Xun Zhao

Dec 6, 2024 | bmccancer.biomedcentral.com | Xun Zhao |Wanfang Xie |Zijian Qin |Litao Zhao |Cheng Liu |Shudong Zhang | +2 more

This retrospective study included consecutive patients diagnosed with complex renal cysts who underwent renal cyst unroofing, nephron-sparing surgery and radical nephrectomy from January 2010 to December 2019. This study protocol was approved by the ethics committee of our hospital (IRB00006761-M2022169).

Nov 20, 2024 | medrxiv.org | Michael Cooper |Xiang Gao |Xun Zhao |Dariia Khoroshchuk

The authors have declared no competing interest. This study was funded by the Canadian Institutes of Health Research. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Sep 30, 2024 | nature.com | Xun Zhao

AbstractThe severe mismatch between solid bioelectronics and dynamic biological tissues has posed enduring challenges in the biomonitoring community. Here, we developed a reconfigurable liquid cardiac sensor capable of adapting to dynamic biological tissues, facilitating ambulatory cardiac monitoring unhindered by motion artifacts or interference from other biological activities. We employed an ultrahigh-resolution 3D scanning technique to capture tomographic images of the skin on the wrist.

Sep 23, 2024 | nature.com | Xun Zhao |Yihao Zhou |Aaron Li |Jing Xu |Shreesh Karjagi |Justin Li | +5 more

Wearable acoustic sensors can be used for voice recognition. However, the capabilities of such devices, which are typically based on solid materials, are often restricted by ambient noise, motion artefacts and low conformability to the skin. Here we report a liquid acoustic sensor for voice recognition. The approach is based on a three-dimensional oriented and ramified magnetic network structure of neodymium–iron–boron magnetic nanoparticles suspended in a carrier fluid, which behaves like a permanent magnet. The sensor can discriminate small pressures (0.9 Pa), has a high signal-to-noise ratio (69.1 dB) and provides self-filtering capabilities that can remove low-frequency biomechanical motion artefact (less than 30 Hz). We use the liquid acoustic sensor—together with a machine learning algorithm—to create a wearable voice recognition system that offers a recognition accuracy of 99% in a noisy environment. An acoustic sensor that is based on a network of magnetic nanoparticles suspended in a carrier fluid can be used—together with a machine learning algorithm—to create a wearable voice recognition system with an accuracy of 99% in a noisy environment.

Apr 26, 2024 | nature.com | Xun Zhao |Yihao Zhou

AbstractBrownian motion allows microscopically dispersed nanoparticles to be stable in ferrofluids, as well as causes magnetization relaxation and prohibits permanent magnetism. Here we decoupled the particle Brownian motion from colloidal stability to achieve a permanent fluidic magnet with high magnetization, flowability and reconfigurability.