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Abstract Background The lipid-lowering effects of Omega-3 fatty acids have been widely reported, yet their impact on ischemic stroke remains controversial. Reports on the protective effects of unsaturated fatty acids, such as Omega-6 and Omega-7, as well as saturated fatty acids in cardiovascular diseases, including hypertension and ischemic stroke, are less frequent. Objectives This study aims to identify fatty acids associated with blood pressure and ischemic stroke through Mendelian randomization. Besides, it seeks to determine whether specific fatty acids can prevent ischemic stroke by managing blood pressure and revealing the specific mechanisms of this action. Methods This research involved downloading relevant data from websites and extracting SNPs that met the standard criteria as instrumental variables. Simultaneously, the ‘MR-PRESSO’ package and ‘Mendelian Randomization’ package were used to eliminate confounding SNPs that could bias the study results. Then, inverse variance weighting and the weighted median were employed as primary analysis methods, accompanied by sensitivity analysis to assess the validity of the causal relationships. Initially, multivariable Mendelian randomization was used to identify fatty acids linked to blood pressure and the incidence of ischemic stroke. The causal link between certain fatty acids and the initiation of ischemic stroke was then investigated using bidirectional and mediator Mendelian randomization techniques. Stepwise Regression and the Product of Coefficients Method in mediator Mendelian randomization were utilized to ascertain whether specific fatty acids reduce ischemic stroke risk by lowering blood pressure. Results Multivariable Mendelian randomization analysis indicated a potential inverse correlation between Omega-3 intake and both blood pressure and ischemic stroke. Consequently, Omega-3 was selected as the exposure, with blood pressure and ischemic stroke-related data as outcomes, for further bidirectional and mediation Mendelian Randomization analyses. Bidirectional Mendelian Randomization revealed that Omega-3 significantly influences DBP (P = 1.01e-04) and IS (P = 0.016). It also showed that DBP and SBP significantly affect LAS, SVS, CES, IS, and LS. Mediator Mendelian Randomization identified five established mediating pathways: Omega-3-Diastolic blood pressure-Small vessel stroke, Omega-3-Diastolic blood pressure-Cardioembolic stroke, Omega-3-Diastolic blood pressure-Lacunar stroke, Omega-3-Diastolic blood pressure-Large artery atherosclerosis stroke, and Omega-3-Diastolic blood pressure-Ischemic stroke. Of these, four pathways are complete mediation, and one pathway is partial mediation. Conclusions The findings suggest that Omega-3 may indirectly reduce the incidence of ischemic stroke by lowering blood pressure. Thus, blood pressure modulation might be one of the mechanisms through which Omega-3 prevents ischemic stroke. In summary, incorporating an increased intake of Omega-3 in the diet can serve as one of the dietary intervention strategies for patients with hypertension. Additionally, it can act as an adjunctive therapy for the prevention of ischemic strokes and their complications.

BackgroundThe gut-brain axis and gut microbiota have emerged as key players in emotional disorders. Recent studies suggest that alterations in gut microbiota may impact psychiatric symptoms through brain miRNA along the gut-brain axis. However, direct evidence linking gut microbiota to the pathophysiology of generalized anxiety disorder (GAD) via brain miRNA is limited. In this study, we explored the effects of fecal microbiota transplantation (FMT) from GAD donors on gut microbiota and prefrontal cortex miRNA in recipient mice, aiming to understand the relationship between these two factors.MethodsAnxiety scores and gut microbiota composition were assessed in GAD patients, and their fecal samples were utilized for FMT in C57BL/6J mice. Anxiety-like behavior in mice was evaluated using open field and elevated plus maze tests. High-throughput sequencing of gut microbiota 16S rRNA and prefrontal cortex miRNA was performed.ResultsThe fecal microbiota of GAD patients exhibited a distinct microbial structure compared to the healthy group, characterized by a significant decrease in Verrucomicrobia and Akkermansia, and a significant increase in Actinobacteria and Bacteroides. Subsequent FMT from GAD patients to mice induced anxiety-like behavior in recipients. Detailed analysis of gut microbiota composition revealed lower abundances of Verrucomicrobia, Akkermansia, Bifidobacterium, and Butyricimonas, and higher abundances of Deferribacteres, Allobaculum, Bacteroides, and Clostridium in mice that received FMT from GAD patients. MiRNA analysis identified five key miRNAs affecting GAD pathogenesis, including mmu-miR-10a-5p, mmu-miR-1224-5p, mmu-miR-218-5p, mmu-miR-10b-5p, and mmu-miR-488-3p. Notably, mmu-miR-488-3p showed a strong negative correlation with Verrucomicrobia and Akkermansia.ConclusionThis study demonstrates that anxiety-like behavior induced by human FMT can be transmitted through gut microbiota and is associated with miRNA expression in the prefrontal cortex. It is inferred that the reduction of Akkermansia caused by FMT from GAD patients leads to the upregulation of mmu-miR-488-3p expression, resulting in the downregulation of its downstream target gene Creb1 and interference with its related signaling pathway. These findings highlight the gut microbiota’s crucial role in the GAD pathophysiology.

Fire remains a pressing issue that requires urgent attention. Due to its uncontrollable and unpredictable nature, it can easily trigger chain reactions and increase the difficulty of extinguishing, posing a significant threat to people’s lives and property. The effectiveness of traditional photoelectric- or ionization-based detectors is inhibited when detecting fire smoke due to the variable shape, characteristics, and scale of the detected objects and the small size of the fire source in the early stages. Additionally, the uneven distribution of fire and smoke and the complexity and variety of the surroundings in which they occur contribute to inconspicuous pixel-level-based feature information, making identification difficult. We propose a real-time fire smoke detection algorithm based on multi-scale feature information and an attention mechanism. Firstly, the feature information layers extracted from the network are fused into a radial connection to enhance the semantic and location information of the features. Secondly, to address the challenge of recognizing harsh fire sources, we designed a permutation self-attention mechanism to concentrate on features in channel and spatial directions to gather contextual information as accurately as possible. Thirdly, we constructed a new feature extraction module to increase the detection efficiency of the network while retaining feature information. Finally, we propose a cross-grid sample matching approach and a weighted decay loss function to handle the issue of imbalanced samples. Our model achieves the best detection results compared to standard detection methods using a handcrafted fire smoke detection dataset, with APval reaching 62.5%, APSval reaching 58.5%, and FPS reaching 113.6.