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Record W7062404660

Unraveling the potential for the novel agent, VR23, and its use as an anti-inflammatory for both acute and chronic inflammatory conditions

2021· dissertation· en· W7062404660 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2021
Typedissertation
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsProinflammatory cytokineMechanism (biology)In vivoContext (archaeology)Inflammatory responseRheumatoid arthritisMechanism of action
DOInot available

Abstract

fetched live from OpenAlex

Inflammatory conditions continue to be on the rise in Canada, due in part to the increase in aging \npopulation. Although effective in some cases, the anti-inflammatory drugs that are currently \navailable have their own pitfalls, with toxic side effects and being non-selective in their \nmechanisms of action. In an attempt to develop an effective anti-inflammatory drug, I have \ncharacterized VR23, a novel 4-aminoquinoline derived sulfonyl hybrid compound. VR23 was \ninitially developed in our laboratory as potentially an effective and safe anticancer agent. \nPreviously, data obtained from an in vivo study for its anticancer effects raised a possibility that \nVR23 might also possess anti-inflammatory property. In a nutshell, data presented in this thesis \nconfirm that the hypothesis is correct. In the study, I used both acute and chronic inflammatory \nmodels. In Chapter 1, I have shown that VR23 is able to effectively down-regulate proinflammatory cytokines comparably to dexamethasone, a well-known anti-inflammatory agent. \nSpecifically, VR23 was able to down-regulate IL-6 with great sensitivity. In rheumatoid arthritis \ncell models of chronic inflammation, VR23 demonstrated superiority over the anti-rheumatic \nhydroxychloroquine in its ability to regulate pro-inflammatory cytokines. In Chapter 2, I \ndemonstrated VR23’s anti-inflammatory mechanism is likely through its prevention of the \nphosphorylation of STAT3, leading to a decrease in the production of its down-stream targets, \nIL-6 and MCP-1. Lastly, in Chapter 3 I describe the discovery that VR23 is rapidly metabolized \ninto CPQ and DK23. CPQ is not an active compound with respect to its anti-inflammatory \nactivity, indicating that it is a by-product of the VR23 detoxification process. On the contrary, \nDK23 possesses active anti-inflammatory property, as potent as VR23 at their respective IC50 \nconcentrations. Data from an acute lung injury model showed that the anti-inflammatory activity \nof VR23 is comparable to that of dexamethasone, a well-known corticosteroid. Data obtained \nfrom the rheumatoid arthritis study showed that VR23 is much more superior to \nhydroxychloroquine, a commonly used anti-rheumatic drug. Overall, this study demonstrates the \npotential for the novel compound, VR23, to be used as a non-toxic specific anti-inflammatory \ndrug to treat IL-6 driven conditions such as rheumatoid arthritis.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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