Academic Articles & Research Papers

Ovarian cancer treatment and natural killer cell-based immunotherapy

BackgroundOvarian cancer (OC) is one of the malignant tumors that poses a serious threat to women’s health. Natural killer (NK) cells are an integral part of the immune system and have the ability to kill tumor cells directly or participate...

Photocatalytic degradation using Bi-doped SnS Quantum dots and phytotoxicity evaluation of treated effluents through seed germination

Quantum dots (QDs) are employed in photocatalytic applications because of their distinctive optical characteristics, such as high absorption coefficients and tunable bandgaps, enabling efficient visible light absorption and charge carrier g...

GROWTH AND YIELD RESPONSES OF SOYBEAN (Glycine max L.) TO ZINC OXIDE (ZnO) NANOPARTICLES FOLIAR APPLICATION

Aim. This study was purposed to investigate the effects of zinc oxide nanoparticles (ZnO NPs) on the growth and yield performance of two soybean (Glycine max L.) varieties, TGX1904-6F and TGX1951-3F, under controlled experimental conditions...

Ethnomedicinal uses, phytochemistry and pharmacological aspects of the genus Premna: a review

Context: The genus Premna (Lamiaceae), distributed throughout tropical and subtropical Asia, Africa, Australia and the Pacific Islands, is used in folk medicine primarily to treat inflammation, immune-related diseases, stomach disorders, wo...

Heme iron amplifies azoxymethane initiating effect on rat colon preneoplastic lesions

Objective: Colorectal cancer is a major public health issue for which dietary factors such as red and processed meat consumption seem to play a prominent role. Heme iron, which is present in important concentration in those food products, w...

Efficacy and safety of biofilm dispersal by glycoside hydrolases in wounds

Novel anti-biofilm and dispersal agents are currently being investigated in an attempt to combat biofilm-associated wound infections. Glycoside hydrolases (GHs) are enzymes that hydrolyze the glycosidic bonds between sugars, such as those f...

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cance...

DeepTox: Toxicity Prediction using Deep Learning

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...

Using deep learning to predict tyrosinase inhibitors for treating hyperpigmentation disorders

Background In recent years, skin health has garnered widespread attention, with hyperpigmentation disorders caused by excessive melanin deposition emerging as a particularly prominent concern. Tyrosinase, as the rate-limiting enzyme in the ...

Artificial Intelligence for Drug Toxicity and Safety

Optimizing Ultrasonic-Assisted Extraction Process of <i>Paralepista flaccida</i>: A Comparative Study of Antioxidant, Anticholinesterase, and Antiproliferative Activities via Response Surface Methodology and Artificial Neural Network Modeling

In this study, extraction conditions were optimized to maximize the biological activities of extracts obtained from Paralepista flaccida, an edible mushroom species. Extraction processes were carried out using an ultrasonically assisted sys...

Advances in ligand-targeted nanodelivery systems for leukemia therapy: from single- to dual-ligand strategies

Abstract Background Leukemia is a highly heterogeneous and aggressive hematological malignancy. Current therapeutic approaches (e.g. chemotherapy, radiotherapy, small-molecule targeted therapies, and hematopoietic stem cell transplantation,...

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties ...

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this w...