard images by three experts. Three models were developed based on different image feature extraction methods：a single-branch model，a multi-branch weighted model，and a multi-branch ensemble model. The diagnostic performance of each model was evaluated using the area under the ROC curve（AUC），sensitivity，specificity，accuracy，positive predictive value，and negative predictive value，respectively. The optimal model was selected and compared with the performance of 4 doctors with varying experience levels. Sixty days later，the images with the assistance of the model's output were reassessed by the doctors to evaluate its impact on manual quality control.Results:① Among the 3 models，the multi-branch ensemble model demonstrated the highest AUC and sensitivity，with an AUC of 0.966（95% CI=0.958 - 0.974），a sensitivity of 91.83%，and a specificity of 91.41%. This model was named M quality. ② M quality's AUC was slightly lower than that of Senior A and B（0.966 vs. 0.976，0.976，and P<0.05），its sensitivity was slightly lower than that of Senior A（91.83% vs. 95.61%， P<0.001）but comparable to Senior B（91.83% vs. 92.89%， P=0.315），its specificity was slightly lower than Senior A and B（91.41% vs. 94.44%，98.18%，and P<0.05）. However，M quality significantly outperformed Junior A and B in AUC and sensitivity（AUC：0.966 vs. 0.850，0.818；sensitivity：91.83% vs. 84.90%，61.46%；all P<0.001），its specificity was higher than that of Junior A（91.41% vs. 80.28%， P<0.001）but lower than that of Junior B（91.41% vs. 95.96%， P<0.001）. ③ With model assistance，Senior B's sensitivity（92.89% vs. 94.20%， P=0.001）and Senior A's specificity（94.44% vs. 96.56%， P<0.001）improved significantly. Junior A and B showed significant improvements in AUC and sensitivity（AUC：0.931 vs. 0.850，0.914 vs. 0.818；sensitivity：91.83% vs. 84.90%，89.53% vs. 61.46%；all P<0.001）. After model assistance，Junior A's specificity increased（93.62% vs. 80.28%， P<0.001），while Junior B's specificity decreased（91.60% vs. 95.96%， P=0.013）. Conclusions:This study develops a quality control model for anal sphincter ultrasound images with robust diagnostic performance，approaching the level of seniors. The model significantly enhances the image quality assessment capabilities of juniors，demonstrating promising clinical application potential.