Biology subjects

Rezaei, F.

Publications and source records attributed to Rezaei, F..

4 recordsLinked to original sources

bioRxiv2026

Beyond Mean Pooling: Statistical Meta-Aggregation for Subject-Level EEG Classification in Parkinson's Disease

Electroencephalography (EEG) provides a non-invasive, temporally precise tool for studying neural alterations associated with Parkinson's disease (PD). When labels are defined at the subject level but inputs are epoch level, a key design choice is how epoch-wise predictions are aggregated to a single subject-level decision. Most prior work uses mean pooling, which assumes all epochs contribute equally and independently. This study proposes a statistical meta-aggregation strategy that represents each subject by 14 descriptors of the epoch-wise prediction distribution, including measures of central tendency, dispersion, shape, and high-confidence concentration. Using a self-supervised contrastive encoder trained on task-evoked EEG from the Simon conflict task, and a sample of 90 subjects including 57 PD and 33 control, this work evaluates the proposed framework under subject-wise five-fold cross-validation. Replacing mean pooling with statistical meta-aggregation lifts subject-level ROC-AUC from 0.655 to 0.872 for logistic regression and from 0.724 to 0.840 for support vector machines, without modifying the encoder or the classifier family. Averaging predictions across three independent self-supervised pretraining seeds yields competitive performance and improved balanced accuracy. These results suggest that, in this dataset, the way epoch-level predictions are summarized at the subject level has a substantial effect on final PD classification performance.

neuroscience↗

bioRxiv2026

A Sterol-Binding Cavity Underlies Sterol Recognition and Differential Activation of the ABCG5/G8

Sterol homeostasis depends on the coordinated regulation of endogenous cholesterol synthesis, dietary sterol absorption, and sterol excretion. The heterodimeric ATP-binding cassette sterol transporter ABCG5/G8 plays an important role in eliminating excess sterols by participating in reverse cholesterol transport and transintestinal cholesterol efflux. The molecular mechanism of sterol recognition and transport by ABCG5/G8 remains poorly understood. Here, we determined the cryo-electron microscopy (cryo-EM) structure of human ABCG5/G8 in complex with ergosterol. The structure revealed a sterol-binding site at the transmembrane domain (TMD) interface between the subunits ABCG5 and ABCG8, adjacent to the conserved aromatic clamp motif. Tyrosine 432 (Y432) on ABCG5, a key residue within the aromatic clamp, lies near the tetracyclic ring of ergosterol. Additionally, to assess the effect of different sterols on transporter activity, we performed molecular dynamic simulations and in vitro ATPase assays in the presence of cholesterol, cholesteryl hemisuccinate (CHS), and ergosterol. Ergosterol exhibited more favorable interactions with ABCG5/G8 and stimulated ATPase activity more effectively than either cholesterol or CHS, representing the first biochemical characterization of ABCG5/G8 activity in response to a non-cholesterol sterol. Furthermore, substitution of Y432 with the canonical phenylalanine in ABCG family abolished the differential ATPase response to ergosterol, with the mutant displaying similar activity levels in the presence of ergosterol and cholesterol. Together, our structural and biochemical findings reveal a conserved sterol-binding site within ABCG5/G8 and demonstrate direct evidence that distinct sterols differentially modulate ABCG sterol transporter activity and that the degenerative aromatic clamp motif in ABCG5 contributes to sterol-dependent functional selectivity.

biochemistry↗

bioRxiv2025

Implementation and Evaluation of Support Vector Machine-Based Models for Cancer Detection Using Multi-Omic Data: A Systematic Review

IntroductionCancer is a major source of mortality and morbidity all over the world that has caused more than 19 million new cases and nearly 10 million deaths in 2020. Although there are so many advances in cancer diagnosis, previous methods such as imaging and serum biomarkers more often lack the necessary sensitivity and specificity, particularly for early-stage detection. However, most of the studies depend on internal validation that increases concerns about the generalizability of these findings. To improve the dependability of SVM applications in clinical fields, the review emphasizes the necessity of external validation and established techniques. Due to all the things mixing AI with omics technology suggests a hopeful way to improve cancer detection, that could end up in better results and more affordable medical treatments. MethodThis systematic review was conducted using the PRISMA2020 principles and registered on The Open Science Framework. A comprehensive search of several databases was conducted, including PubMed/MEDLINE, Scopus, Google Scholar, and Web Of Science. Data was screened using RAYYAN.ai, which uses artificial intelligence methods to help with decision-making and screening. All original English-language studies that employed SVM to build a model for diagnosing a type human malignancy were included. The full text of the articles was extracted, and the quality of the articles and risk of bias were assessed using the PROBAST tool. ResultA total of 104 studies were identified, of which 99 articles have been included after 5 were excluded because full text was unavailable. The studies covered various types of omics, such as proteomics (41 studies), transcriptomics (30 studies), genomics (19 studies), metabolomics (11 studies), epigenomics (4 studies), radiomics (2 studies), immunomics (1 study), and multi-omics (8 studies). 63 studies were internally validated, and 29 were externally validated; however, 2 studies were both internally and externally validated. ConclusionThe review of 99 studies on Support Vector Machine-based models highlights their potential in improving cancer diagnosis. The study emphasizes the importance of proteomics studies in understanding tumor biology and developing effective diagnostic methods. However, concerns about their generalizability and trustworthiness in medical settings persist.

cell biology↗

bioRxiv2022

Structural analysis of cholesterol binding and sterol selectivity by ABCG5/G8

The ATP-binding cassette (ABC) sterol transporters are responsible for maintaining cholesterol homeostasis in mammals by participating in reverse cholesterol transport (RCT) or transintestinal cholesterol efflux (TICE). The heterodimeric ABCG5/G8 carries out selective sterol excretion, preventing the abnormal accumulation of plant sterols in human bodies, while homodimeric ABCG1 contributes to the biogenesis and metabolism of high-density lipoproteins. A sterol-binding site on ABCG5/G8 was proposed at the interface of the transmembrane domain and the core of lipid bilayers. In this study, we have determined the crystal structure of ABCG5/G8 in a cholesterol-bound state. The structure combined with amino acid sequence analysis shows that in the proximity of the sterol-binding site, a highly conserved phenylalanine array supports functional implications for ABCG cholesterol/sterol transporters. Lastly, in silico docking analysis of cholesterol and stigmasterol (a plant sterol) suggests sterol-binding selectivity on ABCG5/G8, but not ABCG1. Together, our results provide a structural basis for cholesterol binding on ABCG5/G8 and the sterol selectivity by ABCG transporters.

biochemistry↗