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Severe soft tissue defects and amputated digits are clinically common injuries. Primary treatments include surgical free flap transfer and digit replantation, but these can fail because of vascular compromise. Postoperative monitoring is therefore crucial for timely detection of vessel obstruction and survival of replanted digits and free flaps. However, current postoperative clinical monitoring methods are labor intensive and highly dependent on the experience of nurses and surgeons. Here, we developed on-skin biosensors for noninvasive and wireless postoperative monitoring based on pulse oximetry. The on-skin biosensor was made of polydimethylsiloxane with gradient cross-linking to create a self-adhesive and mechanically robust substrate that interfaces with skin. The substrate was shown to exhibit appropriate adhesion on one side for both high-fidelity measurements of the sensor and low risk of peeling injury to delicate tissues. The other side demonstrated mechanical integrity to facilitate flexible hybrid integration of the sensor. Validation studies using a model of vascular obstruction in rats demonstrated the effectiveness of the sensor in vivo. Clinical studies indicated that the on-skin biosensor was accurate and more responsive than current clinical monitoring methods in identifying microvascular conditions. Comparisons with existing monitoring techniques, including laser Doppler flowmetry and micro-lightguide spectrophotometry, further verified the sensor’s accuracy and ability to identify both arterial and venous insufficiency. These findings suggest that this on-skin biosensor may improve postoperative outcomes in free flap and replanted digit surgeries by providing sensitive and unbiased data directly from the surgical site that can be remotely monitored.

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e. total surface area, mean cortical thickness) using a genetically-informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, among which 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recounted loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant overlap of global and regional genetic modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

The arrival of surgical robots in high-end medical equipment is a landmark, and the realization of tactile sensation a major challenge in this important cutting-edge research field. Aiming to address this issue, we present ultra-sensitive ionic electronic skin in the form of flexible capacitive pressure sensors, which incorporate multistage bionic microstructures in ion gels for the purpose of monitoring the delicate operations of surgical robots. Significantly, the ionic skin exhibits an ultra-high sensitivity of 9484.3 kPa−1 (

In this paper, a new class of five parameter gamma-exponentiated or generalized modified Weibull (GEMW) distribution which includes exponential, Rayleigh, Weibull, modified Weibull, exponentiated Weibull, exponentiated exponential, exponentiated modified Weibull, exponentiated modified exponential, gamma-exponentiated exponential, gammaexponentiated Rayleigh, gamma-modified Weibull, gamma-modified exponential, gamma-Weibull, gamma-Rayleigh and gamma-exponential distributions as special cases is proposed and studied. Mathematical properties of this new class of distributions including moments, mean deviations, Bonferroni and Lorenz curves, distribution of order statistics and Renyi entropy are presented. Maximum likelihood estimation technique is used to estimate the model parameters and applications to real data sets presented in order to illustrate the usefulness of this new class of distributions and its sub-models.

Purpose: Epidemiological studies have assessed the association between Caveolin-1 polymorphism and cancer risk. However, published data are still inconclusive. To clarify this inconsistency, we conducted a meta-analysis to evaluate the relationship between Caveolin-1 polymorphism (rs7804372) and cancer susceptibility. Methods: We conducted a comprehensive literature search, using PubMed, Embase, Medline, Web of Science, CNKI, and Wanfang database, which included English and Chinese literatures. The latest updated date was January 2018. The following search terms were performed to retrieve the relevant articles: ((CAV1) OR (Caveolin-1) OR (rs7804372)) AND (cancer OR tumor OR carcinoma OR neoplasms OR malignancy) AND (polymorphism OR mutation OR variant OR genotype). Odds ratio (OR) with 95% confidence interval (CI) was used to estimate the pooled effect. Results: In the overall analysis, this kind of polymorphism showed a significant association with increased risk of cancer: allelic model (T/A; OR = 1.33, 95% CI: 1.19 - 1.49; P < 0.0001), homozygous (TT/AA; OR = 1.73, 95% CI: 1.37 - 2.18; P < 0.0001), and heterozygous genetic models (TT/TA; OR = 1.23, 95% CI: 1.14 - 1.33; P < 0.0001), the dominant genetic model (TT + TA/AA; OR = 1.58, 95% CI: 1.28 - 1.96; P < 0.0001), and the recessive genetic model (TT/TA + AA; OR = 1.34, 95% CI: 1.20 - 1.50; P < 0.0001). In addition, the stratified analysis of the results was carried out by ethnicity, HWE status, and cancer types. The outcome indicated that Caveolin-1 rs7804372 polymorphism was associated with an increased risk of cancer. Conclusion: The present study demonstrated that the allele T of Caveolin-1 (rs7804372) polymorphism might associate with increased susceptibility to cancer, and might predict worse survival in patients with various types of cancer. However, further well-designed studies are required to evaluate this association.

Background The COVID-19 pandemic disrupted Alzheimer disease randomized clinical trials (RCTs), forcing investigators to make changes in the conduct of such trials while endeavoring to maintain their validity. Changing ongoing RCTs carries risks for biases and threats to validity. To understand the impact of exigent modifications due to COVID-19, we examined several scenarios in symptomatic and disease modification trials that could be made. Methods We identified both symptomatic and disease modification Alzheimer disease RCTs as exemplars of those that would be affected by the pandemic and considered the types of changes that sponsors could make to each. We modeled three scenarios for each of the types of trials using existing datasets, adjusting enrollment, follow-ups, and dropouts to examine the potential effects COVID-19-related changes. Simulations were performed that accounted for completion and dropout patterns using linear mixed effects models, modeling time as continuous and categorical. The statistical power of the scenarios was determined. Results Truncating both symptomatic and disease modification trials led to underpowered trials. By contrast, adapting the trials by extending the treatment period, temporarily stopping treatment, delaying outcomes assessments, and performing remote assessment allowed for increased statistical power nearly to the level originally planned. Discussion These analyses support the idea that disrupted trials under common scenarios are better continued and extended even in the face of dropouts, treatment disruptions, missing outcomes, and other exigencies and that adaptations can be made that maintain the trials’ validity. We suggest some adaptive methods to do this noting that some changes become under-powered to detect the original effect sizes and expected outcomes. These analyses provide insight to better plan trials that are resilient to unexpected changes to the medical, social, and political milieu.

BackgroundThe COVID-19 pandemic disrupted Alzheimer disease randomized clinical trials (RCTs)forcing investigators to make changes in the conduct of such trials while endeavoring to maintain their validity. Changing ongoing RCTs carries risks for biases and threats to validity. To understand the impact of exigent modifications due to COVID-19 we examined several scenarios in symptomatic and disease modification trials that could be made.MethodsWe identified both symptomatic and disease modification Alzheimer disease RCTs as exemplars of those that would be affected by the pandemic and considered the types of changes that sponsors could make to each. We modeled three scenarios for each of the types of trialsusing existing datasets, adjusting enrollment, follow-ups, and dropouts to examine the potential effects COVID-19-related changes.Simulations were performed that accounted for completion and dropout patterns using linear mixed effects models, modeling time as continuous and categorical. The statistical power of the scenarios was determined.ResultsTruncating both symptomatic and disease modification trials, led to underpowered trials.By contrast, adapting the trials byextending the treatment period, temporarily stopping treatment, delaying outcomes assessments, and performing remote assessment allowed for increased statistical power nearly to the level originally planned.DiscussionThese analyses support the idea that disrupted trials under common scenarios are better continued and extended even in the face of dropouts, treatment disruptions, missing outcomes, and other exigencies, and that adaptations can be made that maintain the trials validity. We suggest some adaptive methods to do this noting that some changes become under-powered to detect theoriginal effect sizes and expected outcomes. These analyses provide insight to better plan trials that are resilient to unexpected changes to the medical, social, and political milieu.

This research proposes deep learning methods to approach the diagnosis of Parkinson’s Disease (PD) with more accurate results than those of existing methods. This automation approach also reduce the diagnosis expenses and make it possible to be applied in developing countries. In order to find a more accurate and cheaper solution to the problem, I have employed and contrasted three different experimental algorithms—Kmeans clustering, Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The best performance, with accuracy of 100%, can be achieved by the SVM model and convolutional neural network model.

Author(s): Banks, Sarah J; Qiu, Yuqi; Fan, Chun Chieh; Dale, Anders M; Zou, Jingjing; Askew, Brianna; Feldman, Howard H; Alzheimer's Disease Neuroimaging Initiative | Abstract: IntroductionSelecting individuals at high risk of Alzheimer's disease (AD) dementia and using the most sensitive outcome measures are important aspects of trial design.MethodsWe divided participants from Alzheimer's Disease Neuroimaging Initiative at the 50th percentile of the predicted absolute risk of the polygenic hazard score (PHS). Outcome measures were the Alzheimer's Disease Assessment Schedule-Cognitive Subscale (ADAS-Cog), ADNI-Mem, Clinical Dementia Rating-Sum of Boxes (CDR SB), and Cognitive Function Composite 2 (CFC2). In addition to modeling, we use a power analysis compare numbers needed with each technique.ResultsData from 188 cognitively normal and 319 mild cognitively impaired (MCI) participants were analyzed. Using the ADAS-Cog to estimate sample sizes, without stratification over 24 months, would require 930 participants with MCI, while using the CFC2 and restricting participants to those in the upper 50th percentile would require only 284 participants.DiscussionCombining stratification by PHS and selection of a sensitive combined outcome measure in a cohort of patients with MCI can allow trial design that is more efficient, potentially less burdensome on participants, and more cost effective.

Pancreatic ductal adenocarcinoma (PDAC) typically has nonspecific symptoms and is often found too late to treat. Because diagnosis of PDAC involves complex, invasive, and expensive procedures, screening populations at increased risk will depend on developing rapid, sensitive, specific, and cost-effective tests. Exosomes, which are nanoscale vesicles shed into blood from tumors, have come into focus as valuable entities for noninvasive liquid biopsy diagnostics. However, rapid capture and analysis of exosomes with their protein and other biomarkers have proven difficult. Here, we present a simple method integrating capture and analysis of exosomes and other extracellular vesicles directly from whole blood, plasma, or serum onto an AC electrokinetic microarray chip. In this process, no pretreatment or dilution of sample is required, nor is it necessary to use capture antibodies or other affinity techniques. Subsequent on-chip immunofluorescence analysis permits specific identification and quantification of target biomarkers within as little as 30 min total time. In this initial validation study, the biomarkers glypican-1 and CD63 were found to reflect the presence of PDAC and thus were used to develop a bivariate model for detecting PDAC. Twenty PDAC patient samples could be distinguished from 11 healthy subjects with 99% sensitivity and 82% specificity. In a smaller group of colon cancer patient samples, elevated glypican-1 was observed for metastatic but not for nonmetastatic disease. The speed and simplicity of ACE exosome capture and on-chip biomarker detection, combined with the ability to use whole blood, will enable seamless "sample-to-answer" liquid biopsy screening and improve early stage cancer diagnostics.

The diverse immunomodulatory effects of vitamin D are increasingly being recognized. However, the ability of oral vitamin D to modulate acute inflammation in vivo has not been established in humans. In a double-blinded, placebo-controlled interventional trial, 20 healthy adults were randomized to receive either placebo or a high dose of vitamin D3 (cholecalciferol) one hour after experimental sunburn induced by an erythemogenic dose of UVR. Compared with placebo, participants receiving vitamin D3 (200,000 international units) demonstrated reduced expression of proinflammatory mediators tumor necrosis factor-α (P = 0.04) and inducible nitric oxide synthase (P = 0.02) in skin biopsy specimens 48 hours after experimental sunburn. A blinded, unsupervised hierarchical clustering of participants based on global gene expression profiles revealed that participants with significantly higher serum vitamin D3 levels after treatment (P = 0.007) demonstrated increased skin expression of the anti-inflammatory mediator arginase-1 (P = 0.005), and a sustained reduction in skin redness (P = 0.02), correlating with significant expression of genes related to skin barrier repair. In contrast, participants with lower serum vitamin D3 levels had significant expression of proinflammatory genes. Together the data may have broad implications for the immunotherapeutic properties of vitamin D in skin homeostasis, and implicate arginase-1 upregulation as a previously unreported mechanism by which vitamin D exerts anti-inflammatory effects in humans.

It is common to encounter missing data among the potential predictor variables in the setting of model selection. For example, in a recent study we attempted to improve the US guidelines for risk stratification after screening colonoscopy (Cancer Causes Control 27 (2016) 1175–1185), with the aim to help reduce both overuse and underuse of follow-on surveillance colonoscopy. The goal was to incorporate selected additional informative variables into a neoplasia risk-prediction model, going beyond the three currently established risk factors, using a large dataset pooled from seven different prospective studies in North America. Unfortunately, not all candidate variables were collected in all studies, so that one or more important potential predictors were missing on over half of the subjects. Thus, while variable selection was a main focus of the study, it was necessary to address the substantial amount of missing data. Multiple imputation can effectively address missing data, and there are also good approaches to incorporate the variable selection process into model-based confidence intervals. However, there is not consensus on appropriate methods of inference which address both issues simultaneously. Our goal here is to study the properties of model-based confidence intervals in the setting of imputation for missing data followed by variable selection. We use both simulation and theory to compare three approaches to such post-imputation-selection inference: a multiple-imputation approach based on Rubin’s Rules for variance estimation (Comput. Statist. Data Anal. 71 (2014) 758–770); a single imputation-selection followed by bootstrap percentile confidence intervals; and a new bootstrap model-averaging approach presented here, following Efron (J. Amer. Statist. Assoc. 109 (2014) 991–1007). We investigate relative strengths and weaknesses of each method. The “Rubin’s Rules” multiple imputation estimator can have severe undercoverage, and is not recommended. The imputation-selection estimator with bootstrap percentile confidence intervals works well. The bootstrap-model-averaged estimator, with the “Efron’s Rules” estimated variance, may be preferred if the true effect sizes are moderate. We apply these results to the colorectal neoplasia risk-prediction problem which motivated the present work.

Author(s): Banks, Sarah Jane; Dale, Anders M; Askew, Brianna; Berg, Jody-Lynn; Qiu, Yuqi; Messer, Karen; Feldman, Howard H

A copy number variant (CNV) of 16p11.2, which encompasses 30 genes, is associated with developmental and psychiatric disorders, head size and body mass. The genetic mechanisms that underlie these associations are not understood. To elucidate the effects of genes on development, we exploited the quantitative effects of CNV on craniofacial structure in humans and model organisms. We show that reciprocal deletion and duplication of 16p11.2 have characteristic “mirror” effects on craniofacial features that are conserved in human, rat and mouse. By testing gene dosage effects on the shape of the mandible in zebrafish, we show that the distribution of effects for all individual genes is consistent with that of the CNV, and some combinations have non-additive effects. Our results suggest that, at minimum, one third of genes within the 16p11.2 region influence craniofacial development, and the facial gestalt of each CNV represents a product of 30 dosage effects.

This research analyses 10 universities from the United Kingdom and China respectively to make a comparison between TESOL and TCSOL curriculum. Based on the analysis, the compulsory courses, and optional courses, some similarities and differences have been analyzed. By referring to the curriculum of TESOL, some suggestions have been put forward. This research aims to inject broader approaches to the study of Master of Teaching Chinese to Speakers of Other Language (TCSOL), which would result in an enhanced understanding and enlargement of the subject matter, provide new thinking direction, promote the development of TCSOL, and reduce the possible confusion on the future development.

Presents the supplementary methods. (DOC 54 kb)