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Objective: Ankylosing spondylitis (AS) is a chronic autoimmune disease characterized by systemic inflammation, often resulting in fusion of the spine and peripheral joints. This study aimed to investigate the role of innate lymphoid cells (ILCs) in AS patients with high disease activity.

Methods: Blood samples were collected from healthy controls and AS patients categorized by high or low disease activity. Systemic inflammation was quantified through C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR), alongside disease activity scores such as Ankylosing Spondylitis Disease Activity Score(ASDAS) and Bath Ankylosing Spondylitis Disease Activity Index(BASDAI). The levels of different ILC subsets and the expression of T-box transcription factor 21 (T-bet) and retinoic-acid-receptor-related orphan receptor gamma (RORγt) in peripheral blood were analyzed via flow cytometry. Additionally, 24 cytokines in plasma were measured using a Luminex liquid suspension chip.

Results: The proportion of total ILCs and the distribution of ILC subsets in peripheral blood varied with AS disease activity scores. Specifically, the frequencies of total ILCs and ILC3s were significantly elevated in AS patients with high disease activity (AS-HA). The frequency and absolute number of ILC3s showed a positively correlation with disease severity scores. Furthermore, T-bet⁺ILC3s were significantly increased in the AS-HA group. Plasma levels of IL-17A and IFN-γ were positively correlated with the frequency of circulating-ILC3 in AS patients.

Conclusions: These findings highlight the critical role of T-bet⁺ILC3s in the inflammatory process of AS, suggesting their potential as a therapeutic target for managing AS disease.

Today's globalised agricultural sector poses significant environmental challenges that are expected to worsen with population growth, increased urbanisation, and with the effects of climate change. In this context, vertical farming systems have gained traction as potential solutions to create a more resilient and sustainable food system. This study aims to evaluate the environmental performance of mixed salad bags from a conventional supply chain and compare it with that of mixed salad supplied by a large-scale vertical farm. Different locations for the vertical farm are also investigated. To compare the environmental impacts, life cycle assessment was conducted for various scenarios employing a cradle-to-grave perspective. The results suggest that the vertical-farmed supply can have lower environmental impacts compared to the conventional supply of mixed salad bags, e.g. having roughly 44 % lower CO₂-eq emissions. However, in five of the eight impact categories assessed, the vertical farm was found to have higher emissions, notably for resource use. Furthermore, it was found that the location of the vertical farm can play a critical role in its sustainability due to varying electricity mix compositions and transportation distances to final processing or consumers. Furthermore, the results are sensitive to the conventional supply data for comparisons. The findings contribute to the growing field of vertical farming and provide valuable information for transitioning toward a more sustainable food system.

Droplet coalescence in microchannels is a complex phenomenon influenced by various parameters such as droplet size, velocity, liquid surface tension, and droplet-droplet spacing. In this study, we thoroughly investigate the impact of these control parameters on droplet coalescence dynamics within a sudden expansion microchannel using two distinct numerical methods. Initially, we employ the boundary element method to solve the Brinkman integral equation, providing detailed insights into the underlying physics of droplet coalescence. Furthermore, we integrate Response Surface Methodology (RSM) to effectively optimize droplet coalescence dynamics, harnessing the power of machine learning algorithms. Our results showcase the efficacy of these computational techniques in enhancing experimental efficiency. Through rigorous evaluation utilizing Regression Coefficient and Mean Absolute Error metrics, we ascertain the accuracy of our estimations. Our findings highlight the significant influence of key parameters, specifically the non-dimensional initial distance of the droplets (D), viscosity ratio ( $\mu$ ), Capillary number (Ca), and width (w), as identified by the non-dimensional final droplet-droplet spacing (DD), velocity of the first droplet (V_FD), and velocity of the second droplet (V_BD), respectively. This comprehensive approach provides valuable insights into droplet coalescence phenomena and offers a robust framework for optimizing microfluidic systems. The most influential parameters on DD are the values of A_d and D, while viscosity has the lowest influence on DD. The most influential parameters on droplet velocity are viscosity and channel width, whereas the initial distance and Ca have the least influence on droplet velocity. The comparison of different machine learning algorithms indicates that the best ones for predicting DD, V_FD, and V_BD are function, SMOreg, Lazy-IBK, and Meta-Bagging, respectively.

Ya That Somdun (YTS) is a traditional Thai medicine composed of six herbs used as a strengthening tonic. Some of the herbs constituting YTS have antihyperlipidemic and anti-obesity activities. The objective of this study was to elucidate the antihyperlipidemic properties of YTS extract in rats with cholesterol suspension-induced hyperlipidemia. Male Sprague-Dawley rats were subdivided into four groups: normal control (NC), hyperlipidemic control (HC), and those who were administered 100 (YTS100) and 200 mg (YTS200) of YTS/kg body weight (BW). Hyperlipidemic rats were orally administered YTS extract for four consecutive weeks from the fifth week of cholesterol suspension. Serum lipid profiles, body weights, liver and renal functional markers, gene expression involved in lipid metabolism, and liver histopathology were examined. The HC and YTS groups showed a significant increase in body weights compared with the NC group. The YTS100 and YTS200 groups showed no significant difference in serum triglyceride, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels compared with the NC and HC groups. YTS treatment (100 mg/kg BW) downregulated sterol regulatory element binding protein 1c (SREBP-1c) mRNA expression and alleviated hepatic steatosis. In conclusion, the YTS extract reduced hepatic lipid accumulation in hyperlipidemic rats by reducing SREBP-1c expression levels.

•DOACs are effective and safe in very morbidly obese AF patients (BMI ≥50 kg/m²).•DOACs show similar stroke and bleeding risks as warfarin in this population.•Findings support DOACs in anticoagulation guidelines for very morbidly obese patients.

The last decennia have witnessed spectacular advances in our knowledge about the influence of the gut microbiome on the development of a wide swathe of diseases that extend beyond the digestive tract, including skin diseases like psoriasis, atopic dermatitis, acne vulgaris, rosacea, alopecia areata, and hidradenitis suppurativa. The novel concept of the gut-skin axis delves into how skin diseases and the microbiome interact through inflammatory mediators, metabolites, and the intestinal barrier. Elucidating the effects of the gut microbiome on skin health could provide new opportunities for developing innovative treatments for dermatological diseases. Psoriasis is a complex disease with multiple factors contributing to its development, such as diet, lifestyle, genetic predisposition, and the microbiome. This paper has a dual purpose. First, we outline the current knowledge on the unique gut microbiota patterns implicated in the pathogenesis of psoriasis. Second, and of equal importance, we briefly discuss the reciprocal impact of psoriasis treatment and gut microbiome. In addition, this review explores potential therapeutic targets based on microbial interventions, which hold promise for providing new treatment options for psoriasis.

The anxieties and concerns about health hazards caused by microwave has been growing recently. Previous studies have reported microwave induced structural and functional injuries to brain. However, the biological effects caused by compound microwave were largely unexplored. In this study, we investigated the biological effects on rat's hippocampus after sequentially exposure to 2.8 GHz and 9.3 GHz at average power density of 10 mW/cm². Morris water maze (MWM) navigation tests showed that microwave exposure significantly extended the average escape latency (AEL) at 1d and 3d after exposure, suggesting reduced learning and memory ability. Importantly, compound microwave produced strongest responses at 3 d after exposure. Moreover, microwave also could induce abnormal electroencephalogram (EEG), such as increasing the power of θ wave and δ wave, suggesting brain damage or dysfunction. Histopathological analysis suggested that microwave resulted in obvious structural injuries in hippocampus at 7 d after exposure, and most serious injuries were observed in compound microwave exposed rats. Moreover, disorder of mitochondria and reduced Nissl bodies in hippocampus might contribute to the decrease of cognitive function. However, both the cognitive function and hippocampal structure restored to normal at 28 d after exposure, which might be attributed to self-recovery mechanisms. Gene ontology (GO) and Protein-protein interaction (PPI) analyses of differential expressed genes (DEGs) in peripheral blood suggested that Htt and Bdnf might be potential indicators to predict microwave. In conclusion, compound microwave of 2.8 GHz and 9.3 GHz could elicit reversible structural injuries to hippocampus, which could decrease the cognitive function of rats.

Background: While electroconvulsive therapy and antidepressants are standard treatments for depressed pregnant women, they are not without threats. The objective of this study was to quantitative synthesis of the literature regarding the effect of yoga interventions on depressive symptoms in pregnant women.

Methods: Nine electronic databases were searched for primary studies with pregnant women with depression measured as outcomes and written in English. Based on the random-effects model, we used Hedges' g to compute the effect size and examined the subgroup analysis.

Results: We found twelve primary studies which included 738 participants who were 28.43 ± 1.92 years old across studies. The overall effect size using random-effects model was g = 1.120 (95%CI .52, 1.72, p < .001). Providing yoga to pregnant women with mood disorder had a lesser effect size (g = .10) than providing yoga to pregnant women without a mood disorder (g = 1.45). Funded studies had a lesser ES (g = .51) than those with unfunded (g = 1.98). Proving yoga in Eastern countries had a greater ES (g = 1.82) than providing yoga in Western (g = .40). No quality indicators showed moderator effects.

Conclusion: When compared to controls, pregnant women who practiced yoga reported significantly improved depressive symptoms. Yoga might be an alternative complementary therapy that clinicians and other healthcare professionals might think about utilizing to help pregnant women who are depressed.

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and "response to radiation" as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing common pathways and potential therapeutic targets for developing novel treatment strategies and repurposing existing drugs for these prevalent skin cancers. Therefore, these findings may guide future research in understanding and developing targeted therapies for both conditions.

Extant research has widely acknowledged the role of digital innovation as a facilitator of digital transformation, presenting solutions for various challenges in various industries. However, prior research demonstrates inadequate discussions on the determinants of digital innovation adoption for digital transformation in developing countries, particularly in the agricultural sector. To address this gap, this study investigates the effect of food security awareness, innovation characteristics, and the moderating role of agricultural experience on behavioral intention to adopt digital innovation in the agricultural sector. A dyadic model based on diffusion innovation theory and the technology acceptance model is proposed to investigate the phenomenon. This study employed a cross-sectional quantitative approach to investigate the phenomenon based on survey data collected from 207 study participants in Ghana's agricultural sector and the partial least square structural equation modeling technique. The study's findings revealed that personal innovativeness significantly affects food security awareness (β = 0.574; p < 0.000), relative advantage (β = 0.699; p < 0.000), compatibility (β = 0.687; p < 0.000), and complexity (β = 0.312; p < 0.000). In addition, food security awareness (β = 0.336; p < 0.000), compatibility (β = 0.257; p < 0.000), and agricultural experience (β = 0.238; p < 0.003) significantly affect behavioral intention to adopt digital innovation. Furthermore, the study revealed that agricultural experience (β = -0.145; p < 0.036) moderates the relationship between compatibility and behavioral intention. Together, these variables explain 78.9 % of the variance in behavioral intention to adopt digital innovation in the agricultural sector in Ghana. The study contributes to the literature on digital innovation adoption in the agricultural sector in developing countries and proffers actionable insights for practitioners.