Highly sensitive and ultrastable skin sensors for biopressure and bioforce measurements based on hierarchical microstructures

Piezoresistive microsensors are considered to be essential components of the future wearable electronic devices. However, the expensive cost, complex fabrication technology, poor stability, and low yield have limited their developments for practical applications. Here, we present a cost-effective, relatively simple, and high-yield fabrication approach to construct highly sensitive and ultrastable piezoresistive sensors using a bioinspired hierarchically structured graphite/polydimethylsiloxane composite as the active layer. In this fabrication, a commercially available sandpaper is employed as the mold to develop the hierarchical structure. Our devices exhibit fascinating performance including an ultrahigh sensitivity (64.3 kPa^–1), fast response time (<8 ms), low limit of detection of 0.9 Pa, long-term durability (>100 000 cycles), and high ambient stability (>1 year). The applications of these devices in sensing radial artery pulses, acoustic vibrations, and human body motion are demonstrated, exhibiting their enormous potential use in real-time healthcare monitoring and robotic tactile sensing

Study on Varicella-Zoster Virus Antibody Levels among Children Aged 1–7 Years in Changzhou, China

We aim to understand the varicella-zoster virus (VZV) antibody levels in children after vaccination and to construct VZV-IgG centile curves and reference values for children aged 1–7 years. From September to October 2023, a total of 806 children were recruited according to the time intervals of 1 month, 6 months, 1 year, 2 years, and 3 years after vaccination, as well as age groups. A generalized additive model for location, shape, and scale (GAMLSS) was applied to estimate P3, P10, P25, P50, P75, P90, and P97 centile reference values of VZV-IgG, and 95% reference intervals were calculated. A total of 785 children were included in the analysis, with an overall positivity rate of 70.3%, a median antibody concentration of 192.05 (82.89–571.14) mIU/mL, and a positivity rate of 57.7% for one dose of vaccine and 84.2% for two doses. Antibody positivity rates at 1 month, 6 months, 1 year, 2 years, and 3 years after vaccination were 65.1%, 74.4%, 80.4%, 67.7%, and 63.0%, respectively. The GAMLSS results showed that VZV-IgG had a tendency to increase and then decrease after vaccination, and the second dose of vaccination could significantly increase VZV-IgG. Two doses of varicella vaccine should be administered to children in a timely manner and included in the routine vaccination programs

Low-voltage Extended Gate Organic Thin Film Transistors for Ion Sensing Based on Semi-conducting Polymer Electrodes

We report a low-voltage organic field-effect transistor consisting of an extended gate sensory area to detect various ions in a solution. The device distinguishes various ions by the shift in threshold voltage and is sensitive to multiple ions with various concentrations. X-ray photoelectron spectroscopy measurements and the resistance changes at the sensor area prove that the ions are doped into the sensitive film at the sensor area. Because of the effect of doping, the conductivity of the semiconductor polymer film changes thus causing a threshold voltage shift

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset