Laparoscopic Sugarbaker repair of parastomal hernia following radical cystectomy and ileal conduit: a single-center experience.

PURPOSE: Parastomal hernia (PH) is a frequent complication following radical cystectomy and ileal conduit. The purpose of this study was to summarize the clinical experience and technical characteristics of laparoscopic Sugarbaker repair of PH following radical cystectomy and ileal conduit. METHODS: We retrospectively evaluated all patients who underwent laparoscopic treatment of PH following radical cystectomy and ileal conduit at Huashan Hospital, Fudan University from May 2013 to December 2022. RESULTS: Thirty-five patients were included in the study. Median follow up was 32months (IQR, 25-38 months). Three patients presented with a recurrence (8.6%), with a median time to recurrence of 14 months. Out of the 35 patients, Thirty-two underwent totally laparoscopic repair using the Sugarbaker technique, Three patients required open surgery to repair the intestinal injury after laparoscopic exploration. One patient died 9 months post-surgery due to COVID-19. During the follow-up period, two patients developed a peristomal abscess, and one patient experienced partial intestinal obstruction 10 days after surgery. CONCLUSION: Surgical management of PH following radical cystectomy and ileal conduit is challenging. The laparoscopic Sugarbaker technique for repairing PH following radical cystectomy and ileal conduit has low complication and recurrence rate.

Structure-inclusive similarity based directed GNN: A method that can control information flow to predict drug-target binding affinity.

MOTIVATION: Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A. RESULTS: To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets. AVAILABILITY: Source codes and data are available at https://github.com/HuangStomach/SISDTA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lap-re-Do Keyhole versus Lap-re-Do Sugarbaker techniques in large parastomal hernia repair: a retrospective cohort study.

PURPOSE: To review the safety and efficacy of Lap-re-Do technique in the treatment of large parastomal hernia. METHODS: We retrospectively analyzed the recurrence and complications of 81 patients with large parastomal hernia who underwent Lap-re-Do technique in Huashan Hospital of Fudan University from May 2010 to December 2019. And the patients should be able to complete follow-up. With such criteria, we included 40 Lap-re-Do Keyhole patients and 41 Lap-re-Do Sugarbaker patients. Observation time was defined as time to recurrence, death, or last nonevent visit. RESULTS: In large parastomal hernias, Lap-re-Do had a recurrence rate of 25.9% and complication rate of 16.0%, and reoperation rate of 9.9% during the average follow-up time of 41.1 ± 17.8 months. Recurrence rates were 40% (16/40) after Lap-re-Do Keyhole repair and 12.2% (5/41) after Lap-re-Do Sugarbaker repair. Complication rates were 12.5% after Lap-re-Do keyhole and 19.5% after Lap-re-Do Sugarbaker repair Re-operation rates referred to Lap-re-Do keyhole repair were 15% and Lap-re-Do Sugarbaker repair 4.9% during follow-up.The majority of reoperations were indicated by recurrence. CONCLUSIONS: Large parastomal hernias are still difficult to be treated. Lap-re-Do Sugarbaker is recommended as an appropriate procedure to close the hernia ring, removing the lengthy colostomy, and effectively reduce recurrence and complication rates.

IKZF2 Degradation: It's Time to Take into Account it When Designing Cereblon-Based PROTACs.

Proteolysis-targeting chimera (PROTAC) has become a very important means of protein degradation and a new way of disease treatment. In particular, PROTACs constructed with ligands for E3 ligase cereblon account for more than 90 % of the PROTACs currently in clinical research. Notably, CRBN ligands themselves are a class of molecular glue compounds capable of degrading neo-substrate proteins. Compared to the target proteins degradation, the degradation of neo-substrates, especially IKZF2, has not received enough attention. Therefore, this review summarizes the currently published IKZF2 degraders derived from articles and patents, which are conducive to the design of PROTACs with desired IKZF2 degradation from the perspective of medicinal chemistry.

Evaluation and clinical significance of serum neurospecific enolase in children with pneumonia: a case-control study.

BACKGROUND: Neurospecific Enolase (NSE), a multifunctional protein, is present in various tissues of the body and plays an important role in many disease processes, such as infection, inflammation, tumours, injury, and immunity. In recent years, the application of NSE in respiratory diseases has become increasingly widespread and a research hotspot. OBJECTIVE: This study aims to explore the relationship between NSE and childhood pneumonia, providing assistance for the diagnosis and assessment of pneumonia. METHODS: Using prospective research and case-control methods, We selected 129 children with pneumonia hospitalised in Weifang People's Hospital from September 2020 to April 2022 as the case group. Among them were 67 cases of Mycoplasma pneumoniae pneumonia (MP+), 62 cases of non-Mycoplasma pneumoniae pneumonia (MP -), and 21 cases of severe pneumonia. At the same time, 136 children who underwent outpatient health examinations were selected as the control group. The levels of NSE, ESR, CRP in cases group and NSE in control group were measured separately. RESULT: The NSE levels in the MP + group were 17.86 (14.29-22.54) ng/mL, while those in the MP- group were 17.89 (14.10-21.66) ng/mL, both of which were higher than the control group's NSE levels of 13.26(12.18,14.44) ng/mL (H = 46.92, P = 0.000). There was no statistically significant difference in NSE levels between the MP + and MP - groups (P > 0.05). The NSE level in the severe pneumonia group was 27.38 (13.95-34.06) ng/mL, higher than that in the mild pneumonia group, which was 17.68 (14.27-21.04) ng/mL, (P = 0.024). The AUC values for diagnosing pneumonia are NSE0.714, CRP0.539, and ESR0.535, with NSE having the highest diagnostic value. CONCLUSION: Serum NSE can serve as an inflammatory indicator for paediatric pneumonia, which has important clinical guidance significance for the diagnosis, condition evaluation, and prognosis of paediatric pneumonia.

Study on Reducing Water Absorption of Recycled Aggregates (RAs) by Microbial Mineralization.

Crushing waste concrete and using it directly as RAs has the disadvantages of high porosity and high water absorption. To achieve the reuse of resources, the researchers use microbial mineralization methods to further reinforce RAs. In this paper, the effect of the microbial carbonic anhydrase mineralization method on the water absorption of RAs was investigated, and the macroscopic analysis was performed by determining the indexes of water absorption and apparent density of RAs before and after the modification, and the microscopic analysis of RAs by using the methods of SEM, XRD, DSC, and EDS as well. According to the microscopic analysis, the mineralization products of microorganisms are calcium carbonate crystals, and with the increase in microbial liquid concentration, the water absorption rate of RAs shows a trend of decreasing and then increasing, and it can be found through the microscopic morphology that abundant mineralization products attached to the surface of the aggregate lead to the surface of the aggregate becoming rougher and more porous. The method of soaking the RAs in 3% bacterial solution and 0.1 mol/L calcium acetate solution followed by carbonation with 20% CO2 resulted in a 4.85% reduction in water absorption.

Gender determination and long-time follow-up analysis of mixed gonadal dysgenesis.

INTRODUCTION: Mixed gonadal dysgenesis (MGD) is a rare disorder of sexual development. The management of MGD is challenging since the disease significantly impacts a patient's growth, hormone balance, and gonadal development. This article used a large population and a long follow-up period for its analysis. OBJECTIVES: This study aims to summarize the gender determination basis and analyze the long-term follow-up of mixed gonadal dysgenesis. METHODS: A total of 45 patients' clinical data were summarized and analyzed. Patients were divided by gender. Next, we followed up regarding the occurrence of complications after surgery, the patients' satisfaction with external genitalia appearance, the growth of the patients, counting the surgical pattern the incidence of surgical complications and the development of the patients' growth. All patients included in this study underwent chromosomal karyotype analysis, abdomen exploration, and pathological biopsy. After sex determination, 7 patients who were raised as female underwent clitoroplasty, and bilateral gonadectomy. 38 male patients underwent urethroplasty + one-sided gonadectomy. RESULTS: Patient follow-up started in the third month after surgery. Female patients reported no surgery-related complications, while 14 male patients showed surgery-related complications. Additionally, 20 male patients (60.6 %) had a lower height compared to normal peers, 12 of which (36.4 %) were lower than the second standard deviation of the height of normal peers. CONCLUSION: The clinical manifestations of mixed gonadal dysgenesis are variable, and the management is complicated. Children's gonadal function, external genital conditions, psychological evaluation, and parents' wishes should be considered before sex determination. In China, most patients are raised as males with a high incidence of postoperative complications. We found that short stature is a common feature in male patients, thus their height and growth should be carefully supervised. Patients should pay attention to their sexual function and sexual potential during adulthood.

Assessment of clinical prognosis improvement in children with concomitant anterior and posterior urethral valves: A case series report.

BACKGROUND: Concomitant anterior urethral valves (AUVs) and posterior urethral valves (PUVs) is an extremely rare congenital urologic anomaly, which may be easily overlooked in the clinic. OBJECTIVE: This study assessed the prognosis of children with concomitant PUVs and AUVs. METHODS: The clinical data of inpatients with concomitant AUVs and PUVs in our hospital were collected from January 1983 to June 2022. The clinical manifestations, auxiliary inspection, and treatment were described in detail. RESULTS: In total, 6 cases of concomitant AUVs and PUVs in boys were found in our hospital, with ages ranging from 3 months to 9 years; the main clinical manifestation was abnormal urination. Four patients exhibited concomitant AUVs and PUVs preoperatively and underwent simultaneous anterior and posterior urethral valvotomy. Follow-up studies showed that 3 patients' clinical symptoms substantially improved with well-maintained renal function. One patient died of renal failure. In the other 2 patients, PUVs were initially identified and excised, but their clinical symptoms did not show substantial improvement. Following voiding cystourethrography (VCUG), the AUVs were found and obstructions were then completely relieved. However, 2 patients died of renal failure. CONCLUSIONS: If urinary symptoms cannot be substantially relieved after posterior urethral valvotomy, VCUG and cystoscopy should be repeated to shorten the interval between anterior and posterior urethral valvotomies to improve patient prognosis.

SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images.

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.

Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network.

Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.

Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules.

BACKGROUND: To develop a fully automated CNN detection system based on magnetic resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury detection on MRI images. METHODS: Including 313 patients aged 16 - 65 years old, the raw data are 368 pieces with injured ACL and 100 pieces with intact ACL. By adding flipping, rotation, scaling and other methods to expand the data, the final data set is 630 pieces including 355 pieces of injured ACL and 275 pieces of intact ACL. Using the proposed CNN model with two attention mechanism modules, data sets are trained and tested with fivefold cross-validation. RESULTS: The performance is evaluated using accuracy, precision, sensitivity, specificity and F1 score of our proposed CNN model, with results of 0.8063, 0.7741, 0.9268, 0.6509 and 0.8436. The average accuracy in the fivefold cross-validation is 0.8064. For our model, the average area under curves (AUC) for detecting injured ACL has results of 0.8886. CONCLUSION: We propose an effective and automatic CNN model to detect ACL injury from MRI of human knees. This model can effectively help clinicians diagnose ACL injury, improving diagnostic efficiency and reducing misdiagnosis and missed diagnosis.

PKDN: Prior Knowledge Distillation Network for bronchoscopy diagnosis.

Bronchoscopy plays a crucial role in diagnosing and treating lung diseases. The deep learning-based diagnostic system for bronchoscopic images can assist physicians in accurately and efficiently diagnosing lung diseases, enabling patients to undergo timely pathological examinations and receive appropriate treatment. However, the existing diagnostic methods overlook the utilization of prior knowledge of medical images, and the limited feature extraction capability hinders precise focus on lesion regions, consequently affecting the overall diagnostic effectiveness. To address these challenges, this paper proposes a prior knowledge distillation network (PKDN) for identifying lung diseases through bronchoscopic images. The proposed method extracts color and edge features from lesion images using the prior knowledge guidance module, and subsequently enhances spatial and channel features by employing the dynamic spatial attention module and gated channel attention module, respectively. Finally, the extracted features undergo refinement and self-regulation through feature distillation. Furthermore, decoupled distillation is implemented to balance the importance of target and non-target class distillation, thereby enhancing the diagnostic performance of the network. The effectiveness of the proposed method is validated on the bronchoscopic dataset provided by Harbin Medical University Cancer Hospital, which consists of 2,029 bronchoscopic images from 200 patients. Experimental results demonstrate that the proposed method achieves an accuracy of 94.78% and an AUC of 98.17%, outperforming other methods significantly in diagnostic performance. These results indicate that the computer-aided diagnostic system based on PKDN provides satisfactory accuracy in diagnosing lung diseases during bronchoscopy.

Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury.

OBJECTIVE: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. METHODS: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. RESULTS: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. CONCLUSION: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.

SDMT: Spatial Dependence Multi-Task Transformer Network for 3D Knee MRI Segmentation and Landmark Localization.

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon's clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

Advancing Strategies for Proteolysis-Targeting Chimera Design.

Proteolysis-targeting chimeras (PROTACs) have shown great therapeutic potential by degrading various disease-causing proteins, particularly those related to tumors. Therefore, the introduction of PROTACs has ushered in a new chapter of antitumor drug development, marked by significant advances over recent years. Herein, we describe recent developments in PROTAC technology, focusing on design strategy, development workflow, and future outlooks. We also discuss potential opportunities and challenges for PROTAC research.

How does Nephroblastomatosis impact the prognosis of unilateral Wilms tumor?

PURPOSE: We aimed to explore whether Nephroblastomatosis (Nbm) has an effect on the prognosis of Wilms tumor (WT), and compare the relapse-free survival (RFS) and overall survival (OS) after surgery of WT patients with or without Nbm. METHODS: We retrospectively analyzed the clinical data of children with WT admitted to our department from April 2010 to September 2021. The enrolled patients were divided into two groups according to whether they were combined with Nbm histologically or not, the Nbm group for patients accompany with Nbm and the non-Nbm group for pure WT. All patients underwent a standardized comprehensive treatment according to National Wilms Tumor Study 5. Clinical variables, pathological results, and the prognosis were collected during follow-up. RESULTS: A total of 345 patients were finally enrolled in this study, of which 299 patients in the non-Nbm group and 46 patients in the Nbm group. Univariate Cox analysis revealed that Nbm was not the risk factor of either OS or RFS. The difference of postoperative RFS (P = 0.66) and OS (P = 0.68) between two groups was not significant. Subgroup analysis revealed that the RFS and OS between the non-Nbm group and the Nbm group had no statistical difference under the condition of same stage (low grade and high grade), same histology (favorable histology and unfavorable histology), same surgical approach (nephrectomy and nephron-sparing surgery), with or without lymph node metastasis (P > 0.05). CONCLUSION: Although Nbm had a strong correlation with the occurrence of WT, a combined Nbm did not increase the risk of metastasis and poor prognosis of WT. After complete surgical resection followed by standardized treatment, the long-term RFS and OS were not different from pure WT. IEC-C-006-A04-V.06 retrospectively registered.

MsgeCNN: Multiscale geometric embedded convolutional neural network for ONFH segmentation and grading.

BACKGROUND: The incidence of osteonecrosis of the femoral head (ONFH) is increasing gradually, rapid and accurate grading of ONFH is critical. The existing Steinberg staging criteria grades ONFH according to the proportion of necrosis area to femoral head area. PURPOSE: In the clinical practice, the necrosis region and femoral head region are mainly estimated by the observation and experience of doctor. This paper proposes a two-stage segmentation and grading framework, which can be used to segment the femoral head and necrosis, as well as to diagnosis. METHODS: The core of the proposed two-stage framework is the multiscale geometric embedded convolutional neural network (MsgeCNN), which integrates geometric information into the training process and accurately segments the femoral head region. Then, the necrosis regions are segmented by the adaptive threshold method taking femoral head as the background. The area and proportion of the two are calculated to determine the grade. RESULTS: The accuracy of the proposed MsgeCNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, dice score is 93.34%. And the segmentation performance is better than the existing five segmentation algorithms. The diagnostic accuracy of the overall framework is 90.80%. CONCLUSIONS: The proposed framework can accurately segment the femoral head region and the necrosis region. The area, proportion, and other pathological information of the framework output provide auxiliary strategies for subsequent clinical treatment.

Development of a nomogram for severe influenza in previously healthy children: a retrospective cohort study.

OBJECTIVE: We aimed to develop a nomogram to predict the risk of severe influenza in previously healthy children. METHODS: In this retrospective cohort study, we reviewed the clinical data of 1135 previously healthy children infected with influenza who were hospitalized in the Children's Hospital of Soochow University between 1 January 2017 and 30 June 2021. Children were randomly assigned in a 7:3 ratio to a training or validation cohort. In the training cohort, univariate and multivariate logistic regression analyses were used to identify risk factors, and a nomogram was established. The validation cohort was used to evaluate the predictive ability of the model. RESULT: Wheezing rales, neutrophils, procalcitonin > 0.25 ng/mL, Mycoplasma pneumoniae infection, fever, and albumin were selected as predictors. The areas under the curve were 0.725 (95% CI: 0.686-0.765) and 0.721 (95% CI: 0.659-0.784) for the training and validation cohorts, respectively. The calibration curve showed that the nomogram was well calibrated. CONCLUSION: The nomogram may predict the risk of severe influenza in previously healthy children.

HDAC6-G3BP2 promotes lysosomal-TSC2 and suppresses mTORC1 under ETV4 targeting-induced low-lactate stress in non-small cell lung cancer.

TSC-mTORC1 inhibition-mediated translational reprogramming is a major adaptation mechanism upon many stresses, such as low-oxygen, -ATP, and -amino acids. But how cancer cells hijack the adaptive pathway to survive under low-lactate stress when targeting glycolysis-related signaling remains uncertain. ETV4 is an oncogenic transcription factor frequently dysregulated in human cancer. We previously found that ETV4 is associated with tumor progression and poor prognosis in non-small cell lung cancer (NSCLC). In this study, we report that ETV4 controls HK1 expression and glycolysis-lactate production to activate mTORC1 by relieving TSC2 repression of Rheb in NSCLC cells. Targeting ETV4-induced low-lactate stress is an important input for TSC2 to inhibit mTORC1 and global protein synthesis, while the core stress granule components G3BP2 and HDAC6 are selectively translated. Mechanistically, G3BP2 recruits lysosomal-TSC2 to suppress mTORC1. HDAC6 deacetylates TSC2 to sustain protein stability and associates with G3BP2 to facilitate more recruiting of TSC2 to inactivate mTORC1. In addition, the microtubule retrograde transport activity of HDAC6 drives the aggregate-like perinuclear-mTOR distribution paralleled by lower mTORC1 activity under stress. Thus, HDAC6-G3BP2 is the key complex that promotes lysosomal-TSC2 and suppresses mTORC1 when targeting ETV4, which might represent a critical adaptive mechanism for cell survival under low-lactate challenges.

Urodynamic changes before and after endoscopic valve ablation in boys diagnosed with the posterior urethral valve without chronic renal failure.

INTRODUCTION: Current research on the posterior urethral valve (PUV) mainly focuses on the follow-up of bladder function after valve ablation. However, few studies exist on the changes in bladder function before and after valve ablation. OBJECTIVES: To investigate the urodynamic changes before and after PUV ablation and determine the effect of operation on bladder function, in patients. MATERIALS AND METHODS: The clinical records of 38 boys diagnosed with PUV and undergone urodynamic exams before and after valve ablation were retrospectively reviewed. In addition, differences in patients' radiographic studies and urodynamic characteristics between pre- and post-operation were evaluated. Moreover, the urodynamic data was compared using the paired t-test and all the data was expressed as means ± SEM. Additionally, p values less than 0.05 were considered to be statistically significant. RESULTS: All the patients were diagnosed with PUV and the follow-up period after operation ranged between 9 and 114 months. The urodynamic exams were performed about 6 months after operation. The results revealed that bladder compliance improved from 8.49 ± 4.73 to 13.31 ± 6.78 ml/cmH2O while the maximum detrusor pressure decreased from 95.18 ± 37.59 to 50.71 ± 21.71 cmH2O, after valve ablation. Additionally, there were significant differences in the pre- and post-operation values of bladder compliance and maximum detrusor pressure (p < 0.05). However, there were no significant differences in the pre- and post-operation values with regard to the residual urine volume, maximum bladder volume and maximum urinary flow rate (p > 0.05). CONCLUSIONS: The adequacy of the COPUM incision is necessary. But the study showed that endoscopic valve ablation couldn't by itself completely improve the bladder function of patients diagnosed with PUV. However, it was able to improve bladder compliance and decrease maximum detrusor pressure to a certain extent. However, bladder compliance still couldn't reach the normal level.