6 research outputs found
In vitro antiproliferative, anti-inflammatory effects and molecular docking studies of natural compounds isolated from Sarcocephalus pobeguinii (Hua ex Pobég)
DATA AVAILABILITY STATEMENT : The original contributions presented in the study are included in
the article/Supplementary Material, further inquiries can be directed
to the corresponding authors.BACKGROUND : Sarcocephalus pobeguinii (Hua ex Pobég) is used in folk medicine to
treat oxidative-stress related diseases, thereby warranting the investigation of its
anticancer and anti-inflammatory properties. In our previous study, the leaf extract
of S. pobeguinii induced significant cytotoxic effect against several cancerous cells
with high selectivity indexes towards non-cancerous cells.
AIM : The current study aims to isolate natural compounds from S. pobeguinii, and
to evaluate their cytotoxicity, selectivity and anti-inflammatory effects as well as
searching for potential target proteins of bioactive compounds.
METHODS : Natural compounds were isolated from leaf, fruit and bark extracts of S.
pobeguinii and their chemical structures were elucidated using appropriate
spectroscopic methods. The antiproliferative effect of isolated compounds was
determined on four human cancerous cells (MCF-7, HepG2, Caco-2 and
A549 cells) and non-cancerous Vero cells. Additionally, the anti-inflammatory
activity of these compounds was determined by evaluating the nitric oxide (NO)
production inhibitory potential and the 15-lipoxygenase (15-LOX) inhibitory
activity. Furthermore, molecular docking studies were carried out on six
putative target proteins found in common signaling pathways of inflammation
and cancer.
RESULTS : Hederagenin (2), quinovic acid 3-O-[α-D-quinovopyranoside] (6) and
quinovic acid 3-O-[β-D-quinovopyranoside] (9) exhibited significant cytotoxic
effect against all cancerous cells, and they induced apoptosis in MCF-7 cells by
increasing caspase-3/-7 activity. (6) showed the highest efficacy against all
cancerous cells with poor selectivity (except for A549 cells) towards noncancerous
Vero cells; while (2) showed the highest selectivity warranting its potential safety as a chemotherapeutic agent. Moreover, (6) and (9) significantly inhibited NO production in LPS-stimulated RAW 264.7 cells which
could mainly be attributed to their high cytotoxic effect. Besides, the mixture
nauclealatifoline G and naucleofficine D (1), hederagenin (2) and chletric acid (3)
were active against 15-LOX as compared to quercetin. Docking results showed that
JAK2 and COX-2, with the highest binding scores, are the potential molecular
targets involved in the antiproliferative and anti-inflammatory effects of bioactive
compounds.
CONCLUSION : Overall, hederagenin (2), which selectively killed cancer cells with
additional anti-inflammatory effect, is the most prominent lead compound which
may be further investigated as a drug candidate to tackle cancer progression.The Central University of Technology operational expenses and the National Research Foundation (NRF), South Africa. The APC was funded by the Central University of Technology research expenses (TM).http://www.frontiersin.org/Pharmacologyam2024Paraclinical SciencesSDG-03:Good heatlh and well-bein
Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey
Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study
Review: machine learning techniques applied to cybersecurity
Machine learning techniques are a set of mathematical models to solve high non-linearity problems of different topics: prediction, classification, data association, data conceptualization. In this work, the authors review the applications of machine learning techniques in the field of cybersecurity describing before the different classifications of the models based on (1) their structure, network-based or not, (2) their learning process, supervised or unsupervised and (3) their complexity. All the capabilities of machine learning techniques are to be regarded, but authors focus on prediction and classification, highlighting the possibilities of improving the models in order to minimize the error rates in the applications developed and available in the literature. This work presents the importance of different error criteria as the confusion matrix or mean absolute error in classification problems, and relative error in regression problems. Furthermore, special attention is paid to the application of the models in this review work. There are a wide variety of possibilities, applying these models to intrusion detection, or to detection and classification of attacks, to name a few. However, other important and innovative applications in the field of cybersecurity are presented. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity