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Future wireless networks require channel coding schemes that can provide high reliability, low latency, and strong adaptability under finite-blocklength and structurally heterogeneous transmission scenarios. Globally coupled low-density parity-check (GC-LDPC) codes are promising for such systems because their coupled structure can enhance error-correction capability, but the additional global constraints also increase decoding complexity and make conventional fixed-parameter decoders less effective. This paper proposes a prior-assisted hierarchical alternating direction method of multipliers (ADMMs) decoding framework for GC-LDPC codes. The proposed decoder first partitions the GC-LDPC parity-check structure into two local subgraphs and performs tuned ADMM decoding on the local blocks in parallel. The local decoding outputs are then merged and verified by the full GC-LDPC parity-check matrix. If the merged local decision satisfies all global constraints, it is directly accepted, thereby avoiding unnecessary full-graph decoding. Otherwise, a global fallback ADMM decoder is activated. In this stage, the channel log-likelihood ratios are fused with soft priors extracted from the local ADMM outputs, where prior clipping and conflict scaling are introduced to control unreliable or contradictory local information. The resulting fused reliability information is used to guide full-matrix ADMM decoding. This local-to-global strategy reduces unnecessary global iterations while preserving the ability to enforce global consistency when local decoding is insufficient. Simulation-oriented metrics, including bit error rate, frame error rate, local pass rate, global fallback rate, global fallback success rate, and average iteration count, are used to evaluate reliability and decoding efficiency. The proposed framework provides an average-complexity-aware and reliability-aware decoding approach for advanced channel coding in future wireless networks.

We suggest that accelerated reactions in confined volumes, specifically in microdroplets that contain water in contact with air, represent a remarkable, indeed a revolutionary chemical phenomenon. This strong claim is justified by the magnitude of the acceleration effect, by evidence for impact in key areas like reaction screening for drug discovery, by its alignment with sustainable chemistry and by the range of natural phenomena, including geological as well as atmospheric processes, where the effect operates. Accelerated processes in microdroplets enable the rapid conversion of quartz microparticles into hydrophilic silica nanoparticles, the late-stage functionalization of complex biomolecules, and the synthesis of heterocyclic compounds through rapid, environmentally benign 'green' processes that contrast strongly with conventional metal-catalyzed routes. The phenomenon is so unexpected, so unusual, that it has raised skepticism ─ as indeed it should. Compelling evidence for its operation is found in automated high-throughput (HT) experiments in which thousands of reactions are carried out at rates of 1 reaction/second and where accelerated processes occur in microdroplets generated from reaction mixtures during their millisecond flight times to give products that are identified by online mass spectrometry (MS) or deposited on surfaces and then bioassayed. Additional evidence comes from early studies of organic 'name' reactions while parallel evidence for just how remarkable this 'water and air' chemistry is comes from the transformations of N2. This classically unreactive, diatomic molecule can be oxidized to NO2 or reduced to NH3 using nothing but the special interfacial properties of wet microdroplets. The oxidation state of nitrogen ranges from +4 to -3 in these products, and intermediates with oxidation numbers lying within this range are also observed. Just as remarkably, microparticles of minerals, suspended in microdroplets of water, are broken down to generate nanoparticles simply by spraying the mixture and collecting the spray. Evidence that Si-O bonds are cleaved by the superacidic character of the microdroplets stands in contrast with the fact that nanomaterials can be built up by deposition of solvated ions ─ both processes occurring in sprayed microdroplets. Further evidence for the remarkable range of chemistry that occurs under apparently mild conditions lies in the fact that amino acids can be condensed to create peptides in sprayed droplets, while other esters and PFAS 'forever' chemicals can be rapidly hydrolyzed in the same medium. Microdroplet chemistry lies at the convergence of three chronologically distinct but methodologically intersecting domains: accelerated molecular reactions, interfacial inorganic chemistry, and materials evolution under nonequilibrium conditions. Each strand emerged independently, yet microdroplets provide a common physical platform in which they merge through shared interfacial and dynamical principles. From these foundations, the field has naturally expanded in diverse chemical and materials directions. One strand emphasizes accelerated chemical reactivity, where bond-forming reactions proceed on millisecond time scales with selectivity and efficiency, enabling rapid synthetic transformations relevant to pharmaceuticals and fine chemicals. A second strand focuses on inorganic and gas-liquid interfacial chemistry, including the formation of small molecules and ions such as NH3, NO2, sulfates, and nitrates, linking microdroplet chemistry to atmospheric, environmental, and geochemical processes. A third strand centers on materials chemistry, where droplets act as transient reactors for the formation of nanoparticles, nanostructures, and solid-phase materials, driven by coupled evaporation, charge and interfacial stresses, and mechanical deformation, connecting microdroplet phenomena to solid-state chemistry, rock weathering, soil formation, and environmental catalysis. These domains and their varied manifestations and interconnections are evident in the literature and will be discussed sequentially.

Geriatric syndromes (GS), such as falls, dementia, delirium and malnutrition, are complex clinical conditions affecting older adults which involve multiple organ systems and have major impact on quality of life and care. GS cut across disease categories, and are poorly represented in structured electronic health records. Natural language processing (NLP) offers an opportunity to extract valuable GS-related information from unstructured clinical text, such as hospital discharge summaries. However, the lack of high-quality annotated datasets limits the effectiveness of NLP models in this domain. This study introduces a manually annotated corpus designed for GS detection, enabling more accurate identification and classification of GS. We developed a comprehensive and detailed annotation scheme to label 12 common GS from hospital discharge summaries, incorporating key attributes such as diagnosis type, negation and event occurrence. The corpus consists of 2,040 manually annotated discharge summaries from National Health Service (NHS) Lothian hospitals in Scotland. To assess the effectiveness of NLP in extracting GS, we experimented with multiple pretrained transformer-based models, including base BERT (general-domain), BioBERT (biomedical-domain), BioClinicalBERT (clinical-domain) and BERT-cased (the cased English BERT checkpoint). The models were fine-tuned and tested for two types of tasks: named entity recognition (NER) and document-level labelling. We also considered an extra task of detecting contextual information with each GS mention (e.g., history, suspected, in-hospital). When context information is considered, two new tasks are called NER-C and DL-C, for NER and document-level labelling with context respectively. Our evaluation showed that, for the document-level labelling task, BERT-cased achieved the highest F1-score (0.897) and BioClinicalBERT performed best when negation was considered (F1-score: 0.888). For the NER task, BioClinicalBERT and BERT-cased achieved an F1-score of 0.883. Frailty (F1 = 1.0), Falls (F1 = 0.973) and Delirium (F1 = 0.946) are the GS entities with the best performing results. For NER-C, BERT-cased achieved the best F1 of 0.692 and BioBERT performed the worst (F1 = 0.658). In NER-C, the best results were achieved for context-aware falls and frailty labels, particularly when the syndrome was implied rather than explicitly stated. Document-level aggregation helped reduce inconsistencies, but the NER experiments used a flattened CoNLL-compatible representation of the original annotations, in which discontinuous mentions were converted into shortest covering spans and overlapping mentions were merged. Therefore, the reported NER results should be interpreted as baseline performance on a simplified representation of these structures, while low-frequency GS categories and sparse contextual labels also negatively affected model accuracy. This study demonstrates the effectiveness of NLP for extracting geriatric syndromes from unstructured clinical text and introduces a manually annotated corpus with detailed guidelines to support this task. The results also show that model performance is strongly shaped by dataset characteristics. More frequent and lexically clearer syndromes, such as frailty, falls and delirium, achieved the strongest results, whereas rarer categories and low-frequency attribute combinations, such as suspected, referral and some negated or context-specific labels, were harder to learn and yielded lower and less stable scores. Likewise, fine-grained annotation was more challenging than coarse-grained annotation because it increases label sparsity and requires the model to distinguish subtle contextual differences, such as current vs. historical mentions, implicit mentions and in-hospital onset. Entity-level extraction was further affected by discontinuous and overlapping mentions, which are common in clinical narratives and make boundary detection harder, whereas document-level aggregation reduced the impact of these local errors and therefore produced higher scores. These findings underline that data distribution, annotation complexity and mention structure directly influence model performance, and should be central considerations in future work on geriatric syndrome extraction.

Hospital delivery volume has been linked to severe maternal morbidity (SMM), which increases medical costs, readmissions, hospital stays, and mortality risk. During Fiscal Years&#xa0;(FY) 2019-2023, the Military Health System (MHS) rolled out a new electronic health record, merged facilities under Defense Health Agency (DHA) management, and weathered the COVID-19 pandemic. This study aimed to examine the association between delivery volume and odds of SMM and 30-day readmissions for deliveries at U.S. military hospitals for FY&#xa0;2019-2023. This cross-sectional study analyzed healthcare claims data from MHS beneficiary women aged 15-54 years delivering FY&#xa0;2019-2023. Delivery volume was measured using International Classification of Diseases-10 (ICD-10) and Medicare Severity-Diagnosis Related Group codes. Military hospitals were grouped into quartiles by volume over the study period. SMM was measured by 21 ICD-10 indicators (any/none). Any postpartum readmission (yes/no) to the same hospital within 30 days of discharge was included. Covariables included hospital identifier and maternal comorbidities and demographics associated with each delivery. Stepwise logistic regression was performed. Women delivering at hospitals in the lowest, lower-middle, and highest volume quartiles had significantly lower (19-30%) odds of SMM compared with the upper-middle quartile (p < 0.05). Those delivering at lower and lower-middle quartile hospitals had significantly higher (29-46%) odds of 30-day readmissions compared with the upper-middle quartile (p < 0.05), with no significant difference found among those in the highest quartile. Delivery volume was significantly associated with SMM and 30-day readmission odds among MHS deliveries FY&#xa0;2019-2023. This differs from previous FY&#xa0;2015-2018 findings, therefore concurrent effects of the DHA transition and COVID-19 pandemic cannot be ruled out.

The iliopsoas muscle is important functionally and is often encountered during various surgical procedures. During routine cadaveric dissection, a previously undescribed muscular structure was located deep to the iliacus muscle and coursed inferomedially to merge with the posterior aspect of the psoas major, running over the iliopectineal bursa. A cadaveric study was performed to better understand this muscle attachment. Two hundred embalmed adult human cadaver donor sides were dissected. The iliacus and psoas major muscles were carefully detached from their origins and reflected anteriorly to expose the iliac fossa. Particular attention was directed toward identifying accessory muscular slips, distinct structures from the medial aspect of the iliac fossa. When present, the accessory muscles were documented according to presence and anatomy. Selected measurements were made of the muscle and its distance to surrounding structures. Histological analysis of the muscle was performed. An accessory attachment of the psoas major was identified in 28% of sides. This structure was located deep to the iliacus and distinct from the more laterally placed iliocapsularis muscle, although the two muscles traveled in the same plane. This muscle's vertically oriented fibers were lateral to the anterior inferior iliac spine, the lumbosacral trunk, and the obturator nerve. The iliopectineal arch gave rise to this muscle's medial attachment. Its origin was from the medial aspect of the ilium near the arcuate line. The muscle had a variable length, but distally, always attached to the deep surface of the psoas major, usually near the level of the inguinal ligament. Its mean length and width were 83.4&#x2009;mm (52-100.4&#x2009;mm, SD 10.4) and 11.3&#x2009;mm (9.6-15.4&#x2009;mm, SD 3.1), respectively. Histologically, the iliac attachment of the psoas major was composed of typical skeletal muscle fibers that had an intimate relationship to the underlying iliopectineal bursa. An accessory muscular structure located along the arcuate line of the ilium was identified in 28% of hemipelves examined. We have termed this muscle the iliac attachment of the psoas major. It demonstrated a consistent fascial separation from the overlying iliacus and merged with the psoas major inferiorly. Its anatomical position suggests a potential role in stabilizing the psoas major muscle and is related to the anterior hip joint. Further investigation using imaging correlation and biomechanical modeling is necessary to clarify its functional significance. Recognition of this variation may be relevant for surgeons treating hip pathology and for anatomists describing muscular variations of the iliac fossa. To our knowledge, this is the first report of an iliac attachment of the psoas major in humans.

Cementitious sand and gravel (CSG) is commonly used in construction engineering; however, its mix proportion design is complex, and traditional physical experiments face limitations such as long cycles, high costs, and susceptibility to external factors when obtaining high-quality sample data. In this study, a foundational dataset was first acquired through physical experiments: 100 sets of CSG specimens with different mix proportions (cement content 40, 50, 60, 70 kg/m3; water-to-binder ratio 1.0, 1.2, 1.4; sand ratio 0.1, 0.2, 0.3, 0.4; fly ash content 20, 30, 40, 50 kg/m3) were prepared. After 28 days of standard curing, compressive strength and splitting tensile strength tests were conducted using a WAW-1000 electro-hydraulic servo universal testing machine, yielding 100 sets of real mechanical property data. The coefficients of variation for all test groups were below 10%, confirming the reliability and repeatability of the experimental data. On this basis, a data augmentation method based on Conditional Tabular Generative Adversarial Networks (CTGAN) is proposed. Through adversarial training between the generator and the discriminator, the model learns the multi-dimensional distribution characteristics of the original CSG data and generates 100 synthetic samples, which are then merged with the original data to expand the dataset to 200 samples. The quality of the synthetic data is evaluated using Wasserstein distance and correlation matrix heatmaps. Furthermore, a hybrid XGBoost-LSTM prediction model is proposed-XGBoost is used for feature construction to capture nonlinear interactions among mix proportion variables, and the constructed features are then fed into an LSTM network for sequential learning and regression prediction. The results show that the CTGAN-generated data are highly consistent with the original data in terms of kernel density distributions and variable correlations, with Wasserstein distance significantly superior to four comparative methods: Bootstrap, SMOTE, GaussianCopula, and TVAE. After augmentation, the XGBoost-LSTM model achieves a coefficient of determination (R2) of 0.9897 for compressive strength prediction (vs. 0.9793 before augmentation) and 0.9801 for splitting tensile strength (vs. 0.9882 before augmentation, a slight decrease). The mean absolute percentage errors (MAPE) are 4.49% and 4.11%, and the root mean square errors (RMSE) are 0.201 and 0.049, respectively; both error metrics are reduced compared with those before augmentation. Compared with baseline models including XGBoost, LSTM, Random Forest (RF), and Support Vector Regression (SVR), the XGBoost-LSTM model exhibits the best performance across all evaluation metrics, and Wilcoxon signed-rank tests confirm that the performance differences are statistically significant (p < 0.05). The proposed method of CTGAN-based data augmentation combined with the XGBoost-LSTM hybrid model provides an effective solution to the problem of insufficient CSG sample data and offers a reference for data enhancement and performance prediction of other small-sample materials.

The estimation of postmortem interval (PMI) is a long-standing scientific challenge in forensic pathology, and its accuracy is susceptible to environmental conditions. Environmental temperature can affect processes such as corneal tissue dehydration, water migration, and degradation of biological macromolecules, altering the spectral evolution characteristics of the cornea after death. Therefore, the relationship between PMI and spectral features may change with temperature conditions. This study collected attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) spectra of 144 rat corneal homogenate samples under three controlled temperature conditions (4&#xb0;C, 20&#xb0;C, and 30&#xb0;C) and six PMI time points (0, 6, 12, 24, 36, and 48&#x202f;h). Firstly, partial least squares regression (PLSR) models were established for different temperature groups and combined data to compare the modellability of the PMI-spectral relationship under different temperature conditions; subsequently, variance-synchronized component analysis (ANOVA-simultaneous component analysis, ASCA) was used to decompose spectral variations into PMI, temperature, and PMI&#x202f;&#xd7;&#x202f;temperature interaction effects, and dual screening was conducted using SHapley Additive exPlanations (SHAP) and other equivalent contribution analyses based on PLSR linear coefficients. The results showed that the single-temperature model was superior model performance to the combined, suggesting that temperature affects the relationship between PMI and spectra. The ASCA results indicated that PMI, temperature, and PMI&#x202f;&#xd7;&#x202f;temperature interaction effects explained 27.38%, 5.94%, and 21.57% of the total spectral variation, respectively. The higher interaction effect suggests that temperature does not merely introduce constant background differences but also alters the spectral evolution trajectory related to PMI. Based on this, this study constructed a dual-screening strategy integrating ASCA effect decomposition and SHAP contribution analysis to identify interpretable PMI-related spectral bands under temperature-dependent variations. Through this screening framework, 56 wave numbers were identified and merged into 9 consecutive candidate spectral bands. These spectral bands are mainly related to protein, amide region, carbonyl group and fingerprint region molecular changes, suggesting that they may reflect the molecular degradation process of the cornea after death. The above spectral bands exhibit strong PMI-related effects, relatively low temperature main effects, and relatively consistent cross-temperature change trends under the current controlled conditions. However, they should not be interpreted as completely independent biomarkers that are completely unaffected by temperature. The proposed ASCA-SHAP dual screening framework in this study can provide candidate spectral features for cross-temperature and interpretable corneal PMI models.

Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on less practical measures. This study developed and validated machine learning models to predict H&Y scores at 5&#x2009;years post-123I-ioflupane single-photon emission computed tomography (SPECT) imaging, leveraging both a real-world dataset and a subset of the PPMI cohort. The goal was to assess the utility of routinely collected clinical and imaging data for prognostic modeling. Data from medical records and imaging were harmonized from 343 real-world patients and 134 PPMI patients, resulting in a merged dataset with 83 overlapping features. Random Forest and Gradient Boosting models were trained to predict 5-year H&Y scores using varying amounts of longitudinal data and imaging features. Models using 2&#x2009;years of clinical follow-up data achieved the highest predictive accuracy. The most important predictors were early H&Y scores, gait symptom severity, and select imaging features. Machine learning models can predict 5-year H&Y scores in PD using real-world clinical data, but imaging features add limited prognostic value. This study demonstrated that implementing machine learning models, when using real-world data, did not significantly improve the already known gap between prognostic modeling and real-world implementation. Improvement of models is, however, a promising prospect and further studies are encouraged.

The aim of this study was to evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions. This retrospective study included 787 patients with stage IA endometrial cancer (EC) or benign endometrial lesions from four centers. Tumor regions of interest (ROIs) were manually delineated on MRI. Employing Python, the following peritumoral ROIs were automatically generated: 3-mm dilated and eroded peritumoral loops (LDE3), 3-mm eroded peritumoral loops (LE3), and intratumoral regions merged with 3-mm dilated peritumoral loops (RD3) Habitat clustering was performed using K-means and Gaussian Mixture Model (GMM) algorithms. Logistic regression was utilized to identify independent predictors and construct habitat-only and combined (clinical + habitat) models. Performance was evaluated using the area under the curve (AUC). Age (P = 0.002) and vaginal bleeding (P&#xa0;<&#xa0;0.001) were identified as independent clinical predictors of stage IA EC. Habitat-based models outperformed the clinical model in some external validation cohorts. Peritumoral features, particularly K-means_RD3, exhibited favorable robustness, achieving an average external validation AUC of 0.740. The integration of habitat features with clinical predictors yielded synergistic improvements, with the Clinical+K-means_RD3 model achieving the highest diagnostic performance (peak AUC: 0.921). Notably, this combined framework effectively mitigated the limitations of clinical factors in challenging subsets, elevating the AUC from 0.644 to 0.850 in validation group D. K-means demonstrated superior robustness and stability compared to GMM. Intratumoral and peritumoral habitat imaging based on multiparametric MRI can non-invasively reveal the microstructural characteristics of stage IA EC. Integrating clinical predictors with habitat models showed good diagnostic performance. The peritumoral habitat model exceeded the intratumoral habitat model.

Rigidity percolation provides an important basis for understanding the onset of mechanical stability in disordered materials. While most studies on the triangular lattice have focused on static properties at fixed bond (site) occupation probabilities, the dynamics of the rigidity transition remain less explored. In this work we formulate a dynamic pebble game algorithm that monitors how rigid clusters emerge and evolve as bonds are added sequentially to an empty lattice, with computational efficiency comparable to the standard static pebble game. We uncover a previously overlooked temporal self-similarity exhibited in multiple quantities, including the cluster-size changes and merged cluster sizes during bond addition, as well as the number of simultaneously merging clusters. We identify large-scale cascade events in which a single bond addition triggers the merger of an extensive number of clusters that scales with system size with inverse correlation-length exponent. Using an event-based ensemble approach, we obtain high-precision estimates of the critical point p_{c}=0.6602778(10), the inverse correlation-length exponent 1/&#x3bd;=0.850(3), and the fractal dimension d_{f}=1.850(2), representing substantial improvements over existing values.

To compare four complete-arch implant impression workflows-segmented right/left IOS merged via a palatal marker (IOS-LEGO-Seg), continuous full-arch IOS with a palatal marker (IOS-LEGO-Full), continuous full-arch IOS without a palatal marker (IOS-Full), and a conventional open-tray PVS impression followed by cast fabrication and cast scanning (CI)-and to evaluate whether segmentation combined with a palatal marker improves agreement with a laboratory reference dataset in a maxillary edentulous model. A maxillary edentulous dental cast with six implants was used to compare IOS-LEGO-Seg, IOS-LEGO-Full, IOS-Full, and CI. Intraoral scans were acquired, with 10 repetitions per condition. Datasets were superimposed onto a master reference scan using three alveolar-ridge markers; the palatal marker was excluded. The primary outcome was the concordance rate, defined as the proportion of intraoral scan body surface points within &#xb1;50&#x2009;&#x3bc;m of the reference. A linear mixed-effects model was used to compare workflows (&#x3b1;&#x2009;=&#x2009;0.05). IOS-LEGO-Seg achieved the highest concordance, followed by IOS-LEGO-Full, IOS-Full, and CI (p&#x2009;<&#x2009;0.001). The concordance rate was comparable between the IOS-LEGO-Full and IOS-Full groups, indicating that the addition of a palatal marker alone did not improve accuracy. Site-specific analysis revealed lower concordance at the distal molars (#16 and #26) in the non-segmented IOS workflows; however, this effect was mitigated by the segmented approach. The CI group did not show site-specific differences. The segmented workflow with a palatal marker may represent an accessible strategy for improving the accuracy of maxillary complete-arch implant impressions.

Health professions education (HPE) scholarship is expanding but remains unevenly distributed by region and language. Scientometrics can guide efforts to strengthen bibliodiversity, particularly for Global South regions underrepresented in HPE meta-research. This study aims to characterise publications from Hispanic America across medicine, nursing, dentistry and veterinary medicine, examining productivity, venues, collaboration, gender and thematic evolution. We analysed HPE journal articles (1992-2025) indexed in PubMed, Scopus and SciELO Citation Index (Web of Science), requiring &#x2265;1 Hispanic American author affiliation. Records were merged, deduplicated and curated, and then analysed in Bibliometrix/Biblioshiny (Bradford concentration, author productivity, networks). Author gender was inferred from names using gender-guesser. We retrieved 3598 publications (annual growth 7.8%) authored by 11&#x2009;240 individuals from 1563 institutions and published in 796 journals. Medicine accounted for most articles (n&#x2009;=&#x2009;2182), followed by nursing (n&#x2009;=&#x2009;637), dentistry (n&#x2009;=&#x2009;318), veterinary medicine (n&#x2009;=&#x2009;68) and mixed health sciences (n&#x2009;=&#x2009;403). The year 2023 was most productive (319 articles). Four hundred thirty-seven papers were single-authored. Author productivity was long-tailed (83.4% published once; 1.2% published >5 articles). International co-authorship was 17.2%; collaboration formed cohesive clusters with limited bridging. Publishing was concentrated: 15 core journals (1.9%) produced 34% of articles. Chile, Mexico, Peru, Colombia and Cuba led the output. Two thousand three hundred and six (64%) were in Spanish, 1260 (34.9%) in English, and 42 (1.1%) in both. Gender was near balanced (female 50.7%, male 47.3%; 0.35% androgynous and 1.7% unclassified), but women were underrepresented among the most prolific authors. Keywords emphasised teaching/learning and curricula/assessment, with post-2020 prominence of mental health and distance education/simulation training. HPE scholarship in Hispanic America is growing, but expansion is occurring within an ecosystem marked by concentration and uneven connectivity ('growth with fragility') and centre-periphery dynamics in scholarly communication. Recognising multilingual evidence and investing in bibliodiversity through bilingual dissemination/metadata, open identifiers and equitable collaboration can broaden the evidence base and support educational impact.

Following the introduction of direct-acting antivirals (DAAs), elimination of hepatitis C virus (HCV) became a target of the World Health Organisation (WHO) in 2016. Denmark had a HCV prevalence of 0.21% in 2016, and in 2018, unrestricted access to DAAs was introduced together with intensified testing strategies. To estimate adult HCV diagnostic and treatment coverage in Denmark by the end of 2022. Four national HCV source registers (laboratory reports, surveillance notifications, treatment database and hospital diagnoses) were merged. Capture-recapture analysis was used to estimate diagnosed individuals not identified in the registers, and estimates were adjusted for treated patients. The undiagnosed population was estimated using HCV testing data among high-risk individuals recorded in the Register for Treatment of Drug Use and compared with Danish studies on the diagnosed fraction of newly detected HCV cases. Across the four registers, 1,649 valid records of HCV-infected adults alive and resident in Denmark were identified. Capture-recapture analysis estimated a total of 1,700 (95% CI: 1,677-1,739) diagnosed individuals. With an undiagnosed fraction of 22-32%, the total HCV-infected population was estimated at 2,471-2,582 individuals, corresponding to a prevalence of 0.05-0.06% by the end of 2022. Compared to the 2016 baseline 82% had been diagnosed and 77% of diagnosed patients had been treated for HCV. HCV prevalence in Denmark has declined substantially, and with ongoing national initiatives to test and treat the remaining HCV patients we are likely to achieve WHO targets of diagnostic coverage and treatment before 2030.

Young adults aging out of foster care face a higher risk for negative adult outcomes, including homelessness, substance abuse, mental health issues, lower levels of education and employment, and financial instability, all of which affect their transition to adulthood. Extended foster care provides ongoing support to these young adults, yet participation remains low. This study explores associated factors, both demographic and foster care-related, of extended foster care participation. The study sample included 45,292 young adults in the United States who aged out of foster care between 2011 and 2020. A secondary analysis of merged data from the National Youth in Transition and the Adoption and Foster Care Analysis and Reporting System databases from 2011 to 2024 was conducted. A multivariable binomial logistic regression analysis examined whether demographic or foster care factors, including total time in foster care, age at first removal, placement setting type, and removal reasons, were associated with participation in extended foster care. Results indicate that total time in foster care, placement setting at age 17, and rural-urban context are most strongly associated with participation. Race and ethnicity, prior adoption, and the removal reasons of relinquishment, neglect, caretaker's inability to cope, and a child's disability, also show significant associations, though with smaller effect sizes. Implications for future research include the need for geographically focused studies that examine structural influences alongside individual factors and include the voices of those with lived experience.

Ammonia serves as a fundamental precursor for nitrogen-based chemicals in both agriculture and industry, which is mainly produced by the Haber-Bosch process. Lithium-mediated nitrogen reduction (LiNR) has emerged as a promising alternative to the Haber-Bosch process, though further optimization of its performance is required. Based on the reaction mechanism of the LiNR process, we contend that electrolyte engineering is a pivotal method for optimization in LiNR and other metal-mediated nitrogen reduction processes (MNR). By merging insights from both LiNR and battery research, several strategies have been proposed, including composition regulation, development of non-liquid electrolytes, and decoupling the catholyte and anolyte. The perspective highlights the critical role of electrolyte engineering and inspires further refinement of MNR, contributing to greener and more efficient ammonia synthesis.

Wheelchair skills training is essential for optimising mobility and participation in meaningful activities. Peer-training is becoming a largely studied approach to meet the training needs of wheelchair users. The Training to Enhance Adaptation and Management for Wheelchair users (TEAM Wheels), a peer-led eHealth program, was evaluated in three Canadian cities. Peer-trainers in each city were recruited and trained to deliver TEAM Wheels. The aim of this study was to explore experiences and perceptions of peer-trainers after delivering the TEAM Wheels program. A descriptive qualitative design employed semi-structured interviews with peer-trainers who delivered TEAM Wheels in a randomised controlled trial. All peer-trainers were contacted by study investigators and invited to participate. Open-ended questions explored wheelchair experiences of peer-trainers, previous peer mentorship, preparation for the peer-trainer role, and experiences with the TEAM Wheels study. Interview recordings were transcribed verbatim, content coded and inductively constructed into themes. All TEAM Wheels peer-trainers (n&#x2009;=&#x2009;7) participated in this study. The first theme uncovered how merging lived experience and personal skills influenced their peer-trainer role, highlighting what peer-trainers perceived as important to accomplish their role. The second theme explored the building blocks of quality experiences for peer-trainers, suggesting flexibility and training as important aspects influencing their experience. When peer-trainers are adequately prepared, and there is a good mentor-mentee match, peer-trainers could play an important role in supporting the training to improve wheelchair mobility and general mentoring needs of wheelchair users. Lived experience and personal skillset of peer-trainers influence wheelchair skills training experiences and eHealth intervention deliveryAdequate training and defining roles allow a better experience for peer-trainers and traineesThe use of an eHealth intervention led by peers could help support wheelchair mobility and general mentoring needs for wheelchair users, and nurture a sense of community.

High-flow nasal oxygen (HFNO), also known as high-flow nasal cannula, is an important noninvasive respiratory support strategy in perioperative and critical care practice. As the literature has expanded across multiple clinical contexts, a structured overview is needed to clarify the development, knowledge base, and emerging priorities of the field. We conducted a bibliometric and visual analysis of HFNO research using English-language articles and reviews retrieved from the Web of Science Core Collection, Scopus, and PubMed for the period 2000-2025. After screening, merging, and deduplication, 2,314 unique publications were included. Bibliometrix in R was used for performance analysis, thematic mapping, and thematic evolution; VOSviewer for collaboration analysis; and CiteSpace for co-citation analysis, keyword clustering, timeline visualization, burst detection, and dual-map overlay. HFNO research showed sustained exponential growth, with marked acceleration after 2018 and especially after 2020. The literature was concentrated in respiratory medicine, critical care, and anesthesiology journals, with Respiratory Care ranking first in publication output. The United States and China were the leading contributors, while several French institutions, particularly Assistance Publique-H&#xf4;pitaux de Paris, were prominent in the institutional network. Co-citation analysis identified major clusters related to acute respiratory failure, perioperative oxygen therapy, preoxygenation strategies, and coronavirus disease. Keyword and thematic analyses indicated a shift from early emphasis on perioperative oxygenation and postoperative respiratory support toward broader critical care application and, more recently, toward context-specific deployment, acute hypoxemic respiratory failure, awake prone positioning, treatment monitoring, and the ROX index. HFNO research has evolved from a focused literature on oxygenation support into a broader and more clinically differentiated field spanning perioperative and critical care practice. Current hotspots are increasingly centered on context-specific use, monitoring, failure prediction, and escalation decisions. Future research should prioritize clinically actionable patient stratification, standardized outcome definitions, and protocol-based integration of HFNO across different care pathways.

Automated detection of lunar impact craters from digital elevation model (DEM) data is important for lunar geological analysis, landing-site selection, and crater catalog updating. However, this task remains challenging because lunar craters exhibit large scale variations, weak or degraded rims, ambiguous boundaries, and complex topographic backgrounds. In addition, large-scale lunar remote sensing applications require detection models to achieve a reasonable balance among accuracy, model complexity, and inference efficiency. To address these challenges, this study proposes FPW-YOLO11n, a frequency-perception crater detection method developed based on YOLO11n. First, a Frequency-Directional Attention Module (FDA-Module) is introduced into the shallow stage of the backbone. This module combines frequency-aware channel attention and direction-aware spatial attention to enhance the representation of crater rim structures, elevation variations, and directional topographic cues in DEM data. Second, a C2PSA-LRSA module is designed by embedding Local Region Self-Attention into the C2PSA framework, thereby improving local contextual feature interaction while reducing the excessive cost associated with global self-attention. Third, Inner-WIoU is adopted to replace the original CIoU loss in YOLO11n. By combining the auxiliary-box mechanism of Inner-IoU with the sample-quality-aware weighting strategy of WIoU, Inner-WIoU provides a more flexible bounding-box regression objective for craters with weak rims, scale variations, and uncertain boundaries. A DEM-based lunar crater dataset was constructed from the Moon LRO LOLA-SELENE Kaguya TC DEM Merge 60N60S 59m product and the Robbins lunar crater catalog, covering the non-polar region from 60&#xb0; S to 60&#xb0; N and containing 4760 image tiles. Under the random data-splitting strategy, FPW-YOLO11n achieves 78.3% Precision, 66.2% Recall, 75.1% mAP@0.5, and 50.2% mAP@0.5:0.95, outperforming the YOLO11n baseline by 1.2, 2.0, 1.6, and 4.0 percentage points, respectively. Additional experiments based on geographically disjoint data splitting further show that the proposed method consistently performs better than YOLO11n on DEM data, indicating that the proposed structural improvements remain effective under a more rigorous spatially independent evaluation setting. Although the computational cost increases from 6.3 to 24.0 GFLOPs, FPW-YOLO11n maintains a compact parameter size of 2.59 M and a high inference speed, demonstrating an improved accuracy-efficiency trade-off for lunar crater detection from DEM data.

Aptamers are single-stranded DNA or RNA molecules that exhibit remarkable affinity and specificity for a broad spectrum of biological targets, positioning them as compelling alternatives to antibodies in both diagnostics and therapeutics. Their ease of synthesis, chemical stability, and tunable binding properties make them highly adaptable for molecular recognition applications. Carbon Nanotubes (CNTs), on the other hand, are renowned for their unique structural, electrical, optical, and mechanical properties, ultimately providing an ideal nanoscale scaffold for aptamer conjugation. When combined with aptamers, CNTs form multifunctional hybrid nanoplatforms that merge the molecular selectivity of aptamers with the high surface area, conductivity, and mechanical strength of CNTs. This review discusses the fundamental concepts, functionalization strategies, and biomedical potential of aptamer-CNT hybrids. Both covalent and non-covalent conjugation approaches are examined, highlighting their impact on stability, sensitivity, and biocompatibility at the biointerface. Recent progress in molecular recognition, biosensing, targeted drug delivery, imaging, and theranostic applications is also summarized. Particular attention is given to the role of CNTs in enhancing electron transfer, signal amplification, and controlled therapeutic release. Moreover, diagnostic and therapeutic applications across various disease models, including cancer, infectious diseases, and neurodegenerative disorders, are highlighted. Furthermore, emerging challenges related to the toxicity, biodegradability, and pharmacokinetics of CNT-based hybrids are addressed, while considering regulatory and ethical perspectives that govern their clinical translation. Overall, aptamer-CNT hybrids hold immense promise as versatile, tunable, and multifunctional platforms that could fundamentally transform next-generation precision medicine and nanotherapeutic systems.