Effect of crocin of Crocus sativus L. on serum inflammatory markers (IL-6 and TNF-α) in chronic obstructive pulmonary disease patients: a randomised, double-blind, placebo-controlled trial.

Different factors, such as inflammation, oxidative stress, extracellular matrix degradation and apoptosis, affect the pathophysiology of chronic obstructive pulmonary disease (COPD), as a progressive disease characterised by permanent airflow limitation. Herbal supplements with anti-inflammatory and antioxidant properties can help treat certain chronic diseases. The current study aimed at investigating the preventive effects of crocin supplementation on the serum concentrations of IL-6, TNF-α, exercise capacity and pulmonary function tests (PFT) in patients with COPD. The present prospective randomised clinical trial equally divided fifty-seven patients with COPD into a placebo and an intervention group, who respectively received a placebo and crocin (15 mg twice day for 12 weeks) as a supplement. ELISA was used to measure serum levels of IL-6 and TNF-α, also PFT and exercise capacity based on 6-min walking distance test (6MWD), which was performed at the beginning and end of the study. Crocin improved the results of PFT (P < 0·05) and 6-MWD (P < 0·001) and exerted preventive effects by increasing the serum levels of IL-6 in patients with COPD compared with those in the placebo group (P < 0·05). Intervention with crocin significantly lowered serum levels of TNF-α at the end of the study (P < 0·01). The present findings suggest crocin supplementation improves exercise capacity and PFT in patients with COPD by reducing serum levels of inflammatory factors.

Effect of Crocin From Saffron (Crocus sativus L.) Supplementation on Oxidant/Antioxidant Markers, Exercise Capacity, and Pulmonary Function Tests in COPD Patients: A Randomized, Double-Blind, Placebo-Controlled Trial.

Background: Chronic obstructive pulmonary disease (COPD) is a progressive and chronic respiratory disorder characterized by reversible airflow limitation and lung parenchyma destruction. The main feature of COPD is inflammation and disturbance of the oxidant/antioxidant balance in the airways. The therapeutic use of herbal supplements with antioxidant and anti-inflammatory properties seems to be very useful in the medical management of patients with COPD. Method: COPD patients were divided into placebo and intervention groups (each group n = 23) in a clinical trial study. The intervention group received crocin supplementation (30 mg/day for 12 weeks), and the control group received a placebo. Pre- and after the intervention, pulmonary function tests (PFTs), exercise capacity (using a 6-min walking distance test (6MWD)), and serum levels of total oxidant status (TOS), total antioxidant capacity (TAOC), and NF-kB were assessed using the ELISA test. Results: Intervention with crocin for 12 weeks in COPD patients decreased serum levels of TOS and NF-κB as well as increased TAOC. In addition, the results of the 6MWD test reveal an improvement in patients' exercise capacity. Conclusion: Crocin supplementation appears to effectively establish oxidant/antioxidant balance and improve inflammatory conditions in patients with COPD.

Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning.

Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue.