Search Results (1,633)

In complex urban traffic scenes, reliable pedestrian trajectory prediction is essential for Automated and Connected Electric Vehicles (ACEVs) and active safety systems. Despite recent progress, many existing approaches still suffer from limited long-term prediction accuracy, redundant temporal features, and high computational cost, which restricts their deployment on vehicles with constrained onboard resources. To address these issues, this paper presents a lightweight trajectory prediction framework named BiTraP-DGF. The model adopts parallel Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) temporal encoders to extract motion information at different time scales, allowing both short-term motion changes and longer-term movement tendencies to be captured from observed trajectories. A conditional variational autoencoder (CVAE) with a bidirectional GRU decoder is further employed to model multimodal uncertainty, where forward prediction is combined with backward goal estimation to guide trajectory generation. In addition, a gated sparse attention mechanism is introduced to suppress irrelevant temporal responses and focus on informative time segments, thereby reducing unnecessary computation. Experimental results on the JAAD dataset show that BiTraP-DGF consistently outperforms the BiTraP-NP baseline. For a prediction horizon of 1.5 s, CADE is reduced by 20.9% and CFDE by 22.8%. These results indicate that the proposed framework achieves a practical balance between prediction accuracy and computational efficiency for autonomous driving applications. Full article

A new category of polyphenolic compounds, like flavonoids, phenolic acids, phenylpropanoids, terpenoids, and others, referred to as food nutrients, may counteract the harmful effects of micro- and nanoplastics (MNPs) by enhancing cellular stress resilience response and overall human health. These compounds found in functional food help mitigate the cellular damage, inflammation, and oxidative stress caused by MNP exposure, which can contribute to pathological conditions, including diabetes. Importantly, specific food nutrients are able to activate, at the minimum dose, the nuclear factor erythroid-derived 2-like 2 (Nrf2) to prevent or block MNP-induced damage. The Nfe2l2 gene encodes the Nrf2 transcription factor, acting as a master regulator of redox homeostasis by inducing antioxidant response element (ARE)-driven resilience genes, which in turn, promote the expression of detoxification enzymes like heme oxygenase-1 (HO-1), NAD(P)H: quinone oxidoreductase 1 (NQO1), and glutathione S-transferase (GST) to scavenge reactive oxygen species (ROS) and shield cells from environmental damage and toxicity. Deregulation of the Nfe2l2 gene due to the accumulation of MNP pollutants may exacerbate the inflammatory conditions associated with diabetes and its chronic complications by rendering cells more sensitive to oxidative stress, apoptosis, and pyroptosis. Furthermore, epigenetic modifications influence gene regulation; chromatin remodeling directly impacts DNA accessibility, allowing or limiting transcription factor access to regulate gene expression. This mechanism may also play a pivotal role in the progression of oxidative stress-related diseases, as it modulates the Nrf2 pathway and the expression levels of its target genes. In contrast to the current literature, which has only addressed the pathological mechanisms induced by MNPs, this research explores, for the first time, how food nutrients interacting with the Nfe2l2 gene can combat or reverse the toxic effects of MNPs in cells, tissues, and organs. The goal is to improve health by attenuating MNP toxicity, which is influenced by individual genetic variations and cellular stress resilience. Full article

Chenopodium album L. is a highly problematic weed in agricultural systems, exhibiting resistance or tolerance to multiple herbicides. This weed significantly impacts crop growth and yield, threatening global agricultural production. Since the introduction of genetically modified herbicide-resistant crops, glyphosate has become a primary option for controlling C. album. However, the continuous application of glyphosate has led to shifts in weed community composition, favoring species that are more challenging to manage, and thus complicating weed control efforts. Although glyphosate resistance in C. album has not been confirmed, varying tolerance among populations brings practical problems to weed evolution. This review provides a synthesis of the progress on the mechanisms of glyphosate tolerance in C. album. Key factors influencing plant responses to glyphosate are examined, including target proteins, encoding genes, morphological and physiological traits, transport capacity, and metabolic detoxification processes. The existing evidence indicates that glyphosate tolerance in C. album is driven primarily by non-target-site adaptations or morpho-physiological changes, not target-site mutations. The insights gained from this review will aid in designing precision approaches to manage glyphosate-tolerant weeds in agricultural systems. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Multi-agent trajectory prediction is essential for autonomous driving systems, as its performance heavily depends on the quality of feature representations. This paper proposes PFR-HiVT, a lightweight and effective approach for multi-agent trajectory prediction, and evaluates it on the Argoverse 1.1 motion forecasting dataset. Although existing methods such as the Hierarchical Vector Transformer (HiVT) have achieved strong performance, they still exhibit limitations in feature extraction and feature transition across different stages of the network. To address these limitations, a collaborative feature enhancement framework is introduced, consisting of two encoder-side modules and a Progressive Feature Refinement Global Interactor (PFR-Global Interactor). Specifically, the Feature Enhancement Module (FEM) and the Attention Enhancement Module (AEM) are employed to refine local spatiotemporal features before global interaction. In addition, the PFR-Global Interactor integrates three lightweight components—the Simple Feature Refinement Module (SFR), the Lightweight Gate Module (LG), and the Residual Connection Module (RC)—to progressively refine globally interacted features prior to trajectory decoding. All proposed modules adopt lightweight designs, introducing only 230.5 k additional parameters (approximately 8.7% of the total parameters of HiVT-128). Experiments on the Argoverse 1.1 dataset show that PFR-HiVT achieves a minADE of 0.703, a minFDE of 1.041, and an MR of 0.112, outperforming the baseline HiVT model. Ablation studies further validate the effectiveness and synergy of the proposed modules. Full article

Background: Interstitial lung diseases (ILDs) are a heterogeneous group of disorders characterized by variable degrees of inflammation and fibrosis affecting the pulmonary interstitium. Advances in molecular biology and genetics have greatly expanded our understanding of ILD pathogenesis, uncovering novel mechanisms and supporting precision medicine approaches. Genetic Insights: Genetic factors play a pivotal role in ILD heterogeneity, influencing disease onset, severity, and progression. To date, more than 30 genes with different inheritance patterns (autosomal dominant, recessive, or X-linked) have been associated with ILDs. These genes are primarily involved in surfactant metabolism, telomere maintenance, immune regulation, and epithelial repair. Emerging evidence also implicates genes encoding aminoacyl-tRNA synthetases. This review summarizes the main genetic alterations underlying ILD pathogenesis and discusses their impact on diagnostic and therapeutic approaches, highlighting how identification of disease-causing variants can improve diagnostic accuracy, refine prognostic assessment, and inform recurrence risk. Methods: A narrative review was conducted through targeted PubMed and Embase searches using disease- and gene-related keywords. Studies were prioritized based on predefined conceptual criteria, including clinical relevance, strength and replication of genetic associations, and availability of functional or translational evidence. Conclusions: This synthesis brings together the latest genetic insights into pediatric ILDs and their clinical implications. Integrating genomic data into clinical practice may enable earlier diagnosis, tailored follow-up, individualized therapeutic strategies, and more informed genetic counseling. However, important challenges remain, including incomplete genotype–phenotype correlations and limited functional validation for several disease-associated genes, which currently constrain full clinical translation. Full article

Recent advances in deep learning (DL) have enabled the integration of diverse biomedical data for disease prediction and risk stratification. Building on this progress, the overall objective of this study was to develop and evaluate a multimodal DL framework for robust multi-label classification (MLC) of major comorbidities in patients with obstructive sleep apnea (OSA) using physiological time series signals and clinical data. This study proposes a robust framework for multi-label classification (MLC) of comorbidities in patients with OSA using diverse physiological and clinical data sources. We conducted a retrospective observational study including a convenience sample of 144 patients referred for overnight polysomnography at the Sleep Medicine Center (SleepLab Split), University Hospital Centre Split (KBC Split), Split, Croatia. Patients were selected based on predefined inclusion criteria and data availability. A one-dimensional Convolutional Neural Network (1D-CNN) was developed to process and fuse time series signals, oxygen saturation ($Sp{O}_2$), derived $Sp{O}_2$ features, and nasal airflow (FP0), with demographic and physiological parameters, enabling the identification of key comorbidities such as arterial hypertension, diabetes mellitus, and asthma/COPD. The instruments included polysomnography-derived signals (SpO₂ and FP0 airflow) and structured demographic/physiological parameters. Signals were preprocessed and used as inputs to the proposed fusion model. The proposed model was trained and fine-tuned using the Optuna hyperparameter optimization framework, addressing class imbalance through weighted loss adjustments. Its performance was comprehensively assessed using multi-label evaluation metrics, including macro/micro F1-score, AUC-ROC, AUC-PR, subset and partial accuracy, Hamming loss, and multi-label confusion matrix (MLCM). The study protocol was approved by the Ethics Committee of the School of Medicine, University of Split (Approval No. 003-08/23-03/0015, Date: 17 October 2023). The 1D-CNN achieved superior predictive performance compared to traditional machine learning (ML) classifiers with macro AUC-ROC = 0.731 and AUC-PR = 0.750. The model demonstrated consistent behavior across age, gender, and BMI groups, indicating strong generalization and minimal demographic bias. In conclusion, the results confirm that $Sp{O}_2$ and airflow signals inherently encode comorbidity-specific physiological patterns, enabling efficient and scalable screening of OSA-related comorbidities without the need for full polysomnography. Although the study is limited by data set size, it provides a methodological basis for the application of multi-label DL models in clinical decision support systems. Future research should focus on the expansion of multi-center datasets, thereby improving model interpretability and potential clinical adoption. Full article

Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they still face challenges in complex medical imaging scenarios, particularly around simultaneously modeling fine-grained local details and capturing long-range global contextual information, which limits segmentation accuracy and structural consistency. To address these challenges, this paper proposes a novel medical image segmentation framework termed DA-TransResUNet. Built upon a ResUNet backbone, the proposed network integrates residual learning, Transformer-based encoding, and a dual-attention (DA) mechanism in a unified manner. Residual blocks facilitate stable optimization and progressive feature refinement in deep networks, while the Transformer module effectively models long-range dependencies to enhance global context representation. Meanwhile, the proposed DA-Block jointly exploits local and global features as well as spatial and channel-wise dependencies, leading to more discriminative feature representations. Furthermore, embedding DA-Blocks into both the feature embedding stage and skip connections strengthens information interaction between the encoder and decoder, thereby improving overall segmentation performance. Experimental results on the LiTS2017 dataset and Sliver07 dataset demonstrate that the proposed method achieves incremental improvement in liver segmentation. In particular, on the LiTS2017 dataset, DA-TransResUNet achieves a Dice score of 97.39%, a VOE of 5.08%, and an RVD of −0.74%, validating its effectiveness for liver segmentation. Full article

Driven by rapid socioeconomic progress and changing lifestyles, the global burden of cardiovascular diseases (CVDs) continues to escalate, with surging morbidity and mortality rates imposing a severe threat to public health. Clinical treatments are focused on the alleviation of treatments, highlighting the need for a deeper understanding of CVDs pathogenesis and the development of targeted therapies. Recent studies have identified imbalances in intracellular Ca²⁺ homeostasis as a key pathological mechanism in the progression of CVDs. Notably, sarcoplasmic/endoplasmic reticulum Ca²⁺-ATPase 2 (SERCA2), a membrane protein encoded by the ATP2A2 gene and ranging from 97 to 115 kDa in molecular weight, plays a pivotal role in regulating intracellular Ca²⁺ levels. Extensive evidence links abnormal SERCA2 function to various CVDs, including heart failure, cardiac hypertrophy, atherosclerosis, and diabetic cardiomyopathy. This review systematically explores the regulatory mechanisms of SERCA2 expression and its functional regulation—including transcriptional regulation, post-translational modifications, and protein–protein interactions—and further investigates its pathological roles in cardiovascular diseases as well as its potential as a therapeutic target. By synthesizing current knowledge, this article aims to provide new insights for future basic research and establish a theoretical foundation for clinical applications. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

Semi-supervised joint classification of Hyperspectral Images (HSIs) and LiDAR-derived Digital Surface Models (DSMs) remains challenging due to scarcity of labeled pixels, strong intra-class variability, and the heterogeneous nature of spectral and elevation features. In this work, we propose a Hybrid Mamba–Graph Fusion Network (HMGF-Net) with Multi-Stage Pseudo-Label Refinement (MS-PLR) for semi-supervised hyperspectral–LiDAR classification. The framework employs a spectral–spatial HSI backbone combining 3D–2D convolutions, a compact LiDAR CNN encoder, Mamba-style state-space sequence blocks for long-range spectral and cross-modal dependency modeling, and a graph fusion module that propagates information over a heterogeneous pixel graph. Semi-supervised learning is realized via a three-stage pseudolabeling pipeline that progressively filters, smooths, and re-weights pseudolabels based on prediction confidence, spatial–spectral consistency, and graph neighborhood agreement. We validate HMGF-Net on three benchmark hyperspectral–LiDAR datasets. Compared with a set of eight state-of-the-art (SOTA) baselines, including 3D-CNNs, SSRN, HybridSN, transformer-based models such as SpectralFormer, multimodal CNN–GCN fusion networks, and recent semi-supervised methods, the proposed approach delivers consistent gains in overall accuracy, average accuracy, and Cohen’s kappa, especially in low-label regimes (10% labeled pixels). The results highlight that the synergy between sequence modeling and graph reasoning in combination with carefully designed pseudolabel refinement is essential to maximizing the benefit of abundant unlabeled samples in multimodal remote sensing scenarios. Full article

Laser Doppler vibrometer (LDV) has the characteristics of long-distance, non-contact, and high sensitivity, and plays an increasingly important role in industrial, military, and security fields. Remote speech acquisition technology based on LDV has progressed significantly in recent years. However, unlike microphone receivers, LDV-captured signals have severe signal distortion, which affects the quality of the LDV-captured speech. This paper proposes a nested U-network with gated temporal convolution (TCNUNet) to enhance monaural speech based on LDV. Specifically, the network is based on an encoder-decoder structure with skip connections and introduces nested U-Net (NUNet) in the encoder to better reconstruct speech signals. In addition, a temporal convolutional network with a gating mechanism is inserted between the encoder and decoder. The gating mechanism helps to control the information flow, while temporal convolution helps to model the long-range temporal dependencies. In a real-world environment, we designed an LDV monitoring system to collect and enhance voice signals remotely. Different datasets were collected from various target objects to fully validate the performance of the proposed network. Compared with baseline models, the proposed model achieves state-of-the-art performance. Finally, the results of the generalization experiment also indicate that the proposed model has a certain degree of generalization ability for different languages. Full article

This paper proposes a novel motion-aware framework to address key challenges in 4D millimeter-wave radar detection for autonomous driving. While existing methods struggle with sparse point clouds and dynamic object characterization, our approach introduces three key innovations: (1) A Bird’s Eye View (BEV) fusion network incorporating velocity vector decomposition and dynamic gating mechanisms, effectively encoding motion patterns through independent XY-component convolutions; (2) a gradient-aware multi-task balancing scheme using learnable uncertainty parameters and dynamic weight normalization, resolving optimization conflicts between classification and regression tasks; and (3) a two-phase progressive training strategy combining multi-frame pre-training with sparse single-frame refinement. Evaluated on the TJ4D benchmark, our method achieves 33.25% mean Average Precision (mAP)_3D with minimal parameter overhead (1.73 M), showing particular superiority in pedestrian detection (+4.16% AP) while maintaining real-time performance (24.4 FPS on embedded platforms). Comprehensive ablation studies validate each component’s contribution, with thermal map visualization demonstrating effective motion pattern learning. This work advances robust perception under challenging conditions through principled motion modeling and efficient architecture design. Full article

Background/Objectives: Ovarian cancer has the highest mortality among gynecological malignancies, with platinum resistance significantly contributing to poor prognosis. We aimed to develop a multimodal model (MMHC-OCPR) to predict platinum response and recurrence risk, enabling earlier personalized treatment and improved outcomes. Methods: This multicenter retrospective study included a combined cohort of 431 patients, comprising 1182 whole slide images (WSIs) curated from two independent datasets. The primary cohort consisted of 376 patients from the National Cancer Center (China), which was further partitioned into training, validation and internal test sets to ensure model development and evaluation. An additional external test cohort was incorporated using publicly available data from TCGA, enhancing the generalizability of our findings. We implemented a weakly supervised multiple instance learning framework to integrate histopathological imaging with clinicopathological variables, further strengthened by the incorporation of the transformer-based pretrained encoder UNI2-h, which enhanced the model’s predictive performance. Results: All patients in the primary cohort had pathology slides collected from primary ovarian tumors and metastatic tumor, along with clinical factors related to prognosis and treatment response. The baseline platinum response classifier using primary WSIs achieved an AUC of 0.896 in the internal test group and 0.876 in the external test group. Integration of metastatic WSIs and clinical data inputs yielded a superior AUC of 0.914 in the internal test set. The recurrence risk model demonstrated a C-index of 0.801, rising to 0.838 after multimodal enhancement. The model stratified patients into low-, intermediate- and high-risk groups with 2-year progression-free survival rates of 77.3%, 48.0% and 2.0%, respectively. Conclusions: Our model enables the early detection of platinum resistance, guiding timely treatment intensification. The recurrence risk stratification supports personalized management by identifying patients with favorable outcomes following surgery and chemotherapy, potentially sparing them from maintenance therapy to reduce associated toxicity, cost, and enhance quality of life. Full article

Fabry disease (FD) is an X-linked systemic lysosomal storage disease caused by mutations in the galactosidase-α (GLA) gene, which encodes the α-galactosidase A (α-AGAL) enzyme. FD can lead to serious complications, including early death, if left untreated. For over 20 years, enzyme replacement therapy (ERT) based on the use of agalsidase-α and agalsidase-β has been the standard treatment for FD, alongside new molecules that have enriched the therapeutic armamentarium and others that are being tested to expand it further. Unfortunately, ERT can be associated with the formation of inhibiting antidrug antibodies (ADAs), which impact ERT clinical efficacy and have consequences affecting safety and therapeutic adherence. A group of FD specialists discussed the problem of immunogenicity in FD, analyzing the most recent literature and the strategies that are currently being used to address it. Once formed, fluctuating levels of ADAs persist and have an impact on the clinical picture and prognosis of the disease that is still the subject of lively scientific debate. The critical nature of ADAs is demonstrated by their ability to bind to the enzyme, increasing drug clearance while forming immune complexes that can build up in the tissues causing chronic inflammation that aggravates the progression of the disease and affects the onset of acute reactions after the infusion, impacting therapeutic adherence. Although similar in their therapeutic mechanism, agalsidase-α and agalsidase-β differ in their production process, with resulting differences from a pharmacokinetic and pharmacodynamic point of view and diverse immunological implications: despite showing rather overlapping efficacy outcomes, agalsidase-α demonstrates a better tolerability profile, with a lower frequency of ADAs, than agalsidase-β. Given the extreme variability of the clinical picture, it is crucial for optimal FD management that the most appropriate molecule is chosen by taking into account the unique immunological risk profile of each single patient, and particular attention should be paid to naïve subjects by periodic measurement of ADAs during therapy and cross-referencing data to correlate serological and clinical patterns. Full article