Objectives: Convalescent plasma (CP) as a passive source of neutralizing antibodies and immunomodulators is a century-old therapeutic option used for the management of viral diseases. We investigated its effectiveness for the treatment of COVID-19.
Design: Open-label, parallel-arm, phase II, multicentre, randomized controlled trial.
Setting: Thirty-nine public and private hospitals across India.
Participants: Hospitalized, moderately ill confirmed COVID-19 patients (PaO2/FiO2: 200-300 or respiratory rate > 24/min and SpO2 ≤ 93% on room air).
Intervention: Participants were randomized to either control (best standard of care (BSC)) or intervention (CP + BSC) arm. Two doses of 200 mL CP was transfused 24 hours apart in the intervention arm.
Main Outcome Measure: Composite of progression to severe disease (PaO2/FiO2<100) or all-cause mortality at 28 days post-enrolment.
Results: Between 22 nd April to 14 th July 2020, 464 participants were enrolled; 235 and 229 in intervention and control arm, respectively. Composite primary outcome was achieved in 44 (18.7%) participants in the intervention arm and 41 (17.9%) in the control arm [aOR: 1.09; 95%
CI: 0.67, 1.77]. Mortality was documented in 34 (13.6%) and 31 (14.6%) participants in intervention and control arm, respectively [aOR) 1.06 95% CI: -0.61 to 1.83].
Interpretation: CP was not associated with reduction in mortality or progression to severe COVID-19. This trial has high generalizability and approximates real-life setting of CP therapy in settings with limited laboratory capacity. A priori measurement of neutralizing antibody titres
in donors and participants may further clarify the role of CP in management of COVID-19.
S ign language recognition has been an active area of research for around two decades and numerous different sign languages have been extensively studied in order to design reliable sign language recognition systems. Urdu, the national language of Pakistan, and its corresponding sign language, has so far been largely neglected by the academia, which is one of the reasons, it has been chosen for developing an Urdu sign language recognition system. A comprehensive database of static images depicting the signs for different Urdu alphabets is being used as reference and input images are being compared to perform Urdu alphabet recognition. Cross-correlation technique is being used for image registration between input image and images from the database to find the closest match. The tolerance level ensures a trade -off between computational complexity and accuracy of the match between set of images. The algorithm tests on the images have been around 75% successful and attempts are being made for more efficient and robust performance.
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