This article presents a reproducible field-to-simulation workflow that translates real football plays into solver-ready boundary conditions for a full-body finite-element human model. Single-view video reconstructs six-degree-of-freedom kinematics at the skull CG; these signals are applied to a helmet-head-body assembly that preserves event-specific hardware. The protocol codifies quality-control gates before interpretation: (I) helmet readiness (mesh integrity, contact stability), (ii) mass and center-of-gravity agreement between the physical configuration and its FE surrogate, (iii) energy balance with bounded spurious energies, (iv) driver-fidelity metrics comparing target versus solver-applied motion (RMSE, peak magnitudes, time-to-peak), and (v) versioned inputs for auditability. A demonstration replay verifies numerical stability and driver fidelity, and reports tissue-level response and diagnostic damage metrics as process outputs without making injury claims. For studies requiring internal response, the framework supports physics-based constitutive models (Internal State Variable formulations) and treats them as process diagnostics unless separately validated. By separating readiness checks from injury interpretation, the method provides a practical foundation to standardize helmet integration, enable cross-laboratory reproducibility for event scenarios, and inform safer design and policy. Although developed for sport, the workflow generalizes to transportation and defense settings where ethical constraints prevent human experimentation.
Electrophoretic deposition is a method of choice for generating coatings thanks to its ease of implementation and its ability to produce coatings of relatively large thicknesses in a single-step process. While this process also benefits from a large number of tunable parameters to adapt the coating to each application (such as applied electric field, particle concentration, and viscosity of the suspension), such freedom can make selecting parameters an overwhelming task. A better fundamental understanding of the microscopic phenomena and mechanisms at play during deposition can provide clues for a more efficient design of optimized coatings. Particle-based models, which allow for the systematic simulation of deposit microstructures across various process parameters, are particularly interesting for gaining insights into such systems. Nevertheless, such studies are rare and usually do not include the possibility of self-cohesion between particles, which is crucial for the final structure of the deposit. Here, we use particle-based simulations to study how barrier-limited aggregation influences the deposits formed under different applied electric fields. We show that self-cohesion indeed leads to different microstructures, both in the close vicinity of the substrate and in the bulk of the deposit, and we relate this to the mechanical signature of the deposits. Our results reveal that at high electric fields, the influence of self-cohesion on the resulting microstructures essentially vanishes beyond a critical field strength. This marks the transition from a deposition regime affected by aggregation to a regime largely dominated by volume-exclusion effects.
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine learning (ML) and deep learning (DL) have enabled the development of computational tools for ASD screening, classification, severity assessment, and intervention monitoring. This review synthesizes findings from 50 recent studies that applied ML and DL techniques to ASD-related datasets, including electroencephalography (EEG), eye-tracking, behavioral video, microbiome, voice acoustic, demographic, and multimodal data. The review addresses three key questions: (i) which data modalities and computational approaches are most frequently used, (ii) how diagnostic performance is evaluated across different study designs, and (iii) what methodological challenges limit clinical translation. The literature is organized according to data modality, algorithmic approach, and clinical readiness. Approaches examined include conventional ML methods, convolutional neural networks, graph neural networks, hybrid deep learning architectures, federated learning, explainable artificial intelligence, topological data analysis, and multimodal fusion. The findings suggest that multimodal and graph-based approaches provide a more comprehensive representation of ASD phenotypes than single-modality methods. Explainability and privacy-preserving learning have also emerged as important considerations for clinical deployment. However, many reported high-performance models are based on small sample sizes, repeated use of the ABIDE dataset, class imbalance, single-site validation, or limited external testing, raising concerns regarding generalizability. Beyond diagnostic accuracy, this review evaluates model interpretability, calibration, scalability, validation rigor, and clinical applicability. Overall, the analysis highlights the need for standardized benchmarks, externally validated multimodal datasets, clinically relevant evaluation metrics, and decision-support systems that complement rather than replace expert clinical assessment in ASD diagnosis and management.
To describe a less invasive laser therapy for retinopathy of prematurity, referred to as ridge-adjacent laser treatment (RALT), and its outcome regarding the need for re-treatment. This was a retrospective observational review of medical records of all infants treated with RALT as a primary intervention for retinopathy of prematurity between June 2016 and June 2021 at a tertiary care center in Sweden. The intervention consisted of RALT with intraoperative wide-field imaging, and most patients also received postoperative dexamethasone eye drops. The primary outcomes measured were the number of laser spots applied, re-treatment rates, and the maximum stage of retinopathy of prematurity observed. Sixty-one eyes of 31 infants were treated with RALT. The median follow-up time was 24 weeks (range: 17 to 45 weeks) after birth. The mean number of laser spots administered was 859 ± 280. Re-treatment was required in 3% of the eyes (6% of the infants). After excluding infants with aggressive retinopathy of prematurity the re-treatment rate for the eyes was 2% (4% of the infants). Seven of the 61 eyes were diagnosed as having aggressive retinopathy of prematurity. In three of these eyes, the retinopathy progressed to a maximum stage greater than 3 following RALT treatment. Treatment of retinopathy of prematurity using the RALT technique, in conjunction with intraoperative wide-field imaging and, in most infants, postoperative dexamethasone eye drops, was associated with a low number of laser spots and a low re-treatment rate. All eyes that did not have aggressive retinopathy of prematurity regressed after the RALT treatment. Further longitudinal studies are required to assess the long-term structural and functional outcomes.
Advances in Earth observation (EO) remote sensing technologies have delivered a range of aerosol and trace gas pollution data with ever-improving spatial and temporal resolution, significantly benefitting assessments of global air quality (AQ). Furthermore, the application of data synthesis techniques incorporating satellite EO with other information sources has improved the availability of satellite-derived estimates of pollutant exposure at local to global scales. These data have been applied to address a diversity of use cases in AQ monitoring and public health, from long-term trend tracking, exposure assessment, and epidemiological analysis to short-term emissions identification and early warning. Successful application of satellite EO to address AQ and AQ-related health problems requires an alignment between (1) the technical capabilities of satellite data to provide relevant information, (2) a defined case for using this information to address a particular need, and (3) the human capacity, computational resources, operational plans, and policy and governance frameworks to implement a solution and take action, and to sustain the solution for as long as the need remains. Only when there is substantial alignment across all these factors can satellite EO information be effectively translated into public health benefits. This paper surveys applications of satellite EO to AQ assessment and AQ-related health management globally, synthesizing key commonalities into recommendations for how satellite EO can effectively support health needs. We also identify gaps in current satellite EO capabilities, use-case applications, and feasibility factors where future research and investment could reduce barriers to increased application of satellite EO to address pressing public health concerns related to AQ worldwide.Implications: This paper summarizes insights collected through the Group on Earth Observations (GEO) Health Community of Practice Air Quality and Respiratory Health Work Group on the current state and gaps in the use of satellite EO to support air quality and related health decision-making globally. We synthesize these insights into general recommendations for how satellite EO capabilities, use cases, and feasibility considerations can be aligned towards effective use of satellite EO data for air quality and related health effects. We also identify barriers and gaps in current capabilities, uses, and capacities, making recommendations for how these might be addressed.
Type 2 diabetes and hypertension are major public health challenges in sub-Saharan Africa, yet treatment control remains poor. Patient adherence to key treatment pillars (medication, lifestyle advice, and self-monitoring) is essential for treatment control, yet adherence across these pillars has not been comprehensively synthesized. We conducted a systematic review and meta-analysis to map adherence to medication, lifestyle advice, and self-monitoring for type 2 diabetes and hypertension in sub-Saharan Africa, and applied a network analysis to identify cross-cutting determinants as priority candidates for future interventional studies. We searched six academic databases, and Google Scholar, for observational and interventional studies in adults with type 2 diabetes or hypertension in sub-Saharan Africa published between January 1, 2004, and May 14, 2026. Risk of bias was assessed using Newcastle-Ottawa Scale and Cochrane RoB 2. We used random-effects models to pool adherence proportions and conducted subgroup analyses and meta-regressions. Cross-cutting determinants were identified using interactive network analysis with noteworthiness scores. We included 312 studies with 108,014 participants from 28 countries. Pooled adherence was 67% (95% CI [60, 73]) for antidiabetic medications, 51% (95% CI [44, 58]) for antihypertensive medications, 44% (95% CI [38, 49]) for dietary recommendations, 42% (95% CI [37, 47]) for physical activity, 85% (95% CI [82, 88]) for alcohol abstinence, 95% (95% CI [94, 96]) for smoking cessation, 18% (95% CI [12, 27]) for glucose monitoring, and 28% (95% CI [16, 45]) for blood pressure monitoring. Overall self-care adherence was 37% (95% CI [30, 46]) for type 2 diabetes and 35% (95% CI [29, 42]) for hypertension. Heterogeneity was high (I2 > 95%, p < 0.001 throughout). Education, self-efficacy, and social support emerged as cross-cutting determinants most consistently associated with adherence. A key limitation is the high statistical heterogeneity, which persisted despite random-effects modeling and subgroup analyses. Adherence across treatment pillars is suboptimal. Education, self-efficacy, and social support represent priority candidates for future interventional studies aimed at improving adherence. The review was registered with PROSPERO (CRD42024626793).
Statistical misreasoning is a key mechanism through which anti-vaccine narratives distort scientific information and undermine public confidence in immunisation. Although prior research has examined thematic and ideological features of vaccine misinformation, little is known about the specific errors in numerical reasoning that shape users' interpretations of vaccine-related data. A total of 597 Polish-language Facebook posts expressing anti-vaccine views and containing references to statistical information were analysed. Based on previous research on statistical cognition and an inductive review of the material, a coding scheme comprising ten categories of statistical misreasoning was developed and applied to all posts. Quantitative analyses were then conducted to examine how frequently these categories occurred and which combinations of errors appeared together. The most prevalent forms of misreasoning were the correlation-causation fallacy (70%, p < 0.001) and base rate neglect (58%, p < 0.001). Denominator neglect and cherry picking appeared in half of the posts, while the remaining categories were less frequent. Most posts contained multiple errors (median = 4), and the most common configuration involved the correlation-causation fallacy, base rate neglect and denominator neglect. The distribution of error counts further showed that posts most often exhibited four distinct categories of misreasoning (23%), followed by three (19%) and five (17%), and overall a majority of posts (62%, p < 0.001) contained between one and four different types of errors. Co-occurrence analysis revealed stable structural patterns, with the strongest association observed between denominator neglect and intuitive reasoning error (ϕ = 0.23; p < 0.001). Anti-vaccine discourse exhibits systematic patterns of statistical misreasoning that shape erroneous interpretations of vaccine-related data, highlighting the need to address cognitive and statistical misunderstandings through targeted public health communication.
Multi-spectral image stitching reconstructs the informative and broader scene by aligning the image pairs captured from different viewpoints. This task is significantly limited by the registered sources. Stitching accuracy deteriorates notably with misaligned multi-modality images. To address the challenges imposed by the registration requirement, we propose robust multi-spectral image stitching for misaligned infrared-visible pairs, in which the intrinsic attributes of multi-modality registration and multi-view alignment are employed concurrently, rather than independent processing. Specifically, the proposed method develops a hierarchical versatile transformation to conduct the correspondence matching progressively, where the sparse foundational homography is utilized for global adjustment and point-flexible splines are applied for local fine-tuning. In the registration of infrared and visible images, a dual consistency-driven modality transfer is introduced to alleviate feature variance and facilitate registration within the shared information matching. During cross-view alignment, multi-spectral image pairs are encoded into a latent domain to exploit the complementary information adaptively, and the planar deformation is conducted through correspondence matching processes identical to those in the registration procedure. In this way, by leveraging the consistency between multi-modality and multi-view matching, we achieve a robust reconstruction of the multi-spectral panorama. Extensive experiments in registration and stitching demonstrate the superiority of our method. Code is available at https://github.com/ZengxiZhang/VTMS.
Sugar-sweetened beverages, a subset of ultra-processed foods, are consumed frequently by children in Jamaica. Given the abundance of time children spend in school and the evidence for school-related determinants of dietary intake, school food environments can be key influencers of children's beverage consumption and drive their dietary behaviors and preferences. 907 primary school students (ages 7-11) in Jamaica were surveyed on their demographic information, nutrition-related preferences, knowledge, and consumptions patterns of sweetened and unsweetened beverages in 2018. School environment audits, documenting internal and external factors from the 30 schools which children attended, were completed at the same time. A two-part model was applied that considered children's probability of consuming each beverage type and the amount they consumed. Models accounted for both child-level characteristics and school food environment measures. Descriptive statistics revealed a large proportion of non-consumers of water (20.9%) and unsweetened beverages (19.8%). Collectively, child-level factors contributed to the modeling of all beverage types and external school environment measures were highly significant to the sweetened beverage model fit. External school measures were jointly associated with sweetened beverage consumption volume while unsweetened beverage intake is more likely impacted by classroom rules, school wide policies, and the internal school environment. Results support the inclusion of beverage regulations, both the promotion of unsweetened and the de-incentivization of sweetened drinks, in Jamaica's recently approved national school nutrition policy.
Patellofemoral complications after total knee arthroplasty (TKA) are linked to femoral component design and trochlear alignment. This study compared the effects of a kinematically aligned (KA)-optimized femoral component versus a standard design on patellofemoral kinematics, contact mechanics and quadriceps function, using native knee biomechanics as reference. Seventeen fresh-frozen cadaveric knees (10 valgus, 7 varus) were tested in a dynamic knee rig (30°-130° flexion) under controlled loading, divided into a valgus and a varus group. Patellofemoral kinematics, contact area and peak pressure patterns were recorded in the native state and after KA TKA using either a standard component (prosthetic trochlear angle [PTA] 6°) or a KA-optimized design (PTA 20°). One-dimensional statistical parametric mapping (SPM1D) independent two-sample t tests were applied (α = 0.05). The KA-optimized component reduced medialization between 30° and 70° in both groups (varus: 2.5 mm; valgus 2.0 mm). Patellar tilt remained unchanged. Contact area decreased after TKA without consistent differences between components (valgus: p < 0.001 until 80°; varus: p < 0.001 at 60°-80°). Peak pressure was not reduced with the KA-optimized design; slightly higher values occurred up to mid-flexion (+1.1 MPa at 90°), normalizing at higher flexion angles in valgus knees. In varus knees, both designs increased peak pressure at high flexion (+1.5 MPa at 120°). Quadriceps force showed no significant differences. The KA-optimized femoral component reduces patellar medialization but does not improve patellofemoral loads or quadriceps efficiency, suggesting that isolated geometric optimization may be insufficient to restore physiological biomechanics after KA TKA. N/A.
The role of the tissue microenvironment in the transition from acute kidney injury to chronic kidney disease remains poorly understood. While persistence of failed-repair proximal tubule cells is postulated to hamper kidney regeneration, the spatial metabolic architecture of injured tissue and its effect on regenerative capacity have not been fully characterized. We analyzed mouse kidneys 14 days post bilateral ischemia-reperfusion injury using a multimodal spatial omics approach. Internal standard normalized mass spectrometry imaging (MSI) quantified metabolite abundances, followed by unsupervised spatial domain analysis using the BANKSY algorithm to identify tissue niches based on lipidome profiles. Consecutive sections underwent high-resolution spatial transcriptomics (Stereo-seq), and we applied niche projection to integrate metabolomic and transcriptomic data, enabling comparison of proximal tubule cells in healthy versus injured niches. Unsupervised spatial domain analysis revealed distinct healthy and injured niches, with injured niches exhibiting diffusely spread metabolic abnormalities extending beyond failed-repair proximal tubule cells. Quantitative metabolomics demonstrated that seemingly healthy proximal tubule cells residing in injured niches exhibited elevated succinic acid and depleted linoleic acid compared with cells in healthy niches. Spatial transcriptomics confirmed these metabolic defects at the transcriptional level, revealing downregulation of oxidative phosphorylation and fatty acid β-oxidation pathways in proximal tubule cells within injured microenvironments. Combined spatially resolved analysis of internal standard-normalized MSI and spatial transcriptomics revealed distinct healthy and injured tissue niches following ischemia-reperfusion injury. Metabolic abnormalities, including defects in oxidative phosphorylation and fatty acid β-oxidation, were not restricted to failed-repair proximal tubule cells but extended into seemingly healthy epithelial cells embedded within injured microenvironments.
As elite women's football evolves tactically, understanding goalkeeper distribution behaviour is increasingly important for performance analysis and coaching. Traditional approaches rely on isolated passing metrics, providing limited insight into how goalkeeper actions are organised within possession structures. This study applied a graph-based clustering framework to classify goalkeeper passing behaviours in the English Women's Super League across three seasons. The dataset comprised 29,911 goalkeeper passes from 27,895 possessions. Pass embeddings were represented within a relational graph and clustered using K-means (k = 14), identifying recurrent distribution patterns shaped by spatial context, defensive pressure, and possession phase. Results may indicate that behaviours were organised along continuous gradients rather than discrete categories. Central clusters reflected adaptable distributions, whereas peripheral clusters captured more specialised actions, including long passes under defensive pressure. Lateral tendencies suggested interactions between dominant-side preferences and spatial availability. Phase analysis showed goalkeeper involvement was most prominent during transitional moments, particularly at possession initiation and termination, with comparatively limited involvement in sustained attacking sequences. Collectively, these findings provide a structured account of goalkeeper distribution in elite women's football and demonstrate how graph-based modelling can support behavioural profiling, tactical interpretation, and evidence-based coaching practice.
A quantitative polymerase chain reaction (qPCR) assay was developed for the species-specific detection and quantification of Gambierdiscus holmesii, recently reported to produce known ciguatoxin (CTX) analogues in north Queensland. The assay was designed to target the ITS region and calibrated using synthetic gBlocks standards and cell-based standard curves to enable accurate quantification. The G. holmesii-specific qPCR assay demonstrated high specificity, efficiency, and sensitivity, with no cross-reactivity observed against closely related Gambierdiscus species. The assay was subsequently applied to environmental samples collected from Heron Island (Great Barrier Reef, Queensland), detecting G. holmesii in 5 out of 12 sites. This newly developed molecular tool enhances the capacity to monitor G. holmesii in the environment and supports improved surveillance of ciguatoxin-producing Gambierdiscus species in Australian waters, enabling earlier detection and more effective management of ciguatera poisoning risks to public health and fisheries.
Early identification of Parkinson's disease is critical for timely intervention. Disruptions in sleep architecture and changes in physical activity patterns have been reported years before motor symptom onset, yet prodromal behavioral changes spanning sleep and daytime activity patterns are subtle and difficult to detect with conventional clinical tools. Wearable sensors provide a scalable means of monitoring these behaviors in natural settings, but extracting meaningful, interpretable features from high-frequency, unlabeled time series remains a major challenge. We present an end-to-end framework for assessing Parkinson's disease risk from wrist-worn accelerometry via automated feature extraction and survival modeling of time to diagnosis. Behavioral states are derived from unlabeled data using pretrained models including random forests with hidden Markov models for physical activity classification and a self-supervised learning based sleep staging model. We jointly model both sleep stage and physical activity sequences using hierarchical nonstationary Markov chains stratified by time of day and temporal resolution, characterizing individual-level behavioral rhythms and transition dynamics. Gradient-boosted Cox proportional hazards models are then used to estimate Parkinson's disease risk from these transition-based features. Applied to accelerometer data from the UK Biobank, our approach outperforms baselines that exclude dynamic modeling or rely on traditional functional data analysis, while providing interpretable predictors from long sequences of wearable sensor data. This demonstrates the potential of integrating pretrained AI models, temporal sequence modeling, and survival analysis to detect early behavioral signatures of neurodegeneration.
Biomolecular condensates create dynamic subcellular compartments that alter systems-level properties of the networks surrounding them. Standard reaction-diffusion models of systems biology cannot define where these compartments emerge nor track how they evolve. One alternative physicochemical model of soluble and condensed states in space and time is the Cahn-Hilliard equation, which specifies a diffuse interface between the two phases. Customized numerical approaches required to solve this equation are absent from computing environments often used for systems biology, however, and the equation's interfacial energy coefficient lacks empirical constraints. Here, using two complementary numerical strategies, we built stable, self-consistent Cahn-Hilliard solvers in three common systems-biology programming languages. The algorithms simulated the complete time evolution of condensed droplets as they dissolved or persisted, relating critical equilibrium droplet size to the Cahn-Hilliard interfacial energy coefficient. We applied this universal relationship to the chromosomal passenger complex, a multi-protein assembly that reportedly condenses on mitotic chromosomes. The fully constrained Cahn-Hilliard simulations predicted spatiotemporal dewetting and coarsening behaviors that matched experiments in cell types with different interfacial energy coefficients. Together, these results suggest how initially variegated recruitment yields robust localization of the chromosomal passenger complex to the inner centromere by the end of prometaphase. More generally, the Cahn-Hilliard equation tests whether condensate dynamics behave as a simple phase-separated liquid, and its numerical solutions advance generalized modeling of biomolecular systems.
The objective of this study is to evaluate how minced cartilage implantation (MCI) is currently applied in clinical settings, focusing on the selection criteria, procedural execution, handling of concomitant joint pathologies, and postoperative management. An online survey was conducted among all 4,915 members of the German Society for Arthroscopy and Joint Surgery (AGA). Invitations with a survey link were emailed, and the questionnaire was accessible anonymously from January 10 to February 20, 2024. A total of 927 responses were received and analyzed, resulting in a 19% response rate. Demographic data were described descriptively; survey answers were reported as percentages. The 25-item questionnaire, developed by AGA and the Quality Circle for Cartilage Repair & Joint Preservation (QKG e.V.), included single- and multiple-choice questions on experience with cartilage therapy, minced cartilage procedure application and postoperative care, and the overall assessment of this technique within cartilage repair. MCI has emerged as one of the three most commonly used cartilage repair techniques in the knee joint. Defect sizes up to 4 cm² represent the largest treated group. The application of the procedure is quite heterogeneous, with some practitioners using a shaver and others manually mincing with a scalpel. Fixation methods also vary, including the use of autologous and allogeneic fibrin as well as collagen membranes. The targeted fragment size is largely consistent across users. Postoperative management is similar, with most recommending partial weight-bearing for six weeks. Minced cartilage implantation has become an increasingly utilized technique for cartilage repair. Although the current evidence has improved - particularly due to the growing number of case series - there are still notable variations in how the procedure is performed.
The effect of genetic polymorphisms on survival outcomes in nasopharyngeal carcinoma (NPC) has not been fully elucidated. This study aimed to identify genetic variants associated with NPC survival using a genome-wide association study (GWAS) approach. We performed a two-stage GWAS involving a total of 783 NPC patients. In the discovery phase, 303 individuals were genotyped for 688,783 single-nucleotide polymorphisms (SNPs) using the whole-genome screening microarray. The Cox proportional hazards regression model was applied to screen for SNPs associated with overall survival (OS) or progression-free survival (PFS). The candidate SNPs were subsequently validated in an independent cohort of 480 patients using the MassARRAY system. Bioinformatic analyses were conducted using bioinformatics software and databases including LocusZoom, LDBlockShow, eQTLGen, and RegulomeDB. Finally, we integrated the genetic variant and clinical factors to develop survival prediction models for NPC. Our results demonstrated that rs17771861 on chromosome 6p22.3 was significantly associated with OS and PFS in NPC patients following validation. Subgroup analyses further indicated that rs17771861 was correlated with poor OS and PFS in male patients, those with advanced-stage disease (Stage III + IV), and patients who received concurrent chemoradiotherapy. Additionally, survival prediction models integrating the genetic variant and clinical factors were constructed for OS and PFS, and these models exhibited superior predictive performance compared to models based only on clinical factors. Our study identified that rs17771861 was associated with OS and PFS in patients with NPC, suggesting that this locus may serve as a novel genetic biomarker for survival stratification in NPC.
Metal‑nitrogen-carbon (M-N-C) materials originating from prussian blue analogs (PBAs) are regarded as attractive catalysts toward oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Nevertheless, they suffer from drawbacks of poor conductivity and catalyst deactivation owing to metal aggregation. In this work, through a dual optimization strategy depending on structure regulation and interface engineering, we prepared a bead-like iron diselenide/nickel diselenide‑nitrogen-doped carbon@carbon nanofibers (FeSe2/NiSe2-NC@CNF) electrocatalyst with FeSe2/NiSe2-NC evenly anchored in the carbon nanofibers (CNF) one by one. FeSe2/NiSe2-NC@CNF demonstrates outstanding dual-functional electrocatalytic activity toward OER (Ej=10 = 254 mV, Ej=300 = 403 mV) and HER (Ej=450 = 601 mV) at high current densities. In addition, when applied to overall water splitting, the potential of FeSe2/NiSe2-NC@CNF at 10 mA·cm-2 is only 1.63 V, indicating its outstanding water electrolysis capability. X-ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations indicate that the additional unsaturated coordinated NiSe bonds in FeSe2/NiSe2-NC@CNF are beneficial to optimizing the adsorption/desorption behaviors of intermediates and accelerating the rate-determining step (*O transforming into *OOH). The present study proposes a rational design strategy to optimize the performance of M-N-C catalysts, and lays the foundation for the advancement of superior bifunctional non-noble metal catalysts toward overall water electrolysis.
This protocol describes an ex vivo mouse model that recapitulates selected cellular and inflammatory features observed in visceral adipose tissue (VAT) following high-fat diet (HFD) feeding, without dietary intervention in vivo. Small VAT explants from lean mice are cultured for 7-10 days, during which they spontaneously develop an inflammatory phenotype that closely resembles that of VAT from HFD-fed animals. Importantly, the model preserves adipose tissue (AT) architecture, enabling the investigation of inflammatory processes in a physiologically relevant context. During culture, key hallmarks of obesity-related inflammation emerge, including the formation of crown-like structures (CLS), the accumulation of metabolically activated macrophages (MMes), and increased inflammatory cytokine production. Despite adipocyte death, other drivers of obesity-related inflammation, including metabolic stressors such as saturated fatty acids, altered lipid handling, glucose excess, oxidative stress, hypoxia, adipokine imbalance, and paracrine adipocyte-immune cell signaling, can be partially represented in the model. The system supports a wide range of downstream applications, including live imaging, flow cytometry, quantitative PCR, ELISA, immunostaining, and bulk or single-cell RNA sequencing. In addition, pharmacological and cytokine-based treatments can be applied to investigate signaling pathways and cell type-specific responses. This easily customizable model aligns with the 3R principles by reducing animal use for in vivo dietary studies. The model is reproducible, cost-effective, and straightforward to implement, providing a versatile platform for studying adipocyte death, immune cell dynamics, and therapeutic strategies in AT inflammation.
To address the limited tissue specificity of extracellular vesicles (EVs) derived from blood and other body fluids, this study isolated small EVs (sEVs) from the tissue interstitial fluid (TIF) of hepatocellular carcinoma (HCC) and adjacent tissues. The expression profiles of non-coding RNAs (ncRNAs) were analyzed to identify more specific diagnostic biomarkers. An optimized protocol for TIF-sEV extraction was established, which combined enzymatic digestion (Collagenase D and DNase I) with differential and ultracentrifugation. The isolated sEVs were characterized using nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and western blotting (WB). The expression of 33 candidate ncRNAs (11 lncRNAs from TCGA-LIHC and 22 miRNAs from TCGA-LIHC and GSE302990) in TIF-sEVs was analyzed by qRT-PCR and integrated with clinical parameters via LASSO regression. The optimal extraction conditions were determined to be digestion with Collagenase D (2 mg/mL) and DNase I (40 U/mL) for 30 minutes. The obtained EVs exhibited typical morphology, a particle size below 200 nm, and expressed canonical EV marker proteins (ALIX, CD63) as well as the hepatocyte-specific membrane protein ASGPR. qRT-PCR analysis revealed specific enrichment of six lncRNAs (e.g., AL031985, TMCC1-AS1) and eleven miRNAs (e.g., miR-1224-5p, miR-483-5p) in TIF-sEVs. LASSO regression applied to all 33 candidate ncRNAs and clinical parameters identified a combined diagnostic signature comprising ALB, PLT, DBIL, lncRNA GAS5, and miR-194-5p, which achieved an area under the curve (AUC) of 0.960. This study establishes an efficient and stable system for the isolation and characterization of TIF-sEVs from liver tissue. Furthermore, it identifies multiple non-coding RNAs (ncRNAs) that are enriched in TIF-sEVs, thereby providing potential novel diagnostic biomarkers for the differential diagnosis and prognosis evaluation of HCC.