#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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#NeurologicalDisease
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#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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your gut is either fighting your MS or feeding it. every single day. 🧠
did anyone ever tell you this? YES or NO 👇
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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they never told me that stress is literally touching my MS every single day 😤
did YOUR doctor explain this? YES or NO 👇
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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MS gets diagnosed in one appointment. understanding it takes considerably longer.
comment MS and I'll send you everything in one place 👇
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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send this to anyone who said 'I'd be so depressed if I had MS' 🧡
you are more than their worst case scenario.
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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if you have MS you know the exact moment a good day turns 💔
has this ever happened to you? 👇
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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nobody talks about mourning the version of you that existed before MS 🧡
type ME if you know exactly who she was 👇
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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you are not lazy. save this for the next time someone says you are 🧡
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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your neurologist has 12 minutes. MS has the other 525,948. 🧡
comment YES or NO — did yours explain everything you needed?
#multiplesclerosis #mstiktok #mswarrior #mssupport #invisibleillness #chronicillness #msfatigue #mslife #neurologicaldisease #livewithms
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A novel pathway of gut bacterial translocation to the brain via the vagus nerve observed in models of high-fat diet or distinct neurological conditions, such as Alzheimer's, Parkinson's and autism spectrum disorder. The image shows a mouse, with its internal organs visible, and bacteria (black) travelling up the vagus nerve (red) from the gut to the brain. The images were created using Biorender software.
Gut #dysbiosis has been linked to #NeurologicalDisease, but what's the mechanism? @grakoui.bsky.social @dweisslab.bsky.social &co show that a high-fat diet increases gut permeability, enabling bacterial translocation from gut to brain via the vagus nerve in mice @plosbiology.org 🧪 plos.io/4ruE6EP
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A novel pathway of gut bacterial translocation to the brain via the vagus nerve observed in models of high-fat diet or distinct neurological conditions, such as Alzheimer's, Parkinson's and autism spectrum disorder. The image shows a mouse, with its internal organs visible, and bacteria (black) travelling up the vagus nerve (red) from the gut to the brain. The images were created using Biorender software.
Gut #dysbiosis has been linked to #NeurologicalDisease, but what's the mechanism? @grakoui.bsky.social @dweisslab.bsky.social &co show that a high-fat diet increases gut permeability, enabling bacterial translocation from gut to brain via the vagus nerve in mice @plosbiology.org 🧪 plos.io/4ruE6EP
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A novel pathway of gut bacterial translocation to the brain via the vagus nerve observed in models of high-fat diet or distinct neurological conditions, such as Alzheimer's, Parkinson's and autism spectrum disorder. The image shows a mouse, with its internal organs visible, and bacteria (black) travelling up the vagus nerve (red) from the gut to the brain. The images were created using Biorender software.
Gut #dysbiosis has been linked to #NeurologicalDisease, but what's the mechanism? @grakoui.bsky.social @dweisslab.bsky.social &co show that a high-fat diet increases gut permeability, enabling bacterial translocation from gut to brain via the vagus nerve in mice @plosbiology.org 🧪 plos.io/4ruE6EP
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#neurology #Ipsen #Dysport #abobotulinumtoxinA #neurologicaldisease #clinicaldata #stroke #neurologicaldisorders #EPITOMEstudy #poststrokespasticity #PSS #strokepatients #realworlddata #strokesurvivors #botulinumtoxintypeA #BoNTA #PSStreatment #BoNTAinjectable #biopharmaceuticals
zurl.co/nLqKq
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tinyurl.com/2z339yz3
#ChronicTraumaticEncephalopathy is a progressive #neurologicaldisease caused by repetitive #braintrauma, which is commonly seen in contact sports like #rugby, #MMA, #boxing, and American #football.
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![Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:5522ztebtekoor5efelihqhb/bafkreiflbushsogb76c77gndw6ccvjg2lg52k2l6rr36zneeyufcshwapu)
Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.
Imaging-based models are being used to assess #BrainAge, but can they predict #NeurologicalDisease? @ritterkerstin.bsky.social &co show that simpler models, with lower age prediction accuracy, are paradoxically more sensitive to disease-related brain changes @plosbiology.org 🧪 plos.io/47NcIew
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![Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:5522ztebtekoor5efelihqhb/bafkreibw45hmyj52kqntfkaw4wwm35ccih3rp2dbhzeqdnrmkaznvhtgta)
Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.
Imaging-based models are being used to assess #BrainAge, but can they predict #NeurologicalDisease? @ritterkerstin.bsky.social &co show that simpler models, with lower age prediction accuracy, are paradoxically more sensitive to disease-related brain changes @plosbiology.org 🧪 plos.io/47NcIew
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![Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:5522ztebtekoor5efelihqhb/bafkreidbug5gkmsmbisx2efl6ibzvquaozjtdfdimluid5jw7u7h23pdqe)
Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.
Imaging-based models are being used to assess #BrainAge, but can they predict #NeurologicalDisease? @ritterkerstin.bsky.social &co show that simpler models, with lower age prediction accuracy, are paradoxically more sensitive to disease-related brain changes @plosbiology.org 🧪 plos.io/47NcIew
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as well as vascular conditions including #CVD , high BP & congestive heart failure.
www.science.org/content/arti...
www.science.org/doi/epdf/10....
Studies on air pollution & disease have also connected PM2.5 exposure to #neurologicaldisease, specifically #dementias & #Parkinsondisease
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Speakers announced and call for abstracts open (until Oct 31)
Present at the 6th RNA Metabolism in #NeurologicalDisease Conference (Mar 29-31, 2026 in San Diego, USA). Speakers announced and call for abstracts open (until Oct 31) at spkl.io/63327APzNL #RNAMetaConf
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When your chronic illnessness and neuro disorder decide to throw a party.
#chronicillness
#chronicfatigue #raredisease #hereditaryangioedema #mastcellactivationsyndrome #mcas #cidp #neurologicaldisease #ehlersdanlossyndrome #carpa #ivinfusion #ivig
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Innovative, powerful tools will help us conquer #neurologicaldisease. See how AI trained with 10x #singlecell & #spatialbiology data is helping decode brain complexity and accelerate therapeutic development in this Drug Target Review article: www.drugtargetreview.com/article/1696...
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This complex #neurologicaldisease affects nearly every system in the body.
It’s not “just in your head”
Activity can trigger days-long crashes
Women are 2–3x more likely to be affected…
@latrobeuni.bsky.social
@theconversation.com
theconversation.com/its-not-just...
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A YT Video on the Parkinson's Belt:
#Parkinson's
#pollution
#neurologicaldisease
#enviromentaldisease triggers
youtube.com/watch?v=q9vo...
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🧠 💻 Have you taken the opportunity to complete one of our learning modules?
Module two covers acute presentations of #NeurologicalDisease, specialist transfer to #ICU and much more. Find out now 👉 www.scottishintensiv...
#IntensiveCare #IntensiveCareSociety
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#Medsky🧪 #neuroSky #publichealth Aging in humans is associated with increased risk of #neurologicaldisease, cognitive impairment, and worsened outcomes following infection or trauma…
molecularneurodegeneration.biomedcentral.com/articles/10....
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This message with so many people in the United States! If you have a family member or friend that has any rare disease, once again you've been off your nose despite your face! #dystoniawarrior #NIH #lupus #ptsd #fuTrump #neurologicaldisease #rarediseaseday
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