Evaluation of Vertical Level Differences Between Left and Right Vocal Folds Using Artificial Intelligence System in Excised Canine Larynx.

OBJECTIVES: This study aimed to establish an artificial intelligence (AI) system to classify vertical level differences between vocal folds during vocalization and to evaluate the accuracy of the classification. METHODS: We designed models with different depths between the right and left vocal folds using an excised canine larynx. Video files for the data set were obtained using a high-speed camera system and a color complementary metal oxide semiconductor camera with global shutter. The data sets were divided into training, validation, and testing. We used 20,000 images for building the model and 8000 images for testing. To perform deep learning multiclass classification and to estimate the vertical level difference, we introduced DenseNet121-ConvLSTM. RESULTS: The model was trained several times using different numbers of epochs. We achieved the most optimal results at 100 epochs, and the batch size used during training was 16. The proposed DenseNet121-ConvLSTM model achieved classification accuracies of 99.5% and 88.0% for training and testing, respectively. After verification using an external data set, the overall accuracy, precision, recall, and f1-score were 90.8%, 91.6%, 90.9%, and 91.2%, respectively. CONCLUSIONS: The newly developed AI system may be an easy and accurate method for classifying superior and inferior vertical level differences between vocal folds. Thus, this AI system can be applied and may help in the assessment of vertical level differences in patients with unilateral vocal fold paralysis.

Stereoselective Alder-Ene Reactions of Bicyclo[1.1.0]butanes: Facile Synthesis of Cyclopropyl- and Aryl-Substituted Cyclobutenes.

Bicyclo[1.1.0]butanes (BCBs), strained carbocycles comprising two fused cyclopropane rings, have become well-established building blocks in organic synthesis, medicinal chemistry, and chemical biology due to their diverse reactivity profile with radicals, nucleophiles, cations, and carbenes. The constraints of the bicyclic ring system confer high p-character on the interbridgehead C-C bond, leading to this broad reaction profile; however, the use of BCBs in pericyclic processes has to date been largely overlooked in favor of such stepwise, non-concerted additions. Here, we describe the use of BCBs as substrates for ene-like reactions with strained alkenes and alkynes, which give rise to cyclobutenes decorated with highly substituted cyclopropanes and arenes. The former products are obtained from highly stereoselective reactions with cyclopropenes, generated in situ from vinyl diazoacetates under blue light irradiation (440 nm). Cyclobutenes featuring a quaternary aryl-bearing carbon atom are prepared from equivalent reactions with arynes, which proceed in high yields under mild conditions. Mechanistic studies highlight the importance of electronic effects in this chemistry, while computational investigations support a concerted pathway and rationalize the excellent stereoselectivity of reactions with cyclopropenes.

Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology.

Introduction: Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology. Methods: The original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset - MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin-stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique. Results: To prioritize the performance of nuclei segmentation, we evaluated the model's outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 - 96.6), 81.8 (95% CI: 80.8 - 83.0), and 69.3 (95% CI: 68.2 - 70.0), respectively, on the internal test data set. Conclusion: Our proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis.

Lewis acid-catalyzed diastereoselective carbofunctionalization of bicyclobutanes employing naphthols.

Traditional radical-mediated ring-opening of bicyclo[1.1.0]butanes (BCBs) for cyclobutane synthesis suffers from poor diastereoselectivity. Although few reports on BCB ring-opening via polar mechanisms are available, the Lewis acid-catalyzed diastereoselective ring-opening of BCBs using carbon nucleophiles is still underdeveloped. Herein, we report a mild and diastereoselective Bi(OTf)3-catalyzed ring-opening of BCBs employing 2-naphthols. The anticipated carbofunctionalized trisubstituted cyclobutanes were obtained via a bicoordinated bismuth complex and the products are formed in good to excellent yields with high regio- and diastereoselectivity. The scope of the reaction was further extended using electron-rich phenols and naphthylamine. The functionalization of the synthesized trisubstituted cyclobutanes shows the synthetic utility of the present method.

Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism.

Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist's level of expertise. In this research, we implemented a DL model using transfer learning on a set of histopathological images to segment cancerous and noncancerous areas in whole-slide images (WSIs). In this approach, the proposed Ensemble U-net model was applied for the segmentation of stroma, cancerous, and benign areas. The WSI dataset of prostate cancer was collected from the Kaggle repository, which is publicly available online. A total of 1000 WSIs were used for region segmentation. From this, 8100 patch images were used for training, and 900 for testing. The proposed model demonstrated an average dice coefficient (DC), intersection over union (IoU), and Hausdorff distance of 0.891, 0.811, and 15.9, respectively, on the test set, with corresponding masks of patch images. The manipulation of the proposed segmentation model improves the ability of the pathologist to predict disease outcomes, thus enhancing treatment efficacy by isolating the cancerous regions in WSIs.

Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model.

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently.

Synthesis of Trisubstituted Oxazoles via Aryne Induced [2,3] Sigmatropic Rearrangement-Annulation Cascade.

A transition-metal-free, [2,3] sigmatropic rearrangement-annulation cascade of 2-substituted thio/amino acetonitriles with arynes allowing the synthesis of 2,4,5-trisubstituted oxazoles under mild conditions has been demonstrated. The key sulfur/nitrogen ylides were generated by the initial S/N arylation followed by proton transfer, which was followed by the selective [2,3] sigmatropic rearrangement involving the -CN moiety and a subsequent annulation to afford the desired products in reasonable yields.

Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques.

OBJECTIVE: A primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normal cells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical features of brain tumors and to perform a classification using artificial intelligence (AI) techniques. METHODS: AI techniques can help radiologists to diagnose primary brain tumors without using any invasive measurement techniques. In this paper, we focused on deep learning (DL) and machine learning (ML) techniques for texture, morphological, and statistical feature classification of three tumor types (namely, glioma, meningioma, and pituitary). T1-weighted magnetic resonance imaging (MRI) 2D scans were used for analysis and classification (multiclass and binary). A total of 102 features were calculated for each tumor, and the 20 most significant features were selected using the three-step feature selection method, which included removing duplicate features, Pearson correlations, and recursive feature elimination. RESULTS: From the predicted results of multiclass and binary classification, a long short-term memory binary classification (glioma vs. meningioma) showed the best performance, with an average accuracy, recall, precision, F1-score, and kappa coefficient of 97.7%, 97.2%, 97.5%, 97.0%, and 94.7%, respectively. CONCLUSIONS: The early diagnosis of primary brain tumors is very important because it can be the key to effective treatment. Therefore, this research presents a method for early diagnoses by effectively classifying three types of primary brain tumors.

Aryne Three-Component Coupling Involving CS2 for the Synthesis of S-Aryl Dithiocarbamates.

A facile synthesis of biologically important S-aryl dithiocarbamates has been demonstrated by the aryne three-component coupling involving CS2 and aliphatic amines. This transition-metal-free and mild reaction is scalable and operates with good functional group compatibility. Preliminary mechanistic experiments, including density functional theory studies, are also provided. Moreover, with 3-triflyloxybenzynes, a unique four-component coupling incorporating tetrahydrofuran was observed.

Transition-Metal-Free C2-Functionalization of Pyridines through Aryne Three-Component Coupling.

The direct C2-functionalization of pyridines through a transition-metal-free protocol by using aryne multicomponent coupling is demonstrated. The reaction allowed a broad-scope synthesis of C2-substituted pyridine derivatives bearing the -CF3 group in good yields with α,α,α-trifluoroacetophenones as the third component. Activated keto esters could also be employed as the third component in this formal 1,2-di(hetero)arylation of ketones. Performing the reaction under dilute conditions inhibited the competing pyridine-aryne polymerization pathway. Nucleophilic attack by the initially generated pyridylidene intermediate on the carbonyl followed by an SN Ar process resembling the Smiles rearrangement affords the desired products.

Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering.

The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.

An Umpolung Oxa-[2,3] Sigmatropic Rearrangement Employing Arynes for the Synthesis of Functionalized Enol Ethers.

An oxa-[2,3] sigmatropic rearrangement involving arynes is reported featuring the umpolung of ketones, where the C═O bond polarity is reversed. The in situ-generated sulfur ylides from ß-keto thioethers and arynes undergo efficient rearrangement allowing the facile and robust synthesis of functionalized enol ethers in high yields and excellent functional group compatibility. Preliminary mechanistic studies rule out the possibility of Pummerer-type rearrangement operating in this case.

Diagnosing Alzheimer's Disease Based on Multiclass MRI Scans using Transfer Learning Techniques.

AIMS: To prevent Alzheimer's disease (AD) from progressing to dementia, early prediction and classification of AD are important and they play a crucial role in medical image analysis. BACKGROUND: In this study, we employed a transfer learning technique to classify magnetic resonance (MR) images using a pre-trained convolutional neural network (CNN). OBJECTIVE: To address the early diagnosis of AD, we employed a computer-assisted technique, specifically the deep learning (DL) model, to detect AD. METHODS: In particular, we classified Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects using whole slide two-dimensional (2D) images. To illustrate this approach, we made use of state-of-the-art CNN base models, i.e., the residual networks Res- Net-101, ResNet-50, and ResNet-18, and compared their effectiveness in identifying AD. To evaluate this approach, an AD Neuroimaging Initiative (ADNI) dataset was utilized. We also showed uniqueness by using MR images selected only from the central slice containing left and right hippocampus regions to evaluate the models. RESULTS: All three models used randomly split data in the ratio of 70:30 for training and testing. Among the three, ResNet-101 showed 98.37% accuracy, better than the other two ResNet models, and performed well in multiclass classification. The promising results emphasize the benefit of using transfer learning, specifically when the dataset is low. CONCLUSION: From this study, we know that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD.

A Paradigm Shift in Nuclear Chromatin Interpretation: From Qualitative Intuitive Recognition to Quantitative Texture Analysis of Breast Cancer Cell Nuclei.

Assessing the pattern of nuclear chromatin is essential for pathological investigations. However, the interpretation of nuclear pattern is subjective. In this study, we performed the texture analysis of nuclear chromatin in breast cancer samples to determine the nuclear pleomorphism score thereof. We used three different algorithms for extracting high-level texture features: the gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and gray-level size zone matrix (GLSZM). Using these algorithms, 12 GLCM, 11 GLRLM, and 16 GLSZM features were extracted from three scores of breast carcinoma (Scores 1-3). Classification accuracy was assessed using the support vector machine (SVM) and k-nearest neighbor (KNN) classification models. Three features of GLCM, 11 of GLRLM, and 12 of GLSZM were consistent across the three nuclear pleomorphism scores of breast cancer. Comparing Scores 1 and 3, the GLSZM feature large zone high gray-level emphasis showed the largest difference among breast cancer nuclear scores among all features of the three algorithms. The SVM and KNN classifiers showed favorable results for all three algorithms. A multiclass classification was performed to compare and distinguish between the scores of breast cancer. Texture features of nuclear chromatin can provide useful information for nuclear scoring. However, further validation of the correlations of histopathologic features, and standardization of the texture analysis process, are required to achieve better classification results. © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques.

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

Thiophenols as Protic Nucleophilic Triggers in Aryne Three-Component Coupling.

The synthetic potential of thiophenols as a protic nucleophilic trigger in the transition-metal-free and Grignard-reagent-free three-component coupling involving arynes is demonstrated. Employing aldehydes as the third component, the reaction allowed the mild and broad scope synthesis of 2-arylthio benzyl alcohol derivatives in good yields. Moreover, selenophenol could be used as the nucleophilic trigger, and activated ketones could be used as the third component in this reaction.

Rapid Synthesis of Zwitterionic Phosphonium Benzoates by a Three-Component Coupling Involving Phosphines, Arynes and CO2.

A mild and easy to perform multicomponent coupling involving phosphines, arynes generated from 2-(trimethylsilyl)aryl triflates, and CO2 allowing the transition-metal-free synthesis of zwitterionic phosphonium benzoates has been developed. The reaction proceeds via the generation of 1 : 1 zwitterionic intermediates from phosphines and arynes followed by the interception with CO2 to deliver the carboxylates in moderate to good yields instead of the anticipated benzooxaphosphol-3(1H)-ones.

Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

BACKGROUND: In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain. METHODS: The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images. RESULTS: The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC. CONCLUSION: The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features.

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.

Three-Component Aminoselenation of Arynes.

The three-component coupling of tertiary amines, arynes, and aryl selenium bromide or diaryl diselenide as an electrophilic selenium source allowing the synthesis of 2-selanyl aniline derivatives is reported. This aminoselenation reaction of arynes installs a C-N and C-Se bond under mild conditions, and the products are formed in moderate to good yields. This reaction is compatible with various functional groups, and the preliminary studies on the mechanism of the reaction is also provided.