Design, Synthesis, and Biological Evaluation of a Novel NIK Inhibitor with Anti-Inflammatory and Hepatoprotective Effects for Sepsis Treatment.

NIK plays a crucial role in the noncanonical NF-κB signaling pathway associated with diverse inflammatory and autoimmune diseases. Our study presents compound 54, a novel NIK inhibitor, designed through a structure-based scaffold-hopping approach from the previously identified B022. Compound 54 demonstrates remarkable selectivity and potency against NIK both in vitro and in vivo, effectively suppressing pro-inflammatory cytokines and nitric oxide production. In mouse models, compound 54 protected against LPS-induced systemic sepsis, reducing AST, ALT, and AKP liver injury markers. Additionally, it also attenuates sepsis-induced lung and kidney damage. Mechanistically, compound 54 blocks the noncanonical NF-κB signaling pathway by targeting NIK, preventing p100 to p52 processing. This work reveals a novel class of NIK inhibitors with significant potential for sepsis therapy.

[Genetic characterization and drug resistance analysis of Salmonella Kentucky ST314 in Shenzhen in 2010-2021].

OBJECTIVE: To understand the prevalence, genetic characteristics and drug resistance features of Salmonella Kentucky ST314 in Shenzhen. METHODS: Whole genome sequencing of 14 strains of Salmonella Kentucky ST314 collected from 2010-2021 by the Foodborne Disease Surveillance Network of Shenzhen Center for Disease Control and Prevention for phylogenetic evolutionary analysis, drug resistance gene and plasmid detection; drug susceptibility experiments were performed by micro-broth dilution method. RESULTS: A total of 57 strains of Salmonella Kentucky were collected from the foodborne disease surveillance network, 14 of which were ST314. The Shenzhen isolates were clustered with isolates from Southeast Asian countries such as Vietnam and Thailand on clade 314.2, and the single nucleotide polymorphism distance between local strains in Shenzhen was large, indicating dissemination. In this study, a total of 17 drug resistance genes/mutations in 9 categories were detected in the genome of Salmonella Kentucky ST314, carrying 3 extended spectrum beta-lactamases(ESBLs), including bla_(CTX-M-24)(14.3%, 2/14), bla_(CTX-M-55)(7.1%, 1/14), and bla_(CTX-M-130)(14.3%, 2/14), all located on plasmids. Regarding quinolone resistance factors, two plasmid-mediated quinolone resistance(PMQR) genes were identified in the genome: qnrB6(71.4%, 10/14) and aac(6')Ib-cr(78.6%, 11/14), a quinolone resistance quinolone resistance-determining regions(QRDR) mutation T57 S(100%, 14/14). The multi-drug resistance rate of Salmonella Kentucky ST314 in Shenzhen was 92.86%(13/14)with the highest rate of resistance to tetracycline and cotrimoxazole(100%, 14/14), followed by chloramphenicol(92.86%, 13/14), cefotaxime and ampicillin(78.57%, 11/14), ciprofloxacin and nalidixic acid(71.43%, 10/14), and ampicillin-sulbactam had the lowest resistance rate(21.43%, 3/14). CONCLUSION: ST314 is the second most prevalent ST type among Salmonella Kentucky in Shenzhen, mainly isolated from food, especially poultry; phylogenetic analysis suggests that ST314 is a disseminated infection and the genome shows a highly genetically conserved phenotype. Drug resistance of Salmonella Kentucky ST314 is very serious, especially QRDR mutation, PMQR gene co-mediated quinolone resistance and plasmid-mediated cephalosporin resistance are prominent and deserve extensive attention.

Comprehensive insights into potential roles of purinergic P2 receptors on diseases: Signaling pathways involved and potential therapeutics.

BACKGROUND: Purinergic P2 receptors, which can be divided into ionotropic P2X receptors and metabotropic P2Y receptors, mediate cellular signal transduction of purine or pyrimidine nucleoside triphosphates and diphosphate. Based on the wide expression of purinergic P2 receptors in tissues and organs, their significance in homeostatic maintenance, metabolism, nociceptive transmission, and other physiological processes is becoming increasingly evident, suggesting that targeting purinergic P2 receptors to regulate biological functions and signal transmission holds significant promise for disease treatment. AIM OF REVIEW: This review highlights the detailed mechanisms by which purinergic P2 receptors engage in physiological and pathological progress, as well as providing prospective strategies for discovering clinical drug candidates. KEY SCIENTIFIC CONCEPTS OF REVIEW: The purinergic P2 receptors regulate complex signaling and molecular mechanisms in nervous system, digestive system, immune system and as a result, controlling physical health states and disease progression. There has been a significant rise in research and development focused on purinergic P2 receptors, contributing to an increased number of drug candidates in clinical trials. A few influential pioneers have laid the foundation for advancements in the evaluation, development, and of novel purinergic P2 receptors modulators, including agonists, antagonists, pharmaceutical compositions and combination strategies, despite the different scaffolds of these drug candidates. These advancements hold great potential for improving therapeutic outcomes by specifically targeting purinergic P2 receptors.

Multiview Deep Subspace Clustering Networks.

Multiview subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. Most existing methods first extract multiple types of handcrafted features and then learn a joint affinity matrix for clustering. The disadvantage of this approach lies in two aspects: 1) multiview relations are not embedded into feature learning and 2) the end-to-end learning manner of deep learning is not suitable for multiview clustering. Even when deep features have been extracted, it is a nontrivial problem to choose a proper backbone for clustering on different datasets. To address these issues, we propose the multiview deep subspace clustering networks (MvDSCNs), which learns a multiview self-representation matrix in an end-to-end manner. The MvDSCN consists of two subnetworks, i.e., a diversity network (Dnet) and a universality network (Unet). A latent space is built using deep convolutional autoencoders, and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices, whereas Unet learns a common self-representation matrix for all views. To exploit the complementarity of multiview representations, the Hilbert-Schmidt independence criterion (HSIC) is introduced as a diversity regularizer that captures the nonlinear, high-order interview relations. Because different views share the same label space, the self-representation matrices of each view are aligned to the common one by universality regularization. The MvDSCN also unifies multiple backbones to boost clustering performance and avoid the need for model selection. Experiments demonstrate the superiority of the MvDSCN.

A hybrid denoising approach for PPG signals utilizing variational mode decomposition and improved wavelet thresholding.

BACKGROUND: Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements. OBJECTIVE: The objective of our study is to effectively eliminate baseline drift and high-frequency noise from PPG signals, ensuring that the signal's critical frequency components remain within the range of 1 ∼ 10 Hz. METHODS: This paper introduces a novel hybrid denoising method for PPG signals, integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. The method initially employs VMD to decompose PPG signals into a set of narrowband intrinsic mode function (IMF) components, effectively removing low-frequency baseline drift. Subsequently, an improved wavelet thresholding algorithm is applied to eliminate high-frequency noise, resulting in denoised PPG signals. The effectiveness of the denoising method was rigorously assessed through a comprehensive validation process. It was tested on real-world PPG measurements, PPG signals generated by the Fluke ProSim™ 8 Vital Signs Simulator with synthesized noise, and extended to the MIMIC-III waveform database. RESULTS: The application of the improved threshold function let to a substantial 11.47% increase in signal-to-noise ratio (SNR) and an impressive 26.75% reduction in root mean square error (RMSE) compared to the soft threshold function. Furthermore, the hybrid denoising method improved SNR by 15.54% and reduced RMSE by 37.43% compared to the improved threshold function. CONCLUSION: This study proposes an effective PPG denoising algorithm based on VMD and an improved wavelet threshold function, capable of simultaneously eliminating low-frequency baseline drift and high-frequency noise in PPG signals while faithfully preserving their morphological characteristics. This advancement establishes the foundation for time-domain feature extraction and model development in the domain of PPG signal analysis.

Layer-Specific Knowledge Distillation for Class Incremental Semantic Segmentation.

Recently, class incremental semantic segmentation (CISS) towards the practical open-world setting has attracted increasing research interest, which is mainly challenged by the well-known issue of catastrophic forgetting. Particularly, knowledge distillation (KD) techniques have been widely studied to alleviate catastrophic forgetting. Despite the promising performance, existing KD-based methods generally use the same distillation schemes for different intermediate layers to transfer old knowledge, while employing manually tuned and fixed trade-off weights to control the effect of KD. These KD-based methods take no consideration of feature characteristics from different intermediate layers, limiting the effectiveness of KD for CISS. In this paper, we propose a layer-specific knowledge distillation (LSKD) method to assign appropriate knowledge schemes and weights for various intermediate layers by considering feature characteristics, aiming to further explore the potential of KD in improving the performance of CISS. Specifically, we present a mask-guided distillation (MD) to alleviate the background shift on semantic features, which performs distillation by masking the features affected by the background. Furthermore, a mask-guided context distillation (MCD) is presented to explore global context information lying in high-level semantic features. Based on them, our LSKD assigns different distillation schemes according to feature characteristics. To adjust the effect of layer-specific distillation adaptively, LSKD introduces a regularized gradient equilibrium method to learn dynamic trade-off weights. Additionally, our LSKD makes an attempt to simultaneously learn distillation schemes and trade-off weights of different layers by developing a bi-level optimization method. Extensive experiments on widely used Pascal VOC 12 and ADE20K show our LSKD clearly outperforms its counterparts while achieving state-of-the-art results.

Genome-wide investigation of SQUAMOSA promoter binding protein-like genes in Liriodendron and functional characterization of LcSPL2.

The plant-specific SQUAMOSA promoter-binding protein-like (SPL) transcription factors play a pivotal role in various developmental processes, including leaf morphogenesis and vegetative to reproductive phase transition. Liriodendron chinense and Liriodendron tulipifera are widely used in landscaping due to their tulip-like flowers and peculiar leaves. However, the SPL gene family in Liriodendron has not been identified and systematically characterized. We systematically identified and characterized the SPL family members in Liriodendron, including phylogeny, gene structure and syntenic analyses. Subsequently, we quantified the expression patterns of LcSPLs across various tissue sites through transcription-quantitative polymerase chain reaction (RT-qPCR) assays and identified the target gene, LcSPL2. Finally, we characterized the functions of LcSPL2 via ectopic transformation. Altogether, 17 LcSPL and 18 LtSPL genes were genome-widely identified in L. chinense and L. tulipifera, respectively. All the 35 SPLs were grouped into 9 clades. Both species had three SPL gene pairs arising from segmental duplication events, and the LcSPLs displayed high collinearity with the L. tulipifera genome. RT-qPCR assays showed that SPL genes were differentially expressed in different tissues, especially. Because LcSPL2 is highly expressed in pistils and leaves, it was selected to describe the SPL gene family of L. chinense by ectopic expression. We showed that overexpression of LcSPL2 in Arabidopsis thaliana resulted in earlier flowering and fewer rosette leaves. Moreover, we observed that overexpression of LcSPL2 in A. thaliana up-regulated the expression levels of four genes related to flower development. This study identified SPL genes in Liriodendron and characterized the function of LcSPL2 in advancing flower development.

Targeting P2Y14R protects against necroptosis of intestinal epithelial cells through PKA/CREB/RIPK1 axis in ulcerative colitis.

Purinergic signaling plays a causal role in the pathogenesis of inflammatory bowel disease. Among purinoceptors, only P2Y14R is positively correlated with inflammatory score in mucosal biopsies of ulcerative colitis patients, nevertheless, the role of P2Y14R in ulcerative colitis remains unclear. Here, based on the over-expressions of P2Y14R in the intestinal epithelium of mice with experimental colitis, we find that male mice lacking P2Y14R in intestinal epithelial cells exhibit less intestinal injury induced by dextran sulfate sodium. Mechanistically, P2Y14R deletion limits the transcriptional activity of cAMP-response element binding protein through cAMP/PKA axis, which binds to the promoter of Ripk1, inhibiting necroptosis of intestinal epithelial cells. Furthermore, we design a hierarchical strategy combining virtual screening and chemical optimization to develop a P2Y14R antagonist HDL-16, which exhibits remarkable anti-colitis effects. Summarily, our study elucidates a previously unknown mechanism whereby P2Y14R participates in ulcerative colitis, providing a promising therapeutic target for inflammatory bowel disease.

Rationally designed nanotrap structures for efficient separation of rare earth elements over a single step.

Extracting rare earth elements (REEs) from wastewater is essential for the growth and an eco-friendly sustainable economy. However, it is a daunting challenge to separate individual rare earth elements by their subtle differences. To overcome this difficulty, we report a unique REE nanotrap that features dense uncoordinated carboxyl groups and triazole N atoms in a two-fold interpenetrated metal-organic framework (named NCU-1). Notably, the synergistic effect of suitable pore sizes and REE nanotraps in NCU-1 is highly responsive to the size variation of rare-earth ions and shows high selectivity toward light REE. As a proof of concept, Pr/Lu and Nd/Er are used as binary models, which give a high separation factor of SFPr/Lu = 796 and SFNd/Er = 273, demonstrating highly efficient separation over a single step. This ability achieves efficient and selective extraction and separation of REEs from mine tailings, establishing this platform as an important advance for sustainable obtaining high-purity REEs.

The identification and functional characterization of the LcMCT gene from Liriodendron chinense reveals its potenatial role in carotenoids biosyanthesis.

Terpenoids are not only important component of plant floral scent, but also indispensable elements in the formation of floral color. The petals of Liriodendron chinense are rich in tetraterpene carotenoids and release large amounts of volatile monoterpene and sesquiterpene compounds during full blooming stage. However, the mechanism of terpenoid synthesis is not clear in L. chinense. In this study, we identified a LcMCT gene and characterized its potential function in carotenoids biosynthesis. A total of 2947 up-regulated differentially expressed genes (DEGs) were discerned from the transcriptomic data of L. chinense petals, with a significant enrichment of DEGs related to plant hormone signal transduction and terpenoid backbone biosynthesis. After comprehensive analysis on these DEGs, the LcMCT gene was selected for subsequent function characterization. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results showed that LcMCT was expressed at the highest level in the petals during full blooming stage, suggesting a possible role in carotenoids biosynthesis and volatile terpenoid biosynthesis. Subcellular localization showed that the LcMCT protein was localized in the chloroplast. Overexpression of LcMCT in Arabidopsis thaliana affected the expression levels of MEP pathway genes. Moreover, the MCT enzyme activity and carotenoids contents in transgenic A. thaliana were increased by 69.27% and 15.57%, respectively. These results suggest that LcMCT promotes the biosynthesis of terpenoid precursors via the MEP pathway. Our work lays a foundation for exploring the mechanism of terpenoid synthesis in L. chinense.

Calcium sensing receptor: A promising therapeutic target in pulmonary hypertension.

Pulmonary hypertension (PH) is characterized by elevation of pulmonary arterial pressure and pulmonary vascular resistance. The increased pulmonary arterial pressure and pulmonary vascular resistance due to sustained pulmonary vasoconstriction and pulmonary vascular remodeling can lead to right heart failure and eventual death. A rise in intracellular Ca2+ concentration ([Ca2+]i) and enhanced pulmonary arterial smooth muscle cells (PASMCs) proliferation contribute to pulmonary vasoconstriction and pulmonary vascular remodeling. Recent studies demonstrated that extracellular calcium sensing receptor (CaSR) as a G-protein coupled receptor participates in [Ca2+]i increase induced by hypoxia in the experimental animals of PH and in PH patients. Pharmacological blockade or gene knockout of CaSR significantly attenuates the development of PH. This review will aim to discuss and update the pathogenicity of CaSR attributed to onset and progression in PH.

TRIM14 suppressed the progression of NSCLC via hexosamine biosynthesis pathway.

Tripartite Motif 14 (TRIM14) is an oncoprotein that belongs to the E3 ligase TRIM family, which is involved in the progression of various tumors except for non-small-cell-lung carcinoma (NSCLC). However, little is currently known regarding the function and related mechanisms of TRIM14 in NSCLC. Here, we found that the TRIM14 protein was downregulated in lung adenocarcinoma tissues compared with the adjacent tissues, which can suppress tumor cell proliferation and migration both in vitro and in vivo. Moreover, TRIM14 can directly bind to glutamine fructose-6-phosphate amidotransferase 1 (GFAT1), which in turn results in the degradation of GFAT1 and reduced O-glycosylation levels. GFAT1 is a key enzyme in the rate-limiting step of the hexosamine biosynthetic pathway (HBP). Replenishment of N-Acetyl-D-glucosamine can successfully reverse the inhibitory effect of TRIM14 on the NSCLC cell growth and migration as expected. Collectively, our data revealed that TRIM14 suppressed NSCLC cell proliferation and migration through ubiquitination and degradation of GFAT1, providing a new regulatory role for TRIM14 on HBP.

Genome-wide identification of GROWTH-REGULATING FACTORs in Liriodendron chinense and functional characterization of LcGRF2 in leaf size regulation.

GROWTH-REGULATING FACTORs (GRFs) play a pivotal role in the regulation of leaf size in plants and have been widely reported in plants. However, their specific functions in leaf size regulation in Liriodendron chinense remains unclear. Therefore, in this study, we identified GRF genes on a genome-wide scale in L. chinense to characterize the roles of LcGRFs in regulating leaf size. A total of nine LcGRF genes were identified, and these genes exhibited weak expression in mature leaves but strong expression in shoot apex. Notably, LcGRF2 exhibited the highest expression level in the shoot apex of L. chinense. Further RT-qPCR assay revealed that the expression level of LcGRF2 gradually decreased along with the leaf development process, and also displayed a gradient along the leaf proximo-distal and medio-lateral axes. Furthermore, overexpression of LcGRF2 in Arabidopsis thaliana resulted in increased leaf size, and significantly up-regulated the expression of genes involved in cell division like AtCYCD3;1, AtKNOLLE, and AtCYCB1;1, indicating that LcGRF2 may influence leaf size by promoting cell proliferation. This work contributes to a better understanding of the roles and molecular mechanisms of LcGRFs in the regulation of leaf size in L. chinense.

Macrophage P2Y6 receptor deletion attenuates atherosclerosis by limiting foam cell formation through phospholipase Cß/store-operated calcium entry/calreticulin/scavenger receptor A pathways.

BACKGROUND AND AIMS: Macrophage-derived foam cells play a causal role during the pathogenesis of atherosclerosis. P2Y6 receptor (P2Y6R) highly expressed has been considered as a disease-causing factor in atherogenesis, but the detailed mechanism remains unknown. This study aims to explore P2Y6R in regulation of macrophage foaming, atherogenesis, and its downstream pathways. Furthermore, the present study sought to find a potent P2Y6R antagonist and investigate the feasibility of P2Y6R-targeting therapy for atherosclerosis. METHODS: The P2Y6R expression was examined in human atherosclerotic plaques and mouse artery. Atherosclerosis animal models were established in whole-body P2Y6R or macrophage-specific P2Y6R knockout mice to evaluate the role of P2Y6R. RNA sequencing, DNA pull-down experiments, and proteomic approaches were performed to investigate the downstream mechanisms. High-throughput Glide docking pipeline from repurposing drug library was performed to find potent P2Y6R antagonists. RESULTS: The P2Y6R deficiency alleviated atherogenesis characterized by decreasing plaque formation and lipid deposition of the aorta. Mechanically, deletion of macrophage P2Y6R significantly inhibited uptake of oxidized low-density lipoprotein through decreasing scavenger receptor A expression mediated by phospholipase Cß/store-operated calcium entry pathways. More importantly, P2Y6R deficiency reduced the binding of scavenger receptor A to CALR, accompanied by dissociation of calreticulin and STIM1. Interestingly, thiamine pyrophosphate was found as a potent P2Y6R antagonist with excellent P2Y6R antagonistic activity and binding affinity, of which the pharmacodynamic effect and mechanism on atherosclerosis were verified. CONCLUSIONS: Macrophage P2Y6R regulates phospholipase Cß/store-operated calcium entry/calreticulin signalling pathway to increase scavenger receptor A protein level, thereby improving foam cell formation and atherosclerosis, indicating that the P2Y6R may be a potential therapeutic target for intervention of atherosclerotic diseases using P2Y6R antagonists including thiamine pyrophosphate.

Network pharmacology-based analysis of Jin-Si-Wei on the treatment of Alzheimer's disease.

ETHNOPHARMACOLOGICAL RELEVANCE: Jin-Si-Wei (JSW), a traditional Chinese medicine (TCM) formula, have cognitive enhancing effect and delay the memory decline in an animal model of AD， which has been reported. However, the therapeutic mechanism of JSW in the treatment of AD remains unclear. AIM OF THE STUDY: This study aimed to verify the pharmacodynamics of JSW in the treatment of AD, and to explore its potential mechanism based on network pharmacology, molecular docking and experimental validation both in vitro and in vivo. MATERIALS AND METHODS: In this study, the underlying mechanism of JSW against AD was investigated by the integration of network pharmacology. Then, the core pathways and biological process of JSW were verified by experiment, including behavioral test and pathological and biochemical assays with 6-month-old APPswe/PS1ΔE9 transgenic (APP/PS1) mice in vivo and verified with Aß1-42-stimulated SH-SY5Y cells in vitro. At last, molecular docking was used to show the binding activity of each active ingredient to the core genes of JSW treatment in AD. RESULTS: A Drug-Ingredient-Target network was established, which included 363 ingredients and 116 targets related to the JSW treatment of AD. The main metabolic pathway of JSW treatment for AD is neuroactive ligand-receptor interaction pathway, and biological processes are mainly involved in Aß metabolic process. In vivo experiments, compared with APP/PS1 mice, the cognitive and memory ability of mice was significantly improved after JSW administration. In brain tissue of APP/PS1 mice, JSW could increase the contents of low-density lipoprotein receptor-related protein 1 (LRP-1), enkephalinase (NEP) and Acetyl choline (ACh), and decrease the contents of Aß1-42, amyloid precursor protein (APP) and receptor for advanced glycation end products (RAGE), decrease the vitality of cholinesterase (AChE) and choline acetyltransferase (ChAT). Besides, JSW could increase α-secretase expression and decrease ß/γ-secretase expression, and improve the number and morphology of synapses in CA1 region of the hippocampus of APP/PS1 mice. In vitro experiments, Drug-Containing Serum (JSW-serum) has a neuroprotective effect by reducing the apoptosis on Aß1-42-stimulated SH-SY5Y cells. Molecular docking results showed that 2-Isopropyl-8-methylphenanthrene-3,4-dione had strong binding activity with PTGS2, which maybe a potential ingredient for the treatment of AD. CONCLUSIONS: JSW improves AD in APP/PS1 mice, and this therapeutic effect may be achieved in part by altering the neuroactive ligand-receptor interaction pathway.

Extracellular vesicles in venous thromboembolism and pulmonary hypertension.

Venous thromboembolism (VTE) is a multifactorial disease, and pulmonary hypertension (PH) is a serious condition characterized by pulmonary vascular remodeling leading with increased pulmonary vascular resistance, ultimately leading to right heart failure and death. Although VTE and PH have distinct primary etiologies, they share some pathophysiologic similarities such as dysfunctional vasculature and thrombosis. In both conditions there is solid evidence that EVs derived from a variety of cell types including platelets, monocytes, endothelial cells and smooth muscle cells contribute to vascular endothelial dysfunction, inflammation, thrombosis, cellular activation and communications. However, the roles and importance of EVs substantially differ between studies depending on experimental conditions and parent cell origins of EVs that modify the nature of their cargo. Numerous studies have confirmed that EVs contribute to the pathophysiology of VTE and PH and increased levels of various EVs in relation with the severity of VTE and PH, confirming its potential pathophysiological role and its utility as a biomarker of disease severity and as potential therapeutic targets.

China's public health initiatives for climate change adaptation.

China's health gains over the past decades face potential reversals if climate change adaptation is not prioritized. China's temperature rise surpasses the global average due to urban heat islands and ecological changes, and demands urgent actions to safeguard public health. Effective adaptation need to consider China's urbanization trends, underlying non-communicable diseases, an aging population, and future pandemic threats. Climate change adaptation initiatives and strategies include urban green space, healthy indoor environments, spatial planning for cities, advance location-specific early warning systems for extreme weather events, and a holistic approach for linking carbon neutrality to health co-benefits. Innovation and technology uptake is a crucial opportunity. China's successful climate adaptation can foster international collaboration regionally and beyond.

Epidemiological Insights into the Omicron Outbreak via MeltArray-Assisted Real-Time Tracking of SARS-CoV-2 Variants.

The prolonged course of the COVID-19 pandemic necessitates sustained surveillance of emerging variants. This study aimed to develop a multiplex real-time polymerase chain reaction (rt-PCR) suitable for the real-time tracking of Omicron subvariants in clinical and wastewater samples. Plasmids containing variant-specific mutations were used to develop a MeltArray assay. After a comprehensive evaluation of both analytical and clinical performance, the established assay was used to detect Omicron variants in clinical and wastewater samples, and the results were compared with those of next-generation sequencing (NGS) and droplet digital PCR (ddPCR). The MeltArray assay identified 14 variant-specific mutations, enabling the detection of five Omicron sublineages (BA.2*, BA.5.2*, BA.2.75*, BQ.1*, and XBB.1*) and eight subvariants (BF.7, BN.1, BR.2, BQ.1.1, XBB.1.5, XBB.1.16, XBB.1.9, and BA.4.6). The limit of detection (LOD) of the assay was 50 copies/reaction, and no cross-reactivity was observed with 15 other respiratory viruses. Using NGS as the reference method, the clinical evaluation of 232 swab samples exhibited a clinical sensitivity of > 95.12% (95% CI 89.77-97.75%) and a specificity of > 95.21% (95% CI, 91.15-97.46%). When used to evaluate the Omicron outbreak from late 2022 to early 2023, the MeltArray assay performed on 1408 samples revealed that the epidemic was driven by BA.5.2* (883, 62.71%) and BF.7 (525, 37.29%). Additionally, the MeltArray assay demonstrated potential for estimating variant abundance in wastewater samples. The MeltArray assay is a rapid and scalable method for identifying SARS-CoV-2 variants. Integrating this approach with NGS and ddPCR will improve variant surveillance capabilities and ensure preparedness for future variants.

Pathogenetic detection, retrospective and pathogenicity analysis of a fatal case of Vibrio vulnificus in Shenzhen, China.

We report a 36-year-old male patient died of V. vulnificus-induced septicaemia and multiple organ failure syndrome after oyster consumption at a restaurant. We isolated and identified V. vulnificus vv16015 from the patient's blood sample and antibiotic susceptibility tests indicated sensitivity to all 21 antibiotics. Oyster samples were subsequently collected from the restaurant's supplier and three strains of V. vulnificus were isolated. Whole genome sequencing and analysis revealed vv16015 to be distantly related to these strains and confirmed that V. vulnificus contamination was present in the seafood of the restaurant and supplier. Using a Galleria mellonella larvae infection model, the virulence of vv16015 was determined to be higher than that of comparison strains isolated from a surviving patient (vv15018) and an oyster (vv220015). The human and environment distribution of V. vulnificus in Shenzhen is sporadic and heterogeneous, and vv16015 is highly virulent compared to other strains.

Bayesian Hierarchical Graph Neural Networks With Uncertainty Feedback for Trustworthy Fault Diagnosis of Industrial Processes.

Deep learning (DL) methods have been widely applied to intelligent fault diagnosis of industrial processes and achieved state-of-the-art performance. However, fault diagnosis with point estimate may provide untrustworthy decisions. Recently, Bayesian inference shows to be a promising approach to trustworthy fault diagnosis by quantifying the uncertainty of the decisions with a DL model. The uncertainty information is not involved in the training process, which does not help the learning of highly uncertain samples and has little effect on improving the fault diagnosis performance. To address this challenge, we propose a Bayesian hierarchical graph neural network (BHGNN) with an uncertainty feedback mechanism, which formulates a trustworthy fault diagnosis on the Bayesian DL (BDL) framework. Specifically, BHGNN captures the epistemic uncertainty and aleatoric uncertainty via a variational dropout approach and utilizes the uncertainty information of each sample to adjust the strength of the temporal consistency (TC) constraint for robust feature learning. Meanwhile, the BHGNN method models the process data as a hierarchical graph (HG) by leveraging the interaction-aware module and physical topology knowledge of the industrial process, which integrates data with domain knowledge to learn fault representation. Moreover, the experiments on a three-phase flow facility (TFF) and secure water treatment (SWaT) show superior and competitive performance in fault diagnosis and verify the trustworthiness of the proposed method.