Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

This study aimed to explore the therapeutic mechanisms of Colquhounia Root Tablets(CRT) in treating diabetic kidney disease(DKD) by integrating biomolecular network mining with animal model verification. By analyzing clinical transcriptomics data, an interaction network was constructed between candidate targets of CRT and DKD-related genes. Based on the topological eigenvalues of network nodes, 101 core network targets of CRT against DKD were identified. These targets were found to be closely related to multiple pathways associated with type 2 diabetes, immune response, and metabolic reprogramming. Given that immune-inflammatory imbalance driven by metabolic reprogramming is one of the key pathogenic mechanisms of DKD, and that many core network targets of CRT are involved in this pathological process, receptor for advanced glycation end products(RAGE)-reactive oxygen species(ROS)-phosphatidylinositol 3-kinase(PI3K)-protein kinase B(AKT)-nuclear factor-κB(NF-κB)-NOD-like receptor family pyrin domain containing 3(NLRP3) signaling axis was selected as a candidate target for in-depth research. Further, a rat model of DKD induced by a high-sugar, high-fat diet and streptozotocin was established to evaluate the pharmacological effects of CRT and verify the expression of related targets. The experimental results showed that CRT could effectively correct metabolic disturbances in DKD, restore immune-inflammatory balance, and improve renal function and its pathological changes by inhibiting the activation of the RAGE-ROS-PI3K-AKT-NF-κB-NLRP3 signaling axis. In conclusion, this study reveals that CRT alleviates the progression of DKD through dual regulation of metabolic reprogramming and immune-inflammatory responses, providing strong experimental evidence for its clinical application in DKD.
Animals ; Diabetic Nephropathies/metabolism* ; Receptor for Advanced Glycation End Products/genetics* ; NF-kappa B/genetics* ; Signal Transduction/drug effects* ; Rats ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Phosphatidylinositol 3-Kinases/genetics* ; Reactive Oxygen Species/metabolism* ; Humans ; Plant Roots/chemistry* ; Rats, Sprague-Dawley ; Tablets/administration & dosage*

OBJECTIVES: To evaluate the application value of genetic newborn screening (gNBS) in the Yunnan region. METHODS: A prospective study was conducted with a random selection of 3 001 newborns born in the Yunnan region from February to December 2021. Traditional newborn screening (tNBS) was used to test biochemical indicators, and targeted next-generation sequencing was employed to screen 159 genes related to 156 diseases. Positive-screened newborns underwent validation and confirmation tests, and confirmed cases received standardized treatment and long-term follow-up. RESULTS: Among the 3 001 newborns, 166 (5.53%) were initially positive for genetic screening, and 1 435 (47.82%) were genetic carriers. The top ten genes with the highest variation frequency were GJB2 (21.29%), DUOX2 (7.27%), HBA (6.14%), GALC (3.63%), SLC12A3 (3.33%), HBB (3.03%), G6PD (2.94%), SLC25A13 (2.90%), PAH (2.73%), and UNC13D (2.68%). Among the initially positive newborns from tNBS and gNBS, 33 (1.10%) and 47 (1.57%) cases were confirmed, respectively. A total of 48 (1.60%) cases were confirmed using gNBS+tNBS. The receiver operating characteristic curve analysis demonstrated that the areas under the curve for tNBS, gNBS, and gNBS+tNBS in diagnosing diseases were 0.866, 0.982, and 0.968, respectively (P<0.05). DeLong's test showed that the area under the curve for gNBS and gNBS+tNBS was higher than that for tNBS (P<0.05). CONCLUSIONS gNBS can expand the range of disease detection, and its combined use with tNBS can significantly shorten diagnosis time, enabling early intervention and treatment.
Humans ; Infant, Newborn ; Neonatal Screening ; Genetic Testing ; Female ; Male ; Follow-Up Studies ; Prospective Studies ; China

Objective To investigate the predictive value of preoperative plasma total cholesterol levels for postoperative delirium(POD)following esophagectomy and to analyze its associated risk factors.Methods The complete perioperative electronic medical records of 187 patients with POD who underwent esophagectomy at the 960 Hospital of the People's Liberation Army between January 2019 and December 2023 were retrospectively analyzed.According to the occurrence of POD within one week postoperatively,the patients were categorized into a delirium group and a non-delirium group.The preoperative plasma total cholesterol levels were used to construct a receiver operating characteristic(ROC)curve for predicting delirium after esophagectomy.The area under the curve(AUC)and its 95%confidence interval(CI)were calculated.Additionally,the plasma total cholesterol level corresponding to the maximum Youden index(sensitivity+specificity-1)was determined as the optimal critical value.Results POD occurred in 24 of 187 patients who underwent esophageal cancer resection,with an incidence rate of 12.83%.Compared to the non-delirium group,the delirium group exhibited older age,lower intraoperative blood pressure,lower plasma total cholesterol and low-density lipoprotein levels,longer anesthesia duration,and a higher preva-lence of coronary heart disease,which was identified as a risk factor for POD.Dexmedetomidine administration was associated with a reduced occurrence of POD.The AUC,95%CI,cut-off value,sensitivity,and specificity of preop-erative plasma total cholesterol level for predicting POD were 0.761,0.657～0.865,≤4.11 mmol/L,70.8%,and 77.3%,respectively.Conclusions Among non-hyperlipidemic patients,low preoperative plasma total cholesterol levels and intraoperative hypotension are identified as independent risk factors for POD following esophageal cancer resection.Preoperative plasma total cholesterol levels exhibit moderate predictive value for the occurrence of POD.

Objective To investigate the predictive value of preoperative plasma total cholesterol levels for postoperative delirium(POD)following esophagectomy and to analyze its associated risk factors.Methods The complete perioperative electronic medical records of 187 patients with POD who underwent esophagectomy at the 960 Hospital of the People's Liberation Army between January 2019 and December 2023 were retrospectively analyzed.According to the occurrence of POD within one week postoperatively,the patients were categorized into a delirium group and a non-delirium group.The preoperative plasma total cholesterol levels were used to construct a receiver operating characteristic(ROC)curve for predicting delirium after esophagectomy.The area under the curve(AUC)and its 95%confidence interval(CI)were calculated.Additionally,the plasma total cholesterol level corresponding to the maximum Youden index(sensitivity+specificity-1)was determined as the optimal critical value.Results POD occurred in 24 of 187 patients who underwent esophageal cancer resection,with an incidence rate of 12.83%.Compared to the non-delirium group,the delirium group exhibited older age,lower intraoperative blood pressure,lower plasma total cholesterol and low-density lipoprotein levels,longer anesthesia duration,and a higher preva-lence of coronary heart disease,which was identified as a risk factor for POD.Dexmedetomidine administration was associated with a reduced occurrence of POD.The AUC,95%CI,cut-off value,sensitivity,and specificity of preop-erative plasma total cholesterol level for predicting POD were 0.761,0.657～0.865,≤4.11 mmol/L,70.8%,and 77.3%,respectively.Conclusions Among non-hyperlipidemic patients,low preoperative plasma total cholesterol levels and intraoperative hypotension are identified as independent risk factors for POD following esophageal cancer resection.Preoperative plasma total cholesterol levels exhibit moderate predictive value for the occurrence of POD.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Objective:To identify the relationship between the CT arterial radiomics score and the treatment response to neoadjuvant therapy for pancreatic cancer.Methods:The clinical data of 243 pancreatic cancer patients who received surgical resection after neo-adjuvant therapy in the First Affiliated Hospital of Naval Medical University from March 2017 to March 2023 were retrospectively analyzed. Based on the tumor regression grade (TRG), the patients were divided into good response group (TRG 0-1, n=30) and non-good response group (TRG 2-3, n=213). The clinical, radiological and pathological features were compared between two groups. Fully-automated segmentation tool was used for segmenting the arterial CT scan of pancreatic tumor before and after treatment. Python package was applied to extract the radiomics features of tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the arterial radiomics score. Univariate and multivariate logistic regression models were used to analyze the association between arterial radiomics score and treatment response to neoadjucant therapy. Receiver operating-characteristics (ROC) curve was drawn and area under curve (AUC), specificity, sensitivity and accuracy for evaluating the treatment response were calculated. The clinical usefulness of arterial radiomics score for diagnosing the response of neoadjuvant treatment for pancreatic cancer were determined by decision curve analysis (DCA) . Results:A total of 330 arterial radiomics CT features were obtained, and 9-selected arterial phase features associated with treatment response were determined after being reduced by the Lasso logistic regression algorithm. Univariate analysis showed that the arterial radiomics score, three-dimensional diameter after neoadjuvant therapy, pancreatic contour, T stage, N stage, Peri-pancreatic nerve invasion, lymph-vascular space invasion (LVSI) and invasion of duodenum were all associated with treatment response (all P value <0.05). Multivariate logistic regression analyses confirmed that arterial radiomics score was obviously associated with the neoadjuvant treatment response ( P<0.001). At the cut-off value of 1.93, AUC of the arterial radiomics score for diagnosing neoadjuvant treatment response was 0.92, and the specificity, sensitivity and accuracy was 86.7%, 84.5% and 84.8%. DCA demonstrated that when the percentage for predicting the treatment response by using the arterial radiomics score was >0.2, the patients could benefit from the application of arterial radiomics score for evaluating neoadjuvant therapy response. Conclusions:The arterial radiomics score was strongly correlated with the neoadjuvant treatment response of pancreatic cancer, and can accurately predict neoadjuant treatment efficacy.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Objective To explore the relationship between atherosclerotic index of plasma(AIP)and cognitive function in older adults.Methods A cross-sectional study method was used to include the National Health and Nutrition Examination Survey(NHANES)2011 to 2014 population aged>60 years as study subjects,and the population was divided into>-2.158 to≤-0.624 AIP group,>-0.624 to≤-0.123 AIP group,>-0.123 to≤0.309 AIP group,>0.309 AIP group 4 groups.Demographic data(including gender,age,race,education,marriage,height,and body mass),chronic disease-related data(including angina pectoris,emphysema,depression score,chronic bronchitis,coronary heart disease,history of stroke,hypertension,and diabetes mellitus),lifestyle-related data(including smoking and sleep duration),and cognitive function assessment[including the United Registry for consortium to establish a registry for Alzheimer's disease word list(CERAD W-L)score,animal fluency test(AFT)score and digit symbol substitution test(DSST)score],and to compare the differences in the above information between groups with different levels of AIP;and to analyze the factors affecting the cognitive function of the population by using multifactorial Logistic regression.Results A total of 1 335 participants were included in the analysis,and the results of the multivariate linear regression suggested that in model 3 after adjusting for all covariates,no linear relationship existed between AIP and CERAD W-L score for the>-0.624-≤-0.123 AIP group,the>-0.123-≤-0.309 AIP group,and the>-0.309 AIP group,compared with the>-2.158-≤-0.624 AIP group have a linear relationship(P=0.500 for the>-0.624-≤-0.123 AIP group,P=0.110 for the>-0.123-≤0.309 AIP group,and P=0.200 for the>0.309 AIP group).Weighted multifactor Logistic regression analyses after adjusting for covariates showed that AIP was a risk correlate for decline in representing immediate and delayed recall CERAD W-L score[odds ratio(OR)=0.970,95％confidence interval(95％CI)of 0.950-1.000,P=0.050].Restricted Cubic Splines of AIP versus CERAD W-L score restricted cubic spline(RCS)curves suggested a significant overall effect of CERAD W-L score on AIP(overall P=0.005),but not a significant nonlinear relationship(nonlinear P=0.278);and subgroup analyses showed that among 65-70 years old who were college-educated or better,married,and free of hypertension,AIP was the most prevalent among CERAD W-L scores.AIP is an associated risk factor for the development of CERAD W-L decline in people(OR<1,P<0.05).Conclusion AIP is a risk factor for cognitive decline associated with delayed and immediate memory,suggesting that AIP can be used as a predictor or assessment of cognitive function.

Objective To explore the relationship between atherosclerotic index of plasma(AIP)and cognitive function in older adults.Methods A cross-sectional study method was used to include the National Health and Nutrition Examination Survey(NHANES)2011 to 2014 population aged>60 years as study subjects,and the population was divided into>-2.158 to≤-0.624 AIP group,>-0.624 to≤-0.123 AIP group,>-0.123 to≤0.309 AIP group,>0.309 AIP group 4 groups.Demographic data(including gender,age,race,education,marriage,height,and body mass),chronic disease-related data(including angina pectoris,emphysema,depression score,chronic bronchitis,coronary heart disease,history of stroke,hypertension,and diabetes mellitus),lifestyle-related data(including smoking and sleep duration),and cognitive function assessment[including the United Registry for consortium to establish a registry for Alzheimer's disease word list(CERAD W-L)score,animal fluency test(AFT)score and digit symbol substitution test(DSST)score],and to compare the differences in the above information between groups with different levels of AIP;and to analyze the factors affecting the cognitive function of the population by using multifactorial Logistic regression.Results A total of 1 335 participants were included in the analysis,and the results of the multivariate linear regression suggested that in model 3 after adjusting for all covariates,no linear relationship existed between AIP and CERAD W-L score for the>-0.624-≤-0.123 AIP group,the>-0.123-≤-0.309 AIP group,and the>-0.309 AIP group,compared with the>-2.158-≤-0.624 AIP group have a linear relationship(P=0.500 for the>-0.624-≤-0.123 AIP group,P=0.110 for the>-0.123-≤0.309 AIP group,and P=0.200 for the>0.309 AIP group).Weighted multifactor Logistic regression analyses after adjusting for covariates showed that AIP was a risk correlate for decline in representing immediate and delayed recall CERAD W-L score[odds ratio(OR)=0.970,95％confidence interval(95％CI)of 0.950-1.000,P=0.050].Restricted Cubic Splines of AIP versus CERAD W-L score restricted cubic spline(RCS)curves suggested a significant overall effect of CERAD W-L score on AIP(overall P=0.005),but not a significant nonlinear relationship(nonlinear P=0.278);and subgroup analyses showed that among 65-70 years old who were college-educated or better,married,and free of hypertension,AIP was the most prevalent among CERAD W-L scores.AIP is an associated risk factor for the development of CERAD W-L decline in people(OR<1,P<0.05).Conclusion AIP is a risk factor for cognitive decline associated with delayed and immediate memory,suggesting that AIP can be used as a predictor or assessment of cognitive function.