A comprehensive systematic review of fMRI studies on brain connectivity in healthy children and adolescents: Current insights and future directions.

This systematic review considered evidence of children's and adolescents' typical brain connectivity development studied through resting-state functional magnetic resonance imaging (rs-fMRI). With aim of understanding the state of the art, what has been researched thus far and what remains unknown, this paper reviews 58 studies from 2013 to 2023. Considering the results, rs-fMRI stands out as an appropriate technique for studying language and attention within cognitive domains, and personality traits such as impulsivity and empathy. The most used analyses encompass seed-based, independent component analysis (ICA), the amplitude of the low frequency fluctuations (ALFF), and fractional ALFF (fALFF). The findings highlight key themes, including age-related changes in intrinsic connectivity, sex-specific patterns, and the relevance of the Default Mode Network (DMN). Overall, there is a need for longitudinal approaches to trace the typical developmental trajectory of neural networks from childhood through adolescence with fMRI at rest.

Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking.

One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10-4, 1.6 × 10-3, and 2.3 × 10-3, respectively. The test error values are 4.6 × 10-4, 1.6 × 10-3, and 2.4 × 10-3, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

A multifunctional long-term release system for treatment of hypothyroidism.

Hypothyroidism is an autoimmune disease associated with underactive thyroid gland. In this study, a dual effect polymeric system was designed to release Cepharanthine (CEP) to block T cell activation and Selenium (Se) to decrease the anti-thyroid peroxidase (TPOAb) concentration in order to treat hypothyroidism. For this purpose, poly(ethylene-vinyl acetate) (PEVA) and polyethylene glycol (PEG) nanoparticles (NPs) including CEP were synthesized by emulsion solvent evaporation method and they were loaded to polyurethane (PU)/PEG-PUSe-PEG block copolymer blends which were fabricated by particulate leaching technique as porous sponges. Fourier-Transform Infrared (FTIR), Raman, and Nuclear Magnetic Resonance (NMR) analysis showed successful synthesis of PEG-PUSe-PEG block copolymer. A long-term zero-order release profile was obtained for CEP. Se release rate from matrices showed an oxidative stress-mediated release which can be used to adjust Se amount. According to MTS results conducted by NIH 3T3 fibroblasts, both NPs and matrices have no adverse effect on cell viability. Fluorescence microscopy and SEM images confirm the MTS results. The dual release system has potential to be effectively used in long-term treatment of hypothyroidism by addressing both auto-immune response and hormone regulation.

Synthesis and Antidepressant Activity Profile of Some Novel Benzothiazole Derivatives.

Within the scope of our new antidepressant drug development efforts, in this study, we synthesized eight novel benzothiazole derivatives 3a-3h. The chemical structures of the synthesized compounds were elucidated by spectroscopic methods. Test compounds were administered orally at a dose of 40 mg/kg to mice 24, 5 and 1 h before performing tail suspension, modified forced swimming, and activity cage tests. The obtained results showed that compounds 3c, 3d, 3f-3h reduced the immobility time of mice as assessed in the tail suspension test. Moreover, in the modified forced swimming tests, the same compounds significantly decreased the immobility, but increased the swimming frequencies of mice, without any alteration in the climbing frequencies. These results, similar to the results induced by the reference drug fluoxetine (20 mg/kg, po), indicated the antidepressant-like activities of the compounds 3c, 3d, 3f-3h. Owing to the fact that test compounds did not induce any significant alteration in the total number of spontaneous locomotor activities, the antidepressant-like effects of these derivatives seemed to be specific. In order to predict ADME parameters of the synthesized compounds 3a-3h, some physicochemical parameters were calculated. The ADME prediction study revealed that all synthesized compounds may possess good pharmacokinetic profiles.

Expression of small RNAs in Rhizobiales and protection of a small RNA and its degradation products by Hfq in Sinorhizobium meliloti.

Regulatory RNA plays a pivotal role in the regulation of bacterial gene expression. Here, five small RNAs were studied in Sinorhizobium meliloti - SmrC15, SmrC16, Sra33, 6S and the signal recognition particle (SRP) RNA, which are conserved among at least seven different Rhizobium and Sinorhizobium species. The amount of SmrC16 decreased in stationary phase, while the other RNAs were up-regulated. The smallest changes, maximally 2-fold, were observed for 6S RNA. In the distantly related Bradyrhizobium japonicum, the amount of 6S RNA was not increased in stationary phase, suggesting some functional divergence in the roles of this molecule in Rhizobiales in comparison to Escherichia coli. Different decay rates were observed for SmrC15 and SmrC16 of S. meliloti upon rifampicin treatment, revealing posttranscriptional regulation during growth. The use of a Deltahfq mutant showed that Hfq protects full-length SmrC16 from degradation and stabilises its specific degradation products.