Patterning amyloid-ß aggregation under the effect of acetylcholinesterase using a biological nanopore - an in vitro study.

Aggregation of amyloid-ß peptide (Aß) is hypothesized to be the primary cause of Alzheimer's disease (AD) progression. Aß aggregation has been widely studied using conventional sensing tools like emission fluorescence, electron microscopy, mass spectroscopy, and circular dichroism. However, none of these techniques can provide cost-efficient, highly sensitive quantification of Aß aggregation kinetics at the molecular level. Among the influences on Aß aggregation of interest to disease progression is the acceleration of Aß aggregation by acetylcholinesterase (AChE), which is present in the brain and inflicts the fast progression of disease due to its direct interaction with Aß. In this work, we demonstrate the ability of a biological nanopore to map and quantify AChE accelerated aggregation of Aß monomers to mixed oligomers and small soluble aggregates with single-molecule precision. This method will allow future work on testing direct and indirect effects of therapeutic drugs on AChE accelerated Aß aggregation as well as disease prognosis.

Quantitation of Circulating Mycobacterium tuberculosis Antigens by Nanopore Biosensing in Children Evaluated for Pulmonary Tuberculosis in South Africa.

Nanopore sensing of proteomic biomarkers lacks accuracy due to the ultralow abundance of targets, a wide variety of interferents in clinical samples, and the mismatch between pore and analyte sizes. By converting antigens to DNA probes via click chemistry and quantifying their characteristic signals, we show a nanopore assay with several amplification mechanisms to achieve an attomolar level limit of detection that enables quantitation of the circulating Mycobacterium tuberculosis (Mtb) antigen ESAT-6/CFP-10 complex in human serum. The assay's nonsputum-based feature and low-volume sample requirements make it particularly well-suited for detecting pediatric tuberculosis (TB) disease, where establishing an accurate diagnosis is greatly complicated by the paucibacillary nature of respiratory secretions, nonspecific symptoms, and challenges with sample collection. In the clinical assessment, the assay was applied to analyze ESAT-6/CFP-10 levels in serum samples collected during baseline investigation for TB in 75 children, aged 0-12 years, enrolled in a diagnostic study conducted in Cape Town, South Africa. This nanopore assay showed superior sensitivity in children with confirmed TB (94.4%) compared to clinical "gold standard" diagnostic technologies (Xpert MTB/RIF 44.4% and Mtb culture 72.2%) and filled the diagnostic gap for children with unconfirmed TB, where these traditional technologies fell short. We envision that, in combination with automated sample processing and portable nanopore devices, this methodology will offer a powerful tool to support the diagnosis of pulmonary TB in children.

A review of deep learning-based detection methods for COVID-19.

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.

An Encoder-Decoder-Based Method for Segmentation of COVID-19 Lung Infection in CT Images.

The novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen-Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78..6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.