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Research led by Sanford Burnham Prebys professor Francesca Marassi, Ph.D., is helping to reveal the molecular secrets of macular degeneration, which causes almost 90% of all age-related vision loss.

The study, published recently in the Biophysical Journal, describes the flexible structure of a key blood protein involved in macular degeneration and other age-related diseases, such as Alzheimer’s and atherosclerosis.

“Proteins in the blood are under constant and changing pressure because of the different ways blood flows throughout the body,” says Marassi. “For example, blood flows more slowly through small blood vessels in the eyes compared to larger arteries around the heart. Blood proteins need to be able to respond to these changes, and this study gives us fundamental truths about how they adapt to their environment, which is critical to targeting those proteins for future treatments.”

Researchers in China show that a technique used to replace diseased mitochondria does not affect early development.

Highlights and Key Developments of the Current Study

In this study, the researchers used the biological synthesis approach to analyze Sargassum polycystum aquatic extract to produce silver seaweed nanoparticles. Various spectroscopic methods, including absorption spectrophotometer (UV-VIS), scanning electron Microscope (SEM), and Fourier transforms infrared spectroscopy (FTIR), were applied to characterize the silver seaweed nanoparticles.

The antibacterial effects of seaweed nanoparticles against several microbial infections, including tuberculosis, were investigated. Zebrafish larvae were used to test the toxicity of the produced silver seaweed nanoparticles.

Researchers are working on an injectable that could get dormant follicles growing again. Trials on mice show promise.

Scientists at McMaster University have identified a toxin used by bacteria to kill other bacteria through a never-before-seen mechanism. The toxin is the first found to directly target RNA molecules in what the team describes as “a total assault on the cell,” which could lead to a new class of antibiotics.

There’s a huge war raging on the microscopic scale as microbes battle each other for resources, and sometimes the weapons they use against each other can prove useful for antibiotics. Most of these toxins target proteins or DNA molecules, killing bacteria by interrupting vital functions.

So that’s the checklist that the team ran through while studying a toxin called RhsP2 that’s produced by Pseudomonas aeruginosa, a common bug behind hospital-acquired infections like pneumonia. Over three years, the researchers investigated the effects of the toxin on these common targets, to no avail.

2077 — 10 Seconds to the Future — Mutation | Science Documentary.

2077 — 10 Seconds to the Future | Global Estrangement: https://youtu.be/CTOduDIkcdM

Continue reading “10 Seconds to the Future | Mutation | Free Documentary” »

Based on the live fire Map from nasa south America has too many fires to count probably estimating in the millions of acres burned circa 2022.

The Amazon rainforest is shrinking. The fires in the Amazon are growing.

Continue reading “Amazon rainforest fires 2022: Facts, causes, and climate impacts” »

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Background: Breast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole-slide pathology features of breast cancer H&E stains and the patients’ gBRCA mutation status.

Methods: In this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. Since the dimensions are too large for slide-based training, we divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification.

Results: A trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the external validation dataset, AUCs (95% CI) of DL models that used 40×, 20×, 10×, and 5× magnification tiles among all cases were 0.766 (0.763–0.769), 0.763 (0.758–0.769), 0.750 (0.738–0.761), and 0.551 (0.526–0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642–0.905), 0.804 (0.676–0.931), 0.828 (0.691–0.966), and 0.635 (0.471–0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction.