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Los Angeles, Feb 23: According to researchers, if administered quickly, a common medication that reduces bleeding could be a treatment for bleeding stroke.

The Spot Sign and Tranexamic Acid on Preventing ICH Growth - Australasia Trial (STOP-AUST) was a multicenter, prospective, randomized, double-blind, placebo-controlled, phase 2 clinical trial using the antifibrinolytic agent tranexamic acid in people with intracerebral hemorrhage (ICH).

ICH is a severe form of acute stroke with few treatment options.

Tranexamic acid is currently used to treat or prevent excessive blood loss from trauma, surgery, tooth removal, nosebleeds and heavy menstruation. For this study, one hundred patients with active brain bleeding were given either intravenous tranexamic acid or placebo within 4.5 hours of symptom onset.

Researchers analyzed brain CT scans taken during the 24-hour period after treatment with tranexamic acid or placebo.

Researchers found a trend towards reduced hemorrhage expansion in the group treated with tranexamic acid, especially in those treated within 3 hours of the brain bleed. However, this trend was not statistically significant. The finding was consistent with previous research using the medication.

"Further trials using tranexamic acid are ongoing and focusing on ultra-early treatment - within 2 hours. 

This is where the greatest opportunity for intervention appears to be. Tranexamic acid is inexpensive, safe and widely available. Our results and others provide great impetus for further, focused research using this treatment," Nawaf Yassi said.

Larger trials focused on patient outcomes are required for this therapy to enter routine clinical practice.

New Delhi, Jun 24: Expanding the testing criterion for coronavirus, the Indian Council of Medical Research has said it should be made widely available to all symptomatic individuals across the country.

"Since test, track and treat' is the only way to prevent spread of infection and save lives, it is imperative that testing should be made widely available to all symptomatic individuals in every part of the country and contact tracing mechanisms for containment of infection are further strengthened," it said in an advisory on 'Newer Additional Strategies for COVID-19 Testing' on Tuesday.

In its revised testing strategy for COVID-19 issued on May 18, the Indian Council of Medical Research (ICMR) had advised testing for all symptomatic Influenza-like illness (ILI) among returnees and migrants within seven days of illness.

All hospitalised patients who develop ILI symptoms, symptomatic individuals living within hotspots or containment zones and healthcare and frontline workers involved in containment and mitigation of coronavirus were also advised testing.

The apex health research body has also advised authorities to enable all government and private hospitals, offices and public sector units to perform antibody-based COVID-19 testing for surveillance to help allay fears and anxiety of healthcare workers and office employees.

The earlier advisories on rapid antibody testing advisories had focused on areas reporting clusters (containment zones), large migration gatherings/evacuees centers and testing of symptomatic ILI individuals at facility level.

Besides, the ICMR on Tuesday also recommended deployment of rapid antigen detection tests for COVID-19 in combination with RT-PCR tests in all containment zones, all central and state government medical colleges and government hospitals, all private hospitals approved by the National Accreditation Board for Hospitals and Healthcare (NABH), all NABL-accredited and ICMR approved private labs, for COVID-19 testing.

All hospitals, laboratories and state governments intending to perform the point-of-care antigen tests need to register with ICMR to obtain the login credentials for data entry.

"ICMR advises all state governments, public and private institutions concerned to take required steps to scale up testing for COVID-19 by deploying combination of various tests as advised," the advisory added.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.