Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

OBJECTIVE To analyze the microbial community structure in Bronchoalveolar Lavage Fluid(BALF)u-sing high-throughput sequencing technology,with a particular focus on the abundance and distribution of Staphy-lococcus aureus.METHODS A total of 1,650 bronchoalveolar lavage fluid(BALF)samples were collected from the First People's Hospital of Yunnan Province between 2022 and 2023.S.aureus gene detection was performed using BALF culture,isothermal amplification and microfluidic technology.Representative 21 S.aureus-positive samples were grouped into S.aureus pneumonia group,SARS-CoV-2 co-infection group and chronic obstructive pulmona-ry disease(COPD)group,followed by 16S rDNA amplicon high-throughput sequencing.RESULTS The microbial community compositions among the three groups exhibited significant differences.Species-level hierarchical cluste-ring analysis revealed that S.aureus was more frequently detected and accounted for a higher proportion in the S.aureus pneumonia group,while the COPD group was dominated by Achromobacter.In contrast,the SARS-CoV-2 co-infection group was mainly composed of Methylobacterium aquatique and Achromobacter.Only in the S.au-reus pneumonia group was S.aureus the dominant bacterium.CONCLUSION The composition of the BALF micro-biota is closely related to different disease states,providing an important reference for a deeper understanding of the microbiological basis of pulmonary infectious diseases.

Carbon nanomaterials have unique physicochemical properties and great application potential, and great potential for medical applications, often in the form of biosensor systems. The development of novel carbon nanomaterials such as carbon nanotubes and graphene, has led to the emergence of new nanobiosensors capable of performing functions such as minimally invasive surgery, disease monitoring, tumor detection and drug delivery. In this review, the application progress of biosensors based on carbon nanomaterials was mainly introduced from the aspects of minimally invasive surgery, disease monitoring, tumor detection and drug delivery. It is of great significance to develop electronic devices or portable detection devices based on carbon nanomaterials for use in the biomedical field.

Objective To investigate the therapeutic effects and mechanisms of Alisma orientale extract(zexie)on gestational diabetes mellitus(GDM)rats by regulating the miR-4417/TGF-β pathway.Methods GDM model rats were randomly divided into a diabetic group,low,medium,high-dose zexie group,insulin group,miR-NC group,miR-4417 mimic group,miR-4417 inhibitor group,and zexie+miR-4417 mimic+LY364947 group,with 12 rats in each group.Additionally,12 normal pregnant rats served as the control group.Hematoxylin and eosin(HE)staining was used to evaluate pancreatic islet pathology.RT-qPCR and Western blot were employed to measure the mRNA and protein levels of various factors.Results Compared with the diabetic group,the glucose,lipid,oxida-tive stress indicators,and pancreatic islet pathological damage were significantly improved(P＜0.05)in the low,medium and high-dose zexie groups and the insulin group.The expression levels of miR-4417 and TGF-β mRNA were notably reduced(P＜0.05),with the most significant changes observed in the high-dose zexie group(P＜0.05).Compared with the miR-NC group,the TGF-β protein level was significantly increased(P＜0.05)in the miR-4417 mimic group and decreased(P＜0.05)in the miR-4417 inhibitor group.Compared with the diabetic group,the oxidative stress and reactive oxygen species(ROS)accumulation were significantly lower(P＜0.05)in the zexie,zexie+miR-4417 mimic,and zexie+miR-4417 mimic+LY364947 groups.Compared with the miR-4417 mimic group,the oxidative stress and ROS accumulation were significantly reduced(P＜0.05)in the zexie+miR-4417 mimic group.No significant differences were found between the zexie group and the zexie+miR-4417 mimic+LY364947 group.Conclusions Zexie can improve glucose and lipid levels,reduce insulin resistance,and control oxidative stress and damage in GDM rats,possibly through the regulation of the miR-4417/TGF-β pathway.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

OBJECTIVE To analyze the microbial community structure in Bronchoalveolar Lavage Fluid(BALF)u-sing high-throughput sequencing technology,with a particular focus on the abundance and distribution of Staphy-lococcus aureus.METHODS A total of 1,650 bronchoalveolar lavage fluid(BALF)samples were collected from the First People's Hospital of Yunnan Province between 2022 and 2023.S.aureus gene detection was performed using BALF culture,isothermal amplification and microfluidic technology.Representative 21 S.aureus-positive samples were grouped into S.aureus pneumonia group,SARS-CoV-2 co-infection group and chronic obstructive pulmona-ry disease(COPD)group,followed by 16S rDNA amplicon high-throughput sequencing.RESULTS The microbial community compositions among the three groups exhibited significant differences.Species-level hierarchical cluste-ring analysis revealed that S.aureus was more frequently detected and accounted for a higher proportion in the S.aureus pneumonia group,while the COPD group was dominated by Achromobacter.In contrast,the SARS-CoV-2 co-infection group was mainly composed of Methylobacterium aquatique and Achromobacter.Only in the S.au-reus pneumonia group was S.aureus the dominant bacterium.CONCLUSION The composition of the BALF micro-biota is closely related to different disease states,providing an important reference for a deeper understanding of the microbiological basis of pulmonary infectious diseases.

Objective:To explore the correlation of bone marrow polychonal plasma cell proportion (pPC% ) and clinical features in newly diagnosed multiple myeloma (NDMM) patients.Methods:A retrospective analysis of 317 patients with NDMM admitted to Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from January 2018 to January 2023 was performed. The results of the pPC% in all patients were clear. The relationship between the pPC% and clinical characteristics was analyzed.Results:A total of 317 patients were included, comprising 180 males and 137 females. The median age at diagnosis was 61 (26-91) years, and 55.8% were 60 years or older. The pPC% in the bone marrow of patients with NDMM was different in the DS, International Staging System (ISS), and revised ISS (R-ISS) stages ( P=0.002, 0.010, and 0.049, respectively), whereas no statistical difference in pPC% was observed among patients with different FISH risk stratigrams ( P=0.971). The correlation coefficient between pPC% and hemoglobin (HGB) at the first diagnosis in patients was 0.211 ( P<0.01). The correlation coefficients with serum calcium, serum creatinine, M protein level, and β 2-microglobulin were -0.141, -0.120, -0.181, and -0.207, respectively, and the results of the significance test were P=0.012, 0.033, 0.004, and 0.002, respectively, indicating a negative correlation. Compared with the patients with a pPC% of ≥2.5%, the group of patients with a pPC% of <2.5% had significantly higher levels of light chain, serum calcium, serum creatinine, M protein, and β 2-microglobulin at the initial diagnosis ( P<0.05) ; lower HGB level ( P<0.001) ; and a higher proportion of patients in ISS stage Ⅲ ( P=0.034) . Conclusion:In this study, the pPC% in patients with NDMM was associated with clinical features of good prognosis, including higher HGB, lower serum calcium, serum creatinine, M protein quantity, β 2-microglobulin, light chain involvement, lower proportion of advanced disease (DS stage and ISS stage Ⅲ), and clinical features showing lower tumor burden.

Objective To analyze the correlation between air pollutants and pediatric atopic dermatitis outpatient visits in Lanzhou,and provide scientific insights for the life guidance of the affected children and dis-ease prevention by relevant departments.Methods A generalized additive model was employed to analyze the effects and lagged effects of air pollutants on pediatric atopic dermatitis outpatient visits in Lanzhou while control-ling for confounding factors such as long-term trends,holiday effects,day of the week effects and meteorological factors.Results The effects of NO2,PM2.5,PM10,and SO2 on pediatric atopic dermatitis outpatient visits were most significant on the current day(Lag0),but were not statistically significant(all P＞0.05);CO also had the most significant effect on Lag0,and for every 10 μg/m3 increase in its concentration,the excess risk(ER)for pediatric atopic dermatitis outpatient visits was 0.05％(95％CI=0-0.10％,P=0.049);and O3 exhibi-ted the most significant effect on day 7 of the cumulative lag(Lag07),with a statistically significant increase in the ER for each 10 μg/m3 increase in its concentration of 7.40％(95％CI=5.31％-9.53％,P＜0.001)for pediatric atopic dermatitis outpatient visits.Age stratification showed that children aged 0-3 years with atopic dermatitis were the most sensitive to CO,with an increased ER of 0.09％(95％CI=0.04％-0.15％,P＜0.001)for every 10 μg/m3 increase in concentration,and children aged 7-14 years with atopic dermatitis were the most sensitive to O3,with an increased ER of 8.26％(95％CI=4.99％-11.64％,P＜0.001)for every 10 μg/m3 increase in concentration.Seasonal stratification showed that CO exerted a stronger effect on pediatric atopic dermatitis outpatient visits in summer and fall,with ER values of 0.45％and 0.16％(both P＜0.001),respectively,while O3 had a significant effect on outpatient visits in winter,with an ER value of 20.48％(P＜0.001).Conclusion Elevated daily average concentrations of air pollutants CO and O3 in Lanzhou were posi-tively correlated with the number of outpatient visits for atopic dermatitis in children,with significant seasonal effects and age-stratified sensitivities.

Background::Limited information exists regarding the impact of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection on psoriasis patients. The objective of this study was to identify clinical factors associated with the prognosis of psoriasis following SARS-CoV-2 infection.Methods::A retrospective, multicenter study was conducted between March and May 2023. Univariable and multivariable logistic regression analyses were employed to identify factors associated with coronavirus disease 2019 (COVID-19)-related psoriasis outcomes. The study included 2371 psoriasis patients from 12 clinical centers, with 2049 of them having been infected with SARS-CoV-2.Results::Among the infected groups, lower exacerbation rates were observed in individuals treated with biologics compared to those receiving traditional systemic or nonsystemic treatments (22.3% [236/1058] vs. 39.8% [92/231] vs. 37.5% [140/373], P <0.001). Psoriasis progression with lesions (adjusted odds ratio [OR] = 8.197, 95% confidence interval [95% CI] = 5.685–11.820, compared to no lesions), hypertension (adjusted OR = 1.582, 95% CI = 1.068–2.343), traditional systemic (adjusted OR = 1.887, 95% CI= 1.263–2.818), and nonsystemic treatment (adjusted OR= 1.602, 95% CI= 1.117–2.297) were found to be associated with exacerbation of psoriasis after SARS-CoV-2 infection, but not biologics (adjusted OR = 0.931, 95% CI = 0.680–1.274, compared to no treatment), according to multivariable logistic regression analysis. Conclusions::A reduced risk of psoriasis exacerbation after SARS-CoV-2 infection was observed with biologics compared to traditional systemic and nonsystemic treatments. Significant risk factors for exacerbation after infection were identified as existing psoriatic lesions and hypertension.

OBJECTIVE: To explore the clinical phenotype and genetic features of a child with dilated cardiomyopathy (DCM). METHODS: Clinical data of the child who had presented at the Zhengzhou Children's Hospital on April 28, 2020 was collected. Trio-whole exome sequencing (trio-WES) was carried out for the child and her parents, and candidate variants were validated by Sanger sequencing. "FHL2" was taken as the key word to retrieve related literature from January 1, 1997 to October 31, 2021 in the PubMed database and was also searched in the ClinVar database as a supplement to analyze the correlation between genetic variants and clinical features. RESULTS: The patient was a 5-month-old female infant presented with left ventricular enlargement and reduced systolic function. A heterozygous missense variant c.391C>T (p.Arg131Cys) in FHL2 gene was identified through trio-WES. The same variant was not detected in either of her parents. A total of 10 patients with FHL2 gene variants have been reported in the literature, 6 of them had presented with DCM, 2 with hypertrophic cardiomyopathy (HCM), and 2 with sudden unexplained death (SUD). Phenotypic analysis revealed that patients with variants in the LIM 3 domain presented hypertrophic cardiomyopathy and those with variants of the LIM 0~2 and LIM 4 domains had mainly presented DCM. The c.391C>T (p.Arg131Cys) has been identified in a child with DCM, though it has not been validated among the patient's family members. Based on the guidelines of the American College of Medical Genetics and Genomics, the c.391C>T(p.Arg131Cys) variant was re-classified as likely pathogenic (PS2+PM2_Supporting+PP3+PP5). CONCLUSION The heterozygous missense variant of c.391C>T (p.Arg131Cys) in the FHL2 gene probably predisposed to the DCM in this child, which has highlighted the importance of WES in the clinical diagnosis and genetic counseling.
Female ; Humans ; Cardiomyopathy, Dilated/genetics* ; Cardiomyopathy, Hypertrophic ; Genetic Counseling ; Genomics ; Heterozygote ; Muscle Proteins/genetics* ; Transcription Factors ; LIM-Homeodomain Proteins/genetics*