Search Results (45)

Star polymers with dense shell structures exhibit unique advantages in molecule encapsulation. The incorporation of dendronized polymers as arms into star polymers enables the formation of spherical core–shell structures with high-density chain stacking, which is of great significance for enhancing their encapsulation capabilities. Here, we report on the synthesis of a new type of star dendronized polymer consisting of oligoethylene glycol (OEG)-based dendronized polymers as the arms and gold nanoparticles (AuNPs) as the core. Due to the thickness of individual dendronized polymer arms, the morphology of star dendronized polymers was directly visualized by an atomic force microscope (AFM). These star polymers inherit characteristic thermoresponsiveness from the OEG-based dendronized linear polymers, and their thermoresponsive behavior depends mainly on the grafting density of polymer chains on the AuNP cores and the molecular weights of the polymer arms. More importantly, these star dendronized polymers exhibit a tunable encapsulation capacity to guest molecules, which can be modulated through thermally induced aggregation. By virtue of these peculiarities, these thermoresponsive star dendronized polymers with tailorable release properties hold promise as smart nanoboxes for bio-applications, including drug delivery and biosensing. Full article

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications. Full article

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R² = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 10⁴ CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 10³ CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications. Full article

Background: Greater trochanteric pain syndrome (GTPS) is associated with structural tendon alterations and functional impairment. Extracorporeal shockwave therapy (ESWT) is a common treatment, but objective monitoring of tendon remodeling and motor recovery remains limited. Objective: This study aimed to integrate shear-wave elastography (SWE) expressed in m/s and wearable inertial measurement unit (IMU) as biosensing tools for the quantitative assessment of tendon elasticity, morphology, and hip motion after ESWT in GTPS. Methods: In a prospective cohort of adults with chronic GTPS, shear wave elastography (SWE) quantified gluteus medius tendon (GMT) elasticity and thickness, while hip abduction range of motion (ROM) was measured using a triaxial inertial measurement unit. Clinical scores on the Visual Analogue Scale (VAS), Harris Hip Score (HHS), Low Extremity Functional Scale (LEFS), and Roles and Maudsley score (RM) were collected at baseline (T0) and at 6 months (T1). Results: Thirty-five patients completed follow-up. Pain and function improved significantly (VAS, HHS, LEFS, RM; all p < 0.05). SWE values of the affected GMT increased, while tendon thickness decreased yet remained greater than on the contralateral side. Hip abduction ROM increased significantly from T0 to T1 (p < 0.05). Correlation analysis showed a negative association between abduction and pain at T1 (r = −0.424; p = 0.011) and, at baseline, between abduction and VAS (r = −0.428; p = 0.010) and RM (r = −0.346; p = 0.042), and a positive association with LEFS (r = 0.366; p = 0.031). SWE correlated negatively with VAS at T1 (r = −0.600; p < 0.05) and positively with HHS at T1 (r = 0.400; p < 0.05). Conclusions: Integrating elastography with inertial sensor-based motion analysis provides complementary, quantitative insights into tendon remodeling and functional recovery after ESWT in GTPS. These findings support combined imaging and wearable motion measures to monitor treatment response over time. Full article

Currently, in influenza vaccine production via the chicken embryo splitting method, embryo viability detection is a pivotal quality control step—non-viable embryos are prone to microbial contamination, directly endangering the vaccine batch quality. However, the predominant manual candling method suffers from unstable accuracy and occupational visual health risks. To address this challenge, we developed a novel real-time embryo viability detection system based on photoplethysmography (PPG) technology, comprising a hardware circuit for chicken embryo PPG signal collection and customized software for real-time signal filtering and time–frequency-domain analysis. Based on this system, we conducted three pivotal experiments: (1) impact of the source–detector spatial arrangement on PPG signal acquisition, (2) viable/non-viable embryo discrimination, and (3) embryo PPG signal detection performance for days 10–14. The experimental results show that within the sample size (15 viable, 5 non-viable embryos), the system achieved a 100% discrimination accuracy; meanwhile, it realized 100% successful multi-day (days 10–14) PPG signal capture for the 15 viable embryos, with consistent performance across the developmental stages. This PPG-based system overcomes limitations of traditional and existing automated methods, provides a non-invasive alternative for embryo viability detection, and presents significant implications for standardizing vaccine production quality control and advancing optical biosensing for biological viability detection. Full article

Flexible sensors have emerged as critical interfaces for information exchange between soft biological tissues and machines. Here, we present a dual-mode stretchable sensor system capable of synchronous strain and electromyography (EMG) signal detection, integrated with wireless WIFI transmission for real-time joint movement monitoring. The system consists of two key components: (1) A multi-channel gel electrode array for high-fidelity EMG signal acquisition from target muscle groups, and (2) a novel capacitive strain sensor made of stretchable micro-cracked gold film based on Styrene Ethylene Butylene Styrene (SEBS) that exhibits exceptional performance, including >80% stretchability, >4000-cycle durability, and fast response time (<100 ms). The strain sensor demonstrates position-independent measurement accuracy, enabling robust joint angle detection regardless of placement variations. Through synchronized mechanical deformation and electrophysiological monitoring, this platform provides comprehensive movement quantification, with data visualization interfaces compatible with mobile and desktop applications. The proposed technology establishes a generalizable framework for multimodal biosensing in human motion analysis, robotics, and human–machine interaction systems. Full article

Early detection of high-risk human papillomavirus (HPV), particularly HPV16 E7, is critical for cervical cancer prevention. Here, we report a novel, portable, and instrument-free biosensing platform that integrates recombinase polymerase amplification (RPA) with CRISPR/Cas12a-mediated detection on a microfluidic paper-based analytical device (μPAD) for colorimetric, visual readout of double-stranded DNA (dsDNA). The μPAD features seven functional zones, including lyophilized RPA and CRISPR reagents, and immobilized streptavidin and anti-FAM antibodies for signal generation. Upon target recognition, Cas12a’s trans-cleavage activity releases biotinylated-FAM-labeled reporters that form a sandwich complex with gold nanoparticle (AuNP)-conjugated anti-FAM antibodies, producing a visible red signal at the test zone. The gray value of the colorimetric signal correlates linearly with target concentration, enabling the quantitative detection of HPV16 E7 dsDNA down to 100 pM within 60 min. The assay demonstrated high accuracy and reproducibility in spiked samples. By combining isothermal amplification, CRISPR specificity, and paper-based microfluidics, this platform offers a rapid, low-cost, and user-friendly solution for point-of-care HPV screening in resource-limited settings. This work advances the integration of CRISPR diagnostics with μPAD, paving the way for scalable point-of-care molecular diagnostics beyond HPV. Full article

Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture. Full article

Mycoplasma hominis (MH) is a prevalent opportunistic pathogen that is strongly associated with a wide range of urogenital tract infections and severe adverse pregnancy outcomes in clinical settings. Current MH detection methods, including microbial culture and qPCR, are time-consuming and rely on complex equipment, making them unsuitable for scenarios requiring rapid or simplified testing. In this study, we developed a visual readout biosensing platform by synergistically integrating recombinase polymerase amplification (RPA), CRISPR/Cas12a-mediated target nucleic acid recognition, and lateral flow biosensors for the rapid, sensitive, and specific identification of MH. The assay specifically targets the MH-specific 16S rRNA gene, achieving a limit of detection as low as 2 copies/reaction of recombinant plasmid containing the target gene with a total assay time of 60 min. Critical reaction parameters, including Cas12a-crRNA molar ratio, volume of RPA amplicon input, and Cas12a cleavage time, were systematically optimized to maximize the biosensor’s response efficiency and detection reliability. The platform exhibited exceptional specificity, with no cross-reactivity observed against common co-occurring urogenital pathogens, and effectively minimized aerosol contamination risks via a rigorous decontamination workflow. Furthermore, this work represents the first documented implementation of a contamination-control protocol for an MH-specific CRISPR-LFA assay. Notably, testing results from 18 clinical samples demonstrated the high specificity of this assay, highlighting its promising potential for clinical application. Full article

Breast cancer is one of the most prevalent malignant tumors worldwide, with the highest incidence and mortality among women. Early precise diagnosis and the development of efficient treatment regimens remain major clinical challenges. Harnessing the programmable size, surface chemistry, and tumor microenvironment (TME) responsiveness of nanomaterials, there is tremendous potential for their applications in breast cancer diagnosis and therapy. In the diagnostic arena, nanomaterials serve as core components of novel contrast agents (e.g., gold nanorods, quantum dots, superparamagnetic iron oxide nanoparticles) and biosensing platforms, substantially enhancing the sensitivity and specificity of molecular imaging modalities—such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging (FLI)—and enabling high-sensitivity detection of circulating tumor cells and tumor-derived exosomes, among various liquid biopsy biomarkers. In therapy, nanoscale carriers (e.g., liposomes, polymeric micelles) improve tumor targeting and accumulation efficiency through passive and active targeting strategies, thereby augmenting anticancer efficacy while effectively reducing systemic toxicity. Furthermore, nanotechnology has spurred the rapid advancement of emerging modalities, including photothermal therapy (PTT), photodynamic therapy (PDT), and immunotherapy. Notably, the construction of theranostic platforms that integrate diagnostic and therapeutic units within a single nanosystem enables in vivo, real-time visualization of drug delivery, treatment monitoring, and therapeutic response feedback, providing a powerful toolkit for advancing breast cancer toward personalized, precision medicine. Despite challenges that remain before clinical translation—such as biocompatibility, scalable manufacturing, and standardized evaluation—nanomaterials are undoubtedly reshaping the paradigm of breast cancer diagnosis and treatment. Full article

Brain–computer interfaces (BCIs) have emerged as an option that allows better communication between humans and some technological devices. This article presents a BCI based on the steady-state visual evoked potentials (SSVEP) paradigm and low-cost hardware to control a virtual prototype of a robotic hand. A LED-based device is proposed as a visual stimulator, and the Open BCI Ultracortex Biosensing Headset is used to acquire the electroencephalographic (EEG) signals for the BCI. The processing and classification of the obtained signals are described. Classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) are compared, demonstrating that the classifiers based on SVM have superior performance to those based on ANN. The classified EEG signals are used to implement different movements in a virtual prosthetic hand using a co-simulation approach, showing the feasibility of BCI being implemented in the control of robotic hands. Full article

Accurate assessment of extraocular muscle mechanics is crucial for diagnosing and treating ocular motility disorders, yet current methods, such as the forced duction test, rely on subjective tactile sensation and gross visual observation. To overcome the limitations of subjectivity and the impracticality of previous quantitative devices, we developed a novel biosensing system capable of simultaneously and objectively measuring passive ocular tension and rotation angle during forced duction. The system integrates custom-engineered surgical forceps equipped with dual strain gauges and an infrared video camera that precisely tracks pupil displacement to calculate real-time rotation angle. We clinically validated this system in a prospective study involving 10 patients (20 eyes) with intermittent exotropia, with measurements performed under general anesthesia. Reliable tension–angle curves were successfully obtained in all cases without complications. Passive tension increased progressively with ocular rotation, following a linear-parabolic trajectory up to 40°. The mean duction force of the medial and lateral rectus muscles showed comparable symmetry. This lightweight, practical, and objective biosensing system offers a reliable tool for quantifying ocular mechanics, with the potential to enhance diagnostic accuracy, enable individualized surgical planning, and support fundamental research in ocular motility disorders. Full article

Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious dimensions. Psychoanalysis—particularly its constructs of setting, rêverie, and transference—offers a unique epistemological basis for designing therapeutic environments that engage implicit emotional processes. Aim: This paper aims to develop a conceptual framework for modeling IVR-based therapeutic settings inspired by psychoanalytic theory and enhanced through IoT-enabled biosensing technologies. Methods/Approach: We propose a three-layer architecture: (1) a somatic layer involving IoT-based real-time physiological monitoring (e.g., heart rate variability, galvanic skin response, eye-tracking, EEG); (2) a symbolic-narrative layer where the VR environment dynamically adapts to the user’s affective state through immersive visual and auditory stimuli; and (3) a relational layer where AI-driven avatars simulate transferential dynamics. The model is theoretically grounded in psychoanalytic literature and informed by current advances in affective computing and bioengineering. Conclusions: By bridging psychoanalytic metapsychology and bioengineering design, this framework proposes a novel approach to therapeutic IVR systems that move beyond explicit cognition to engage the embodied unconscious. The integration of IoT biosignals enables the mapping and modulation of internal states within a structured symbolic space, opening new pathways for the clinical application of digital psychoanalysis. Full article

Rapid blood type detection in point-of-care testing (POCT) scenarios is crucial for various clinical treatments. In this study, we present a sensitive, cost-effective, and straightforward biosensing approach for visual blood typing that notably simplifies the procedure by eliminating any need for blood sample pretreatment. Our technique achieves this by directly trapping and accumulating red blood cell (RBC) clusters within a photolithography-based microfluidic chip, thereby bypassing complex preprocessing. By employing an antigen–antibody assay involving isoagglutinins A, B, and/or D on the RBC surface and their corresponding antibodies, we effectively determine blood types. When antibodies are present, the corresponding RBCs bind to the antibody-conjugated RBC clusters, which are subsequently trapped within the microfluidic accumulation chip, resulting in the formation of a visible bar. The blood group can then be readily identified by observing this visual bar with the naked eye or under microscopy. Notably, we integrate two continuous mixing units (Z and S) at the entrance of the biochip to improve mixing efficiency and accelerate the antigen–antibody interaction. This method demonstrates high selectivity, accuracy, and stability across various clinical blood samples. Moreover, the sensor operates with minimal sample volume (as low as 10 μL) and delivers results within 5 min. The fabrication cost of the PDMS-based biochip is approximately $0.2 per chip, and the limit of detection (LOD) is determined to be 3 × 10⁶ cells/mL, indicating excellent sensitivity and affordability for practical use. Overall, this biochip provides a fast, low-cost, and reliable solution for emergency blood typing, particularly in resource-limited settings. Full article

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements—up to an 8128-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors. Full article