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Hydrogen-bonded organic frameworks (HOFs) have emerged as promising materials for biomedical applications owing to their metal-free biocompatibility and recyclability. Notably, most HOFs are synthesized and utilized in organic solvents, limiting their biomedical translation. Although water is a biologically compatible alternative, it competes for hydrogen bonding and disrupts interactions between building blocks, making the construction of stable aqueous HOFs challenging. Inspired by the DNA base pairing structure, the first nucleoside-based HOF (N-HOF-1) was developed using a multi-hydrogen bonding strategy. This framework is synthesized entirely in water by simply mixing 2-amino-2'-fluoro-2'-deoxyadenosine (2FA) and cyanuric acid (CA), enabling grade production while maintaining stability under physiological conditions. Microcrystal electron diffraction (MicroED) and single-crystal X-ray diffraction (SCXRD) studies revealed the confinement of M-shaped water clusters within the channels of N-HOF-1, mimicking DNA hydration and preserving the HOF architecture. Notably, the porous and positively charged properties of N-HOF-1 enable interaction with bacteria to form the bacteria-nanoparticle biohybrid systems. Leveraging the intrinsic bioactivity of nucleoside building blocks, this system enhances engineered bacterial colonization in the periodontium, periodontal tissue regeneration, and lymphoma therapy. These findings highlighted the potential of nucleosides as versatile building blocks for hydrophilically stable HOFs, offering new possibilities for their biomedical applications.

Health promotion in correctional settings faces challenges due to limited healthcare access and restricted environments. Nurses are primary healthcare providers in correctional institutions; however, evidence on nurse-led capacity-building programs for inmate health volunteers remains limited. Developing the capacity of Prison Public Health Volunteer Leaders (PPHVs) through a holistic healthcare approach can enhance their self-reliance. This study aimed to collaboratively develop, implement, and evaluate a holistic health care program to strengthen the capacity of PPHVs at the Central Correctional Institution for Young Offenders in Pathum Thani Province, Thailand. This study employed a Participatory Action Research (PAR) design. A total of 100 well-behaved inmates were purposively selected as PPHVs and actively engaged as co-participants throughout the PAR process. The intervention was collaboratively designed and implemented over five weeks, comprising ten participatory training sessions that addressed physical, mental, social, and environmental health dimensions. Quantitative data were collected using validated questionnaires assessing knowledge, attitudes, health practice skills, data management skills, and overall capacity at baseline, post-intervention, and at 1-month follow-up. Data were analyzed using repeated measures analysis of variance (ANOVA). The findings revealed statistically significant improvements (p <0.001) in participants' knowledge (F = 778.41, &#x3b7;p2 = 0.887), attitudes (F = 889.09, &#x3b7;p2 = 0.900), health practice skills (F = 1241.89, &#x3b7;p2 = 0.917), data management skills (F = 546.81, &#x3b7;p2 = 0.847), and overall capacity (F = 727.48, &#x3b7;p2 = 0.880). These outcomes indicate substantial enhancement in PPHVs' ability to perform health promotion and data management roles proficiently. The holistic health care program significantly improved PPHVs' capacity, suggesting promise for strengthening inmate health volunteer programs in correctional settings. These findings have significant implications for nursing practice in designing capacity-building programs, nursing education in preparing nurses for correctional care, and nursing policy in developing standards for correctional nursing in Thailand. Thai Clinical Trials Registry (TCTR20260115003).

The development of artificial intelligence (AI) has created new opportunities for AI-supported foreign language teaching and applications. This study investigates the interrelationships among foreign language enjoyment (FLE), writing self-efficacy, self-regulated learning (SRL) strategies, and learner engagement within the context of AI-assisted English as a foreign language (EFL) writing. A cross-sectional survey design was employed, involving 535 Chinese university students with prior experience in AI-assisted writing. Participants completed adapted and validated scales measuring FLE, writing self-efficacy, SRL strategies, and learner engagement. Structural equation modeling (SEM) was used to test a hypothesized partial mediation model linking these constructs. The results supported a well-fitting partial mediation model. FLE was found to be a direct and significant positive factor of learner engagement. Furthermore, the analysis confirmed that self-efficacy and SRL strategies act as sequential mediators in the relationship between FLE and engagement. Specifically, FLE is positively associated with learners' self-efficacy, which in turn promotes the use of SRL strategies, ultimately leading to deeper cognitive, behavioral, emotional, and agentic engagement in the writing task. The study extends Control-Value Theory, Broaden-and-Build Theory, Social Cognitive Theory, and Self-Regulated Learning Theory to the novel domain of AI-assisted language learning, highlighting the critical affective and cognitive pathways that foster engaged learning. Pedagogical implications are discussed, emphasizing the importance of designing enjoyable, confidence-building, and strategy-rich AI-assisted writing environments.

Interstitial lung disease (ILD) is a serious irreversible, often progressive, lung condition that can lead to respiratory failure and early death. Indian data on epidemiology of the disease is scarce. To better understand the nation-wide burden, population characteristics and treatment outcomes, and capacity building of clinicians for management of patients with ILDs, ICMR's Network of Pulmonary Fibrosis (INPF) was established. Objective: To describe the rationale, protocol, and current status of INPF. The network, consisting of 23 centres across the country, was formally launched in August 2022. Governed by the Indian Council of Medical Research (ICMR) and its task force (TF) committee, INPF has been recruiting all new and previously diagnosed adult patients (age >18 years) with ILDs. The diagnosis and sub classification of ILD was based on multi-disciplinary team discussion (MDD). The data is secured in the online database incorporated in the INPF website. Progress of the network is continuously monitored by the investigators at the centres and by the TF at the ICMR. As on February 2026, 11,544 have been enrolled in INPF. Connective disease associated-ILD (CTD-ILD) was the most common ILD constituting 28.46% (3,286) of the patients. Currently, patients are being enrolled for four proposed sub-studies with satisfactory progress. Four capacity building sessions has been conducted. INPF is one of the largest ILD registries in the world. It also provides pulmonologists a platform to conduct research in ILD and better equip themselves to manage patients.

To evaluate the recent literature on differential exposure to and health risks from wildfire smoke across subpopulations, whether wildfire-derived PM2.5 affects health differently from non-wildfire-derived PM2.5, and how wildfire smoke composition affects health. We found inconsistent evidence of differential exposure to and health risks from wildfire PM2.5 by population subgroups. This could be due to variation in wildfire PM2.5 infiltration into buildings and ability to take individual protective actions, both of which have been noted to be related to socio-economic status in the recent scientific literature. Respiratory health endpoints have been the most consistent and commonly evaluated health outcome in studies of wildfire smoke; additional research is needed to resolve conflicting findings for non-respiratory health outcomes (e.g., cardiovascular disease). Although some recent studies have documented larger health risks from wildfire-derived as compared to non-wildfire-derived PM2.5, we document how further research could evaluate whether these findings are confounded by type of fuel burned, due to methodological concerns, or are true. We also conclude that more research is necessary to elucidate potential differences in health risks of constituents of wildfire smoke other than PM2.5 or from burning of different fuels. Wildfire smoke is projected to continue to increase. We encourage future research to move away from further documentation of respiratory health impacts of wildfire smoke, which has been very well established, into studies of other health endpoints that have been less well studied to date, more exploration into health effects from wildfire smoke constituents other than PM2.5 and from different types of fires (i.e., wildland urban interface (WUI) fires versus wildland fires), and additional exploration of remaining uncertainties with a goal of further supporting public health protection from wildfire smoke.

To investigate the impact of spatial positions of the transfer fork registration markers on the accuracy of generating a virtual dentofacial patient. An in vitro study was conducted using a mannequin head with a standard maxillary dentition model. Radiopaque gauge markers were fixed on the face and dentition of the mannequin head. CBCT was performed and the distance and angle between the dentition and facial markers were measured in the CBCT as reference values. Intraoral scanners were used to obtain 3D morphological data of the maxilla. Two types of transfer fork were designed and fabricated. The registration markers on transfer fork A were positioned in the midline area, while those on transfer fork B were located at the corners of the mouth on both sides. The transfer forks were digitised and connected to the maxillary dentition within the mannequin head, and facial scanning was performed using a facial scanner five times in each group. A virtual dentofacial patient was built through matching and integration of digital dentition, face and transfer fork data using 3D reverse engineering software (Geomagic Wrap 2021, 3D Systems, Rock Hill, SC, USA). Measurement values including feature lengths and feature angles between six facial gauge markers and three dentition gauge markers were obtained in the virtual patients. The mean trueness and precision of linear difference for virtual patients established using transfer fork A were -1.00 &#xb1; 0.11 mm and 0.27 &#xb1; 0.02 mm and the angle deviation was -1.88 &#xb1; 0.27 degrees, whereas for transfer fork B, the mean trueness and precision of linear difference were 2.66 &#xb1; 0.25 mm and 0.83 &#xb1; 0.06 mm, and the angle deviation was 3.74 &#xb1; 0.87 degrees. There is an overall significant difference in the trueness values of feature lengths (t = -13.963, P = 0.000) and angles (t = -5.985, P = 0.004) between transfer fork groups A and B, with group A showing better trueness and precision. Linear and angular errors will be introduced in the process of building up a virtual dentofacial patient using a transfer fork. The trueness and precision of the transfer fork with the matching markers at the centre of the lips are more precise than the transfer fork, with matching markers on both sides of the mouth.

Building on previous research and implementations, this study identifies the essential structural and functional components required to develop a Learning Health System (LHS) for oncology in the Netherlands. Drawing on work related to the Netherlands Cancer Registry (NCR), we systematically mapped our studies onto the LHS framework to examine component coverage and integration. Medical informatics advances LHS development by enabling structured data capture, computable guideline representation, continues real-world data analysis, and data-driven guideline refinement. In the Netherlands, strong integration with the NCR provides a robust foundation for continuous learning, moving the vision of a fast, data-driven health system closer to reality. Our findings show that while the methodological and technical foundations of LHS components are well established, integration gaps remain. The next step is engaging multidisciplinary learning communities, supported by clear governance, to enable continuous learning, and sustained improvement in oncology care.

The field of lignin-based polymeric materials is undergoing rapid development, driven by increasing sustainability demands. However, progress in lignin-derived materials is often pursued from different disciplinary perspectives&#x2500;biomass chemistry, organic synthesis, and polymer materials science&#x2500;using field-specific metrics, resulting in fragmented knowledge. This Perspective examines the lignin-to-materials pathway by connecting advances in the conversion of lignin into platform molecules, their transformation into monomers, and the synthesis of polymeric materials through representative examples. We perform rough estimates of sustainably available lignin streams and compare them with current polymer production, indicating that lignin could potentially supply aromatic monomers at scales comparable to existing markets. Through analysis of key literature on lignin-to-monomers and monomers-to-polymer strategies, we identify critical directions for lignin-to-materials development. These include refinery concepts that utilize complex lignin-derived substrates as primary building blocks, prioritizing the use of their inherent functionality before stepwise defunctionalization, and adopting application-driven materials design, in which the requirements of a target application guide monomer and polymer selection rather than attempting to reproduce the molecular structures of the petroleum-derived polymers currently used for those applications.

Current decision-making for slope-reducing osteotomy (SRO) often relies on isolated posterior tibial slope (PTS) thresholds, potentially misidentifying patients with acquired soft-tissue decompensation or possibly overtreating those with an asymptomatic, inherently hyperlax baseline. Furthermore, rigid point-based scoring systems oversimplify the synergistic biomechanics of the anterior cruciate ligament-deficient knee. Building on the foundational 'Set-Point' theory established in Part 1, this paper introduces the assessment-led personalization (ALP) system. This unified clinical algorithm integrates the normalized percentage of absolute static anterior tibial translation (sATT%) and its side-to-side difference (&#x394;sATT%) with PTS laterality, generalized hyperlaxity, and injury chronicity. By mathematically calibrating raw translation data to isolate true soft-tissue decompensation from underlying osseous asymmetry, the ALP system provides a proactive, joint-preserving framework to identify high-risk phenotypes likely to fail isolated soft-tissue reconstruction and precisely refines the indications for SRO. LEVEL OF EVIDENCE: Level V.

Ohio has been severely affected by the opioid epidemic for more than a decade, leading to the development of diversion and deflection programs that seek to intervene with individuals experiencing substance use disorder in the community. The Comprehensive Opioid, Stimulant, and Substance Use Program provided funding to develop or enhance 9 first responder-led diversion/deflection efforts across the state of Ohio and examine their operations and impact on client outcomes from 2021 to 2024. Although heterogeneously implemented, each of the 9 funded deflection teams engaged in overdose response or proactive outreach, targeting people with substance use disorder to connect them to community-based treatment resources, overdose prevention materials, and other supports based on the needs of the individual. Demographics for referrals across the sites, number of referrals/unique individuals recorded per team, deflection team activities (ie, contact attempts, successful contact rates, overdose prevention material distribution), and clients' proximal outcomes of connection to treatment are reported. In addition, successful rates of contact and connection to treatment were examined across demographic groups. Less than 25% of all teams had any significant differences in rates of contact or connections to treatment between demographic groups. Nearly 80% of the teams showed significant differences in mean number of contact attempts between successful and nonsuccessful contacts. This multisite evaluation contributes to a small but growing body of research on deflection programs. It emphasizes 2 proximal outcomes that are directly tied to team activities. It also builds on past research by examining how client demographics and team activities (ie, contact attempts) may influence client outcomes.

Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R2) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.

Sociocultural barriers are powerful forces that may shape how young people experience, interpret, and respond to psychological distress in Zimbabwe. For many youth (aged 15-24&#x202f;years), symptoms of common mental disorders (CMDs) are navigated within social environments where disclosure risks labelling, social exclusion, and moral judgment. This stigma is rarely experienced in isolation; it intersects with pressures to protect family reputation, distrust in formal and informal support systems, and gendered expectations governing emotional expression. While existing research has documented individual barriers to mental health care, limited evidence examines how these sociocultural forces converge to shape help-seeking pathways among Zimbabwean youth. This qualitative study explored how sociocultural factors influence decisions to seek or avoid care among young people with lived experience of CMDs. A descriptive qualitative research design was employed. We utilised in-depth interviews and focus group discussions with young people aged 15-24&#x202f;years with lived experience of CMDs, their caregivers, peer counsellors, lay health workers, and church leaders in Harare and Bindura, Zimbabwe. Purposive sampling was employed to recruit participants until data saturation was reached. All interviews were audiotaped and transcribed verbatim, and all the data were analysed using inductive thematic analysis. The analysis revealed that help-seeking decisions were shaped by four interconnected sociocultural barriers; pervasive stigma; profound distrust in community and institutional spaces; family reputation pressures that prioritise concealment over disclosure; and restrictive gender norms that equate emotional expression with weakness, particularly among young men. Participants described how these factors intersect to create a dynamic where mental health concerns remain hidden rather than addressed. Our findings demonstrate that effective mental health interventions for Zimbabwean youth must address the multifaceted sociocultural landscape in which help-seeking decisions are made. Programmes need to simultaneously combat stigma, build institutional trust, engage families in destigmatisation efforts, as well as challenge restrictive gender norms. These strategies offer the most promising pathway to closing the mental health treatment gap for young people in Zimbabwe.

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to pathological conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. To address these challenges, we introduce a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once (YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), such as Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, thereby minimizing the impact of cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and cell phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.

Nuclear medicine (NM) often functions as an isolated diagnostic island within hospitals, resulting in the underutilization of high-value molecular imaging resources despite their significant clinical potential. This paper proposes a conceptual management-driven framework, termed the Management-Driven Integration Loop, to break down these silos and integrate NM into the broader hospital ecosystem to potentially enhance clinical decision-making and operational efficiency. A comprehensive integration framework was developed based on four strategic pillars: strategic resource allocation, process re-engineering, performance leverage, and brand building. The framework introduces specific administrative interventions, including the establishment of satellite workstations to improve clinical proximity, the creation of a Clinical-NM Liaison role to bridge interdisciplinary gaps, and the implementation of a multidimensional Total Performance Index (TPI) to align incentives. Integration is envisioned through protocol-based clinical pathways embedded in electronic health records and structured Multidisciplinary Team (MDT) collaboration, ensuring NM expertise may inform treatment planning. The framework incorporates a continuous feedback loop to track clinical impact, including diagnostic upstaging and the avoidance of unnecessary procedures. By proposing a shift from volume-based metrics to the TPI, administrators can weight qualitative clinical contributions and interdisciplinary collaboration alongside traditional procedural throughput. Transforming nuclear medicine into a central strategic asset requires deliberate administrative intervention. While implementation depends on overcoming institutional, technical, and regulatory barriers, this management-driven integration loop aims to bridge the gap between technological potential and clinical impact, improve patient outcomes through precision diagnostics, and strengthen institutional positioning as centers of excellence.

Neuronal synchronization can emerge through various coupling mechanisms, but its expression depends strongly on how these connections are organized. Gap junctions, in particular, can reshape inhibitory synchrony, sometimes reinforcing coherence, other times fragmenting it. Building on the classic Rinzel-Golomb model of the thalamic reticular nucleus (TRN), we extend an inhibitory network to include gap-junction coupling arranged in biologically-motivated clustered patterns. In particular, we explore the effects of the size, strength, and spatial distribution of gap-junction clusters on synchronization, and how these effects are modulated by the level of background inhibition. Across conductance regimes, weak electrical coupling can transiently destabilize synchrony, while stronger or more extensive clustering promotes coherence or dampens oscillations. These results suggest that the spatial organization of electrical connectivity, together with inhibitory tone, plays a decisive role in shaping rhythmic coordination within TRN-like networks.

Urea electrolysis plays a key role in various urea waste valorization processes. In this regard, the complex six-electron transfer process, urea oxidation reaction, is the key to achieving maximum efficiency. In this study, we developed multilayered &#x3b1;-Ni-(OH)2 sheets through a sonochemical approach. We used Fe3+ to dope the &#x3b1;-Ni-(OH)2 sheets in three different compositions. The synthesized catalysts were analyzed by using XRD, SEM, EDX, FTIR, and XPS. Using EDX and XPS, successful doping of iron was confirmed. The electrocatalytic activities were characterized by CV, LSV, EIS, and CA techniques. Through CV analysis, it was identified that Fe3+ did not directly participate in the electrochemical reaction; rather, due to high Lewis acidity, it was modulating the electronic environment of Ni2+ ions. Iron doping induced a reduction in the onset potential and synergistically increased the catalytic current by promoting a higher number of active sites for urea adsorption. Tafel analysis concluded that the improved reaction kinetics was due to iron incorporation. Using Nyquist and Bode plots, it was identified that iron doping promoted CO2 production as RDS. Therefore, catalyst poisoning due to prolonged adsorption of CO2 was diminished, and subsequently, catalyst stability was increased. Overall, while Fe3+ doping has proved to be a significant method to enhance catalytic activity, careful optimization of concentration is required to build a state-of-the-art catalytic architecture for urea waste valorization.

Mosquito population dynamics are a key determinant of West Nile virus (WNV) transmission risk in temperate regions, as they regulate the timing, intensity, and spatial distribution of seasonal outbreaks. In Europe, Culex pipiens s.l. is the primary WNV vector, yet a comprehensive understanding of how its populations respond to environmental variability across multiple temporal and spatial scales remains limited. Using a nine-year entomological dataset (2014-2022) from an extensive monitoring network in Northern Italy, we investigated the spatio-temporal dynamics of Cx. pipiens s.l. across the heterogeneous landscapes of the Po Valley, a WNV-endemic area. We applied complementary statistical and machine-learning approaches to characterize between-year trends, within-year (summer) fluctuations, and long-term spatial patterns in mosquito abundance. Results showed strong persistence in population states across both years and months, consistently modulated by hydroclimatic conditions. Precipitation during the pre-activity period emerged as a dominant driver of inter-annual variability, highlighting its potential as an early indicator of summer population build-up. Within seasons, short-term temperature exerted a strong, nonlinear influence on Cx. pipiens s.l. abundance, with declines observed under extreme heat conditions. Spatial analyses identified persistent hotspots associated with irrigated agricultural systems, wetlands, and major river corridors, whereas upland and forest-dominated areas exhibited lower suitability. Overall, this study advances current knowledge of Cx. pipiens s.l. spatio-temporal dynamics and demonstrates how climatically and environmentally driven indicators can be translated into actionable tools for risk assessment and adaptive vector surveillance. These insights support improved early detection and targeted control within integrated One Health surveillance frameworks for WNV.