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OBJECTIVES: To describe a bladder cuff excision method modified with ureteral catheterization to better visualize the ureteral orifice during robot-assisted nephroureterectomy (RANU). METHODS: We retrospectively analyzed 66 patients with upper urinary tract urothelial carcinoma of the renal pelvis and/or upper-mid ureter treated between January 2020 and January 2023. Among them, 32 patients (group A) underwent RANU supported by ureteral catheterization, and the remaining patients (group B) received routine transperitoneal RANU. Postoperative cystoscopy was performed routinely to compare the rates of residual ureteral orifice between the two groups. RESULTS: Surgeries were completed uneventfully in all 66 patients, without blood transfusion or conversion to open procedures. The operative time, estimated blood loss, and postoperative length of hospital stay were similar between both groups. However, the mean time required for BCE in group A was shorter than that in group B (9.5 min vs. 16.0 min, p = 0.006). Cystoscopy at postoperative three months showed no ipsilateral ureteral orifice in group A, but residual ureteral orifice was found in 23.5% of patients in group B. During a short follow-up period of 16 months, no patients in group A experienced bladder tumor recurrence. However, two patients (5.9%) in group B developed bladder tumor recurrence, with one experiencing local tumor recurrence at the level of the ureteral stump. CONCLUSIONS: Our novel technique enables complete ureteral retrieval, accurate and rapid bladder cuff excision, which makes the procedure less invasive and safely reproducible during robot-assisted nephroureterectomy.

BackgroundBladder cancer is a common disease worldwide with most patients presenting with the non-muscle-invasive form (NMIBC) at initial diagnosis. Postoperational intravesical instillation of BCG is carried out for patients with high-risk disease to reduce tumor recurrence and progression to muscle invasive disease. However, BCG can also have side effects or be ineffective in some patients because it cannot enter the cancer cells. Thus, to improve the efficacy of BCG immunotherapy is the long-term pursuit of the bladder cancer field.MethodsTo increase the adhesion of BCG to the urothelium we overexpressed FimH, a mannose binding protein naturally used by uropathogenic Escherichia coli to adhere to human urothelium, onto the surface of BCG. The adhesion/internalization ability of rBCG-S.FimH was examined in mouse bladder by fluorescence microscopy. Preclinical evaluation of antitumor efficacy was carried out in orthotopic mouse models of bladder cancer and in human peripheral blood mononuclear cells. Mechanistic studies were carried out using toll-like receptor 4 (TLR4) knockout mice. Immune cells and cytokines in the serum, tumor and lymph nodes were analyzed by flow cytometry, PCR, ELISA and ELISPOT.ResultsrBCG-S.FimH exhibited markedly improved adhesion and more rapid internalization into urothelial cells than wild-type BCG, resulting in more potent antitumor activity in orthotopic murine models of bladder cancer. To our surprise, rBCG-S.FimH elicited a much more prominent Th1-biased immune response known to be positively correlated with BCG efficacy. Mechanistic studies using TLR4 knockout mouse showed that rBCG-S.FimH could induce enhanced dendritic cell activation and tumor antigen-specific immune response in a TLR4-dependent manner. Furthermore, human peripheral blood mononuclear cells stimulated by rBCG-S.FimH also showed better tumoricidal effects than those using wild-type BCG.ConclusionrBCG-S.FimH is a novel BCG strain with significantly improved efficacy against bladder cancer. Since intravesical BCG immunotherapy is the first-line treatment for NMIBC, which accounts for more than 70% of all bladder cancer cases, our results provide a compelling rationale for clinical development.

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

Rock Young’s modulus is an essential parameter for formation stress characterization and oil and gas reservoir evaluation work and plays an important role in oil drilling-related engineering type work. Aiming at the problems of doubtful confidence in Young’s modulus measurements, time-consuming computation, and high measurement cost in oil drilling, this paper proposed Young’s modulus estimation method based on the Stacking fusion model. The method first processed the downhole vibration data to obtain its time-domain feature data and then used the time-domain feature data as the input to the fusion model while used the rock Young’s modulus data as the model output. The model learner used consists of three base learners, ANN, XGBoost, and CatBoost, with MLR as the model meta-learner. The mapping relationship between the time-domain features and Young’s modulus was established by this method, and the prediction and estimation of Young’s modulus parameters of the rock were finally realized. The results showed that the average absolute error (MAE) of the fused Stacking model was 0.2502 and the goodness-of-fit (R2) was 0.9691. Compared with other single models, the fused model based on Stacking had the advantage of being able to combine each single model, which provided a new method for estimation and prediction of Young’s modulus of rocks.