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Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.

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Background: Functional Ankle Instability (FAI) is a pervasive condition that can emerge following inadequate management of lateral ankle sprains. It is hallmarked by chronic joint instability and a subsequent deterioration in physical performance. The modulation of motor patterns through attentional focus is a well-established concept in the realm of motor learning and performance optimization. However, the precise manner in which attentional focus can rehabilitate or refine movement patterns in individuals with FAI remains to be fully elucidated. Objective: The primary aim of this study was to evaluate the impact of attentional focus strategies on the biomechanics of single-leg drop landing movements among individuals with FAI. Methods: Eighteen males with unilateral FAI were recruited. Kinematic and kinetic data were collected using an infrared three-dimensional motion capture system and force plates. Participants performed single-leg drop landing tasks under no focus (baseline), internal focus (IF), and external focus (EF) conditions. Biomechanical characteristics, including joint angles, ground reaction forces, and leg stiffness, were assessed. A 2 × 3 [side (unstable and stable) × focus (baseline, IF, and EF)] Repeated Measures Analysis of Variance (RM-ANOVA) analyzed the effects of attentional focus on biomechanical variables in individuals with FAI. Results: No significant interaction effects were observed in this study. At peak vertical ground reaction force (vGRF), the knee flexion angle was significantly influenced by attentional focus, with a markedly greater angle under EF compared to IF (p < 0.001). Additionally, at peak vGRF, the ankle joint plantarflexion angle was significantly smaller with EF than with IF (p < 0.001). Significant main effects of focus were found for peak vGRF and the time to reach peak vGRF, with higher peak vGRF values observed under baseline and IF conditions compared to EF (p < 0.001). Participants reached peak vGRF more quickly under IF (p < 0.001). Leg Stiffness (kleg) was significantly higher under IF compared to EF (p = 0.001). Conclusion: IF enhances joint stability in FAI, whereas EF promotes a conservative landing strategy with increased knee flexion, dispersing impact and minimizing joint stress. Integrating these strategies into FAI rehabilitation programs can optimize lower limb biomechanics and reduce the risk of reinjury.

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Hepatocellular carcinoma (HCC), a hypervascular tumor, is the most frequent primary malignant tumor of the liver. Angiogenesis inhibitors, such as endogenous angiogenesis inhibitors, are essential for HCC therapy and have generated significant interest owing to their safety, efficacy, and multitargeting attributes. Canstatin is an angiogenesis inhibitor derived from the basement membrane and exerts anti-tumor effects. However, the inhibitory effects and underlying mechanisms of action of canstatin on HCC remain unclear. Therefore, in this study, HepG2 and Huh7 cells were used to investigate the inhibitory effects of recombinant canstatin on HCC cells. Subsequently, the biosafety and inhibitory effects of recombinant canstatin on tumor growth were investigated in a xenograft animal model of liver cancer. Canstatin inhibited the growth of liver cancer cells by regulating their proliferation, apoptosis, and migration. Additionally, it suppressed the occurrence and progression of HCC by modulating the HIF-1α/VEGF signaling pathway. In mice, canstatin exerted no discernible harmful side effects and suppressed the growth of HCC subcutaneous xenograft tumors. Overall, our findings shed light on the molecular pathways underlying canstatin-induced HCC cell death that may help develop novel HCC treatments.

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Glycosylation is an important post-modification reaction in plant secondary metabolism, and contributes to structural diversity of bioactive natural products. In plants, glycosylation is usually catalyzed by UDP-glycosyltransferases. Flavonoid 2'-O-glycosides are rare glycosides. However, no UGTs have been reported, thus far, to specifically catalyze 2'-O-glycosylation of flavonoids. In this work, UGT71AP2 was identified from the medicinal plant Scutellaria baicalensis as the first flavonoid 2'-O-glycosyltransferase. It could preferentially transfer a glycosyl moiety to 2'-hydroxy of at least nine flavonoids to yield six new compounds. Some of the 2'-O-glycosides showed noticeable inhibitory activities against cyclooxygenase 2. The crystal structure of UGT71AP2 (2.15 Å) was solved, and mechanisms of its regio-selectivity was interpreted by pK a calculations, molecular docking, MD simulation, MM/GBSA binding free energy, QM/MM, and hydrogen‒deuterium exchange mass spectrometry analysis. Through structure-guided rational design, we obtained the L138T/V179D/M180T mutant with remarkably enhanced regio-selectivity (the ratio of 7-O-glycosylation byproducts decreased from 48% to 4%) and catalytic efficiency of 2'-O-glycosylation (k cat/K m, 0.23 L/(s·µmol), 12-fold higher than the native). Moreover, UGT71AP2 also possesses moderate UDP-dependent de-glycosylation activity, and is a dual function glycosyltransferase. This work provides an efficient biocatalyst and sets a good example for protein engineering to optimize enzyme catalytic features through rational design.

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Nominally anhydrous minerals (NAMs) composing Earth's and planetary rocks incorporate microscopic amounts of volatiles. However, volatile distribution in NAMs and their effect on physical properties of rocks remain controversial. Thus, constraining trace volatile concentrations in NAMs is tantamount to our understanding of the evolution of rocky planets and planetesimals. Here, we present an approach of trace-element quantification using micro-scale Nuclear Magnetic Resonance (NMR) spectroscopy. This approach employs the principle of enhanced mass-sensitivity in NMR microcoils. We were able to demonstrate that this method is in excellent agreement with standard methods across their respective detection capabilities. We show that by simultaneous detection of internal reference nuclei, the quantification sensitivity can be substantially increased, leading to quantifiable trace volatile element amounts of about 50 ng/g measured in a micro-meter sized single anorthitic mineral grain, greatly enhancing detection capabilities of volatiles in geologically important systems.

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Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.

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Fine roots and absorptive roots play key roles in acquiring resources throughout soil profiles and determining plant functions along environmental gradients. Yet, the geographical pattern of carbon allocation in fine roots, particularly in absorptive roots, and their relations with plant sizes and evironment are less understood. We sampled 243 xerophytic shrubs from 63 species distributed along the latitudinal gradient (23°N to 32°N) in dry valleys of southwest China and synthetically measured biomass fractions of plant organs, especially fine roots and absorptive roots (1st to 3rd root order). We identified latitudinal patterns of biomass allocation fractions of organs and their relationships with plant sizes and environmental factors. The latitudinal patterns of both absorptive root and fine-root fractions followed weak unimodal distributions; stem biomass fraction increased with the latitude, while the leaf biomass fraction decreased. The fraction of fine-root biomass had negative relationships with plant height and root depth. The fractions of root, fine root, and absorptive root biomass were largely explained by soil moisture. Furthermore, fraction of fine-root biomass increased in a relatively humid environment. Overall, soil moisture was the most important factor in driving latitudinal patterns of biomass fraction. Our study highlighted that functional redistribution of root system biomass was the critical adaptive strategy along a latitudinal gradient.

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Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss. While current deep learning-based methods outperform traditional ones, they still face challenges such as the lack of local anatomical landmarks around the fovea, the inability to robustly handle diseased retinal images, and the variations in image conditions. In this paper, we propose a novel transformer-based architecture called DualStreamFoveaNet (DSFN) for multi-cue fusion. This architecture explicitly incorporates long-range connections and global features using retina and vessel distributions for robust fovea localization. We introduce a spatial attention mechanism in the dual-stream encoder to extract and fuse self-learned anatomical information, focusing more on features distributed along blood vessels and significantly reducing computational costs by decreasing token numbers. Our extensive experiments show that the proposed architecture achieves state-of-the-art performance on two public datasets and one large-scale private dataset. Furthermore, we demonstrate that the DSFN is more robust on both normal and diseased retina images and has better generalization capacity in cross-dataset experiments.

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Fine-root architecture is critical feature reflecting root explorative and exploitative strategies for soil resources and soil space occupancy. Yet, studies on the variation of fine-root architecture across different species are scare and little work has been done to integrate the potential drivers on these variations along a biogeographical gradient in arid ecosystems. We measured root branching intensity, topological index, and root branching ratios as well as morphological traits (root diameter and length) in dry valley along a 1000 km latitudinal gradient. Influence of phylogeny, environmental factors on fine-root architecture and trade-offs among root traits were evaluated. With increasing latitude, the topological index and second to third root order branching ratio decreased, whereas first-to-second branching ratio increased. Root branching intensity was associated with short and thin fine roots, but has no significant latitudinal pattern. As a whole, soil microbial biomass was the most important driver in the variation of root branching intensity, and soil texture was the strongest predictor of topological index. Additionally, mean annual temperature was an important factor influencing first-to-second branching ratio. Our results suggest variations in fine-root architectures were more dependent on environmental variables than phylogeny, signifying that fine-root architecture is sensitive to environmental variations.

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Astragaloside IV is a significant chemical component derived from the medicinal plant Astragalus membranaceus. Despite the characterization of several glycosyltransferases from A. membranaceus, the complete biosynthetic pathway of astragaloside IV has not been fully elucidated. In this study, we propose a biosynthetic pathway for astragaloside IV that involves a sequence of oxidation-reduction reactions. The biosynthesis pathway from cycloastragenol to astragaloside IV encompasses four key steps: C-3 oxidation, 6-O-glucosylation, C-3 reduction, and 3-O-xylosylation. We identified a hydroxysteroid dehydrogenase AmHSD1 from A. membranaceus. AmHSD1 catalyzes the C-3 oxidation of cycloastragenol, yielding cycloastragenol-3-one, and the C-3 reduction of cycloastragenol-3-one-6-O-glucoside, resulting in cycloastragenol-6-O-glucoside. Additionally, the glycosyltransferases AmGT8 and AmGT1, previously reported by our groups, were identified as catalyzing the 6-O-glucosylation and 3-O-xylosylation steps, respectively. Astragaloside IV was successfully synthesized in transient expression in Nicotiana benthamiana using the combination of AmHSD1, AmGT8 and AmGT1. These results support the proposed four-step biosynthetic pathway and suggest that AmHSD1 probably plays a crucial role in the biosynthesis of astragaloside IV within A. membranaceus.

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Mathematical learning and ability are crucial for individual and national economic and technological development, but the neural mechanisms underlying advanced mathematical learning remain unclear. The current study used functional magnetic resonance imaging (fMRI) to investigate how brain networks were involved in advanced mathematical learning and transfer. We recorded fMRI data from 24 undergraduate students as they learned the advanced mathematical concept of a commutative mathematical group. After learning, participants were required to complete learning and transfer behavioural tests. Results of single-trial interindividual brain-behaviour correlation analysis found that brain activity in the semantic and visuospatial networks, and the functional connectivity within the semantic network during advanced mathematical learning were positively correlated with learning and transfer effects. Additionally, the functional connectivity between the semantic and visuospatial networks was negatively correlated with the learning and transfer effects. These findings suggest that advanced mathematical learning relies on both semantic and visuospatial networks.

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The human cerebellum is increasingly recognized to be involved in nonmotor and higher-order cognitive functions. Yet, its ties with the entire cerebral cortex have not been holistically studied in a whole brain exploration with a unified analytical framework. Here, we characterized dissociable cortical-cerebellar structural covariation patterns based on regional gray matter volume (GMV) across the brain in n = 38,527 UK Biobank participants. Our results invigorate previous observations in that important shares of cortical-cerebellar structural covariation are described as 1) a dissociation between the higher-level cognitive system and lower-level sensorimotor system and 2) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neuroscience view paves the way for future studies to explore the implications of these structural covariations, potentially illuminating new pathways in our understanding of neurological and cognitive disorders.NEW & NOTEWORTHY Cerebellum's association with the entire cerebral cortex has not been holistically studied in a unified way. Here, we conjointly characterize the population-level cortical-cerebellar structural covariation patterns leveraging ∼40,000 UK Biobank participants whole brain structural scans and ∼1,000 phenotypes. We revitalize the previous hypothesis of an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel ipsilateral cerebral-cerebellar associations. Phenome-wide association (PheWAS) revealed real-world implications of the structural covariation patterns.

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Recent climate warming has significantly affected the sensitivity of Gross Primary Productivity (GPP) to precipitation within China's agricultural ecosystems. Nonetheless, the spatial and temporal nonlinear evolution patterns of GPP-precipitation sensitivity under climate change, as well as the underlying drivers and long-term trends of this sensitivity, are not well understood. This study employs correlation analysis to quantify the sensitivity between GPP and precipitation in China's agricultural ecosystems, and utilizes nonlinear detection algorithms to examine the long-term changes in this sensitivity. Advanced machine learning techniques and frameworks are subsequently applied to analyze the driving factors of GPP-precipitation sensitivity in China's agricultural ecosystems. The findings reveal that approximately 49.00 % of the analyzed pixels exhibit a significant positive correlation between GPP and precipitation. Nonlinear change analysis indicates spatial heterogeneity in GPP-precipitation sensitivity across China's agricultural ecosystems, with patterns showing initial increases followed by decreases accounting for 25.12 %, and patterns of initial decreases followed by increases at 13.27 %. Machine learning analysis identifies temperature, soil moisture, and crop water footprint as the primary factors influencing GPP-precipitation sensitivity in agricultural ecosystems. This study is the first to introduce crop water footprint as a significant factor in the analysis of GPP-precipitation sensitivity. It not only offers new insights into the temporal nonlinear changes and driving factors of GPP-precipitation sensitivity but also underscores the importance of enhancing agricultural water efficiency to maintain agricultural ecosystem health and ensure food security under climate change.

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Objectively In objective terms, the return of rural labor force shortens the spatial distance with parents, leading to changes in caregiving support, emotional support, and financial support for parents, thereby affecting the health status of parents. This article, using data from the Chinese Family Panel Studies, analyzes the characteristics of the health status of parents with and without returning migrant children. By employing multiple linear regression models, PSM models, and IV-2SLS methods to address endogeneity bias, the study preliminarily explores the impact of rural labor force return on parental health. The results show that: (1) among the 5,760 older adult individuals, 1866 of them have returning migrant chil-dren, while the remaining 3,894 do not have returning migrant children. (2) Parents' health status generally follows a normal distribution, with a small proportion of parents having very poor or very good health. The proportions of parents with relatively poor, fair, and relatively good health status range between 20 and 40%. Among parents with returning chil-dren, 40.12% have relatively poor health status, 45.01% have fair health status, and a small proportion have very poor or very good health status. In contrast, among parents without returning children, the proportions of parents with relatively poor, fair, and rela-tively good health status are 21.69, 33.21, and 38.45%, respectively. When parents tran-sition from not having returning children to having returning children, their health status decreases by 0.541 levels, indicating a negative impact of rural labor force return on par-ents' health. Based on the analysis results, this article provides policy recommendations from three aspects: how to increase the income of returning labor force, improve the rural pension system, and enhance the concept of children supporting their parents.

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Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on unknown samples. However, current Raman spectroscopy analysis algorithms still have many drawbacks. They struggled to handle quantitative analysis between different instruments. Their prediction accuracy for concentration is generally low, with poor robustness. Therefore, this study addresses these deficiencies by designing the cross instrument-sparse Bayesian learning (CI-SBL) Raman spectroscopy analysis algorithm. CI-SBL can facilitate spectroscopic analysis between different instruments through the cross instrument module. CI-SBL converts data from portable instruments into data from scientific instruments, with high similarity between the converted spectrum and the spectrum from the scientific instruments reaching 98.6%. The similarity between the raw portable instrument spectrum and the scientific instrument spectrum is often lower than 90%. The cross instrument effect of the CI-SBL is remarkable. Moreover, CI-SBL employs sparse Bayesian learning (SBL) as the core module for analysis. Through multiple iterations, the SBL algorithm effectively identified various components within mixtures. In experiments, CI-SBL can achieve a qualitative accuracy of 100% for the majority of binary and multicomponent mixtures. On the other hand, the previous Raman spectroscopy analysis algorithms predominantly yield a qualitative accuracy below 80% for the same data. Additionally, CI-SBL incorporates a quantitative module to calculate the concentration of each component within the mixed samples. In the experiment, the quantification error for all substances was below 3%, with the majority of the substances exhibiting an error of approximately 1%. These experimental results illustrate that CI-SBL significantly enhances the accuracy of qualitative judgment of mixture spectra and the prediction of mixture concentrations compared with previous Raman spectroscopy analysis algorithms. Furthermore, the cross instrument module of CI-SBL allows for a flexible handling of data acquired from different instruments.

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The decision-making of soccer referees is one of the typical forms influenced by factors such as environmental pressure and individual emotions. While previous studies have explored how common factors like personal anxiety and on-field pressure affect the decisions of soccer referees, the mechanisms by which anxiety influences decision-making under pressure remain unclear. This study developed a penalty task based on real soccer match scenarios and recruited 76 experienced soccer referees. These referees were divided into two groups, high anxiety and low anxiety, based on their anxiety levels, to perform decision-making tasks under different pressure environments simulated to mimic real matches. Additionally, this research employed Event-Related Potential (ERP) technology to compare the brain signals of soccer referees with different levels of anxiety when facing foul play under various pressure environments. It was found that referees with high levels of anxiety displayed larger P300 and N400 amplitudes in a low-pressure environment (p = 0.0059, t = 2.9437). However, no significant differences in P300 and N400 amplitudes were observed between referees with high and low levels of anxiety under high-pressure conditions (p = 0.1890, t = 1.3411). This study not only reveals the complex mechanisms of anxiety in the decision-making process of referees but also emphasizes the importance of understanding and managing the psychological state of referees in competitive sports to improve the quality of their decisions. Our findings provide an empirical basis for future efforts to mitigate the impact of anxiety and optimize the decision-making process in similar high-pressure environments.

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The incidence of autoimmune liver diseases (ALDs) and research on their pathogenesis are increasing annually. However, except for autoimmune hepatitis, which responds well to immunosuppression, primary biliary cholangitis and primary sclerosing cholangitis are insensitive to immunosuppressive therapy. Besides the known effects of the environment, genetics, and immunity on ALDs, the heterogeneity of target cells provides new insights into their pathogenesis. This review started by exploring the heterogeneity in the development, structures, and functions of hepatocytes and epithelial cells of the small and large bile ducts. For example, cytokeratin (CK) 8 and CK18 are primarily expressed in hepatocytes, while CK7 and CK19 are primarily expressed in intrahepatic cholangiocytes. Additionally, emerging technologies of single-cell RNA sequencing and spatial transcriptomic are being applied to study ALDs. This review offered a new perspective on understanding the pathogenic mechanisms and potential treatment strategies for ALDs.

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Results of measurable residual disease (MRD)-testing by next-generation sequencing (NGS) correlate with relapse risk in adults with B-cell acute lymphoblastic leukemia (ALL) receiving chemotherapy or an allotransplant from a human leukocyte antigen (HLA)-identical relative or HLA-matched unrelated donor. We studied cumulative incidence of relapse (CIR) and survival prediction accuracy using a NGS-based MRD-assay targeting immunoglobulin genes after 2 courses of consolidation chemotherapy cycles in 93 adults with B-cell ALL most receiving HLA-haplotype-matched related transplants. Prediction accuracy was compared with MRD-testing using multi-parameter flow cytometry (MPFC). NGS-based MRD-testing detected residual leukemia in 28 of 65 subjects with a negative MPFC-based MRD-test. In Cox regression multi-variable analyses subjects with a positive NGS-based MRD-test had a higher 3-year CIR (Hazard Ratio [HR] = 3.37; 95 % Confidence Interval [CI], 1.34-8.5; P = 0.01) and worse survival (HR = 4.87 [1.53-15.53]; P = 0.007). Some data suggest a lower CIR and better survival in NGS-MRD-test-positive transplant recipients but allocation to transplant was not random. Our data indicate MRD-testing by NGS is more accurate compared with testing by MPFC in adults with B-cell ALL in predicting CIR and survival. (Registered in the Beijing Municipal Health Bureau Registration N 2007-1007 and in the Chinese Clinical Trial Registry [ChiCTR-OCH-10000940 and ChiCTROPC-14005546]).

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BACKGROUND: Observational studies have elucidated the associations between dietary factors and hypertension. Nevertheless, the exploration of these relationships using Mendelian randomization remains scarce currently. METHODS: The Mendelian randomization approach investigated the potential causal relationships between 16 dietary factors and hypertension. To achieve this, we identified genetic variants associated with these dietary factors by utilizing data from European-descent genome-wide association studies with a stringent significance threshold (P < 5 × 10 - 8). Subsequently, we obtained genetic associations with hypertension from the extensive FinnGen Study, encompassing 92,462 cases and 265,626 controls. Our primary analytical method was the inverse variance weighted method, and we also conducted assessments for heterogeneity and pleiotropy to ensure the robustness and reliability of our findings. RESULTS: The study revealed significant associations with hypertension risk for various dietary factors. Specifically, higher weekly alcohol consumption (OR: 1.53, 95% CI: 1.19-1.96) and more frequent alcohol intake (OR: 1.20, 95% CI: 1.08-1.33) were positively correlated with an increased risk of hypertension. Likewise, increased poultry intake (OR: 3.25, 95% CI: 1.83-5.78) and beef intake (OR: 1.80, 95% CI: 1.09-2.97) were also linked to a higher risk of hypertension. Conversely, there were protective factors associated with a decreased risk of hypertension. These included consuming salad and raw vegetables, dried fruits, cheese, and cereals. It is important to note that no evidence of pleiotropy was detected, underscoring the robustness of these findings. CONCLUSIONS: This study uncovered causal relationships between various dietary factors and hypertension risk. Specifically, alcohol consumption in terms of drinks per week and intake frequency, as well as poultry and beef intake, were causally associated with an elevated risk of hypertension. In contrast, consuming salad/raw vegetables, dried fruits, cheese, and cereals demonstrated an inverse causal association with hypertension, suggesting a potential protective effect.

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