Objective To analyze the influenza-like illness (ILI) data in Ordos City from 2017 to 2023 and conduct nucleic acid detection of the virus to understand the local influenza epidemic situation, and to provide a reliable basis for influenza prevention and control in the city. Methods Real-time quantitative polymerase chain reaction (qPCR) was used to identify virus subtypes in ILI throat swab samples. Comparisons of positive rates were conducted using the chi-square test, with a significance level of α=0.05. Results From 2017 to 2023, a total of 3,283,434 outpatient and emergency visits were recorded at the Ordos City Central Hospital, including 74,159 ILI cases, with an ILI proportion of 2.26%. The majority of ILI cases (74.43%) occurred in children aged 0~14 years old. The overall positive rate of influenza virus nucleic acid detection was 10.87%, with the highest proportion being subtype A (seasonal H3) at 43.03%. The highest detection rate was observed in the 5~14 years age group, with statistically significant differences in positive rates across age groups (χ²=155.638, P<0.001). Influenza peaks occurred mainly from November to March of the following year. From January to April, three types of influenza were prevalent alternately or mixed, while from October to December, subtype A (seasonal H3) predominated. Positive rates varied significantly across months (χ²=250.923, P<0.001). The temporal trends of ILI proportions and PCR-positive rates were consistent. Conclusion Influenza in Ordos City exhibits distinct seasonal and age distribution characteristics, with alternating or mixed circulation of three virus types. Continued efforts are needed to strengthen influenza surveillance, especially the prevention and control of influenza in infants and adolescents.

Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

ObjectiveTo observe the clinical efficacy and possible mechanisms of Xianglian Huazhuo Granules （香连化浊颗粒， XHG） in the treatment of chronic atrophic gastritis with intestinal metaplasia. MethodsA total of 140 patients with chronic atrophic gastritis and intestinal metaplasia were randomly divided into a treatment group and a control group， with 70 cases in each group. The treatment group received 12.5 g of XHG orally， twice daily. The control group received 12.5 g of placebo orally， twice daily. Both groups were treated for 6 months. The traditional Chinese medicine （TCM） symptom scores， pathological types， serum tumor markers of the digestive system， and serum bile acids （TBA）， interleukin-23 （IL-23）， and Dickkopf-related protein 1 （DKK-1） levels were observed before and after treatment. Safety indicators and adverse events were recorded. After treatment， TCM syndrome efficacy and pathological types were evaluated， and patients were followed up for 18 months with gastric endoscopy and pathological results， which were compared with the results after treatment finished. ResultsTwo patients dropped out in the control group， and a total of 168 cases were included in the final analysis， 70 in the treatment group and 68 in the control group. The treatment group showed a significant reduction in TCM symptom scores， serum TBA， IL-23， and DKK-1 levels， and a significant increase in alpha-fetoprotein （AFP）， carbohydrate antigen 125 （CA125）， carbohydrate antigen 199 （CA199） levels； in the control group， carcinoembryonic antigen （CEA）， CA125， CA199 levels significantly increased （P＜0.05 or P＜0.01）； and carbohydrate antigen 242 （CA242） level in both the treatment group and the control group decreased significantly （P＜0.01）. The treatment group had lower TCM symptom scores and lower levels of serum TBA， IL-23， and DKK-1 compared to the control group （P＜0.05）. The effective rate for TCM syndrome efficacy in the treatment group was 80.00% （56/70）， significantly higher than the 20.59% （14/68） in the control group （P < 0.05）. The effective rate for pathological classification in the treatment group was 72.73% （8/11） for mixed intestinal metaplasia， significantly better than 46.15% （6/13） in the control group （P＜0.05）. No adverse events were reported in either group. Among 40 patients who had a follow-up endoscopy after one year， 21 were from the treatment group， of whom 11 showed reduced intestinal metaplasia， 9 showed no significant changes， and 1 had worsened； while 19 patients in the control group had 4 with reduced intestinal metaplasia， 13 with no significant changes， and 2 with worsened conditions. No cancer was detected in either group. The treatment group showed significantly better improvement in intestinal metaplasia on follow-up gastric endoscopy pathology than the control group （P＜0.05）. ConclusionXHG can significantly improve the clinical symptoms in patients with chronic atrophic gastritis and intestinal metaplasia and reduce the degree of mixed intestinal metaplasia. The mechanism may involve lowering serum TBA， DKK-1， and IL-23 levles， thus delaying the progression from inflammation to cancer.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

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.

Fetal malformation of cortical development(MCD)is a group of structural neurological disorders caused by abnormalities in development of cortical layer during embryogenesis,characterized by significant heterogeneity and diversity,which may lead to adverse clinical outcomes such as epilepsy and intellectual disabilities.The progresses in prenatal evaluation on fetal MCD were reviewed in this article.

Objective To predict the lymphovascular invasion(LVI)status of breast cancer patients based on digital breast tomo-synthesis(DBT)intratumoral and peritumoral radiomics nomogram.Methods A total of 192 breast cancer patients from 2 institu-tions were retrospectively selected,in which institution 1 was used for train(n=113)and test(n=49),while institution 2 was used for external validation(n=30).Radiomics features were extracted and selected based on intratumoral and peritumoral 1 mm regions from DBT images.Different machine learning algorithms were used to construct intratumoral,peritumoral,and combined intratumoral and peritumoral models,respectively.Patient clinical data were analyzed by both univariate and multivariate logistic regression analy-ses to identify independent risk factors for the clinical imaging model.The performance of the models was evaluated using the receiver operating characteristic(ROC)curve.The radiomics features with the optimal diagnostic performance and the selected clinical imaging features were combined to construct a comprehensive clinical-radiomics model,and a nomogram was drawn.Results The combined intratumoral and peritumoral model was the optimal radiomics model.Maximum tumor diameter[odds ratio(OR)=1.486,P=0.014],suspicious malignant calcifications(OR=2.898,P=0.015),and axillary lymph node(ALN)metastasis(OR=3.615,P<0.001)were independent risk factors for LVI positive.Furthermore,the area under the curve(AUC)of the comprehensive clinical-radiomics model in the training set,test set and external valida-tion set was 0.889,0.916,and 0.862,respectively,which was higher than those of the combined intratumoral and peritumoral model(0.858,0.849,0.844)and the clinical imaging model(0.743,0.759,0.732).Conclusion The predictive nomogram,derived from both radiomics and clinical imaging features,is relatively accurate in identifying future LVI occurrence in breast cancer,demonstra-ting its potential as an assistive tool for clinicians to devise individualized treatment regimes.

Objective To evaluate the clinical outcome of neurosurgical robot-assisted stereotactic puncture and drainage for brain abscess.Methods A retrospective analysis was conducted on the clinical data of 53 patients with brain abscess admitted to our hospital from January 2018 to December 2024.Among them,29 cases underwent craniotomy for abscess resection(craniotomy group),while 24 cases received neurosurgical robot-assisted stereotactic puncture and drainage(robot-assisted group).The operation time,intraoperative blood loss,decompressive craniectomy rate,proportion of postoperative antibiotic regimen adjustment,postoperative hospital stay,incidence of postoperative complications,mortality rate and Glasgow outcome scale(GOS)scores 6 months after surgery of patients were compared between the two groups.Results Compared with the craniotomy group,the robot-assisted group demonstrated significantly shorter operation time,less intraoperative blood loss,and lower incidence of postoperative complication,the differences were all statistically significant(P<0.05).However,there were no statistically significant differences in terms of decompressive craniectomy rate,postoperative hospital stay,mortality rate,GOS score,or proportion of the postoperative antibiotic regimen adjustment between the two groups(P>0.05).Conclusion As a precise and minimally invasive surgical method,neurosurgical robot-assisted stereotactic puncture and drainage for patients with brain abscess can effectively improve the operational efficiency,shorten the operation time,reduce intraoperative injury,and lower the risk of postoperative complications.It has high clinical application value and potential for widespread adoption.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.