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.

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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.

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Early diagnosis of autism certainly stands as one of the most important determinants to ensure a better prognosis of the disorder, it is common that the screening programs to ensure this, end up not being implemented in health systems of many countries. This may stem from the disadvantages of classically suggested scale‐based screening (SBS) programs. This study presents a nationwide recognition and referral model for early diagnosis of autism spectrum disorders (ASD), in order to meet the obvious need for new methods. The model consists of interactive video‐based training (IVBAT) of health care workers (HCW), a system where family physicians (FPs) record five probable indicators of autism in their family medicine information system; and is therefore, a practical referral system in which the FP may refer a child with any suggestive finding to a child psychiatrist and may well continue to monitor recently diagnosed cases. The autism teams consist of trained child psychiatrists and coordinators, who delivered trainings to 29,612 FPs and 23,511 nurses countrywide. Of 98.8% FPs were trained throughout the country. Total of 1,863,096 children were reported to have a brief examination of autism signs in Family Medicine Units by trained FPs and nurses. A total of 55,314 (2.96%) these children were deemed at risk for ASD and were referred to child psychiatrists. In the evaluation of 55,314 children by child psychiatrists, 10,087 cases were diagnosed with developmental disorders, while 3226 of children at risk were diagnosed with autism. The results of this study, which reached to the largest sample to date, suggest that some other alternative methods, in addition to SBS should also be tested in order to screen ASD. Lay Summary In this study, a nationwide recognition and referral model for early identification of autism spectrum disorders (ASD) is presented. Scale‐based screening (SBS) is the most recommended model for autism, however, it is clear that most countries can not implement this model in their health system. The results of this study, which reached to the largest sample to date, suggest that SBS may not be the only me for screening ASD and that alternative methods should be tried, as there is an obvious need for exploratory approaches.

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