It was published lately in 2016 that there are approximately 3.7 million of deaths caused by communicable diseases annually. Unfortunately, currently there is no automated method for the detection and tracking of communicable diseases progression. In this paper, a framework is proposed, that is based on social network analysis, different biological sensors, and big data analytics as for predicting and analyzing communicable disease and to facilitate the process of managing, preventing and predicting risks of communicable disease progression. The proposed framework is largely based on graph theory and social network analysis algorithms to model and dynamically predict communicable disease risk for diagnosed and non-diagnosed patients. In this research, a global graph structure that maps a whole friendship network is proposed, and the suitable algorithms to identify and continuously monitor a certain communicable disease progression rate. This research can potentially be useful for forming a methodology for early intervention and prevention policies targeted at patients that can potentially divert them from the disease pathway. The interpretation and dynamic utilities offered by the framework and its predictive capability are considered a remarkable and promising broad model highlighting potential pathways linking social support, biological sensors and data sciences to physical health.
Background
Patent foramen ovale closure in the setting of stroke was debatable until the recent data from the long-term follow-up of multiple randomized control trials. These recent data have led to increase the number of the procedure worldwide.
To our knowledge, there was no previous formal structured program in Egypt between cardiologists and neurologists for investigation and management of patients with cryptogenic stroke.
The first Egyptian-dedicated stroke team was created in two large tertiary centers with collaboration between cardiologists, dedicated cardiac imagers, and neurologists for investigation and management of patients with cryptogenic stroke.
Results
Sixty-three patients with cryptogenic stroke were identified from a total of 520 patients admitted to the stroke units between 2016 and 2019. Twenty-five patients had a proven PFO-related stroke. Three patients were referred for surgical closure, 19 patients underwent transcatheter PFO closure, and procedural success was met in 18 patients (94.7%). We did not experience any major procedure-related complication. Complete closure was achieved in 83.3% of patients at 6 months. One patient had a single attack TIA within the first 3 months after device closure; one patient had a device-related thrombosis; both were managed successfully.
Conclusion
Our initial experience in collaboration between cardiologist and neurologist with the establishment of a dedicated cryptogenic stroke team added significantly to the management of patients with stroke.
The results of the first Egyptian cohort who underwent transcatheter PFO closure demonstrated procedural feasibility, safety, and efficacy with very low incidence of major complications.
A nationwide program is needed to reduce the ischemic stroke disease burden and the risk of recurrence.
Background:Ischemic heart diseaseis one of the heaviest health-related burdens worldwide.We aimed to identify the common hub mRNA and pathways that are involved in pathological progression of ischemic cardiomyopathy(ICM). Methods:To explore potential differentially expressed genes (DEGs) of all ischemic heart disease stages, we used chipster and GEO2R tools to analyze of retrieved eight high throughput RNA datasets obtained from GEO database. Gene Ontology functional annotation and Pathways enrichment analyses were used to obtain the common functional enriched DEGs which were visualized in protein–protein interactions (PPI) network to explore the hub mRNA according to the interaction scores. Validation qRT-PCR was carried out for blood and cardiac biopsies compared with controls to validate the determined four hub mRNAs and subsequently reviewed inside comprehensive published meta-analysis database. The validated mRNAs were visualized in two interaction modules. Finally screening of approved drugs was applied.
Results: 15 common DEGs with p value ≤ 0.01 were identified and carbohydrate &amino acids metabolism and inflammatory responses were significantly enriched. STAT3, CEBPD, GLUL and CD163 were hub enriched mRNAs with interaction score ≥ 0.50. Our qRT-PCR analysis showed increased expression of STAT3 over all patients groups and CD163 mainly in cardiac samples with remarked ascending manner. Interaction modules showed co-regulators supporting high STAT3-CD163 connectivity providing potential role of STAT3-CD163 crosstalk mediated inflammatory responses in ICM progression. We determined two reported drugs targeting STAT3.
Conclusion:Post analysis of the used GEO datasets and qRT-PCR data pointed that STAT3-CD163 crosstalk was potential biomarkers for ICM progression.
Clinical trial registration: www.clinicaltrials.gov, Identifier: NCT05508269
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