ObjectiveTo elucidate the potential mechanisms by which the classic prescription Anmeidan alleviates cognitive impairment in insomnia model rats through metabolic profiling. MethodsA total of 60 SD rats were randomly divided into six groups： blank group， model group， low-， medium-， and high-dose Anmeidan groups， and the Suvorexant group， with 10 rats in each group. Except for the blank group， the insomnia model was established in all other groups via intraperitoneal injection of para-chlorophenylalanine. The Suvorexant group was administered Suvorexant solution （30 mg·kg^-1·d^-1） by gavage， while the low-， medium-， and high-dose Anmeidan groups received Anmeidan decoction （4.55， 9.09， 18.18 g·kg^-1·d^-1） by gavage. The blank group received an equivalent volume of normal saline. The open field test was used to assess spatial exploration and anxiety/depressive-like behaviors in rats. Serum levels of epidermal growth factor （EGF）， brain-derived neurotrophic factor （BDNF）， and vasoactive intestinal peptide （VIP） were measured using enzyme-linked immunosorbent assay （ELISA）. Untargeted metabolomics was employed to identify differential metabolites in rat serum， and systematic biological methods were applied to analyze the potential targets and pathways of Anmeidan. ResultsCompared to the blank group， the model group exhibited significant reductions in total distance traveled， average speed， number of entries into the central area， time spent in the central area， and frequency of upright events （P<0.01）， along with significant decreases in VIP， EGF， and BDNF levels （P<0.05，P<0.01）. A total of 100 differential metabolites were identified between the model and blank groups. Compared to the model group， the low-， medium-， and high-dose Anmeidan groups showed significant increases in total distance traveled， average speed， number of entries into the central area， time spent in the central area， and frequency of upright events （P<0.05，P<0.01）， as well as a significant increase in VIP levels （P<0.05，P<0.01）. Anmeidan significantly reversed abnormal changes in 67 metabolites compared to the model group. A combined analysis identified 134 potential targets of Anmeidan， with network topology analysis suggesting that Caspase-3， B-cell lymphoma 2 （Bcl-2）， nuclear transcription factor-κB （NF-κB）， interleukin-1β （IL-1β）， interleukin-2 （IL-2）， matrix metalloproteinase-9 （MMP-9）， and Toll-like receptor 4 （TLR4）， among others， may serve as key targets of Anmeidan. Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis revealed major enriched pathways， including the cyclic adenosine monophosphate （cAMP） signaling pathway， hypoxia inducible factor-1 （HIF-1） signaling pathway， and IL-17 signaling pathway. ConclusionThis study demonstrates that Anmeidan can recalibrate abnormal metabolic profiles in insomnia model rats to mitigate cognitive impairment， with its mechanisms of action potentially involving the regulation of immune-inflammatory responses， energy metabolism， and apoptosis-related pathways.

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

OBJECTIVES: To construct a Z-score regression model for coronary artery diameter based on echocardiographic data from children in Sichuan Province and to establish a Z-score calculation formula. METHODS: A total of 744 healthy children who underwent physical examinations at Sichuan Provincial People's Hospital from January 2020 to December 2022 were selected as the modeling group, while 251 children diagnosed with Kawasaki disease at the same hospital from January 2018 to December 2022 were selected as the validation group. Pearson correlation analysis was conducted to analyze the relationships between coronary artery diameter values and age, height, weight, and body surface area. A regression model was constructed using function transformation to identify the optimal regression model and establish the Z-score calculation formula, which was then validated. RESULTS: The Pearson correlation analysis showed that the correlation coefficients for the diameters of the left main coronary artery, left anterior descending artery, left circumflex artery, and right coronary artery with body surface area were 0.815, 0.793, 0.704, and 0.802, respectively (P<0.05). Among the constructed regression models, the power function regression model demonstrated the best performance and was therefore chosen as the optimal model for establishing the Z-score calculation formula. Based on this Z-score calculation formula, the detection rate of coronary artery lesions was found to be 21.5% (54/251), which was higher than the detection rate based on absolute values of coronary artery diameter. Notably, in the left anterior descending and left circumflex arteries, the detection rate of coronary artery lesions using this Z-score calculation formula was higher than that of previous classic Z-score calculation formulas. CONCLUSIONS The Z-score calculation formula established based on the power function regression model has a higher detection rate for coronary artery lesions, providing a strong reference for clinicians, particularly in assessing coronary artery lesions in children with Kawasaki disease.
Humans ; Male ; Female ; Child, Preschool ; Child ; Coronary Artery Disease/diagnostic imaging* ; Infant ; Mucocutaneous Lymph Node Syndrome ; Regression Analysis ; Coronary Vessels/diagnostic imaging* ; Echocardiography ; Adolescent

Peri-implant diseases are characterized by the resorption of hard tissue and the inflammation of soft tissue. Epigenetics refers to alterations in the expression of genes that are not encoded in the DNA sequence, influencing diverse physiological activities, including immune response, inflammation, and bone metabolism. Epigenetic modifications can lead to tissue-specific gene expression variations among individuals and may initiate or exacerbate inflammation and disease predisposition. However, the impact of these factors on peri-implantitis remains inconclusive. To address this gap, we conducted a comprehensive review to investigate the associations between epigenetic mechanisms and peri-implantitis, specifically focusing on DNA methylation and microRNAs (miRNAs or miRs). We searched for relevant literature on PubMed, Web of Science, Scopus, and Google Scholar with keywords including "epigenetics," "peri-implantitis," "DNA methylation," and "microRNA." DNA methylation and miRNAs present a dynamic epigenetic mechanism operating around implants. Epigenetic modifications of genes related to inflammation and osteogenesis provide a new perspective for understanding how local and environmental factors influence the pathogenesis of peri-implantitis. In addition, we assessed the potential application of DNA methylation and miRNAs in the prevention, diagnosis, and treatment of peri-implantitis, aiming to provide a foundation for future studies to explore potential therapeutic targets and develop more effective management strategies for this condition. These findings also have broader implications for understanding the pathogenesis of other inflammation-related oral diseases like periodontitis.
Peri-Implantitis/genetics* ; Humans ; Epigenesis, Genetic ; DNA Methylation ; MicroRNAs/genetics*

Addressing the enduring challenge of evaluating traditional Chinese medicines (TCMs), the integrated evidence chain-based effectiveness evaluation of TCMs (Eff-iEC) has emerged. This paper explored its capacity through a demonstration study that evaluated the effectiveness evidence of six commonly used anti-hepatic fibrosis Chinese patent medicines (CPMs), including Biejiajian Pill (BP), Dahuang Zhechong Pill (DZP), Biejia Ruangan Compound (BRC), Fuzheng Huayu Capsule (FHC), Anluo Huaxian Pill (AHP), and Heluo Shugan Capsule (HSC), using both Eff-iEC and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. The recognition of these CPMs within the TCM academic community was also assessed through their inclusion in relevant medical documents. Results showed that the evidence of BRC and FHC received higher assessments in both Eff-iEC and GRADE system, while the assessments for others varied. Analysis of community recognition revealed that Eff-iEC more accurately reflects the clinical value of these CPMs, exhibiting superior evaluative capabilities. By breaking through the conventional pattern of TCMs effectiveness evaluation, Eff-iEC offers a novel epistemology that better aligns with the clinical realities and reasoning of TCMs, providing a coherent methodology for clinical decision-making, new drug evaluations, and health policy formulation.

Deactivation of the mitochondrial pyruvate dehydrogenase complex (PDC) is important for the metabolic switching of cancer cell from oxidative phosphorylation to aerobic glycolysis. Studies examining PDC activity regulation have mainly focused on the phosphorylation of pyruvate dehydrogenase (E1), leaving other post-translational modifications largely unexplored. Here, we demonstrate that the acetylation of Lys 488 of pyruvate dehydrogenase complex component X (PDHX) commonly occurs in hepatocellular carcinoma, disrupting PDC assembly and contributing to lactate-driven epigenetic control of gene expression. PDHX, an E3-binding protein in the PDC, is acetylated by the p300 at Lys 488, impeding the interaction between PDHX and dihydrolipoyl transacetylase (E2), thereby disrupting PDC assembly to inhibit its activation. PDC disruption results in the conversion of most glucose to lactate, contributing to the aerobic glycolysis and H3K56 lactylation-mediated gene expression, facilitating tumor progression. These findings highlight a previously unrecognized role of PDHX acetylation in regulating PDC assembly and activity, linking PDHX Lys 488 acetylation and histone lactylation during hepatocellular carcinoma progression and providing a potential biomarker and therapeutic target for further development.
Humans ; Acetylation ; Carcinoma, Hepatocellular/genetics* ; Liver Neoplasms/genetics* ; Pyruvate Dehydrogenase Complex/genetics* ; Gene Expression Regulation, Neoplastic ; Animals ; Mice ; Cell Line, Tumor ; Protein Processing, Post-Translational ; Histones/metabolism* ; Disease Progression