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Background The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. Objective We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. Methods We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. Results The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. Conclusion The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

Malignant transformation of melanocytes, the pigment cells of human skin, causes formation of melanoma, a highly aggressive cancer with increased metastatic potential. Recently, mono-chemotherapies continue to improve by melanoma specific combination therapies with targeted kinase inhibitors. Still, metastatic melanoma remains a life-threatening disease because tumors exhibit primary resistance or develop resistance to novel therapies, thereby regaining tumorigenic capacity. In order to improve the therapeutic success of malignant melanoma, the determination of molecular mechanisms conferring resistance against conventional treatment approaches is necessary; however, it requires innovative cellular in vitro models. Here, we introduce an in vitro three-dimensional (3D) organotypic melanoma spheroid model that can portray the in vivo architecture of malignant melanoma and may warrant new insights into intra-tumoral as well as tumor-host interactions. The model incorporates defined numbers of mature and differentiated melanoma spheroids in a 3D human full skin reconstruction model consisting of primary skin cells. The cellular composition and differentiation status of the embedded melanoma spheroids is similar to the one of cutaneous melanoma metastasis in vivo. Using this organotypic melanoma spheroid model as a drug screening platform may support the identification of responders to selected combination therapies, while sparing the unnecessary treatment burden for non-responders, thereby increasing the benefit of therapeutic interventions.

Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K-fold cross validation (K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953–1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994–1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%–100.0%]) and 99.1% (95% CI [96.1%–99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%–100%]) and 99.5% (95% CI [96.8%–99.9%]), respectively. Heatmaps were in accordance with the clinician’s observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images.

The use of membrane reactors for enzymatic and co-factor regenerating reactions offers versatile advantages such as higher conversion rates and space-time-yields and is therefore often applied in industry. However, currently available screening and kinetics characterization systems are based on batch and fed-batch operated reactors and were developed for whole cell biotransformations rather than for enzymatic catalysis. Therefore, the data obtained from such systems has only limited transferability for continuous membrane reactors. The aim of this study is to evaluate and to improve a novel screening and characterization system based on the membrane reactor concept using the enzymatic hydrolysis of cellulose as a model reaction. Important aspects for the applicability of the developed system such as long-term stability and reproducibility of continuous experiments were very high. The concept used for flow control and fouling suppression allowed control of the residence time with a high degree of precision (±1% accuracy) in a long-term study (>100 h).

In this paper a solution to reduce magnetic field emission levels generated by MV/LV substation power cables is proposed. The reduction is obtained by the arrangement of the phases in a proper way and by shielding the cables with magnetic or conductive materials. The effects introduced by these two options have been analyzed by means of simulations, using finite elements method calculation software, and by experimental measurements. The introduced results allow selecting an optimal arrangement and the best screening material in order to reduce the magnetic fields in those directions required to protect. © 2007 Elsevier B.V. All rights reserved.

The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs well for images containing both a single cell and many overlapping cells. © 2010 IEEE.