MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

Enhanced water treatment performance of ceramic-based forward osmosis membranes via MOF interlayer

Forward osmosis (FO) membrane processes could operate without hydraulic pressures, enabling the efficient treatment of wastewaters with mitigated membrane fouling and enhanced efficiency. Designing a high-performance polyamide (PA) layer on ceramic substrates remains a challenge for FO desalination applications. Herein, we report the enhanced water treatment performance of thin-film nanocomposite ceramic-based FO membranes via an in situ grown Zr-MOF (UiO-66-NH2) interlayer. With the Zr-MOF interlayer, the ceramic-based FO membranes exhibit lower thickness, higher cross-linking degree, and increased surface roughness, leading to higher water flux of 27.38 L m-2 h-1 and lower reverse salt flux of 3.45 g m-2 h-1. The ceramic-based FO membranes with Zr-MOF interlayer not only have an application potential in harsh environments such as acidic solution (pH 3) and alkaline solution (pH 11), but also exhibit promising water and reverse salt transport properties, which are better than most MOF-incorporated PA membranes. Furthermore, the membranes could reject major species (ions, oil and organics) with rejections ＞94 % and water flux of 22.62-14.35 L m-2 h-1 in the treatment of actual alkaline industrial wastewater (pH 8.6). This rational design proposed in this study is not only applicable for the development of a high-quality ceramic-based FO membrane with enhanced performance but also can be potentially extended to more challenging water treatment applications.

Proteomic analysis of multiple organ dysfunction induced by rhabdomyolysis

Rhabdomyolysis (RM) leads to dysfunction in the core organs of kidney, lung and heart, which is an important reason for the high mortality and disability rate of this disease. However, there is a lack of systematic research on the characteristics of rhabdomyolysis-induced injury in various organs and the underlying pathogenetic mechanisms, and especially the interaction between organs. We established a rhabdomyolysis model, observed the structural and functional changes in kidney, heart, and lung. It is observed that rhabdomyolysis results in significant damage in kidney, lung and heart of rats, among which the pathological damage of kidney and lung was significant, and of heart was relatively light. Meanwhile, we analyzed the differentially expressed proteins (DEPs) in the kidney, heart and lung between the RM group and the sham group based on liquid chromatography-tandem mass spectrometry (LC-MS/MS). In our study, Serpina3n was significantly up-regulated in the kidney, heart and lung. Serpina3n is a secreted protein and specifically inhibits a variety of proteases and participates in multiple physiological processes such as complement activation, inflammatory responses, apoptosis pathways, and extracellular matrix metabolism. It is inferred that Serpina3n may play an important role in multiple organ damage caused by rhabdomyolysis and could be used as a potential biomarker. This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis. SIGNIFICANCE: This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis.

Detoxification and underlying mechanisms towards toxic alkaloids by Traditional Chinese Medicine processing: A comprehensive review

BACKGROUND

Alkaloids have attracted enduring interest worldwide due to their remarkable therapeutic effects, including analgesic, anti-inflammatory, and anti-tumor properties, thus offering a rich source for lead compound design and new drug discovery. However, some of these alkaloids possess intrinsic toxicity. Processing (Paozhi) is a pre-treatment step before the application of herbal medicines in traditional Chinese medicine (TCM) clinics, which has been employed for centuries to mitigate the toxicity of alkaloid-rich TCMs.

PURPOSE

To explore the toxicity phenotypes, chemical basis, mode of action, detoxification processing methods, and underlying mechanisms, we can gain crucial insights into the safe and rational use of these toxic alkaloid-rich herbs. Such insights have the great potential to offer new strategies for drug discovery and development, ultimately improving the quality of life for millions of people.

METHODS

Literatures published or early accessed until December 31, 2023, were retrieved from databases including PubMed, Web of Science, and CNKI. The following keywords, such as "toxicity", "alkaloid", "detoxification", "processing", "traditional Chinese medicine", "medicinal plant", and "plant", were used in combination or separately for screening.

RESULTS

Toxicity of alkaloids in TCM includes hepatotoxicity, nephrotoxicity, neurotoxicity, cardiotoxicity, and other forms of toxicity, primarily induced by pyrrolizidines, quinolizidines, isoquinolines, indoles, pyridines, terpenoids, and amines. Factors such as whether the toxic-alkaloid enriched part is limited or heat-sensitive, and whether toxic alkaloids are also therapeutic components, are critical for choosing appropriate detoxification processing methods. Mechanisms of alkaloid detoxification includes physical removal, chemical decomposition or transformation, as well as biological modifications.

CONCLUSION

Through this exploration, we review toxic alkaloids and the mechanisms underlying their toxicity, discuss methods to reduce toxicity, and unravel the intricate mechanisms behind detoxification. These offers insights into the quality control of herbs containing toxic alkaloids, safe and rational use of alkaloid-rich TCMs in clinics, new strategies for drug discovery and development, and ultimately helping improve the quality of life for millions of people.

Intermittent short-duration reoxygenation relieves high-altitude pulmonary hypertension via NOX4/H2O2/PPAR-γ axis

High-altitude pulmonary hypertension (HAPH) is a severe and progressive disease that can lead to right heart failure. Intermittent short-duration reoxygenation at high altitude is effective in alleviating HAPH; however, the underlying mechanisms are unclear. In the present study, a simulated 5,000-m hypoxia rat model and hypoxic cultured pulmonary artery smooth muscle cells (PASMCs) were used to evaluate the effect and mechanisms of intermittent short-duration reoxygenation. The results showed that intermittent 3-h/per day reoxygenation (I3) effectively attenuated chronic hypoxia-induced pulmonary hypertension and reduced the content of H2O2 and the expression of NADPH oxidase 4 (NOX4) in lung tissues. In combination with I3, while the NOX inhibitor apocynin did not further alleviate HAPH, the mitochondrial antioxidant MitoQ did. Furthermore, in PASMCs, I3 attenuated hypoxia-induced PASMCs proliferation and reversed the activated HIF-1α/NOX4/PPAR-γ axis under hypoxia. Targeting this axis offset the protective effect of I3 on hypoxia-induced PASMCs proliferation. The present study is novel in revealing a new mechanism for preventing HAPH and provides insights into the optimization of intermittent short-duration reoxygenation.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding

MOTIVATION

Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.

RESULTS

In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development.

AVAILABILITY

The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.

Bifidobacterium alleviate metabolic disorders via converting methionine to 5'-methylthioadenosine

Dietary patterns and corresponding gut microbiota profiles are associated with various health conditions. A diet rich in polyphenols, primarily plant-based, has been shown to promote the growth of probiotic bacteria in the gastrointestinal tract, subsequently reducing the risk of metabolic disorders in the host. The beneficial effects of these bacteria are largely due to the specific metabolites they produce, such as short-chain fatty acids and membrane proteins. In this study, we employed a metabolomics-guided bioactive metabolite identification platform that included bioactivity testing using in vitro and in vivo assays to discover a bioactive metabolite produced from probiotic bacteria. Through this approach, we identified 5'-methylthioadenosine (MTA) as a probiotic bacterial-derived metabolite with anti-obesity properties. Furthermore, our findings indicate that MTA administration has several regulatory impacts on liver functions, including modulating fatty acid synthesis and glucose metabolism. The present study elucidates the intricate interplay between dietary habits, gut microbiota, and their resultant metabolites.

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features

Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of protein phosphorylation sites. However, most existing methods rely on simple protein sequence features that provide limited contextual information. To overcome this limitation, we propose DeepMPSF, a phosphorylation site prediction model based on multiple protein sequence features. There are two types of features: sequence semantic features, which comprise protein residue type information and relative position information within protein sequence, and protein background biophysical features, which include global semantic information containing more comprehensive protein background information obtained from pretrained models. To extract these features, DeepMPSF employs two separate subnetworks: the S71SFE module and the BBFE module, which automatically extract high-level semantic features. Our model incorporates a learning strategy for handling imbalanced datasets through ensemble learning during training and prediction. DeepMPSF is trained and evaluated on a well-established dataset of human proteins. Comparing the analysis with other benchmark methods reveals that DeepMPSF outperforms in predicting both S/T residues and Y residues. In particular, DeepMPSF showed excellent generalization performance in cross-species blind test performance, with an average improvement of 5.63%/5.72%, 22.28%/25.94%, 20.11%/17.49%, and 26.40%/28.33% for Mus musculus/Rattus norvegicus test sets in area under curves (AUCs) of ROC curve, AUC of the PR curve, F1-score, and MCC metrics, respectively. Furthermore, it also shows excellent performance in the latest updated case of natural proteins with functional phosphorylation sites. Through an ablation study and visual analysis, we uncover that the design of different feature modules significantly contributes to the accurate classification of DeepMPSF, which provides valuable insights for predicting phosphorylation sites and offers effective support for future downstream research.

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer

Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurate prediction of RNA modifications. However, those methods generally predicted only a single type of RNA modification. In addition, such methods suffered from the scarcity of the interpretability for their predicted results. In this work, a new Transformer-based deep learning method was proposed to predict multiple RNA modifications simultaneously, referred to as TransRNAm. More specifically, TransRNAm employs Transformer to extract contextual feature and convolutional neural networks to further learn high-latent feature representations of RNA sequences relevant for RNA modifications. Importantly, by integrating the self-attention mechanism in Transformer with convolutional neural network, TransRNAm is capable of not only capturing the critical nucleotide sites that contribute significantly to RNA modification prediction, but also revealing the underlying association among different types of RNA modifications. Consequently, this work provided an accurate and interpretable predictor for multiple RNA modification prediction, which may contribute to uncovering the sequence-based forming mechanism of RNA modification sites.

A forensic population database of autosomal STR and X-STR markers in the Qiang ethnic minority of China

The Qiang ethnic group is one of the oldest ethnic groups in China and is the most active ethnic group among all the populations along the Tibetan-Yi corridor. They have had a profound impact nationally and internationally. The paternal and maternal genetic feature of the Qiang ethnic group has been revealed, leaving the question of the genetic characteristics from autosomes and X chromosome not answered. The aim of this study was to explore the potential of 36 A-STR (Microreader™ 36A ID System) and 19 X-STR (Microreader™ 19X System) for application in the Qiang population and to elucidate their genetic diversity in southwest China. The cumulative probability of exclusion (CPE) for autosomal STRs is 1-1.3814 × 10-15 and the mean paternity exclusion chance (MEC) for X-STRs is 1-1.7323 × 10-6. Forensic parameters suggest that the STRs analyzed here are well-suited for forensic applications. The results of phylogenetic, interpopulation differentiation, and principal coordinates analysis (PCoA) indicate that the Qiang people have extensive connections with ethnic minorities in China, supporting the view that the Qiang people are the oldest group in the entire Sino-Tibetan language family. The Qiang appeared genetically more associated with most ethnic groups in China, especially the Han. The calculation of random matching probability (RMP) was improved by Fst correction of allele frequencies to make RMP more accurate and reasonable. This study can fill in the gaps in the Qiang STR reference database, providing valuable frequency data for forensic applications and evidence for the Qiang's genetic pattern as an important ancestral position in the Sino-Tibetan populations.

Expanding annotation of chemical compounds in hawthorn fruits and their variations in thermal processing using integrated mass spectral similarity networking

Chemical structural characterization of chemical compounds from hawthorn fruits and its thermal processed products was carried out in present study. By linking Global Natural Products Social (GNPS) Molecular Networking and MolNetEnhancer workflow, seventy-four chemical compounds in hawthorn fruits and its thermal processed products were tentatively identified. Three quercetagetin derivatives (quercetagetin-3-O-glucoside, quercetagetin-di-glucoside and its isomer), five quercetin or kaempferol derivatives (quercetin-acetylapiosyl-hexoside, quercetin-3-O-(6″-malonyl-hexoside), quercetin-3-O-(6″-malonyl-hexoside)-(1 → 2)-O-hexoside, quercetin-3-O-(6″-malonyl-hexoside)-(1 → 2)-O-deoxyhexoside, kaempferol-3-O-(6″-malonyl-hexoside)), six procyanidins including four (E)C-ethyl-procyanidins and two A-type procyanidins digallate, as well as 13 triterpenoids including ursolic aldehyde, triterpenoid glycosides, and triterpene acids were reported for the first time in hawthorn fruits. In addition, triterpenoids exhibited considerable thermal stability, while all of flavonoid glycosides, proanthocyanidins and 10 in 13 organic acids showed dramatic decrease after thermal processing.

Fixed-Field IMRT for Cervix Carcinoma Patients on an MR-LINAC Platform: Dosimetric Feasibility and Challenges

PURPOSE/OBJECTIVE(S)

To investigate the impact of MR-LINAC performance characteristics and inverse planning implementation on the feasibility of fixed-field IMRT for cervix carcinoma patients by benchmarking MR-LINAC plans against clinically used VMAT plans in a single institution study and multi-institutional treatment planning challenge.

MATERIALS/METHODS

For 10 cervix cancer patients who had previously received Linac-based VMAT, new treatment plans were optimized for MR-LINAC IMRT using 6X FFF fixed fields with maximum available field size of 27.4 x 24.1 cm2. Dose optimization was performed on the clinically used planning CT and structure set. Prescribed dose was 48.6 Gy in 27 fractions for all patients with 6 patients receiving an additional integrated boost for a total of 58.05 Gy to involved nodes. Constraints were based on our institutional protocol as per Table 1. IMRT delivery time was limited to 20 min. Original clinically used VMAT plans were generated on Eclipse (Varian Medical System) using 3 to 4 arcs. For the multi-institutional planning challenge, the data set from a single patient was anonymized and shared to participants. Participants used a single MR-based Linac planning platform to generate a plan based on our institutional constraints, with maximum treatment time limited to 20 min. For all analyses, a paired samples t-test was used to compare the significance defined at p < 0.05.

RESULTS

For MR-LINAC plans, the mean number of fields used was 23, mean number of segments 229, and the average estimated treatment delivery time was 17.3 minutes. MR-LINAC plans showed a significantly higher heterogeneity and dose to organs at risk compared to VMAT plans (Table 1). For the planning challenge, a total of 30 participants registered interest. Of this, seven plans were submitted to the challenge. On average, participants generated a plan that would be acceptable based on our institutional constraints (Table 1). However, the volumetric dose to bowel and pelvic bones were higher on MR-LINAC plans compared to the reference VMAT plan.

CONCLUSION

MR-LINAC fixed-field IMRT for cervix cancer patients is feasible but system constraints and optimization implementation result in greater dose heterogeneity and worse organ-at-risk sparing compared to Linac based VMAT. Further research is needed to determine if potential reduction of treatment margins, allowed by better MRI soft-tissue visualization, will result in MR-LINAC IMRT superior to Linac VMAT.

SFTSV infection is associated with transient overproliferation of monoclonal lambda-type plasma cells

The impairment of antibody-mediated immunity is a major factor associated with fatal cases of severe fever with thrombocytopenia syndrome (SFTS). By collating the clinical diagnosis reports of 30 SFTS cases, we discovered the overproliferation of monoclonal plasma cells (MCP cells, CD38⁺cLambda⁺cKappa^-) in bone marrow, which has only been reported previously in multiple myeloma. The ratio of CD38⁺cLambda⁺ versus CD38⁺cKappa⁺ in SFTS cases with MCP cells was significantly higher than that in normal cases. MCP cells presented transient expression in the bone marrow, which was distinctly different from multiple myeloma. Moreover, the SFTS patients with MCP cells had higher clinical severity. Further, the overproliferation of MCP cells was also observed in SFTS virus (SFTSV)-infected mice with lethal infectious doses. Together, SFTSV infection induces transient overproliferation of monoclonal lambda-type plasma cells, which have important implications for the study of SFTSV pathogenesis, prognosis, and the rational development of therapeutics.

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC-MS/MS Data Analysis

Untargeted mass spectrometry is a robust tool for biology, but it usually requires a large amount of time on data analysis, especially for system biology. A framework called Multiple-Chemical nebula (MCnebula) was developed herein to facilitate the LC-MS data analysis process by focusing on critical chemical classes and visualization in multiple dimensions. This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify "features" (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. Notably, MCnebula can be used to explore the classification and structural characteristic of unknown compounds beyond the limit of the spectral library. Moreover, it is intuitive and convenient for pathway analysis and biomarker discovery because of its function of ABC selection and visualization. MCnebula was implemented in the R language. A series of tools in R packages were provided to facilitate downstream analysis in an MCnebula-featured way, including feature selection, homology tracing of top features, pathway enrichment analysis, heat map clustering analysis, spectral visualization analysis, chemical information query, and output analysis reports. The broad utility of MCnebula was illustrated by a human-derived serum data set for metabolomics analysis. The results indicated that "Acyl carnitines" were screened out by tracing structural classes of biomarkers, which was consistent with the reference. A plant-derived data set was investigated to achieve a rapid annotation and discovery of compounds in E. ulmoides.

[Analysis on missed diagnosis or misdiagnosis of anomalous origin of left coronary artery from pulmonary artery by echocardiography from one single medical center]

Objectives: To analyze the reasons of missed diagnosis or misdiagnosis on anomalous origin of left coronary artery from pulmonary artery (ALCAPA) by echocardiography. Methods: This is a retrospective study. Patients with ALCAPA who underwent surgical treatment in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from August 2008 to December 2021 were included. According to the results of preoperative echocardiography and surgical diagnosis, the patients were divided into confirmed group or missed diagnosis/misdiagnosis group. The results of preoperative echocardiography were collected, and the specific echocardiographic signs were analyzed. According to the experience of the doctors, the echocardiographic signs were divided into four types, namely clear displayed, vague/doubtful displayed, no display and no notice, and the display rate of each sign was calculated (display rate=number of clearly displayed cases/total number of cases×100%). By referring the surgical data, we analyzed and recorded the pathological anatomy and pathophysiological characteristics of the patients, and the rate of missed diagnosis/misdiagnosis of echocardiography in patients with different characteristics was compared. Results: A total of 21 patients were enrolled, including 11 males, aged 1.8 (0.8, 12.3) years (range 1 month to 47 years). Except for one patient with anomalous origin of left anterior descending artery, the others were all originated from the main left coronary artery (LCA). There were 13 cases of ALCAPA in infant and children, and 8 cases of adult ALCAPA. There were 15 cases in the confirmed group (diagnostic accuracy was 71.4% (15/21)), and 6 cases in the missed diagnosis/misdiagnosis group (three cases were misdiagnosed as primary endocardial fibroelastosis, two cases were misdiagnosed as coronary-pulmonary artery fistula; and one case was missed diagnosis). The working years of the physicians in the confirmed group were longer than those in the missed diagnosis/misdiagnosed group ((12.8±5.6) years vs. (8.3±4.7) years, P=0.045). In infants with ALCAPA, the detection rate of LCA-pulmonary shunt (8/10 vs. 0, P=0.035) and coronary collateral circulation (7/10 vs. 0, P=0.042) in confirmed group was higher than that in missed diagnosis/misdiagnosed group. In adult ALCAPA patients, the detection rate of LCA-pulmonary artery shunt was higher in confirmed group than that in missed diagnosis/misdiagnosed group (4/5 vs. 0, P=0.021). The missed diagnosis/misdiagnosis rate of adult type was higher than that of infant type (3/8 vs. 3/13, P=0.410). The rate of missed diagnosis/misdiagnosis was higher in patients with abnormal origin of branches than that of abnormal origin of main trunk (1/1 vs. 5/21, P=0.028). The rate of missed diagnosis/misdiagnosis in patients with LCA running between the main and pulmonary arteries was higher than that distant from the main pulmonary artery septum (4/7 vs. 2/14, P=0.064). The rate of missed diagnosis/misdiagnosis in patients with severe pulmonary hypertension was higher than that in patients without severe pulmonary hypertension (2/3 vs. 4/18, P=0.184). The reasons with an echocardiography missed diagnosis/misdiagnosis rate of≥50% included that (1) the proximal segment of LCA ran between the main and pulmonary arteries; (2) abnormal opening of LCA at the right posterior part of the pulmonary artery; (3) abnormal origin of LCA branches; (4) complicated with severe pulmonary hypertension. Conclusions: Echocardiography physicians' knowledge of ALCAPA and diagnostic vigilance are critical to the accuracy of diagnosis. Attention should be paid to the pediatric cases with no obvious precipitating factors of left ventricular enlargement, regardless of whether the left ventricular function is normal or not, the origin of coronary artery should be routinely explored.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets

The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.

Nanodrug rescues liver fibrosis via synergistic therapy with H2O2 depletion and Saikosaponin b1 sustained release

Hypoxia and hydrogen peroxide (H₂O₂) accumulation form the profibrogenic liver environment, which involves fibrogenesis and chronic stimulation of hepatic stellate cells (HSCs). Catalase (CAT) is the major antioxidant enzyme that catalyzes H₂O₂ into oxygen and water, which loses its activity in different liver diseases, especially in liver fibrosis. Clinical specimens of cirrhosis patients and liver fibrotic mice are collected in this work, and results show that CAT decrease is closely correlated with hypoxia-induced transforminmg growth factor β1 (TGF-β1). A multifunctional nanosystem combining CAT-like MnO₂ and anti-fibrosis Saikosaponin b1 (Ssb1) is subsequently constructed for antifibrotic therapy. MnO₂ catalyzes the accumulated H₂O₂ into oxygen, thereby ameliorating the hypoxic and oxidative stress to prevent activation of HSCs, and assists to enhance the antifibrotic pharmaceutical effect of Ssb1. This work suggests that TGF-β1 is responsible for the diminished CAT in liver fibrosis, and our designed MnO₂@PLGA/Ssb1 nanosystem displays enhanced antifibrotic efficiency through removing excess H₂O₂ and hypoxic stress, which may be a promising therapeutic approach for liver fibrosis treatment.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism

MOTIVATION

Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

RESULTS

In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.

AVAILABILITY AND IMPLEMENTATION

The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Safety of sugammadex for reversal of neuromuscular block: A postmarketing study based on the World Health Organization pharmacovigilance database

AIM

Residual neuromuscular blockade is a common complication after general anaesthesia. Sugammadex can reverse the action of aminosteroid neuromuscular blockers. This study aimed to explore sugammadex safety issues in the real world and determine the spectrum of adverse reactions.

METHODS

All sugammadex-related adverse events reported in VigiBase between 2010 and 2019 were classified by group queries according to the Medical Dictionary for Regulatory Activities. A disproportionality analysis of data was performed using the information component (IC); positive IC values were deemed significant.

RESULTS

Overall, 16 219 410 adverse events were reported and 2032 were associated with sugammadex. The frequent reactions were recurrence of neuromuscular blockade (n = 54, IC 6.74, IC₀₂₅ 6.33), laryngospasm (n = 53, IC 6.05, IC₀₂₅ 5.64), bronchospasm (n = 119, IC 5.63, IC₀₂₅ 5.36) and bradycardia (n = 169, IC 5.13, IC₀₂₅ 4.90). Fatal cases were more likely among patients with cardiac disorders, especially those over 65 years. In addition, the common adverse drug reactions (ADRs) differed between different age groups (P < .01). ADRs were higher in the 0-17 years age group than in other age groups. The onset time of common ADRs was typically within 1 day and 68.9% occurred within half an hour after sugammadex administration.

CONCLUSIONS

Anaesthesiologists should carefully monitor the anaesthesia recovery period to correct the ADRs caused by sugammadex and recommend monitoring neuromuscular function throughout the anaesthesia process. Sugammadex should be used carefully in patients with cardiovascular diseases, and electrocardiography and hemodynamic changes should be monitored after medication.

Hypoxia Inhibits Cell Cycle Progression and Cell Proliferation in Brain Microvascular Endothelial Cells via the miR-212-3p/MCM2 Axis

Hypoxia impairs blood-brain barrier (BBB) structure and function, causing pathophysiological changes in the context of stroke and high-altitude brain edema. Brain microvascular endothelial cells (BMECs) are major structural and functional elements of the BBB, and their exact role in hypoxia remains unknown. Here, we first deciphered the molecular events that occur in BMECs under 24 h hypoxia by whole-transcriptome sequencing assay. We found that hypoxia inhibited BMEC cell cycle progression and proliferation and downregulated minichromosome maintenance complex component 2 (Mcm2) expression. Mcm2 overexpression attenuated the inhibition of cell cycle progression and proliferation caused by hypoxia. Then, we predicted the upstream miRNAs of MCM2 through TargetScan and miRanDa and selected miR-212-3p, whose expression was significantly increased under hypoxia. Moreover, the miR-212-3p inhibitor attenuated the inhibition of cell cycle progression and cell proliferation caused by hypoxia by regulating MCM2. Taken together, these results suggest that the miR-212-3p/MCM2 axis plays an important role in BMECs under hypoxia and provide a potential target for the treatment of BBB disorder-related cerebrovascular disease.

Synthesis of Crystalline Phosphine-Graphdiyne with Self-Adaptive p-π Conjugation

"Dynamic" behavior materials with high surface activity and the ability of chemical bond conversion are the frontier materials in the field of renewable energy. The outstanding feature of these materials is that they have adaptive electronic properties that external stimuli can adjust. An original discovery in a new crystalline two-dimensional phosphine-graphdiyne (P-GDY) material is described here. Although the p-π conjugation of most trivalent phosphorus π-systems is insignificant because of the pyramidal configuration, the lone pair electrons of phosphorus atoms participate strongly in the delocalization under the influence of the interlayer van der Waals forces in P-GDY. Due to the dynamically reversible nature of noncovalent interactions (p-π conjugation), P-GDY exhibits a specific adaptive behavior and realizes the responsive reversible transport of a lithium ion by regulating p-π interactions. Our findings would provide the potential to develop a new family of responsive materials with tunable structures.

The Role of eIF5A1 in LPS-Induced Neuronal Remodeling of the Nucleus Accumbens in the Depression

BACKGROUND

The pathogenesis of depression is complex, with the brain's reward system likely to play an important role. The nucleus accumbens (NAc) is a key region in the brain that integrates reward signals. Lipopolysaccharides (LPS) can induce depressive-like behaviors and enhance neuroplasticity in NAc, but the underlying mechanism is still unknown. We previously found that eukaryotic translation initiation factor A1 (eIF5A1) acts as a ribosome-binding protein to regulate protein translation and to promote neuroplasticity.

METHODS

In the present study, LPS was administered intraperitoneally to rats and the expression and cellular location of eIF5A1 was then investigated by RT-PCR, Western blotting and immunofluorescence. Subsequently, a neuron-specific lentivirus was used to regulate eIF5A1 expression in vivo and in vitro. Neuroplasticity was then examined by Golgi staining and by measurement of neuronal processes. Finally, proteomic analysis was used to identify proteins regulated by eIF5A1.

RESULTS

The results showed that eIF5A1 expression was significantly increased in the NAc neurons of LPS rats. Following the knockdown of eIF5A1 in NAc neurons, the LPS-induced increases in neuronal arbors and spine density were significantly attenuated. Depression-like behaviors were also reduced. Neurite outgrowth of NAc neurons in vitro also increased or decreased in parallel with the increase or decrease in eIF5A1 expression, respectively. The proteomic results showed that eIF5A1 regulates the expression of many neuroplasticity-related proteins in neurons.

CONCLUSIONS

These results confirm that eIF5A1 is involved in LPS-induced depression-like behavior by increasing neuroplasticity in the NAc. Our study also suggests the brain's reward system may play an important role in the pathogenesis of depression.

Terahertz spectroscopic monitoring and analysis of citrus leaf water status under low temperature stress

Low temperature stress, in the form of chilling and freezing, is one of the major environmental factors impacting on citrus yield, which changes plant's water state and results in the crops' sub-health or injury. The innovative terahertz (THz) spectroscopy and imaging based sensing technology has been shown to be a suitable tool for plant leaf water status determination, due to THz radiation's innate sensitivity to hydrogen bond vibration in aqueous solutions, which is usually related to plant phenotype change. We demonstrate experimentally that the THz absorption coefficient of leaf could be used for distinguishing plant's physiological stress status, exhibiting clear decreasing or increasing trend under chilling or freezing stress respectively. The underlying rationale might be that membrane damage shows a diverse pattern, changing the intra- or extra-cellular liquid environments, likely being linked to the various THz spectral characteristics. There were different adaptations in leaf morphology, leading to different leaf density, which in turn affects the water volume fraction. Moreover, different patterns of the dynamic equilibrium state of free water and bound water under chilling and freezing treatment were revealed by THz spectroscopy. Here, THz spectroscopic monitoring has shown unique potential for judging citrus's low temperature stress state through bio-water detection and discrimination.

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP²ISP to improve the prediction of protein-protein interaction sites. ctP²ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP²ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP²ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

A Review on Thermal Properties of Hydrogels for Electronic Devices Applications

Hydrogels, as a series of three-dimensional, crosslinked, hydrophilic network polymers, exhibit extraordinary properties in softness, mechanical robustness and biocompatibility, which have been extensively utilized in various fields, especially for electronic devices. However, since hydrogels contain plenty of water, the mechanical and electrochemical properties are susceptible to temperature. The thermal characteristics of hydrogels can significantly affect the performance of flexible electronic devices. In this review, recent research on the thermal characteristics of hydrogels and their applications in electronic devices is summarized. The focus of future work is also proposed. The thermal stability, thermoresponsiveness and thermal conductivity of hydrogels are discussed in detail. Anti-freezing and anti-drying properties are the critical points for the thermal stability of hydrogels. Methods such as introducing soluble ions and organic solvents into hydrogels, forming ionogels, modifying polymer chains and incorporating nanomaterials can improve the thermal stability of hydrogels under extreme environments. In addition, the critical solution temperature is crucial for thermoresponsive hydrogels. The thermoresponsive capacity of hydrogels is usually affected by the composition, concentration, crosslinking degree and hydrophilic/hydrophobic characteristics of copolymers. In addition, the thermal conductivity of hydrogels plays a vital role in the electronics applications. Adding nanocomposites into hydrogels is an effective way to enhance the thermal conductivity of hydrogels.

Multisource Attention-Mechanism-Based Encoder-Decoder Model for Predicting Drug-Drug Interaction Events

Many computational methods have been proposed to predict drug-drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder-decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network. Experiments showed that both MAEDDI and MAEDDIE performed better than some state-of-the-art methods in various validation attempts at different experimental tasks. The visualization analysis showed that the semantic features of drug pairs learned from our models had a good drug representation. In practice, MAEDDIE successfully screened 43 DDI events on favipiravir, an influenza antiviral drug, with a success rate of nearly 50%. Our model achieved competitive results, mainly owing to the design of sequence-based, structural, biochemical, and statistical multisource features. Moreover, different encoders constructed based on different features learn the interrelationship information between drug pairs, and the different representations of these drug pairs are incorporated to predict the target problem. All of these encoders were designed to better characterize the complex DDI relationships, allowing us to achieve high generalization in DDI and DDI-associated event predations.

Integrated gut microbiota and serum metabolomics reveal the protective effect of oleanolic acid on liver and kidney-injured rats induced by Euphorbia pekinensis

Euphorbia pekinensis (EP) is a commonly used Chinese medicine treating edema with potential hepatorenal toxicity. However, its toxic mechanism and prevention are remained to be explored. Oleanolic acid (OA) is a triterpene acid with potential hepatorenal protective activities. We investigated the protective effect and potential mechanism of OA on EP-induced hepatorenal toxicity. In this study, rats were given total diterpenes from EP (TDEP, 16 mg/kg) combined with OA (10, 20, 40 mg/kg) by gavage for 4 weeks. The results showed that TDEP administration could lead to a 3-4-fold increasement in hepatorenal biochemical parameters with histopathological injuries, while OA treatment could ameliorate them in a dose-dependent manner. At microbial and metabolic levels, intestinal flora and host metabolism were perturbed after TDEP administration. The disturbance of bile acid metabolism was the most significant metabolic pathway, with secondary bile acids increasing while conjugated bile acids decreased. OA treatment can improve the disorder of intestinal flora and metabolic bile acid spectrum. Further correlation analysis screened out that Escherichia-Shigella, Phascolarctobacterium, Acetatifactor, and Akkermansia were closely related to the bile acid metabolic disorder. In conclusion, oleanolic acid could prevent hepatorenal toxicity induced by EP by regulating bile acids metabolic disorder via intestinal flora improvement.

Paternal genetic structure of the Qiang ethnic group in China revealed by high-resolution Y-chromosome STRs and SNPs

The Qiang population mainly lived in Beichuan Qiang Autonomous County of Sichuan Province. It is one of the nomads in China, distributed along the Minjiang River. The Qiang population was assumed to have great affinity with the Han, the largest ethnic group in China, when it refers to the genetic origin. Whereas, it is deeply understudied, especially from the Y chromosome. Here in this study, we used validated high-resolution Y-chromosome single nucleotide polymorphisms (Y-SNPs) and short tandem repeats (Y-STRs) panels to study the Qiang ethnic group to unravel their paternal genetic, forensic and phylogenetic characteristics. A total of 422 male samples of the Qiang ethnic group were genotyped by 233 Y-SNPs and 29 Y-STRs. Haplogroup O-M175 (N = 312) was the most predominant haplogroup in the Qiang ethnic group, followed by D-M174 (N = 32) and C-M130 (N = 32), N-M231 (N = 27), and Q-M242 (N = 15). After further subdivision, O2a-M324 (N = 213) accounted for the majority of haplogroup O. Haplogroup C2b-Z1338 (N = 29), D1a-CTS11577 (N = 30). O2a2b1a1a1-F42 (N = 48), O2a1b1a1a1a-F11 (N = 35), and O2a2b1a1-M117 (N = 21) represented other large terminal haplogroups. The results unveiled that Qiang ethnic group was a population with a high percentage of haplogroup O2a2b1a1a1-F42 (48/422) and O2a1b1a1a1a-F11 (35/422), and O2a2b1a1-M117 (21/422), which has never been reported. Its haplogroup distribution pattern was different from any of the Han populations, implying that the Qiang ethnic group had its unique genetic pattern. Mismatch analysis indicated that the biggest mismatch number in haplogroup O2a2b1a1a1-F42 was 21, while that of haplogroup O2a1b1a1a1a-F11 was 20. The haplotype diversity of the Qiang ethnic group equaled 0.999788, with 392 haplotypes observed, of which 367 haplotypes were unique. The haplogroup diversity of the Qiang ethnic group reached 0.9767, and 53 terminal haplogroups were observed (The haplogroup diversity of the Qiang ethnic group was the highest among Qiang and all Han subgroups, indicating the larger genetic diversity of the Qiang ethnic group.). Haplogroup O2a2b1a1a1-F42 was the most predominant haplogroup, including 11.37 % of the Qiang individuals. Median-joining trees showed gene flow between the Qiang and Han individuals. Our results indicated that 1) the highest genetic diversity was observed in the Qiang ethnic group compared to any of the former studied Chinese population, suggesting that the Qiang might be an older paternal branch; 2) the haplogroup D-M174 individuals of Qiang, Tibetans and Japanese distributed in three different subclades, which was unable to identify through low-resolution Y-SNP panel; and 3) the Qiang had lower proportion of haplogroup D compared to Yi and Tibetan ethnic groups, showing that the Qiang had less genetic communication with them than with Han Chinese.

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence

MOTIVATION

Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology.

RESULTS

This article proposes a deep learning-based multi-objective computational approach, iHMnBS, to identify which of the seven typical histone modifications a DNA sequence may choose to bind, and which parts of the DNA sequence bind to them. iHMnBS employs a customized dataset that allows the marking of modifications contained in histones that may bind to any position in the DNA sequence. iHMnBS tries to mine the information implicit in this richer data by means of deep neural networks. In comprehensive comparisons, iHMnBS outperforms a baseline method, and the probability of binding to modified histones assigned to a representative nucleotide of a DNA sequence can serve as a reference for biological experiments. Since the interaction between transcription factors and histone modifications has an important role in gene expression, we extracted a number of sequence patterns that may bind to transcription factors, and explored their possible impact on disease.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/lennylv/iHMnBS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Effect analysis of treating intracranial wide-neck bifurcation aneurysms through Woven EndoBridge]

Objective: To explore the clinical effect of Woven EndoBridge (WEB) in the treatment of wide-neck bifurcation aneurysms. Methods: The clinical and imaging data of 11 patients with intracranial wide-neck bifurcation aneurysms treated by WEB alone at Department of Neurosurgery of the Northern Theater General Hospital from September 2017 to May 2018, were retrospectively analyzed. The patients were 7 males and 4 females, aged (54±11) years (ranged from 31 to 66 years). The aneurysms of 5 patients were located in the anterior communicating artery, 3 in the top of the basilar artery, and 3 in the bifurcation of the middle cerebral artery. The intraoperative and postoperative conditions of the patients were recorded, and the degree of aneurysm embolization was evaluated by WEB embolization aneurysm occlusion scale (WOS). Results: The intraoperative WEB release of all the 11 patients was good, with 3 cases of WOS grade A, 1 of grade B and 7 of grade C, with no intraoperative acute complications occurring. The imaging follow-up was not carried out in 1 patient due to economic reason, and the clinical follow-up was good until 3 years after the operation; 10 patients were followed up by imaging for 6 months to 3 years, and no postoperative complications occurred in the target treatment area. Among the 2 patients with WOS grade A and 1 patient with grade B during operation, according to the postoperative follow-up, all were WOS grade A; among the 7 patients with WOS grade C during operation, 4 were still of grade C and 3 were of grade D according to the follow-up. Among the 3 patients with WOS grade D, 1 patient received secondary embolization due to poor recurrence morphology, unstable hemodynamics and high possibility of rupture of aneurysm, stent assisted coil embolization was adopted, with good immediate effect; the other 2 cases had recurrent aneurysms, but the aneurysms had good morphology and stable hemodynamics, therefore, clinical follow-up was continued and no secondary surgery was performed. No complications occurred in all these 11 patients. Conclusions: The operation of treating unruptured intracranial wide-neck bifurcation aneurysms with WEB device alone is simple, and there is no need for anticoagulation and antiplatelet treatment before and after the operation, the clinical effect is being good. WEB device provides a new treatment option for intracranial wide-neck bifurcation aneurysms.

Visualization and Heat Transfer Performance of Mini-Grooved Flat Heat Pipe Filled with Different Working Fluids

Mini-grooved flat heat pipe (MGFHP) possesses the advantages of high compactness, no mechanical component, super thermal conductivity, and excellent temperature uniformity, which can meet the demand for electronic devices efficiently cooling. In this research, visual and heat transfer experiments were performed to investigate the flow and thermal characteristics inside the MGFHP. Fluid flow and distribution are observed to be quite different in the MGFHP containing different working fluids, which is affected by the physical properties of working fluid, the surface state of the grooved wick, and limited working space. Additionally, the input heat, working fluid type, filling ratio, and wettability obviously affect the thermal conductivity and temperature uniformity of the MGFHP. The deionized water-filled MGFHP possesses lower thermal resistance and higher heat transfer capacity than anhydrous ethanol or hexane filled MGFHP, especially for the copper oxide MGFHP filled with deionized water with a filling ratio of 1.0. Thermal resistance, maximum temperature, and temperature nonuniformity at the condensation section of deionized water-filled copper oxide MGFHP are lower than those of the original copper MGFHP by 31.1%, 3.7 °C, and 0.11 °C for the anhydrous ethanol filled MGFHP and 34.4%, 3.1 °C, and 0.13 °C for the hexane filled MGFHP, respectively.

Tunable rare-earth metal-organic frameworks for ultra-high selenite capture

Linkers and clusters with various conformations present challenges for the design and prediction of highly porous and stable rare-earth metal-organic frameworks (RE-MOFs) for trapping toxic ions in aqueous solutions. Herein, we designed and synthesized a series of RE-MOFs based on a malleable ligand to explore the effects of ligands, clusters, and configurations on structural stability. The results showed that the nonanuclear high-connected UPC-183 exhibited better stability than the hexanuclear low-connected RE-MOF (UPC-181/182 series). Due to the syngenetic effect of chemi- and physisorption, the adsorption capacity of UPC-183-Eu for selenite (SeO₃^2-) is as high as 308.39 mg/g, recorded one of the highest ever reported for MOFs. Furthermore, we accurately analyzed the adsorption site of UPC-183-Eu for SeO₃^2- through single-crystal structure and theoretical simulation. The ultra-high selenite adsorption capacity and removal efficiency endow UPC-183-Eu an excellent porous adsorbent for removing pollutants.

Stress conditions induced circRNAs profile of extracellular vesicles in brain microvascular endothelial cells

Cerebral ischemia causes hypoxic injury and inflammation, and brain microvascular endothelial cells (BMVECs) dysfunction is an initial stage of blood-brain barrier disruption. Endothelial cells secrete extracellular vesicles (EVs) that are involved in intercellular signal transduction. EVs contain a variety of RNAs, proteins, and metabolites. Circular RNA (circRNA) is a member of the non-coding RNA. The expression profile and potential function of circRNAs in BMVECs are unknown. Here, human BMVECs have undergone hypoxia or TNF-α induction, and the changes in circRNAs were measured by RNA sequencing. A total of 70 circRNAs showed differential expression, including 43 previously unrecorded circRNAs and 27 recorded circRNAs. Since astrocyte end-feet encircle endothelial cells, they are considered the main targets of the EVs from BMVEC. The miRNA sequence data and bioinformatics were used to predict the circRNA-miRNA-mRNA networks in astrocytes. The gene ontology (GO) analysis showed the main downstream targets of circRNAs are DNA transcription regulation and protein kinase-related signaling pathways. These results suggest that altering circRNAs may be a potential therapeutic target for cerebral ischemia induced hypoxic injury and inflammation.

Chronic Hypoxia in Ovine Pregnancy Recapitulates Physiological and Molecular Markers of Preeclampsia in the Mother, Placenta, and Offspring

BACKGROUND

Preeclampsia continues to be a prevalent pregnancy complication and underlying mechanisms remain controversial. A common feature of preeclampsia is utero-placenta hypoxia. In contrast to the impact of hypoxia on the placenta and fetus, comparatively little is known about the maternal physiology.

METHODS

We adopted an integrative approach to investigate the inter-relationship between chronic hypoxia during pregnancy with maternal, placental, and fetal outcomes, common in preeclampsia. We exploited a novel technique using isobaric hypoxic chambers and in vivo continuous cardiovascular recording technology for measurement of blood pressure in sheep and studied the placental stress in response to hypoxia at cellular and subcellular levels.

RESULTS

Chronic hypoxia in ovine pregnancy promoted fetal growth restriction (FGR) with evidence of fetal brain-sparing, increased placental hypoxia-mediated oxidative damage, and activated placental stress response pathways. These changes were linked with dilation of the placental endoplasmic reticulum (ER) cisternae and increased placental expression of the antiangiogenic factors sFlt-1 (soluble fms-like tyrosine kinase 1) and sEng (soluble endoglin), combined with a shift towards an angiogenic imbalance in the maternal circulation. Chronic hypoxia further led to an increase in uteroplacental vascular resistance and the fall in maternal blood pressure with advancing gestation measured in normoxic pregnancy did not occur in hypoxic pregnancy.

CONCLUSIONS

Therefore, we show in an ovine model of sea-level adverse pregnancy that chronic hypoxia recapitulates physiological and molecular features of preeclampsia in the mother, placenta, and offspring.

TransPPMP: predicting pathogenicity of frameshift and non-sense mutations by a Transformer based on protein features

MOTIVATION

Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases can also be found in healthy individuals. A method to distinguish neutral and potentially disease-associated frameshift and non-sense mutations is of practical and fundamental importance. It would allow researchers to rapidly screen out the potentially pathogenic sites from a large number of mutated genes and then use these sites as drug targets to speed up diagnosis and improve access to treatment. The problem of how to distinguish between neutral and potentially disease-associated frameshift and non-sense mutations remains under-researched.

RESULTS

We built a Transformer-based neural network model to predict the pathogenicity of frameshift and non-sense mutations on protein features and named it TransPPMP. The feature matrix of contextual sequences computed by the ESM pre-training model, type of mutation residue and the auxiliary features, including structure and function information, are combined as input features, and the focal loss function is designed to solve the sample imbalance problem during the training. In 10-fold cross-validation and independent blind test set, TransPPMP showed good robust performance and absolute advantages in all evaluation metrics compared with four other advanced methods, namely, ENTPRISE-X, VEST-indel, DDIG-in and CADD. In addition, we demonstrate the usefulness of the multi-head attention mechanism in Transformer to predict the pathogenicity of mutations-not only can multiple self-attention heads learn local and global interactions but also functional sites with a large influence on the mutated residue can be captured by attention focus. These could offer useful clues to study the pathogenicity mechanism of human complex diseases for which traditional machine learning methods fall short.

AVAILABILITY AND IMPLEMENTATION

TransPPMP is available at https://github.com/lennylv/TransPPMP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Superhydrophobic Carbon Nanotube Network Membranes for Membrane Distillation: High-Throughput Performance and Transport Mechanism

Despite increasing sustainable water purification, current desalination membranes still suffer from insufficient permeability and treatment efficiency, greatly hindering extensive practical applications. In this work, we provide a new membrane design protocol and molecule-level mechanistic understanding of vapor transport for the treatment of hypersaline waters via a membrane distillation process by rationally fabricating more robust metal-based carbon nanotube (CNT) network membranes, featuring a superhydrophobic superporous surface (80.0 ± 2.3% surface porosity). With highly permeable ductile metal hollow fibers as substrates, the construction of a superhydrophobic (water contact angle ∼170°) CNT network layer endows the membranes with not only almost perfect salt rejection (over 99.9%) but a promising water flux (43.6 L·m^-2·h^-1), which outperforms most existing inorganic distillation membranes. Both experimental and molecular dynamics simulation results indicate that such an enhanced water flux can be ascribed to an ultra-low liquid-solid contact interface (∼3.23%), allowing water vapor to rapidly transport across the membrane structure via a combined mechanism of Knudsen diffusion (more dominant) and viscous flow while efficiently repelling high-salinity feed via forming a Cassie-Baxter state. A more hydrophobic surface is more in favor of not only water desorption from the CNT outer surface but superfast and frictionless water vapor transport. By constructing a new superhydrophobic triple-phase interface, the conceptional design strategy proposed in this work can be expected to be extended to other membrane material systems as well as more water treatment applications.

Identification and assessment method of cable joint health status based on traveling wave reflection

Due to the difficulty in accurately detecting the health status of cable joints, a new method of identifying and assessing the cable joints' health status based on traveling wave reflection is proposed. By transmitting nanosecond pulse signals at the head end of the cable, the health status characteristics of the cable joints can be obtained, and the noise reduction of the measurement signals is performed by the wavelet function to identify the reflected wave at the joint and the end of the cable. The equivalent circuits and simulation models of contact resistance and leakage resistance are established, and the contact resistance and leakage resistance of the cable joints are calculated according to the amplitude and phase of the reflected signals at the joint and the end of the cable. By comparing with the resistance when the joint is in healthy operation, any measured parameter exceeding the corresponding standard is considered a joint fault. Simulations and test results show that the method can accurately identify the reflected signals of the cable joints in different operating states, and with changes in joint health, clear regular features can be extracted from the reflected signals, and the quality and health of the joints can be determined. The traveling wave method can be used to detect cable joint faults and provide an early warning of defects, which provides a new idea for online monitoring and early warning of cable joints in both theory and engineering practice.

Screening of Bioactive Fraction of Radix Paeoniae Alba and Enhancing Anti-Allergic Asthma by Stir-Frying Through Regulating PI3K/AKT Signaling Pathway

Allergic asthma is a common respiratory inflammation disease. The crude Radix Paeoniae Alba (RPA) and its processed products have been used frequently as antipyretic and anti-inflammatory agents in traditional medicine. To evaluate the effect of honey and bran processing, different fractions of RPA were used for treating anti-allergic asthma in the ovalbumin (OVA)-induced mice model, and then, the most effective fraction of RPA and stir-frying Radix Paeoniae Alba with honey and bran (FRPA) for treating anti-allergic asthma were compared mutually for pharmacological effects. The results showed that the treatment of the dichloromethane fraction of RPA significantly improved the pathological condition of lung tissues, decreased the number of eosinophils and other cells in bronchoalveolar lavage fluid (BALF), and the increased the expression of various inflammatory factors. Furthermore, the study discovered that the lung pathological conditions, compared with the high dose of dichloromethane RPA fraction, could be ameliorated by high dose of dichloromethane FRPA fraction treatment. Moreover, the expression of inflammatory factors and the phosphorylation of the PI3K/AKT signaling pathway could be diminished by FRPA. Finally, the contents of compounds with a significant difference in the FRPA dichloromethane fraction were paeoniflorin, ethyl gallate, pentagalloylglucose, galloylpaeoniflorin, and others by UPLC/Q-TOF-MS analysis. These findings suggest that the dichloromethane fraction of FRPA has an enhancement effect on anti-allergic asthma and provide the experimental basis for exploring the processed mechanism of RPA.

Control one-dimensional length of rectangular pore on graphene membrane for better desalination performance

At present, there is a general contradiction between permeability and selectivity of reverse osmosis (RO) membranes for desalination; a membrane with higher water permeability will give a lower salt rejection or selectivity, and vice versa. In this work, single-layer nanoporous graphene is used as RO membrane to investigate the effects of pore shape to reduce this contradiction by molecular dynamics simulations. Two kinds of pores (round and rectangular pores) with different sizes are simulated. For round pore, although the water permeability increases with the increase of the pore size, the salt rejection rate drops rapidly. For rectangular pore, reasonable designed pore structure can achieve improved water permeability and high salt rejection of graphene membrane by keeping one-dimensional length (i.e. the width) of the pore less than the size of the hydrated ions and increasing the other dimensional length. The restriction of one dimension can prevent the passage of hydrated ions through the pore effectively. This 'one-dimensional restriction' provides a simple strategy for designing RO membrane with variable pore structures to obtain a better desalination performance.

Learning Useful Representations of DNA Sequences From ChIP-Seq Datasets for Exploring Transcription Factor Binding Specificities

Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequences. Here, we designed a deep learning model named SemanticCS (Semantic ChIP-seq)to predict TF binding specificities. We trained our learning model on an ensemble of ChIP-seq datasets (Multi-TF-cell)to learn useful intermediate features across multiple TFs and cells. To interpret these feature vectors, visualization analysis was used. Our results indicate that these learned representations can be used to train shallow machines for other tasks. Using diverse experimental data and evaluation metrics, we show that SemanticCS outperforms other popular methods. In addition, from experimental data, SemanticCS can help to identify the substitutions that cause regulatory abnormalities and to evaluate the effect of substitutions on the binding affinity for the RXR transcription factor. The online server for SemanticCS is freely available at http://qianglab.scst.suda.edu.cn/semanticCS/.

A deep clustering-based mass spectral data visualization strategy for anti-renal fibrotic lead compound identification from natural products

We present a deep clustering-based MS data visualization strategy (MCnebula), integrated with the influential open-source automatic MS annotation platform SIRIUS and in vivo and in vitro methods, to screen and validate potential lead compounds from natural products.

[Comparison of single incision robot-assisted laparoscopic radical prostatectomy with and without extraperitoneal special channel device]

Objective: To compare the clinical effects of single-incision robot-assisted laparoscopic radical prostatectomy (RARP) with and without extraperitoneal special channel device. Methods: The clinical data of 70 patients who had undergone RARP in the Robotic Minimally Invasive Surgery Center of Sichuan Provincial People's Hospital from September 2020 to February 2021 were analyzed retrospectively, including 29 cases who were operated on without special channel device (group A) and 41 cases with special channel device (group B). All operations were performed by robot-assisted single-incision retrograde bladder neck exfoliation via extraperitoneal approach in patients by the same operator. The operation time, intraoperative blood loss, the bladder neck urethral anastomosis time, postoperative hospital stay, postoperative exhaust time, positive rate of incisal margin, indwelling time of urinary catheter, retention rate of postoperative erectile function, satisfaction rate of immediate postoperative urine control, positive rate of postoperative lymph node pathology, incision length, treatment cost and the rate of prostate specific antigen (PSA)lower than 0.2 μg/L at 6 weeks after operation were compared between the two groups. Results: All 70 cases were operated successfully. The difference of age[ (68.9±3.9) vs (69.4±5.4) years], preoperative PSA level[14.1(6.3, 19.8)vs13.7(5.8, 18.1)μg/L], prostate volume[44.8(30.7,172.6)vs 56.3(40.9,163.4)ml ] of the two groups was not statistically significant(all P>0.05). The difference of operation time [ (59.1±18.5) vs (59.6±18.0) min ], intraoperative blood loss [93(66,198)vs 95(68,203) ml ], bladder neck urethral anastomosis time [ (12.6±1.3) vs (13.7±2.8) min ], postoperative hospital stay [ (8.1±2.3) vs (9.1±1.3) d], postoperative exhaust time [ (1.4±0.6) vs (1.3±0.6) d], positive rate of incisal margin (20.7% vs 19.5%), indwelling time of the urinary catheter after operation [ (6.8±1.5) vs (7.1±2.0) d ], the retention rate of postoperative erectile function (31.0% vs 27.0%), the satisfaction rate of immediate postoperative urine control (79.3% vs 75.6%), the positive rate of postoperative lymph node pathology (17.2% vs 14.6%), the length of incision [ (5.1±0.5) vs (6.1±0.4) cm ], the rate of PSA lower than 0.2 μg/L at 6 weeks after operation (86.2% vs 83.0%) of the two groups was not statistically significant(all P>0.05). The operation cost of group A[(62 000±4 000) yuan]was lower than group B[(68 000±4 000) yuan] (P<0.05). Conclusion: Extraperitoneal non-special channel device single-incision RARP is safe and feasible.

The OPG/RANKL/RANK system modulates calcification of common carotid artery in simulated microgravity rats by regulating NF-κB pathway

Functional and structural adaptation of common carotid artery could be one of the important causes of postflight orthostatic intolerance after microgravity exposure, the mechanisms of which remain unclear. Recent evidence indicates that long-term spaceflight increases carotid artery stiffness, which might present a high risk to astronaut health and postflight working ability. Studies have suggested that vascular calcification is a common pathological change in cardiovascular diseases that is mainly manifested as an increase in vascular stiffness. Therefore, this study aimed to investigate whether simulated microgravity induces calcification of common carotid artery and to elucidate the underlying mechanisms. Four-week hindlimb-unweighted (HU) rats were used to simulate the deconditioning effects of microgravity on cardiovascular system. We found that simulated microgravity induced vascular smooth muscle cell (VSMC) osteogenic differentiation and medial calcification, increased receptor activator of nuclear factor κB ligand (RANKL) and RANK expression, and enhanced NF-κB activation in rat common carotid artery. In vitro activation of the RANK pathway with exogenous RANKL, a RANK ligand, increased RANK and osteoprotegerin (OPG) expression in HU rats. Moreover, the expression of osteogenic markers and activation of NF-κB in HU rats were further enhanced by exogenous RANKL but suppressed by the RANK inhibitor OPG-Fc. These results indicated that the OPG/RANKL/RANK system modulates VSMC osteogenic differentiation and medial calcification of common carotid artery in simulated microgravity rats by regulating NF-kB pathway.