Objective To explore factors associated with early postoperative hormonal remission in patients with prolactinoma and to develop prediction models based on clinical and radiological features. Methods Data from 107 patients with prolactinoma who underwent transsphenoidal surgery at The First People’s Hospital of Huai’an, Nanjing Medical University between January 2020 and December 2024 was collected, included general clinical characteristics, preoperative laboratory indicators, and imaging features. Based on whether early postoperative prolactin (PRL) levels normalized, patients were divided into a remission group (n=76) and a non-remission group (n=31). Univariate logistic regression was used for preliminary evaluation of candidate variables, followed by LASSO regression for feature selection. Multiple machine learning models were constructed, including logistic regression, random forest, support vector machine, K-nearest neighbors, naive Bayes, decision tree, neural network, and gradient boosting decision tree (GBDT). All models were trained and evaluated using ten-fold cross-validation, with comprehensive assessment of model performance based on the area under the ROC curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score. Results The maximum diameter of tumors in the non remission group was larger than that in the remission group (P=0.019), and the incidence of tumor stroke and preoperative PRL levels were significantly higher than those in the remission group (P＜0.001). Univariate analysis showed that sex, maximum tumor diameter, tumor stroke, and preoperative PRL levels were influencing factors for early postoperative hormone response in patients with prolactinoma (P＜0.05). The comparison results of machine learning models show that the neural network model performs the best (AUC=0.921) and has good clinical application value, followed by the GBDT model (AUC=0.893) and the support vector machine model (AUC=0.884). Other models also show certain predictive ability. Conclusions Sex, preoperative PRL levels, maximum tumor diameter, Hounsfield unit value, and tumor stroke are important factors affecting early hormone response after prolactinoma surgery. The machine learning model constructed based on the above variables has good predictive performance, and performs the best and has good clinical application value.

Aim To investigate the role and regulatory mechanism of CREB regulated transcription coactivator 2(CRTC2)in cardiomyocyte hypertrophy.Methods A pathological cardiomyocyte hypertrophy model was established in C57BL/6 mice by intraperitoneal injection of isoproterenol(ISO),the expression of CRTC2 in cardiac tissue was detec-ted by Western blot.The CRTC2 knockout mice model was constructed,the cardiac function of mice was detected by small animal echocardiography,the collagen fiber content in mice cardiac tissue was detected by Masson staining,the car-diomyocyte hypertrophy related proteins:skeletal muscle α1-actin(ACTA1)and brain natriuretic peptide(BNP),as well as ferroptosis related proteins:acyl-CoA synthetase long chain family member 4(ACSL4),solute carrier family 7 member 11(SLC7A11)and glutathione peroxidase 4(GPX4)in mice cardiac tissue were detected by Western blot,the iron ion content in mice cardiac tissue was detected by iron ion kit,to evaluate the correlation between CRTC2 and cardiomyocyte hypertrophy and ferroptosis.H9c2 cells were induced by ISO to construct an in vitro model of cardiomyocyte hypertrophy,the protein expressions of CRTC2,ACTA1,BNP,ACSL4,SLC7A11 and GPX4 were detected after intervention with fer-roptosis inhibitor ferrostatin-1(Fer-1).H9c2 cells with CRTC2 overexpression induced by ISO were used to construct an in vitro model of cardiomyocyte hypertrophy,the related indicators of cardiomyocyte hypertrophy and ferroptosis were detec-ted to explore the mechanism of CRTC2 in cardiomyocyte hypertrophy.Results Compared with the control group,the expression of CRTC2 protein in the cardiac tissue of ISO induced cardiomyocyte hypertrophy mice was increased(P＜0.05).Compared with wild-type mice,CRTC2-/-mice showed worsened cardiac function,manifested as increased left ventricular end-diastolic diameter(LVEDD),left ventricular end-systolic diameter(LVESD),left ventricular posterior wall thickness(LVPWT),heart weight/tibia length(HW/TL)and heart weight/body weight(HW/BW),decreased short axis shortening(FS)and ejection fraction(EF),increased collagen fiber content in cardiac tissue,upregulated ex-pression of cardiomyocyte hypertrophy-related proteins ACTA1 and BNP,increased mRNA and protein expression of ferrop-tosis-related protein ACSL4,decreased mRNA and protein expression of SLC7A11 and GPX4,and elevated iron ion content in cardiac tissue(P＜0.05 or P＜0.01).In vitro experiments showed that compared with ISO group,the ISO+Fer-1 group had no significant change in CRTC2 protein expression(P＞0.05),the expression of ACTA1 and BNP protein decreased,the surface area of cardiomyocyte reduced,the expression of ACSL4 protein decreased,and the expression of SLC7A11 and GPX4 proteins increased(P＜0.05 or P＜0.01).Compared with the ISO group,the LV-CRTC2+ISO group showed a decrease in surface area of cardiomyocytes(P＜0.01),a decrease in ACTA1,BNP and ACSL4 protein ex-pression,an increase in SLC7A11 and GPX4 protein expression,and a decrease in ROS and iron ion content(P＜0.05 or P＜0.01).Conclusion CRTC2 alleviates cardiomyocyte hypertrophy and protect cardiac function by suppressing fer-roptosis in cardiomyocytes.

OBJECTIVE To explore the effects of meropenem exposure and degradation levels on clinical efficacy in patients with purulent meningitis （PM）. METHODS A total of 131 PM patients treated with meropenem at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2022 to June 2025 were prospectively included. Relevant data were collected and divided into a cured group （91 cases） and a non-cured group （40 cases） based on the efficacy. High-performance liquid chromatography-tandem mass spectrometry was used to determine the concentration of meropenem and its open-loop metabolites. Risk factors that affect efficacy were screened， and their predictive power and correlation were evaluated by univariate analysis， and multivariate Logistic regression analysis， receiver operating characteristic （ROC） curves， and correlation analysis. RESULTS Univariate analysis showed that serum creatinine， creatinine clearance rate， minimum inhibitory concentration of meropenem ≥16 μg/mL， cerebrospinal fluid red blood cell count， cerebrospinal fluid white blood cell count， cerebrospinal fluid glucose content， blood trough concentration， blood open-loop metabolite concentration/trough concentration ratio， and intrathecal injection were all correlated with efficacy （P＜0.05）. The results of multiple Logistic regression analysis showed that serum creatinine blood open-loop metabolite concentration/trough concentration ratio， intrathecal injection， and cerebrospinal fluid glucose content were influencing factors for suboptimal anti-infective ltt efficacy （P＜0.05）. ROC curve analysis showed that when the blood open-loop metabolite concentration/trough concentration ratio was greater than 2.854 （AUC＝0.647）， serum creatinine was less than 59.5 μmol/L （AUC＝0.647）， and cerebrospinal fluid glucose content was less than 3.37 mmol/L （AUC＝0.709）， the risk of treatment failure significantly increased （P＜0.05）. Correlation analysis showed that the blood trough concentration of meropenem was positively correlated with the concentration of its open-loop metabolites （R 2＝0.134 5， P＜0.000 1）. CONCLUSIONS Insufficient exposure level and rapid degradation of meropenem are key mechanisms affecting the anti-infective efficacy of PM. Elevated blood open-loop metabolite concentration/ trough concentration ratio， low serum creatinine level， lack of intrathecal injection， and low cerebrospinal fluid glucose content are independent risk factors for poor efficacy.

Aim To investigate the role and regulatory mechanism of CREB regulated transcription coactivator 2(CRTC2)in cardiomyocyte hypertrophy.Methods A pathological cardiomyocyte hypertrophy model was established in C57BL/6 mice by intraperitoneal injection of isoproterenol(ISO),the expression of CRTC2 in cardiac tissue was detec-ted by Western blot.The CRTC2 knockout mice model was constructed,the cardiac function of mice was detected by small animal echocardiography,the collagen fiber content in mice cardiac tissue was detected by Masson staining,the car-diomyocyte hypertrophy related proteins:skeletal muscle α1-actin(ACTA1)and brain natriuretic peptide(BNP),as well as ferroptosis related proteins:acyl-CoA synthetase long chain family member 4(ACSL4),solute carrier family 7 member 11(SLC7A11)and glutathione peroxidase 4(GPX4)in mice cardiac tissue were detected by Western blot,the iron ion content in mice cardiac tissue was detected by iron ion kit,to evaluate the correlation between CRTC2 and cardiomyocyte hypertrophy and ferroptosis.H9c2 cells were induced by ISO to construct an in vitro model of cardiomyocyte hypertrophy,the protein expressions of CRTC2,ACTA1,BNP,ACSL4,SLC7A11 and GPX4 were detected after intervention with fer-roptosis inhibitor ferrostatin-1(Fer-1).H9c2 cells with CRTC2 overexpression induced by ISO were used to construct an in vitro model of cardiomyocyte hypertrophy,the related indicators of cardiomyocyte hypertrophy and ferroptosis were detec-ted to explore the mechanism of CRTC2 in cardiomyocyte hypertrophy.Results Compared with the control group,the expression of CRTC2 protein in the cardiac tissue of ISO induced cardiomyocyte hypertrophy mice was increased(P＜0.05).Compared with wild-type mice,CRTC2-/-mice showed worsened cardiac function,manifested as increased left ventricular end-diastolic diameter(LVEDD),left ventricular end-systolic diameter(LVESD),left ventricular posterior wall thickness(LVPWT),heart weight/tibia length(HW/TL)and heart weight/body weight(HW/BW),decreased short axis shortening(FS)and ejection fraction(EF),increased collagen fiber content in cardiac tissue,upregulated ex-pression of cardiomyocyte hypertrophy-related proteins ACTA1 and BNP,increased mRNA and protein expression of ferrop-tosis-related protein ACSL4,decreased mRNA and protein expression of SLC7A11 and GPX4,and elevated iron ion content in cardiac tissue(P＜0.05 or P＜0.01).In vitro experiments showed that compared with ISO group,the ISO+Fer-1 group had no significant change in CRTC2 protein expression(P＞0.05),the expression of ACTA1 and BNP protein decreased,the surface area of cardiomyocyte reduced,the expression of ACSL4 protein decreased,and the expression of SLC7A11 and GPX4 proteins increased(P＜0.05 or P＜0.01).Compared with the ISO group,the LV-CRTC2+ISO group showed a decrease in surface area of cardiomyocytes(P＜0.01),a decrease in ACTA1,BNP and ACSL4 protein ex-pression,an increase in SLC7A11 and GPX4 protein expression,and a decrease in ROS and iron ion content(P＜0.05 or P＜0.01).Conclusion CRTC2 alleviates cardiomyocyte hypertrophy and protect cardiac function by suppressing fer-roptosis in cardiomyocytes.

TCM believes that the main pathogenesis of gastrointestinal dysfunction (GD) after laparoscopic cholecystectomy (LC) is spleen and stomach weakness, liver and stomach disharmony, liver depression and spleen deficiency, and intestinal depression. Moxibustion in the treatment of GD after LC can avoid the aggravation of gastrointestinal burden caused by oral drugs. The intervention methods mainly include suspension moxibustion, umbilical moxibustion, heat sensitive moxibustion, thunder fire moxibustion, warm acupuncture, partition moxibustion, etc. Moxibustion is often performed on the acupoints in liver meridian, spleen meridian, stomach meridian, small intestine meridian, large intestine meridian and Conception Vessel, such as Taichong (LR3), Ganshu (BL18), Yinlingquan (SP9), Zusanli (ST36), Tianshu (ST25), Wangu (SI4), Hegu (LI4), Zhongwan (CV12), Shenque (CV8) and Qihai (CV6). At present, most studies combined with moxibustion on the basis of conventional Western medicine treatment can significantly improve the efficacy, and have certain advantages in improving gastrointestinal motility decline, intestinal flora imbalance, first exhaust time, gastrointestinal hormone level disorder and intestinal mucosal barrier dysfunction. However, there are still some problems in the existing research: small sample size of clinical research, not perfect scoring scale, not unified treatment plan and operation standard, relatively scarce basic research, relatively simple acupoint research, lack of biochemical evaluation indicators, and the research of national moxibustion needs to be explored and improved in the future.

Objective:To construct and validate an artificial intelligence diagnostic system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions under white-light endoscopy.Methods:Gastroscopic images from Renmin Hospital of Wuhan University and the Seventh Medical Center of Chinese PLA General Hospital were collected from November 2012 to July 2021. A total of 823 images of gastric whitish lesions from 267 patients were finally selected. Five white-light endoscopic features associated with gastric whitish lesions were selected through a literature search, including lesion location, boundary clarity, surface texture, roundness, and depression status. Images with manually annotated features were used to train machine learning models, with the optimal model selected as the multi-feature fitting diagnostic system, which assigned diagnostic weights to each feature. A conventional deep learning model was trained with the same dataset. The diagnostic performance of the two models were compared, and eight endoscopists of varying expertise were invited to participate in human-machine comparisons.Results:Accuracy, sensitivity, and specificity of the multi-feature fitting diagnostic system were 82.11% (101/123), 78.43% (40/51), and 84.72% (61/72), respectively. Feature weights in descending order were depression (0.71), lesion location (0.11), surface roughness (0.08), boundary clarity (0.06), and subcircular shape (0.04). The diagnostic accuracy of the system was significantly higher than that of non-expert endoscopists (82.11% VS 74.31%, Z=-2.785, P=0.008) and comparable to that of expert endoscopists (82.11% VS 83.20%, Z=-0.696, P=0.700). There was no significant difference in accuracy between the multi-feature fitting diagnostic system and the traditional deep learning model [82.11% (101/123) VS 82.93% (102/123), P=1.000]. Conclusion:The feature-weighted artificial intelligence diagnostic system for gastric whitish neoplastic lesions demonstrates clinically relevant diagnostic accuracy under white-light endoscopy.

Objective:To construct and validate an artificial intelligence diagnostic system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions under white-light endoscopy.Methods:Gastroscopic images from Renmin Hospital of Wuhan University and the Seventh Medical Center of Chinese PLA General Hospital were collected from November 2012 to July 2021. A total of 823 images of gastric whitish lesions from 267 patients were finally selected. Five white-light endoscopic features associated with gastric whitish lesions were selected through a literature search, including lesion location, boundary clarity, surface texture, roundness, and depression status. Images with manually annotated features were used to train machine learning models, with the optimal model selected as the multi-feature fitting diagnostic system, which assigned diagnostic weights to each feature. A conventional deep learning model was trained with the same dataset. The diagnostic performance of the two models were compared, and eight endoscopists of varying expertise were invited to participate in human-machine comparisons.Results:Accuracy, sensitivity, and specificity of the multi-feature fitting diagnostic system were 82.11% (101/123), 78.43% (40/51), and 84.72% (61/72), respectively. Feature weights in descending order were depression (0.71), lesion location (0.11), surface roughness (0.08), boundary clarity (0.06), and subcircular shape (0.04). The diagnostic accuracy of the system was significantly higher than that of non-expert endoscopists (82.11% VS 74.31%, Z=-2.785, P=0.008) and comparable to that of expert endoscopists (82.11% VS 83.20%, Z=-0.696, P=0.700). There was no significant difference in accuracy between the multi-feature fitting diagnostic system and the traditional deep learning model [82.11% (101/123) VS 82.93% (102/123), P=1.000]. Conclusion:The feature-weighted artificial intelligence diagnostic system for gastric whitish neoplastic lesions demonstrates clinically relevant diagnostic accuracy under white-light endoscopy.

Objective:To investigate the clinical characteristics and potential predisposing factors of the external cervical resorption(ECR).Methods:22 ECR cases with 38 affected teeth from 2016 to 2022 were retrospectively reviwed.Descriptive analysis combined with single factor analysis was used to study the clinical characteristics and influencing factors of ECR.Results:Maxillary anterior teeth(34.2％)were the most affected by ECR.Univariate analysis showed that ECR was more commonly noted in teeth without percussion pain and palpation pain,the probing depth of the periodontal pocket was greater than 3mm,with pulp activity reaction,without forma-tion of abscess and/or sinus tract,and without periapical lesions.There were statistically significant differences in percussion tender-ness,palpation tenderness and probing depth among the different Heithersary stages(P＜0.05).In the advanced cases,deep periodon-tal pockets and abscess formation were observed.The most common related dental factors of ECR were orthodontic treatment(15.87％)and dental traumatic injury(28.57％).Conclusion:ECR affected teeth often lack of clinical signs and symptoms.Radiology is the key to early diagnosis.

Objective:To construct an intelligent endoscopic image analysis system that could monitor the blind spot of the upper gastrointestinal tract, and to test its performance.Methods:A total of 87 167 upper gastrointestinal endoscopy images (dataset 1) including 75 551 for training and 11 616 for testing, and a total of 2 414 pharyngeal images (dataset 2) including 2 233 for training and 181 for testing were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University between 2016 to 2020. A 27-category-classification model for blind spot monitoring in the upper gastrointestinal tract (model 1, which distinguished 27 anatomical sites such as the pharynx, esophagus, and stomach) and a 5-category-classification model for blind spot monitoring in the pharynx (model 2, which distinguished palate, posterior pharyngeal wall, larynx, left and right pyriform sinuses) were constructed. The above models were trained and tested based on dataset 1 and 2, respectively, and trained based on the EfficientNet-B4, ResNet50 and VGG16 models of the keras framework. Thirty complete upper gastrointestinal endoscopy videos were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University in 2021 to test model 2 blind spot monitoring performance.Results:The cross-sectional comparison results of the accuracy of model 1 in identifying 27 anatomical sites of the upper gastrointestinal tract in images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 90.90%, 90.24%, and 89.22%, respectively, with the EfficientNet-B4 model performance the best, and the accuracy of EfficientNet-B4 model for each site ranged from 80.49% to 97.80%. The cross-sectional comparison results of the accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 99.40%, 98.56%, and 97.01%, respectively, in which the EfficientNet-B4 model had the best performance, and the accuracy of EfficientNet-B4 model for each site ranged from 96.15% to 100.00%. The overall accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the video was 97.33% (146/150).Conclusion:The intelligent endoscopic image analysis system based on deep learning can monitor blind spots in the upper gastrointestinal tract, coupled with pharyngeal blind spot monitoring and esophagogastroduodenal blind spot monitoring functions. The system shows high accuracy in both images and videos, which is expected to have a potential role in clinical practice and assisting endoscopists to achieve full observation of the upper gastrointestinal tract.

China has classified the Corona Virus Disease 2019(COVID-19) as a statutory category B infectious disease and managed it according to Category B since January 8, 2023.In view that Omicron variant is currently the main epidemic strain in China, in order to guide the treatment of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) infection in children with the times, refer to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial 10 th Edition), Expert Consensus on Diagnosis, Treatment and Prevention of Novel Coronavirus Infection in Children (Fourth Edition) and the Diagnosis and Treatment Strategy for Pediatric Related Viral Infections.The Expert Consensus on the Diagnosis, Treatment and Prevention of Novel Coronavirus Infection in Children (Fifth Edition) has been formulated and updated accordingly on related etiology, epidemiology, pathogenic mechanism, clinical manifestations, auxiliary examination, diagnosis and treatment, and added key points for the treatment of COVID-19 related encephalopathy, fulminating myocarditis and other serious complications for clinical reference.