We Like: BioScreen, Figure 1, Fall-Detecting Sensors Reply

bioscreen

BioScreen

MS is an unpredictable disease, and no two people experience the same symptoms or level of disability. Just 20 years ago, there was no effective treatment for MS. Patients were diagnosed and sent on their way, powerless to alter the course of their disease. Now there are 10 disease modifying therapies (DMTs) to choose from, but no clear criteria on which to base that choice. Stephen Hauser, M.D., Chair of Neurology at the UCSF School of Medicine, and his co-investigator Pierre-Antoine Gourraud, Ph.D., MPH, have been gathering data from 800 patients over more than eight years. The enormous amount of information they’ve compiled resides in a database. Using an algorithm they developed, BioScreen processes the data into a visual interpretation of the MS population as a whole, allowing individuals to compare their symptoms and disease course to those of other patients. [Source: Healthline]

figure1

Figure 1

Figure1 is a new photo-sharing network specifically for health professionals: cute cats, vacation scenes and other mainstays of social media are nowhere to be found. As a kind of Instagram for medicine, the app lets practitioners quickly upload unusual and informative images for the edification of colleagues near and far. It may lack the factual detail of some other medical apps and web sites, but gives overworked professionals a simple and surprisingly rich resource, argues Dr. Josh Landy, a Toronto-based critical-care specialist and Figure1 co-founder. [Source: National Post]

Fall Detecting Sensors

Fall-Detecting Sensors

Falling is the second leading cause of injury and death for people ages 65 and older, according to the World Health Organization. Falls often occur when a person is home alone and not within reach of a phone. In some cases, it can be hours until help arrives. To solve this problem, two IEEE members from the department of electrical and computer engineering at the University of Utah, in Salt Lake City, have developed a wireless network of sensors that detect when a person has fallen. Unlike present systems, it would not require a person to wear a sensor, push a button, or install a video camera to send an alert. Instead, the system, being developed by IEEE Member Neal Patwari and IEEE Student Member Brad Mager, can be installed in or on walls to automatically alert an emergency center or loved one when help is needed. [Source: The Institute]

 

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s