Gas-Phase Protonation Thermodynamics of Biological Lipids: Experiment, Theory, and Implications.

Phospholipids are important to cellular function and are a vital structural component of plasma and organelle membranes. These membranes isolate the cell from its environment, allow regulation of the internal concentrations of ions and small molecules, and host diverse types of membrane proteins. It remains extremely challenging to identify specific membrane protein-lipid interactions and their relative strengths. Native mass spectrometry, an intrinsically gas-phase method, has recently been demonstrated as a promising tool for identifying endogenous protein-lipid interactions. However, to what extent the identified interactions reflect solution- versus gas-phase binding strengths is not known. Here, the "Extended" Kinetic Method and ab initio computations at three different levels of theory are used to experimentally and theoretically determine intrinsic gas-phase basicities (GB, ΔG for deprotonation of the protonated base) and proton affinities (PA, ΔH for deprotonation of the protonated base) of six lipids representing common phospholipid types. Gas-phase acidities (ΔG and ΔH for deprotonation) of neutral phospholipids are also evaluated computationally and ranked experimentally. Intriguingly, it is found that two of these phospholipids, sphingomyelin and phosphatidylcholine, have the highest GB of any small, monomeric biomolecules measured to date and are more basic than arginine. Phosphatidylethanolamine and phosphatidylserine are found to be similar in GB to basic amino acids lysine and histidine, and phosphatidic acid and phosphatidylglycerol are the least basic of the six lipid types studied, though still more basic than alanine. Kinetic Method experiments and theory show that the gas-phase acidities of these phospholipids are high but less extreme than their GB values, with phosphatidylserine and phosphatidylglycerol being the most acidic. These results indicate that sphingomyelin and phosphatidylcholine lipids can act as charge-reducing agents when dissociated from native membrane protein-lipid complexes in the gas phase and provide a straightforward model to explain the results of several recent native mass spectrometry studies of protein-lipid complexes.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson's Disease.

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and "noisy". Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without a priori feature selection. The performance of the NN approach for predicting Parkinson's disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted 'omics methods.

Illuminating Music: Impact of Color Hue for Background Lighting on Emotional Arousal in Piano Performance Videos.

This study sought to determine if hues overlayed on a video recording of a piano performance would systematically influence perception of its emotional arousal level. The hues were artificially added to a series of four short video excerpts of different performances using video editing software. Over two experiments 106 participants were sorted into 4 conditions, with each viewing different combinations of musical excerpts (two excerpts with nominally high arousal and two excerpts with nominally low arousal) and hue (red or blue) combinations. Participants rated the emotional arousal depicted by each excerpt. Results indicated that the overall arousal ratings were consistent with the nominal arousal of the selected excerpts. However, hues added to video produced no significant effect on arousal ratings, contrary to predictions. This could be due to the domination of the combined effects of other channels of information (e.g., the music and player movement) over the emotional effects of the hypothesized influence of hue on perceived performance (red expected to enhance and blue to reduce arousal of the performance). To our knowledge this is the first study to investigate the impact of these hues upon perceived arousal of music performance, and has implications for musical performers and stage lighting. Further research that investigates reactions during live performance and manipulation of a wider range of lighting hues, saturation and brightness levels, and editing techniques, is recommended to further scrutinize the veracity of the findings.

Identifying robust and reliable volatile organic compounds in human sebum for biomarker discovery.

Sebum from sebaceous glands is a rich source of volatile organic compounds (VOCs) that can readily be sampled non-invasively from the surface of skin. The VOC profiles of sebum can then be used to obtain information regarding different medical conditions including diabetes and Parkinson's Disease. However, the effects of sampling approaches and environmental factors on sebum VOC profiles are not established and the confident attribution of VOCs to disease states needs to be free of extraneous influences such as sampling materials and preparatory conditions. Here, we investigated a more standardised skin swab sampling approach for profiling sebum VOCs from healthy human subjects using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Using a standard GC-MS method for the chemical analysis of sebum swabs, a surprisingly high number of VOCs originate from 'blank' medical swab material alone (up to 74 VOCs) and from the ambient environment (up to 29 VOCs) based on control experiments. We found that heat-treatment of medical swabs prior to GC-MS reduced the number of VOCs detected from 'blank' swabs and improved the reproducibility of VOC profiling, however significant VOC absorption can still occur from environmental exposure to ambient air. VOCs identified in 'blank' swabs consisted predominantly of hydrocarbons, esters, and silicon-based compounds and depended strongly on the material used (cotton and polyester-rayon). Environmental VOCs found to absorb to swabs from the ambient air during sampling included 1-butylheptyl-benzene and hexadecanoic acid methyl ester as well as exogenous VOCs such as isopropyl palmitate and isopropyl myristate. In contrast, sebum VOCs consisted primarily of esters, alcohols, ketones, and aldehydes. 23 and 18 VOCs were identified in sebum collected using polyester-rayon and cotton-based medical swabs, respectively, with 14 VOCs common to both swabs. The effect of subject bathing prior to sebum sampling had minimal impact on the VOC profiles. However, individual differences owing to external factors such as skin type, diet, and exercise will likely influence sebum production. This study highlights the importance of using rigorous controls in sebum sampling, and recommendations are provided for future research involving sebum VOC analysis. For example, the use of sebum sample replicates across multiple days, and the use of control swabs during sample collection is required to confirm the origin and reliability of sebum VOCs. It is anticipated that these recommendations in conjunction with a library of well-established VOCs from medical swabs will further strengthen biomarker identification resulting from sebum VOC analysis.

Medical diagnosis at the point-of-care by portable high-field asymmetric waveform ion mobility spectrometry: a systematic review and meta-analysis.

Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%-85%,I2= 62%), and specificity of 78% (95% CI, 70%-84%,I2= 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.

Metal-ion free chiral analysis of amino acids as small as proline using high-definition differential ion mobility mass spectrometry.

The chiral analysis of enantiomers is important because bioactivity can depend strongly on stereochemistry as ligand-protein binding motifs are typically chiral. Ion mobility mass spectrometry-based methods are emerging for the rapid and sensitive chiral analysis of molecules. However, such methods are typically limited by the use of metal-bound trimers, which can be challenging to form owing to ion suppression and the need for extensive pre-screening experiments to identify suitable metal ions. Moreover, the chiral separation of very small molecules, such as cysteine and proline, using ion mobility has remained challenging. Here, using electrospray ionisation high-resolution differential ion mobility mass spectrometry (ESI-DMS-MS), we demonstrate that the enantiomers of benchmark amino acids as small as proline can be rapidly distinguished without the use of metal ions for the first time. ESI-DMS-MS of proton-bound diastereomeric dimer complexes, containing enantiomers of amino acids and a 'chiral selector' (N-tert-butoxycarbonyl-O-benzyl-l-serine; BBS) corresponding to [L/D-X(BBS)+H]+ (X = cysteine and proline) resulted in the separation of L and D-enantiomers. By use of DMS-MS and standard solutions of chiral mixtures, these data indicate that the enantiomeric excess of proline can be accurately quantified by differential ion mobility mass spectrometry. Overall, these results provide further evidence that DMS-MS can be used for the rapid and accurate 'metal-ion free' chiral analysis of many other biologically important molecules.