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Metabolic Fingerprinting of Autism Spectrum Disorders in Breath by Mass Spectrom...

Metabolic Fingerprinting of Autism Spectrum Disorders in Breath by Mass Spectrometry Current diagnosis of individuals suffering from disorders on the autism spectrum is based on behavioural and developmental observations, usually in their early life. Even so, there is a significant amount of people having to live... see more

31/01/2026

UCY

148K€

Project Budget: 148K€

Project leader

UNIVERSITY OF CYPRUS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2023-04-24

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 Expected Outcome:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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2 Participantes

UCY

148.49K€ | Leader

UJA

N.D. | participant

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Project duration: 33 months Date Start: 2023-04-24
End date: 2026-01-31

Project leader

UNIVERSITY OF CYPRUS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 148K€

participation deadline Sin fecha límite de participación.

Project description Current diagnosis of individuals suffering from disorders on the autism spectrum is based on behavioural and developmental observations, usually in their early life. Even so, there is a significant amount of people having to live with these life-long disorders that remain undetected and no treatment is provided to them. However, it is already established that such disorders are associated with metabolic changes. There is now a growing amount of evidence suggesting that such metabolic alterations can be detected in samples such as blood or urine taken from children early in their life. These routines however can cause significant discomfort to the child. The primary aim of the proposal will involve the use of less invasive sampling techniques such as exhaled breath and exhaled breath condensate for the early diagnosis of metabolic hallmarks of autism spectrum disorders. These samples will be analysed using already established chemical analysis techniques for the detection of small metabolic changes in the obtained samples. Tandem mass spectrometry techniques will be used for the unambiguous identification of these metabolites. Data analysis tools such as principal component analysis (PCA) and hierarchical clustering will be used, to associate these metabolic changes with existing medical records obtained from the patients, to establish a potential diagnostic tool. The potential for the rapid detection of these metabolites will be examined through the integration of novel ambient mass spectrometric ionisation methods such as plasma ionisation. The ability to selectively ionise specific metabolites associated with autism will be investigated with the aim of generating an early prototype of a rapid screening method for routine use. Data generated during the initial stage of the work will be used to create a database that will be used for near real-time characterisation of the exhaled breath through the analysis of metabolites and volatile organic compounds (VOCs).

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