A use of platelet additives solution (PAS) improves storage conditions so as to give increased shelf life to platelets and to maintain hemostatic function.
To develop a research overview of brain tumor classification using machine learning, we conducted a systematic review with a bibliometric analysis. Our systematic review and bibliometric analysis included 1747 studies of automated brain tumor detection using machine learning reported in the previous 5 years (2019-2023) from 679 different sources and authored by 6632 investigators. Bibliographic data were collected from the Scopus database, and a comprehensive bibliometric analysis was conducted using Biblioshiny and the R platform. The most productive and collaborative institutes, reports, journals, and countries were determined using citation analysis. In addition, various collaboration metrics were determined at the institute, country, and author level. Lotka's law was tested using the authors' performance. Analysis showed that the authors' publication trends followed Lotka's inverse square law. An annual publication analysis showed that 36.46% of the studies had been reported in 2022, with steady growth from previous years. Most of the cited authors had focused on multiclass classification and novel convolutional neural network models that are efficient for small training sets. A keyword analysis showed that "deep learning," "magnetic resonance imaging," "nuclear magnetic resonance imaging," and "glioma" appeared most often, proving that of the several brain tumor types, most studies had focused on glioma. India, China, and the United States were among the highest collaborative countries in terms of both authors and institutes. The University of Toronto and Harvard Medical School had the highest number of affiliations with 132 and 87 publications, respectively.
β-Thalassemia/HbE disease has a wide spectrum of clinical phenotypes ranging from asymptomatic to dependent on regular blood transfusions. Ability to predict disease severity is helpful for clinical management and treatment decision making. A thalassemia severity score has been developed from Mediterranean β-thalassemia patients. However, different ethnic groups may have different allele frequency and linkage disequilibrium structures. Here, Thai β0-thalassemia/HbE disease genome-wild association studies (GWAS) data of 487 patients were analyzed by SNP interaction prioritization algorithm, interacting Loci (iLoci), to find predictive SNPs for disease severity. Three SNPs from two SNP interaction pairs associated with disease severity were identifies. The three-SNP disease severity risk score composed of rs766432 in BCL11A, rs9399137 in HBS1L-MYB and rs72872548 in HBE1 showed more than 85% specificity and 75% accuracy. The three-SNP predictive score was then validated in two independent cohorts of Thai and Malaysian β0-thalassemia/HbE patients with comparable specificity and accuracy. The SNP risk score could be used for prediction of clinical severity for Southeast Asia β0-thalassemia/HbE population.
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.