Objective To construct machine learning models based on preoperative inflammatory and radiological features for the prediction of glioma grading and isocitrate dehydrogenase (IDH) mutation status, and to analyze application values of these models and identify the optimal predictive models. Methods A retrospective analysis was conducted on the data of pathologically confirmed glioma patients admitted to Tongji Hospital Affiliated to Tongji University from March 2019 to March 2023. LASSO regression was used to screen feature variables, and predictive models were constructed based on logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting decision tree (XGBoost) and K-nearest neighbor (KNN) algorithms. The model performance was comprehensively evaluated using metrics including discrimination ability, area under the precision-recall curve (AUC), accuracy, F1 score and Brier score. The DeLong test was adopted to compare the AUC values among different models; Friedman rank-sum test was used to determine the overall performance differences of the models, with the Nemenyi test applied for multiple comparison correction. Results In the task of glioma grading prediction, the LR model achieved the highest comprehensive score (0.726), and no significant difference was observed between the LR model and the other four models; age was positively correlated with glioma grading (P=0.003). In the task of IDH mutation status prediction, the XGBoost model obtained the highest comprehensive score (0.832), which was superior to the LR (0.762, P=0.035) and KNN models (0.754, P=0.025), while no statistical differences were found between the XGBoost model and the RF or SVM models. Conclusions The LR model for glioma grading prediction and XGBoost model for IDH mutation prediction constructed based on a task-oriented strategy achieve a favorable interpretability while ensuring optimized performance, thereby providing reliable decision support for the individualized diagnosis and treatment of glioma.

OBJECTIVE Melatonin(MEL),a natural hormone with broad-spectrum anticancer effects,has been shown to potentiate the therapeutic outcomes of conventional chemotherapy.This study aims to investigate the combined effects of Doxorubicin(DOX)and MEL on head and neck squamous cell carcinoma(HNSCC)cell lines.METHODS The effects of MEL and DOX on cell proliferation in HNSCC cell lines TU686 and CAL-27 were assessed using the MTT assay.The effects of MEL and DOX on reactive oxygen species(ROS)expression levels in HNSCC cell lines were detected using DCFH-DA fluorescent probe.The effects of MEL and DOX on 8-hydroxy-2′-deoxyguanosine(8-oxodG)levels in HNSCC cell lines were measured by enzyme-linked immunosorbent assay(ELISA).The effects of MEL and DOX on γ-H2AX protein levels in HNSCC cell lines were analyzed via Western blot.The effects of MEL and DOX on antioxidant enzymes levels in HNSCC cell lines were evaluated based on spectrophotometry.The effects of MEL and DOX on cell apoptosis in HNSCC cell lines were detected using an ELISA cell death detection kit.RESULTS The MTT assay revealed that MEL significantly enhanced the cytotoxic effects of DOX in HNSCC cell lines(tTU686=13.51,tCAL-27=17.580,all P<0.05).The combination of MEL and DOX markedly increased intracellular ROS levels(FTU686=89.984,FCAL-27=102.853,all P<0.05),while the expression of antioxidant enzymes was significantly reduced(TU686:the F values of CAT,GPx,GR,GST and SOD are 176.035,34.662,20.260,120.105 and 184.254,all P<0.05;CAL-27:the F values are 96.801,177.398,97.849,102.750 and 186.608 respectively,all P<0.05).Furthermore,this combination treatment significantly elevated the expression levels of 8-oxodG(FTU686=200.078,FCAL-27=663.982,all P<0.05)and γ-H2AX(FTU686=192.500,FCAL-27=285.700,all P<0.01)in HNSCC cell lines.Additionally,compared to single-agent treatment,MEL significantly enhanced DOX-induced apoptosis in HNSCC cell lines(FTU686=2718.253,FCAL-27=5185.334,all P<0.01).CONCLUSION The combination of MEL and DOX can enhance cytotoxic effects on HNSCC cell lines,increase intracellular ROS levels,induce DNA oxidative damage,and impair cellular antioxidant defense capacity,thereby effectively promoting HNSCC cell apoptosis.This combination may represent a novel therapeutic approach to improve clinical outcomes in HNSCC.

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

OBJECTIVE: Recombination events are common and serve as the primary driving force of diverse human adenovirus (HAdV), particularly in children with acute respiratory tract infections (ARIs). Therefore, continual monitoring of these events is essential for effective viral surveillance and control. METHODS: Respiratory specimens were collected from children with ARIs between January 2022 and December 2023. The penton base, hexon, and fiber genes were amplified from HAdV-positive specimens and sequenced to determine the virus type. In cases with inconsistent typing results, genes were cloned into the pGEM-T vector to detect recombination events. Metagenomic next-generation sequencing (mNGS) was performed to characterize the recombinant HAdV genomes. RESULTS: Among 6,771 specimens, 277 (4.09%, 277/6,771) were positvie for HAdV, of which 157 (56.68%, 157/277) were successfully typed, with HAdV-B3 being the dominant type (91.08%, 143/157), and 14 (5.05%, 14/277) exhibited inconsistent typing results, six of which belonged to species B. The penton base genes of these six specimens were classified as HAdV-B7, whereas their hexon and fiber genes were classified as HAdV-B3, resulting in a recombinant genotype designated P7H3F3, which closely resembled HAdV-B114. Additionally, a partial gene encoding L1 52/55 kD was identified, which originated from HAdV-B16. CONCLUSION A novel recombinant, P7H3F3, was identified, containing sequences derived from HAdV-B3 and HAdV-B7, which is similar to HAdV-B114, along with additional sequences from HAdV-B16.
Humans ; Adenoviruses, Human/isolation & purification* ; Respiratory Tract Infections/epidemiology* ; Child, Preschool ; Child ; Recombination, Genetic ; Male ; Beijing/epidemiology* ; Infant ; Female ; Phylogeny ; Adenovirus Infections, Human/epidemiology* ; Acute Disease ; Genome, Viral

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

In recent years,the incidence rate of type 1 diabetes is on the rise.The causes of the disease are extremely complex,and the pathogenesis has not yet been fully clarified.Different types of studies have confirmed that the occurrence and evolution of type 1 diabetes is a typical process of polygene,multifactor,multi-stage and multi-channel,which is considered to be caused by the combined effect of genetic and environ-mental factors.At present,it is believed that environmental factors are related to the interaction of infection factors,diet factors,early exposure events,intestinal flora,immune factors,other factors and genetic factors.This article reviews the research on environmental factors of type 1 diabetes in recent years.

OBJECTIVES: To study the clinical and genetic characteristics of osteopetrosis (OPT) in children. METHODS: A retrospective analysis was performed on the clinical data of 14 children with OPT. Whole-exome sequencing was used to detect pathogenic genes, and clinical phenotypes and genotypic features were summarized. RESULTS: Among the 14 children (10 males and 4 females), the median age at diagnosis was 8 months. Clinical manifestations included systemic osteosclerosis (14 cases, 100%), anemia (12 cases, 86%), infections (10 cases, 71%), thrombocytopenia (9 cases, 64%), hepatosplenomegaly (8 cases, 57%), and developmental delay (5 cases, 36%). Malignant osteopetrosis (MOP) cases had lower platelet counts, creatine kinase isoenzyme, and serum calcium levels, but higher white blood cell counts, lactate dehydrogenase, and alkaline phosphatase levels compared to non-MOP cases (P<0.05). Genetic testing identified 15 variants in 12 patients, including 8 variants in the CLCN7 gene (53%), 6 in the TCIRG1 gene (40%), and 1 in the TNFRSF11A gene (7%). Three novel CLCN7 variants were identified: c.2351G>C, c.1215-43C>T, and c.1534G>A. All four patients with TCIRG1 variants exhibited MOP clinical phenotypes. Of the seven patients with CLCN7 variants, 4 presented with intermediate OPT, 2 with benign OPT, and 1 with MOP. CONCLUSIONS Clinical phenotypes of OPT in children are heterogeneous, predominantly involving CLCN7 and TCIRG1 gene variants, with a correlation between clinical phenotypes and genotypes.
Humans ; Osteopetrosis/genetics* ; Male ; Female ; Infant ; Child, Preschool ; Retrospective Studies ; Vacuolar Proton-Translocating ATPases/genetics* ; Child ; Chloride Channels/genetics* ; Mutation ; Receptor Activator of Nuclear Factor-kappa B

Hypercholesterolemia is a significant risk factor for the development of atherosclerosis. 2',3',5'-Tri-O-acetyl-N ⁶-(3-hydroxyphenyl) adenosine (IMM-H007), a novel AMPK agonist, has shown protective effects in metabolic diseases. However, its impact on cholesterol and triglyceride metabolism in hypercholesterolemia remains unclear. In this study, we aimed to elucidate the effects and specific mechanisms by which IMM-H007 regulates cholesterol and triglyceride metabolism. To achieve this goal, we used Apoe ^-/- and Ldlr ^-/- mice to establish a hypercholesterolemia/atherosclerosis model. Additionally, hepatocyte-specific Ampka1/2 knockout mice were subjected to a 5-week high-cholesterol diet to establish hypercholesterolemia, while atherosclerosis was induced via AAV-PCSK9 injection combined with a 16-week high-cholesterol diet. Our results demonstrated that IMM-H007 improved cholesterol and triglyceride metabolism in mice with hypercholesterolemia. Mechanistically, IMM-H007 modulated the AMPKα1/2-LDLR signaling pathway, increasing cholesterol uptake in the liver. Furthermore, IMM-H007 activated the AMPKα1-FXR pathway, promoting the conversion of hepatic cholesterol to bile acids. Additionally, IMM-H007 prevented hepatic steatosis by activating the AMPKα1/2-ATGL pathway. In conclusion, our study suggests that IMM-H007 is a promising therapeutic agent for improving hypercholesterolemia and atherosclerosis through the activation of AMPKα.