Perioperative outcomes and continence following robotic-assisted radical cystectomy with mainz pouch II urinary diversion in patients with bladder cancer.

PURPOSE: To present the widely unknown perioperative outcomes and continence status of bladder cancer patients following robotic-assisted radical cystectomy (RARC) with Mainz pouch II urinary diversion (UD). MATERIALS AND METHODS: From November 2020 to December 2023, 37 bladder cancer patients who underwent RARC with Mainz pouch II UD were retrospectively assessed (ChiCTR2300070279). The results, which included patient demographics, perioperative data, continence, and complications (early ≤ 30 days and late ≤ 30 days) were reported using the RC-pentafecta criteria. RC-pentafecta criteria included ≥ 16 lymph nodes removed, negative soft tissue surgical margins, absence of major (Grade III-IV) complication at 90 days, absence of clinical recurrence at ≤ 12 months, and absence of long-term UD-related sequelae. A numeric rating scale assessed patient satisfaction with urinary continence 30 days after surgery. The validated Patient Assessment of Constipation Symptoms (PAC-SYM) questionnaire was used to evaluate bowel function. The Kaplan-Meier curve was used to evaluate overall survival (OS). RESULTS: Of the 37 patients evaluated over a median (range) follow-up period of 23.0 (12.0-36.5) months. The median (range) age was 65 (40-81) years. The median (range) time to urinary continence after surgery was 2.3 (1.5-6) months. Of the 37 patients, 31 (83.8%) were continent both during the day and at night, 34 (91.9%) were continent during the day, 32 (86.5%) were continent at night, 35 (94.6%) were satisfied with their urinary continence status, and 21 (56.8%) were very satisfied. The mean (range) voiding frequency was 6 (4-10) during the day and 3 (2-5.5) at night. The mean (range) PAC-SYM total score was 9.50 (4.00-15.00). In 12 (32.4%) of the patients, RC-pentafecta was achieved, and achieving RC-pentafecta was linked to better satisfaction scores (7.3 vs. 5.5, p = 0.034). There was no significant difference between RC-pentafecta and No RC-pentafecta groups in terms of OS (25.6 vs. 21.5 months, p = 0.16). 7 (19.4%) patients experienced late complications. CONCLUSIONS: Mainz pouch II UD following RARC in bladder cancer patients results in a satisfactory continence rate. Achieving RC-pentafecta was correlated with better satisfaction scores. The intracorporeal approach to Mainz pouch II UD is beneficial for female patients due to its reduced invasiveness. TRIAL REGISTRATION: ChiCTR2300070279; Registration: 07/04/2023, Last updated version: 01/06/2023. Retrospectively registered.

Transperitoneal vs retroperitoneal laparoscopic radical nephrectomy: a double-arm, parallel-group randomized clinical trial.

OBJECTIVE: To compare the outcomes of patients undergoing Retroperitoneal laparoscopic Radical nephrectomy (RLRN) and Transperitoneal laparoscopic Radical nephrectomy (TLRN). METHODS: A total of 120 patients with localized renal cell carcinoma were randomized into either RLRN or TLRN group. Mainly by comparing the patient perioperative related data, surgical specimen integrity, pathological results and tumor results. RESULTS: Each group comprised 60 patients. The two group were equivalent in terms of perioperative and pathological outcomes. The mean integrity score was significantly lower in the RLRN group than TLRN group. With a median follow-up of 36.4 months after the operation, Kaplan-Meier survival analysis showed no significant difference between RLRN and TLRN in overall survival (89.8% vs. 88.5%; P = 0.898), recurrence-free survival (77.9% vs. 87.7%; P = 0.180), and cancer-specific survival (91.4% vs. 98.3%; P = 0.153). In clinical T2 subgroup, the recurrence rate and recurrence-free survival in the RLRN group was significantly worse than that in the TLRN group (43.2% vs. 76.7%, P = 0.046). Univariate and multivariate COX regression analysis showed that RLRN (HR: 3.35; 95%CI: 1.12-10.03; P = 0.030), male (HR: 4.01; 95%CI: 1.07-14.99; P = 0.039) and tumor size (HR: 1.23; 95%CI: 1.01-1.51; P = 0.042) were independent risk factor for recurrence-free survival. CONCLUSIONS: Our study showed that although RLRN versus TLRN had roughly similar efficacy, TLRN outperformed RLRN in terms of surgical specimen integrity. TLRN was also significantly better than RLRN in controlling tumor recurrence for clinical T2 and above cases. TRIAL REGISTRATION: Chinese Clinical Trial Registry ( https://www.chictr.org.cn/showproj.html?proj=24400 ), identifier: ChiCTR1800014431, date: 13/01/2018.

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis.

Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM) for mammography image classification. The SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, with an accuracy of 94.10% and a sensitivity of 94.30%. A 10-fold cross-validation was performed to ensure the robustness of the results, and the mean and standard deviation of various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all performance indicators, indicating its superior performance. This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images. The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis. This may have significant implications for reducing breast cancer mortality rates.

LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition.

In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

DeepDate: A deep fusion model based on whale optimization and artificial neural network for Arabian date classification.

PURPOSE: As agricultural technology continues to develop, the scale of planting and production of date fruit is increasing, which brings higher yields. However, the increasing yields also put a lot of pressure on the classification step afterward. Image recognition based on deep learning algorithms can help to identify and classify the date fruit species, even in natural light. METHOD: In this paper, a deep fusion model based on whale optimization and an artificial neural network for Arabian date classification is proposed. The dataset used in this study includes five classes of date fruit images (Barhi, Khalas, Meneifi, Naboot Saif, Sullaj). The process of designing each model can be divided into three phases. The first phase is feature extraction. The second phase is feature selection. The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50). RESULTS: The experimental results show that, after trying different combinations of optimization algorithms and classifiers, the highest test accuracy achieved by DeepDate was 95.9%. It takes less time to achieve a balance between classification accuracy and time consumption. In addition, the performance of DeepDate is better than that of many deep transfer learning models such as Alexnet, Squeezenet, Googlenet, VGG-19, NasNet, and Inception-V3. CONCLUSION: The proposed DeepDate improves the accuracy and efficiency of classifying date fruits and achieves better results in classification metrics such as accuracy and F1. DeepDate provides a promising classification solution for date fruit classification with higher accuracy. To further advance the industry, it is recommended that stakeholders invest in technology transfer programs to bring advanced image recognition and AI tools to smaller producers, enhancing sustainability and productivity across the sector. Collaborations between agricultural technologists and growers could also foster more tailored solutions that address specific regional challenges in date fruit production.

Research on the Low-Temperature Impact Toughness of a New 100-mm Ultra-Thick Offshore Steel Fabricated Using the Narrow-Gap Laser Wire Filling Welding Process.

Ultra-thick offshore steel, known for its high strength, high toughness, and corrosion resistance, is commonly used in marine platforms and ship components. However, when offshore steel is in service for an extended period under conditions of high pressure, extreme cold, and high-frequency impact loads, the weld joints are prone to fatigue failure or even fractures. Addressing these issues, this study designed a narrow-gap laser wire filling welding process and successfully welded a 100-mm new type of ultra-thick offshore steel. Using finite element simulation, EBSD testing, SEM analysis, and impact experiments, this study investigates the weld's microstructure, impact toughness, and fracture mechanisms. The research found that at -80 °C, the welded joint exhibited good impact toughness (>80 J), with the impact absorption energy on the surface of the weld being 217.7 J, similar to that of the base material (225.3 J), and the fracture mechanism was primarily a ductile fracture. The impact absorption energy in the core of the weld was 103.7 J, with the fracture mechanism mainly being a brittle fracture. The EBSD results indicated that due to the influence of the welding thermal cycle and the cooling effect of the narrow-gap process, the grains gradually coarsened from the surface of the welded plate to the core of the weld, which was the main reason for the decreased impact toughness at the joint core. This study demonstrates the feasibility of using narrow-gap laser wire filling welding for 100-mm new type ultra-thick offshore steel and provides a new approach for the joining of ultra-thick steel plates.

Comparing Business, Innovation, and Platform Ecosystems: A Systematic Review of the Literature.

In recent decades, the term "ecosystem" has garnered substantial attention in scholarly and managerial discourse, featuring prominently in academic and applied contexts. While individual scholars have made significant contributions to the study of various types of ecosystem, there appears to be a research gap marked by a lack of comprehensive synthesis and refinement of findings across diverse ecosystems. This paper systematically addresses this gap through a hybrid methodology, employing bibliometric and content analyses to systematically review the literature from 1993 to 2023. The primary research aim is to critically examine theoretical studies on different ecosystem types, specifically focusing on business, innovation, and platform ecosystems. The methodology of this study involves a content review of the identified literature, combining quantitative bibliometric analyses to differentiate patterns and content analysis for in-depth exploration. The core findings center on refining and summarizing the definitions of business, innovation, and platform ecosystems, shedding light on both commonalities and distinctions. Notably, the research unveils shared characteristics such as openness and diversity across these ecosystems while highlighting significant differences in terms of participants and objectives. Furthermore, the paper delves into the interconnections within these three ecosystem types, offering insights into their dynamics and paving the way for discussions on future research directions. This comprehensive examination not only advances our understanding of business, innovation, and platform ecosystems but also lays the groundwork for future scholarly inquiries in this dynamic and evolving field.