The mean Kappa of 1000 iteration tests was 0.49 ( confidence interval   ( CI ) = [ 0.23 , 0.74 ] ) when comparing three dermatologists and our model, which is comparable to the agreement in similarity assessment among the dermatologists themselves (the mean Kappa of 1000 iteration tests for three dermatologists was 0.48, CI = [ 0.19 , 0.77 ] .) By contrast, the mean Kappa was 0.22 ( CI = [ - 0.00 , 0.43 ] ) when comparing the similarity assessments of three dermatologists and random guesses. Conclusions Our study showed that our image feature-engineering-based algorithm can effectively assess the similarity of moles as dermatologists do. Such a similarity assessment could serve as the foundation for computer-assisted intra-patient evaluation of moles.Purpose To assess acute ischemic stroke (AIS) severity, infarct is segmented using computed tomography perfusion (CTP) software, such as RAPID, Sphere, and Vitrea, relying on contralateral hemisphere thresholds. Since this approach is potentially patient dependent, we investigated whether convolutional neural networks (CNNs) could achieve better performances without the need for contralateral hemisphere thresholds. Approach CTP and diffusion-weighted imaging (DWI) data were retrospectively collected for 63 AIS patients. Cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak, mean-transit-time (MTT), and delay time maps were generated using Vitrea CTP software. U-net shaped CNNs were developed, trained, and tested for 26 different input CTP parameter combinations. Infarct labels were segmented from DWI volumes registered with CTP volumes. Infarct volumes were reconstructed from two-dimensional CTP infarct segmentations. To remove erroneous segmentations, conditional random field (CRF) postprocessing was applied and compared with prior results. Spatial and volumetric infarct agreement was assessed between DWI and CTP (CNNs and commercial software) using median infarct difference, median absolute error, dice coefficient, positive predictive value. Results The most accurate combination of parameters for CNN segmenting infarct using CRF postprocessing was CBF, CBV, and MTT (4.83 mL, 10.14 mL, 0.66, 0.73). Commercial software results are RAPID = (2.25 mL, 21.48 mL, 0.63, 0.70), Sphere = (7.57 mL, 17.74 mL, 0.64, 0.70), Vitrea = (6.79 mL, 15.28 mL, 0.63, 0.72). Conclusions Use of CNNs with multiple input perfusion parameters has shown to be accurate in segmenting infarcts and has the ability to improve clinical workflow by eliminating the need for contralateral hemisphere comparisons.Purpose A brain tumor is deadly as its exact extraction is tricky. However, at times, its removal is the only way to save a patient, leaving very little room for the doctors to make a mistake. Image segmentation algorithms can be used to detect tumor in magnetic resonance imaging (MRI). Irregularity in size, location, and shape of tumor in brain with imbalanced distribution of classes in the dataset make this a challenging task. To deal with these challenges, a region of interest (ROI) is extracted from images by removing redundant information. Approach We present a process to extract ROIs by converting images into neutrosophic domain. Two modalities FLAIR and T2 were diffused to reduce inhomogeneity in nontumorous regions and then anisotropic diffusion is applied to reduce the noise. The ROIs, which are tumorous regions, were extracted using neutrosophic technique based on the modified S-function. The extracted ROIs were refined by applying the morphological operations in the end. Results We evaluated our proposed method using three datasets including BraTS 2019 and compared the results with state-of-the-art methods. The parameters sensitivity, false negative rate, and ratio of ROI area to slice area were calculated to evaluate the proposed method. These parameters indicate that the proposed method achieved more than 98% sensitivity, 1.5% false negative rate, and removed more than 80% redundancy. Conclusions Evaluating parameters indicate that the method proposed has removed most of the redundant data from MRI images and extracted ROIs are composed of tumorous region.Osteosarcoma (OS) is a familiar malignant bone tumor that occurs mainly in adolescents. Immature colon carcinoma transcript-1 (ICT1) is an important member of the large mitoribosomal subunit in mitochondrial ribosomes, which has been shown to be closely related to tumorigenesis. Its expression and function in OS, however, remained unclear. Here, we showed that ICT1 was significantly upregulated in OS and promoted the growth of OS cells. Mechanistically, ICT1 acted as an oncogene in OS and promoted proliferation and inhibited apoptosis of OS cells through the STAT3/BCL-2 axis. These results reveal a novel insight into the role of the ICT1/STAT3/BCL-2 axis in OS and therefore may represent a novel molecular target for novel treatments.Bronchodilator reversibility (BDR) is often used as a diagnostic test for adult asthma. However, there has been limited assessment of its diagnostic utility. We aimed to determine the discriminatory accuracy of common BDR cut-offs in the context of current asthma and asthma-COPD overlap (ACO) in a middle-aged community sample. The Tasmanian Longitudinal Health Study is a population-based cohort first studied in 1968 (n=8583). In 2012, participants completed respiratory questionnaires and spirometry (n=3609; mean age 53 years). Receiver operating characteristic (ROC) curves were fitted for current asthma and ACO using continuous BDR measurements. Diagnostic parameters were calculated for different categorical cut-offs. Area under the ROC curve (AUC) was highest when BDR was expressed as change in forced expiratory volume in 1 s (FEV1) as a percentage of initial FEV1, as compared with predicted FEV1. The corresponding AUC was 59% (95% CI 54-64%) for current asthma and 87% (95% CI 81-93%) for ACO. Of the categorical cut-offs examined, the European Respiratory Society/American Thoracic Society threshold (≥12% from baseline and ≥200 mL) was assessed as providing the best balance between positive and negative likelihood ratios (LR+ and LR-, respectively), with corresponding sensitivities and specificities of 9% and 97%, respectively, for current asthma (LR+ 3.26, LR- 0.93), and 47% and 97%, respectively, for ACO (LR+ 16.05, LR- 0.55). U0126 manufacturer With a threshold of ≥12% and ≥200 mL from baseline, a positive BDR test provided a clinically meaningful change in the post-test probability of disease, whereas a negative test did not. BDR was more useful as a diagnostic test in those with co-existent post-bronchodilator airflow obstruction (ACO).U0126 manufacturer