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ABSTRACTUlcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.

ObjectiveExecutive dysfunction is a core symptom of vascular cognitive impairment (VCI), which seriously affects patients’ prognosis. This paper aims to investigate the effectiveness of rTMS on executive function in VCI.MethodsThe databases selected for this study included Pubmed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, China Science and Technology Journal Database (VIP), and China Biology Medicine Disc (CBM). The screening times were conducted from the time of library construction until August 23, 2023. The inclusion criteria for this meta-analysis were randomized controlled trials (RCTs) on rTMS for VCI, which include executive function scores. The primary metrics were executive subscale scores of the Cognitive Comprehensive Scale and total scores of the Executive Specificity Scale. The secondary metrics were subscale scores of the Executive Specificity Scale. The quality of each eligible study was assessed using the Cochrane Risk of Bias tool. Meta-analysis and bias analysis were performed using Stata (version 16.0) and RevMan (version 5.3).ResultsA total of 20 high-quality clinical RCTs with 1,049 samples were included in this paper. The findings from the primary outcomes revealed that within the rTMS group, there were significantly higher scores observed for the executive sub-item on the cognitive composite scale (SMD = 0.93, 95% CI = 0.77–1.08, p

Abstract Background The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host’s health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated. Methods We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets. Results We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ( $$\rho = 0.07$$ ρ = 0.07 ) and species richness ( $$\rho = 0.44$$ ρ = 0.44 ) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ( $$\rho = 0.12$$ ρ = 0.12 ), species richness ( $$\rho = 0.46$$ ρ = 0.46 ), and functional redundancy ( $$\rho = - 0.16$$ ρ = - 0.16 ). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases. Conclusions Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract

Near‐infrared (NIR) radiation has demonstrated significant promise in skin tissue engineering and wound healing. However, the high cost and operational complexity associated with current specialized apparatuses have hindered their practical household use. In response to these issues, a flexible, battery‐free, wireless sensor–actuator NIR therapy system, offering real‐time physiological monitoring and active treatment to cutaneous wounds, is developed. The wound management system can be wirelessly powered, and transmits data to any near‐field communication‐compatible smartphone. The therapeutic prowess of our system for wound closure is computationally and experimentally demonstrated by promoting epithelial migration and modulating inflammation. Moreover, a novel feature is reported: the smartphone‐enabled system can precisely predict the wound stage via a temperature sensor pair, which can provide an effective timely intervention strategy for NIR wound therapy. Studies based on rat wound model successfully confirm the efficacy of the proposed strategy, showcasing a remarkable 30% enhancement in wound closure compared to control groups. The user‐friendly, effective wound management platform promises a revolutionary step toward accessible and efficient at‐home wound care.

ObjectiveThis study aimed to elucidate the efficacy, safety, and long-term implications of vagus nerve stimulation (VNS) as a viable therapeutic option for patients with upper limb dysfunction following a stroke.MethodsData from the following libraries were searched from inception to December 2022: PubMed, Wanfang, Scopus, China Science and Technology Journal Database, Embase, Web of Science, China Biology Medicine Disc, Cochrane Library, and China National Knowledge Infrastructure. Outcomes included indicators of upper limb motor function, indicators of prognosis, and indicators of safety (incidence of adverse events [AEs] and serious AEs [SAEs]). Two of the authors extracted the data independently. A third researcher arbitrated when disputes occurred. The quality of each eligible study was evaluated using the Cochrane Risk of Bias tool. Meta-analysis and bias analysis were performed using Stata (version 16.0) and RevMan (version 5.3).ResultsTen trials (VNS combined with rehabilitation group vs. no or sham VNS combined with rehabilitation group) with 335 patients were included in the meta-analysis. Regarding upper extremity motor function, based on Fugl–Meyer assessment scores, VNS combined with other treatment options had immediate (mean difference [MD] = 2.82, 95% confidence interval [CI] = 1.78–3.91, I2 = 62%, p < 0.00001) and long-term (day-30 MD = 4.20, 95% CI = 2.90–5.50, p < 0.00001; day-90 MD = 3.27, 95% CI = 1.67–4.87, p < 0.00001) beneficial effects compared with that of the control treatment. Subgroup analyses showed that transcutaneous VNS (MD = 2.87, 95% CI = 1.78–3.91, I2 = 62%, p < 0.00001) may be superior to invasive VNS (MD = 3.56, 95% CI = 1.99–5.13, I2 = 77%, p < 0.0001) and that VNS combined with integrated treatment (MD = 2.87, 95% CI = 1.78–3.91, I2 = 62%, p < 0.00001) is superior to VNS combined with upper extremity training alone (MD = 2.24, 95% CI = 0.55–3.93, I2 = 48%, p = 0.009). Moreover, lower frequency VNS (20 Hz) (MD = 3.39, 95% CI = 2.06–4.73, I2 = 65%, p < 0.00001) may be superior to higher frequency VNS (25 Hz or 30 Hz) (MD = 2.29, 95% CI = 0.27–4.32, I2 = 58%, p = 0,03). Regarding prognosis, the VNS group outperformed the control group in the activities of daily living (standardized MD = 1.50, 95% CI = 1.10–1.90, I2 = 0%, p < 0.00001) and depression reduction. In contrast, quality of life did not improve (p = 0.51). Safety was not significantly different between the experimental and control groups (AE p = 0.25; SAE p = 0.26).ConclusionVNS is an effective and safe treatment for upper extremity motor dysfunction after a stroke. For the functional restoration of the upper extremities, noninvasive integrated therapy and lower-frequency VNS may be more effective. In the future, further high-quality studies with larger study populations, more comprehensive indicators, and thorough data are required to advance the clinical application of VNS.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42023399820.

Abstract In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in the operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses a cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by a vision system. Then, it extracts the route of processed images based on the improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments and has good practicability and applicability.

Abstract Background Aberrant hippocampal neurogenesis is an important pathological feature of sepsis-associated encephalopathy. In the current study, we examined the potential role of the long noncoding RNA (lncRNA) sex-determining region Y-box 2 (SOX2) overlapping transcript (SOX2OT), a known regulator of adult neurogenesis in sepsis-induced deficits in hippocampal neurogenesis and cognitive function. Methods Sepsis was induced in adult C57BL/6 J male mice by cecal ligation and perforation (CLP) surgery. Randomly selected CLP mice were transfected with short interfering RNAs (siRNAs) against SOX2OT or SOX2, or with scrambled control siRNA. Cognitive behavior was tested 8–12 days post-surgery using a Morris water maze. Western blotting and RT-qPCR were used to determine expression of SOX2, Ki67, doublecortin (DCX), nestin, brain lipid-binding protein, and glial fibrillary acidic protein (GFAP) in the hippocampus. The number of bromodeoxyuridine (BrdU)+/DCX+ cells, BrdU+/neuronal nuclei (NeuN)+ neurons, and BrdU+/GFAP+ glial cells in the dentate gyrus were assessed by immunofluorescence. Results CLP mice showed progressive increases in SOX2OT and SOX2 mRNA levels on days 3, 7, and 14 after CLP surgery, accompanied by impaired cognitive function. Sepsis led to decrease in all neuronal markers in the hippocampus, except GFAP. Immunofluorescence confirmed the decreased numbers of BrdU+/DCX+ cells and BrdU+/NeuN+ neurons, and increased numbers of BrdU+/GFAP+ cells. SOX2OT knockdown partially inhibited the effects of CLP on levels of SOX2 and neuronal markers, neuronal populations in the hippocampus, and cognitive function. SOX2 deficiency recapitulated the effects of SOX2OT knockdown. Conclusion SOX2OT knockdown improves sepsis-induced deficits in hippocampal neurogenesis and cognitive function by downregulating SOX2 in mice. Inhibiting SOX2OT/SOX2 signaling may be effective for treating or preventing neurodegeneration in sepsis-associated encephalopathy.