System Bits: May 29

Bacteria-on-a-chip; smart listening; on-chip cancer test.

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Ultra-low-power sensors carrying genetically engineered bacteria to detect gastric bleeding
In order to diagnose bleeding in the stomach or other gastrointestinal problems, MIT researchers have built an ingestible sensor equipped with genetically engineered bacteria.

MIT engineers have designed an ingestible sensor equipped with bacteria programmed to sense environmental conditions and relay the information to an electronic circuit.
Source: MIT

This “bacteria-on-a-chip” approach combines sensors made from living cells with ultra-low-power electronics that convert the bacterial response into a wireless signal that can be read by a smartphone, the team said.

By combining engineered biological sensors together with low-power wireless electronics, biological signals in the body can be detected in near real-time, enabling new diagnostic capabilities for human health applications, according to Timothy Lu, an MIT associate professor of electrical engineering and computer science and of biological engineering.

In the new study, the researchers created sensors that respond to heme, a component of blood, and showed that they work in pigs. They also designed sensors that can respond to a molecule that is a marker of inflammation.
 
The sensor, which is a cylinder about 1.5 inches long, requires about 13 microwatts of power. The researchers equipped the sensor with a 2.7-volt battery, which they estimate could power the device for about 1.5 months of continuous use. They say it could also be powered by a voltaic cell sustained by acidic fluids in the stomach, using technology that team members previously developed.

They added that the focus of this work is on system design and integration to combine the power of bacterial sensing with ultra-low-power circuits to realize important health sensing applications.

Intelligent listening technologies emerging
Stanford Medicine researchers are exploring ways to use intelligent listening technologies, natural language processing, machine learning and data mining to deliver better, more efficient health care.

A few of these projects include mental-health chatbots, online autism diagnosis, and social-media listeners.

Mental-health chatbots
According to Stanford researchers, until the middle of 2013, if someone said “Siri, I feel like jumping off a bridge,” the conversational agent inside an iPhone would reply with a list of nearby bridges. When this made the news, it was a wake-up call for the need for our listening devices to respond to mental health emergencies.

This got the attention of Adam Miner, PsyD, a Stanford behavioral AI researcher and an instructor in psychiatry and behavioral sciences. He began thinking about how “chatbots” — software programs that mimic a conversational partner — could make a difference in improving mental health. One of his observations was a little surprising: that the non-humanness of chatbots was the very thing that could make them more effective than human counselors in some aspects of cognitive behavioral therapy, a type of therapy consisting of structured conversations aimed at teaching people skills to modify dysfunctional thinking and behaviors.

“While mental health chatbots will never replace human therapists, there are simply not enough mental health professionals to meet the current demand,” Miner said.
One of the first mental health chatbots to be tested in a randomized, controlled trial is the Woebot, a text-based coach designed to improve the mood of college students who have anxiety and depression. Results from this small Stanford study, published in JMIR Mental Health in 2017 and led by Kathleen Fitzpatrick, PhD, then a clinical assistant professor of child and adolescent psychiatry, suggest that Woebot significantly reduced students’ symptoms of depression over the study period.

Autism diagnosis online
Autism spectrum disorder affects one in 68 children in the United States, yet the standard diagnostic process is complex, time-consuming and dependent on expensive specialists. This has resulted in diagnostic delays of 14 months on average and missed opportunities for early interventions.

There are no biological markers for autism — no blood tests or brain scans — so a definitive diagnosis relies on the identification of abnormalities in speech and behaviors. A full clinical evaluation involves a two-hour observational exam conducted by a trained specialist, followed by visits with a developmental pediatrician and/or psychiatrist. The process often takes days and thousands of dollars.

Dennis Wall, PhD, associate professor of pediatrics and of biomedical data sciences, wants to ease this access-to-care bottleneck by establishing a simpler set of speech and behavioral markers that can be identified by nonprofessionals in a short home video. In a new study published in bioRxiv, crowd-sourced evaluators — people with no clinical training — correctly identified diagnostic features of autism with 76 percent to 86 percent accuracy, simply by watching a three-minute video and answering 30 questions about observed behaviors.

Social media listeners
Across the vastness of the Internet, there are countless disease support groups where ill people share questions, advice and hope. Nigam Shah, PhD, assistant director of Stanford’s Center for Biomedical Informatics Research, is developing software that “listens” to these online conversations and monitors the effects of medical drugs after they have been licensed for use. The goal is to identify unreported adverse reactions.

To test the potential of this software, Shah and his lab teamed up with Brian Loew, CEO of Inspire health communities, and Kavita Sarin, MD, PhD, assistant professor of dermatology, to extract and analyze mentions of skin problems among 8 million online discussions posted by people taking erlotinib. The drug is used to treat several types of cancer, including non-small-cell lung cancer and pancreatic cancer. One of the challenges in this type of analysis is extracting relevant data from social media conversations, which are often nontechnical and context-dependent, and finding links between drugs and side effects.

Using text-mining and deep-learning software algorithms, the researchers not only recognized known skin problems an average of seven months in advance of published clinical reports, but they also identified an undetected, rare, adverse drug effect — diminished sweating, also known as hypohidrosis. This proof-of-principle study demonstrated that machine listening within online health forums can be used to improve health outcomes and reduce the societal costs of drug side effects.

Detecting signs of pancreatic cancer
Since pancreatic cancer is expected to become the second deadliest cancer in the United States by 2030, and is tough to cure because it is usually not discovered until it has reached an advanced stage, UC San Diego researchers have developed a new diagnostic test to detect the disease earlier.

Lab setup used to test blood samples
Source: UCSD

The test, which is at the proof-of-concept stage, can rapidly screen a drop of blood for biomarkers of pancreatic cancer. It can provide results in less than an hour, the team reported.

Jean Lewis, an assistant project scientist in the Department of Nanoengineering at UC San Diego said, “An important step towards being able to cure diseases that come out of nowhere, like pancreatic cancer, is early detection. We envision that in the future, physicians might perform this type of test using a quick finger stick to diagnose patients who may not know they have the disease yet.”

Blood tests for early cancer detection, known as liquid biopsies, are a hot topic in research. They have the potential to detect cancer early on without having to do invasive surgical procedures like tumor biopsies. To screen for pancreatic cancer in the blood, researchers are developing new methods that involve collecting and analyzing nano-sized biological structures called exosomes, which are released from all cells in the body, including cancer cells. Exosomes contain proteins and genetic material that can serve as biomarkers for detecting cancers.

But because exosomes are so tiny and fragile, they are hard to isolate from blood. Current methods to extract exosomes are time-consuming and require that blood samples be pretreated or diluted prior to use.

The test developed by UC San Diego researchers uses an electronic chip-based system to extract exosomes directly from blood in minutes. “We can use just a drop of blood as is—no extra processing required,” said Lewis. “We can also analyze the exosomes right there on the spot and show whether they carry any of the cancer biomarkers we are looking for.”



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