Complex wounds with exposed bone, muscle, tendon, or hardware continue to be a therapeutic challenge for wound care providers. Wounds with exposed structures are more susceptible to infection, necrosis, and amputation. As such, rapid granulation to cover exposed deep tissue structures is essential for patient recovery.

In this prospective, pilot study, the authors evaluate the clinical outcomes of a cryopreserved umbilical tissue graft containing viable cells (vCUT) in the treatment of complex wounds.

Ten patients with 12 wounds each received 1 application of vCUT. Two patients did not complete the study and were removed from the per-protocol population. Data analyses were performed on the remaining 8 patients with 10 wounds. The average wound area was 16.5 cm2 with an average duration of 10 months. Post-application, patients were followed for an additional 4 weeks for granulation, closure, and safety outcomes.

By the end of the study, 8 of 10 (80.0%) vCUT-treated wounds achieved 100% granulation, and 3 wounds (30.0%) went on to achieve complete closure. The median area reduction was 40.5% and the median volume reduction was 59.4%.

The results of this study suggest vCUT in conjunction with standard of care can be a viable treatment option for acute and chronic lower extremity complex wounds.

The results of this study suggest vCUT in conjunction with standard of care can be a viable treatment option for acute and chronic lower extremity complex wounds.

Access to high-quality, comprehensive contraceptive care is an inherent component of reproductive human rights. However, hindrances to specific aspects of contraceptive provision, including availability, accessibility, acceptability, and quality, continue to perpetuate unmet needs. The state of Utah has recently passed a series of contraceptive policies intended to improve contraceptive access. Despite these positive changes to theoretical access, fiscal appropriations to support the implementation of these policies have been minimal, and many individuals still struggle to access contraception.

The Family Planning Elevated Contraceptive Access Program (FPE CAP), part of a larger statewide contraceptive initiative, specifically aims to improve contraceptive access within health clinics. This paper describes the study protocol for evaluating the success of FPE CAP.

Health clinics apply for membership in the FPE CAP. On acceptance in the program, they receive a cash grant for clinical supplies, equipment, 2-year duration of the intervention, and for the subsequent 12 months following the intervention.

We found that the study is adequately powered (>80% power) with our planned number of clinics and the number of months of data available in the study. To date, we have successfully completed the recruitment and enrollment of 8 of the expected 9 health organizations and 4 of the control clinics. Completed health organization enrollment for both intervention and control organizations is expected to be completed in December 2020.

The study aims to provide insight into a new approach to contraceptive initiatives by addressing comprehensive aspects of contraceptive care at the health system level. Ongoing state policy changes and implementation components may affect the evaluation outcomes.

DERR1-10.2196/18308.

DERR1-10.2196/18308.[This corrects the article DOI 10.2196/15779.].Lightweight and real-time multi-lead electrocardiogram (MECG) compression on wearable devices is important and challenging for long-term health monitoring. To make use all three kinds of correlations of MECG data simultaneouly, we construct 3-order incremental tensor and formulate data compression problem as tensor decomposition. However, the conventional tensor decomposition algorithms for large-scale tensor are usually too computationally expensive to apply to wearable devices. To reduce the computation complexity, we develop online compression approach by incrimental tracking the CANDECOMP/PARAFAC (CP) decomposition of dynamic incremental tensors, which can efficiently utilize the tensor compression result based on the previous MECG data to derive the tensor compression upon arriving of new data. We evaluate the performance of our method with the Physikalisch-Technische Bundesanstalt MECG diagnostic dataset. Our method can achieve the averaged percentage root-mean-square difference (PRD) of 8.35 2.28% and the compression ratio (CR) of 43.05 2.01, which is better than five state-of-the-art of methods. Additionally, it can also well preserve the information of R-peak. Our method is suitable for near real-time MECG compression on wearable devices.In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H∞ state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped with more design freedom leading to better steady-state accuracy compared with the conventional Luenberger observer. The phenomena of randomly occurring gain variations, which are characterized by the Bernoulli distributed random variables with certain probabilities, are taken into consideration in the implementation of the addressed PIO. Attention is focused on the design of a nonfragile PIO such that the error dynamics of the state estimation is exponentially stable in a mean-square sense, and the prescribed H∞ performance index is also achieved. Sufficient conditions for the existence of the desired PIO are established by virtue of the Lyapunov-Krasovskii functional approach and the matrix inequality technique. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed PIO design scheme.We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous samples that are quite different from existing labeled novel data can inevitably emerge in the testing phase. To this end, we consider augmenting an uncertainty assessment module into low-shot learning system to account into the disturbance of those out-of-distribution (OOD) samples. Once detected, these OOD samples are passed to human beings for active labeling. Due to the discrete nature of this uncertainty assessment process, the whole Human-In-the-Loop Low-shot (HILL) learning framework is not end-to-end trainable. We hence revisited the learning system from the aspect of reinforcement learning and introduced the REINFORCE algorithm to optimize model parameters via policy gradient. The whole system gains noticeable improvements over existing low-shot learning approaches.Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of high-quality medical expertise and infrastructure. Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community. Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities. Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings. This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments. When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively. Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing „Mild Cognitive Impairment (MCI) due to Alzheimer’s Disease (AD)” patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2%. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81% in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.Face photo-sketch style transfer aims to convert a representation of a face from the photo (or sketch) domain to the sketch (respectively, photo) domain while preserving the character of the subject. It has wide-ranging applications in law enforcement, forensic investigation and digital entertainment. However, conventional face photo-sketch synthesis methods usually require training images from both the source domain and the target domain, and are limited in that they cannot be applied to universal conditions where collecting training images in the source domain that match the style of the test image is unpractical. This problem entails two major challenges 1) designing an effective and robust domain translation model for the universal situation in which images of the source domain needed for training are unavailable, and 2) preserving the facial character while performing a transfer to the style of an entire image collection in the target domain. To this end, we present a novel universal face photo-sketch style transfer method that does not need any image from the source domain for training.