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Record W4200394945 · doi:10.31983/jlm.v3i1.8005

Microscopic Description of Mus Musculus Kidney Preparation Deparafinized with Olive Oil in Eosin (HE) Hematoxylin (HE) Staining

2021· article· en· W4200394945 on OpenAlexaff
Ela Nur Pratiwi, Desy Armalina

Bibliographic record

VenueJaringan Laboratorium Medis · 2021
Typearticle
Languageen
FieldMedicine
TopicBiological Stains and Phytochemicals
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsChemistryStainingH&E stainEosinKidneyChromatographyOlive oilPathologyMedicineFood scienceInternal medicine

Abstract

fetched live from OpenAlex

Deparaffinization is a stage before the staining process to remove/dissolve paraffin so that the absorption of color in tissue preparations is maximized. Deparaffinization is usually carried out using xylol and toluol. Xylol has toxic effects including acute neurotoxicity, heart and kidney damage, hepatotoxicity, fatal blood dyscrasias, skin erythema, dry skin, peeling skin, and also has a carcinogenic effect. The toxicity effect of olive oil is lower than that of xylol. Oils that have non-polar properties can remove the remaining paraffin contained in the tissue. The purpose of this study was to determine the microscopic appearance of the kidney tissue preparations of mice deparaffinized with olive oil on hematoxylin eosin (HE) staining. The type of research used is experimental research which is analyzed with a descriptive approach. The results of the assessment of preparations deparaffinized with xylol in 80 visual fields obtained 100% good preparations and preparations deparaffinized with olive oil in 80 visual fields obtained 0% poor preparations, 11.3% poor preparations, and 88.7% good preparation. So it can be said that better results are found in the microscopic picture of the kidney preparations of mice (Mus musculus) deparaffinized with xylol.

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)0.000
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 score0.059
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.278
Teacher spread0.262 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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