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Gut microbiome implicated in healthy aging and longevity: Data from over 9,000 people reveal a distinct gut microbiome signature that is associated with healthy aging and survival in the latest decades of life -- ScienceDaily

The data showed that gut microbiomes became increasingly unique (i.e. increasingly divergent from others) as individuals aged, starting in mid-to-late adulthood, which corresponded with a steady decline in the abundance of core bacterial genera (e.g. Bacteroides) that tend to be shared across humans. Strikingly, while microbiomes became increasingly unique to each individual in healthy aging, the metabolic functions the microbiomes were carrying out shared common traits. This gut uniqueness signature was highly correlated with several microbially-derived metabolites in blood plasma, including one -- tryptophan-derived indole -- that has previously been shown to extend lifespan in mice. Blood levels of another metabolite -- phenylacetylglutamine -- showed the strongest association with uniqueness, and prior work has shown that this metabolite is indeed highly elevated in the blood of centenarians. "This uniqueness signature can predict patient survival in the latest decades of life," said ISB Research Scientist Dr. Tomasz Wilmanski, who led the study. Healthy individuals around 80 years of age showed continued microbial drift toward a unique compositional state, but this drift was absent in less healthy individuals. "Interestingly, this uniqueness pattern appears to start in mid-life -- 40-50 years old -- and is associated with a clear blood metabolomic signature, suggesting that these microbiome changes may not simply be diagnostic of healthy aging, but that they may also contribute directly to health as we age," Wilmanski said. For example, indoles are known to reduce inflammation in the gut, and chronic inflammation is thought to be a major driver in the progression of aging-related morbidities.

Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries | npj Digital Medicine

Temporal correlation analyses were conducted to characterize the relationships between a range of COVID-19 symptom-specific search terms and reported COVID-19 cases and deaths for each country from January 1 through April 20, 2020. Increases in COVID-19 symptom-related searches preceded increases in reported COVID-19 cases and deaths by an average of 18.53 days (95% CI 15.98–21.08) and 22.16 days (20.33–23.99), respectively.

AI can predict early death risk: Algorithm using echocardiogram videos of the heart outperforms other predictors of mortality -- ScienceDaily

Researchers at Geisinger have found that a computer algorithm developed using echocardiogram videos of the heart can predict mortality within a year. The algorithm -- an example of what is known as machine learning, or artificial intelligence (AI) -- outperformed other clinically used predictors, including pooled cohort equations and the Seattle Heart Failure score. The results of the study were published in Nature Biomedical Engineering. "We were excited to find that machine learning can leverage unstructured datasets such as medical images and videos to improve on a wide range of clinical prediction models," said Chris Haggerty, Ph.D., co-senior author and assistant professor in the Department of Translational Data Science and Informatics at Geisinger. Imaging is critical to treatment decisions in most medical specialties and has become one of the most data-rich components of the electronic health record (EHR). For example, a single ultrasound of the heart yields approximately 3,000 images, and cardiologists have limited time to interpret these images within the context of numerous other diagnostic data. This creates a substantial opportunity to leverage technology, such as machine learning, to manage and analyze this data and ultimately provide intelligent computer assistance to physicians.

Anonymous cell phone data can quantify behavioral changes for flu-like illnesses: Iceland study links cell phone metadata with public health data -- ScienceDaily

The researchers collaborated with a major cell phone service provider in Iceland, along with public health officials of the island nation. They analyzed data for more than 90,000 encrypted cell phone numbers, which represents about a quarter of Iceland's population. They were permitted to link the encrypted cell phone metadata to 1,400 anonymous individuals who received a clinical diagnosis of a flu-like illness during the H1N1 outbreak. "The individual linkage is key," Vigfusson says. "Many public-health applications for smartphone data have emerged during the COVID-19 pandemic but tend to be based around correlations. In contrast, we can definitively measure the differences in routine behavior between the diagnosed group and the rest of the population."

dmca/2019-11-08-abbott.md at master · github/dmca · GitHub

It has come to Abbott’s attention that a software project titled “Libre2-patched-App” has been uploaded to GitHub, Inc.’s (“GitHub”) website and creates unauthorized derivative works of Abbott’s LibreLink program (the “Infringing Software”). The Infringing Software is available at https://github.com/user987654321resu/Libre2-patched-App. In addition to offering the Infringing Software, the project provides instructions on how to download the Infringing Software, circumvent Abbott’s technological protection measures by disassembling the LibreLink program, and use the Infringing Software to modify the LibreLink program.

Abbott Labs kills free tool that lets you own the blood-sugar data from your glucose monitor, saying it violates copyright law / Boing Boing

First, they say that creating a tool that interoperates with the Freestyle Libre's data is a copyright infringement, because the new code is a derivative work of Abbott's existing product. But code that can operate on another program's data is not a derivative work of the first program -- just because Apple's Pages can read Word docs, it doesn't mean that Pages is a derivative of MS Office. In addition, as Diabettech points out, EU copyright law explicitly contains an exemption for reverse engineering in order to create interoperability between medical devices (EU Software Directive, Article 6). More disturbing is Kirkland/Abbott's claim that the project violates Section 1201 of the Digital Millennium Copyright Act, which prohibits bypassing "access controls" for copyrighted works. Factual data (like your blood sugar levels) are not copyrightable -- and if they were, you would hold that copyright. It's your blood. What's more, DMCA 1201 also contains an interoperability exemption.

Machine learning results: pay attention to what you don't see - STAT

Beyond examining multiple overall metrics of performance for machine learning, we should also assess how tools perform in subgroups as a step toward avoiding bias and discrimination. For example, artificial intelligence-based facial recognition software performed poorly when analyzing darker-skinned women. Many measures of algorithmic fairness center on performance in subgroups. Bias in algorithms has largely not been a focus in health care research. That needs to change. A new study found substantial racial bias against black patients in a commercial algorithm used by many hospitals and other health care systems. Other work developed algorithms to improve fairness for subgroups in health care spending formulas.

AI and Compute

The total amount of compute, in petaflop/s-days, used to train selected results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used. A petaflop/s-day (pfs-day) consists of performing 1015 neural net operations per second for one day, or a total of about 1020 operations. The compute-time product serves as a mental convenience, similar to kW-hr for energy. We don’t measure peak theoretical FLOPS of the hardware but instead try to estimate the number of actual operations performed. We count adds and multiplies as separate operations, we count any add or multiply as a single operation regardless of numerical precision (making “FLOP” a slight misnomer), and we ignore ensemble models. Example calculations that went into this graph are provided in this appendix. Doubling time for line of best fit shown is 3.4 months.

Urgent Need to Improve PAP Management: The Devil Is in Two (Fixable) Details

Several high-profile, large prospective sleep apnea therapy trials have failed to meet expected outcomes: Apnea Positive Pressure Long-term Efficacy Study (APPLES) (cognition),1 the Treatment of Predominant Central Sleep Apnoea by Adaptive Servo Ventilation in Patients With Heart Failure (SERVE-HF) trial (heart failure),2 the Canadian Positive Airway Pressure Trial (CANPAP), the Sleep Apnea cardioVascular Endpoints (SAVE) study (general cardiovascular),3 and the Heart Bio-marker Evaluation in Apnea Treatment (HeartBEAT) (metabolic/hemodynamic).4 Each theoretically had the power to positively influence practice, but instead have cast doubt on the staple of our field: positive airway pressure. Struggling to navigate these findings, experts have invoked explanations ranging from inadequate use, too-short duration of therapy, overwhelming disease pathophysiology, treatment initiated too late in evolution of disease, and unknown pathophysiological constructs.

Using science to sell apps: Evaluation of mental health app store quality claims | npj Digital Medicine

Seventy-three apps were coded, and the majority (64%) claimed effectiveness at diagnosing a mental health condition, or improving symptoms, mood or self-management. Scientific language was most frequently used to support these effectiveness claims (44%), although this included techniques not validated by literature searches (8/24 = 33%). Two apps described low-quality, primary evidence to support the use of the app. Only one app included a citation to published literature. A minority of apps (14%) described design or development involving lived experience, and none referenced certification or accreditation processes such as app libraries.

Period-Tracking Apps Say You May Have a Disorder. What if They’re Wrong? - The New York Times

“It’s certainly become confusing as a consumer if you go onto these app marketplaces and these apps are making claims about helping you learn about mental health, PCOS, heart disease, diabetes,” said Dr. John Torous, director of the digital psychiatry division at Beth Israel Deaconess Medical Center in Boston, one of the authors of the Nature study. “Do we know this helps or it doesn’t help?”

An AI startup tries to take better pictures of the heart

Caption Health provided me with unpublished data from a study in which 8 nurses with no previous experience in cardiac ultrasound performed four different types of scans on 240 patients. For assessing patients’ left ventricular size and function, as well as assessment of pericardial effusion, or fluid around the heart, the AI took the same number of usable images. For each, 240 scans were performed, and 237, or 98.8%, were of sufficient quality, according to a panel of five cardiologists. For images of the right ventricle, which is harder to see, the results were a bit worse: 222 images, or 92.5% of them, were of adequate quality. Eric Topol, the director and founder of the Scripps Research Translational Institute, commented that this was still a small number of samples for AI work; Caption Health said it “respectfully disagrees” because the study was prospective. The goal of the study was to show the test was 80% accurate.

Data standards may be wonky, but they will transform health care - STAT

The proposed rule creates a highly promising road map toward the easy exchange of electronic health information that exemplifies a minimalist regulatory approach for creating the standardization and uniformity needed to spark an apps marketplace. It would also create economic and commercial guardrails to promote a level playing field between electronic health record vendors and app developers. These regulations are an essential ingredient for a burgeoning apps market. All six individuals who previously served as the national coordinator of health information technology have endorsed the rule. It has sparked robust conversation: During the public comment period on the proposed rule, nearly 2,000 comments were submitted about interoperability and information blocking. As might be expected, there is pushback from the electronic health record industry on timelines and price controls. The proposed timeline — two years of development— has proven highly realistic, given the successful implementation of SMART on FHIR among the major brands of electronic health records by the Argonaut working group in just one year, and the work of the CARIN alliance to help connect patient apps to the SMART API.

App could help diagnose ear infections more accurately (developing countries)

The funnel is placed on the outside of the ear, at which point the app sends a bird chirp-like sound into the ear. Depending on the sounds that the app picks up in return, a machine learning algorithm built into the app is able to tell whether or not there is liquid in the ear. “It’s like tapping on a wine glass,” Chan said. “Depending on whether it’s empty or not, it’s going to sound different.”

First smart speaker system that uses white noise to monitor infants' breathing - ScienceBlog.com

Detecting breathing in babies has an extra wrinkle: the movement of their chests is so tiny that the smart speaker needs to know exactly where the babies are to be able to “see” them breathing. “The breathing signal is so weak that we can’t just look for a change in the overall signal we get back,” Wang said. “We needed a way to scan the room and pinpoint where the baby is to maximize changes in the white noise signal. Our algorithm takes advantage of the fact that smart speakers have an array of microphones that can be used to focus in the direction of the infant’s chest. It starts listening for changes in a bunch of potential directions, and then continues the search toward the direction that gives the clearest signal.”

Time in range: a new blood sugar metric for people with diabetes - STAT

With nearly 300 blood sugar measurements a day, CGMs offer a new way to evaluate how well an individual is controlling his or her diabetes: time in range. This is expressed as a percentage of the time an individual’s blood sugar is within the target values. This metric, recently endorsed by the American Diabetes Association and by an international consensus committee, correlates nicely with control of diabetes and the implied development of complications such as vision loss, kidney problems, and low blood sugar excursions. Greater time in range has been linked to more stable glucose control, which should lead to fewer complications.

Living a longer, healthier life: A systems approach to medicine at WesternU's Pumerantz Lecture | Benzinga

"We think that clinical trials in the future ought to be done as N-of-1 (single subject) experiments," Hood said. "In a cancer trial we can use the individual data clouds to actually identify biomarkers that distinguish the responders from the non-responders. Then what we will do is a second trial of 50 patients with all responders. And if you get a 98 percent response rate, the FDA will approve your drug in the blink of an eye. You go from spending $1.5 billion on a clinical trial to spending hundreds of thousands of dollars on a clinical trial." Hood and his collaborators completed a study that used dynamic data clouds to improve wellness. "A wellness study of 108 individuals using personal, dense, dynamic data clouds," published in Nature Biotechnology in 2017, involved collecting the complete genome sequences of 108 volunteers, including Hood. The subjects each gave blood draws every three months to measure 1,200 analytes of three classes: clinical chemistries, metabolites and proteins. They measured gut microbiome every three months and used Fitbit and other devices for digital health measurements. "What these gave us for each individual was longitudinal data clouds that, when analyzed, led to actionable possibilities that if executed by the individual could either improve their wellness and/or let them ameliorate or avoid disease," Hood said. "The big question in all of this is: How do you change social behavior? What we used were wellness coaches, trained in psychology and nutrition and nursing, who were magnificent. They would elicit from the individual exactly what they wanted in their health objectives, and this is not easy to do."

Gut bacteria 'fingerprint' predicts radiotherapy side effects: First clinical study to show link between types of gut bacteria and radiotherapy-induced gut damage -- ScienceDaily

The researchers found that patients who had a high risk of gut damage had 30-50 per cent higher levels of three bacteria types, and lower overall diversity in their gut microbiome, than patients who had not undergone any radiotherapy. This suggests that patients with less diverse gut microbiomes and high levels of the bacteria -- Clostridium IV, Roseburia and Phascolarctobacterium -- are more susceptible to gut damage. The researchers also believe these patients may require more 'good bacteria' to maintain a healthy gut -- and so may be more susceptible to side effects when these bacteria are killed by radiation.

Fragmented physical activity linked to greater mortality risk -- ScienceDaily

The researchers found that for each 10 percent higher activity fragmentation there was a 49 percent increase in the risk of mortality. The researchers defined activity fragmentation as the probability of transitioning from an active state to a sedentary state for each participant, so shorter average activity periods meant higher fragmentation. The researchers also analyzed the duration of each participant's bouts of activity, and found that "percent of activity spent in bouts of less than five minutes" appeared to be another good marker of mortality risk. Each additional 10 percent of active time spent in such short bouts was associated with a 28 percent increase in the chance of mortality. Percent of active time spent in 5- to 10-minute bouts was not a significant indicator of mortality risk.

AI system accurately detects key findings in chest X-rays of pneumonia patients within 10 seconds -- ScienceDaily

Researchers found that the CheXpert model outperformed the current system of using a radiologist to create radiology reports for all key pneumonia findings, plus NLP. It also did so in less than 10 seconds, compared to the 20 minutes to hours from NLP. NLP of radiology reports was the most frequent cause of errors within ePNa. "A 2013 study published in JAMA Internal Medicine found that 59 percent of errors made by ePNA were due to NLP processing of radiologist reports, so we're eager to replace it with a better, faster system," Dr. Dean said. Outside of ePNa concerns, emergency department physicians looking at radiology reports often are challenged to understand the unstructured language used by radiologists in interpreting shadows on chest X-rays, Dr. Dead added.

Artificial intelligence needs patients' voice to remake health care - STAT

Health care AI companies currently harness data from electronic health records (EHRs) to build their products. EHRs are incomplete at best, dangerous at worst. They are so saturated with answers to questions required by insurance companies’ reimbursement rules and core measures from the Centers for Medicare and Medicaid Services that they end up having little to do with actual patient care.

Wrist-worn step trackers accurate in predicting patient health outcomes -- ScienceDaily

In the study, researchers conducted a 12-week, blinded, randomized, cross-over trial with 52 patients, a group that included adults with a history of respiratory problems during periods of elevated air pollution. Wrist step counters tracked patient steps for those 12 weeks; and patients also filled out respiratory symptom questionnaires. Researchers found they could effectively estimate a patient's 6MWD results by using step counters, instead of having patients come in a clinical setting to do the 6MWD test. "Instead of having one measurement every few months, you could have weekly measurements, and have information at disease progression at more frequent intervals. This is a significant improvement and enhanced convenience for our patients," said Dr. Blagev. The implications? Using wrist step counters will allow physicians to track how their patients are doing, the progression of the disease, and whether a patient requires an immediate intervention. "Being able to distill step counts into this clinically important metric is a first step in being able to think about how to use step counters in order to better manage health and detect deterioration earlier," Dr. Blagev added.

A bathroom scale could monitor millions with heart failure -- ScienceDaily

The pulsing and bobbing signal is called a ballistocardiogram (BCG), a measurement researchers took more commonly about 100 years ago but gave up on as imaging technology far surpassed it. The researchers are making it useful again with modern computation. "Our work is the first time that BCGs have been used to classify the status of heart failure patients," said Omer Inan, the study's principal investigator and an associate professor in Georgia Tech's School of Electrical and Computer Engineering.

For the first time walking patterns identify specific types of dementia -- ScienceDaily

For the study, researchers analysed the walk of 110 people, including 29 older adults whose cognition was intact, 36 with Alzheimer's disease and 45 with Lewy body dementia. The participants took part in a simple walking test at the Gait Lab of the Clinical Ageing Research Unit, an NIHR-funded research initiative jointly run by Newcastle Hospitals NHS Foundation Trust and Newcastle University. Participants moved along a walkway -- a mat with thousands of sensors inside -- which captured their footsteps as they walked across it at their normal speed and this revealed their walking patterns. People with Lewy body dementia had a unique walking pattern in that they changed how long it took to take a step or the length of their steps more frequently than someone with Alzheimer's disease, whose walking patterns rarely changed. When a person has Lewy body dementia, their steps are more irregular and this is associated with increased falls risk. Their walking is more asymmetric in step time and stride length, meaning their left and right footsteps look different to each other. Scientists found that analysing both step length variability and step time asymmetry could accurately identify 60% of all dementia subtypes -- which has never been shown before.

Pattern Analysis of Oxygen Saturation Variability in Healthy Individuals: Entropy of Pulse Oximetry Signals Carries Information about Mean Oxygen Saturation

Pulse oximetry is routinely used for monitoring patients' oxygen saturation levels with little regard to the variability of this physiological variable. There are few published studies on oxygen saturation variability (OSV), with none describing the variability and its pattern in a healthy adult population.

The Heroism of Incremental Care | The New Yorker

Instead of once-a-year checkups, in which people are like bridges undergoing annual inspection, we will increasingly be able to use smartphones and wearables to continuously monitor our heart rhythm, breathing, sleep, and activity, registering signs of illness as well as the effectiveness and the side effects of treatments. Engineers have proposed bathtub scanners that could track your internal organs for minute changes over time. We can decode our entire genome for less than the cost of an iPad and, increasingly, tune our care to the exact makeup we were born with. Our health-care system is not designed for this future—or, indeed, for this present. We built it at a time when such capabilities were virtually nonexistent. When illness was experienced as a random catastrophe, and medical discoveries focussed on rescue, insurance for unanticipated, episodic needs was what we needed. Hospitals and heroic interventions got the large investments; incrementalists were scanted. After all, in the nineteen-fifties and sixties, they had little to offer that made a major difference in people’s lives. But the more capacity we develop to monitor the body and the brain for signs of future breakdown and to correct course along the way—to deliver “precision medicine,” as the lingo goes—the greater the difference health care can make in people’s lives, as well as in reducing future costs. This potential for incremental medicine to improve and save lives, however, is dramatically at odds with our system’s allocation of rewards. According to a 2016 compensation survey, the five highest-paid specialties in American medicine are orthopedics, cardiology, dermatology, gastroenterology, and radiology. Practitioners in these fields have an average income of four hundred thousand dollars a year. All are interventionists: they make most of their income on defined, minutes- to hours-long procedures—replacing hips, excising basal-cell carcinomas, doing endoscopies, conducting and reading MRIs—and then move on. (One clear indicator: the starting income for cardiologists who perform invasive procedures is twice that of cardiologists who mainly provide preventive, longitudinal care.)