Biology subjects

Milne, J.

Publications and source records attributed to Milne, J..

3 recordsLinked to original sources

bioRxiv2023

Investigation of Volatile Metabolites in Sebum as Prodromal Indicators of Parkinson's Disease

Parkinsons Disease (PD) has been associated with a distinct odour, which emanates from the skin and is strongest in sebum-rich areas. In this study, sebum was sampled from participants using cotton gauze and the volatile components emanating from these swabs were analysed directly with thermal desorption gas chromatography - mass spectrometry (TD GC-MS). We analysed subjects with clinically established PD (n=46) along with healthy controls (n=28) sampled from two sites. The volatilome profiles obtained for PD and control cohorts were compared with the profile of participants (n=9) with isolated REM sleep behaviour disorder (iRBD) to investigate metabolite changes in probable prodromal PD. We also compared PD participants sampled at yearly intervals for a total of three years. Volatile compounds from TD GC-MS analysis were found in different quantities between PD, control and iRBD subjects. We found 55 significant features where abundance in samples from individuals with iRBD was intermediate between that found for PD and control samples. Significant features were found to be alkanes and fatty acid methyl esters (FAMEs), with other metabolites identified as an aldehyde, purine and tropinone. In olfactory analysis of the iRBD samples three out of nine were classified PD, and on clinical follow up two of these showed PD symptoms. Further, when analysing the volatilome from longitudinal PD sampling, almost two-thirds of the significant features showed differential regulation over the three visits. Our findings support the use of sebum as an accessible biofluid rich with measurable volatile compounds which alter in abundance in individuals with PD and iRBD, as the disease progresses.

neuroscience↗

bioRxiv2022

Not getting in too deep: a practical deep learning approach to routine crystallisation image classification

Using a relatively small training set of [~]16 thousand images from macromolecular crystallisation experiments, we compare classification results obtained with four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative. We use eight classes to effectively rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions. Author summaryX-ray crystallography can provide structural information on the molecules that play important roles in cell function and allow interactions vital for drug design to be elucidated. However, the technique requires the molecules to be crystallised and obtaining sufficiently high-quality crystals can require hundreds of experiments under different conditions. Robots have been designed to perform the microscopic experiments with imaging systems to record the results over time but automated classification of all these images is becoming essential. In this work we compare different classifiers that can be used without the need for vast computational resources and show that an ensemble classifier which combines the different strengths of four deep-learning networks is able to correctly classify the experimental results into one of eight different categories 94% of the time.

molecular biology↗

bioRxiv2018

Discovery of volatile biomarkers of Parkinsons disease from sebum

Parkinsons disease (PD) is a progressive, neurodegenerative disease that presents with significant motor symptoms, for which there is no diagnostic test (1-3). We have serendipitously identified a hyperosmic individual, a Super Smeller that can detect PD by odor alone, and our early pilot studies have indicated that the odor was present in the sebum from the skin of PD subjects(4). Here, we have employed an unbiased approach to investigate the volatile metabolites of sebum samples obtained non-invasively from the upper back of 64 participants in total (21 controls and 43 PD subjects). Our results, validated by an independent cohort, identified a distinct volatiles-associated signature of PD, including altered levels of perillic aldehyde and eicosane, the smell of which was then described as being highly similar to the scent of PD by our Super Smeller.\n\n1 sentence summaryMetabolomics identifies volatile odorous compounds from patient sebum that associate with the smell of Parkinsons.

neuroscience↗