Objective: To analyze the epidemiological characteristics and influencing factors of severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang Province from 2019 to 2023, so as to provide the reference for strengthening SFTS prevention and control. Methods: Data on laboratory-confirmed SFTS cases in Zhejiang Province from 2019 to 2023 were collected through the Infectious Disease Reporting Information System of Chinese Disease Prevention and Control Information System. Meteorological data, geographic environment and socioeconomic factors during the same period were collected from the fifth-generation European Centre for Medium-Range Weather Forecasts, Geospatial Data Cloud, and Zhejiang Statistical Yearbook, respectively. Descriptive epidemiological methods were used to analyze the epidemiological characteristics of SFTS from 2019 to 2023, and a Bayesian spatio-temporal model was constructed to analyze the influencing factors of SFTS incidence. Results: A total of 578 SFTS cases were reported in Zhejiang Province from 2019 to 2023, with an annual average incidence of 0.23/105. The peak period was from May to July, accounting for 52.60%. There were 309 males and 269 females, with a male-to-female ratio of 1.15∶1. The cases were mainly aged 50-<80 years, farmers, and in rural areas, accounting for 82.53%, 77.34%, and 75.43%, respectively. Taizhou City and Shaoxing City reported more SFTS cases, while Shaoxing City and Zhoushan City had higher annual average incidences of SFTS. The Bayesian spatio-temporal interaction model showed good goodness of fit. The results showed that mean temperature (RR=1.626, 95%CI: 1.111-2.378) and mean wind speed (RR=1.814, 95%CI: 1.321-2.492) were positively correlated with SFTS risk, while altitude (RR=0.432, 95%CI: 0.230-0.829) and population density (RR=0.443, 95%CI: 0.207-0.964) were negatively correlated with SFTS risk. Conclusions SFTS in Zhejiang Province peaks from May to July. Middle-aged and elderly people and farmers are high-risk populations. Taizhou City, Shaoxing City, and Zhoushan City are high-incidence areas. Mean temperature, mean wind speed, altitude, and population density can all affect the risk of SFTS incidence.

Objective:To investigate the longitudinal relationships among resilience, social support, and negative emotions in college students.Methods:Questionnaire surveys were administered to 1 739 college students from a university in Hebei Province born in the same year in November 2020 and November 2021 respectively, and 1 183 valid responses were finally obtained. The survey battery comprised the 11-item resilience scale, the social support questionnaire, and the 21-item depression anxiety stress scales. Cross-lagged panel analysis was conducted using Mplus 8.3 software. Mediating effects were tested via the Bootstrap method.Results:The scores of resilience, social support and negative emotion at T1 were 58 (51, 65), 57 (51, 66), and 3 (0, 10), respectively. The scores of resilience, social support and negative emotion at T2 were 59 (48, 66), 56 (44, 65), and 11 (1, 24), respectively. Resilience at T1 and T2 were significantly and positively correlated with social support at T1 and T2 ( r=0.66, 0.75, 0.40, 0.33, all P<0.01), while negatively correlated with negative emotion at T1 and T2( r=-0.45, -0.23, -0.26, -0.24, all P<0.01). Social support at T1 and T2 were negatively correlated with negative emotion at T1 and T2( r=-0.44, -0.28, -0.28, -0.28, all P<0.01). T1 resilience significantly and positively predicted T2 resilience ( β=0.35) and T2 social support ( β=0.15). T1 social support significantly and positively predicted T2 resilience ( β=0.07) and T2 social support ( β=0.35), while negatively predicted T2 negative emotion ( β=-0.15). T1 negative emotion significantly and negatively predicted T2 resilience ( β=-0.06) and T2 social support ( β=-0.07). The total effect of T1 resilience on T2 negative emotion was -0.35, and the mediating effect of T2 social support was -0.09, accounting for 25.71% of the total effect. Conclusion:T2 social support partially mediates the relationship between T1 resilience and T2 negative emotions.

Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Objective To compare five time series models and predict the monthly incidence of bacillary dysentery in Qinghai Province in 2024,and provide reference for the prevention and control.Methods The epidemic charac-teristics of bacterial dysentery in Qinghai Province from 2014 to 2023 were analyzed.R4.3.1 software was used for establishing seasonal autoregressive integrated moving average(SARIMA)model,Holt-Winters triple exponential smoothing(Holt Winters)model,exponential smoothing(ETS)model,neural network autoregression(NNAR)model,and trigonometric seasonality,Box-Cox transformation,ARMA errors,trend and seasonal components(TBATS)model.Fitting effect of the models was analyzed and accuracy was compared.Results From 2014 to 2023,a total of 5 833 cases of bacterial dysentery were reported in Qinghai Province,without deaths,male to fe-male ratio being 1.23∶1.The highest incidence was reported in 2016(15.45 per 100 000 people),and the lowest in-cidence was reported in 2023(3.68 per 100 000 people).Incidence increased from 2014 to 2016,then decreased,showing an obvious overall downward trend.Case number in＜5 years age group was the highest,accounting for 29.76％of the total cases(n=1 736).Regarding population distribution,the top three were children in childcare institutions and scattered children(35.56％),farmers(24.65％),and students(12.62％).Except the additive Holt-Winters model,the predicted trends of the other four models were consistent with actuality.The ETS model had the best fitting effect,with a relatively balanced overall performance(training set:MAE=0.13,RMSE=0.21,MAPE=19.55％;testing set:MAE=0.11,RMSE=0.16,MAPE=28.66％).It is recommended to pre-dict the incidence of bacillary dysentery in Qinghai Province based on ETS model.Conclusion From 2014 to 2023,bacterial dysentery in Qinghai Province showed a downward trend,with the peak of the epidemic from June to Au-gust.Preschool and scattered children were high-risk groups.Among the five prediction models,ETS model has the best fitting effect,and can be used to predict the incidence of bacillary dysentery.

Objective:To investigate the longitudinal relationships among resilience, social support, and negative emotions in college students.Methods:Questionnaire surveys were administered to 1 739 college students from a university in Hebei Province born in the same year in November 2020 and November 2021 respectively, and 1 183 valid responses were finally obtained. The survey battery comprised the 11-item resilience scale, the social support questionnaire, and the 21-item depression anxiety stress scales. Cross-lagged panel analysis was conducted using Mplus 8.3 software. Mediating effects were tested via the Bootstrap method.Results:The scores of resilience, social support and negative emotion at T1 were 58 (51, 65), 57 (51, 66), and 3 (0, 10), respectively. The scores of resilience, social support and negative emotion at T2 were 59 (48, 66), 56 (44, 65), and 11 (1, 24), respectively. Resilience at T1 and T2 were significantly and positively correlated with social support at T1 and T2 ( r=0.66, 0.75, 0.40, 0.33, all P<0.01), while negatively correlated with negative emotion at T1 and T2( r=-0.45, -0.23, -0.26, -0.24, all P<0.01). Social support at T1 and T2 were negatively correlated with negative emotion at T1 and T2( r=-0.44, -0.28, -0.28, -0.28, all P<0.01). T1 resilience significantly and positively predicted T2 resilience ( β=0.35) and T2 social support ( β=0.15). T1 social support significantly and positively predicted T2 resilience ( β=0.07) and T2 social support ( β=0.35), while negatively predicted T2 negative emotion ( β=-0.15). T1 negative emotion significantly and negatively predicted T2 resilience ( β=-0.06) and T2 social support ( β=-0.07). The total effect of T1 resilience on T2 negative emotion was -0.35, and the mediating effect of T2 social support was -0.09, accounting for 25.71% of the total effect. Conclusion:T2 social support partially mediates the relationship between T1 resilience and T2 negative emotions.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.

Oral potentially malignant disorders (OPMD) refer to a group of diseases occurring on the oral mucosa that harbor the potential to progress into oral squamous cell carcinoma, including oral leukoplakia, oral erythroplakia, discoid lupus erythematosus of the oral mucosa, oral submucous fibrosis, oral lichen planus, actinic cheilitis, etc. Diabetes mellitus (DM) is one type of diseases characterized by chronic hyperglycemia, with a high incidence and mortality rate worldwide. Hyperglycemia is the characteristic metabolic change in DM patients and those in the pre-diabetic stage, playing a determinative role in many complications related to DM. A number of clinical studies had revealed an association between hyperglycemia and OPMD, as well as its malignant transformation. This article will review the potential regulatory effects and mechanisms of high glucose states, such as diabetes, on OPMD, and assess the correlation between hyperglycemia and the malignant transformation of OPMD.

Objective To examine the correlation between fetal rectal ampulla diameter and gestational age,and to establish reference value ranges for low-risk fetuses between 18 and 40 weeks of gestation in Yibin region.Methods A total of 1,103 low-risk singleton pregnant women between 18 and 40 weeks of gestation were recruited from five hospitals in Yibin City(the Second People's Hospital,the First People's Hospital,the Fifth People's Hospital,the Maternal and Child Health Hospital of Yibin City,and the Maternal and Child Health Hospital of Cuiping District)for routine level Ⅰ,Ⅱ,and Ⅲ prenatal ultrasound screening from October 2022 to March 2024.Fetal rectal ampulla diameters,including anteroposterior diameter,transverse diameter,and area,were measured using prenatal ultrasound.The normality of these measurements was assessed using the Shapiro-Wilk test.Scatter plots depicting the relationship between fetal rectal ampulla diameter parameters and gestational age were generated using the"Overlap Scatter Plot"function in SPSS.Percentiles were calculated using the"Explore"function in SPSS,with reference value ranges described by P5,P10,P50,P90,and P95.Results The visual-ization rate of the fetal rectal ampulla diameter was 55%at 18～20 weeks of gestation,100%at 21～37 weeks,and 96%at greater than 37 weeks.The fetal rectal ampulla diameter exhibited a significant positive correlation with gestational age(r=0.925～0.949,P<0.01).Conclusions Prenatal ultrasound measurement of fetal rectal ampulla diameter demonstrates a robust correlation with gestational age.The reference intervals for the rectal ampulla diameter of low-risk fetuses between 18 and 40 weeks of gestation,established in this study,may offer valuable theoretical guidance for prenatal diagnosis of fetal rectal and anal abnormalities in Yibin region.

Objective To evaluate the applicability and guideline concordance of the Chinese Society of Clinical Oncology(CSCO)arti-ficial intelligence(AI)system in clinical decision-making for colorectal cancer(CRC)patients,and to explore its feasibility in real-world clinical applications.Methods A total of 972 CRC patients diagnosed and treated at the Second Affiliated Hospital,Zhejiang University School of Medicine,from January 2010 to December 2021,were included.Patient data were analyzed by the CSCO AI system to gener-ate treatment decisions,and decision concordance was assessed by a blinded independent central review(BICR)panel.The applicability and guideline concordance rates of the CSCO AI system were calculated for different treatment stages,and a logistic regression model was used to analyze factors influencing the system's decision discrepancies with actual treatments.Results The overall applicability rate of the CSCO AI system was 96.2%,and the overall guideline concordance rate was 94.9%.In the adjuvant and palliative treatment stages,the system's applicability rates were 95.8%and 96.7%,respectively,and the guideline concordance rates were 95.0%and 94.9%,respective-ly.Multivariate logistic regression analysis showed that age≥65 years and high-risk stage Ⅱ treatment were significant factors affecting guideline concordance in the adjuvant treatment stage(both P<0.05).Conclusions The CSCO AI system demonstrated high applicability and guideline concordance in the adjuvant and palliative treatment stages for CRC.The system's clinical decision-making potential is sig-nificant,and it can be further optimized for specific clinical scenarios and promoted for use across various medical institutions.

Objective:To explore the distribution of HIV-1 genetic subtypes and drug resistance profiles among HIV-1 infected patients with antiretroviral treatment (ART) failure in Henan Province and to provide evidence for optimizing ART regimens.Methods:HIV-1 infected patients who had received ART for at least 6 months with viral loads (VL) ≥200 copies/ml in 18 cities of Henan from January to December 2023. The plasma samples were collected, and partial pol gene sequences and full-length integrase ( int) gene sequences of HIV-1 were amplified using nested RT-PCR. HIV-1 subtypes were determined using the REGA HIV-1 subtyping tool, and drug resistance mutations were analyzed using the Stanford University HIV Drug Resistance Database ( http://hivdb.stanford.edu/). Chi-square tests and multivariate logistic regression were used to identify risk factors associated with drug resistance of HIV-1 infected patients. Results:Among 697 HIV-1 infected patients with ART failure, 14 HIV-1 genetic subtypes were identified. Subtype B was predominant (58.68%, 409/697), followed by CRF01_AE (21.95%, 153/697) and CRF07_BC (12.91%, 90/697). The overall drug resistance rate was 72.31% (504/697), with CRF55_01B exhibiting a resistance rate of 91.30% (21/23). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) had the highest resistance mutation rate (67.29%, 469/697), followed by nucleoside reverse transcriptase inhibitors (NRTIs)(56.81%, 396/697), protease inhibitors (PIs)(5.74%, 40/697), and integrase strand transfer inhibitors (INSTIs)(2.75%, 19/691). The results of multivariate analysis showed that the positive correlation factor for drug resistance in HIV-1 infected individuals with failed ART was baseline CD4 +T lymphocyte counts <200 cells/μl (a OR=3.84, 95% CI: 1.69-8.72), and the negative correlation factor was ART duration of 3-5 years (a OR=0.32, 95% CI: 0.13-0.77), the initial treatment ART protocol used two types of NRTIs plus one type of PIs (a OR=0.14, 95% CI: 0.05-0.43) and two types of NRTIs plus one type of INSTIs protocol (a OR=0.12, 95% CI: 0.03-0.57). Conclusions:The drug resistance rate of HIV-1 infected patients with ART failure was relatively higher in Henan Province in 2023. Strengthening the monitoring of HIV-1 drug resistance is of great significance to improve the ART effect of HIV-1 infected patients.