Pseudomonas Aeruginosa Is One Of The Most Pleasant Bacteria To Identify From A Microbiologist's Perspective.

Scientists Use Electricity to Make Wounds Heal 3x Faster

Scientists have developed a specially engineered biochip that uses electricity to heal wounds up to three times faster than normal.

It’s well known that electric fields can guide the movements of skin cells, nudging them towards the site of an injury for instance. In fact, the human body generates an electric field that does this naturally. So researchers from the University of Freiburg in Germany set out to amplify the effect.

While it might not heal severe injuries with the speed of a Marvel superhero, it could radically reduce the time it takes for small tears and lacerations to recover.

For people with chronic wounds that take a long time to heal, such as in elderly folk, those with diabetes, or people with poor blood circulation, recovering quickly from frequent small, open cuts could be a literal lifesaver.

“Chronic wounds are a huge societal problem that we don’t hear a lot about,” says Maria Asplund, a bioelectronics scientist at the University of Freiburg and Chalmers University of Technology in Sweden.

“Our discovery of a method that may heal wounds up to three times faster can be a game changer for diabetic and elderly people, among others, who often suffer greatly from wounds that won’t heal.”

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diatom- any member of a large group comprising several genera of algea, specifically microalgea, found in oceans, waterways, and soils of the world. there's something beautiful about the little details of the world.

"Microbial Rainbow" (detail), Tal Danino, 2018

source

Comatricha nigra

by andysandsphotography

CRISPR–Cas encoding of a digital movie into the genomes of a population of living bacteria. (2017)

• nature url: https://nature.com/articles/nature23017 • sci-hub url: http://sci-hub.st/10.1038/nature • https://github.com/churchlab/crispr-images

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Broad's Scientist Introduce a Machine Learning Model to Identify Genetic Factors for Cardiovascular Diseases

A machine learning model has been introduced by Broad's Scientists to identify genetic factors associated with cardiovascular diseases.

Using the heart as an investigational model, scientists at the Broad Institute of MIT and Harvard have designed an autoencoder-based machine-learning pipeline that can effectively predict a patient’s heart condition based on image data from ECGs and MRIs. The approach could also be used to detect markers related to cardiovascular diseases.

Nearly all areas of medical science have utilized artificial intelligence (AI) over the years. It has been effectively diagnosing diseases and predicting their transmission and prognosis. AI has been used to design therapeutic approaches effectively and has been helpful in the field of drug design. The use of AI in studying cardiovascular diseases has come a long way, especially machine learning-based systems. AI-based algorithms can be trained to predict cardiovascular disease outcomes using available diagnostic imaging technology.

Currently, the field of cardiology uses a variety of imaging technologies, such as ultrasound imaging, magnetic resonance imaging (MRI), computed tomography (CT), etc. The Electrocardiogram (ECG) is a widely used test to monitor the heart’s rhythm. These technologies generate a lot of data that can be utilized to analyze the condition of a person’s heart. The availability of several diagnostic modalities has raised the need for standardized tools for analyzing imaging data effectively. A multi-modal framework built on machine learning techniques has been suggested by researchers from The Broad Institute of MIT and Harvard. The proposed system can help doctors to understand the cardiovascular state of a person using data from MRIs and ECGs. In practice, clinicians can use data generated from the machine learning program to diagnose a patient appropriately.

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It's not the best "microbiology" art, but it has a very interesting background. Two bacteria from two different clinical cases were inoculated on the TSCB medium. This metallic blue spilling bacterium is of course Pseudomonas aeruginosa. The yellow one (positive reaction on TSCB medium) is Vibrio metschnikovii isolated from chronic UTI in a dog. It was an unusual microbiological diagnosis. But what can you do when even your dog has a better holiday than you? Problems with urination (in this dog) began just after returning from the Mediterranean, the owners and the dog intensively used the charms of warm and salty water.

by TheMicrobiology09 on yt