MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Discovering trends and hotspots of biosafety and biosecurity research via machine learning.

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

Aberrant HO-1/NQO1-Reactive Oxygen Species-ERK Signaling Pathway Contributes to Aggravation of TPA-Induced Irritant Contact Dermatitis in Nrf2-Deficient Mice.

NF-erythroid 2-related factor 2 (Nrf2) is a major transcription factor to protect cells against reactive oxygen species (ROS) and reactive toxicants. Meanwhile, Nrf2 can inhibit contact dermatitis through redox-dependent and -independent pathways. However, the underlying mechanisms of how Nrf2 mediates irritant contact dermatitis (ICD) are still unclear. In this article, we elucidated the role of Nrf2 in 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced acute ICD. Our study demonstrated that the ear thickness, redness, swelling, and neutrophil infiltration were significantly increased, accompanied by increased expression of inflammatory cytokines (IL-1α, IL-1ß, IL-6, etc.) and decreased expression of antioxidant genes (HO-1 and NQO1) in Nrf2 knockout mice. Moreover, ERK phosphorylation was elevated in mouse embryonic fibroblasts (MEFs) from Nrf2 knockout mouse. Inhibition of ERK significantly alleviated TPA-induced cutaneous inflammation and ROS accumulation in MEFs derived from mouse. Conversely, ROS scavenging inhibited the ERK activation and TPA-induced inflammation in MEFs. Taken together, the findings illustrate the key role of the Nrf2/ROS/ERK signaling pathway in TPA-induced acute ICD.

Muc2 mucin O-glycosylation interacts with enteropathogenic Escherichia coli to influence the development of ulcerative colitis based on the NF-kB signaling pathway.

BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine characterized by a compromised intestinal epithelial barrier. Mucin glycans are crucial in preserving barrier function during bacterial infections, although the underlying mechanisms remain largely unexplored. METHODS: A cohort comprising 15 patients diagnosed with UC and 15 healthy individuals was recruited. Stool samples were collected to perform 16S rRNA gene sequencing, while biopsy samples were subjected to nanocapillary liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) to assess O-glycosylation. Gene expression was evaluated through qPCR analysis and Western blotting. Furthermore, animal experiments were conducted to investigate the effects of Escherichia coli and/or O-glycan inhibitor benzyl-α-GalNAc on the development of colitis in mice. RESULTS: Our findings revealed that the mucus barrier was disrupted during the early stages of UC, while the MUC2 protein content remained unaltered. Additionally, a noteworthy reduction in the O-glycosylation of MUC2 was observed, along with significant changes in the intestinal microbiota during the early stages of UC. These changes included a decrease in intestinal species richness and an increase in the abundance of Escherichia coli (E. coli). Moreover, subsequent to the administration of galactose or O-glycan inhibitor to intestinal epithelial cells, it was observed that the cell culture supernatant had the ability to modify the proliferation and adhesive capacity of E. coli. Furthermore, when pathogenic E. coli or commensal E. coli were cocultured with intestinal epithelium, both strains elicited activation of the NF-KB signaling pathway in epithelial cells and facilitated the expression of serine protease in comparison to the untreated control. Consistently, the inhibition of O-glycans has been observed to enhance the pathogenicity of E. coli in vivo. Furthermore, a correlation has been established between the level of O-glycans and the development of ulcerative colitis. Specifically, a reduction in the O-glycan content of MUC2 cells has been found to increase the virulence of E. coli, thereby compromising the integrity of the intestinal epithelial barrier. CONCLUSIONS: Together, there exist complex interactions between the intestinal epithelium, O-glycans, and the intestinal microbiota, which may inform the development of novel therapeutic strategies for the treatment of ulcerative colitis.

Feature selection may improve deep neural networks for the bioinformatics problems.

MOTIVATION: Deep neural network (DNN) algorithms were utilized in predicting various biomedical phenotypes recently, and demonstrated very good prediction performances without selecting features. This study proposed a hypothesis that the DNN models may be further improved by feature selection algorithms. RESULTS: A comprehensive comparative study was carried out by evaluating 11 feature selection algorithms on three conventional DNN algorithms, i.e. convolution neural network (CNN), deep belief network (DBN) and recurrent neural network (RNN), and three recent DNNs, i.e. MobilenetV2, ShufflenetV2 and Squeezenet. Five binary classification methylomic datasets were chosen to calculate the prediction performances of CNN/DBN/RNN models using feature selected by the 11 feature selection algorithms. Seventeen binary classification transcriptome and two multi-class transcriptome datasets were also utilized to evaluate how the hypothesis may generalize to different data types. The experimental data supported our hypothesis that feature selection algorithms may improve DNN models, and the DBN models using features selected by SVM-RFE usually achieved the best prediction accuracies on the five methylomic datasets. AVAILABILITY AND IMPLEMENTATION: All the algorithms were implemented and tested under the programming environment Python version 3.6.6. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer Research Trend Analysis Based on Fusion Feature Representation.

Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research.

Broadband indium tin oxide nanowire arrays as saturable absorbers for solid-state lasers.

Indium Tin Oxide nanowire arrays (ITO-NWAs), as epsilon-near-zero (ENZ) materials, exhibit a fast response time and a low saturable absorption intensity, which make them promising photoelectric materials. In this study, ITO-NWAs were successfully fabricated using a chemical vapor deposition (CVD) method, and the saturable absorption properties of this material were characterized in the near-infrared region. Further, passively Q-switched all-solid-state lasers were realized at wavelengths of 1.0, 1.3, and 2.0 µm using the as-prepared saturable absorber (SA). To the best of our knowledge, we present the first application of ITO-NWAs in all-solid-state lasers. The results reveal that ITO-NWAs may be applied as an SA while developing Q-switched lasers and that they exhibit a broad application prospect as broadband saturable absorption materials.

Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

A topical heparinoid-containing product improves epidermal permeability barrier homeostasis in mice.

Because of the importance of epidermal functions, including stratum corneum hydration and maintenance of permeability barrier homeostasis, in the pathogenesis of a variety of cutaneous and systemic disorders, a wide range of products has been developed to improve epidermal functions. However, the underlying mechanisms whereby certain products, including heparinoid-containing product, are far little understood. In the present study, we assessed the impact of a heparinoid-containing product, Hirudoid® cream, on epidermal permeability barrier function and expression levels of a panel of epidermal mRNA related to the formation/maintenance of the permeability barrier in mouse skin. Our results showed that while the baseline levels of transepidermal water rates remained unchanged, treatment with Hirudoid® cream twice daily for 7 days significantly accelerated permeability barrier recovery and increased stratum corneum hydration. In parallel, expression levels of epidermal mRNA for certain differentiation marker-related proteins, lipid synthetic enzymes, keratinocyte proliferation and antimicrobial peptides also increased significantly. Together, these results provide the underlying mechanisms by which topical Hirudoid® cream improves epidermal permeability barrier and antimicrobial function. Because of its benefits for epidermal functions, heparinoid-containing product could be more useful in the management of skin conditions, characterized by abnormal permeability barrier and antimicrobial function.

Continuous-wave and Q-switched Nd:BGSO lasers based on bismuth nanosheets absorber.

This Nd:BG1-xSxO (Nd:BGSO) crystal was grown using the micro-pulling-down method, and the continuous-wave laser operation of this crystal was demonstrated for the first time, to the best of our knowledge. The maximum output power of 1.038 W was obtained under the absorbed pump power of 3.01 W, which corresponds to a slope efficiency of 31.3%. Bismuth nanosheets were first employed as saturable absorbers to generate a passively Q-switched Nd:BGSO laser. Stable Q-switched pulses with the shortest pulse width of 376.5 ns and the maximum repetition rate of 136.6 kHz were achieved at the absorbed pump power of 3.01 W. The largest pulse energy and highest peak power achieved were 0.94 µJ and 2.48 W, respectively.

The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study.

BACKGROUND: It is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the most suitable publication venue, given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music, and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature. OBJECTIVE: We aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper's abstract. METHODS: In Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals. RESULTS: We collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering-based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/. CONCLUSIONS: Our deep learning-based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

Diagnosis of Breast Hyperplasia and Evaluation of RuXian-I Based on Metabolomics Deep Belief Networks.

Breast cancer is estimated to be the leading cancer type among new cases in American women. Core biopsy data have shown a close association between breast hyperplasia and breast cancer. The early diagnosis and treatment of breast hyperplasia are extremely important to prevent breast cancer. The Mongolian medicine RuXian-I is a traditional drug that has achieved a high level of efficacy and a low incidence of side effects in its clinical use. However, for detecting the efficacy of RuXian-I, a rapid and accurate evaluation method based on metabolomic data is still lacking. Therefore, we proposed a framework, named the metabolomics deep belief network (MDBN), to analyze breast hyperplasia metabolomic data. We obtained 168 samples of metabolomic data from an animal model experiment of RuXian-I, which were averaged from control groups, treatment groups, and model groups. In the process of training, unlabelled data were used to pretrain the Deep Belief Networks models, and then labelled data were used to complete fine-tuning based on a limited-memory Broyden Fletcher Goldfarb Shanno (L-BFGS) algorithm. To prevent overfitting, a dropout method was added to the pretraining and fine-tuning procedures. The experimental results showed that the proposed model is superior to other classical classification methods that are based on positive and negative spectra data. Further, the proposed model can be used as an extension of the classification method for metabolomic data. For the high accuracy of classification of the three groups, the model indicates obvious differences and boundaries between the three groups. It can be inferred that the animal model of RuXian-I is well established, which can lay a foundation for subsequent related experiments. This also shows that metabolomic data can be used as a means to verify the effectiveness of RuXian-I in the treatment of breast hyperplasia.

Efficient continuous-wave and passive Q-switched mode-locked Er3+ : CaF2-SrF2 lasers in the mid-infrared region.

An Er3+-doped CaF2-SrF2 mixed crystal was grown using the temperature gradient technique, and its laser characteristics were studied. In a compact linear cavity, a continuous-wave output power of 712 mW was obtained with the highest slope efficiency of 41.4%. Using a semiconductor saturable absorber mirror, a passive Q-switched mode-locked Er3+:CaF2-SrF2 laser emitting at 2729.5 nm was demonstrated for the first time, to the best of our knowledge. The maximum average output power of 125 mW was obtained at an absorbed pump power of 1.81 W, and the repetition rate of the Q-switched envelope was 4.17 kHz. The mode-locked pulses in the Q-switched pulse envelope had a repetition rate of 136.3 MHz, and its duration was estimated to be approximately 1.78 ns. These results indicate that the Er3+-doped CaF2-SrF2 mixed crystal is promising for the development of an ultrafast laser in the mid-infrared regime.

Potential role of inflammaging mediated by the complement system in enlarged facial pores.

BACKGROUND: Enlarged facial pores are a common cosmetic concern of the skin, rather than a disease, and have not received much attention from dermatologists in recent years. Consequently, progress in understanding their pathogenesis has been limited, and current cosmetic solutions have limitations. Given that the complement system has regained interest as a key player in chronic inflammatory skin conditions, various mechanisms involving this system are being investigated. OBJECTIVE: We aimed to shed light on the mechanism underlying enlarged facial pores by examining the role of the complement system in skin. METHODS: We conducted a comprehensive literature search utilizing various academic databases including PubMed, Web of Science, and Google Scholar. Employing keywords such as "complement system," "inflammation," "facial pores," "enlarged," and "mechanisms," we compiled a selection of relevant studies. These studies provided a comprehensive understanding of the intricate mechanisms underlying the relationship between the "complement system" and "inflammation" within the context of facial pore enlargement. RESULTS: Our findings suggest that inflammaging mediated by complement activation may be a critical player in the formation of enlarged facial pores. Specifically, overactivation of the complement system leading to the accumulation of complement fragments could be a major contributor to this process. Notably, the complement system in skin may be involved in a range of skin issues, including aging. CONCLUSION: Modulating the complement system presents a promising avenue for future research in improving skin health. Further basic and clinical research is necessary to validate these findings, but we hope that this study can serve as a theoretical foundation for the development of targeted cosmetics.

Azelaic Acid: Mechanisms of Action and Clinical Applications.

AZA is a non-phenolic, saturated dicarboxylic acid with nine carbon atoms, naturally produced by the yeast Malassezia. It has diverse physiological activities, including antibacterial, anti-keratinizing, antimelanogenic, antioxidant and anti-inflammatory effects. AZA is widely used in dermatology and is FDA-approved for treating papulopustular rosacea. It also shows significant efficacy in acne vulgaris and melasma. This review summarizes the mechanisms of action and clinical applications of AZA, aiming to provide theoretical support for its clinical and cosmetic use and to facilitate further research.

Human lip vermilion: Physiology and age-related changes.

BACKGROUND: The human lip vermilion, also known as the red lip, is important to the quality of life and has long attracted the attention of cosmetic researchers. However, there is limited existing literature on the physiological characteristics and age-related alterations in the human vermilion. OBJECTIVE: We aim to provide an overview of the physiological characteristics and age-related alterations in the human vermilion. METHODS: This article is a result of previous research. We conducted a literature search using various academic databases such as Google Scholar, Web of Science, and PubMed. Our findings provided a comprehensive understanding of the physiological characteristics and age-related changes of the human lip vermilion. RESULTS: The human lip vermilion has a unique structure and physiological characteristics, and during the aging process, a few changes may occur in the human lip vermilion. CONCLUSION: Understanding the human lip vermilion's physiological characteristics and age-related changes can provide key information for the future innovation of lip vermilion care products. Further investigations are necessary to reach a consensus on the physiological characteristics and age-related alterations in the human vermilion.

Lactic Acid Chemical Peeling in Skin Disorders.

Lactic acid is the most widely occurring natural organic acid in nature. It not only exhibits mild and safe properties but also possesses multiple physiological activities, such as antibacterial effects, immune regulation, and promotion of wound healing, making it one of the most popular chemical peeling agents. Chemical peels are commonly used in the field of aesthetic dermatology as a non-invasive therapeutic approach. This research aims to provide valuable references for clinical dermatologists by summarizing the characteristics of lactic acid, elucidating its mechanism of action in peeling, and investigating the clinical applications of this compound. Furthermore, it anticipates the potential for lactic acid to be the most suitable chemical peeling agent for Chinese skin.

Long-term clinical outcomes of extracorporeal shockwave lithotripsy and endoscopic retrograde cholangiopancreatography for pancreatic duct stone treatment in patients with chronic pancreatitis.

BACKGROUND AND AIMS: Extracorporeal shock wave lithotripsy for pancreatic stones (P-ESWL) and endoscopic retrograde cholangiopancreatography (ERCP) are the preferred therapeutic approaches for painful chronic pancreatitis (CP) with pancreatic stones. This study aimed to report the short- and long-term outcomes following P-ESWL and ERCP in a large cohort with CP. METHODS: Patients with painful CP and pancreatic stones >5 mm in size, who underwent P-ESWL and subsequent ERCP between March 2011 and June 2018, were included in this retrospective-prospective mixed observational study. The total stone clearance rates were recorded. All patients were followed up until the end of March 2024, with the visual analogue scale (VAS) for pain, pain type, quality-of-life scores and other relevant information recorded. RESULTS: A total of 2071 patients underwent P-ESWL, and 93.1% of them subsequently underwent ERCP during the study period. Patients were followed up for an average of 11.8 years from the onset of CP and 6.7 years from the first P-ESWL procedure. Complete stone clearance was achieved in 73.7% of the patients. At the end of the follow-up period, 70.1% of the patients achieved complete pain remission. Significant pain type conversion and lower VAS scores were observed in the patients after treatment. Quality-of-life scores and body mass indices increased after P-ESWL and ERCP. CONCLUSIONS: P-ESWL and ERCP are effective and minimally invasive treatments for pancreatic stones in patients with painful CP. Most patients achieved complete pain relief, and pain-type conversion was common after treatment. (ClinicalTrials.gov: NCT05916547).

Deep Learning Model for Coronary Angiography.

The visual inspection of coronary artery stenosis is known to be significantly affected by variation, due to the presence of other tissues, camera movements, and uneven illumination. More accurate and intelligent coronary angiography diagnostic models are necessary for improving the above problems. In this study, 2980 medical images from 949 patients are collected and a novel deep learning-based coronary angiography (DLCAG) diagnose system is proposed. Firstly, we design a module of coronary classification. Then, we introduce RetinaNet to balance positive and negative samples and improve the recognition accuracy. Additionally, DLCAG adopts instance segmentation to segment the stenosis of vessels and depict the degree of the stenosis vessels. Our DLCAG is available at http://101.132.120.184:8077/ . When doctors use our system, all they need to do is login to the system, upload the coronary angiography videos. Then, a diagnose report is automatically generated.

Efficacy and safety of allogeneic umbilical cord-derived mesenchymal stem cells for the treatment of complex perianal fistula in Crohn's disease: a pilot study.

OBJECTIVES: The aim of the study was to evaluate the efficacy and safety of allogeneic umbilical cord-derived mesenchymal stem cells (TH-SC01) for complex perianal fistula in patients with Crohn's disease (CD). METHODS: This was an open-label, single-arm clinical trial conducted at Jinling Hospital. Adult patients with complex treatment-refractory CD perianal fistulas (pfCD) were enrolled and received a single intralesional injection of 120 million TH-SC01 cells. Combined remission was defined as an absence of suppuration through an external orifice, complete re-epithelization, and absence of collections larger than 2 cm measured by magnetic resonance imaging (MRI) at 24 weeks after cell administration. RESULTS: A total of 10 patients were enrolled. Six patients (60.0%) achieved combined remission at 24 weeks. The number of draining fistulas decreased in 9 (90.0%) and 7 (70.0%) patients at weeks 12 and 24, respectively. Significant improvement in Perianal Crohn Disease Activity Index, Pelvic MRI-Based Score, Crohn Disease Activity Index, and quality of life score were observed at 24 weeks. No serious adverse events occurred. The probability of remaining recurrence-free was 70% at week 52. CONCLUSION: The study demonstrated that local injection of TH-SC01 cells might be an effective and safe treatment for complex treatment-refractory pfCD after conventional and/or biological treatments fail (ClinicalTrials.gov ID, NCT04939337). TRIAL REGISTRATION: The study was retrospectively registered on www. CLINICALTRIALS: gov (NCT04939337) on June 25, 2021.