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Record W3118062921 · doi:10.5539/ijc.v13n1p12

Evaluation of the Effects of Hypo-Magnetic Fields on Mouse Macrophage RAW264 Cells

2020· article· en· W3118062921 on OpenAlexvenueno aff
Nipitpon Srimai, Wasawat Kiatarkom, Hidetake Miyata

Bibliographic record

VenueInternational Journal of Chemistry · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
FundersNaresuan UniversityMinistry of Education, Culture, Sports, Science and Technology
KeywordsChemistryMitochondrionReactive oxygen speciesMitochondrial ROSMembrane potentialBiophysicsCell biologyBiochemistryBiology

Abstract

fetched live from OpenAlex

Hypo-magnetic fields (HMFs) are static magnetic fields that are significantly weaker than the geomagnetic field and are found in interplanetary space, on Mars, and the Moon. Understanding the mechanism of the biological effects of HMFs is an important topic in magnetobiology. Human physiology studies have shown that HMFs cause various adverse health effects such as increased blood pressure and blood flow rate. Previous studies on the effects of HMFs on cells revealed a variety of effects, such as a change in growth rate, a decrease in mitochondrial membrane potential, and an increase in reactive oxygen species. A decrease in mitochondrial membrane potential (DFm) may seriously impair cellular activity by interfering with the production of ATP. Thus, a deeper investigation of the effects of HMFs on mitochondrial function could provide clues to understanding the adverse effects of HMFs. In the present study, we used mouse macrophage RAW264 cells to investigate the effects of an HMF on mitochondrial membrane potential, mitochondrial superoxide anion (O2-) production, and cell proliferation.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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 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
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of ChemistrySame topicMagnetic and Electromagnetic EffectsFrench-language works237,207