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.

Aim To investigate the effect of high-fat diet on the tight junction function injury of Sertoli cells through the NF-κB/NLRP3 signaling pathway in mice and to explore the underlying mechanism.Methods Male C57BL/6J mice were fed with high-fat or normal diet for five months.The body and gonadal organ weight of mice were measured,and their indices were calculated.The sperm concentration,the sperm viabili-ty,the testicular histomorphology and the expression levels of tight junction proteins ZO-1,Occludin and Claudin-11 were measured.TM4 cells were treated with palmitic acid(PA)for 24 h.Cell viability was detected by CCK-8 method.Then,TM4 cells were di-vided into different groups treated with PA(0,50,100,200 and 300 μmnol·L-1),and the expression lev-els of tight junction proteins ZO-1,Occludin and Clau-din-11 were detected by Western blot.The tight junc-tion permeability of TM4 cells were detected by transepithelial electrical resistance(TEER)and FITC-dextran.The expression levels of mRNA and proteins for the NF-κB/NLRP3 pathway-related factors were de-tected by RT-qPCR and Western blot.Results The results from animal experiments showed that high-fat diet increased body weight and seminal vesicle weight of mice,and decreased testicular index,epididymal in-dex,sperm concentration and sperm motility of mice.High-fat diet also caused testicular tissue structure damage and down-regulated the expression levels of tight junction proteins ZO-1 and Occludin,without af-fecting the expression of Claudin-11.In vitro,PA sig-nificantly down-regulated the expression levels of ZO-1,Occludin and Claudin-11 in TM4 cells,increased the cell permeability,as well as up-regulated the mRNA and protein expression levels of NLRP3/NF-κB signa-ling pathway-related factors in TM4 cells.Conclusions High-fat diet can impair the function of tight junction of testicualr Sertoli cells,and the machanism may be related to the activation of the NF-κB/NLRP3 signaling pathway,resulting in Sertoli cell inflammation in mice.

Aim To investigate the effects of Agrimonia pilosa(AP)on the proliferation and apoptosis of non-small cell lung cancer(NSCLC)H1299 cells using non-targeted metabolomics and other methods,and to explore the underlying molecular mechanisms.Meth-ods Taking H1299 cells as the research object,the effect of AP on cell proliferation and apoptosis was de-tected through CCK-8 method,colony formation,LDH,Hoechst 33258 staining,AO/EB staining,flow cytometry detection,RT qPCR and other experiments.The main differential metabolites were detected by the metabolomics method of ultra-high phase liquid chro-matography and mass spectrometry(UHPLC-Q-Orbi-trap MS),and related metabolic pathways were ana-lyzed.Results Compared with the control group,AP treatment was able to significantly inhibit the prolifera-tion and colony formation of H1299 cells,while the re-lease of LDH increased in a dose-dependent manner.Fluorescence microscopy and flow cytometry and RT-qPCR analysis revealed that H1299 cells underwent crumpling and increased nuclear fragmentation after AP administration,blocked in G0/G1 phase,up-regulated apoptotic genes caspase-3 and Bax,and down-regulated apoptosis-inducing effects of Bcl-2.Metabolomics anal-ysis screened 35 differential metabolites,which were PC(O-30∶1),D-Glutamic acid,PE(18∶0/15∶0),etc.The main metabolic pathways involved includ-ed amino acid metabolism,glycerophospholipid metabo-lism and purine metabolism so on.Conclusions AP may exert its pharmacological effects by interfering with multiple metabolic pathways in H1299 cells,inhibiting cell proliferation and promoting apoptosis.

Cyclic GMP-AMP synthase(cGAS)is a congenital immune sensor that can recognize cytoplasm abnormal dsDNA.By catalyzing the second messenger cyclic GMP-AMP(cGAMP)formation,it activates stimulator of interferon genes(STING),releases type Ⅰ interferon and inflammatory cytokines,activates the host immune response,and participates in cerebral ischemia reperfusion injury(CIRI)cascade reaction.This article reviews the research progress of the mechanism of cGAS-STING signaling pathway participation in CIRI,hoping to provide ideas for its treatment.

Aim To investigate the interventional effects of polysaccharides from Dicliptera chinensis(L.)Juss.(DCP)on acute liver failure(ALF)in-duced by lipopolysaccharide(LPS)combined with D-galactosamine(D-GalN)in mice,and on LPS-induced inflammatory responses in RAW264.7 cells,based on the TLR-4/MyD88/NF-κB signaling pathway.Meth-ods Mice were randomly divided into the control,model,silymarin,DCP low,medium,and high dose groups,and toxicity test groups.After 10 consecutive days of treatment,ALF models were established by in-jecting mice with LPS+D-GalN.Additionally,an in-flammatory response model was established by stimula-ting RAW264.7 cells with LPS.Results Biochemical assays showed that compared with the model group,the medium-and high-dose DCP groups exhibited de-creased serum ALT,AST,ALP,TBIL,and γ-GT activi-ties(P＜0.05),reduced levels of ROS,MPO and MDA in liver(P＜0.05),increased activities of SOD,GSH-Px,CAT,and elevated T-AOC levels(P＜0.05).ELISA revealed lower levels of ICAM-1,VCAM-1,IL-6,IL-1β,and TNF-α in liver(P＜0.05).HE staining indicated reduced inflammatory cell infiltration and improved hepatocyte necrosis in liv-er after DCP administration.The use of DCP alone showed no significant organ toxicity.qRT-PCR and Western blot results indicated that DCP inhibited the expression of key factors in TLR-4/MyD88/NF-κB sig-naling pathway(P＜0.05).Cell validation experi-ments also confirmed that this pathway was inhibited by DCP.Conclusion DCP alleviates ALF primarily by inhibiting oxidative stress and blocking the activation of the TLR-4/MyD88/NF-κB signaling pathway.

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: We aimed to investigate the patterns of fasting blood glucose (FBG) trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers. METHODS: The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort (TGOC) between 2017 and 2022. A group-based trajectory model was used to identify the FBG trajectories. Environmental risk scores (ERS) were constructed using regression coefficients from the occupational hazard model as weights. Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories. RESULTS: FBG trajectories were categorized into three groups. An association was observed between high temperature, noise exposure, and FBG trajectory ( P < 0.05). Using the first quartile group of ERS1 as a reference, the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90 and 2.21 times, respectively (odds ratio [ OR] = 1.90, 95% confidence interval [ CI]: 1.17-3.10; OR = 2.21, 95% CI: 1.09-4.45). CONCLUSION An association was observed between occupational hazards based on ERS and FBG trajectories. The risk of FBG trajectory levels increase with an increase in ERS.
Humans ; Male ; Adult ; Blood Glucose/analysis* ; China ; Prospective Studies ; Occupational Exposure/adverse effects* ; Risk Factors ; Middle Aged ; Steel ; Fasting/blood* ; Metal Workers ; East Asian People

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Aim To investigate the effect of high-fat diet on the tight junction function injury of Sertoli cells through the NF-κB/NLRP3 signaling pathway in mice and to explore the underlying mechanism.Methods Male C57BL/6J mice were fed with high-fat or normal diet for five months.The body and gonadal organ weight of mice were measured,and their indices were calculated.The sperm concentration,the sperm viabili-ty,the testicular histomorphology and the expression levels of tight junction proteins ZO-1,Occludin and Claudin-11 were measured.TM4 cells were treated with palmitic acid(PA)for 24 h.Cell viability was detected by CCK-8 method.Then,TM4 cells were di-vided into different groups treated with PA(0,50,100,200 and 300 μmnol·L-1),and the expression lev-els of tight junction proteins ZO-1,Occludin and Clau-din-11 were detected by Western blot.The tight junc-tion permeability of TM4 cells were detected by transepithelial electrical resistance(TEER)and FITC-dextran.The expression levels of mRNA and proteins for the NF-κB/NLRP3 pathway-related factors were de-tected by RT-qPCR and Western blot.Results The results from animal experiments showed that high-fat diet increased body weight and seminal vesicle weight of mice,and decreased testicular index,epididymal in-dex,sperm concentration and sperm motility of mice.High-fat diet also caused testicular tissue structure damage and down-regulated the expression levels of tight junction proteins ZO-1 and Occludin,without af-fecting the expression of Claudin-11.In vitro,PA sig-nificantly down-regulated the expression levels of ZO-1,Occludin and Claudin-11 in TM4 cells,increased the cell permeability,as well as up-regulated the mRNA and protein expression levels of NLRP3/NF-κB signa-ling pathway-related factors in TM4 cells.Conclusions High-fat diet can impair the function of tight junction of testicualr Sertoli cells,and the machanism may be related to the activation of the NF-κB/NLRP3 signaling pathway,resulting in Sertoli cell inflammation in mice.