Sooma Oy secured U.S. FDA investigational device exemption approval to initiate a pivotal study of its transcranial direct current stimulation medical device in people with major depressive disorder. The study will examine the efficacy of the non-invasive Sooma 2Gen device in improving MDD as an at-home treatment.
In an advance that could significantly lighten the load for caretakers in the “sandwich generation” and reduce loneliness in elderly patients, Aspargo Labs Inc. developed a metered delivery device that optimizes absorption of pharmaceuticals and reminds users to take their medications.
Precision Neuroscience Inc. recently partnered with Beth Israel Deaconess Medical Center to test its brain computer interface, the Layer 7 Cortical Interface, during craniotomy procedures.
Researchers from Weill Cornell University filed for protection of discoveries made from investigations into the mechanisms underlying depression, which revealed that a specific brain network is significantly larger in individuals affected by depression.
The first patenting from Theta Neurotech Inc. sees the company’s co-founders describe their development of a wearable earpiece that uses an electroencephalography technology and machine learning algorithms to alert epilepsy patients 30 to 60 minutes before they have a seizure.
Med-tech companies focusing on cardiovascular diseases or neurological conditions, women’s health or robotic surgery, will find European investors willing to deploy capital into their stories. European venture capital firms are excited about the continuing innovation and opportunities in the sector.
Dongkook Life Science Co. Ltd. (DKLS) priced a ₩18 billion (US$12.5 million) IPO on the Korea Exchange as South Korea’s first med-tech listing of the year.
Biotronik Neuro’s Prospera spinal cord stimulation system achieved more than 50% pain reduction for 86% of patients with back pain and 89% of patients with leg pain over two years, according to new study results.
A recent patent application from Laleh Rad, associate professor of Biomedical Engineering and Radiology at Northwestern University, describes the use of machine learning for real-time risk assessment of magnetic resonance imaging in patients with conductive implants for whom tissue heating from radiofrequency excitation fields remains a major concern.
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