Evidence map›Paper›PMID 35756718›Full record

ArticleBiomedical signal processing and control2022

Identification of characteristics frequency and hot-spots in protein sequence of COVID-19 disease.

Vikas Pathak, Satyasai Jagannath Nanda, Amit Mahesh Joshi, Sitanshu Sekhar Sahu

Open access · greenAbstract read
In one paragraph

Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 50% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Graphene-Based Wearable Temperature Sensors: A Review.Nanomaterials (Basel, Switzerland) · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Vikas PathakMalaviya National Institute of Technology, Jaipur 302017, Rajasthan, India.
Satyasai Jagannath NandaMalaviya National Institute of Technology, Jaipur 302017, Rajasthan, India.
Amit Mahesh JoshiMalaviya National Institute of Technology, Jaipur 302017, Rajasthan, India.
Sitanshu Sekhar SahuBirla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India.
Malaviya National Institute of Technology Jaipur · INBirla Institute of Technology, Mesra · INInstitute of Technology Management · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has threatened the whole world since December 2019 and has also infected millions of people around the globe. It has been transmitted through the SARS CoV-2 virus. Various proteins of the SARS CoV-2 virus have an important role in its interaction with human cells. Specifically, the interaction of S-protein with human ACE-2 protein helps in entering of SARS CoV-2 virus into a human cell. This interaction take-place at some specific amino-acid locations called as hot-spots. Understanding of this interaction is helpful for drug designing and vaccine development for new variants of COVID-19 disease. An attempt has been made in this paper for understanding this interaction by finding the characteristics frequency of SARS-related protein families using the resonance recognition model (RRM). Hardware implementation of Bandpass notch (BPN) lattice IIR filter system architecture is also carried out, which is used for hot-spots identification in SARS CoV-2 proteins. Various signal processing techniques like retiming, pipelining, etc. are explored for performance improvement. Synthesis of proposed BPN filter system has been done using Xilinx ISE EDA tool on Zynq-series (Zybo-board) FPGA family. It is found that retimed and pipelined architecture of hardware-implemented BPN lattice IIR filter-based hot-spots detection system improves the speed (computational time) by 14 to 31 times for different SARS CoV2 related proteins as compared to its MATLAB simulation with similar functionality.

Indexed as

COVID-19Hot-spots identificationIIR digital filterSARS CoV2

Identifiers

PMID35756718
PMCPMC9212940
OpenAlexW4282958486

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.