A flexible particle swarm optimization based on global best and global worst information

A reverse direction supported particle swarm optimization (RDS-PSO) method was proposed in this paper. The main idea to create such a method relies that on benefiting from global worst particle in reverse direction. It offers avoiding from local optimal solutions and providing diversity thanks to its flexible velocity update equation. Various experimental studies have been done in order to evaluate the effect of variable inertia weight parameter on RDS-PSO by using of Rosenbrock, Rastrigin, Griewangk and Ackley test functions. Experimental results showed that RDS-PSO, executed with increasing inertia weight, offered relatively better performance than RDS-PSO with decreasing one. RDS-PSO executed with increasing inertia weight produced remarkable improvements except on Rastrigin function when it is compared with original PSO

A particle swarm optimizer with modified velocity update and adaptive diversity regulation

This study introduces reverse direction supported particle swarm optimization (RDS-PSO) with an adaptive regulation procedure. It benefits from identifying the global worst and global best particles to increase the diversity of the PSO. The velocity update equation of the original PSO was changed according to this idea. To control the impacts of the global best and global worst particles on the velocity update equation, the alpha parameter was added to the velocity update equation. Moreover, a procedure for diversity regulation based on cosine amplitude or max–min methods was introduced. Alpha value was changed adaptively with respect to this diversity measure. Besides, RDS-PSO was implemented with both linearly increasing and decreasing inertia weight (with 1,000 and 2,000 iterations) in order to survey the effects of these variations on RDS-PSO performances. Six most commonly used benchmark functions and three medical classification problems were selected as experimental data sets. All experimental results showed that when the grain searching ability is not so small in the last generations, the algorithm performance continues to increase. Experimental proof of it was showed up especially in RDS-PSO using the cosine amplitude approach. Because the best results among all the RDS-PSO types for decreasing inertia weight modes were obtained with 2,000 maximal iterations rather than 1,000 ones. © 2018 John Wiley & Sons, Lt

Factors affecting the accuracy of 18F‑FDG PET/CT in evaluating axillary metastases in invasive breast cancer

Background and Aim: There are conflicting results of studies on accuracy of positron emission tomography (PET)/computed tomography (CT) for axillary staging. The aim of this study is to determine the factors affecting the efficacy of 18F‑fluorodeoxyglucose (18F‑FDG) PET/CT in detecting axillary metastases in invasive breast cancer.Materials and Methods: Data of 232 patients with invasive breast cancer who underwent 18F‑FDG PET/CT  examination before surgery between January 2013 and September 2017 were reviewed retrospectively.  Histopathological examination of axillary lymph nodes (ALNs) was used as a reference to assess the efficacy of 18F‑FDG PET/CT in detecting axillary metastases. Results: While 134 (57.8%) patients had axillary metastases as detected in 18F‑FDG PET/CT scans,  histopathologically axillary metastases were detected in 164 (70.7%) patients. The sensitivity, specificity, positive  predictive value, negative predictive value, and overall accuracy of 18F‑FDG PET/CT in detection of axillary metastasis were 72.56%, 77.94%, 88.8%, 54%, and 74.1%, respectively. The false‑negative and false‑positive rates were 27.4% and 22%, respectively. In univariate analysis, patients’ age, estrogen receptor positivity, higher ALN SUVmax, greater tumor size, and lymph node size determined by 18F‑FDG PET/CT were all significantly associated with accuracy of 18F‑FDG PET/CT for axillary metastasis. In multivariate analysis, tumor size determined by 18F‑FDG PET/CT and ALN SUVmax were independent variables associated with axillary metastasis. The accuracy of 18F‑FDG PET/CT for axillary metastasis was higher in  patients with a larger tumor (≥19.5 mm) and a higher ALN SUVmax (≥3.2).Conclusion: 18F‑FDG PET/CT should not be routinely used for axillary  staging, especially in patients with small tumors. It cannot eliminiate the necessity of sentinel lymph node biopsy. When it is used, both visual information and optimal cut‑off value of axillary node SUVmax should be taken into consideration.Keywords: Axillary metastasis, breast cancer, positron emission tomography/computed tomography, sentinel lymph node, sentinel lymph node biops

Frequency of mtDNA A1555G and A7445G mutations among children with prelingual deafness in Turkey

Considerable differences on the frequencies of the mitochondrial 12S rRNA A1555G and tRNA(Ser(UCN)) A7445G mutations have been reported in different populations. Our screening of 168 patients coming from independent Turkish families with prelingual sensorineural non-syndromic deafness revealed three deaf children with A1555G (1.8%) but no examples of A7445G. One proband with the mitochondrial A1555G mutation has also evidence for right parietal infarct on a brain imaging study, for which common thrombotic mutations were found to be negative. This study shows that the mitochondrial A1555G mutation is among the significant causes of prelingual non-syndromic deafness in the Turkish population

Using machine learning algorithm for diagnosis of stomach disorders

Medicine is one of the rich sources of data, generating and storing massive data, begin from description of clinical symptoms and end by different types of biochemical data and images from devices. Manual search and detecting biomedical patterns is complicated task from massive data. Data mining can improve the process of detecting patterns. Stomach disorders are the most common disorders that affect over 60% of the human population. In this work, the classification performance of four non-linear supervised learning algorithms i.e. Logit, K-Nearest Neighbour, XGBoost and LightGBM for five types of stomach disorders are compared and discussed. The objectives of this research are to find trends of using or improvements of machine learning algorithms for detecting symptoms of stomach disorders, to research problems of using machine learning algorithms for detecting stomach disorders. Bayesian optimization is considered to find optimal hyperparameters in the algorithms, which is faster than the grid search method. Results of the research show algorithms that base on gradient boosting technique (XGBoost and LightGBM) gets better accuracy more 95% on the test dataset. For diagnostic and confirmation of diseases need to improve accuracy, in the article, we propose to use optimization methods for accuracy improvement with using machine learning algorithms

Transplantation in pediatric aHUS within the era of eculizumab therapy

aHUS is caused by the over-activation and dysregulation of the alternative complement pathway. Data regarding outcomes of pediatric aHUS patients after kidney transplantation are still very scarce. Accordingly, the aim of this study was to describe the clinical findings and outcomes of pediatric aHUS patients after renal transplantation. This is a retrospective, multicenter study including 12 patients from the national registry system. Among the 12 patients, eight had received prophylactic eculizumab and none of those patients (except one) had experienced aHUS recurrence during a median follow-up period of 58.5 (min-max, 4-94) months. Although eculizumab had been started on the day before transplantation in one of them, aHUS recurrence occurred during the transplantation procedure. Eculizumab had been stopped in only one patient who had no complement gene mutation after 35 months of therapy, and recurrence had not been observed during the 19 months of follow-up. In three patients, maintenance doses had been spaced out without any recurrence. One additional patient with anti-CFH antibody received only two doses of eculizumab for transplantation and had been followed for 46 months without aHUS recurrence. The remaining three patients had not received anti-C5 therapy and none of those patients experienced aHUS recurrence during a median follow-up period of 21 (min-max, 9-42) months. Prophylactic eculizumab is a safe and effective treatment for the prevention of aHUS recurrence. Eculizumab interval prolongation, discontinuation, and transplantation without eculizumab prophylaxis can be tried in selected patients with close follow-up. © 2020 Wiley Periodicals LL