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The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

Using machine learning to predict the risk of algae bloom

Here the authors aimed to compare the effects of artificial Allura Red AC dye and natural cochineal dye on the growth of Escherichia coli and Bacillus coagulans bacteria. Their research found that only Allura Red AC dye significantly affected bacterial growth, specifically amplifying E. coli growth. Based on their results, they suggest that Allura Red AC dye may increase the growth of E. coli bacteria within the human gut.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

Impact of carbon tax and dividend on financial security

Current osteosarcoma (OS) treatments rely on surgery and chemotherapy, but drug resistance remains a major challenge that lowers patient survival rates. Ferroptosis, a form of regulated cell death, has shown promise in cancer therapy but is not well understood in OS. This study explores the use of Ferroptosis in OS.

The authors looked at different treatments to clean up rainwater collected at home. They found that chlorine treatment and treatment with hydrogen peroxide catalyzed by yeast showed similar potential for cleaning up contaminated rainwater, but that further studies are needed to better assess impact on specific contaminant levels still present.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

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.