Project description:The tensor veli palatini muscle is involved in opening of the Eustachian tube during chewing and swallowing, allowing for equilibration of pressure between the middle ear and external environment. In patients with cleft palate, abnormal musculature in the region of the cleft results in Eustachian tube dysfunction. A palatoplasty with muscle repositioning is advocated to reduce the incidence of otitis media, which is a result of this. A special suture is described which can be incorporated into a palatoplasty procedure to further reduce the incidence of otitis media. This suture is inserted around the tendon of the tensor veli palatini muscle bilaterally, and when activated under tension in the midline, it results in opening of the Eustachian tube with improved middle ear ventilation. This novel suture results in a reduction in the incidence of otitis media due to improved middle ear ventilation and reduces tension across the suture margins.
Project description:BackgroundPrevious meta-analyses examining skin closure methods for all surgical wounds have found suture to have significantly decreased rates of wound dehiscence compared to tissue adhesive; however, this was not specific to laparoscopic wounds alone. This study aims to determine the best method of skin closure in patients undergoing laparoscopic abdominopelvic surgery in order to minimize wound complications and pain, while maximize cosmesis, time and cost efficiency.MethodsA comprehensive search of EMBASE, Medline, Pubmed, and CENTRAL was conducted from inception to 1st May 2020 for randomized controlled trials (RCTs). Two independent reviewers extracted data and assessed risk of bias. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was used to describe the quality of evidence. Meta-analysis was performed using a random-effects model. A summary relative risk (RR) was calculated for dichotomous outcomes where data could be pooled. (Prospero registration number: CRD42019122639).ResultsThe literature search identified 11,628 potentially eligible studies. Twelve RCTs met inclusion criteria. There was no difference in wound complications (infection, dehiscence, and drainage) between sutures, tissue adhesives nor adhesive papertape. Low-quality evidence found transcutaneous suture had lower rates of wound complications compared with subcuticular sutures (RR 0.22, 95%: CI 0.05-0.98). There was no evidence of a difference in patient-evaluated cosmesis, prolonged pain, or patient satisfaction between the three groups. Closure with tissue adhesive and adhesive papertape was faster and cheaper than suture.ConclusionTissue adhesive and adhesive papertape offer safe, cost and time-saving alternatives to closure of laparoscopic port sites compared to suture.
Project description:BackgroundIt is of biological interest to make genome-wide predictions of the locations of DNA melting bubbles using statistical mechanics models. Computationally, this poses the challenge that a generic search through all combinations of bubble starts and ends is quadratic.ResultsAn efficient algorithm is described, which shows that the time complexity of the task is O(NlogN) rather than quadratic. The algorithm exploits that bubble lengths may be limited, but without a prior assumption of a maximal bubble length. No approximations, such as windowing, have been introduced to reduce the time complexity. More than just finding the bubbles, the algorithm produces a stitch profile, which is a probabilistic graphical model of bubbles and helical regions. The algorithm applies a probability peak finding method based on a hierarchical analysis of the energy barriers in the Poland-Scheraga model.ConclusionExact and fast computation of genomic stitch profiles is thus feasible. Sequences of several megabases have been computed, only limited by computer memory. Possible applications are the genome-wide comparisons of bubbles with promotors, TSS, viral integration sites, and other melting-related regions.
Project description:BackgroundIncisional hernia remains a frequent problem after midline laparotomy. This study compared a short stitch to standard loop closure using an ultra-long-term absorbent elastic suture material.MethodsA prospective, multicentre, parallel-group, double-blind, randomized, controlled superiority trial was designed for the elective setting. Adult patients were randomly assigned by computer-generated sequence to fascial closure using a short stitch (5 to 8 mm every 5 mm, USP 2-0, single thread HR 26 mm needle) or long stitch technique (10 mm every 10 mm, USP 1, double loop, HR 48 mm needle) with a poly-4-hydroxybutyrate-based suture material (Monomax®). Incisional hernia assessed by ultrasound 1 year after surgery was the primary outcome.ResultsThe trial randomized 425 patients to short (n = 215) or long stitch technique (n = 210) of whom 414 (97.4 per cent) completed 1 year of follow-up. In the short stitch group, the fascia was closed with more stitches (46 (12 s.d.) versus 25 (7 s.d.); P < 0.001) and higher suture-to-wound length ratio (5.3 (2.2 s.d.) versus 4.0 (1.3 s.d.); P < 0.001). At 1 year, seven of 210 (3.3 per cent) patients in the short and 13 of 204 (6.4 per cent) patients in the long stitch group developed incisional hernia (odds ratio 1.97, 95 per cent confidence interval 0.77 to 5.05; P = 0.173).ConclusionThe 1-year incisional hernia development was relatively low with clinical but not statistical difference between short and long stitches. Registration number: NCT01965249 (http://www.clinicaltrials.gov).
Project description:Knitting turns yarn, a 1D material, into a 2D fabric that is flexible, durable, and can be patterned to adopt a wide range of 3D geometries. Like other mechanical metamaterials, the elasticity of knitted fabrics is an emergent property of the local stitch topology and pattern that cannot solely be attributed to the yarn itself. Thus, knitting can be viewed as an additive manufacturing technique that allows for stitch-by-stitch programming of elastic properties and has applications in many fields ranging from soft robotics and wearable electronics to engineered tissue and architected materials. However, predicting these mechanical properties based on the stitch type remains elusive. Here we untangle the relationship between changes in stitch topology and emergent elasticity in several types of knitted fabrics. We combine experiment and simulation to construct a constitutive model for the nonlinear bulk response of these fabrics. This model serves as a basis for composite fabrics with bespoke mechanical properties, which crucially do not depend on the constituent yarn.
Project description:Several devices have been designed and tried over the years to percutaneously close atrial septal defects (ASDs). Most of the devices were first experimented in animal models with subsequent clinical testing in human subjects. Some devices were discontinued or withdrawn from further clinical use for varied reasons and other devices received Food and Drug Administration (FDA) approval with consequent continued usage. The outcomes of both discontinued and currently used devices was presented in some detail. The results of device implantation are generally good when appropriate care and precautions are undertaken. At this time, Amplatzer Septal Occluder is most frequently utilized device for occlusion of secundum ASD around the world.
Project description:Random vector functional link and extreme learning machine have been extended by the type-2 fuzzy sets with vector stacked methods, this extension leads to a new way to use tensor to construct learning structure for the type-2 fuzzy sets-based learning framework. In this paper, type-2 fuzzy sets-based random vector functional link, type-2 fuzzy sets-based extreme learning machine and Tikhonov-regularized extreme learning machine are fused into one network, a tensor way of stacking data is used to incorporate the nonlinear mappings when using type-2 fuzzy sets. In this way, the network could learn the sub-structure by three sub-structures' algorithms, which are merged into one tensor structure via the type-2 fuzzy mapping results. To the stacked single fuzzy neural network, the consequent part parameters learning is implemented by unfolding tensor-based matrix regression. The newly proposed stacked single fuzzy neural network shows a new way to design the hybrid fuzzy neural network with the higher order fuzzy sets and higher order data structure. The effective of the proposed stacked single fuzzy neural network are verified by the classical testing benchmarks and several statistical testing methods.
Project description:Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of n stacked transformations are applied to each image during network training. The underlying assumption is that the "expected" domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented "big" data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with n=9 transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n = 10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11%(Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than "shallower" stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n = 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.