Safety and efficacy of beta-3 adrenergic agonists in treating neurogenic lower urinary tract dysfunction: A systematic review and meta-analysis.

PURPOSE: To evaluate efficacy and safety of beta-3 adrenergic agonists in adults with neurogenic lower urinary tract dysfunction. MATERIALS AND METHODS: According to a protocol (CRD42022350079), we searched multiple data sources for published and unpublished randomized controlled trials (RCTs) up to 2nd August 2022. Two review authors independently screened studies and abstracted data from the included studies. We performed statistical analyses by using a random-effects model and interpreted them according to the Cochrane Handbook for Systematic Reviews of Interventions. We used GRADE guidance to rate the certainty of evidence (CoE). RESULTS: We found data to inform two comparisons: beta-3 adrenergic agonists versus placebo (4 RCTs) and anticholinergics (2 RCTs). Only mirabegron was used for intervention in all included studies. Compared to placebo, beta-3 adrenergic agonists may have a clinically unimportant effect on urinary symptoms score (mean difference [MD] -2.50, 95% confidence interval [CI] -4.78 to -0.22; I²=92%; 2 RCTs; 192 participants; low CoE) based on minimal clinically important difference of 3. We are very uncertain of the effects of beta-3 adrenergic agonists on quality of life (MD 10.86, 95% CI 1.21 to 20.50; I²=41%; 2 RCTs; 98 participants; very low CoE). Beta-3 adrenergic agonists may result in little to no difference in major adverse events (cardiovascular adverse events) (risk ratio 0.57, 95% CI 0.14 to 2.37; I²=0%; 4 RCTs; 310 participants; low CoE). Compared to anticholinergics, no study reported urinary symptom scores and quality of life. There were no major adverse events (cardiovascular adverse events) in either study group (1 study; 60 participants; very low CoE). CONCLUSIONS: Compared to placebo, beta-3 adrenergic agonists may have similar effects on urinary symptom scores and major adverse events. There were uncertainties about their effects on quality of life. Compared to anticholinergics, we are either very uncertain or have no evidence about urinary symptom scores, quality of life, and major adverse events.

Peripheral zone thickness in preoperative MRI is predictive of Trifecta achievement after Holmium laser enucleation of the prostate (HoLEP).

PURPOSE: To investigate various anatomical features of the prostate using preoperative MRI and patients' clinical factors to identify predictors of successful Holmium:YAG laser enucleation of the prostate (HoLEP). METHODS: 71 patients who had received HoLEP and undergone a 3.0-T prostate MRI scan within 6 months before surgery were retrospectively enrolled. MRI features (e.g., total prostate and transitional zone volume, peripheral zone thickness [PZT], BPH patterns, prostatic urethral angle, intravesical prostatic protrusion, etc.) and clinical data (e.g., age, body mass index, surgical technique, etc.) were analyzed using univariable and multivariable logistic regression to identify predictors of successful HoLEP. Successful HoLEP was defined as achieving the Trifecta, characterized by the contemporary absence of postoperative complications within 3 months, a 3-month postoperative maximum flow rate (Qmax) > 15 mL/s, and no urinary incontinence at 3 months postoperatively. RESULTS: Trifecta achievement at 3 months post-surgery was observed in 37 (52%) patients. Patients with Trifecta achievement exhibited a lower preoperative IPSS-quality of life score (QoL) (4.1 vs. 4.5, P = 0.016) and a thinner preoperative peripheral zone thickness (PZT) on MRI (7.9 vs.10.3 mm, P < 0.001). In the multivariable regression analysis, a preoperative IPSS-QoL score < 5 (OR 3.98; 95% CI, 1.21-13.07; P = 0.017) and PZT < 9 mm (OR 11.51; 95% CI, 3.51-37.74; P < 0.001) were significant predictors of Trifecta achievement after HoLEP. CONCLUSIONS: Alongside the preoperative QoL score, PZT measurement in prostate MRI can serve as an objective predictor of successful HoLEP. Our results underscore an additional utility of prostate MRI beyond its role in excluding concurrent prostate cancer.

Water Vapor Therapy and Zonal Anatomy of the Prostate.

Water vapor therapy using Rezum has been recently introduced as a minimally invasive surgery for benign prostatic hyperplasia and is being increasingly performed. However, there is a lack of real-time images showing this practice and how convective water vapor acts in the prostate gland. In real-time ultrasonography, convective water vapor rapidly spreads throughout the ipsilateral transitional zone and is mostly limited within the transitional zone. For educational purposes, we would like to present a case to help readers understand water vapor therapy by visualizing convective water vapor using real-time ultrasound.

Binding-Free Taste Visualization with Plasmonic Metasurfaces.

Taste sensors using photonics, termed artificial photonic tongues, have emerged as a promising platform for intuitive taste discrimination. However, the need for complex binding protocols for each taste profile limits their applicability to a narrow range of taste molecules. Here, we introduce an intriguing "binding-free" approach to molecular taste sensing using plasmonics, eliminating the requirement for physical or chemical binding protocols. We develop a wafer-scale plasmonic metasurface constructed by coating metallic nanoparticles in a scalable manner onto a metallic mirror. This metasurface functions to detect molecular refractive indices and surface tensions via 2D projection optical images of an array of liquid droplets containing the taste molecules on top, which can immediately visualize and distinguish between the five basic tastes of molecules (including their mixtures) as well as other additional spicy and alcoholic tastes. We anticipate that this intuitive and rapid taste-sensing approach has the potential to establish a user-friendly and portable taste-sensing platform.

Programmable directional color dynamics using plasmonics.

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual devices. However, the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural designs. Here, we present a multicolor nanofilter consisting of multilayered 'active' plasmonic nanocomposites, wherein metallic nanoparticles are embedded within a conductive polymer nanofilm. These nanocomposites are fabricated with a total thickness below 100 nm using a 'lithography-free' method at the wafer level, and they inherently exhibit three prominent optical modes, accompanying scattering phenomena that produce distinct dichroic reflection and transmission colors. Here, a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed, encompassing the entire visible spectrum. Furthermore, this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum (3250 K-6250 K). This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun's elevation and prevailing weather conditions.

Proton-Assisted Assembly of Colloidal Nanoparticles into Wafer-Scale Monolayers in Seconds.

Underwater adhesion processes in nature promise controllable assembly of functional nanoparticles for industrial mass production; However, their artificial strategies have faced challenges to uniformly transfer nanoparticles into a monolayer, particularly those below 100 nm in size, over large areas. Here a scalable "one-shot" self-limiting nanoparticle transfer technique is presented, enabling the efficient transport of nanoparticles from water in microscopic volumes to an entire 2-inch wafer in a remarkably short time of 10 seconds to reach near-maximal surface coverage (≈40%) in a 2D mono-layered fashion. Employing proton engineering in electrostatic assembly accelerates the diffusion of nanoparticles (over 50 µm2/s), resulting in a hundredfold faster coating speed than the previously reported results in the literature. This charge-sensitive process further enables "pick-and-place" nanoparticle patterning at the wafer scale, with large flexibility in surface materials, including flexible metal oxides and 3D-printed polymers. As a result, the fabrication of wafer-scale disordered plasmonic metasurfaces in seconds is successfully demonstrated. These metasurfaces exhibit consistent resonating colors across diverse material and geometrical platforms, showcasing their potential for applications in full-color painting and optical encryption devices.

An unsupervised two-step training framework for low-dose computed tomography denoising.

BACKGROUND: Although low-dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT images tend to have more noise, which impedes accurate diagnosis. Recently, deep neural networks using convolutional neural networks to reduce noise in the reconstructed low-dose CT images have shown considerable improvement. However, they need a large number of paired normal- and low-dose CT images to fully train the network via supervised learning methods. PURPOSE: To propose an unsupervised two-step training framework for image denoising that uses low-dose CT images of one dataset and unpaired high-dose CT images from another dataset. METHODS: Our proposed framework trains the denoising network in two steps. In the first training step, we train the network using 3D volumes of CT images and predict the center CT slice from them. This pre-trained network is used in the second training step to train the denoising network and is combined with the memory-efficient denoising generative adversarial network (DenoisingGAN), which further enhances both objective and perceptual quality. RESULTS: The experimental results on phantom and clinical datasets show superior performance over the existing traditional machine learning and self-supervised deep learning methods, and the results are comparable to the fully supervised learning methods. CONCLUSIONS: We proposed a new unsupervised learning framework for low-dose CT denoising, convincingly improving noisy CT images from both objective and perceptual quality perspectives. Because our denoising framework does not require physics-based noise models or system-dependent assumptions, our proposed method can be easily reproduced; consequently, it can also be generally applicable to various CT scanners or dose levels.

Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data.

Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these complex data types. We applied advanced techniques of data imputation (bidirectional recurrent imputation for time series; BRITS) and feature selection (the least absolute shrinkage and selection operator; LASSO). Additionally, we utilized self-supervised algorithms and transfer learning to address the common issues with medical datasets, such as irregular data collection and sparsity. We also proposed a novel approach for discrete time-series data preprocessing, utilizing both shifting and rolling time windows and modifying time resolution. Our study evaluated the performance of a progressive self-transfer network for predicting diabetes, which demonstrated a significant improvement in metrics compared to non-progressive and single self-transfer prediction tasks, particularly in AUC, recall, and F1 score. These findings suggest that the proposed approach can mitigate accumulated errors and reflect temporal information, making it an effective tool for accurate diagnosis and disease management. In summary, our study highlights the importance of considering the complexities of multivariate, multi-instance, and time-series data in diabetes prediction.

The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review.

Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.

Supplementary Effects of Allium hookeri Extract on Glucose Tolerance in Prediabetic Subjects and C57BL/KsJ-db/db Mice.

Allium hookeri (AH) has been used as a nutritional and medicinal food in Asia for many years. Our previous studies have described its anti-diabetic, anti-obesity, and anti-inflammatory activities in animal models and prediabetes. This study investigated whether AH could improve glycemia by modulating insulin secretion in prediabetic subjects through an in-depth study. Eighty prediabetic subjects (100 ≤ fasting plasma glucose < 140 mg/dL) were randomly assigned to a placebo (n = 40) group or an ethanol AH extract (500 mg/day, n = 40) group for 12 weeks. Dietary intake and physical activity, blood glucose (an oral glucose tolerance test for 120 min), insulin (insulin response to oral glucose for 120 min), area under the curve (AUC) of glucose or insulin after oral glucose intake, insulin sensitivity markers, C-peptide, adiponectin, glycated hemoglobin A1c (HbA1c) levels, hematological tests (WBC, RBC, hemoglobin, hematocrit, and platelet count), blood biochemical parameters (ALP, AST, total bilirubin, total protein, albumin, gamma-GT, BUN, creatinine, LD, CK, and hs-CRP), and urine parameters (specific gravity and pH) were examined at both baseline and 12 weeks after supplementation with placebo or AH capsules. Fifty-eight participants (placebo group: 20 men and 10 women; AH group: 13 men and 15 women) completed the study. AH supplementation moderately reduced postprandial blood glucose at 60 min (-6.14 mg/dL, p = 0.061), postprandial insulin levels at 90 min (-16.69 µU/mL, p = 0.017), the glucose AUC at 90 min (-412.52 mg*min/dL, p = 0.021), as well as the insulin AUC at 90 min (-978.77 µU*min/mL, p = 0.021) and 120 min (-1426.41 µU*min/mL, p = 0.015) when compared with the placebo group. However, there were no effects of AH on dietary intake and physical activity; HOMA index; HbAlc; C-peptide; or adiponectin, hematological-, blood biochemical-, and urinary markers. To confirm the effects of AH extract on blood glucose insulin sensitivity, C57BL/6J or C57BL/KsJ-db/db mice were used (n = 8/group). Body weight, fasting plasma glucose level, lipid profiles, liver and renal function, pancreatic histology, and insulin immunoreactivity were assessed. In the diabetic db/db mice, hyperglycemia, which was accompanied by an increase in insulin secretion in diabetic mice, was significantly reduced by AH treatment, resulting in the alleviation of ß-cell overcompensation and insulin resistance. We confirmed that AH supplementation can effectively control blood glucose and insulin levels by improving insulin sensitivity and may be a potential agent for glycemic control in subjects with prediabetes and type 2 diabetes mellitus.

Effects of anthocyanin supplementation on blood lipid levels: a systematic review and meta-analysis.

Introduction: Dyslipidemia is a major cardiovascular disease risk factor associated with increased mortality. The intake of plant food-derived bioactive compounds is associated with beneficial cardiovascular effects, including decreased blood lipid levels and cardiovascular risk. We aimed to evaluate the effects of anthocyanin intake on blood lipid levels by analyzing relevant randomized controlled trials. Methods: We searched the PubMed and Embase databases using the "Patient/Population, Intervention, Comparison, and Outcomes" format to determine whether anthocyanin supplementation intervention affected blood lipid levels compared with placebo supplementation in human participants. Results: A total of 41 studies with 2,788 participants were included in the meta-analysis. Anthocyanin supplementation significantly reduced triglyceride [standardized mean difference (SMD) = -0.10; 95% confidence interval [CI], -0.18, -0.01) and low-density lipoprotein-cholesterol (SMD = -0.16; 95% CI -0.26, -0.07) levels and increased high-density lipoprotein-cholesterol levels (SMD = 0.42; 95% CI 0.20, 0.65). Discussion: Anthocyanin supplementation significantly improved blood lipid component levels in the included studies. Larger, well-designed clinical trials are needed to further investigate the effects of anthocyanin intake on blood lipid levels and the safety of anthocyanin supplementation for treating dyslipidemia. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021257087, identifier: CRD42021257087.

Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study.

The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.

A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer.

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multi-scale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 × 5 and 6 × 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.

Effects of Puffing, Acid, and High Hydrostatic Pressure Treatments on Ginsenoside Profile and Antioxidant Capacity of Mountain-Cultivated Panax ginseng.

The purpose of this study was to investigate the effects of puffing, acid, and high hydrostatic pressure (HHP) treatments on the ginsenoside profile and antioxidant capacity of mountain-cultivated Panax ginseng (MCPG) before and after treatments. Puffing and HHP treatments decreased extraction yield and increased crude saponin content. The combination of puffing and HHP treatment showed significantly higher crude saponin content than each single treatment. Puffing treatment showed the highest ginsenoside conversion compared with HHP and acid treatments. Significant ginsenoside conversion was not observed in HHP treatment but was in acid treatment. When the puffing and acid treatments were combined, Rg3 and compound K content (1.31 mg and 10.25 mg) was significantly higher than that of the control (0.13 mg and 0.16 mg) and acid treatment (0.27 mg and 0.76 mg). No synergistic effect was observed between acid and HHP treatments. In the case of functional properties, the puffing treatment showed a significant increase in TFC (29.6%), TPC (1072%), and DPPH radical scavenging capacity (2132.9%) compared to the control, while acid and HHP combined treatments did not significantly increase; therefore, the synergistic effects of HHP/puffing and acid/puffing treatments were observed in crude saponin content and ginsenoside conversion, respectively. Consequently, puffing combined with acid or HHP treatments may provide new ways to produce high-value-added MCPG with a higher content of Rg3 and compound K or crude saponin compared to untreated MCPG.

Unsupervised Visual Representation Learning Based on Segmentation of Geometric Pseudo-Shapes for Transformer-Based Medical Tasks.

Recently, transformer-based architectures have been shown to outperform classic convolutional architectures and have rapidly been established as state-of-the-art models for many medical vision tasks. Their superior performance can be explained by their ability to capture long-range dependencies of their multi-head self-attention mechanism. However, they tend to overfit on small- or even medium-sized datasets because of their weak inductive bias. As a result, they require massive, labeled datasets, which are expensive to obtain, especially in the medical domain. This motivated us to explore unsupervised semantic feature learning without any form of annotation. In this work, we aimed to learn semantic features in a self-supervised manner by training transformer-based models to segment the numerical signals of geometric shapes inserted on original computed tomography (CT) images. Moreover, we developed a Convolutional Pyramid vision Transformer (CPT) that leverages multi-kernel convolutional patch embedding and local spatial reduction in each of its layer to generate multi-scale features, capture local information, and reduce computational cost. Using these approaches, we were able to noticeably outperformed state-of-the-art deep learning-based segmentation or classification models of liver cancer CT datasets of 5,237 patients, the pancreatic cancer CT datasets of 6,063 patients, and breast cancer MRI dataset of 127 patients.

Antioxidant and Immune Stimulating Effects of Allium cepa Skin in the RAW 264.7 Cells and in the C57BL/6 Mouse Immunosuppressed by Cyclophosphamide.

Allium cepa L. (onion) has been reported to have various pharmacological effects, such as preventing heart disease, and improving antimicrobial activity and immunological effects. The Republic of Korea produced 1,195,563 tons of onions (2022). The flesh of onion is used as food while the onion skin (OS) is thrown away as an agro-food by-product and is considered to induce environmental pollution. Thus, we hypothesize that increasing usage of OS as functional food material could help protect from the environment pollution. The antioxidant effects and immune-enhancing effects of OS were evaluated as functional activities of OS. In this study, OS showed high 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activities and xanthine oxidase (XO) inhibitory activity. The antioxidant activities increased in a dose-dependent manner. The IC50 values of DPPH, ABTS radical scavenging activity, and XO inhibitory activity were 954.9 µg/mL, 28.0 µg/mL, and 10.7 µg/mL, respectively. Superoxide dismutase and catalase activities of OS in RAW 264.7 cells were higher than those of the media control. There was no cytotoxicity of OS found in RAW 264.7 cells. Nitric oxide and cytokines (IL-1ß, IL-6, IFN-γ, and TNF-α) concentrations in RAW 264.7 cells significantly increased in a dose dependent manner. Immune-stimulating effects of OS were evaluated in immunosuppressed mice induced by cyclophosphamide. White blood cell count and the B cell proliferation of splenocytes were higher in OS100 (OS extract 100 mg/kg body weight) and OS200 (OS extract 200 mg/kg body weight) groups than in the negative control (NC) group. Serum IgG and cytokine (IL-1ß and IFN-γ) levels were also higher in OS100 and OS200 groups than in the NC group. OS treatment increased NK cell activity compared with the NC group. The results suggested that OS can improve antioxidant and immune stimulating effects. The use of OS as functional supplement can reduce the agro-food by-product and it may contribute to carbon neutrality.

Intra-person multi-task learning method for chronic-disease prediction.

In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps.

Effect of ß3-adrenoceptor agonist on the micromotion of bilateral major pelvic ganglion-excised rat bladder.

AIMS: Micromotion is an autonomous intramural movement of the bladder, and is believed to be an initial step in the generation of urinary urgency. Therefore, controlling micromotion may be a novel target in overactive bladder (OAB) treatment. However, developing micromotion treatment has been limited by the absence of a standardized animal model. We attempted to create a micromotion animal model and investigated the effectiveness of a ß3 -adrenoceptor agonist (CL316,243) on micromotion. METHODS: Bilateral major pelvic ganglia (MPGs) were excised in 18 male Sprague-Dawley rats, resulting in an almost completely denervated bladder. On postoperative Day 7, cystometry was performed. Rats were divided into three treatment groups: CL316,243; ß3- adrenoceptor antagonist (SR59230A) pretreated CL316,243; and a nonselective antimuscarinic agent (oxybutynin). Changes in micromotion were evaluated after the intra-arterial administration of each agent. RESULTS: Low-amplitude oscillations in intravesical pressure (micromotion) were observed 1 week after MPGs excision. Micromotion frequency significantly (p = 0.003) decreased (2.17 ± 3.54 times/5 min) with CL316,243 compared with vehicle (6.33 ± 1.97 times/5 min). Micromotion amplitude also decreased with CL316,243 (1.15 ± 1.93 cmH2 O) compared with vehicle (5.96 ± 5.12 cmH2 O), approaching conventional significance (p = 0.090). No significant decreases in frequency or amplitude were observed with oxybutynin treatment. CONCLUSIONS: Systemic administration of the ß3 -adrenoceptor agonist CL316,243 effectively controlled micromotion in bilateral MPGs-excised, almost completely denervated rat bladders. This result indicates that ß3 -adrenoceptor agonist may affect the bladder directly, suggesting that it might be effective for overall OAB, regardless of the presence or level of neurological deficits. Bilateral MPGs-excised rats are considered a plausible micromotion animal model suitable for future research.