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Record W1577439976

Erratum: Histological characteristics following a long-term nitrate-rich diet in miniature pigs with parotid atrophy.

2015· erratum· en· W1577439976 on OpenAlexaff
Dengsheng Xia, Xingmin Qu, Simon D. Tran, Laura L Schmidt, Lizheng Qin, Chunmei Zhang, Xiuyu Cui, Dajun Deng, Songlin Wang

Bibliographic record

VenuePubMed · 2015
Typeerratum
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsMcGill University
Fundersnot available
KeywordsAtrophyNitrateParotid glandUrineInternal medicinePathologyToxicityKidneyEndocrinologyBiologyMedicineEcology
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the histological characteristics following a 2-year nitrate-rich diet in miniature pigs with parotid atrophy. Using averages collected data from three time points at 6, 12, and 24 months following the induction of parotid gland atrophy, salivary nitrate levels of the nitrate-diet parotid-atrophied group (17.3 ± 3.9 ng/µl) were close to those of the control group (19.6 ± 5.1 ng/µl). Compared to the control group, the nitrate-diet group had significantly higher nitrate levels in blood (P < 0.05) and urine (P < 0.001). Histological and electron microscopy analyses showed no abnormalities in the organs of experimental or control animals. No significant differences on apoptosis rate were found in liver and kidney tissues between the standard- and nitrate-diet groups. Therefore, dietary nitrate supplementation could restore salivary nitrate levels. High-dose nitrate loading for 2 years had no observed systemic toxicity in miniature pigs with parotid atrophy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.242
Teacher spread0.213 · 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
GenreOther

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

Citations6
Published2015
Admission routes1
Has abstractyes

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