Search Results (353)

Surface-enhanced Raman spectroscopy (SERS) has evolved from a fundamental optical phenomenon to a powerful, molecule-specific analytical technique capable of detecting ultra-trace-level species across biomedicine, catalysis, environmental monitoring, and national security applications. In this review, we summarize recent advances in SERS probe design and fabrication along three major directions: (i) engineering plasmonic hotspots with enhanced field confinement to achieve stronger and more uniform signals; (ii) analyte-directed strategies that precisely position and retain target molecules via tailored surface chemistries, nanoscale confinement, and on-surface reactions for single hotspot SERS; and (iii) hybrid architectures integrating plasmonic metals with functional materials, including high entropy materials, semiconductors, and graphene and other 2D materials, to synergistically couple electromagnetic and chemical enhancement mechanisms. Despite significant progress, key challenges remain for practical applications outside laboratories, including substrate reproducibility and stability, diverse analyte compatibility, unknown molecule identification and standardized quantitative performance in complex environments. We highlight emerging solutions, such as large-area nanomanufacturing for controlled nanoscale gaps, high-resolution Raman mapping for spatial–temporal characterization, density-functional-theory-guided molecular interpretation, and machine-learning-enabled spectral analysis. Advances in foundational AI models and data-driven discovery are positioning SERS to become an increasingly versatile platform, from decoding unknown molecular structures to analyzing complicated multi-component systems for environmental, biomedical, and national security applications with high sensitivity and selectivity. Full article

Contactless vital signs monitoring is becoming increasingly relevant in scenarios where conventional sensors are impractical or not recommended. In this manuscript, a radar-based contactless system for the joint reconstruction of phonocardiogram (PCG) waveforms and cardiac state segmentation is illustrated. The proposed method exploits a self-attention one-dimensional (1D) U-Net fed by a pre-processed radar-derived input to estimate a PCG-like waveform, its envelope, and the four main cardiac phases: S1, systole, S2, and diastole. The accuracy of our method has been assessed on a public synchronized radar–PCG dataset acquired by means of a 24 GHz Doppler radar and a digital stethoscope. On the test subset, the proposed model achieved a 13.4885 dB reduction in log-spectral distance relative to the radar input signal, indicating a marked improvement in waveform fidelity. Segmentation performance also improved, with Micro-${\mathrm{F}}_1$ increasing from 74.41% to 84.17% and Macro-${\mathrm{F}}_1$ from 68.40% to 80.43% on average. Experimental results demonstrated the viability of real-time low-power embedded hardware deployment for contactless auscultation and continuous cardiac monitoring applications. The findings confirm that respiratory interference and low-amplitude signals complicate S2 detection, especially when exacerbated by subject motion. Full article

Additive manufacturing (AM) has emerged as a transformative technology across multiple industries, from aerospace to biomedical applications. The integration of artificial intelligence (AI) and machine learning (ML) into AM processes represents a paradigm shift toward intelligent, autonomous manufacturing systems. This comprehensive review synthesizes recent advances in AI/ML applications across the entire AM life cycle—from design optimization and process planning through in situ monitoring, closed-loop control, and post-process qualification. The analysis is organized by ISO/ASTM AM process families, including powder bed fusion (PBF), directed energy deposition (DED), material extrusion (MEX), vat photopolymerization (VP), binder jetting (BJ), material jetting (MJT), and sheet lamination (SL). For each process family, the review examines the specific AI/ML techniques employed, the data modalities utilized (thermal imaging, acoustic signals, in situ cameras, CT/NDE data), and the current state of deployment from research prototypes to industrial implementation. The analysis reveals that while significant progress has been made in single-stage ML applications such as defect detection and parameter optimization, truly integrated life cycle-wide AI-driven AM workflows remain largely aspirational. Key challenges are identified including data scarcity, model generalization across machines and materials, real-time control constraints, and certification requirements. Finally, future research directions are outlined toward autonomous AM systems enabled by physics-informed ML, digital twins, and hierarchical AI architectures. Full article

Magnetic resonance techniques have evolved beyond conventional proton-based imaging, enabling access to a broader range of nuclei that provide complementary structural, functional, and molecular information. This review presents a comprehensive overview of multinuclear NMR and MRI in solid and soft materials as well as in biomedical applications, with particular emphasis on ¹H, ¹³C, ³¹P, ²³Na, and ¹⁹F nuclei. Proton-based methods remain the foundation of magnetic resonance due to their high sensitivity and widespread applicability, offering insights into molecular mobility, hydration, and microstructural heterogeneity. In contrast, heteronuclear approaches enable more specific characterization of chemical structure (¹³C), phosphorus-containing functional groups and membranes (³¹P), ionic homeostasis and transport (²³Na), and exogenous tracers with negligible biological background (¹⁹F). Together, these techniques extend magnetic resonance from primarily anatomical imaging toward functional, metabolic, and molecular-level analysis. The review further discusses key hardware aspects, including magnetic field strength and radiofrequency coil design, highlighting the trade-offs between low- and high-field systems and the growing importance of multinuclear coil architectures. For example, because ¹H, ²³Na, ³¹P, and ¹⁹F resonate at different Larmor frequencies, multinuclear experiments require dedicated or multi-tuned RF coils that balance sensitivity, field homogeneity, and decoupling between channels. Mechanisms of contrast generation are examined in detail, distinguishing between endogenous sources—such as water, ions, and metabolites—and exogenous contrast agents, including gadolinium-, manganese-, and fluorine-based compounds, as well as targeted and theranostic platforms. A comparative framework of endogenous and exogenous signals is presented, emphasizing their complementary roles in balancing safety, specificity, and sensitivity. Finally, the opportunities and challenges of multinuclear magnetic resonance are critically evaluated, including limitations in sensitivity, signal-to-noise ratio, data interpretation in heterogeneous systems, and technical complexity. Emerging directions such as ultrahigh-field imaging, advanced RF technologies, hyperpolarization, and artificial intelligence-assisted reconstruction are discussed as key drivers for future development. Overall, multinuclear NMR and MRI represent a powerful and expanding toolbox for probing complex material and biological systems, with the potential to significantly enhance diagnostic capabilities and deepen our understanding of structure–function relationships across multiple scales. Full article

Today, the rapid progress in non-invasive light–matter interaction analysis is transforming the landscape of biomedical and life sciences driven by low-intensity light detection technologies. As the complexity of photonic applications continues to grow, the importance of single-photon detection techniques becomes pivotal. Among them, Time-Correlated Single-Photon Counting (TCSPC) has become the gold standard for precise, time-resolved reconstruction of rapid and faint optical signals. However, TCSPC has long been constrained by pile-up distortion, which worsens with increasing acquisition speed, typically limiting it to 5% of the excitation frequency. To overcome the operational constraints of conventional implementations, a novel TCSPC acquisition methodology has been introduced, independent of photodetector dead time, excitation intensity, and prior optical signal knowledge, still enabling distortion-free reconstruction of the measured light profiles. In this context, the development of single-photon detectors with short dead time and low timing jitter becomes crucial. This work presents a single-photon detection module based on a Silicon Photomultiplier, which delivers 750 ps FWHM output pulses with a 33.5 ps RMS IRF. Its performance is showcased through fluorescence measurements employing the constraint-free TCSPC methodology, achieving a photon count rate up to 166% of the excitation frequency with a minimal lifetime estimation error of just −1.46%. Full article

Plant pathogens can result in massive crop destruction globally, thereby increasing starvation, while conventional or synthetic pesticides are harmful to the environment and human health. The urgent need for sustainable and eco-friendly disease management strategies has driven interest in natural biocontrol agents. Ulva sp. produce a sulfated polysaccharide named ulvan, which serves as a multifunctional biostimulant with pronounced antibacterial, antiviral, and antifungal properties against a broad spectrum of phytopathogens. Its complex anionic structure plays a dual role by directly inhibiting pathogen growth through cell membrane disruption and biofilm suppression, while simultaneously inducing plant defense mechanisms through reactive oxygen species (ROS) signaling and activation of pathogenesis-related (PR) proteins. Recent advances in ulvan extraction, purification, structural analysis, and inhibitory mechanisms of phytopathogens are discussed in this review. Furthermore, the biodegradability and biocompatibility of ulvan highlight its potential applications beyond agriculture, including biomedical and sustainable biomaterial development. By comprehensively analyzing the bioactivity spectrum and mechanistic pathways of ulvan, this review proposes strategic approaches for integrating ulvan into environmentally friendly plant disease management systems, supporting its role in advancing a circular bioeconomy. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder affecting millions worldwide, characterized by motor symptoms such as tremor, rigidity, and bradykinesia that significantly impair daily life. The current diagnosis and monitoring rely primarily on clinical observations and rating scales (e.g., the MDS-UPDRS), which are subjective and limited in detecting subtle motor alterations, leading to inter- and intra-rater variability. In recent years, wearable sensors such as surface electromyography (sEMG) and inertial measurement units (IMUs) have emerged as non-invasive tools for quantifying neuromuscular activity and motor performance in PD. When combined with machine learning (ML) and deep learning (DL) techniques, these signals enable the development of models for disease detection, patient classification, and symptom severity assessment. This review provides a structured overview of recent ML and DL approaches applied to surface electromyography for PD monitoring, addressing a gap in the current literature. It analyzes data acquisition strategies, preprocessing techniques, feature extraction methods, model architectures, and evaluation protocols across tasks such as diagnosis, tremor analysis, freezing of gait detection, and gait assessment. Despite promising results, key challenges remain, including limited dataset size, lack of standardization, and poor generalization. Finally, this work highlights emerging trends and identifies a representative processing pipeline to support real-world clinical translation. Full article

Accurate analysis of prostate cancer (PC)-related biomarkers requires sensing platforms capable of sensitive and multiplex detection in complex biological environments. Herein, we propose a signal-on electrochemical aptamer-based sensor (E-AB) for the simultaneous detection of L-lactate (L-Lac) and prostate-specific antigen (PSA). To maximize analytical performance, two Lac aptamer sensing configurations, single-stranded (ssLac201) and double-stranded (dsLac201), were constructed and comparatively evaluated. The dsLac201 structure displayed more effective background suppression and enhanced target induced signal response. Under optimized conditions, the dsLac201-based sensor exhibited a wide linear range from 500 nM to 10 mM for L-Lac, with a low detection limit of 157 nM and high selectivity. Based on this optimized design, a dual-aptamer electrochemical platform was further engineered through programmable nucleic acid assembly, enabling simultaneous detection of L-Lac and PSA via dual-input signal integration. The dual-target sensor showed broad analytical ranges for both biomarkers (L-Lac: 500 nM–10 mM; PSA: 10 pg mL⁻¹–500 ng mL⁻¹) and retained promising performance in serum samples. This work demonstrates a simple and versatile strategy for multiplex electrochemical biosensing and provides a promising platform for PC-related biomarker monitoring and clinical biomedical analysis. Full article

Biosensors have emerged as a rapidly evolving area of research, offering transformative potential across biomedical diagnostics, environmental monitoring, and pharmaceutical applications. Among the diverse range of biosensing technologies, graphene-based surface plasmonic resonance (SPR) biosensors have attracted particular interest due to their exceptional sensitivity, scalability for mass production, and cost-effective fabrication processes. This study explores the operational principles and current design methodologies of graphene-based SPR biosensors, with a special emphasis on the role of electrolyte gating and its impact on sensor performance. Furthermore, the influence of graphene’s quantum capacitance is investigated as a critical parameter for improving the accuracy and reliability of performance predictions in the proposed sensor configuration. Computational analysis of sensitivity and key performance metrics was conducted. Notably, key performance metrics of the sensor improved upon incorporating quantum capacitance effects into the simulation framework. At a β₂-microglobulin concentration of 0.00118 g/L, the sensitivity increased to 174 GHz·g/L, the figure of merit reached 0.55 L/g, the quality factor was 0.01, the signal-to-noise ratio (SNR) rose to 0.008, and the detection accuracy (DA) reached 0.08 L/THz, demonstrating the significant impact of quantum capacitance on the sensor’s performance. These findings highlight the potential of quantum-electrostatic considerations to enhance the precision and efficacy of graphene-based SPR biosensors, paving the way for the development of next-generation biosensing platforms with improved analytical capabilities. Unlike conventional graphene SPR biosensors, which primarily detect refractive index changes near the graphene surface, our model explicitly considers the electrostatic effect of biomolecules on graphene’s Fermi energy. By modelling β2-microglobulin as a charged species, we compute the resulting electric double layer and incorporate quantum capacitance in series. This amplifies the charge-induced modulation of graphene’s optical conductivity, and, combined with a graphene perfect absorber design, leads to enhanced plasmonic resonance shifts. Consequently, our approach achieves higher sensitivity and more precise detection of biomolecular interactions compared to traditional simulations. Full article

Polyurethanes (PUs) are widely used in biomedical applications; however, their surface properties critically determine bacterial colonization and cell response. In this study, medical-grade PU films were modified using low-pressure C₃H₂F₄ plasma (50 W, 300 s, 0.2 mbar), and the resulting changes in surface chemistry, wettability, topography, bacterial adhesion, and cell compatibility were evaluated. X-ray photoelectron spectroscopy (XPS) analysis confirmed the incorporation of fluorine-containing groups (CF₂, CF₃) and the appearance of an F 1s signal at ~688.3 eV. Plasma treatment increased the water contact angle from 92.6° ± 5.6° to 97.9° ± 3.1° and elevated the root mean square (RMS) surface roughness (Sq) from 39.0 nm to 77.3 nm. Surface free energy slightly decreased after plasma treatment due to reductions in both polar and dispersive components. Quantitative adhesion assays revealed strain-dependent effects. For S. aureus DSM 4910, S. epidermidis DSM 28319, and P. aeruginosa DSM 22644, no consistent reduction in adhesion was observed on plasma-treated surfaces. In contrast, E. coli DSM 18039 demonstrated significantly higher adhesion on modified PU at all incubation times, reaching 5.96 ± 0.44 logCFU/mL after 240 min compared to 5.05 ± 0.27 log colony-forming units per milliliter (logCFU/mL) on unmodified PU. Fluorescence microscopy confirmed increased surface coverage by E. coli on fluorinated samples. Biocompatibility studies using A549 cells showed no cytotoxic effects. Cell spreading area remained comparable between surfaces (1188.6 vs. 1185.1 µm²; p = 0.958). However, cells on plasma-treated PU exhibited reduced major axis length (38.6 vs. 46.7 µm; p < 0.001) and decreased focal adhesion area (8.88 vs. 10.94 µm²; p = 0.002), indicating moderate alterations in cell morphology without compromised viability. These results demonstrate that C₃H₂F₄ plasma fluorination moderately increases PU hydrophobicity and nanoscale roughness, induces strain-dependent changes in bacterial adhesion—particularly enhancing E. coli colonization—while fully preserving mammalian cell viability and showing no cytotoxic effects of the modified surface. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Lipidomics has emerged as a transformative discipline in biomedical research, providing high-resolution insights into metabolic signaling and disease pathophysiology. The R programming language provides a widely adopted framework for extensible analysis of complex lipidomic datasets due to its robust biostatistical infrastructure. Herein, we present a comprehensive roadmap for lipidomics in R, structured around a standardized analytical lifecycle: from raw data acquisition and preprocessing to structural annotation, statistical modeling and functional interpretation. We critically contextualize and integrate a curated suite of widely adopted R packages (version 4.3.0), including xcms and MSnbase for feature extraction, LipidMS 3.0 for fragmentation-based identification, and lipidr for quality control and normalization. Furthermore, we demonstrate how advanced tools such as mixOmics and clusterProfiler can be integrated to bridge the gap between differential lipid abundance and systems-level biological insights. Particular emphasis is placed on reproducibility, nomenclature standardization and the emerging role of machine learning in biomarker discovery. By synthesizing these resources into a coherent pipeline, this guide provides a structured reference for researchers. Further discussion addresses methodological pitfalls, statistical assumptions and reproducibility constraints that frequently compromise lipidomics studies. Ultimately, this structured approach facilitates systematic tool selection, accelerating the translation of complex lipidomic signatures into reproducible and clinically meaningful discoveries. Full article

This study focuses on the development and characterization of advanced composite materials based on poly(ε-caprolactone) (PCL) and poly(vinylidene fluoride) (PVDF), with or without silver nanoparticles (AgNPs), planned for peripheral nerve or bone regeneration. The complementary properties of PCL (biocompatibility and biodegradability) and PVDF (mechanical stability and piezoelectric functionality) were exploited by blending the polymers in different ratios, resulting in binary (PCL/PVDF) and ternary (PCL/PVDF/AgNPs) composites. Green-synthesized AgNPs were integrated to enhance antimicrobial activity and to support tissue repair through improved signal transmission. Functional thin films and electrospun fibres were obtained and subjected to advanced characterization techniques, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermal analysis. The results demonstrated appropriate morphology, chemical composition, structural stability, and favourable interactions with simulated physiological media. Preliminary biocompatibility assays confirmed good cell viability, supporting the biomedical applicability of the designed scaffolds. Overall, the obtained results highlight the potential of AgNPs-functionalized PCL/PVDF binary and ternary composites as promising candidates for flexible, durable, and bioactive implants in peripheral nerve or bone regeneration. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

Near-infrared spectroscopy (NIRS) is increasingly studied as a non-invasive optical investigation tool for in vivo tissue characterization, including applications to skeletal muscle and brain regions. In this context, previous studies have demonstrated reliability in differentiating muscle sites, typically relying on dense acquisition schemes (≥50 spectra acquired per site) to ensure signal stability. However, this requirement may limit throughput and hinder real-world clinical translation. Optimizing the trade-off between acquisition burden and classification performance represents a key design problem for device scalability and feasibility of bedside deployment. In this study, we explored the impact of spectral sampling density on machine learning-based muscle discrimination. Thirty healthy adults provided 50 Vis–SWIR (Visible–Short-Wave Infrared; 350–2500 nm) reflectance spectra per biceps and triceps muscle sites (3000 spectra). Seven datasets were generated by random subsampling, progressively reducing the number of spectra (from 50 to 1 spectra/muscle/subject). All datasets underwent an identical preprocessing pipeline and were subjected to Partial Least-Squares Discriminant Analysis (PLS-DA) classification. PLS-DA achieved near-perfect discrimination from 50 to 5 spectra per muscle with a mean cross-validation (CV) accuracy ≥ 99.5%, whereas performance collapsed abruptly at three spectra (CV accuracy ~39%) and one spectrum (CV accuracy ~15%). Therefore, high machine learning classification performance is retained even when the number of acquired spectra is substantially reduced. These findings support the feasibility of acquisition-efficient protocols that may enhance device portability and reduce measurement time, thus enabling NIRS integration into clinical workflows. From a biomedical engineering standpoint, spectra number reduction without loss of predictive performance represents a key step toward scalable, real-time, and patient-centered Vis–SWIR diagnostic platforms. Full article