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A high classification accuracy of 99.99% is achieved for both validation techniques. This is one of the first few studies to have employed deep learning algorithm coupled with ECG signals for the detection of HPT. Our results imply that the developed tool is useful in a hospital setting as an automated diagnostic tool, enabling the effortless detection of HPT using ECG signals.Most colorectal cancer (CRC) are characterized by allele loss of the genes located on the short arm of chromosome 17 (17p13.1), including the tumor suppressor p53 gene. Although important, p53 is not the only driver of chromosome 17p loss. In this study, we explored the biological and prognostic significance of genes around p53 on 17p13.1 in CRC. The Cancer Genome Atlas (TCGA) were used to identify differentially expressed genes located between 1000 kb upstream and downstream of p53 gene. The function of CLDN7 was evaluated by both in vitro and in vivo experiments. Quantitative real-time PCR, western blot, and promoter luciferase activity, immunohistochemistry were used to explore the molecular drivers responsible for the development and progression of CRC. The results showed that CLDN7, located between 1000 kb upstream and downstream of p53 gene, were remarkably differentially expressed in tumor and normal tissues. CLDN7 expression also positively associated with p53 level in different stages of the adenoma-carcinoma sequence. Both in vitro and in vivo assays showed that CLDN7 inhibited cell proliferation in p53 wild type CRC cells, but had no effects on p53 mutant CRC cells. Mechanistically, p53 could bind to CLDN7 promoter region and regulate its expression. Clinically, high CLDN7 expression was negatively correlated with tumor size, invasion depth, lymphatic metastasis and AJCC III/IV stage, but was positively associated with favorable prognosis of CRC patients. Collectively, our work uncovers the tumor suppressive function for CLDN7 in a p53-dependent manner, which may mediate colorectal tumorigenesis induced by p53 deletion or mutation.

This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.

The CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).

This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.

This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.Cortisol concentration of hair (HCC) is an established biomarker in stress research that can provide valuable retrospective information on subjects’ long-term cortisol levels. Using a population-wide sample of in total N = 482 participants this study aimed to examine whether there are differences in HCC when participants collect the required samples by themselves with the help of a partner in domestic settings compared to professionally collected hair strands in the lab. Potential confounding factors that may affect HCC and might obfuscate the outcomes were considered. The results suggest that the two compared sample collection methods did not significantly differ from each other in terms of HCC (p = .307). A somewhat larger sample loss in the domestic setting was observed due to hair samples where HCC could not be determined (5.3 % vs. 1.8 % in the lab). Similarly, in a sample of N = 50 using a within-subjects design (Sample 2) no significant HCC differences between collection methods occurred (p = .206). In addition, potential moderating effects of personality traits of the Five-Factor-Model on the relationship between hair collection method and HCC were investigated. In Sample 1 personality data of the hair donor were available, while in Sample 2 personality data (n = 40) were available for the hair donor and the hair sample collector. Interestingly, none of the Big Five traits significantly moderated the relationship between HCC and hair collection method (all p > .20). Overall, these findings suggest that the self-collection of hair in domestic settings is a viable and economical method for measuring long-term cortisol concentrations in hair.Cell diversity in a multicellular organism relies on cell-cell communication where cells must receive positional information as input signals to adopt their proper cell fate in the right place and at the right time. This process is achieved through triggering signaling cascades that drive cellular changes during development. In plants, signaling through mobile transcription factors (TF) plays a central role in development. Rather than acting cell-autonomously and exclusive to their expression domains, many TFs move between cells and deploy regulatory networks and cell type-specific effectors to achieve their biological functions. Selleck JNJ-75276617 Here, we highlight a few examples of mobile TFs central to cell fate specification in Arabidopsis.