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Usage of Saliva as Alternative Biological Fluid to Serum for Minerals, Energetic and Hormones Assessment in Lactating Egyptian Water Buffaloes

2013· article· en· W2096708278 on OpenAlexvenueno aff
Abdelghany Hefnawy, Saad Shousha, Omnia Abdel-Hamid, Seham Youssef

Bibliographic record

VenueJournal of Buffalo Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsSalivaChemistrySodiumUreaPotassiumEndocrinologyCreatinineInternal medicineCalciumBlood serumRadioimmunoassayInsulinHormoneMedicineBiochemistry

Abstract

fetched live from OpenAlex

Blood sample is the most common biological fluid utilized for diagnosis and monitoring of diseases. Saliva contains locally produced substances as well as serum component, so the aim of this study is to compare the profile of minerals, energetic and hormones in Egyptian water buffaloes. Blood serum and saliva samples were collected from 80 healthy multiparous, non- pregnant lactating Egyptian water buffaloes. Both fluids were tested for sodium, potassium, chloride, calcium, phosphorous, magnesium, insulin, cortisol, ACTH, glucose, urea, creatinine, total protein and immunoglobulin [IgA]. The results revealed that, serum concentrations of calcium, glucose, total protein, sodium, chloride, Insulin, cortisol, ACTH and IgA were significantly higher than saliva. In contrast, the concentrations of potassium and phosphorous in the saliva were significantly higher than that of serum. On the other hand no significant change in respect of urea, creatinine and magnesium was noted between saliva and serum. The relationships between saliva and serum of the estimated parameters were significantly positive except the concentrations of insulin in saliva and blood serum did not correlate. In conclusion, the saliva sample can be used in clinical practice with high level of reliability and provide non-invasive biological fluid for monitoring of different parameters in Egyptian water buffaloes.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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
Published2013
Admission routes1
Has abstractyes

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