A rapid and accurate method for textile fiber identification was developed for quality control and consumer protection.This method utilized electric soldering iron burning-mesh collision enhanced microtube plasma ionization mass spectrometry(ESIB-MC-μTP-MS)to acquire textile fiber MS data and used a random forest(RF)prediction model to identify fiber composition based on these MS data.The MC-μTP device involved in the method was a homemade low-temperature plasma ionization device constructed using cost-effective and readily available components.The system was applicable for direct analysis of small amount of textile samples without any complex sample pretreatment processes.Characteristic thermal decomposition products of different fibers were generated via soldering iron burning(350℃)in ambient atmosphere,and were subsequently analyzed by a mass spectrometer,with each analysis completed within 5 s.Raw MS data underwent noise reduction,normalization,and global binning steps to form a dataset,and its intrinsic class separability was evaluated using principal component analysis(PCA)combined with k-means clustering.Then,the RF model was trained based on the dimensionality-reduced textile fiber dataset.After grid search optimization,this model demonstrated robust performance with a 0.9762 out-of-bag score,a 0.9683 cross-validation accuracy(5-fold),and a 0.9636 test accuracy,supported by precision,recall,and F1-scores exceeding 0.889 for all fiber classes.The method was applied to analysis of 30 luxury apparel samples from eight brands,among which 20 samples achieved 100%prediction confidence,aligning with labeled compositions.The identification result of two low-confidence samples was further confirmed using attenuated total reflection Fourier transform infrared spectroscopy(ATR-FT-IR).The method has been proven to be simple,portable and with minimal sample requirements for on-site customs inspections,providing a viable tool in the fight against counterfeit products,therefore supporting regulatory enforcement and consumer trust in the textile goods market.

Due to the poor ionization efficiency and the weak mass spectrometry(MS)intensity of weakly polar substances,direct analysis using the traditional electrospray ionization mass spectrometry(ESI-MS)is a big challenge.In this study,a novel rapid on-site detection method of four prohibited sex hormones in cosmetics was proposed using online derivatization strategy coupled with a miniature mass spectrometer.The target substances in the samples were extracted by a custom-made polyaniline/multi-walled carbon nanotube solid-phase microextraction(SPME)probe.The stirring speed was 200 r/min,the extraction temperature was 40℃,and the extraction time was 2 min.A pulled dual-channel θ borosilicate glass capillary emitter was used as the nano-ESI ion source.The SPME probe was inserted into the channel containing methanol in theθborosilicate glass capillary.When the spray voltage was applied,the four sex hormones were desorbed and formed spray microdroplets,which then collided with the hydroxylamine microdroplets generated from the other channel.The microdroplets of reaction product entered into the miniature mass spectrometer for direct analysis.The limits of detection(LOD)and limits of quantification(LOQ)for the four sex hormones were 10-20 ng/mL and 20-50 ng/mL,respectively.The recoveries were from 84.6%to 107.8%with the relative standard deviations(RSD)from 4.1%to 11.6%.Compared to detection without derivatization,the MS signals of the four target substances were increased by 3 to 15 times.This method was simple,rapid,highly efficient and sensitive,and suitable for on-site rapid analysis of weakly polar sex hormones in cosmetics.

BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.

Diabetic cardiomyopathy is a distinct form of cardiomyopathy that can lead to heart failure, arrhythmias, cardiogenic shock, and sudden death. It has become a major cause of mortality in diabetic patients. The pathogenesis of diabetic cardiomyopathy is complex, involving increased oxidative stress, activation of inflammatory responses, disturbances in glucose and lipid metabolism, accumulation of advanced glycation end products (AGEs), abnormal autophagy and apoptosis, insulin resistance, and impaired intracellular Ca²⁺ homeostasis. Recent studies have shown that adenosine monophosphate-activated protein kinase (AMPK) plays a crucial protective role by lowering blood glucose levels, promoting lipolysis, inhibiting lipid synthesis, and exerting antioxidant, anti-inflammatory, anti-apoptotic, and anti-ferroptotic effects. It also enhances autophagy, thereby alleviating myocardial injury under hyperglycemic conditions. Consequently, AMPK is considered a key protective factor in diabetic cardiomyopathy. As part of diabetes prevention and treatment strategies, both pharmacological and exercise interventions have been shown to mitigate diabetic cardiomyopathy by modulating the AMPK signaling pathway. However, the precise regulatory mechanisms, optimal intervention strategies, and clinical translation require further investigation. This review summarizes the role of AMPK in the prevention and treatment of diabetic cardiomyopathy through drug and/or exercise interventions, aiming to provide a reference for the development and application of AMPK-targeted therapies. First, several classical AMPK activators (e.g., AICAR, A-769662, O-304, and metformin) have been shown to enhance autophagy and glucose uptake while inhibiting oxidative stress and inflammatory responses by increasing the phosphorylation of AMPK and its downstream target, mammalian target of rapamycin (mTOR), and/or by upregulating the gene expression of glucose transporters GLUT1 and GLUT4. Second, many antidiabetic agents (e.g., teneligliptin, liraglutide, exenatide, semaglutide, canagliflozin, dapagliflozin, and empagliflozin) can promote autophagy, reverse excessive apoptosis and autophagy, and alleviate oxidative stress and inflammation by enhancing AMPK phosphorylation and its downstream targets, such as mTOR, or by increasing the expression of silent information regulator 1 (SIRT1) and peroxisome proliferator-activated receptor‑α (PPAR‑α). Third, certain anti-anginal (e.g., trimetazidine, nicorandil), anti-asthmatic (e.g., farrerol), antibacterial (e.g., sodium houttuyfonate), and antibiotic (e.g., minocycline) agents have been shown to promote autophagy/mitophagy, mitochondrial biogenesis, and inhibit oxidative stress and lipid accumulation via AMPK phosphorylation and its downstream targets such as protein kinase B (PKB/AKT) and/or PPAR‑α. Fourth, natural compounds (e.g., dihydromyricetin, quercetin, resveratrol, berberine, platycodin D, asiaticoside, cinnamaldehyde, and icariin) can upregulate AMPK phosphorylation and downstream targets such as AKT, mTOR, and/or the expression of nuclear factor erythroid 2-related factor 2 (Nrf2), thereby exerting anti-inflammatory, anti-apoptotic, anti-pyroptotic, antioxidant, and pro-autophagic effects. Fifth, moderate exercise (e.g., continuous or intermittent aerobic exercise, aerobic combined with resistance training, or high-intensity interval training) can activate AMPK and its downstream targets (e.g., acetyl-CoA carboxylase (ACC), GLUT4, PPARγ coactivator-1α (PGC-1α), PPAR-α, and forkhead box protein O3 (FOXO3)) to promote fatty acid oxidation and glucose uptake, and to inhibit oxidative stress and excessive mitochondrial fission. Finally, the combination of liraglutide and aerobic interval training has been shown to activate the AMPK/FOXO1 pathway, thereby reducing excessive myocardial fatty acid uptake and oxidation. This combination therapy offers superior improvement in cardiac dysfunction, myocardial hypertrophy, and fibrosis in diabetic conditions compared to liraglutide or exercise alone.

Ginsenoside Rg_2(GRg2) is a triterpenoid compound found in Panax notoginseng. This study explored its effects and mechanisms on diabetic retinopathy and angiogenesis. The study employed endothelial cell models induced by glucose or vascular endothelial growth factor(VEGF), the chorioallantoic membrane(CAM) model, the oxygen-induced retinopathy(OIR) mouse model, and the db/db mouse model to evaluate the therapeutic effects of GRg2 on diabetic retinopathy and angiogenesis. Transwell assays and endothelial tube formation experiments were conducted to assess cell migration and tube formation, while vascular area measurements were applied to detect angiogenesis. The impact of GRg2 on the retinal structure and function of db/db mice was evaluated through retinal thickness and electroretinogram(ERG) analyses. The study investigated the mechanisms of GRg2 by analyzing the activation of Yes-associated protein(YAP) and Toll-like receptors(TLRs) pathways. The results indicated that GRg2 significantly reduced cell migration numbers and tube formation lengths in vitro. In the CAM model, GRg2 exhibited a dose-dependent decrease in the vascular area ratio. In the OIR model, GRg2 notably decreased the avascular and neovascular areas, ameliorating retinal structural disarray. In the db/db mouse model, GRg2 increased the total retinal thickness and enhanced the amplitudes of the a-wave, b-wave, and oscillatory potentials(OPs) in the ERG, improving retinal structural disarray. Transcriptomic analysis revealed that the TLR signaling pathway was significantly down-regulated following YAP knockdown, with PCR results consistent with the transcriptome sequencing findings. Concurrently, GRg2 downregulated the expression of Toll-like receptor 4(TLR4), TNF receptor-associated factor 6(TRAF6), and nuclear factor-kappaB(NF-κB) proteins in high-glucose-induced endothelial cells. Collectively, GRg2 inhibits cell migration and tube formation and significantly reduces angiogenesis in CAM and OIR models, improving retinal structure and function in db/db mice, with its pharmacological mechanism likely involving the down-regulation of YAP expression.
Animals ; Ginsenosides/pharmacology* ; Diabetic Retinopathy/physiopathology* ; Mice ; YAP-Signaling Proteins ; Humans ; Male ; Signal Transduction/drug effects* ; Cell Movement/drug effects* ; Adaptor Proteins, Signal Transducing/genetics* ; Mice, Inbred C57BL ; Neovascularization, Pathologic/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Panax notoginseng/chemistry* ; Endothelial Cells/metabolism* ; Transcription Factors/genetics* ; Angiogenesis

OBJECTIVE To establish and verify the risk prediction models for postoperative multidrug-resistant or-ganisms(MDROs)infections in the liver transplantation patients based on the machine learning algorithms so as to provide bases for identification of the population at high risk of postoperative MDROs infections.METHODS The liver transplantation patients who were retrospectively collected from intensive care Ⅳ database(MIMIC-Ⅳ)and eICU collaborative research database(eICU)were recruited as the research subjects,meanwhile,the patients who underwent liver transplantation in the First Hospital of Shanxi Medical University from Jan.2021 to Jul.2024 were assigned as the external verification group.The variables were selected by Lasso regression,and the models were established based on 7 types of machine learning algorithms such as extreme gradient boosting algorithm and random forest.The predictive performances of the models were evaluated by comparing the areas under receiver operating characteristic(ROC)curves and the accuracy,the characteristic variables were interpreted by Shapley additive explanations(SHAP),and the risk prediction calculator was established.RESULTS A total of 637 pa-tients were finally enrolled in the study,and the incidence of postoperative MDROs infections was 35.79％.Total-ly 15 variables were finally selected for construction of the model.The area under the receiver operating character-istic curve of XGBoost model was 0.82 for the internal test set,0.78 for the external test set;the predictive per-formance of XGBoost model was better than that of the rest of 6 models.SHAP algorithm indicated that the top 5 important predictive factors were as follows:hepatic encephalopathy,length of intensive care unit(ICU)stay,albumin,model of end-stage liver disease(MELD)and total length of hospital stay.CONCLUSION The risk pre-diction models that are established based on the machine learning algorithms have remarkable effect on prediction of the postoperative MDROs infections and can accurately identify the liver transplantation patients at high risk of postoperative MDROs infections,which may provide guidance for the identification of high-risk population and the development of prevention and treatment measures for infections.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

OBJECTIVE To establish and verify the risk prediction models for postoperative multidrug-resistant or-ganisms(MDROs)infections in the liver transplantation patients based on the machine learning algorithms so as to provide bases for identification of the population at high risk of postoperative MDROs infections.METHODS The liver transplantation patients who were retrospectively collected from intensive care Ⅳ database(MIMIC-Ⅳ)and eICU collaborative research database(eICU)were recruited as the research subjects,meanwhile,the patients who underwent liver transplantation in the First Hospital of Shanxi Medical University from Jan.2021 to Jul.2024 were assigned as the external verification group.The variables were selected by Lasso regression,and the models were established based on 7 types of machine learning algorithms such as extreme gradient boosting algorithm and random forest.The predictive performances of the models were evaluated by comparing the areas under receiver operating characteristic(ROC)curves and the accuracy,the characteristic variables were interpreted by Shapley additive explanations(SHAP),and the risk prediction calculator was established.RESULTS A total of 637 pa-tients were finally enrolled in the study,and the incidence of postoperative MDROs infections was 35.79％.Total-ly 15 variables were finally selected for construction of the model.The area under the receiver operating character-istic curve of XGBoost model was 0.82 for the internal test set,0.78 for the external test set;the predictive per-formance of XGBoost model was better than that of the rest of 6 models.SHAP algorithm indicated that the top 5 important predictive factors were as follows:hepatic encephalopathy,length of intensive care unit(ICU)stay,albumin,model of end-stage liver disease(MELD)and total length of hospital stay.CONCLUSION The risk pre-diction models that are established based on the machine learning algorithms have remarkable effect on prediction of the postoperative MDROs infections and can accurately identify the liver transplantation patients at high risk of postoperative MDROs infections,which may provide guidance for the identification of high-risk population and the development of prevention and treatment measures for infections.