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Metabolomic profiling in heart failure as a new tool for diagnosis and phenotyping
Abstract Classifying heart failure (HF) by stages and ejection fraction (EF) remains a debated topic in cardiology. Metabolomic profiling (MP) offers a means to identify unique pathophysiological changes across different phenotypes, presenting a promising approach for the diagnosis and prognosis of HF, as well as for the development of targeted therapies. In our study, MP was performed on 408 HF patients (54.9% male). The mean ages of patients were 62 [53;68], 67 [65;74], 68 [61;72], and 69 [65;73] years for stages A, B, C, and D, respectively. This study demonstrates high accuracy in HF stage classification, distinguishing Stage A from Stage B with an AUC ROC of 0.91 and Stage B from Stage C with an AUC ROC of 0.97, by integrating chromatography-mass spectrometry data through multiparametric machine learning models. The observed metabolic similarities between HF with mildly reduced EF and HF with reduced EF phenotypes (AUC ROC 0.96) once again highlight the fundamental differences at the cellular and molecular levels between HF with preserved EF and HF with EF < 50%. Hierarchical clustering based on MP identified four distinct HF phenotypes and 26 key metabolites, including metabolites of tryptophan catabolism, glutamine, riboflavin, norepinephrine, serine, and long- and medium-chain acylcarnitines. The average follow-up period was 542.37 [16;1271] days. A downward change in the trajectory of EF [HR 3,008, 95% CI 1,035 to 8,743, p = 0,043] and metabolomic cluster 3 [HR 2,880; 95% CI 1,062 to 7,810, p = 0,0376] were associated with increased risk of all-cause mortality. MP can refine HF phenotyping and deepen the understanding of its underlying mechanisms. Metabolomic analysis illuminates the biochemical landscape of HF, aiding in its classification and suggesting new therapeutic pathways.
CMPK2 promotes microglial activation through the cGAS-STING pathway in the neuroinflammatory mechanism
Efficacy and factors related to prognosis of combination chemotherapy with different radiotherapy methods in patients with unresectable hepatocellular carcinoma
The use of dendritic fibrous nano-titanium to enhance the initial characteristics and durability of lightweight concrete
Optimized DINO model for accurate object detection of sesame seedlings and weeds
Abstract The application of intelligent agricultural machinery is crucial in modern agricultural production. However, in environments where the target and the surrounding morphology are highly similar, such as distinguishing sesame seedlings from weeds, the problem essentially becomes one of optimizing edge detection algorithms for similar targets. To address this issue in agricultural object detection, we developed a custom dataset containing 1,300 images of sesame seedlings and weeds. To overcome the high complexity and low detection accuracy limitations of the original DINO model for this problem, the backbone network was replaced with MobileNet V3, the SENet attention mechanism and neck structure were optimized, and the H-Swish6 activation function was introduced to suit edge devices. Given the higher degree of lignification in the stems of sesame seedlings, these modifications improved the overall Average Precision (AP) of the model on the COCO dataset by 5.1% compared to the original DINO model. Specifically, $$\text {AP}_{S}$$ and $$\text {AP}_{M}$$ increased by 3.3% and 3.8%, respectively, while $$\text {AP}_{50}$$ and $$\text {AP}_{75}$$ increased by 2.3% and 3.2%. The model’s parameter count was reduced to 29M, inference time was lowered by 60%, and computational cost in FLOPs decreased by 43.72%. To verify the effectiveness of the improvements, we developed a custom dataset containing 1,300 images of sesame seedlings and weeds. On this model, the improved DINO model achieved a maximum AP of 81.8%, outperforming the YOLOv7 model by 5.6%, with an FPS of 24 frames per second. Ablation experiments verified the effectiveness of the model improvements.However, the aforementioned studies have not addressed the issue of low detection accuracy in scenarios with similar targets in the agricultural domain.
Lithium isotopic composition of the carbonate type salt lake in Tibet and its implication for origin and hydrological processes
Abstract The implementation of the carbon peaking and carbon neutrality strategy has led to a steady increase in the supply of lithium resources. Brine is one of the important sources of lithium, and the extraction of Li from carbonate-type brine is particularly straightforward. Research into the source of materials and hydrological processes of brine is crucial for the sustainable development of lithium in carbonate-type brine. As a fluid-mobile and metallogenic element, lithium has a significant mass difference between its stable isotopes (7Li and 6Li), leading to isotopic fractionation. In this study, we analyzed the hydrochemistry and Li isotope compositions of samples collected from a Li-rich salt lake (Bangor Co) in the Qinghai-Tibetan Plateau. The samples included lake brines, recharge rivers, cold springs, and salt minerals (hydromagnesites). The Li content in the various types of water varied significantly, ranging from 0.06 mg/L to 198.10 mg/L, showing a variation of 4 orders of magnitude. Water samples exhibit a wide range of δ7Li values, varying from 4.89‰ to 16.02‰. Notably, the lowest and highest values are observed in cold springs. Additionally, the concentrations and δ7Li values in hydromagnesite differ across various relative ages. The hydrochemistry indicated that the recharge water is influenced by rock weathering, but the lake brine is influenced by evaporation concentration. The analysis of trace elements and Li isotopic data reveals that rock weathering, geothermal systems, salt minerals, and freshwater, primarily from early geothermal activities and the redissolution of carbonate minerals, contribute to the Li in salt lake brine. Boron isotopes and lithium isotopes of lake brines are found to vary differently. The δ7Li in brine is increased significantly by adsorption of hydromagnesite. And 11B gradually accumulates in hydromagnesite. This study has demonstrated that hydromagnesite plays a crucial role in influencing the characteristics of Li in brine.
Energy efficient task scheduling for heterogeneous multicore processors in edge computing
The mosquitocidal activity of isoxazoline derivatives afoxolaner, lotilaner, and fluralaner are not affected by mosquito sugar or antibiotic treatment
Assessing particle count in electron microscopy measurements of nanomaterials to support regulatory guidance
Abstract In the European Union, nanomaterials are regulated through different pieces of sectoral legislation. This legislation often requires risk assessments and thus reliable characterization data, for which regulatory guidance generally recommend electron microscopy. The guidance provides best practices for measurements but lacks requirements on how many particles to measure. Using transmission electron microscopy data of nanomaterials, a strategy based on repeated subsampling is proposed to establish, for different particle size and shape measurands, mathematical relationships between particle count and precision, and subsequently to determine the minimum particle count. Our results confirm that the minimum particle count generally depends on the width of the size and shape distributions and that the median of the distribution can be determined with the highest precision compared to other percentiles. Upon combining the precision uncertainty related to particle number with uncertainties from other sources, such as sample preparation, calibration and trueness, we reach an optimal particle count above which additional particle measurements only yield negligible improvements to the combined measurement uncertainty. Our findings offer an experimental approach for determining the minimum particle count to measure particle size and shape by electron microscopy. It enables efficient analyses and facilitates compliance with legislation addressing nanomaterials across various application domains.
Optimizing radiation monitoring networks to improve emergency response strategies during nuclear power plant accidents
Client engagement solution for post implementation issues in software industry using blockchain
Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
A holistic approach to enhance reliability and profitability through reliable efficiency index optimization and renewable energy integration
Molecular detection, seasonality, epidemiology and effect of Brucella melitensis infection on the hematological profile of cattle breeds
Separable reversible data hiding by vacating room after encryption using encrypted pixel difference
Identification and experimental verification of biomarkers related to butyrate metabolism in osteoarthritis
Abstract Butyrate plays a crucial role in osteoarthritis (OA) development. However, the relationship between butyrate metabolism-related genes (BMRGs) and OA remains unclear. This study investigates the potential correlation between BMRGs and OA using OA-related datasets (GSE55235, GSE12021 and GSE143514). Differential expression analysis identified 38 differentially expressed butyrate metabolism-related genes (DE-BMRGs) from the overlap of 782 OA-related differentially expressed genes (DEGs) and 385 BMRGs in GSE55235. Enrichment analysis indicated that these DE-BMRGs were tightly associated with cell proliferation, differentiation, and apoptosis, which are key processes in OA pathogenesis. Six candidate biomarkers (IL1B, IGF1, CXCL8, PTGS2, SERPINE1, MMP9) were identified through two machine-learning algorithms. IL1B, CXCL8, and PTGS2 were upregulated in controls, exhibiting consistent patterns across validation datasets. Gene set enrichment analysis (GSEA) revealed that dysregulated expression of these biomarkers lead to abnormal cell proliferation and differentiation, contributing to OA progression. Furthermore, significant differences in immune cell infiltration—particularly activated and resting mast cells—along with correlations to immune regulatory factors (CD86, CXCL12, TNFSF9, IL6), highlighted potential therapeutic targets. Quantitative RT-PCR further confirmed elevated expression of IL1B, CXCL8 and PTGS2 in control group. This study identifies IL1B, CXCL8 and PTGS2 as OA-related biomarkers linked to butyrate metabolism, offering a theoretical foundation and potential therapeutic strategies.
Effects of Tai Chi on executive function, single-leg dynamic balance, and brain functional connectivity in older adults
Abstract Although previous studies have confirmed the beneficial effects of Tai Chi Chuan (TCC) on improving balance ability and cognitive function in older adults, the synergistic regulatory mechanisms of TCC on bilateral lower limb muscle activation symmetry, brain functional connectivity(FC) strength, and dynamic balance ability remain unclear. This study aims to investigate the effects of a 9-week TCC training program on the executive function (EF), bilateral lower limb muscle activation symmetry, single-leg dynamic balance ability, and brain FC strength in elderly individuals. It provides valuable new insights into a field that has not been extensively explored before. After 9-week training intervention, (1) The TCC group showed significant improvements in the Y-balance Test (YBTs), with enhanced symmetry in single-leg dynamic balance between the bilateral lower limbs and a significant reduction in the laterality of lower limb muscle activation. (2) TCC training strengthened the FC of related brain regions during YBTs. When performing YBTs with the left and right legs separately, the laterality of the average brain FC strength between the two tasks was significantly reduced. (3) During EF tasks, the reaction time was significantly reduced, and the concentration of oxygenated hemoglobin in the prefrontal cortex increased. Studies have shown that TCC has significantly superior intervention effects compared to brisk walking in improving neuromuscular function in older adults. Through its multisensory integration training model, TCC simultaneously enhances neuromuscular coordination and brain network collaboration efficiency, promoting the adaptive reorganization of dynamic balance control.