Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Objective The prevalence of drug-resistant Mycobacterium tuberculosis (Mtb) strains is exacerbating the global burden of tuberculosis (TB), highlighting the urgent need for new treatment strategies for TB. Methods The recombinant adenovirus vaccine expressing cyclic di-adenosine monophosphate (c-di-AMP) phosphodiesterase B (CnpB) (rAd-CnpB), was administered to normal mice via mucosal immunization, either alone or in combination with drug therapy, to treat Mtb respiratory infections in mice.Enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of antibodies in serum and bronchoalveolar lavage fluid (BALF). Real-time quantitative PCR was performed to assess the transcription levels of cytokines interferon γ(IFN-γ) and interleukin 10(IL-10) in mouse lungs. Flow cytometry was used to determine the proportions of CD4⁺ and CD8⁺ T cell subsets in the lungs and spleens. ELISA was employed to measure the levels of cytokines IFN-γ, IL-2, IL-10, inflammatory factors IL-6, and tumor necrosis factor α (TNF-α) secreted by spleen cells following antigen stimulation. The bacteria loads in the lungs and spleens of Mtb-infected mice were enumerated by plate counting methods. Resluts Intranasal immunization with rAd-CnpB induced high titers of IgG in mouse serum and the production of IgG and IgA in BALF, along with alterations in T lymphocyte subsets in the lungs and spleens. Administration of rAd-CnpB, either alone or in combination with drugs, to Mtb-infected mice significantly increased serum IgG levels as well as IgA and IgG levels in BALF. rAd-CnpB immunization promoted the secretion of CnpB-specific cytokines and inflammatory factors by splenocytes in Mtb-infected mice. However, rAd-CnpB immunotherapy, either alone or combined with drugs, did not significantly affect the bacterial loads in the lungs and spleens of mice with Mtb respiratory infections. Conclusion Mucosal immunization with rAd-CnpB induced significant mucosal, humoral and cellular immune responses in mice, and significantly enhanced CnpB-specific cellular immune responses in Mtb-infected mice.
Animals ; Adenoviridae/immunology* ; Mycobacterium tuberculosis/genetics* ; Mice ; Female ; Phosphoric Diester Hydrolases/genetics* ; Tuberculosis Vaccines/administration & dosage* ; Tuberculosis/prevention & control* ; Mice, Inbred BALB C ; Cytokines ; Lung/microbiology* ; Immunization ; Bronchoalveolar Lavage Fluid/immunology* ; Immunity, Mucosal

Objective:To investigate the relationship between lipid levels and cognitive impairment in the elderly Chinese population using prospective cohort data.Methods:Based on the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) cohort, this study included 24 380 individuals aged ≥60 years who participated in the cognitive function follow-up survey from 2018 to 2019. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), with cognitive impairment defined according to different educational levels: MMSE ≤17 for illiterate individuals, MMSE ≤20 for those with primary education and MMSE ≤24 for those with secondary education or above. Multivariable linear regression and logistic regression models were employed to examine the associations between six baseline lipid indicators and cognitive scores, as well as cognitive impairment. Additionally, restricted cubic splines were used to explore the exposure-dose relationship between lipid levels and cognitive function.Results:The study population had a median follow-up time of 11.6 years, with a baseline age of (59.7±6.8) years. Among the participants, 9 510 (39.0%) were males, and the mean MMSE score was 24.7±6.8. A total of 3 887 individuals (15.9%) were identified as cognitively impaired. The results of multivariable linear regression and logistic regression indicated that total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels were not only significantly positively associated with cognitive scores but also significantly associated with a lower risk of cognitive impairment. Each 1 mmol/L increase in these lipid levels corresponded to β values (95% CI) of 0.267 (0.173-0.361), 0.385(0.271-0.499) and 0.331(0.231-0.431), respectively. Each 1 mmol/L increase in these lipid levels corresponded to odds ratio ( OR) (95% CI) values of 0.915 (0.876-0.956), 0.875 (0.830-0.923) and 0.886 (0.848-0.927), respectively. The dose-response curve demonstrated that the negative association was primarily observed within the guideline-recommended optimal lipid level range. Specifically, when LDL-C was less than 3.4 mmol/L and non-HDL-C was less than 4.1 mmol/L, the corresponding OR (95% CI) values were 0.859 (0.796-0.926) and 0.876 (0.818-0.939). Conclusion:Lipid levels exhibit a certain linear negative association with cognitive impairment in elderly Chinese adults, with LDL-C and non-HDL-C demonstrating a stronger effect, particularly within the guideline-recommended optimal range.

Computational analysis can accurately detect drug-gene interactions(DGIs)cost-effectively.However,transductive learning models are the hotspot to reveal the promising performance for unknown DGIs(both drugs and genes are present in the training model),without special attention to the unseen DGIs(both drugs and genes are absent in the training model).In view of this,this study,for the first time,proposed an inductive learning-based model for the precise identification of unseen DGIs.In our study,by integrating disease nodes to avoid data sparsity,a multi-relational drug-disease-gene(DDG)graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions.Following the extraction of graph features by utilizing graph embedding algorithms,our next step was the retrieval of the attributes of individual gene and drug nodes.In this way,a hybrid feature charac-terization was represented by integrating graph features and node attributes.Machine learning(ML)models were built,enabling the fulfillment of transductive predictions of unknown DGIs.To realize inductive learning,this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights,enabling inductive predictions for the unseen DGIs.Consequently,the final model was superior to existing models,with significant improvement in predicting both external unknown and unseen DGIs.The practical feasibility of our model was further confirmed through case study and molecular docking.In summary,this study establishes an efficient data-driven approach through the proposed modeling,suggesting its value as a promising tool for accelerating drug discovery and repurposing.

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 investigate the relationship between lipid levels and cognitive impairment in the elderly Chinese population using prospective cohort data.Methods:Based on the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) cohort, this study included 24 380 individuals aged ≥60 years who participated in the cognitive function follow-up survey from 2018 to 2019. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), with cognitive impairment defined according to different educational levels: MMSE ≤17 for illiterate individuals, MMSE ≤20 for those with primary education and MMSE ≤24 for those with secondary education or above. Multivariable linear regression and logistic regression models were employed to examine the associations between six baseline lipid indicators and cognitive scores, as well as cognitive impairment. Additionally, restricted cubic splines were used to explore the exposure-dose relationship between lipid levels and cognitive function.Results:The study population had a median follow-up time of 11.6 years, with a baseline age of (59.7±6.8) years. Among the participants, 9 510 (39.0%) were males, and the mean MMSE score was 24.7±6.8. A total of 3 887 individuals (15.9%) were identified as cognitively impaired. The results of multivariable linear regression and logistic regression indicated that total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels were not only significantly positively associated with cognitive scores but also significantly associated with a lower risk of cognitive impairment. Each 1 mmol/L increase in these lipid levels corresponded to β values (95% CI) of 0.267 (0.173-0.361), 0.385(0.271-0.499) and 0.331(0.231-0.431), respectively. Each 1 mmol/L increase in these lipid levels corresponded to odds ratio ( OR) (95% CI) values of 0.915 (0.876-0.956), 0.875 (0.830-0.923) and 0.886 (0.848-0.927), respectively. The dose-response curve demonstrated that the negative association was primarily observed within the guideline-recommended optimal lipid level range. Specifically, when LDL-C was less than 3.4 mmol/L and non-HDL-C was less than 4.1 mmol/L, the corresponding OR (95% CI) values were 0.859 (0.796-0.926) and 0.876 (0.818-0.939). Conclusion:Lipid levels exhibit a certain linear negative association with cognitive impairment in elderly Chinese adults, with LDL-C and non-HDL-C demonstrating a stronger effect, particularly within the guideline-recommended optimal range.

Purpose To explore the forensic pathological features of deaths caused by craniocerebral injury in traf-fic accidents,in order to provide forensic practitioners with a more rigorous approach to identification.Methods A retrospective analysis was performed on 225 autopsy reports of traffic accident fatalities resulting from craniocerebral in-jury.The causes of death were classified,tabulated,and analyzed.Results Among the 225 autopsy reports,the main causes of death included primary brain injury,secondary brainstem injury,and complications.The main types of injuries craniocerebral injuries observed were skull fractures,cerebral hemorrhage,cerebral contusion,cerebral edema and so on.Conclusion The occurrence of craniocerebral injury is related to factors such as age,vehicle,collision speed,and road conditions,and there is an inherent regularity to these factors.Forensic practitioners should combine comprehesive and systematic pathological examinations,clinical data,and the inherent regularities of related factors to make objective,comprehensive,and accurate determinations of the cause of death in traffic accident-related craniocere-bral injuries.

Purpose To explore the forensic pathological features of deaths caused by craniocerebral injury in traf-fic accidents,in order to provide forensic practitioners with a more rigorous approach to identification.Methods A retrospective analysis was performed on 225 autopsy reports of traffic accident fatalities resulting from craniocerebral in-jury.The causes of death were classified,tabulated,and analyzed.Results Among the 225 autopsy reports,the main causes of death included primary brain injury,secondary brainstem injury,and complications.The main types of injuries craniocerebral injuries observed were skull fractures,cerebral hemorrhage,cerebral contusion,cerebral edema and so on.Conclusion The occurrence of craniocerebral injury is related to factors such as age,vehicle,collision speed,and road conditions,and there is an inherent regularity to these factors.Forensic practitioners should combine comprehesive and systematic pathological examinations,clinical data,and the inherent regularities of related factors to make objective,comprehensive,and accurate determinations of the cause of death in traffic accident-related craniocere-bral injuries.

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.

The family readiness for discharge of premature infants in the Neonatal Intensive Care Unit (NICU) is an important index to evaluate the safe discharge of premature infants, and a good family discharge readiness is the basic guarantee for the smooth recovery and healthy growth of premature infants. This article summarizes the concept, influencing factors, and intervention strategies of family discharge readiness for premature infants in NICU, in order to provide reference for the formulation and improvement of discharge readiness measures for premature infants in NICU.