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Localization and induced release of potentially therapeutic components of the rat submandibular salivary gland

2019· article· en· W3174690227 on OpenAlexaff
Michiko Watanabe, Amy Lin, Yong Qiu Doughman, Omar Al‐Adhami, Paulina M. Getsy, Gregory A. Coffee, Alessandra Giarola, Arun Sridhar, Ruth E. Siegel, Yehe Liu, Michael W. Jenkins, A. Dean Befus, Stephen J. Lewis

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSubmandibular glandSalivaSalivary glandChemistryInternal medicineSublingual glandEndocrinologyMedicinePharmacology

Abstract

fetched live from OpenAlex

The salivary glands release multiple compounds into the saliva and blood that have potentially therapeutic properties. One aim of this study was to localize rat submandibular gland protein SMR1, a prohormone known to contain anti‐inflammatory and analgesic peptides, and compare to the distribution of other known components within the rat submandibular gland (SMG). We also explored the feasibility of releasing SMR1 by local neurostimulation using electrodes. Our hypothesis is that SMR1 would be found in discrete regions of the SMG and could be stimulated to be released into the saliva and blood using localized electrostimulation. Salivary glands serve as a source of multiple compounds that are essential to health. The prohormone SMR1 is an example of such a compound that is present in the rat SMG and is secreted into the saliva or blood as peptides that have been shown to have analgesic and anti‐inflammatory properties. Neuroactivation using systemically applied drugs have been shown to release these and other SMG components. Focused electrode‐based neurostimulation of specific sites of the autonomic system may be a better strategy to more specifically release therapeutic compounds without affecting off‐target systems. Previous studies supported differential release of components from the acini compared to the duct system. In this study we evaluated the tissue distribution of SMR1 compared to other known components of the rat SMG, a major salivary gland. SMGs from young and mature rats were fixed, cryosectioned and immunostained. SMR1 peptides were immunohistologically detected in the serous acinar cells of the rat SMG with heterogeneous distribution within any particular acinus. The location of other components alpha‐amylase, EGF (marker of the tubogranular duct system), lactoferrin, lysozyme, were compared to the location of SMR1. Smooth muscle cell (anti‐smooth muscle actin), neuronal components (anti‐TuJ1, anti‐TH, anti‐ChAT, and anti‐synapsin) were also localized or co‐localized. Neurostimulation of the superior cervical ganglion (SCG) and adjacent nerve trunk by bipolar electrodes induced marked increases in SMR1 content in the saliva as determined by Western blot. We also discovered that placement of the electrode altered the level of peptide secretion. Intriguingly, the electrode stimulation increased levels of SMR1 collected from adjacent blood vessels. These findings set the stage for additional studies to refine the stimulation parameters and to map the SCG innervation of the SMG. Support or Funding Information Supported by grants from GSK‐Galvani This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.232
Teacher spread0.215 · 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 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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