A pilot study of the effects of crocin on high‐density lipoprotein cholesterol uptake capacity in patients with metabolic syndrome: A randomized clinical trial

Saberi‐Karimian, Maryam; Safarian-Bana, Hamideh; Mohammadzadeh, Elham; Kazemi, Tooba; Mansoori, Amin; Ghazizadeh, Hamideh; Samadi, Sara; Nikbakht-Jam, Irandokht; Nosrati, Mina; Esmaily, Habibollah; Aghasizadeh, Malihe; Ghayour‐Mobarhan, Majid

doi:10.1002/biof.1783

Cited by 21 publications

(14 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accumulating data revealed that ginsenosides as an antioxidant and the main active constituent of ginseng, had a potential to e ciently alter TAM to the M1 subset of macrophages [30,31]. Since crocin has been used as a supplement in a number of clinical trial studies [32][33][34][35][36], these ndings highlight that crocin may possibly be able to modify the immune system of the recipient even in the absence of any in ammatory condition and also put emphasis on reassessing crocin administration as a preventive approach toward the diseases with a well-known etiology of M1 macrophage dominancy.…”

Section: Discussionmentioning

confidence: 99%

The effect of crocin on phenotype switching of murine macrophages depends on their polarization state at the time of exposure to crocin

Abdi

Roshanravan

Hosseinzadeh

et al. 2022

Preprint

View full text Add to dashboard Cite

Background Macrophages have plastic phenotypes. M1 macrophages release inflammatory cytokines and have microbicidal activities. M2 cells release anti-inflammatory cytokines and repair tissue damages. The imbalance between M1 and M2 responses has an important effect on many pathological events. Crocin is an antioxidant and can eliminate free radicals, the properties involved in its anti-inflammatory capacities. We investigated the effects of crocin on macrophages phenotype switching in view of their primary activation states. Methods The effect of crocin different concentrations on macrophage viability was evaluated by MTT. TNF-α and IL-6 secretion as well as iNOS/Arg-I ratio were measured in cells treated with crocin and or LPS + INF-γ (M1 inducers). The cytokine secretion levels and the ratio were also determined in cells concurrently treated with crocin and LPS + INF-γ or cells pretreated with crocin before M1 induction. Results Crocin did not show any toxicity at a concentration of 500 µM and lower. When M0 uncommitted macrophages were exposed to crocin (25–100 µM), it raised some M1 activity indicators including iNOS/Arg-I ratio and TNF-α secretion, but not IL-6. Crocin in concurrent treatment with LPS + INF-γ could halt raising in M1 indicators, iNOS/Arg-I ratio and TNF-α secretion. Pretreatment of cells by crocin before the addition of LPS + INF-γ, could not converse the M1 induction in macrophages and caused a further increase in iNOS/Arg-I ratio and TNF-α secretion. Conclusion It seems that crocin modulatory effects on macrophage M1/M2 phenotype switching at least partly depends on the presence or absence of inflammatory mediators and accordingly the preliminary state of macrophage commitment.

show abstract

Section: Discussionmentioning

confidence: 99%

The effect of crocin on phenotype switching of murine macrophages depends on their polarization state at the time of exposure to crocin

Abdi

Roshanravan

Hosseinzadeh

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The aim of a decision tree is to form a predictive model in terms of predictor variables. In order to form the tree, decision tree algorithms make splitting criteria at internal nodes 20,21 . The split of a node tries to minimize the impurity of the node.…”

Section: Methodsmentioning

confidence: 99%

“…In order to form the tree, decision tree algorithms make splitting criteria at internal nodes. 20,21 The split of a node tries to minimize the impurity of the node. The node is not split and is characterized as a leaf node if a split is not able to achieve any enhancements with regard to reducing impurity.…”

Section: Decision Tree Modelmentioning

confidence: 99%

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements

Saberi‐Karimian

Mansoori

Bajgiran

et al. 2022

Clinical Laboratory Analysis

Self Cite

View full text Add to dashboard Cite

Background The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches. Methods A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35–65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid‐arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist‐to‐height Ratio (WHtR), and Waist‐to‐hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software. Results Traditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p‐value < 0.001) and in females, demispan and WC (p‐value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI. Conclusions Our results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.

show abstract

“…There are different techniques for classification problems 16 . DT can be applied in various applications in the medical field [17][18][19] . Due to the simplicity in understanding and clarity and extracting simple and understandable rules, it is widely applied and studied in these fields 16 .…”

Section: Logistic Regression (Lr) Modelingmentioning

confidence: 99%

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis

Mansoori

Sahranavard

Hosseini

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35–65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.

show abstract

A pilot study of the effects of crocin on high‐density lipoprotein cholesterol uptake capacity in patients with metabolic syndrome: A randomized clinical trial

Cited by 21 publications

References 38 publications

The effect of crocin on phenotype switching of murine macrophages depends on their polarization state at the time of exposure to crocin

The effect of crocin on phenotype switching of murine macrophages depends on their polarization state at the time of exposure to crocin

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis

Contact Info

Product

Resources

About