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Biomucilage‐Mediated Self‐Sintering Liquid Metal Ink for Robust Electronic Textiles With Soft‐Rigid Interfaces
ABSTRACT The advancement of electronic textiles (e‐textiles) depends on seamless integration of conductive materials that match the soft, breathable, and deformable nature of fabrics. However, achieving durable circuits on textiles remains challenging due to poor interfacial stability, limited stretchability, and inadequate wettability of conventional conductive materials. Liquid metal, while conductive and deformable, is difficult to adhere to textiles because of high surface tension and poor wettability. Here, we report a biomucilage‐mediated liquid metal (bio‐LM) ink that can be directly printed onto various textiles and, upon drying, autonomously self‐sinters into highly conductive and robust circuits. The bio‐LM ink forms strong bonds with textile, resulting in deformable interconnects that exhibit stable electrical performance, with resistance variations below 4% under repeated 15% stretching and across 20°C–50°C. Furthermore, we developed hole‐like electrical interconnect (e‐hole) structures that combine physical anchoring with chemical alloying between bio‐LM and copper, creating solder‐like, robust electrical junctions between soft textiles and rigid components. To verify its applicability, we demonstrate an integrated, closed‐loop e‐textile system capable of real‐time muscle fatigue monitoring and on‐demand heat therapy. This bio‐LM ink establishes a new material foundation for robust, scalable, and skin‐comfortable e‐textiles, moving beyond conventional circuit integration toward breathable, elastic, and durable health‐monitoring platforms.
Comparative fluorimetric strategies for determination of landiolol hydrochloride using native fluorescence and green synthesized silver nanoparticles
Abstract This study introduces the first reported stepwise fluorimetric strategy for the sensitive and rapid determination of the ultra-short-acting β₁-blocker, landiolol hydrochloride. Initially, an environmentally benign method was developed by exploiting the drug’s native fluorescence in water (λ ex /λ em = 217/298 nm). While this cost-effective approach successfully utilized water as a green solvent, it demonstrated limited LOD of 32.57 ng/mL. To enhance fluorescence performance, various organic solvents were investigated, with acetonitrile providing the highest signal at λ ex /λ em = 222/300 nm and improving the LOD to 16.31 ng/mL. To enable ultra-trace clinical monitoring, a third “turn-on” method was developed using silver nanoparticles (AgNPs). By measuring fluorescence enhancement at λ ex /λ em = 260/524.6 nm upon interaction with landiolol, this platform amplifies the signal, achieving an outstanding LOD of 3.10 ng/mL. AgNPs were prepared using a green synthesis approach employing Aloe vera extract as a natural reducing and stabilizing agent in an aqueous medium avoiding using hazardous chemicals. Water was used throughout both nanoparticle preparation and the AgNP-enhanced determination, reinforcing the eco-friendly profile of the method. Unlike native fluorescence, the AgNP-assisted method enhances sensitivity through nanoparticle-mediated surface passivation, with a possible auxiliary contribution from resonance energy transfer (RET)-like processes. Selectivity is improved through preferential adsorption of landiolol on the AgNP surface and a large Stokes shift that minimizes interference from UV-absorbing matrix components. The resulting enhanced sensitivity and reduced matrix interference make this method a powerful tool for ultra-trace, selective, and sustainable determination of landiolol in pharmaceutical formulations and human plasma, demonstrating its suitability for routine quality control and bioanalytical applications.
Australians recognise the effects of climate change but not its consequences for health system resilience and workforce capacity
Evaluating the regulatory effects of Icariin and Puerarin combination from shenge bushen capsules on renal-gonadal axis in beagle dogs
Test-retest reliability and concurrent validity of gait variability and trunk stability dual task effect in people with chronic stroke
Improved β-carotene synthesis in fas-knockout Mucor circinelloides strains
Leveraging G$$\times$$E interaction to optimize multi-trait selection in lima bean
Adaptive learning path generation in virtual reality music education via deep reinforcement learning
Subsistence input rates limit farming systems’ potential in a longterm on-farm trial in Central Kenya
Efficient privacy preserving cloud data sharing with blockchain-assisted access control and periodic integrity checking
Abstract Cloud-assisted data sharing has become the preferred approach for data exchange. Despite its popularity, the loss of physical control over outsourced data has intensified data owners’ concerns regarding data privacy and security. Existing attribute-based encryption schemes achieve fine-grained access control, but face an efficiency-security trade-off in policy updates, key privacy leakage risks, and a lack of proactive periodic integrity verification, limiting their practical application. To address these issues, this paper proposes EPPCDS, an efficient privacy-preserving cloud data sharing scheme with blockchain-assisted access control and periodic integrity checking. EPPCDS implements key-level fine-grained access control via multi-keyword searchable encryption for low-overhead secure policy updates, and adopts a double-layer digital envelope mechanism combined with certificateless cryptography and identity randomization to protect decryption tokens and user identity privacy. It also integrates provable data possession with a deposit-based reward-penalty mechanism to realize proactive periodic integrity verification. We conduct a thorough security analysis confirming EPPCDS satisfies diverse security demands in cloud data sharing. Extensive experiments on a Hyperledger Fabric prototype demonstrate that EPPCDS incurs low runtime overhead for all participating parties and outperforms representative existing schemes in terms of policy creation, policy update, and data access efficiency.
Multi-decadal remote sensing assessment of Urban heat Island intensification and biophysical transformation in Al Taif, Saudi Arabia
Abstract Urban environments in arid regions face escalating environmental pressures from rapid development and climate change, making systematic monitoring of their thermal and biophysical dynamics essential for sustainable planning. This study quantifies two decades of land-surface transformation in Al Taif City, Saudi Arabia, using multi-epoch analysis of Landsat imagery (2003, 2010, 2017, 2023) processed in Google Earth Engine, with Random Forest classification achieving accuracies of 92.1 to 97.1%. Built-up areas expanded by 597% (62.1 to 433.0 km²), accompanied by reductions in water bodies (66%), vegetation cover (49%), and mountainous terrain (7%). The thermal analysis reveals a characteristic UHI-intensification signature that would have been concealed by any single statistic: scene-mean land-surface temperature changed by only − 1.6 °C (50.0 to 48.4 °C, within measurement uncertainty), whereas maximum LST rose by 3.1 °C (61.9 to 65.0 °C) and the overall thermal range widened by 9.4 °C, indicating that heat is concentrating in specific urban zones rather than warming the city uniformly. Together, correlation and principal component analyses identify vegetation- and water-related biophysical indicators as accounting for 61.9% of the thermal variance, with NDVI showing the strongest cooling association ( r = − 0.6), while built-up and bare-soil indicators show the strongest warming associations. These findings support a zone-specific planning framework calibrated to hot-arid, water-scarce contexts: the central urban core requires cool-material and shading interventions; transitional peripheries require compact growth management and passive, water-sensitive urban design; and existing natural thermal refugia require formal preservation. The methodological framework presented here offers a transferable approach for characterizing UHI intensification in rapidly expanding hot-arid cities across the Middle East, North Africa, and comparable regions, where hydrologically realistic mitigation strategies, including drought-tolerant greening, treated-wastewater irrigation, and cool-material interventions, are more feasible than generic citywide greening prescriptions.
A systematic stepwise optimization framework for rapid multi-analyte UPLC–MS/MS plasma analysis with integrated sustainability assessment
Abstract A stepwise optimization guidance model for rapid multi-analyte UPLC–MS/MS is proposed to report recurring limitations in bioanalytical method development. The model involves six stages: (1) physicochemical profiling, (2) extraction strategy selection, (3) chromatographic optimization, (4) mobile phase selection, (5) method validation, and (6) greenness evaluation. The applicability of this framework was demonstrated by determining Rifaximin, Ciprofloxacin, and Fluconazole in spiked human plasma, using Ibuprofen as an internal standard. Physicochemical profiling guided the selection of ionization mode and prediction of solubility behavior. Based on the model recommendations, liquid–liquid extraction using dichloromethane was selected to achieve effective analyte recovery. Chromatographic optimization and mobile phase selection through the proposed framework supported the use of rapid isocratic separation on a C18 column using a methanol/water mixture (95:5, v/v) as mobile phase. The method validation, performed as an integral stage of the workflow according to regulatory guidelines, demonstrated exceptional linearity (r² ≥ 0.999). The final stage of the model involved evaluating environmental applicability using AGSA and EPPI analyses. The proposed sequential framework is intended to serve as a methodological reference for analysts employing LC–MS/MS platforms, to establish a structured and reproducible analytical workflow that advances ecological sustainability in multi-analyte LC–MS/MS bioanalytical applications.
Professional quality of life among migrant and non-migrant mental health staff in Germany: a gender-sensitive cross-sectional study
Abstract Germany increasingly relies on an internationally recruited migrant workforce, like Vietnamese migrants, to address shortages in the mental healthcare sector. This cross-sectional study examined differences in the Professional Quality of Life (ProQOL), measured through the subscales burnout (BO), secondary traumatic stress (STS) and compassion satisfaction (CS), among Vietnamese migrant and non-migrant mental health professionals ( N = 312) in Germany. Participants completed an online survey in German or Vietnamese including the ProQOL – 5 Scale, as well as sociodemographic and work-related variables. Multiple linear regression models identified salary across all subscales and psychiatric pre-conditions for BO and STS as significant predictors for ProQOL outcomes. Analyses of covariance compared adjusted group means across migration and gender. Vietnamese migrant professionals report significantly lower CS and higher STS compared to non-migrant professionals, while no significant migration-related differences were observed for BO. Considering gender, women exhibit significantly higher CS than men, whereas no significant gender differences emerged for BO or STS. No significant migration × gender interaction effects were found. Findings indicate migration background is associated with relevant differences in ProQOL among mental health staff in Germany, underscoring the need for targeted support, like an inclusive workplace culture and work-related social network. Further research should incorporate direct measures of migration-related stressors to better understand underlying mechanisms and longitudinal designs.
Cross-organ transfer learning for tumor microenvironment classification from colorectal to gastric cancer histopathology
Abstract Transfer learning offers a promising strategy for extending computational pathology models from data-rich to annotation-scarce cancer types, yet the transferability of tumor microenvironment (TME) representations across gastrointestinal organs remains underexplored. Here, we trained Swin Transformer, ConvNeXtV2, and UNI2-h deep learning models on ~ 100,000 annotated colorectal cancer histopathology patches (NCT-CRC dataset) and evaluated their generalization to the HMU-GC gastric cancer dataset (31,096 patches) under zero-shot and few-shot conditions. In the zero-shot setting, evaluation was performed on the full HMU-GC dataset, whereas each few-shot experiment used independently generated training, validation, and test splits. Zero-shot transfer yielded limited performance (macro-F1: 49.15–53.63%), revealing a substantial domain gap between colorectal and gastric histology. Few-shot adaptation with only 5% labeled gastric patches improved macro-F1 to 63.25–68.53%. The best configuration—Swin Transformer with Reinhard stain normalization and 20% labeled target data—achieved 72.26% macro-F1 and 72.22% accuracy, corresponding to an absolute accuracy gain of 18.49% points over the same model’s zero-shot baseline. These findings provide preliminary, single-pair evidence that cross-organ transfer of TME representations between colorectal and gastric histology is feasible but constrained by domain differences. Establishing generalizable cross-organ transfer will require validation across multiple source and target cohorts.
Water access, sanitation, and hygiene (WASH): exploring the interplay of practices and diarrhea in children under five in North Kordofan State, Sudan
Machine learning-based prognostic model for trauma and surgical ICU patients: the role of peak glucose and glycemic variability
Pi-Loc: a Pareto indoor localization using deep learning in 5GB
Abstract Ultra-accurate indoor localization is essential for critical applications such as emergency drone tracking, healthcare operations, and autonomous systems that rely on 5G New Radio (NR) and beyond networks. Sub-centimeter positioning accuracy is increasingly necessary for safe and effective operation in complex indoor environments. However, existing localization methods, including global positioning systems (GPS), face fundamental limitations when dealing with complex blockage conditions, signal heterogeneity, and the coexistence of line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions, particularly in single-cell positioning scenarios. In response to these challenges, this work introduces Pi-Loc, a novel Pareto indoor localization framework that explicitly addresses the trade-off between LoS and NLoS signal conditions through multi-objective optimization. Pi-Loc operates in two phases: first, Pareto optimization is applied to min-max normalized 5G NR channel state information (CSI) features, minimizing the competing LoS and NLoS localization errors simultaneously; second, the Pi-Loc convolutional network is trained on the Pareto-selected optimal CSI feature weightings. Extensive experiments on multiple simulated 5G NR benchmark datasets including three DeepMIMO indoor ray-tracing scenarios compliant with the 3GPP 5G NR cluster delay line (CDL) channel model, a 3GPP Indoor Office scenario, and an NYUSIM complex blockage dataset demonstrate that Pi-Loc outperforms established deep learning baselines (NN, DNN, LSTM, BiLSTM) in both accuracy and convergence efficiency, achieving near-mm-level positioning accuracy on simulated DeepMIMO datasets. Ablation studies confirm the framework’s stability and the individual contribution of each CSI feature category.