A soft body area sensor network presents a promising direction in wearable devices to integrate on-body sensors for physiological signal monitoring and flexible printed circuit boards (FPCBs) for signal conditioning/readout and wireless transmission. However, its realization currently relies on various sophisticated fabrication approaches such as lithography or direct printing on a carrier substrate before attaching to the body. Here, we report a universal fabrication scheme to enable printing and room-temperature sintering of the metal nanoparticle on paper/fabric for FPCBs and directly on the human skin for onbody sensors with a novel sintering aid layer. Consisting of polyvinyl alcohol (PVA) paste and nanoadditives in the water, the sintering aid layer reduces the sintering temperature. Together with the significantly decreased surface roughness, it allows for the integration of a submicron-thick conductive pattern with enhanced electromechanical performance. Various on-body sensors integrated with an FPCB to detect health conditions illustrate a system-level example.

Purpose Lung metastasis (LM) is one of the most frequent distant metastases of thyroid cancer (TC). This study aimed to develop a machine learning algorithm model to predict lung metastasis of thyroid cancer for providing relative information in clinical decision‐making. Methods Data comprising of demographic and clinicopathological characteristics of patients with thyroid cancer were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, which is employed to develop six machine learning algorithm models support vector machine (SVM), logistic regression (LR), eXtreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), and k‐nearest neighbor (KNN). Compared and evaluated models by the following indicators: accuracy, precision, recall rate, F1‐score, the area under the ROC curve (AUC) value and Brier score, and interpreted the association between clinicopathological characteristics and target variables based on the best model. Results Nine thousand nine hundred and fifty patients were selected, which including 212 patients (2.1%) with lung metastasis, and 9738 patients without lung metastasis (97.9%). Multivariate logistic regression showed that age, T stage, N stage, and histological type were independent factors in TC with LM. Evaluation indicators of the best model‐ RF were as following: accuracy (0.99), recall rate (0.88), precision (0.61), F1‐score (0.72), AUC value (0.99), and the Brier score (0.016). Conclusion RF learning model performed better and can be applied to forecast lung metastasis of thyroid cancer, and offer valuable and significant reference for clinicians' decision‐making in advance.

Highly flexible electromagnetic interference (EMI) shielding material with excellent shielding performance is of great significance to practical applications in nextgeneration flexible devices. However, most EMI materials suffer from insufficient flexibility and complicated preparation methods. In this study, we propose a new scheme to fabricate a magnetic Ni particle/Ag matrix composite ultrathin film on a paper surface. For a ~2 µm thick film on paper, the EMI shielding effectiveness (SE) was found to be 46.2 dB at 8.1 GHz after bending 200,000 times over a radius of ~2 mm. The sheet resistance (R□) remained lower than 2.30 Ω after bending 200,000 times.Contrary to the change in R□, the EMI SE of the film generally increased as the weight ratio of Ag to Ni increased, in accordance with the principle that EMI SE is positively related with an increase in electrical conductivity. Desirable EMI shielding ability, 2 ultrahigh flexibility, and simple processing provide this material with excellent application prospects.

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