Hypoxia-preconditioned bone marrow-derived mesenchymal stem cells protect neurons from cardiac arrest-induced pyroptosis

Cardiac arrest can lead to severe neurological impairment as a result of inflammation,mitochondrial dysfunction,and post-cardiopulmonary resuscitation neurological damage.Hypoxic preconditioning has been shown to improve migration and survival of bone marrow–derived mesenchymal stem cells and reduce pyroptosis after cardiac arrest,but the specific mechanisms by which hypoxia-preconditioned bone marrow–derived mesenchymal stem cells protect against brain injury after cardiac arrest are unknown.To this end,we established an in vitro co-culture model of bone marrow–derived mesenchymal stem cells and oxygen–glucose deprived primary neurons and found that hypoxic preconditioning enhanced the protective effect of bone marrow stromal stem cells against neuronal pyroptosis,possibly through inhibition of the MAPK and nuclear factor κB pathways.Subsequently,we transplanted hypoxia-preconditioned bone marrow–derived mesenchymal stem cells into the lateral ventricle after the return of spontaneous circulation in an 8-minute cardiac arrest rat model induced by asphyxia.The results showed that hypoxia-preconditioned bone marrow–derived mesenchymal stem cells significantly reduced cardiac arrest–induced neuronal pyroptosis,oxidative stress,and mitochondrial damage,whereas knockdown of the liver isoform of phosphofructokinase in bone marrow–derived mesenchymal stem cells inhibited these effects.To conclude,hypoxia-preconditioned bone marrow–derived mesenchymal stem cells offer a promising therapeutic approach for neuronal injury following cardiac arrest,and their beneficial effects are potentially associated with increased expression of the liver isoform of phosphofructokinase following hypoxic preconditioning.

废锂离子电池的热处理:过程污染物迁移和转化

废锂离子电池中不仅富含我国高对外依存度的关键金属,还含有重金属、有机污染物等有毒有害物质,具有资源与环境的双重属性。推进其高效循环利用是保障新能源汽车等战略新兴产业可持续发展的关键。锂离子电池组成结构复杂,有机物成分变化大、种类多,常规的火法和湿法冶金过程容易产生二次环境危害,不利于资源的清洁循环利用。热处理作为保障废锂离子电池中有价金属资源有效回收的重要技术,近年来受到了行业的广泛关注。热处理技术具有二次污染小、设备简单、过程易放大、经济性高等诸多优势。结合热处理技术对废锂离子电池回收中的污染物进行源头治理,既能实现清洁生产,也能强化后续深度处理。本工作立足于行业现状和战略需求,重点讨论了废锂离子电池预处理中的污染物产生、迁移和转化规律,对比总结了热处理在杂质去除和污染防控等方面的技术优势。同时,对废锂离子电池的热处理工艺进行了系统分类,总结了不同热处理条件下的物质转化规律。要点:(1)概述了废锂离子电池的主要回收工艺及预处理过程,综述了热处理技术的研究进展。(2)重点讨论了废锂离子电池在预处理过程中的污染物来源、迁移和转化规律。(3)分析了热处理技术在废锂离子电池回收中的污染物源头防治优势,提出了热处理技术的发展方向。

High-speed three-dimensional shape measurement with inner shifting-phase fringe projection profilometry

Fringe projection profilometry(FPP)has been extensively studied in the field of three-dimensional(3D)measurement.Although FPP always uses high-frequency fringes to ensure high measurement accuracy,too many patterns are projected to unwrap the phase,which affects the speed of 3D reconstruction.We propose a high-speed 3D shape measurement method using only three high-frequency inner shifting-phase patterns(70 periods),which satisfies both high precision and high measuring speed requirements.Besides,our proposed method obtains the wrapped phase and the fringe order simultaneously without any other information and constraints.The proposed method has successfully reconstructed moving objects with high speed at the camera's full frame rate(1700 frames per second).

Stretchable and anti-impact iontronic pressure sensor with an ultrabroad linear range for biophysical monitoring and deep learning-aided knee rehabilitation

Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation.However,stretchable flexible pressure sensors with high sensitivity and a broad range that can meet these requirements are still lacking.Herein,we successfully monitored various vital biophysical features and implemented in-sensor dynamic deep learning for knee rehabilitation using an ultrabroad linear range and highsensitivity stretchable iontronic pressure sensor(SIPS).We optimized the topological structure and material composition of the electrode to build a fully stretching on-skin sensor.The high sensitivity(12.43 kPa^(−1)),ultrabroad linear sensing range(1 MPa),high pressure resolution(6.4 Pa),long-term durability(no decay after 12000 cycles),and excellent stretchability(up to 20%)allow the sensor to maintain operating stability,even in emergency cases with a high sudden impact force(near 1 MPa)applied to the sensor.As a practical demonstration,the SIPS can positively track biophysical signals such as pulse waves,muscle movements,and plantar pressure.Importantly,with the help of a neuro-inspired fully convolutional network algorithm,the SIPS can accurately predict knee joint postures for better rehabilitation after orthopedic surgery.Our SIPS has potential as a promising candidate for wearable electronics and artificial intelligent medical engineering owing to its unique high signal-to-noise ratio and ultrabroad linear range.