To assess the effectiveness and safety of autologous intraparenchymal blood patch (IBP) application in reducing the frequency of pneumothorax (PTX) after percutaneous transthoracic pulmonary core needle biopsy.
 
  The records of patients who underwent the transthoracic pulmonary core needle biopsy procedure under CT guidance between January 2015 and October 2018 were screened retrospectively. Patients whose traversed pulmonary parenchymal length was ≥20 mm during biopsy were included in the study irrespective of lesion size. The IBP procedure was made a department policy in November 2017; patients who underwent biopsy after this date comprised the IBP group, while those who underwent the procedure before this date comprised the control group. IBP recipients received 2-5 mL of autologous blood injection to the needle tract. Demographic data, procedural reports, tomography images, and the follow-up records of patients were assessed.
 
  A total of 262 patients were included in the study. Of the 91 patients that received an IBP, PTX developed in 13 (14.1%), with 7 (7.7%) requiring a thoracic tube. Of the 171 patients who did not receive an IBP, PTX developed in 45 (26.3%), with 19 (11.1%) requiring a thoracic tube. Patients who received an autologous IBP showed a significantly lower rate of PTX development versus those who did not (P = 0.01). Similarly, a significantly lower number of patients who received the blood patch required chest tube placement (P = 0.015).
 
  Autologous IBP is a safe, inexpensive and easy to use method that reduces the rate of PTX development and thoracic tube application after percutaneous core needle biopsies of the lung.
 Autologous IBP is a safe, inexpensive and easy to use method that reduces the rate of PTX development and thoracic tube application after percutaneous core needle biopsies of the lung.
  Earlier imaging techniques for coronary artery disease (CAD) focused primarily on either morphological or functional assessment of CAD. However, dual-energy computed tomography (DECT) can be used to assess myocardial blood supply both morphologically and functionally.  https://www.selleckchem.com/products/cl-amidine.html  We aimed to evaluate the diagnostic accuracy of DECT in detecting morphological and functional components of CAD, using invasive coronary angiography (ICA) and single photon emission computed tomography (SPECT) as reference standards.
 
  Twenty-five patients with known or suspicious CAD and scheduled for ICA were investigated by DECT and SPECT. DECT was performed during the resting state using retrospective electrocardiography (ECG) gating. CT coronary angiography and perfusion images were generated from the same raw data. All patients were evaluated for significant stenosis (≥50%) on both ICA and DECT coronary angiography, and for myocardial perfusion defects on SPECT and DECT perfusion. Comparison was done between ICA and DECT coronary angiogand SPECT as reference standards. In the same scan, DECT can accurately provide integrative imaging of coronary artery morphology and myocardial perfusion.Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n=10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.Few-shot learning aims to learn a well-performing model from a few labeled examples. Recently, quite a few works propose to learn a predictor to directly generate model parameter weights with episodic training strategy of meta-learning and achieve fairly promising performance. However, the predictor in these works is task-agnostic, which means that the predictor cannot adjust to novel tasks in the testing phase. In this article, we propose a novel meta-learning method to learn how to learn task-adaptive classifier-predictor to generate classifier weights for few-shot classification. Specifically, a meta classifier-predictor module, (MPM) is introduced to learn how to adaptively update a task-agnostic classifier-predictor to a task-specialized one on a novel task with a newly proposed center-uniqueness loss function. Compared with previous works, our task-adaptive classifier-predictor can better capture characteristics of each category in a novel task and thus generate a more accurate and effective classifier.