Browse Articles

Reverse engineering of BNIP3 identifies a mitochondrial protective peptide

Abstract Recent advances in mitochondrial network dynamic and signalling highlight mitochondria as key therapeutic targets across diverse diseases. Yet, high drug development failure rates reflect an incomplete understanding of upstream molecular regulators of mitochondrial fate. Here, we address this gap by reverse engineering of the BH3-only protein BNIP3. Structural modelling and sequence–function analyses of its N-terminus identify a critical functional domain and amino acid hotspots that directly activate BCL-2 executioner proteins, triggering mitochondrial cell death. Leveraging these insights, we develop a BNIP3 antagonist peptide (B-017) that disrupts interactions between BNIP3 and BCL-2 executioner proteins, preserving mitochondrial integrity. B-017 demonstrates target specificity, a favourable safety profile, and robust suppression of cell death signalling in human cells. In clinically relevant animal models, it reduces tissue damage in the heart, brain, and liver. Together, these findings position B-017 as a promising therapeutic candidate targeting mitochondrial dysfunction.

Redox Asymmetry Enables Fe–H Bonds in Perovskite Oxyhydrides

Associations of the insulinemic potential of diet and lifestyle with metabolic dysfunction-associated steatotic liver disease (MASLD) risk in adults with type 2 diabetes

Probing non-ergodicity and symmetry via direct measurement of coherent scattering in a shaken rotor

Combinatorial Design of Benzodithiophene–Benzothiadiazole Building Blocks for Ultralarge Pore Optoelectronically Tunable Covalent Organic Frameworks

Research on the spectral detection effect and data fusion of small white apricot quality based on different detection distances

A translational approach to airway reconstruction leveraging decellularized meniscus and cartilage progenitor cells

Electrostatic-Driven Nucleobase Discrimination by Covalent Organic Framework Nanosheets for Deoxyribonucleic Acid Methylation Profiling

DCBM-Tri: a dual-channel bilinear mapping triplet model for early recognition of acute kidney injury in imbalanced cohorts

Intermediate accumulated upon interruption of fatty acid oxidation flux promotes tumoral ferroptosis and improves immunotherapy

Synergistic Covalent and Hydrogen-Bonding Interactions Drive the Assembly of a Gigantic Snub Cube

Early cancer biomarker detection using a prism-based multi-resonant Ag/BaTiO₃/BP plasmonic sensor

Abstract Early and precise detection of cancer biomarkers with a ultra low concentration is a critical issue in clinical diagnostics mainly because of limited sensitivity, low selectivity as well as variable workability of the conventional surface plasmon resonance (SPR) biosensors. In order to deal with these drawbacks, this paper suggests a new prism-based, multi-resonant SPR biosensor with an Ag/BaTiO3/Black Phosphorus (BP) heterostructure. The goal is to improve field confinement, responsive refractive index and multiplexing capability to detected cancer biomarkers at an early-stage. The transfer matrix method was used to model the biosensor design and this was confirmed by finite element-based electromagnetic finite element simulations. The main plasmonic coating is Silver, BaTiO3 is a high-permittivity dielectric spacer to stabilize field coupling and the anisotropic BP is the active sensing material. Results of the simulation show an angular sensitivity of 412.14°/RIU and a figure of merit (FOM) of 352.26 RIU − 1 and a perception of 1.06/°, which is better than most state-of-the-art designs. Also, the sensor has multi-resonance behavior, which allows detecting multiple biomarkers with characteristic angular shifts simultaneously, thereby improving the diagnostic reliability and specificity. The paper shows that ferroelectric spacer of BaTiO 3 and 2D BP layer integration results in a synergistic increase in resolution of sensors, environmental stability and spectral sharpness. The characteristics of the sensor also qualify it as a good contender of real-time, label-free, and non-invasive cancer diagnostics. This platform has future application of extending this to the lab-on-chip level, wearable sensors, and AI based spectral classification with widespread biomedical use. In contrast to more traditional Ag–dielectric 2D material SPR designs that require enhancement of a single mode resonance, the presented sensor is based on a prism-based multi-resonant heterostructure using Ag/BaTiO 3 /black phosphorus and allows simultaneous excitation of numerous sharp plasmonic modes. High permittivity BaTiO 3 as a middle-level layer offers a great benefit to enhancing the confining of electromagnetic fields and splitting of the modes to generate ultra-high angular sensitivity and a better figure of merit. The multi-resonant approach offers a paradigm shift in sensing that can be used in multi-purpose plexus of early cancer biomarkers sensing.

Guide RNA reprogramming facilitates minimized tracrRNA-dependent off-target and versatile CRISPR/Cas9 engineering

Impact of Changjiang-Hanjiang Water Diversion Project on the runoff process in the lower reach of Hanjiang River

The confounding effects of skin colour in photoacoustic imaging

Abstract Skin colour is known to confound optical devices, adversely impacting care for patients with darker skin. Photoacoustic imaging (PAI) combines optics and ultrasound for deep tissue imaging, creating a complex relationship between PAI-derived biomarkers and skin melanin concentration, yet no generalisable bias correction has been demonstrated. Drawing on a healthy volunteer cohort of 42 participants spanning Fitzpatrick types I–VI and vitiligo – the most diverse ever assembled in PAI – we characterise optical and acoustic mechanisms driving skin colour-dependent degradation in image quality and biomarker quantification. Wavelength-dependent melanin absorption causes spectral colouring, dominating at low melanin levels, while epidermal ultrasound backscattering dominates at high melanin levels, producing a non-linear relationship between unmixed sO₂ and skin tone. Leveraging this understanding, we propose a practical spectral colouring correction and adapt a plane-wave reconstruction algorithm to resolve backscattered ultrasound artefacts. Our findings underscore the need for advanced reconstruction methods to enable equitable clinical PAI.

An optimization-driven hierarchical deep learning approach using the Gray Langurs algorithm for data-driven seismic activity prediction

Abstract The statistical prediction of seismic activity patterns from historical earthquake catalog data remains a major challenge in data-centered seismic hazard analysis because seismic time series are non-stationary, multi-scale, and clustered in nature. Existing data-driven seismic prediction pipelines often emphasize architectural innovation while giving less attention to systematic hyperparameter optimization, which is essential for achieving strong predictive performance. This work is motivated by the need for an integrated and computationally efficient data-driven time-series modeling framework. Accordingly, a hierarchical deep learning-metaheuristic optimization paradigm is proposed based on the Neural Hierarchical Interpolation for Time Series Forecasting (N-HITS) algorithm and the Gray Langurs Optimizer (GLO). We conduct a systematic benchmarking of N-HITS against state-of-the-art deep time-series prediction models trained under identical preprocessing and training conditions, followed by adaptive hyperparameter optimization. Baseline analysis showed that N-HITS, with a coefficient of determination ( $$R^2$$ ) of 0.921 and a Mean Squared Error (MSE) of 0.00234, was the strongest standalone model. Following GLO-based hyperparameter optimization, performance improved to an $$R^2$$ of $$0.9892 \pm 0.0049$$ and an MSE of 7.980e-05 ± 7.980e-07, indicating substantial error reduction and higher convergence stability. These results highlight the importance of optimization intelligence in catalog-based statistical seismic activity prediction and position hierarchical deep learning with adaptive metaheuristic search as a scalable architecture for seismic trend monitoring. However, the proposed model relies only on historical seismic catalog patterns and does not incorporate tectonic processes or geophysical drivers; therefore, its outputs should be interpreted as statistical trend estimates rather than physically reliable earthquake predictions.

Cortical and white matter myelination proceed in concert during early infancy

Abstract The infant brain undergoes rapid myelination that is critical for healthy brain function. This development has been characterized for gray and white matter independently, but the link between gray and white matter myelination remains unexplored. To close this knowledge gap, we evaluated two complementary myelin-sensitive imaging metrics: Large-scale (N = 273) T1w/T2w and quantitative (N = 21) R1 data. Automated software was employed to identify 26 white matter bundles and map their cortical terminations, before evaluating T1w/T2w and R1 development shortly after birth. Here we show that for both metrics mean values as well as developmental slopes are correlated across tissues. The synchrony of brain T1w/T2w is impacted by postmenstrual age and prematurity, whereas inter-individual differences in this synchrony predict motor outcomes at 17 − 25 months of age. As T1w/T2w and R1 are associated with myelin content, our results reveal an intricate relationship between gray and white matter myelination.

Triazenyl Furans as Diels–Alder Dienes

Effects of black soldier (Hermetia illucens) larvae meal inclusion in commercial diet on performance and economic feasibility of Sasso chicken in Arba Minch

Inequality in human development amplifies climate-related disaster risk

Abstract The impacts of climate-related disasters are shaped by the interaction between hazard intensity, exposure, and vulnerability. However, the influence of hazard intensity and within-country inequality on impact magnitudes remains poorly quantified. Here, we present a global multi-hazard study of over 7000 climate-related disasters reported by the Emergency Events Database from 1990 to 2020. Using subnational indicators, we show that human development drives major shifts in global exposure and impact patterns, with societal vulnerability outweighing hazard intensity in shaping impacts. Despite a declining share of global exposure over the past three decades, regions with low subnational Human Development Index scores experience disproportionately higher human losses across most disaster types. For instance, individuals in these regions face an 8.2-fold higher risk of fatality associated with storms (95% confidence interval: 2.16-23.06) compared to those in very high human development regions. Our findings also indicate that within-country inequality in human development exacerbates disaster risk in regions with low and medium levels of human development. These results underscore the critical role of human development in managing disaster risks and highlight the link between socioeconomic conditions and vulnerability to climate-related hazards.