Lifeboat Foundation

Tiny biohybrid robots on the micrometer scale can swim through the body and deliver drugs to tumors or provide other cargo-carrying functions. The natural environmental sensing tendencies of bacteria mean they can navigate toward certain chemicals or be remotely controlled using magnetic or sound signals.

To be successful, these tiny biological robots must consist of materials that can pass clearance through the body’s immune response. They also have to be able to swim quickly through viscous environments and penetrate tissue cells to deliver cargo.

In a paper published this week in APL Bioengineering, from AIP Publishing, researchers fabricated biohybrid bacterial microswimmers by combining a genetically engineered E. coli MG1655 substrain and nanoerythrosomes, small structures made from red blood cells.

A targeted therapy, currently being studied for treatment of certain cancers including glioblastoma, may also be beneficial in treating other neurologic diseases, a study at the University of Cincinnati shows.

The study, being published online April 6 in the journal EBioMedicine, revealed that the effects of a therapy delivery system using microscopic components of a cell (nanovesicles) called SapC-DOPS may be able to provide targeted treatment without harming healthy cells. This method could even prove to be successful in treating other neurologic conditions, like Parkinson’s disease.

This study is led by Xiaoyang Qi, professor in the Division of Hematology Oncology, UC Department of Internal Medicine, and Ying Sun, research professor in the UC Department of Pediatrics and a member of the Division of Human Genetics at Cincinnati Children’s Hospital Medical Center.

🏺Super Pink Moon

Fyodor R., Credits article input Oscar Cainer, photography taken in North America, yesterday, by Richard S., one of our members.

Continue reading “Supermoon LIVE stream: How to watch the Pink Supermoon here TONIGHT” »

The symptoms match up with coronavirus infection. But like many people in the US, because they’re relatively mild I’m unable to get a test. Testing rates have finally skyrocketed recently, yet per capita we’re still lagging behind, leaving many wondering—myself included—if we truly caught the Covid-19 bug.

That’s a problem.

Knowing the amount of people who have already recovered from the disease would be a game-changer, not just for personal peace of mind but for society as a whole. Although we don’t yet fully understand how long the virus confers immunity for, it’s likely—though severely understudied —that infected and recovered people already have protective immunity. It means that virus-killing antibodies are circulating in our blood, ones that could potentially be harnessed for people with more severe cases; we could be walking anti-coronavirus drug factories.

A new form of magnetic brain stimulation rapidly relieved symptoms of severe depression in 90% of participants in a small study conducted by researchers at the Stanford University School of Medicine.

The researchers are conducting a larger, double-blinded trial in which half the participants are receiving fake treatment. The researchers are optimistic the second trial will prove to be similarly effective in treating people whose condition hasn’t improved with medication, talk therapy or other forms of electromagnetic stimulation.

The treatment is called Stanford Accelerated Intelligent Neuromodulation Therapy, or SAINT. It is a form of transcranial magnetic stimulation, which is approved by the Food and Drug Administration for treatment of depression. The researchers reported that the therapy improves on current FDA-approved protocols by increasing the number of magnetic pulses, speeding up the pace of the treatment and targeting the pulses according to each individual’s neurocircuitry.

The world generates over six million tons of coffee grounds, according to the International Coffee Organization. The journal Agriculture and Food Chemistry reported in 2012 that over half of spent coffee grounds end up in landfills. Cellulose nanofibers are the building blocks for plastic resins that can be made into biodegradable plastic products.

The YNU team, led by Izuru Kawamura, an associate professor at the Graduate School of Engineering Science, set out to build upon previous research into extracting cellulose nanofibers from coffee grounds. They published their findings on April 1 in the journal Cellulose.

“Our ultimate goal is to establish a sustainable recycling system with our cellulose nanofibers in the coffee industry,” Kawamura said. “Now, more and more restaurants and cafés have been banned from using single-use straws. Following that movement, we aim to make a transparent disposable coffee cup and straw with an additive comprising cellulose nanofibers from spent coffee grounds.”

Glass has an unusual atomic structure that resembles a liquid frozen in place, making it hard to predict how it will behave. DeepMind has developed an AI capable of doing so, which may also be able to predict traffic jams.

As capable as robots are, the original animals after which they tend to be designed are always much, much better. That’s partly because it’s difficult to learn how to walk like a dog directly from a dog — but this research from Google’s AI labs make it considerably easier.

The goal of this research, a collaboration with UC Berkeley, was to find a way to efficiently and automatically transfer “agile behaviors” like a light-footed trot or spin from their source (a good dog) to a quadrupedal robot. This sort of thing has been done before, but as the researchers’ blog post points out, the established training process can often “require a great deal of expert insight, and often involves a lengthy reward tuning process for each desired skill.”

That doesn’t scale well, naturally, but that manual tuning is necessary to make sure the animal’s movements are approximated well by the robot. Even a very doglike robot isn’t actually a dog, and the way a dog moves may not be exactly the way the robot should, leading the latter to fall down, lock up or otherwise fail.

We established an ultrasensitive method for identifying multiple enzymes in biological samples by using a multiplexed microdevice-based single-molecule enzymatic assay. We used a paradigm in which we “count” the number of enzyme molecules by profiling their single enzyme activity characteristics toward multiple substrates. In this proof-of-concept study of the single enzyme activity–based protein profiling (SEAP), we were able to detect the activities of various phosphoric ester–hydrolyzing enzymes such as alkaline phosphatases, tyrosine phosphatases, and ectonucleotide pyrophosphatases in blood samples at the single-molecule level and in a subtype-discriminating manner, demonstrating its potential usefulness for the diagnosis of diseases based on ultrasensitive detection of enzymes.

Cellular functions are mediated by the activities of diverse enzymes, and hence, determining the functional changes that occur in these enzymes during pathogenesis is crucial for understanding and detecting diseases (1). However, the detection sensitivity of conventional assays for discovering and using enzyme biomarkers for diagnosis needs to be improved. In case of DNA and RNA analysis, enhancing the sensitivity of detection to the single-molecule level has revolutionized biomarker discovery and usage (2). However, the detection methods for proteins, which are thought to contain more functionality-oriented information that can be directly linked to the phenotypes, are yet to attain such a high degree of sensitivity (3).

In this study, we developed a novel assay platform for comprehensively detecting multiple enzymes in biological samples at single protein level for ultrasensitive and quantitative profiling of the disease-related enzymatic activities. This method is based on single-molecule enzyme analysis performed in a microfabricated chamber device, in which single-molecule enzymes in a diluted biological sample are separately loaded into individual microchambers for measuring and detecting its activity (4, 5). Although conventional single-molecule analysis is commonly used to study the biochemical properties of specific enzymes, their application for analyzing biological samples containing complex mixtures of characterized and uncharacterized proteins remains challenging, as it is difficult to predict which enzyme is loaded into each chamber due to random distribution.