In Vitro and In Silico Anthelmintic Activity of Extracts of and on .

The aim of this study was to assess the anthelmintic activity of and , on a nematode model, to promote their use in the Cameroonian pharmacopoeia for the treatment of helminthiases. One nematode was used, . First, the effect of the extracts on the eggs and larval stages (L1, L2, and L3) of was evaluated, 100 L of extract and 100 L of parasite suspension (containing 50 eggs) were mixed in a 96-well microplate.

Anthelmintic Activity of Ethanolic and Aqueous Extracts of Stem Bark against : and Approaches.

Background: Parasitic infection remains a serious health trade for humans and livestock. The purpose of this study was to present scientific proof of the anthelmintic properties of , which the native population uses to cure helminthiasis.

Method: Fresh eggs were isolated from faecal samples of experimentally infected mice.

Anthelmintic Activities of and Extracts Used in Chad by Traditional Healers for the Treatment of Helminthiasis and Study of Phytoconstituents.

Background: Helminthiasis is endemic in Chad and constitutes a public health problem, particularly among school-age children. The aim of this study was to evaluate the anthelmintic activity of extracts of and used in Chad by traditional healers for the treatment of helminthiasis.

Methods: The anthelmintic activity was assessed against and larvae using the Worm Microtracker.

Metabolic profiling of UHPLC-HRMS-MS with computer-assisted structure elucidation and its antimicrobial activity.

is used as Hakka traditional herb to treat cold, flu, etc. So far, the phytochemistry and antimicrobial compounds have not been well investigated. In this study, the orbitrap-ion trap MS was used to characterize its metabolites, combined with a computer-assisted structure elucidation method, and the antimicrobial activities were assessed by a broth dilution method against 21 human pathogens, as well as the bioassay-guided purification work to clarify its main antimicrobial compounds.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.

Background: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

Methods: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA).

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

Process optimisation for green synthesis of zero-valent iron nanoparticles using .

The potential of in the iron nanoparticles (FeNPs) production was evaluated for the first time. The influences of the variables such as incubation time, temperature, and volume ratio of the extract to metal ions on the nanoparticle size were investigated using central composite design. The appearance of SPR bands at 284 nm in UV-Vis spectra of the mixtures verified the nanoparticle formation.

Predicting protein structural classes based on complex networks and recurrence analysis.

Protein sequences are divided into four structural classes. The determination of class is a challenging and beneficial task in the bioinformatics field. Several methods have been proposed to this end, but most utilize too many features and produce unsuitable results.