The two core causes of obesity in modern lifestyle are high-fat diet (HFD) and insufficient physical activity. HFD can lead to disruption of gut microbiota and abnormal lipid metabolism, further exacerbating the process of obesity. The small intestine, as the “first checkpoint” for the digestion and absorption of dietary lipids into the body, plays a pivotal role in lipid metabolism. The small intestine is involved in the digestion, absorption, transport, and synthesis of dietary lipids. The absorption of lipids in the small intestine is a crucial step, as overactive absorption leads to a large amount of lipids entering the bloodstream, which affects the occurrence of obesity. HFD can lead to insulin resistance, disruption of gut microbiota, and inflammatory response in the body, which can further induce lipid absorption and metabolism disorders in the small intestine, thereby promoting the occurrence of chronic metabolic diseases such as obesity. Long term HFD can accelerate pathological structural remodeling and lipid absorption dysfunction of the small intestine: after high-fat diet, the small intestine becomes longer and heavier, with excessive villi elongation and microvilli elongation, thereby increasing the surface area of lipid absorption and causing lipid overload in the small intestine. In addition, overexpression of small intestine uptake transporters, intestinal mucosal damage induced “intestinal leakage”, dysbiosis of intestinal microbiota, ultimately leading to abnormal lipid absorption and chronic inflammation, accelerating lipid accumulation and obesity. Exercise, as one of the important means of simple, economical, and effective proactive health interventions, has always been highly regarded for its role in improving lipid metabolism homeostasis. The effect of exercise on small intestine lipid absorption shows a dose-dependent effect. Moderate to low-intensity aerobic exercise can improve the intestinal microenvironment, regulate the structure and lipid absorption function of the small intestine, promote lipid metabolism and health, while vigorous exercise, excessive exercise, and long-term high-intensity training can cause intestinal discomfort, leading to the destruction of intestinal structure and related symptoms, affecting lipid absorption. Long term regular exercise can regulate the diversity of intestinal microbiota, inhibit inflammatory signal transduction such as NF-κB, enhance intestinal mucosal barrier function, and improve intestinal lipid metabolism disorders, further enhancing the process of small intestinal lipid absorption. Exercise also participates in the remodeling process of small intestinal epithelial cells, regulating epithelial structural homeostasis by activating cell proliferation related pathways such as Wnt/β-catenin. Exercise can regulate the expression of lipid transport proteins CD36, FATP, and NPC1L1, and regulate the function of small intestine lipid absorption. However, the research on the effects of long-term exercise on small intestine structure, villus structure, absorption surface area, and lipid absorption related proteins is not systematic enough, the results are inconsistent, and the relevant mechanisms are not clear. In the future, experimental research can be conducted on the dose-response relationship of different intensities and forms of exercise, exploring the mechanisms of exercise improving small intestine lipid absorption and providing theoretical reference for scientific weight loss. It should be noted that the intestine is an organ that is sensitive to exercise response. How to determine the appropriate range, threshold, and form of exercise intensity to ensure beneficial regulation of intestinal lipid metabolism induced by exercise should become an important research direction in the future.

Metaflammation is a crucial mechanism in the onset and advancement of metabolic disorders, primarily defined by the activation of immune cells and increased concentrations of pro-inflammatory substances. The function of brain-derived neurotrophic factor (BDNF) in modulating immune and metabolic processes has garnered heightened interest, as BDNF suppresses glial cell activation and orchestrates inflammatory responses in the central nervous system via its receptor tyrosine kinase receptor B (TrkB), while also diminishing local inflammation in peripheral tissues by influencing macrophage polarization. Exercise, as a non-pharmacological intervention, is extensively employed to enhance metabolic disorders. A crucial mechanism underlying its efficacy is the significant induction of BDNF expression in central (hypothalamus, hippocampus, prefrontal cortex, and brainstem) and peripheral (liver, adipose tissue, intestines, and skeletal muscle) tissues and organs. This induction subsequently regulates inflammatory responses, ameliorates metabolic conditions, and decelerates disease progression. Consequently, BDNF is considered a pivotal molecule in the motor-metabolic regulation axis. Despite prior suggestions that BDNF may have a role in the regulation of exercise-induced inflammation, systematic data remains inadequate. Since that time, the field continues to lack structured descriptions and conversations pertinent to it. As exercise physiology research has advanced, the academic community has increasingly recognized that exercise is a multifaceted activity regulated by various systems, with its effects contingent upon the interplay of elements such as type, intensity, and frequency of exercise. Consequently, it is imperative to transcend the prior study paradigm that concentrated solely on localized effects and singular mechanisms and transition towards a comprehensive understanding of the systemic advantages of exercise. A multitude of investigations has validated that exercise confers health advantages for individuals with metabolic disorders, encompassing youngsters, adolescents, middle-aged individuals, and older persons, and typically enhances health via BDNF secretion. However, exercise is a double-edged sword; the relationship between exercise and health is not linearly positive. Insufficient exercise is ineffective, while excessive exercise can be detrimental to health. Consequently, it is crucial to scientifically develop exercise prescriptions, define appropriate exercise loads, and optimize health benefits to regulate bodily metabolism. BDNF mitigates metaflammation via many pathways during exercise. Initially, BDNF suppresses pro-inflammatory factors and facilitates the production of anti-inflammatory factors by modulating bidirectional transmission between neural and immune cells, therefore diminishing the inflammatory response. Secondly, exercise stimulates the PI3K/Akt, AMPK, and other signaling pathways via BDNF, enhancing insulin sensitivity, reducing lipotoxicity, and fostering mitochondrial production, so further optimizing the body’s metabolic condition. Moreover, exercise-induced BDNF contributes to the attenuation of systemic inflammation by collaborating with several organs, enhancing hepatic antioxidant capacity, regulating immunological response, and optimizing “gut-brain” axis functionality. These processes underscore the efficacy of exercise as a non-pharmacological intervention for enhancing anti-inflammatory and metabolic health. Despite substantial experimental evidence demonstrating the efficacy of exercise in mitigating inflammation and enhancing BDNF levels, numerous limitations persist in the existing studies. Primarily, the majority of studies have concentrated on molecular biology and lack causal experimental evidence that explicitly confirms BDNF as a crucial mediator in the exercise regulation of metaflammation. Furthermore, the outcomes of current molecular investigations are inadequately applicable to clinical practice, and a definitive pathway of “exercise-BDNF-metaflammation” remains unestablished. Moreover, the existing research methodology, reliant on animal models or limited human subject samples, constrains the broad dissemination of the findings. Future research should progressively transition from investigating isolated and localized pathways to a comprehensive multilevel and multidimensional framework that incorporates systems biology and exercise physiology. Practically, there is an immediate necessity to undertake extensive, double-blind, randomized controlled longitudinal human studies utilizing multi-omics technologies (e.g., transcriptomics, proteomics, and metabolomics) to investigate the principal signaling pathways of BDNF-mediated metaflammation and to elucidate the causal relationships and molecular mechanisms involved. Establishing a more comprehensive scientific evidence system aims to furnish a robust theoretical framework and practical guidance for the mechanistic interpretation, clinical application, and pharmaceutical development of exercise in the prevention and treatment of metabolic diseases.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Objectives: To investigate whether the protective actions of ginsenoside Rb1 (Rb1) on astrocytes are mediated through the Gs-type G-protein-coupled receptor (GPCR-Gs). Methods: Primary astrocyte cultures derived from neonatal mouse brain were used. Astrocyte injury was induced via oxygen-glucose deprivation/re-oxygenation (OGD/R). Cell morphology, viability, lactate dehydrogenase (LDH) leakage, apoptosis, glutamate uptake, and brain-derived neurotrophic factor (BDNF) secretion were assessed to gauge cell survival and functionality. Western blot was used to investigate the cyclic adenosine monophosphate (cAMP) and protein kinase B (Akt) signaling pathways. GPCR-Gs-specific inhibitors and molecular docking were used to identify target receptors. Results: Rb1 at concentrations ranging from 0.8 to 5 μM did not significantly affect the viability, glutamate uptake, or BDNF secretion in normal astrocytes. OGD/R reduced astrocyte viability, increasing their LDH leakage and apoptosis rate. It also decreased glutamate uptake and BDNF secretion by these cells. Rb1 had protective effects of astrocytes challenged by OGD/R, by improving viability, reducing apoptosis, and enhancing glutamate uptake and BDNF secretion. Additionally, Rb1 activated the cAMP and Akt pathways in these cells. When the GPCR-Gs inhibitor NF449 was introduced, the protective effects of Rb1 completely disappeared, and its activation of cAMP and Akt signaling pathways was significantly inhibited. Conclusion Rb1 protects against astrocytes from OGD/R-induced injury through GPCR-Gs mediation.

Objective To screen the risk factors and risk prediction model for coronary atherosclerotic heart disease in Xining area. Methods Five hundred and eighteen patients with coronary atherosclerotic heart disease who attended the Qinghai Specialized Hospital for Cardiovascular and Cerebrovascular Diseases and Cerebrovascular Diseases from May 2018 to September 2022 were selected as the observation group, and another 421 patients with non-coronary heart disease were set as control group. The general information of patients were collected. The risk factors affecting the development of coronary heart disease were screened using Logistic regression analysis, and a risk prediction model was constructed, then receiver operating characteristic (ROC) curve was plotted to validate the predictive value of risk model. Results The gender ratio and smoking history yielded no statistical difference between two groups (P>0.05), but statistical difference was found in age, body mass index (BMI), diabetes history, hypertension history, smoking history and family history between two groups (P<0.05). Serum levels of total cholesterol (TC), low-density lipoprotein (LDL) and uric acid (UA) were all higher than the control group, and serum high-density lipoprotein (HDL) level was lower than the control group, with statistical difference (all P<0.05). Multivariate Logistic regression analysis denoted that age, BMI, diabetes history, hypertension history, smoking history, family history, TC, LDL, and UA were risk factors for the development of coronary heart disease (P<0.05). ROC curve analysis yielded an AUC of 0.890 for the risk model, with a sensitivity and specificity of 72.03% and 91.14%, respectively. Conclusion Patient's age, BMI, and the presence of diabetes mellitus, hypertension and smoking, family history, abnormal blood lipid profiles, and abnormal blood uric acid are all risk factors for the development of coronary heart disease, and the risk model constructed on the basis of the above risk factors has a high degree of sensitivity and specificity, which is of great value in accurately evaluating the risk of coronary heart disease.

BackgroundPrevention and intervention of procrastination in college students are of great practical significance， and studies have illustrated a pairwise correlation among mindfulness， self-control， insecure attachment and procrastination， whereas the mechanism by which insecure attachment leads to procrastination remains unclear， and the related mediation path is quite understudied. ObjectiveTo investigate the effect of insecure attachment on procrastination among college students and the pathway of mindfulness and self-control， so as to inform the design of interventions for procrastination among college students. MethodsRandom and cluster sampling method were utilized to enroll 514 college students from 4 colleges in Guangdong Province From February to April 2023. Subjects were assessed using Irrational Procrastination Scale （IPS）， Adult Attachment Scale （AAS）， Mindful Attention Awareness Scale （MAAS） and Brief Self-Control Scale （BSCS）. Pearson correlation analysis were adopted to identify the correlation among above scales， and the mediation effect was examined via Bootstrap procedure. ResultsAAS score was positively correlated with IPS score （r=0.382， P<0.01）， and negatively correlated with MAAS and BSCS scores （r=-0.242， -0.353， P<0.01）. IPS score was negatively correlated with MAAS and BSCS scores （r=-0.314， -0.682， P<0.01）. MAAS score was positively correlated with BSCS score （r=0.439， P<0.01）. Insecure attachment positively predicted procrastination （β=0.377， P<0.01）， and the prediction of procrastination by insecure attachment was mediated by self-control， with an indirect effect value of 0.163 （95% CI： 0.105~0.223）， accounting for 43.24% of the total effect value. The mindfulness and self-control exerted a chained mediation effect on the relationship between insecure attachment and procrastination， and the indirect effect value was 0.056 （95% CI： 0.028~0.089）， accounting for 14.85% of the total effect value. ConclusionInsecure attachment can influence procrastination among college students both directly and indirectly through the single mediation of self-control or the chained mediation of mindfulness and self-control. ［Funded by 2023 National College Student Innovation and Entrepreneurship Training Program （number， 202310570023）］

ObjectiveTo investigate the influence of concentration ratio（CR） between the internal reference and target components on the quantitative accuracy of quantitative analysis of multi-components by single marker（QAMS） by taking ginsenosides as an example. MethodUltra performance liquid chromatography（UPLC） was employed， the contents of nine components in Ginseng Radix et Rhizoma（ginsenosides Rg₁， Re， Rf， Rh₁， Rb₁， Rc， Rb₂， Rb₃， Rd） were determined by external standard method（ES）. Using ginsenoside Rg₁ as the internal reference， the contents of the remaining 8 ginsenosides were determined by QAMS， and the quantitative results were compared with those of ES to evaluate the quantitative accuracy of the established QAMS. According to the contents of these 9 ginsenosides， the simulated sample solutions with different CRs of ginsenoside Rg₁ to ginsenosides Rf， Rb₂， Rd were prepared with the reference substance（CR=100∶1， 10∶1， 5∶1， 2∶1， 1∶1， 0.5∶1， 0.25∶1）， in order to validate the effect of the CRs between the internal reference and other components on the quantitative accuracy of the QAMS. ResultThe results of ginsenosides Re， Rf， Rb₁， Rc， Rb₂ calculated by the two methods were the same with the relative standard deviation（RSD）<3%， however， the results of ginsenosides Rh₁， Rb₃ and Rd calculated by the two methods were different with RSDs of 7.06%-9.61%. According to the result of the simulated sample solution， the difference between the calculated results of ES and QAMS was large when the CR between the internal reference（ginsenoside Rg₁） and other components was 5 or 10 or even higher. ConclusionThe quantitative error of QAMS will increase when the CR between the quantitative component and the internal reference is too large， so it is suggested that when establishing the QAMS， the components with close concentration to the internal reference should be selected for quantification.

In recent years, obesity has emerged as a significant risk factor jeopardizing human health and stands out as an urgent issue demanding attention from the global public health sector. The factors influencing obesity are intricate, making it challenging to comprehensively elucidate its causes. Recent studies indicate that food reward significantly contributes to the genesis and progression of obesity. Food reward comprises three integral components: hedonic value (liking), eating motivation (wanting), and learning and memory. Each facet is governed by the corresponding neural pathway. The mesocorticolimbic system (MS) plays a pivotal role in regulating food reward, wherein the MS encompasses dopamine (DA) neurons originating from the ventral tegmental area (VTA) projecting into various brain regions or nuclei such as the nucleus accumbens (NAc), prefrontal cortex (PFC), amygdala, and hippocampus. On one hand, prolonged consumption of palatable foods induces adaptive alterations and synaptic remodeling in neural circuits regulating food reward. This includes the attenuation of neuronal connections and signal transmission among the PFC, visual cortex, hypothalamus, midbrain, and brain stem, resulting in aberrant food reward and compelling the body to compensate for satisfaction deficiency by increasing food consumption. Studies involving humans and animals reveal that compulsive eating bears resemblance to the behavior observed in individuals with substance addictions, encompassing aspects such as food cravings, loss of eating control, and dieting failures. Propelled by food reward, individuals often opt for their preferred palatable foods during meals, potentially leading to excessive energy intake. Coupled with a sedentary lifestyle, this surplus energy is stored in the body, transforming into fat and culminating in obesity. While evidence supports the notion that prolonged exposure to a high-energy-density diet contributes to abnormal food reward, the internal mechanisms remain somewhat unclear. In previous research on depression, substance abuse, and alcohol dependence, it has been confirmed that there is a close connection between inflammation and reward. For example, obese people show a higher tendency toward depression, and white blood cell count is an important mediating variable between intake and depressive symptoms. In addition, it has been found in individuals with alcohol dependence and drug abuse that long-term opioid overdose or alcohol abuse will activate glial cells to release pro-inflammatory cytokines that affect neuronal function, and disrupt synaptic transmission of neurotransmitters to promote addictive behaviors. Comprehensive analysis suggests that inflammation may play an important role in the reward regulation process. Recent studies indicate that metaflammation within the central or peripheral system, triggered by excess nutrients and energy, can disrupt the normal transmission of reward signals. This disruption affects various elements, such as DA signaling (synthesis, release, reuptake, receptor function, and expression), mu opioid receptor function, glutamate excitatory synaptic transmission, Toll-like receptor 4 (TLR4) signal activation, and central insulin/leptin receptor signal transduction. Consequently, this disruption induces food reward dysfunction, thereby fostering the onset and progression of obesity. Building upon these findings, we hypothesized that obesity may be linked to abnormal food reward induced by metaflammation. This review aims to delve deeply into the intricate relationship between obesity, food reward, and metaflammation. Additionally, it seeks to summarize the potential mechanisms through which metaflammation induces food reward dysfunction, offering novel insights and a theoretical foundation for preventing and treating obesity.

Humans ; Transcatheter Aortic Valve Replacement ; Aortic Valve/surgery* ; Heart Valve Prosthesis Implantation ; Renal Dialysis ; Aortic Valve Stenosis/surgery* ; Treatment Outcome ; Heart Valve Prosthesis

Humans ; Transcatheter Aortic Valve Replacement ; Aortic Valve/surgery* ; Heart Valve Prosthesis Implantation ; Renal Dialysis ; Aortic Valve Stenosis/surgery* ; Treatment Outcome ; Heart Valve Prosthesis