Alzheimer's disease(AD),a neurodegenerative disorder,manifests pathological changes in the brain even during the asymptomatic stage.As the pathological burden intensifies,patients experience functional decline in multiple cognitive domains,including memory,language,spatial perception,executive function,and calculation,and may also exhibit emotional abnormalities.Once AD progresses,treatment becomes extremely chal-lenging.Therefore,early diagnosis and accurate prediction of disease conversion are core tasks in the prevention and treatment of AD,and they are also urgent scientific research challenges to be overcome.Deep learning(DL)models demonstrate considerable advantages in the diagnosis,prediction,classification,and feature extraction of AD,offering new hope for solving this challenging problem.This research commences with a concise introduction to the outcomes of AD and the fundamental knowledge of deep learning.Subsequently,it offers an overview of the imaging studies on the utilization of deep learning for predicting disease transformation from two perspectives.Firstly,it systematically summarizes the existing DL models that have demonstrated innovation in the classification and prediction performance of AD.Secondly,it provides a comprehensive outline of the DL fusion models applied to the diagnosis,classification,and prediction of AD.Finally,this paper expounds upon the impending challenges in the research of this domain.This article demonstrates that deep learning models is cutting-edge trends in the ex-ploration of AD research.

Objective:To establish a patient-derived tumor xenograft (PDX) model of primary liver cancer (PLC), and to analyze the pathological features, proliferation and drug-related gene mutation characteristics of the primary tumor and the PDX model of primary liver cancer.Methods:A retrospective analysis was conducted on the tumor samples of 64 PLC patients who underwent hepatectomy in the Department of Hepatobiliary Surgery of Tianjin First Central Hospital from January 2019 to December 2020. Among them, there were 46 males and 18 females, with an average age of (60.7±9.4) years. The degree of tumor differentiation and whether there was vascular invasion were recorded. The pathology of the primary tumor and each generation of PDX models was observed. Immunohistochemical staining was used to detect the characteristic proteins and proliferation-related protein of PLC. The genes related to drug use in PLC PDX models were analyzed in the primary tumor.Results:Among the tumor tissues of 64 PLC patients, 31 cases (48.4%) were successfully transplanted into nude mice and passaged to subsequent generations. In the primary tumor tissues and PDX models of hepatocellular carcinoma (HCC), the cancer nodules were clearly distinguishable, the cancer cells were arranged disorderly, and distributed in nests or cords. The tumor cell nuclei were large and deeply stained. In the primary tumor tissues and each generation of PDX models of intrahepatic cholangiocarcinoma (ICC), there were ICC adenocarcinoma-like structures, with well-differentiated tumor glands and glandular structures composed of cuboidal or columnar tumor cells, presenting as small individual glands or interwoven glands. The degree of differentiation of the PDX models of HCC and ICC patients was basically consistent with that of the primary tumors. Immunohistochemistry showed that proliferating cell nuclear antigen staining of the primary tumors and PDX model transplanted tumors of HCC and ICC patients was strongly positive. The Ki-67 staining positive rate of the primary tumors and PDX model transplanted tumors of HCC was > 80%, and that of ICC was > 70%. Alpha-fetoprotein was strongly positive in the primary tumors and PDX model transplanted tumors of HCC and ICC. The common mutations of transplanted tumors and the primary tumors of P24 HCC patients were 90%, and those of P43 ICC patients were 89%, 94%, and 94%, respectively.Conclusion:The constructed PDX model is highly consistent with the biological characteristics of the primary tumor.

Human Respiratory Stem Cells (RSCs) play a crucial role in the maintenance, repair and regeneration of the respiratory system. As a novel therapeutic method, stem cell therapy is a popular research direction in the medical field. And with the in-depth research on the mechanism of pneumonia caused by respiratory infections in recent years, the use of RSCs to explore pneumonia caused by respiratory infections and its therapeutic strategies has become a hot topic. In this paper, we firstly outlined the types of RSCs, summarized the mechanism of pneumonia caused by respiratory tract infections, discussed the advantages of RSCs application and the progress of culture differentiation, and elaborated the therapeutic exploration of RSCs in pneumonia caused by respiratory tract infections.

Objective:To establish 30-day of age transcutaneous bilirubin (TcB) reference curves for healthy neonates, and to investigate regional variations in bilirubin dynamics.Methods:A multicenter retrospective cohort study was conducted. A total of 220 950 healthy neonates born at a gestational age of 35-<42 weeks, with a birth weight ≥2 000 g, who did not receive phototherapy within 60 h after birth were recruited. All of them underwent remote TcB monitoring using the Bilibaby remote jaundice monitoring system between August 1 st, 2020 and December 31 st, 2024 in 426 hospitals. TcB data were collected within the period from birth to 30-day of age. The P40, P75, and P95 of TcB values were calculated, and dynamic TcB curves for 30-day of age were constructed. Patterns of bilirubin change, rates of change, and transition outcomes were described. Regional comparisons between South and North were conducted using linear mixed-effects models for TcB trajectories and Pearson′s chi-square test for outcome differences. Results:A total of 220 950 neonates were included, of whom 101 711 (46.03%) were female. Gestational age at birth was (38.75±1.12) weeks, and birth weight was (3 272±417) g. TcB levels increased rapidly within 3-day of age, peaked at 4-6-day of age, with peak values at P40, P75, and P95 of 200.6, 239.7 and 275.4 μmol/L (11.8, 14.1 and 16.2 mg/dl), respectively. TcB levels gradually declined thereafter and stabilized after 13-day of age, with values at P40, P75, and P95 fluctuating between 147.9-159.8, 190.4-200.6, and 231.2-239.7 μmol/L (8.7-9.4, 11.2-11.8, 13.6-14.1 mg/dl), respectively. Notably, among neonates categorized as low-or low-intermediate-risk within 3-day of age, 6 700 (12.76%) progressed to intermediate-high or high risk between 4 and 30 days of age. Before 13-day of age, TcB levels in the southern regions were consistently higher than those in the northern regions ( P=0.039); from 14 to 30 days of age, the overall TcB levels had no statistically difference, but the temporal changes in TcB still showed regional differences (degrees of freedom=3, all interaction P<0.05). Among neonates classified as low-or low-intermediate risk within 3-day of age, 25 326 were from southern regions, of whom 4 254 (16.80%) progressed to intermediate-high or high risk between 4 and 30 days of age. In northern regions, 27 193 neonates were classified as low-or low-intermediate risk within 3-day of age, among whom 2 446 (8.99%) progressed to intermediate-high or high risk. The risk progression between the 2 regions had statistically difference ( χ2=716.49, P<0.001). Conclusions:A TcB percentile curve for neonates within 30-day of age was established, revealing that both the overall TcB level and its temporal trend were higher in southern than in northern newborns. These findings provide baseline data to support continuous management of neonatal jaundice.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To investigate the effect of dorsal repulsive axon guidance protein(Draxin)on the migration of trunk neural crest cells during the early development of embryonic mouse spinal cord.Methods Immunohistochemistry and in situ hybridization were used to detect the expression characteristics of Draxin in early embryonic spinal cord(8 mice each group);In situ hybridization was used to detect the change of migration characteristics of trunk neural crest cells in early embryonic spinal cord of different types of mouse(5 mice each group);in vitro culture method was used to check the effect of Draxin on the migration characteristics of embryonic mouse trunk neural crest cells(16 mice each group).Resultsβ-galactosidase gene Z(LacZ)gene was introduced when Draxin gene was knocked out to produce Draxin gene knockout mice.β-galactosidase staining was used to detect LacZ gene expression in Draxin knockout embryonic mice,and the result showed that Draxin expression was observed in the spinal cord of early embryonic mice since 9.5 days(E9.5).Draxin expression was obvious in the embryonic mice spinal cord in E10.5 period.In situ hybridization was used to detect the expression of Draxin gene in the spinal cord of wild type embryonic mice,and the result further verified the obvious expression of Draxin in the early embryonic mice spinal cord in El0.5 period.Sox10 in situ hybridization was used to detect neural crest cell migration in the spinal cord of embryonic mice in E10.5 period.The result showed that segmental migration of neural crest cells in the early embryonic spinal cord of some Draxin knockout mice was delayed compared with the wild type mice.The effect of Draxin on the migration of wild type early embryonic mice trunk neural crest cells in vitro was tested.The result showed that Draxin reduced the migration distance of neural crest cells in vitro.Conclusion In the early developmental stage of embryonic spinal cord(E9.5-E10.5),neural crest cells migrated exuberant.At the same time,Draxin plays an important inhibitory function in the formation of the specific migration pathways of trunk neural crest cells by promoting neural crest cells migrating away from Draxin expressing regions.

Objective:To establish a patient-derived tumor xenograft (PDX) model of primary liver cancer (PLC), and to analyze the pathological features, proliferation and drug-related gene mutation characteristics of the primary tumor and the PDX model of primary liver cancer.Methods:A retrospective analysis was conducted on the tumor samples of 64 PLC patients who underwent hepatectomy in the Department of Hepatobiliary Surgery of Tianjin First Central Hospital from January 2019 to December 2020. Among them, there were 46 males and 18 females, with an average age of (60.7±9.4) years. The degree of tumor differentiation and whether there was vascular invasion were recorded. The pathology of the primary tumor and each generation of PDX models was observed. Immunohistochemical staining was used to detect the characteristic proteins and proliferation-related protein of PLC. The genes related to drug use in PLC PDX models were analyzed in the primary tumor.Results:Among the tumor tissues of 64 PLC patients, 31 cases (48.4%) were successfully transplanted into nude mice and passaged to subsequent generations. In the primary tumor tissues and PDX models of hepatocellular carcinoma (HCC), the cancer nodules were clearly distinguishable, the cancer cells were arranged disorderly, and distributed in nests or cords. The tumor cell nuclei were large and deeply stained. In the primary tumor tissues and each generation of PDX models of intrahepatic cholangiocarcinoma (ICC), there were ICC adenocarcinoma-like structures, with well-differentiated tumor glands and glandular structures composed of cuboidal or columnar tumor cells, presenting as small individual glands or interwoven glands. The degree of differentiation of the PDX models of HCC and ICC patients was basically consistent with that of the primary tumors. Immunohistochemistry showed that proliferating cell nuclear antigen staining of the primary tumors and PDX model transplanted tumors of HCC and ICC patients was strongly positive. The Ki-67 staining positive rate of the primary tumors and PDX model transplanted tumors of HCC was > 80%, and that of ICC was > 70%. Alpha-fetoprotein was strongly positive in the primary tumors and PDX model transplanted tumors of HCC and ICC. The common mutations of transplanted tumors and the primary tumors of P24 HCC patients were 90%, and those of P43 ICC patients were 89%, 94%, and 94%, respectively.Conclusion:The constructed PDX model is highly consistent with the biological characteristics of the primary tumor.

Alzheimer's disease(AD),a neurodegenerative disorder,manifests pathological changes in the brain even during the asymptomatic stage.As the pathological burden intensifies,patients experience functional decline in multiple cognitive domains,including memory,language,spatial perception,executive function,and calculation,and may also exhibit emotional abnormalities.Once AD progresses,treatment becomes extremely chal-lenging.Therefore,early diagnosis and accurate prediction of disease conversion are core tasks in the prevention and treatment of AD,and they are also urgent scientific research challenges to be overcome.Deep learning(DL)models demonstrate considerable advantages in the diagnosis,prediction,classification,and feature extraction of AD,offering new hope for solving this challenging problem.This research commences with a concise introduction to the outcomes of AD and the fundamental knowledge of deep learning.Subsequently,it offers an overview of the imaging studies on the utilization of deep learning for predicting disease transformation from two perspectives.Firstly,it systematically summarizes the existing DL models that have demonstrated innovation in the classification and prediction performance of AD.Secondly,it provides a comprehensive outline of the DL fusion models applied to the diagnosis,classification,and prediction of AD.Finally,this paper expounds upon the impending challenges in the research of this domain.This article demonstrates that deep learning models is cutting-edge trends in the ex-ploration of AD research.

Objective To investigate the mechanism of Achyranthoside Ⅰ inhibits pyroptosis in chondrocytes through the nuclear factor-κB(NF-κB)/NOD receptor protein structure domain related proteins 3(NLRP3)/cystine containing aspartate specific proteins-1(caspase-1)signaling pathway.Methods Primary mouse chondrocytes were divided into blank group(phosphate buffered solution with the same volume),model group[10 ng·mL-1 interleukin-1β(IL-1 β)],control group(10 ng·mL-1 IL-1β+20 μmol·L-1 celecoxib)and experimental group(10 ng·mL-1 IL-1β+3 μg·mL-1 Achyranthoside Ⅰ).After 24 hours of intervention,the cell proliferation was measured by cell counting kit 8,the levels of superoxide dismutase(SOD),malondialdehyde(MDA),IL-1 and IL-6 were detected by enzyme-linked immunosorbent assay,the protein expression levels of NF-κB p65,NLRP3 and caspase-1 were detected by Western Blot.Results The apoptosis rates in experimental,control,model and blank groups were(13.34±0.61)％,(15.64±1.01)％,(21.81±1.10)％and 0;the SOD levels were(147.03±16.49),(130.09±7.33),(122.03±10.71)and(164.40±22.74)nU·mL-1;the MDA levels were(6.43±0.71),(7.63±1.01),(8.89±1.84)and(5.69±0.81)nmol·L-1;the IL-1 levels were(338.69±40.95),(361.78±32.15),(391.44±30.59)and(289.23±25.19)pg·mL-1;the IL-6 levels were(89.96±8.81),(101.10±11.59),(120.39±14.71)and(60.29±6.03)pg·mL-1;the relative expression levels of NF-κB p65 were 0.68±0.05,0.97±0.05,1.26±0.05 and 0.57±0.05;the relative expression levels of NLRP3 were 0.71±0.08,1.02±0.10,1.50±0.06 and 0.31±0.05;the relative expression levels of caspase-1 were 0.70±0.07,1.29±0.08,1.66±0.07 and 0.51±0.07,respectively.Compared with the model group,the differences of above indexes were statistically significant in the experimental group(all P＜0.05).Conclusion Achyranthoside Ⅰ can improve the oxidative stress status induced by IL-1 β in chondrocytes,reduce the expression of proteins related to the NF-κB signaling pathway,and thereby decrease the occurrence of caspase-1 dependent pyroptosis,providing a protective effect on chondrocytes.

Human Respiratory Stem Cells (RSCs) play a crucial role in the maintenance, repair and regeneration of the respiratory system. As a novel therapeutic method, stem cell therapy is a popular research direction in the medical field. And with the in-depth research on the mechanism of pneumonia caused by respiratory infections in recent years, the use of RSCs to explore pneumonia caused by respiratory infections and its therapeutic strategies has become a hot topic. In this paper, we firstly outlined the types of RSCs, summarized the mechanism of pneumonia caused by respiratory tract infections, discussed the advantages of RSCs application and the progress of culture differentiation, and elaborated the therapeutic exploration of RSCs in pneumonia caused by respiratory tract infections.