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Tebuconazole, one of the most widely used triazole fungicides, is reported to potentially pose a risk of inducing neurological disorders in human beings. Considering the increasing exposure, whether it could influence cognitive function remains to be elucidated. Herein, we used a mouse model to evaluate the potential cognitive risks and possible mechanisms from the continuous edible application of tebuconazole at low concentrations. Our study revealed that tebuconazole deteriorated spatial learning and memory and downregulated the expression of glutamate receptor subunits. Importantly, metagenomic analysis indicated that tebuconazole not only led to significant shifts in the composition and diversity of the gut microbiota but also changed intestinal homeostasis. Specifically, after exposure, tebuconazole circulated in the bloodstream and largely entered the gut–brain axis for disruption, including disturbing the Firmicutes/Bacteroidetes ratio, interrelated neurotransmitters and systemic immune factors. Moreover, pretreatment with probiotics improved immune factor expression and restored the deterioration of synaptic function and spatial learning and memory. The current study provides novel insights concerning perturbations of the gut microbiome and its functions as a potential new mechanism by which tebuconazole exposes cognitive function-related human health.

Background and purpose: Serine/arginine-rich splicing factor 1 (SRSF1) is closely related to the development of tumor. Esophageal carcinoma is the common malignant tumor of digestive system. However, the role of SRSF1 in esophageal carcinoma is rarely reported. This study aimed to detect the expression of SRSF1 in esophageal squamous cell carcinoma (ESCC) and its effects on the proliferation, invasion and migration of ESCC cells, and to explore its mechanism. Methods: Forty pairs of ESCC tissues and paracancerous tissues resected at the Fourth Hospital of Hebei Medical University from January 2020 to December 2021 were collected for this study. The expression of SRSF1 in ESCC was detected by immunohistochemistry. The mRNA expression and protein level of SRSF1 in ESCC cells were detected by real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and Western blot, respectively. Eca9706 cell with high expression of SRSF1 was selected. The mRNA expression of SRSF1 was reduced by small interfering RNA (siRNA). The proliferation, migration and invasion of Eca9706 cells were detected by cell counting kit-8 (CCK-8), transwell and Matrigel matrix gel assays, respectively. Database was used to analyze the expression of vascular endothelial growth factor A (VEGFA) in ESCC tissues, and the correlation between expressions of SRSF1 and VEGFA was also analyzed. RTFQ-PCR was used to detect the expression levels of VEGFA Iso8a and Iso8b isoform in Eca9706 cells, and the change in VEGFA Iso8a and Iso8b isoform expressions was also detected after knocking down SRSF1. Results: The expression of SRSF1 was significantly higher in ESCC tissues than in normal esophageal tissues (P

Abstract Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as $$\mathop {\mathtt {CTKG}}\limits$$ CTKG . $$\mathop {\mathtt {CTKG}}\limits$$ CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of $$\mathop {\mathtt {CTKG}}\limits$$ CTKG in various applications such as drug repurposing and similarity search, among others.

The highly anisotropic crystal structure of β-Ga2O3 gives rise to a variety of crystal planes, among which the (310) plane is a potentially stable close-packed plane for the O sublattice. In this paper, we report the β-Ga2O3 single crystal and substrates with a (310) major plane grown by the spontaneous nucleation technique in the casting method. High-quality crystal growth and substrate processing were confirmed by the 25.67 arc sec full width at half maximum and the 0.25 nm surface roughness. The nanoindentation experiments revealed the (310) substrate's better elastic recovery than that of (100) substrate. The Young's modulus and hardness of (310) substrates were 200 and 7.6 GPa, respectively. The surface barrier height and the Schottky barrier height were 1.25 and 0.92 eV, respectively. First principles calculations identified the (310)-Ga-I plane as the most stable surface configuration of the (310) plane under oxygen-poor condition, with a surface energy density of 1.48 J/m2. The (310) twin boundary formation around the O sublattice has a high energy density of 0.55 J/m2, suggesting its unlikelihood of spontaneous formation. These properties of (310) plane facilitate a high-quality crystal processing and epitaxial growth, thus endowing potential applications in high-quality power devices. Furthermore, the growth and fabrication of the (310) plane provide a route toward understanding the properties of β-Ga2O3 and advancing the growth techniques of oxide crystals. [ABSTRACT FROM AUTHOR]

Organic frameworks face a trade‐off between the framework stability and the bond dynamics, which necessitates the development of innovative linkages that can generate stable frameworks without hindering efficient synthesis. Although iodine(I)‐based halogen‐bonded organic frameworks (XOFs) have been developed, constructing XOFs based on bromine(I) is desirable yet challenging due to the high sensitivity of bromine(I) species. In this work, we present the inaugural construction of stable bromine(I)‐bridged two‐dimensional (2D) halogen‐bonded organic frameworks, XOF(Br)−TPy−BF4/OTf, based on sensitive [N⋅⋅⋅Br⋅⋅⋅N]+ halogen bonds. The formation of XOF(Br)−TPy−BF4/OTf was monitored by 1H NMR, XPS, IR, SEM, TEM, HR‐TEM, SEAD. Their framework structures were established by the results from PXRD, theoretical simulations and SAXS. More importantly, XOF(Br) displayed excellent chemical and thermal stabilities. They exhibited stable two‐dimensional framework structures in various organic solvents and aqueous media, even over a wide pH range (pH 3–12), while the corresponding model compounds BrPy2BF4/OTf decomposed quickly even in the presence of minimal water. Furthermore, the influence of the counterions were investigated by replacing BF4 with OTf, which improved the stability of XOF(Br). This characteristic enabled XOF(Br) to serve as an efficient oxidizing reagent in aqueous environments, in contrast with the sensitivity of BrPy2BF4/OTf, which performed well only in organic media. This study not only deepens our fundamental understanding of organic frameworks but also opens new avenues for the development and application of multifunctional XOFs. [ABSTRACT FROM AUTHOR]

In this work, we report on an electrochemical method for the signal-on detection of caspase-3 and the evaluation of apoptosis based on the biotinylation reaction and the signal amplification of methylene blue (MB)-loaded metal–organic frameworks (MOFs). Zr-based UiO-66-NH2 MOFs were used as the nanocarriers to load electroactive MB molecules. Recombinant hexahistidine (His6)-tagged streptavidin (rSA) was attached to the MOFs through the coordination interaction between the His6 tag in rSA and the metal ions on the surface of the MOFs. The acetylated peptide substrate Ac-GDEVDGGGPPPPC was immobilized on the gold electrode. In the presence of caspase-3, the peptide was specifically cleaved, leading to the release of the Ac-GDEVD sequence. A N-terminal amine group was generated and then biotinylated in the presence of biotin-NHS. Based on the strong interaction between rSA and biotin, rSA@MOF@MB was captured by the biotinylated peptide-modified electrode, producing a significantly amplified electrochemical signal. Caspase-3 was sensitively determined with a linear range from 0.1 to 25 pg/mL and a limit of detection down to 0.04 pg/mL. Further, the active caspase-3 in apoptosis inducer-treated HeLa cells was further quantified by this method. The proposed signal-on biosensor is compatible with the complex biological samples and shows great potential for apoptosis-related diagnosis and the screening of caspase-targeting drugs. [ABSTRACT FROM AUTHOR]

To investigate current status of quality of life and the association between depression and symptom burden in a sample of Chinese maintenance hemodialysis (MHD) patients. Methods: A self-designed patient general information questionnaire, disease-related information questionnaire, dialysis patient symptom burden scale, depression scale, and quality of survival scale were used to investigate 380 maintenance haemodialysis patients in haemodialysis centres. A regression model of the factors affecting the quality of survival was established using structural equation modelling. Results: The regression model data had a high goodness of fit: c2/df = 4.736, RMSEA = 0.099, GFI = 0.918, CFI = 0.972, TLI = 0.962, SRMR = 0.0469. Structural equation model analysis showed that depression had a positive predictive effect on symptom burden, β = 0.398, P < 0.001; Symptom burden had a negative predictive effect on the quality of life, β =− 0.851, P < 0.001; and Depression had a negative predictive effect on the quality of life, β =− 0.151, P < 0.001. Depression indirectly affects the quality of life through symptom burdens. Conclusion: Depression and symptom burden directly or indirectly affect the quality of life in patients with maintenance hemodialysis. Symptom burden moderates the relationship between depression and quality of life as a mediating variable. [ABSTRACT FROM AUTHOR]

As one of the important traditional medicinal plants listed in the Chinese pharmacopoeia, Vernonia anthelmintica (L.) Willd has been shown to possess various biological activities. In this study, we characterized culturable endophytic bacteria associated with the medicinal plant V. anthelmintica collected from Hotan within the Xinjiang autonomous region of China. Bacterial endophytes were identified via 16S rRNA gene sequence analysis and compared to similar sequences from the GenBank. Isolated strains exhibited 99.08–100% similarity to Bacillus haynesii XJB-5, Bacillus proteolyticus XJB-16, Bacillus halotolerans XJB-35, Bacillus safensis XJB-71, Pseudomonas punonensis XJB-7, Lysinibacillus fusiformis XJB-17, Streptococcus lutetiensis XJB-66, Leclercia adecarboxylata XJB-12, Paenibacillus alvei XJB-14, and Pantoea agglomerans XJB-62. The ethyl acetate extracts of the bacterial endophytes demonstrated various pharmacological properties, such as antimicrobial, cytotoxic, antidiabetic, and antioxidant activity, according to a melanin content assay and have shown tyrosinase activity in murine B16 cells. A crude extract of B. halotolerans XJB-35 displayed more powerful biological activities than other bacterial endophytes; therefore, this strain was studied further in order to select the optimized parameters for enhancing the synthesis of bioactive compounds. The optimal culture medium was found to be nutrient broth (NB) medium, using peptone as its carbon source and yeast extract as its nitrogen source. A 24 h incubation time produced the optimal conditions for the maximum growth of B. halotolerans XJB-35 and the production of bioactive compounds. Moreover, we investigated the volatile chemical component of the dichloromethane fraction using GC-MS analysis. Our findings provide valuable information regarding the synthesize of bioactive natural products by B. halotolerans XJB-35 for use by the medicinal and pharmaceutical industries.

Abstract Introduction Applied health informatics infrastructure is a requirement for learning health systems and it is imperative that we train a workforce that can support this infrastructure. Our department offers courses in several interdisciplinary programs with topics ranging from bioinformatics to population health informatics. Due to changes in the field and our faculty members, we sought to assess our courses relevant to applied health informatics. Methods In this paper, we discuss the three‐phase evaluation of our program and include the survey we developed to identify the skills and knowledge base of our faculty. Results We show how this assessment allowed us to identify gaps and develop strategies for program expansion. Conclusions A focus on workforce development can help to guide and focus curricular review in an interdisciplinary graduate program.

Summary: In this work, we propose a new deep-learning model, MHCrank, to predict the probability that a peptide will be processed for presentation by MHC class I molecules. We find that the performance of our model is significantly higher than that of two previously published baseline methods: MHCflurry and netMHCpan. This improvement arises from utilizing both cleavage site-specific kernels and learned embeddings for amino acids. By visualizing site-specific amino acid enrichment patterns, we observe that MHCrank’s top-ranked peptides exhibit enrichments at biologically relevant positions and are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank’s learned embeddings for amino acids correlates highly with physiochemical properties that have been experimentally demonstrated to be instrumental in determining a peptide’s favorability for processing. Altogether, the results reported in this work indicate that MHCrank demonstrates strong performance compared with existing methods and could have vast applicability in aiding drug and vaccine development. Motivation: More than binding affinity, designing effective drugs and vaccines to stimulate the adaptive immune response requires accurate predictions regarding which antigens will be produced through processing and ultimately presented by MHC class I molecules. Constructing a model that is more acutely aware of biologically relevant features for processing and presentation will improve these predictions. However, future predictions will benefit from considering amino acid structure in addition to the antigen sequence.