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The COVID-19 pandemic and the subsequent lockdown saw many people turning chefs overnight, but those who could not turned to online delivery of food. And not just any food, as per a new report, Indians "craved the most for Biryani" during the lockdown.

The "StatEATistics report: The Quarantine Edition" from food delivery platform Swiggy found that Indians ordered biryani over "5.5 lakh times" from their favourite restaurants.

The new normal might have opened a pandora's box of behavioral changes, but some old habits die hard like the love for Biryani, which took the top spot for overall orders. It was followed by butter naan and masala dosa at 3,35,185 and 3,31,423, respectively.

Biryani has topped the list of most ordered dishes for the fourth year in a row, the food delivery platform noted.

Indians didn't forget to indulge their sweet tooth in the uncertain months of lockdown. Their favourite comfort food during the lockdown period was the moist and decadent Choco Lava cake, ordered around 1,29,000 times.

"The humble Gulab Jamun (84,558) and chic Butterscotch Mousse cake (27,317) followed suit," said the report derived from Swiggy's order analysis in the past few months across cities that it is present in.

Also, as birthday parties moved to video calls, and virtual cake cutting sessions, according to the food delivery platform, it delivered nearly "1,20,000 cakes" to complete these celebrations.

According to the report, on average, "65,000 meal orders" were placed by 8 pm each day to make sure food arrived in time for dinner.

"It was the busiest hour for Swiggy delivery partners and restaurants. On average, they (customers) chose to tip Rs.23.65, with one particularly generous customer tipping Rs. 2500!," it added.

For those who only relied on home-made food during the quarantine, Swiggy delivered a whooping 323 million kgs of onions and 56 million kgs of bananas through its grocery section and hence ensured that its consumers were all stocked up.

That said, it also took care of the 'quick-fix meal' tribe -- consumers who resort to the evergreen college hacks of living on instant noodles.

"Around 3,50,000 packets of this ideal easy to cook meal were ordered during the lockdown," it said.

In all, Swiggy delivered 40 million orders across food, groceries, medicines and other household items during India's lockdowns. It also delivered over 73,000 bottles of sanitizers and hand wash along with 47,000 face masks as the definition of essentials' changed during these uncertain times.

Clinician-scientists have found that Irish patients admitted to hospital with severe coronavirus (COVID-19) infection are experiencing abnormal blood clotting that contributes to death in some patients.

The research team from the Royal College of Surgeons in Ireland found that abnormal blood clotting occurs in Irish patients with severe COVID-19 infection, causing micro-clots within the lungs.

According to the study, they also found that Irish patients with higher levels of blood clotting activity had a significantly worse prognosis and were more likely to require ICU admission.

"Our novel findings demonstrate that COVID-19 is associated with a unique type of blood clotting disorder that is primarily focussed within the lungs and which undoubtedly contributes to the high levels of mortality being seen in patients with COVID-19," said Professor James O'Donnell from St James's Hospital in Ireland.

In addition to pneumonia affecting the small air sacs within the lungs, the research team has also hundreds of small blood clots throughout the lungs.

This scenario is not seen with other types of lung infection and explains why blood oxygen levels fall dramatically in severe COVID-19 infection, the study, published in the British Journal of Haematology said.

"Understanding how these micro-clots are being formed within the lung is critical so that we can develop more effective treatments for our patients, particularly those in high-risk groups," O'Donnell said.

"Further studies will be required to investigate whether different blood-thinning treatments may have a role in selected high-risk patients in order to reduce the risk of clot formation," Professor O'Donnell added.

According to the study, emerging evidence also shows that the abnormal blood-clotting problem in COVID-19 results in a significantly increased risk of heart attacks and strokes.

As of Friday morning, the cases increased to 20,612 cases in Ireland, with 1,232 deaths so far, according to the Johns Hopkins University.

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.