ObjectiveTo investigate the pharmacological effects of modified Xiexintang ointment on anti-infection and promotion of wound healing in mice with deep second-degree scalds， as well as its underlying mechanisms. MethodsA mouse model of deep second-degree scald was established and mice were randomly divided into six groups： blank group， model group， positive control group （Jingwanhong ointment， 0.1 g·cm^-2）， low-， medium-， and high-dose modified Xiexintang ointment group （0.05， 0.1， and 0.2 g·cm^-2）， with 10 mice in each group. Treatment was administered for 16 days. Wound recovery was recorded by photography， and the wound healing rate was calculated. Hematoxylin-eosin （HE） staining was used to observe histopathological changes in the scalded skin tissues. The levels of inflammatory cytokines， including interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α）， were measured by enzyme-linked immunosorbent assay （ELISA）. Immunohistochemistry was used to evaluate the expression levels of vascular endothelial growth factor （VEGF） and cluster of differentiation 31 （CD31） in wound tissues. Western blot analysis was performed to determine the protein expression levels of nerve growth factor （NGF）， its high-affinity receptor tropomyosin receptor kinase A （TrkA）， and its low-affinity receptor nerve growth factor receptor （NGFR）. ResultsCompared with the blank group， the burn site in the model group showed swelling and whitening on the day of modeling， followed by hardening and scab formation on the next day. HE staining on day 2 revealed dermal tissue damage， swelling and degeneration of collagen fibers with homogeneous eosinophilic staining， partial destruction of hair follicles and sebaceous glands， and inflammatory cell infiltration in the dermis， indicating successful model establishment. Compared with the model group， from day 8 of treatment， the low-， medium-， and high-dose groups of modified Xiexintang ointment significantly increased the wound healing rate （P<0.01）. Compared with the model group， all dose groups significantly promoted thickening of the newly formed epidermis， reduced hemorrhage， decreased inflammatory cell infiltration， and increased neovascularization. Compared with the model group， all dose groups significantly reduced the levels of pro-inflammatory cytokines IL-1β， IL-6， and TNF-α in wound tissues （P<0.01）. Meanwhile， the expression levels of angiogenesis-related proteins VEGF and CD31 in wound tissues were significantly increased in all dose groups （P<0.01）. Compared with the model group， the protein expression levels of NGF， NGFR， and TrkA were increased in the medium-dose group （P<0.05， P<0.01）. With increasing dosage， the high-dose group showed a more pronounced increase in NGF， NGFR， and TrkA expression compared with the model group （P<0.01）. ConclusionModified Xiexintang ointment exerts anti-infective and wound-healing effects on skin wounds in mice with deep second-degree scalds. The underlying mechanisms may be related to the regulation of VEGF， CD31， NGF， TrkA， and NGFR protein expression， thereby reducing inflammation and promoting angiogenesis and nerve regeneration at the wound site.

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 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 summarize the best evidence concerning the prevention and management of indwelling line complications in patients with hepatocellular carcinoma(HCC)receiving hepatic artery infusion chemotherapy(HAIC),and to standardize the key contents of clinical observation of complications during HAIC treatment.Methods By using the"6S"pyramid model system,the relevant literature was searched in the order from high to low.Two professionals evaluated the quality of the literature,summarized the evidence and conducted the analysis and summarization.Results Ten literature articles were finally enrolled in this study,including one article of guideline,one article of systematic review,five articles of expert consensus,one article of meta-analysis,and two articles of randomized controlled trials.Six complications(catheter displacement or falling off,catheter obstruction,unplanned extubation,arterial spasm or occlusion,infection,puncture site bleeding/local hematoma)and 22 pieces of best evidence for prevention management were summarized.Conclusion This study systematically summarizes 6 complications and their prevention and treatment in patients with HCC receiving HAIC,providing a reliable basis for clinical practice.

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.

This paper analyzed the clinical data of a patient with recurrent infection of chlamydia psittaci pneumonia within 7 months. The patient had a clear history of contact with live poultry, and the clinical manifestations were dry cough, persistent fever, and respiratory failure. Chest CT imaging changes showed lobar consolidation of infected lung lobes, ground-glass shadows, bronchial air-filling signs, and pleural effusion. The two infections were detected in bronchoalveolar lavage fluid by metagenomic next-generation sequencing (mNGS) and pathogen targeted next-generation sequencing (tNGS), respectively, to achieve early diagnosis of chlamydia psittaci pneumonia. New tetracycline drugs were used as the core of treatment for both infections, and rapid improvement was achieved after anti-infection treatment.

Objective:To develop an automatic diagnostic model for temporomandibular joint osteoarthritis(TMJOA)imaging based on deep learning technology,and to assist clinical diagnosis and improve the efficiency and accuracy of TMJOA diagnosis.Methods:CBCT data of 220 patients were collected,and 2 052 sagittal images were exported.Regions of interest were delineated according to the imaging analysis criteria for temporomandibular joint disorders,and the images were classified into TMJOA-free,TMJOA-uncer-tain and TMJOA.The data were randomly divided into a training set and a validation set according to 8∶2 ratio,and the training set data were used to train a TMJOA detection model based on three lightweight YOLOV5 deep learning frameworks,and the models' performance was evaluated on the validation set.Results:The Yolov5N model demonstrated the best performance,achieving a de-tection accuracy,recall,and precision of 92.5％,90.1％and 85.7％on the validation set,respectively.Conclusion:The auto-matic detection model for TMJOA imaging developed in this study can effectively identify arthritic lesions.Artificial intelligence tools are expected to become a powerful auxiliary tool for the clinical diagnosis of TMJOA.

Objective:To develop an automatic diagnostic model for temporomandibular joint osteoarthritis(TMJOA)imaging based on deep learning technology,and to assist clinical diagnosis and improve the efficiency and accuracy of TMJOA diagnosis.Methods:CBCT data of 220 patients were collected,and 2 052 sagittal images were exported.Regions of interest were delineated according to the imaging analysis criteria for temporomandibular joint disorders,and the images were classified into TMJOA-free,TMJOA-uncer-tain and TMJOA.The data were randomly divided into a training set and a validation set according to 8∶2 ratio,and the training set data were used to train a TMJOA detection model based on three lightweight YOLOV5 deep learning frameworks,and the models' performance was evaluated on the validation set.Results:The Yolov5N model demonstrated the best performance,achieving a de-tection accuracy,recall,and precision of 92.5％,90.1％and 85.7％on the validation set,respectively.Conclusion:The auto-matic detection model for TMJOA imaging developed in this study can effectively identify arthritic lesions.Artificial intelligence tools are expected to become a powerful auxiliary tool for the clinical diagnosis of TMJOA.

This paper analyzed the clinical data of a patient with recurrent infection of chlamydia psittaci pneumonia within 7 months. The patient had a clear history of contact with live poultry, and the clinical manifestations were dry cough, persistent fever, and respiratory failure. Chest CT imaging changes showed lobar consolidation of infected lung lobes, ground-glass shadows, bronchial air-filling signs, and pleural effusion. The two infections were detected in bronchoalveolar lavage fluid by metagenomic next-generation sequencing (mNGS) and pathogen targeted next-generation sequencing (tNGS), respectively, to achieve early diagnosis of chlamydia psittaci pneumonia. New tetracycline drugs were used as the core of treatment for both infections, and rapid improvement was achieved after anti-infection treatment.