A novel deep learning model for predicting marine pollution for sustainable ocean management.

Climate change has become a major source of concern to the global community. The steady pollution of the environment including our waters is gradually increasing the effects of climate change. The disposal of plastics in the seas alters aquatic life.

Super learner model for classifying leukemia through gene expression monitoring.

Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive.

Transfer learned deep feature based crack detection using support vector machine: a comparative study.

Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations.

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53.

Postnatal depression in British mothers of African and Caribbean origin: a randomised controlled trial of learning through play plus culturally adapted cognitive behaviour therapy compared with psychoeducation.

Background: One in every three women worldwide experiences postnatal depression after childbirth, with long-term negative consequences on their children. The mainstream mental healthcare provision for British mothers of African/Caribbean origin is mostly unsuccessful due to a lack of culturally appropriate care.

Methods: The study adopts a mixed-methods randomised controlled trial (RCT) design.

Medical ethics and compliance amongst physician groups: a self-assessed survey in a hospital in Southeast Nigeria.

Background: Being a doctor remains a moral enterprise as he is expected to make some medical decisions based on ethical principles during encounter with patients.

Objective: The objective of this study was to investigate the knowledge and application of medical ethical principles amongst physician groups in a Hospital in Enugu, Nigeria.

Methods: This was a cross-sectional self-assessed study conducted amongst medical doctors in five specialty groups in a teaching hospital in Enugu, Nigeria.

Remediation of spent engine oil-contaminated soil through biostimulation and bioaugmentation with sodium dodecyl sulphate (SDS) and indigenous hydrocarbonoclastic bacterial isolates.

Pollution caused by spent engine oil has become a major global ecological concern as it constitutes a big threat to plants, animals, microorganisms and the soil ecosystem. This study was undertaken to examine the remediation of spent engine oil-contaminated soil through biostimulation and bioaugmentation with sodium dodecyl sulphate and indigenous hydrocarbonoclastic bacterial isolates. Twelve mesocosms were organized into four groups designated G1, G2, G3 and G4 and each filled with 2.

CHAPERON: A tool for automated GROMACS-based molecular dynamics simulations and trajectory analyses.

Molecular dynamics (MD) simulation is a powerful computational tool used in biomolecular studies to investigate the dynamics, energetics, and interactions of a wide range of biological systems at the atomic level. GROMACS is a widely used free and open-source biomolecular MD simulation software recognized for its efficiency, accuracy, and extensive range of simulation options. However, the complexity of setting up, running, and analyzing MD simulations for diverse systems often poses a significant challenge, requiring considerable time, effort, and expertise.

Delineation of the CENP-LN sub-complex dissociation mechanism upon multisite phosphorylation during mitosis.

Phosphorylation is the most prevalent form of regulation in cells, organizing virtually all cellular functions, including survival, motility, differentiation, proliferation, and metabolism. This regulatory function has been largely conserved from the primitive single-cell to the more complex multicellular organisms. More than a third of proteins in eukaryotes are phosphorylated, and essentially every class of protein undergoes regulation by phosphorylation.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach.

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted.

MasitinibL shows promise as a drug-like analog of masitinib that elicits comparable SARS-Cov-2 3CLpro inhibition with low kinase preference.

SARS-CoV-2 infection has led to several million deaths worldwide and ravaged the economies of many countries. Hence, developing therapeutics against SARS-CoV-2 remains a core priority in the fight against COVID-19. Most of the drugs that have received emergency use authorization for treating SARS-CoV-2 infection exhibit a number of limitations, including side effects and questionable efficacy.

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.

Background: Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection.

Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers.

Technology is playing an important role is healthcare particularly as it relates to disease prevention and detection. This is evident in the COVID-19 era as different technologies were deployed to test, detect and track patients and ensure COVID-19 protocol compliance. The White Spot Disease (WSD) is a very contagious disease caused by virus.

Moderating Role of Initiative on the Relationship Between Intrinsic Motivation, and Self-Efficacy on Entrepreneurial Intention.

The growing population of new graduates and the increasing scarcity of employment opportunities have made entrepreneurship an unavoidable option for employment and self-sustenance. This study investigates the effect of the initiative in moderating the relationship between intrinsic motivation, self-efficacy, and entrepreneurship intention through the integrated framework of theory of planned behaviour, self-determination, and humanism. This study contributes insights to how these factors moderated by initiative influence entrepreneurial intention among graduating students of tertiary institutions in Nigeria.

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets.

There are growing concerns about the mortality due to Breast cancer many of which often result from delayed detection and treatment. So an effective computational approach is needed to develop a predictive model which will help patients and physicians to manage the situation timely. This study presented a Weighted Bayesian Belief Network (WBBN) modeling for breast cancer prediction using the UCI breast cancer dataset.

Prospects and Challenges of Using Machine Learning for Academic Forecasting.

The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events.

A New Variant of Mutational and Polymorphic Signatures in the Gene of Fluconazole-Resistant .

Background: Resistance to antifungal drugs for treating infections remains a major concern globally despite the range of medications available. Most of these drugs target key proteins essential to the life cycle of the organism. An enzyme essential for fungal cell membrane integrity, lanosterol 14-α demethylase (CYP51), is encoded by the gene in species.

Plant-derived compounds for the treatment of schistosomiasis: Improving efficacy via nano-drug delivery.

Schistosomiasis is a neglected infectious tropical disease that is second in occurrence only to hookworm infection in sub-Saharan Africa. Presently, chemotherapy is the main method of control and treatment of this disease due to the absence of a vaccine. However, Praziquantel, which is the only chemotherapeutic option, lacks efficacy against the early developmental stages of schistosomes.

Identification of a Potential mRNA-based Vaccine Candidate against the SARS-CoV-2 Spike Glycoprotein: A Reverse Vaccinology Approach.

The emergence of the novel coronavirus (SARS-CoV-2) in December 2019 has generated a devastating global consequence which makes the development of a rapidly deployable, effective and safe vaccine candidate an imminent global health priority. The design of most vaccine candidates has been directed at the induction of antibody responses against the trimeric spike glycoprotein of SARS-CoV-2, a class I fusion protein that aids ACE2 (angiotensin-converting enzyme 2) receptor binding. A variety of formulations and vaccinology approaches are being pursued for targeting the spike glycoprotein, including simian and human replication-defective adenoviral vaccines, subunit protein vaccines, nucleic acid vaccines and whole-inactivated SARS-CoV-2.

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease.

Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical.

Covid-19 fake news sentiment analysis.

'Fake news' refers to the misinformation presented about issues or events, such as COVID-19. Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously, however, they have been ineffectual. This research uses Information Fusion to obtain real news data from News Broadcasting, Health, and Government websites, while fake news data are collected from social media sites.

A Classification Algorithm-Based Hybrid Diabetes Prediction Model.

Diabetes is considered to be one of the leading causes of death globally. If diabetes is not treated and detected early, it can lead to a variety of complications. The aim of this study was to develop a model that can accurately predict the likelihood of developing diabetes in patients with the greatest amount of precision.

Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence-Based Granger Panel Model Approach.

Sleep apnea is a serious sleep disorder that occurs when a person's breathing is interrupted during sleep. People with untreated sleep apnea stop breathing repeatedly during their sleep. This study provides an empirical analysis of apnea syndrome using the AI-based Granger panel model approach.