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The antiadipogenic effect of the pentacyclic triterpenoid isoarborinol is mediated by LKB1-AMPK activation
Obesity and overweight are two highly prevalent conditions worldwide, which can lead to death or produce chronic and degenerative diseases. The search for alternative therapies to control these morbidities can involve the study of metabolites obtained from plants. Particularly, pentacyclic triterpenes produce an antiadipogenic effect by affecting the expression of master regulators of adipogenesis and their signaling pathways, including LKB1-AMPK pathway. In this work, we evaluated the effect of the pentacyclic triterpene isoarborinol on adipogenesis in 3T3-L1 cells. This molecule inhibits differentiation and decreases lipid accumulation during cell differentiation in a dose-dependent manner, deregulates the expression of C/EBPβ, C/EBPδ, C/EBPα, PPARγ and SREBP-1C, causes an increase in the phosphorylation of LKB1 and AMPK, as well as a down regulation of the lipogenic factors ACC1, FAS and FABP4. These findings show that the antiadipogenic effect of isoarborinol is associated with the activation of LKB1-AMPK, leading to changes in the expression of master regulators of adipogenesis and lipogenic factors.
Identification of immune-related biomarkers associated with allergic rhinitis and development of a sample diagnostic model
This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.
Hidden community interlayer spillover detection in financial multilayer networks: Generalization of hierarchical clustering to multilayer networks
Interdependent networks structurally influence each other so that the source network imposes hidden community structures into the target network. We propose a mathematical model so that when introducing an interlayer similarity function we generalize hierarchical clustering approaches for multilayer networks. The proposed methodology shows how a “source” network influences the “target” network via structural spillovers that are hidden and are not detectable by conventional community detection methods. The methodology reveals evidence that hidden interlayer interactions consequently generate hidden links on the target network. These hidden links construct hidden community structures on the target network (imposed from the source network) that are distinct from the community structures of the solo target network (without the presence of the source network). This model applies to systems with hidden interlayer interactions, such as, e.g., covert criminal groups, inter-platform social network interactions, scientific research groups, and financial markets. Financial markets are well known for complicated endogenous and exogenous, but often hidden, not to say the least, asymmetric layer interactions. We implement our model on multilayer financial networks: in particular, we find that trading value logarithmic changes (source) impose hidden community structures on the price return network (target). The main finding is that adding another relevant layer, such as the trading value layer, adds more information to systemic behaviors throughout the price return network. Dismissing it may yield less systemic information and underestimation of systemic risk because the footprint of some structures on the target network originated from another layer and is not detectable by singling out the target layer. As an empirical application, we exploit the methodology to define another perspective on portfolio diversification.
Redefining player roles in professional women’s basketball: From traditional positions to functional profiles
The analysis of box-score performance indicators has traditionally been used to classify player roles in women’s basketball based on the five conventional positions: point guard, shooting guard, small forward, power forward, and center. However, this framework may not reflect the current tactical and functional demands of the game. The aim of this study was to identify and redefine functional player roles in professional women’s basketball using performance data derived from actual competition. A total of 36,204 individual player records from 3,894 games in the Spanish Liga Femenina Endesa (2012–2022) were analyzed. Game-related statistics were normalized by effective playing time and scaled to a 40-minute format. One-way ANOVA revealed significant differences across traditional positions, but also indicated considerable functional overlap. Unsupervised learning techniques, including k-means and Gaussian mixture models, were applied to identify underlying performance-based player profiles. The analysis yielded nine stable and interpretable functional roles, offering a more nuanced classification than the traditional five-position model. These roles capture offensive, defensive, and hybrid specializations, providing coaches and analysts with a practical framework for tactical planning, scouting, and individualized player development. The findings support a shift toward data-driven classification systems that better reflect the functional realities of modern elite women’s basketball.
How loneliness relates to health, wellbeing, quality of life, and healthcare resource utilisation and costs across multiple age groups in the UK
Increasing evidence of its detrimental impact has brought loneliness to the forefront of public health in recent years. Loneliness has been recognised as a cross-cutting theme for Healthy Ageing by the World Health Organisation and there is increasing need to better understand its wide-ranging health, wellbeing, and economic impacts across the wider population. This study utilises data from wave 13(2021–2023) of the Understanding Society UK Household Longitudinal Study to evaluate health and economic outcomes associated to loneliness (UCLA 3-item scale). Outcomes include the General Health Questionnaire, Short Form Health Survey, Short Warwick-Edinburgh Mental Well-being Scale, and costed GP, outpatient, and inpatient visits. Generalised Linear Modelling is applied to adjust for demographic characteristics, and subgroup analysis is conducted to consider costs in different age groups. Complete data provided observations for 23,071 respondents. Average marginal effects found loneliness is associated to higher mental distress, lower positive mental wellbeing, poorer physical and mental functioning, and higher healthcare service use. Overall, there is approximately a £900 cost difference in healthcare use between lonely and non-lonely respondents. Cost difference increases with age, and for more severe loneliness forms a U-shape with the greatest costs in younger and older adults. Additionally, difference in mean is only statistically significant across all models for 16- to 24-year-olds, suggesting importance in targeting young adult healthcare resource use. This is the first study to consider age-based analysis of health-related costs of loneliness in the UK. It also adds to the literature by considering validated wellbeing and health-related outcomes in a large UK based population. Findings motivate tackling young adult loneliness to support their health, wellbeing, quality of life, and potential overuse of healthcare services. This study also supports the pressing need for greater economic evaluation of loneliness and loneliness interventions.
Effect of planned preoperative oral care implemented at least 2 weeks before surgery on postoperative infections: A single-center retrospective observational study
This study aimed to investigate whether initiating oral care more than 2 weeks before surgery could prevent postoperative infections, particularly pneumonia. This retrospective observational study analyzed 1,806 patients who underwent surgery at the Ehime University Hospital between April 2019 and March 2023. The patients were divided into two groups: those receiving structured oral care at least 2 weeks before surgery (n = 257) and those receiving late or no oral care (n = 1,549). Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to minimize selection bias. Nevertheless, residual confounding factors, especially confounding by indication, may remain, because as patients who received early oral care may differ systematically from those who did not. The primary outcome measure was the incidence of postoperative pneumonia. After PSM and IPTW analyses, the early oral care group showed significantly lower rates of postoperative pneumonia than the control group (risk difference: −3.56%, 95% confidence interval [CI]: −4.89% to −2.23%, p = 0.0004 in the matched analysis; −3.68%, 95% CI: −4.61% to −2.75%, p < 0.001 in the IPTW analysis). IPTW analysis demonstrated shorter hospital stays in the early oral care than in the control group (mean difference: −2.65 days, 95% CI: −4.75 to −0.55, p = 0.013). Implementing structured oral care at least 2 weeks before surgery reduced postoperative pneumonia and shortened hospital stays across various surgical procedures, suggesting its value as a preventive strategy for improving surgical outcomes.
Social determinants of health in lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) older adults: Impact of socioeconomic disadvantage on inpatient hospitalizations
Introduction Little is known about the impact of socioeconomic disadvantage on lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) older adults (≥50 years). The aim of this study is to determine whether the distribution of LGBTQ+ inpatient hospitalizations are related to structural socioeconomic factors. Methods A secondary analysis of retrospective electronic health record data for LGBTQ+ older adults hospitalized from 2018 to 2022 was conducted at one large health system. The average county area deprivation index where the patient resided was calculated. Results The analysis included 2270 LGBTQ+ older adult inpatient hospitalizations, with 1508 (66.4%) from low socioeconomic disadvantage, 595 (26.3%) from moderate socioeconomic disadvantage; and 17 (7.4%) from high socioeconomic disadvantage counties (p < .0001). LGBTQ+ older adults who resided in moderate and high socioeconomic disadvantaged counties had a significant proportion of patients identifying as asexual (a posteriori contrasts, p < .05) compared to the low socioeconomic disadvantaged group. Those from moderate socioeconomic disadvantaged counties had a significantly higher proportion of patients identifying as bisexual (a posteriori contrasts, p < .05) compared to the high socioeconomic disadvantaged group. Discussion This analysis highlights socioeconomic disadvantage of LGBTQ+ older adults who utilized one large health system. More work needs to be done to understand use of the hospital system by LGBTQ+ older adults in moderate to high socioeconomic disadvantaged areas.
Cheminformatics-based screening and evaluation of phytochemicals as CDK2 inhibitors in colorectal cancer therapy
Colorectal cancer (CRC) poses a significant global health issue. It ranks as the third most common type of cancer and the second leading cause of cancer-related deaths. Among the molecular factors driving its progression, cyclin-dependent kinase 2 (CDK2) plays a key role. CDK2 is a protein kinase essential for regulating the cell cycle, and its dysregulation is implicated in the development of various cancers, notably CRC. Fruquintinib is an already available drug against CRC. However, this study is being performed in search of better drug-like compounds. Some studies have shown that phytochemicals are less toxic and have fewer adverse effects than commercially available medications. With the vision of detecting CDK2 inhibitors, phytochemicals with anticancer activity can be used as alternatives to develop the drug candidate. Cheminformatics-based analysis is used for this purpose. This approach includes molecular docking, adsorption, distribution, metabolism, excretion/toxicity (ADME/T), post-docking molecular mechanism generalized born surface area (MM-GBSA), structural activity relationship (SAR), frontier molecular orbital (FMO), and molecular dynamics (MD) simulations. Molecular docking was employed to determine the binding strength of 4433 phytochemicals with anti-cancer properties sourced from the IMPPAT database. The top five candidates, CIDs-135438111, 6474893, 44257567, 10469828, and 353825, were selected based on their docking scores. Later, three lead compounds, CIDs-6474893, 10469828, and 135438111, were finalized depending on their favorable ADME/T profiles. All three selected pharmaceuticals demonstrated excellent post-dock MM-GBSA scores and HOMO-LUMO energy gaps, which served as confirmation of their efficacy and safety. The SAR analysis also revealed anti-mutagenic, antineoplastic, and apoptosis-inducing properties of the compounds. Finally, the rigidity of the protein-ligand complex structures was verified by MD simulations. Overall, the study suggests these three phytochemicals exhibit stronger binding and better pharmacological profiles than the control (fruquintinib), offering a promising direction for CRC treatment development.
The formation mechanism of “sports fandom circle” in the digital media era: An analysis from the perspective of fan emotional dynamic development
The rise of the “sports fandom circle” has become a significant phenomenon in the digital media era, reshaping the emotional and social dynamics of sports fan communities. This study employs grounded theory methodology to analyze web-scraped data from 40 selected accounts on major Chinese social media platforms [Weibo, Xiaohongshu, and Bilibili] over a two-year period. These accounts were identified based on their focus on celebrity athletes [e.g., Fan Zhendong, Sun Yingsha, Wang Chuqin] and their engagement in fan-specific activities such as support, fan clubs, and voting. The analysis involved open coding, axial coding, and selective coding to construct an emotional interaction model. Our findings reveal that international sports events serve as emotional catalysts, where competition, nationalism, and athlete narratives trigger fans’ emotional arousal. Digital media platforms, including spatiotemporal extension, virtualization, and selectivity, amplify these emotions, fostering proximal emotional attachment to athletes. Through symbolic interactions and digital consumption, fans transition from individual engagement to collective emotional aggregation, solidifying their sense of group identity. However, in the reproduction process, the anonymity of digital media platforms can lead to emotional polarization, intensifying conflicts among fan groups, or facilitate emotional resocialization, fostering more rational and inclusive fandom behaviors. This study provides a theoretical framework for understanding the emotional evolution of sports fandom in the digital media era and offers practical insights for managing online fan communities, mitigating conflicts, and promoting a healthier digital sports culture.