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Cannabidiol (CBD) is a chemical extracted from cannabis and shown by some studies to alleviate the symptoms of many mental disorders, especially major depressive disorder. The authors hypothesized that chronic treatments with purified CBD through oral administration would relieve depression-associated behaviors in normal healthy rats under adverse conditions. A statistical analysis of the experimental data suggested that long-term consumption of CBD could elicit depression associated symptoms in normal rats without depression. The results imply that people should consume CBD-containing products with extreme caution and highlight the need to carefully monitor the use of CBD in health care products.

The misfolding of proteins leads to numerous diseases including Akzheimer’s, Parkinson’s and Type II Diabetes. Understanding of exactly how proteins fold is crucial for many medical advancements. Chenna and Englander addressed this problem by measuring the rate of hydrogen-deuterium exchange within proteins exposed to deuterium oxide in order to further elucidate the process of protein folding. Here, mass spectrometry was used to measure exchange in Cytochrome c and was compared to archived 1H NMR data.

Stem cells are at the forefront of research in regenerative medicine and cell therapy. Two essential properties of stem cells are self-renewal and potency, having the ability to specialize into different types of cells. Here, Park and Jeong took advantage of previously identified stem cell transcription factors associated with potency to differentiate umbilical cord mesenchymal stem cells (US-MSCs) from morphologically similar fibroblasts. Western blot analysis of the transcription factors Klf4, Nanog, and Sox2 revealed their expression was unique to US-MSCs providing insight for future methods of differentiating between these cell lines.

The authors looked at whether traditional Chinese medicine remedies that target the lungs and liver would reduce inflammation in a planaria model. They found that the two active compounds they tested were able to decrease induced inflammation by 97-98%.

This article explores the largely unstudied impact of noise pollution on plant life. By exposing Arabidopsis thaliana seedlings to urban traffic noise, the study found a significant increase in seedling growth, alongside substantial changes in gene expression. This research reveals critical insights into how noise pollution affects plant physiology and contributes to a broader understanding of its ecological impacts, helping to guide future efforts in ecosystem conservation.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

The authors tested the effects of reiki on stress levels in middle schoolers.

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

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.