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Record W3113149330 · doi:10.1002/ptr.6978

Phytotherapics in <scp>COVID19</scp>: Why palmitoylethanolamide?

2020· review· en· W3113149330 on OpenAlexaboutno aff
Marcella Pesce, Luisa Seguella, Sara Cassarano, Laura Aurino, Walter Sanseverino, Jie Lu, Chiara Corpetti, Alessandro Re, Martina Vincenzi, Giovanni Sarnelli, Giuseppe Esposito

Bibliographic record

VenuePhytotherapy Research · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsNutraceuticalPalmitoylethanolamideCoronavirus disease 2019 (COVID-19)PandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakInfectious disease (medical specialty)MedicineBiotechnologyDiseaseBiologyVirology

Abstract

fetched live from OpenAlex

At present, googling the search terms "COVID-19" and "Functional foods" yields nearly 500,000,000 hits, witnessing the growing interest of the scientific community and the general public in the role of nutrition and nutraceuticals during the COVID-19 pandemic. Many compounds have been proposed as phytotherapics in the prevention and/or treatment of COVID-19. The extensive interest of the general public and the enormous social media coverage on this topic urges the scientific community to address the question of whether which nutraceuticals can actually be employed in preventing and treating this newly described coronavirus-related disease. Recently, the Canadian biotech pharma company "FSD Pharma" received the green light from the Food and Drug Administration to design a proof-of-concept study evaluating the effects of ultramicronized palmitoylethanolamide (PEA) in COVID-19 patients. The story of PEA as a nutraceutical to prevent and treat infectious diseases dates back to the 1970s where the molecule was branded under the name Impulsin and was used for its immunomodulatory properties in influenza virus infection. The present paper aims at analyzing the potential of PEA as a nutraceutical and the previous evidence suggesting its anti-inflammatory and immunomodulatory properties in infectious and respiratory diseases and how these could translate to COVID-19 care.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.001

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 head0.154
GPT teacher head0.457
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 teacher head, not a consensus.

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

Citations17
Published2020
Admission routes1
Has abstractyes

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