In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics.Similarities and variations of components present significant analytical challenges.A two-dimensional(2D)liquid chromatography-mass spectrometry(LC-MS)method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium(CMS).A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated.For efficient and high-accuracy screening of CMS,a targeted method based on a self-constructed high resolution(HR)mass spectrum database of CMS components was established.The database was built based on the commercial MassHunter Personal Compound Database and Library(PCDL)software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening.On this basis,the unknown peaks in the CMS chromatograms were deduced and assigned.The molecular formula,group composition,and origins of a total of 99 compounds,of which the combined area percentage accounted for more than 95％of CMS components,were deduced by this 2D-LC-MS method combined with the MassHunter PCDL.This profiling method was highly efficient and could distinguish hundreds of components within 3 h,providing reliable results for quality control of this kind of complex drugs.

Objective To analysis the correlations of cataract patients'age,axial length(AL)with anterior chamber depth(ACD),lens thickness(LT),and postoperative effective lens position(ELP).Methods A retrospective study was employed to analyze the biometric parameters of 7458 cataract patients(7458 eyes)who underwent surgical treatment in the First Affiliated Hospital of Kunming Medical University from January 2016 to December 2022.All measurements were obtained using the Lenstar LS900 optical biometer via optical low-coherence reflectance technology.Pearson or Spearman correlation analyses was performed between patients'age,sex,and AL with ACD,LT,and postoperative ELP measurements.Results The mean AL of patients was 23.41(22.68,24.31)mm,categorized into 4 groups:short AL group(AL≤22 mm,593 cases,8.0%),normal AL group(AL 22-24 mm,4520 cases,60.5%),longer AL group(AL 24-26 mm,1579 cases,21.2%),and long AL group(AL>26 mm,766 cases,10.3%).The ACD was(2.65±0.43)mm,and the LT was(4.29±0.46)mm.Compared with female patients,male patients had significantly longer AL,deeper ACD,and thicker LT(P<0.001).Postoperative ELPs,measured in 209 eyes 3 months after surgery,was 5.03(4.57,5.21)mm.Correlation analysis revealed a positive correlation between AL and ACD in short,normal,and longer AL groups(ρ=0.155,ρ=0.311,ρ=0.074,P<0.001 or P<0.05),while a negative correlation was found in the long AL group(ρ=-0.089,P<0.05).No statistically significant correlation was observed between AL and LT in short AL group(P>0.05),but a negative correlation was noted in normal and longer AL groups(ρ=-0.104,ρ=-0.095,P<0.001),and a positive correlation in long AL group(ρ=0.165,P<0.001).In short,normal,and longer AL groups,postoperative ELP was positively correlated with preoperative ACD(ρ=1.000,ρ=0.826,ρ=0.441,P<0.001),with no significant correlation in long AL group(P>0.05).Conclusions In cataract patients,AL,ACD,and LT are associated with both age and sex.LT and AL significantly influence ACD,while ACD and AL significantly influence LT.Preoperative ACD is the primary factor affecting postoperative ELP.

Objective To investigate the correlation of corneal α angle and κ angle with lens tilt in normal human eyes using a new swept-source optical biometer.Methods A cross-sectional study was conducted,involving 303 healthy eyes(148 right eyes and 155 left eyes)of patients who visited the First Affiliated Hospital of Kunming Medical University from June to August 2024.ZW-30 swept-source optical biometer was used to collect the lens tilt angle,κ angle(distance from the corneal light reflex to the pupil center),and α angle(distance from corneal light reflex to the corneal geometric center,which is the midpoint of the horizontal white to white(WTW)diameter).The degrees(°)and directions of κ angle and α angle were calculated by the ratio of the above measurements to the anterior chamber depth(ACD)respectively.Spearman correlation analysis and linear regression analysis were employed to evaluate the correlations between the magnitude and direction of corneal α angle,κ angle and lens tilt angle.Results The magnitude and direction of corneal α angle,κ angle,and lens tilt angle in the right eye were as follows respectively:(0.54±0.19)mm(7.81°±3.88°),194.43°±39.75°;(0.27±0.23)mm(4.72°±3.90°),181.07°±79.59°;5.52°±1.67°,188.21°±25.73°.For the left eye,the corresponding values were:(0.47±0.27)mm(8.12°±5.26°),336.04°±46.64°;(0.26±0.27)mm(4.45°±4.80°),322.86°±107.79°;5.50°±1.61°,340.65°±32.84°.Spearman's correlation analysis showed that the correlation coefficients between corneal α angle and lens tilt angle in the right eye were 0.609(distance correlation)and 0.625(angle correlation),while those for κ angle were 0.559(distance correlation)and 0.578(angle correlation).In the left eye,the correlation coefficients between corneal α angle and lens tilt angle were 0.545(distance correlation)and 0.552(angle correlation),and those for κ angle were 0.377(distance correlation)and 0.395(angle correlation).In addition,the correlation coefficient between the direction of corneal α angle and the direction of lens tilt angle in the right eye was 0.343,and that for κ angle direction was 0.284;in the left eye,the correlation coefficients were 0.216(α angle direction)and 0.198(κ angle direction),all with statistical significance(P<0.05).Univariate linear regression analysis showed that lens tilt was positively correlated with both corneal α angle and Kappa angle(P<0.05).Conclusions Corneal α angle and κ angle are highly correlated with lens tilt angle in both eyes,and the correlation of corneal α angle is stronger than that of κ angle in both left and right eyes.The correlation expressed by degree(°)is better than that by distance(mm).It is recommended to refer to the corneal α angle and κ angle expressed in degrees during preoperative evaluation.

Acute ischemic stroke(AIS)is associated with high mortality and disability rates,presenting a substantial challenge to global public health challenge.Intravenous thrombolysis(IVT)is recognized as a cornerstone of early AIS treatment and is recommended as the standard therapeutic approach by both national and international guidelines.However,the clinical efficacy of IVT remains suboptimal due to several limitations,including a narrow therapeutic time window and the inevitable activation of the coagulation system and platelet aggregagation during thrombolysis.These factors may contribute to adverse outcomes such as early neurological deterioration(END)and vascular re-occlusion.Antiplatelet therapy(APT),which inhibits platelet aggregations,reduces microthrombus formation,and stabilizes the vascular endothelium with multifaceted mechanisms,has emerged as a promising adjunctive strategy to IVT,offering potential synergistic effects.This review summarized the latest evidence from both domestic and international studies,focusing on the mechanisms of APT,recent clinical advancements in IVT combined with APT,and the safety and efficacy of APT administration at different time windows relative to IVT.Emphasis is placed on the influence of various antiplatelet agents,dosing regimens,and initiation timing on therapeutic outcomes,alongside a comprehensive evaluation in the context of current guideline recommendations and clinical practice.Current guidelines recommend initiating APT 24 h after IVT,following imaging confirmation to exclude the risk of intracranial hemorrhage.However,the efficacy and safety of earlier APT initiation remain inconclusive.Individualized treatment strategies,such as early administration of low-dose,short-acting APT or combination therapy in specific patient subgroups,may effectively balance therapeutic benefits and risks.The adjunctive use of APT in IVT holds promise for enhancing efficacy and improving clinical outcomes,but precise stratification of safety and efficacy is essential.Future research should focus on optimizing combination IVT and APT strategies through individualized patient profiling,appropriate drug selection,and dynamic imaging monitoring to achieve precision management in AIS treatment.

Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67％and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]

Aim To systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics. Similarities and variations of components present significant analytical challenges. A two-dimensional (2D) liquid chromatography-mass spectrometr (LC-MS) method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium (CMS). A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated. For efficient and high-accuracy screening of CMS, a targeted method based on a self-constructed high resolution (HR) mass spectrum database of CMS components was established. The database was built based on the commercial MassHunter Personal Compound Database and Library (PCDL) software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening. On this basis, the unknown peaks in the CMS chromatograms were deduced and assigned. The molecular formula, group composition, and origins of a total of 99 compounds, of which the combined area percentage accounted for more than 95% of CMS components, were deduced by this 2D-LC-MS method combined with the MassHunter PCDL. This profiling method was highly efficient and could distinguish hundreds of components within 3 h, providing reliable results for quality control of this kind of complex drugs.

OBJECTIVE: To determine the prevalence of lumbar spondylolysis (LS) and the proportion of spondylolytic spondylolisthesis (SS) in China, and to evaluate the musculoskeletal status of patients with LS and SS. METHODS: Spine Computed Tomography (CT) images were collected from community populations aged 40 and above in a nationwide multi-center project. LS was diagnosed, and SS was graded by an experienced radiologist. Bone mineral density (BMD) and paraspinal muscle parameters were quantified based on CT images. RESULTS: One hundred and seventeen patients of a total of 3,317 individuals were diagnosed with LS, corresponding to a prevalence rate of 3.53%. 63 of the 1,214 males (5.18%) and 54 of the 2,103 females (2.57%) were diagnosed with LS. SS occurred in 64/121 vertebrae (52.89%). BMD was not associated with LS ( P = 0.341). The L5 extensor paraspinal muscle density was higher in the LS group than in the non-LS group. In the LS group, patients with SS had a smaller L5 paraspinal extensor muscle cross-sectional area than those without SS ( P = 0.003). CONCLUSION The prevalence of LS in Chinese adults was 3.53%, with prevalence rates of 5.18% in males and 2.57% in females. Patients with LS have higher muscle density, whereas those with SS have smaller muscle cross-sectional areas at the L5 level.
Humans ; Male ; Female ; Middle Aged ; China/epidemiology* ; Prevalence ; Adult ; Lumbar Vertebrae/diagnostic imaging* ; Spondylolysis/diagnostic imaging* ; Aged ; Bone Density ; Tomography, X-Ray Computed ; Aged, 80 and over ; Spondylolisthesis/epidemiology* ; East Asian People

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Objective To analyze the influencing factors of death in the elderly with coronavirus disease 2019(COVID-19).Methods The case data of death caused by COVID-19 in West China Fourth Hospital from January 1 to July 8,2023 were collected,and surviving cases from the West China Elderly Health Cohort infected with COVID-19 during the same period were selected as the control.LASSO-Logistic regression was adopted to analyze the data after propensity score matching and the validity of the model was verified by drawing the receiver operating characteristic curve.Results A total of 3 239 COVID-19 survivors and 142 deaths with COVID-19 were included.The results of LASSO-Logistic regression showed that smoking(OR=3.33,95%CI=1.46-7.59,P=0.004),stroke(OR=3.55,95%CI=1.15-10.30,P=0.022),malignant tumors(OR=19.93, 95%CI=8.52-49.23, P<0.001),coronary heart disease(OR=7.68, 95%CI=3.52-17.07, P<0.001),fever(OR=0.51, 95%CI=0.26-0.96, P=0.042),difficulty breathing or asthma symptoms(OR=21.48, 95%CI=9.44-51.95, P<0.001),and vomiting(OR=8.19,95%CI=2.87-23.58, P<0.001)increased the risk of death with COVID-19.The prediction model constructed based on the influencing factors achieved an area under the curve of 0.889 in the test set.Conclusions Smoking,stroke,malignant tumors,coronary heart disease,fever,breathing difficulty or asthma symptoms,and vomiting were identified as key factors influencing the death risk in COVID-19.
Humans ; COVID-19/mortality* ; Aged ; Propensity Score ; China/epidemiology* ; Risk Factors ; Logistic Models ; Smoking ; SARS-CoV-2 ; Male ; Female ; Stroke ; Neoplasms