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Clarification of the metabolic mechanisms underlying salt stress responses in plants will allow further optimization of crop breeding and cultivation to obtain high yields in saline-alkali land. Here, we characterized 68 differential metabolites of cultivated soybean (Glycine max) and wild soybean (Glycine soja) under neutral-salt and alkali-salt stresses using gas chromatography-mass spectrometry (GC-MS)-based metabolomics, to reveal the physiological and molecular differences in salt tolerance. According to comparisons of growth parameters under the two kinds of salt stresses, the level of inhibition in wild soybean was lower than in cultivated soybean, especially under alkali-salt stress. Moreover, wild soybean contained significantly higher amounts of phenylalanine, asparagine, citraconic acid, citramalic acid, citric acid and α-ketoglutaric acid under neutral-salt stress, and higher amounts of palmitic acid, lignoceric acid, glucose, citric acid and α-ketoglutaric acid under alkali-salt stress, than cultivated soybean. Further investigations demonstrated that the ability of wild soybean to salt tolerance was mainly based on the synthesis of organic and amino acids, and the more active tricarboxylic acid cycle under neutral-salt stress. In addition, the metabolite profiling analysis suggested that the energy generation from β-oxidation, glycolysis and the citric acid cycle plays important roles under alkali-salt stress. Our results extend the understanding of mechanisms involved in wild soybean salt tolerance and provide an important reference for increasing yields and developing salt-tolerant soybean cultivars.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.

The key purpose of the existing article is to discuss the effects of various hybrid nanofluids and a simple nanofluid over the heat transfer and friction drags along a stretched surface. The various kinds of hybrid nanofluids and a simple nanofluid together with the effects of aligned magnetic field, nonlinear radiation and suction have been taken into consideration. These hybrid nanofluids are prepared by suspending a couple of distinct nanoparticles Cu and A l 2 O 3 into the base fluids H 2 O and C 2 H 6 O 2 . The comparison of various graphical results of skin friction coefficient, rate of heat transfer, velocity and temperature for two different hybrid nanofluids C u - A l 2 O 3 / H 2 O , C u - A l 2 O 3 / H 2 O - C 2 H 6 O 2 and a simple nanofluid A l 2 O 3 / H 2 O is considered. Moreover, the impact of surface stretching, aligned magnetic field and thermal radiation over the velocity, temperature, skin friction coefficient and local Nusselt number are also considered. The outcomes drawn from this modern research is that the hybrid nanofluid C u - A l 2 O 3 / H 2 O - C 2 H 6 O 2 is quite effective in cooling and heating in comparison to the other hybrid nanofluids C u - A l 2 O 3 / H 2 O , C u - A l 2 O 3 / C 2 H 6 O 2 and a simple nanofluid A l 2 O 3 / H 2 O . Based on these findings we could say that the suspension of multiple particles in the composition of two or more base fluids provides a better rate of heat transfer and limits the friction drag.

Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps. For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO ). We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

Maxillary supernumerary teeth (ST) frequently induce complications (e.g., dental irregularities, bone destruction), but precise risk stratification remains challenging. This retrospective study analysed cone-beam computed tomography (CBCT) data from 217 patients, stratified into childhood (6–12 years) and adulthood (≥ 19 years). Morphological assessment showed that 77.1% of ST were conical, with a significantly higher proportion in females (88.3% vs. 73.4% in males). Age-stratified risk modelling revealed that root curvature was strongly associated with adult bone destruction (adjusted odds ratio [OR] = 3.5), while ST number drove childhood dental anomalies (adjusted OR = 4.2). The adult bone destruction model achieved an area under the curve (AUC) of 0.80 (outperforming non-stratified models), whereas the childhood dental anomaly model had modest performance (AUC = 0.69). These findings support age-specific clinical strategies: early extraction for high-risk children and prioritised surgical intervention for adults with ≥ 2 ST plus root curvature, thereby enhancing precision and reducing unnecessary treatments.

Vitamin D3 plays a crucial role in female reproduction. As research progresses, the mechanisms of action of vitamin D3 on follicular development have been widely discussed. Firstly, key enzymes involved in the synthesis and metabolism of vitamin D3 have been discovered in the ovary, suggesting that vitamin D3 can be synthesized and metabolized locally within the ovary. Additionally, the detection of vitamin D3 receptors (VDR) in follicles suggests that vitamin D3 may exert its effects by binding specifically to these receptors during follicular development. Further research indicates that vitamin D3 promotes follicular growth by enhancing the development of granulosa cells (GCs) and oocytes. Currently, the mechanism of action of vitamin D3 in follicular development is becoming increasingly clear. Vitamin D3 promotes oocyte development by regulating molecules involved in meiotic arrest in oocytes. It also enhances granulosa cell proliferation by stimulating steroid hormone synthesis and cell cycle regulation. Additionally, vitamin D3 exerts anti-inflammatory effects by reducing oxidative stress and advanced glycation end-products (AGEs), mitigating the detrimental effects of inflammation on follicular development. These functions of vitamin D3 have clinical applications, such as in treating polycystic ovary syndrome (PCOS), improving female fertility, and enhancing outcomes in in vitro fertilization (IVF). This review summarizes the research progress on the role and mechanisms of vitamin D3 in follicular development and briefly summarizes its clinical applications.

Background Airway epithelial damage is a pathologic feature commonly observed in the individuals with asthma, and it primarily triggers bronchial hyperresponsiveness and airway remodeling. The present study aimed to investigate the protective effect of Netrin-1 against asthmatic airway inflammation by modulating the NLRP3 inflammasome. Methods Lentiviral transfection was performed to regulate Netrin-1 expression in house dust mite (HDM)-induced human airway epithelial cells and a murine asthma model. ROS levels and cell apoptosis were quantified via flow cytometry. The levels of inflammatory cytokines were assessed using ELISA. The expression of NLRP3 inflammasome components and activation of the HMGB1/RAGE/NF-κB axis were examined via qRT-PCR and Western blotting. The protective effects of Netrin-1 overexpression on asthmatic mice was assessed by HE, PAS and immunohistochemical staining. Results Netrin-1 overexpression notably inhibited cell apoptosis, while decreasing the level of ROS and the proinflammatory cytokines including IL-4, IL-6, and IL-13. These effects were associated with modulation of the HMGB1/RAGE/NF-κB pathway and attenuation of the NLRP3 inflammasome response. In asthmatic mice, Netrin-1 activation significantly reduced airway inflammation and mucus hypersecretion. Conversely, co-overexpression of HMGB1 or administration of the NLRP3 activator abolished these protective effects, underscoring the complex interaction between these molecules. Conclusions Netrin-1 exerts anti-inflammatory effect by inhibiting the NLRP3 inflammasome activation and suppressing HMGB1/RAGE/NF-κB signaling pathway activation. Our preliminary findings demonstrate that Netrin-1 may serve as a potential therapeutic target for asthma, offering novel insights into its pathogenic mechanisms. Graphical Abstract

Soybean is an important economic crop that is continually threatened by abiotic stresses, especially salt stress. Wild soybean is an important germplasm resource for the breeding of cultivated soybean. The root system plays a very important role in plant salt tolerance. To explore the salt tolerance-related mechanisms among , we have demonstrated the seedling roots' growth and metabolomics in wild soybean, semi-wild soybean, and cultivated soybean under two types of salt stress by using gas chromatography-mass spectrometry. We characterized 47 kinds of differential metabolites under neutral salt stress, and isoleucine, serine, l-allothreonine, glutamic acid, phenylalanine, asparagines, aspartic acid, pentadecanoic acid, lignoceric acid, oleic acid, galactose, tagatose, d-arabitol, dihydroxyacetone, 3-hydroxybutyric acid, and glucuronic acid increased significantly in the roots of wild soybean seedlings. However, these metabolites were suppressed in semi-wild and cultivated soybeans. Amino acid, fatty acid, sugars, and organic acid synthesis and the secondary metabolism of antioxidants increased significantly in the roots of wild soybean seedling. Under alkaline salt stress, wild soybean contained significantly higher amounts of proline, glutamic acid, aspartic acid, l-allothreonine, isoleucine, serine, alanine, arachidic acid, oleic acid, cis-gondoic acid, fumaric acid, l-malic acid, citric acid, malonic acid, gluconic acid, 5-methoxytryptamine, salicylic acid, and fluorene than semi-wild and cultivated soybeans. Our study demonstrated that carbon and nitrogen metabolism, and the tricarboxylic acid (TCA) cycle and receiver operating characteristics (especially the metabolism of phenolic substances) of the seedling roots were important to resisting salt stress and showed a regular decreasing trend from wild soybean to cultivated soybean. The metabolomics's changes were critical factors in the evolution of salt tolerance among . This study provides new insights into salt tolerance in soybean, and presents quantitative parameters for a salt tolerant soybean breeding system, which is conducive to the rational use and protection of wild soybean resources.

Cervical carcinoma (CC) remains one of the significant cancers threatening women's health globally. Increasing evidence suggests that alterations in the microbiota are closely associated with cancer development. However, the understanding of reliable biomarkers and underlying mechanisms during the aggravation of cervical neoplasia such as cervical intraepithelial neoplasia (CIN) and CC is still relatively limited. In this study, cervical swab samples from 53 healthy controls, 51 high-grade squamous intraepithelial lesion (HSIL), and 52 CC patients were subjected to 16S rDNA sequencing and metabolomics analysis. We observed significant differences in the cervical microbiota between CC patients and healthy controls or HSIL groups. Compared to the healthy controls, CC patients exhibited increased microbial diversity, decreased abundance of , and notable changes in microbial composition. Metabolomics analysis revealed significantly elevated levels of the inflammatory mediator Prostaglandin E2 (PGE2) in CC samples. Through random forest modeling and ROC curve analysis, we identified a combination of key microbiota ( , ) and metabolites (Cellopentaose, PGE2) as diagnostic biomarkers with high diagnostic value for CC. Furthermore, we found a significant correlation between the cervical microbiota and the metabolite PGE2, suggesting a potential role of key microbiota in inducing inflammation. These findings indicate that alterations in cervical microbiota and metabolites may be closely associated with the occurrence and aggravation of cervical neoplasia, providing new insights for further understanding the mechanisms of cervical neoplasia progression and developing novel diagnostic markers and therapeutic approaches.

In order to reduce the difficulty of concrete preparation, reduce the environmental hazards caused by concrete materials and their preparation process, and meet the development trend of energy saving and emission reduction, this study proposes a low-carbon gel material composed of slag powder, fly ash and gypsum for concrete preparation. Low-carbon gel concrete was made with the ingredients of slag powder, fly ash and gypsum with a water-cement ratio of 0.38 and a ratio of slag powder to fly ash of 1:1.The compressive, flexural and tensile strengths of low-carbon gel concrete samples with different ratios were tested by standard concrete strength test methods to assess their durability properties, and the effects of different ratios of slag powder, admixture and gypsum on the material properties of low-carbon gel were analyzed. concrete material properties with different proportions of slag powder, admixtures and gypsum were analyzed. The experimental results showed that low-carbon gel concrete materials outperformed traditional concrete materials in terms of compressive strength, flexural strength, and tensile strength. After 28 days of curing, the compressive strength of low-carbon gel concrete was 20% higher than that of conventional concrete, and the carbonation depth was reduced by 30.77%. The results indicate that the low-carbon gel concrete materials proposed in this study have excellent strength and durability, and can improve the performance of conventional concrete. This material provides a green and sustainable solution for the modern construction industry, contributing to the sustainable development of the construction sector and the global fight against climate change.