Muscle atrophy, characterized by the loss of muscle mass and function, is a hallmark of sarcopenia and cachexia and is frequently associated with aging, malignant tumors, chronic heart failure, and malnutrition. Moreover, it poses significant challenges to human health, leading to increased frailty, reduced quality of life, and heightened mortality risks. Despite extensive research on sarcopenia and cachexia, consensus in their assessment remains elusive, with inconsistent conclusions regarding their molecular mechanisms. Muscle atrophy models are crucial tools for advancing research in this field. Currently, animal models of muscle atrophy used for clinical and basic scientific studies are induced through various methods, including aging, genetic editing, nutritional modification, exercise, chronic wasting diseases, and drug administration. Muscle atrophy models also include in vitro and small organism models. Despite their value, each of these models has certain limitations. This review focuses on the limitations and diverse applications of muscle atrophy models to understand sarcopenia and cachexia, and encourage their rational use in future research, therefore deepening the understanding of underlying pathophysiological mechanisms, and ultimately advance the exploration of therapeutic strategies for sarcopenia and cachexia.

In the research, they benchmark FIFAWC on two tasks: traditional GAR and innovative GA video captioning. For GAR, they evaluate the classical detector-based approach ARG, and the state-of-the-art detector-free DFWSGAR.

Due to the advantages of high energy density and environmentally clean combustion, hydrogen is an ideal energy source for addressing global energy crises and environmental challenges. This review offers a brief summary and outlook on the interface in alkaline hydrogen evolution reaction between multiple components and the interaction between catalyst and solution.

With the continuous growth of global energy demand and the intensification of environmental issues, CO₂ emissions have surged dramatically, reaching up to 54 billion tons annually, drawing worldwide attention. Converting CO₂ into high-value chemicals not only helps mitigate global climate change but also promotes the development of the carbon cycle and green chemistry. Electrochemical CO₂ reduction reactions offer an effective pathway for this conversion, and porous materials, with their unique physicochemical properties, have shown tremendous potential in these reactions.

In this work, we present a chemoenzymatic cascade for selective synthesis of chiral N-arylated aspartic acids from biomass-derived furfural and waste nitrophenols by merging robust photo- and electrocatalysis with stereoselective biocatalysis. Briefly, concurrent photoelectrocatalytic oxidation of furfural into maleic acid/fumaric acid at the anode and electrocatalytic hydrogenation of nitrophenols into value-added aminophenols at the cathode proceeded simultaneously. The products formed at the two electrodes were converted into N-arylated (S)-aspartic acids by a bienzymatic cascade.

Researchers at KAUST, Saudi Arabia, have made significant strides in neuromorphic computing by developing a reconfigurable metal-oxide-semiconductor capacitor (MOSCaps), demonstrating optoelectronic synaptic features and memcapacitive behavior. These devices can perform stimulus-associated learning and exhibit tunable volatility, enabling transitions from light sensing to optical data-retention. Integrated into a leaky integrate-and-fire neuron model, the MOSCaps show potential for dynamically adjusting firing patterns based on light stimuli, with applications in exoplanet detection towards more adaptive and energy-efficient systems.

Stroke, a leading cause of global mortality, poses significant challenges due to limited therapeutic options and devastating impact on brain health. This review delves into the role of efferocytosis, a vital cellular process responsible for clearing apoptotic cells, in both neurodevelopment and ischemic stroke recovery. By examining its molecular mechanisms and therapeutic potential, the study offers critical insights into innovative strategies aimed at mitigating ischemic brain damage and improving patient outcomes.

Identifying biomarkers for predicting radiotherapy efficacy is crucial for optimizing personalized treatments. We previously reported that rs1553867776 in the miR-4274 seed region can predict survival in patients with rectal cancer receiving postoperative chemoradiation therapy. Hence, to investigate the molecular mechanism of the genetic variation and its impact on the radiosensitivity of colorectal cancer (CRC), in this study, bioinformatics analysis is combined with functional experiments to confirm peroxisomal biogenesis factor 5 (PEX5) as a direct target of miR-4274. The miR-4274 rs1553867776 variant influences the binding of miR-4274 and PEX5 mRNA, which subsequently regulates PEX5 protein expression. The interaction between PEX5 and Ku70 was verified by co-immunoprecipitation and immunofluorescence. A xenograft tumor model was established to validate the effects of miR-4274 and PEX5 on CRC progression and radiosensitivity in vivo. The overexpression of PEX5 enhances radiosensitivity by preventing Ku70 from entering the nucleus and reducing the repair of ionizing radiation (IR)-induced DNA damage by the Ku70/Ku80 complex in the nucleus. In addition, the enhanced expression of PEX5 is associated with increased IR-induced ferroptosis. Thus, targeting this mechanism might effectively increase the radiosensitivity of CRC. These findings offer novel insights into the mechanism of cancer radioresistance and have important implications for clinical radiotherapy.

The team proposed a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model.