Objective: To analyze the association of eating dining locations and their association with nutritional status among Chinese children aged 6-17 years,so as to provide reference for guiding children s reasonable diet. Methods: Stratified random cluster sampling was used to select children aged 6 to 17 years from 28 cities and rural areas of 14 provinces in East, North, Central, South, Southwest, Northwest, Northeast of China, and a total of 52 535 children were included in the study from 2019 to 2021. Information including dining locations, demographic characteristics, dietary intakes and physical activity were collected through a questionnaire survey. Fasting body height and weight were measured in the morning. Unordered multiclass Logistic regression analysis was conducted to assess the relationship between dining locations and nutritional status in children. Results: Regarding children s dining locations, 66.3% ate breakfast at home,25.8% ate breakfast at school,7.9% ate breakfast outside (small dining tables, restaurants, stalls, etc.); 67.7% ate dinner at home,29.0% ate dinner at school,3.3% ate dinner outside; and 63.6% ate lunch at school,30.8% ate lunch at home,5.7% ate lunch outside. The prevalence rates of overweight/obesity and undernutrition were 28.6% and 9.3%, respectively. The adjusted multiclass Logistic regression analysis (controlling for age, region, parental education, household income, total energy intake, and moderate-to-vigorous physical activity) demonstrated that, compared to eating at home, school based breakfast and dinner consumption was associated with significantly lower overweight/obesity risks for both genders (boys: breakfast OR =0.70, 95% CI =0.65-0.75; dinner OR =0.80, 95% CI = 0.74- 0.86; girls: breakfast OR = 0.89 , 95% CI = 0.82-0.96; dinner OR =0.88, 95% CI =0.81-0.95), whereas eating lunch away from home significantly increased overweight/obesity risks (boys: OR =1.32, 95% CI =1.17-1.48; girls: OR =1.43, 95% CI =1.26- 1.62 ), with all associations being statistically significant ( P <0.05). After adjusting for confounding factors, boys who ate breakfast away from home showed a significantly reduced risk of undernutrition ( OR =0.80,95% CI =0.66-0.97), while those consuming lunch away from home had an increased risk ( OR =1.26, 95% CI =1.01-1.57) ( P <0.05). Conclusions The choice of dining locations for children is becoming more diverse, and a relatively high proportion of children eat meals outside the home and at school. Eating out have a higher risk of malnutrition for children. School feeding may be beneficial to children s physical health.

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

Cuproptosis, a recently discovered form of regulated cell death involving copper ion metabolism, has emerged as a promising approach for tumor therapy. This pathway not only directly eliminates tumor cells but also promotes immunogenic cell death (ICD), reshaping the tumor microenvironment (TME) and initiating robust anti-tumor immune responses. However, translating cuproptosis-based therapies into clinical applications is hindered by challenges, including complex metabolic regulation, TME heterogeneity, and the precision required for effective drug delivery. To address these limitations, nanoparticles offer transformative solutions by providing precise delivery of cuproptosis-inducing agents, controlled drug release, and enhanced therapeutic efficacy through simultaneous modulation of metabolic pathways and immune responses. This review systematically discusses recent advancements in nanoparticle-based cuproptosis delivery systems, highlighting nanoparticle design principles and their synergistic effects when integrated with other therapeutic modalities such as ICB, PTT, and CDT. Furthermore, we explore the potential of cuproptosis-based nanomedicine for personalized cancer treatment by emphasizing strategies for TME stratification and therapeutic optimization tailored to patient profiles. By integrating current insights from metabolic reprogramming, tumor immunotherapy, and nanotechnology, this review aims to facilitate the clinical translation of cuproptosis nanomedicine and significantly contribute to the advancement of precision oncology.

In recent decades, the prevalence of hyperuricemia and gout has increased dramatically due to lifestyle changes. The drugs currently recommended for hyperuricemia are associated with adverse reactions that limit their clinical use. In this study, we report that berberine (BBR) is an effective drug candidate for the treatment of hyperuricemia, with its mechanism potentially involving the modulation of gut microbiota and its metabolite, succinic acid. BBR has demonstrated good therapeutic effects in both acute and chronic animal models of hyperuricemia. In a clinical trial, oral administration of BBR for 6 months reduced blood uric acid levels in 22 participants by modulating the gut microbiota, which led to an increase in the abundance of Bacteroides and a decrease in Clostridium sensu stricto_1. Furthermore, Bacteroides fragilis was transplanted into ICR mice, and the results showed that Bacteroides fragilis exerted a therapeutic effect on uric acid similar to that of BBR. Notably, succinic acid, a metabolite of Bacteroides, significantly reduced uric acid levels. Subsequent cell and animal experiments revealed that the intestinal metabolite, succinic acid, regulated the upstream uric acid synthesis pathway in the liver by inhibiting adenosine monophosphate deaminase 2 (AMPD2), an enzyme responsible for converting adenosine monophosphate (AMP) to inosine monophosphate (IMP). This inhibition resulted in a decrease in IMP levels and an increase in phosphate levels. The reduction in IMP led to a decreased downstream production of hypoxanthine, xanthine, and uric acid. BBR also demonstrated excellent renoprotective effects, improving nephropathy associated with hyperuricemia. In summary, BBR has the potential to be an effective treatment for hyperuricemia through the gut-liver axis.

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis

Objective To construct a diagnostic and prognostic model for malignant pleural effusion (MPE) in patients with non-M1b stage (AJCC 7th edition) non-small cell lung cancer (NSCLC) by machine learning. Methods Retrospective analysis was conducted on patients diagnosed with NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2015, excluding those in the M1b stage. Two sets of data were collected: data 1 (patients with non-M1b stage NSCLC, n=47 392) was used to construct the MPE diagnostic model; and data 2 (patients with M1a stage NSCLC and MPE, n=2 422) was used to construct a prognostic model. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to screen feature variables, with a training set and validation set ratio of 7:3. Models were built using eight machine learning algorithms, with evaluation metrics including accuracy, precision, recall, F1 score, area under the ROC curve (AUC), decision curve, calibration curve, and precision recall curve (PR), with ROC-AUC as the main evaluation metric. Results The incidence of MPE in patients with non-M1b stage NSCLC was 5.12%, and the 1-year survival rate of patients with MPE was 32.5%. LASSO regression identified nine diagnostic-related variables and 12 prognostic-related variables. The AUC values of the models constructed by eight machine learning algorithms all exceeded 0.70. The random forest model performed the best in the diagnostic model (training set AUC=0.908, validation set AUC=0.897), and the XGBoost model showed the best performance in the prognostic model (training set AUC=0.905, validation set AUC=0.875). Other evaluation indicators showed good results and balanced distribution. SHAP feature importance analysis showed that tumor size, lymph node metastasis, and histological type were important influencing factors for the occurrence of MPE, and chemotherapy intervention was the most remarkably prognostic factor. Conclusion The random forest diagnostic model constructed in this study can effectively predict the risk of MPE in patients with non-M1b stage NSCLC, and the XGBoost prognostic model can predict the prognosis of M1a-stage NSCLC patients with concurrent MPE.

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.

Objective To investigate the expressions of 12 cytokines(IL-1β,IL-2,IL-4,IL-5,IL-6,IL-8,IL-10,IL-12p70,IL-17,IFN-α,IFN-γ,TNF-α)and procalcitonin in patients with infective endocarditis(IE).Methods Ten IE patients admitted to our hospital from December 2021 to December 2022 were included into the IE group,10 patients with non-infectious and non-rheumatic valvular diseases who were admitted to our hospital at the same period were randomly selected as the control group,and blood sampling of all patients were conducted at admission.The expressions of 12 cytokines and blood routine indexes were detected by flow cytometry,and the level of procalcitonin was detected by ELISA.The correlations among the expression levels of cytokines in IE patients were analyzed by Pearson method and the correlations of IL-8 level and white blood cell count with procalcitonin in IE patients were analyzed by Spearman method.Results Compared with the control group,the levels of cytokines of IL-1β,IL-2,IL-6,IL-10,TNF-α,IFN-α,IFN-γ and IL-12p70 in the IE group were significantly increased(P<0.05),the white blood cell count,neutrophil percentage and procalcitonin were significantly increased(P<0.05).There was no significant difference in the percentage of monocytes between the two groups(P>0.05).IFN-α of IE patients was positively correlated with IL-2,TNF-α,IL-1β and IL-12p70,IL-2 was positively correlated with TNF-α and IL-1β,IL-12p70 was positively correlated with IFN-γ,and procalcitonin was significantly positively correlated with IL-8 and white blood cell count,with statistically significant differences(P<0.05).Conclusion The levels of IL-1β,IL-2,IL-6,IL-10,TNF-α,IFN-α,IFN-γ,IL-12p70 and procalcitonin in IE patients are significantly higher than those in the normal population,and the detections of these indicators are of guiding significance for the early diagnosis of IE and the evaluation of the severity of the disease.

As a new strategy for the application of sacubitril/valsartan (LCZ696) in patients with CKD, much evidence showed that it improved the prognosis of patients with CKD. This review summarizes the efficacy and safety of sacubitril/valsartan in physiology, pathology, pharmacology and clinical application by searching Wanfang, CNKI, PubMed and other databases for related articles on the application of sacubitril/valsartan in CKD patients. Although LBQ657, the active product of sacubitril, has a high drug accumulation in patients with moderate, severe renal injury, and ESRD, it is not cleared in hemodialysis, and has very little eliminated in peritoneal dialysis, which does not affect its safety. Compared with angiotensin converting enzyme inhibitor and angiotensin receptor blocker drugs, LCZ696 could increase the blood pressure control rate, improve cardiac function, slow down the decline of glomerular filtration rate, and significantly improve cardiovascular outcomes without more adverse events. Sacubitril/valsartan can be used in all levels of CKD patients complicated with hypertension and/or heart failure, with reliable safety and tolerance.