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Key Points Perturbations of the endoplasmic reticulum (ER) caused by accumulation of unfolded proteins in this organelle trigger signal-transduction responses that assist with restoration of homeostasis during short-term but contribute to pathology when prolonged, including causing cell death. Among the stimuli that trigger ER stress are hypoxia, oxidative injury, a high-fat diet, hypoglycaemia, protein-inclusion bodies and viral infection, thus linking these organelle-initiated responses to a diversity of diseases, including cancer, autoimmunity, diabetes, heart disease, stroke and neurodegeneration. With increasing recognition of ER stress in association with human diseases and with improving understanding of the underlying molecular mechanisms, novel targets for drug discovery and new strategies for therapeutic intervention are beginning to emerge from the study of ER stress. Scenarios in which ER stress contributes to disease are outlined and prospects for drug discovery are discussed. Among the cell death mechanisms addressed are: pro-apoptotic signals resulting from activation of the ER-associated kinase IRE1, an upstream activator of apoptotic signalling kinase 1 (ASK1) that activates a stress kinase pathway affecting the activity or expression of several apoptosis regulators including BCL-2, BIM and CHOP; cytoprotective ER-membrane-associated proteins that modulate ER stress signalling; and the interplay among ER-initiated signal-transduction mechanisms that control apoptosis, necrosis and autophagy. Endoplasmic reticulum (ER) stress is induced following the accumulation of unfolded proteins in the ER. This triggers the unfolded protein response, which initially acts to compensate for damage, but if prolonged or excessive can trigger cell death. Here, Reed and colleagues discuss the role of ER-initiated cell death pathways in diseases including neurodegeneration, hypoxia, heart disease, diabetes and immune disorders, while identifying promising therapeutic targets. The accumulation of unfolded proteins in the endoplasmic reticulum (ER) represents a cellular stress induced by multiple stimuli and pathological conditions. These include hypoxia, oxidative injury, high-fat diet, hypoglycaemia, protein inclusion bodies and viral infection. ER stress triggers an evolutionarily conserved series of signal-transduction events, which constitutes the unfolded protein response. These signalling events aim to ameliorate the accumulation of unfolded proteins in the ER; however, when these events are severe or protracted they can induce cell death. With the increasing recognition of an association between ER stress and human diseases, and with the improved understanding of the diverse underlying molecular mechanisms, novel targets for drug discovery and new strategies for therapeutic intervention are beginning to emerge.

Developing highly efficient, selective and low-overpotential electrocatalysts for carbon dioxide (CO 2 ) reduction is crucial. This study reports an efficient Ni single-atom catalyst coordinated with pyrrolic nitrogen and pyridinic nitrogen for CO 2 reduction to carbon monoxide (CO). In flow cell experiments, the catalyst achieves a CO partial current density of 20.1 mA cm geo −2 at −0.15 V vs. reversible hydrogen electrode (V RHE ). It exhibits a high turnover frequency of over 274,000 site −1 h −1 at −1.0 V RHE and maintains high Faradaic efficiency of CO (FE CO ) exceeding 90% within −0.15 to −0.9 V RHE . Operando synchrotron-based infrared and X-ray absorption spectra, and theoretical calculations reveal that mono CO-adsorbed Ni single sites formed during electrochemical processes contribute to the balance between key intermediates formation and CO desorption, providing insights into the catalyst’s origin of catalytic activity. Overall, this work presents a Ni single-atom catalyst with good selectivity and activity for CO 2 reduction while shedding light on its underlying mechanism. Rational design of electrocatalysts for selective CO 2 conversion is of great interests. Here the authors show that Ni single atom with asymmetric pyrrolic and pyridinic nitrogen for efficient CO 2 electroconversion to CO.

Exosomes are lipid bilayer membrane vesicles and are emerging as competent nanocarriers for drug delivery. The clinical translation of exosomes faces many challenges such as massive production, standard isolation, drug loading, stability and quality control. In recent years, artificial exosomes are emerging based on nanobiotechnology to overcome the limitations of natural exosomes. Major types of artificial exosomes include ‘nanovesicles (NVs)’, ‘exosome-mimetic (EM)’ and ‘hybrid exosomes (HEs)’, which are obtained by top-down, bottom-up and biohybrid strategies, respectively. Artificial exosomes are powerful alternatives to natural exosomes for drug delivery. Here, we outline recent advances in artificial exosomes through nanobiotechnology and discuss their strengths, limitations and future perspectives. The development of artificial exosomes holds great values for translational nanomedicine.

This study tackles the numerical challenges posed by solutions with steep gradients in the Burgers equation, particularly poor stability in high-gradient regions and the ill-posedness of inverse problems in shock wave modeling. We propose a Weak-Form Physics-Informed Neural Network (WF-PINN) that fundamentally enhances both forward and inverse problem solving. Key innovations include: (i) a weak-form integral formulation of the PDE loss, which improves training stability near shocks; (ii) enforcement of an entropy condition to ensure unique and physically consistent shock capture; (iii) a dual-network architecture for inverse problems, where an auxiliary network dedicated to initial condition reconstruction is coupled with the main solver via consistency constraints. Numerical experiments show that WF-PINNs achieve significantly higher accuracy and convergence robustness compared to strong-form PINNs, accurately resolving shock locations and amplitudes while enabling precise identification of unknown initial conditions and viscosity coefficients. The framework offers a unified and generalizable approach for solving conservation laws with discontinuities.

Rock mass deformation and failure are macroscopic manifestations of crack initiation, propagation, and coalescence. However, simulating the transition of rocks from continuous to discontinuous media under cyclic dynamic loading remains challenging. This study proposes a hybrid finite-discrete element method (HFDEM) to model crack propagation, incorporating a frequency-dependent cohesive-zone model. The mechanical properties of standard sandy mudstone under quasi-static and cyclic dynamic loading were simulated using HFDEM, and the method's reliability was verified through experimental comparison. The comparative analysis demonstrates that HFDEM successfully captures crack interaction mechanisms and accurately simulates the overall failure behavior of specimens. Additionally, the effects of pre-existing flaw inclination angle and dynamic loading frequency on rock failure mechanisms were investigated. The numerical results reveal that rock samples exhibit significantly higher compressive strength under dynamic loading compared to quasi-static loading, with compressive strength increasing with higher cyclic dynamic load frequencies. Furthermore, by analyzing the strength characteristics, crack propagation, and failure modes of the samples, insights into the failure mechanisms of rocks under different frequency loads were obtained. This study provides valuable insights into crack development and failure of rocks under seismic loads, offering guidance for engineering practices.

To enable high performance of all solid-state batteries, a catholyte should demonstrate high ionic conductivity, good compressibility and oxidative stability. Here, a LaCl 3 -based Na + superionic conductor (Na 1 − x Zr x La 1 − x Cl 4 ) with high ionic conductivity of 2.9 × 10 −4  S cm −1 (30 °C), good compressibility and high oxidative potential (3.80 V vs . Na 2 Sn) is prepared via solid state reaction combining mechanochemical method. X-ray diffraction reveals a hexagonal structure ( P6 3 /m ) of Na 1 − x Zr x La 1 − x Cl 4 , with Na + ions forming a one-dimensional diffusion channel along the c- axis. First-principle calculations combining with X-ray absorption fine structure characterization etc. reveal that the ionic conductivity of Na 1 − x Zr x La 1 − x Cl 4 is mainly determined by the size of Na + -channels and the Na + /La 3+ mixing in the one-dimensional diffusion channels. When applied as a catholyte, the NaCrO 2 ||Na 0.7 Zr 0.3 La 0.7 Cl 4 ||Na 3 PS 4 ||Na 2 Sn all-solid-state batteries demonstrate an initial capacity of 114 mA h g −1 and 88% retention after 70 cycles at 0.3 C. In addition, a high capacity of 94 mA h g −1 can be maintained at 1 C current density. The advancement of sodium halide solid-state electrolytes can propel high-energy-density all-solid-state sodium-ion batteries. Here, the authors present a LaCl 3 -based electrolyte facilitating rapid Na + ion conduction along the c -axis diffusion channel, ensuring high capacity even at elevated current densities.

Background The newly described inflammatory burden index (IBI) reflects a patient’s inflammatory burden. This study aimed to estimate the association between IBI, osteoarthritis (OA), and all-cause mortality in patients with OA. Methods We extracted the data of adults from the National Health and Nutrition Examination Survey database between 1999 and 2018. After using appropriate survey weights to correct for sample bias, we conducted multivariate logistic regression analyses to explore the association between IBI and OA across three models: in the unadjusted model, partially adjusted model (adjusting age, sex, race, education level, marital status, PIR, BMI, smoking status, drinking status, stroke, CVD, DM, and hypertension) and fully adjusted model (which included additional variables: HBA1C, ALT, AST, BUN, TC, and HDL). And the odds ratios (OR) and 95% confidence intervals (CI) were calculated. Similarly, using comparable survey weights and covariates adjustments, we employed Cox proportional hazards regression analysis to investigate the association between IBI and all-cause mortality in the other 3 models. The Cox proportional hazards regression models were fitted to calculate the hazard ratios (HR) and 95% CI of the association between IBI and all-cause mortality. A restricted cubic spline (RCS) was used to explore the nonlinear relationships between association effects. Subgroup analysis was performed to validate the reliability of their effects. Results In total, 22,343 eligible participants were included. Multiple logistic regression models revealed that participants with the highest IBI had 2.54 times (95%CI, 2.23, 2.90)) higher risk of OA than those with the lowest IBI in Model 1, whereas the OR was 1.21 (95%CI, 1.03, 1.42) in Model 2 and 1.23 (95%CI,1.05, 1.45) in Model 3. Multiple Cox regression models showed participants with the highest IBI had 186% (95%CI, 1.50, 2.31) times risk of developing all-cause death than those with the lowest IBI in Model 1. This trend remained stable in Models 2 (HR,1.54; 95%CI,1.22, 1.95) and 3 (HR, 1.41; 95%CI, 1.10, 1.80). The RCS revealed a significant positive association between IBI and OA risk. With respect to the association between IBI and all-cause mortality, a slight decrease in mortality was observed from the lowest quartile to the second quartile of IBI, and the mortality risk increased with increasing IBI. Subgroup analyses showed that age, cardiovascular disease, and hypertension were pivotal in the association of IBI with all-cause mortality, whereas the association of IBI with OA remained stable after stratification by other factors such as sex, race, education level, marital, smoking, and drinking status, hypertension, and most serological indices. Conclusions This study provides evidence of a positive association between IBI, OA, and all-cause mortality. IBI may be a promising signature for assessing the inflammatory burden in patients with OA, which, in turn, is conducive to precise references for high-risk population recognition, anti-inflammatory guidance, and reducing mortality intervention.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types 1 , 2 , yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits. Sparse labelling and whole-brain imaging are used to reconstruct and classify brain-wide complete morphologies of 1,741 individual neurons in the mouse brain, revealing a dependence on both brain region and transcriptomic profile.

Solution-processable perovskites show highly emissive and good charge transport, making them attractive for low-cost light-emitting diodes (LEDs) with high energy conversion efficiencies. Despite recent advances in device efficiency, the stability of perovskite LEDs is still a major obstacle. Here, we demonstrate stable and bright perovskite LEDs with high energy conversion efficiencies by optimizing formamidinium lead iodide films. Our LEDs show an energy conversion efficiency of 10.7%, and an external quantum efficiency of 14.2% without outcoupling enhancement through controlling the concentration of the precursor solutions. The device shows low efficiency droop, i.e. 8.3% energy conversion efficiency and 14.0% external quantum efficiency at a current density of 300 mA cm −2 , making the device more efficient than state-of-the-art organic and quantum-dot LEDs at high current densities. Furthermore, the half-lifetime of device with benzylamine treatment is 23.7 hr under a current density of 100 mA cm −2 , comparable to the lifetime of near-infrared organic LEDs. Light-emitting diodes based on halide perovskites have made remarkable progress but their stability is the bottleneck. Here Miao et al . show 14% quantum efficiency at a current density of 300 mA cm-2 and 23.7 hr lifetime under a current density of 100 mA cm-2, making it promising for actual application.

Solution-processed metal-halide perovskites are emerging as one of the most promising materials for displays, lighting and energy generation. Currently, the best-performing perovskite optoelectronic devices are based on lead halides and the lead toxicity severely restricts their practical applications. Moreover, efficient white electroluminescence from broadband-emission metal halides remains a challenge. Here we demonstrate efficient and bright lead-free LEDs based on cesium copper halides enabled by introducing an organic additive (Tween, polyethylene glycol sorbitan monooleate) into the precursor solutions. We find the additive can reduce the trap states, enhancing the photoluminescence quantum efficiency of the metal halide films, and increase the surface potential, facilitating the hole injection and transport in the LEDs. Consequently, we achieve warm-white LEDs reaching an external quantum efficiency of 3.1% and a luminance of 1570 cd m −2 at a low voltage of 5.4 V, showing great promise of lead-free metal halides for solution-processed white LED applications. Designing efficient light-emitting diodes with white-light-emission from broadband-emission metal halides remains a challenge. Here, the authors demonstrate bright and efficient lead-free LEDs based on cesium copper halides enabled by introducing Tween organic additive in the precursor.