Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Objective:To investigate the expressions of HIF-1α, BRD4, Beclin1, LC3B and p62 in breast cancer tissues and their clinicopathological significance, and to study alterations of their expression in breast cancer cells under hypoxic microenvironment.Methods:Immunohistochemistry was used to detect HIF-1α, BRD4, Beclin1, LC3B and p62 protein expressions in 125 breast cancer tissues and 50 para-cancer normal breast tissues, and their correlation with clinicopathologic characteristics were analyzed. The expression of these proteins were also measured after 24 hours of hypoxia stimulation was detected in different breast cancer cell lines and normal breast epithelial cells.Results:The expression of HIF-1α, BRD4, Beclin1 and LC3B proteins in breast cancer tissues were significantly higher than in para-cancer normal breast tissues ( P<0.05). There was a positive association between histologic grade, the expression of HIF-1α, BRD4, Beclin1 and LC3B ( P<0.05). High expressions of HIF-1a and Beclin1 were often correlated with lymph node metastasis and lymphatic invasion ( P<0.05). Increased HIF-1α, BRD4, Beclin1 and LC3B expression was associated with ER or PR negativity, but only HIF-1α was associated with HER2 positivity ( P<0.05). HIF-1α, BRD4, Beclin1, and LC3B were positively correlated with each other in breast cancer tissues ( P<0.01). After 24 hours of hypoxic stimulation, the expression of HIF-1α, BRD4, Beclin1 and LC3B was up-regulated in breast cancer cells. Conclusions:Hypoxia induces autophagy in breast cancer tissues. HIF-1α is positively correlated with BRD4, suggesting that BRD4 is involved in the regulation of autophagy by hypoxic microenvironment in breast cancer. High expression of HIF-1α, BRD4 and autophagy may play an important role in the development of breast cancer.

Objective To investigate the risk factors of cerebral hemorrhage in hypertensive intracerebral hemorrhage,and to provide a reference for clinical treatment.Methods The clinical data of 118 patients with hyper-tensive cerebral hemorrhage treated in our hospital were analyzed retrospectively.The clinical data of patients was col-lected and statistical analysis was carried out,and the risk factors of edema around the hematoma were analyzed by Logistic regression analysis.Results Multivariate non conditional logistic regression analysis showed that,the course of hypertensive cerebral hemorrhage of edema around the hematoma was the risk factor,the longer the duration,the more risk of hypertensive cerebral hemorrhage edema around the hematoma enlargement.There was no significant correlation between sex,age,bleeding site,broken into ventricles and the edema around the hematoma in hypertensive intracerebral hemorrhage.Diastolic blood pressure was a risk factor for the edema around the hematoma in hypertensive cerebral hemorrhage,the diastolic blood pressure control was not good,and the swelling of the edema around the hema-toma was increasing.While the systolic blood pressure,pulse pressure difference and hypertensive cerebral hemorrhage hematoma around the hematoma showed no obvious correlation.Use of amlodipine and vascular tension angiotensin converting enzyme inhibitor in hypertensive cerebral hemorrhage were the protective factors of edema around the hematoma, early application of amlodipine,vascular and nervous angiotensin converting enzyme inhibitor to control blood pressure helped to reduce hypertensive cerebral hemorrhage edema around the hematoma volume.Conclusion Amlodipine and vascular tension angiotensin converting enzyme inhibitors help to reduce hypertensive cerebral hemorrhage edema around the hematoma volume,while long course,poor control of diastolic blood pressure can promote hypertension cerebral hemorrhage edema around the hematoma volume increase.We should pay attention to the development of hypertensive cerebral hemorrhage and the control of diastolic blood pressure,as soon as possible to stabilize the patient's condition and avoid the expansion of the volume of edema around the hematoma.

Humans ; Maxillofacial Injuries ; therapy ; Patient Care Team