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Record W3165575082 · doi:10.1002/bab.2203

Age and sex‐specific reference intervals for prooxidant‐antioxidant balance, anti‐heat‐shock protein 27 (anti‐hsp27), and routine laboratory tests in the middle‐aged adult population

2021· article· en· W3165575082 on OpenAlexaff
Hamideh Ghazizadeh, Mary Kathryn Bohn, Mahdiyeh Yaghooti‐Khorasani, Roshanak Ghaffarian‐Zirak, Mohsen Valizadeh, Maryam Saberi‐Karimian, Hamideh Safarian, Atieh Kamel‐Khodabandeh, Reza Zare‐Feyzabadi, Ameneh Timar, Maryam Mohammadi‐Bajgiran, Mohammad Reza Oladi, Meysam Gachpazan, Mohadeseh Rohban, Habibollah Esmaily, Gordon A. Ferns, Khosrow Adeli, Majid Ghayour‐Mobarhan

Bibliographic record

VenueBiotechnology and Applied Biochemistry · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineUric acidBody mass indexPopulationPhysiologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Abstract Introduction We aimed to define specific reference intervals (RIs) for 11 biomarkers including inflammatory and oxidative stress biomarkers, liver, and renal function tests in a healthy Iranian adult population for the first time. Methods CLSI Ep28‐A3 guidelines were then used to calculate accurate age‐ and sex‐ as well as body mass index (BMI)‐specific RIs. Results RIs for studied biomarkers showed no significant age and sex‐specific differences, except for uric acid, which had higher concentrations in men when compared to women. Additionally, after partitioning the participants based on the BMI with a cutoff point of 25 kg/m 2 , only the levels of hs‐CRP were positively associated with higher BMI (RI for BMI>25: 0.51–7.85 mg/L and for BMI<25: 0.40–4.46 mg/L). RI for PAB and anti‐hsp‐27 were reported 4.69–155.36 HK and 0.01–0.70 OD in men and women aged 35–65 years old. Conclusion Partitioning by sex and BMI was only required for uric acid and hs‐CRP, respectively, while other biomarkers required no partitioning. These results can be expected to valuably contribute to improve laboratory test result interpretation in adults for improved monitoring of various diseases in the Iranian population.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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