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.

The distribution of ABO and Rh-D blood groups was studied among 150,536 blood donors screened at the Dr John Scudder Memorial Blood Bank, Christian Medical College Hospital, Vellore, over a period of 11 years (April 1988 to March 1999). The most common blood group was found to be group O [58,330 (38.75%)], followed by group B [49,202 (32.69%)], and group A [28,372 (18.85%)]. The least common blood group was AB group [7,930 (5.27%)]. A2 or A2B groups were found in 3.01% and 1.43% of donors, respectively. The prevalence of Rh-D negative group was found in 8,225 (5.47%) donors. Bombay group (H negative non-secretor, genotype hh phenotype Oh) was found in six donors (0.004%). Although the incidence of Rh-D negative group was identical to previously published data from North India, the most common blood group was O group in our study as opposed to B group.

To the Editor: Vaccination has played a major role in eradicating communicable diseases. 1 Because health care workers (HCWs) serve in the forefront during pandemics, they are particularly vulnerable. Thus, in the coronavirus disease 2019 (COVID-19) pandemic, it was imperative to vaccinate frontline workers as quickly as possible and ascertain the extent of protection offered by vaccination.

The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.

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