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GregoryLab Profile
GregoryLab

@GregoryLab7

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Our lab are interested in understanding the non-motor manifestations of motor neuron disease including cognition and the gut-brain axis.

Aberdeen, Scotland
Joined August 2021
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@TargetALS_fdn
Target ALS
6 months
At our Annual Meeting, Dr. Jenna Gregory shared how her team is bringing oncology-style precision to ALS—studying how T cells behave differently with #ALS. Her findings, through research we fund, offer clues for better treatment. Sign up to learn more: https://t.co/cIsItEAG1S
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@jennagregory488
Prof Jenna Gregory
6 months
#ENCALS2025 in Turin is over, but we’ve had an amazing time presenting our team’s (@GregoryLab7) work as posters and oral presentations. A wonderful demonstration of the excellent translational neuroscience research going on @aberdeenuni. Can’t wait for Madrid next year! ⭐️🤩⭐️
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@jennagregory488
Prof Jenna Gregory
6 months
@GregoryLab7 will be in Turin next week for #ENCALS2025 🇮🇹⭐️🤓 Interested in biomarkers for early disease detection (precision prevention) and treatment stratification (precision medicine) in ALS/FTD? Come and find us in the session detailed below. @FergalWaldron @HollySneuro
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@FergalWaldron
Fergal Waldron
8 months
In our new preprint we report improved detection of TDP-43 in non-CNS tissues prior to symptom onset in #ALS #MND, using an RNA aptamer– from peripheral organs including skin, GI tract and lymph node, between 1-14 years prior to clinical diagnosis. https://t.co/cVRd6mO13B
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biorxiv.org
A recently developed TDP-43 RNA aptamer (TDP-43APT) with greater sensitivity and specificity for detecting pathological TDP-43, compared to currently available antibodies, has revealed novel pathol...
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@HollySneuro
Holly Spence
8 months
There is still time to register for #BNA2025. Be sure not to miss our symposium highlighting UK MND translational research. Thanks to @MNDScotland, @MNDoddie5 & @mndassoc for organising a great session.
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@jennagregory488
Prof Jenna Gregory
8 months
Take a look at our recent Lancet Neurology review. Amyotrophic lateral sclerosis caused by TARDBP mutations: https://t.co/yXkRg4ePPe It’s been a huge privilege working with this incredible writing team and we hope you enjoy reading it. ⭐️🤩🤓🤩⭐️
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@HornsteinLab
Hornstein Lab
9 months
Excited to share our preprint: "𝐎𝐫𝐠𝐚𝐧𝐞𝐥𝐥𝐨𝐦𝐢𝐜𝐬: 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐞𝐞𝐩 𝐨𝐫𝐠𝐚𝐧𝐞𝐥𝐥𝐚𝐫 𝐩𝐡𝐞𝐧𝐨𝐭𝐲𝐩𝐢𝐧𝐠 𝐫𝐞𝐯𝐞𝐚𝐥𝐬 𝐧𝐨𝐯𝐞𝐥 𝐀𝐋𝐒 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬 𝐢𝐧 𝐡𝐮𝐦𝐚𝐧 𝐧𝐞𝐮𝐫𝐨𝐧𝐬"
@NancyYaco
Nancy Yacovzada
9 months
🚀𝐕𝐢𝐬𝐢𝐨𝐧-𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬-𝐛𝐚𝐬𝐞𝐝 𝐨𝐫𝐠𝐚𝐧𝐞𝐥𝐥𝐚𝐫 𝐩𝐡𝐞𝐧𝐨𝐭𝐲𝐩𝐢𝐧𝐠 𝐮𝐧𝐯𝐞𝐢𝐥𝐬 𝐡𝐢𝐝𝐝𝐞𝐧 𝐀𝐋𝐒 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬! 🧵 Thread on 𝘕𝘖𝘝𝘈 (𝘕𝘦𝘶𝘳𝘰𝘯𝘢𝘭 𝘖𝘳𝘨𝘢𝘯𝘦𝘭𝘭𝘰𝘮𝘪𝘤𝘴 𝘝𝘪𝘴𝘪𝘰𝘯 𝘈𝘵𝘭𝘢𝘴)🎗️ our deep-learning model trained with
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@SciReports
Scientific Reports
9 months
Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models @GregoryLab7 https://t.co/4N45XyThJr
nature.com
Scientific Reports - Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models
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@jennagregory488
Prof Jenna Gregory
10 months
Our team (@GregoryLab7, @HollySneuro & @FergalWaldron) is delighted to share some recent work from machine learning expert @MV_Research. It’s always a pleasure to contribute to collaborative projects especially with Marta and her incredible team. https://t.co/DLVr5dhjlp.
nature.com
Scientific Reports - Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models
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@MNDScotland
MND Scotland
10 months
MND Scotland expresses gratitude to the female researchers contributing to our understanding of MND and enhancing the quality of life for those affected. Some have received funding from us, while others are part of our Scientific Advisory Panel 💙 #WomenInScienceDay #WomenInSTEM
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@FergalWaldron
Fergal Waldron
11 months
Fantastic work lead by Holly Spence @HollySneuro - happy to have contributed!
@HollySneuro
Holly Spence
11 months
⭐️New preprint - Machine learning identifies predictors of cognitive dysfunction⭐ (1/4) Population-level risk factors exist for dementia & related neurodegenerative diseases – how do these relate to individual risk? 🤔 https://t.co/O5LM5QD4x7
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@jennagregory488
Prof Jenna Gregory
11 months
⭐️New preprint - Machine learning identifies predictors of cognitive dysfunction⭐ Population-level risk factors exist for dementia & related neurodegenerative diseases – how do these relate to individual risk? 🤔
@biorxiv_neursci
bioRxiv Neuroscience
11 months
Machine learning identifies routine blood tests as accurate predictive measures of pollution-dependent poor cognitive function https://t.co/eXsqAm1WvJ #biorxiv_neursci
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@HollySneuro
Holly Spence
11 months
⭐️New preprint - Machine learning identifies predictors of cognitive dysfunction⭐ (1/4) Population-level risk factors exist for dementia & related neurodegenerative diseases – how do these relate to individual risk? 🤔 https://t.co/O5LM5QD4x7
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biorxiv.org
Background Several modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures –...
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@HollySneuro
Holly Spence
11 months
(4/4) Here, we demonstrate how routine, inexpensive medical testing & local authority initiatives could help to identify and protect at-risk individuals. This work was supported by generous funding from @NIH @CSO_Scotland @TargetALS_fdn @wellcometrust 🤓 https://t.co/O5LM5QD4x7
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biorxiv.org
Background Several modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures –...
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@HollySneuro
Holly Spence
11 months
(3/4) The most predictive risk factors of individual cognitive dysfunction out of >450 features evaluated in our deeply phenotyped cohort were: 💭Low and high haemoglobin levels 💭Pollutants typical of vehicle emissions 💭Region specific brain volumes https://t.co/O5LM5QD4x7
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biorxiv.org
Background Several modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures –...
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@HollySneuro
Holly Spence
11 months
(2/4) Here, we use a machine learning approach to identify predictors of poor cognition in a deeply phenotyped @genscot cohort of 324 individuals – we assess >450 environmental, social, and biometric risk factors, and their impact on cognitive health. https://t.co/O5LM5QD4x7
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biorxiv.org
Background Several modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures –...
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@GregoryLab7
GregoryLab
11 months
⭐️New preprint - Machine learning identifies predictors of cognitive dysfunction⭐ Population-level risk factors exist for dementia & related neurodegenerative diseases – how do these relate to individual risk? 🤔
@HollySneuro
Holly Spence
11 months
⭐️New preprint - Machine learning identifies predictors of cognitive dysfunction⭐ (1/4) Population-level risk factors exist for dementia & related neurodegenerative diseases – how do these relate to individual risk? 🤔 https://t.co/O5LM5QD4x7
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@jennagregory488
Prof Jenna Gregory
1 year
So lucky to work with such an amazing team of hard working scientists. Here’s a snapshot of our year in review from the Gregory Lab (and friends) - working hard towards our dream of a world where we can beat #ALS #MND
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@AbdnLifeScience
UoA Medicine, Medical Sciences and Nutrition
1 year
Research highlight for 2024: Dr Jenna Gregory's team found new ways to detect MND. The TDP-43 aptamer can show early signs of the disease in brain tissue samples before the cells malfunction when standard symptoms would start to appear. Read more here:
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abdn.ac.uk
Scientists from the University of Aberdeen in collaboration with the University of Edinburgh and international partners, have identified a new way to detect signs of motor neurone disease (MND) in...
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@FergalWaldron
Fergal Waldron
1 year
#ALS/#MND Precision-prevention concepts: 💭Perspectives>Learning from cancer 💭Detection>Before motor symptoms arise 💭Precision>Understanding individual’s risk factors & heterogenous disease manifestations is key 💭Prevention>Safe interventions & early triage to clinical trials
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