مجله دانشکده پزشکی اصفهان
پیاپی 661 (هفته اول اردیبهشت 1401)

Increased NK cells or their cytokine activity in endometrium and peripheral blood of mothers with pre-eclampsia have been reported. NKG2D is a cell surface receptor that can be used to detect NK cell and lymphocytic activity. Plant flavonoids such as silymarin has immunomodulatory effects and potentially inhibit the disease progression. Therefore, an in vitro experiment was designed to investigate the immunomodulatory effects of silymarin in pre-eclampsia.

Blood samples from preeclamptic and pregnant and non-pregnant women were analyzed by Isolation of peripheral blood mononuclear cells (PBMCs) using Ficoll-Paque. Flow cytometry was used for marker assay including NKG2D expression on CD56+CD3- and CD3+ cells. Also, different concentrations of silymarin were applied, andNKG2D expression on the cells were considered, and IFNγ production in the cell culture medium were evaluated using ELISA.

CD3-CD56+ cells and NKG2D receptor expression were augmented in PBMCs in the pre-eclampsia group (P = 0.03). However, treatment of PBMCs with silymarin solutions decrease NKG2D markers on the lymphocytes. PBMCs treated cells by silymarin in culture medium indicates reduce IFNɣ production even in the presence of IL-2 stimulation by 2-3 folds (P = 0.0001).

Increase in NK cell population, NKG2D receptor expression and IFNɣ production play a crucial role in the samples of preeclamptic patients, and well express involvement of immunological factors of the disease. The effectiveness of silymarin in cell culture medium and modulation of inflammatory factors necessitate the design of clinical model for using silymarin in patients by risk of increased NK activity in pre-eclampsia patients.

Nephrolithiasis (kidney stone) is a systemic disease associated with metabolic disorders. Regarding the beneficial properties of Nigella sativa, including diuretic and its inhibitory effects of calcium oxalate crystals deposition, this study aimed to investigate the effect of the alcoholic extract of Nigella sativa on kidney stone size in patients.

This study was a clinical trial conducted from June 2020 to December 2021 in Qazvin, Iran. 50 patients with kidney stones were allocated to two groups using the randomized block sampling method. For 12 weeks, the intervention group received a capsule containing alcoholic extract Nigella sativa (100 mg) two times per day and the control group received their usual treatment. pH, urinary oxalate and citrate, the size of the stone (sonography) were measured before and after the intervention. Paired t-test and independent t-test were used to compare the quantitative mean of the data.

Despite the decrease in stone size in the intervention group, no statistical significance in difference between both groups was observed. The amount of urinary oxalate in the intervention group was significantly reduced compared to the control group. There were no significant differences in terms of urine volume, pH, citrate levels between the two groups.

Findings of this study showed that alcoholic extract of Nigella sativa can be effective to prevent the accumulation of calcium oxalate crystals by reducing urinary oxalate. Although a complete understanding of the mechanism is unknown.

Cardiovascular diseases are one of the leading causes of death worldwide. Therefore, early diagnosis of heart disease, evaluation of cardiovascular system using cardiac hearing and Phonocardiogram (PCG) analysis which is a low cost, non-invasive, rapid method, and automatic screening of cardiovascular patients in remote areas is crucial. The aim of this study is to present a new method for screening heart patients based on signal processing (PCG) that is cheap and fast and has sufficient accuracy.

In this study, for screening 2062 labeled PCG signals, by extracting new features and applying them in 1- Random forest network 2- K-nearest neighbors 3- Decision tree 4- Linear discriminant analysis 5- Logistic regression and 6- Deep neural network, six different models were constructed and each of them was evaluated by k fold cross-validation method (K = 10). The test data were applied to the mentioned models and based on the outputs of these models, three indicators of accuracy, sensitivity and specificity were calculated. We showed and developed a new solution in differentiating and screening some heart patients from healthy individuals using PCG analysis.

Evaluation on the mentioned models was calculated by the three indicators, repeated 5 times and their mean and variance values were calculated. The highest sensitivity value is related to deep neural network (DNN) with sensitivity of 96.4 ± 0.14 and accuracy of 93.4 ± 0.11.

The new differential features along with the success of the proposed deep neural network in differentiating and screening between PCGs of healthy individuals and heart patients, shows the efficiency of the proposed algorithm. This method can be further improved with simultaneous multimodal classifier and the application of the voting rule.