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Record W4310558308 · doi:10.1111/jnc.15736

<scp>SARS‐CoV</scp>‐2, aging, and <scp>Post‐COVID</scp>‐19 neurodegeneration

2022· review· en· W4310558308 on OpenAlexafffund
Michael J. Strong

Bibliographic record

VenueJournal of Neurochemistry · 2022
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsRobarts Clinical TrialsWestern University
FundersWestern University
KeywordsNeurodegenerationCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakNeuroscienceVirologyBiologyMedicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

As the world continues to experience the effects of SARS-CoV-2, there is evidence to suggest that the sequelae of viral infection (the post-COVID-19 condition; PCC) at both an individual and population level will be significant and long-lasting. The history of pandemics or epidemics in the last 100 years caused by members of the RNA virus family, of which coronaviruses are a member, provides ample evidence of the acute neurological effects. However, except for the H1N1 influenza pandemic of 1918/1919 (the Spanish flu) with its associated encephalitis lethargica, there is little information on long-term neurological sequelae. COVID-19 is the first pandemic that has occurred in a setting of an aging population, especially in several high-income countries. Its survivors are at the greatest risk for developing neurodegenerative conditions as they age, rendering the current pandemic a unique paradigm not previously witnessed. The SARS-CoV-2 virus, among the largest of the RNA viruses, is a single-stranded RNA that encodes for 29 proteins that include the spike protein that contains the key domains required for ACE2 binding, and a complex array of nonstructural proteins (NSPs) and accessory proteins that ensure the escape of the virus from the innate immune response, allowing for its efficient replication, translation, and exocytosis as a fully functional virion. Increasingly, these proteins are also recognized as potentially contributing to biochemical and molecular processes underlying neurodegeneration. In addition to directly being taken up by brain endothelium, the virus or key protein constituents can be transported to neurons, astrocytes, and microglia by extracellular vesicles and can accelerate pathological fibril formation. The SARS-CoV-2 nucleocapsid protein is intrinsically disordered and can participate in liquid condensate formation, including as pathological heteropolymers with neurodegenerative disease-associated RNA-binding proteins such as TDP-43, FUS, and hnRNP1A. As the SARS-CoV-2 virus continues to mutate under the immune pressure exerted by highly efficacious vaccines, it is evolving into a virus with greater transmissibility but less severity compared with the original strain. The potential of its lingering impact on the nervous system thus has the potential to represent an ongoing legacy of an even greater global health challenge than acute infection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.339
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2022
Admission routes2
Has abstractyes

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