Objectives: To assess stone free rates following URS for renal calculi at our institution using low dose renal only CT (LDCT). Methods: A retrospective review of patients undergoing flexible URS for renal stones only with subsequent CT scan within 3 months. Meticulous basketing of all stone fragments was performed whenever possible. A "true" zero fragment SFR was determined by reviewing the CT scan and radiologist's report. Patients with nephrocalcinosis (as determined by visual inspection of papilla at the time of URS) were assigned the "stone free" category. Results: Flexible URS was performed in 288 renal units of 214 patients with renal calculi from 2013 to 2016. Median pre-operative stone size was 6.2mm with the average kidney containing 6.4 stones. An access sheath was used in 92% of cases. A total of 73% (209/288) renal units were completely stone free by CT assessment. Patients with residual fragments were as follows: 1mm in 2% (7/288), 2-4 mm in 16% (46/288), and >4 mm in 9% of kidneys (26/288). Conclusions: The true stone free rate in patients undergoing flexible URS for renal calculi utilizing active basketing of fragments as determined by strict CT assessment was 73%. In patients with residual fragments, the majority are 2-4 mm in size making URS a treatment option for renal calculi with excellent stone free results.
This paper, selecting 775 listed companies from Shanghai and Shenzhen stock markets during three years from 2010 to 2012 as samples, studies the relationship between agency cost and capital structure, using two econometrics methods which are ordinary least squares (OLS) and panel data respectively. Capital structure is calculated by debt-to-asset ratio and long-term liability rate while agency cost is measured by overhead expenses rate and asset turnover rate. The result shows agency cost has a slightly negative correlation to debt-to-asset ratio and there is a positive and insignificant correlation relationship between long-term liability rate and agency cost.
Deep neural networks (DNNs) have promoted much of the recent progress in hyperspectral image (HSI) classification, which depends on extensive labeled samples and deep network structure and has achieved surprisingly good generalization capacity. However, due to the expensive labeling cost, the labeled samples are scarce in most practice cases, which causes these DNN-based methods to be prone to over-fitting and influences the classification result. To mitigate this problem, we present a clustering-inspired active learning method for enhancing the HSI classification result, which mainly contributes to two aspects. On one hand, the modified clustering by fast search and find of peaks clustering method is utilized to select highly informative and diverse samples from unlabeled samples in the candidate set for manual labeling, which empowers us to appropriately augment the limited training set (i.e., labeled samples) and thus improves the generalization capacity of the baseline DNN model. On the other hand, another K-means clustering-based pseudo-labeling scheme is utilized to pre-train the DNN model with all samples in the candidate set. By doing this, the pre-trained model can be effectively generalized to unlabeled samples in the testing set after being fine tuned-based on the augmented training set. The experiment accuracies on two benchmark HSI datasets show the effectiveness of the proposed method.
Deep neural networks have underpinned much of the recent progress in the field of hyperspectral image (HSI) classification owing to their powerful ability to learn discriminative features. However, training a deep neural network often requires the availability of a large number of labeled samples to mitigate over-fitting, and these labeled samples are not always available in practical applications. To adapt the deep neural network-based HSI classification approach to cases in which only a very limited number of labeled samples (i.e., few or even only one labeled sample) are provided, we propose a novel few-shot deep learning framework for HSI classification. In order to mitigate over-fitting, the framework borrows supervision from an auxiliary set of unlabeled samples with soft pseudo-labels to assist the training of the feature extractor on few labeled samples. By considering each labeled sample as a reference agent, the soft pseudo-label is assigned by computing the distances between the unlabeled sample and all agents. To demonstrate the effectiveness of the proposed method, we evaluate it on three benchmark HSI classification datasets. The results indicate that our method achieves better performance relative to existing competitors in few-shot and one-shot settings.
Background: Previous classification of renal pelvicalyceal anatomical structure may be difficult to intuitively understand and unpractical for endourological surgery. We aim to put forward a modified Takazawa anatomical classification of renal pelvicalyceal system based on three-dimensional (3D) virtual reconstruction models for endourological surgery.Methods: We retrospectively collected data on 225 patients (320 kidneys) in total between Apr. 2017 and Dec. 2020, spatial anatomical structure of renal pelvis and calyces were modeled and corresponding morphological parameters were measured after 3D virtual reconstruction of computed tomography urography (CTU). The modified Takazawa renal pelvicalyceal anatomical classification was advanced based on the renal pelvicalyceal morphological parameters [bifurcated branches of renal pelvis, cross sectional area of renal pelvis and ureteropelvic junction (UPJ), infundibuloureteral angle (IUA), lower pole infundibular calyceal length (IL)] by 3D virtual reconstruction models, and comparison of renal pelvicalyceal system morphological parameters were performed to evaluate the differences in various classification types of renal pelvis and calyces.Results: Anatomical structure of renal pelvis and calyces were divided into two main types (Type A and Type B) according to renal pelvic branch patterns. A single pelvis without bifurcated branch was regarded as Type A (62%) and subclassified into three subtypes: Type A1 (22%), Type A2 (27%) and Type A3 (13%), the slimline pelvis was classified as Type A1, the typical pelvis as Type A2 and the broad pelvis as Type A3. A divided pelvis with bifurcated branches was seen as Type B (38%) and subclassified into two subtypes: Type B1 (15%) with the wide and flat lower calyx branch, Type B2 (23%) with the narrow and steep lower calyx branch.Conclusions: Previous studies have reported that the visualization and classification of renal pelvicalyceal anatomical structure by endocast, autopsy, ultrasonography and excretory urography, the modified Takazawa classification system based on 3D virtual reconstruction models enables to standardized different anatomical morphology of renal pelvicalyceal system and provide intuitive and concise information on anatomy, thus leading to the improvement in treatment modality.
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