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Electrochemical tyrosine-click bioconjugation enables multiplexed cytokine sensing and immunoprofiling in native serum

Features of mobile apps for diabetic kidney disease self-management: A scoping review

Background Diabetic kidney disease (DKD) is a chronic complication of diabetes mellitus (DM). DKD and chronic kidney disease (CKD) are both long-term conditions requiring ongoing patient care. Individuals living with DKD or CKD can benefit from mobile apps that support effective self-management. However, limited evidence is available about what mobile apps features are commonly included for DKD. Objective This scoping review aimed to identify the features of mobile applications on self-management for individuals with DKD, DM or CKD. Methods The review followed the Joanna Briggs Institute (JBI) methodology for scoping reviews and adhered to PRISMA-ScR reporting guidelines. Five databases (PubMed, Scopus, SAGE Journals, ScienceDirect, and Web of Science) were searched from inception to February 26, 2025. Studies were included if they reported on mobile apps supporting self-management in adults with DKD, DM or CKD. DM and CKD apps were included due to similar self-management needs such as blood sugar or blood pressure tracking that are also relevant to people with DKD. Data were extracted on study characteristics, app features, use of technology, integration with care teams, and reported outcomes. Results Out of 3521 records identified, eleven studies met the inclusion criteria. Five studies focused on CKD, two on DKD, and four on diabetes. Across the eleven mobile apps reviewed, four core domains of self-management were identified: self-care monitoring (91%), educational components (64%), patient support and motivation (100%), and performance incentives (18%). Four apps employed wearable devices and incorporated supportive devices such as Bluetooth glucometers. However, only two apps included real-time communication features with providers integration with healthcare teams and gamification strategies. Conclusions Mobile apps targeting DKD frequently incorporate monitoring, education, and motivational features. However, consistent integration with healthcare providers and incentive-based engagement strategies remains limited. Future app development should emphasise personalised feedback, clinical integration, and sustained engagement mechanisms to enhance usability and impact.

Pre-splicing conformation and stepwise circularization of a group I intron in Azoarcus pre-tRNA

Effects of attachment designs on clear aligner tooth movement: A finite element analysis

This study investigates the impact of different attachment shapes and configurations on the displacement, stress, and strain profiles of maxillary first molar during clear aligner-based orthodontic treatment. A subject-specific 3D maxillary model was developed from CBCT imaging, incorporating cortical and trabecular bone, periodontal ligament (PDL), teeth, attachments, and aligner geometry. Five attachment shapes square, rectangle, trapezoid, ellipse, and semicircle were analyzed in single and dual (buccal-lingual) configurations across four clinically relevant movements: mesialization, intrusion, extrusion, and rotation. Finite-element simulation results indicated that flat-shaped attachments (rectangular and trapezoidal) generated the greatest crown displacement but induced higher PDL strain (up to 0.390 mm/mm) and localized bone stress (7.11 MPa), particularly at the root apex and alveolar crest. Curved attachments provided more diffused load distribution but significantly reduced movement efficiency. Dual attachments improved root engagement and bodily displacement in all movement types, mitigating undesired tipping and enhancing force symmetry, albeit with elevated strain. Rotational control was most influenced by attachment geometry, with flat designs producing greater angular movement. Overall, attachment shape and placement exert a substantial influence on orthodontic biomechanics during aligner therapy. The findings underscore the need for evidence-based attachment protocols tailored to specific movement goals and patient risk profiles. These insights can guide clinicians toward optimizing clear aligner treatments for improved movement precision, minimized biological risk, and enhanced treatment outcomes in complex orthodontic cases.

Room temperature photochemical synthesis of metal–organic frameworks for enhanced photocatalysis

Abstract The function of metal–organic frameworks (MOFs) is fundamentally governed by their synthesis precision. Here, we report a light-driven strategy enabling ambient-temperature MOFs synthesis (15 °C, 4 hours) for cobalt-porphyrin frameworks (phoPPF-3), overcoming traditional thermal constraints. This approach achieves multidimensional control, manifested in two-dimensional hourglass morphologies and selective Co 2 ⁺-carboxylate coordination that preserves free-base porphyrin cores unattainable conventionally. Resulting phoPPF-3 exhibits enhanced thermal stability and higher photocatalytic activity in benzyl alcohol oxidation and H 2 evolution comparing to solvothermal analogues. The methodology demonstrates a certain generality through successful extension to other MOFs. This work marks the demonstration of using photons to initiate and guide MOFs synthesis and establishes a sustainable approach for atomically precise MOFs engineering via photochemical control.

Isolation methods influence the biological properties of Wharton’s Jelly-derived mesenchymal stem cells: A comparative study of yield, viability, proliferation, differentiation potential, and proteomic profiles

Background Mesenchymal stem cells derived from Wharton’s Jelly (WJ-MSCs) are an attractive cell source for regenerative medicine due to high proliferative capacity, non-invasive accessibility, and minimal ethical constraints. However, their therapeutic efficacy may vary with isolation technique and culture conditions. Methods We compared three WJ-MSC isolation methods; two explant approaches (non-scraped and scraped) and one enzymatic method – each cultured with or without basic fibroblast growth factor (bFGF). WJ-MSCs were obtained from three full-term umbilical cords, and subsequently evaluated for cell viability, proliferation kinetics, immunophenotypic surface marker expression, multilineage differentiation potential, and proteomic profiles through mass spectrometry coupled with bioinformatics analyses. Results All methods produced viable WJ-MSCs, although enzymatic isolation without bFGF resulted in early culture failure in 2/3 donors and was excluded from downstream analyses. Highest viability was observed with the non-scraped explant method supplemented with bFGF, and bFGF significantly enhanced proliferation by reducing cell doubling time. All groups consistently expressed canonical MSC markers, along with WJ-MSC-specific surface proteins. Osteogenic differentiation was robust across all groups, whereas adipogenic differentiation was limited. Proteomic profiling revealed 2,372 proteins commonly expressed across all groups, indicating a largely stable core proteome, with isolation- and bFGF-dependent modulation observed primarily at the pathway level. Gene set enrichment analysis showed that bFGF-treated cultures were enriched for metabolic pathways, including oxidative phosphorylation and fatty acid metabolism, whereas bFGF-free and enzymatic isolation methods showed increased inflammatory and stress-related signatures. Differential expression analysis further identified 36 proteins uniquely regulated by isolation method and bFGF treatment, associated with cell adhesion, tissue morphogenesis, and immunomodulatory functions . Conclusion This study clarifies how isolation- and growth factor–driven effects shape the functional properties and paracrine identity of WJ-MSCs. The non-scraped explant method with bFGF emerges as a robust, reproducible approach, yielding high-viability, phenotypically stable, and metabolically resilient MSCs. These findings provide a framework for standardized WJ-MSC production optimized for regenerative and immunomodulatory applications.

Active learning in latent spaces enables rapid inverse design of ferroelectric ceramics for energy storage

Channel reconstruction and dual attention dynamic fusion for remote sensing image semantic segmentation

As the spatial resolution of remote sensing imagery continues to be improved, the complexity of the information also increases. Remote sensing images generally have characteristics such as wide imaging ranges, dispersed distribution of similar land objects, complex boundary shapes, and dense small targets, which pose severe challenges to semantic segmentation tasks. To address these challenges, we propose a channel reconstruction and dual attention dynamic fusion network (CRDFNet), which is a semantic segmentation network for remote sensing image that can effectively integrate global and local contexts. To better handle complex boundary shapes, we designed a channel feature aggregation module (CFAM), which can extract spatially redundant information during feature fusion and enhance high-resolution detail features. Through a channel reconstruction block, it promotes the alignment of fine-grained information from the encoder with high-level semantic information from the decoder, effectively aggregating multi-scale features extracted by the encoder and significantly improving segmentation accuracy. At the same time, to optimize the segmentation performance of small targets, we propose a dual attention feature refinement module (DAFRM), which achieves precise segmentation of small targets by effectively fuses the shallow spatial features of the encoder and the deep semantic features of the decoder through a dynamic fusion mechanism guided by dual attention. Experimental results on the Potsdam, Vaihingen, UAVid, and MSIDBG datasets demonstrate that CRDFNet outperforms existing methods in terms of F1 score, OA, and mIoU (Intersection over Union), validating its excellent performance.

Design of robust networks via reinforcement learning prompts the emergence of multi-backbones

A socio-ecological approach to the determinants of animal health management: A scoping review

With approximately 60% of human infectious diseases originating from zoonotic sources, an integrated approach to animal health management is critical. Significant barriers persist in optimizing disease control strategies, particularly regarding diagnostics, surveillance, biosecurity, and vaccination systems. Beyond technical and health-related aspects, socio-economic factors substantially influence both adoption and efficacy of intervention measures. While these determinants have been increasingly explored through integration of social sciences, such as economics, sociology, political science, into veterinary public health, most research remains confined to individual-level assessments focusing on epidemiological aspects and behavioral determinants, often neglecting broader social dynamics governing decision-making processes. Thus, this study develops a novel typology of determinants affecting the adoption of disease management measures using the socio-ecological model framework. We refer to these as “applicability factors”, defined as elements that either facilitate or hinder the implementation of animal health management measures. A scoping review was conducted to refine the classification of these factors, leading to the identification of 22,683 articles, of which 593 were analyzed. The scoping review was performed in accordance with the methodology defined by the PRISMA-ScR guidelines. Five key factors emerged: (1) individual and socio-cognitive, (2) socio-political and institutional, (3) economic, (4) organizational and professional, and (5) infrastructural. Our findings highlight critical barriers and facilitators while identifying research biases, including a predominant focus on zoonotic diseases and those with significant economic impact. The overrepresentation of studies from high-income countries underscores an imbalance in research efforts. Most studies emphasize individual-level determinants using epidemiological approaches, creating a notable gap in addressing systemic influences. Framing animal disease management within a socio-ecological model demonstrates the necessity of integrating these determinants into policy development. A systems-based approach proves essential for strengthening One Health governance. However, the current absence of cohesive and equitable global governance structures hampers strategy effectiveness. This calls for the implementation of intelligent governance mechanisms at local and global levels, coupled with appropriate tools and infrastructure to enhance disease management efficacy. The study provides a comprehensive framework for addressing multi-level determinants in animal health policy and practice.

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

Abstract Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. Physics-inspired approximate models offer greater data efficiency and intuitive understanding, but often sacrifice accuracy and transferability. Here we present HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. Starting from an approximate model encoding essential physical effects, HAMSTER captures the critical influence of dynamic environments on Hamiltonians using only few explicit first-principles calculations. We demonstrate our approach on halide perovskites, achieving accurate prediction of optoelectronic properties across temperature and compositional variations, and scalability to systems containing tens of thousands of atoms. This work highlights the power of physics-informed Hamiltonian learning for accurate and interpretable optoelectronic property prediction in large, complex systems.

Prevalence and characteristics of scoliosis among ethiopian schoolchildren aged 6–15 Years: A school-based cross-sectional study

Background Scoliosis is a progressive spinal deformity that often develops during childhood and adolescence. In Ethiopia, population-level prevalence data are scarce, and school-based screening, though practical, may overestimate cases without radiographic confirmation. Understanding its distribution and severity is critical for guiding clinical and public health strategies. Objectives To estimate the prevalence of scoliosis among Ethiopian schoolchildren, characterize its types and severity, and examine associations with clinical and anthropometric variables. Methods A cross-sectional school-based screening was conducted from March 2024 to June 2025 across 42 public primary schools in six regions. Children aged 6–15 years were screened using the Adam’s Forward Bend Test and scoliometer; suspected cases (ATR ≥ 7°) were referred for radiographic confirmation. Prevalence estimates and associations were analyzed using chi-square tests and t-tests. Data were analyzed in Python, with quality control ensured through standardized training, pilot testing, and double-entry verification. Results Of 32,000 children screened, 48 were suspected of scoliosis (0.15%; 95% CI: 0.11–0.20%), and 21 were radiographically confirmed (0.066%; 95% CI: 0.04–0.10%). Congenital scoliosis was most common (61.9%), with male predominance (69%), while idiopathic scoliosis (23.8%) was more frequent in females (60%). Neuromuscular and syndromic scoliosis were rare. Severity analysis showed male predominance in mild and very severe cases, with equal sex distribution in severe scoliosis. The mean Cobb angle was 47.4° (SD ± 28.9), most cases involved the thoracic spine (52.4%), and the rib hump was typically right-sided (61.9%). Cobb angle correlated positively with ATR (r = 0.61) and thoracic loss (r = 0.48), and negatively with age (r = –0.24) and thoracic height (r = –0.46). Conclusion This study shows that scoliosis prevalence within the school population is low, with adolescent idiopathic cases markedly underrepresented compared to other school-based screening reports. These findings suggest that nationwide school screening programs are not recommended. Instead, efforts should prioritize strengthening diagnostic and referral pathways for clinically evident cases to ensure timely access to specialized care.

Single-pot mechanochemically-enabled fluorine atom closed-loop economy using PFASs as fluorinating agents

Abstract Per- and polyfluoroalkyl substances (PFASs), also known as “forever chemicals”, pose an increasing threat to the environment and human health. Despite recent advancements in PFASs destruction, the recycling processes for such molecules remain limited to methods using high-temperatures or strong reducing agents. Sustainable degradation methods for PFASs, along with the subsequent utilization or recycling of the resulting fluorides, are indeed highly beneficial. In this study, we present a user-friendly, single-pot mechanochemical defluorination approach for fluorine transfer from PFASs to organic molecules. The high efficiency of this mechanochemical system obviates subsequent purification steps, requiring only minimal solvent filtration, even on a decagram scale. Furthermore, this strategy can be extended to the defluorination of everyday fluoroplastic and fluorinated polymers, such as PVDF membranes, pipes, and PTFE, thus addressing a critical challenge in sustainably breaking down persistent and environmentally harmful “forever chemicals”.

Higher temperatures are associated with increased risk of police violence: A nationwide county-level study in the United States, 2013–2024

Ambient temperature has been demonstrated to be associated with a variety of violent or conflict events. However, few studies have so far linked temperature to the risk of police violence, where the quantitative estimate of the temperature effect is still unclear. In this study, we comprehensively explore the relationship between temperature variations and police violence risk based on a series of panel regression models with high-dimensional fixed effects in the United States. The results indicate a generally positive association between temperature and the police violence, with higher temperatures corresponding to elevated risks. The heterogeneity analysis exhibits that lower levels of precipitation and larger population sizes are associated with increased risks of police violence. Specifically, under the conditions of less than 50 mm precipitation and a population of larger than 5 million, each 1°C rise in monthly average temperature is associated with an increased death rate of 2.06 (95% CI: 0.92–3.20) and 2.01 (95% CI: 1.08–2.93), respectively. The temperature effect on police violence risk presents notable spatiotemporal variation, with elevated risks observed in certain states with experiencing high temperature and particularly during the year of 2024. Our research projects that by the year 2050, under the highest greenhouse gas emissions scenario of SSP5–8.5, the cumulative additional deaths from police violence in the United States due to expected temperature change would achieve 479 (95% CI: 183–836). Given the profound and widespread societal impact of deaths related to police violence, these projected additional deaths may pose further challenges to public health and social stability in the United States. Our research reveals the linkage between temperature variation and the risk of police violence, highlighting the urgent need for targeted intervention strategies in the practices of police law enforcement, particularly under the high-temperature environmental conditions.

Cooperativity in E. coli aspartate transcarbamoylase is tuned by allosteric breathing

A nationwide survey on the prevalence of asbestos-related lung cancer in Japan

The nationwide prevalence of asbestos-related lung cancer (ARLC) needs to be accurately estimated to adequately operate a compensation subsidy program for patients with ARLC. In the present study, we aimed to estimate the proportion of patients with ARLC among patients with primary lung cancer according to the criteria established in the Japanese national compensation system, and described the characteristics and distribution of ARLC,. All facilities that treated patients diagnosed with lung cancer in 2016 were requested to submit computed tomography (CT) images of ten patients who were randomly selected from the national databases of hospital-based cancer registries. ARLC was defined as pleural plaques (PPs) extending over one-quarter of the inner lateral chest wall or existing PPs accompanied by obvious lung fibrosis. We estimated the proportion and distribution of ARLC among primary lung cancer cases and compared the characteristics of ARLC with those of primary lung cancer. Of the 772 facilities that treated at least one patient with lung cancer, 370 facilities provided 3,565 sets of CT images. Among them, 216 (6.1%) patients had PPs, and 86 (2.4%) patients met the compensation criteria. After sample weighting, 2.0% of all primary lung cancers were classified as ARLC in Japan. Compared with other primary lung cancers, a higher percentage of patients with ARLC were male (94.2% vs. 68.6%; P  < 0.01) and had more advanced-stage disease (stage III: 22.1% vs. 16.0%; stage IV: 44.2% vs. 39.8%; P  = 0.05). Most patients with ARLC (53.5%) were diagnosed at designated cancer hospitals. The proportion of patients with squamous cell carcinoma was higher in patients with ARLC than in those with primary lung cancer (25.6% vs. 18.6%; P  < 0.01). The estimated number of patients with ARLC was higher than expected from the number of applicants in the Asbestos Health Damage Relief System for asbestos-related health damages. Thus, countermeasures are needed to accurately identify eligible compensation recipients.

When bubbles bounce or stick

Quantitative profiling of lifespan-dependent cell-cell communication potential reveals dynamic ligand-receptor network shifts across mouse tissues

Cell-to-cell communication (CCC) is a tightly regulated process essential for tissue development and homeostasis, but can become dysregulated during ageing. While CCC is inherently complex and remains incompletely characterised, advances in single-cell RNA sequencing (scRNA-seq) have enabled large-scale, unbiased inference of intercellular interactions which offers broad-spectrum information that complements traditional protein-based assays. Unlike these targeted assays, transcriptomic approaches enable systematic inference and exploration of both known and potentially novel ligand-receptor (LR) interactions. In this study, we applied LIgand-receptor ANalysis frAmework (LIANA), which integrates multiple inference methods to derive consensus CCC predictions, to scRNA-seq data for four mouse organs (liver, lung, heart, and kidney), spanning key life stages: post-natal development, adulthood and ageing. Our analysis revealed dynamic, organ-specific CCC patterns characterised by both gains and losses of LR interactions over time, reflecting lifespan-dependent shifts in transcriptome-inferred intercellular communication potential. To quantify these shifts, we developed a two-phase comparative framework and introduced the Shrink and Expand (SE) score to capture directional changes in inferred LR interaction sets between any two biological states. Applying this framework generated a curated dataset of LR pairs and their predicted changes, capturing the repertoire of putative interactions across organs and states and enabling robust, interpretable comparisons of organ-specific and coinciding patterns of change across multiple organs. For instance, CD44 and ITGB1 were found to undergo highly dynamic changes across timepoints and organs, suggesting that they may act as central nodes in predicted age-dependent communication changes. This generalisable approach supports quantitative comparisons of inferred CCC across diverse states, including development, ageing, disease, or treatment conditions, and provides a resource for prioritising candidate interactions for drug target discovery for further experimental validation while exploring context-specific shifts in predicted intercellular communication.

Observation of partonic flow in proton—proton and proton—nucleus collisions

Abstract Quantum Chromodynamics predicts a phase transition from hadronic matter to quark–gluon plasma (QGP) at high temperatures and energy densities, where quarks and gluons (partons) are no longer confined within hadrons. The QGP forms in ultrarelativistic heavy-ion collisions. Anisotropic flow coefficients, quantifying the azimuthal expansion of produced matter, probe QGP properties. Flow measurements in high-energy heavy-ion collisions show a distinctive grouping of anisotropic flow for baryons and mesons at intermediate transverse momentum – a feature associated with flow imparted at the quark level, confirming QGP existence. The observation of QGP-like features in proton–proton and proton–ion collisions has sparked debate about QGP formation in smaller systems. For the first time, we demonstrate the distinctive grouping of anisotropic flow for baryons and mesons in high-multiplicity proton–lead and proton–proton collisions at the Large Hadron Collider (LHC). These results are described by a model including hydrodynamic flow followed by hadron formation via quark coalescence, consistent with the formation of partonic flowing systems in these collisions.

Forecasting auditor’s going concern opinion using with hybrid robust machine learning model

The importance of forecasting company bankruptcies makes the auditor’s reporting of the going concern opinion (GCO) a focal point for interested parties. Therefore, researchers have recently turned to predicting GCO using various machine learning (ML) methods. The aim of this research is to propose a novel hybrid model that integrates ML models to enhance the prediction accuracy of the system. We use a combination of traditional (classical) and hybrid ML approaches to identify the superior model among 30 models based on empirical data of Turkish companies listed on Borsa Istanbul (BIST) for the period 2017–2021. Given that the distribution of classes in the analysed dataset is balanced, it can be confidently stated that the research is reliable. The ML models are selected in accordance with the non-linear system since the equation system under consideration is the non-linear system. To minimise deviations and errors caused by distribution and fragmentation, the k-fold method is used to separate the training and test data sets. The experimental results show that the Random Forest based AdaBoost hybrid model outperforms traditional and other hybrid ML models in terms of accuracy by 89%.