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Record W3045161065 · doi:10.1186/s13223-020-00463-9

Differences in serum markers of oxidative stress in well controlled and poorly controlled asthma in Sri Lankan children: a pilot study

2020· article· en· W3045161065 on OpenAlexvenueno aff
Yenuli Fernando, Pujitha Wickramasinghe, Udani De Silva, Malintha Alahakoon, K. W. D. A. Anuradha, Shiroma Handunnetti

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2020
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsAsthmaNitriteOxidative stressNitric oxideMedicineAntioxidantExhaled nitric oxideNitrateNOxInternal medicineImmunologyBronchoconstrictionChemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background Asthma is a disease characterised by hyper responsiveness and bronchoconstriction of airways, and is a major health burden globally. A dysfunction of the oxidant-antioxidant balance, termed oxidative stress, has been implicated in the pathophysiology of asthma. The present study aims to assess the changes in oxidative stress markers, namely nitric oxide metabolites and antioxidant capacity, in children with poorly controlled and well controlled asthma, in comparison to healthy controls. Methods The present study enrolled 72 children (ages 5-15 years) classified into three groups: (1) poorly controlled asthma (n = 20), (2) well controlled asthma (n = 24) and (3) healthy controls (n = 27). An interviewer-administered questionnaire was used to record socio-demographic data of the participants. The serum concentrations of the oxidant markers (nitrite, nitrate and total nitric oxide metabolites [NOx]) were determined using the Griess test, and the total antioxidant capacity (TAOC) was determined using the ABTS decolorisation method. The concentrations of these markers were compared across the three groups. Results The three study groups were similar in terms of socio-demographic data. The differences across the three groups were statistically significant for serum concentrations of nitrate and NOx(but not nitrite) and serum TAOC. Further analyses showed that the disparity for nitrate and NOx concentrations was greatest between poorly controlled asthma and healthy controls (p = 0.001 and p < 0.001) compared to the well-controlled asthmatics and healthy controls (p = 0.036 and p = 0.049). A significant difference in serum nitrate and NOxconcentrations was not observed between the two asthma groups (p = 0.311 and 0.203). The TAOC were significantly lower in poorly controlled asthmatics as compared to well-controlled asthmatics (p = 0.003) and healthy controls (p < 0.001). However, there was no significant difference in the serum TAOC between healthy controls and well-controlled asthmatics (p = 0.496). These findings may indicate that it is perhaps the higher TAOC that contributes to the well controlled state of asthma. Conclusions The present study indicated that an imbalance of oxidants and antioxidants in the serum may have an underlying role in asthma pathophysiology, and how these markers may be effective in asthma management.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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