There is a lack of predictive markers for early and rapid identification of disease progression in COVID-19 patients. Our study aims at identifying microRNAs (miRNAs)/small nucleolar RNAs (snoRNAs) as potential biomarkers of COVID-19 severity. Using differential expression analysis of microarray data (n = 29), we identified hsa-miR-1246, ACA40, hsa-miR-4532, hsa-miR-145-5p, and ACA18 as the top five differentially expressed transcripts in severe versus asymptomatic, and ACA40, hsa-miR-3609, ENSG00000212378 (SNORD78), hsa-miR-1231, hsa-miR-885-3p as the most significant five in severe versus mild cases. Moreover, we found that white blood cell (WBC) count, absolute neutrophil count (ANC), neutrophil (%), lymphocyte (%), red blood cell (RBC) count, hemoglobin, hematocrit, D-Dimer, and albumin are significantly correlated with the identified differentially expressed miRNAs and snoRNAs. We report a unique miRNA and snoRNA profile that is associated with a higher risk of severity in a cohort of SARS-CoV-2 infected patients. Altogether, we present a differential expression analysis of COVID-19-associated microRNA (miRNA)/small nucleolar RNA (snoRNA) signature, highlighting their importance in SARS-CoV-2 infection.

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r²) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

Histological structure of thrombi is a strong determinant of the outcome of vascular recanalization therapy, the only treatment option for acute ischemic stroke (AIS) patients. A total of 21 AIS patients from this study after undergoing non-enhanced CT scan and multimodal MRI were treated with mechanical stent-based and manual aspiration thrombectomy, and thromboembolic retrieved from a cerebral artery. Complementary histopathological and imaging analyses were performed to understand their composition with a specific focus on fibrin, von Willebrand factor, and neutrophil extracellular traps (NETs). Though distinct RBC-rich and platelet-rich areas were found, AIS patient thrombi were overwhelmingly platelet-rich, with 90% of thrombi containing <40% total RBC-rich contents (1.5 to 37%). Structurally, RBC-rich areas were simple, consisting of tightly packed RBCs in thin fibrin meshwork with sparsely populated nucleated cells and lacked any substantial von Willebrand factor (VWF). Platelet-rich areas were structurally more complex with thick fibrin meshwork associated with VWF. Plenty of leukocytes populated the platelet-rich areas, particularly in the periphery and border areas between platelet-rich and RBC-rich areas. Platelet-rich areas showed abundant activated neutrophils (myeloperoxidase⁺ and neutrophil-elastase⁺) containing citrullinated histone-decorated DNA. Citrullinated histone-decorated DNA also accumulated extracellularly, pointing to NETosis by the activated neutrophils. Notably, NETs-containing areas showed strong reactivity to VWF, platelets, and high-mobility group box 1 (HMGB1), signifying a close interplay between these components.

Transient ischemic attack (TIA) refers to a momentary neurologic deficit caused by focal cerebral, spinal or retinal ischemic insult. TIA is associated with a high risk of impending acute ischemic stroke (AIS), a neurologic dysfunction characterized by focal cerebral, spinal or retinal infarction. Understanding the differences in molecular pathways in AIS and TIA has merit for deciphering the underlying cause for neuronal deficits with long-term effects and high risks of morbidity and mortality. In this study, we performed comprehensive investigations into the circulating microRNA (miRNA) profiles of AIS (n = 191) and TIA (n = 61) patients. We performed RNA-Seq on serum samples collected within 24 hrs of clinical diagnosis and randomly divided the study populations into discovery and validation cohorts. We identified a panel of 11 differentially regulated miRNAs at FDR < 0.05. Hsa-miR-548c-5p, -20a-5p, -18a-5p, -484, -652-3p, -486-3p, -24-3p, -181a-5p and -222-3p were upregulated, while hsa-miR-500a-3p and -206 were downregulated in AIS patients compared to TIA patients. We also probed the previously validated gene targets of our identified miRNA panel to highlight the molecular pathways affected in AIS. Moreover, we developed a multivariate classifier with potential utilization as a discriminative biomarker for AIS and TIA patients. The underlying molecular pathways in AIS compared to TIA may be explored further in functional studies for therapeutic targeting in clinical translation.

MicroRNAs (miRNAs) participate in diverse cellular changes following acute ischemic stroke (AIS). Circulating miRNAs, stabilized and delivered to target cells via extracellular vesicles (EVs), are potential biomarkers to facilitate diagnosis, prognosis, and therapeutic modulation. We aimed to identify distinctive expression patterns of circulating EV-miRNAs in AIS patients. miRNA profiles from EVs, isolated from plasma samples collected within 24 h following AIS diagnosis, were examined between a dataset of 10 age-, gender- and existing comorbidities-matched subjects (5 AIS and 5 healthy controls, HC). We measured 2578 miRNAs and identified differentially expressed miRNAs between AIS and HC. An enrichment analysis was conducted to delineate the networks and biological pathways implicated by differentially expressed microRNAs. An enrichment analysis was conducted to delineate the networks and biological pathways implicated by differentially expressed microRNAs. Five miRNAs were differentially expressed between stroke (AIS) versus control (HC). hsa-let-7b-5p, hsa-miR-16-5p, and hsa-miR-320c were upregulated, whereas hsa-miR-548a-3p and hsa-miR-6808-3p, with no previously reported changes in stroke were downregulated. The target genes of these miRNAs affect various cellular pathways including, RNA transport, autophagy, cell cycle progression, cellular senescence, and signaling pathways like mTOR, PI3K-Akt, and p53. Key hub genes within these networks include TP53, BCL2, Akt, CCND1, and NF-κB. These pathways are crucial for cellular function and stress response, and their dysregulation can have significant implications for the disease processes. Our findings reveal distinct circulating EV-miRNA expression patterns in AIS patients from Qatar, highlighting potential biomarkers that could aid in stroke diagnosis and therapeutic strategies. The identified miRNAs are involved in critical cellular pathways, offering novel insights into the molecular mechanisms underlying stroke pathology. Circulating EV-miRNAs differentially expressed in AIS may have a pathophysiological role and may guide further research to elucidate their precise mechanisms.

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography–mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.