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Record W4313454305 · doi:10.22034/iji.2022.90456.2007

The Effect of Oral Administration of Silymarin on Serum Levels of Tumor Necrosis Factor-α and Interleukin-1ß in Patients with Rheumatoid Arthritis.

2022· article· en· W4313454305 on OpenAlexaff
Mehrdad Shavandi, Yasaman Yazdani, Shirin Asar, Arash Mohammadi, Ehsan Mohammadi‐Noori, Amir Kiani

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicSilymarin and Mushroom Poisoning
Canadian institutionsWestern University
Fundersnot available
KeywordsRheumatoid arthritisMedicineTumor necrosis factor alphaInternal medicineRegimenGastroenterologyPathogenesisInterleukinInterleukin 6ArthritisNecrosisImmunologyPharmacologyCytokine

Abstract

fetched live from OpenAlex

BACKGROUND: Rheumatoid Arthritis (RA) is a systemic chronic autoimmune disease. Several inflammatory agents play key roles in RA pathogenesis, among which tumor necrosis factor-alpha (TNF-α) and interleukin 1 beta (IL-1β) are of great importance. Silymarin is a potent anti-oxidant extracted from Silybummarianum L. seeds. OBJECTIVE: To study the effect of silymarin on serum levels of TNF-α and IL-1β in patients with RA. METHODS: Patients with stable RA received 140 mg of silymarin, 3 times a day, for 3 months. Serum samples were collected before and after the treatment. Both TNF-α and IL-1β serum levels were measured by ELISA. RESULTS: 42 patients (14.3% male, and 85.7% female, with a mean age of 47.59±12.8 years old) completed the treatment course. There was no significant difference in the overall mean concentration of either TNF-α (p=0.14) or IL-1β (p=0.27) in all 42 patients after the treatment with silymarin. CONCLUSION: The addition of silymarin to the treatment regimen of patients with stable RA has no significant effect on the serum levels of TNF-α and IL-1β, however, this study needs further evaluation with a larger sample size.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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