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Background MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn’t been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. Results The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. Conclusions The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others. Graphical Abstract

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and is rapidly spreading among the human population 1 . Although recent reports reveal that the Omicron variant robustly escapes vaccine-associated and therapeutic neutralization antibodies 2 – 10 , the pathogenicity of the virus remains unknown. Here we show that the replication of Omicron is substantially attenuated in human Calu3 and Caco2 cells. Further mechanistic investigations reveal that Omicron is inefficient in its use of transmembrane serine protease 2 (TMPRSS2) compared with wild-type SARS-CoV-2 (HKU-001a) and previous variants, which may explain its reduced replication in Calu3 and Caco2 cells. The replication of Omicron is markedly attenuated in both the upper and lower respiratory tracts of infected K18-hACE2 mice compared with that of the wild-type strain and Delta (B.1.617.2) variant, resulting in its substantially ameliorated lung pathology. Compared with wild-type SARS-CoV-2 and the Alpha (B.1.1.7), Beta (1.351) and Delta variants, infection by Omicron causes the lowest reduction in body weight and the lowest mortality rate. Overall, our study demonstrates that the replication and pathogenicity of the Omicron variant of SARS-CoV-2 in mice is attenuated compared with the wild-type strain and other variants. The replication and pathogenicity of the Omicron variant of SARS-CoV-2 is attenuated compared with the original strain and other variants.

Hybrid models which combine the convolution and transformer model achieve impressive performance on human pose estimation. However, the existing hybrid models on human pose estimation, which typically stack self-attention modules after convolution, are prone to mutual conflict. The mutual conflict enforces one type of module to dominate over these hybrid sequential models. Consequently, the performance of higher-precision keypoints localization is not consistent with overall performance. To alleviate this mutual conflict, we developed a hybrid parallel network by parallelizing the self-attention modules and the convolution modules, which conduce to leverage the complementary capabilities effectively. The parallel network ensures that the self-attention branch tends to model the long-range dependency to enhance the semantic representation, whereas the local sensitivity of the convolution branch contributes to high-precision localization simultaneously. To further mitigate the conflict, we proposed a cross-branches attention module to gate the features generated by both branches along the channel dimension. The hybrid parallel network achieves 75.6% and 75.4%AP on COCO validation and test-dev sets and achieves consistent performance on both higher-precision localization and overall performance. The experiments show that our hybrid parallel network is on par with the state-of-the-art human pose estimation models.

Heroin is a highly addictive drug that exerts its primary effects through activation of μ-opioid receptors. Its principal active metabolite, 6-monoacetylmorphine (6-MAM), significantly contributes to heroin’s neurological effects and acute toxicity. Current pharmacotherapies for heroin use disorder, employing opioid receptor agonist or antagonist, are often limited by risks of dependence, tolerance, and/or adverse side effects. In this context, enzyme-based therapy emerges as a promising alternative by rapidly converting drugs into inactive or less harmful metabolites in the blood. As a macromolecule, the enzyme does not cross the blood–brain barrier, thereby avoiding side effects in CNS. Through structure-based computational screening, Xoo-PepA (PDB ID: 3JRU), a leucine aminopeptidase from Xanthomonas oryzae pv. oryzae, was identified as a potential enzyme capable of hydrolyzing heroin and 6-MAM. Computational and experimental analyses confirm that Xoo-PepA hydrolyzes heroin sequentially to 6-MAM and subsequently to morphine. Enzymatic properties including dependence on metal ions, optimal pH, thermal stability, and substrate specificity were characterized accordingly. Notably, supplementation with Ni2+ or Zn2+ and TCEP extended Xoo-PepA’s half-life at 37 °C from 1 h to over 24 h, highlighting the essential role of metal ions in maintaining structural stability. Moreover, Ni2+ enhanced Xoo-PepA’s hydrolysis toward peptidase substrate L-leucine-p-nitroaniline by 770-fold, yet conferred no significant activation toward heroin. Mutations in metal ion-coordination residues (e.g., K262A, D267A/E346L) exhibited different activity profiles toward these two types of substrates, suggesting a distinct regulatory mechanism of metal ions may be involved in these activities. This study provides the first demonstration that Xoo-PepA, a non-mammalian, metal-dependent aminopeptidase, can hydrolyze heroin and 6-MAM, shedding light on its functional versatility and biochemical characteristics.

Understanding the factors that contribute to efficient SARS-CoV-2 infection of human cells may provide insights on SARS-CoV-2 transmissibility and pathogenesis, and reveal targets of intervention. Here, we analyze host and viral determinants essential for efficient SARS-CoV-2 infection in both human lung epithelial cells and ex vivo human lung tissues. We identify heparan sulfate as an important attachment factor for SARS-CoV-2 infection. Next, we show that sialic acids present on ACE2 prevent efficient spike/ACE2-interaction. While SARS-CoV infection is substantially limited by the sialic acid-mediated restriction in both human lung epithelial cells and ex vivo human lung tissues, infection by SARS-CoV-2 is limited to a lesser extent. We further demonstrate that the furin-like cleavage site in SARS-CoV-2 spike is required for efficient virus replication in human lung but not intestinal tissues. These findings provide insights on the efficient SARS-CoV-2 infection of human lungs. Here, using lung epithelial cells and ex vivo tissue explants, the authors show that, in addition to ACE2, host heparan sulfate is directly involved in SARS-CoV-2 attachment and entry and provide data suggesting that host sialic acids may act as viral restriction factor in lung tissues.

Emerging SARS-CoV-2 variants continue to cause waves of new infections globally. Developing effective antivirals against SARS-CoV-2 and its variants is an urgent task. The main protease (M pro ) of SARS-CoV-2 is an attractive drug target because of its central role in viral replication and its conservation among variants. We herein report a series of potent α-ketoamide-containing M pro inhibitors obtained using the Ugi four-component reaction. The prioritized compound, Y180, showed an IC 50 of 8.1 nM against SARS-CoV-2 M pro and had oral bioavailability of 92.9%, 31.9% and 85.7% in mice, rats and dogs, respectively. Y180 protected against wild-type SARS-CoV-2, B.1.1.7 (Alpha), B.1.617.1 (Kappa) and P.3 (Theta), with EC 50 of 11.4, 20.3, 34.4 and 23.7 nM, respectively. Oral treatment with Y180 displayed a remarkable antiviral potency and substantially ameliorated the virus-induced tissue damage in both nasal turbinate and lung of B.1.1.7-infected K18-human ACE2 (K18-hACE2) transgenic mice. Therapeutic treatment with Y180 improved the survival of mice from 0 to 44.4% ( P  = 0.0086) upon B.1.617.1 infection in the lethal infection model. Importantly, Y180 was also highly effective against the B.1.1.529 (Omicron) variant both in vitro and in vivo. Overall, our study provides a promising lead compound for oral drug development against SARS-CoV-2. An inhibitor of the SARS-CoV-2 main protease (Mpro), Y180, showed therapeutic efficacy against wild-type SARS-CoV-2 and its variants including Omicron after oral administration and improved survival in a humanized mouse model.

Pyroptosis is a host immune strategy to defend against Mycobacterium tuberculosis (Mtb) infection. S100A4, a calcium-binding protein that plays an important role in promoting cancer progression as well as the pathophysiological development of various non-tumor diseases, has not been explored in Mtb-infected hosts. In this study, transcriptome analysis of the peripheral blood of patients with pulmonary tuberculosis (PTB) revealed that S100A4 and GSDMD were significantly up-regulated in PTB patients’ peripheral blood. Furthermore, there was a positive correlation between the expression of GSDMD and S100A4. KEGG pathway enrichment analysis showed that differentially expressed genes between PTB patients and healthy controls were significantly related to inflammation, such as the NOD-like receptor signaling pathway and NF-κB signaling pathway. To investigate the regulatory effects of S100A4 on macrophage pyroptosis, THP-1 macrophages infected with Bacillus Calmette-Guérin (BCG) were pre-treated with exogenous S100A4, S100A4 inhibitor or si-S100A4. This research study has shown that S100A4 promotes the pyroptosis of THP-1 macrophages caused by BCG infection and activates NLRP3 inflammasome and NF-κB signaling pathways, which can be inhibited by knockdown or inhibition of S100A4. In addition, inhibition of NF-κB or NLRP3 blocks the promotion effect of S100A4 on BCG-induced pyroptosis of THP-1 macrophages. In conclusion, S100A4 activates the NF-κB/NLRP3 inflammasome signaling pathway to promote macrophage pyroptosis induced by Mtb infection. These data provide new insights into how S100A4 affects Mtb-induced macrophage pyroptosis.

In the original publication [...]

The extensibility of dough and its resistance to extension (toughness) are important indicators, since they are directly linked to dough quality. Therefore, this paper used an independently developed device to blow sheeted dough, and then a three-dimensional (3D) camera was used to continuously collect point cloud images of sheeted dough forming bubbles. After data collection, the rotation algorithm, region of interest (ROI) extraction algorithm, and statistical filtering algorithm were used to process the original point cloud images. Lastly, the oriented bounding box (OBB) algorithm was proposed to calculate the deformation height of each data point. And the point cloud image with the largest deformation depth was selected as the data to input into the 3D convolutional neural network (CNN) models. The Convolutional Block Attention Module (CBAM) was introduced into the 3D Visual Geometry Group 11 (Vgg11) model to build the enhanced Vgg11. And we compared it with the other classical 3D CNN models (MobileNet, ResNet18, and Vgg11) by inputting the voxel-point-based data and the voxel-based data separately into these models. The results showed that the enhanced 3D Vgg11 model using voxel-point-based data was superior to the other models. For prediction of dough extensibility and toughness, the Rp was 0.893 and 0.878, respectively.

SARS-CoV-2 B.1.1.529.1 (Omicron BA.1) emerged in November 2021 and quickly became the predominant circulating SARS-CoV-2 variant globally. Omicron BA.1 contains more than 30 mutations in the spike protein, which contribute to its altered virological features when compared to the ancestral SARS-CoV-2 or previous SARS-CoV-2 variants. Recent studies by us and others demonstrated that Omicron BA.1 is less dependent on transmembrane serine protease 2 (TMPRSS2), less efficient in spike cleavage, less fusogenic, and adopts an altered propensity to utilize the plasma membrane and endosomal pathways for virus entry. Ongoing studies suggest that these virological features of Omicron BA.1 are in part retained by the subsequent Omicron sublineages. However, the exact spike determinants that contribute to these altered features of Omicron remain incompletely understood. In this study, we investigated the spike determinants for the observed virological characteristics of Omicron. By screening for the individual changes on Omicron BA.1 and BA.2 spike, we identify that 69-70 deletion, E484A, and H655Y contribute to the reduced TMPRSS2 usage while 25-27 deletion, S375F, and T376A result in less efficient spike cleavage. Among the shared spike mutations of BA.1 and BA.2, S375F and H655Y reduce spike-mediated fusogenicity. Interestingly, the H655Y change consistently reduces serine protease usage while increases the use of endosomal proteases. In keeping with these findings, the H655Y substitution alone reduces plasma membrane entry and facilitates endosomal entry when compared to SARS-CoV-2 WT. Overall, our study identifies key changes in Omicron spike that contributes to our understanding on the virological determinant and pathogenicity of Omicron.