Ischemic perinatal stroke (IPS) is common, resulting in significant mortality and morbidity. In such cases, the incidence of unilateral arterial cerebral infarction is often occluded in the middle cerebral artery (MCA), leading to focal ischemia. In adult rodents, blockage of MCA is the most frequently used strategy for ischemic stroke study. However, modeling MCA occlusion (MCAo) in postnatal day 0–7 (P0–7) mouse pups for IPS study has not been accomplished. Here we occluded the dMCA by inducing the accumulation of magnetic particles (MPs) administered through the superficial temporal vein of mice between P0 and P7, which we called neonatal or perinatal SIMPLE (Stroke Induced with Magnetic Particles). SIMPLE produced either permanent or transient occlusion in the dMCA of perinatal and neonatal mice. Permanent MCA occlusion with SIMPLE resulted in cerebral infarction and neuronal death in the brain. SIMPLE can also be used to reliably produce focal ischemic stroke in neonatal or perinatal mouse brains. As a result, SIMPLE allows the modeling of IPS or focal ischemic stroke for further mechanistic studies in mice, with particular utility for mimicking transient focal ischemia in human pre-term babies, which for the first time here has been accomplished in mice.Electronic supplementary materialThe online version of this article (10.1186/s13041-018-0389-0) contains supplementary material, which is available to authorized users.
Thermal metamaterials are mixture‐based materials that are engineered to manipulate, control, and process the flow of heat, enabling numerous advanced thermal metadevices. Conventional thermal metamaterials are predominantly designed with tractable regular geometries owing to the delicate analytical solution and easy‐to‐implement effective structures. Nevertheless, it is challenging to achieve the design of thermal metamaterials with arbitrary geometry, letting alone intelligent (automatic, real‐time, and customizable) design of thermal metamaterials. Here, an intelligent design framework of thermal metamaterials is presented via a pre‐trained deep learning model, which gracefully achieves the desired functional structures of thermal metamaterials with exceptional speed and efficiency, regardless of arbitrary geometry. It possesses incomparable versatility and is of great flexibility to achieve the corresponding design of thermal metamaterials with different background materials, anisotropic geometries, and thermal functionalities. The transformation thermotics‐induced, freeform, background‐independent, and omnidirectional thermal cloaks, whose structural configurations are automatically designed in real‐time according to shape and background, are numerically and experimentally demonstrated. This study sets up a novel paradigm for an automatic and real‐time design of thermal metamaterials in a new design scenario. More generally, it may open a door to the realization of an intelligent design of metamaterials in also other physical domains.
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