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.

Objective: To investigate the effects of polydatin on a high-fat diet-induced non-alcoholic fatty liver disease(NAFLD) mouse model and hepatoma G2(HepG2) cell model, and to reveal its potential molecular mechanisms. Methods: Thirty 6-week-old male SPF C57BL/6J mice were randomly divided into a normal diet group and a high-fat diet group. After the NAFLD mouse model was established in the high-fat diet group, they were further divided into a model group and a polydatin treatment group. The polydatin treatment group was administered polydatin by gavage at a dose of 250 mg/(kg·d) for 10 weeks, during which body weight was monitored and oral glucose and insulin tolerance tests were performed. At the end of the experiment, a series of tests to evaluate the effects of polydatin on mouse liver weight, blood lipids, liver lipid accumulation, and liver injury markers were performed. The expression of G0/G1 switch gene 2(G0S2) and adipose triglyceride lipase(ATGL) was measured by qRT-PCR and Western blot, and gene expression was further verified using immunohistochemical staining. The effects of polydatin on HepG2 cell activity was assessed by CCK-8 assay, lipid accumulation was observed by oil red O staining, and the expression of G0S2 and ATGL was detected by qRT-PCR and Western blot. Results: Polydatin significantly reduced the body weight, liver weight, and serum and liver tissue levels of aspartate aminotransferase(AST), alanine aminotransferase(ALT), triglyceride(TG), and total cholesterol (TC) in mice (P < 0. 05) , al⁃leviated pathological liver damage , decreased G0S2 expression (P < 0. 05) , and increased ATGL expression (P <0. 05) . At the cellular level , polydatin reduced lipid droplet accumulation , improved lipid metabolism , decreased G0S2 expression ( P < 0. 05 ) , and increased ATGL expression ( P < 0. 05 ) . Even in cells with knockdown of G0S2 , polydatin still promoted fat decomposition (P < 0. 01) . Conclusion Polydatin promotes hepatic fat break⁃down by regulating the expression of G0S2 and ATGL , helping to alleviate metabolic disorders and liver damage in the NAFLD mouse model caused by a high⁃fat diet , offering a new strategy for treating NAFLD.

Objective To explore the impact of early discharge guidance,based on an intelligent education system,on postoperative pain management,functional recovery,and discharge readiness in lung cancer patients.Methods A total of 170 lung cancer patients were selected from Jinhua Municipal Central Hospital from July 2022 to June 2023.According to the time of admission,patients admitted between July to November 2022 were assigned to control group(n=82),while patients admitted between December 2022 to June 2023 were assigned to observation group(n=88).Control group received routine perioperative health education,while observation group received additional intelligent education system.Following outcomes were compared between two groups:Time to first ambulation after surgery,pain numeric rating scale(NRS)scores on postoperative 1 to 3 days and on day of discharge,performance on 2-minute step test on day of discharge,and readiness for hospital discharge scale(RHDS).Results Time to first ambulation post operative in observation group was shorter than that in control group.NRS scores at 72 hours post operative and on day of discharge were lower in observation group compared to control group.Number of steps completed in 2-minute step test in observation group was higher than that in control group.Additionally,RHDS scores in observation group was higher than that in control group,and all differences were statistically significant(P＜0.05).Conclusion The discharge planning advancement driven by the intelligent education system significantly improved early postoperative ambulation,reduced postoperative pain,enhanced physical function,and increased discharge readiness in lung cancer patients,thereby effectively promoting postoperative recovery.

Objective:To establish a combined model of tumor heterogeneity metabolic parameters using 18F-FDG PET/CT and explore its value in differentiating benign from malignant pulmonary lesions. Methods:A total of 251 patients (157 males, 94 females; age 15-88 years) who were diagnosed with malignant lung lesions by 18F-FDG PET/CT and with definitive pathological results at Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2017 to February 2024 were retrospectively enrolled. Analysis was conducted on clinical data, traditional parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG)) of primary lesions on 18F-FDG PET/CT, and intra-tumoral metabolic heterogeneity index (HI; such as cumulative SUV volume histogram AUC (AUC-CSH), linear regression slope, CV). AUC-CSH and CV were calculated using SUV thresholds of 2.5 and 40%SUV max. Logistic univariate and multivariate regression analyses were used to extract independent predictors in clinical features and PET/CT parameters for the differential diagnosis of pulmonary lesions. A multi-parameter combined model was established through logistic regression and validated for diagnostic efficacy using ROC curve analysis. Results:Among 251 patients, 101 were benign and 150 were malignant. In univariate analysis, gender, age, tumor markers, spiculation sign, lobulation sign, vessel convergence sign, air bronchogram, long diameter, short diameter, SUV max, AUC-CSH 2.5, AUC-CSH 40%, CV2.5, and CV40% were predictive factors for the diagnosis of benign and malignant tumors (odds ratio ( OR): 0.57-17.39, all P<0.05). In multivariate analysis, gender, age, tumor markers, lobulation sign, vessel convergence sign, SUV max, AUC-CSH 40%, and CV40% were independent predictors for the diagnosis of benign and malignant tumors ( OR: 2.30-13.18, all P<0.05). The AUC, sensitivity, specificity, and accuracy of the multi-parameter combined model established with the above independent predictors were 0.89, 77.33%(116/150), 84.16%(85/101), 80.08%(201/251), respectively. Conclusion:18F-FDG PET/CT multi-parameter combined model has high value in the differentiation of benign and malignant pulmonary lesions.

Objective:To evaluate the value of a combined model based on primary lesion 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer (NSCLC) . Methods:A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set ( n=142) and the validation set ( n=61) at a ratio of 7∶3. A predictive model was developed in the training set, and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score (Radscore). Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic (ROC) curves and clinical application value was assessed by calibration curves and decision curve analysis (DCA) . Results:Among 203 NSCLC patients, 116 had lymph node metastasis, with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening, and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that, sex ( OR=0.48, 95% CI: 0.24-0.95, P=0.036), tumor marker levels ( OR=3.81, 95% CI: 1.84-7.91, P<0.001), long diameter of tumor ( OR=2.56, 95% CI: 1.27-5.16, P=0.009), short diameter of tumor ( OR=3.73, 95% CI: 1.75-7.92, P=0.001), vacuolar sign ( OR=0.32, 95% CI: 0.12-0.86, P=0.024), ring-like metabolism ( OR=3.67, 95% CI: 1.33-10.13, P=0.012), maximum standardized uptake value (SUV max) ( OR=6.57, 95% CI: 3.03-14.25, P<0.001), metabolic tumor volume (MTV) ( OR=2.91, 95% CI: 1.43-5.92, P=0.003), total lesion glycolysis (TLG) ( OR=4.23, 95% CI: 2.08-8.59, P<0.001), Radscore-PET ( OR=21.93, 95% CI: 9.04-53.20, P<0.001) and Radscore-CT ( OR=13.72, 95% CI: 6.12-30.76, P<0.001) were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that, tumor marker levels ( OR=2.55, 95% CI: 1.11-5.90, P=0.028), vacuolar sign ( OR=0.26, 95% CI: 0.08-0.83, P=0.023), SUV max ( OR=5.94, 95% CI: 1.99-17.75, P=0.001), Radscore-PET ( OR=25.51, 95% CI: 5.92-110.22, P<0.001), and Radscore-CT ( OR=8.68, 95% CI: 2.73-27.61, P<0.001) were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors, models were constructed: the traditional model (tumor marker levels, vacuolar sign, SUV max), the PET model (SUV max, Radscore-PET), the CT model (vacuolar sign, Radscore-CT), and the combined model (tumor marker levels, vacuolar sign, SUV max, Radscore-PET, Radscore-CT). ROC curve analysis showed that, the area under curve (AUC) of the traditional, PET, CT, and combined models in the training set were 0.75 (95% CI: 0.67-0.82), 0.90 (95% CI: 0.84-0.95), 0.85 (95% CI: 0.78-0.90), and 0.94 (95% CI: 0.88-0.97), respectively. The predictive value of the combined model was higher than that of the traditional model ( Z=5.01, P<0.001), the PET model ( Z=1.99, P=0.047), and the CT model ( Z=3.25, P=0.001). In the validation set, the AUCs for the traditional model, PET model, CT model, and combined model were 0.65 (95% CI: 0.52-0.77), 0.86 (95% CI: 0.74-0.93), 0.85 (95% CI: 0.73-0.93), and 0.90 (95% CI: 0.80-0.96), respectively. The predictive value of the combined model was superior to that of the traditional model ( Z=3.23, P=0.001). The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%, while in the validation set, the sensitivity and specificity were 82.61% and 94.74%, respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions:Tumor marker levels, vacuolar sign, SUV max, Radscore-PET, and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.

Objective:To evaluate the prognostic value of 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT metabolic parameters in small cell lung cancer (SCLC) . Methods:A retrospective analysis was conducted on the clinical and imaging data of 156 SCLC patients, who underwent 18F-FDG PET/CT imaging and were diagnosed by histopathological examination at Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from September 2013 to February 2024. The metabolic tumor volume (MTV), total lesion glycolysis (TLG), linear regression slope, area under the curve of cumulative standard uptake value (SUV) volume histogram (AUC-CSH), and coefficient of variation (CV) were calculated using LIFEx software with different SUV thresholds. Univariate and multivariate analyses were performed using Cox proportional hazards model. Patient stratification was based on the critical values determined by receiver operator characteristic (ROC) curve analysis. The survival curve was plotted using the Kaplan-Meier method and log-rank test was performed. Results:Univariate analysis showed that MTV 40% ( HR=2.91, 95% CI: 1.55-5.47, P=0.001), MTV 60% ( HR=2.31, 95% CI: 1.29-4.17, P=0.005), TLG 40% ( HR=2.07, 95% CI: 1.19-3.60, P=0.010), linear regression slope ( HR=0.45, 95% CI: 0.26-0.79, P=0.005), and CV 40% ( HR=0.27, 95% CI: 0.08-0.84, P=0.024) were factors affecting progression-free survival (PFS) in SCLC patients. MTV 40% ( HR=1.98, 95% CI: 1.22-3.22, P=0.005), MTV 60% ( HR=1.80, 95% CI: 1.12-2.88, P=0.015), MTV 80% ( HR=1.71, 95% CI: 1.08-2.74, P=0.024), TLG 40% ( HR=3.68, 95% CI: 1.59-8.49, P=0.002), linear regression slope ( HR=0.49, 95% CI: 0.30-0.80, P=0.004), and AUC-CSH 80% ( HR=0.44, 95% CI: 0.23-0.84, P=0.013) were found to be factors affecting overall survival (OS) in SCLC patients. Multivariate analysis revealed that MTV 40% ( HR=4.76, 95% CI: 1.11-20.50, P=0.036) was an independent factor influencing PFS, and TLG 40% ( HR=3.19, 95% CI: 1.02-9.92, P=0.046) was an independent factor influencing OS in SCLC patients. ROC curve analysis identified the optimal cutoff value for MTV 40% in predicting PFS as 5.5cm 3 and the optimal cutoff value for TLG 40% in predicting OS as 41.5 g in SCLC patients. Survival analysis showed that patients with MTV 40%≤5.5 cm 3 ( n=33) had a median PFS that was not reached, while patients with MTV 40%>5.5 cm 3 ( n=123) had a median PFS of 10.3 months, with a statistically significant difference ( χ2=12.09, P=0.001). For patients with TLG 40%≤41.5 g ( n=35), the median OS was not reached, whereas for TLG 40%>41.5 g ( n=121), the median OS was 31.6 months, with a statistically significant difference ( χ2=10.55, P=0.001) . Conclusions:The 18F-FDG PET/CT metabolic parameter MTV 40% is an independent factor influencing PFS, while TLG 40% is an independent factor influencing OS in SCLC patients. The above two parameters may serve as indicators for assessing the prognosis of SCLC patients.

Objective: To evaluate the radiation dose, operational standardization, and image quality of computed tomography (CT) Ningbo City Medical Imaging Cloud Platform, so as to provide references for optimizing the quality of CT examinations. Methods: Six CT devices were randomly selected from the Ningbo City Medical Imaging Cloud Platform. Digital Imaging and Communication in Medicine (DICOM) image data from CT examinations of the head, neck, chest, and abdomen in males aged 36 to 60 years were collected from January 2023 to December 2024. The radiation dose levels were evaluated using the volume CT dose index (CTDIvol) and dose length product (DLP). The coefficient of variation (CV) of CTDIvol and scan length were calculated to assess scan stability. Operational standardization was evaluated using the redundancy rate of scan length and protocol matching degree. Imaging quality was assessed using the signal to noise ratio (SNR) and contrast to noise ratio (CNR). Results: A total of 28 897 DICOM images were collected, including 6 730 axial scans of the skull, 2 778 plain scans of the neck, 15 496 plain scans of the chest, and 3 893 plain scans of the abdomen. The typical values of CTDIvol and DLP radiation doses for the head, neck, and chest were all lower than the diagnostic reference levels. The maximum typical values of CTDIvol and DLP for the abdomen were 22.49 mGy and 941.45 mGy·cm, respectively, which were higher than the diagnostic reference levels. The CV values of CTDIvol and scan length ranged from 14.59% to 37.88% and from 8.27% to 44.96%, respectively. The scan stability of head CT was relatively poor, with CV values ranging from 21.74% to 37.88% and from 12.66% to 44.96%, respectively. The redundancy rate of scan length ranged from 6.02% to 74.40%, and the protocol matching degree ranged from 79.80% to 100.00%. The operational standardization of neck CT was relatively poor, with redundancy rates ranging from 45.70% to 74.40% and protocol matching degrees ranging from 79.80% to 95.36%. The mean SNR and mean CNR of the pulmonary arteries in the chest were relatively high, ranging from 15.81 to 17.65 and from 6.33 to 7.41, respectively. Conclusions The radiation doses from abdominal CT examinations on some CT devices exceed the diagnostic reference levels. The scan stability of head CT examinations and the operational standardization of neck CT examinations represent weak points in quality control. It is recommended to carry out targeted quality control training to enhance the overall quality level of CT examinations.

Objective To explore the impact of early discharge guidance,based on an intelligent education system,on postoperative pain management,functional recovery,and discharge readiness in lung cancer patients.Methods A total of 170 lung cancer patients were selected from Jinhua Municipal Central Hospital from July 2022 to June 2023.According to the time of admission,patients admitted between July to November 2022 were assigned to control group(n=82),while patients admitted between December 2022 to June 2023 were assigned to observation group(n=88).Control group received routine perioperative health education,while observation group received additional intelligent education system.Following outcomes were compared between two groups:Time to first ambulation after surgery,pain numeric rating scale(NRS)scores on postoperative 1 to 3 days and on day of discharge,performance on 2-minute step test on day of discharge,and readiness for hospital discharge scale(RHDS).Results Time to first ambulation post operative in observation group was shorter than that in control group.NRS scores at 72 hours post operative and on day of discharge were lower in observation group compared to control group.Number of steps completed in 2-minute step test in observation group was higher than that in control group.Additionally,RHDS scores in observation group was higher than that in control group,and all differences were statistically significant(P＜0.05).Conclusion The discharge planning advancement driven by the intelligent education system significantly improved early postoperative ambulation,reduced postoperative pain,enhanced physical function,and increased discharge readiness in lung cancer patients,thereby effectively promoting postoperative recovery.

Objective:To establish a combined model of tumor heterogeneity metabolic parameters using 18F-FDG PET/CT and explore its value in differentiating benign from malignant pulmonary lesions. Methods:A total of 251 patients (157 males, 94 females; age 15-88 years) who were diagnosed with malignant lung lesions by 18F-FDG PET/CT and with definitive pathological results at Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2017 to February 2024 were retrospectively enrolled. Analysis was conducted on clinical data, traditional parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG)) of primary lesions on 18F-FDG PET/CT, and intra-tumoral metabolic heterogeneity index (HI; such as cumulative SUV volume histogram AUC (AUC-CSH), linear regression slope, CV). AUC-CSH and CV were calculated using SUV thresholds of 2.5 and 40%SUV max. Logistic univariate and multivariate regression analyses were used to extract independent predictors in clinical features and PET/CT parameters for the differential diagnosis of pulmonary lesions. A multi-parameter combined model was established through logistic regression and validated for diagnostic efficacy using ROC curve analysis. Results:Among 251 patients, 101 were benign and 150 were malignant. In univariate analysis, gender, age, tumor markers, spiculation sign, lobulation sign, vessel convergence sign, air bronchogram, long diameter, short diameter, SUV max, AUC-CSH 2.5, AUC-CSH 40%, CV2.5, and CV40% were predictive factors for the diagnosis of benign and malignant tumors (odds ratio ( OR): 0.57-17.39, all P<0.05). In multivariate analysis, gender, age, tumor markers, lobulation sign, vessel convergence sign, SUV max, AUC-CSH 40%, and CV40% were independent predictors for the diagnosis of benign and malignant tumors ( OR: 2.30-13.18, all P<0.05). The AUC, sensitivity, specificity, and accuracy of the multi-parameter combined model established with the above independent predictors were 0.89, 77.33%(116/150), 84.16%(85/101), 80.08%(201/251), respectively. Conclusion:18F-FDG PET/CT multi-parameter combined model has high value in the differentiation of benign and malignant pulmonary lesions.

ObjectiveTo study the characteristics of imprinting template of flavonoid clusters in four Chinese medicines attributed to the lung meridian， and to establish an in vitro experimental approach for the study of the attribution of Chinese medicines to the lung meridian. MethodBased on 13 Chinese medicines， including Xanthii Fructus， Houttuyniae Herba， Fagopyri Dibotryis Rhizoma， Belamcandae Rhizoma and so on， which only belong to the lung meridian in Chinese Materia Medica（the 13^th Five-Year planning textbook of general higher education）， we identified four representative Chinese medicines， namely Houttuyniae Herba， Fagopyri Dibotryis Rhizoma， Belamcandae Rhizoma and Mori Cortex， and set up their fingerprints by high performance liquid chromatography（HPLC） and calculated the molecular connectivity indices of various components in the four Chinese medicines， the similarity to their mean value was calculated by included angle cosine method， so as to establish the quantitative relationship of construction versus imprinting ability， and to determine the order of each component in the lung meridian. A total of 7 reference substances， including chlorogenic acid， rutin， quercetin， isoquercitrin， hyperoside， epicatechin， and iridin， were selected to validate the overall conformational relationships of flavonoids of the model， as well as its predictive ability. ResultHouttuyniae Herba， Fagopyri Dibotryis Rhizoma， Belamcandae Rhizoma and Mori Cortex contained a total of 437 chemical components with an average molecular connectivity index similarity of 0.995 6. The four Chinese medicines contained a total of 204 flavonoids with an average molecular connectivity index similarity of 0.978 0， which was second only to the alkaloids with 0.985 1. The retention time（t_R） of the 7 reference substances showed a good conformational relationship with the similarity of the molecular connectivity index（t_R=831.4×S-790.3， r=0.861 4， P<0.01）， which was applicable to the in vitro attribution study of the position， similarity， and relative similarity with t_R of the cluster of 98.04% of flavonoids. Accordingly， the 1^st position was kuwanon D， with a similarity of molecular connectivity index of 0.987 7 and a t_R of 30.88 min， the 200^th position was chlorogenic acid， with a similarity of molecular connectivity index of 0.958 2 and a t_R of 6.36 min. The total first-order moment of the four Chinese medicines calculated by total statistical moment method of fingerprint was 24.26 min， ranked 21， which could characterize 99.19% of the whole， and the total first-order moment of the total peak area of the 7 reference substances in the four Chinese medicines was 20.00 min， with a rank of 46， which could characterize 98.68% of the whole. ConclusionFlavonoid clusters are suitable probes for the characterization of imprinting template for the study of the lung meridian， which can be established a quantitative imprinting method for meridian tropism of Chinese medicines in vitro.