Medical artificial intelligence （AI） is a new type of application formed by the combination of machine learning， computer vision， natural language processing， and other technologies with clinical medical treatment. With the continuous iteration and development of relevant technologies， medical AI has shown great potential in improving the efficiency of diagnosis and treatment， and service quality， but it also increases the possibility of triggering ethical issues. Ethical issues resulting from the clinical application of medical AI were analyzed， including the lack of algorithmic interpretability and transparency of medical AI， leading to information asymmetry and cognitive discrepancies； the concerning status of security and privacy protection of medical data； and the complex and unclear division of responsibilities due to the collaborative participation of multiple subjects in the clinical application of medical AI， resulting in increased difficulty in the identification of medical accidents and clarification of responsibilities. The paper proposed the principles of not harming patients’ interests， physician’s subjectivity， fairness and inclusiveness， and rapid response. It also explored the strategies and implementation paths for responding to the ethical issues of medical AI from multiple perspectives， including standardizing the environment and processes， clarifying responsibility attribution， continuously assessing the impact of data protection， guaranteeing data security， ensuring model transparency and interpretability， carrying out multi-subject collaboration， as well as the principles of being driven by ethical values and adhering to the “human health-centeredness.” It aimed to provide guidance for the healthy development of medical AI， ensuring technological progress while effectively managing and mitigating accompanying ethical risks， thereby promoting the benign development of medical AI technology and better serving the healthcare industry and patients.

This study aims to systematically characterize the targeted effects of Quanduzhong Capsules on cartilage lesions in knee osteoarthritis by integrating spatial transcriptomics data mining and animal experiments validation, thereby elucidating the related molecular mechanisms. A knee osteoarthritis model was established using Sprague-Dawley(SD) rats, via a modified Hulth method. Hematoxylin and eosin(HE) staining was employed to detect knee osteoarthritis-associated pathological changes in knee cartilage. Candidate targets of Quanduzhong Capsules were collected from the HIT 2.0 database, followed by bioinformatics analysis of spatial transcriptomics datasets(GSE254844) from cartilage tissues in clinical knee osteoarthritis patients to identify spatially specific disease genes. Furthermore, a "formula candidate targets-spatially specific genes in cartilage lesions" interaction network was constructed to explore the effects and major mechanisms of Quanduzhong Capsules in distinct cartilage regions. Experimental validation was conducted through immunohistochemistry using animal-derived biospecimens. The results indicated that Quanduzhong Capsules effectively inhibited the degenerative changes in the cartilage of affected joints in rats, which was associated with the regulation of Quanduzhong Capsules on the thioredoxin-interacting protein(TXNIP)-NOD-like receptor family pyrin domain containing 3(NLRP3)-bone morphogenetic protein receptor type 2(BMPR2)-fibronectin 1(FN1)-matrix metallopeptidase 2(MMP2) signal axis in the articular cartilage surface and superficial zones, subsequently inhibiting cartilage matrix degradation leading to oxidative stress and inflammatory diffusion. In summary, this study clarifies the spatially specific targeted effects and protective mechanisms of Quanduzhong Capsules within pathological cartilage regions in knee osteoarthritis, providing theoretical and experimental support for the clinical application of this drug in the targeted therapy on the inflamed cartilage.
Animals ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Male ; Humans ; Capsules ; Female ; Disease Models, Animal

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

OBJECTIVES: To investigate the epidemiological characteristics of human metapneumovirus (hMPV) and the risk factors for severe pneumonia in hospitalized children. METHODS: The epidemiological characteristics of hMPV in hospitalized children at Hebei Children's Hospital from January 2019 to December 2023 were retrospectively analyzed. The clinical data of hospitalized children with hMPV infection from April to December 2023 were included, and independent risk factors for severe pneumonia were identified through logistic regression. RESULTS: A total of 44 092 children were tested, with an hMPV positive rate of 7.30% (3 220/44 092). Children aged 3-6 years constituted the largest proportion (40.93%, 1 318/3 220) among hMPV-positive cases. The detection rate varied significantly by year (P<0.001), peaking in 2022 (12.35%, 978/7 919). The peak season of the epidemic was winter and spring from 2019 to 2021, but shifted to spring and summer from 2022 to 2023. The proportion of co-infection was 38.70% (1 246/3 220), primarily with rhinovirus (600/1 246, 48.15%), Mycoplasma pneumoniae (217/1 246, 17.42%), and respiratory syncytial virus (182/1 246, 14.61%). The main manifestations of hMPV pneumonia were cough, expectoration, and fever. Children with severe pneumonia were significantly younger (P<0.05). Wheezing, underlying diseases, co-infection, and younger age were identified as independent risk factors for severe pneumonia (P<0.05). CONCLUSIONS There are significant annual and seasonal differences in the epidemiological characteristics of hMPV in hospitalized children. Young age, underlying diseases, wheezing, and co-infection are independent risk factors for severe pneumonia.
Humans ; Risk Factors ; Metapneumovirus ; Child, Preschool ; Child ; Male ; Female ; Paramyxoviridae Infections/complications* ; Pneumonia/epidemiology* ; Retrospective Studies ; Child, Hospitalized ; Infant ; Logistic Models ; Seasons ; Hospitalization

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

Objective To achieve comprehensive quality control throughout the entire process of electronic medical re-cords,a large general hospital has implemented an artificial intelligence-based closed-loop system for medical record quality con-trol.Methods By establishing a database of medical record quality control rules and integrating it with artificial intelligence technology and a closed-loop management mode,an intelligent closed-loop system for medical record quality control was formed.This system comprised pre-emptive alerts,real-time monitoring and warning during patient care,post-event feedback and im-provement mechanisms,as well as multi-dimensional statistical analysis.Results The system achieved full-sample coverage for medical record quality control,with quality indicators related to medical records showing a steady improvement.Conclusion The application of the artificial intelligence-based closed-loop system for medical record quality control can significantly improve the quality of electronic medical records,as well as the efficiency and effectiveness of the medical records department,enhancing the hospitals level of refined management.

Objective:To further improve the understanding of paroxysmal nocturnal hemoglobinuria (PNH), we retrospectively analyzed and summarized the clinical characteristics, treatment status, and survival status of patients with PNH in Zhejiang Province.Methods:This study included 289 patients with PNH who visited 20 hospitals in Zhejiang Province. Their clinical characteristics, comorbidity, laboratory test results, and medications were analyzed and summarized.Results:Among the 289 patients with PNH, 148 males and 141 females, with a median onset age of 45 (16-87) years and a peak onset age of 20-49 years (57.8% ). The median lactic dehydrogenase (LDH) level was 1 142 (604-1 925) U/L. Classified by type, 70.9% (166/234) were classical, 24.4% (57/234) were PNH/bone marrow failure (BMF), and 4.7% (11/234) were subclinical. The main clinical manifestations included fatigue or weakness (80.8%, 235/289), dizziness (73.4%, 212/289), darkened urine color (66.2%, 179/272), and jaundice (46.2%, 126/270). Common comorbidities were hemoglobinuria (58.7% ), renal dysfunction (17.6% ), and thrombosis (15.0% ). Moreover, 82.3% of the patients received glucocorticoid therapy, 70.9% required blood transfusion, 30.7% used immunosuppressive agents, 13.8% received anticoagulant therapy, and 6.3% received allogeneic hematopoietic stem cell transplantation. The 10-year overall survival (OS) rate was 84.4% (95% CI 78.0% -91.3% ) . Conclusion:Patients with PNH are more common in young and middle-aged people, with a similar incidence rate between men and women. Common clinical manifestations include fatigue, hemoglobinuria, jaundice, renal dysfunction, and recurrent thrombosis. The 10-year OS of this group is similar to reports from other centers in China.