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Abstract Background Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in AS, remain largely unexplored and are a subject of ongoing research. Methods This study employed advanced bioinformatics techniques to identify and validate FAMGs associated with AS. We conducted differential expression analysis on a select list of 49 candidate FAMGs. GSEA and GSVA were utilized to elucidate the potential biological roles and pathways of these FAMGs. Subsequently, Lasso regression and SVM-RFE were applied to identify key hub genes and assess the diagnostic efficacy of seven FAMGs in distinguishing AS. The study also explored the correlation between these hub FAMGs and clinical features of AS. Validation of the expression levels of the seven FAMGs was performed using datasets GSE43292 and GSE9820. Results The study pinpointed seven FAMGs with a close association to AS: ACSBG2, ELOVL4, ACSL3, CPT2, ALDH2, HSD17B10, and CPT1B. Analysis of their biological functions underscored their significant involvement in critical processes such as fatty acid metabolism, small molecule catabolism, and nucleoside bisphosphate metabolism. The diagnostic potential of these seven FAMGs in AS differentiation showed promising results. Conclusions This research has successfully identified seven key FAMGs implicated in AS, offering novel insights into the pathophysiology of the disease. These findings not only contribute to our understanding of AS but also present potential biomarkers for the disease, opening avenues for more effective monitoring and progression tracking of AS.

Abstract Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Abstract Background Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)—a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. Methods Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. Results Our analyses revealed that eleven PyMGs—DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2—are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. Conclusions This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma’s molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.

Abstract Background Effective interprofessional teamwork is essential for the efficiency, safety and quality of healthcare system services and requires interprofessional education for medical students. Physical education is a simple and easy way to teach teamwork, which translates into team performance in the work environment. This study was conducted to examine the effectiveness of the physical education competition model, instead of the exams model, for improving teamwork skills among medical students. Methods A quasiexperimental intervention design was used to measure the effect of a 16-week cheerleading programme on subjects' teamwork skills by completing a teamwork scale comprising four subdimensions, namely, personal characteristics, teamwork, leadership, and conflict management, before the start and at the end of the programme, and by comparing nonwinning to winning students to measure the effect of teamwork skills on team performance. Results A total of 179 students completed the valid baseline and posttest (effective rate = 95.21%). The teamwork scale scores (B M = 4.81, R M = 5.05, p < 0.001) and 4 subdimension scores (personal characteristics p = 0.002, teamwork p = 0.028, leadership p < 0.001, conflict management p < 0.001) were statistically significant. Twenty-two of the 44 items in the scale improved significantly. The differences between students who won the competition and those who did not (N M=4.86, W M=5.14, p

Abstract Aims Additional evidence has indicated a correlation between N6-methyladenosine (m6A) RNA methylation and cardiovascular disease. Nevertheless, the alterations in RNA methylation modification and the expression of numerous genes remains unclear. This study aimed to identify the role of m6A in ST-segment elevation myocardial infarction (STEMI). Methods Two microarray datasets (GSE123342 and GSE59867) were downloaded from the GEO database. After merging the data and batch normalization, differentially expressed regulators were identified using the limma package. Subtyping consistency analysis was performed to group samples. The random forest algorithm and support vector machine were used to identify diagnostic biomarkers. Immune infiltration and inflammation levels among the subtypes were assessed using a single-sample gene set enrichment analysis. Results A total of 15 key differential m6A regulators (RBM15B, ELAVL1, ALKBH5, METTL16, ZC3H13, RBM15, YTHDC1, YTHDC2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, RBMX, FTO) were identified using the random forest classifier and were found to be highly correlated by PPI analysis. Two distinct RNA modification patterns (cluster A and B) were validated based on the expression levels of the 15 key m6A regulators. GO and KEGG annotations showed that immunity and inflammation pathways were enriched. Immune infiltration analysis revealed that cluster 2 had higher immune activation than cluster 1. Further analysis showed that cluster 2 had a higher inflammation level, with IL-4 and IL-33 showing differential expression (p

Abstract Background This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS. Methods A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets. Results This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results. Conclusion In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.

To compare the effects of different exercise training on executive function (EF) in obese or overweight individuals. PubMed, Web of Science, SPORTDiscus, MEDLINE, and CINAHL. The included articles, in English, should have been published from January 2000 to February 2024. All included studies were randomized controlled trials (RCTs) of exercise intervention in overweight or obese populations. The primary outcomes are EFs, which encompass core functions (e.g., inhibitory control, working memory (WM), and cognitive flexibility (CF)) and higher-level functions (e.g., responding, planning, and problem-solving). Therefore, the primary outcomes should include at least one of the above indicators. Additionally, given the focus of many exercise intervention studies on academic performance (AP) in obese adolescents, a secondary outcome includes AP. This meta-analysis synthesizes findings from 20 RCTs published between 2010 and 2023, encompassing a total of 1183 overweight or obese participants. Interventions were categorized into seven types: control training (CT), aerobic exercise (AE), resistance training (RT), coordinated physical activity (CPA), prolonged time of exercise (PTE), high-intensity interval training (HIIT), and AE combined with RT (mix mode, MIX). The surface under the cumulative ranking curve (SUCRA) results demonstrated the preferable effects of various interventions on EF improvement. SUCRA values indicate that CPA performs best in improving the accuracy and reaction speed of CF, as well as the reaction speed of inhibitory control in children and adolescents. AE shows significant effects in enhancing AP in this population. Additionally, PTE excels in improving CF and inhibitory control in middle-aged and older adults. Through subgroup analysis based on age and intervention duration, we found AE exhibited a significant effect on interventions for the 0–17 age group (SMD = 0.84, 95%CI = 0.31~1.38, p = 0.002) and interventions lasting 8–16 weeks showed significant improvement in EFs (SMD = 0.53, 95%CI = 0.00~1.05, p = 0.048). There was also a significant difference between CPA intervention and CT (SMD = 1.12, 95%CI = 0.45~1.80, p = 0.001) in children and adolescents. Additionally, PTE showed significant effects for middle-aged adults aged 17–59 (SMD = 0.93, 95%CI = 0.11~1.96, p < 0.027). Conclusions: This NMA found that CPA and AE have significant benefits for CF, inhibitory control, and AP in children and adolescents. Furthermore, PTE improves EFs in adults and older adults. Combining the findings of this study with previous related research, we recommend that OW/OB begin by interrupting prolonged sedentary behavior and increasing fragmented physical activity, gradually incorporating AE, RT, and CPA (such as jump rope).

Team sports require athletes’ exceptional perceptual-cognitive skills, such as anticipation and decision-making. Perceptual-cognitive training in laboratories aims to enhance these abilities. However, its effectiveness in real-game performance remains controversial, necessitating a systematic review and meta-analysis to determine optimal training methods. Following the PRISMA guidelines, we searched databases (e.g., PubMed, WOS, Scopus, and EBSCO) for relevant studies published before November 2023, assessed study quality, extracted important characteristics, and conducted a meta-analysis using Stata 15.1. This study was registered in PROSPERO (CRD42023494324). A total of 22 quantitative studies involving 45 effect sizes were included. Perceptual-cognitive training positively influenced elite athletes’ anticipation and decision-making. However, its transfer effect on real-game performance improvement (ES = 0.65) was inferior to laboratory performance improvement (ES = 1.51). Sub-group analyses indicated that the effects of training interventions varied based on stimulus presentation and intervention duration. Based on our findings, we concluded that while perceptual-cognitive training improved on-court performance, its transfer effects were limited. To maximize effectiveness, future interventions should use virtual reality to present training stimuli and incorporate participants’ sport-specific responses to reflect real-game scenarios.

The additional local stress concentration caused by undercut and misaligned defects could lead to lower fatigue strength of welded joints. The paper aims to investigate the effects of undercuts and misalignments on fatigue performance and reliability of Load‐carrying Cruciform Welded Joint (LCWJ) quantitatively by probabilistic statistics theory and fracture mechanics theory. Firstly, the geometrical characteristics of undercut and misalignment are summarized from the literature and our experiments. Subsequently, the probabilistic distributions of material fracture properties, the configurations of geometries, and flaws were evaluated under specific loading conditions. The fatigue strengths are further estimated by the probabilistic reliability theory by combining the actual fatigue data. The results demonstrate a clear disparity of fatigue crack growth threshold and resistances for the tested data of base metal and weldment. The level of defects determines the interval of fatigue life, and the larger the dispersion accompanying earlier fatigue failure occurs. Highlights: The effects of weld defects on fatigue strength and reliability were assessed quantitatively by the probabilistic method.FCGRs of HAZ and U‐WM are more conservative than the base metal.The SIFs in mismatched LCWJs were evaluated by the weight function theory. [ABSTRACT FROM AUTHOR]

Robotic arms are frequently utilized in contemporary industrial production since they offer great qualities like high precision and low mass. How to minimize robotic arm tremors in order to maximize their control effect has emerged as one of the most critical issues to be resolved with the continued development of industrial intelligence. The study uses a combination of PID control and an artificial fish swarm technique to optimize the parameters and confirm the simulation control effect based on the kinematic analysis of a six-degree-of-freedom (Six-DOF) modular robotic arm. The findings demonstrated that the suggested fusion approach converges to zero in 80 iterations and has a recall of 0.893 and 0.785 at an accuracy of 0.8 and 0.9, respectively. The robotic arm control system's average control effect is 42.96 %, which is a respectable control performance. In the second and third studies, the fusion approach stabilized actuator end tremor suppression after 0.01 s and 0.0001 s, respectively. It shows that the technique can effectively suppress robotic arm bearing tremor and has high flexibility for robotic arm tremor suppression, which offers trustworthy technological support for improving the motion control system of industrial robots. [ABSTRACT FROM AUTHOR]

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