Medinformatics

Spatio-Temporal Attributes of Varicella-Zoster Case Number Trends Assist with Optimizing Machine Learning Predictions

David A. Wood — Wed, 25 Oct 2023 00:00:00 +0800

The varicella-zoster virus (VZV) (chickenpox) is a problematic infectious disease with regular outbreaks occurring seasonally in most countries. Being able to predict with accuracy the expected number of cases in future weeks based on historical case trend information is an important goal both locally and nationally. Space and time-related attributes extracted from the case number trends for the previous 12 weeks of historical VZV cases recorded in Hungary. These attributes are able to generate reliable predictions for expected VZV cases for multiple weeks ahead. Supervised machine learning (SML) combined with feature selection optimizers can identify combinations of the most effective of 15 local trend time-series attributes supported. These features are complemented with an additional 10 regional trend attributes providing the spatial dimension. The most practical combination of influential trend attributes varies depending on the number of weeks ahead being forecast. SML models are developed using weekly VZV case data (2005–2014) for the regions of Hungary focusing on the region of Komarom-Esztergom (Kom) northwest of Budapest. SML predictions for up to 4 weeks ahead are most strongly influenced by the local time-series attributes including moving averages (MAs) and seasonality components from recently past weeks. However, for predictions further forward (up to 13 weeks) the SML models also exploit regional trend attributes related to recent past rate-of-change in VZV case numbers to provide effective predictions. The proposed trend-attribute method provides more accurate case predictions than the commonly used univariate case-forecasting methods relying on MA and autoregressive integrated moving models. The applied method also provides a means of data mining the most influential trend attributes and the time ranges of their effectiveness. The flexibility and transparency of the technique provide a robust method that could be applied for forecasting short-term epidemiological case numbers associated with other infectious diseases.

Received: 3 September 2023 | Revised: 4 October 2023 | Accepted: 19 October 2023

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in UC Irvine Machine Learning Repository at https://doi.org/10.24432/C5103B

In Silico Annotation and Immunoinformatics Guided Epitope Mapping of Potential Antigenic Proteins of Trichomonas foetus

Thu, 21 Mar 2024 00:00:00 +0800

Bovine trichomonosis is one of the neglected tropical diseases of cattle that is resulting in severe reproductive failure. With present knowledge, disease diagnosis and maintaining the infected animals in the quarantine are the only available strategies. Several spillover incidences of Trichomonas foetus had also resulted in zoonotic transmission to humans. In spite of above circumstances, till date there are no point of care diagnostics developed for screening bovine trichomoniasis in cattle. In the light of above circumstances, there exists a demand for cost-effective diagnostic kits to be provided to farming community. This current study highlights evaluation of few surface proteins of Trichomonas foetus for the suitability as sero-diagnostic markers. Few target Proteins such as Adhesin, Immuno-dominant variable surface antigen-like protein, Polymorphic membrane protein - like protein, GP-63-like (Clan MA, family M8) protein and Hypothetical protein (OHS95735.1) were evaluated for suitable pH, Signal Peptide, protein glycosylation pattern using freely available Bioinformatics tools. Mapping of potential epitopes of all the target proteins was done using immunoinformatics tools. Among the above proteins, GP63-like protein, immuno-dominant variable antigenic domain-like protein, and polymorphic membrane protein-like proteins are most suitable as diagnostic targets, owing to their higher levels of glycosylation, large epitope domains, and showing structural similarities with the domains of known toxic proteins. On the other hand, adhesin protein has the potential to be exploited as a vaccine candidate. The above proteins are suitable to be expressed in suitable host system and validated the immunogenic potential by animal inoculation and by testing with the real samples.

Received: 25 November 2023 | Revised: 28 February 2024 | Accepted: 15 March 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Molecular Docking Studies of Bioactive Constituents of Long Pepper, Ginger, Clove, and Black Pepper to Target the Human Cathepsin L Protease: As a Natural Therapeutic Strategy Against SARS-Cov-2

Kaushal Kishor Sharma, Brijendra Singh, P. S. Bisen, D. D. Agarwal — Sun, 31 Dec 2023 00:00:00 +0800

Human cathepsin L protease is involved in the cleavage of S protein of SARS-Cov-2 virus and activates the membrane fusion, which mediates the entry of the virus into the host cell. Thus, it suggests the cathepsin L protease is critical for the entry of SARS-Cov-2. Currently, chemically synthesized cathepsin L inhibitors are present, but the consumption of chemically synthesized drugs is also an alarming stage due to its side effects, illness, and age reduction. In this study, natural bioactive constituents of long pepper, ginger, clove, and black pepper that has been widely known for antiviral effect and other medicinal properties were used for molecular docking against the human cathepsin L receptor (PDB ID 2XU1). Molecular docking (using a software, AutoDock 4.2) was performed on bioactive constituents of long pepper, ginger, clove, and black pepper against the human cathepsin L protease and elucidates the binding energies, visualization, and analysis of interacting residue (using Discovery studio) at the docking site of cathepsin L protease and compared the docking analysis of these bioactive constituents with preclinical cathepsin L inhibitor (Pub Chem CID: 16725315). The pharmacokinetic properties and toxicity evaluation were calculated by Datawarrior and Osiris Molecular Property explorer software, respectively. Many bioactive constituents from long pepper, ginger, clove, and black pepper have shown significant binding affinity, docking interactions and acceptable pharmacokinetic properties with the human cathepsin L protease. Piperolactam A constituent of long pepper and Kaempferol constituent of clove were found to be more acceptable natural therapeutic compounds among other selected bioactive constituents with the highest binding affinity (Kcal/mol) −9.4 and −9.3, respectively.

Received: 21 August 2023 | Revised: 22 December 2023 | Accepted: 25 December 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Elastic Net – MLP – SMOTE (EMS)-Based Model for Enhancing Stroke Prediction

Hussam Mezher Merdas — Wed, 03 Apr 2024 00:00:00 +0800

A stroke is a sudden disruption in the blood supply to the brain, affecting one or more blood vessels that nourish the brain. This results in a disturbance or deficiency in the brain’s oxygen supply, causing damage or impairment to brain cells. In some cases, determining the timing and severity of a stroke can be challenging. This study proposes an EMS (Elastic Net – MLP – SMOTE) model built on artificial intelligence, specifically utilizing two machine learning algorithms, Elastic Net and multilayer perceptron (MLP) by using Synthetic Minority Over-sampling Technique (SMOTE). The Elastic Net algorithm was employed for feature selection to identify crucial features, followed by prediction using the MLP algorithm. The Elastic Net algorithm was used due to its incorporation of both L2 and L1 regularization, providing good results in discerning influential features in model performance. The MLP algorithm was employed for its reliance on deep learning techniques, which yield promising results in such cases. This algorithm classified data from a comprehensive dataset containing essential features related to stroke. SMOTE is used to increase the performance of the model. Notably, no previous research study has integrated these three techniques together (Elastic Net – MLP – SMOTE). EMS achieved a prediction accuracy of 95% and MSE = 0.05. This model facilitates predicting the occurrence of stroke by relying on the patient’s historical data, mitigating the sudden onset of this serious disease.

Received: 15 January 2024 | Revised: 22 February 2024 | Accepted: 28 March 2024

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Meenal Joshi, Bhupesh Kumar Singh — Mon, 04 Mar 2024 00:00:00 +0800

Vagueness in the determination of the tumor size creates significant hindrances in planning and quantitatively assessing brain tumor (BT) treatments. Non-invasive magnetic resonance imaging (MRI) has become a primary non-ionizing radiation diagnostic tool for brain cancers. It takes a long time to manually segment the extent of a BT from 3D MRI volumes, and the performance heavily depends on the operator’s skill. A precise and automated BT segmentation tool is needed desperately. In this case, an accurate assessment of the tumor’s extent requires a reliable automated segmentation method for the BT. The multimodal BT image segmentation (BRATS 2020) dataset is used in this paper to demonstrate an automated deep convolutional network, or U-Net, method for BT segmentation. Deep learning and transfer learning are utilized to improve the accuracy and effectiveness in detecting and recognizing different types of brain cancers. The unobserved images’ F1 scores were 98% and 99%, respectively.

Received: 4 September 2023 | Revised: 4 February 2024 | Accepted: 27 February 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author. The dataset analyzed for another part of the study can be found in the BraTS 2020.

Genetic Variant rs1800795 (G>C) in the Interleukin 6 (IL6) Gene and Susceptibility to Coronary Artery Diseases, Type 2 Diabetes, Acute Pancreatitis, Rheumatoid Arthritis, and Bronchial Asthma in Asians: A Comprehensive Meta-Analysis Based on 30154 Subjects

Md. Harun-Or-Roshid, Md. Nurul Haque Mollah, Jesmin — Sat, 06 Jan 2024 00:00:00 +0800

Numerous studies conducted globally have explored the possible connection between the IL6 gene variant rs1800795 (G>C) and the risk of several diseases. Nonetheless, the correlation specifically within the Asian population remains inconclusive. Hence, this extensive meta-analysis aims to establish a conclusive correlation between the rs1800795 variant and susceptibility to various diseases among Asians. Fifty eligible articles were chosen from Google Scholar, PubMed, Web of Science, and PMC based on specific inclusion criteria. Odds ratios alongside 95% confidence intervals (CI) were utilized. Additionally, subgroup analysis, publication bias, and sensitivity evaluation were conducted. The analysis, of 14,737 cases and 15,417 controls, showed a notable correlation between the rs1800795 (G>C) single-nucleotide polymorphism and the overall disease susceptibility to all models (p-value <2.5E-05). The ethnicity-specific stratified findings indicated that the C-allele of (C vs. G) model of −174G/C polymorphism expressively elevated the overall disease susceptibility in both East and South Asian populations. The disease-based stratified analyses suggested that the C variant of rs1800795 was related to coronary artery diseases and bronchial asthma (under all models), type 2 diabetes (CG vs. GG), acute pancreatitis (AP) (C vs. G; and CC vs. GG), rheumatoid arthritis (CC+CG vs. GG; CC vs. CG+GG; and C vs. G), and AP (C or CC vs. G or GG respectively). IL6 rs1800795 polymorphism is a highly significant disease risk factor in Asians and can potentially serve as a prognostic biomarker for future disease screening and evaluation.

Received: 3 November 2023 | Revised: 18 December 2023 | Accepted: 31 December 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.