Lycopene from tomatoes and tomato products exerts renoprotective effects by ameliorating oxidative stress, apoptosis, pyroptosis, fibrosis, and inflammatory injury in calcium oxalate nephrolithiasis: the underlying mechanisms.

Several mechanisms underlying nephrolithiasis, one of the most common urological diseases, involve calcium oxalate formation, including oxidative stress, inflammatory reactions, fibrosis, pyroptosis, and apoptosis. Although lycopene has strong antioxidant activity, its protective effects against CaOx-induced injury have not yet been reported. This study aimed to systematically investigate the protective effects of lycopene and explore its mechanisms and molecular targets. Crystal deposition, renal function, oxidative stress, inflammatory response, fibrosis, pyroptosis, and apoptosis were assessed to evaluate the renoprotective effects of lycopene against crystal formation in a CaOx rat model and oxalate-stimulated NRK-52E and HK-2 cells. Lycopene markedly ameliorated crystal deposition, restored renal function, and suppressed kidney injury by reducing oxidative stress, apoptosis, inflammation, fibrosis, and pyroptosis in the rats. In cell models, lycopene pretreatment reversed reactive oxygen species increase, apoptotic damage, intracellular lactate dehydrogenase release, cytotoxicity, pyroptosis, and extracellular matrix deposition. Network pharmacology and proteomic analyses were performed to identify lycopene target proteins under CaOx-exposed conditions, and the results showed that Trappc4 might be a pivotal target gene for lycopene, as identified by cellular thermal shift assay and surface plasmon resonance analyses. Based on molecular docking, molecular dynamics simulations, alanine scanning mutagenesis, and saturation mutagenesis, we observed that lycopene directly interacts with Trappc4 via hydrophobic bonds, which may be attributed to the PHE4 and PHE142 residues, preventing ERK1/2 or elevating AMPK signaling pathway phosphorylation events. In conclusion, lycopene might ameliorate oxalate-induced renal tubular epithelial cell injury via the Trappc4/ERK1/2/AMPK pathway, indicating its potential for the treatment of nephrolithiasis.

Qbnb: An innovative C2B2C2B2C e-commerce mode for integrated on-demand services.

The traditional supply-side-driven e-commerce modes provide consumers with massive amounts of structured information about goods and services. Due to the lack of a tailor-made ability to describe, explain, and accurately understand unstructured service demands, existing technologies and service modes still struggle to fully explore, articulate, and meet the personalized, high-quality, and urgent service demands of the disadvantaged groups in e-commerce. This paper innovatively proposes the concept of "integrated services on demand" and develops its C2B2C2B2C-based "Qbnb" e-commerce mode, operation mechanism and intermediary platform architecture. After conducting exploratory operation training, it demonstrates that the Qbnb mode can effectively achieve centralized allocation of idle social surplus service capacity to cater to the needs of disadvantaged groups in e-commerce. Compared to the traditional e-commerce service mode, Qbnb will significantly expand service demand and capacity, achieve higher service efficiency, and create a broader service consumption market, resulting in many more job opportunities.

Variation in VEGFA and risk of cardiovascular disease in the UK Biobank.

Background: Cardiovascular disease (CVD) is an escalating global health crisis, contributing significantly to worldwide mortality and morbidity. Dyslipidemia stands as a critical risk factor for CVD. Vascular endothelial growth factor A (VEGFA) is pivotal in angiogenesis and represents a clinical target for CVD intervention. However, the impact of genetic modulation of VEGFA on lipid levels and the subsequent risk of cardiovascular events remains unclear. Methods: We used LDpred2 to calculate genetic scores for lipid levels based on VEGFA variation, serving as instrumental variables to simulate the effect of VEGFA inhibitors. We then assessed the associations between genetic risk for lipid levels and CVD risk by conducting One-sample Mendelian randomization. Results: Our results indicated that low-density lipoprotein cholesterol [LDL-C; odds ratio (OR) = 1.09, 95% CI: 1.06-1.11], remnant cholesterol (RC; OR = 1.24, 95% CI: 1.13-1.36), and triglycerides (TG; OR = 1.14, 95% CI: 1.07-1.22) were positively associated with the incidence of CVD. In contrast, high-density lipoprotein cholesterol (HDL-C) was inversely associated with the incidence of CVD (OR = 0.80, 95% CI: 0.76-0.86). When considering the genetic score for LDL-C constructed based on VEGFA, the group with a high genetic score demonstrated an elevated CVD risk (OR = 1.11, 95% CI: 1.04-1.19) compared to those with a low genetic score. Notably, One-sample Mendelian randomization results provided evidence of a causal relationship between LDL-C and CVD (p = 8.4×10-3) when using genetic variation in VEGFA as an instrumental variable. Conclusions: Genetic variation mimicking the effect of VEGFA inhibition, which lowers LDL-C levels, was causally associated with a reduced risk of cardiovascular events. These findings offer insight into the potential therapeutic relevance of modulating VEGFA-mediated lipid changes in the prevention and management of CVD.

Effects of dietary fibers or probiotics on functional constipation symptoms and roles of gut microbiota: a double-blinded randomized placebo trial.

Dietary fibers/probiotics may relieve constipation via optimizing gut microbiome, yet with limited trial-based evidences. We aimed to evaluate the effects of formulas with dietary fibers or probiotics on functional constipation symptoms, and to identify modulations of gut microbiota of relevance. We conducted a 4-week double-blinded randomized placebo-controlled trial in 250 adults with functional constipation. Intervention: A: polydextrose; B: psyllium husk; C: wheat bran + psyllium husk; D: Bifidobacterium animalis subsp. lactis HN019 + Lacticaseibacillus rhamnosus HN001; Placebo: maltodextrin. Oligosaccharides were also included in group A to D. 16S rRNA sequencing was used to assess the gut microbiota at weeks 0, 2, and 4. A total of 242 participants completed the study. No time-by-group effect was observed for bowel movement frequency (BMF), Bristol stool scale score (BSS), and degree of defecation straining (DDS), while BSS showed mean increases of 0.95-1.05 in group A to D (all P < 0.05), but not significantly changed in placebo (P = 0.170), and 4-week change of BSS showed similarly superior effects of the interventions as compared placebo. Group D showed a marginal reduction in plasma 5-hydroxytryptamine. Group A resulted in a higher Bifidobacterium abundance than placebo at week 2 and 4. Fourteen genera showed intervention-specific increasing or decreasing trends continuously, among which Anaerostipes showed increasing trends in groups B and C, associated with BMF increase. Random forest models identified specific baseline microbial genera panels predicting intervention responders. In conclusion, we found that the dietary fibers or probiotics may relieve hard stool, with intervention-specific changes in gut microbiota relevant to constipation relief. Baseline gut microbiota may predispose the intervention responsiveness. ClincialTrials.gov number, NCT04667884.

What is the context?Supplementation of dietary fibers, such as psyllium husk or wheat bran (10 ~ 15 g/day) may relieve constipation symptoms, but bloating and flatulence are major concerns on a high fiber intake.Functional constipation patients had alternated gut microbiota profiles, while meta-analysis suggested that multispecies probiotics may increase bowel movement frequency and relieve hard stool in functional constipation.Dietary fibers or probiotics may lead to before-after changes of gut microbiota in patients with functional constipation, but time-series continued changes of gut microbiota during the intervention are unknown.Elevation of 5-hydroxytryptamine synthesis in enterochromaffin cells may affect bowel movement. And the elevated plasma 5-hydroxytryptamine was observed in functional constipation patients.What is new? Daily supplement of three prebiotic formulas with dietary fibers (polydextrose, psyllium husk, wheat bran, together with oligosaccharides), or a probiotic formula with Bifidobacterium animalis subsp. lactis HN019 + Lacticaseibacillus rhamnosus HN001 effectively relieved hard stool in functional constipation patients after 4 weeks intervention.We identified continued increasing or decreasing gut microbial genera over the intervention. Dietary fiber ­ gut microbiota (Anaerostipes)­constipation relieve (bowel movement frequency) evidence axis was identified in this human trial.Probiotic supplementation marginally reduced plasma 5-hydroxytryptamine, possibly associated with changes in BMF-related gut microbial genera.Intervention-specific baseline gut microbiota well predicted the responsiveness of constipation symptom relief.What is the impact? We provided references for the dosage and duration of dietary fiber/probiotics recommendations for adults with functional constipation, and advanced the microbial genera evidences of the fibers/probiotics-microbiota-laxation theory in humans.

Next-Day Medical Activities Recommendation Model with Double Attention Mechanism Using Generative Adversarial Network.

Medical activities recommendation is a key aspect of an intelligent healthcare system, which can assist doctors with little clinical experience in clinical decision making. Medical activities recommendation can be seen as a kind of temporal set prediction. Previous studies about them are based on Recurrent Neural Network (RNN), which does not incorporate personalized medical history or differentiate between the impact of medical activities. To address the above-given issues, this paper proposes a Next-Day Medical Activities Recommendation (NDMARec) model. Specifically, our model firstly proposes an inpatient day embedding method based on soft-attention which balances the impact of different medical activities to get a joint representation of medical activities that occurred within the same day. Then, a fusion module is designed to combine features of inpatient day and medical history to achieve personalization. These features are learned by the self-attention mechanism that solves the long-term dependency problem of RNNs. Last, adversarial training is introduced to improve the generalization ability of our model. Extensive experiments on a real dataset from a hospital are conducted to show that NDMARec outperformed both classical and state-of-the-art methods.

Effects of tongue hair flexural deformation on viscous fluid transport by bees.

A bee's tongue is coated in dynamic hairs that gradually unfold to entrain the viscid nectar, during which hairs inevitably deflect as a result of fluid drag. The hair deflection induced decline in nectar capture rate may be a coupled elastoviscous problem and remains poorly understood. Here we employed geometric beam theory coupled with the effective viscous force to derive a dynamic model for a rotary tongue hair deflection in a viscous fluid. Considering deflection of the tongue hair, we rationalized the nectar capture rate by takingBombusterrestrisas a model system. When the nectar concentration increases from 20% to 70%, the nectar capture rate declines by 87%, indicating that hair erection is more severely impeded in thicker nectar. Based on this model, we predicted an optimal hair length with which the bee can reach the maximum nectar capture rate. This work may provide a new theoretical framework for quantifying viscous liquid transport by hairy surfaces and shed light on design methodologies for fluid transport devices using hairy beds.

Self-supervised graph neural network with pre-training generative learning for recommendation systems.

The case assignment system is an essential system of case management and assignment within the procuratorate and is an important aspect of judicial fairness and efficiency. However, existing methods mostly use manual or random case assignment, which leads to unbalanced case distribution. Moreover, the relationship between prosecutors and case categories usually shows a power-law distribution in real-world data. Therefore, in this paper, we describe the case rationality assignment as a recommendation problem under the power-law distributed data. To solve the above problems, we propose an end-to-end Self-supervised Graph neural network model with Pre-training Generative learning for Recommendation (SGPGRec), the main idea of which is to capture self-supervised signals using intra-node features and inter-node correlations in the data, and generate the data representation by pre-training to improve the recommendation results. To be specific, we designed three auxiliary self-supervised tasks based on the prosecutor-case category interaction graph and the data distribution to obtain feature representations of prosecutors, case categories, and the interaction information between them. Then we constructed an end-to-end graph neural network recommendation model by the interaction information based on the data characteristics of the power-law distribution. Finally, extensive experimental consistency on a real-world dataset from three procuratorates shows that our method is effective compared to several yet competing baseline methods and further supports the development of an intelligent case assignment system with adequate performance.

Dual-level diagnostic feature learning with recurrent neural networks for treatment sequence recommendation.

In recent years, the massive electronic medical records (EMRs) have supported the development of intelligent medical services such as treatment recommendations. However, existing treatment recommendations usually follow the traditional sequential recommendation strategies, ignoring the partial temporality of the practical process and the patient's diagnostic features. To this end, in this paper, we propose a new Dual-level Diagnostic Feature Learning with Recurrent Neural Network for treatment sequence recommendation (DDFL-RNN), where the dual-level diagnostic features including patients' historical medical records and current treatment results. Firstly, we divide the dataset into several sequential sets of treatment item based on the patient's treatment days. Secondly, we propose two kinds of attention mechanisms to learn diagnostic features, including the elemental attention mechanism and the sequential attention mechanism. Finally, the dual-level learned diagnostic features are brought into the recurrent neural network for encoding and recommendation. Extensive experiments on a breast cancer dataset from a first-rate hospital have shown that our model achieves significantly better performance than several classical and state-of-the-art baseline methods.

The Effect of Single-Dose Ougan Juice Application on the Pharmacokinetics of Erlotinib.

The aim of our study was to investigate the effects of single-dose Ougan (Citrus reticulata cv. Suavissima) juice application on the pharmacokinetics of erlotinib in vivo. Twelve Sprague-Dawley rats were randomly divided into the Ougan juice and control groups (n = 6 each). The rats were given a single dose of 1 mL/100 g Ougan juice or 1 mL/100 g normal saline (NS) by intragastric administration, followed by a single oral administration of 20 mg/kg erlotinib. The plasma concentration of erlotinib in rats was determined using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Erlotinib-d6 was used as the internal standard for chromatographic analysis on the UPLC BEH C18 analysis column (2.1 mm × 50 mm, 1.7 µm). The mobile phase was composed of acetonitrile and 0.1% formic acid eluting by gradient. Different pharmacokinetic (PK) parameters of erlotinib were calculated. The Ougan juice promoted the absorption of erlotinib and reduced the clearance of the drug. The area under the curve of erlotinib in the single-dose Ougan juice pretreatment group was approximately 1.87 times higher, and the maximum blood concentration (Cmax) was approximately 1.34 times higher than that in the control group. The mean residence time of erlotinib in the Ougan juice group was larger, and the clearance rate was smaller than those in the control group; the difference was statistically significant (P < 0.05). Ougan juice affected the PK spectrum of erlotinib in rats by improving the bioavailability of the drug and significantly increasing its plasma concentration.

Pharmacokinetics of 10-Hydroxy Mesaconitine in Rat Plasma by Ultra-Performance Liquid Chromatography-Tandem Quadrupole Mass Spectrometry.

Mesaconitine is the predominant active ingredient in Aconitum carmichaelii Debx. The compound 10-hydroxy mesaconitine is one known metabolite of mesaconitine and is toxic. In order to better understand its pharmacokinetics, UPLC-MS/MS was used in this paper to measure the concentration of 10-hydroxy mesaconitine in the plasma of rats after oral (5 mg/kg) and intravenous (0.1 mg/kg) administration of 10-hydroxy mesaconitine. The concentrations of 10-hydroxy mesaconitine in rat plasma measured in the standard curve covered the range of 0.3-60 ng/mL. The intraday and interday precisions of the samples of 10-hydroxy mesaconitine in rat plasma were lower than 15%. In addition, the accuracies ranged between 96.0% and 109.3%, the matrix effects ranged between 88.9% and 98.1%, and the recoveries were all higher than 79.1%. The AUC(0 - t) values were 23.6 ± 5.9 and 207.6 ± 72.9 ng/mL·h for intravenous and oral administration, respectively, and the bioavailability of 10-hydroxy mesaconitine was 17.6%. Lastly, t 1/2 was 1.3 ± 0.6 h and 3.1 ± 0.4 h for intravenous and oral administration, respectively.

Co-benefits of carbon and pollution control policies on air quality and health till 2030 in China.

Facing the dual challenges of climate change and air pollution, China has made great efforts to explore the co-control strategies for the both. We assessed the benefits of carbon and pollution control policies on air quality and human health, with an integrated framework combining an energy-economic model, an air quality model and a concentration-response model. With a base year 2015, seven combined scenarios were developed for 2030 based on three energy scenarios and three end-of-pipe control ones. Policy-specific benefits were then evaluated, indicated by the reduced emissions, surface concentrations of major pollutants, and premature deaths between scenarios. Compared to the 2030 baseline scenario, the nationwide PM2.5- and O3-related mortality was expected to decline 23% or 289 (95% confidence interval: 220-360) thousand in the most stringent scenario, and three quarters of the avoided deaths were attributed to the end-of-pipe control measures. Provinces in heavily polluted and densely populated regions would benefit more from carbon and pollution control strategies. The population fractions with PM2.5 exposure under the national air quality standard (35 µg/m3) and WHO guideline (10 µg/m3) would be doubled from 2015 to 2030 (the most stringent scenario), while still very few people would live in areas with the WHO guideline achieved for O3 (100 µg/m3). Increased health impact of O3 suggested a great significance of joint control of PM2.5 and O3 in future policy-making.