Expression of toll like receptors before and after coronary angiography with isomolar contrast among those with and without coronary atherosclerosis.

Angiography is a safe technique for the detection of and treatment of cardiovascular diseases. However, the effects of the technique on the molecular response of the immune system are yet to be clarified. Toll like receptors (TLRs) are the important molecule participate in the innate immunity responses and induction of inflammation. This project was designed to explore the effects of angiography on the expression of TLR1, TLR2, TLR3 and TLR4. Fifty-five participants, including three separate groups (without artery stenosis, with one artery stenosis and more than one artery stenosis), were assessed in this project. TLR1, TLR2, TLR3 and TLR4 expression levels were evaluated in peripheral blood immune cells by measuring mRNA before and after angiography using Real-Time PCR techniques. mRNA levels of TLR1, TLR2 and TLR3 were significantly increased following angiography. Expression of TLR4 did not change after angiography. Other criteria also showed no correlation on TLR expression after angiography. TLR4 mRNA levels had a positive correlation with age in the participants without artery stenosis. Angiography may induce inflammation in subjects without artery stenosis via up-regulation of TLR1, 2 and 3 which may lead to cardiovascular diseases related complications.

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.

Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model's architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model's accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.

The Association Between C424c/A Polymorphism Within the IL-25 Gene and Multiple Sclerosis.

BACKGROUND: Multiple Sclerosis (MS) is a common autoimmune system disease which affects the central nervous system. It has been documented that interleukin-25 (IL-25) plays key roles in suppressing Th1 responses, which is increased during MS. OBJECTIVES: The aim of this study was to investigate the c424C/A polymorphism within the IL-25 gene in MS patients in comparison to healthy controls. PATIENTS AND METHODS: In this case-control study, 74 patients with MS and 75 healthy controls were selected. Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) was used in order to determine c424C/A polymorphism within the IL-25 gene. RESULTS: The results showed that there was no statistical significant difference in distribution of genotype (AA, AC and CC) and allele (A and C) frequencies between MS patients and healthy controls (P = 0.901 and P = 0.728, respectively). CONCLUSIONS: In conclusion, it appears that the c424C/A polymorphism within the IL-25 gene has no significant relationship with MS, and this polymorphism is probably not associated with MS complications, its onset and gender distribution.