On the use of local and global search paradigms for computer-aided diagnosis of breast cancer

Cancer is one of the most dangerous diseases around the world and the most common cancer among women is breast cancer. Although not all the cancer types are curable upon diagnosis, breast cancer can be cured if it is diagnosed early. The most reliable way of diagnosing reast cancer is mammographic screening which can diagnose the disease 1.5 to 4 years before it is clinically diagnosed. Double Reading is the important diagnostic process in which two experts/radiologists should read the same mammogram image to make an accurate diagnosis. But this process is not a cost-effective approach for early detection of breast cancer. Computer-Aided Diagnosis (CAD) can act as the second expert and therefore one expert would be enough for breast cancer diagnosis. In this study, we use the data extracted from low-resolution as well as high resolution mammography images. The attributes extracted from mammographic images are imported into Support Vector Machine (SVM) to classify the patients. An important point about the attributes is that sometimes there may be some irrelevant or even noisy attributes that have negative effect on the classification accuracy. Therefore, the main objective of this study is to apply local and global search paradigms in order to find the best subset of attributes to construct the most accurate CAD system that can effectively distinguish between benign and healthy patients. Artificial Bee Colony (ABC) is a population-based swarm intelligence algorithm with good global exploration ability, and Simulated Annealing (SA) is a robust local-search algorithm. Thus, we utilize a hybrid global and local search algorithm (named ABCSA) to simultaneously benefit from the advantages of both ABC and SA. In this approach, ABC is firstly performed for the global exploration in the search space. Then, SA is utilized to search locally in the vicinity of the best solution found via ABC, in order to improve the quality of the final solution. Obtained simulation results over four different mammographic datasets show that the proposed algorithm outperforms the existing metaheuristic feature selection approaches in terms of minimizing the number of features, while maximizing the detection accuracy

Maternal exposure to ambient air pollution during pregnancy and lipid profile in umbilical cord blood samples; a cross-sectional study

Adverse health effects of exposure to air pollution have been investigated in many previous studies. However, there is no study available on the association between maternal exposure to air pollution during pregnancy and cord blood lipid profile. This study, based on 150 mother-newborn pairs residing in Sabzevar, Iran (2018), evaluated the association of exposure to ambient air pollution as well as traffic indicators (total street length in different buffers around residential address and distance to major roads) during entire pregnancy with lipid levels cord blood lipid profile. Concentrations of PM10, PM2.5, and PM1 at maternal residential address were estimated using land use regression (LUR) models. We measured triglyceride (TAG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) levels and TC/HDL-C and TAG/HDL-C ratio in the cord blood samples to characterize their lipid profile. Multiple linear regression models were developed to estimate the association of exposure to air pollution and traffic indicators with cord blood lipid profile controlled for relevant covariates. Higher concentrations of PM2.5 and PM10 were associated with higher levels of TAG, TC, HDL-C, TC/HDL-C, and TAG/HDL-C in cord blood samples. Moreover, higher concentration of PM1 was associated with higher levels of TAG, TC and LDL-C. There was also a positive association between total street length in 100 m buffer around home and serum levels of TC, TAG, LDL-C and TC/HDL ratio (β = 3.73, 95 confidence intervals (CI): 1.76, 5.71; β = 2.75, 95 CI: 0.97, 4.53; β = 1.87, 95 CI: 0.64, 3.09; β = 0.06, 95 CI: 0.01, 0.11, respectively). However, the associations for total street length in larger buffers and distance to major roads were not statistically significant. Our findings support a relationship between exposure to air pollution during pregnancy and increase in cord blood lipid levels. Exposure to air pollution during entire pregnancy was associated with changes in lipid profile in cord blood samples. © 2020 Elsevier Lt

Metformin as a protective agent against natural or chemical toxicities : a comprehensive review on drug repositioning

International audienceBackground Metformin is the first prescribed drug for hyperglycemia in type 2 diabetes mellitus. Mainly by activating AMPK pathway, this drug exerts various functions that among them protective effects are of the interest. Purpose Herein, we aimed to gather data about the protective impacts of metformin against various natural or chemical toxicities. Results An extensive search among PubMed, Scopus, and Google Scholar was conducted by keywords related to protection, toxicity, natural and chemical toxins and, metformin. Our literature review showed metformin alongside its anti-hyperglycemic effect has a wide range of anti-toxic effects against anti-tumour and routine drugs, natural and chemical toxins, herbicides and, heavy metals. Conclusion It is evident that metformin is a potent drug against the toxicity of a broad spectrum of natural, chemical toxic agents which is proved by a vast number of studies. Metformin mainly through AMPK axis can protect different organs against toxicities. Moreover, metformin preserves DNA integrity and can be an option for adjuvant therapy to ameliorate side effect of other therapeutics