ObjectiveTo investigate the relationship between the composition of traditional Chinese medicine (TCM) and depression/anxiety/sleep disturbances (D/A/S) in patients with chronic pain.MethodsThis cross-sectional study was conducted at 13 tertiary hospitals across China, enrolling patients who experienced chronic pain between November 2023 and May 2024. The Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Pittsburgh Sleep Quality Index, and TCM constitution categories were used to assess the patients. The association between the TCM constitution and the D/A/S ratio was analyzed using multivariable logistic regression.ResultsA total of 1107 patients (63.2% women) were analyzed. Compared with those with a balanced constitution, patients who had qi-deficiency and yin-deficiency were at a higher risk of depression. Qi-deficiency and yin-deficiency were associated with anxiety. Sleep disturbances were common in patients with qi-deficiency constitution (odds ratio [OR]: 2.32, 95% confidence interval [CI]: 1.42–3.81), yang-deficiency constitution (OR: 1.94, 95% CI: 1.26–2.98), yin-deficiency constitution (OR: 2.03, 95% CI: 1.24–3.32), blood stasis constitution (OR: 2.07, 95% CI: 1.01–4.22), and qi-stagnation constitution (OR: 2.66, 95% CI: 1.35–5.25).ConclusionIn patients with chronic pain, specific TCM constitutions may be associated with D/A/S. Further longitudinal studies are needed to clarify the potential causal relationships between TCM constitution types and these conditions.

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Pyroptosis is an identified programmed cell death that has been highly linked to endoplasmic reticulum (ER) dynamics. However, the crucial proteins for modulating dynamic ER membrane curvature change that trigger pyroptosis are currently not well understood. In this study, a biotin-labeled chemical probe of potent pyroptosis inducer α-mangostin (α-MG) was synthesized. Through protein microarray analysis, reticulon-4 (RTN4/Nogo), a crucial regulator of ER membrane curvature, was identified as a target of α-MG. We observed that chemically induced proteasome degradation of RTN4 by α-MG through recruiting E3 ligase UBR5 significantly enhances the pyroptosis phenotype in cancer cells. Interestingly, the downregulation of RTN4 expression significantly facilitated a dynamic remodeling of ER membrane curvature through a transition from tubules to sheets, consequently leading to rapid fusion of the ER with the cell plasma membrane. In particular, the ER-to-plasma membrane fusion process is supported by the observed translocation of several crucial ER markers to the "bubble" structures of pyroptotic cells. Furthermore, α-MG-induced RTN4 knockdown leads to pyruvate kinase M2 (PKM2)-dependent conventional caspase-3/gasdermin E (GSDME) cleavages for pyroptosis progression. In vivo, we observed that chemical or genetic RTN4 knockdown significantly inhibited cancer cells growth, which further exhibited an antitumor immune response with anti-programmed death-1 (anti-PD-1). In translational research, RTN4 high expression was closely correlated with the tumor metastasis and death of patients. Taken together, RTN4 plays a fundamental role in inducing pyroptosis through the modulation of ER membrane curvature remodeling, thus representing a prospective druggable target for anticancer immunotherapy.
Pyroptosis/immunology* ; Humans ; Endoplasmic Reticulum/immunology* ; Animals ; Nogo Proteins/antagonists & inhibitors* ; Mice ; Cell Line, Tumor ; Xanthones/pharmacology* ; Neoplasms/pathology* ; Mice, Nude

BACKGROUND: The mechanisms distinguishing metabolically healthy from unhealthy phenotypes within the same BMI categories remain unclear. This study aimed to investigate the associations between regional fat distribution and metabolically unhealthy phenotypes in Chinese adults across different BMI categories. METHODS: This cross-sectional study involving 11833 Chinese adults aged 20 years and older. Covariance analysis, adjusted for age, compared the percentage of regional fat (trunk, leg, or arm fat divided by whole-body fat) between metabolically healthy and unhealthy participants. Trends in regional fat percentage with the number of metabolic abnormalities were assessed by the Jonckheere-Terpstra test. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated by logistic regression models. All analyses were performed separately by sex. RESULTS: In non-obese individuals, metabolically unhealthy participants exhibited higher percent trunk fat and lower percent leg fat compared to healthy participants. Additionally, percent trunk fat increased and percent leg fat decreased with the number of metabolic abnormalities. After adjustment for demographic and lifestyle factors, as well as BMI, higher percent trunk fat was associated with increased odds of being metabolically unhealthy [highest vs. lowest quartile: ORs (95%CI) of 1.64 (1.35, 2.00) for men and 2.00 (1.63, 2.46) for women]. Conversely, compared with the lowest quartile, the ORs (95%CI) of metabolically unhealthy phenotype in the highest quartile for percent arm and leg fat were 0.64 (0.53, 0.78) and 0.60 (0.49, 0.74) for men, and 0.72 (0.56, 0.93) and 0.46 (0.36, 0.59) for women, respectively. Significant interactions between BMI and percentage of trunk and leg fat were observed in both sexes, with stronger associations found in individuals with normal weight and overweight. CONCLUSIONS Trunk fat is associated with a higher risk of metabolically unhealthy phenotype, while leg and arm fat are protective factors. Regional fat distribution assessments are crucial for identifying metabolically unhealthy phenotypes, particularly in non-obese individuals.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Adipose Tissue ; Body Fat Distribution ; Body Mass Index ; China/epidemiology* ; Cross-Sectional Studies ; Health Surveys ; Phenotype

Objective:This study aims to investigate the differences in hemostatic efficacy and patient comfort between an innovative domestically produced biodegradable nasal packing sponge and a traditional absorbent sponge following endoscopic nasal surgery. Methods:A prospective, randomized controlled trial design was utilized, including 30 patients who were divided into two groups according to random allocation, each receiving one of the two types of nasal packing. The study assessed the hemostatic efficacy, comfort, and safety of the materials by comparing the rates of no bleeding within 24 hours after packing, re-bleeding rates after 48 hours, pain ratings in the head and nasal areas, scores on a visual analog scale for nasal ocular symptoms, and safety indicators between the two groups. Results:The rates of no bleeding within 24 hours post-packing were 73.33% for both the experimental and control groups, with a no-bleeding rate of 100% after 48 hours in both groups. The pain rating in the head and nasal areas at various times post-packing was Grade Ⅰ（100%） in both groups, with no statistically significant difference（P=1.000）. The experimental groups sneezing score on the day of packing was（0.73±1.03）, lower than the control groups（2.27±1.67）, （P=0.007）; after 48 hours, the experimental groups sneezing score was（0.67±0.98）, also lower than the control groups（1.67±1.18）, （P=0.019）. There was no significant difference between the two groups in the Lund-Kennedy scoring during endoscopic examinations at the screening period, 7 days, 1 month, and 3 months post-packing（P>0.05）. Laboratory tests for other examination indicators were normal in both groups. Conclusion:The innovative domestically produced biodegradable nasal packing sponge not only provides hemostatic efficacy comparable to imported materials but also significantly improves patient comfort after surgery. It represents an economical and effective choice for nasal packing materials.
Humans ; Prospective Studies ; Surgical Sponges ; Endoscopy/methods* ; Male ; Female ; Epistaxis/prevention & control* ; Middle Aged ; Nasal Surgical Procedures/methods* ; Adult

Objective:A bidirectional Mendelian randomization approach was applied to dissect the causal association between dairy product consumption and osteoarthritis (OA), so to offering novel insights for the prevention, treatment, and prognosis assessment of knee osteoarthritis (KOA).Methods:A two-sample Mendelian randomization (MR) analysis was conducted using public available genome-wide association studies. The primary analysis was performed using the inverse variance weighted (IVW) method. To assess the stability and reliability of the results, we compared IVW and MR-Egger regression using Cochran′s Q test to evaluate SNP heterogeneity. MR-Egger regression was employed to assess pleiotropy, while a leave-one-out approach was used for sensitivity analysis. Results:Based on 207 instrumental variables (IVs), two-sample MR analysis revealed that for each standard deviation increase in genetically predicted cheese intake, the risk of OA was significantly negatively associated [ OR(95% CI)=0.98(0.98, 0.99), P<0.001], so did the risk of KOA [ OR(95% CI)=0.73(0.63, 0.85), P<0.001]. However, no significant association was found between OA risk and the intake of full-fat milk (30 IVs), semi-skimmed milk (15 IVs), skimmed milk (32 IVs), soy milk (25 IVs), or other types of milk (13 IVs), yogurt (11 IVs), or butter (3 IVs). Conclusion:The two-sample MR analysis revealed causally inverse associations between cheese intake and OA as well as KOA.

BACKGROUND:Secondary screw perforation of the articular surface is one of the major complications after locking plate internal fixation of proximal humerus fracture,and cut-out screws can damage shoulder function by abrading the glenoid and causing impingement of the acromion.Therefore,accurate risk prediction has positive clinical significance.OBJECTIVE:To screen risk factors for secondary screw perforation after proximal humerus fracture plating by machine learning methods,and to develop and validate a risk prediction model that facilitates clinicians to identify and intervene in high-risk patients at an early stage.METHODS:Clinical data of 214 patients with proximal humerus fractures who underwent locking plate internal fixation from June 2013 to June 2022 were collected as a training group to establish the model,and 61 similar patients from another hospital in the same period were included in the external validation group.The patients were divided into secondary screw perforation and screw maintenance groups according to whether they developed secondary screw perforation after surgery.The training group used three machine learning algorithms,namely,random forest,support vector machine,and logistic regression,to construct the prediction model.The recursive feature elimination method was used,and 10-fold cross-validation resampling was used as the screening method for the variables,and the intersection of the variables that were included when the accuracy of the three models was the highest was taken as the highly correlated with the secondary screw perforation reliable risk variables.The dynamic predictive model was constructed by R language software and presented as a web calculator,and the model was internally and externally validated.The internal test of the model was conducted by the Bootstrap method with 1 000 resamples,and the area under the receiver operating characteristic curve,the calibration curve,and the clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk threshold of the prediction model,according to which the patients in the external validation group were divided into high-and low-risk groups,and the stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The machine learning algorithm identified four risk variables that were highly correlated with secondary screw perforation,namely cortical support of the proximal medial humeral column,deltoid tuberosity index,fracture type,and postoperative reduction.(2)The constructed risk prediction model showed good discrimination and accuracy[area under the curve=0.874,95％confidence interval(0.827,0.922)],and the calibration curve showed good agreement between the model predicted risk and the actual occurrence risk.(3)The clinical decision curve suggested that the model had good clinical applicability when the probability of the risk threshold was in the 0.1-0.75 range.(4)A risk probability of 26％was the optimal threshold for model risk stratification,and the external validation group used model risk stratification to predict secondary screw perforation with an overall accuracy rate of 84％.(5)The risk prediction model has good accuracy and extrapolation,and may provide a basis for guiding clinical treatment.

Background:The incidence and mortality rates of severe acute pancreatitis(SAP)have been increasing year by year.Therefore,early and rapid identification,along with timely intervention in the progression of acute pancreatitis(AP),is of particular importance.Aims:To explore the value of red blood cell distribution width(RDW)combined with BISAP score in the early prediction of SAP.Methods:A total of 561 AP patients admitted from January 2019 to December 2021 at the General Hospital of the Central Theater Command of the PLA were enrolled and divided into SAP group and non-SAP group according to the disease severity.Venous blood samples were collected within 24 hours of admission.The relevant clinical data,laboratory indices,BISAP score,and MCTSI score were compared between the two groups.Logistic regression analysis was used to identify the risk factors for SAP.Spearman correlation coefficient was employed to assess the correlation of these risk factors with the severity of AP,as well as the correlation of RDW with BISAP score and MCTSI score.The predictive values of these risk factors for SAP were evaluated by ROC curve analysis.Results:Compared with the non-SAP group,the prevalence of hypertension,length and cost of hospital stay,neutrophil count(NEUT),neutrophil-to-lymphocyte ratio(NLR),RDW,serum potassium,aspartate transaminase(AST),blood urea nitrogen(BUN),serum creatinine(SCr),BISAP score and MCTSI score were significantly increased in the SAP group(all P<0.05),while the lymphocyte count(LYM),serum calcium and albumin(ALB)were significantly decreased(all P<0.05).RDW(OR=1.582,95%CI:1.066-2.348,P=0.023),SCr(OR=1.018,95%CI:1.001-1.035,P=0.040),BISAP score(OR=6.210,95%CI:3.121-12.356,P<0.001),and MCTSI score(OR=2.917,95%CI:2.160-3.939,P<0.001)were the independent risk factors for SAP.RDW(rs=0.320,P<0.001),SCr(rs=0.103,P=0.015),BISAP score(rs=0.516,P<0.001),and MCTSI score(rs=0.512,P<0.001)were positively correlated with the severity of AP.Moreover,RDW was positively correlated with BISAP score(rs=0.428,P<0.001)and MCTSI score(rs=0.408,P<0.001).ROC curve analysis revealed that the areas under the ROC curve of RDW,SCr,BISAP score,MCTSI score,and combination of RDW and BISAP score for predicting SAP were 0.753,0.581,0.889,0.888,and 0.905,respectively.Conclusions:RDW,SCr,BISAP score,and MCTSI score are the independent risk factors for SAP.RDW combined with BISAP score can enhance the predictive value for SAP.

OBJECTIVE To investigate the current status of cleaning and disinfection of medical bedding items in public medical institutions of Liangshan ethnic minority area and analyze the risk of infections so as to provide ba-ses for optimizing the infection control strategies in the ethnic minority area.METHODS On the basis of question-naire survey,onsite sampling method and bacterial isolation and identification technique,a cross-sectional survey was conducted for the current status of cleaning and disinfection of medical bedding items in 15 different grades of public medical institutions of Liangshan ethnic minority area by stratified random sampling method from Sep.2021 to Sep.2023.RESULTS A total of 66 valid questionnaire were recycled,and 540 bedding item samples were col-lected.Totally 93.33％of the medical institutions relied on laundries for centralized cleaning of the medical bed-ding items.The implementation rates of management items such as policy formulation(100.00％)and pre-job training were remarkably higher in the tertiary hospitals than in the secondary and primary hospitals(allP＜0.05).There were no significant differences in the qualified rates of surveillance of microbiological indexes for the medical bedding items among the medical institutions,the qualified rate of surveillance of microbiological inde-xes was highest(71.30％)in the general hospitals and was lowest(61.11％)in the infectious diseases specialized hospital.The qualified rate of surveillance of microbiological indexes was lowest(3.33％)in pediatrics department or internal medicine department,and it raised most remarkably(80.00％)after the disinfection,and there was significant difference(x2=36.274,P＜0.001).CONCLUSIONS The current status of disinfection of the medical bedding items is not optimistic in the medical institutions of Liangshan ethnic minority area.There are the prob-lems such as imperfect system,insufficient equipment and absence of personnel training.It is necessary to com-plete the execution and supervision system for cleaning and disinfection standards,increase the cleaning facilities,promote the use of antibacterial textile and bring in smart management system so as to improve the cleaning and disinfection quality and reduce the risk of hospital-associated infections.