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. RESULTS: Histopathological examination revealed 35 positive cases in 50 patients, including 15 cases of high-grade uroepithelial carcinoma (HGUC) and 20 cases of low-grade uroepithelial carcinoma (LGUC.) The sensitivity of ELLBC was 82.86%, the specificity was 93.33%, the positive predictive value (PPV) was 96.67%, the negative predictive value (NPV) was 70.00%, and the accuracy was 86.00%; CC had a sensitivity of 37.14%, specificity of 80.00%, PPV of 81.25%, NPV of 35.29%, and accuracy of 50%; ELLBC combined with CC had a sensitivity of 88.57%, specificity of 73.33%, PPV of 88.57%, NPV of 73.33%, and accuracy of 84.00%. The sensitivity and specificity of ELLBC were higher than that of CC, and the difference was statistically significant (p < 0.05), ELLBC combined with CC achieved higher sensitivity, but the diagnostic accuracy decreased. For clinical staging, the diagnostic accuracy was 86.36% for ELLBC and 40.91% for CC in patients in Stage I, and 90.91% for ELLBC and 36.36% for CC in patients in Stage II. CONCLUSION: ELLBC has high clinical application value for the diagnosis of bladder cancer and can provide new options and methods for the early screening of 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. Logistic regression was used to analyze the risk factors of recurrence stenosis after UPJO. RESULTS: Out of these patients, 119 had complete clinical data and follow-up records. Among these patients, restenosis occurred in nine cases after the operation. There was a significant statistical difference in blood loss (P < 0.05). Univariate and multivariate logistic regression analysis revealed that the preoperative separation degree of the renal pelvis, cystatin C, and intraoperative blood loss were potential risk factors for recurrent stenosis after primary LP. When divided by split renal function (SRF), the odds ratio (OR) was 7.850 (P = 0.044), indicating that it was an independent risk factor for postoperative restenosis. Similarly, the OR for stenotic segment length was 0.025 (P = 0.011), also indicating it as an independent risk factor for restenosis. The areas under the receiver operating characteristic curve for stenotic segment length and SRF were 0.9056 and 0.7697, respectively. CONCLUSION: In our study, we identified that preoperative renal pelvis separation, cystatin C, and intraoperative blood loss were potential risk factors for postoperative restenosis. SRF and stenosis segment length were independent risk factors for postoperative restenosis.

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.We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species (binary classification) and secondly to distinguish each of six animal species (multiclassification). We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage Mycobacterium bovis (the cause of bovine tuberculosis) transmission between badgers and cattle.We used two deep learning frameworks for automatic image recognition. They achieved high accuracies, in the order of 98.05% for binary classification and 90.32% for multiclassification. Based on the deep learning framework, a detection process was also developed for identifying animals of interest in video footage, which to our knowledge is the first application for this purpose.The algorithms developed here have wide applications in wildlife monitoring where large quantities of visual data require screening for certain species.