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Faecalibacterium prausnitzii (F. prausnitzii) has been recognized for its various intestinal and extraintestinal benefits to human. And reduction of F. prausnitzii has been linked to an increased risk of intestinal fibrosis in patients of Crohn’s disease (CD). In this study, oral administration of either live F. prausnitzii or its extracellular vesicles (FEVs) can markedly mitigate the severity of fibrosis in mice induced by repetitive administration of DSS. In vitro experiment revealed that FEVs were capable of directing the polarization of peripheral blood mononuclear cells (PBMCs) towards an M2b macrophage phenotype, which has been associated with anti-fibrotic activities. This effect of FEV was found to be stable under various conditions that promote the development of pro-fibrotic M1/M2a/M2c macrophages. Proteomics and RNA sequencing were performed to uncover the molecular modulation of macrophages by FEVs. Notably, we found that FEVs reprogramed every metabolism of macrophages by damaging the mitochondria, and inhibited oxidative phosphorylation and glycolysis. Moreover, FEV-treated macrophages showed a decreased expression of PPARγ and an altered lipid processing phenotype characterized by decreased cholesterol efflux, which may promote energy reprogramming. Taken together, these findings identify FEV as a driver of macrophage reprogramming, suggesting that triggering M2b macrophage polarization by oral admiration of FEV may serve as strategy to alleviate hyperfibrotic intestine conditions in CD.

Mannan oligosaccharides (MOSs) have been implicated in the animal growth rate, health indices, and lipid oxidative stability. MOSs have been indicated to maintain intestinal health and anti-inflammatory effects via modulation of gut microbiota. Furthermore, the role of MOSs in modulating skeletal muscle function is largely unknown. Here, this study aimed to investigate the effects of MOS supplementation on muscle function and muscle mass in mice. Additionally, the possible underlying mechanisms, including the contributions of gut microbiota and microbial metabolites, were explored. In our study, 3-week-old C57BL/6J male mice (body weight of approximately 10.7 ± 1.1 g) were given pure water or pure water with 1% MOS. To study the effect of MOSs on gut-microbiota-derived metabolites, serum metabolic profiles were analyzed through untargeted metabolomic profiling. Moreover, we detected the downstream signals of differential metabolites, and decanoic acid (DA) was selected as our target spot. Then, DA was used to treat C2C12 cells, and we found that DA promotes C2C12 cell differentiation via the GPR84 and PI3K/AKT signaling pathways. In conclusion, these results showed that MOS supplementation improves muscle function and muscle mass. Additionally, gut microbiome and microbial metabolites were regulated by MOSs, and DA may be one of the most important links between the gut microbiome and skeletal muscle function regulation.

Abstract Purposes Gastric cancer (GC) is one of the most common malignant tumors threatening human beings and has a poor prognosis. Therefore, exploring unveiled biomarkers or therapeutic targets for the diagnosis and treatment of GC is crucial. Methods A total of 5772 protein quantitative trait loci (pQTL) were aggregated from four latest large-scale proteomics cohorts. Two-sample Mendelian randomization (two-sample MR) was utilized to identify the causal effect of blood plasma proteins on GC. Heterogeneity, pleiotropy, and directionality analyses were employed to evaluate proteins identified via two-sample MR. The robustness of results was further validated via colocalization. The drug targets of proteins were evaluated to reveal the compounds that can interfere with these proteins. Results Ten proteins with potential causations in relation to GC were identified: LY6D, SLURP1, MLN, PSCA, THSD1, CFTR, PPM1B, KDM3A, TSC1, and HCG22. Among these proteins, LY6D, SLURP1, and THSD1 were considered as the most reliable biomarkers of GC due to their significant H4 posterior probabilities in colocalization analysis and absence of pleiotropy. Compound 35, nitrosamide, and 0175029-0000 were potential drugs or small molecules targeting LY6D, SLURP1, and THSD1, respectively. Conclusion This study identified several plasma proteins as potential biomarkers of GC and provided data support and new insights into the early diagnosis, intervention, and therapeutic targets of GC.

Abstract The application of near-infrared spectroscopy (NIRS) for rapid quantitative analysis of soil total nitrogen (STN) is of great significance to recycling nitrogen in the ecosystem and crops growth. However, collecting thousands of soil samples and chemical analysis are impracticable, more importantly a deviation from NIRS advantages of rapid, inexpensive and nondestructive. To more efficiently improve the estimation performance and reduce uncertainty of the model when working with small sample sizes (less than 100), solutions from soil particle size decomposition and model fusion were investigated. Elaborately, 123 Latosols samples were collected and decomposed them according to particle sizes to extend limited data at multiple scales. Based on all soil groups decomposed, a hyperspectral data recapture and model decision fusion method were implemented. The results demonstrated that the proposed method increased the scale of spectral data, extracted more STN-related spectral information, improved estimation accuracy, and reduced uncertainty. The fused model based on data from all decomposed groups yielded the best estimated results (root mean square error $$(RMSE) = 0.075g.kg^{-1}$$ ( R M S E ) = 0.075 g . k g - 1 , $$R^2 = 0.784$$ R 2 = 0.784 , and a ratio of performance to inter-quartile distance $$(RPIQ) = 3.787$$ ( R P I Q ) = 3.787 ) on the validation set. Through a tenfold cross-validation, the weighted fusion model with six groups of particle sizes data showed an improvement of 0.307 in $$R^2_cv$$ R c 2 v and an improved RPIQ of 1.015 compared to models constructed using conventional machine learning (ML) techniques and limited pristine data ( $$R^2_cv = 0.442, RMSE = 0.119$$ R c 2 v = 0.442 , R M S E = 0.119 ). Therefore, when utilizing NIRS to build rapid and accurate STN predictive models, the proposed method demonstrates great potential in improving the reliability of soil spectral models under small sample sizes. Graphical Abstract

The nutritional status of rubber trees (Hevea brasiliensis) is inseparable from the production of natural rubber. Nitrogen (N) and potassium (K) levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree. Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly. However, high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model. A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech. Therefore, a less intensive and streamlined method, remining information from hyperspectral image data, was assessed. From this new perspective, a semisupervised learning (SSL) method and resampling techniques were employed for generating pseudo-labeling data and class rebalancing. Subsequently, a 5-classification spectral model of the N and K statuses of rubber leaves was established. The SSL model based on random forest classifiers and mean sampling techniques yielded optimal classification results both on imbalance/balance dataset (weighted average precision 67.8/78.6%, macro averaged precision 61.2/74.4%, and weighted recall 65.7/78.5% for the N status). All data and code could be viewed on the:Github https://github.com/WeehowTang/SSL-rebalancingtest. Ultimately, we proposed an efficient way to rapidly and accurately monitor the N and K levels in rubber leaves, especially in the scenario of small annotation and imbalance categories ratios.

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure–activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

Background: Significant anxiety symptoms are associated with poor clinical course and outcome in major depressive disorder (MDD). This single-arm, open-label study aimed to evaluate the efficacy and tolerability of escitalopram treatment in patients with MDD and anxiety symptoms. Methods: Adult patients with MDD and anxiety symptoms (Montgomery-Asberg Depression Rating Scale [MADRS] ≥22 and Hamilton Anxiety Rating Scale [HAM-A] ≥14) were enrolled and received escitalopram (10-20 mg/day) treatment for 24 weeks. Symptom status was assessed by MADRS, 17-item-Hamilton Depression Rating Scale, HAM-A, and Clinical Global Impression Scale at baseline and the following visits. Quality of life was assessed by Short Form-12, and safety was evaluated by adverse events, laboratory investigations, vital signs, and physical findings. Results: Overall, 200 of 318 (66.2%) enrolled patients completed the 24-week treatment. The remission (MADRS ≤10 and HAM-A ≤7) rate in the full analysis set (N=285) was 73.3% (95% confidence interval: 67.80, 78.38) at week 24. Mean (± standard deviation) MADRS total score was 33.4 (±7.13) and HAM-A score was 27.6 (±7.26) at baseline, which reduced to 6.6 (±10.18) and 6.0 (±8.39), respectively, at week 24. Patients with higher baseline depression and anxiety level took longer to achieve similar remission rates. Overall, 80 of the 302 (26.5%) patients included in the safety set reported at least 1 treatment-emergent adverse event (TEAE). Most frequently reported TEAEs (.2%) were headache (4.0%), nasopharyngitis (3.6%), nausea (3.0%), and dizziness (2.6%). Serious TEAEs were reported by 1.3% patients; no deaths were reported. Conclusion: Escitalopram 10-20 mg/day was effective and well-tolerated in the long-term treatment of MDD with anxiety symptoms in adult Chinese population. [ABSTRACT FROM AUTHOR]

Bacillus cereus is a well-characterized human pathogen that produces toxins associated with diarrheal and emetic foodborne diseases. To investigate the possible transmission of B. cereus on lettuce farms in China and determine its enterotoxicity, (I) a total of 524 samples (lettuce: 332, soil: 69, water: 57, manure: 57, pesticide: 9) were collected from 46 lettuce farms in five Chinese provinces, (II) multilocus sequence typing (MLST) was used to classify B. cereus isolates and for trace analysis, and (III) the presence of toxin genes and enterotoxins (Hbl and Nhe) was detected in 68 strains. The results showed that one hundred and sixty-one lettuce samples (48.5%) tested positive for B. cereus at levels ranging from 10 to 5.3 × 104 CFU/g. Among the environmental sample categories surveyed, the highest positive rate was that of the pesticide samples at 55.6%, followed by soil samples at 52.2% and manure samples at 12.3%. Moreover, one hundred isolates of B. cereus yielded 68 different sequence types (STs) and were classified into five phylogenetic clades. Furthermore, Nhe toxin genes (nheA, nheB, nheC) were broadly distributed and identified in all 68 strains (100%), while Hbl toxin genes (hblA, hblC, hblD) were present in 61 strains (89.7%), entFM was detected in 62 strains (91.2%), and cytK was found in 29 strains (42.6%). All strains were negative for ces. As for the enterotoxin, Nhe was observed in all 68 isolates carrying nheB, while Hbl was present in 76.5% (52/68) of the strains harboring hblC. This study is the first report of possible B. cereus transmission and of its potential enterotoxicity on lettuce farms in China. The results showed that soil and pesticides are the main sources of B. cereus on lettuce farms in China, and the possible transmission routes are as follows: soil-lettuce, manure-lettuce, pesticide-lettuce, manure-soil-lettuce, and water-manure-soil-lettuce. Furthermore, the B. cereus isolates, whether from lettuce or the environment, pose a potential risk to health.

Abstract Background Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN. Results Datasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues from living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Five types of immune cells revealed important associations with LN, and monocytes emerged as having the most prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8, were closely related to clinical features. Conclusions This study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.