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Background: The accumulation of ectopic fats is related to metabolic syndromes with insulin resistance, which is considered as the first hit in obesity-related diseases. However, systematic understanding of the occurrence of ectopic fats is limited, since organisms are capable of orchestrating complicated intracellular signaling pathways to ensure that the correct nutritional components reach the tissues where they are needed. Interestingly, tissue-specific mechanisms lead to different consequences of fat metabolism with different insulin sensitivities. Aim of Review: To summarize the mechanisms of fat deposition in different tissues including adipose tissue, subcutis, liver, muscle and intestines, in an attempt to elucidate interactive mechanisms involving insulin actions and establish a potential reference for the rational uptake of fat. Key Scientific Concepts of Review: Tissue-specific fat metabolism serves as a trigger for developing abnormal fat metabolism or as a compensatory agent for regulating normal fat metabolism. Outcomes of de novo lipogenesis and adipogenesis differ in the subcutaneous adipose tissue (SAT), liver and muscle, with the participation of insulin actions. Overload of lipid metabolic capability results in SAT fat expansion, and ectopic fat accumulation implicates impaired lipo-/adipogenesis in SAT. Regulating insulin actions may be a key measure on fat deposition and metabolism in individuals.

ABSTRACTGut microbiota–diet interaction has been identified as a key factor of metabolic associated fatty liver disease (MAFLD). Recent studies suggested that dietary polyphenols may protect against MAFLD by regulating gut microbiota; however, the underlying mechanisms remain elusive. We first investigated the effects of cyanidin 3-glucoside and its phenolic metabolites on high-fat diet induced MAFLD in C57BL/6J mice, and protocatechuic acid (PCA) showed a significant positive effect. Next, regulation of PCA on lipid metabolism and gut microbiota were explored by MAFLD mouse model and fecal microbiota transplantation (FMT) experiment. Dietary PCA reduced intraperitoneal and hepatic fat deposition with lower levels of transaminases (AST & ALT) and inflammatory cytokines (IL-1β, IL-2, IL-6, TNF-α & MCP-1), but higher HDL-c/LDL-c ratio. Characterization of gut microbiota indicated that PCA decreased the Firmicutes/Bacteroidetes ratio mainly by reducing the relative abundance of genus Enterococcus, which was positively correlated with the levels of LDL-c, AST, ALT and most of the up-regulated hepatic lipids by lipidomics analysis. FMT experiments showed that Enterococcus faecalis caused hepatic inflammation, fat deposition and insulin resistance with decreased expression of carnitine palmitoyltransferase-1 alpha (CPT1α), which can be reversed by PCA through inhibiting Enterococcus faecalis. Transcriptomics analysis suggested that Enterococcus faecalis caused a significant decrease in the expression of fibroblast growth factor 1 (Fgf1), and PCA recovered the expression of Fgf1 with insulin-like growth factor binding protein 2 (Igfbp2), insulin receptor substrate 1 (Irs1) and insulin receptor substrate 2 (Irs2). These results demonstrated that high proportion of gut Enterococcus faecalis accelerates MAFLD with decreased expression of CPT1α and Fgf1, which can be prevented by dietary supplementation of PCA.

Ferulic acid (FA) and vanillic acid (VA) are considered as major phenolic metabolites of cyanidin 3-glucoside, a polyphenol that widely exists in plants that possess a protective effect against oxidative stress and inflammation in our previous study. This study aimed to investigate the effect of FA and VA on inflammation, gut barrier function, and growth performance in a weaned piglet model challenged with lipopolysaccharide (LPS). Thirty-six piglets (PIC 337 × C48, 28 d of age) were randomly allocated into 3 treatments with 6 replicate pens (2 piglets per pen). They were fed with a basal diet or a diet containing 4,000 mg/kg of FA or VA. Dietary supplementation of VA significantly increased average daily gain (ADG) (P

Placental function is vital to the fetal growth of sows, and resveratrol (RES) can protect cells against oxidative stress, which is one of the major factors impairing placental function. This study aimed to investigate the effect of dietary resveratrol (RES) on placental function and reproductive performance during late pregnancy in a sow model from the aspects of oxidative stress, insulin resistance, and gut microbiota. A total of 26 hybrid pregnant sows (Landrace × Yorkshire) with similar parity were randomly allocated into two groups (n = 13) and fed with a basal diet or a diet containing 200 mg/kg of resveratrol from day 85 of gestation until parturition. The dietary supplementation of RES increased the litter weight at parturition by 12.53% (p = 0.145), with ameliorated insulin resistance (HOMA-IR), increased triglyceride (TG) levels, and decreased interleukin (IL)-1β and IL-6 levels in serum (p < 0.05). Moreover, resveratrol increased the placental vascular density (p < 0.05) with the enhanced expression of nutrient transporter genes (SLC2A1 and SLC2A3) and antioxidant genes, such as superoxide dismutase 2 (SOD2) and heme oxygenase-1 (HO-1) but declined the expression of inflammatory genes, such as IL-1β and IL-6 (p < 0.05). The characterization of the fecal microbiota revealed that resveratrol decreased the relative abundance of the Christensensllaceae R-7 group and Ruminococcaceae UCG-008 (p < 0.05), which had a positive linear correlation with the expression of IL-1β and IL-6 (p < 0.05), but had a negative linear correlation with the expression of SOD2, HO-1, SLC2A1, and SCL2A3 genes (p < 0.05). These data demonstrated that dietary supplementation with resveratrol can improve placental function with ameliorated insulin resistance, oxidative stress, and inflammation potentially by regulating Ruminococcaceae UCG-008 and the Christensensllaceae R-7 group in sows.

Eucommia ulmoides bark has been traditionally used as a Chinese medicine to attenuate stress, but the leaf, which is rich in polyphenols and polysaccharides, has been rarely used. This study aimed to investigate the effect of Eucommia ulmoides leaf extracts (EULEs) on oxidative stress and meat quality of broilers. A total of 252 broilers were randomly divided into 3 treatments and fed with a control basal diet (CON), or a diet containing 250 mg/kg or 1,000 mg/kg of EULE for 51 days. Results showed that dietary supplementation of 250 mg/kg EULE increased significantly the average daily gain of broilers in the early stage (1–21 days), while 250 mg/kg or 1,000 mg/kg of EULE decreased the feed conversion ratio in the whole period (P < 0.05). Supplementation of 250 mg/kg EULE reduced the level of MDA in the liver (P < 0.05), while 1,000 mg/kg EULE decreased the serum level of MDA (P < 0.05), and the HDL level in serum was increased by 250 mg/kg or 1,000 mg/kg EULE (P < 0.05). Additionally, 250 mg/kg EULE decreased abdominal fat ratio and serum triglyceride (TC) level in broilers, while 250 or 1,000 mg/kg of EULE reduced drip loss in breast muscle (P < 0.05), and 1,000 mg/kg EULE reduced the cooking loss in thigh muscle (P < 0.05). In conclusion, dietary supplementation of 250 mg/kg of EULE could attenuate oxidative stress and improve the growth performance and meat quality in broilers.

ObjectiveThis study aimed to evaluate the effect of an antibiotic cocktail on gut microbiota and provide a reference for establishing an available mouse model for fecal microbiota transplantation (FMT) of specific microbes.DesignC57BL/6J mice (n = 24) had free access to an antibiotic cocktail containing vancomycin (0.5 g/L), ampicillin (1 g/L), neomycin (1 g/L), and metronidazole (1 g/L) in drinking water for 3 weeks. Fecal microbiota was characterized by 16S rDNA gene sequencing at the beginning, 1st week, and 3rd week, respectively. The mice were then treated with fecal microbiota from normal mice for 1 week to verify the efficiency of FMT.ResultsThe diversity of microbiota including chao1, observed species, phylogenetic diversity (PD) whole tree, and Shannon index were decreased significantly (P < 0.05) after being treated with the antibiotic cocktail for 1 or 3 weeks. The relative abundance of Bacteroidetes, Actinobacteria, and Verrucomicrobia was decreased by 99.94, 92.09, and 100%, respectively, while Firmicutes dominated the microbiota at the phylum level after 3 weeks of treatment. Meanwhile, Lactococcus, a genus belonging to the phylum of Firmicutes dominated the microbiota at the genus level with a relative abundance of 80.63%. Further FMT experiment indicated that the fecal microbiota from the receptor mice had a similar composition to the donor mice after 1 week.ConclusionThe antibiotic cocktail containing vancomycin, ampicillin, neomycin, and metronidazole eliminates microbes belonging to Bacteroidetes, Actinobacteria, and Verrucomicrobia, which can be recovered by FMT in mice.

In recent years, nonsteroidal anti-inflammatory drug (NSAIDs), which are considered to affect the prognosis of spinal surgery, have been widely used in perioperative analgesia in spinal surgery, but the relationship between these two factors remains unclear. The purpose of this study was to explore the effect of perioperative use of NSAIDs on the prognosis of patients treated with spinal surgery. We systematically searched PubMed, Embase, and Cochrane Library for relevant articles published on or before July 14, 2023. We used a random-effect model for the meta-analysis to calculate the standardized mean difference (SMD) with a 95% confidence interval (CI). Sensitivity analyses were conducted to analyze stability. A total of 23 randomized clinical trials including 1457 participants met the inclusion criteria. Meta-analysis showed that NSAIDs were significantly associated with postoperative morphine use (mg) (SMD = -0.90, 95% CI -1.12 to -0.68) and postoperative pain (SMD = -0.71, 95% CI -0.85 to -0.58). These results were further confirmed by the trim-and-fill procedure and leave-one-out sensitivity analyses. The current study shows that perioperative use of NSAIDs appears to be an important factor in reducing postoperative pain and morphine use in patients undergoing spinal surgery. However, well-designed, high-quality randomized controlled trials (RCTs) are still required. [ABSTRACT FROM AUTHOR]

Early dental caries detection by endoscope can prevent complications, such as pulpitis and apical infection. However, automatically identifying dental caries remains challenging due to the uncertainty in size, contrast, low saliency, and high interclass similarity of dental caries. To address these problems, we propose the Global Feature Detector (GFDet) that integrates the proposed Feature Selection Pyramid Network (FSPN) and Adaptive Assignment-Balanced Mechanism (AABM). Specifically, FSPN performs upsampling with the semantic information of adjacent feature layers to mitigate the semantic information loss due to sharp channel reduction and enhance discriminative features by aggregating fine-grained details and high-level semantics. In addition, a new label assignment mechanism is proposed that enables the model to select more high-quality samples as positive samples, which can address the problem of easily ignored small objects. Meanwhile, we have built an endoscopic dataset for caries detection, consisting of 1318 images labeled by five dentists. For experiments on the collected dataset, the F1-score of our model is 75.6%, which out-performances the state-of-the-art models by 7.1%.

Prediction of enhancer-promoter interactions (EPIs) is key to regulating gene expression and diagnosing genetic diseases. Due to limited resolution, biological experiments perform not as well as expected while precisely identifying specific interactions, giving rise to computational biology approaches. Many EPI predictors have been developed, but their prediction accuracy still needs to be enhanced. Here, we design a new model named EPIMR to identify enhancer-promoter interactions. First, Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial information. Second, a multi-scale residual neural network (ResNet) is used to learn the distinguishing features of different abstraction levels. Finally, matching heuristics are adopted to concatenate the learned features of enhancers and promoters, which pays attention to their potential interaction information. Experimental results on six cell lines indicate that EPIMR performs better than existing methods, with higher area under the precision-recall curve (AUPR) and area under the receiver operating characteristic (AUROC) results on benchmark and under-sampling datasets. Furthermore, our model is pre-trained on all cell lines, which improves not only the transferability of cross-cell line prediction, but also cell line-specific prediction ability. In conclusion, our method serves as a valuable technical tool for predicting enhancer-promoter interactions, contributing to the understanding of gene transcription mechanisms. Our code and results are available at https://github.com/guofei-tju/EPIMR.

The efficient removal of antibiotic residues from water has become a significant concern in environmental management. This study addresses the low tetracycline removal efficiency of MIL-125(Ti) due to its limited adsorption capacity and weak ultraviolet light absorption. To enhance the photocatalytic performance of MIL-125(Ti), Co3O4 with a suitable band gap was selected for designing and preparing the heterojunction catalyst. By leveraging the porous nature of metal–organic frameworks, Co3O4 was derived from ZIF-67(Co) to provide additional adsorption sites. Notably, the addition of just 0.5 % Co3O4, which exhibits negligible photocatalytic activity on its own, enabled MIL-125(Ti) to form a visible-light responsive Z-scheme heterojunction with superior adsorption capacity. The adsorption of tetracycline increased from 18.1 % for MIL-125(Ti) to 34.3 % for the MIL-125(Ti)/Co3O4 composite. Furthermore, the photocatalytic degradation rate of tetracycline increased from 41.1 % for MIL-125(Ti) to 57.6 % for MIL-125(Ti)/Co3O4, resulting in a remarkable overall tetracycline removal efficiency of 91.9 % due to the combined effects of adsorption and photocatalysis. The Z-scheme heterojunction demonstrated excellent stability and reusability after undergoing five cycles. This study provides new insights into the design of MIL-125(Ti)-based composites for efficient synergistic adsorption and photocatalysis.

Abstract Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis.

Abstract Plant phenotypic characteristics, especially leaf morphology of leaves, are an important indicator for species identification. However, leaf shape can be extraordinarily complex in some species, such as oaks. The great variation in leaf morphology and difficulty of species identification in oaks have attracted the attention of scientists since Charles Darwin. Recent advances in discrimination technology have provided opportunities to understand leaf morphology variation in oaks. Here, we aimed to compare the accuracy and efficiency of species identification in two closely related deciduous oaks by geometric morphometric method (GMM) and deep learning using preliminary identification of simple sequence repeats (nSSRs) as a prior. A total of 538 Asian deciduous oak trees, 16 Q. aliena and 23 Q. dentata populations, were firstly assigned by nSSRs Bayesian clustering analysis to one of the two species or admixture and this grouping served as a priori identification of these trees. Then we analyzed the shapes of 2328 leaves from the 538 trees in terms of 13 characters (landmarks) by GMM. Finally, we trained and classified 2221 leaf‐scanned images with Xception architecture using deep learning. The two species can be identified by GMM and deep learning using genetic analysis as a priori. Deep learning is the most cost‐efficient method in terms of time‐consuming, while GMM can confirm the admixture individuals' leaf shape. These various methods provide high classification accuracy, highlight the application in plant classification research, and are ready to be applied to other morphology analysis.

DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. Here the authors present a strand assembly algorithm (DBGPS) using de Bruijn graph and greedy path search.

Core-shell thermally expandable microspheres (TEMs) were prepared via Pickering suspension polymerization. Acrylonitrile (AN), methacrylic acid (MAA) and N,N-dimethylacrylamide (DMAA) were used as the comonomers, the 2,2′-azobisisobutyronitrile (AIBN) was used as the initiator and the ethyleneglycol dimethacrylate (EGDMA) was used as the crosslinker. The effects of initiator concentration, cross-linking agent concentration and modified monomer type on the morphology, thermal properties and encapsulation content of TEMs were investigated. The results show that the small change in initiator concentration had a significant effect on the properties of TEMs. When the initiator concentration was 0.55%, the microspheres showed core-shell structure and the maximum expansion ratio was 3.06 times. According to the scanning electron microscope (SEM) image, the heat resistance of the microspheres increased with the increase of the content of crosslinking agent. The lower the water solubility of the modified monomer, the more stable the foaming of the TEMs. When methyl acrylate (MA) was used as a modified monomer, the initial expansion temperature of the microspheres was 163.2°C, the maximum expansion temperature was 223.5 °C, and the encapsulation content of the blowing agent was 14.70%.

Cotton production makes an important contribution to the income of rural residents and the economy in Xinjiang province, which leads other provinces in terms of planted area, total production, and average yield of cotton in China. This study analyzed the competitiveness of cotton production in the study area using the efficiency advantage index (EAI), scale advantage index (SAI), and aggregated advantage index (AAI). Moreover, the factors influencing the productivity of cotton have been investigated by the use of ridge regression and correlation matrix using a dataset for the period 2005 to 2018. The results showed that cotton production had a large comparative advantage in Xinjiang from 2005 to 2018. The average of efficiency advantage index (EAI), scale advantage index (SAI), and aggregated advantage index (AAI) are 1.50, 12.96, and 4.35, respectively. Overall, Xinjiang cotton production has a higher planting scale advantage and productivity. By using ridge regression to calculate the impact of cotton production on agricultural output value in Xinjiang, the results showed that total cotton production, fiscal expenditure on agricultural support, total agricultural machinery power, and fertilizer use had significant positive effects, whereas cotton sown area, average cotton yield, and the proportion of affected area by insects and diseases had negative impact agricultural output value. The study implies the need for a implementing a well-thought and empirically backed plan to support cotton production based on comparative advantage for a specific area, building a cotton production standard system, reducing the cost of cotton production, and building a cotton risk-protection system to protect the interests of cotton farmers and promote the sustainable development of the cotton industry.

As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use.

Blood pressure is one of the most basic health screenings and it has a complex relationship with chronic kidney disease (CKD). Controlling blood pressure for CKD patients is crucial for curbing kidney function decline and reducing the risk of cardiovascular disease. Two independent CKD cohorts, including matched controls (discovery n = 824; validation n = 552), were recruited. High-throughput metabolomics was conducted with the patients’ serum samples using mass spectrometry. After controlling for CKD severity and other clinical hypertension risk factors, we identified ten metabolites that have significant associations with blood pressure. The quantitative importance of these metabolites was verified in a fully connected neural network model. Of the ten metabolites, seven have not previously been associated with blood pressure. The metabolites that had the strongest positive association with blood pressure were aspartylglycosamine (p = 4.58 × 10−5), fructose-1,6-diphosphate (p = 1.19 × 10−4) and N-Acetylserine (p = 3.27 × 10−4). Three metabolites that were negatively associated with blood pressure (phosphocreatine, p = 6.39 × 10−3; dodecanedioic acid, p = 0.01; phosphate, p = 0.04) have been reported previously to have beneficial effects on hypertension. These results suggest that intake of metabolites as supplements may help to control blood pressure in CKD patients.