These Dogs Know When Their Owners Are About to Have an MS Flare
https://www.healthline.com/health/multi ... -with-MS#1
Health and wellness touch everyone’s life differently. Here are some stories.
No matter what type of pet you have — dog, cat, bunny, or hamster — they can calm you, make you laugh, and lift your spirits when you’re down.
But for those of us with MS, or another chronic condition, pets can provide so much more than entertainment and love — as if that wasn’t enough. In my experience, they can actually alert us to an impending flare.
I don’t know exactly when I noticed that my dog seemed to have a sixth-sense about my disease, but he’s proven repeatedly that sometimes he knows what I need even before I do.
This furry little Morkie is so sensitive to me and my health, he alerts me prior to a flare or relapse.
When I’m about to experience a flare, he follows on my heels absolutely everywhere and gets extremely agitated if I’m out of his field of view. He’ll lie on me and try to keep me seated or lying down during a flare, or in the time just prior to one occurring.
How does he know? I have no idea. But he helps me more than I could’ve ever imagined a dog would. And it’s not just his advanced flare warnings.
His unconditional acceptance, judgment-free companionship, and unmistakable adoration comfort me during some of my toughest days dealing with MS symptoms.
I’m the community manager for the Healthline: Living with MS Facebook page. I posted about Rascal and my experience with him, and asked community members if they have pets that helped them with their MS.
I knew there had to be others, but I wasn’t prepared for the numerous messages I received.
Dogs can smell MS
MS signature in breath
And related to this, there is a MS signature in breath:
https://multiple-sclerosis-research.org ... int-in-ms/
Exhaled Breath Markers for Non-Imaging and Non-Invasive Measures for Detection of Multiple Sclerosis.
Broza YY, Har-Shai L, Jeries R, Cancilla JC, Glass-Marmor L, Lejbkowicz I, Torrecilla JS, Yao X, Feng X, Narita A, Müllen K, Miller A, Haick H.
Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids or by magnetic resonance imaging of the brain and/or spinal cord. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease progression. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on non-invasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 volunteers, 164 MS and 58 control individuals. Analysis was performed by: gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensors array. Predictive models were derived from the sensors, using Artificial Neural Networks. GC-MS analysis revealed significant differences in VOCs abundance between MS patients and Controls. Sensor data analysis on training sets were able to binary discriminate between MS patients and Controls with accuracies up-to 90%. Blinded sets showed 95% positive predictive value between MS-remission and control and 100% sensitivity with 100% negative predictive value between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.
https://multiple-sclerosis-research.org ... int-in-ms/
Exhaled Breath Markers for Non-Imaging and Non-Invasive Measures for Detection of Multiple Sclerosis.
Broza YY, Har-Shai L, Jeries R, Cancilla JC, Glass-Marmor L, Lejbkowicz I, Torrecilla JS, Yao X, Feng X, Narita A, Müllen K, Miller A, Haick H.
Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids or by magnetic resonance imaging of the brain and/or spinal cord. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease progression. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on non-invasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 volunteers, 164 MS and 58 control individuals. Analysis was performed by: gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensors array. Predictive models were derived from the sensors, using Artificial Neural Networks. GC-MS analysis revealed significant differences in VOCs abundance between MS patients and Controls. Sensor data analysis on training sets were able to binary discriminate between MS patients and Controls with accuracies up-to 90%. Blinded sets showed 95% positive predictive value between MS-remission and control and 100% sensitivity with 100% negative predictive value between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.
Gas Chromatography Mass Spectrometry (GC-MS) for MS.
Using Gas Chromatography Mass Spectrometry (GC-MS) for MS diagnosis.
Diagnosing Multiple Sclerosis Using Breath Biomarkers and GC-MS.
https://go.gale.com/ps/anonymous?id=GAL ... =AONE&sw=w
As a chronic neurological disease, multiple sclerosis affects the brain and spinal cord causing a wide range of symptoms, from problems with vision and movement to sensation or balance. The condition can cause serious disability, but the range and severity of symptoms varies with mild forms also existing.
Diagnosis of the disease is based upon visible symptoms of the disease and confirmed by examination of the cerebrospinal fluids (CSF), an invasive and painful procedure. Magnetic resonance imaging of the brain and spinal cord can also be used for diagnosis, and according to a recent publication, could also predict how their condition will progress, including how disabled they are likely to become (1). However, as routine diagnostic...
Diagnosing Multiple Sclerosis Using Breath Biomarkers and GC-MS.
https://go.gale.com/ps/anonymous?id=GAL ... =AONE&sw=w
As a chronic neurological disease, multiple sclerosis affects the brain and spinal cord causing a wide range of symptoms, from problems with vision and movement to sensation or balance. The condition can cause serious disability, but the range and severity of symptoms varies with mild forms also existing.
Diagnosis of the disease is based upon visible symptoms of the disease and confirmed by examination of the cerebrospinal fluids (CSF), an invasive and painful procedure. Magnetic resonance imaging of the brain and spinal cord can also be used for diagnosis, and according to a recent publication, could also predict how their condition will progress, including how disabled they are likely to become (1). However, as routine diagnostic...
An artificial nose can smell it too
Detecting Multiple Sclerosis via breath analysis using an eNose, a pilot study
https://iopscience.iop.org/article/10.1 ... xyLzNVRjGx
Abstract
Objective: In the present study we investigated whether Multiple Sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose. The AeonoseTM (an electronic nose, The eNose Company, Zutphen, The Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the AeonoseTM can make a distinction between the breath patterns of patients with MS and healthy control subjects. Methods: In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the AeonoseTM for 5 minutes. Exhaled breath data was used to train an artificial neural network (ANN) predictive model. To investigate the influence of medication intake we created a second predictive model with a subgroup of MS patients without medication prescribed for MS. Results: The ANN model based on the entire dataset was able to distinguish MS patients from healthy controls with a sensitivity of 0.75 [95% CI: 0.66-0.82] and specificity of 0.60 [0.51-0.69]. The model created with the subgroup of MS patients not using medication and the healthy control subjects had a sensitivity of 0.93 [0.82-0.98] and a specificity of 0.74 [0.65-0.81]. Conclusion: The study showed that the AeonoseTM is able to make a distinction between MS patients and healthy control subjects, and could potentially provide a quick screening test to assist in diagnosing MS. Further research is needed to determine whether the AeonoseTM is able to differentiate new MS patients from subjects who will not get the diagnosis.
https://iopscience.iop.org/article/10.1 ... xyLzNVRjGx
Abstract
Objective: In the present study we investigated whether Multiple Sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose. The AeonoseTM (an electronic nose, The eNose Company, Zutphen, The Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the AeonoseTM can make a distinction between the breath patterns of patients with MS and healthy control subjects. Methods: In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the AeonoseTM for 5 minutes. Exhaled breath data was used to train an artificial neural network (ANN) predictive model. To investigate the influence of medication intake we created a second predictive model with a subgroup of MS patients without medication prescribed for MS. Results: The ANN model based on the entire dataset was able to distinguish MS patients from healthy controls with a sensitivity of 0.75 [95% CI: 0.66-0.82] and specificity of 0.60 [0.51-0.69]. The model created with the subgroup of MS patients not using medication and the healthy control subjects had a sensitivity of 0.93 [0.82-0.98] and a specificity of 0.74 [0.65-0.81]. Conclusion: The study showed that the AeonoseTM is able to make a distinction between MS patients and healthy control subjects, and could potentially provide a quick screening test to assist in diagnosing MS. Further research is needed to determine whether the AeonoseTM is able to differentiate new MS patients from subjects who will not get the diagnosis.