Molecular Detection and Characterization of spp. in Cattle in Nigeria.

Background: Haemonchosis is a major parasitic infestation in ruminant livestock, causing significant economic losses annually. The causative organisms are helminths of the genus spp. Detection of the causative agent is important for effective management and control of the disease.

Pathology and molecular detection of influenza A subtype H9N2 virus in commercial poultry in Nigeria, 2024.

Background: After the first Avian Influenza H5N1 outbreak in Nigerian poultry in 2006, subsequent waves of outbreaks occurred, causing substantial losses. Despite effective control measures by 2008, a resurgence in 2015 led to further losses and required depopulation efforts.

Aim: The aim of this study was to do pathology and molecular detection of influenza A subtype H9N2 virus in commercial poultry in Nigeria during 2024.

Frondoside A of Cucumaria frondosa (Gennerus, 1767): Chemistry, biosynthesis, medicinal applications, and mechanism of actions.

Cucumaria frondosa (Gennerus, 1767) or orange-footed sea cucumbers are traditional food and are used as natural sources of anti-diabetic, anti-inflammatory, antioxidant, anti-angiogenic, antimicrobial, and anticancer agents. Currently, the introduction of value-added sea cucumber products to the global market has inspired basic research on frondoside A and other saponins in sea cucumbers. These saponins serve as a means of their chemical defence.

Sea Cucumber and Blueberry Extracts Suppress Inflammation and Reduce Acute Lung Injury through the Regulation of NF-κB/MAPK/JNK Signaling Pathway in Lipopolysaccharide-Treated C57BL/6 Mice.

Acute lung injury (ALI) represents a life-threatening condition with high morbidity and mortality despite modern mechanical ventilators and multiple pharmacological strategies. Therefore, there is a need to develop efficacious interventions with minimal side effects. The anti-inflammatory activities of sea cucumber () and wild blueberry () extracts have been reported recently.

A supervised machine learning approach for the prediction of antioxidant activities of seed.

This research aimed at modelling and predicting the antioxidant activities of seed extract using four (4) data-driven models. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest Neighbour (k-NN), and Decision Tree (DT) were used as modelling algorithms for the construction of a non-linear empirical model to predict the antioxidant properties of seed extract. Datasets for the modelling operation were obtained from a Box Behnken design while the hyperparameters of the ANN, SVM, k-NN and DT were determined using a 10-fold cross-validation technique.