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Context Urodynamic study (UDS) provides the most objective assessment of bladder outlet obstruction (BOO) but is impractical to be recommended routinely in outpatient services. Intravesical prostatic protrusion (IPP) had been described to obstruct urinary flow by creating an anatomical ball-valve effect, but there remains a lack of pooled evidence that can objectively correlate with BOO in benign prostatic hyperplasia. Objective To update the current evidence on the predictive role of IPP in determining BOO and unsuccessful trial without catheter (TWOC). Evidence acquisition A comprehensive literature search was performed to identify studies that evaluated IPP in diagnosing UDS-determined BOO and TWOC. The search included the PubMed/MEDLINE, EMBASE, and Cochrane Library up to January 2021. An updated systemic review and meta-analysis was performed. Evidence synthesis A total of 18 studies with 4128 patients were examined. Eleven studies with 1478 patients examined the role of IPP in UDS-determined BOO. The pooled area under the curve (AUC) was 0.83 (95% confidence interval [CI]: 0.79–0.86), and at a cut-off of >10 mm, the sensitivity (Sn) and specificity (Sp) were 0.71 (95% CI: 0.61–0.78) and 0.77 (95% CI: 0.68–0.84), respectively. The probability-modifying plot revealed positive and negative likelihood ratios of 3.34 (95% CI: 2.56–4.36) and 0.35 (95% CI: 0.26–0.45), respectively. Seven studies with 2650 patients examined IPP in predicting unsuccessful TWOC, with a pooled AUC of 0.74 (95% CI: 0.70–0.84), with Sn of 0.51 (95% CI: 0.43–0.60) and Sp of 0.79 (95% CI: 0.73–0.84) at an IPP cut-off of >10 mm. Five studies compared prostate volume (PV) and IPP and revealed a lower AUC of PV at 0.71 (95% CI: 0.67–0.75), which was an inferior parameter in diagnosing BOO (p Conclusions This systemic review provided evidence that IPP is a reliable clinical parameter that correlates strongly with underlying BOO and unsuccessful TWOC. Patient summary In this review, we comprehensively reviewed all the literature to date on evaluating the clinical utility of intravesical prostatic protrusion (IPP). We have demonstrated that IPP correlates strongly with urodynamic study (UDS)-determined bladder outlet obstruction and failure of trial without catheter (TWOC). Outpatient IPP measurement is a quick, inexpensive, and reproducible clinical parameter that can determine the severity of benign prostatic hyperplasia. The clinical role of IPP in predicting failure of TWOC selects patients who are best treated with aggressive surgical approaches rather than conservative medical therapies. More importantly, IPP can facilitate the discriminatory use of invasive UDS, reserved for patients with a strong suspicion of concomitant detrusor abnormalities.

Introduction Autosomal Dominant Polycystic Kidney Disease(ADPKD) is a genetic disorder characterized by development of multiple renal cysts. Significantly enlarged cysts can encroach on anatomical space required for future renal transplantation and frequently cause recurrent infective and haematuria episodes throughout the patient's lifetime. This presentation aims to present the step-by-step surgical technique of modified supine hand assisted laparoscopic (HAL) bilateral nephrectomy for ADPKD in our series of 8 consecutive patients, as well as its advantages. Materials From May 2016 to October 2020, a total of 8 patients who underwent HAL bilateral nephrectomy for ADPKD, by a single surgeon, was retrospectively evaluated in this study. A step-by-step HAL bilateral nephrectomy technique is as follows: (1) Modified supine positioning, (2) Midline GelPort® (Applied Medical) creation, (3) 10 × 12 mm upper midline and lumbar ports insertion for camera and working instruments, (4) Nephrectomy, (5) Operating team and equipment re-positioning, (6) Contralateral nephrectomy. The specimen can be extracted via the same incision for the hand-port, facilitating quick recovery with minimal post-operative pain and excellent cosmesis. Patient demographics, peri‐ and postoperative short-term outcomes were analysed. Results 8 HAL bilateral nephrectomies were performed. Median operative time was 250 mins (IQR 182-230). Mean length of stay was 4 days (s=1.2 days) with a median blood loss of 200 millilitres (IQR 50 – 250 millilitres). Mean VAS analogue score on post-operative day 1 (POD) was 2.8 (s=1.4). The mean combined weight on histology was 3167g (s=1179g). Adrenal glands were preserved in all patients. There were no open conversions, no vascular or bowel injuries, with no grade 3 and above modified Clavien-Dindo classification complications. Conclusions HAL bilateral nephrectomy for ADPKD is a safe and feasible technique which provides good postoperative outcomes with limited risk of complications while ensuring cosmetic satisfaction.

Sign language recognition has made significant advances in recent years. Many researchers show interest in encouraging the development of different applications to simplify the daily life of deaf people and to integrate them into the hearing society. The use of the Kinect sensor (developed by Microsoft) for sign language recognition is steadily increasing. However, there are limited publicly available RGB-D and skeleton joint datasets that provide complete information for dynamic signs captured by Kinect sensor, most of them lack effective and accurate labeling or stored in a single data format. Given the limitations of existing datasets, this article presents a challenging public dataset, named LIBRAS-UFOP. The dataset is based on the concept of minimal pairs, which follows specific categorization criteria; the signs are labeled correctly, and validated by an expert in sign language; the dataset presents complete RGB-D and skeleton data. The dataset consists of 56 different signs with high similarity grouped into four categories. Besides, a baseline method is presented that consists of the generation of dynamic images from each multimodal data, which are the input to two flow stream CNN architectures. Finally, we propose an experimental protocol to conduct evaluations on the proposed dataset. Due to the high similarity between signs, the experimental results using a baseline method reports a recognition rate of 74.25% on the proposed dataset. This result highlights how challenging this dataset is for sign language recognition and let room for future research works to improve the recognition rate.

In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.