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The Effects of Noradrenergic Antagonism on Skin Vascular Control in Females Using Oral Contraceptives

2017· article· en· W4389022699 on OpenAlexaff
Zachary T. Martin, Gary J. Hodges, Andrew T. Del Pozzi

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsBrock University
Fundersnot available
KeywordsMicrodialysisThermoregulationVasodilationVasoconstrictionInternal medicineMedicineSodium nitroprussideEndocrinologyLaser Doppler velocimetryMicrocirculationAnesthesiaBlood flowNitric oxideCentral nervous system

Abstract

fetched live from OpenAlex

Vasodilation and vasoconstriction of the cutaneous microcirculation play a major role in human thermoregulation. Since cutaneous vascular function is known to differ between males and females, the mechanisms behind these differences need to be investigated. Additionally, female oral contraceptive (OCP) use is quite common and its effects on thermoregulation are unknown. PURPOSE To determine the contributions of noradrenaline (NA) and neuropeptide Y (NPY) to basal vascular tone and vasodilation in response to local forearm heating. In addition, we aimed to determine the effects of estrogen and progesterone on vascular responses by testing the female participants during low (LH) and high hormone (HH) phases of OCP use. METHODS Six female and 6 male volunteers had 4 sites on 1 forearm prepared with microdialysis fibers, local skin heaters, and laser‐Doppler probes. Blood pressure was assessed every 5 min throughout the study. Microdialysis fibers were infused with one of the following solutions: lactated Ringer's solution (study vehicle), yohimbine and propranolol (YP), BIBP‐3226 (BIBP), and bretylium tosylate (BT). Baseline (33 °C) SkBF data were collected for 10 min via laser‐Doppler flowmetry (LDF). Local heating was initiated at a rate of 1 °C · 20 s −1 from 33 °C to 42 °C and was held for 35 min. Heaters were then set to 43 °C, in combination with an infusion of sodium nitroprusside, to induce maximal SkBF. LDF data were continuously collected at 50 Hz and processed offline. Raw LDF (mV), were converted to cutaneous vascular conductance (CVC) by dividing LDF values by the mean arterial pressure (mm Hg), then expressed as a percentage of maximal CVC (%CVCmax). Repeated measure ANOVAs were utilized to analyze differences in cutaneous vascular conductance. Statistical significance was set at p < 0.05. RESULTS Baseline (33 °C): For BIBP, compared to control, CVC for females in the HH phase (9 ± 1%max vs 10 ± 1%max) LH phase (7 ± 1% max vs 8 ± 1%max) did not differ (p > 0.05 for both); CVC in males was significantly higher (12 ± 1%max vs 8 ± 1%max; p < 0.05). With YP treatment, HH females had significantly higher, p < 0.05, CVC (19 ± 2%max) compared to LH females (12 ± 2%max). With BT treatment, females in the HH phase had significantly higher, p < 0.05, (19 ± 3%max) CVC than LH females (11 ± 1%max). Initial Peak (42 °C): For BIBP treatment, CVC in males was lower (65 ± 1%max) than HH (75 ± 2%max) and LH (72 ± 2%max) females. CONCLUSION NPY Y1 receptor antagonism resulted in significantly higher baseline SkBF in males compared to females. Thus, NPY does not appear to play a role in basal vascular function in females. NA receptor antagonism and presynaptic blockade of sympathetic neurotransmitters at baseline resulted in an increased SkBF response for HH females compared to LH females. Thus, NA appears to play a far greater role in vascular function during the HH phase of OCP use. The SkBF response to local heating was reduced in males who had NPY blockade when compared to females in both hormone phases. Therefore, NPY does not appear to play a role in the increase of SkBF in response to local heating nor in basal vascular function of females during LH and HH phases of OCP use.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.320
Teacher spread0.286 · 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 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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