Bone adapts according to the mechanical environment, and this adaptation can be visualized by altering its shape, size, and microarchitecture. Bone adaptation was recognized more than a century ago, with a description presented in The Law of Bone Remodeling. Furthermore, the conceptual model of “The Mechanostat” provides a quantitative relationship between the magnitude of bone tissue deformation (strain) and bone adaptive responses. However, upon maintaining a constant strain magnitude, various bone responses were observed experimentally under different loading parameters (e.g., frequency, rate, number of load cycles, rest insertion, and waveform). Nevertheless, the precise relationship between mechanical signals and bone adaptation remains unclear. Accordingly, we reviewed in vivo loading studies to determine the quantitative relationships between various mechanical signals and bone adaptive responses in various animal loading models. Additionally, we explored how these relationships are influenced by pathophysiological factors, such as age, sex, and estrogen deficiency. Moreover, mechanistic studies that consider cellular mechanical microenvironments to explain these quantitative relationships are discussed. A general formula that considers the bone adaptive response as a function of different loading parameters was proposed. This review may enhance our understanding of bone adaptation and offer guidance for clinicians to develop effective mechanotherapies to prevent bone loss.

Professor Jihyun Hong’s team at POSTECH uncovers a hidden surface degradation mechanism in the cathodes of lithium-ion batteries.

Autism is a neurodevelopmental disorder that affects social communication and development. A key characteristic in its clinical diagnosis is that autistic children rarely look at others’ faces, which is internationally recognized as a significant behavioral marker. Now, however, a new study aims to challenge this idea by using a combination of child-friendly testing environments and artificial intelligence to better understand the relationship between eye contact, cognitive development, and children’s attention skills.