Objective:To evaluate the clinical efficacy of Tianma Xionglin Zhixuan Tablets in treating hypertension patients with liver yang hyperactivity or comorbid phlegm-stasis syndrome and explore its therapeutic mechanisms through plasma metabolomics.Methods:Thirty-six hypertension patients(4 dropouts)diagnosed with liver yang hyperactivity or phlegm-stasis syndrome were enrolled as the treatment group from June 2022 to September 2023 at the First Affiliated Hospital of Hunan University of Chinese Medicine,while 30 healthy volunteers with balanced constitutions were recruited as the blank group.Plasma samples were collected from patients pre-and post-treatment and from healthy volunteers.Clinical outcomes,including syndrome scores,office blood pressure(BP),and 24-hour ambulatory BP,were recorded.Plasma metabolomic profiling was performed using liquid chromatography-mass spectrometry(LC-MS).Results:Compared with baseline,Tianma Xionglin Zhixuan Tablets significantly reduced traditional Chinese medicine syndrome scores(P＜0.01),office systolic/diastolic BP(P＜0.01),and 24-hour ambulatory BP parameters(24-hour mean BP,daytime/nighttime mean BP;all P＜0.01).Metabolomic analysis identified 45 differential metabolites between the blank group and pretreatment patients,and 64 metabolites altered post-treatment(VIP＞1,P＜0.05).Enrichment analysis of 16 overlapping endogenous metabolites revealed that Tianma Xionglin Zhixuan Tablets primarily modulated arachidonic acid metabolism and sphingolipid metabolism pathways.Conclusion:Tianma Xionglin Zhixuan Tablets demonstrates significant clinical efficacy in hypertension patients with liver yang hyperactivity or phlegm-stasis syndrome,potentially mediated through regulation of arachidonic acid and sphingolipid metabolism.

Objective:To evaluate the clinical efficacy of Tianma Xionglin Zhixuan Tablets in treating hypertension patients with liver yang hyperactivity or comorbid phlegm-stasis syndrome and explore its therapeutic mechanisms through plasma metabolomics.Methods:Thirty-six hypertension patients(4 dropouts)diagnosed with liver yang hyperactivity or phlegm-stasis syndrome were enrolled as the treatment group from June 2022 to September 2023 at the First Affiliated Hospital of Hunan University of Chinese Medicine,while 30 healthy volunteers with balanced constitutions were recruited as the blank group.Plasma samples were collected from patients pre-and post-treatment and from healthy volunteers.Clinical outcomes,including syndrome scores,office blood pressure(BP),and 24-hour ambulatory BP,were recorded.Plasma metabolomic profiling was performed using liquid chromatography-mass spectrometry(LC-MS).Results:Compared with baseline,Tianma Xionglin Zhixuan Tablets significantly reduced traditional Chinese medicine syndrome scores(P＜0.01),office systolic/diastolic BP(P＜0.01),and 24-hour ambulatory BP parameters(24-hour mean BP,daytime/nighttime mean BP;all P＜0.01).Metabolomic analysis identified 45 differential metabolites between the blank group and pretreatment patients,and 64 metabolites altered post-treatment(VIP＞1,P＜0.05).Enrichment analysis of 16 overlapping endogenous metabolites revealed that Tianma Xionglin Zhixuan Tablets primarily modulated arachidonic acid metabolism and sphingolipid metabolism pathways.Conclusion:Tianma Xionglin Zhixuan Tablets demonstrates significant clinical efficacy in hypertension patients with liver yang hyperactivity or phlegm-stasis syndrome,potentially mediated through regulation of arachidonic acid and sphingolipid metabolism.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

OBJECTIVES: To investigate the predictive factors for plastic bronchitis (PB) in children with Mycoplasma pneumoniae pneumonia (MPP) and to establish a nomogram prediction model for PB occurrence. METHODS: A retrospective analysis was conducted on children with MPP hospitalized at The Affiliated Hospital of Xuzhou Medical University from January 2023 to June 2024. The patients were randomly divided into a training set (n=562) and a validation set (n=240) at a ratio of 7:3 using simple random sampling. In the training set, patients were categorized into a PB group (n=70) and a non-PB group (n=492) based on the occurrence of PB. Spearman correlation analysis was performed to exclude collinearity among variables, followed by univariate analysis and LASSO regression to identify predictive factors. A nomogram prediction model for PB in children with MPP was constructed. The discriminative ability of the model was assessed using receiver operating characteristic (ROC) curve analysis, model calibration was evaluated with calibration curves, and clinical utility was appraised through decision curve analysis. RESULTS: Compared with the non-PB group, the PB group exhibited significantly longer disease duration prior to bronchoscopy, prolonged fever duration, higher fever peaks, higher proportions of patients with a family history of allergy and personal allergy history, and a higher proportion of patients with pleural effusion, as well as significantly elevated levels of white blood cell count, neutrophil percentage, C-reactive protein, procalcitonin, fibrinogen, D-dimer, aspartate aminotransferase, alanine aminotransferase, creatine kinase, lactate dehydrogenase, immunoglobulin A, and interleukin-6, along with a significantly lower lymphocyte percentage (all P<0.05). LASSO regression analysis identified pleural effusion, procalcitonin, D-dimer, and lactate dehydrogenase as major predictive factors for PB occurrence in children with MPP. The nomogram model based on these factors demonstrated good discriminative ability (area under the ROC curve: 0.852 in the training set and 0.830 in the validation set), with satisfactory calibration and clinical benefit. CONCLUSIONS The nomogram prediction model based on pleural effusion, procalcitonin, D-dimer, and lactate dehydrogenase provides effective predictive performance for the occurrence of PB in children with MPP.
Humans ; Pneumonia, Mycoplasma/complications* ; Nomograms ; Male ; Female ; Child ; Child, Preschool ; Retrospective Studies ; Bronchitis/etiology* ; Infant ; ROC Curve ; Adolescent

Transarterial radioembolization (TARE) is a widely utilized therapeutic approach for hepatocellular carcinoma (HCC), however, the clinical implementation is constrained by the stringent preparation conditions of radioembolization agents. Herein, we incorporated the superstable homogeneous iodinated formulation technology (SHIFT), simultaneously utilizing an enhanced solvent form in a carbon dioxide supercritical fluid environment, to encapsulate radionuclides (such as ¹³¹I,¹⁷⁷Lu, or ¹⁸F) with lipiodol for the preparation of radiolipiodol. The resulting radiolipiodol exhibited exceptional stability and ultra-high labeling efficiency (≥99%) and displayed notable intratumoral radionuclide retention and in vivo stability more than 2 weeks following locoregional injection in subcutaneous tumors in mice and orthotopic liver tumors in rats and rabbits. Given these encouraging findings, ¹⁸F was authorized as a radiotracer in radiolipiodol for clinical trials in HCC patients, and showed a favorable tumor accumulation, with a tumor-to-liver uptake ratio of ≥50 and minimal radionuclide leakage, confirming the feasibility of SHIFT for TARE applications. In the context of transforming from preclinical to clinical screening, the preparation of radiolipiodol by SHIFT represents an innovative physical strategy for radionuclide encapsulation. Hence, this work offers a reliable and efficient approach for TARE in HCC, showing considerable promise for clinical application (ChiCTR2400087731).

Objective:To systematically evaluate the clinical efficacy and safety of Tonifying kidney and activating blood thera-py for the treatment of diabetic mellitus erectile dysfunction.Methods:China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP,Chinese Biomedical Database(CBM),PubMed,Cochrane Library,Embase and Web of Science were searched from incep-tion until October 20th of 2024,for randomized controlled trials of Tonifying kidney and activating blood therapy for the treatment of dia-betic erectile dysfunction.Literature screening,quality evaluation,and data extraction were carried out in accordance with relevant standards.The software of RevMan5.4 was used for the analysis of publication bias.And meta-analysis was conducted to assess the im-pact of this therapy on IIEF-5,total effective rate,adverse reactions.The evidence levels according to the analysis results were evalua-ted.Results:Totally 19 RCTs were included,involving 1 612 patients.The result of meta-analysis indicated that Tonifying kidney and activating blood therapy had advantages on the improvement of IIEF-5 scores(MD=3.59,95％CI[2.14,5.03],P＜0.01),total effective rate(OR=4.30,95％CI[3.29,5.32],P＜0.000 01).However,there was no statistically significant difference in the inci-dence of adverse reactions(OR=0.98,95％CI[0.48,2.01],P=0.96)between the two groups.Conclusions:Tonifying kidney and activating blood therapy can improve the clinical efficacy and IIEF-5 score for the patients with diabetic erectile dysfunction.But considering the limited quantity of included studies,more high-quality studies still be needed to validate the therapeutic effect.

Objective To explore the value of droplet digital polymerase chain reaction(ddPCR)in the etiological diagnosis of severe acute pancreatitis(SAP)patients with suspected bloodstream infection(BSI).Methods SAP patients admitted to the department of critical care medicine in a hospital July to September 2022 were enrolled.When BSI was suspected,venous blood was collected for both ddPCR detection and blood culture(BC)with antimi-crobial susceptibility testing(AST)simultaneously.The time required for two detection methods was recorded,and the detection results of ddPCR and BC were compared.The etiological diagnostic efficacy of ddPCR was calculated,and the correlation between the value of pathogen load detected by ddPCR and the level of infection parameters was explored.Results A total of 22 patients were included in the analysis,and 52 venous blood specimens were collec-ted for detection.BC revealed 17 positive specimens(32.7％)and 29 pathogenic strains,while ddPCR showed 41 positive specimens(78.8％)and 73 pathogenic strains.Detection time required for ddPCR was significantly lower than that of BC([0.16±0.03]days vs[5.92±1.20]days,P＜0.001).Within the detection range of ddPCR and taking BC results as the gold standard,the sensitivity and specificity of ddPCR were 80.0％and 28.6％,respective-ly.With the combined assessment of BSI based on non-blood specimen microbial evidence within a week,the sensi-tivity and specificity of ddPCR detection increased to 91.9％and 76.9％,respectively.ddPCR detected resistance genes of blaKPC,blaNDM/IMP,VanA/VanM,and mecA from 19,9,6,and 5 specimens,respectively.Correlation analysis showed a positive correlation between pathogen load and levels of C-reactive protein as well as procalcitonin(r=0.347,0.414,P＜0.05).Conclusion As a supplementary detection method for BC in BSI diagnosis,ddPCR has the advantages of higher sensitivity and shorter detection time,and is worthy of further exploration in clinical application.

In the study of age estimation in living individuals,a lot of data needs to be analyzed by mathematical statistics,and reasonable medical statistical methods play an important role in data design and analysis.The selection of accurate and appropriate statistical methods is one of the key factors af-fecting the quality of research results.This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics,difference analysis,consistency test and multivariate statistical analysis,as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals,and summarizes the relevance and application prospects between medical statistical methods and machine learning methods.This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Most patients with differentiated thyroid cancer have a good prognosis after radioiodine-131 therapy,but a small number of patients are insensitive to radioiodine-131 therapy and even continue to develop disease.At present,some targeted drugs can improve progression-free survival in patients with radioactive iodine-refractory differentiated thyroid cancer(RAIR-DTC),such as sorafenib and levatinib,have been approved for the treatment of RAIR-DTC.However,due to the presence of primary and acquired drug resistance,drug efficacy in these patients is unsatisfactory.This review introduces the acquired drug resistance mechanism of sorafenib in the regulation of mitogen-activated protein kinase(MAPK)and phosphatidylinositol-3-kinase(PI3K)pathways and proposes related treatment strategies,in order to provide a reference for similar drug resistance mechanism of sorafenib and effective treatment of RAIR-DTC.