OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

OBJECTIVE: To observe the effects of Tiaoshu Anshen (regulating the hinge and calming the mind) acupuncture on sleep quality and serum levels of 5-hydroxytryptamine (5-HT) and dopamine (DA) in patients with chronic insomnia. METHODS: A total of 58 patients with chronic insomnia were randomly divided into an acupuncture group and a medication group, 29 cases in each group. Tiaoshu Anshen acupuncture was applied at Baihui (GV20) and bilateral Shenmen (HT7), Sanyinjiao (SP6), Benshen (GB13) in the acupuncture group, once a day, 1-day interval was taken after 6 consecutive days of treatment. Estazolam tablet was given orally before bed in the medication group, 1 mg each time. The 4-week treatment was required in both groups. Before and after treatment, the sleep quality was assessed by Pittsburgh sleep quality index (PSQI) and polysomnography (PSG), the serum levels of 5-HT and DA were detected by ELISA. RESULTS: After treatment, the item scores and total scores of PSQI were decreased compared with those before treatment in the two groups (P<0.05); in the acupuncture group, the scores of sleep quality, sleep latency, sleep time, sleep efficiency, sleep disorders and total score of PSQI were lower than those in the medication group (P<0.05). After treatment, the total sleep time (TST) was prolonged (P<0.05), the sleep latency (SL) and wake after sleep onset (WASO) were shortened (P<0.05), the sleep efficiency (SE%), percentage of non-rapid eye movement stage 3 (N3%), percentage of rapid eye movement stage (REM%) and serum levels of 5-HT were increased (P<0.05) compared with those before treatment; the percentage of non-rapid eye movement stage 1 (N1%), percentage of non-rapid eye movement stage 2 (N2%) and serum levels of DA were decreased (P<0.05) compared with those before treatment in the two groups. After treatment, in the acupuncture group, TST was longer, while SL and WASO were shorter than those in the medication group (P<0.05), SE%, N3%, REM% and serum level of 5-HT were higher, while N1%, N2% and serum level of DA were lower than those in the medication group (P<0.05). CONCLUSION Tiaoshu Anshen acupuncture may improve the sleep quality by regulating the serum neurotransmitter levels i.e. 5-HT and DA in patients with chronic insomnia.
Humans ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Male ; Acupuncture Therapy ; Female ; Middle Aged ; Adult ; Serotonin/blood* ; Sleep Quality ; Acupuncture Points ; Dopamine/blood* ; Aged ; Neurotransmitter Agents/blood* ; Young Adult

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons and the accumulation of Lewy bodies, leading to motor and nonmotor symptoms. While both genetic and environmental factors contribute to PD, recent studies highlight the crucial role of lipid metabolism disturbances in disease progression. Altered lipid homeostasis promotes protein aggregation and oxidative stress, with significant changes in lipid classes such as sphingolipids and glycerolipids observed in patients with PD. These disturbances are involved in key pathological processes, such as α-synuclein aggregation, organelle dysfunction, lipid-mediated neuroinflammation, and impaired lipid homeostasis. This review examines the relationship between lipid species and PD progression, focusing on the physiological roles of lipids in the central nervous system. It explores the mechanistic links between lipid metabolism and PD pathology, along with lipid-related genetic risk factors. Furthermore, this review discusses lipid-targeting therapeutic strategies to mitigate PD progression, emphasizing the potential of lipid modulation for effective treatment development.
Humans ; Parkinson Disease/metabolism* ; Lipid Metabolism/physiology* ; Animals ; Oxidative Stress/physiology* ; alpha-Synuclein/metabolism*

Processing for deodorization is widely used in the production of animal-derived Chinese medicinal materials. In this study, Heracles Neo ultra-fast gas-phase electronic nose combined with chemometrics was employed to analyze the overall odor difference of Cervi Cornu Pantotrichum(focusing on that derived from Cervus nippon Temminck in this study) before and after processing. The results showed that the electronic nose effectively distinguished between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. HS-GC-MS was used to identify and quantify the volatile components in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum, and 35 and 37 volatile components were detected in the medicinal materials and decoction pieces, respectively. The medicinal materials and decoction pieces contained 28 common volatile components contributing to the odor of Cervi Cornu Pantotrichum. The odor activity value(OAV) of each volatile component was calculated based on the olfactory threshold and relative content. The results showed that there were 17 key odor substances such as isovaleraldehyde, 2-methylbutanal, isobutyraldehyde, hexanal, and methanethiol in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. All of them had bad odor and were the main source of the odor of Cervi Cornu Pantotrichum. The results of principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) showed that there were significant differences in volatile components between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. Based on the thresholds of P<0.05 and Variable Importance in Projection(VIP)>1, 21 differential volatile odor components were screened out. Among them, isopentanol, isovaleraldehyde, 2-methylbutanal, n-nonanal, and dimethylamine were the key differential odor compounds between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. The odor compounds and their relative content reduced, and some flavor substances such as esters were produced after processing with wine, which was the main reason for the reduction of the odor after processing of Cervi Cornu Pantotrichum.
Odorants/analysis* ; Electronic Nose ; Gas Chromatography-Mass Spectrometry/methods* ; Animals ; Volatile Organic Compounds/analysis* ; Deer ; Drugs, Chinese Herbal/chemistry*

A chemical investigation of secondary metabolites (SMs) from Aspergillus nidulans resulted in the identification of five novel dioxopiperazine (DKP)-diphenyl ether hybrids, designated as diphenylemestrins A-E (1-5). These compounds 1-5 represent the first known dimers combining DKP and diphenyl ether structures, with compound 4 featuring an uncommon dibenzofuran as the diphenyl ether component. The structural elucidation and determination of absolute stereochemistry were accomplished through spectroscopic analysis and electronic circular dichroism (ECD) calculations. Notably, diphenylemestrin C (3) exhibited moderate cytostatic activity against NB4 cells, with a half maximal inhibitory concentration (IC₅₀) value of 21.99 μmol·L^-1, and induced apoptosis at higher concentrations.
Aspergillus nidulans/metabolism* ; Diketopiperazines/pharmacology* ; Molecular Structure ; Phenyl Ethers/pharmacology* ; Humans ; Apoptosis/drug effects* ; Cell Line, Tumor

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection,with a high mortality rate.Sirtuin 3,a deacetylase within mitochondria,plays an important regulatory role in cellular metabolism,oxidative stress,and inflammatory responses.In recent years,significant progress has been made in the study of the function and regulatory role of sirtuin 3 in sepsis-related diseases.Research has shown that sirtuin 3 can alleviate organ damage caused by sepsis by regulating mitochondrial function,reducing oxidative stress,and inhibiting inflammatory responses.The specific mechanisms include the regulation of mitochondrial bioenergetics,activation of antioxidant enzyme systems,and inhibition of inflammatory mediator expression.In addition,sirtuin 3 plays a protective role in the pathological process of sepsis by interacting with multiple signaling pathways.This article summarizes the functions and regulatory mechanisms of sirtuin 3 in various sepsis-related diseases,aiming to provide new targets and strategies for the prevention and treatment of sepsis in the future.
Sepsis/metabolism* ; Sirtuin 3/physiology* ; Humans ; Animals ; Oxidative Stress ; Mitochondria/metabolism* ; Signal Transduction

Objective: To review the current development of artificial intelligence (AI) technology in the field of transfusion medicine. Methods: A systematic search was conducted in the Clarivate Web of Science Database from inception to December 2024 for literature related to AI and transfusion. A total of 4 775 publications were identified. Based on inclusion and exclusion criteria, 133 original studies were ultimately included and analyzed using a narrative synthesis approach. Results: Research on AI in transfusion has surged since 2020 (accounting for 77% of all publications), with China ranking second globally in publication volume. Among the included studies, 69.2% focused on predicting individual transfusion needs, followed by inventory management (8.3%), diagnosis and prediction of adverse transfusion reactions (6.0%), factors influencing transfusion outcomes (5.3%), blood group identification (5.3%), blood quality testing (4.5%), and precise blood volume measurement (1.5%). Additionally, 4.5% of the studies were published in journals with an impact factor greater than 10; 19.5% developed software or applications; 31.5% were multi-center studies; 48.1% utilized decision tree methods, while 31.5% employed neural network approaches; and 14.2% conducted external validation of the algorithms. Conclusion: AI demonstrates significant potential in transfusion risk prediction, decision support, and blood management. However, challenges remain, including limited model generalizability, insufficient algorithm interpretability, and barriers to clinical translation. The deep integration of AI with transfusion medicine will accelerate the advent of precision transfusion era, maximizing blood resource utilization, reducing waste, and ensuring transfusion safety.

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential