COVID-19's Radiologic, Functional, and Serologic Consequences at 6-Month and 18-Month Follow-up: A Prospective Cohort Study.

BACKGROUND: We evaluated the radiologic, pulmonary functional, and antibody statuses of coronavirus disease 2019 (COVID-19) patients 6 and 18 months after discharge, comparing changes in status and focusing on risk factors for residual computed tomography (CT) abnormalities. METHODS: This prospective cohort study was conducted on COVID-19 patients discharged between April 2020 and January 2021. Chest CT, pulmonary function testing (PFT), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) measurements were performed 6 and 18 months after discharge. We evaluated factors associated with residual CT abnormalities and the correlation between lesion volume in CT (lesionvolume), PFT, and IgG levels. RESULTS: This study included 68 and 42 participants evaluated 6 and 18 months, respectively, after hospitalizations for COVID-19. CT abnormalities were noted in 22 participants (32.4%) at 6 months and 13 participants (31.0%) at 18 months. Lesionvolume was significantly lower at 18 months than 6 months (P < 0.001). Patients with CT abnormalities at 6 months showed lower forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC), and patients with CT abnormalities at 18 months exhibited lower FVC. FVC significantly improved between 6 and 18 months of follow-up (all P < 0.0001). SARS-CoV-2 IgG levels were significantly higher in patients with CT abnormalities at 6 and 18 months (P < 0.001). At 18-month follow-up assessments, age was associated with CT abnormalities (odds ratio, 1.17; 95% confidence interval, 1.03-1.32; P = 0.01), and lesionvolume showed a positive correlation with IgG level (r = 0.643, P < 0.001). CONCLUSION: At 18-month follow-up assessments, 31.0% of participants exhibited residual CT abnormalities. Age and higher SARS-CoV-2 IgG levels were significant predictors, and FVC was related to abnormal CT findings at 18 months. Lesionvolume and FVC improved between 6 and 18 months. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0008573.

Comparative Analysis of Clinical Characteristics and Prognosis Between Primary Lung Cancers Identified as Screening Detected and Incidentally Detected Pulmonary Nodules.

BACKGROUND/AIM: Although the importance of low-dose computed tomography (LDCT) screening is increasingly emphasized and implemented, many lung cancers continue to be incidentally detected during routine medical practices, and data on incidentally detected lung cancer (IDLC) remain scarce. This study aimed to investigate the clinical characteristics and prognosis of IDLCs by comparing them with screening-detected lung cancers (SDLCs). PATIENTS AND METHODS: In this retrospective study, subjects with cT1 (≤3 cm) pulmonary nodules detected on baseline computed tomography (CT), later pathologically confirmed as primary lung cancer in 2015, were included. Patients were categorized into IDLC and SDLC groups based on the setting of the first pulmonary nodule detection. RESULTS: Out of 457 subjects, 129 (28.2%) were IDLCs and 328 (71.8%) were SDLCs. The IDLC group, consisted of older individuals with a higher prevalence of smokers and underlying pulmonary disease, compared to the SDLC group. Adenocarcinomas were more frequently detected in SDLCs (87.5%) than in IDLCs (76.7%, p<0.001). The time to treatment initiation (TTI) and 5-year overall survival (OS) rates were similar. Multivariate analyses revealed underlying interstitial lung disease, DLCO, solidity of nodules and TNM stage as independent risk factors associated with mortality. Less than 30% of study participants would have been eligible for the current lung cancer screening program. CONCLUSION: The IDLC group was associated with older age, higher rate of smokers, underlying pulmonary disease, and non-adenocarcinoma histology. However, prognosis was similar to that of the SDLC group, attributable to the similarity in TNM stage, strict adherence to guidelines, and short TTI. Furthermore, less than 30% of the participants would have been suitable for the existing lung cancer screening program, indicating a potential need to reconsider the scope for screening candidates.

Albumin-binding recombinant human IL-18BP ameliorates macrophage activation syndrome and atopic dermatitis via direct IL-18 inactivation.

Given the clinical success of cytokine blockade in managing diverse inflammatory human conditions, this approach could be exploited for numerous refractory or uncontrolled inflammatory conditions by identifying novel targets for functional blockade. Interleukin (IL)-18, a pro-inflammatory cytokine, is relatively underestimated as a therapeutic target, despite accumulated evidence indicating the unique roles of IL-18 in acute and chronic inflammatory conditions, such as macrophage activation syndrome. Herein, we designed a new form of IL-18 blockade, i.e., APB-R3, a long-acting recombinant human IL-18BP linked to human albumin-binding Fab fragment, SL335, for extending half-life. We then explored the pharmacokinetics and pharmacodynamics of APB-R3. In addition to an extended serum half-life, APB-R3 alleviates liver inflammation and splenomegaly in a model of the macrophage activation syndrome induced in IL-18BP knockout mice. Moreover, APB-R3 substantially controlled skin inflammation in a model of atopic dermatitis. Thus, we report APB-R3 as a new potent IL-18 blocking agent that could be applied to treat IL-18-mediated inflammatory diseases.

Missed and Detected Incidental Breast Cancers on Contrast Enhanced Chest CT: Detection Rates and CT Features.

This study investigated the rate at which radiologists miss or detect incidental breast cancers on chest CT and to compare the CT features between the two groups. This retrospective study evaluated chest CT examinations and medical records of patients who registered with the diagnosis code of "breast cancer" between January 2016 and December 2020, and who had undergone contrast enhanced chest CT 3-18 months before registration, during which they were unaware of any breast lesions. This study found that out of 84 patients, incidental breast cancer lesions were missed in 54 (64.3%) and detected in 30 (53.7%). The initial treatment was delayed in the missed breast lesions group (p = 0.004). Breast lesions of smaller sizes (<9.0 mm, p = 0.01), or with lower enhancement ratios (<1.4, p = 0.009), were more likely to be missed. When three radiologists re-read the CTs with more attention to breast area, they detected breast cancers with higher accuracies (90.1%, 87.9%, and 81.3%). In summary, this study revealed that radiologists miss 64.3% of incidental breast cancers on chest CT, especially those of sub-centimeter sizes and weak enhancements.

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.

A novel strategy to promote liver regeneration: utilization of secretome obtained from survivin-overexpressing adipose-derived stem cells.

PURPOSE: Survivin is a typical antiapoptotic protein. It is copiously expressed during human fetal development but is infrequently present in adult tissues. In this experiment, we researched the treatment effect of the secretome that adipose-derived stem cells (ASCs) transfected with survivin. METHODS: First of all, we generated survivin-overexpressing ASCs transfected with a plasmid comprising a gene encoding survivin. The secreted substances released from survivin-overexpressing ASCs (survivin-secretome) were collected, and were determined their in vitro and in vivo therapeutic potential, especially in the model of liver impairment. RESULTS: In vitro, the survivin-secretome significantly increased cell viability and promoted the expression of proliferation-related markers (proliferating cell nuclear antigen [PCNA], phospho-signal transducer and activator of transcription 3 (p-STAT3), hepatocyte growth factor [HGF], vascular endothelial growth factor [VEGF]) and anti-apoptosis-related markers (myeloid cell leukemia-1 [Mcl-1] and survivin) (P < 0.05). In vivo using 70% hepatectomy mice, the survivin-secretome group exhibited the lowest serum levels of interleukin-6, tumor necrosis factor-α (P < 0.05). The serum levels of liver transaminases (alanine aminotransferase and aspartate aminotransferase) were also the lowest in the survivin-secretome group (P < 0.05). The survivin-secretome group also exhibited the highest liver regeneration on the 7th day after 70% partial hepatectomy (P < 0.05). In the subsequent liver specimen analysis, the specimens of survivin-secretome exhibited the highest expression of p-STAT3, HGF, VEGF, PCNA, and Mcl-1 and the lowest expression of bcl-2-like protein 4 (P < 0.05). CONCLUSION: Taken together, secretome secreted by survivin-overexpressing ASCs could be an effective way to improve liver regeneration and repair for liver injury treatment.

Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients.

INTRODUCTION: To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). METHODS: We included 85 patients (M:F = 71:14; age, 35-88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots. RESULTS: Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]). CONCLUSION: In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.

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.

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.

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Vasculitis is a systemic disease, characterized by inflammation of the vascular wall. Although rare, it is sometimes life-theatening due to diffuse pulmonary hemorrhage or acute glomerulonephritis. Besides primary vasculitis, whose cause is unknown, numerous conditions such as autoimmune diseases, drugs, infections, and tumors can cause secondary vasculitis. Vasculitis displays various non-specific symptoms, signs, and laboratory findings; hence, diagnosis of the disease requires integration of various results including clinical features, imaging findings, autoantibody tests, and pathological findings. In this review, we have discussed the clinical, radiologic, and pathological features of vasculitis. Further, we elaborated the imaging findings and differential diagnosis of typical vasculitis that frequently involves the lung and introduced a new international classification of vasculitis, the Diagnostic and Classification Criteria in Vasculitis.

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.

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.

Pediatric Deaths Attributed to Complex Chronic Conditions over 10 Years in Korea: Evidence for the Need to Provide Pediatric Palliative Care.

BACKGROUND: Pediatric palliative care (PPC) is the active total care of children suffering from life-threatening illnesses. Palliative care includes symptom management, psychosocial support, and end-of-life care. Despite significant advances in disease diagnosis and treatment, resources for PPC of children with serious illnesses are limited in Korea. This study aimed to investigate the scale, time trends, disease composition, regional distribution, and unmet needs of children dying from complex chronic conditions (CCCs). METHODS: We examined available information on children who died of CCCs from 2005 to 2014 using the cause of death statistics in Korea. RESULTS: There were 36,808 cases of pediatric deaths in Korea during that 10-year period, one-third (12,515 cases, 34.0%) of which were due to CCCs. In 2014, there were 1,044 cases of pediatric deaths due to CCCs (9.8 deaths per 100,000 children) in Korea. The rate of pediatric deaths due to CCCs has declined over this 10-year period. Among CCCs, malignancy was the most common cause of death overall, as well as in children and adolescents, whereas neonatal disorders were the most common cause of death in infants. Although over 1,000 children die from chronic illnesses each year, there are no hospitals or institutes in Korea that meet the minimum standards for specialized PPC. CONCLUSION: To improve the quality of life of children suffering from CCCs and to support their families who face enormous distress, children with CCCs should be able to access adequate palliative care services. Health authorities should consider supporting the establishment of PPC centers and increasing PPC accessibility in Korea.

Three-dimensional migration of neutrophils through an electrospun nanofibrous membrane.

The study of immune cell migration is important for understanding the immune system network, which is associated with the response to foreign cells. Neutrophils act against foreign cells before any other immune cell, and they must be able to change shape and squeeze through narrow spaces in the extracellular matrix (ECM) during migration to sites of infection. Conventional in vitro migration assays are typically performed on two-dimensional substrates that fail to reproduce the three-dimensional (3-D) nature of the ECM. Here we present an in vitro method to simulate the 3-D migration of neutrophils using an electrospun nanofibrous membrane, which is similar to the ECM in terms of morphology. We examined the properties of neutrophil movement and the effects of gravity and the presence of IL-8, which has been widely used as a chemotactic attractant for neutrophils. The number of neutrophils passing through the nanofibrous membrane were higher, and their movement was more active in the presence of IL-8. Also, we confirmed that neutrophils could migrate against gravity toward IL-8 through a nanofibrous membrane.