Olmos, P. M. | bioRxiv Science

bioRxiv2025

Non-linear and time-domain sleep qEEG features predict CSF protein damage markers in early Alzheimers disease

Study ObjectivesIn preclinical Alzheimers disease (AD), oxidative stress plays a key role in the pathogenesis by promoting non-enzymatic protein modifications that lead to the accumulation of oxidized species, established biomarkers of oxidative damage detectable in cerebrospinal fluid (CSF). These molecular alterations contribute to impaired proteostasis and the dysfunction of sleep-regulating neural circuits, resulting in altered sleep electroencephalographic patterns. Due to the invasiveness of CSF sampling, quantitative electroencephalography (qEEG) is proposed as a non-invasive alternative for assessing oxidatively modified protein levels via Machine Learning (ML). MethodsForty-two mild-to-moderate AD patients underwent polysomnography (PSG). QEEG features were extracted. CSF protein oxidation markers levels --glutamic semialdehyde, aminoadipic semialdehyde, N{varepsilon}-carboxyethyl-lysine, N{varepsilon}-carboxymethyl-lysine, and N{varepsilon}-malondialdehyde-lysine --were assessed by gas chromatography/mass spectrometry, and ML models trained to predict CSF biomarker levels. Model generalizability was validated using EEG data from healthy controls. ResultsqEEG features from slow-wave sleep (SWS) and rapid eye movement (REM) sleep, particularly over frontal and central regions, yielded R2 > 0.625 for patients biomarker prediction. ConclusionqEEG is a non-invasive, scalable tool for detecting AD-related oxidative processes, with potential implications for early diagnosis and risk stratification. HighlightsO_LISleep EEG features predict CSF oxidative biomarkers in early Alzheimers disease. C_LIO_LIPeak-to-peak amplitude, variance, and entropy were the top predictive features. C_LIO_LINREM sleep, especially SWS, and REM sleep provided the most informative EEG signals. C_LIO_LIEEG-based models achieved accurate, non-invasive biomarker estimation. C_LIO_LISleep qEEG may enable early detection and risk stratification in Alzheimers. C_LI

neuroscience↗

bioRxiv2024

Rapid, automatic typing of Clostridioides difficile Ribotypes Using MALDI-TOF MS

Clostridioides difficile is a major cause of hospital-acquired diarrhea, posing significant clinical challenges due to its high morbi-mortality rates and its involvement in nosocomial outbreaks. Detecting its toxigenic ribotypes rapidly and accurately is crucial for effective outbreak control. This study aimed to create a rapid diagnostic methodology based on MALDI-TOF MS and Machine Learning algorithms to differentiate toxigenic C. difficile RTs. MALDI-TOF spectra were acquired from 379 clinical isolates sourcing from 10 Spanish hospitals and analysed using Clover MSDAS, a specific software for MALDI-TOF spectra analysis, considered as the state-of-the-art tool for this purpose, and AutoCdiff, an ad hoc software developed in this study. Seven biomarker peaks were found to differentiate epidemic RT027 and RT181 strains from other RTs (2463, 3353, 4933, 4993, 6187, 6651 and 6710 m/z). Two peaks (2463 and 4993 m/z) were specifically found in RT027 isolates while combinations of the other 5 peaks allowed the differentiation of RT181 from other ribotypes. Automatic classification tools developed in Clover MSDAS and AutoCdiff using the specific peaks and the entire protein spectra, respectively, showed up to 100% balanced accuracy. Both methods allowed correct ribotype assignment for isolates sourcing from real-time outbreaks. The developed models, available from Clover MSDAS and the AutoCdiff website -https://bacteria.id-offer researchers a valuable tool for quick C. difficile ribotype determination based on MALDI-TOF spectra analysis. Although further validation of the models is still required, they represent rapid and cost-effective methods for standardized C. difficile ribotype assignment.

microbiology↗