Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique

Objectives/Hypothesis: To develop a deep-learning–based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P <.001), polyps (91% vs. 86%, P <.001), leukoplakia (91% vs. 65%, P <.001), and malignancy (90% vs. 54%, P <.001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. Level of Evidence: NA Laryngoscope, 130:E686–E693, 2020

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Tuning Dielectric Loss of SiO2@CNTs for Electromagnetic Wave Absorption

We developed a simple method to fabricate SiO2-sphere-supported N-doped CNTs (NCNTs) for electromagnetic wave (EMW) absorption. EMW absorption was tuned by adsorption of the organic agent on the precursor of the catalysts. The experimental results show that the conductivity loss and polarization loss of the sample are improved. Meanwhile, the impedance matching characteristics can also be adjusted. When the matching thickness was only 1.5 mm, the optimal 3D structure shows excellent EMW absorption performance, which is better than most magnetic carbon matrix composites. Our current approach opens up an effective way to develop low-cost, high-performance EMW absorbers

Importation of Mumps Virus Genotype K to China from Vietnam

During May–August 2016, mumps virus genotype K was detected in 12 Vietnam citizens who entered China at the Shuikou border crossing and 1 girl from China. We provide evidence that mumps genotype K is circulating in Vietnam and was imported to China from Vietnam

Establishing a tumor-suppressive microenvironment with glycine and valine supplement

A tumorigenic microenvironment can give rise to neoplasm. A shift from this condition to a tumor-suppressive microenvironment is of significant benefit to susceptible individuals. The carbonyl groups of glycine and valine have long bond lengths, consequently generating potent affinities to divalent cations such as calcium. We hypothesize that the formation of insoluble and rigid calcium oxalate augmented by glycine and valine counteracts strong acids such as HCl chemically, thus reducing cancer risks. The anticancer effects of the two amino acids reported previously can be explained from a chemical and biochemical perspective. A tumor-suppressive microenvironment could be established via the modification of the proteome without genome editing at the DNA level

Isolation of novel sequences targeting highly variable viral protein hemagglutinin

Rapid evolution is a hallmark of the viral kingdom and a major concern for developing universal vaccines. The isolation of substantial numbers of viral sequence variants at highly variable viral protein domains remains a major challenge. We previously developed a combinatorial method for the isolation of novel sequences to cope with rapid viral variations at the G-H loop of Foot and Mouth Disease virus VP1 protein [1]. Here we present a modification of that method in its application in the randomization of the hemagglutinin gene from a H5N2 virus, namely: • removal of potentially stressful region which harbored a stretch of basic amino acids to increase the success rates of gene cloning, and to streamline the process of future engineering of novel viral variants. • clustered randomization in a full-length gene, as the positive rate for partial gene fragment libraries was extremely low before enrichment in the previous FMDV studies. • the use of fusion partner was avoided, which was used previously for protein expression, stabilization of clones and reduction of stresses on host cells. • the use of Poisson distribution is proposed to approximate sequencing output to achieve cost effectiveness

Celastrol improves the therapeutic efficacy of EGFR-TKIs for non-small-cell lung cancer by overcoming EGFR T790M drug resistance

International audienceThe development of resistance to therapy continues to be a serious clinical problem in lung cancer management. Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) is one of the most common chemotherapy drugs to treat non-small-cell lung cancer. However, almost all treatments fail after ∼1 year of treatment because of drug tolerance, probably occurring from the threonine 790 mutation (T790M) of the EGFR, resulting in overactivation of the EGFR. Celastrol is a natural compound that exhibits antiproliferative activity. In this study, we showed that celastrol combined with EGFR-TKIs significantly suppressed cell invasion of lung cancer cells with a T790M mutation by suppressing the EGFR pathway. Combined therapy with celastrol and EGFR-TKIs inhibited tumor growth in vivo. Together, these results suggested that combined therapy with EGFR-TKIs and celastrol may be a more effective treatment of patients with non-small-cell lung cancer with T790M mutations of the EGFR

A Combinatorial Yeast Overlay Method for the Isolation of Antibacterial Oligopeptides

The abuse of antibiotics has led to the emergence of many resistant bacteria, which threaten public health on a global scale. Conventional pipelines for antibiotic discovery are gradually exhausted. Here a combinatorial yeast overlay method is presented for the isolation of antibacterial oligopeptides. Three antibacterial dipeptide and tripeptides were discovered from 20 attempts, two of which possess glycosylation sites. Although the antibacterial effects were modest against several tested bacteria including a multi-resistant Staphylococcus aureus strain Y5, they are amenable to chemical modifications to enhance potency. This cost-effective method can circumvent the expensive chemical peptide synthesis process. The eukaryotic host Saccharomyces cerevisiae adopted for peptide expression could serve as a surrogate biosafety assay system for antibacterial peptides. The small size of the oligopeptides render them attractive candidates for drug development. Given the vast combinatorial diversity of peptides, the reservoir may serve as a rich source of drug leads