Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

OBJECTIVE To compare the efficacy and safety of different classes of antibacterial agents in hospitalized adult patients with community-acquired pneumonia （CAP）.METHODS PubMed， Embase，the Cochrane Library，Web of Science，CNKI，and Wanfang data were searched from inception to January 1，2026，to identify randomized controlled trials that evaluated different classes of antibacterial agents in hospitalized adults with CAP.After screening the literature， extracting the data， and evaluating the quality of the literature， a network meta-analysis was performed using Stata 18.0.RESULTS A total of 25 studies were included，involving 7 863 patients. In terms of efficacy，compared with carbapenems，penicillins，cephalosporins，macrolides，and pleuromutilins were associated with significantly higher treatment failure rates （ P ＜0.05），carbapenems and traditional tetracyclines were ranked as the top two by the surface under the cumulative ranking curve area （SUCRA）. No statistically significant differences were observed in mortality among the interventions （ P ＞0.05），carbapenems and other quinolones were ranked as the top two by SUCRA. In terms of safety，no statistically significant differences were observed among the interventions in the incidence of adverse events or discontinuation rates （ P ＞0.05）；traditional tetracyclines and novel tetracyclines were ranked as the top two by SUCRA for the incidence of adverse events，while novel tetracyclines and pleuromutilins were ranked as the top two by SUCRA for discontinuation rates.CONCLUSIONS Carbapenems significantly reduced the treatment failure rate in hospitalized adults with CAP. The safety profiles of all antibacterial agents were comparable， with novel tetracyclines showing a more favorable safety profile.

Objective To explore the effect of cognitive behavioral therapy(CBT)on improving anxiety,depression,sleep,and fatigue in patients with post-intensive care syndrome(PICS).Methods Sixty PICS patients transferred from the department of respiratory intensive care unit(RICU)of Puyang Oilfield General Hospital from September 2022 to February 2023 were selected as the study subjects.Patients were divided into a control group and an intervention group using a random number table method,with 30 cases in each group.The control group only received routine health education,while the intervention group received CBT on the basis of routine health education.After 3 months of intervention,the changes in hospital anxiety and depression scale(HADS),Pittsburgh sleep quality index(PSQI),and fatigue assessment scale(FAS)before and after intervention were observed in both groups of patients.Results There was no statistically significant difference in anxiety,depression,PSQI,and FAS scores between the two groups of patients before intervention.The scores gradually decreased over time and reached the lowest level at 3 months after intervention.After intervention,the above scores in the intervention group were significantly lower than those in the control group(anxiety score:5.06±1.76 vs.9.33±1.42,depression score:6.93±1.64 vs.7.63±1.61,PSQI score:5.27±1.37 vs.8.43±2.56,FAS score:5.67±0.47 vs.9.97±0.21,all P<0.05).Conclusion Implementing CBT for PICS patients can alleviate anxiety and depression,improve their sleep quality and fatigue symptoms,thereby enhancing their quality of life.

Objective:To investigate the main influencing factors of mild cognitive impairment(MCI)in the elderly and explore the interaction between high fluoride exposure and related co-factors on MCI.Methods:A cross-sectional study was conducted from May to December 2024 in four towns in the rural areas of Tianjin(two historically high-fluoride towns and two non-high-fluoride towns). A total of 125 elderly people aged 60 years and above were randomly selected from each township.MCI was diagnosed using the Mini-Mental State Examination(MMSE)and Montreal Cognitive Assessment-Basic(MoCA-B)according to the diagnostic criteria for MCI.Binary logistic regression was used to analyze the influencing factors of MCI, and conditional logistic regression was employed to assess multiplicative and additive interaction effects.Results:A total of 481 participants were included, with 354 in the normal group and 127 in the MCI group, and the incidence of MCI was 26.40%.Univariate analysis showed that MCI was associated with age, annual household income per capita, high fluoride exposure, life stress, depression, and abnormal urinalysis( χ2=4.241, 4.017, 4.552, 7.143, 4.151, 5.113, all P<0.05). Multivariate logistic regression revealed that after adjusting for other confounders, high fluoride exposure( OR=1.816, 95% CI: 1.177-2.802), age ≥70 years( OR=1.584, 95% CI: 1.034-2.428), depression( OR=2.106, 95% CI: 1.042-4.254), and abnormal urinalysis( OR=1.595, 95% CI: 1.041-2.444)increased the risk of MCI.Compared with severe life stress, moderate stress( OR=0.254, 95% CI: 0.082-0.790)reduced the risk of MCI.No multiplicative or additive interaction was found between high fluoride exposure and depression, life stress, or abnormal urinalysis. Conclusions:High fluoride exposure, age ≥70 years, depression, abnormal urinalysis, and severe life stress may increase the risk of MCI in the elderly, but there is no interaction among them.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Objective To explore the value of nomogram model based on MR T1WI enhanced radiomics features and clinical fac-tors in predicting the prognosis of gliomas.Methods A retrospective selection was conducted on 135 patients with postoperative pathologically confirmed gliomas,who were categorized into poor prognosis group(n=59)and good prognosis group(n=76)according to survival condition at 20 months postoperatively.All patients were randomly divided into training group(n=94)and validation group(n=41)in a 7︰3 ratio.Radiomics features were extracted by 3D Slicer software,and the extracted radiomics features were downscaled by intraclass correlation coefficient(ICC),t-test,least absolute shrinkage and selection operator(LASSO)regression,and a total of 1 058 features were extracted for each patient,and 10 optimal radiomics features were obtained,to finally get the Radiomics score(Radscore).After combining clinical features and Radscore,a nomogram model was constructed.Results In the training group,Radscore was significantly higher in the poor prognosis group than that in the good prognosis group(t=8.773,P<0.05).The area under the curve(AUC)of receiver operating characteristic(ROC)curve of the training and validation groups of the radiomics model were 0.751[95%confidence interval(CI)0.654-0.849]and 0.606(95%CI 0.426-0.787),respectively.The AUC of the nomogram model were 0.899(95%CI 0.839-0.960)and 0.908(95%CI 0.823-0.994)in the training and validation groups,respectively,with much better predictive efficacy of the nomogram model.Conclusion A nomogram model based on MR T1WI enhanced radiomics features and clinical factors has good predictive efficacy in the prognosis of gliomas after surgery.

Age-related macular degeneration(AMD)is a degenerative disease caused by multiple factors,and it is one of the leading causes of severe vision loss.Mendelian randomization(MR)uses genetic variation as an instrumental varia-ble to detect and quantify the causal relationship between exposure and outcomes,overcoming the limitations of observa-tional studies and randomized controlled trials.In recent years,MR has been widely used in the etiological inference of AMD.In this paper,MR studies related to AMD are summarized,focusing on the causal relationship between different risk factors and the onset of AMD,so as to provide a basis for the etiological research and prevention strategy development of AMD.

Objective:To develop and validate a diagnostic model for pancreatic benign and malignant tumors using radiomics technology.Methods:A retrospective analysis was conducted on the clinical data of 113 patients with pancreatic tumors who underwent surgical treatment at the Hepatobiliary and Pancreatic Surgery Departments of the Second Hospital and the First Hospital of Hebei Medical University from January 2020 to December 2022. There were 59 male and 54 female patients, aged (55.3±16.8) years. Preoperative enhanced thin-slice computed tomography (CT) data and postoperative pathological diagnosis results were collected. Data from 74 patients at the Second Hospital were selected, and according to the random classification principle of 7∶3, the data of 52 patients were determined as the training set for model construction, while the remaining 22 patients' data served as the internal validation set. Data from 39 patients at the First Hospital of Hebei Medical University were used as the external validation set to assess the generali-zability of the established model. The region of interest in the lesions on CT images was analyzed using three-dimensional radiomics feature extraction, and the top 5 features were selected using feature selection methods. Radiomics models were established for the selected features using 17 classifiers. The performance of the models was evaluated using the area under curve (AUC) of the receiver operating characteristic (ROC).Results:Two hundred and fifty-five models were established with 15 feature selection methods and 17 classifiers. 7 models with the AUC greater than 0.7 were selected, among which the best one was LASSO-K neighbors classifier model, constructed using the LASSO feature selection method and the k-nearest neighbors algorithm, achieving AUC values of 0.933 (95% CI: 0.859-0.984) in the training set, 0.973 (95% CI: 0.896-1.000) in the validation set, and 0.774 (95% CI: 0.624-0.908) in the external validation set, with satisfactoryclassification and generalization ability. Conclusion:The radiomics-based diagnostic model for pancreatic benign and malignant tumors can effectively distinguish the benignancy and malignancy of tumors. The LASSO-K neighbors classifier model demonstrated high accuracy and reliability in this study.

Breast cancer(BC)is one of the most prevalent malignant tumors affecting women worldwide,with its incidence rate continuously increasing.As a result,treatment strategies for this disease have received considerable attention.Research has highlighted the crucial role of the Hedgehog(Hh)signaling pathway in the initiation and progression of BC,particularly in promoting tumor growth and metastasis.There-fore,molecular targets within this pathway represent promising opportunities for the development of novel BC therapies.This study aims to elucidate the therapeutic mechanisms by which natural com-pounds modulate the Hh signaling pathway in BC.By conducting a comprehensive review of various natural compounds,including polyphenols,terpenes,and alkaloids,we reveal both common and unique regulatory mechanisms that influence this pathway.This investigation represents the first comprehen-sive analysis of five distinct mechanisms through which natural compounds modulate key molecules within the Hh pathway and their impact on the aggressive behaviors of BC.Furthermore,by exploring the structure-activity relationships between these compounds and their molecular targets,we shed light on the specific structural features that enable natural compounds to interact with various components of the Hh pathway.These novel insights contribute to advancing the development and clinical application of natural compound-based therapeutics.Our thorough review not only lays the groundwork for exploring innovative BC treatments but also opens new avenues for leveraging natural compounds in cancer therapy.

Objective: To understand the dose response relationship between urinary fluoride and dental fluorosis among children from drinking water borne endemic fluorosis areas in Tianjin, so as to provide a scientific basis for assessing fluoride exposure risk among children from endemic areas and establishing reference values for urinary fluoride. Methods: From January to December 2024, 83 endemic villages were selected in Tianjin. A total of 2 382 children aged 8-12 years from these villages underwent dental fluorosis examination, along with water fluoride and urinary fluoride testing. Additionally, data from areas where the drinking water fluorosis control target was achieved for more than 12 years (10 villages, 50 people per village) were included as controls. A restricted cubic spline model was used to analyze the relationship between urinary fluoride levels and dental fluorosis prevalence, and benchmark dose (BMD) and benchmark dose lower bound (BMDL), as well as reference dose (RfD), were calculated using the benchmark dose method. Results: The prevalence rates of dental fluorosis among children in drinking water borne endemic fluorosis areas and areas of normal fluoride content in water in Tianjin were 10.58%, 7.60%, with a geometric mean urinary fluoride level of 0.72 and 0.60 mg/L,respectively. There were statistically significant differences in both dental fluorosis prevalence and geometric mean urinary fluoride levels between the drinking water borne endemic fluorosis areas group and the areas of normal fluoride content in water ( χ 2/Z = 4.05 , -7.31, both P <0.05). Across different periods of water source improvement, there were statistically significant differences in overall population, male, and female dental fluorosis prevalence rates and geometric mean urinary fluoride levels ( χ 2/H =44.95, 23.96, 21.05; 168.39, 63.93, 107.50, all P <0.01). Significant differences were also observed across age groups among children from drinking water borne endemic fluorosis areas in terms of dental fluorosis prevalence and geometric mean urinary fluoride levels ( χ 2/H =32.14, 79.73, both P <0.01). The results of the restricted cubic spline model showed that the risk of dental fluorosis in different sex, age and overall children in the drinking water borne endemic fluorosis areas increased significantly with rising urinary fluoride concentration(all P-general trend <0.05). The BMD value for the drinking water borne endemic fluorosis areas was 1.72 mg/L, the BMDL was 1.31 mg/L, and the RfD was 1.31 mg/L. Conclusions The prevalence of dental fluorosis among children in the drinking water borne endemic fluorosis areas in Tianjin has been effectively controlled, but it is still higher than that in the areas of normal fluoride content in water. Moreover, urinary fluoride levels and dental fluorosis prevalence among children from drinking water borne endemic fluorosis areas show a clear dose response relationship.