Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

Analysis of the climatic trends and heat stress periods for ruminants rearing in Bangladesh.

The production of ruminant livestock is greatly impacted by climate change, as it is anticipated to jeopardise food security due to the increasing heat stress experienced by the animals, which can be measured using the Temperature Humidity Index (THI). The objective of our study was to analyze climatic patterns, identify influential variables and evaluate heat stress episodes through the utilization of the THI to establish a rearing system for ruminants in Bangladesh. The THI value was determined by analyzing meteorological station data spanning from 1995 to 2022 across various climatic zones in Bangladesh. The Mann-Kendall evaluation was used to analyze the THI patterns throughout the study. Our findings indicated that heat stress problems are expected to occur in Bangladesh when THI for ruminant rearing exceeds 74, particularly from February to December. The severity of heat stress in THIruminant 71-90 varied significantly, ranging from normal to extremely severe. We observed that June (90) was the hottest month in the west central region, while January (71) was the coldest in the northwest area. When examining the impact of climatic factors on the THI, we found that air temperature has the highest influence, while relative humidity had the second-highest influence on THI in all areas of Bangladesh. Sunlight length and wind speed influenced the yearly THI marginally but not seasonally. Our findings highlighted a seasonal threat associated with heat stress in the climatic conditions of Bangladesh. It is essential to identify heat stress in ruminants, especially considering the continuing global warming issue. Our results recommend the implementation of heat stress mitigation strategies for ruminant farmers in Bangladesh.

Assessment of knowledge, attitudes, and practices on vaccine usage among small ruminant farmers in the Northern Region of Bangladesh.

Background and Aim: Small ruminants require vaccines to prevent and manage diseases. Unfortunately, no studies have been conducted in Bangladesh to assess the knowledge, attitudes, and practices (KAP) of small ruminant farmers (SRF) regarding vaccine use against infectious diseases, affecting the success of vaccination campaigns. The present study aims to assess SRF's KAP regarding vaccines, revealing gaps and barriers to efficient vaccination. Materials and Methods: Two hundred and twenty-eight SRF in northern Bangladesh were surveyed in a cross-sectional study. Data were collected from random participants through face-to-face interviews using a structured questionnaire. KAP levels were categorized as "good" or "poor" and "positive" or "negative" using a scoring method with a 60% cutoff. The analysis comprised the utilization of descriptive statistics as well as logistic regression models. Results: Results showed that most participants were female (60.5%), aged 31-40 (34.2%), with secondary education (28.1%), and vaccination training (22.8%). While 75% knew about vaccines, only 37.3% understood their role in preventing infectious diseases, and 63.6% in reducing antibiotic use 68.4% of farmers were aware of negative drawbacks, and 61.8% reported vaccinating their herds. About 42.1% of the farmers had good knowledge, 52.6% had a positive attitude, and 22.8% followed good practices. Female farmers with graduate degrees and 6-10 years of goat farming experience, but not those with vaccination training, demonstrated stronger knowledge. Female farmers with a graduate degree and 6-10 years of goat farming experience displayed positive attitudes. Female goat farmers from Thakurgaon had a higher likelihood of following good vaccination practices than those with vaccination training. Conclusion: The study unearths disparities in KAP scores among farmers. To effectively address KAP gaps concerning vaccine usage and prevent potential infectious diseases, it is essential to design focused educational and training programs. About 52.6% of SRF hold a positive view toward vaccines.

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.

Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach.

COVID-19 has emerged as global health threats. Chronic kidney disease (CKD) patients are immune-compromised and may have a high risk of infection by the SARS-CoV-2. We aimed to detect common transcriptomic signatures and pathways between COVID-19 and CKD by systems biology analysis. We analyzed transcriptomic data obtained from peripheral blood mononuclear cells (PBMC) infected with SARS-CoV-2 and PBMC of CKD patients. We identified 49 differentially expressed genes (DEGs) which were common between COVID-19 and CKD. The gene ontology and pathways analysis showed the DEGs were associated with "platelet degranulation", "regulation of wound healing", "platelet activation", "focal adhesion", "regulation of actin cytoskeleton" and "PI3K-Akt signalling pathway". The protein-protein interaction (PPI) network encoded by the common DEGs showed ten hub proteins (EPHB2, PRKAR2B, CAV1, ARHGEF12, HSP90B1, ITGA2B, BCL2L1, E2F1, TUBB1, and C3). Besides, we identified significant transcription factors and microRNAs that may regulate the common DEGs. We investigated protein-drug interaction analysis and identified potential drugs namely, aspirin, estradiol, rapamycin, and nebivolol. The identified common gene signature and pathways between COVID-19 and CKD may be therapeutic targets in COVID-19 patients with CKD comorbidity.

rMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data.

Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.