Search

Abstract Background and aim Ulcerative colitis (UC) is characterized by complex immunological interactions involving CD4 T cell subsets and the NLRP3 inflammasome, which influence inflammatory responses. This investigation focused on delineating the activation profiles of these components and their correlation with disease severity and activity, assessing their diagnostic implications in UC. Methods We conducted immunohistochemistry and ELISA assays to measure markers expression of CD4 T cell subsets and the NLRP3 inflammasome in UC patients versus controls. Findings were validated using correlation analysis, molecular docking and ROC curves. Results UC patients displayed increased Th1 (T-bet, TNF-α), Th2 (GATA3, IL-6), and Th17 (RORγt, IL-17, IL-22, IL-23) markers versus controls. Additionally, Th1 and Th2 cytokines (IL-2 and IL-4) were significantly elevated in severe UC, while Treg markers (FOXP3, IL-10, TGF-β1) were elevated only in mild-to-moderate UC. Enhanced NLRP3 inflammasome activation, indicated by elevated NLRP3, Caspase-1, and IL-1β levels. These molecular patterns, confirmed through correlation analysis and molecular docking, underscored strong correlations among NLRP3, T-bet, and GATA3, supporting the proposed NLRP3/T-bet/GATA3 axis. This axis, along with other biomarkers, showed strong associations with UC severity, Mayo score, UCEIS, demonstrated relatively high diagnostic value. Conclusion The NLRP3/T-bet/GATA3 axis provides a referable strategy for multi-targeted combined treatment of UC and may serve as potential biomarkers for enhancing diagnostic accuracy and guiding therapy.

Abstract This paper addresses the challenge of large-scale electric vehicle (EV) charging scheduling during peak demand periods, such as holidays or rush hours. The growing EV industry has highlighted the shortcomings of current scheduling plans, which struggle to manage surge large-scale charging demands effectively, thus posing challenges to the EV charging management system. Deep reinforcement learning, known for its effectiveness in solving complex decision-making problems, holds promise for addressing this issue. To this end, we formulate the problem as a Markov decision process (MDP). We propose a deep Q-learning (DQN) based algorithm to improve EV charging service quality as well as minimizing average queueing times for EVs and average idling times for charging devices (CDs). In our proposed methodology, we design two types of states to encompass global scheduling information, and two types of rewards to reflect scheduling performance. Based on this designing, we developed three modules: a fine-grained feature extraction module for effectively extracting state features, an improved noise-based exploration module for thorough exploration of the solution space, and a dueling block for enhancing Q value evaluation. To assess the effectiveness of our proposal, we conduct three case studies within a complex urban scenario featuring 34 charging stations and 899 scheduled EVs. The results of these experiments demonstrate the advantages of our proposal, showcasing its superiority in effectively locating superior solutions compared to current methods in the literature, as well as its efficiency in generating feasible charging scheduling plans for large-scale EVs. The code and data are available by accessing the hyperlink: https://github.com/paperscodeyouneed/A-Noisy-Dueling-Architecture-for-Large-Scale-EV-ChargingScheduling/tree/main/EV%20Charging%20Scheduling .

High-Al-content n-AlGaN ohmic contact is very important for deep ultraviolet optoelectrical devices. However, it often suffers from the etching damages formed in inductively coupled plasma (ICP) etching. In this paper, the effects of ICP etching parameters on n-Al0.5Ga0.5N ohmic contact, including RF power, ICP power, and etching gas, were systematically investigated and analyzed by X-ray photoelectron spectroscopy and circular transmission line model. Finally, n-Al0.5Ga0.5N ohmic contact was achieved with a low specific contact resistivity of 8.7×10-4 Ω·cm2, and AlGaN-based UVC light-emitting diodes (LEDs) showed a low operation voltage of only 5.6 V at the injection current density of 16 A/cm2.

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires.

Abstract AlphaFold is making great progress in protein structure prediction, not only for single-chain proteins but also for multi-chain protein complexes. When using AlphaFold-Multimer to predict protein‒protein complexes, we observed some unusual structures in which chains are looped around each other to form topologically intertwining links at the interface. Based on physical principles, such topological links should generally not exist in native protein complex structures unless covalent modifications of residues are involved. Although it is well known and has been well studied that protein structures may have topologically complex shapes such as knots and links, existing methods are hampered by the chain closure problem and show poor performance in identifying topologically linked structures in protein‒protein complexes. Therefore, we address the chain closure problem by using sliding windows from a local perspective and propose an algorithm to measure the topological–geometric features that can be used to identify topologically linked structures. An application of the method to AlphaFold-Multimer-predicted protein complex structures finds that approximately 1.72% of the predicted structures contain topological links. The method presented in this work will facilitate the computational study of protein‒protein interactions and help further improve the structural prediction of multi-chain protein complexes.

Organophosphorus flame retardants (OPFRs) have been frequently detected with relatively high concentrations in various environmental media and are considered emerging environmental pollutants. However, their biological effect and underlying mechanism is still unclear, and whether chlorinated OPFRs (Cl-OPFRs) cause adverse outcomes with the same molecular initial events or share the same key events (KEs) remains unknown. In this study, in vitro bioassays were conducted to analyze the cytotoxicity, mitochondrial impairment, DNA damage and molecular mechanisms of two Cl-OPFRs. The results showed that these two Cl-OPFRs, which have similar structures, induced severe cellular and molecular damages via different underlying mechanisms. Both tris(2-chloroethyl) phosphate (TCEP) and tris(1-chloro-2-propyl) (TCPP) induced oxidative stress-mediated mitochondrial impairment and DNA damage, as shown by the overproduction of intracellular reactive oxygen species (ROS) and mitochondrial superoxide. Furthermore, the DNA damage caused by TCPP resulted in p53/p21-mediated cell cycle arrest, as evidenced by flow cytometry and real-time PCR. At the cellular and molecular levels, TCPP increased the sub-G1 apoptotic peak and upregulated the p53/Bax apoptosis pathway, possibly resulted in apoptosis associated with its stronger cytotoxicity. Although structurally similar to TCPP, TCEP did not induce mitochondrial impairment and DNA damage by the same KEs. These results provide insight into the toxicity of Cl-OPFRs with similar structures but different mechanisms, which is of great significance for constructing adverse outcome pathways or determining intermediate KEs.

Abstract Electrocatalytic water splitting that is coupled with electrocatalytic chemical oxidation is considered as one of the promising methods for efficiently obtaining hydrogen energy and fine chemicals. Herein, we focus on an electrochemical redox activation strategy to rationally manipulate the microstructure and surface valence states of copper foam (CF) and boost the corresponding performance towards electrocatalytic benzyl alcohol oxidation (EBA), accompanied by the efficient hydrogen production. Correspondingly, the Cu(II)‐dominated species are gradually formed on the CF surface with the dissolution and redeposition of copper in the suitable potential range. The new species containing Cu2O, CuO, and Cu(OH)2 during surface reconstruction process of the CF were confirmed by multiple characterization techniques. After 220‐cycled activation (CF‐220), the activated CF achieves an increase of current density for EBA in anode from 9.5 for the original CF to 29.3 mmol/cm2, while the pure hydrogen yield increases threefold than that of the original CF at 1.5 VRHE. The produced new species can endow the CF‐220 with abundant acidity sites, which can enhance the adsorption toward Lewis‐basicity benzyl alcohol, confirmed by NH3‐temperature‐programmed desorption. In situ Raman result further reveals that the as‐produced CuO, Cu(OH)2, and Cu(OH)42− are the main active species toward the EBA process.

Summary: The lateral root angle or gravitropic set-point angle (GSA) is an important trait for root system architecture (RSA) that determines the radial expansion of the root system. The GSA therefore plays a crucial role for the ability of plants to access nutrients and water in the soil. Only a few regulatory pathways and mechanisms that determine GSA are known. These mostly relate to auxin and cytokinin pathways. Here, we report the identification of a small molecule, mebendazole (MBZ), that modulates GSA in Arabidopsis thaliana roots and acts via the activation of ethylene signaling. MBZ directly acts on the serine/threonine protein kinase CTR1, which is a negative regulator of ethylene signaling. Our study not only shows that the ethylene signaling pathway is essential for GSA regulation but also identifies a small molecular modulator of RSA that acts downstream of ethylene receptors and that directly activates ethylene signaling.

ObjectiveThe purpose of this article is to explore the effectiveness of B-Mode ultrasound as an auxiliary diagnostic tool for carpal tunnel syndrome (CTS). It aims to demonstrate the advantages of B-Mode ultrasound, including its non-invasive nature and its ability to provide real-time imaging, in localizing nerve compression and predicting postoperative outcomes.MethodsThe study included 40 patients who were subjected to preoperative B-ultrasonography. The approach focused on evaluating the consistency of B-Mode ultrasound results with intraoperative findings. It also assessed the importance of employing standardized imaging techniques and emphasized the need for cooperation between hand surgeons and sonographers for accurate diagnosis.ResultsB-Mode ultrasound findings in the study were consistent with intraoperative observations, indicating its reliability. Additionally, B-Mode ultrasound was able to identify other anatomical abnormalities within the carpal canal that may contribute to CTS symptoms, such as persistent median arteries, median nerve bifurcation, and space-occupying lesions like cysts and tumors.ConclusionThe article concludes that B-Mode ultrasound should be considered a valuable supplementary diagnostic tool for CTS, particularly in instances where clinical signs and electrophysiological studies do not offer clear results. However, it should not replace established diagnostic methods for CTS.

Abstract Background The application of clinical mNGS for diagnosing respiratory infections improves etiology diagnosis, however at the same time, it brings new challenges as an unbiased sequencing method informing all identified microbiomes in the specimen. Methods Strategy evaluation and metagenomic analysis were performed for the mNGS data generated between March 2017 and October 2019. Diagnostic strengths of four specimen types were assessed to pinpoint the more appropriate type for mNGS diagnosis of respiratory infections. Microbiome complexity was revealed between patient cohorts and infection types. A bioinformatic pipeline resembling diagnosis results was built based upon multiple bioinformatic parameters. Results The positive predictive values (PPVs) for mNGS diagnosing of non-mycobacterium, Nontuberculous Mycobacteria (NTM), and Aspergillus were obviously higher in bronchoalveolar lavage fluid (BALF) demonstrating the potency of BALF in mNGS diagnosis. Lung tissues and sputum were acceptable for diagnosis of the Mycobacterium tuberculosis (MTB) infections. Interestingly, significant taxonomy differences were identified in sufficient BALF specimens, and unique bacteriome and virome compositions were found in the BALF specimens of tumor patients. Our pipeline showed comparative diagnostic strength with the clinical microbiological diagnosis. Conclusions To achieve reliable mNGS diagnosis result, BALF specimens for suspicious common infections, and lung tissues and sputum for doubtful MTB infections are recommended to avoid the false results given by the complexed respiratory microbiomes. Our developed bioinformatic pipeline successful helps mNGS data interpretation and reduces manual corrections for etiology diagnosis.

The number of people with Alzheimer's disease (AD) is increasing annually, with the nidus mainly concentrated in the cortex and hippocampus. Despite of numerous efforts, effective treatment of AD is still facing great challenges due to the blood brain barrier (BBB) and limited drug distribution in the AD nidus sites. Thus, in this study, using vinpocetine (VIN) as a model drug, the objective is to explore the feasibility of tackling the above bottleneck via intranasal drug delivery in combination with a brain guider, borneol (BOR), using nanoemulsion (NE) as the carrier. First of all, the NE were prepared and characterized. In vivo behavior of the NE after intranasal administration was investigated. Influence of BOR dose, BOR administration route on drug brain targeting behavior was evaluated, and the influence of BOR addition on drug brain subregion distribution was probed. It was demonstrated that all the NE had comparable size and similar retention behavior after intranasal delivery. Compared to intravenous injection, improved brain targeting effect was observed by intranasal route, and drug targeting index (DTI) of the VINNE group was 154.1%, with the nose-to-brain direct transport percentage (DTP) 35.1%. Especially, remarkably enhanced brain distribution was achieved after BOR addition in the NE, with the extent depending on BOR dose. VIN brain concentration was the highest in the VIN-1-BOR-NE group at BOR dose of 1 mg/kg, with the DTI reaching 596.1% and the DTP increased to 83.1%. BOR could exert better nose to brain delivery when administrated together with the drug via intranasal route. Notably, BOR can remarkably enhance drug distribution in both hippocampus and cortex, the nidus areas of AD. In conclusion, in combination with intranasal delivery and the intrinsic brain guiding effect of BOR, drug distribution not only in the brain but also in the cortex and hippocampus can be enhanced significantly, providing the perquisite for improved therapeutic efficacy of AD.

Abstract Background The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease features with machine learning models to further uncover druggable disease to target indications. Results We derived novel target-disease associations as supplemental features to OT platform-based associations using three data sources: (1) target tissue specificity from GTEx expression profiles; (2) target semantic similarities based on gene ontology; and (3) functional interactions among targets by embedding them from protein–protein interaction (PPI) networks. Machine learning models were applied to evaluate feature importance and performance benchmarks for predicting targets with known drug indications. The evaluation results show the newly integrated features demonstrate higher importance than current features in OT. In addition, these also show superior performance over association benchmarks and may support discovery of novel therapeutic indications for highly pursued targets. Conclusion Our newly generated features can be used to represent additional underlying biological relatedness among targets and diseases to further empower improved performance for predicting novel indications for drug targets through advanced machine learning models. The proposed methodology enables a powerful new approach for systematic evaluation of drug targets with novel indications.

There are increasing concerns over the threat of nanoplastics to environmental and human health. However, multidisciplinary barriers persist between the communities assessing the risks to environmental and human health. As a result, the hazards and risks of nanoplastics remain uncertain. Here, we identify key knowledge gaps by evaluating the exposure of nanoplastics in the environment, assessing their bio-nano interactions, and examining their potential risks to humans and the environment. We suggest considering nanoplastics a complex and dynamic mixture of polymers, additives, and contaminants, with interconnected risks to environmental and human health. We call for comprehensive integration of One Health approach to produce robust multidisciplinary evidence to nanoplastics threats at the planetary level. Although there are many challenges, this holistic approach incorporates the relevance of environmental exposure and multi-sectoral responses, which provide the opportunity to identify the risk mitigation strategies of nanoplastics to build resilient health systems.

Abstract Background The important role of long noncoding RNAs (lncRNAs) in cancer has been demonstrated in many studies. Prostate cancer gene expression marker 1 (PCGEM1) is a lncRNA specifically expressed within the prostate and overexpressed in many cancer cells. Numerous studies have shown that PCGEM1 promotes cell proliferation, invasion and migration. However, the specific mechanism of PCGEM1 within prostate cancer (PCa) has not been elucidated. MicroRNA-506-3p (miR-506-3p) is a noncoding RNA, and studies have indicated that miR-506-3p is downregulated in prostate cancer cell lines and functions as a tumor suppressor. Methods The TCGA (GEPIA) database ( http://gepia.cancer-pku.cn/ ) was employed to measure PCGEM1 levels in PCa. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to determine the PCGEM1 gene level. CCK-8 (Cell Counting Kit-8) and colony formation assays were used to detect cell proliferation, and Transwell assays were applied to assess cell invasion and migration. The interacting ability of miR-506-3p with PCGEM1 or TRIAP1 was validated through a dual-luciferase reporter assay. TRIAP1 protein expression was detected by Western blotting. Results PCGEM1 expression was increased in PCa tissues and cells. In PCa tissues, High PCGEM1 expression was associated with high Gleason score, distant metastasis and extracapsular extension. In addition, PCGEM1 knockdown inhibited PCa cell (C4-2B and PC-3) proliferation, invasion and migration. miR-506-3p may interact with PCGEM1 or TRIAP1, and the suppressive effect of PCGEM1 knockdown was reversed when TRIAP1 or a miR-506-3p inhibitor was cotransfected. Conclusion PCGEM1 expression increased in PCa cells and tissues, enhancing PCa cell proliferation, migration and invasion by sponging miR-506 to upregulate TRIAP1.

The fungal pathogen Candida auris has emerged as a new threat to human health. We previously reported the first isolate of C. auris (BJCA001) in China, which belongs to the South Asian clade (I) and was susceptible to all antifungals tested. In this study, we report the isolation of a drug-resistant C. auris strain (BJCA002) from the same city (Beijing). Strain BJCA002 belongs to the South African clade (III) and is resistant to fluconazole and amphotericin B based on the tentative MIC breakpoints. Taking advantage of the two isolates with distinct antifungal susceptibility and genetic origins, we performed a biological and genomic comparative study. Besides antifungal susceptibility, strains BJCA001 and BJCA002 showed differences in multiple aspects including morphologies, expression of virulence factors, virulence, mating type, and genomic sequence and organization. Notably, strain BJCA002 was less virulent than BJCA001 in both the Galleria mellonella and mouse systemic infection models. Genomic analysis demonstrated that strain BJCA002 but not BJCA001 had multiple mutations in drug resistance-associated genes, including a hot-spot mutation of ERG11 (VF125AL, namely V125A and F126L) and some missense mutations in CDR1, MDR1, and TAC1. Notably, strain BJCA001 carried 64 copies of the Zorro3 retrotransposon, whereas BJCA002 had only 3 copies in the genome. Taken together, our findings not only reveal the genetic and phenotypic diversities of the two isolates from Beijing, China, but also shed new light on the genetic basis of the antifungal resistance and virulence of C. auris.

Recovery from gastrointestinal (GI) surgery is often interrupted by the unpredictable occurrence of postoperative complications, including infections, anastomotic leak, GI dysmotility, malabsorption, cancer development, and cancer recurrence, in which the implication of gut microbiota is beginning to emerge. Gut microbiota can be imbalanced before surgery due to the underlying disease and its treatment. The immediate preparations for GI surgery, including fasting, mechanical bowel cleaning, and antibiotic intervention, disrupt gut microbiota. Surgical removal of GI segments also perturbs gut microbiota due to GI tract reconstruction and epithelial barrier destruction. In return, the altered gut microbiota contributes to the occurrence of postoperative complications. Therefore, understanding how to balance the gut microbiota during the perioperative period is important for surgeons. We aim to overview the current knowledge to investigate the role of gut microbiota in recovery from GI surgery, focusing on the crosstalk between gut microbiota and host in the pathogenesis of postoperative complications. A comprehensive understanding of the postoperative response of the GI tract to the altered gut microbiota provides valuable cues for surgeons to preserve the beneficial functions and suppress the adverse effects of gut microbiota, which will help to enhance recovery from GI surgery.

The aim of this study was to compare the clinical safety and efficacy of robotic hepatectomy (RH) versus conventional laparoscopic hepatectomy (LH) for malignancy using meta-analysis. A systematic literature search was performed using PubMed, EMBASE, Medline and the Cochrane Library databases up to September 2020 for studies, which limited to comparative articles of RH or LH for malignant tumors. Stata14.0 was performed in the meta-analysis. Six studies with a total of 1093 patients (345 RH and 748 LH) were eligible for inclusion. Operative time, tumor size, open procedure rate and the proportion of right hepatectomy were found to be significantly different between RH and LH in the pooled analysis (P 0.05). For malignant tumors that require hepatectomy, robotic approaches have demonstrated similar safety and feasibility to laparoscopy, with lower open procedure rate, were suitable for larger tumor size, and have a high right hepatectomy utilization rate. These results still need to be confirmed by multicenter, high-quality randomized controlled studies.

The gastrointestinal (GI) tract harbors trillions of commensal microbes, called the gut microbiota, which plays a significant role in the regulation of GI physiology, particularly GI motility. The GI tract expresses an array of receptors, such as toll-like receptors (TLRs), G-protein coupled receptors, aryl hydrocarbon receptor (AhR), and ligand-gated ion channels, that sense different gut microbiota-derived bioactive substances. Specifically, microbial cell wall components and metabolites, including lipopeptides, peptidoglycan, lipopolysaccharides (LPS), bile acids (BAs), short-chain fatty acids (SCFAs), and tryptophan metabolites, mediate the effect of gut microbiota on GI motility through their close interactions with the enteroendocrine system, enteric nervous system, intestinal smooth muscle, and immune system. In turn, GI motility affects the colonization within the gut microbiota. However, the mechanisms by which gut microbiota interacts with GI motility remain to be elucidated. Deciphering the underlying mechanisms is greatly important for the prevention or treatment of GI dysmotility, which is a complication associated with many GI diseases, such as irritable bowel syndrome (IBS) and constipation. In this perspective, we overview the current knowledge on the role of gut microbiota and its metabolites in the regulation of GI motility, highlighting the potential mechanisms, in an attempt to provide valuable clues for the development of gut microbiota-dependent therapy to improve GI motility.

Background: Metagenomic next-generation sequencing (mNGS), with its comprehensiveness, is widely applied in microbiological diagnosis. Etiological diagnosis is of paramount clinical importance in patients with skin and soft tissue infections (SSTIs). However, the clinical application of mNGS in SSTIs is relatively less studied. Materials and methods: From April 1, 2017 to December 31, 2019, 96 SSTI cases were collected. The positive rates of pathogens detected by mNGS and culture were compared by analyzing tissue samples, pus, swabs, and/or interstitial fluids obtained from the infected parts. Modification of the antibiotic treatment strategy due to mNGS was also assessed. Results: The sensitivity of mNGS for detecting pathogens in SSTI cases was superior to that of culture testing (67.7% vs 35.4%; p < 0.01). Significantly higher identification rates for viruses (10.4% vs 0.0%; p < 0.01) and anaerobes (11.5% vs 1.0%; p < 0.01) were obtained with mNGS compared to culture. Of note, rare pathogens such as Vibrio vulnificus and Bartonella henselae were also detected by mNGS. Importantly, the proportion of multi-pathogen SSTIs detected by mNGS was higher than that of multi-pathogen SSTIs detected by culture (16.7% vs 6.3%; p = 0.035). The rate of targeted antibiotic treatment was significantly higher in mNGS-positive cases than in mNGS-negative cases (41.7% vs 3.8%; p < 0.01). In culture-negative and mNGS-positive cases, the improvement rate was higher than that in mNGS-negative cases, but this was not statistically significant (75.0% vs 73.1%; p = 0.864). Conclusions: mNGS is a promising tool for the etiological diagnosis of SSTIs, particularly in identifying viruses, anaerobes, and multi-pathogen infections. The application of mNGS testing in clinical practice could change antibiotic treatment strategies and partly benefit clinical outcomes.

Heteroepitaxial growth of high Al-content AlGaN often results in a high density of threading dislocations and surface hexagonal hillocks, which degrade the performance and reliability of AlGaN-based UVC light emitting diodes (LEDs). In this study, the degradation mechanism and impurity/defect behavior of UVC LEDs in relation to the hexagonal hillocks have been studied in detail. It was found that the early degradation of UVC LEDs is primarily caused by electron leakage. The prominent contribution of the hillock edges to the electron leakage is unambiguously evidenced by the transmission electron microscopy measurements, time-of-flight secondary ion mass spectrometry, and conductive atomic force microscopy. Dislocations bunching and segregation of impurities, including C, O, and Si, at the hillock edges are clearly observed, which facilitate the trap-assisted carrier tunneling in the multiple quantum wells and subsequent recombination in the p-AlGaN. This work sheds light on one possible degradation mechanism of AlGaN-based UVC LEDs.

We report on the epitaxial lateral overgrowth (ELO) of high-quality AlN on stripe-patterned Si(111) substrates with various trench widths. By narrowing down the trench and ridge widths of patterned Si substrates, crack-free, 6-micrometer-thick, high-quality AlN films on Si substrates were produced. The full-width-at-half-maximum values of the X-ray-diffraction rocking curves for the AlN (0002) and (101¯2) planes were as low as 260 and 374 arcsec, respectively, corresponding to a record low dislocation density of 1.3 × 109 cm−2. Through the combination of a micro-Raman study and the X-ray diffraction analysis, it was found that narrowing the stripe width from 5 μm to 3 μm can reduce the vertical growth thickness before coalescence, resulting in a large decrease in the internal tensile stress and tilt angle, and, therefore, better suppression in the cracks and dislocations of the ELO–AlN. This work paves the way for the fabrication of high-performance Al(Ga)N-based thin-film devices such as ultraviolet light-emitting diodes and AlN bulk acoustic resonators grown on Si.

In the present work, hematite–boron-carbonitride (Fe2O3–BCN) nanosheets were synthesized by a simple hydrothermal reaction and the following high temperature treatment. The morphology, structure and chemical composition of the as-prepared material were carefully characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The Fe2O3–BCN nanosheets were used to modified on the surface of the glassy carbon electrode to fabricate an electrochemical sensor for lead ions (Pb(II)) via differential pulse anodic stripping voltammetry (DPASV). At the same time, the influence of the modification concentration, solution acidity, deposition potential and deposition time on response peak current of Pb(II) at the Fe2O3–BCN-based electrochemical sensor was well investigated. Under the optimized conditions, the electrochemical signal and concentration of Pb(II) show two-stage linear relationship in the range of 0.5 – 40 μg/L and 40 –140 μg/L, with a limit of detection (LOD) of 0.129 μg/L. The Fe2O3–BCN-based electrochemical sensor shows excellent selectivity and anti-interference ability in the anti-interference experiments and actual sample analysis experiments, revealing its broad application in environmental monitoring of Pb(II).

BackgroundThe identification of a target-indication pair is regarded as the first step in a traditional drug discovery and development process. Significant investment and attrition occur during discovery and development before a molecule is shown to be safe and efficacious for the selected indication and becomes an approved drug. Many drug targets are functionally pleiotropic and might be good targets for multiple indications. Methodologies that leverage years of scientific contributions on drug targets to allow systematic evaluation of other indication opportunities are critical for both patients and drug discovery and development scientists.MethodsWe introduced a network-based approach to systematically screen and prioritize disease indications for drug targets. The approach fundamentally integrates disease genomics data and protein interaction network. Further, the methodology allows for indication identification by leveraging state-of-art network algorithms to generate and compare the target and disease subnetworks.ResultsWe first evaluated the performance of our method on recovering FDA approved indications for 15 randomly selected drug targets. The results showed superior performance when compared with other state-of-art approaches. Using this approach, we predicted novel indications supported by literature evidence for several highly pursued drug targets such as IL12/IL23 combination.ConclusionsOur results demonstrated a potential global approach for indication expansion strategies. The proposed methodology enables rapid and systematic evaluation of both individual and combined drug targets for novel indications. Additionally, this approach provides novel insights on expanding the role of genes and pathways for developing therapeutic intervention strategies.

A small RNA-based signaling cascade prevents the induction of plant resistance genes (R-genes) in the absence of pathogen challenge. Here Cai et al. show that nuclear accumulation of the R protein SNC1 can activate immunity by suppressing small RNA production and releasing R-gene repression.

Abstract Chronic persistent stress is an important cause of gastritis, but the underlying mechanism remains to be further researched, especially the role of the gastric microbiota in this process. Here, we used the water avoidance stress (WAS) test in mouse models for chronic stress‐induced gastritis to investigate the underlying mechanisms of this disease. The effect of stress on the gastric microbiota was analyzed based on 16S rRNA sequencing; the changes in hydrogen sulfide (H2S) and inflammatory cytokine levels in gastric tissues were detected by Western blotting, ELISA, immunofluorescence, and qRT‐PCR. Hematoxylin and eosin staining was used as an indicator of the gastritis histological score. This finding is consistent with previous studies showing that gastric H2S is negatively associated with the inflammatory index and might protect the gastrointestinal tract from inflammation. WAS‐induced gastritis was associated with a reduction in H2S release, which appeared to affect the homeostasis of the gastric microbiota of mice. Inflammation and microbial dysbiosis were partially reversed by sodium hydrosulfide (NaHS) and vitamin B6 (VB6) supplementation, suggesting the therapeutic potential of VB6 supplementation for the treatment of stress‐induced gastritis. Gastritis has a serious impact on health and quality of life. An increasing number of people are suffering from chronic gastritis linked to a high‐stress lifestyle, and our research provides clues for the prevention and treatment of stress‐induced gastritis.

GaN Schottky barrier ultraviolet photodetectors with unintentionally doped GaN and lightly Si-doped n−-GaN absorption layers were successfully fabricated, respectively. The high-quality GaN films on the Si substrate both have a fairly low dislocation density and point defect concentration. More importantly, the effect of Si doping on the performance of the GaN-on-Si Schottky barrier ultraviolet photodetector was studied. It was found that light Si doping in the absorption layer can significantly increase the responsivity under reverse bias, which might be attributed to the persistent photoconductivity that originates from the lowering of the Schottky barrier height. In addition, the devices with unintentionally doped GaN demonstrated a relatively high-speed photo response. We briefly studied the mechanism of changes in Schottky barrier, dark current and the characteristic of response time.

The original version of this Article contained an error in the spelling of the author Beixin Mo, which was incorrectly given as Beixing Mo. This has now been corrected in both the PDF and HTML versions of the Article.

AIM:This study is to explore the different expressions of serum N-glycoproteins and glycosylation sites between hepatocellular carcinoma (HCC) patients and healthy controls. METHOD:We combined high abundant proteins depletion and hydrophilic affinity method to enrich the glycoproteins. Through liquid chromatography-tandem mass spectrometry (LC-MS/MS), we extensively surveyed different expressions of glycosylation sites and glycoproteins between the two groups. RESULT:This approach identified 152 glycosylation sites and 54 glycoproteins expressed differently between HCC patients and healthy controls. With the absolute values of Pearson coefficients of at least 0.8, eight proteins were identified significantly up or down regulated in HCC serum. Those proteins are supposed to be involved in several biological processes, cellular components and molecular functions of hepatocarcinogenesis. Several of them had been reported abnormally regulated in several kinds of malignant tumors, and may be promising biomarkers of HCC. CONCLUSION:Our work provides a systematic and quantitative method of glycoproteomics and demonstrates some key changes in clinical HCC serum. These proteomic signatures may help to unveil the underlying mechanisms of hepatocarcinogenesis and may be useful for the exploration of candidate biomarkers.

Background and Purpose: abdominal tuberculosis (TB) is a common form of extrapulmonary TB but it is still a diagnostic dilemma in clinical practice. This study aimed to highlight the clinical features and diagnostic approaches for abdominal TB., Methods: seventy cases of diagnosed abdominal TB were retrospectively collected between August 1st, 2015 and June 30th, 2020. They were classified as peritoneal TB, lymph node TB, gastrointestinal TB, visceral TB or mixed TB., Results: eighteen patients were diagnosed with peritoneal TB, nine with lymph node TB, five with gastrointestinal TB, two with visceral TB and 36 with mixed TB. More than 65 % of the patients had tuberculosis of other sites except the abdomen. The median diagnosis time was 60 days. Ascites (58.6 %), abdominal distension (48.6 %), weight loss (44.3 %) and fever (42.9 %) were the most common symptoms. The overall microbiological and histological detection rates were 70.0 % and 38.6 %, respectively. The non-ascite samples yielded a higher microbiological confirmation rate (63.6 %) than the total samples (40.8 %). Diagnosis was confirmed histologically in 18 patients (69.2 %). Forty-five cases (64.3 %) were clinically diagnosed. Invasive procedures such as surgery (6/7), percutaneous biopsy (7/7) and endoscopy in lymph node TB (4/5) had high confirmation rates., Conclusions: the diagnosis of abdominal TB should be reached by a combination of clinical, laboratory, radiological, microbiological and pathological findings.