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Aberrant AKT activation occurs in a number of cancers, metabolic syndrome, and immune disorders, making it an important target for the treatment of many diseases. To monitor spatial and temporal AKT activity in a live setting, we generated an Akt-FRET biosensor mouse that allows longitudinal assessment of AKT activity using intravital imaging in conjunction with image stabilization and optical window technology. We demonstrate the sensitivity of the Akt-FRET biosensor mouse using various cancer models and verify its suitability to monitor response to drug targeting in spheroid and organotypic models. We also show that the dynamics of AKT activation can be monitored in real time in diverse tissues, including in individual islets of the pancreas, in the brown and white adipose tissue, and in the skeletal muscle. Thus, the Akt-FRET biosensor mouse provides an important tool to study AKT dynamics in live tissue contexts and has broad preclinical applications. ispartof: SCIENCE ADVANCES vol:9 issue:17 ispartof: location:United States status: published

Granular acute lymphoblastic leukemia (ALL) is defined by the presence of intracytoplasmic granules in lymphoblastic blasts, mimicking acute myeloblastic leukemia. The disease is extremely rare in adults, and hence, the characteristics thereof are poorly understood. We report a case of a 70-year-old man diagnosed with granular ALL. Bone marrow examination showed blasts with azurophilic granules in the cytoplasm, but immunophenotyping showed B-ALL with aberrant expression of myeloid antigens CD13 and CD33. Karyotyping revealed monosomy 7, and targeted NGS showed

International audience; PURPOSE: Common CT abnormalities of pulmonary aspergillosis represent a cavity with air-meniscus sign, nodule, mass, and consolidation having an angio-invasive pattern. This study aims to conduct a systematic review and an individual patient-level image analysis of CT findings of COVID-19-associated pulmonary aspergillosis (CAPA). METHODS: A systematic literature search was conducted to identify studies reporting CT findings of CAPA as of January 7, 2021. We summarized study-level clinical and CT findings of CAPA and collected individual patient CT images by inviting corresponding authors. The CT findings were categorized into four groups: group 1, typical appearance of COVID-19; group 2, indeterminate appearance of COVID-19; group 3, atypical for COVID-19 without cavities; and group 4, atypical for COVID-19 with cavities. In group 2, cases had only minor discrepant findings including solid nodules, isolated airspace consolidation with negligible ground-glass opacities, centrilobular micronodules, bronchial abnormalities, and cavities. RESULTS: The literature search identified 89 patients from 25 studies, and we collected CT images from 35 CAPA patients (mean age 62.4 ± 14.6 years; 21 men): group 1, thirteen patients (37.1%); group 2, eight patients (22.9%); group 3, six patients (17.1%); and group 4, eight patients (22.9%). Eight of the 14 patients (57.1%) with an atypical appearance had bronchial abnormalities, whereas only one (7.1%) had an angio-invasive fungal pattern. In the study-level analysis, cavities were reported in 12 of 54 patients (22.2%). CONCLUSION: CAPA can frequently manifest as COVID-19 pneumonia without common CT abnormalities of pulmonary aspergillosis. If abnormalities exist on CT images, CAPA may frequently accompany bronchial abnormalities.

BACKGROUND An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images.This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115).In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035).Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

The coronavirus disease (COVID-19) outbreak affected the utilization and management of blood products in hospitals. Blood shortages occurred owing to social distancing policies and reduction in blood donors. However, only a few studies examined whether these changes affected blood usage and transfusion patterns. We retrospectively reviewed blood component usage according to hospital departments and phases of surgery in transfused patients admitted between 1 March 2019 and 28 February 2021, in a single center in Anyang, Korea. We also analyzed the length of hospital stay and mortality to determine prognosis. In 2020, 32,050 blood components were transfused to 2877 patients, corresponding to 15.8% and 11.8% less than the rates in 2019, respectively. Postoperative usage of blood products significantly decreased in 2020 (3.87 ± 6.50) compared to 2019 (7.12 ± 21.71) (p = 0.047). The length of hospital stay of the patients who underwent postoperative transfusion in 2019 (n = 197) was 13.97 ± 11.95 days, which was not significantly different from that in 2020 (n = 167), i.e., 16.44 ± 17.90 days (p = 0.118). Further, 9 of 197 postoperative transfusion patients died in 2019, while 8 of 167 patients died in 2020 (p = 0.920). The COVID-19 pandemic resulted in limited blood supply and reduced postoperative transfusions; however, patient prognosis was not affected.

Thalassemia is the most common form of hereditary anemia. Here, we aimed to investigate the 13-year trend of the epidemiologic profiles and risk of comorbidities in thalassemia using a nationwide population-based registry in Korea. Diagnosis of thalassemia, the comorbidities and transfusion events in patients with thalassemia were identified in the Korean National Health Insurance database, which includes the entire population. The prevalence of thalassemia increased from 0.74/100,000 in 2006 to 2.76/100,000 in 2018. Notably, the incidence rate has nearly doubled in the last 2 years from 0.22/100,000 in 2016 to 0.41/100,000 in 2018. The annual transfusion rate gradually decreased from 34.7% in 2006 to 20.6% in 2018. Transfusion events in patients with thalassemia were significantly associated with the risk of comorbidities (diabetes: odds ratio [OR] = 3.68, 95% confidence interval [CI] = 2.59–5.22; hypertension: OR = 3.06, 95% CI = 2.35–4.00; dyslipidemia: OR = 1.72, 95% CI = 1.22–2.43; atrial fibrillation: OR = 3.52, 95% CI = 1.69–7.32; myocardial infarction: OR = 3.02, 95% CI = 1.09–8.38; stroke: OR = 3.32, 95% CI = 2.05–5.36; congestive heart failure: OR = 2.83, 95% CI = 1.62–4.97; end-stage renal disease: OR = 3.25, 95% CI = 1.96–5.37). Early detection of comorbidities and timely intervention are required for the management of thalassemia.