Search Results (76)

Lipid-based nanomedicines are already widely used in antitumor therapy and gene delivery. However, their complex structural features demand advanced mesoscopic structural characterization tools for effective research and development (R&D) and quality control. Synchrotron small-angle X-ray scattering (SAXS) is a powerful, non-invasive technique for probing nanoscale membrane organizations, monitoring in situ dynamic membrane assembly, and exploring the interactions of components in lipid-based drug delivery systems, including liposomes, lipoplexes, lipid nanoparticles (LNPs), and lyotropic liquid crystals (LLCs). Recent advances in high-flux synchrotron facilities, high-frequency detectors, and automated SAXS data processing pipelines permit a detailed structural characterization of lamellarity, bilayer spacing, internal phases, core–shell morphology, as well as “pump-probe” dynamic process studies for lipid nanomedicines. Though major challenges remain in sample polydispersity and model fitting, the advances in time-resolved synchrotron SAXS, high-throughput automation, and artificial intelligence (AI)-assisted modeling are rapidly reducing this barrier. This review summarizes SAXS methodology and introduces representative case studies in the field of lipid nanomedicines. The performance of BioSAXS beamline BL19U2 in the Shanghai synchrotron radiation facility (SSRF) and prospects of AI-guided drug screening at BL19U2 are highlighted to advance intelligent R&D and quality control for lipid nanomedicines. Full article

Accurate biological detection is crucial for autonomous navigation of underwater robots, yet severely challenged by optical degradation and scale variation in marine environments. While image enhancement and domain adaptation methods offer some mitigation, they often operate as disjointed preprocessing steps, potentially introducing artifacts and compromising downstream detection performance. Furthermore, existing architectures struggle to balance accuracy, computational efficiency, and robustness across the extreme scale variability of marine organisms in challenging underwater conditions. To overcome these limitations, we propose UW-YOLO-Bio, a novel framework built upon the YOLOv8 architecture. Our approach integrates three dedicated modules: (1) The Global Context 3D Perception Module (GCPM), which captures long-range dependencies to mitigate occlusion and noise without the quadratic cost of self-attention; (2) The Channel-Aggregation Efficient Downsampling Block (CAEDB), which preserves critical information from low-contrast targets during spatial reduction; (3) The Regional Context Feature Pyramid Network (RCFPN), which optimizes multi-scale fusion with contextual awareness for small marine organisms. Extensive evaluations on DUO, RUOD, and URPC datasets demonstrate state-of-the-art performance, achieving an average improvement in mAP50 of up to 2.0% across benchmarks while simultaneously reducing model parameters by 8.3%. Notably, it maintains a real-time inference speed of 61.8 FPS, rendering it highly suitable for deployment on resource-constrained autonomous underwater vehicles (AUVs). Full article

The discovery of the fifth gravitational wave, GW170817, and its electromagnetic counterpart, resulting from the merger of neutron stars by the LIGO and Virgo teams, marked a major milestone in astronomy. It was the first time that gravitational waves and light from the same cosmic event were observed simultaneously. The LIGO detectors in the United States recorded the signal for 100 s, longer than in previous detections. The merging of neutron stars emits both gravitational and electromagnetic waves across all frequencies—from radio to gamma rays. However, pinpointing the exact source remains difficult, requiring rapid sky scanning to locate it. To address this challenge, the Gravitational-Wave Optical Transient Observer (GOTO) project was established. It is specifically designed to detect optical light from transient events associated with gravitational waves, enabling faster follow-up observations and a deeper study of these short-lived astronomical phenomena, which appear and disappear quickly in the universe. In astrophysics, it has become more important to find astronomical transient events like supernovae, gamma-ray bursts, and stellar flares because they are linked to extreme cosmic processes. However, finding these short-lived events in huge sky survey datasets, like those from the GOTO project, is very hard for traditional analysis methods. This study suggests a deep learning methodology employing Convolutional Neural Networks (CNNs) to enhance transient classification. CNNs are based on how biological vision systems work and how they are structured. They mimic how animal brains hierarchically process visual information, making it possible to automatically find complex spatial patterns in astronomical images. Transfer learning and fine-tuning on pretrained ImageNet models are utilized to emulate adaptive learning observed in biological organisms, enabling swift adaptation to new tasks with minimal data. Data augmentation methods like rotation, flipping, and noise injection mimic changes in the environment to improve model generalization. Dropout and different batch sizes are used to stop overfitting, which is similar to how biological systems use redundancy and noise tolerance. Ensemble learning strategies, such as Soft Voting and Weighted Voting, draw inspiration from collective intelligence in biological systems, integrating multiple CNN models to enhance decision-making robustness. Our findings indicate that this bio-inspired framework substantially improves the precision and dependability of transient detection, providing a scalable solution for real-time applications in extensive sky surveys such as GOTO. Full article

This study conducts a comprehensive examination of a GaN channel-based nanobiosensor featuring a dielectrically modulated trigate FinFET structure, incorporating both uniform and Gaussian channel doping. The proposed device incorporates a nanocavity structure situated beneath the gate region, intended for the analysis of diverse biomolecules in biosensing applications. The proposed biosensor employs HfO₂ as the gate dielectric, characterized by a dielectric constant of 25, leading to an enhanced switching ratio for the device. This study examines the electrical properties relevant to biomolecule identification, including the switching ratio, DIBL, threshold swing, threshold voltage, and transconductance. The sensitivity of these properties concerning the drain current is subsequently assessed. Enhanced sensitivity increases the likelihood of detecting biomolecules. The electrical property of a biomolecule is examined in the absence of another biomolecule within the cavity. The apparatus is designed to detect neutral biomolecules. Simultaneously, further investigational research has been undertaken regarding the linearity behavior of GAA FET, nanobiosensors, and dielectrically modulated TGFinFET. This study’s results have been compared with those of GaN-based FinFET and GaN SOI FinFET technologies. The data indicates approximately ∼103% and ∼42% improvements in I_OFF and Switching ratio, respectively, when compared to IRDS 2025. The nanobiosensor (GAA FET) demonstrates enhanced linear performance concerning higher-order voltage and current intercept points, including VIP2, VIP3, IIP3, and P1dB. Full article

This study provides a detailed comparison of three optical sensing approaches for colourimetric bio/chemical detection, focusing on cost, scalability, and performance. We examine laboratory-grade spectrophotometry, portable camera-based imaging, and low-cost LED photometry using Paired Emitter–Detector Diode (PEDD) charge–discharge methodology. Our findings reveal that while the LED-based PEDD system outperforms the other two methods in key sensory metrics—such as sensitivity, resolution, and limit of detection—its cost-effectiveness and scalability make it a promising solution for widespread industrial and field applications. Compared to the spectrophotometer, the LED/PEDD approach demonstrates improvements in measurement range (×16.39), dynamic range (×147.06), accuracy (×1.79), and sensitivity (×107.53). The results highlight the potential for industrial-scale adoption of LED photometry, especially for cost-effective applications in bio/chemical sensing sectors. Full article

Betalains are hydrophilic natural pigments commonly found in plants of the Caryophyllales order, as well as in specific species and genera of fungi, such as Hygrocybe, Hygrophorus, and Amanita muscaria. Betalains are sorted into two groups: betacyanins, which form red-violet pigments, and betaxanthins, which form yellow-orange pigments. These compounds can be employed as colorimetric sensors and biosensors. This paper provides a review of the isolation methods of betalains and the various applications of betalains as colorimetric sensors and biosensors. The review was conducted by collecting publications over the last decade. The results show that betalains can be used as a colorimetric sensor to identify metal compounds in water and nonmetal compounds that indicate the quality of food. In addition, betaxanthin has been used for developing cell-based biosensors from yeast and bacteria. Furthermore, betalain as a colorimetric sensor and biosensor is developed by using an innovative digital detector, such as a smartphone. Nevertheless, the fragile stability of betalains presents a significant barrier during the extraction. As a result, future studies could focus on adding innovative technologies for optimizing extraction and also developing betalain as novel bio-indicators for specific analytes. Full article

Automated tasks, mainly in the biomedical field, help to develop new technics to provide faster solutions for monitoring patients’ health status. For instance, they help to measure different types of human bio-signal, perform fast data analysis, and enable overall patient status monitoring. Eulerian Video Magnification (EVM) can reveal small-scale and hidden changes in real life such as color and motion changes that are used to detect actual pulse. However, due to patient movement during the measurement, the EVM process will result in the wrong estimation of the pulse. In this research, we provide a working prototype for effective artefact elimination using a face movement compensated EVM (MC-EVM) which aims to track the human face as the main Region Of Interest (ROI) and then use EVM to estimate the pulse. Our primary contribution lays on the development and training of two face detection models using TensorFlow Lite: the Single-Shot MultiBox Detector (SSD) and the EfficientDet-Lite0 models that are used based on the computational capabilities of the device in use. By employing one of these models, we can crop the face accurately from the video, which is then processed using EVM to estimate the pulse. MC-EVM showed very promising results and ensured robust pulse measurement by effectively mitigating the impact of patient movement. The results were compared and validated against ground-truth data that were made available online and against pre-existing solutions from the state-of-the-art. Full article

Freeze-drying fresh vegetables and fruits may not only prevent post-harvest losses but also provide a concentrated source of nutrients and phytochemicals. This study focused on the phenolic composition of different freeze-dried products derived from horticultural crop remains (HCRs) in the vegetable and fruit production chain. These products may be considered as a potential health-promoting solution for preventing post-harvest fruit spoiling and losses. The total polyphenolic content (TPC) and the main phenolics were studied using high-performance liquid chromatography (HPLC) with a diode array detector (DAD). Additionally, an in vitro chemical screening of the antioxidant capacity was carried out using the Ferric Reducing Antioxidant Power (FRAP) assay. These analyses were performed together with an investigation of the correlations among phenolics and their antioxidant properties, and a bioinformatic approach was used to estimate the main potential bio-targets in human beings. Furthermore, a statistical approach using Principal Component Analysis (PCA) was carried out for a multivariate characterization of these products. Catechins, flavonols, and phenolic acids were the predominant and most discriminating classes in different products. The TPC values obtained in this study ranged from 366.86 ± 71.30 mg GAE/100 g DW (apple, MD) to 1077.13 ± 35.47 mg GAE/100 g DW (blueberry, MID) and 1102.25 ± 219.71 mg GAE/100 g DW (kaki, KD). The FRAP values ranged from 49.28 ± 2.88 mmol Fe²⁺/kg DW (apple, MD) to 80.43 ± 0.02 mmol Fe²⁺/kg DW (blueberry, MID) and 79.05 ± 0.21 mmol Fe²⁺/kg DW (kaki, KD). The proposed approach may be an effective tool for quality control and valorization of these products. This study showed that the utilization of crop remains can potentially lead to the development of new functional foods, providing additional economic benefits for farmers. Finally, the use of freeze-drying may potentially be a sustainable and beneficial solution for growers who may directly utilize this technology to produce dried products from the crop remains of their fruit productions. Full article

Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized collision sensors across various scenes. Recent progress in neuroscience has revealed the energetic advantages of dendritic arrangements presynaptic to the LGMDs, which receive contrast polarity-specific signals on separate dendritic fields. Specifically, feed-forward inhibitory inputs arise from parallel ON/OFF pathways interacting with excitation. However, none of the previous research has investigated the evolution of a computational LGMD model with feed-forward inhibition (FFI) separated by opposite polarity. This study fills this vacancy by presenting an optimized neuronal model where FFI is divided into ON/OFF channels, each with distinct synaptic connections. To align with the energy efficiency of biological systems, we introduce an activation function associated with neural computation of FFI and interactions between local excitation and lateral inhibition within ON/OFF channels, ignoring non-active signal processing. This approach significantly improves the time efficiency of the LGMD model, focusing only on substantial luminance changes in image streams. The proposed neuronal model not only accelerates visual processing in relatively stationary scenes but also maintains robust selectivity to ON/OFF-contrast looming stimuli. Additionally, it can suppress translational motion to a moderate extent. Comparative testing with state-of-the-art based on ON/OFF channels was conducted systematically using a range of visual stimuli, including indoor structured and complex outdoor scenes. The results demonstrated significant time savings in silico while retaining original collision selectivity. Furthermore, the optimized model was implemented in the embedded vision system of a micro-mobile robot, achieving the highest success ratio of collision avoidance at 97.51% while nearly halving the processing time compared with previous models. This highlights a robust and parsimonious collision-sensing mode that effectively addresses real-world challenges. Full article

Agri-food by-products, obtained as waste from the food industry, negatively impact the global economy and the environment. In order to valorize waste materials from fruit juices and tomato sauces as upcycled materials rich in health-promoting compounds, they were characterized in terms of polyphenolic and protein content. The results obtained were compared with those collected for their final products. The recovery of polyphenols was performed via ultrasound-assisted extraction (UAE). A high-performance liquid chromatography–diode array detector (HPLC-DAD) method was developed and validated to depict the quali-quantitative polyphenolic profile of both the by-products and the final products. The antioxidant capacity of the resulting extracts was characterized by UV-Vis spectrophotometric assays in terms of total phenolic content (TPC) and total antioxidant status (TAS). Moreover, the protein content was assessed with the Kjeldahl method too. The results highlighted a significant quantity of polyphenols remaining in peach, apricot, and apple by-products, which were able to exert an antioxidant activity (in the range of 4.95 ± 5.69 × 10⁻¹ to 7.06 ± 7.96 × 10⁻¹ mmol Trolox 100 g⁻¹ of dry weight (DW) sample). Conversely, the tomato by-products were highly rich in proteins (11.0 ± 2.00 to 14.4 ± 2.60 g of proteins 100 g⁻¹ DW). The results proved that all by-products may potentially be sustainable ingredients with nutritional and functional value in a circular bio-economy prospect. Full article

This paper presents a low-power, energy-efficient, 12-bit incremental zoom analog-to-digital converter (ADC) for multi-channel bio-signal acquisitions. The ADC consists of a 7-stage ring voltage-controlled oscillator (VCO)-based incremental ΔΣ modulator (I-ΔΣM) and an 8-bit successive approximation register (SAR) ADC. The proposed VCO-based I-ΔΣM can provide fast phase-alignment of the ring-VCO to reduce the interval settling time; thereby, the I-ΔΣM can accommodate time-division-multiplexed input signals without phase leakage between consecutive measurements. The SAR ADC also adopts splitting unit capacitors that can support V_CM-free tri-level switching and prevent invalid states from the phase frequency detector with minimal logic gates and switches. The proposed ADC has been fabricated in a standard 180 nm standard 1P6M CMOS process, exhibiting a 67-dB peak signal-to-noise ratio, a 74-dB dynamic range, and a Walden figure of merit of 19.12 fJ/c-s, while consuming a power of 3.51 μW with a sampling rate of 100 kS/s. Full article

In the current study, chromatographic and in silico techniques were applied to investigate the biotransformation of ethyl 5-(4-bromophenyl)-1-(2-(2-(2-hydroxybenzylidene) hydrazinyl)-2-oxoethyl)-2-methyl-1H-pyrrole-3-carboxylate (11b) in hepatocytic media. The initial chromatographic procedure was based on the employment of the conventional octadecyl stationary phase method for estimation of the chemical stability. Subsequently, a novel and rapid chromatographic approach based on a phenyl–hexyl column was developed, aiming to separate the possible metabolites. Both methods were performed on a Dionex 3000 ThermoScientific (ACM 2, Sofia, Bulgaria) device equipped with a diode array detector set up at 272 and 279 nm for analytes detection. An acetonitrile: phosphate buffer of pH 3.5: methanol (17:30:53 v/v/v) was eluted isocratically as a mobile phase with a 1 mL/min flow rate. A preliminary purification from the biological media was achieved by protein precipitation with methanol. A validation procedure was carried out, where the method was found to correspond to all ICH (Q2) and M10 set criteria. Additionally, an in silico-based approach with the online server BioTransformer 3.0 was applied in an attempt to predict the possible metabolites of the title compound 11b. It was hypothesized that four CYP450 isoforms (1A2, 2C9, 3A4, and 2C8) were involved in the phase I metabolism, resulting in the formation of 12 metabolites. Moreover, docking studies were conducted to evaluate the formation of stable complexes between 11b and the aforementioned isoforms. The obtained data indicated three metabolites as the most probable products, two of which (M9_11b and M10_11b) were synthesized by a classical approach for verification. Finally, liquid chromatography with a mass detector was implemented for comprehensive and summarized analysis, and the obtained results revealed that the metabolism of the 11b proceeds possibly with the formation of glucuronide and glycine conjugate of M11_11b. Full article

Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust’s visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model’s performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments. Full article

This paper presents the development of a miniaturized sensor device for selective detection of pathogens, specifically Influenza A Influenza virus, as an enveloped virus is relatively vulnerable to damaging environmental impacts. In consideration of environmental factors such as humidity and temperature, this particular pathogen proves to be an ideal choice for our study. It falls into the category of pathogens that pose greater challenges due to their susceptibility. An impedance biosensor was integrated into an existing platform and effectively separated and detected high concentrations of airborne pathogens. Bio-functionalized hydrogel-based detectors were utilized to analyze virus-containing particles. The sensor device demonstrated high sensitivity and specificity when exposed to varying concentrations of Influenza A virus ranging from 0.5 to 50 $\mathsf{\mu }$g/mL. The sensitivity of the device for a 0.5 $\mathsf{\mu }$g/mL analyte concentration was measured to be 695 $\mathsf{\Omega }·$ mL/$\mathsf{\mu }$g. Integration of this pathogen detector into a compact-design air quality monitoring device could foster the advancement of personal exposure monitoring applications. The proposed sensor device offers a promising approach for real-time pathogen detection in complex environmental settings. Full article

Biological macromolecules, the fundamental building blocks of life, exhibit dynamic structures in their natural environment. Traditional structure determination techniques often oversimplify these multifarious conformational spectra by capturing only ensemble- and time-averaged molecular structures. Addressing this gap, in this work, we extend the application of the single-object scattering sampling (SOSS) method to diverse biological molecules, including RNAs and proteins. Our approach, referred to as “Bio-SOSS”, leverages ultrashort X-ray pulses to capture instantaneous structures. In Bio-SOSS, we employ two gold nanoparticles (AuNPs) as labels, which provide strong contrast in the X-ray scattering signal, to ensure precise distance determinations between labeled sites. We generated hypothetical Bio-SOSS images for RNAs, proteins, and an RNA–protein complex, each labeled with two AuNPs at specified positions. Subsequently, to validate the accuracy of Bio-SOSS, we extracted distances between these nanoparticle labels from the images and compared them with the actual values used to generate the Bio-SOSS images. Specifically, for a representative RNA (1KXK), the standard deviation in distance discrepancies between molecular dynamics snapshots and Bio-SOSS retrievals was found to be optimally around 0.2 Å, typically within 1 Å under practical experimental conditions at state-of-the-art X-ray free-electron laser facilities. Furthermore, we conducted an in-depth analysis of how various experimental factors, such as AuNP size, X-ray properties, and detector geometry, influence the accuracy of Bio-SOSS. This comprehensive investigation highlights the practicality and potential of Bio-SOSS in accurately capturing the diverse conformation spectrum of biological macromolecules, paving the way for deeper insights into their dynamic natures. Full article