Make the Mask a Way to Detect Disease

Had seen something similar presented,  sanitizing more complex masks was mentioned as an issue.  ,

Smart Mask Could Be Early Warning System   By South China Morning Post (Hong Kong), September 22, 2022

A team of Chinese scientists have developed a wearable bioelectric mask that can detect respiratory diseases in the air, including Covid-19 and influenza, and report results in 10 minutes.

Once connected to a wireless network, the mask can transmit real-time data to a user's mobile device, including detection alerts, according to a study published in the peer-reviewed journal Matter on Monday.

The mask is intended to be used as an early warning system to prevent future outbreaks of respiratory infectious diseases, researchers said.

These diseases are spread through the air by droplets or aerosols. But direct detection of viruses in the air can be difficult as the concentrations can be extremely low.

From South China Morning Post (Hong Kong)

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AI Mapping Bacteria

 Intrigued by the idea of mapping some complex, yet still varying topography.   The morphology and how that can be used to understand its functional characteristics.  

AI to Map Our Intestinal Bacteria   University of Copenhagen (Denmark)  January 12, 2021

Researchers at Denmark's University of Copenhagen (UCPH) are applying artificial intelligence (AI) to the exploration of human intestinal bacteria and its relationship to disease. UCPH's Simon Rasmussen and colleagues developed an algorithm that uses AI to complete the DNA strings of the approximately 1 billion bacteria found in feces. Said Rasmussen, "If we are able to reconstruct their DNA, it will give us an idea of the types of bacteria we are dealing with, what they are capable of, and what they actually do. It is not the complete picture, but it is a huge step forward." ... ' 

Organs on a Chip

Had seem examples of this some years ago, to what degree has it been predictively accurate?

Using “organs-on-a-chip” to model complicated diseases
A new approach reveals how different tissues contribute to inflammatory diseases such as ulcerative colitis.

Anne Trafton | MIT News Office

Press Inquiries
MIT biological engineers have created a multitissue model that lets them study the relationships between different organs and the immune system, on a specialized microfluidic platform seeded with human cells.

Using this type of model, sometimes called “organs-on-a-chip” or “physiome on-a-chip,” the research team was able to explore the role of circulating immune cells in ulcerative colitis and other inflammatory diseases. They also discovered that a metabolic byproduct generated by bacteria living in the human gut plays an important role under these inflammatory conditions.

“We’ve shown that now you can start to attack some of these really thorny, chronic inflammatory diseases by designing experiments in these organs on chips,” says Linda Griffith, the School of Engineering Professor of Teaching Innovation, a professor of biological engineering and mechanical engineering, and the senior author of the study.

This approach, described today in the journal Cell Systems, could also lend itself to studying many other complex diseases, the researchers say.

“Now we have options to really decrease or increase the level of disease complexity, under controlled and systematic conditions,” says Martin Trapecar, an MIT postdoc and the lead author of the paper.

Complex models

Nearly 20 years ago, Griffith’s lab first began working on a model of the human liver known as the “liver chip.” This system, which consists of engineered human liver tissue grown on a specialized scaffold, can be used to test drug toxicity. More recently, she has been working on small-scale replicas of many interconnected organs, also known as microphysiological systems (MPS). In 2018, she reported the development of a platform that could be used to model interactions between up to 10 organs at a time. .... ' 

Epidemics and use of Personal GPS Data

Recall our work with bioterrorism modeling, link below.  Not just a matter of disease epidemics.

During Epidemics, Access to GPS Data from Smartphones Can Be Crucial
Ecole Polytechnique Fédérale de Lausanne (Switzerland)
By Sandrine Perroud

Researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and the Massachusetts Institute of Technology have found that human mobility is a major factor in the spread of vector-borne diseases such as malaria and dengue. The researchers used mobile phone data and census models to effectively predict the spatial distribution of dengue cases in Singapore, based on data from actual reported cases in 2013 and 2014. The team also demonstrated that the types of data used in their study could be obtained without infringing on people's privacy. Said EPFL's Emanuele Massaro, "We need to think seriously about changing the law around accessing this kind of information – not just for scientific research, but for wider prevention and public health reasons." ... '

Flu Foreasting with Smart Thermometers

We also looked at epidemic forecasting.   Note the smart 'thermometers' mentioned here are taking human temperatures.  A slight confusion when I first read this.

Smart Thermometers Improve Flu Forecasting    By Joe Dysar

Researchers at the University of Iowa (UI) have found a way to get a jump on forecasting outbreaks of influenza-like illnesses by using real-time data from smart thermometers .

"Using simple forecasting models, we showed that thermometer data could be effectively used to predict influenza levels up to two to three weeks into the future," says Aaron Miller, an assistant professor or epidemiology at UI.

Miller's team secured its study data from Kinsa Inc., a maker of smart thermometer products.  The U.S. Food and Drug Administration-approved devices plug into Android or Apple smartphones and can send anonymized fever readings to Kinsa corporate headquarters in San Francisco.

Thanks to Kinsa, Miller's team was able to study more than 8 million temperature readings from all 50 U.S. states, which were provided over a period of nearly two years.

The team found that by using real-time data from the off-the-shelf thermometers, they were able to forecast outbreaks of flu-like illness in various parts of the country up to three weeks earlier than conventional forecasting methods. .... " 

Identifiying Plant Disease

Out of the fall garden and back to the possibilities of pattern recognition.  Back to my alternate botanic tech universe.  Penn State work using deep learning to identifying plant disease.  Great example:

 Phone Powered AI Spots Sock Plants with Remarkable Accuracy,   by Matt Simon, Science

Researchers at Pennsylvania State University (PSU) say they have designed a smartphone-based neural-network program that can automatically identify diseases in the cassava plant with near-flawless accuracy. The network is based on Google's open source TensorFlow machine-learning library, and Google's Pete Warden notes the TensorFlow Mobile app requires only about 25 million parameters, versus the hundreds of millions some networks need. "It only requires about 11 billion floating point operations to actually calculate its result, and some other networks require hundreds of billions of operations to do a similar job," Warden notes. He also says thanks to transfer learning, the network was trained to recognize cassava leaves on much less data. "It really comes down to the data, because garbage in, garbage out," says PSU's Amanda Ramcharan. She believes the growing affordability of smartphones and the continuing simplification of algorithms will combine to make such tools more widely available to farmers. ... "