ORCID Profile
0000-0002-1192-3682
Current Organisation
University of Oxford
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Publisher: Wiley
Date: 13-08-2019
DOI: 10.1111/JPN.13167
Abstract: A biological assay was carried out to evaluate the impact of dietary tryptophan (TRP) in aflatoxin B
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 05-2023
DOI: 10.1161/STROKEAHA.122.040529
Abstract: Anti-inflammatory therapies reduce recurrent vascular events in coronary disease. Existing studies have reported highly conflicting findings for the association of blood inflammatory markers with vascular recurrence after stroke leading to uncertainty about the potential of anti-inflammatory therapies after stroke and no consensus about the utility of measurement of inflammatory markers in current guidelines. We investigated the association between hsCRP (high-sensitivity C-reactive protein), IL-6 (interluekin-6), and recurrent major adverse cardiovascular events (MACE), and stroke from in idual participant data from 8420 patients with ischemic stroke/transient ischemic attack from 10 prospective studies. We did within-study multivariable regression analyses and then combined adjusted risk ratio (RR) by random-effects meta-analysis. During 18 920 person-years of follow-up, 1407 (16.7% [95% CI, 15.9–17.5]) patients had MACE and 1191 (14.1% [95% CI, 13.4–14.9]) patients had recurrent stroke. On bivariate analysis, baseline IL-6 was associated with MACE (RR, 1.26 [95% CI, 1.10–1.43]) and recurrent stroke (RR, 1.18 [95% CI, 1.05–1.32]), per unit increase log e IL-6. Similar associations were observed for hsCRP (MACE RR, 1.19 [95% CI, 1.09–1.29] recurrent stroke RR, 1.12 [95% CI, 1.04–1.21], per unit increase log e hsCRP). After adjustment for vascular risk factors and treatment, independent associations remained with MACE (IL-6, RR, 1.12 [95% CI, 1.04–1.21] hsCRP, RR, 1.09 [95% CI, 1.04–1.15]) and recurrent stroke (IL-6, RR, 1.09 [95% CI, 1.00–1.19] hsCRP, RR, 1.05 [95% CI, 1.00–1.11]). Comparing the top with the bottom quarters (Q4 versus Q1), IL-6 (RR, 1.35 [95% CI, 1.09–1.67]) and hsCRP (RR, 1.31 [95% CI, 1.07–1.61]) were associated with MACE after adjustment. Similar results were observed for recurrent stroke for IL-6 (RR, 1.33 [95% CI, 1.08–1.65]) but not hsCRP (RR, 1.16 [95% CI, 0.93–1.43]). Blood markers of inflammation were independently associated with vascular recurrence after stroke, strengthening the rationale for randomized trials of anti-inflammatory therapies for secondary prevention after ischemic stroke/TIA.
Publisher: Oxford University Press (OUP)
Date: 05-2023
DOI: 10.1093/BIB/BBAD182
Abstract: MicroRNAs are small regulatory RNAs that decrease gene expression after transcription in various biological disciplines. In bioinformatics, identifying microRNAs and predicting their functionalities is critical. Finding motifs is one of the most well-known and important methods for identifying the functionalities of microRNAs. Several motif discovery techniques have been proposed, some of which rely on artificial intelligence-based techniques. However, in the case of few or no training data, their accuracy is low. In this research, we propose a new computational approach, called DiMo, for identifying motifs in microRNAs and generally macromolecules of small length. We employ word embedding techniques and deep learning models to improve the accuracy of motif discovery results. Also, we rely on transfer learning models to pre-train a model and use it in cases of a lack of (enough) training data. We compare our approach with five state-of-the-art works using three real-world datasets. DiMo outperforms the selected related works in terms of precision, recall, accuracy and f1-score.
Publisher: Cold Spring Harbor Laboratory
Date: 12-03-2021
DOI: 10.1101/2021.03.12.435171
Abstract: Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We evaluated the domain adaptation techniques on source and target domains consisting of 5 different datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for finetuning in the target domain. On comparing the performance of different techniques on the target dataset, unsupervised domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Ali Maghsoudi.