Correction to: Amelioration of radiation­induced liver damage by p-coumaric acid in mice.

[This corrects the article DOI: 10.1007/s10068-022-01118-8.].

Amelioration of radiation-induced liver damage by p-coumaric acid in mice.

Radiation-induced liver damage (RILD) is a spiny problem in radiotherapy or other circumstances that exposure to radiation. The need for radioprotective agent is increasing to protect liver tissue. This study aimed to explore the hepatoprotective effect of p-coumaric acid (CA) against RILD. C57BL/6 male mice were exposed to 4 Gy irradiation and administrated with CA for 4 days starting on the same day of irradiation. Mice were sacrificed to obtain blood and liver tissues on day 3.5 or 14 post irradiation, respectively. The blood and liver tissues were collected. As compared with the only irradiated group, CA supplementation improved liver morphology, decreased serum alanine aminotransferase and aspartate aminotransferase, inhibited BCL2-associated X (BAX) protein expression, and improved the mice hematopoietic function. CA at the dose of 100 mg/kg body weight showed better effect compared to the other doses. Thus, CA might possess potential to protect against RILD.

Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing.

Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of "learned sensing" applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden.