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Record W2129841728

Role of micronutrients and natural antioxidants in fighting against HIV; a quick mini-review

2014· article· en· W2129841728 on OpenAlexaff
Soodabeh Saeidnia, Mohammad Abdollahı

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMicronutrientNatural (archaeology)Human immunodeficiency virus (HIV)BiologyMedicineImmunologyPathology
DOInot available

Abstract

fetched live from OpenAlex

Oxidative stress has been implicated in the progression of HIV to AIDS, since HIV usually replicates in a highly oxidized condition and CD4+ T lymphocytes can be activated via a cascade of internal oxidative pathways, which enhances the formation of proteins and enzymes. Thus, antioxidants should potentially be useful for the treatment and prevention of HIV infection as a new alternative strategy. Regarding the point that there are various approaches for treating the HIV-positive patients, antioxidant supplementation therapy alongside with other medications possesses many benefits. In fact, antioxidants and micronutrient supplements have been considered as a costly and short-term strategy to improve antioxidant deficiency. If diets come with sufficient education and scientific recommendations, they can provide a low-cost and long-term strategy to reduce oxidative stress, prevent micronutrient deficiency, and slow down HIV progression. This strategy may be applicable and beneficial particularly in countries around coast of Africa, where HIV is most common. Meantime these countries are rich of natural food resources. It seems that a healthy diet is the best way to insure proper nutrient intake, since it contains many nutrients not available in pills.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.096
GPT teacher head0.509
Teacher spread0.413 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2014
Admission routes1
Has abstractyes

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