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.

Objective To investigate the factors associated with unplanned readmission within 30 days after discharge in adult patients who underwent coronary artery bypass grafting (CABG) and to develop and validate a risk prediction model. Methods A retrospective analysis was conducted on the clinical data of patients who underwent isolated CABG at the Nanjing First Hospital between January 2020 and June 2024. Data from January 2020 to August 2023 were used as a training set, and data from September 2023 to June 2024 were used as a validation set. In the training set, patients were divided into a readmission group and a non-readmission group based on whether they had unplanned readmission within 30 days post-discharge. Clinical data between the two groups were compared, and logistic regression was performed to identify independent risk factors for unplanned readmission. A risk prediction model and a nomogram were constructed, and internal validation was performed to assess the model’s performance. The validation set was used for validation. Results A total of 2 460 patients were included, comprising 1 787 males and 673 females, with a median age of 70 (34, 89) years. The training set included 1 932 patients, and the validation set included 528 patients. In the training set, there were statistically significant differences between the readmission group (79 patients) and the non-readmission group (1 853 patients) in terms of gender, age, carotid artery stenosis, history of myocardial infarction, preoperative anemia, and heart failure classification (P<0.05). The main causes of readmission were poor wound healing, postoperative pulmonary infections, and new-onset atrial fibrillation. Multivariable logistic regression analysis revealed that females [OR=1.659, 95%CI (1.022, 2.692), P=0.041], age [OR=1.042, 95%CI (1.011, 1.075), P=0.008], carotid artery stenosis [OR=1.680, 95%CI (1.130, 2.496), P=0.010], duration of first ICU stay [OR=1.359, 95%CI (1.195, 1.545), P<0.001], and the second ICU admission [OR=4.142, 95%CI (1.507, 11.383), P=0.006] were independent risk factors for unplanned readmission. In the internal validation, the area under the curve (AUC) was 0.806, and the net benefit rate of the clinical decision curve analysis (DCA) was >3%. In the validation set, the AUC was 0.732, and the DCA net benefit rate ranged from 3% to 48%. Conclusion Females, age, carotid artery stenosis, duration of first ICU stay, and second ICU admission are independent risk factors for unplanned readmission within 30 days after isolated CABG. The constructed nomogram demonstrates good predictive power.

Perceptual decision making refers to the process by which individuals make choices and judgments based on sensory information, serving as a fundamental ability for human adaptation to complex environments. While traditional research has focused on perceptual decision making in isolated contexts, growing evidence highlights the profound influence of social contexts prevalent in real-world scenarios. As a crucial factor supporting individual survival and development, social context not only provides rich information sources but also shapes perceptual decision making through top-down processing mechanisms, prompting researchers to recognize the inherently social nature of human decisions. Empirical studies have demonstrated that social information, such as others’ choices or group norms, can systematically bias individuals’ perceptual decisions, often manifesting as conformity behaviors. Social influence can also facilitate performance under certain conditions, particularly when individuals can accurately identify and adopt high-quality social information. The impact of social context on perceptual decisions is modulated by a variety of external and internal factors, including group characteristics(e.g., group size, response consistency), attributes of peers (e.g., familiarity, social status, distinctions between human and artificial agents), as well as individual differences such as confidence, personality traits, and developmental stage. The motivations driving social influence encompass three primary mechanisms: improving decision accuracy through informational influence, gaining social acceptance through normative influence, and maintaining positive self-concept. Recent computational approaches have employed diverse theoretical frameworks to provide valuable insights into the cognitive mechanisms underlying social influence in perceptual decision making. Reinforcement learning models demonstrate how social feedback shapes future choices through reward-based updating. Bayesian inference frameworks describe how individuals integrate personal beliefs with social information based on their respective reliabilities, dynamically updating beliefs to optimize decisions under uncertainty. Drift diffusion models offer powerful tools to decompose social influence into distinct cognitive components, allowing researchers to differentiate between changes in perceptual processing and shifts in decision criteria. Collectively, these models establish a comprehensive methodological foundation for disentangling the multiple pathways by which social context shapes perceptual decisions. Neuroimaging and electrophysiological studies provide converging evidence that social context influences perceptual decision making through multi-level neural mechanisms. At early perceptual processing stages, social influence modulates sensory evidence accumulation in parietal cortex and directly alters primary visual cortex activity, while guiding selective attention to stimulus features consistent with social norms through attentional alignment mechanisms. At higher cognitive levels, the reward system (ventral striatum, ventromedial prefrontal cortex) is activated during group-consistent decisions; emotion-processing networks (anterior cingulate cortex, insula, amygdala) regulate experiences of social acceptance and rejection; and mentalizing-related brain regions (dorsomedial prefrontal cortex, temporoparietal junction) support inference of others’ mental states and social information integration. These neural circuits work synergistically to achieve top-down multi-level modulation of perceptual decision making. Understanding the mechanisms by which social context shapes perceptual decision making has broad theoretical and practical implications. These insights inform the optimization of collective decision-making, the design of socially adaptive human-computer interaction systems, and interventions for cognitive disorders such as autism spectrum disorder and anorexia nervosa. Future studies should combine computational modeling and neuroimaging approaches to systematically investigate the multi-level and dynamic nature of social influences on perceptual decision making.

Aging is an inevitable, physiological process of the human body, leading to deterioration in bodily function and increased susceptibility to various diseases. Effective endogenous therapeutic strategies for anti-aging and related diseases remain limited. Exercise confers multifaceted benefits to physical health by augmenting osteogenic and myogenic processes, enhancing cardiovascular and nervous system function, and attenuating chronic inflammation. Angiogenesis and lymphangiogenesis play pivotal roles in anti-aging, tissue repair, and immune response modulation, underscoring their potential as therapeutic targets for age-related diseases. Modulating angiogenic and lymphangiogenic pathways may provide a promising strategy for mitigating vascular decline and immune system dysfunction associated with aging. Exercise-induced endogenous angiogenesis and lymphangiogenesis can exert beneficial effects on physiological function, thereby representing a potential therapeutic paradigm for combating age-related decline and diseases. This review offers a thorough summary of the present knowledge regarding angiogenesis and lymphangiogenesis induced by exercise, encompassing the underlying mechanisms and the effects in different organs. In addition, it explores the potential of physical activity as a non-pharmacological intervention for anti-aging strategies and disease management, offering novel insights into the intersection of physical activity, aging, and disease progression.
Humans ; Lymphangiogenesis/physiology* ; Aging/physiology* ; Exercise/physiology* ; Animals ; Neovascularization, Physiologic/physiology* ; Angiogenesis

Objective: To systematically evaluate the influencing factors for autism spectrum disorder (ASD) in Chinese children, so as to provide the evidence for risk prediction and intervention of ASD. Methods: The publications pertaining to the influencing factors for ASD in Chinese children were retrieved from CNKI, Wanfang Data, VIP, PubMed and Embase database from inception to August 2024. A meta-analysis was performed using R package version 4.4.1. Sensitivity analysis was performed using the "leave-one-out" evaluation procedure. Publication bias was assessed using Egger regression test. Results: A total of 38 high-quality articles out of 9 015 articles were finally included, covering 149 607 individuals, with 5 974 cases of ASD. The meta-analysis showed that demographic factors including family history of related diseases (OR=14.958), maternal age of ≥35 years (OR=2.287) and parental history of hazardous occupations (OR=3.511); pregnancy-related factors including history of abortion (OR=5.832), no folate supplementation before and during pregnancy (OR=4.566), tobacco exposure before and during pregnancy (OR=2.596), history of other adverse exposures before and during pregnancy (OR=3.533), history of infectious diseases during pregnancy (OR=3.753), history of non-infectious diseases during pregnancy (OR=2.563), psychological problems during pregnancy (OR=3.864), history of medication during pregnancy (OR=6.651), adverse environmental exposures during pregnancy (OR=3.754), severe pregnancy reactions (OR=5.082), abnormal perinatal period (OR=2.987), cesarean delivery (OR=1.659), other perinatal adverse factors (OR=3.856), history of neonatal asphyxia (OR=2.792) and neonatal jaundice (OR=3.687); parenting factors including non-exclusive breastfeeding (OR=2.510), early/excessive screen exposure (OR=3.589) and feeding problems (OR=3.113); and individual factors including being male (OR=3.333) and history of convulsions/epilepsy (OR=7.035) were influencing factors for ASD in Chinese children. Conclusions The prevalence of ASD in Chinese children is primarily associated with 23 influencing factors, including family history of related diseases, history of abortion, no folate supplementation before and during pregnancy, medication during pregnancy, early/excessive screen exposure and history of convulsions/epilepsy.

As the biggest tissue of human body, skin is the first barrier of resisting external aggression. Collagen is one of important parts of the skin, which could not only affect the aesthetics of skin, but also influence the health and normal function of skin. It is the great significance to find ways that could inhibit the loss of collagen. The mechanisms of the collagen degradation in skin are complex and multifaceted. Natural bioactive products have unique advantages in treating the loss of collagen, which have multi-targets and mechanisms. In this review, the mechanisms of skin collagen degradation are discussed, and the research progress of natural bioactive products in resisting skin aging through promoting collagen synthesis are reviewed, in order to provide references for futural research.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

Traditional Chinese medicine(TCM) processing is a specialized pharmaceutical technique with the primary objective of reducing the toxicity of medicinal substances. Euphorbia pekinensis, E. ebracteolata, and E. fischeriana, all belonging to Euphorbiaceae, are classified as drastic purgative herbs, traditionally used for eliminating retained water, reducing swelling, resolving toxicity, and dispersing masses. However, these herbs are also associated with adverse effects such as abdominal pain and diarrhea. Accordingly, they are commonly processed with vinegar, milk, or Terminalia chebula decoction to reduce the toxicity. This review summarizes the chemical constituents, pharmacological activities, historical evolution of processing methods, and detoxification mechanisms of the three toxic Euphorbia species. The primary toxic constituents are terpenoids. Specifically, E. ebracteolata and E. fischeriana are rich in diterpenoids, while E. pekinensis contains diterpenoids, triterpenoids, and sesquiterpenoids. Studies have shown that vinegar processing promotes structural transformations of diterpenoids, including ether bond hydrolysis, lactone ring opening, esterification, oxidation, and epoxide ring cleavage, thereby reducing the content and toxicity of these compounds. Milk processing facilitates the dissolution of toxic components into the residual liquid of excipients, leading to decreases in their concentrations in the final decoction pieces. Processing with T. chebula decoction raises the levels of tannin-derived phenolic acids, which antagonize the adverse effects of the intestine. These findings reveal a shared detoxification pattern among the three toxic herbs. Accordingly, this review proposes the concept of a shared detoxification mechanism for toxic herbs belonging to the same family or genus. That is, toxic herbs belonging to the same taxon often exhibit similar toxicological profiles and can undergo detoxification through the same processing methods, reflecting common underlying mechanisms. Investigating such shared mechanisms across multiple species of the same genus offers a promising research strategy. Ultimately, the research into processing-induced detoxification mechanisms provides both theoretical and practical support for ensuring the safety of toxic TCM.
Euphorbia/classification* ; Drugs, Chinese Herbal/metabolism* ; Humans ; Animals ; Inactivation, Metabolic ; Medicine, Chinese Traditional

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

To address the challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the "You Only Look Once" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.
Algorithms ; Humans ; Blood Cells/cytology* ; Blood Cell Count/methods* ; Neural Networks, Computer ; Deep Learning ; Detection Algorithms