SARS-CoV-2 genome clusters analyzed by Deep Learning

We report on a method for analyzing the variant of coronavirus genes using autoencoder. Since coronaviruses have mutated rapidly and generated a large number of genotypes, an appropriate method for understanding the entire population is required. The method using autoencoder meets this requirement and is suitable for understanding how and when the variants emarge and disappear. For the over 30,000 SARS-CoV-2 ORF1ab gene sequences sampled globally from December 2019 to February 2021, we were able to represent a summary of their characteristics in a 3D plot and show the expansion, decline, and transformation of the virus types over time and by region. Based on ORF1ab genes, the SARS-CoV-2 viruses were classified into five major types (A, B, C, D, and E in the order of appearance): the virus type that originated in China at the end of 2019 (type A) practically disappeared in June 2020; two virus types (types B and C) have emerged in the United States and Europe since February 2020, and type B has become a global phenomenon. Type C is only prevalent in the U.S. and is suspected to be associated with high mortality, but this type also disappeared at the end of June. Type D is only found in Australia. Currently, the epidemic is dominated by types B and E

An autoencoder-classified cluster of SARS-CoV-2 strain with two mutations in helicase

Using an autoencoder-based analysis to classify genomes of SARS-CoV-2 coronaviruses, we found a cluster consisting only of a specific genotype with two mutations in the helicase. This virus genotype, called C-type SARS-CoV-2, was almost exclusively prevalent in the United States from March to July 2020. This type of virus, characterized by a pair of the C17747T (P504L) and A17858G (Y541C) mutations on the nsp13 gene, had never been highly prevalent at any other time or in any other part of the world. In the U.S., Washington State was the center of the epidemic, and the C-type viruses, along with the viruses with wild-type helicase, seemed to have aroused the pandemic. In Washington State, USA, the CoViD-19 epidemic during the first two months of the year, starting at the end of February 2020, was mainly caused by the type-C virus. During this period, the infection spread rapidly; from May onwards, the number of viruses with wild-type helicases became higher than that of type-C viruses, and no type-C viruses have been collected since early July. The involvement of the helicase in this COVID-19 disease was discussed

Photo-Induced Cell Damage Analysis for Single- and Multifocus Coherent Anti-Stokes Raman Scattering Microscopy

In this study, we investigated photo-induced damage to living cells during single- and multifocus excitations for coherent anti-Stokes Raman scattering (CARS) imaging. A near-infrared pulsed laser (709 nm) was used to induce cell damage. We compared the photo-induced cell damage in the single- and the multifocus excitation schemes with the condition to obtain the same CARS signal in the same frame rate. For the evaluation of cell viability, we employed 4',6-diamidino-2-phenylindole (DAPI) fluorophores that predominantly stained the damaged cells. One- and two-photon fluorescence of DAPI fluorophores were, respectively, excited by an ultraviolet light source and the same near-infrared light source and were monitored to evaluate the cell viability during near-infrared pulsed laser irradiation. We found lower uptake of DAPI fluorophores into HeLa cells during the multifocus excitation compared with the single-focus excitation scheme in both the one- and the two-photon fluorescence examinations. This indicates a reduction of photo-induced cell damage in the multifocus excitation. Our findings suggested that the multifocus excitation scheme is expected to be suitable for CARS microscopy in terms of minimal invasiveness

Photo-Induced Cell Damage Analysis for Single- and Multifocus Coherent Anti-Stokes Raman Scattering Microscopy

In this study, we investigated photo-induced damage to living cells during single-and multifocus excitations for coherent anti-Stokes Raman scattering (CARS) imaging. A near-infrared pulsed laser (709 nm) was used to induce cell damage. We compared the photo-induced cell damage in the single- and the multifocus excitation schemes with the condition to obtain the same CARS signal in the same frame rate. For the evaluation of cell viability, we employed 4', 6-diamidino-2-phenylindole (DAPI) fluorophores that predominantly stained the damaged cells. One-and two-photon fluorescence of DAPI fluorophores were, respectively, excited by an ultraviolet light source and the same near-infrared light source and were monitored to evaluate the cell viability during near-infrared pulsed laser irradiation. We found lower uptake of DAPI fluorophores into HeLa cells during the multifocus excitation compared with the single- focus excitation scheme in both the one- and the two-photon fluorescence examinations. This indicates a reduction of photo-induced cell damage in the multifocus excitation. Our findings suggested that the multifocus excitation scheme is expected to be suitable for CARS microscopy in terms of minimal invasiveness

Convolutional neural network can recognize drug resistance of single cancer cells

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P>. 11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P=. 98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P=. 0005), but significantly superior specificity (P=. 02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists

Improvement of nerve imaging speed with coherent anti-Stokes Raman scattering rigid endoscope using deep-learning noise reduction

A coherent anti-Stokes Raman scattering (CARS) rigid endoscope was developed to visualize peripheral nerves without labeling for nerve-sparing endoscopic surgery. The developed CARS endoscope had a problem with low imaging speed, i.e. low imaging rate. In this study, we demonstrate that noise reduction with deep learning boosts the nerve imaging speed with CARS endoscopy. We employ fine-tuning and ensemble learning and compare deep learning models with three different architectures. In the fine-tuning strategy, deep learning models are pre-trained with CARS microscopy nerve images and retrained with CARS endoscopy nerve images to compensate for the small dataset of CARS endoscopy images. We propose using the equivalent imaging rate (EIR) as a new evaluation metric for quantitatively and directly assessing the imaging rate improvement by deep learning models. The highest EIR of the deep learning model was 7.0 images/min, which was 5 times higher than that of the raw endoscopic image of 1.4 images/min. We believe that the improvement of the nerve imaging speed will open up the possibility of reducing postoperative dysfunction by intraoperative nerve identification

Invited Article: Label-free nerve imaging with a coherent anti-Stokes Raman scattering rigid endoscope using two optical fibers for laser delivery

A coherent anti-Stokes Raman scattering (CARS) rigid endoscope using two optical fibers to deliver excitation beams individually is developed. The use of two optical fibers allows the correction of longitudinal chromatic aberration and enhances the CARS signal by a factor of 2.59. The endoscope is used to image rat sciatic nerves with an imaging time of 10 s. Imaging of the rabbit prostatic fascia without sample slicing is also demonstrated, which reveals the potential for the application of the CARS endoscope to robot-assisted surgery

Coherent anti-Stokes Raman scattering rigid endoscope toward robot-assisted surgery

Label-free visualization of nerves and nervous plexuses will improve the preservation of neurological functions in nerve-sparing robot-assisted surgery. We have developed a coherent anti-Stokes Raman scattering (CARS) rigid endoscope to distinguish nerves from other tissues during surgery. The developed endoscope, which has a tube with a diameter of 12 mm and a length of 270 mm, achieved 0.91% image distortion and 8.6% non-uniformity of CARS intensity in the whole field of view (650 μm diameter). We demonstrated CARS imaging of a rat sciatic nerve and visualization of the fine structure of nerve fibers