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Record W4224300206 · doi:10.1016/j.sciaf.2022.e01184

Anti-COVID-19 potential of Azadirachta indica (Neem) leaf extract

2022· review· en· W4224300206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific African · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsUniversity of OttawaUniversity of Winnipeg
Fundersnot available
KeywordsAzadirachtaCoronavirus disease 2019 (COVID-19)MeliaceaeTraditional medicineBiologyToxicologyBotanyMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19 is caused by infection with the “severe acute respiratory syndrome coronavirus-2″ (i.e., SARS-CoV-2). This is an enveloped virus having a positive sense, single-stranded RNA genome; like the two earlier viruses SARS-CoV and the Middle East respiratory syndrome (MERS) virus. COVID-19 is unique in that, in the severe case, it has the propensity to affect multiple organs, leading to multiple organ distress syndrome (MODS), and causing high morbidity and mortality in the extreme case. In addition, comorbidities like age, cardiovascular disease, diabetes and its complications, obesity, are risk factors for severe COVID-19. It turns out that a most plausible, simple, single explanation for this propensity for MODS is the pivotal involvement of the vascular endothelium (VE). This is a consequence of the fact that the VE seamlessly connects all the entire vascular bed in the body, thus linking all the target organs (heart, lungs, kidney, liver, brain) and systems. Infection with SARS-CoV-2 leads to hyper-inflammation yielding uncontrolled production of a mixture of cytokines, chemokines, reactive oxygen species, nitric oxide, oxidative stress, acute phase proteins (e.g., C-reactive protein), and other pro-inflammatory substances. In the extreme case, a cytokine storm is created. Displacement of the virus bound to the VE, and/or inhibition of binding of the virus, would constitute an effective strategy for preventing COVID-19. In this regard, the acetone-water extract of the leaf of the Neem (Azadirachta indica) plant has been known to prevent the adherence of malaria parasitized red blood cells (pRBCs) to VE; prevent cytoadherence of cancer cells in metastasis; and prevent HIV from invading target T lymphocytes. We therefore hypothesize that this Neem leaf acetone-water extract will prevent the binding of SARS-CoV-2 to the VE, and therefore be an effective therapeutic formulation against COVID-19. It is therefore advocated herein that this extract be investigated through rigorous clinical trials for this purpose. It has the advantages of being (i) readily available, and renewable in favor of the populations positioned to benefit from it; (ii) simple to prepare; and (iii) devoid of any detectable toxicity.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.994
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.077
GPT teacher head0.298
Teacher spread0.220 · 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