With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
Humans ; Precision Medicine/methods* ; Medical Oncology/methods* ; Artificial Intelligence ; Neoplasms/pathology* ; Computational Biology/methods*

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.

The access evaluation of new medical technology is an important part of the preclinical application of medical technology and plays a vital role in ensuring the quality and safety of medical services.However,in the con-crete practice of access evaluation,there are still some problems such as imperfect access theoretical framework,imperfect evaluation index system.With the strategic support of health policies,laws,and regulations,the theory and method of HB-HTA are used for reference,core elements such as assessment subject,assessment object,and assessment content are comprehensively considered,the index system is designed from the dimensions of tech-nical characteristics,safety,effectiveness,economy and applicability,and the access evaluation framework of im-ported medical new technologies is constructed.To offer a theoretical framework and evidence-based basis for medi-cal facility medical technology access management.

Objective:To study the effect of first drainage failure on the prognosis of perihilar cholangiocarcinoma (PHCC).Methods:The clinical data of 68 patients with PHCC undergoing surgery in the Department of Pancreatic and Metabolic Surgery of Nanjing Drum Tower Hospital, Affiliated to the Medical School of Nanjing University, from April 2014 to December 2022 were retrospectively analyzed, including 46 males and 22 females, aged (63±9) (range, 39-80) years old. The patients were divided into two groups based on whether the first drainage was successful: successful group ( n=34) and failed group ( n=34). The patient's age, gender, first drainage, complications and other clinical data were collected. Patients were followed up by outpatient or telephone review. Kaplan-Meier method was used for survival analysis, and log-rank test was used for survival comparison. Results:Compared to the successful group, the drainage time [41(28, 52) d vs. 20(14, 28) d], the drainage tube adjustment rate [32.4%(11/34) vs. 0(0/34)], and the incidence of complications after drainage [88.2% (30/34) vs. 0(0/34)] were all increased in the failed group, with the adjustment rate of drainage position decreased [82.4%(28/34) vs. 100%(34/34)] (all P<0.05). Compared to the successful group, the incidence of abdominal infection was higher [70.6%(24/34) vs. 44.1%(15/34)] in the failed group ( P=0.027). The 5-year cumulative survival rates of the failed group and the successful group were 61.4% and 44.1%, respectively ( P>0.05). Conclusion:Compared to the patients of PHCC with first successful drainage, the risk of abdominal infection is increased when first drainage failed, while the incidence of postoperative complications, in-hospital mortality and long-term survival rate are comparable.

Objective:To investigate whether intestinal rehabilitation therapy (IRT)could optimize the effectiveness of IRT in patients with short bowel syndrome (SBS) caused by acute mesenteric ischemia (AMI) with chronic multivessel lesions.Methods:Clinical data of 18 hospitalized patients diagnosed with AMI leading to SBS and undergoing IRT at the Eastern Theater General Hospital of the People's Liberation Army from Jan 2012 to Oct 2023 was retrospectively analyzed.Result:Following IRT, the revascularization group showed significantly greater increases in ASMI and grip strength compared to the control group [(0.28±0.26) kg/m 2vs. (0.02±0.21) kg/m 2, P=0.033, and (0.97±0.33) kg vs. (0.48±0.34) kg, P=0.007, respectively]. Similarly, the increase in EN intake was significantly higher in the revascularization group compared to the control group [(572.5±93.6) ml/d vs. (375.2±176.3) ml/d, P=0.012], accompanied by a greater improvement in intestinal nitrogen absorption rate [(25.06±14.06)% vs. (13.84±4.62)%, P=0.034] and a more substantial decrease in GSRS scores [(-15.88±3.94) vs. (-6.33±5.13), P=0.030]. Moreover, there were significant differences in the composition of EN formulations between the two groups after IRT ( P=0.046). Additionally, SF-36 scores at discharge were significantly higher in the revascularization group than that in the control group for five indicators including BP, GH, VT, SF, and MH ( P<0.05). Conclusions:For patients with SBS resulting from AMI by chronic multivessel lesions, revascularization therapy may not leading to higher growth in weight and hematological nutritional indicators during IRT, but it is beneficial for improving muscle function, improving EN absorption, increasing the likelihood of PN independence, relieving gastrointestinal symptoms, and enhancing overall quality of life.

ObjectiveTo identify the chemical constituents of Ruyi Zhenbaowan in vitro and in vivo. MethodThe chemical constituents of Ruyi Zhenbaowan were identified based on UHPLC-Q Exactive Orbitrap HRMS. A total of 12 male SD rats were randomized into two groups： control （pure water） and Ruyi Zhenbaowan （1.8 g·kg^-1）. The rats were administrated with the suspension of Ruyi Zhenbaowan or pure water by gavage. After 1.5 h， the plasma and cerebrospinal fluid were collected. Chromatographic separation was performed on a Waters ACQUITY UPLC BEH C₁₈ column （2.1 mm × 150 mm， 1.7 μm） with a mixture of 0.1% formic acid aqueous solution （A） and acetonitrile （B） as the mobile phase. Gradient elution was carried out according to the procedure of 0~15 min，97%~80%A；15~30 min ，80%~60%A；30~40 min，60%~30%A；40~45 min，30%~5%A. The ion source was electrospray ionization， and scan range was m/z 100-1 500. The prototype components and the components in the plasma and cerebrospinal fluid were analyzed qualitatively by scanning in positive and negative ion modes and identified by comparison with the data in published literature and the information of standard substances. ResultA total of 126 chemical constituents were identified from the 80% methanol solution of Ruyi Zhenbaowan， and 14 and 7 prototype constituents were detected in the plasma and the cerebrospinal fluid， respectively. In addition， the fragmentation rules of apigenin， apigenin-7-O-glucuronide， galangin， liquiritin， piperine， glycyrrhizic acid， eugenol， gallic acid， and cholic acid were deduced. ConclusionThis study achieved rapid multicomponent characterization and identification of Ruyi Zhenbaowan in vitro and in vivo， providing theoretical support for exploring active substances and performing quality control.l.

Different porous structures were studied through finite element analysis and then optimal porous structure was selected for the orthopedic applications.The optimal Voronoi structure was designed and fabricated using 3D printing.The mechanical properties and osseointegration ability were both investigated.The mechanical tests showed that the tensile strength,compressive strength and bending strength of Voronoi structures were obviously higher than that of the human bone,and the modulus of Voronoi structures were similar to human bone.In addition,the animal experimental results exhibited that obvious bone ingrowth was found from Month 1 to Month 6.This study provides some theoretical references for the orthopedic application of porous structures.

This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images,composed of 1761 ultrasound images from 1165 participants.A deep learning architecture that combines bilinear convolutional neural networks with residual neural networks,known as the single-input BCNN-ResNet model,was utilized to aid clinical doctors in diagnosing plaques using carotid ultrasound images.Following training,internal validation,and external validation,the model yielded an ROC AUC of 0.99(95%confidence interval:0.91 to 0.84)in internal validation and 0.95(95%confidence interval:0.96 to 0.94)in external validation,surpassing the ResNet-34 network model,which achieved an AUC of 0.98(95%confidence interval:0.99 to 0.95)in internal validation and 0.94(95%confidence interval:0.95 to 0.92)in external validation.Consequently,the single-input BCNN-ResNet network model has shown remarkable diagnostic capabilities and offers an innovative solution for the automatic detection of carotid artery plaques.

Objective:To explore the impact of sleep duration, physical exercise, and their interactions on the risk of dyslipidemia in older adults aged ≥80 (the oldest old) in China.Methods:The study subjects were the oldest old from four rounds of Healthy Aging and Biomarkers Cohort Study (2008-2009, 2011-2012, 2014 and 2017-2018). The information about their demographic characteristics, lifestyles, physical examination results and others were collected, and fasting venous blood samples were collected from them for blood lipid testing. Competing risk model was used to analyze the causal associations of sleep duration and physical exercise with the risk for dyslipidemia. Restricted cubic spline (RCS) function was used to explore the dose-response relationship between sleep duration and the risk for dyslipidemia. Additive and multiplicative interaction model were used to explore the interaction of sleep duration and physical exercise on the risk for dyslipidemia.Results:The average age of 1 809 subjects was (93.1±7.7) years, 65.1% of them were women. The average sleep duration of the subjects was (8.0±2.5) hours/day, 28.1% of them had sleep duration for less than 7 hours/day, and 27.2% had sleep for duration more than 9 hours/day at baseline survey. During the 9-year cumulative follow-up of 6 150.6 person years (follow-up of average 3.4 years for one person), there were 304 new cases of dyslipidemia, with an incidence density of 4 942.6/100 000 person years. The results of competitive risk model analysis showed that compared with those who slept for 7-9 hours/day, the risk for dyslipidemia in oldest old with sleep duration >9 hours/day increased by 22% ( HR=1.22, 95% CI: 1.07-1.39). Compared with the oldest old having no physical exercise, the risk for dyslipidemia in the oldest old having physical exercise decreased by 33% ( HR=0.67, 95% CI: 0.57-0.78). The RCS function showed a linear positive dose-response relationship between sleep duration and the risk for hyperlipidemia. The interaction analysis showed that physical exercise and sleep duration had an antagonistic effect on the risk for hyperlipidemia. Conclusion:Physical exercise could reduce the adverse effects of prolonged sleep on blood lipids in the oldest old.