Effect of Intermediate-Dose vs Standard-Dose Prophylactic Anticoagulation on Thrombotic Events, Extracorporeal Membrane Oxygenation Treatment, or Mortality among Patients with COVID-19 Admitted to the Intensive Care Unit: The INSPIRATION Randomized Clinical Trial

Importance: Thrombotic events are commonly reported in critically ill patients with COVID-19. Limited data exist to guide the intensity of antithrombotic prophylaxis. Objective: To evaluate the effects of intermediate-dose vs standard-dose prophylactic anticoagulation among patients with COVID-19 admitted to the intensive care unit (ICU). Design, Setting, and Participants: Multicenter randomized trial with a 2 � 2 factorial design performed in 10 academic centers in Iran comparing intermediate-dose vs standard-dose prophylactic anticoagulation (first hypothesis) and statin therapy vs matching placebo (second hypothesis; not reported in this article) among adult patients admitted to the ICU with COVID-19. Patients were recruited between July 29, 2020, and November 19, 2020. The final follow-up date for the 30-day primary outcome was December 19, 2020. Interventions: Intermediate-dose (enoxaparin, 1 mg/kg daily) (n = 276) vs standard prophylactic anticoagulation (enoxaparin, 40 mg daily) (n = 286), with modification according to body weight and creatinine clearance. The assigned treatments were planned to be continued until completion of 30-day follow-up. Main Outcomes and Measures: The primary efficacy outcome was a composite of venous or arterial thrombosis, treatment with extracorporeal membrane oxygenation, or mortality within 30 days, assessed in randomized patients who met the eligibility criteria and received at least 1 dose of the assigned treatment. Prespecified safety outcomes included major bleeding according to the Bleeding Academic Research Consortium (type 3 or 5 definition), powered for noninferiority (a noninferiority margin of 1.8 based on odds ratio), and severe thrombocytopenia (platelet count <20 �103/µL). All outcomes were blindly adjudicated. Results: Among 600 randomized patients, 562 (93.7) were included in the primary analysis (median interquartile range age, 62 50-71 years; 237 42.2% women). The primary efficacy outcome occurred in 126 patients (45.7%) in the intermediate-dose group and 126 patients (44.1%) in the standard-dose prophylaxis group (absolute risk difference, 1.5% 95% CI,-6.6% to 9.8%; odds ratio, 1.06 95% CI, 0.76-1.48; P =.70). Major bleeding occurred in 7 patients (2.5%) in the intermediate-dose group and 4 patients (1.4%) in the standard-dose prophylaxis group (risk difference, 1.1% 1-sided 97.5% CI,-� to 3.4%; odds ratio, 1.83 1-sided 97.5% CI, 0.00-5.93), not meeting the noninferiority criteria (P for noninferiority >.99). Severe thrombocytopenia occurred only in patients assigned to the intermediate-dose group (6 vs 0 patients; risk difference, 2.2% 95% CI, 0.4%-3.8%; P =.01). Conclusions and Relevance: Among patients admitted to the ICU with COVID-19, intermediate-dose prophylactic anticoagulation, compared with standard-dose prophylactic anticoagulation, did not result in a significant difference in the primary outcome of a composite of adjudicated venous or arterial thrombosis, treatment with extracorporeal membrane oxygenation, or mortality within 30 days. These results do not support the routine empirical use of intermediate-dose prophylactic anticoagulation in unselected patients admitted to the ICU with COVID-19. Trial Registration: ClinicalTrials.gov Identifier: NCT04486508. © 2021 American Medical Association. All rights reserved

Simulation of vanadium-48 production using MCNPX code

Vanadium-48 was produced through the irradiation of the natural titanium target via the natTi(p, xn)48V reaction. The titanium target was irradiated at 1 mA current and by a 21 MeV proton beam for 4 hours. In this paper, the activity of 48V, 43Sc, and 46Sc radionuclides and the efficacy of the 47Ti(p, g), 48Ti(p, n), and 49Ti(p, 2n) channel reactions to form 48V radionuclide were determined using MCNPX code. Furthermore, the experimental activity of 48V was compared with the estimated value for the thick target yield produced in the irradiation time according to MCNPX code. Good agreement between production yield of the 48V and the simulation yield was observed. In conclusion, MCNPX code can be used for the estimation of the production yield

Quantity and quality of deadwood in the mid-successional stage in oriental Beech (Fagus orientalis Lipsky) stands (Case study: Kheyrood forest, Nowshahr)

Deadwood is widely recog­nized as an extremely important structural and functional component of forest communities. Therefore, understanding its role and dynamics are important to improve forest management strategies in both managed and unmanaged forests. The aim of this study was to analyze the qualitative and quantitative characteristics of dead trees in the mid-succession stage in intact mixed oriental beech (Fagus orientalis Lipsky) forests of Kheyrood,, Mazandaran province. Three one-ha sample plots were laid out in compartment 310 of Gorazbon district, in which a number of quantitative (diameter at breast height≥7.5cm and height) and qualitative (species, type of deadwood (log or snag) and decay class) characteristics were recorded. Our results indicated the mean volume of deadwood of 37.8 m3 ha-1. In addition, common hornbeam (Carpinus betulus L.) possessed the highest frequency (64%) of the deadwood among the species. The frequency and volume proportions of logs were 74.7% and 69.3%, respectively, and the maximum amount of deadwood was observed in the large timber size (50-75 cm). As conclusion, forest management planning should pass an adequate attention to succession stage and the amount of deadwood to guarantee the health, long lasting productivity and sustainability of forest ecosystem

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals

Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study

Abstract This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process