Search

Analytical observations (in silico) indicate molecular features of SARS-Cov2 genome that potentially explains the high prevalence of asymptomatic cases in Covid-19 pandemic. We observed that the virus maintains a low preference for ‘GGG’ codon for glycine (3%) in its genome. We also observed multiple putative introns of 26–44 nucleotide (nt) length in the genomic region between the coding regions of Nsp10 and RPol in the viral ORF1ab, like several other beta-coronaviruses of similar infectivity levels. It appears that the virus employs a dual strategy to ensure unhindered replication within the host. One of the strategies employ a (− )1 frameshift translation event through programmed ribosomal slippage at the ribosomal slippage site in the ORF1ab. The alternate strategy relies on intron excision to generate a read through frame. The presence of ‘GGG’ in this conserved ribosomal slippage site ensures adequate tRNA in cytoplasm to match the codon, implying no additional frameshift translation due to ribosomal stalling. With fewer replication events, viral load remains low and resulting in asymptomatic cases. We suggest that this strategy is the primary reason for the prevalence of asymptomatic cases in the disease, enabling the virus to spread rapidly. Supplementary Information The online version contains supplementary material available at 10.1007/s12064-021-00349-3.

Purpose The timely management of cancer surgery suffered due to COVID-19 and nationwide lockdown. Continuing cancer surgery was a challenge faced by all. We present our experience on cancer surgery in a cancer centre with high volume of patients and limited resources during early pandemic. Methods We retrospectively analysed our operation theatre database on surgery and anaesthesia from 1st April to 30th June 2020. Results A total of 457 surgeries were done—complex major, major, intermediate and minor surgeries constituted 43%, 25%, 12% and 20%, respectively. Median age of patient was 50 years, and 76% were below 60. The median ASA class was I (I–IV), and 97% were ASA I and II. The median Eastern Cooperative Oncology Group score was 0 (0–3), and 92% had score 0 and 1. Major cases done under regional anaesthesia were 30.7%. Median length of intensive care unit stay was 1 (1–6) days, and length of hospital stay was 7 (7–15) days. Clavien–Dindo Grade II complication in patients above 60 years was 16.4% and below 60 years was 17.6% (p = 0.76). 10% in ASA I compared to 26% of ASA II (p = 0.00) and 15.9% with ECOG 0 and 1 compared to 30.9% with ECOG 3 and 4 (p = 0.01) had grade II complication. Four (1%) patients had Grade ≥ III CD complication. Covid testing was undertaken in 52% patients pre-operatively, and there was no positive case in post-operative period. Conclusions Adopting and implementing institutional policy catering to limited resource available at our centre, we facilitated cancer surgery.

This paper proposes a hybridized machine-learning framework called Extreme Learning Machine using self-adaptive multi-population-based Jaya algorithm for forecasting the currency exchange value. This learning technique attempts to take the advantages of generalization ability of Extreme Learning Machines (ELMs) along with the multi-population search scheme of Jaya optimization technique. This model can very well forecast the exchange price of USD–INR and USD–EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. Proposed model has been compared with original ELM and ELM-Jaya along with technical analysis method such as discrete wavelet neural network optimized with self-adaptive multi-population-based Jaya and the comparison of different performance measures like MAPE, Theil’s U, ARV and MAE reveal that ELM using self-adaptive multi-population-based Jaya hybrid models possesses superior compared to the rest predictive models. Comparison of different features demonstrates technical indicators outperform other two features such as statistical measures and combination of both technical indicators and statistical measures.