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Type 2 diabetes mellitus and metabolic disorders have become an epidemic globally. However, the pathogenesis remains largely unclear and the prevention and treatment are still limited. In addition to environmental factors during adulthood, early life is the critical developmental window with high tissue plasticity, which might be modified by external environmental cues. Substantial evidence has demonstrated the vital role of early-life nutrition in programming the metabolic disorders in later life. In this review, we aim to overview the concepts of fetal programming and investigate the effects of early-life nutrition on energy metabolism in later life and the potential epigenetic mechanism. The related studies published on PubMed database up to March 2020 were included. The results showed that both maternal overnutrition and undernutrition increased the riskes of metabolic disorders in offspring and epigenetic modifications, including DNA methylation, miRNAs, and histone modification, might be the vital mediators. The beneficial effects of early-life lifestyle modifications as well as dietary and nutritional interventions on these deleterious metabolic remolding were initially observed. Overall, characterizing the early-life malnutrition that reshapes metabolic disease trajectories may yield novel targets for early prevention and intervention and provide a new point of view to the energy metabolism.

Hepatocellular carcinoma (HCC) is the fifth most common tumor worldwide and has a very poor prognosis. Its occurrence has been on the increase in recent years. Surgical resection and liver transplantation are the primary methods of treatment for HCC patients, but can only be applied to 15% of patients. The median survival time of unresectable or metastasizing HCC patients is only a few months. Existing systemic treatment methods are not effective for advanced HCC patients and a new method of treatment is needed for these patients. It has been established that the HCC occurs in multiple stages, however, the pathogenesis at a molecular level is not clear and many key factors are yet to be determined. In the past 30 years, it has become evident that the Ras/Raf/MEK/extracellular signal-regulated kinase (ERK) signaling pathway plays a significant role in the occurrence and development of HCC. This review focused on the association between the Ras/Raf/MEK/ERK signaling pathway and HCC.

Background Accurately assessing the invasiveness of lung adenocarcinoma with part-solid pulmonary nodules is pivotal for the treatment. Recent studies have highlighted the crucial role of adipose in the growth and invasion of tumors. This study aimed to develop and validate a model by integrating radiomic features from intrathoracic adipose tissue (IAT) with clinical factors to classify lung adenocarcinoma invasiveness. Methods 608 patients with lung adenocarcinomas were enrolled in three different centers. Radiomic features were extracted from nodules and IAT in computed tomography images. Multivariable logistic regression analysis was used to develop a nomogram model. The nomogram's performance was assessed utilizing accuracy, discrimination, and clinical benefits. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate changes in model performance after the addition of IAT features. Results The nomogram, incorporating nodule and IAT signature with nodular diameter, was constructed in the training dataset. The nomogram demonstrated area under the receiver operating curve values of 0.927 (95% CI: 0.872,0.983), 0.945 (95% CI: 0.902,0.988), and 0.917 (95% CI: 0.871,0.962) in the internal validation and two external validation cohorts, respectively. The nomogram showed good calibration (Hosmer-Lemeshow test, P>0.05), and adding the IAT signatures did improve nomogram performance in all cohorts (all NRI and IDI >0, P<0.05). Decision curve and stratification analysis revealed that the nomogram was clinically useful and had potential generalization ability. Conclusions The nomogram, consisting of nodule signature, IAT signature, and clinical factors, could individualize the classification of invasive adenocarcinomas with part-solid pulmonary nodules.

Background and objectives Brain metastasis (BM) significantly affects the prognosis of non-small cell lung cancer (NSCLC) patients. Increasing evidence suggests that adipose tissue influences cancer progression and metastasis. This study aimed to develop a predictive nomogram integrating mediastinal fat area (MFA) and deep learning (DL)-derived tumor characteristics to stratify postoperative‌ BM risk in NSCLC patients. Materials and methods A retrospective cohort of 585 surgically resected NSCLC patients was analyzed. Preoperative computed tomography (CT) scans were utilized to quantify MFA using ImageJ software (radiologist-validated measurements). Concurrently, a DL algorithm extracted tumor radiomic features, generating a deep learning brain metastasis score (DLBMS). Multivariate logistic regression identified independent BM predictors, which were incorporated into a nomogram. Model performance was assessed via area under the receiver operating characteristic curve (AUC), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results Multivariate analysis identified N stage, EGFR mutation status, MFA, and DLBMS as independent predictors of BM. The nomogram achieved superior discriminative capacity (AUC: 0.947 in the test set), significantly outperforming conventional models. MFA contributed substantially to predictive accuracy, with IDI and NRI values confirming its incremental utility (IDI: 0.123, P  < 0.001; NRI: 0.386, P  = 0.023). Calibration analysis demonstrated strong concordance between predicted and observed BM probabilities, while DCA confirmed clinical net benefit across risk thresholds. Conclusion This DL-enhanced nomogram, incorporating MFA and tumor radiomics, represents a robust and clinically useful tool for preoperative prediction of postoperative BM risk in NSCLC. The integration of adipose tissue metrics with advanced imaging analytics advances personalized prognostic assessment in NSCLC patients.

It shows that detrimental exposures and conditions in mothers can lead to the development of obesity and type 2 diabetes in offspring. This can lead to a vicious cycle of metabolic dysfunction, where rising rates of obesity, pre-diabetes, and diabetes in individuals of reproductive age, propagating risks to subsequent generations. It is well established that regular exercise has important health benefits for people with obesity and type 2 diabetes. Recently, increasing studies aim to examine the effects of maternal exercise on metabolic health in offspring. This review aims to demonstrate the evidence linking maternal exercise during critical periods of development and its implications for glucose metabolism in offspring, including intervention timing, sexual dimorphism, different exercise type, and intensity. Then we further examine the potential role of epigenetic modifications in this process.

Obesity has become a global health problem. Lifestyle modification and medical treatment only appear to yield short-term weight loss. Roux-en-Y gastric bypass (RYGB) is the most popular bariatric procedure, and it sustains weight reduction and results in the remission of obesity-associated comorbidities for obese individuals. However, patients who undergo this surgery may develop hypoglycemia. To date, the diagnosis is challenging and the prevalence of post-RYGB hypoglycemia (PRH) is unclear. RYGB alters the anatomy of the upper gastrointestinal tract and has a combined effect of caloric intake restriction and nutrient malabsorption. Nevertheless, the physiologic changes after RYGB are complex. Although hyperinsulinemia, incretin effects, dysfunction of β-cells and α-cells, and some other factors have been widely investigated and are reported to be possible mediators of PRH, the pathogenesis is still not completely understood. In light of the important role of the gut microbiome in metabolism, we hypothesized that the gut microbiome might also be a critical link between RYGB and hypoglycemia. In this review, we mainly highlight the current possible factors predisposing individuals to PRH, particularly related to the gut microbiota, which may yield significant insights into the intestinal regulation of glucose metabolic homeostasis and provide novel clues to improve the treatment of type 2 diabetes mellitus.

Local anesthetics (LAs) are a class of drugs, which have wide applications in the treatment of post-operative pain care management. Long-acting LAs that can be given as a single dose analgesic are desperately needed. The prepared sulfobutylether β-cyclodextrin (SCD)/hyaluronic acid (HA) hydrogels were characterized by Fourier transform infrared and X-ray diffraction patterns. The as-prepared SCD/HA hydrogels greatly enhanced their swelling behavior and in vitro degradation properties. Furthermore, a scanning electron microscope reported that the surface morphology of the Lidocaine (LDC)-loaded SCD/HA hydrogels was smooth, and a significant change in porosity was observed after the addition of LDC (0.5%, 1.0%, and 1.5% w/v). The occurrence of cross-linking between the LDC and SCD/HA hydrogels was studied through rheological analysis. Approximately 94% of lidocaine (LDC) was released from the SCD/HA-LDC formulations by 24 h. The cytotoxicity of SCD/HA-LDC was studied against fibroblast (3T3) cells. SCD/HA-LDC showed a prolonged in vitro release and lower cytotoxicity when compared to free LDC. Furthermore, in vivo evaluation of the anesthetic properties in animal models showed that the SCD/HA-LDC showed a significantly prolonged analgesic effect when compared to free LDC. Moreover, the SCD/HA-LDC exhibited good biodegradability and biocompatibility in histological analyses. Overall, the findings suggest that the LDC-loaded SCD/HA hydrogels had a synergistic impact in prolonging analgesia without generating toxicity, and hence might be used as a long-acting analgesia therapeutic care.

Background and Purpose Prognostic stratification in non‐small cell lung cancer (NSCLC) presents considerable challenges due to tumor heterogeneity. Emerging evidence has proposed that adipose tissue may play a prognostic role in oncological outcomes. This study investigates the integration of deep learning (DL)–derived computed tomography (CT) imaging biomarkers with mediastinal adiposity metrics to develop a multimodal prognostic model for postoperative survival prediction in NSCLC patients. Methods A retrospective cohort of 702 surgically resected NSCLC patients was analyzed. Tumor radiomic features were extracted using a DenseNet121 convolutional neural network architecture, while mediastinal fat area (MFA) was quantified through semiautomated segmentation using ImageJ software. A multimodal survival prediction model was developed through feature‐level fusion of DL‐extracted tumor characteristics and MFA measurements. Model performance was evaluated using Harrell's concordance index (C‐index) and receiver operating characteristic (ROC) analysis. Risk stratification was performed using an optimal threshold derived from training data, with subsequent Kaplan–Meier survival curve comparison between high‐ and low‐risk cohorts. Results The DL‐based tumor model achieved C‐indices of 0.787 (95% CI: 0.742–0.832) for disease‐free survival (DFS) and 0.810 (95% CI: 0.768–0.852) for overall survival (OS) in internal validation. Integration of MFA with DL‐derived tumor features yielded a multimodal model demonstrating enhanced predictive performance, with C‐indices of 0.823 (OS) and 0.803 (DFS). Kaplan–Meier analysis revealed significant survival divergence between risk‐stratified groups (log‐rank p < 0.05). Conclusion The multimodal fusion of DL‐extracted tumor radiomics and mediastinal adiposity metrics represents a significant advancement in postoperative survival prediction for NSCLC patients, demonstrating superior prognostic capability compared to unimodal approaches.

CD40L and statins exhibit pro-inflammatory and anti-inflammatory effects, respectively. They are both pleiotropic and can regulate extracellular matrix (ECM) degeneration in an atherosclerotic plaque. Statins can decrease both the CD40 expression and the resulting inflammation. However, the effects of CD40L and stains on atherosclerotic plaque ECM production and the underlying mechanisms are not well established. Moreover, prolyl-4-hydroxylase α1 (P4Hα1) is involved in collagen synthesis but its correlations with CD40L and statins are unknown. In the present study, CD40L suppressed P4Hα1 expression in human aortic smooth muscle cells (HASMCs) in a dose- and time-dependent manner, with insignificant changes in MMP2 expression and negative enzymatic activity of MMP9. CD40L increased TRAF6 expression, JNK phosphorylation, NF-κB nuclear translocation as well as DNA binding. Furthermore, silencing TRAF6, JNK or NF-κB genes abolished CD40L-induced suppression of P4Hα1. Lower NF-κB nuclear import rates were observed when JNK or TRAF6 silenced HASMCs were stimulated with CD40L compared to HASMCs with active JNK or TRAF6. Together, these results indicate that CD40L suppresses P4Hα1 expression in HASMCs by activating the TRAF6-JNK- NF-κB pathway. We also found that rosuvastatin inhibits CD40L-induced activation of the TRAF6-JNK- NF-κB pathway, thereby significantly rescuing the CD40L stimulated P4Hα1 inhibition. The results from this study will help find potential targets for stabilizing vulnerable atherosclerotic plaques.

Using a 2 (regulatory focus: promotion/prevention-focused) * 2 (decision-making strategies: intuitive/rational strategies) experimental design, the current study explored the influence of regulatory focus and decision-making strategies on moral judgment. The results are as follows: (a) The main influencing effect of regulatory focus was statistically significant. Specifically, participants that were promotion-focused tended to make utilitarian moral judgments while participants that were prevention-focused tended to make deontological moral judgments. (b) The interaction effect of regulatory focus and decision-making strategies was also statistically significant. Specifically, moral judgement scores from participants that were promotion-focused were higher when they adopted intuitive rather than rational strategies while the scores of participants that were prevention-focused were higher when they adopted rational rather than intuitive strategies. These results suggest that the fit between regulatory focus and decision-making strategies can influence moral judgment. KEYWORDS regulatory focus decision-making strategies regulatory fit moral judgment