Optical clearing is a versatile approach to improve imaging quality and depth of optical microscopy by reducing scattered light. However, conventional optical clearing methods are restricted in the efficiency-first applications due to unsatisfied time consumption, irreversible tissue deformation, and fluorescence quenching. Here, we developed an ultrafast optical clearing method (FOCM) with simple protocols and common reagents to overcome these limitations. The results show that FOCM can rapidly clarify 300-μmthick brain slices within 2 min. Besides, the tissue linear expansion can be well controlled by only a 2.12% increase, meanwhile the fluorescence signals of GFP can be preserved up to 86% even after 11 d. By using FOCM, we successfully built the detailed 3D nerve cells model and showed the connection between neuron, astrocyte, and blood vessel. When applied to 3D imaging analysis, we found that the foot shock and morphine stimulation induced distinct c-fos pattern in the paraventricular nucleus of the hypothalamus (PVH). Therefore, FOCM has the potential to be a widely used sample mounting media for biological optical imaging. optical clearing | tissue clearing | deep tissue imaging

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

Wearable strain sensors that detect joint/muscle strain changes become prevalent at human–machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti3C2Tx MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.

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Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal nature. Transarterial chemoembolization, the first-line treatment option of unresectable HCC that employs drug-loaded embolic agents to occlude tumor-feeding arteries and concomitantly delivers chemotherapeutic drugs into the tumor, is still under fierce debate in terms of the treatment parameters. The models that can produce in-depth knowledge of the overall intratumoral drug release behavior are lacking. This study engineers a 3D tumor-mimicking drug release model, which successfully overcomes the substantial limitations of conventional in vitro models through utilizing decellularized liver organ as a drug-testing platform that uniquely incorporates three key features, i.e., complex vasculature systems, drug-diffusible electronegative extracellular matrix, and controlled drug depletion. This drug release model combining with deep learning-based computational analyses for the first time permits quantitative evaluation of all important parameters associated with locoregional drug release, including endovascular embolization distribution, intravascular drug retention, and extravascular drug diffusion, and establishes long-term in vitro-in vivo correlations with in-human results up to 80 d. This model offers a versatile platform incorporating both tumor-specific drug diffusion and elimination settings for quantitative evaluation of spatiotemporal drug release kinetics within solid tumors.