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Analysis of salivary parameters of mucopolysaccharidosis individuals

2022· article· es· W4206933387 on OpenAlexaff
Patrícia Luciana Serra NUNES, Filipe Atahide FONSECA, Luiz Renato Paranhos, Cauane Blumenberg, Valentim Adelino Ricardo Barão, Elizabeth S. Fernandes, Rebeca Garcia FERREIRA, Walter L. Siqueira, Michelle F. Siqueira, Eduardo Buozi Moffa

Bibliographic record

VenueBrazilian Oral Research · 2022
Typearticle
Languagees
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSalivaInternal medicineAlbuminMedicineUrineGastroenterologyGlycosaminoglycanMucopolysaccharidosisImmunologyEndocrinology

Abstract

fetched live from OpenAlex

Mucopolysaccharidosis (MPS) is a heterogeneous group of rare, chronic, progressive and systemic inherited disorders resulting from deficiency or lack of lysosomal enzymes responsible for the degradation of glycosaminoglycans. Products of nitrosative stress have been previously detected in blood and urine samples of patients with MPS. However, it is unclear whether they are present in the saliva of MPS patients and also if they correlate with salivary parameters such as flow and pH. This study compared the salivary levels of NOX (NO2- + NO3-), nitrite (NO2-), protein (albumin), erythrocyte and leukocyte numbers, as well as the salivary flow rate and pH values of samples obtained from 10 MPS patients and 10 healthy subjects. MPS patients exhibited higher salivary levels of NOX and NO2- when compared to healthy subjects (p < 0.05). Albumin was only detected in six saliva samples of MPS patients and, erythrocytes and leukocytes were detected in 60% and 40% of the MPS patients, respectively. In addition, salivary flow rate and pH averages were statistically lower in this group when compared to healthy samples (p < 0.05). Overall, the data indicates that the salivary levels of NO products can be used in combination with other heath indicators to monitor MPS disorders.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.019
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0180.000

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 head0.092
GPT teacher head0.414
Teacher spread0.322 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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