DMAMP: A deep-learning model for detecting antimicrobial peptides and their multi-activities.

Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding activities require a high cost, whereas computational technologies do so for much less. Currently, most computational methods solve the identification of AMPs and their activities as two independent tasks, which ignore the relationship between them. Therefore, the combination and sharing of patterns for two tasks is a crucial problem that needs to be addressed. In this study, we propose a deep learning model, called DMAMP, for detecting AMPs and activities simultaneously, which is benefited from multi-task learning. The first stage is to utilize convolutional neural network models and residual blocks to extract the sharing hidden features from two related tasks. The next stage is to use two fully connected layers to learn the distinct information of two tasks. Meanwhile, the original evolutionary features from the peptide sequence are also fed to the predictor of the second task to complement the forgotten information. The experiments on the independent test dataset demonstrate that our method performs better than the single-task model with 4.28% of Matthews Correlation Coefficient (MCC) on the first task, and achieves 0.2627 of an average MCC which is higher than the single-task model and two existing methods for five activities on the second task. To understand whether features derived from the convolutional layers of models capture the differences between target classes, we visualize these high-dimensional features by projecting into 3D space. In addition, we show that our predictor has the ability to identify peptides that achieve activity against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). We hope that our proposed method can give new insights into the discovery of novel antiviral peptide drugs.

A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.

Cell identity annotation for single-cell transcriptome data is a crucial process for constructing cell atlases, unraveling pathogenesis, and inspiring therapeutic approaches. Currently, the efficacy of existing methodologies is contingent upon specific data sets. Nevertheless, such data are often sourced from various batches, sequencing technologies, tissues, and even species. Notably, the gene regulatory relationship remains unaffected by the aforementioned factors, highlighting the extensive gene interactions within organisms. Therefore, we propose scHGR, an automated annotation tool designed to leverage gene regulatory relationships in constructing gene-mediated cell communication graphs for single-cell transcriptome data. This strategy helps reduce noise from diverse data sources while establishing distant cellular connections, yielding valuable biological insights. Experiments involving 22 scenarios demonstrate that scHGR precisely and consistently annotates cell identities, benchmarked against state-of-the-art methods. Crucially, scHGR uncovers novel subtypes within peripheral blood mononuclear cells, specifically from CD4+ T cells and cytotoxic T cells. Furthermore, by characterizing a cell atlas comprising 56 cell types for COVID-19 patients, scHGR identifies vital factors like IL1 and calcium ions, offering insights for targeted therapeutic interventions.

scRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data.

Mathematical Modeling of NaCl Scaling Development in Long-Distance Membrane Distillation for Improved Scaling Control.

Membrane distillation is a novel membrane-based separation technology with the potential to produce pure water from high-salinity brine. It couples transport behaviors along the membrane and across the membrane. The brine in the feed is gradually concentrated due to the permeate flux across the membrane, which is a significant factor in initiating the scaling behavior on the membrane surface along the feed flow direction. It is of great interest to investigate and estimate the development of scaling on the membrane surface. This work specifically focuses on a long-distance membrane distillation process with a sodium chloride solution as the feed. A modeling approach has been developed to estimate the sodium chloride scaling development on the membrane surface along the flow direction. A set of experiments was conducted to validate the results. Based on mathematical simplification and analytical fitting, a simplified model was summarized to predict the initiating position of sodium chloride scaling on the membrane, which is meaningful for scaling control in industrial-scale applications of membrane distillation.

Simulation study on electroporation of cancer cells in multicellular system.

Electroporation (EP) of the normal cell and cancer cell both in single-cell and multicellular models was investigated by the meshed transport network method (MTNM) in this paper. The simulation results suggest that the cancer cell undergoes faster and more significant local EP than that of the corresponding normal cell induced by nanosecond pulsed electric fields (nsPEFs) both in single-cell and multicellular models. Furthermore, the results of the multicellular model indicate that there is a unidirectional neighboring effect in the multicellular model, meaning that cells at the center are affected and their pore formation is significantly reduced, but this effect is very weak for cells at the edges of the system. This means that the electric field selectively kills cells in different distribution locations. This work can provide guidance for the selection of parameters for the cancer cell EP process.

Primary Ewing's sarcoma of the intestine: case report and literature review.

Ewing sarcoma (ES)/peripheral primitive neuroectodermal tumor is a highly aggressive malignant tumor that typically presents in bone and soft tissue. Primary ES of the intestine is relatively rare, which poses a challenge in distinguishing it from other primary tumors of the small intestine through imaging. This article details a case study of ES originating in the intestine. Computed tomography (CT) imaging suggested a small intestinal stromal tumor, and so the patient underwent resection of the small bowel and omental tumor. Pathology results confirmed the diagnosis of ES of the small intestine. Following surgery, the patient underwent six cycles of chemotherapy, and a follow-up positron emission tomography-CT revealed widespread dissemination of the disease with intraperitoneal metastasis, ultimately resulting in the death of the patient.

Metabolome profiling and transcriptome analysis unveiling the crucial role of magnesium transport system for magnesium homeostasis in tea plants.

Magnesium (Mg2+) is a crucial nutrient for the growth and development of Camellia sinensis and is closely related to the quality of tea. However, the underlying mechanisms responding to low-Mg 2+ stress in tea plants remain largely unknown. In this study, photosynthetic parameters, metabolomics, and transcriptomics were utilized to explore the potential effects of low Mg2+ on the growth and metabolism of C. sinensis. Low-Mg2+ treatment increased the ratio of shoot dry weight to root dry weight but decreased the photosynthesis of C. sinensis. Forty and thirty metabolites were impacted by Mg2+ shortage in C. sinensis shoots and roots, respectively. Integrated transcriptome and metabolome analyses revealed the possible reasons for the decreased contents of chlorophyll and catechins and the increased theanine content in C. sinensis roots. Weighted gene co-expression network analysis indicated that the Mg2+ transport system was essential in the regulation of Mg2+ homeostasis in C. sinensis, in which CsMGT5 was identified to be the key regulator according to CsMGT5-overexpressing and complementary assays in Arabidopsis thaliana. Moreover, silencing of CsMGT5 in vivo reduced the content of chlorophyll in C. sinensis shoots. In addition, CsMGT5 might collaborate with ammonium transporters to keep the amino acid content steady, suggesting its potential application for tea quality improvement. All these findings demonstrate the key roles of CsMGTs for Mg2+ homeostasis in C. sinensis, providing a theoretical basis for Mg2+ efficient utilization in plants.

Structural insights into the molecular effects of the anthelmintics monepantel and betaine on the Caenorhabditis elegans acetylcholine receptor ACR-23.

Anthelmintics are drugs used for controlling pathogenic helminths in animals and plants. The natural compound betaine and the recently developed synthetic compound monepantel are both anthelmintics that target the acetylcholine receptor ACR-23 and its homologs in nematodes. Here, we present cryo-electron microscopy structures of ACR-23 in apo, betaine-bound, and betaine- and monepantel-bound states. We show that ACR-23 forms a homo-pentameric channel, similar to some other pentameric ligand-gated ion channels (pLGICs). While betaine molecules are bound to the classical neurotransmitter sites in the inter-subunit interfaces in the extracellular domain, monepantel molecules are bound to allosteric sites formed in the inter-subunit interfaces in the transmembrane domain of the receptor. Although the pore remains closed in betaine-bound state, monepantel binding results in an open channel by wedging into the cleft between the transmembrane domains of two neighboring subunits, which causes dilation of the ion conduction pore. By combining structural analyses with site-directed mutagenesis, electrophysiology and in vivo locomotion assays, we provide insights into the mechanism of action of the anthelmintics monepantel and betaine.

Phosphodiesterase Inhibition to Sensitize Non-Small-Cell Lung Cancer to Pemetrexed: A Double-Edged Strategy.

Phosphosidesterases (PDEs) are key regulators of cyclic nucleotide signaling, controlling many hallmarks of cancer and playing a role in resistance to chemotherapy in non-small-cell lung cancer (NSCLC). We evaluated the anti-tumor activity of the anti-folate agent pemetrexed (PMX), alone or combined with biochemical inhibitors of PDE5, 8, 9, or 10, against squamous and non-squamous NCSLC cells. Genomic alterations to PDE genes (PDEmut) or PDE biochemical inhibition (PDEi) can sensitize NSCLC to PMX in vitro (observed in 50% NSCLC evaluated). The synergistic activity of PDEi with PMX required microdosing of the anti-folate drug. As single agents, none of the PDEis evaluated have anti-tumor activity. PDE biochemical inhibitors, targeting either cAMP or cGMP signaling (or both), resulted in significant cross-modulation of downstream pathways. The use of PDEi may present a new strategy to overcome PMX resistance of PDEwt NSCLC tumors but comes with important caveats, including the use of subtherapeutic PMX doses.

A Modified Technique for Inducing Polycystic Ovary Syndrome in Mice.

Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility in women. Animal models are widely used to study the etiologic mechanisms of PCOS and for related drug development. Letrozole-induced mouse models replicate the metabolic and reproductive phenotypes of patients with PCOS. The traditional method of letrozole treatment in PCOS mice requires daily dosing over a certain period, which can be labor-intensive and cause significant stress to the mice. This study describes a simple and effective method for inducing PCOS in mice by implanting a controlled letrozole-releasing mini-pump. A mini-pump capable of stable, continuous release of a quantitative amount of letrozole was fabricated and implanted subcutaneously in mice under anesthesia. This study demonstrated that the mouse model successfully mimicked PCOS features after letrozole mini-pump implantation. The materials and equipment used in this study are readily available to most laboratories, requiring no special customization. Collectively, this article provides a unique, easy-to-perform method for inducing PCOS in mice.

A Taxonomic Study of Candolleomyces Specimens from China Revealed Seven New Species.

Based on phylogenetic analysis, Candolleomyces (Psathyrellaceae, Agaricales) was established with Psathyrella candolleana as the type species. The basidiomes range from small to large and are typically terrestrial, lignicolous, and rarely fimicolous. We analysed the Candolleomyces species collected during five years in China, and based on morphological and molecular data (nrITS, nrLSU, and tef-1α), we propose seven new Candolleomyces species viz. C. brevisporus, C. gyirongicus, C. lignicola, C. luridus, C. shennongdingicus, C. shennongjianus, and C. sichuanicus. Full descriptions, colour photographs, illustrations, phylogenetic analyses results, and comparisons with related Candolleomyces species of the new taxa are provided. This study enriches the species diversity of Candolleomyces in China.

Alternative splicing responses to salt stress in Glycyrrhiza uralensis revealed by global profiling of transcriptome RNA-seq datasets.

Excessive reactive oxygen species stress due to salinity poses a significant threat to the growth of Glycyrrhiza uralensis Fisch. To adapt to salt stress, G. uralensis engages in alternative splicing (AS) to generate a variety of proteins that help it withstand the effects of salt stress. While several studies have investigated the impact of alternative splicing on plants stress responses, the mechanisms by which AS interacts with transcriptional regulation to modulate the salt stress response in G. uralensis remain poorly understood. In this study, we utilized high-throughput RNA sequencing data to perform a comprehensive analysis of AS events at various time points in G. uralensis under salt stress, with exon skipping (SE) being the predominant AS type. KEGG enrichment analysis was performed on the different splicing genes (DSG), and pathways associated with AS were significantly enriched, including RNA transport, mRNA surveillance, and spliceosome. This indicated splicing regulation of genes, resulting in AS events under salt stress conditions. Moreover, plant response to salt stress pathways were also enriched, such as mitogen-activated protein kinase signaling pathway - plant, flavonoid biosynthesis, and oxidative phosphorylation. We focused on four differentially significant genes in the MAPK pathway by AS and qRT-PCR analysis. The alternative splicing type of MPK4 and SnRK2 was skipped exon (SE). ETR2 and RbohD were retained intron (RI) and alternative 5'splice site (A5SS), respectively. The expression levels of isoform1 of these four genes displayed different but significant increases in different tissue sites and salt stress treatment times. These findings suggest that MPK4, SnRK2, ETR2, and RbohD in G. uralensis activate the expression of isoform1, leading to the production of more isoform1 protein and thereby enhancing resistance to salt stress. These findings suggest that salt-responsive AS directly and indirectly governs G. uralensis salt response. Further investigations into AS function and mechanism during abiotic stresses may offer novel references for bolstering plant stress tolerance.

Unbiased photoelectrochemical H2O2 coupled to H2 production via dual Sb2S3-based photoelectrodes with ultralow onset potential.

The productions of hydrogen peroxide (H2O2) and hydrogen (H2) in a photoelectrochemical (PEC) water splitting cell suffer from an onset potential that limits solar conversion efficiencies. The formation of H2O2 through two-electron PEC water oxidation reaction competes with four-electron oxidation evolution reaction. Herein, we developed the surface selenium doped antimony trisulfide photoelectrode with the integrated ruthenium cocatalyst (Ru/Sb2(S,Se)3) to achieve the low onset potential and high Faraday efficiency (FE) for selective H2O2 production. The photoanode exhibits an average FE of 85% in the potential range of 0.4-1.6 VRHE and the H2O2 yield of 1.01 µmol cm-2 min-1 at 1.6 VRHE, especially at low potentials of 0.1-0.55 VRHE with 80.4% FE. Impressively, an unassisted PEC system that employs light and electrolyte was constructed to simultaneously produce H2O2 and H2 production on both Ru/Sb2(S,Se)3 photoanode and the Pt/TiO2/Sb2S3 photocathode. The integrated system enables the average PEC H2O2 production rate of 0.637 µmol cm-2 min-1 without applying any addition bias. This is the first demonstration that Sb2S3-based photoelectrodes exhibit H2O2/H2 two-side production with a strict key factor of the system, which represents its powerful platform to achieve high efficiency and productivity and the feasibility to facilitate value-added products in neutral conditions.

Acute therapy-related myelodysplastic syndromes following capecitabine and oxaliplatin therapy in gastric malignant tumor: A case report.

RATIONAL: Patients with gastric cancer show a relatively low incidence of developing secondary myelodysplastic syndrome (MDS). PATIENT CONCERNS: A 60-year-old man was admitted because of pain and discomfort in the upper abdomen and intermittent abdominal pain. DIAGNOSES: Ulcerative moderately poorly differentiated adenocarcinoma (pT2N2M0G3, stage IIB) and MDS. INTERVENTIONS: The patient underwent chemotherapy with oxaliplatin (OXP, intravenously guttae on day 1) plus capecitabine (CAP, bis in die orally on day 1-14). The patient developed degree III myelosuppression after OXP plus CAP chemotherapy and MDS was subsequently confirmed by diagnosis of the bone marrow biopsy. Temporary but significant hematological improvements were observed after the patient received corresponding treatment, which helped achieve remission and improve pancytopenia. OUTCOMES: The patient presented partial remission after corresponding treatment and no other complications have been recorded. LESSONS: Acute MDS is an unusual adverse effect induced by OXP plus CAP chemotherapy. It is urgent to suggest implementing a supplementary assessment or examination for patients receiving these therapies in future cases.

Causal associations of immune cell phenotypes with migraine: A mendelian randomization study.

The interaction between the immune system and the brain, crucial for blood-brain barrier integrity, is a potential factor in migraine development. Although there's evidence of a connection between immune dysregulation and migraine, a clear causal link has been lacking. To bridge this knowledge gap, we performed a two-sample Mendelian randomization (MR) analysis of 731 immune cell phenotypes to determine their causality with migraine, of which parameters included fluorescence, cell abundance, count, and morphology. Sensitivity and pleiotropy checks validated our findings. After applying a false discovery rate correction, our MR study identified 35 of 731 immune phenotypes with a significant causal link to migraine (p < 0.05). Of these, 24 showed a protective effect (inverse variance weighting : p < 0.05, odds ratio <1), and 11 were risk factors (inverse variance weighting : p < 0.05, odds ratio >1). Although limited by population sample size and potential population-specific genetic variations, our study uncovers a significant genetic link between certain immune cell markers and migraine, providing new insights into the disorder's pathophysiology. These discoveries are crucial for developing targeted biomarkers and personalized treatments. The research enhances our understanding of immune cells' role in migraine and may substantially improve patient outcomes and lessen its socio-economic impact.

The Risk for Readmission to Juvenile Detention: The Role of Trauma Exposure and Trauma-related Mental Health Disorders.

The purpose of this study was to examine the impact of childhood trauma exposure, posttraumatic stress disorder, and trauma-related comorbid diagnoses on the risk for readmission to juvenile detention among youth in a large metropolitan area (N = 1282). The following research questions were addressed: 1) Does a greater number of childhood traumas increase the risk for readmission to detention following release? 2) Does the risk for readmission differ by type of trauma? 3) Do PTSD and other co-morbid diagnoses increase the risk for readmission? and 4) What role do demographic factors play in the relationship between trauma-related variables and risk for readmission? This study utilized the screening results of 1282 youth who were voluntarily screened for PTSD, depressive symptoms and substance use during their initial intake to detention. More than half of the sample was readmitted during the three-year study period, with readmissions most likely to occur within one year of release. Returning to detention within one year was also associated with increased risk for multiple readmissions. Youth readmitted to detention were more likely to have a history of sexual abuse and problematic substance use. No other significant relationships were found between risk for readmission and trauma-related variables. Although trauma-related symptoms may be crucial targets for treatment, focusing solely on trauma exposure and traumatic stress symptoms without considering the impact of other risk factors may not be enough to decrease the likelihood of readmission for youth of color in a large urban environment.

Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.

Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein-disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding. The accuracies of the proposed model using five-fold cross-validation on Yeast core and Human datasets are 96.34% and 99.30%, respectively. In the task of predicting interactions in important PPI networks, our model correctly predicted all interactions in one-core, Wnt-related, and cancer-specific networks. The experimental results on cross-species datasets, including Caenorhabditis elegans, Helicobacter pylori, Homo sapiens, Mus musculus, and Escherichia coli, also show that our feature fusion method helps increase the generalization capability of the PPI prediction model.

MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning.

De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model's generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.

Considering Joule heating in coupled electroporation and electrodeformation modeling of glioblastoma cells.

Cells exposed to a pulsed electric field undergo electroporation(EP) and electrodeformation(ED) under electric field stress, and a coupled model of EP and ED of glioblastoma(GBM) taking into account Joule heating is proposed. The model geometry is extracted from real cell boundaries, and the effects of Joule heating-induced temperature rise on the EP and ED processes are considered. The results show that the temperature rise will increase the cell's local conductivity, leading to a decrease in the transmembrane potential(TMP). The temperature rise also causes a decrease in the dynamic Young's modulus of the cell membrane, making the cell less resistant to deformation. In addition, GBM cells are more susceptible to EP in the middle portion of the cell and ED in the three tentacle portions under pulsed electric fields, and the cells undergo significant positional shifts. The ED of the nucleus is similar to spherical cells, but the degree of ED is smaller.