Evidence map›Paper›PMID 35782712›Full record

ArticleIEEE transactions on molecular, biological, and multi-scale communications2021

VIVID:

Saswati Pal, Nabiul Islam, Sudip Misra

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on molecular, biological, and multi-scale communications, 2021. 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
1.2field-weighted citation impact, top 24% 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, 14 citations in OpenAlex.

  1. Article
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

3 authors at 2 institutions in 2 countries.

Saswati PalSchool of Nano-Science and TechnologyIndian Institute of Technology Kharagpur Kharagpur 721302 India.
Nabiul IslamTelecommunications Software and Systems GroupWaterford Institute of Technology Waterford X91 WR86 Ireland.ORCID https://orcid.org/0000-0002-0898-5946
Sudip MisraDepartment of Computer Science and EngineeringIndian Institute of Technology Kharagpur Kharagpur 721302 India.ORCID https://orcid.org/0000-0002-2467-6414
Indian Institute of Technology Kharagpur · INWaterford Institute of Technology · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As an alternative to ongoing efforts for vaccine development, scientists are exploring novel approaches to provide innovative therapeutics, such as nanoparticle- and stem cell-based treatments. Thus, understanding the transmission and propagation dynamics of coronavirus inside the respiratory system has attracted researchers' attention. In this work, we model the transmission and propagation of coronavirus inside the respiratory tract, starting from the nasal area to alveoli using molecular communication theory. We performed experiments using COMSOL, a finite-element multiphysics simulation software, and Python-based simulations to analyze the end-to-end communication model in terms of path loss, delay, and gain. The analytical results show the correlation between the channel characteristics and pathophysiological properties of coronavirus. For the initial 50% of the maximum production rate of virus particles, the path loss increases more than 16 times than the remaining 50%. The delayed response of the immune system and increase in the absorption of virus particles inside the respiratory tract delay the arrival of virus particles at the alveoli. Furthermore, the results reveal that the virus load is more in case of asthmatic patients as compared to the normal subjects.

Indexed as

channel modelingCoronavirusCOVID-19molecular communicationnanonetworktransmission modelvirus propagation

Identifiers

PMID35782712
PMCPMC8544951
OpenAlexW3152749975

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.