Objective:The relationships between folic acid supplementation, folate intake, and GDM remain controversial. We conducted a preliminary investigation using a multimethod approach integrating a retrospective cohort study, Mendelian randomization, and dose-response analysis to explore this association. Methods:We examined the relationship between folic acid supplement use (including a combination preparation) and the GDM risk in a retrospective cohort of 10,479 pregnant women receiving care at Jiangsu Provincial People's Hospital using multivariable logistic regression analysis. MR analysis provides genetic support for a potential causal link between genetically predicted folic acid supplement use and GDM. A cross-sectional analysis of 3,680 pregnant women in the National Health and Nutrition Examination Survey (NHANES) evaluated total folate intake and dietary folate equivalents (DFEs) via 24-h dietary recall; multivariable logistic regression and restricted cubic spline models were used to characterize associations and generate dose-response curves. The models were adjusted for age, BMI, race or ethnic origin, education, and smoking history. Subgroup analyses were performed to assess potential effect modifications. Results:In this retrospective cohort study, compared with non-users, folic acid supplement users had a 46.2% greater likelihood of having GDM (OR = 1.46, 95% CI: 1.339-1.595; p < 0.001). MR analysis supported a potential causal association between genetically predicted folic acid products and GDM risk (OR = 1.40, 95% CI 1.17-1.67, p < 0.001). In the NHANES cohort, higher total folate (OR = 1.42, 95% CI: 1.05-1.92, p = 0.02) and DFE intake (OR = 1.61, 95% CI: 1.23-2.10, p < 0.001) were linked to an increased GDM risk, with non-linear dose-response inflection points at approximately 445 μg/day and 582 μg/day, respectively. These associations were generally maintained after multivariable adjustment, and subgroup analyses revealed consistent trends toward an increased risk. Conclusion:This multimethod study indicates that both supplemental folic acid and dietary folate intake may be associated with an increased GDM risk. These observations support the need for additional research to better understand the potential impact of current recommendations on prenatal folate levels.

Large language models have facilitated knowledge graph (KG) construction from clinical guidelines, but extracted triples vary in structural validity and evidential support. Meanwhile, graph-augmented question answering (QA) systems typically optimize query relevance during retrieval, with limited reuse of quality information produced during KG construction. This creates a disconnect between construction-time quality control and inference-time evidence use. We investigate whether construction-time triple quality can serve as a persistent signal for downstream evidence selection and presentation. We propose a quality-aware framework that models structural conformance (SchemaConf) and evidential support (EvidScore) as complementary dimensions and fuses them into a per-triple quality signal, Q(t). Rather than using quality solely for filtering, the framework retains Q(t) and derived quality tiers as graph attributes and propagates them into quality-weighted subgraph retrieval and tier-conditioned evidence prompting, while preserving passage-level provenance. Experiments on Chinese diabetes clinical guidelines show that the utility of the quality signal is distribution dependent. Under cross-version and cross-model shift, the fused Q(t) provides stronger triple-quality discrimination than either component alone (AUC 0.748 vs. 0.703 for EvidScore and 0.645 for SchemaConf). In guideline-grounded QA, propagating construction-time quality reduces required-knowledge omission from 16.3

BACKGROUND:The S100 family of calcium-binding proteins (S100s) had been tightly related to the biological processes of various cardiovascular diseases. This study aims to investigate the expression of S100s in Atherosclerosis (AS) and explore their potential as diagnostic biomarkers and therapeutic targets. METHODS:We analyzed multiple sequencing datasets from the GEO database to compare the expression profiles of S100s in AS tissues versus normal samples. Employing unsupervised clustering techniques, AS subtypes were discerned based on the intricate variations in S100-related gene expression profiles. Subsequent analyses delved into immune cell infiltration and GSVA pathway enrichment, shedding light on the nuanced immune landscape characterizing diverse AS subtypes. Machine learning techniques were employed to develop a diagnostic model for AS. Single-cell RNA analysis was utilized to investigate the cellular distribution of S100 hub genes in AS. RESULTS:Unsupervised clustering analysis identified two distinct AS subtypes (C1 and C2), characterized by specific S100 gene expression patterns. The RF-based diagnostic model exhibited the highest efficacy (AUC=0.881), and the top five genes (S100A4, S100A10, S100A11, S100A13, S100Z) were used to construct a diagnostic nomogram. CONCLUSION:This study systematically elucidates the roles of S100s in AS, offering insights into molecular subtyping, immune characteristics, and diagnostic model construction. The findings provide valuable implications for the precise treatment and prognosis assessment of AS and pave the way for further research into related mechanisms.

BACKGROUND:This Mendelian Randomization (MR) study investigates the causal relationships between mitochondrial proteins and Diabetic polyneuropathy (DPN). METHODS:Using a two-sample MR design with data from FINNGEN (1048 DPN cases, 374,434 controls) and 63 mitochondrial proteins from GWAS datasets. Analyses used the Inverse Variance Weighted (IVW) method, MR-Egger regression, and weighted medians, with extensive sensitivity tests for robustness. RESULTS:Elevated COA3 levels (OR = 0.5774, 95% CI: 0.4466-0.7465, p < 0.01) decreased DPN risk, while elevated NFU1 (OR: 1.3992, 95% CI: 1.0935-1.7904, p = 0.0075) and SARS2 (OR: 1.3660, 95% CI: 1.0651-1.7520, p = 0.0140) increased risk. CONCLUSION:COA3, NFU1, and SARS2 significantly affect DPN risk, with COA3 lowering and NFU1 and SARS2 increasing risk. These findings highlight potential targets for DPN prevention and treatment, suggesting the importance of mitochondrial proteins in DPN pathogenesis, and providing new insights for future therapeutic strategies to effectively combat this debilitating condition.

The impact of metformin on sarcopenia remains uncertain. This study aimed to investigate whether metformin influences sarcopenia risk and evaluate the effects of potential drug targets on sarcopenia traits. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) (n = 3549) to assess the association between metformin use and sarcopenia risk in elderly patients with type 2 diabetes. Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) from UK Biobank (n = 1,366,167) and FinnGen (n = 218,007), with expression quantitative trait loci (eQTL) as instrumental variables, examined the causal effect of metformin-related targets on sarcopenia traits, while molecular docking explored the interaction between metformin and its drug targets. Metformin use was associated with increased grip strength (OR = 2.46; 95

Objective : The relationship between folate and GDM remains controversial. We conducted a preliminary investigation using a multimethod approach—integrating a retrospective cohort, Mendelian randomization, and dose-response analysis—to explore this association. Methods : In a retrospective cohort of 10,479 pregnant women at Jiangsu Provincial People’s Hospital, we examined the relationship between folate use (including a combination preparation) and GDM risk using multivariable logistic regression. MR analysis leveraged folate-associated single nucleotide polymorphisms from large-scale genome-wide association studies to assess potential causal effects on GDM. A cross-sectional analysis of 3,681 pregnant women in the National Health and Nutrition Examination Survey (NHANES) evaluated total folate intake and dietary folate equivalent (DFE) via 24-hour dietary recall; multivariable logistic regression and restricted cubic spline models characterized associations and dose–response curves. Models adjusted for age, BMI, race, education and smoking history. Subgroup analyses assessed potential effect modification. Results : In the retrospective cohort study, folate users exhibited a 46.2% higher odds of GDM compared to non-users (OR = 1.462, 95% CI: 1.339–1.595, p < 0.001). MR analysis supported a potential causal association between genetically predicted folate levels and GDM risk (β = 33.81, SE = 9.074, p < 0.001). In NHANES, higher total folate (OR = 1.42, 95% CI: 1.05–1.92, p = 0.02) and DFE intake (OR = 1.61, 95% CI: 1.23–2.10, p < 0.001) were linked to increased GDM risk, with nonlinear dose–response inflection points at approximately 445 µg/day and 582 µg/day.These associations were generally maintained after multivariable adjustment, and subgroup analyses showed consistent trends toward increased risk. Conclusion : This multi-method study indicate that higher folate intake may be associated with an elevated risk of GDM. These observations support the need for additional research to better understand the potential impact on current prenatal folate.

BackgroundThe exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features.MethodsUsing transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model.ResultsReceiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion.ConclusionsThe association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.

Endometrial cancer (EC) is associated with significant risk factors such as polycystic ovarian syndrome (PCOS) and sedentary behavior. In our study, we aim to employ machine learning algorithms to investigate the potential molecular processes that underlie their interaction and explore their respective roles in the diagnosis and immunotherapy of EC. The GEO database provides access to microarray data, which was utilized in this study to identify gene expression modules associated with PCOS and sedentary behavior, using weighted gene expression network analysis (WGCNA). Cluego software was then employed to investigate the energy enrichment of shared pathways in both PCOS and sedentary individuals, and differential gene analysis was used to confirm another two databases. The miRNAs-mRNAs controlled network was constructed to verify the pathway. The immune-related factors of the shared pathway in EC were then analyzed. Finally, to validate our findings, we conducted cell experiments using EC cell lines (AN3CA, KLE, Ishikawa, RL95-2, and HEC-1A). We found that increased intracellular aromatic compound anabolism is a common feature of both PCOS and sedentary individuals. We then developed a disease pathway model that was based on the common genetic characteristics of PCOS and sedentary behavior. We utilized pathway typing in EC samples and found a significant survival difference between the two subgroups, with the upregulated expression type exhibiting an immune-hot phenotype. Finally, the experimental results confirmed the expression of the hub gene (NAA15) in EC. The findings of our study suggest that genes related to the intracellular aromatic compound metabolic pathway can be used for immunotherapy of EC.

High salt can induce cardiac damage. The aim of this present study was to explore the effect and the mechanism of microRNA (miR)-142-3p on the cardiac fibrosis induced by high salt. Rats received high salt diet to induce cardiac fibrosis in vivo, and neonatal rat cardiac fibroblasts (NRCF) treated with sodium chloride (NaCl) to induce fibrosis in vitro. The fibrosis and mitochondrial autophagy levels were increased the heart and NRCF treated with NaCl, which were alleviated by miR-142-3p upregulation. The fibrosis and mitochondrial autophagy levels were elevated in NRCF after treating with miR-142-3p antagomiR. Optineurin (OPTN) expression was increased in the mitochondria of NRCF induced by NaCl, which was attenuated by miR-142-3p agomiR. OPTN downregulation inhibited the increases of fibrosis and mitochondrial autophagy levels induced by NaCl in NRCF. These results miR-142-3p could alleviate high salt-induced cardiac fibrosis via downregulation of OPTN to reduce mitophagy.

BackgroundGliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression.MethodsThis study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression.ResultsIn total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient’s MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031.ConclusionThis study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1β, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis.

ObjectivesIn adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors.MethodsIn this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM–based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO–selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values.ResultsLogistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results.ConclusionIn IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

目的 探讨自身免疫性甲状腺疾病(AITD)对患者骨代谢水平的影响.方法 74例初诊AITD患者根据甲状腺功能分为甲状腺功能亢进组(甲亢组,53例)、甲状腺功能减退组(甲减组,12例)和甲状腺功能正常组(甲功正常组,9例).另以11例健康体检者作为对照组.治疗前检测各组血清甲状腺功能(FT3、FT4、TSH)、甲状腺相关抗体[甲状腺过氧化物酶抗体(TPOAb)、甲状腺球蛋白抗体(TGAb)和促甲状腺激素受体抗体(TRAb)]、骨代谢相关标志物[β-Ⅰ型胶原羧基端肽(β-CTx)、总Ⅰ型胶原氨基端前肽(T-P1NP)、核因子κB受体活化因子配体(RANKL)、骨保护素(OPG)]、甲状旁腺激素(PTH)、25-羟维生素D3、ALP、血钙和血磷水平.AITD患者根据病情进行相应治疗,所有甲亢患者接受抗甲状腺药物治疗.分别在治疗3、6个月时检测AITD患者甲状腺功能、甲状腺相关抗体及骨代谢相关标志物水平.结果 治疗前,甲亢组血清β-CTx、T-P1NP、ALP水平均高于甲减组、甲功正常组和对照组(P＜0.05).AITD患者血清β-CTx、T-P1NP水平与FT3、FT4、TRAb水平呈正相关,与TSH水平呈负相关(P＜0.01);血清RANKL水平与FT3水平呈正相关,与TSH水平呈负相关(P＜0.05).治疗6个月时,AITD患者血清β-CTx、T-P1NP水平与TRAb水平呈正相关,与TGAb水平呈负相关(P＜0.05或P＜0.01);RANKL/OPG比值与FT3水平呈正相关(P＜0.05).治疗期间甲亢组血清β-CTx、T-P1NP和RANKL水平逐渐下降,OPG水平逐渐升高.结论 AITD患者甲状腺功能亢进程度与骨转换速率呈正相关.TRAb水平升高可能会加快骨转换速率,而TGAb则相反.

Background Endometrial carcinoma (EC) is the sixth most frequent malignancy in women and is often linked to high estrogen exposure. Polycystic ovarian syndrome (PCOS) is a known risk factor for EC, but the underlying mechanisms remain unclear. Methods We investigated shared gene signals and potential biological pathways to identify effective therapy options for PCOS- and EC-related malignancies. Weighted gene expression network analysis (WGCNA) was used to identify genes associated with PCOS and EC using gene expression data from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) datasets. Enrichment analysis using Cluego software revealed that the steroid hormone biosynthetic process was a critical feature in both PCOS and EC. A predictive signature encompassing genes involved in steroid hormone production was developed using multivariate and least absolute shrinkage and selection operator (LASSO) regression analysis to predict the prognosis of EC. Then, we conducted further experimental verification. Results Patients in the TCGA cohort with high predictive scores had poorer outcomes than those with low scores. We also investigated the relationship between tumor microenvironment (TME) features and predictive risk rating and found that patients with low-risk scores had higher levels of inflammatory and inhibitory immune cells. Also, we found that immunotherapy against anti-CTLA4 and anti-PD-1/PD-L1 was successful in treating individuals with low risk. Low-risk individuals were more responsive to crizotinib therapy, according to further research performed using the “pRRophetic” R package. We further confirmed that IGF2 expression was associated with tumor cell migration, proliferation, and invasion in EC cells. Conclutions By uncovering the pathways and genes linking PCOS and EC, our findings may provide new therapeutic strategies for patients with PCOS-related EC.

Background: S100 Calcium Binding Protein A16 (S100A16), a novel member of S100 protein family, is linked to tumorigenic processes and abundantly expressed in CNS tissues. Our study aimed to explore the biological function and possible mechanism of S100A16 in the progression of glioma. Methods: Sequence data of S100A16 and survival prognosis of glioma patients were initially analyzed using public databases. Glioma tissues were collected to examine S100A16 expression levels. Glioma cell lines and nude mice were subjected to in vitro and in vivo functional experiments. Western blot, immunofluorescence (IF), immunoprecipitation (IP) and ubiquitination assays were done to further elucidate the underlying mechanism. Results: This study firstly revealed that S100A16 was markedly up-regulated in glioma, and patients with higher S100A16 levels have a shorter survival time. S100A16 overexpression promoted the proliferation, invasion and migration of glioma cells, and the tumor formation of nude mice. Importantly, we identified S100A16 as a negative regulator of the Hippo pathway which could decrease LATS1 expression levels, promote the YAP nuclear import and initiate the downstream target genes CYR61 and CTGF. Moreover, our data showed that S100A16 destabilized LATS1 protein by inducing the CUL4A-mediated LATS1 ubiquitination degradation. Conclusions: This study demonstrated a vital biological role of S100A16 in glioma progression mechanism by promoting CUL4A-mediated LATS1 ubiquitination to inhibit Hippo signaling pathway. S100A16 could be a novel biomarker and treatment option for glioma patients.

Background & aims Hyperglycemia is associated with lipid disorders in patients with diabetes. Ceramides are metabolites involved in sphingolipid metabolism that accumulate during lipid disorders and exert deleterious effects on glucose and lipid metabolism. However, the effects of ceramide on glucagon-mediated hepatic gluconeogenesis remain largely unknown. This study was designed to investigate the impact of ceramides on gluconeogenesis in the context of the hepatic glucagon response, with the aim of finding new pharmacological interventions for hyperglycemia in diabetes. Methods Liquid chromatography-mass spectrometry was used to quantify ceramide content in the serum of patients with diabetes. Primary hepatocytes were isolated from male C57BL/6J mice to study the effects of ceramide on hepatic glucose production. Immunofluorescence staining was performed to view cAMP-responsive element-binding protein (CREB)- regulated transcription co-activator 2 (CRTC2) nuclear translocation in hepatocytes. Serine palmitoyl-transferase, long chain base subunit 2 (Sptlc2) knockdown mice were generated using an adeno-associated virus containing shRNA, and hepatic glucose production was assessed glucagon tolerance and pyruvate tolerance tests in mice fed a normal chow diet and high-fat diet. Results Increased ceramide levels were observed in the serum of patients newly diagnosed with type 2 diabetes. De novo ceramide synthesis was activated in mice with metabolic disorders. Ceramide enhanced hepatic glucose production in primary hepatocytes. In contrast, genetic silencing of Sptlc2 prevented this process. Mechanistically, ceramides de-phosphorylate CRTC2 (Ser 171) and facilitate its translocation into the nucleus for CREB activation, thereby augmenting the hepatic glucagon response. Hepatic Sptlc2 silencing blocked ceramide generation in the liver and thus restrained the hepatic glucagon response in mice fed a normal chow diet and high-fat diet. Conclusions These data indicate that ceramide serves as an intracellular messenger that augments hepatic glucose production by regulating CRTC2/CREB activity in the context of the hepatic glucagon response, suggesting that CRTC2 phosphorylation might be a potential node for pharmacological interventions to restrain the hyperglycemic response during fasting in diabetes.

Ferroptosis is a recently identified form of cell death that is distinct from the conventional modes such as necrosis, apoptosis, and autophagy. Its role in bronchopulmonary dysplasia (BPD) remains inadequately understood. To address this gap, we obtained BPD-related RNA-seq data and ferroptosis-related genes (FRGs) from the GEO database and FerrDb, respectively. A total of 171 BPD-related differentially expressed ferroptosis-related genes (DE-FRGs) linked to the regulation of autophagy and immune response were identified. Least absolute shrinkage and selection operator and SVM-RFE algorithms identified 23 and 14 genes, respectively, as marker genes. The intersection of these 2 sets yielded 9 genes (ALOX12B, NR1D1, LGMN, IFNA21, MEG3, AKR1C1, CA9, ABCC5, and GALNT14) with acceptable diagnostic capacity. The results of the functional enrichment analysis indicated that these identified marker genes may be involved in the pathogenesis of BPD through the regulation of immune response, cell cycle, and BPD-related pathways. Additionally, we identified 29 drugs that target 5 of the marker genes, which could have potential therapeutic implications. The ceRNA network we constructed revealed a complex regulatory network based on the marker genes, further highlighting their potential roles in BPD. Our findings offer diagnostic potential and insight into the mechanism underlying BPD. Further research is needed to assess its clinical utility.

Introduction: Bronchopulmonary dysplasia (BPD) is a life-threatening lung illness that affects premature infants and has a high incidence and mortality. Using interpretable machine learning, we aimed to investigate the involvement of endoplasmic reticulum (ER) stress-related genes (ERSGs) in BPD patients. Methods: We evaluated the expression profiles of endoplasmic reticulum stress-related genes and immune features in bronchopulmonary dysplasia using the GSE32472 dataset. The endoplasmic reticulum stress-related gene-based molecular clusters and associated immune cell infiltration were studied using 62 bronchopulmonary dysplasia samples. Cluster-specific differentially expressed genes (DEGs) were identified utilizing the WGCNA technique. The optimum machine model was applied after comparing its performance with that of the generalized linear model, the extreme Gradient Boosting, the support vector machine (SVM) model, and the random forest model. Validation of the prediction efficiency was done by the use of a calibration curve, nomogram, decision curve analysis, and an external data set. Results: The bronchopulmonary dysplasia samples were compared to the control samples, and the dysregulated endoplasmic reticulum stress-related genes and activated immunological responses were analyzed. In bronchopulmonary dysplasia, two distinct molecular clusters associated with endoplasmic reticulum stress were identified. The analysis of immune cell infiltration indicated a considerable difference in levels of immunity between the various clusters. As measured by residual and root mean square error, as well as the area under the curve, the support vector machine machine model showed the greatest discriminative capacity. In the end, an support vector machine model integrating five genes was developed, and its performance was shown to be excellent on an external validation dataset. The effectiveness in predicting bronchopulmonary dysplasia subtypes was further established by decision curves, calibration curves, and nomogram analyses. Conclusion: We developed a potential prediction model to assess the risk of endoplasmic reticulum stress subtypes and the clinical outcomes of bronchopulmonary dysplasia patients, and our work comprehensively revealed the complex association between endoplasmic reticulum stress and bronchopulmonary dysplasia.

BackgroundPrevious studies have shown that increasing the frequency of eating is beneficial in terms of cardiovascular metabolic risk factors; however, limited evidence is available for the association between daily eating frequency and mortality, especially in people with diabetes. Therefore, we aimed to explore the association between eating frequency and long-term mortality in populations with diabetes.MethodsWe selected 4,924 individuals suffering from diabetes (mean age: 57.77 years; 51.3% men) from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2014. Daily eating frequency was used as the exposure factor in this study. We extracted the mortality data from the National Death Index records and matched them with the population of NHANES. All participants were followed up from the date of getting enrolled in NHANES to 31 December 2015. Multivariate Cox proportional hazards regression, Kaplan–Meier survival curves, and restricted cubic spline were used to assess the associations between eating frequency and all-cause and cause-specific mortality among people with diabetes.ResultsDuring 34,950 person–years of follow-up, 1,121 deaths were documented, including 272 cardiovascular disease (CVD)-related deaths and 156 cancer-related deaths. After adjusting for confounding factors, the daily eating frequency was linearly inversely associated with all-cause and CVD-related mortality, and the HR (95% CIs) for per one-time increment of eating frequency was 0.88 (0.80–0.98) and 0.77 (0.63–0.93), respectively. Sensitivity analyses showed that the main results and statistical significance were still stable.ConclusionHigher eating frequency was independently related to lower all-cause and CVD-related mortality in people with diabetes, which can be used as a potential strategy for daily-diet management among populations suffering from diabetes.