The application of the "perinephric fat wrapping" technique with oral mucosal graft for the management of ureter repair and reconstruction.

Objective: The management of long-segment ureteral stenosis has posed a significant challenge for urologists. Ureteroplasty with oral mucosal graft has emerged as an effective approach for treating long-segment ureteral stenosis and defects. A key step in replacement repair surgery involves suturing the surrounding tissue with an adequate blood supply around the reconstructed ureter.

Enzyme-labeled liquid-based cytology (ELLBC): a new noninvasive diagnostic method for bladder cancers.

Background: Based on liquid-based cytology, we performed an enzyme histochemical staining using acid phosphatase as a marker and termed it ELLBC. The aim of this study was to investigate the value of ELLBC in the diagnosis of bladder cancer.

Methods: Fifty patients who were initially diagnosed with suspected bladder cancers (hematuria or bladder irritation symptoms, urinary ultrasound suggestive of bladder mass) at the Second Affiliated Hospital of Anhui Medical University (Anhui, China) from January 2022 to December 2022 were selected as the study subjects, all of whom underwent ELLBC, CC, and histopathology Histopathology was used as the gold standard to calculate the diagnostic efficacy of ELLBC, CC and ELLBC combined with CC in bladder cancer.

Analysis of risk factors for stenosis after laparoscopic pyeloplasty in the treatment of ureteropelvic junction obstruction.

Background: Laparoscopic ureteroplasty is an effective method for managing ureteropelvic junction obstruction. Despite its high success rate, there remains a subset of patients who do not experience improvement in the hydrops.

Methods: The study retrospectively analyzed the data of 143 patients with ureteropelvic junction obstruction (UPJO) who underwent laparoscopic pyeloplasty (LP) in our hospital from January 2015 to May 2022.

Wildlife surveillance using deep learning methods.

Wildlife conservation and the management of human-wildlife conflicts require cost-effective methods of monitoring wild animal behavior. Still and video camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. In the present study, we describe a state-of-the-art, deep learning approach for automatically identifying and isolating species-specific activity from still images and video data.