Nerve injury-induced protein 1 (NINJ1) was originally identified in 1996 as a homophilic adhesion molecule upregulated following nerve injury. For over two decades thereafter, research on NINJ1 primarily focused on areas such as nerve regeneration, immune cell migration, and inflammation regulation. In 2021, the discovery by Kayagaki’s group completely transformed the understanding of NINJ1—the protein was demonstrated to be a key executor of plasma membrane rupture (PMR) during lytic cell death, overturning the long-held view that PMR is a passive osmotic event. This finding rapidly sparked intensive research efforts in structural biology, cell death regulation, and therapeutic target development. This review is organized around the central scientific questions in NINJ1 research, systematically tracing the trajectory from molecular discovery, structural elucidation, and activation regulation to disease associations and therapeutic targeting. We critically analyze the logical relationships among different research avenues, discuss the underlying assumptions and limitations of current findings, and highlight the key knowledge gaps that remain in the field.
Intelligent lower-limb prostheses are evolving from single-joint assistance toward coordinated, system-level control that supports cross-task adaptation, multimodal intent estimation, and verifiable safety. This systematic review surveys powered, semi-active, microprocessor-controlled, and related intelligent lower-limb prosthesis literature published between 1 January 2021 and 1 January 2026, spanning electromechanical design, sensing and human-machine interfaces, state/phase estimation, intent/terrain recognition, control and learning, evaluation endpoints, and translational considerations. Following a PRISMA-style workflow, 180 full-text reports were included and synthesized into a modular taxonomy covering clinical needs and endpoints; actuation and transmission; sensing and human-machine interfaces; phase/state estimation; intent/terrain recognition; impedance and trajectory control, including model predictive control; personalization with explicit safety constraints; real-world validation; and safety, reliability, and standardization. Emerging patterns include backdrivable low-impedance hardware, multimodal sensing with uncertainty-aware gating, and continuous phase-variable control, although the level of validation remains heterogeneous. Key gaps remain in endpoint consistency, external validity across users and contexts, and failure-mode reporting. We recommend benchmark protocols and system-level validation frameworks to support more reproducible evaluation and future clinical translation.
Unmanned aerial vehicles (UAVs) are increasingly used in applications such as agriculture, logistics, mapping, surveillance, and environmental monitoring. However, the limited battery endurance continues to restrict mission duration and operational range. This review examines two sustainable propulsion alternatives, hydrogen fuel cells and solar-powered systems, based on findings reported in the literature. Evidence from peer-reviewed studies, experimental demonstrations, and industrial reports published between 2009 and 2024 is considered. Key parameters, including endurance, payload capacity, and operational altitude, are compared, along with practical aspects such as hydrogen storage, thermal management, and energy control systems. The available data suggest that hydrogen fuel cell (HFC) drones are better suited for low to mid-altitude missions requiring higher payload and rapid refueling. Solar-powered drones are more effective for long-endurance and high-altitude applications under favorable solar conditions. Future developments are expected to focus on hybrid propulsion systems, improved materials, and more efficient energy management strategies.
Adolescence is a crucial developmental stage marked by rapid biological maturation, intense social scrutiny, rising academic pressures, and ongoing development of brain systems linked to reward processing, executive control, stress regulation, and emotion regulation. Depressive symptoms, anxiety, perceived stress, sleep problems, sedentary behavior, and excessive screen exposure often occur during this time. Research has extensively explored physical activity as a modifiable behavior that could enhance adolescent mental health, but much of the evidence still focuses on its link to improved psychological outcomes. Less attention has been given to the psychophysiological pathways through which physical activity may impact mental health. This narrative review examines how physical activity affects adolescent mental health, focusing on autonomic nervous system regulation, HPA axis function, inflammatory and immune pathways, neuroplasticity, and sleep–circadian rhythm regulation. There is evidence that suggests physical activity may support adolescent mental health by increasing autonomic flexibility, facilitating stress recovery, boosting neurotrophic signaling, improving executive control and sleep quality, and fostering social connections, while reducing sedentary time and inflammatory burden. However, these effects are not uniform. Factors such as gender, pubertal development, initial mental health status, body weight, fitness, activity preferences, family support, school climate, peer connections, digital lifestyle, and activity dose might all impact the psychological and physiological outcomes. This review makes the case that physical activity shouldn’t be used as a panacea for adolescent mental health problems. Rather, this should be interpreted as a developmentally integrated psychophysiological regulation approach whose benefits depend on dose, timing, context, individual variation, and its combination with sleep, stress management, and supportive social environments.
The Modified Tardieu Scale is commonly used to assess spasticity by differentiating between neural and mechanical resistance. However, its manual administration may reduce objectivity and reproducibility. This study aimed to automate the Quality of Muscle Reaction (QMR) assessment in the wrist flexors. To this end, we developed a Hand Spasticity Testing (HaST) device and QMR classification model. The device integrates two inertial measurement units, surface electromyography sensors, and a force sensor to record joint angle, angular velocity, muscle activity, and reaction force during passive wrist extension. A classification model was then constructed using decision trees based on the acquired features, with training and evaluation performed via leave-one-out cross-validation. Using the developed device, 19 participants with upper-limb spasticity were evaluated. Key features, such as the number of local maxima in joint angle, velocity, and reaction force, along with other derived parameters, were extracted and classified to estimate QMR grades (0–2). The proposed method achieved an overall accuracy of 76% and a weighted average F1-score of 0.76. These results demonstrate the feasibility of objective and automated QMR quantification using the HaST device. The proposed system may serve as a preliminary screening and documentation tool to support objective spasticity assessment in clinical settings.
