The spatial interactions between malignant and immune cells in the tumor microenvironment are important for tumor immunobiology and patient outcomes. However, analytical tools that can extract rigorous yet interpretable spatial features and link them to patient outcomes remain limited. We propose a framework integrating TDA with statistical approaches to extract interpretable spatial features characterizing malignant-immune interactions. We introduce Topological Malignant Region (TopMR), which uses topological persistence to automatically define regions of malignant cells, providing an objective reference for spatial analysis even when tumor boundaries are ambiguous. Global-scale infiltration is quantified using signed distances from immune cells to the TopMR boundary and local-scale interactions are captured via malignant cell density around individual immune cells. These global and local features are integrated into a unified signed distance-density (sDD) space, enabling comprehensive characterization of spatial patterns. We apply this framework to high-resolution multiplex immunofluorescence images of diffuse large B-cell lymphoma, analyzing both malignant-enriched and tumor border regions. Two-stage hierarchical clustering stratifies patients based on spatial interaction patterns, revealing associations with survival outcomes. This framework provides an end-to-end pipeline from spatial feature extraction to clinical interpretation, suggesting how region-aware spatial analysis can capture biologically meaningful patterns linked to patient survival.
Sexually transmitted diseases (STDs) remain a major public health concern in China. However, existing research on STDs primarily focuses on specific regions or populations, and often overlooks the spatially varying effects of associated factors. Macro-level analyses of sexually transmitted infections across China are relatively limited. This study analyzes the spatiotemporal distribution and associated factors of the three leading STDs, HIV/AIDS, syphilis, and gonorrhea, at the provincial level in mainland China from 2011 to 2020. We utilize a Bayesian disease mapping model to quantify the spatiotemporal trends of STD incidence rates, and examine their spatially varying associations with socioeconomic, sociodemographic, and meteorological factors. The results reveal distinct geographic patterns for each disease. Overall, HIV/AIDS and syphilis had an upward trend over the study period, while gonorrhea remained relatively stable, with a modest mid-period rise. HIV/AIDS was most prevalent in the southwestern provinces, syphilis was concentrated in the northwest, and gonorrhea was most common in the southeastern coastal provinces. The associations between STD incidence and selected factors exhibited substantial spatial heterogeneity and varied by disease. The primary factors associated with regional disparities in STD epidemics in China included GDP per capita, illiteracy proportion, the number of healthcare institutions, and annual average temperature. This study provides a comprehensive analytical framework for understanding the epidemic trends of STDs and their associated factors. The findings offer both scientific insights and practical guidance to policymakers for designing targeted prevention and control strategies in public health practice.
This review provides an overview of analytical techniques that have been published for detecting the (fraudulent) addition of reconstituted bovine milk to liquid bovine milk and identifies which of these techniques are most suitable for identifying partial replacement of liquid bovine milk by reconstituted bovine milk at levels that are economically meaningful. Evaluation of these analytical techniques was done against a detection limit of 10% inclusion of reconstituted milk. In total, 30 studies were included and categorized to deal with type II heat indicators, spectroscopic techniques, omics techniques and other techniques, respectively. The variation in the lowest detectable level of added reconstituted milk powder to liquid milk is very high, ranging from 0.5 to 50%, highly depending on the fat content and temperature treatment of both reconstituted milk and liquid milk. In cases that liquid milk and reconstituted milk with similar fat content and comparable processing histories are combined, the sensitivity of most techniques decreases. In many cases the sensitivity even may be insufficient to reliably identify adulteration at economically relevant levels. Many recent publications describe omics-based techniques which show high potential for detecting fraudulent addition of reconstituted milk in liquid milk, even though these techniques in many cases need further exploration of their potential by applying the techniques to a sample set reflecting the typical adulteration practices. In conclusion, the detection of fraudulent addition of reconstituted bovine milk in bovine liquid milk remains challenging and relevant up to today.
Neuromyths-misconceptions arising from misinterpretations of neuroscientific findings-are widely endorsed by educators and students, including those in psychology. Their persistence has been linked to contextual, cognitive, and personality-related factors, but evidence is mixed, especially among psychology students. This study examined predictors of neuromyth endorsement in Argentine psychology undergraduates. To identify contextual (neuroscience training, interest), personality (Need for Cognition; NFC), and cognitive (Cognitive Reflection Test; CRT) predictors of neuromyth beliefs. A convenience sample of 320 psychology students (82.5% women; M_age = 27.39 years) completed online measures assessing neuromyth endorsement, general brain knowledge, NFC, CRT, and self-reported neuroscience training and interest. Spearman correlations and hierarchical regressions were conducted to examine associations and predictive effects. Participants endorsed 41.22% of neuromyths on average, with learning styles (76.25%) and sensory-rich environments benefits (74.37%) being the most accepted. CRT scores negatively predicted neuromyth endorsement (β = -.175, p < .001), whereas the NFC "enjoyment of thinking" factor positively predicted endorsement (β = 0.145, p = .024). Age also showed a positive effect (β = 0.175, p = .002). Neuroscience interest, courses taken, and general brain knowledge did not predict neuromyth acceptance, although they were positively associated with neuroscience knowledge. Analytical thinking emerged as the strongest protective factor against neuromyths, while enjoyment of thinking unexpectedly predicted higher endorsement, possibly reflecting exposure to low-quality sources of information and/or Dunning-Kruger effects. Factual neuroscience knowledge and training did not decrease neuromyth endorsement, underscoring the importance of fostering critical thinking skills within psychology education.
Clinical oncology increasingly depends on molecular information, but many assays still detach analytes from the histological coordinates in which diagnostic decisions are made. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is being investigated as a spatial molecular adjunct that can measure lipids, metabolites, peptides, proteins, glycans and drugs directly from tissue sections. This review evaluates applications in which spatial molecular evidence may refine, rather than replace, conventional oncopathology: molecularly resolved tumor histology, margin and adjacent-tissue assessment, tumor microenvironment interpretation and therapeutic-response prediction. We emphasize that clinical value requires alignment with hematoxylin and eosin (H&E) morphology, expert annotation, quality control, molecular-identification confidence and patient-level validation. The literature shows progress from visual ion-map comparison toward multimodal registration, machine-learning classification, spatial proteomics and spatial pharmacology. However, routine adoption remains constrained by pre-analytical variability, incomplete metabolite annotation, batch effects, validation leakage in pixel-level models and limited prospective evidence. The most realistic near-term role for MALDI-MSI is therefore a spatial molecular adjudicator for selected, decision-relevant problems such as difficult classification, uncertain margins, field effects, microenvironment-associated risk and heterogeneous drug exposure.
The accurate ability to predict the distribution of contact stress under reinforced concrete (RC) footings is important for the safety and serviceability of shallow foundations. Conventional analytical models idealizing the footing as rigid and soil as homogenous fail to capture the stress concentration and redistribution effects, especially under non-uniform loading. Earlier studies are mostly concentrated on sand; however, basalt soil has different mechanical characteristics as it possesses high stiffness, angularity, and interlocking effects. It also ignores stiffness loss due to concrete cracking. This study aims to fill these gaps through an experimental and numerical investigation of RC square footings resting on basaltic soil and the influence of the reinforcement ratio, yield strength of steel and strength of concrete. Within the laboratory conditions, four footings having different reinforcement ratios of 0.19%, 0.36%, 0.54% and 3.43% were tested under monotonic loading. Central and edge displacements were measured. Using a validated finite element model, a parametric study expanded the investigation to include reinforcement ratios of 0.54% to 4.80%, steel yield stresses of 240 MPa to 450 MPa and concrete compressive strengths of 20 MPa to 60 MPa, allowing systematic consideration of these parameters on central contact stress, ultimate load, deformation and energy absorption. The findings revealed that enhancing the reinforcement ratio from 0.54% to 4.80% resulted in an increase of 73.4% in central contact stress, 34.1% in ultimate load, and 55% in energy absorption, respectively. Increase in steel yield stress from 240 MPa to 450 MPa caused a 25.2% increase in central contact stress, 13% in ultimate load and 3.74% in energy absorption in laminated composite panel. The increase of concrete compressive strength from 20 MPa to 60 MPa increased central contact stress by 117.4% ultimate load by 70.4% and energy absorption by 270% showing this factor as dominant. These results show that the performance of footing on stiff basaltic soil mainly depends on the concrete strength and amount of reinforcement whereas careful use of steel yield stress. The insights provided by the study are critical for practical design. Furthermore, non-uniform contact stresses, stiffness degradation, and soil-structure interaction need to be accounted for optimizing strength and ductility.
The method of standard addition (MSA) is a quantification method in which aliquots of an unknown sample are spiked with increasing concentrations of the analyte targeted for quantification. When a regression is performed on the resulting dataset, the unknown concentration is estimated by obtaining the absolute value of the intercept with the concentration axis. As described by the authors in the paper "Achieving a more accurate method of standard addition (MSA) through mathematical improvements", optimized selection of the spiked concentrations and calibration model (weight and order) yields more accurate MSA quantification results. This is complemented by measurement uncertainty calculation which adds additional confidence to the quantification result. Save for the selection of upspike concentrations, these improvements call for a level of proficiency in mathematics and programming that goes beyond what is considered basic for a forensic or analytical toxicologist. That is a strong impediment to the dissemination of best MSA practices. To remove this barrier, we developed a user-friendly tool: EZMSA. Available as an Excel spreadsheet and an online or local application, this tool carries out all the optimized MSA calculations for the user. The R application even allows the production of a customized report in PDF, DOCX or HTML formats. Validation of both EZMSA platforms was performed by analyzing various datasets testing the full scope of functions and calculations included in the tool. Two full application examples, where EZMSA is used for the quantification of alprazolam and phenazolam in blood, are also presented. The EZMSA tool for simple implementation of accurate MSA calculations is available for download in its Excel and R formats at https://github.com/ToxBrigitte/ezmsa; and as an online application at https://toxbrigitte.shinyapps.io/EZMSA/.
Digital health interventions (DHIs) show considerable promise in supporting hypertension self-management by promoting preventative care and self-monitoring. While their efficacy is increasingly evident, the long-term uptake, acceptance and sustained engagement with these tools are frequently challenged by issues such as usability, trust and varying user experiences. This review aims to synthesise qualitative evidence to identify barriers and facilitators and the key factors that impact the adoption, acceptance and engagement with DHIs for hypertension self-management. Systematic review of qualitative literature using thematic analysis following Cochrane's qualitative and implementation methods guidance. PubMed, PsycInfo, Web of Science and the Cochrane Library were searched in February 2025. The searches included relevant qualitative and mixed-methods studies on the use of digital devices for hypertension management, which described the barriers and facilitators associated with these tools. We included studies published from 2015 to 2025 to capture relevant evidence. Only studies published in English with a qualitative approach were included. From an initial 10 943 identified publications, 15 met our inclusion criteria, primarily originating from Europe and the USA, exploring diverse racial and ethnic group experiences. Our thematic synthesis revealed 7 analytical and 22 descriptive themes detailing barriers and facilitators encountered by patients with hypertension, healthcare providers (HCPs) and caregivers. These themes covered technology utilisation, design components, linguistic and cultural relevance, healthcare factors, trust and credibility and interpersonal interactions. Our analysis underscores that factors such as the usability, design and relevance of social support profoundly influence the uptake and acceptance of DHIs in hypertension self-management among patients, caregivers and HCPs. CRD42023480389.
Circulating tumor DNA (ctDNA) is increasingly investigated in lymphomas because it enables non-invasive molecular profiling, longitudinal assessment of clonal evolution, and quantification of minimal residual disease (MRD), which reflects residual tumor burden and treatment response and serves as a clinically validated prognostic biomarker. The clinical utilities of ctDNA include supporting diagnosis, enabling early detection of relapse, and resolving ambiguous imaging findings. Current approaches for ctDNA assessment in lymphomas include droplet digital PCR, immunoglobulin clonotype sequencing, hybrid-capture next-generation sequencing with unique molecular identifiers or duplex barcoding, and phased sequencing. Establishing ctDNA as a clinical-grade assay requires rigorous quality control and standardization across all technical steps, from blood collection and plasma processing to cfDNA extraction, quantification, and analytically validated genotyping and MRD measurement. Large prospective trials and international standardization efforts are underway to define ctDNA-based MRD assessment as a reproducible and clinically actionable tool in lymphoma care. In this review, we outline key pre-analytical and analytical workflows for ctDNA assessment in lymphomas and discuss unresolved challenges and future directions in the field.
Organic arsenicals such as monosodium methylarsonate (MSMA) remain widely used, yet their mechanistic toxicity in aquatic systems is poorly understood. Here, we integrated fish embryo toxicity (FET) assays with 1H NMR-based metabolomics to characterize the effects of acute MSMA exposure in zebrafish (Danio rerio) embryos. The 96-h LC50 was 74 mg L-1, and embryos were exposed to sublethal concentrations (10, 30, and 60 mg L-1). Exposure induced concentration-dependent embryotoxicity, with severe phenotypic alterations at 60 mg L-1, including reduced survival (p = 0.0316), delayed hatching (p = 0.0331), tachycardia (p < 0.0001), and a reduction in some morphometric parameters, when compared with the control group. Total arsenic analysis confirmed significant bioaccumulation in larvae, reaching 1.34 ± 0.06, 6.51 ± 0.01, and 11.08 ± 0.22 mg kg-1 across increasing concentrations. Metabolomic profiling revealed marked metabolic reprogramming, and multivariate analyses (PCA, PLS-DA, and OPLS-DA) demonstrated clear separation between the control and exposed groups. Perturbations in energy metabolism were evidenced by decreased levels of lactate, glucose, and oxaloacetate, and increased levels of acetate, suggesting impaired glycolysis, mitochondrial dysfunction, and reduced fatty acid oxidation, while elevated glutathione levels indicated activation of antioxidant defenses. Pathway analysis highlighted disruptions in glycolysis/gluconeogenesis, glutathione metabolism, purine metabolism, and aminoacyl-tRNA biosynthesis. Four metabolites (xanthine, lactate, acetoacetate, and L-tryptophan) consistently discriminated between exposed groups, with high predictive performance (AUC = 0.967) supported by cross-validation and permutation testing (p = 0.001). Overall, these findings provide mechanistic insight into MSMA-induced toxicity and demonstrate the potential of metabolomics-derived biomarkers for environmental risk assessment, supporting more sensitive and integrative strategies for evaluating arsenical contamination in aquatic ecosystems and contributing to the development of improved environmental monitoring and regulatory frameworks for arsenical pollutants.
Ericoid mycorrhizal (ErM) fungi play a crucial role across terrestrial ecosystems, forming mutualistic symbiosis with Ericaceae and contributing to soil organic matter dynamics. However, compared to other fungal groups, their biogeography remains unknown. Here, we combined several analytical approaches to analyze a newly compiled, large-scale dataset comprising 39 163 soil samples and more than 13 million ITS rRNA sequences assigned to ErM fungi. Specifically, we asked: What are the global patterns of ErM fungal species richness and relative abundance (out of all fungi) and their predictors, and how is the distribution of ErM fungi associated with soil carbon content at the global scale? We show that ErM fungi reach their highest species richness in very high latitudes. Soil chemistry is a stronger predictor of ErM fungal species richness than climate or ericoid vegetation cover. The relative abundance of ErM fungi is highest in soils with high surface carbon content, supporting their proposed role in soil carbon storage. Furthermore, we predict that climate change will reduce ErM fungal abundance across 38% of the land cover of their current global distribution. Our study shows distinct biogeographic patterns of ErM fungi compared with arbuscular and ectomycorrhizal fungi and indicates the vulnerability of ErM fungi to climate change.
Passive samplers, for instance Chemcatcher, Microporous Polyethylene Tube passive samplers (MPTs) and Diffusive Gradients in Thin Films (DGT), are widely used for monitoring time-weighted average concentrations (CTWA) of contaminants in waters. This study presents ChemTRAP, a novel passive sampler using two macroporous filters (MFs) as both sorbent support and diffusion matrix for analyte uptake. This design enables analyte recovery by solid-phase extraction (SPE), thereby reducing per-unit costs and simplifying workflows. Six organic analytes and six heavy metals were used to evaluate ChemTRAPs embedded with Hydrophilic-Lipophilic Balanced resin or Chelex100 resin. The ChemTRAP, as an SPE device, achieved a mean recovery of 94.3% for all 12 analytes in both simulated and Lake Nanhu water. The performance of Chelex100 resin remained stable over 10 reuse cycles. In a preliminary field test, ChemTRAPs showed an average sampling rate of 2.4 mL/d, comparable to MPTs and DGTs. The average recovery of 12 analytes was 73.4% (95% Confidence Interval: 54-92%), indicating that ChemTRAP-derived CTWA are representative of ambient concentrations, despite a slight negative bias likely attributable to biofouling. While further field validation is necessary, investigation of analyte diffusion in MFs is also desirable to develop a predictive RS model comparable to that established for DGTs.
A novel dual-emission metal-organic framework composite, FS@Eu-TDA, was fabricated for the ratiometric fluorescence detection of phosphate (PO43-). This composite was prepared via an in-situ encapsulation strategy, in which fluorescein sodium molecules were incorporated as guest chromophores into Eu-MOF. The FS@Eu-TDA composite achieves a low limit of detection of 0.12 μM for PO43-. Benefiting from its dual-emission response, the composite enables visual PO43- detection. Specifically, its fluorescence color distinctly changes from red to green under UV light as the PO43- concentration increases. The practical applicability of FS@Eu-TDA was successfully validated in real samples, including tap water and fetal bovine serum. To address the limitations of powder suspension systems and achieve convenient detection, a flexible FS@Eu-TDA@PVP/PES composite hydrogel film was further fabricated, which exhibits a visual detection limit of 0.21 μM for PO43-. This work demonstrates that FS@Eu-TDA serves as a highly sensitive, accurate, and practical sensing platform for PO43- detection in environmental and biological samples.
This work presents a surface-enhanced Raman scattering (SERS)-based molecularly imprinted polymer (MIP) platform for ultrasensitive and selective determination of microcystin-LR (MC-LR) in water. The strategy combines molecular imprinting for selective recognition of MC-LR with SERS as a label-free analytical readout. MC-LR was used as the template, and a sol-gel copolymer based on 3-aminopropyltriethoxysilane (APTES) and tetraethyl orthosilicate (TEOS) was employed for MIP fabrication. The plasmonic effect of silver nanowires (AgNWs) was integrated with the MIP by coating a uniform and thin sol-gel layer onto the AgNW surface (AgNW@MIP). After template removal, the AgNW@MIP/MC-LR system exhibited over 30-fold Raman signal enhancement. A good linear relationship was obtained between Raman intensity and MC-LR concentration, achieving a detection limit of 5.4 ngL-1 over a range of 6.4 ngL-1 to 500 μgL-1. The sensor was successfully applied to reservoir water samples, showing good agreement with ELISA.
Degree-based topological indices are widely used in mathematical chemistry because they provide simple numerical descriptions of molecular graphs and can support structure-property analysis. In this work, we introduce the Inverse Prodeg index [Formula: see text] and its coindex [Formula: see text] as new degree-derived graph invariants. Unlike product-, sum-, and mixed-degree descriptors such as the Randić, sum-connectivity, harmonic, atom-bond connectivity, geometric-arithmetic, Sombor, Nirmala, inverse Nirmala, and misbalance prodeg indices, [Formula: see text] reduces to the vertex-wise concave sum [Formula: see text], whereas [Formula: see text] transfers the same inverse square-root degree weighting to nonedges using the original graph degrees. We establish their main mathematical properties, including bounds involving graph order, size, and degree extrema, equality cases, Nordhaus-Gaddum-type inequalities, exact expressions for standard graph families, and estimates under several graph operations. These results show that the proposed descriptors are analytically tractable and computationally efficient, with linear-time computability in the number of edges. To examine their chemical relevance, we evaluate a small Prodeg-based descriptor family on a dataset of 90 aromatic-carboxylate compounds using training and external test sets generated by the Kennard-Stone algorithm. The models indicate that these descriptors capture useful structure-property information, especially for size- and thermodynamics-related endpoints. Linear regression and partial least-squares regression achieved the strongest average external-test performance among the considered Prodeg-only models, with mean external-test [Formula: see text] across the nine core endpoints, while Ridge regression was close with mean external-test [Formula: see text]. Nonlinear methods did not improve the average prediction accuracy. Additional validation through bootstrap analysis, Y-randomization, residual diagnostics, and applicability-domain assessment supports a non-spurious but dataset-dependent predictive signal. Overall, the Inverse Prodeg index and its coindex provide mathematically well-founded and practically useful graph descriptors, although broader validation and combination with chemically richer descriptors are needed before claiming general predictive superiority.
Effective pandemic response requires large-scale behavioural change. Despite efforts to integrate behavioural science advice in public health policy making, there is a shortage of empirical evidence on the challenges faced by countries that want to professionalise their behavioural science advisory capacity. As senior behavioural science advisers during the COVID-19 pandemic, we explored and compared how behavioural science organised itself across Northwestern Europe in order to draw lessons for integrating behavioural insights into policy during future crises. Using the evidence-based public health framework, we conducted interviews with 21 senior stakeholders from the Netherlands, Ireland, the UK and Finland, followed by a facilitated look-back meeting with a subset of participants. All participants were actively involved in their countries' pandemic responses. Data were analysed using qualitative analysis software (NVivo 15) and validated during the facilitated look-back meeting. Producing actionable and relevant behavioural science advice during crises benefits from: (1) drawing on a wide mix of research methodologies; (2) sustaining continuous dialogue with, and access to, policy and decision-makers; (3) ensuring clear packaging and delivery of advice, including additional efforts to support comprehension; (4) creating functional structures for interdisciplinarity; and (5) maintaining institutional memory of the competencies and relationships developed during crisis response. An improved science for policy practice in the health field involves the difficult tasks of motivating direct dialogue, interdisciplinarity and co-creation, and a functioning governance of behavioural science advice systems. The findings further illustrate the conceptual utility of the evidence-based public health framework.
Low-template DNA mixtures pose substantial analytical challenges owing to limited genetic information, allelic dropout, and increasing complexity arising from multiple contributors and imbalanced mixture proportions. Using highly informative genetic markers in combination with fully continuous probabilistic genotyping (PG) models has been widely recognized as a promising strategy for improving the interpretation of complex DNA mixtures. Accordingly, in this study, we evaluated the evidential performance of low-template DNA mixtures using high-efficiency next-generation sequencing-based microhaplotype (MH) marker systems in combination with PG. Three MH panels (55-, 67-, and 87-plex) were examined under forensically relevant conditions, including low DNA input (down to 0.05 ng), increasing numbers of contributors (up to four), and extreme mixture ratios (up to 1:40). Likelihood ratio (LR) distributions were generated using the EuroForMix software by designating either a true minor contributor or a non-contributor as the person of interest. In two-person balanced mixtures, all panels produced reliable results even at the lowest DNA input, consistently distinguishing true contributors from non-contributors, including close relatives. In complex multi-person mixtures, panels with relatively higher polymorphism improved genotype resolution, reducing spurious LR inflation for relatives and enhancing contributor/non-contributor separation. Under highly imbalanced conditions, panels with higher locus counts partially compensated for allelic information losses and retained limited but informative discriminatory power. These findings indicate that MH panel performance is strongly context-dependent, impacted by mixture composition, locus number, and marker polymorphism. As supplementary analyses, mixture deconvolution accuracy was assessed using a mixture proportion deviation metric (Dratio), and kinship inference was explored in the absence of direct reference profiles by comparing LR support for close relatives versus unrelated individuals. Overall, these results provide valuable insights for guiding panel design, analytical strategies, and interpretation frameworks for increasingly challenging forensic DNA mixture analysis.
We investigate universal features of measurement-and-feedback control of quantum chaotic dynamics by examining the quantum Arnold cat map, a paradigmatic model of quantum chaos. Inspired by probabilistic control of classical chaos, our protocol stochastically alternates between intrinsic instability and engineered control operations that steer trajectories toward a target point. Simulation of exact quantum dynamics and a semiclassical truncated Wigner approximation reveal universal properties of the cat map's control transition. To further characterize this universality, we introduce the inverted harmonic oscillator as an analytically tractable effective model of instability. By integrating numerical simulations, a semiclassical Fokker-Planck description, and a direct spectral analysis of the stochastic quantum channel, we identify quantum signatures absent in classical limits. The close agreement between quantum simulation, truncated Wigner approximation, and inverted oscillator analysis shows that universal features of the transition are set by uncertainty-limited quantum fluctuations and are insensitive to genuine quantum interference.
Iron homeostasis is regulated by bone morphogenetic protein (BMP) signaling which induces hepcidin to negatively regulate serum iron levels and iron import. After a major blood loss event, developing erythroblasts produce erythroferrone (ERFE) which inhibits hepatocyte BMP signaling to increase serum iron levels and drive their maturation to erythrocytes. While ERFE was recently shown to contain a heparin binding motif (HBM), its mechanistic significance remains poorly understood. Here, we establish that the ERFE HBM is essential for BMP inhibition and uncover a novel ternary mechanism for ligand antagonism. Using biophysical assays and molecular dynamics (MD) simulations, we show that heparin/heparan sulfate (HS) simultaneously engages with the HBM of both ERFE and BMP6 to stabilize a high-order inhibitory complex. This complex exerts far greater affinity for HS in the extracellular matrix than ERFE alone, supporting potent, ligand-dependent localization to the cell surface. Importantly, we demonstrate that ERFE preferentially engages the BMP6:HS complex over other BMP ligands, suggesting modes of ligand-HS interactions are key determinants for selectivity. Together, these findings reframe ERFE as a matrix-assisted antagonist that exploits HS as a structural co-factor for BMP antagonism and gives insight into the mechanism for tissue-restricted BMP antagonism of ERFE.
Brain organoids, as three-dimensional cellular models that recapitulate human brain development and function in vitro, have emerged as a pivotal platform for neuroscience research. However, it still faces many technical challenges in aspects such as the structural and physiological relevance, functional regulation and system analysis, and urgently needs the deep integration of multi-disciplinary technologies to drive paradigm-shifting innovations. Here, we systematically summarize the main technological frameworks that support brain organoid research, including bioengineering and organoid construction technologies, biofabrication and microenvironmental modulation strategies, high-precision detection and multimodal analytical methodologies, as well as intelligent algorithms and data analysis platforms. The cross-integration of multiple technologies not only provides solutions to the existing limitations in brain organoid research, but also opens up cross-technological innovation application scenarios. We further discuss key bottlenecks in technology convergence and propose the development direction of future research. This review aims to provide a comprehensive technical roadmap for brain organoid research, promoting its in-depth application and paradigm innovation in cross-disciplinary fields including neuroscience, precision medicine, and brain-inspired computing.