Search

Background: Repeated cross-sectional serosurveys in the same geographic area establish the trend of the evolving pandemic. We present the findings of the third round of a national serosurvey to estimate the seroprevalence of SARS-CoV-2 infection among the general population and health care workers of India. Methods: We conducted the third population-based serosurvey between Dec 18, 2020 and Jan 6, 2021 in the same 700 villages or wards from 70 districts in 21 states across India, which were selected for the first and second serosurveys. We enrolled from each district, at least 400 individuals aged ≥ 10 years from general population and 100 HCWs from sub-district level public health facilities. Serum samples from general population were tested for the presence of IgG antibodies against nucleocapsid (N) and spike protein (S1-RBD) of SARS-CoV-2 using the Abbott and Siemens assays respectively, whereas sera from HCWs were tested for anti-S1-RBD. For general population, sera positive for either of the antibodies were considered positive, while sera positive for anti-S1-RBD were considered as positive for HCW. Weighted seroprevalence estimates were adjusted for sensitivity and specificity of respective assays. Findings: Of the 28,598 sera from general population, 4585 (16%) had IgG antibodies against N, 6647 (23.2%) against S1-RBD and 7436 (26%) against either. The weighted and assay characteristic adjusted seroprevalence against either of the antibodies was 24.1 (95%CI: 23.0% to 25.3%). Seroprevalence was lower in rural areas (21.4%, 95% CI: 20.3% to 22.6%) compared to urban non-slum (29.4%, 95% CI: 26.9% - 32.1%) and slum areas (34.6%, 95% CI: 31.0% to 38.3%). Among 7385 HCWs, the seroprevalence of anti-S1-RBD IgG antibodies was 25.6% (95% CI: 23.5% to 27.8%). Interpretation: Nearly one in four individuals aged 10 years or older from general population as well as HCWs were exposed to SARS-CoV-2 by December 2020 amounting to 271 million infections in India. Funding Statement: Indian Council of Medical Research Declaration of Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare no competing interests Ethics Approval Statement: The project was approved by Institutional Human Ethics Committee at ICMR-National Institute of Epidemiology.

The study was undertaken to perform a systematic review to investigate the diagnostic relevance of primitive reflexes (PR) such as Moro, Rooting, Sucking, Palmar, and Plantar/Babinski reflexes in high-risk newborns. A systematic literature search was carried out using different search engines between the years 1980 and 2018. There were 48 articles considered out of 61 articles, based on screening of the title and abstract. Finally, among 48 articles, a total of 8 articles were considered for the detailed review based on the inclusion criteria. The results of the selected articles on the different PRs have been discussed in the present study. Though most studies have evaluated the moro and the plantar reflex, every reflex considered in the study has been found to be affected in high-risk newborns and consequently, it is difficult to undermine the significance of any of these. All the studies reviewed highlighted the importance of assessing these reflexes in high-risk newborns and indicated that an abnormal reflex is an indication of a neurological abnormality. These findings indicate that the assessment of PRs should be included in the newborn screening protocol because they are not time consuming and can be performed easily with minimum or no tools. Future studies that are aimed at investigating the efficacy of these PRs in identifying children with developmental disabilities are essential.

Background: Rabies is one of the highly fatal diseases, with nearly fifty nine thousand deaths annually globally and almost one third of these are in India. Higher rates of dog bites are common in our country, due to large stray dog population. Despite a preventable disease, unhealthy wound practices are still prevailing in the community due to lot of myths related to the disease. The objectives of the study were to assess the magnitude of unhealthy wound practices and to assess the knowledge regarding health seeking behaviour following animal bite.Methods: The present study was a cross sectional study conducted at SDMC Polyclinic, Mehrauli, Delhi from January to December 2015. A total of 160 cases of animal bite that attended OPD and consented were analysed for knowledge, practices and health seeking behaviour towards animal bite.Results: Out of total 160 respondents almost all were bitten by dogs of which 2/3rd was stray dogs and most common site of bite was lower limb. 55% of the subjects did not wash or clean the wounds after bite. 27% of subjects applied chilli powder or its paste with oil and 2.5% used oil and turmeric paste and 1.9% oil on the wound. Only 41.9% of the subjects believed that vaccination is the treatment of the choice following animal bite.Conclusions: Intensive health education through mass media and mid media can be used to create awareness about the disease transmission and preventive measures like wound management and post-exposure prophylaxis. Also, pet vaccination should be made mandatory for all those who keep pets.

Speaker Recognition is an active area of research for the last few decades for its applications in several national security, and other forensic applications. In this work, we present the details of a speaker recognition system developed using universal background model and support vector machines(UBM-SVM). We explored several techniques to improve the performance of the baseline system developed using mel frequency cepstral coefficients(MFCC) as input features. We developed and tested the speaker recognition system for 200 speakers, using the data collected over 13 different channels, such as handset regular phone, speaker phone, regular phone headphone, regular phone, etc. We experimented with the use of RelAtive SpecTrA (RASTA) processing, and feature warping on the input MFCC features, and nuisance attribute projection (NAP) on the Gaussian mixture model supervectors derived in the system. It was seen that these techniques have helped improve the system performance significantly by minimizing the effect of different channels on the system performance. The details of the system implementation and results are presented in this paper. The complete system is developed in MATLAB and C/C++.

For spoken language processing applications like speaker recognition/verification, not only that the silence segments do not contribute any speaker specific information, but also it dilutes the already available information content in the speech segments in the audio data. It has been experimentally studied that removing silence segments with the help of a voice activity detector(VAD) from the utterance before feature extraction enhances the performance of speaker recognition systems. Empirical algorithms using signal energy and spectral centroid(ESC) is one of the most popular approaches to VAD. In this paper, we show that using spectral matching (SM) to distinguish between silence and speech segments for VAD outperforms the VAD using ESC . We use a neural network with TempoRAl PatternS (TRAPS) of critical band energies as its input for improved performance. We evaluate the performance of VADs using a speaker recognition system developed for 20 speakers.