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Record W2141698400 · doi:10.25011/cim.v31i2.3367

cAMP and cGMP in nasal mucus related to severity of smell loss in patients with smell dysfunction

2008· article· en· W2141698400 on OpenAlexvenueno aff
Robert I. Henkin, Irina Velicu

Bibliographic record

VenueClinical and investigative medicine · 2008
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHyposmiaAnosmiaMucusMedicineTasteOlfactionInternal medicineGastroenterologyEndocrinologyPsychologyChemistryBiologyDiseaseBiochemistryNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate nasal mucus levels of cAMP and cGMP in patients with taste and smell dysfunction with respect to severity of their smell loss. METHODS: cAMP and cGMP were measured in nasal mucus using a sensitive spectrophotometric 96 plate ELISA technique. Smell loss was measured in patients with taste and smell dysfunction by standardized psychophysical measurements of olfactory function and classified by severity of loss into four types from most severe to least severe such that anosmia > Type I hyposmia > Type II hyposmia > Type III hyposmia. Measurements of nasal mucus cyclic nucleotides and smell loss were made independently. RESULTS: As smell loss severity increased stepwise cAMP and cGMP levels decreased stepwise [cAMP, cGMP (in pmol/ml); anosmia - 0.004, 0.008: Type I hyposmia - 0.12+/-0.03, 0.10+/-0.03: Type II hyposmia - 0.15+/-0.02, 0.16+/-0.01: Type III hyposmia - 0.23+/-0.05, 0.20+/-0.15]. CONCLUSIONS: These results confirm the association of biochemical changes in cyclic nucleotides with systematic losses of smell acuity. These results confirm the usefulness of the psychophysical methods we defined to determine the systematic classification of smell loss severity. These changes can form the basis for the biochemical definition of smell loss among some patients with smell loss as well as for their therapy.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
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 head0.218
GPT teacher head0.308
Teacher spread0.090 · 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.

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

Citations41
Published2008
Admission routes1
Has abstractyes

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