Operation management is an important tool to promote the high-quality development of public hospitals in China.Since the founding of New China,based on China's economic and social development and medical and health system reform,the evolution of operation management of public hospitals in China can be divided into four stages,the system building stage(1949-1978),the liberalization and revitalization stage(1979-1996),the operation mechanism reform stage(1997-2020),and the new stage of high-quality development(2021 present).The development trend of public hospital operation management in future should deepen the public welfare-oriented public hospital operation management,explore the value-oriented medical operation management model based on high-quality development and the refined operation and management model of public hospitals,so as to promote the high-quality development of public hospitals in China.

Objective To compare the differences in the average transaction costs per-referral patients under different models of Integrated Delivery Systems(IDS).Methods Using a typical case sampling method,it selected referred patients from three IDS models:the county medical alliance in D City(Qinghai Province),the urban medical consortium in J District(Zhengzhou City,Henan Province),and the health management coalition in N County(Shandong Province).Structured questionnaires collected demographics,average transaction costs per-referral and cost perceptions.t-tests and ANOVA assessed cost differences;generalized linear regression identified influencing factors.Results Among 915 patients,the average transaction costs per-referral were 1 035.05 yuan(county alliance),195.31 yuan(urban consortium),and 700.97 yuan(health management coalition),with statistically significant differences(P＜0.05).The urban consortium exhibited lower time costs and specialized input costs.Key influencing factors included older age(county alliance),education level,employment status,and referral travel time(urban consortium),as well as urban-rural disparities(health management coalition).Patients'cost perceptions significantly differed across models(P＜0.05).Conclusion The urban medical consortium demonstrated the lowest patient the average transaction costs,highlighting its institutional advantage in minimizing financial burdens.

BACKGROUND:More and more scholars are investigating the mechanism of hypoxia-inducible factor-1α in regulating bone homeostasis and oral and maxillofacial diseases to provide new targets and strategies for the treatment of related disorders,but there is no relevant review.OBJECTIVE:To summarize the regulatory potential of hypoxia-inducible factor-1α in a variety of oral and maxillofacial diseases and bone homeostasis with the aim of providing a new research direction for oral and maxillofacial bone tissue engineering.METHODS:A literature review was conducted in databases such as PubMed,Web of Science and CNKI,for articles Published from 2003 to 2024.The keywords were"hypoxia inducible factor-1α,oral cavity,bone formation,osteoclast,angiogenesis,oxidative stress,tissue engineering,periodontitis,pulpitis,temporomandibular joint osteoarthritis"in Chinese and English.Finally,84 articles were included for review.RESULTS AND CONCLUSION:(1)Hypoxia-inducible factor-1α is essential in promoting bone tissue regeneration,facilitating osteogenic-angiogenic coupling,and mitigating damage from oxidative stress in bone tissue.(2)Increasing levels of hypoxia-inducible factor-1α in tissue cells reduces inflammation in periodontitis and promotes periodontal tissue remodeling,pulp regeneration,and involves in joint remodeling after temporomandibular joint osteoarthritis.(3)By stabilizing the level of hypoxia-inducible factor-1α in tissue cells,the micronutrient-carrying biomaterial promotes bone marrow mesenchymal stem cells to migrate and attach to the bone defect area,coupling angiogenesis and osteogenesis to achieve bone regeneration.(4)How to increase the level of hypoxia-inducible factor-1α in oral and maxillofacial tissues using bioactive materials to achieve bone regeneration at maxillofacial bone defects remains to be investigated.

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 re-duces 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(Fdata-set,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 pro-cessing 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 so-lution 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.

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 evaluate the effect of acupuncture on postoperative delirium (POD) in diabetic patients undergoing surgery under general anesthesia.Methods:In this randomized controlled trial, 92 diabetic patients of either sex, aged 30-80 yr, with a body mass index of 18-28 kg/m 2, of American Society of Anesthesiologists Physical Status classification Ⅱ or Ⅲ, scheduled for elective surgery under general anesthesia, were divided into 2 groups ( n=46 each) using a table of random numbers: control group (group C) and acupuncture group (group A). Group A received acupuncture at the Baihui (GV20), Shenting (GV24) and Sishencong (EX-HN1) acupoints before anesthesia. The needles were retained for 30 min, with manual stimulation applied every 10 min for 10 s each time. After 4 stimulations, routine anesthesia was carried out. Group C received routine anesthesia only. Regional cerebral oxygen saturation was recorded on admission to the operating room (T 0), after anesthesia induction (T 1), at the start of surgery (T 2), at the end of surgery (T 3), and immediately after tracheal extubation (T 4). The POD developed within 3 days after surgery was assessed. The occurrence of needle-related adverse effects such as fainting, subcutaneous bleeding, and local paresthesia was recorded. Results:Compared with group C, the incidence of POD was significantly reduced, and the regional cerebral oxygen saturation was increased at T 1, 4 in group A ( P<0.05). Conclusions:Acupuncture can decrease the development of POD in diabetic patients undergoing surgery under general anesthesia, which is related to an increase in regional cerebral oxygen saturation.

Objective:To evaluate the effect of acupuncture on postoperative delirium (POD) in diabetic patients undergoing surgery under general anesthesia.Methods:In this randomized controlled trial, 92 diabetic patients of either sex, aged 30-80 yr, with a body mass index of 18-28 kg/m 2, of American Society of Anesthesiologists Physical Status classification Ⅱ or Ⅲ, scheduled for elective surgery under general anesthesia, were divided into 2 groups ( n=46 each) using a table of random numbers: control group (group C) and acupuncture group (group A). Group A received acupuncture at the Baihui (GV20), Shenting (GV24) and Sishencong (EX-HN1) acupoints before anesthesia. The needles were retained for 30 min, with manual stimulation applied every 10 min for 10 s each time. After 4 stimulations, routine anesthesia was carried out. Group C received routine anesthesia only. Regional cerebral oxygen saturation was recorded on admission to the operating room (T 0), after anesthesia induction (T 1), at the start of surgery (T 2), at the end of surgery (T 3), and immediately after tracheal extubation (T 4). The POD developed within 3 days after surgery was assessed. The occurrence of needle-related adverse effects such as fainting, subcutaneous bleeding, and local paresthesia was recorded. Results:Compared with group C, the incidence of POD was significantly reduced, and the regional cerebral oxygen saturation was increased at T 1, 4 in group A ( P<0.05). Conclusions:Acupuncture can decrease the development of POD in diabetic patients undergoing surgery under general anesthesia, which is related to an increase in regional cerebral oxygen saturation.

Objective:To explore whether perioperative oral probiotic therapy reduces infectious complications following pancreaticoduodenectomy (PD), aiming to obtain higher-level evidence for clinical practice.Methods:A total of 81 participants undergoing PD at the Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital & Affiliated Hospital of Medical School, Nanjing University, from May 2024 to December 2024 were enrolled in this single-center, prospective, randomized controlled trial. The participants were randomly divided into a probiotic treatment group and a control group (receiving conventional treatment without probiotics) using a random number method. The primary outcomes included the incidence of postoperative infectious complications and intra-abdominal infection, and the secondary outcomes were the recovery of gastrointestinal function, postoperative hospital stay, and duration and costs of antibiotic use. The hematological indicators including inflammation and immune markers on postoperative days (POD) 1, 3, 5, and 7 were also compared between these two groups.Results:Finally 72 cases (39 males and 33 females) were analyzed, with 36 patients in the probiotic treatment group and 36 patients in the control group. Compared to the control group, the probiotic treatment group showed statistically significant reductions in the incidence of infectious complications (33.3% vs. 66.7%, P=0.029), intra-abdominal infection (27.8% vs. 58.3%, P=0.030), and incidence of delayed gastric emptying (0 vs. 16.7%, P=0.033). Also, the probiotic treatment group exhibited significantly faster recovery in postoperative bowel movements and shorter time to defecation, liquid diet, and semi-liquid diet (all P<0.05). Additionally, the probiotic treatment group had significantly shorter hospital stay, reduced duration of antibiotic use, and lower antibiotic costs (all P<0.05). Finally, the probiotic treatment group had significantly higher lymphocyte counts on POD 1 ( P<0.05) and showed a significant downward trend in inflammatory markers such as interleukin-6 on PODs 3 and 5 and C-reactive protein on POD 7 (all P<0.05). Conclusions:Perioperative application of probiotic preparations in PD may reduce the incidence of postoperative infectious complications, especially intra-abdominal infection. Additionally, it can prevent delayed gastric emptying, promote the recovery of postoperative gastrointestinal function, shorten hospital stay, and reduce the use of antibiotics. These benefits may be related to the improvement of postoperative inflammatory status.