European Society of Coloproctology: Guidelines for diagnosis and treatment of cryptoglandular anal fistula.

AIM: The primary aim of the European Society of Coloproctology (ESCP) Guideline Development Group (GDG) was to produce high-quality, evidence-based guidelines for the management of cryptoglandular anal fistula with input from a multidisciplinary group and using transparent, reproducible methodology. METHODS: Previously published methodology in guideline development by the ESCP has been replicated in this project. The guideline development process followed the requirements of the AGREE-S tool kit. Six phases can be identified in the methodology. Phase one sets the scope of the guideline, which addresses the diagnostic and therapeutic management of perianal abscess and cryptoglandular anal fistula in adult patients presenting to secondary care. The target population for this guideline are healthcare practitioners in secondary care and patients interested in understanding the clinical evidence available for various surgical interventions for anal fistula. Phase two involved formulation of the GDG. The GDG consisted of 21 coloproctologists, three research fellows, a radiologist and a methodologist. Stakeholders were chosen for their clinical and academic involvement in the management of anal fistula as well as being representative of the geographical variation among the ESCP membership. Five patients were recruited from patient groups to review the draft guideline. These patients attended two virtual meetings to discuss the evidence and suggest amendments. In phase three, patient/population, intervention, comparison and outcomes questions were formulated by the GDG. The GDG ratified 250 questions and chose 45 for inclusion in the guideline. In phase four, critical and important outcomes were confirmed for inclusion. Important outcomes were pain and wound healing. Critical outcomes were fistula healing, fistula recurrence and incontinence. These outcomes formed part of the inclusion criteria for the literature search. In phase five, a literature search was performed of MEDLINE (Ovid), PubMed, Embase (Ovid) and the Cochrane Database of Systematic Reviews by eight teams of the GDG. Data were extracted and submitted for review by the GDG in a draft guideline. The most recent systematic reviews were prioritized for inclusion. Studies published since the most recent systematic review were included in our analysis by conducting a new meta-analysis using Review manager. In phase six, recommendations were formulated, using grading of recommendations, assessment, development, and evaluations, in three virtual meetings of the GDG. RESULTS: In seven sections covering the diagnostic and therapeutic management of perianal abscess and cryptoglandular anal fistula, there are 42 recommendations. CONCLUSION: This is an up-to-date international guideline on the management of cryptoglandular anal fistula using methodology prescribed by the AGREE enterprise.

Tumour grade and stage are associated with specific body composition phenotypes with visceral obesity predisposing the host to a less aggressive tumour in colorectal cancer.

BACKGROUND: Sarcopenia, myosteatosis and visceral obesity (VO) are known to negatively impact on outcomes from colorectal cancer (CRC). Little is known about tumour factors associated with these body composition (BC) phenotypes. We aimed to identify whether histopathological tumour characteristics were associated with various BC phenotypes. METHODS: A prospectively collected database of patients undergoing surgery for primary CRC at a tertiary referral unit in the United Kingdom was analysed. Sarcopenia, myosteatosis and VO were identified on preoperative CT. Binary logistic regression modelling was performed to determine significant associations between tumour stage, grade and BC phenotype. RESULTS: Final analysis included 795 patients; median age 69, 56% male, 65% were sarcopenic, 72% myosteatotic, 52% VO and 20% had sarcopenic obesity (SO). VO patients were significantly less likely to have advanced T Stage (T3-4) OR0.62(95%CI 0.44-0.86, p = 0.005); nodal metastases OR0.60(95%CI 0.44-0.82, p = 0.001); vascular invasion OR0.63(95%CI 0.46-0.88, p = 0.006) and poor tumour differentiation OR0.49(95%CI 0.28-0.86, p = 0.012). Myosteatotic patients were more likely to have metastatic disease OR2.31(95%CI 1.15-4.63, p = 0.018) but less likely to have poorly differentiated tumours OR0.48(95%CI 0.27-0.86, p = 0.013). SO patients were significantly more likely to have poorly differentiated tumours OR2.01(95%CI 1.04-3.87, p = 0.037). CONCLUSION: VO predisposes to earlier stage tumours with a less aggressive tumour phenotype. The SO group have adverse tumour characteristics which may be explained by differences in fat distribution. Myosteatosis relates to increased likelihood of distant metastasis that may be related to a systemic inflammatory response, despite the association with better differentiated tumours.

Quantitative Magnetic Resonance Imaging in Perianal Crohn's Disease at 1.5 and 3.0 T: A Feasibility Study.

Perianal Crohn's Disease (pCD) is a common manifestation of Crohn's Disease. Absence of reliable disease measures makes disease monitoring unreliable. Qualitative MRI has been increasingly used for diagnosing and monitoring pCD and has shown potential for assessing response to treatment. Quantitative MRI sequences, such as diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE) and magnetisation transfer (MT), along with T2 relaxometry, offer opportunities to improve diagnostic capability. Quantitative MRI sequences (DWI, DCE, MT and T2) were used in a cohort of 25 pCD patients before and 12 weeks after biological therapy at two different field strengths (1.5 and 3 T). Disease activity was measured with the Perianal Crohn's Disease Activity index (PDAI) and serum C-reactive protein (CRP). Diseased tissue areas on MRI were defined by a radiologist. A baseline model to predict outcome at 12 weeks was developed. No differences were seen in the quantitative MR measured in the diseased tissue regions from baseline to 12 weeks; however, PDAI and CRP decreased. Baseline PDAI, CRP, T2 relaxometry and surgical history were found to have a moderate ability to predict response after 12 weeks of biological treatment. Validation in larger cohorts with MRI and clinical measures are needed in order to further develop the model.

BiCyCLE NMES-neuromuscular electrical stimulation in the perioperative treatment of sarcopenia and myosteatosis in advanced rectal cancer patients: design and methodology of a phase II randomised controlled trial.

BACKGROUND: Colorectal cancer is associated with secondary sarcopenia (muscle loss) and myosteatosis (fatty infiltration of muscle) and patients who exhibit these host characteristics have poorer outcomes following surgery. Furthermore, patients, who undergo curative advanced rectal cancer surgery such as pelvic exenteration, are at risk of skeletal muscle loss due to immobility, malnutrition and a post-surgical catabolic state. Neuromuscular electrical stimulation (NMES) may be a feasible adjunctive treatment to help ameliorate these adverse side-effects. Hence, the purpose of this study is to investigate NMES as an adjunctive pre- and post-operative treatment for rectal cancer patients in the radical pelvic surgery setting and to provide early indicative evidence of efficacy in relation to key health outcomes. METHOD: In a phase II, double-blind, randomised controlled study, 58 patients will be recruited and randomised (1:1) to either a treatment (NMES plus standard care) or placebo (sham-NMES plus standard care) group. The intervention will begin 2 weeks pre-operatively and continue for 8 weeks after exenterative surgery. The primary outcome will be change in mean skeletal muscle attenuation, a surrogate marker of myosteatosis. Sarcopenia, quality of life, inflammatory status and cancer specific outcomes will also be assessed. DISCUSSION: This phase II randomised controlled trial will provide important preliminary evidence of the potential for this adjunctive treatment. It will provide guidance on subsequent development of phase 3 studies on the clinical benefit of NMES for rectal cancer patients in the radical pelvic surgery setting. TRIAL REGISTRATION: Protocol version 6.0; 05/06/20. ClinicalTrials.gov NCT04065984 . Registered on 22 August 2019; recruiting.

Validation and Update of the Lémann Index to Measure Cumulative Structural Bowel Damage in Crohn's Disease.

BACKGROUND & AIMS: The Lémann Index is a tool measuring cumulative structural bowel damage in Crohn's disease (CD). We reported on its validation and updating. METHODS: This was an international, multicenter, prospective, cross-sectional observational study. At each center, 10 inclusions, stratified by CD duration and location, were planned. For each patient, the digestive tract was divided into 4 organs, upper tract, small bowel, colon/rectum, anus, and subsequently into segments, explored systematically by magnetic resonance imaging and by endoscopies in relation to disease location. For each segment, investigators retrieved information on previous surgical procedures, identified predefined strictures and penetrating lesions of maximal severity (grades 1-3) at each organ investigational method (gastroenterologist and radiologist for magnetic resonance imaging), provided segmental damage evaluation ranging from 0.0 to 10.0 (complete resection). Organ resection-free cumulative damage evaluation was then calculated from the sum of segmental damages. Then investigators provided a 0-10 global damage evaluation from the 4-organ standardized cumulative damage evaluations. Simple linear regressions of investigator damage evaluations on their corresponding Lémann Index were studied, as well as calibration plots. Finally, updated Lémann Index was derived through multiple linear mixed models applied to combined development and validation samples. RESULTS: In 15 centers, 134 patients were included. Correlation coefficients between investigator damage evaluations and Lémann Indexes were >0.80. When analyzing data in 272 patients from both samples and 27 centers, the unbiased correlation estimates were 0.89, 0,97, 0,94, 0.81, and 0.91 for the 4 organs and globally, and stable when applied to one sample or the other. CONCLUSIONS: The updated Lémann Index is a well-established index to assess cumulative bowel damage in CD that can be used in epidemiological studies and disease modification trials.

Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes.

Discriminative features of 3-D meshes are significant to many 3-D shape analysis tasks. However, handcrafted descriptors and traditional unsupervised 3-D feature learning methods suffer from several significant weaknesses: 1) the extensive human intervention is involved; 2) the local and global structure information of 3-D meshes cannot be preserved, which is in fact an important source of discriminability; 3) the irregular vertex topology and arbitrary resolution of 3-D meshes do not allow the direct application of the popular deep learning models; 4) the orientation is ambiguous on the mesh surface; and 5) the effect of rigid and nonrigid transformations on 3-D meshes cannot be eliminated. As a remedy, we propose a deep learning model with a novel irregular model structure, called mesh convolutional restricted Boltzmann machines (MCRBMs). MCRBM aims to simultaneously learn structure-preserving local and global features from a novel raw representation, local function energy distribution. In addition, multiple MCRBMs can be stacked into a deeper model, called mesh convolutional deep belief networks (MCDBNs). MCDBN employs a novel local structure preserving convolution (LSPC) strategy to convolve the geometry and the local structure learned by the lower MCRBM to the upper MCRBM. LSPC facilitates resolving the challenging issue of the orientation ambiguity on the mesh surface in MCDBN. Experiments using the proposed MCRBM and MCDBN were conducted on three common aspects: global shape retrieval, partial shape retrieval, and shape correspondence. Results show that the features learned by the proposed methods outperform the other state-of-the-art 3-D shape features.

Asymmetric Actuator Backlash Compensation in Quantized Adaptive Control of Uncertain Networked Nonlinear Systems.

This paper mainly aims at the problem of adaptive quantized control for a class of uncertain nonlinear systems preceded by asymmetric actuator backlash. One challenging problem that blocks the construction of our control scheme is that the real control signal is wrapped in the coupling of quantization effect and nonsmooth backlash nonlinearity. To resolve this challenge, this paper presents a two-stage separation approach established on two new technical components, which are the approximate asymmetric backlash model and the nonlinear decomposition of quantizer, respectively. Then the real control is successfully separated from the coupling dynamics. Furthermore, by employing the neural networks and adaptation method in control design, a quantized controller is developed to guarantee the asymptotic convergence of tracking error to an adjustable region of zero and uniform ultimate boundedness of all closed-loop signals. Eventually, simulations are conducted to support our theoretical results.

Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights.

In this paper, we propose a new approach to establish a landslide displacement forecasting model based on artificial neural networks (ANNs) with random hidden weights. To quantify the uncertainty associated with the predictions, a framework for probabilistic forecasting of landslide displacement is developed. The aim of this paper is to construct prediction intervals (PIs) instead of deterministic forecasting. A lower-upper bound estimation (LUBE) method is adopted to construct ANN-based PIs, while a new single hidden layer feedforward ANN with random hidden weights for LUBE is proposed. Unlike the original implementation of LUBE, the input weights and hidden biases of the ANN are randomly chosen, and only the output weights need to be adjusted. Combining particle swarm optimization (PSO) and gravitational search algorithm (GSA), a hybrid evolutionary algorithm, PSOGSA, is utilized to optimize the output weights. Furthermore, a new ANN objective function, which combines a modified combinational coverage width-based criterion with one-norm regularization, is proposed. Two benchmark data sets and two real-world landslide data sets are presented to illustrate the capability and merit of our method. Experimental results reveal that the proposed method can construct high-quality PIs.

Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

Weighted Couple Sparse Representation With Classified Regularization for Impulse Noise Removal.

Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Robust Nonnegative Patch Alignment for Dimensionality Reduction.

Dimensionality reduction is an important method to analyze high-dimensional data and has many applications in pattern recognition and computer vision. In this paper, we propose a robust nonnegative patch alignment for dimensionality reduction, which includes a reconstruction error term and a whole alignment term. We use correntropy-induced metric to measure the reconstruction error, in which the weight is learned adaptively for each entry. For the whole alignment, we propose locality-preserving robust nonnegative patch alignment (LP-RNA) and sparsity-preserviing robust nonnegative patch alignment (SP-RNA), which are unsupervised and supervised, respectively. In the LP-RNA, we propose a locally sparse graph to encode the local geometric structure of the manifold embedded in high-dimensional space. In particular, we select large p -nearest neighbors for each sample, then obtain the sparse representation with respect to these neighbors. The sparse representation is used to build a graph, which simultaneously enjoys locality, sparseness, and robustness. In the SP-RNA, we simultaneously use local geometric structure and discriminative information, in which the sparse reconstruction coefficient is used to characterize the local geometric structure and weighted distance is used to measure the separability of different classes. For the induced nonconvex objective function, we formulate it into a weighted nonnegative matrix factorization based on half-quadratic optimization. We propose a multiplicative update rule to solve this function and show that the objective function converges to a local optimum. Several experimental results on synthetic and real data sets demonstrate that the learned representation is more discriminative and robust than most existing dimensionality reduction methods.

Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity.

This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

Energy-efficient SVM learning control system for biped walking robots.

An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.

Novel deployment of a covered duodenal stent in open surgery to facilitate closure of a malignant duodenal perforation.

BACKGROUND: Its a dilemma to attempt a palliative procedure to debulk the tumour and/or prevent future obstructive complications in a locally advanced intra abdominal malignancy. CASE PRESENTATION: A 38 year old Vietnamese man presented with a carcinoma of the colon which had invaded the gallbladder and duodenum with a sealed perforation of the second part of the duodenum. Following surgical exploration, it was evident that primary closure of the perforated duodenum was not possible due to the presence of unresectable residual tumour. CONCLUSION: We describe a novel technique using a covered duodenal stent deployed at open surgery to aid closure of a malignant duodenal perforation.