Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objective:To explore genomic features associated with gemcitabine sensitivity, patient-derived organoid models of biliary tract cancer (BTC) were established and characterized.Methods:This is an experimental study. The tissue specimens of BTC were collected from patients who underwent surgical resection at the Department of Hepatobiliary and Pancreatic Surgery,the Second Affiliated Hospital of Zhejiang University School of Medicine between January 2020 and December 2023. The tumor organoids were cultured in vitro and histologically characterized. Drug sensitivity testing was performed using gemcitabine,cisplatin,paclitaxel,fluorouracil,and lenvatinib etc. to evaluate cell viability. The correlation between the drug sensitivity of organoids and clinical therapeutic response was analyzed.Results:Thirty-eight patient-derived organoids (PDO) models were successfully established from 43 biliary tract malignancy patients with complete follow-up data,including gallbladder cancer PDO 14 cases,distal bile duct cancer PDO 16 cases,intrahepatic cholangiocarcinoma PDO 8 cases,achieving an overall success rate of 88.4%. Drug sensitivity testing (DST) was performed on the successfully generated PDO,with 35 models successfully completing DST experiments. The overall consistency rate between drug responses in PDOs and clinical survival outcomes in corresponding patients was 8/14. Transcriptomic analysis of gemcitabine-sensitive vs. gemcitabine-resistant PDO identified 71 differentially expressed genes in the resistant group,the significantly up-regulated genes including GLDC, LINC01595, IL-27, ANGPTL3, CYP7A1,and AKR1C1;the significantly down-regulated genes including P2RY2,LIPC,and ECHDC3. Conclusion:A biobank of patient-derived organoids of BTC has been established,which demonstrates its potential as preclinical models and tools for predicting chemotherapy responses for BTC patients.

Objective:To explore genomic features associated with gemcitabine sensitivity, patient-derived organoid models of biliary tract cancer (BTC) were established and characterized.Methods:This is an experimental study. The tissue specimens of BTC were collected from patients who underwent surgical resection at the Department of Hepatobiliary and Pancreatic Surgery,the Second Affiliated Hospital of Zhejiang University School of Medicine between January 2020 and December 2023. The tumor organoids were cultured in vitro and histologically characterized. Drug sensitivity testing was performed using gemcitabine,cisplatin,paclitaxel,fluorouracil,and lenvatinib etc. to evaluate cell viability. The correlation between the drug sensitivity of organoids and clinical therapeutic response was analyzed.Results:Thirty-eight patient-derived organoids (PDO) models were successfully established from 43 biliary tract malignancy patients with complete follow-up data,including gallbladder cancer PDO 14 cases,distal bile duct cancer PDO 16 cases,intrahepatic cholangiocarcinoma PDO 8 cases,achieving an overall success rate of 88.4%. Drug sensitivity testing (DST) was performed on the successfully generated PDO,with 35 models successfully completing DST experiments. The overall consistency rate between drug responses in PDOs and clinical survival outcomes in corresponding patients was 8/14. Transcriptomic analysis of gemcitabine-sensitive vs. gemcitabine-resistant PDO identified 71 differentially expressed genes in the resistant group,the significantly up-regulated genes including GLDC, LINC01595, IL-27, ANGPTL3, CYP7A1,and AKR1C1;the significantly down-regulated genes including P2RY2,LIPC,and ECHDC3. Conclusion:A biobank of patient-derived organoids of BTC has been established,which demonstrates its potential as preclinical models and tools for predicting chemotherapy responses for BTC patients.

Objective To describe and analyze the current situation of the four same type of departments in an hospital in order to provide a reference for the construction of"the most cost-effective medical care".Methods The CN-DRG were used to automatically group and compare the medical capacity and inpatient service efficiency of the hospital department groups,and in the refined analysis,one DRG disease group of in situ cancer and non-malignant disease loss uterine surgery and single species uterine fibroid was included,and the Kruskal-Wallis H test was used to further compare the differences in length of stay and various costs.Results It included a total of 22630 patients,whose weights varied from a maximum of 3948.62 in diagnostic group 1 to a minimum of 133.55 in diagnostic group 11.The cost consumption indexes ranged from a minimum of 0.89 in diagnostic group 5 to a maximum of 1.04 in diagnostic group 2,while the time consumption indexes ranged from a minimum of 0.48 in diagnostic group 11 to a maximum of 0.81 in diagnostic group 5.When comparing the diagnostic groups,there were statistically significant differences(P＜0.05)in hospitalization days,total cost,diagnostic cost,therapeutic cost,and cost of supplies.Specifically,when comparing the diagnostic and treatment groups within departments,the differences in hospitalization days and all costs were statistically significant(P＜0.05)in departments 1 and 2,the differences in diagnostic cost,therapeutic cost,and cost of supplies were statistically significant(P＜0.05)in department 3.Conclusion There exists a notable disparity in the extent to which each diagnostic and treatment group contributes to the hospital's service capacity and cost variability.Consequently,it is necessary to reasonably evaluate the length of hospital stay and medical cost of patients to achieve the highest cost-effective medical treatment.

ObjectiveTo observe the clinical efficacy of Tongfu Xiezhuo enema in treating stage 3-4 chronic kidney disease （CKD） and the effect of the therapy on the neutrophil-to-lymphocyte ratio （NLR） as an inflammation marker. MethodSixty patients diagnosed with stage 3-4 CKD who visited the Nephrology Department of the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from December 2022 to June 2023 were included and randomly assigned into observation and control groups in a ratio of 1∶1. The control group received conventional therapy plus Shenkang suppositories， while the observation group received conventional therapy plus Tongfu Xiezhuo enema. After 8 weeks of treatment， the clinical efficacy was assessed based on the changes in traditional Chinese medicine （TCM） symptom scores， renal function indicators， and NLR. Result① Both groups showed decreases in TCM symptom scores after treatment （P<0.01）， and the decreases were more significant in the observation group than in the control group （P<0.05）. The total response rate of TCM symptoms in the observation group was 79.31% （23/29）， which was higher than that （62.96%， 17/27） in the control group （Z=0.604，P<0.05）. ② After treatment， the observation group showed declined serum levels of creatinine （SCr）， blood urea nitrogen （BUN）， and cystatin C （Cys C） and increased glomerular filtration rate （GFR） （P<0.01）， and the control group showed lowered SCr level and increased GFR （P<0.05）. The observation group had lower SCr level and higher GFR than the control group after treatment （P<0.05）. The total response rate of renal function in the observation group was 79.31% （23/29）， which was higher than that （55.56%， 15/27） in the control group （Z=1.127，P<0.01）. ③ The NLR in the observation group decreased after treatment （P<0.05）， and it was lower than that in the control group （P<0.05）. ④ There were no significant differences in safety indicators between the two groups before and after treatment. ConclusionTongfu Xiezhuo enema ameliorated symptoms and improved renal function indicators in the patients with stage 3-4 CKD by reducing the NLR and inhibiting inflammation.

The covalent binding of drugs and their metabolites to proteins forms drug-protein adducts, which may cause adverse reactions in the body. The development of adductomics technology is helpful for the identification of covalent adducts between drugs and human plasma proteins. For many drugs, such as beta-lactam antibiotics, acyl glucuronides, covalent tyrosine kinases inhibitors, and reactive metabolites, human serum albumin (HSA) is a potential target and biomarker for the formation of drug-protein adducts. In this review, we will describe the relevant technical advances, describe the methods for the identification of covalent adducts of drugs and HSA, define the chemical reactions that form adducts, and preliminarily explore the role of drug-HSA adducts in adverse drug reactions and the potential effect on pharmacokinetics.

The purpose of this study was to identify the tick species native to Xindi Township,Yumin County,Xinjiang,China.Preliminary morphological identification of parasitic ticks collected from animals in the area was conducted with an ultra-depth of field three-dimensional VHX 600 digital stereo microscope.Total DNA of the ticks was extracted,amplified by PCR based on the COI and ITS2 gene loci,and the posi-tive PCR products were sequenced.The sequence were a-ligned with reference sequences from the NCBI database were aligned with the Basic Local Alignment Search Tool.A genet-ic phylogenetic tree was generated with the neighbor-joining method of MEGA 7.0 software to determine the evolutionary biological characteristics of ticks.Morphological identification showed that the ticks collected from Xindi Township of Yu-min County were consistent with the characteristics of Hya-lomma asiaticum.An evolutionary tree based on the COI and ITS2 gene sequences showed that the ticks collected in this study were clustered with known H.asiaticum sequences.The PCR products of COI and ITS2 were sequenced and compared,which confirmed that the collected tick species were H.asiaticum,in agreement with the morphological and molecular biological results.These findings help to clarify the distribution of ticks in Xindi Township of Xinjiang,and provide basic data for the analysis of tick genetic and evolutionary characteristics,as reference for surveillance and control of ticks in the Xinjiang Uygur Autonomous Region.

Objectives:To develop a novel prediction model for mitral valve repair failure based on machine learning algorithms.Methods:Clinical and echocardiographic data were analyzed on patients,who underwent mitral valve repair in Fuwai Hospital from 2009 January 1st to 2022 December 31st.End points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or severe mitral regurgitation before discharge).Risk factors of mitral valve repair failure were analyzed by XGBoost and shapley additive explanation (SHAP),and a machine learning model was established based on mixture of experts (MoE) as a risk prediction model and compared with conventional mitral valve repair complexity scores.Results:A total of 2314 patients were included in this study.Mitral repair was unsuccessful in 4.2％ (98 of 2314) of patients.Patient factors such as tricuspid regurgitation pressure gradient,A3 and A3P3 lesions,left ventricular end-systolic volume,and left atrium anterior and posterior diameter are associated with mitral valve repair failure;in addition,surgeon factors,such as cumulative repair failure rate,cumulative repair volume,and surgeon seniority,are also risk factors for mitral valve repair failure.The MoE model has an AUC value of 0.79,and the prediction performance is significantly better than traditional complexity scores.Conclusions:The MoE based machine learning model can predict the risk of mitral valve repair failure well.This evaluation system can effectively assist surgeons in assessing the risk of mitral valve repair failure and in selecting suitable treatment options for patients.

Objectives:To develop a novel prediction model for mitral valve repair failure based on machine learning algorithms.Methods:Clinical and echocardiographic data were analyzed on patients,who underwent mitral valve repair in Fuwai Hospital from 2009 January 1st to 2022 December 31st.End points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or severe mitral regurgitation before discharge).Risk factors of mitral valve repair failure were analyzed by XGBoost and shapley additive explanation (SHAP),and a machine learning model was established based on mixture of experts (MoE) as a risk prediction model and compared with conventional mitral valve repair complexity scores.Results:A total of 2314 patients were included in this study.Mitral repair was unsuccessful in 4.2％ (98 of 2314) of patients.Patient factors such as tricuspid regurgitation pressure gradient,A3 and A3P3 lesions,left ventricular end-systolic volume,and left atrium anterior and posterior diameter are associated with mitral valve repair failure;in addition,surgeon factors,such as cumulative repair failure rate,cumulative repair volume,and surgeon seniority,are also risk factors for mitral valve repair failure.The MoE model has an AUC value of 0.79,and the prediction performance is significantly better than traditional complexity scores.Conclusions:The MoE based machine learning model can predict the risk of mitral valve repair failure well.This evaluation system can effectively assist surgeons in assessing the risk of mitral valve repair failure and in selecting suitable treatment options for patients.

Humans ; Aged ; Processing Speed ; Cross-Sectional Studies ; Cognition ; Hearing Loss/psychology* ; Cohort Studies ; Cognitive Dysfunction