Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.

Objective To evaluate the effect of a multidisciplinary collaborative quality control management (hereinafter referred to as "QC management") on improving the performance of biological safety cabinets in hospital. Methods A total of 63 ClassⅡbiological safety cabinets in active use at Peking University Third Hospital were selected as the study subjects using the before-after study mode. Conventional management was implemented on the biological safety cabinets from 2018 to 2021. QC management was used in 2022. The compliance of biological safety cabinets management norm and performance differences under the two models were compared. Results The median and the 25th and 75th percentiles [M(P₂₅, P₇₅)] of the service life among these 63 biological safety cabinets were 3 (1,6) years. The overall performance pass rate and inflow velocity pass rate of biological safety cabinets were higher in the QC management than that in the conventional management (90.5% vs 65.1%, 96.8% vs 84.1%, both P<0.05). However, there was no significant difference in downflow velocity, high-efficiency particulate air filter integrity, cleanliness, airflow smoke pattern, noise, and illumination pass rates of biosafety cabinets before and after the implementation of QC management (79.4% vs 88.9%, 90.5% vs 100.0%, 96.8% vs 100.0%, 85.7% vs 100.0%, 100.0% vs 100.0%, and 85.7% vs 96.8%, respectively; all P>0.05). Conclusion sQC management improves the standardization of biological safety cabinet management and key performance indicators in hospital.

Objective: To analyze the disease burden of chronic obstructive pulmonary disease (COPD) and changes in its risk factors among residents in Zhejiang Province from 1990 to 2021, so as to identify key priorities for COPD prevention and control. Methods: Data on COPD mortality and disability-adjusted life years (DALY) for residents in Zhejiang Province from 1990 to 2021 were collected from the Global Burden of Disease (GBD) 2021 database. Standardized mortality and standardized DALY rate were calculated using the GBD 2021 world population standard structure. Premature mortality was computed via the life table method. The average annual percent change (AAPC) was applied to analyze trends in COPD mortality, DALY rate, and premature mortality. Changes in deaths of COPD risk factors were evaluated using population attributable fraction (PAF). Results: From 1990 to 2021, the standardized COPD mortality in Zhejiang Province decreased from 272.40/100 000 to 70.56/100 000 (AAPC=-4.395%), and the standardized DALY rate declined from 4 167.37/100 000 to 1 071.89/100 000 (AAPC=-4.396%). Similar downward trends were observed in both males (AAPC=-3.933%, -4.173%) and females (AAPC=-4.785%, -4.480%), all P<0.05. Crude mortality and DALY rates increased with age, and the crude mortality and DALY rates of various age groups in Zhejiang Province showed decreasing trends from 1990 to 2021 (all P<0.05). The premature mortality declined from 4.37% to 0.60% from 1990 to 2021 (AAPC=- 6.206%), with consistent trends across males and females (AAPC=- 6.144%, - 6.379%, all P<0.05). From 1990 to 2021, particulate matter pollution showed the largest reduction in PAF (- 56.76%), while ambient ozone pollution had the largest increase (103.07%) in Zhejiang Province. By 2021, smoking became the leading risk factor for deaths of COPD (PAF=43.32%). Conclusions The standardized mortality, standardized DALY rate, and premature mortality for COPD show consistent declining trends in Zhejiang Province from 1990 to 2021. However, risk factors such as smoking and ambient ozone pollution require intensified focus to further reduce disease burden of COPD.

BACKGROUND: Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS. METHODS: This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide. RESULTS: Eighty-two patients were assigned to variant ( n  = 42) and wild type groups ( n  = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] μg/mL; wild type, 49.1 [32.0] μg/mL) and area under plasma concentration-time curve over a dosing interval (AUC tau ) (variant, 1731.3 [769.0] μg∙h/mL; wild type, 1564.5 [1053.0] μg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study. CONCLUSION: ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients. REGISTRATION NCT04410965, https://clinicaltrials.gov .
Humans ; Crotonates/adverse effects* ; Toluidines/adverse effects* ; Nitriles ; Hydroxybutyrates ; Female ; Male ; Adult ; ATP Binding Cassette Transporter, Subfamily G, Member 2/genetics* ; Middle Aged ; Multiple Sclerosis, Relapsing-Remitting/genetics* ; Prospective Studies ; Young Adult ; Neoplasm Proteins/genetics* ; East Asian People

BACKGROUND: G protein-coupled receptor kinase 2 (GRK2) could participate in the regulation of diverse cells via interacting with non-G-protein-coupled receptors. In the present work, we explored how paroxetine, a GRK2 inhibitor, modulates the differentiation and activation of immune cells in rheumatoid arthritis (RA). METHODS: The blood samples of healthy individuals and RA patients were collected between July 2021 and March 2022 from the First Affiliated Hospital of Anhui Medical University. C57BL/6 mice were used to induce the collagen-induced arthritis (CIA) model. Flow cytometry analysis was used to characterize the differentiation and function of dendritic cells (DCs)/T cells. Co-immunoprecipitation was used to explore the specific molecular mechanism. RESULTS: In patients with RA, high expression of GRK2 in peripheral blood lymphocytes, accompanied by the increases of phosphatidylinositol 3 kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR). In animal model, a decrease in regulatory T cells (T regs ), an increase in the cluster of differentiation 8 positive (CD8 + ) T cells, and maturation of DCs were observed. Paroxetine, when used in vitro and in CIA mice, restrained the maturation of DCs and the differentiation of CD8 + T cells, and induced the proportion of T regs . Paroxetine inhibited the secretion of pro-inflammatory cytokines, the expression of C-C motif chemokine receptor 7 in DCs and T cells. Simultaneously, paroxetine upregulated the expression of programmed death ligand 1, and anti-inflammatory cytokines. Additionally, paroxetine inhibited the PI3K-AKT-mTOR metabolic pathway in both DCs and T cells. This was associated with a reduction in mitochondrial membrane potential and changes in the utilization of glucose and lipids, particularly in DCs. Paroxetine reversed PI3K-AKT pathway activation induced by 740 Y-P (a PI3K agonist) through inhibiting the interaction between GRK2 and PI3K in DCs and T cells. CONCLUSION Paroxetine exerts an immunosuppressive effect by targeting GRK2, which subsequently inhibits the metabolism-related PI3K-AKT-mTOR pathway of DCs and T cells in RA.
G-Protein-Coupled Receptor Kinase 2/metabolism* ; Arthritis, Rheumatoid/immunology* ; Animals ; Dendritic Cells/metabolism* ; Paroxetine/therapeutic use* ; Proto-Oncogene Proteins c-akt/metabolism* ; Mice ; Humans ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Male ; Phosphatidylinositol 3-Kinases/metabolism* ; Lymphocyte Activation/drug effects* ; Female ; T-Lymphocytes/metabolism* ; Middle Aged

The etiology of nervous system diseases is complicated, posing significant harm to patients and often resulting in poor prognoses. In recent years, the role of dopaminergic system in nervous system diseases has attracted much attention, and its complex regulatory mechanism and therapeutic potential have been gradually revealed. This paper reviews the role of dopaminergic neurons, the neurotransmitter dopamine, dopamine receptors and dopamine transporters in neurological diseases (including Alzheimer's disease, Parkinson's disease and schizophrenia), with a view to further elucidating the disease mechanism and providing new insights and strategies for the treatment of neurological diseases.
Humans ; Dopamine/metabolism* ; Nervous System Diseases/physiopathology* ; Parkinson Disease/physiopathology* ; Receptors, Dopamine/metabolism* ; Dopaminergic Neurons/physiology* ; Dopamine Plasma Membrane Transport Proteins/metabolism* ; Alzheimer Disease/physiopathology* ; Schizophrenia/physiopathology* ; Animals

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Abelmoschi Corolla, the dried corolla of Abelmoschus manihot, has anti-inflammatory, antioxidant, and anti-fibrosis activities. Its chemical constituents mainly include flavonoids, organic acids, steroids, and polysaccharides. This study reviewed the research progress in the chemical constituents and pharmacological activities of Abelmoschi Corolla in recent 20 years. According to the concept of quality marker(Q-marker), the Q-markers of Abelmoschi Corolla were predicted from plant phylogeny, chemical constituent specificity, traditional efficacy, chemical constituent measurability, and absorbed constituents. The primary Q-markers for Abelmoschi Corolla were anticipated to include quercetin-3'-O-β-D-glucopyranoside, gossypetin-8-O-β-D-glucuronide, isoquercetin, myricetin,quercetin, and hyperoside, with the aim of providing reference data for improving the quality evaluation system of Abelmoschi Corolla.
Abelmoschus/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Flowers/chemistry* ; Humans ; Animals ; Quality Control ; Flavonoids/chemistry*