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Record W2909690397 · doi:10.5539/jmbr.v9n1p7

Synergistic Effects of Nano-Sized Clinoptilolite and Nigella Sativa on Inflammatory and Hematological Factors in Rats with Type 2 Diabetes

2019· article· en· W2909690397 on OpenAlexvenueno aff
Elahe Bazri, Sirous Khorram, Mehran Mesgari Abbasi, Mohammad Asghari Jafarabadi, Ali Tarighat‐Esfanjani, Yalda Salari

Bibliographic record

VenueJournal of Molecular Biology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicNigella sativa pharmacological applications
Canadian institutionsnot available
FundersUniversity of TabrizTabriz University of Medical Sciences
KeywordsMean corpuscular volumeMean corpuscular hemoglobinHematocritMean corpuscular hemoglobin concentrationHemoglobinNigella sativaInternal medicineWhite blood cellEndocrinologyType 2 diabetesDiabetes mellitusPlateletMedicineChemistryTraditional medicine

Abstract

fetched live from OpenAlex

The inflammatory efficacy of supplementation of natural nano-sized clinoptilolite (NCLN) and Nigella sativa (NS) was evaluated At the end of 7th week in interleukin1β (IL1β), interleukin10 (IL-10), interleukin6 (IL-6) and platelet (PLT), white blood cell (WBC), red blood cell (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV). 42 rats were divided into two groups as diabetic and non-diabetic. Diabetic group divided into 4 subgroups as normal control (NC), NS 1% food, NCLN 2%/food, NS 1%/food + NCLN 2%/food and fed high fat diet (HFD) for 1 month, then injected 35mg/kg BW STZ to induce type 2 diabetes (T2D). Our results showed IL-10 in NCLN and NCLN+NS groups were significantly higher NC group (p<0.05). IL-6 decreased in NS group in comparison with DC group. And PLT decreased significantly in NCLN group in comparison with other groups. Our data suggests NS and NCLN may have synergistic beneficial effects on increasing some of anti-inflammatory factors.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.370
Teacher spread0.345 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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