Objective To investigate the association between dietary patterns and adolescent development in girls in Pudong New Area，Shanghai. Methods Using a stratified cluster sampling method, a total of 998 girls were selected from the cohort of growth, development, and health of children and adolescents in Pudong New Area of Shanghai. Physical development examinations and dietary surveys were conducted on these girls. Principal component analysis was used to determine girls' dietary patterns, and their relationship with adolescent development was analyzed using logistic regression. Results Among the 998 participants, 770 (77.15%) were pubertal girls. The factor analysis identified three dietary patterns, fungi and algae~nuts~legumes pattern, meat-vegetables and fruits-cereals pattern, and milk-eggs pattern. Logistic regression analysis revealed that after adjusting for age, waist circumference, residential district, mother's education level, and father's education level, the Q2 level (OR=0.54, 95% CI: 0.30-0.99), Q3 level (OR=0.50, 95% CI: 0.27-0.91), and Q4 level (OR=0.49, 95% CI: 0.26-0.90) of the fungi and algae-nuts-legumes pattern were negatively correlated with adolescent development. The Q2 level (OR=0.48, 95% CI: 0.27-0.87) and Q3 level (OR=0.49, 95% CI: 0.27-0.88) of the milk-eggs pattern were also negatively correlated with adolescent development. Conclusion The fungi and algae~nuts~legumes pattern and milk-eggs patterns are inversely related to pubertal development.

Objective To investigate the role of the neutrophil-to-lymphocyte ratio(NLR)in predicting the in-hospital mortality risk of patients with acute aortic dissection(AAD)in multicenter hospitals.Methods A multicenter retrospective cohort study was conducted.Clinical data were collected from 2642 AAD patients who were hospitalized in five teaching hospitals:Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology,Henan Provincial People's Hospital,Fuwai Central China Cardiovascular Hospital,the Third Affiliated Hospital of Xinxiang Medical University,and the Second Affiliated Hospital of Chongqing Medical University between August 2010 and December 2021.According to the quartiles of serum NLRlevels,the patients were divided into four groups:first quartile(Q1,n=660),second quartile(Q2,n=661),third quartile(Q3,n=661),and fourth quartile(Q4,n=660).The clinical characteristics and biochemical indicators of each group were compared.Partial correlation analysis was used to assess the relationship between NLR and cardiovascular parameters.Restricted cubic splines,Kaplan-Meier survival analysis,and Cox regression models were employed to evaluate the association between NLR levels and in-hospital mortality risk in AAD patients.Results The median age of all patients was 54[interquartile range(IQR):46-63]years,including 2096 males and 546 females.Compared with Q1-Q3 groups,patients inQ4group had a lower incidence of smoking history and diabetes history,and were more likely to have DeBakey type Ⅰ AAD(P＜0.05).Additionally,the levels of aspartate aminotransferase,high-density lipoprotein cholesterol,creatinine,and D-dimer in Q4 group were higher,while the levels of triglycerides and C-reactive protein(CRP)were lower(P＜0.01).The results of partial correlation analysis showed that the plasma NLR level was positively correlated with D-dimer(r=0.43,P＜0.01)and creatinine(r=0.16,P＜0.01).The restricted cubic spline function in the Cox model revealed a significant non-linear relationship between the plasma NLR level and clinical outcomes in AAD patients(P＜0.01).Kaplan-Meier survival analysis indicated that patients in Q4 group had the highest in-hospital mortality rate compared with Q1-Q3 groups(P＜0.0001).Furthermore,multivariate Cox regression analysis demonstrated that compared with Q1 group,the hazard ratio(HR)of NLR in Q4 group was 1.77(95％CI 1.33-2.37,P＜0.001),which was an independent risk factor for the primary endpoint events.Conclusion A higher plasma NLR level is significantly associated with the occurrence of cardiovascular events in AAD patients,and this association remains significant even after adjusting for potential confounding factors such as the multicenter visiting hospitals.

Huntington's disease (HD) is an inherited neurodegenerative disorder caused by the abnormal expansion of CAG trinucleotide repeats in the Huntingtin gene (HTT) located on chromosome 4. It is transmitted in an autosomal dominant manner and is characterized by motor dysfunction, cognitive decline, and emotional disturbances. To date, there are no curative treatments for HD have been developed; current therapeutic approaches focus on symptom relief and comprehensive care through coordinated pharmacological and nonpharmacological methods to manage the diverse phenotypes of the disease. International clinical guidelines for the treatment of HD are continually being revised in an effort to enhance care within a multidisciplinary framework. Additionally, innovative gene and cell therapy strategies are being actively researched and developed to address the complexities of the disorder and improve treatment outcomes. This review endeavours to elucidate the current and emerging gene and cell therapy strategies for HD, offering a detailed insight into the complexities of the disorder and looking forward to future treatment paradigms. Considering the complexity of the underlying mechanisms driving HD, a synergistic treatment strategy that integrates various factors-such as distinct cell types, epigenetic patterns, genetic components, and methods to improve the cerebral microenvironment-may significantly enhance therapeutic outcomes. In the future, we eagerly anticipate ongoing innovations in interdisciplinary research that will bring profound advancements and refinements in the treatment of HD.
Huntington Disease/pathology* ; Humans ; Genetic Therapy/methods* ; Animals ; Huntingtin Protein/genetics* ; Cell- and Tissue-Based Therapy/methods*

Pyroptosis is an identified programmed cell death that has been highly linked to endoplasmic reticulum (ER) dynamics. However, the crucial proteins for modulating dynamic ER membrane curvature change that trigger pyroptosis are currently not well understood. In this study, a biotin-labeled chemical probe of potent pyroptosis inducer α-mangostin (α-MG) was synthesized. Through protein microarray analysis, reticulon-4 (RTN4/Nogo), a crucial regulator of ER membrane curvature, was identified as a target of α-MG. We observed that chemically induced proteasome degradation of RTN4 by α-MG through recruiting E3 ligase UBR5 significantly enhances the pyroptosis phenotype in cancer cells. Interestingly, the downregulation of RTN4 expression significantly facilitated a dynamic remodeling of ER membrane curvature through a transition from tubules to sheets, consequently leading to rapid fusion of the ER with the cell plasma membrane. In particular, the ER-to-plasma membrane fusion process is supported by the observed translocation of several crucial ER markers to the "bubble" structures of pyroptotic cells. Furthermore, α-MG-induced RTN4 knockdown leads to pyruvate kinase M2 (PKM2)-dependent conventional caspase-3/gasdermin E (GSDME) cleavages for pyroptosis progression. In vivo, we observed that chemical or genetic RTN4 knockdown significantly inhibited cancer cells growth, which further exhibited an antitumor immune response with anti-programmed death-1 (anti-PD-1). In translational research, RTN4 high expression was closely correlated with the tumor metastasis and death of patients. Taken together, RTN4 plays a fundamental role in inducing pyroptosis through the modulation of ER membrane curvature remodeling, thus representing a prospective druggable target for anticancer immunotherapy.
Pyroptosis/immunology* ; Humans ; Endoplasmic Reticulum/immunology* ; Animals ; Nogo Proteins/antagonists & inhibitors* ; Mice ; Cell Line, Tumor ; Xanthones/pharmacology* ; Neoplasms/pathology* ; Mice, Nude

Image 1.

Objective Melatonin has been shown to have neuroprotective effects.This study is aimed at observing the effects of copper deposition on cognitive function in a toxic milk(TX)mouse model of Wilson disease(WD),and investigating the effects and mechanisms of action of Gandou Bushen Decoction(GDBSD)on melatonin synthesis and pineal function in the WD model mice.Methods A total of 30 homozygous TX mice were randomly assigned to 3 groups(n=10 in each group),including a WD group,a GDBSD group,and a dimercaptosuccinic acid(DMSA)group.A total of 10 DL mice were included in the normal control(NC)group.The structure and copper content of pineal gland tissues,oxidative stress and apoptosis-related markers,and serum melatonin levels were evaluated using hematoxylin-eosin(HE)staining,enzyme-linked immunosorbent assay(ELISA),flow cytometry,and Western blot.Results Compared with the NC group,the WD group exhibited decreased learning and cognitive abilities(P＜0.05),damaged pineal gland structure,increased copper content,reactive oxygen species(ROS)levels,and mitochondrial damage rate in the pineal gland(P＜0.01),altered levels of melatonin and oxidative stress-related markers(P＜0.05),upregulated expression levels of pro-apoptotic proteins Bax and Caspase-3,and decreased expression of the anti-apoptotic protein Bcl-2(P＜0.01).After treatment with GDBSD and DMSA,the SIRT3/FOXO3α signaling pathway was activated,the copper content in the pineal gland was reduced,and oxidative stress and apoptosis-related damages were improved,leading to an improvement in learning and memory abilities(P＜0.05).Conclusion GDBSD can alleviate cognitive impairments in WD mice caused by pineal gland copper deposition by inhibiting oxidative stress and apoptosis in the pineal gland.The underlying molecular mechanism is associated with the regulation of the SIRT3/FOXO3α signaling pathway.

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

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.