Explore the vast range of titles available.

Acute ischemic stroke is a common cause of morbidity and mortality worldwide. Thrombolysis with recombinant tissue plasminogen activator and endovascular thrombectomy are the main revascularization therapies for acute ischemic stroke. However, ischemia-reperfusion injury after revascularization therapy can result in worsening outcomes. Among all possible pathological mechanisms of ischemia-reperfusion injury, free radical damage (mainly oxidative/nitrosative stress injury) has been found to play a key role in the process. Free radicals lead to protein dysfunction, DNA damage, and lipid peroxidation, resulting in cell death. Additionally, free radical damage has a strong connection with inducing hemorrhagic transformation and cerebral edema, which are the major complications of revascularization therapy, and mainly influencing neurological outcomes due to the disruption of the blood-brain barrier. In order to get a better clinical prognosis, more and more studies focus on the pharmaceutical and nonpharmaceutical neuroprotective therapies against free radical damage. This review discusses the pathological mechanisms of free radicals in ischemia-reperfusion injury and adjunctive neuroprotective therapies combined with revascularization therapy against free radical damage.

Compliant strain sensors are crucial for soft robots’ perception and autonomy. However, their deformable bodies and dynamic actuation pose challenges in predictive sensor manufacturing and long-term robustness. This necessitates accurate sensor modelling and well-controlled sensor structural changes under strain. Here, we present a computational sensor design featuring a programmed crack array within micro-crumples strategy. By controlling the user-defined structure, the sensing performance becomes highly tunable and can be accurately modelled by physical models. Moreover, they maintain robust responsiveness under various demanding conditions including noise interruptions (50% strain), intermittent cyclic loadings (100,000 cycles), and dynamic frequencies (0–23 Hz), satisfying soft robots of diverse scaling from macro to micro. Finally, machine intelligence is applied to a sensor-integrated origami robot, enabling robotic trajectory prediction (<4% error) and topographical altitude awareness (<10% error). This strategy holds promise for advancing soft robotic capabilities in exploration, rescue operations, and swarming behaviors in complex environments. Due to the deformable nature of soft robots, developing robust sensors that can sustain stability and performance is a challenge. Here, the authors report a computational strain sensor design based on a programmed crack array for predictable, tunable, and stable sensing performance.

A long-standing goal in optics is to produce a solid-state alloptical transistor, in which the transmission of light can be controlled by a single photon that acts as a gate or switch. Sun et al. used a solid-state system comprising a quantum dot embedded in a photonic crystal cavity to show that transmission through the cavity can be controlled with a single photon. The single photon is used to manipulate the occupation of electronic energy levels within the quantum dot, which in turn changes its optical properties. With the gate open, about 28 photons can get through the cavity on average, thus demonstrating single-photon switching and the gain for an optical transistor. Science , this issue p. 57 A solid-state quantum dot memory is used to realize a single-photon switch and optical transistor. Single-photon switches and transistors generate strong photon-photon interactions that are essential for quantum circuits and networks. However, the deterministic control of an optical signal with a single photon requires strong interactions with a quantum memory, which has been challenging to achieve in a solid-state platform. We demonstrate a single-photon switch and transistor enabled by a solid-state quantum memory. Our device consists of a semiconductor spin qubit strongly coupled to a nanophotonic cavity. The spin qubit enables a single 63-picosecond gate photon to switch a signal field containing up to an average of 27.7 photons before the internal state of the device resets. Our results show that semiconductor nanophotonic devices can produce strong and controlled photon-photon interactions that could enable high-bandwidth photonic quantum information processing.

Interactions between single spins and photons are essential for quantum networks and distributed quantum computation. Achieving spin–photon interactions in a solid-state device could enable compact chip-integrated quantum circuits operating at gigahertz bandwidths. Many theoretical works have suggested using spins embedded in nanophotonic structures to attain this high-speed interface. These proposals implement a quantum switch where the spin flips the state of the photon and a photon flips the spin state. However, such a switch has not yet been realized using a solid-state spin system. Here, we report an experimental realization of a spin–photon quantum switch using a single solid-state spin embedded in a nanophotonic cavity. We show that the spin state strongly modulates the polarization of a reflected photon, and a single reflected photon coherently rotates the spin state. These strong spin–photon interactions open up a promising direction for solid-state implementations of high-speed quantum networks and on-chip quantum information processors using nanophotonic devices. Placing a single solid-state spin in an optical nanocavity results in a switch that operates at the fundamental quantum limit, where the spin modulates the polarization of a photon and a single photon flips the spin state.

A central challenge to the development of protein-based therapeutics is the inefficiency of delivery of protein cargo across the mammalian cell membrane, including escape from endosomes. Here we report that combining bioreducible lipid nanoparticles with negatively supercharged Cre recombinase or anionic Cas9:single-guide (sg)RNA complexes drives the electrostatic assembly of nanoparticles that mediate potent protein delivery and genome editing. These bioreducible lipids efficiently deliver protein cargo into cells, facilitate the escape of protein from endosomes in response to the reductive intracellular environment, and direct protein to its intracellular target sites. The delivery of supercharged Cre protein and Cas9:sgRNA complexed with bioreducible lipids into cultured human cells enables gene recombination and genome editing with efficiencies greater than 70%. In addition, we demonstrate that these lipids are effective for functional protein delivery into mouse brain for gene recombination in vivo. Therefore, the integration of this bioreducible lipid platform with protein engineering has the potential to advance the therapeutic relevance of protein-based genome editing.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with continual decline in cognition and ability to perform routine functions such as remembering familiar places or understanding speech. For decades, amyloid beta (Aβ) was viewed as the driver of AD, triggering neurodegenerative processes such as inflammation and formation of neurofibrillary tangles (NFTs). This approach has not yielded therapeutics that cure the disease or significant improvements in long-term cognition through removal of plaques and Aβ oligomers. Some researchers propose alternate mechanisms that drive AD or act in conjunction with amyloid to promote neurodegeneration. This review summarizes the status of AD research and examines research directions including and beyond Aβ, such as tau, inflammation, and protein clearance mechanisms. The effect of aging on microvasculature is highlighted, including its contribution to reduced blood flow that impairs cognition. Microvascular alterations observed in AD are outlined, emphasizing imaging studies of capillary malfunction. The review concludes with a discussion of two therapies to protect tissue without directly targeting Aβ for removal: (1) administration of growth factors to promote vascular recovery in AD; (2) inhibiting activity of a calcium-permeable ion channels to reduce microglial activation and restore cerebral vascular function.

Large lattice expansion/contraction with Li + intercalation/deintercalation of electrode active materials results in severe structural degradation to electrodes and can negatively impact the cycle life of solid-state lithium-based batteries. In case of the layered orthorhombic MoO 3 (α-MoO 3 ), its large lattice variation along the b axis during Li + insertion/extraction induces irreversible phase transition and structural degradation, leading to undesirable cycle life. Herein, we propose a lattice pinning strategy to construct a coherent interface between α-MoO 3 and η-Mo 4 O 11 with epitaxial intergrowth structure. Owing to the minimal lattice change of η-Mo 4 O 11 during Li + insertion/extraction, η-Mo 4 O 11 domains serve as pin centers that can effectively suppress the lattice expansion of α-MoO 3 , evidenced by the noticeably decreased lattice expansion from about 16% to 2% along the b direction. The designed α-MoO 3 /η-Mo 4 O 11 intergrown heterostructure enables robust structural stability during cycling (about 81% capacity retention after 3000 cycles at a specific current of 2 A g −1 and 298 ± 2 K) by harnessing the merits of epitaxial stabilization and the pinning effect. Finally, benefiting from the stable positive electrode–solid electrolyte interface, a highly durable and flexible all-solid-state thin-film lithium microbattery is further demonstrated. This work advances the fundamental understanding of the unstable structure evolution for α-MoO 3 , and may offer a rational strategy to develop highly stable electrode materials for advanced batteries. Large lattice variation of electrode materials upon lithiation and delithiation limits the cycle life of lithium batteries. Here, authors introduce a lattice pinning strategy via heterostructure design to suppress lattice expansion in MoO 3 and improve cycle life in all-solid-state lithium batteries.

Background Physical activity is a key determinant of healthy aging, playing a crucial role in preventing chronic diseases, maintaining mobility, and enhancing the quality of life among older adults. However, significant gender disparities in physical activity levels exist, particularly in low- and middle-income countries like China and India, where cultural norms, socio-economic conditions, and gender roles often restrict women’s participation in physical activities. These disparities are especially concerning in older populations, where women may face compounded barriers due to lifelong inequalities, caregiving responsibilities, and limited access to resources. This study examines gender inequality in physical activity among older adults in China and India, two of the world’s most populous countries with rapidly aging populations. Methods Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) and the 2017–2019 Longitudinal Aging Study in India (LASI) were analysed. We conducted descriptive statistics, chi-square tests, logistic regression, and dominance analysis to examine gender differences and associated factors in physical activities, applying appropriate sampling weights using Stata 17. Results The results reveal significant differences in elderly populations between China and India. In India, 56.5% of the elderly have no education, compared to 29.2% in China. Additionally, 30.8% are employed, whereas in China, the corresponding figure is 13.3%. Health metrics indicate that 82.8% of elderly Indians report good health, compared to 70.8% in China. Vigorous physical activity decreases with age; 34.5% of Chinese men aged 60–69 participate, dropping to 8.5% at age 80+. Gender disparities are also evident, with 27.2% of Chinese males and 25% of Indian males engaging in vigorous activities, compared to 19.4% and 12.1% of females, respectively. In China, place of residence is the most dominant factor for vigorous activity among males (43.8%) and females (40.5%). In India, current employment status is the strongest predictor of vigorous activity, with dominance scores of 73.4% for males and 78.3% for females. Age and health status also play significant roles, but with varying importance across genders and countries. Conclusion The study highlights the need for targeted, gender-sensitive interventions, including community-based programs and public health campaigns, to promote physical activity among older women. By addressing these disparities, China and India can improve the health outcomes of their aging populations and contribute to more equitable public health strategies. The research underscores the importance of a collaborative approach involving governments, healthcare providers, and community organizations in developing and implementing policies that create inclusive opportunities for physical activity among older adults.

This study aims to explore interrelationships among leisure activities and depression and the mediating roles of activities of daily living (ADL) and self-reported health (SRH) among older adults in China. Data were extracted from 9,893 participants aged 50 and older from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) conducted in 2018. Pearson correlation analysis and multiple stepwise regression analysis were conducted by using SPSS, and the mediating effect was tested using the Process plugin. Leisure activities were negatively correlated with depression ( r  = − 0.169, P  < 0.001), and positively correlated with ADL and SRH ( r  = 0.223, r  = 0.123, P  < 0.001). ADL and SRH were negatively correlated with depression ( r  = − 0.120, − 0.376, P  < 0.001). Leisure activities can not only directly and negatively affect depression, but also can indirectly affect depression through positive effects on ADL and SRH. The mediating effect accounts for 30.79% of the total effect. ADL and SRH play mediating roles in the relationship among leisure activities and depression. The participation of elderly people in leisure activities reduces the risk of depression by increasing their level of daily activity ability and perceived health.

During the use and management of lead–acid batteries, it is very important to carry out prediction and study of the state of the health (SOH) of the battery. To this end, this paper proposes a SOH prediction method for lead–acid batteries based on the CNN-BiLSTM-Attention model. The model utilizes the convolutional neural network (CNN) to carry out feature extraction and data dimension reduction in the input factors of model, and then these factors are used as the input of the bidirectional long short-term memory network (BiLSTM). The BiLSTM is used to learn the temporal correlation information in the local features of input time series bidirectionally. The attention mechanism is introduced to assign more attention to key features in the input sequence with more significant influence on the output result by assigning weights to important features, and finally, multi-step prediction of the battery SOH is realized. Compared with the prediction results of battery SOH using other neural network methods, the method proposed in this study can provide higher prediction accuracy and achieve accurate multi-step prediction of battery SOH. Measured results show that most of the multi-step prediction errors of the proposed method are controlled within 3%.