Browse Articles

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

Here, based on the identification of androgapholide as a potential therapeutic treatment against cancer, Alzheimer's disease, diabetes, and multiple sclerosis, due to its ability to inhibit a signaling pathway in immune system function, the authors sought ways to optimize the natural product human systems by manipulating its chemical structure. Through the semisynthesis of a natural product along with computational studies, the authors developed an understanding of the kinetic mechanisms of andrographolide and semisynthetic analogs in the context of Michael additions.

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.

We hypothesize that berberine has broad-spectrum antibacterial properties, along with potency that is comparable to current broad-spectrum antibiotics that are commercially available. Here, we screened berberine against four strains of bacteria and evaluated its antimicrobial activity against five broad-spectrum antibiotics from different classes to better quantify berberine’s antibacterial activity and compare its efficacy as an antibacterial agent to the broad-spectrum antibiotics. Our results indicated that berberine had strain-selective cytotoxic effects and was significantly less potent than most of the broad-spectrum antibiotics

Here the authors investigated the potential of plant-derived smoke water to mitigate the negative impacts of abiotic stressors, such as heat, salinity, and water fluctuations, on the germination and growth of various agricultural crops. Their findings suggest that smoke water, specifically from buckwheat, pea, and radish, significantly improves plant resilience against drought and excessive water stress, offering a viable strategy to bolster food security in the face of climate change.

The authors studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.

This paper investigates the potential anticancer properties of Uvularia perfoliata by testing its effects on the viability of uveal melanoma cells.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

The authors propose a method to recycle Christmas tree needles into a non-toxic reducing agent for synthesizing copper nanoparticles.