Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients.

Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.

Emphysematous Osteomyelitis: Novel Use of PET-MRI and a Review of Characteristic Imaging Findings.

INTRODUCTION: Emphysematous Osteomyelitis (EO) is an extremely rare bone infection caused by gas-forming bacteria with few documented cases in the literature. Our study aims to highlight characteristic imaging features, including the novel use of positron emission tomographymagnetic resonance imaging (PET-MRI) in diagnosing this potentially fatal entity. CASE: Radiography and computed tomography (CT) of the pelvis were performed due to complaints of persistent back pain in a 36-year-old male with a history of recent abdominal aorta surgery. Sacroiliac joint aspiration was performed, and a follow-up PET-MRI was subsequently performed. RESULTS: Radiography and CT demonstrated bilateral sacroiliitis, osteonecrosis and EO in the bony pelvis. Left sacroiliac joint aspiration identified Staphylococcus aureus as the causative organism. PET-MRI revealed EO with left iliopsoas abscess and abdominal aortic graft infection. The patient's symptoms resolved following antibiotic therapy and image-guided abscess drainage. CONCLUSION: EO is a lethal variant of osteomyelitis with a dearth of published cases. Pertinent imaging characteristics of EO on radiography, CT and PET-MRI are discussed here, along with a review of the literature surrounding this rare condition.

A specific biomarker for amyotrophic lateral sclerosis: Quantitative susceptibility mapping.

OBJECTIVE: Accurate and timely diagnosis of amyotrophic lateral sclerosis (ALS) is a diagnostic challenge given the lack of specific diagnostic and imaging biomarkers as well as the significant clinic overlap with mimic syndromes. We hypothesize that MR quantitative susceptibility mapping (QSM) can help differentiate ALS from mimic diagnoses. METHODS: In a blinded retrospective study of MRIs with QSM from 2015 to 2018, we compared motor cortex susceptibility along the hand and face homunculi in ALS patients and patients with similar clinical presentations. Inclusion required a confirmed ALS or a mimic diagnosis. Comparative groups included age-matched patients with MRIs performed for non-motor neuron symptoms that were reported as normal or demonstrated leukoaraiosis. Quantitative susceptibility values were compared with ANOVA and Tukey-Kramer (post-hoc). ROC analysis and Youden's index were used to identify optimal cutoff values. RESULTS: Fifty ALS, 35 mimic, and 70 non-motor neuron symptom patients (35 normal, 35 leukoaraiosis) were included. Hand and face homunculus mean susceptibility values were significantly higher in the ALS group compared to the mimic (p=0.001, p=0.004), leukoaraiosis (p<0.001, p=0.003), and normal (p<0.001, p<0.001) groups. ROC curve analysis comparing ALS to mimics resulted in an area under the curve of 0.71 and 0.67 for the hand and face homunculus measurements, respectively. In differentiating ALS from mimics, Youden's index showed 100% specificity and 36% sensitivity for hand homunculus measurements. CONCLUSIONS: QSM has diagnostic potential in the assessment of suspected ALS patients, demonstrating very high specificity in differentiating ALS from mimic diagnoses.

Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs.

Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.

Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning.

Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity (0-3) and geographic extent (0-4). Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0-6) with a standard deviation of 0.25 (9.9%) across three readers. The average geographic extent score was 3.42 (range: 0-8) with a standard deviation of 0.57 (16.7%) across three readers. The inter-rater agreement yielded a Fleiss' Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R2) of 0.90 (range: 0.73-0.90 for traditional learning and 0.83-0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2-21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.

Malignant Astrocytic Tumor Progression Potentiated by JAK-mediated Recruitment of Myeloid Cells.

Purpose: While the tumor microenvironment has been known to play an integral role in tumor progression, the function of nonresident bone marrow-derived cells (BMDC) remains to be determined in neurologic tumors. Here we identified the contribution of BMDC recruitment in mediating malignant transformation from low- to high-grade gliomas.Experimental Design: We analyzed human blood and tumor samples from patients with low- and high-grade gliomas. A spontaneous platelet-derived growth factor (PDGF) murine glioma model (RCAS) was utilized to recapitulate human disease progression. Levels of CD11b+/GR1+ BMDCs were analyzed at discrete stages of tumor progression. Using bone marrow transplantation, we determined the unique influence of BMDCs in the transition from low- to high-grade glioma. The functional role of these BMDCs was then examined using a JAK 1/2 inhibitor (AZD1480).Results: CD11b+ myeloid cells were significantly increased during tumor progression in peripheral blood and tumors of glioma patients. Increases in CD11b+/GR1+ cells were observed in murine peripheral blood, bone marrow, and tumors during low-grade to high-grade transformation. Transient blockade of CD11b+ cell expansion using a JAK 1/2 Inhibitor (AZD1480) impaired mobilization of these cells and was associated with a reduction in tumor volume, maintenance of a low-grade tumor phenotype, and prolongation in survival.Conclusions: We demonstrate that impaired recruitment of CD11b+ myeloid cells with a JAK1/2 inhibitor inhibits glioma progression in vivo and prolongs survival in a murine glioma model. Clin Cancer Res; 23(12); 3109-19. ©2016 AACR.

Biogenesis of the posterior pole is mediated by the exosome/microvesicle protein-sorting pathway.

Biogenesis of the posterior pole is critical to directed cell migration and other polarity-dependent processes. We show here that proteins are targeted to the posterior pole on the basis of higher order oligomerization and plasma membrane binding, the same elements that target proteins to exosomes/microvesicles (EMVs), HIV, and other retrovirus particles. We also demonstrate that the polarization of the EMV protein-sorting pathway can occur in morphologically non-polarized cells, defines the site of uropod formation, is induced by increased expression of EMV cargo proteins, and is evolutionarily conserved between humans and the protozoan Dictyostelium discoideum. Based on these results, we propose a mechanism of posterior pole biogenesis in which elevated levels of EMV cargoes (i) polarize the EMV protein-sorting pathway, (ii) generate a nascent posterior pole, and (iii) prime cells for signal-induced biogenesis of a uropod. This model also offers a mechanistic explanation for the polarized budding of EMVs and retroviruses, including HIV.

Protein targeting to exosomes/microvesicles by plasma membrane anchors.

Animal cells secrete small vesicles, otherwise known as exosomes and microvesicles (EMVs). A short, N-terminal acylation tag can target a highly oligomeric cytoplasmic protein, TyA, into secreted vesicles (Fang, Y., Wu, N., Gan, X., Yan, W., Morell, J. C., and Gould, S. J. (2007) PLoS Biol. 5, 1267-1283). However, it is not clear whether this is true for other membrane anchors or other highly oligomeric, cytoplasmic proteins. We show here that a variety of plasma membrane anchors can target TyA-GFP to sites of vesicle budding and into EMVs, including: (i) a myristoylation tag; (ii) a phosphatidylinositol-(4,5)-bisphosphate (PIP(2))-binding domain; (iii), a phosphatidylinositol-(3,4,5)-trisphosphate-binding domain; (iv) a prenylation/palmitoylation tag, and (v) a type-1 plasma membrane protein, CD43. However, the relative budding efficiency induced by these plasma membrane anchors varied over a 10-fold range, from 100% of control (AcylTyA-GFP) for the myristoylation tag and PIP(2)-binding domain, to one-third or less for the others, respectively. Targeting TyA-GFP to endosome membranes by fusion to a phosphatidylinositol 3-phosphate-binding domain induced only a slight budding of TyA-GFP, ∼2% of control, and no budding was observed when TyA-GFP was targeted to Golgi membranes via a phosphatidylinositol 4-phosphate-binding domain. We also found that a plasma membrane anchor can target two other highly oligomeric, cytoplasmic proteins to EMVs. These observations support the hypothesis that plasma membrane anchors can target highly oligomeric, cytoplasmic proteins to EMVs. Our data also provide additional parallels between EMV biogenesis and retrovirus budding, as the anchors that induced the greatest budding of TyA-GFP are the same as those that mediate retrovirus budding.

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets.

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.