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Record W4404120946 · doi:10.24908/qap.v1i2.17352

Thapsigargin: An Unlikely Drug in the Battle Against Viral Pandemics

2024· article· en· W4404120946 on OpenAlexaff
Che C. Colpitts, Isabella E Pellizzari Delano

Bibliographic record

VenueQapsule Queen s Undergraduate Health Sciences Journal · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsQueen's University
Fundersnot available
KeywordsBattlePandemicDrugVirologyThapsigarginCoronavirus disease 2019 (COVID-19)MedicinePharmacologyHistoryAncient historyInternal medicineInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Antiviral resistance is an increasingly categorized phenomenon, and with the high pandemic potential of emergent pathogens such as coronaviruses, the need for novel therapeutic agents is dire. Thapsigargin, a potent endoplasmic reticulum stress inducer found in the Mediterranean region, has shown significant promise in combatting replication of several viruses including coronaviruses by harnessing the host's innate immune system, even being more efficacious than some antivirals currently used. There is a major gap in knowledge regarding the exact mechanism of action that permits Thapsigargin to be an incredible endoplasmic reticulum stress inducer and an potent antiviral. We hypothesize that interferon 1 production and SERCA-inhibition caused by thapsigargin mediates the antiviral effects observed. This review outlines what is currently known about this novel compound, the gap in research, and details methodologies including gene expression analyses, protein quantification, and mammalian cell culturing, to address the gap and answer the hypothesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.146
GPT teacher head0.467
Teacher spread0.321 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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