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Regional skin wetness perception and its modulation by warm and cold whole body skin temperatures in people with multiple sclerosis.

Aikaterini ChristogianniRichard J BibbAshleigh FiltnessDavide Filingeri
Published in: American journal of physiology. Regulatory, integrative and comparative physiology (2022)
Skin wetness sensing is important for thermal stress resilience. Individuals with multiple sclerosis (MS) present greater vulnerability to thermal stress; yet, it is unclear whether they present wetness-sensing abnormalities. We investigated the effects of MS on wetness sensing and their modulation with changes in mean skin temperature (T sk ). Twelve participants with MS [5 males (M)/7 females (F); 48.3 ± 10.8 yr; Expanded Disability Status Scale (EDSS) range: 1-7] and 11 healthy controls (4 M/7 F; 47.5 ± 11.3 yr) undertook three trials, during which they performed a quantitative sensory test with either a thermoneutral (30.9°C), warm (34.8°C), or cold (26.5°C) mean T sk . Participants reported on visual analog scales local wetness perceptions arising from the static and dynamic application of a cold-, neutral-, and warm-wet probe (1.32 cm 2 ; water content: 0.8 mL), to the index finger pad, forearm, and forehead. Data were analyzed for the group-level effect of MS, as well as for its individual variability. Our results indicated that MS did not alter skin wetness sensitivity at a group level, across the skin sites and temperature tested, neither under normothermia nor under conditions of shifted thermal state. However, when taking an individualized approach to profiling wetness-sensing abnormalities in MS, we found that 3 of the 12 participants with MS (i.e., 25% of the sample) presented a reduced wetness sensitivity on multiple skin sites and to different wet stimuli (i.e., cold, neutral, and warm wet). We conclude that some individuals with MS may possess reduced wetness sensitivity; however, this sensory symptom may vary greatly at an individual level. Larger-scale studies are warranted to characterize the mechanisms underlying such individual variability.
Keyphrases
  • multiple sclerosis
  • mass spectrometry
  • ms ms
  • soft tissue
  • wound healing
  • white matter
  • primary care
  • healthcare
  • climate change
  • high resolution
  • machine learning
  • big data
  • deep learning
  • artificial intelligence
  • data analysis