Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.
Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.
The demands for transparent, flexible electronic devices are continuously increasing due to their potential applications to the human body. In particular, skin-like, transparent, flexible strain sensors have been developed to realize multifunctional human-machine interfaces. Here, we report a sandwich-like structured strain sensor with excellent optical transparency based on highly purified, solution-processed, 99% metallic CNT-polydimethylsiloxane (PDMS) composite thin films. Our CNT-PDMS composite strain sensors are mechanically compliant, physically robust, and easily fabricated. The fabricated strain sensors exhibit a high optical transparency of over 92% in the visible range with acceptable sensing performances in terms of sensitivity, hysteresis, linearity, and drift. We also found that the sensitivity and linearity of the strain sensors can be controlled by the number of CNT sprays; hence, our sensor can be applied and controlled based on the need of individual applications. Finally, we investigated the detections of human activities and emotions by mounting our transparent strain sensor on various spots of human skins.
NIR fluorescence imaging could safely identify pulmonary neoplasms after the systemic injection of ICG. In addition, low-dose ICG is sufficient for NIR fluorescence imaging of pulmonary neoplasms. However, because the passive accumulation of ICG could not be used to discriminate tumours with inflammation, tumour-targeted fluorescence should be developed to solve this problem in the future.
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