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Bovine milk peptides are the protein fragments with diverse bioactive properties having antioxidant, anticarcinogenic, other therapeutic and nutraceutical potentials. These peptides are formed in milk by enzymatic hydrolysis, gastrointestinal digestion and fermentation processes. They have significant health impact with high potency and low toxicity making them a suitable natural alternative for preventing and managing diseases. Antibiotic resistance has increased the quest for better peptide candidates with antimicrobial effects. This article presents a comprehensive review on well documented antimicrobial, immunological, opioid, and anti-hypertensive activities of bovine milk peptides. It also covers the usage of computational biology tools and databases for prediction and analysis of the food-derived bioactive peptides. In silico analysis of amino acid sequences of Bos taurus milk proteins have been predicted to generate peptides with dipeptidyl peptidase IV inhibitory and ACE inhibitory properties, making them favorable candidates for developing blood sugar lowering drugs and anti-hypertensives. In addition to the prediction of new bioactive peptides, application of bioinformatics tools to predict novel functions of already known peptides is also discussed. Overall, this review focuses on the reported as well as predicted biologically active peptide of casein and whey proteins of bovine milk that can be utilized to develop therapeutic agents. [ABSTRACT FROM AUTHOR]

Importance: Neonatal anemia has a long-term effect on children's growth and development. Anemia during pregnancy is also the most widespread nutritional deficiency among pregnant women in the world; If it leads to anemia in newborns, it will affect a wide range of people and be a public health problem worthy of attention. Objective: To study the relationship between maternal anemia during pregnancy and neonatal hemoglobin levels. Data sources: PubMed, Web of science, Scopus, MEDLINE, Embase, ProQuest, Dissertations & Theses Global, The Cochrane Library, China Biology Medicine Database, Chinese CNKI Database, and Chinese Wanfang Database were systematically searched from inception to August 31, 2022. Study selection: The meta-analysis included all original studies which pertain to cohort studies, case-control studies or cross-sectional studies that investigated the relationship between maternal anemia during pregnancy and neonatal hemoglobin levels. Data extraction and synthesis: Hemoglobin level of both anemic and non-anemic pregnant mothers and their paired newborns were pooled from the selected studies. The random-effects model was used to assess the risk of getting a lower neonatal hemoglobin level between mothers with and without pregnant anemia. Data analyses were performed from September 5, 2022, to March 10, 2023. Main outcomes and measures: Maternal anemia during pregnancy is a risk factor of lower neonatal hemoglobin levels. Results: The initial search yielded 4267 records of which 116 articles underwent full-text evaluation, which identified 18 articles and a total of 1873 patients that were included. The findings of the meta-analysis showed a significant difference between the two groups(MD=-1.38; 95%CI:[-1.96,-0.80]. p<0.01), while the co-effect showed that the neonatal hemoglobin value of anemic mothers was 1.38g/dL lower than that of non-anemic mothers(-1.96,-0.80), suggesting a correlation between maternal anemia lower neonatal hemoglobin levels. Conclusions and relevance: This systematic review and meta-analysis demonstrated that maternal anemia during pregnancy were associated with a lower level of newborn hemoglobin levels. This may enable a better understanding of neonatal anemia and provide guidance towards future development of nutritional supplementation during pregnancy and the prediction of postpartum outcomes. Trial registration: PROSPERO Identifier: CRD42022352759. [ABSTRACT FROM AUTHOR]

Background: Lymph node metastasis (LNM) is a critical factor affecting the outcomes of head and neck squamous cell carcinoma (HNSCC) and the main reason for treatment failure. This study was designed to examine the effects of the key genes involved in the LNM of HNSCC. Methods: Tissue samples (HNSCC) were examined by transcriptome sequencing, and the core genes associated with LNM were detected via bioinformatics analysis. The functions of these core genes were then validated using the TCGA biological database and their effects on the propagation, invasion, and metastasis of HNSCC cells were evaluated through cell culture experiments. Moreover, the effect of core gene expression on the LNM capability of HNSCC was confirmed via a footpad xenograft mice model. Results: In the findings, a key gene involved in the LNM of HNSCC was identified as SLC7A2. It was correlated with adverse clinical prognosis and expressed with low expression in HNSCC tissues. As shown in cell culture experiments, FaDu and SCC15 cell growth, invasion, and migration were inhibited when SLC7A2 was overexpressed. Further, cell apoptosis was stimulated, and lymphangiogenesis was suppressed through the downregulation of CPB2 expression. Animal studies demonstrated that the growth and LNM of HNSCC cells were inhibited by SLC7A2 overexpression. Conclusion: It is concluded that SLC7A2 is involved in HNSCC lymphatic metastasis by controlling CPB2 function. The results are anticipated to offer new directions for the effective treatment of HNSCC. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusion\nIMPLICATIONS FOR REHABILITATIONTo examine the effects of inspiratory muscle training (IMT) on pulmonary and diaphragmatic function, exercise capacity, balance and quality of life (QOL), in post-stroke individuals.A literature search was conducted using MEDLINE, CINAHL, EMBASE, PubMed, PEDro, Web of Science and China Biological Medicine databases. Randomized controlled trials (RCTs) with a PEDro score ≥6 focusing on the effects of IMT were included. The GRADE system was used to determine the certainty of evidence for each outcome.Nine studies (255 participants) were included. IMT significantly increased forced expiratory volume in one second (FEV1) (4 studies, 112 participants, mean difference (MD)=0.18 litre, 95% confidence interval (CI): 0.14–0.23); maximal inspiratory pressure (MIP) (8 studies, 226 participants, MD = 6.37 cm H2O, 95% CI: 1.26–11.49); and diaphragm thickness fraction (DTf) on both sides (MD of affected side vs. unaffected side: 51 vs. 37%). The evidence certainty for diaphragmatic function was moderate. No significant change was observed in forced vital capacity (FVC), balance function, exercise capacity and QOL.This review reveals moderate evidence certainty in support of IMT improving diaphragmatic function. It appears that a training intensity between 30 and 50% MIP results in a significantly improved MIP.Stroke not only impacts motor function but also affects respiratory function, especially diaphragmatic function.Inspiratory muscle training (IMT) is an intervention that has been used to improve the strength and endurance of inspiratory muscles.There is evidence of moderate certainty which cautiously recommends IMT to improve diaphragmatic function after stroke.The evidence supporting IMT for the enhancement of forced vital capacity, balance function, exercise capacity and quality of life in stroke patients is inadequate. [ABSTRACT FROM AUTHOR]

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene–disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities. [ABSTRACT FROM AUTHOR]

Motivation Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. Results In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼ 5.5 % on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%). Availability and implementation All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360. [ABSTRACT FROM AUTHOR]

Compared with analyzing omics data from a single platform, an integrative analysis of multi-omics data provides a more comprehensive understanding of the regulatory relationships among biological features associated with complex diseases. However, most existing frameworks for integrative analysis overlook two crucial aspects of multi-omics data. Firstly, they neglect the known dependencies among biological features that exist in highly credible biological databases. Secondly, most existing integrative frameworks just simply remove the subjects without full omics data to handle block missingness, resulting in decreasing statistical power. To overcome these issues, we propose a network-based integrative Bayesian framework for biomarker selection and disease outcome prediction based on multi-omics data. Our framework utilizes Dirac spike-and-slab variable selection prior to identifying a small subset of biomarkers. The incorporation of gene pathway information improves the interpretability of feature selection. Furthermore, with the strategy in the FBM (stand for "full Bayesian model with missingness") model where missing omics data are augmented via a mechanistic model, our framework handles block missingness in multi-omics data via a data augmentation approach. The real application illustrates that our approach, which incorporates existing gene pathway information and includes subjects without DNA methylation data, results in more interpretable feature selection results and more accurate predictions. [ABSTRACT FROM AUTHOR]

Motivation: Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies. Results: STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git. [ABSTRACT FROM AUTHOR]

Motivation Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. Results We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.   This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge. Availability and implementation https://github.com/jiyuc/de-inconsistency. [ABSTRACT FROM AUTHOR]

Simple Summary: Bacteria increase their genetic diversity by acquiring genetic material from a variety of organisms across different species, a process known as horizontal gene transfer. Recently, there has been an increasing emphasis in the scientific literature on the importance of HGT for the evolution of eukaryotes such as fungi, animals, and plants. However, this observation presents a paradox since eukaryotes have developed mechanisms to prevent HGT. Our work proposes an alternative scenario to explain the abundance of reported cases of HGT in eukaryotes. We suggest that the misidentification of HGT candidates in eukaryotes is due to the lack of similar sequences (genes or proteins) in public databases. To support this hypothesis, we designed specific databases to identify potential HGT candidates within a particular group of fungi. Our results demonstrate that fewer HGT candidates are detected when more similar sequences exist for these candidates in databases. This finding holds significant relevance as public databases continue to grow; consequently, newly available information may refute several previously identified instances of HGT. Our experiments lead us to conclude that database imbalances need consideration before asserting new occurrences of HGT in eukaryotes. Horizontal gene transfer (HGT) is a widely acknowledged phenomenon in prokaryotes for generating genetic diversity. However, the impact of this process in eukaryotes, particularly interdomain HGT, is a topic of debate. Although there have been observed biases in interdomain HGT detection, little exploration has been conducted on the effects of imbalanced databases. In our study, we conducted experiments to assess how different databases affect the detection of interdomain HGT using proteomes from the Pezizomycotina fungal subphylum as our focus group. Our objective was to simulate the database imbalance commonly found in public biological databases, where bacterial and eukaryotic sequences are unevenly represented, and demonstrate that an increase in uploaded eukaryotic sequences leads to a decrease in predicted HGTs. For our experiments, four databases with varying proportions of eukaryotic sequences but consistent proportions of bacterial sequences were utilized. We observed a significant reduction in detected interdomain HGT candidates as the proportion of eukaryotes increased within the database. Our data suggest that the imbalance in databases bias the interdomain HGT detection and highlights challenges associated with confirming the presence of interdomain HGT among Pezizomycotina fungi and potentially other groups within Eukarya. [ABSTRACT FROM AUTHOR]

Analytical performance specifications (APS) are typically established through one of three models: (i) outcome studies, (ii) biological variation (BV), or (iii) state-of-the-art. Presently, The APS can, for most measurands that have a stable concentration, be based on BV. BV based APS, defined for imprecision, bias, total allowable error and allowable measurement uncertainty, are applied to many different processes in the laboratory. When calculating APS, it is important to consider the different APS formulae, for what setting they are to be applied and if they are suitable for the intended purpose. In this opinion paper, we elucidate the background, limitations, strengths, and potential intended applications of the different BV based APS formulas. When using BV data to set APS, it is important to consider that all formulae are contingent on accurate and relevant BV estimates. During the last decade, efficient procedures have been established to obtain reliable BV estimates that are presented in the EFLM biological variation database. The database publishes detailed BV data for numerous measurands, global BV estimates derived from meta-analysis of quality-assured studies of similar study design and automatic calculation of BV based APS. [ABSTRACT FROM AUTHOR]

Several studies have reported the efficacy of traditional Chinese medicine (TCM) for central serous chorioretinopathy (CSC), while some ophthalmologists are concerned that TCM may be a risk factor for CSC as some chinese herbs contain hormonal ingredients. This study aimed to evaluate the efficacy and safety of TCM in treating patients with CSC. Randomized controlled trials (RCTs) and observational studies of TCM for CSC were searched up to July 10, 2023 on the following biological databases without language and publication time restrictions: PubMed, Ovid Medline, Embase, Cochrane Library, The Chinese National Knowledge Infrastructure Database (CNKI), Technology Periodical Database (VIP), Wanfang, and Chinese Biomedical Literature Service System (SinoMed). Review Manager V.5.4.1 and Stata 14 software were used for data analysis. Finally, thirty-eight studies were finally included including 23 RCTs and 15 cohort studies. The meta-analysis showed that compared with the routine treatment alone, the combination of TCM can not only reduce the recurrence rate (OR = 0.29, 95% CI: 0.21,0.40; I2 = 0%) and central retinal thickness (CRT) (MD = - 35.63, 95% CI: - 45.96,-25.30; I2 = 89%) of CSC, but improve patients' best corrected visual acuity (BCVA) (SMD = 0.86, 95% CI: 0.62,1.11; I2 = 77%); additionally, it has no obvious side effects compared with routine treatment (OR = 0.72, 95% CI: 0.39,1.34; I2 = 10%). Overall, this study shows that the use of TCM does not increase the risk of CSC recurrence; on the contrary, the combination of TCM may reduce the recurrence of CSC and improve BCVA and CRT in patients with CSC compared with conventional treatment. [ABSTRACT FROM AUTHOR]

Summary Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. Availability and implementation The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph. [ABSTRACT FROM AUTHOR]

The increase in molecular data and the use of computer technologies in biology have led to the emergence of professional biocurators, who populate biological databases and knowledgebases with high-quality information. Although crucial to life science knowledge production, biocuration is, to a large extent, invisible labour that takes place behind the scenes of data-centric life science. The field suffers from a lack of recognition and status that has been linked to a language of service and a scientific system that is not equipped to recognise and reward new types of scientific practices. However, as the majority of biocurators are highly educated female biologists, biocuration is also reproducing the problematic pattern of women leaving the scientific tenure track in favour of less prestigious positions. Instead of viewing the issue as just another example of 'the leaky pipeline,' the gendering of biocuration could be seen as an interplay of gendered structures in science, organisations and society which makes a career in biocuration attractive for female scientists while at the same time positioning the activity as non-scientific low-status work. By illuminating some of the ways gender works in the processes which render certain kinds of technoscientific work invisible, biocuration serves as an example of how existing social structures influence the emerging data-centric science. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to evaluate the impact of obesity on the treatment response to secukinumab and drug survival rate in patients with ankylosing spondylitis (AS). Methods: We performed an observational cohort study that included AS patients based on the biological drug database in Turkey (TURKBIO) Registry between 2018 and 2021. The patients were divided into three groups: normal [body mass index (BMI) < 25 kg/m2], overweight (BMI: 25–30 kg/m2), and obese (BMI ≥ 30 kg/m2). Disease activity was evaluated at baseline, 3, 6, and 12 months. Drug retention rates at 12 months were also investigated. Results: There were 166 AS patients using secukinumab (56.6% male, mean age: 44.9 ± 11.6 years). The median follow-up time was 17.2 (3–33.2) months. Forty-eight (28.9%) patients were obese. The mean age was higher in the obese group than in others (P = .003). There was no statistically significant difference in Bath Ankylosing Spondylitis Disease Activity Index 50, Assessment of SpondyloArthritis international Society 20 (ASAS20), ASAS40, Ankylosing Spondylitis Disease Activity Score (ASDAS) low disease activity, and ASDAS clinically important improvement responses between the three groups at 3, 6, and 12 months, although they were numerically lower in obese patients. Drug retention rates at 12 months were similar in all groups (P > .05). Conclusions: This study suggested that obesity did not affect secukinumab treatment response and drug retention in AS patients. [ABSTRACT FROM AUTHOR]

Background and Objectives: Connexin 43 (Cx43) is involved in the transfer of small signaling molecules between neighboring cells, thereby exerting a major influence on the initiation and progression of tumorigenesis. However, there is a lack of systematic research on Cx43 expression and its predictive role in clinical diagnosis and prognosis in pan-cancer. Materials and Methods: Several biological databases were used to evaluate the expression levels of GJA1 (encoding Cx43) and its diagnostic and prognostic significance in pan-cancer. We targeted kidney renal clear cell carcinoma (KIRC) and investigated the relationship between GJA1 expression and different clinical features of KIRC patients. Then, we performed cell-based experiments to partially confirm our results and predicted several proteins that were functionally related to Cx43. Results: The expression of GJA1 has a high level of accuracy in predicting KIRC. High GJA1 expression was remarkably correlated with a favorable prognosis, and this expression was reduced in groups with poor clinical features in KIRC. Cell experiments confirmed the inhibitory effects of increased GJA1 expression on the migratory capacity of human renal cancer (RCC) cell lines, and protein–protein interaction (PPI) analysis predicted that CDH1 and CTNNB1 were closely related to Cx43. Conclusions: GJA1 could be a promising independent favorable prognostic factor for KIRC, and upregulation of GJA1 expression could inhibit the migratory capacity of renal cancer cells. [ABSTRACT FROM AUTHOR]

Cannabis has gained popularity in recent years as a substitute treatment for pain following the risks of typical treatments uncovered by the opioid crisis. The active ingredients frequently associated with pain-relieving effects are the phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), but their effectiveness and mechanisms of action are still under research. In this study, we used Caenorhabditis elegans, an ideal model organism for the study of nociception that expresses mammal ortholog cannabinoid (NPR-19 and NPR-32) and vanilloid (OSM-9 and OCR-2) receptors. Here, we evaluated the antinociceptive activity of THC and CBD, identifying receptor targets and several metabolic pathways activated following exposure to these molecules. The thermal avoidance index was used to phenotype each tested C. elegans experimental group. The data revealed for the first time that THC and CBD decreases the nocifensive response of C. elegans to noxious heat (32–35 °C). The effect was reversed 6 h post- CBD exposure but not for THC. Further investigations using specific mutants revealed CBD and THC are targeting different systems, namely the vanilloid and cannabinoid systems, respectively. Proteomic analysis revealed differences following Reactome pathways and gene ontology biological process database enrichment analyses between CBD or THC-treated nematodes and provided insights into potential targets for future drug development. [ABSTRACT FROM AUTHOR]

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying welldefined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the relationship between the dose and efficacy of acupuncture in treating limb dysfunction during acute stroke. Methods: Studies were searched from seven databases, including PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Wanfang Data (WF), VIP information database (VIP), and China Biology Medicine Database (CBM). All databases were searched until August 1, 2023 from inception. The risk of bias was assessed using Cochrane Collaboration's risk of bias tool (RoB2). Meta-analyses were performed using RevMan V.5.4 and Stata 12.0 statistical software. We used Fugl-Meyer Assessment (FMA) to measure recovery of limb dysfunction, NIH Stroke Scale (NIHSS) to measure neurological deficits, and Barthel index, Modified Barthel Index (MBI), and Activities of Daily Living (ADL) to measure activities of daily living. The primary outcome measure is FMA. After examining and integrating the raw data, we performed a metaanalysis using a 3-step process. First, we investigated the dose-related effects of acupuncture at varying doses and determined the optimal dosage for maximum therapeutic benefits. Second, we determined the difference between postintervention and baseline scores on the outcomes of interest to determine minimal clinically important differences (MCID) to provide evidence for clinical treatment. Third, by combining the results of step 1 and step 2, we made the recommendations employing the Grades of Recommendations, Assessment, Development and Evaluations (GRADE) tool. Results: Twenty-six studies containing 1947 participants were included, among which 61.5% of RCTs had a low risk of bias. Through the three-step analysis, the effect in improving limb dysfunction of acute stroke varied across different acupuncture dosages. Regarding the frequency of acupuncture, the results demonstrated a significant improvement in the low (every other day) and moderate-frequency (once a day) groups (low frequency: MD: 9.02, 95%CI: 5.40-12.64, p < 0.00001; moderate frequency: MD: 10.11, 95%CI: 5.05-15.18, p < 0.00001, heterogeneity (p = 0.87), I2 = 0%). For the acupuncture retention time, the results showed no significant difference between the short and medium retention groups (short retention time: MD: 0.05, 95% CI: -0.21-0.31, p = 0.71; medium retention time: MD: -1.16, 95% CI: -2.80-0.48, p = 0.17, heterogeneity (p < 0.00001), I2 = 99%). For the course of acupuncture, the results showed a significant improvement in the short course treatment (less than 2 weeks) group (MD: 14.87, 95% CI: 12.18-17.56, p < 0.00001, heterogeneity (p = 0.45), I2 = 0%). Conclusion: Our study demonstrated the effectiveness of different acupuncture dose in improving limb dysfunction. The pooled data suggested that the optimal intervention dose for acupuncture interval time was low (every other day) and moderate frequency (once a day), the optimal intervention dose for needle course time was short course treatment (less than 2 weeks). But we did not find the optimal intervention dose for needle retention time. Future studies of higher quality are needed to confirm this. [ABSTRACT FROM AUTHOR]

LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A. [ABSTRACT FROM AUTHOR]

Biomedical text document classification is an essential task within Natural Language Processing (NLP), with applications ranging from sentiment analysis to authorship identification. Despite advancements in traditional machine-learning algorithms like Support Vector Machines (SVM) and Logistic Regression, challenges such as data sparsity and high dimensionality persist. Recent years have seen a surge in the use of deep learning models to mitigate these issues. This study aims to conduct a comparative analysis of various machine-learning algorithms for classifying biomedical text documents. The study employs the "Medical Text Dataset - Cancer Doc Classification" from Kaggle, comprising 7570 biomedical text documents labeled into three types of cancer (colon, lung, and thyroid). A preprocessing pipeline involving tokenization, stop-word removal, and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization is applied. Algorithms including Logistic Regression, SVM, and Multinomial Naive Bayes are evaluated through 5-fold cross-validation. Performance metrics like accuracy, precision, recall, F1 score, and area under the ROC curve (AUC ROC) are employed. Logistic Regression outperforms the other algorithms with an accuracy of 78.3% and an AUC ROC of 88.59%. SVM and Multinomial Naive Bayes follow with lower performance metrics. Hyperparameter tuning further enhances the performance of the algorithms, particularly Logistic Regression. The study makes a significant contribution to the field of biomedical text classification by systematically comparing machine-learning algorithms. Logistic Regression emerges as the most effective, emphasizing the importance of algorithm selection and hyperparameter tuning in machine learning applications within this domain. [ABSTRACT FROM AUTHOR]

Recently, consumers have become increasingly interested in natural, health‐promoting, and chronic disease‐preventing medicine and food homology (MFH). There has been accumulating evidence that many herbal medicines, including MFH, are biologically active due to their biotransformation through the intestinal microbiota. The emphasis of scientific investigation has moved from the functionally active role of MFH to the more subtle role of biotransformation of the active ingredients in probiotic‐fermented MFH and their health benefits. This review provides an overview of the current status of research on probiotic‐fermented MFH. Probiotics degrade toxins and anti‐nutritional factors in MFH, improve the flavor of MFH, and increase its bioactive components through their transformative effects. Moreover, MFH can provide a material base for the growth of probiotics and promote the production of their metabolites. In addition, the health benefits of probiotic‐fermented MFH in recent years, including antimicrobial, antioxidant, anti‐inflammatory, anti‐neurodegenerative, skin‐protective, and gut microbiome‐modulating effects, are summarized, and the health risks associated with them are also described. Finally, the future development of probiotic‐fermented MFH is prospected in combination with modern development technologies, such as high‐throughput screening technology, synthetic biology technology, and database construction technology. Overall, probiotic‐fermented MFH has the potential to be used in functional food for preventing and improving people's health. In the future, personalized functional foods can be expected based on synthetic biology technology and a database on the functional role of probiotic‐fermented MFH. [ABSTRACT FROM AUTHOR]

Motivation Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. Results By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α -amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. Availability and implementation All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict. [ABSTRACT FROM AUTHOR]

Background The 2022 Barcelona Clinic Liver Cancer (BCLC) algorithm does not recommend liver resection (LR) in BCLC A patients with oligo-nodular (two or three nodules ≤3   cm) hepatocellular carcinoma (HCC). This sharply contrasts with the therapeutic hierarchy concept, implying a precise treatment order exists within each BCLC stage. This study aimed to compare the outcomes of LR versus radiofrequency ablation (RFA) or trans-arterial chemoembolization (TACE) in BCLC A patients. Methods A meta-analysis adhering to PRISMA guidelines and the Cochrane Handbook was performed. All RCT, cohort and case–control studies that compared LR versus RFA or TACE in oligo-nodular BCLC A HCC published between January 2000 and October 2023 were comprehensively searched on PubMed, Embase, the Cochrane Library and China Biology Medicine databases. Primary outcomes were overall survival (OS) and disease-free survival (DFS) at 3 and 5 years. Risk ratio (RR) was computed as a measure of treatment effect (OS and DFS benefit) to calculate common and random effects estimates for meta-analyses with binary outcome data. Results 2601 patients from 14 included studies were analysed (LR = 1227, RFA = 686, TACE = 688). There was a significant 3- and 5-year OS benefit of LR over TACE (RR = 0.55, 95% c.i. 0.44 to 0.69, P < 0.001 and RR 0.57, 95% c.i. 0.36 to 0.90, P = 0.030, respectively), while there was no significant 3- and 5-year OS benefit of LR over RFA (RR = 0.78, 95% c.i. 0.37 to 1.62, P = 0.452 and RR 0.74, 95% c.i. 0.50 to 1.09, P = 0.103, respectively). However, a significant 3- and 5-year DFS benefit of LR over RFA was found (RR = 0.70, 95% c.i. 0.54 to 0.93, P = 0.020 and RR 0.82, 95% c.i. 0.72 to 0.95, P = 0.015, respectively). A single study comparing LR and TACE regarding DFS showed a significant superiority of LR. The Newcastle–Ottawa Scale quality of studies was high in eight (57%) and moderate in six (43%). Conclusions In BCLC A oligo-nodular HCC patients, LR should be preferred to RFA or TACE (therapeutic hierarchy concept). Additional comparative cohort studies are urgently needed to increase the certainty of this evidence. [ABSTRACT FROM AUTHOR]

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene–disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

Introduction: A common task in the analysis of microbial communities involves assigning taxonomic labels to the sequences derived from organisms found in the communities. Frequently, such labels are assigned using machine learning algorithms that are trained to recognize individual taxonomic groups based on training data sets that comprise sequences with known taxonomic labels. Ideally, the training data should rely on labels that are experimentally verified--formal taxonomic labels require knowledge of physical and biochemical properties of organisms that cannot be directly inferred from sequence alone. However, the labels associated with sequences in biological databases are most commonly computational predictions which themselves may rely on computationallygenerated data--a process commonly referred to as "transitive annotation." Methods: In this manuscript we explore the implications of training a machine learning classifier (the Ribosomal Database Project's Bayesian classifier in our case) on data that itself has been computationally generated. We generate new training examples based on 16S rRNA data from a metagenomic experiment, and evaluate the extent to which the taxonomic labels predicted by the classifier change after re-training. Results: We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed. Discussion and conclusions: We conclude with a discussion of key factors that affect the resilience of classifiers to transitively-annotated training data, and propose best practices to avoid the artifacts described in our paper. [ABSTRACT FROM AUTHOR]

Collection management faces many challenges in keeping stored items preserved and the information associated with them accurate and organized. It is essential for the expansion and use of this biodiversity repository that the database is unambiguous and that errors are quickly identified and corrected. This work aims to show the use of interactive visual representations (IVRs) of the collection's metadata as tools to inspect the data and help solve these challenges. To do this, we used the Annelida collection database from the National Museum (MN) of the Federal University of Rio de Janeiro (UFRJ). Interactive graphs of the metadata within this database (catalog date, taxonomic identification and determiners, sampling, depth, geographic localization, and collector data) were created with the Altair library in the Python 3 language. Data analyses using these graphs made it possible to identify anomalous patterns in the data and fill in missing records. They also provided an understanding of the spatial and bathymetric distribution of the specimens deposited over time, and the growth rate of the collection in each family, thus projecting future growth and solutions for the physical organization of vials. Graphs are an ally in the management of collections with digital entry forms and aim to facilitate the availability of metadata associated with cataloged specimens. Likewise, IVRs can even be used to give credit to the researchers involved in building biological collections. Thus, visualization tools are efficient in recognizing global patterns present in databases and solving biological collection management tasks. [ABSTRACT FROM AUTHOR]

Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that depict the relationships between the diverse items participating in biological procedures, such as genes and chemical compounds. However, the size, complexity, and diversity of these graphs make them difficult to explore and understand, as well as making it difficult to extract a clear conclusion regarding their most important components. In this regard, we present KEGGSum, a system enabling the efficient and effective summarization of KEGG pathways. KEGGSum receives a KEGG identifier (Kid) as an input, connects to the KEGG database, downloads a specialized form of the pathway, and determines the most important nodes in the graph. To identify the most important nodes in the KEGG graphs, we explore multiple centrality measures that have been proposed for generic graphs, showing their applicability to KEGG graphs as well. Then, we link the selected nodes in order to produce a summary graph out of the initial KEGG graph. Finally, our system visualizes the generated summary, enabling an understanding of the most important parts of the initial graph. We experimentally evaluate our system, and we show its advantages and benefits. [ABSTRACT FROM AUTHOR]

Background: Xicotli data is the short name given to the dataset generated within the project framework "Integration of Biodiversity Data for the Management and Conservation of Wild Bee-Plant Interactions in Mexico (2021-2023)", as xicotli is the generic word for a bee in Nahuatl. The team comprised eco-informaticians, ecologists and taxonomists of both native bees and flora. The generated dataset contains so far 4,532 curated records of the plants, which are potential hosts of species of three focal families of bees native to Mexico: Apidae, Halictidae and Megachilidae and morphological and ecological data of the plant-bee interactions. This dataset was integrated and mobilised from citizen observations available at naturalista.mx (iNat), which were compiled through the iNaturalist project. New information: The new information obtained with the Xicotli data project was: 1. Taxonomic information about bee species curated by taxonomists based on the information contained in iNaturalist; 2. Taxonomic identification of the host plants by a botanist from the photos compiled by the Xicotli Data project; 3. Data on the ecomorphological traits of bees and plants based on expert knowledge and literature. All the data were integrated into the Xicotli Data Project via the creation of new "observation fields". The visibility of the information originally contained in iNaturalist was maximized and can be consulted directly on the iNaturalist platform. [ABSTRACT FROM AUTHOR]

Background: More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data. Results: In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage. Conclusions: DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO. [ABSTRACT FROM AUTHOR]

Neosalanx taihuensis is widely distributed in freshwater and brackish water areas in China. Due to its high commercial value, it has been artificially introduced into many lakes and reservoirs, showing strong ecological adaptability. Here, a gap-free chromosome-level reference genome was constructed by combining short reads, PacBio HiFi long reads, Nanopore ultralong reads and Hi-C data. The reference genome of N. taihuensis was 397.29 Mb with a contig N50 of 15.61 Mb. The assembled sequences were anchored to 28 chromosomes. Furthermore, 20,024 protein-coding genes and 98.16% of the predicted genes were annotated in publicly available biological databases. This high-quality gap-free assembled genome will provide an essential reference for studying the evolution and ecological adaptability of N. taihuensis. [ABSTRACT FROM AUTHOR]

Motivation Fungal pathogens are known to cause life threatening invasive infections with rising global mortality rates. Besides, the indiscriminate use of antifungals in both clinics and agriculture has promoted antifungal drug resistance in the last decade. Fungi can show drug resistance by a variety of mechanisms. But primary driver in all these hitherto documented mechanisms is stable and heritable point mutations in the key proteins. Therefore, cataloguing mutations that can confer resistance is the first step toward understanding the mechanisms leading to the emergence of antifungal resistance. Results In the present, work we have described a database of all the mutations responsible for antifungal resistance. Named as antifungal resistance database (AFRbase), it is better than the existing databases of antifungal resistance namely, FunResDB and MARDy which have a limited scope and inadequate information. Data of AFRbase was collected using both text mining and manual curation. AFRbase provides a separate window for visualization of mutations in the 2D and 3D formats making it easy for researchers to analyze the mutation data and ensures interoperability with other standard molecular biology databases like NCBI and UniProtKB. We hope AFRbase can be useful to both clinicians and basic biomedical scientists as we envision it as an important resource for genotypic susceptibility testing of fungi and to study/predict the course of evolution of antifungal resistance. The current version of AFRbase contains manually curated 3691 unique mutations present in 29 proteins of 32 fungal species along with the information of drugs against which resistance is caused. Availability and implementation AFRbase is an open access database available at http://proteininformatics.org/mkumar/afrbase/. [ABSTRACT FROM AUTHOR]

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets. [ABSTRACT FROM AUTHOR]

To systematically evaluate the efficacy of acupuncture in the treatment of schizophrenia.Methods: We searched China National Knowledge Infrastructure (CNKI), Wanfang Database, Chongqing VIP Chinese Science and Technology Periodical Database (VIP), China Biology Medicine Database (CBM), PubMed, Embase, Web of Science, Cochrane Library for relevant literature on the acupuncture treatment of schizophrenia published from database inception to May 17, 2023. The evaluation criteria included total effective rate, incidence of adverse reactions, TESS scale, PANSS scale, BPRS scale, SANA scale, SAPS scale. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. The RevMan 5.4 software was used for meta-analysis, risk of bias (ROB) evaluation tool was used to evaluate the risk of bias of the studies, and the GRADE evaluation tool was used to evaluate the quality of evidence. The study was registered on PROSPERO, CRD42023416438.Results: A total of 38 RCTs involving 3143 patients were included in the meta-analysis. The results showed that acupuncture can improve the total effective rate [OR=3.43 (95% CI: 2.71, 4.35), moderate credibility], reduce the incidence of adverse reactions [OR=0.45 (95% CI: 0.32, 0.63), moderate credibility], reduce the TESS score (side effect scale) [MD=− 1.83 (95% CI: − 2.94, − 0.71), very low credibility]. Acupuncture also reduced the PANSS total score [MD=− 5.75 (95% CI: − 8.08, − 3.42), very low credibility], SANA score [MD=− 2.66 (95% CI: − 6.84, 1.51), very low credibility], SAPS score [MD=− 1.26 (95% CI: − 2.55, − 0.02), very low credibility], and BPRS score [MD=− 7.02 (95% CI: − 10.59, − 3.46), very low credibility].Conclusion: Existing evidence indicates that acupuncture as an adjunctive therapy can improve the total effective rate of SZ patients, reduce the incidence of adverse reactions, improve clinical symptoms, and alleviate depression and anxiety in SZ patients. However, more high-quality clinical research evidence is still needed to support these findings. [ABSTRACT FROM AUTHOR]

Biological databases are essential resources for life science research, but finding and selecting the most relevant and up-to-date databases can be challenging due to the large number and diversity of available databases. The Nucleic Acids Research (NAR) journal publishes annual database issues that provide a comprehensive list of databases in the molecular biology domain. However, the information provided by NAR is limited and sometimes does not reflect the current status and quality of the databases. In this article, we present a web-based platform for the annotation and analysis of NAR-published databases. The platform allows users to manually curate and enrich the NAR entries with additional information such as availability, downloadability, source code links, cross-references, and duplicates. Statistics and visualizations on various aspects of the database landscape, such as recency, status, category, and curation history are also provided. Currently, it contains a total of 2,246 database entries of which 2,025 are unique with the majority updated within the last five years. Around 75% of all databases are still available and more than half provide a download option. Cross references to Database Commons are available for 1,889 entries. The platform is freely available online at https://nardbstatus.kalis-amts.de and aims to help researchers in database selection and decision-making. It also provides insights into the current state and challenges of a subset of all databases in the life sciences. [ABSTRACT FROM AUTHOR]

PX domain containing serine/threonine kinase (PXK) is a multitissue, broad-spectrum expressed gene that plays an important role in immune-related diseases. The objective of this study was to analyze the relationship between PXK and the development of acute myelocytic leukemia (AML) by using bioinformatics methods in multiple databases. The UALCAN database was utilized to conduct an online analysis of PXK expression in various subtypes of AML. LinkedOmics was utilized to analyze the correlation between the expression level of PXK and the clinicopathologic features of AML. The Kaplan-Meier Plotter database was used to analyze the mRNA expression of PXK in AML and to evaluate the effect of PXK on survival in AML. PXK interacting protein network and expression correlation were analyzed by String and GEPIA databases. The correlation between PXK gene and tumor infiltrating immune cells was analyzed by quanTIseq algorithm, and" xCell" R package was used to analyze the correlation between PXK gene expression and multiple immune checkpoints. PXK and its co-expressed genes were functionally enriched in the Metascape database to anticipate their biological roles. The findings revealed that PXK was highly expressed in various cancer tumor tissues when compared to normal tissues, and the expression level of PXK in AML was significantly up-regulated, which had an effect on the clinicopathological features, survival rate, and prognosis of AML patients, whereas the prognosis of AML patients with high PXK expression was better. PXK is highly expressed in AML tissues and is closely related to the initiation and progression of AML, which may be one of the important indicators for the diagnosis, treatment and prognosis evaluation of AML. [ABSTRACT FROM AUTHOR]

Problem: Endometriosis (EMS) is an estrogen‐dependent disease which is characterized with estrogen‐dependent growth of ectopic endometrium and increased local estrogen production. EMS performs tumor‐like biological functions such as invasiveness and angiogenesis. Rab27b is a member of the Rab family of GTPases, which is strongly associated with the growth, invasion and metastasis of a variety of tumors. However, little is known about the function of Rab27b in EMS. In this study, we intended to investigate the impact of Rab27b and its downstream molecule in the development of EMS. Method of study: Normal endometrium and endometriotic lesions were collected to investigate the expression levels of Rab27b. Then, ESCs were transfected with Rab27b siRNA. We analyzed the influence of Rab27b on the proliferation and invasive activity of ESCs. Conditioned media harvested from Rab27b siRNA‐treated ESCs were used to treat HUVECs. HUVEC Tube formation and ELISA were performed to explored the interactions between ESCs and HUVEC. In addition, ESCs were treated with different concentrations of estrogen. Based on biological database predictions, we explored possible mechanisms through which estrogen regulates the expression of Rab27b. Results: The expressions of Rab27b were significantly higher in endometriotic lesions than that in normal endometrium. Rab27b can promote the cell proliferation, migration and invasion of ESCs. The elevated expression of Rab27b, on the one hand, promotes the secretion of MMP9 and increases the invasiveness of ESCs. On the other hand, Rab27b may play a key role in the communication between ESC and endothelial cells, by simulating VEGF secretion and neovascularization. Besides, estrogen upregulated phosphorylated FOXO1 levels in ectopic ESCs, resulting in the promotion of Rab27b expression levels. Conclusion: Rab27b plays a key role in the development of EMS, which may provide new insights into the pathogenesis of EMS. Our findings may also contribute to the development of therapeutic interventions for EMS. [ABSTRACT FROM AUTHOR]

The Eukaryotic Pathogen, Vector and Host Informatics Resources (VEuPathDB), a database containing detailed molecular information on parasites and fungi that cause infectious diseases, has lost funding from the U.S. National Institute of Allergy and Infectious Diseases (NI-AID). The database, which had 46,000 unique users per month in over 150 countries, provided valuable information for researchers studying diseases like malaria and leishmaniasis. While two new platforms funded by NI-AID claim to serve the same needs, many scientists argue that they do not adequately replace VEuPathDB's capabilities. Temporary funding has been secured to keep the database online for one year, but long-term sustainability remains uncertain. [Extracted from the article]

Objective: Steroids-refractory (SR) acute graft-versus-host disease (aGVHD) is a life-threatening condition in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), but the optimal second-line therapy still has not been established. We aimed to perform a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the efficacy and safety of different second-line therapy regimens. Methods: Literature search in MEDLINE, Embase, Cochrane Library and China Biology Medicine databases were performed to retrieve RCTs comparing the efficacy and safety of different therapy regimens for patients with SR aGVHD. Meta-analysis was conducted with Review Manager version 5.3. The primary outcome is the overall response rate (ORR) at day 28. Pooled relative risk (RR) and 95% confidence interval (CI) were calculated with the Mantel-Haenszel method. Results: Eight eligible RCTs were included, involving 1127 patients with SR aGVHD and a broad range of second-line therapy regimens. Meta-analysis of 3 trials investigating the effects of adding mesenchymal stroma cells (MSCs) to other second-line therapy regimens suggested that the addition of MSCs is associated with significantly improvement in ORR at day 28 (RR = 1.15, 95% CI = 1.01-1.32, P = 0.04), especially in patients with severe (grade III-IV or grade C-D) aGVHD (RR = 1.26, 95% CI = 1.04-1.52, P = 0.02) and patients with multiorgan involved (RR = 1.27, 95% CI = 1.05-1.55, P = 0.01). No significant difference was observed betwwen the MSCs group and control group in consideration of overall survival and serious adverse events. Treatment outcomes of the other trials were comprehensively reviewed, ruxolitinib showed significantly higher ORR and complete response rate at day 28, higher durable overall response at day 56 and longer failure-free survival in comparison with other regimens; inolimomab shows similar 1-year therapy success rate but superior long-term overall survial in comparison with anti-thymocyte globulin, other comparisons did not show significant differences in efficacy. Conclusions: Adding MSCs to other second-line therapy regimens is associated with significantly improved ORR, ruxolitinib showed significantly better efficacy outcomes in comparison with other regimens in patients with SR aGVHD. Further well-designed RCTs and integrated studies are required to determine the optimal treatment. [ABSTRACT FROM AUTHOR]

The development of tools for the annotation of genes from newly sequenced species has not evolved much from homologous alignment to prior annotated species. While the quality of gene annotations continues to decline as we sequence and assemble more evolutionary distant gut microbiome species, machine learning presents a high quality alternative to traditional techniques. In this study, we investigate the relative performance of common classical and nonclassical machine learning algorithms in the problem of gene annotation using human microbiome-associated species genes from the KEGG database. The majority of the ensemble, clustering, and deep learning algorithms that we investigated showed higher prediction accuracy than CD-Hit in predicting partial KEGG function. Motif-based, machine-learning methods of annotation in new species were faster and had higher precision–recall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity in reconstructed KEGG pathways, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment alone. [ABSTRACT FROM AUTHOR]

The analysis of the relationship between the growing interest in artificial neural net- work and it's negligent exploitation by students of secondary and higher schools in order to minimize personal intellectual effort is conducted in work. The author has proposed the biographical and literary pattern from the ancient history - Solomon's polyfunctional as one of the self stimulation tools of participation in educational process. That is con- sidered as a universal analogue of comprehensive use of personal potential. The personal development path is confirmed by biological data. [ABSTRACT FROM AUTHOR]

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project. [ABSTRACT FROM AUTHOR]

As biological sequence databases continue growing, so do the insight that they promise to shed on the shape of the genetic diversity of life. However, to fulfil this promise the software must remain usable, be able to accommodate a large amount of data and allow use of modern high performance computing infrastructure. In this study we present a reimplementation as well as an extension of a technique using indicator vectors to compute and visualize similarities between sets of nucleotide sequences. We have a flexible and easy to use python program relying on standard and open-source libraries. Our tool allows analysis of very large complement of sequences using code parallelization, as well as by providing routines to split a computational task in smaller and manageable subtasks whose results are then merged. This implementation also facilitates adding new sequences into an indicator vector-based representation without re-computing the whole set. The efficient synthesis of data into knowledge is no trivial matter given the size and rapid growth of biological sequence databases. Based on previous results regarding the properties of indicator vectors, the open-source approach proposed here efficiently and flexibly supports comparative analysis of genetic diversity at a large scale. Our software is freely available at: https://github.com/WandrilleD/pyKleeBarcode. [ABSTRACT FROM AUTHOR]

Copyright of Journal of University of Anbar for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For decades, query processing over uncertain databases has received much attention from the database community due to the pervasive data uncertainty in many real-world applications such as location-based services (LBS), sensor networks, business planning, biological databases, and so on. In this paper, we will study a novel query type, namely range-constrained probabilistic mutual furthest neighbor query (PMFN), over uncertain databases. PMFN retrieves a set of object pairs, (o i , o j) , within a given query range Q, such that uncertain objects o i and o j are furthest neighbors of each other with high probabilities. In order to efficiently tackle the PMFN problem, we propose effective pruning methods, range, convex hull, and hypersphere pruning, for filtering out uncertain objects that can never appear in the PMFN answer set. Then, we also design spatial and probabilistic pruning methods to rule out false alarms of PMFN candidate pairs. Finally, we utilize a variant of the R ∗ -tree to integrate our proposed pruning methods and efficiently process ad hoc PMFN queries. Extensive experiments show the efficiency and effectiveness of our pruning techniques and PMFN query processing algorithms over real and synthetic data sets. [ABSTRACT FROM AUTHOR]

Background: The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. Results: We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. Conclusions: SynBioTools is freely available at https://synbiotools.lifesynther.com/. It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection. [ABSTRACT FROM AUTHOR]