Schistosomiasis, caused by Schistosoma species, is notoriously difficult to accurately diagnose with conventional methods. In this study, we present an innovative biosensor that integrates CRISPR–Cas12a technology with nucleic acid aptamers for the highly sensitive detection of Schistosoma japonicum eggs. The biosensor leverages a Y-shaped DNA structure (Y-DNA) that incorporates an aptamer specific to S. japonicum eggs, along with an activator DNA and a segment for immobilization on magnetic nanomaterials. Upon target recognition, the Y-DNA releases the activator, which triggers the collateral cleavage activity of Cas12a, enabling the direct detection of eggs. This system demonstrates remarkable sensitivity, being capable of detecting individual eggs in infected rabbit serum and feces. Moreover, it effectively distinguishes the eggs of S. japonicum from those of other parasitic species. The simplicity, high sensitivity, and rapid detection of our biosensor offer significant potential for improving the diagnosis of schistosomiasis, providing a novel, reliable tool for early detection in clinical settings.

The present Special Issue, entitled “Electrochemical (Bio)Sensors as Promising Analytical Tools in the Analysis of Soils, Plants and Environmental Monitoring”, aims to provide an up-to-date overview of recent advances in electroanalytical techniques and electrochemical (bio)sensors, with particular emphasis on their applications in environmental systems, agriculture, and biological matrices [...]

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human-machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m-3. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10-100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa-1, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (Voc = 123 V, Isc = 0.52 μA, Qsc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs.

Short-chain fatty acids (SCFAs) are key biomarkers of gut microbiota activity; however, routine quantification in fecal samples relies largely on chromatography, which is instrument-intensive and throughput-limited chromatography techniques. Herein, we present a rapid machine-learning-assisted electroanalysis platform for SCFAs profiling that integrates a disposable three-electrode planar gold chip with voltammetric fingerprinting and artificial neural network (ANN)-based signal decoupling. To generate orthogonal chemical information and improve the discrimination of structurally similar species, a dual pretreatment strategy combining acid-catalyzed esterification and alkaline dissociation was employed prior to electrochemical analyses. Differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were employed to acquire high-dimensional fingerprints, from which current-, potential-, and area-based descriptors were extracted using a cross-information feature strategy. A hierarchical modeling framework improved total SCFAs prediction by incorporating ANN-predicted propionate and butyrate concentrations as auxiliary inputs. While linear calibration was achievable in standard mixtures, direct linear models performed poorly in real fecal matrices due to strong sample-dependent matrix interference. In contrast, the ANN captured nonlinear relationships among multifeature inputs and suppressed matrix effects. Validation against gas chromatography-mass spectrometry in an independent fecal test cohort (n = 30) demonstrated excellent agreement and low prediction errors, with mean absolute error/root mean square error values of 0.063/0.072 mM (propionic acid), 0.029/0.034 mM (butyric acid), and 0.135/0.202 mM (total SCFAs). The DPV/CV acquisition requires only minutes per sample, whereas pretreatment takes 1~3 h depending on the target route but can be performed in parallel for batch processing; thus, overall throughput is determined mainly by batch pretreatment rather than per-sample instrument time. This electrochemical-ANN workflow provides a portable, high-throughput alternative to chromatography for fecal SCFAs profiling in clinical screening and microbiome research.

Streptococcus suis is a serious zoonotic pathogen responsible for rapid progression and deadly infections in both humans and pigs. With an increasing number of reported cases and considering the limitations of standard routine identification, a simple, rapid, and cost-effective approach is needed. In this study, a label-free colorimetric assay based on gold nanoparticles (AuNPs) was applied with a specific aptamer, R8-su12. This assay offered simplified detection through observable color change, enabling visual analysis by the naked eye or assessment via UV-Vis spectrophotometry. Under the optimal assay conditions, the detection procedure was carried out within 45 min. The reaction of the aptasensor and other bacterial species, including Staphylococcus aureus, S. pneumoniae, S. pyogenes, Pseudomonas aeruginosa, Escherichia coli, Enterococcus faecium, and E. faecalis, was not present, indicating the specificity of this assay. Moreover, the aptasensor exhibited high sensitivity with a limit of detection (LOD) at 1 CFU of S. suis and had broad reactivity with S. suis serotypes 1, 1/2, 9, and 14, as well as with S. suis isolated from clinical specimens. Thus, this aptasensor demonstrates proof-of-concept feasibility including clinical sample testing before practical implementation. It holds promise as a practical tool for the early screening and outbreak management of S. suis in a variety of settings, such as clinical laboratories, food safety, and the environment.

Gesture recognition with electrical impedance tomography (EIT) is an enormous potential tool for human-machine interaction because of its low cost, low complexity and high temporal resolution. Although high-precision EIT-based gesture recognition has been achieved in ideal scenarios, ensuring its consistent performance under interference remains challenging. This article presents a novel method to alleviate the effect of confounding factors on EIT gesture recognition. An EIT armband was designed to mitigate the effect of contact impedance variation based on equivalent circuit analysis, and a spatial-temporal fusion network, named the Fold Atrous Spatial Pyramid Pooling-Gated Recurrent Unit (FASPP-GRU), was developed for robust gesture classification. The results showed that the proposed two-layer electrode maintained a stable contact impedance when its contact force with the skin was changed. Although confounding factors caused significant changes in baseline forearm impedance, FASPP-GRU achieved 80% accuracy under the effect of limb position changes and dynamic changes in muscle state over time, which outperforms conventional classifiers. With an 87 μs inference time, the proposed system shows enormous potential in real-time applications.

This study presents a modular and scalable wearable functional near-infrared spectroscopy (fNIRS) system for high-resolution cerebral hemodynamic signal acquisition. The system is based on compact optoelectronic modules and supports mixed measurements using short-separation and long-separation channels, offering good scalability and spatial adaptability. The integrated quartz light guide structure improves optical coupling efficiency between the probe and scalp. A series of in vivo experiments validated system performance. In a forearm arterial occlusion experiment, the system accurately captured concentration changes in oxygenated and deoxygenated hemoglobin during blood flow blockade and reperfusion, with large effect sizes (Cohen's d > 0.9). In a prefrontal cortex Valsalva experiment, the biphasic response characteristic of neurovascular coupling was successfully resolved. In a 2-back working memory task, the system identified a task-related frequency component (0.0227 Hz) and right-lateralized prefrontal cortex activation (p = 0.023). These results demonstrate that the system exhibits a good signal-to-noise ratio and temporal dynamic response, enabling high-resolution mapping of regional hemodynamic changes. This work provides an effective solution for the development of wearable, modular, and high-precision multi-channel fNIRS systems.

Background: Gene expression and cellular identity are regulated by epigenetics that occurs through chromatin modifications, RNA changes, chromatin accessibility, and three-dimensional genome organization. Although DNA methylation has been the focus of most epigenetics studies in the past, other non-methyl epigenetic processes, including histone post-translational modifications (PTMs), epitranscriptomic marks, and chromatin remodeling, are dynamic, reversible, and context-dependent, and thus are difficult to accurately interrogate using endpoint sequencing-based assays, especially in heterogeneous tissues, developing systems, and therapeutic response environments. Scope and Approach: The present review discusses epigenetic modifications other than DNA methylation regarding sensor-based technologies that can measure live, dynamic, and spatially resolved measurements. Epigenetic sensors include any genetically encoded sensors (GECs) based on resonance energy transfer, CRISPR/dCas-derived sensors, or aptamer-based sensors, and hybrid biochemical/imaging sensors that can be used in live or semi-live settings. It lays emphasis on the technologies, which have been developed recently, that allow real-time kinetic measurements, working in three-dimensional and organoid models, and being applied to disease-relevant perturbations. On these platforms, performance properties such as specificity, sensitivity, spatial and temporal resolution, ability to perform dynamic versus locus-specific interrogation, and perturbed endogenous chromatin states are compared. Key Conclusions and Outlook: Together, these sensing strategies are complementary to the traditional methods of measuring epigenomics in that they show epigenetic dynamics unobservable with static measurements. We list the important technical issues, including specificity, quantitation, multiplexing, and chromatin perturbation, and report the barriers and solutions in development and design. Lastly, we provide a conceptual map of how live epigenetic sensing and multi-omics and translational models can be integrated, and how the two methodologies can be used to develop functional epigenetics and guide disease modeling and drug development.

Sensitive quantitative detection of breast cancer gene synthetic sequences is crucial for related biosensing research. To address the limitations of traditional sensors for detecting ultra-low concentrations, this study developed a novel fiber-optic biosensor by combining nanomaterial sensitization with nanoparticle signal amplification strategies. A fiber optic sensor based on single-mode fiber-thin-core fiber-multimode fiber-single-mode fiber structure was fabricated and functionalized with black phosphorus (BP) nano-interface. The Au@cDNA complex was prepared by covalently immobilizing sulfhydryl-modified complementary DNA (cDNA) on the surface of gold nanoparticles (AuNPs). The complex specifically hybridized with the probe DNA (pDNA) immobilized on the surface of the sensor. The experimental results show that this sensor has a sensitivity of 0.793 nm/lgM and a detection limit of 20.27 fM in the concentration range of 100 fM to 100 nM. Specifically, the BP-functionalized sensor exhibits superior dynamic range, higher sensitivity, and lower detection limits for detecting Au@cDNA. The synergistic effect of interfacial sensitization by BP and signal amplification by AuNPs significantly enhances detection performance, providing a promising platform for ultra-sensitive biosensing applications.

Frontal alpha asymmetry (FAA), a pattern of brain activity that reflects the difference in alpha wave power between the left and right frontal areas of the brain, is considered a stable marker for an individual's tendency to experience either more approach-related or withdrawal-related emotions. On the other hand, electrodermal activity (EDA) measures arousal by tracking changes in skin sweat, which are controlled by the sympathetic nervous system. This study explores the interrelation between EDA features, obtained from time and frequency domains, with FAA by means of the mutual information. Multiple cognitive tasks such as EAT, ship search, PVT and N-Back were analyzed in 10 participants in intervals of two hours over 24 h (12 trials), in which they had to face sleep deprivation conditions. The most informative EDA features about FAA, were used to identify the two main clusters associated to high and low FAA values through the hierarchical agglomerative clustering approach. Once data is labeled, a supervised classifier based on support vector machines (SVMs) is used to identify positive and negative emotional states by using a rigorous one-trial out cross-validation scheme. Results show consistent performance within tasks and trials, achieving accuracy values over 80% on average, giving an important insight about the use of EDA signal as an alternative to the more complex FAA measurement for tracking positive or negative emotional states.