A Novel Exoskeleton Neuromuscular Interface Based on Motor Unit Action Potential Model Using High-Density sEMG

The topical and noninvasive measurement of highdensity surface electromyogram (HD-sEMG) signals enables the estimation of human motor unit action potentials (MUAPs) as important motor function indicators for human-robot interaction. In this paper, a two-dimensional (2D) high-density microneedle electrode array is developed using the KOH bulk etching technique and the flexible printed circuit (FPC). To determine the optimal configuration of the 2D electrode array, an accurate and efficient neuromuscular analytical model for HD-sEMG is proposed to comprehensively analyze the effects of the micro-needle electrode size, inter-electrode distance, and location. The elbow joint angle estimation experiment for the sEMG-based upperlimb exoskeleton has further been conducted to demonstrate the feasibility and performance of the proposed micro-needlebased high-density electrode array, which is compared with a commercial wet electrode. The experimental results showed that the proposed micro-needle electrode with high spatial resolution was comparable to the wet electrode (Wilcoxon rank sum test, p > 0.05). On average, the correlation and root mean squared error (RMSE) of the micro-needle electrode array with high space utilization were 7.56% and 19.83% better than those of the wet electrode, respectively. The 2D high-density microneedle electrode array based on the proposed HD-sEMG model facilitates a novel neural-machine interface for intuitive control of upper-limb exoskeletons

The modified capsular arthroplasty for young patients with developmental dislocation of the hip.

The present study aimed to investigate the clinical results of the modified Codivilla-Hey Groves-Colonna capsular arthroplasty in the treatment of young patients with developmental dislocation of the hip. We retrospectively evaluated 90 patients (92 hips) who underwent the modified capsular arthroplasty from June 2012 to June 2021. Hips were evaluated using the modified hip Harris score (mHHS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and the 12-item International Hip Outcome Tool (iHOT-12). The Tönnis osteoarthritis grade and the Severin classification system were used to assess the radiographic outcomes. The average age was 15.7 years (range: 8-26 years). The mean pre-operative mHHS, the WOMAC score and the iHOT-12 score were 83.03, 14.05 and 52.79, respectively. The patients were followed for a mean of 41.1 months (range: 12.1-120.9 months). The patients had a mean mHHS of 83.61 (range: 31.2-97), a WOMAC score of 16.41 (range: 0-51) and an iHOT-12 score of 64.81 (range: 12.9-98.2) at the final follow-up. Capsular thickness had a positive predication on the final functional outcomes. The excellent/good rate of radiological reduction was 79.3%. More than 60% of patients had no/slight osteoarthritis. A total of 54 hips (58.7%) had superior radiographic outcomes. The risk factors for inferior radiographic outcomes were capsular quality (odds ratio [OR]: 0.358, 95% confidence interval [CI]: 0.113-0.931) and capsular thickness (OR: 0.265, 95% CI: 0.134-0.525). Joint stiffness was the most common complication (14.1%). We confirmed the efficacy of this procedure in the treatment of developmental hip dislocation. Patients with poor capsular quality are not suitable for this procedure. With suitable selection according to indications, this procedure can restore the hip rotation center with a low incidence of femoral head necrosis or severe osteoarthritis

Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment

The Gut Microbiota, Tumorigenesis, and Liver Diseases

In recent decades, diseases concerning the gut microbiota have presented some of the most serious public health problems worldwide. The human host’s physiological status is influenced by the intestinal microbiome, thus integrating external factors, such as diet, with genetic and immune signals. The notion that chronic inflammation drives carcinogenesis has been widely established for various tissues. It is surprising that the role of the microbiota in tumorigenesis has only recently been recognized, given that the presence of bacteria at tumor sites was first described more than a century ago. Extensive epidemiological studies have revealed that there is a strong link between the gut microbiota and some common cancers. However, the exact molecular mechanisms linking the gut microbiota and cancer are not yet fully understood. Changes to the gut microbiota are instrumental in determining the occurrence and progression of hepatocarcinoma, chronic liver diseases related to alcohol, nonalcoholic fatty liver disease (NAFLD), and cirrhosis. To be specific, the gut milieu may play an important role in systemic inflammation, endotoxemia, and vasodilation, which leads to complications such as spontaneous bacterial peritonitis and hepatic encephalopathy. Relevant animal studies involving gut microbiota manipulations, combined with observational studies on patients with NAFLD, have provided ample evidence pointing to the contribution of dysbiosis to the pathogenesis of NAFLD. Given the poor prognosis of these clinical events, their prevention and early management are essential. Studies of the composition and function of the gut microbiota could shed some light on understanding the prognosis because the microbiota serves as an essential component of the gut milieu that can impact the aforementioned clinical events. As far as disease management is concerned, probiotics may provide a novel direction for therapeutics for hepatocellular carcinoma (HCC) and NAFLD, given that probiotics function as a type of medicine that can improve human health by regulating the immune system. Here, we provide an overview of the relationships among the gut microbiota, tumors, and liver diseases. In addition, considering the significance of bacterial homeostasis, we discuss probiotics in this article in order to guide treatments for related diseases

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

<div><p>The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability.</p></div

Impact of training sample size.

<p>Prediction MCC based on different number of training samples for 10 endpoints using <i>NCentroid</i>.</p

Relationship between SSNR and endpoint predictability based on all training samples.

<p>The ex post facto relationship between SSNR values and endpoint predictability (prediction MCC) based on (a) normal and (b) swap modeling using <i>NCentroid</i> on all training samples. Here green (a) and orange columns (b) represent the SSNR values obtained from original training and validation sets, while the rectangles faced yellow are corresponding prediction MCC values of models on original validation and training samples, respectively.</p