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Abstract Background Syntaxin6 (STX6) is a SNARE (Soluble N-ethylmaleimide-sensitive factor attachment protein receptors) protein complex located in the trans-Golgi network and endosomes, which is closely associated with a variety of intracellular membrane transport events. STX6 has been shown to be overexpressed in a variety of human malignant tumors such as esophageal, colorectal, and renal cell carcinomas, and participates in tumorigenesis and development. Methods Based on clinical public database and clinical liver samples analysis, the expression of STX6 in hepatocellular carcinoma (HCC) tissues was investigated. The effects of STX6 on proliferation, migration and invasion of HCC cell in vitro and in vivo were evaluated through gain- and loss-of-function studies. We further performed RNA-seq analysis and protein interactome analysis, to further decifer the detailed mechanisms of STX6 in the regulation of the JAK-STAT pathway in HCC. Results STX6 expression was upregulated in HCC tissues and its expression was highly correlated with the high histological grade of the tumor. STX6 promoted HCC cell proliferation, migration and invasion both in vitro and in vivo. Mechanistically, STX6 mediated tumor progression depending on promoting the activation of JAK-STAT signaling pathway. Receptor for activated protein kinase C (RACK1) as an essential adaptor protein mediating STX6 regulation of JAK-STAT pathway. Specifically, STX6 interacted with RACK1 and then recruited signal transducer and activator of transcription 3 (STAT3) to form a protein-binding complex and activates STAT3 transcriptional activity. Conclusions This study provided a novel concept that STX6 exerted oncogenic effects by activating the STAT3 signaling pathway, and STX6 might be a promising therapeutic target for HCC.

Arsenic (As) is a widespread global pollutant, and there is significant controversy surrounding its complex relationship with obesity, primarily focused on short-term exposure. Recognizing the prolonged nature of dietary arsenic exposure, this study involved feeding mice with arsenic-contained food for 14 months. The results showed that mice exposed to arsenic developed a non-alcoholic fatty liver condition, characterized by a light-yellow hue on the liver surface and various pathological alterations in the liver cells, including enlarged nuclei, cellular necrosis, inflammatory infiltration, dysfunctional mitochondria, and endoplasmic reticulum disorganization. There were also disruptions in biochemistry indices, with a significant increase in total cholesterol (TC) level and a decrease in high-density lipoprotein (HDL) level. However, some contradictory observations occurred, such as a significant decrease in body weight, triglyceride (TG) level, and the numbers of lipid droplets. Several genes related to lipid metabolism were tested, and a model was used to explain these discrepancies. Besides, examinations of the colon revealed compromised intestinal barrier function and signs of inflammation. Fecal 16S rRNA sequencing and pseudo-targeted metabolomics revealed disruptions in internal homeostasis, such as modules, nodes, connections, and lipid-related KEGG pathways. Fecal targeted metabolomics analyses of short-chain fatty acids (SCFAs) and bile acids (BAs) demonstrated a significant upregulation in three primary bile acids (CA, CDCA, TCDCA), four secondary bile acids (TUDCA, DCA, LCA, GUDCA), and total SCFAs level. Oxidative stress and inflammation were also evident. Additionally, based on correlation analysis and mediation analysis, it was assumed that changes in the microbiota (e.g., Dubosiella) can impact the liver metabolites (e.g., TGs) through alterations in fecal metabolites (e.g., LPCs). These findings provide a theoretical reference for the long-term effect of arsenic exposure on liver lipid metabolism.

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

Abstract Zebrafish (Danio rerio) have attracted much attention over the past decade as a reliable model for gut microbiome research. Owing to their low cost, strong genetic and development coherence, efficient preparation of germ‐free (GF) larvae, availability in high‐throughput chemical screening, and fitness for intravital imaging in vivo, zebrafish have been extensively used to investigate microbiome‐host interactions and evaluate the toxicity of environmental pollutants. In this review, the advantages and disadvantages of zebrafish for studying the role of the gut microbiome compared with warm‐blooded animal models are first summarized. Then, the roles of zebrafish gut microbiome on host development, metabolic pathways, gut‐brain axis, and immune disorders and responses are addressed. Furthermore, their applications for the toxicological assessment of aquatic environmental pollutants and exploration of the molecular mechanism of pathogen infections are reviewed. We highlight the great potential of the zebrafish model for developing probiotics for xenobiotic detoxification, resistance against bacterial infection, and disease prevention and cure. Overall, the zebrafish model promises a brighter future for gut microbiome research.

Gut microbiome can participate in arsenic metabolism. However, its efficacy in the host under arsenic stress is still controversial. To clarify their roles in fecal arsenic excretion, tissue arsenic accumulation, host physiological states and metabolism, in this study, ninety-six C57BL/6 male mice were randomly divided to four groups, groups A and B were given sterile water, and groups C and D were given the third generation of broad-spectrum antibiotic (ceftriaxone) to erase the background gut microbiome. Subsequently, groups B and D were subchronicly exposed to arsenic containing feed prepared by adding arsenical mixture (rice arsenic composition) into control feed. In group D, the fecal total arsenic (CtAs) decreased by 25.5 %, iAsIII composition increased by 46.9 %, unclarified As (uAs) composition decreased by 92.4 %, and the liver CtAs increased by 26.7 %; the fecal CtAs was positively correlated with microbial richness and some metabolites (organic acids, amino acids, carbohydrates, SCFAs, hydrophilic bile acids and their derivatives); and fecal DMA was positively correlated with microbial richness and some metabolites (ferulic acid, benzenepropanoic acid and pentanoic acid); network analysis showed that the numbers of modules, nodes, links were decreased and vulnerability was increased; some SCFAs and hydrophilic bile acid decreased, and hydrophobic bile acids increased (Ps

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

Long-term consumption of arsenic-contaminated rice has become a public health issue that urgently needs to be addressed. In this study, mice were exposed to arsenic in rice (low dose, 0.91 mg/kg; medium dose, 9.1 mg/kg) for 30 days and 60 days, respectively, and the effects on pathological structures of spleen and skin, as well as the structure of the fecal microbiome were examined. The findings revealed dose/time cumulative effects on pathological changes, with even a low dose exposure for 30 days causing destruction of splenic follicular structure and thickening of dermal keratinized and epidermal layers. The Firmicutes/Bacteroidetes ratio in the community and the positive/negative ratio in network links were higher in arsenic groups, suggesting that arsenic resulted in a less healthy and unstable microbiome for the host. Thus lifetime consumption of arsenic in rice may have potential health effects on humans and must be carefully assessed to safeguard human health. Furthermore, in arsenic groups, arsenic-resistant bacteria or arsenic hazards remediation bacteria changed to be the dominant bacteria and acted as the core bacteria in the network modules. Some microbial arsenic transforming genes (arsC, arsR, arsA, ACR3, and aoxB) differed, indicating that the gut microbiome changed to withstand arsenic stress. Furthermore, Faecalibaculum, Lachnospiraceae_NK4A136_group, Angelakisella, Ruminiclostridium, and Desulfovibrionaceae are positively associated with arsenic dosage and may be useful in the early detection of arsenicals.

The F1F0-ATPase β-subunit mutation strain L. plantarum LPM21 was screened to resolve the post-acidification of Sichuan pickle products caused by the probiotic adjunct cultures L. plantarum during product handling, transportation or storage at ambient temperature. Ser-268 mutation into Leu-268 in F1F0-ATPase β-subunit of LPM21, located in the interaction interface of F1F0-ATPase α- and β-subunit, triggered a 43.44% drop in the ATPase activity. In MRS broth, LPM21 could not grow under desired pH 4.2 and its acid production was about 1/2 of parent L. plantarum CCTCC 207202. When LPM21 cultures were incorporated into aseptic Sichuan pickle products, the pH of products was 4.3 after 7 d incubation at 30°C, above the critical pH limit of 4.2. Furthermore, LPM21 viable count with 7.27 log CFU mL-1 satisfied the criteria for a probiotic food product. Therefore, L. plantarum LPM21 as probiotic adjunct has the potential for reducing the post-acidification of probiotic Sichuan pickle. [ABSTRACT FROM AUTHOR]