Sensors & diagnostics最新文献

CRISPR-Cas9 enables curative genome editing but requires precise control of target recognition, particularly when single-nucleotide polymorphisms (SNPs) influence specificity. Conventional biochemical and optical assays often rely on endpoint or ensemble-averaged measurements and therefore fail to resolve the real-time binding dynamics underlying off-target interactions. Here, we report a label-free, non-faradaic electrochemical impedance spectroscopy (nfEIS) platform that directly monitors spCas9-gRNA interactions on gold microelectrodes with single-base resolution at the sickle cell disease (SCD) locus. A guide RNA was designed to perfectly match the SCD mutation (A to T) while introducing a single PAM-proximal mismatch with the wild-type DNA (WD) sequence. Using 63-nucleotide synthetic DNA substrates representing SCD and WD targets, concentration-dependent binding assays were performed to extract equilibrium parameters. Hill-model analysis revealed higher affinity for the SCD target (k _D = 0.09 nM) relative to WD (k _D = 0.3 nM), confirming strong on-target binding and weakened interaction at the mismatch site. Magnesium dependence evaluation showed that 5 mM Mg²⁺ enhanced discrimination by stabilizing on-target complexes while destabilizing mismatched binding, whereas at 1 mM Mg²⁺ this selectivity was lost. Time-resolved kinetic measurements using 1 nM spCas9 and exponential fitting of the curve revealed rapid association (t _1/2 = 1.85 min) and dissociation rates (t _1/2 = 5.24 min) for SCD, consistent with efficient R-loop formation. In contrast, the WD target exhibited slower association (t _1/2 = 2.68 min) and recurring transient binding with delayed dissociation (t _1/2 = 34.38 min), corroborated by endpoint gel assays. Cas9 lacking gRNA showed only weak, unstable interactions. Overall, these results demonstrate that Cas9 specificity arises from both affinity differences and binding-residence dynamics. nfEIS thus provides a real-time, label-free platform for probing Cas9 fidelity, Mg²⁺-dependent activation, and gRNA design for therapeutic genome editing and diagnostics.

Hydrogen sulfide (H₂S) is both an important biological signaling molecule and a toxic environmental pollutant, making its precise detection essential for biomedical research and environmental monitoring. Among the various sensing platforms available, amino-1,8-naphthalimide (Nap)-based fluorescent probes have become powerful tools for real-time H₂S detection due to their high sensitivity, excellent selectivity, biocompatibility, and quick response. Nap fluorophores offer several inherent advantages—including strong and tunable emission, prominent intramolecular charge-transfer (ICT) characteristics, large Stokes shifts, and easy structural modification—making them especially attractive as scaffolds for developing activity-based probes. This review highlights recent developments in Nap-derived fluorescent sensors for H₂S detection, categorizing the probes based on their reactive sites and sensing mechanisms, such as thiolysis, reduction, and nucleophilic substitution. For each category, we explore structure–function relationships, photophysical properties, sensing performance, and practical applications in biological and environmental settings. Finally, we address current challenges and future directions in designing next-generation Nap-based probes with enhanced ratiometric responses, targeted subcellular localization, two-photon excitation capabilities, and improved suitability for in vivo imaging. Overall, this review offers a comprehensive perspective to guide the rational development of innovative fluorescent tools for accurate and efficient H₂S detection.

Correction for ‘Highly selective detection of ethanol in biological fluids and alcoholic drinks using indium ethylenediamine functionalized graphene’ by Ramin Boroujerdi et al., Sens. Diagn., 2022, 1, 566–578, https://doi.org/10.1039/D2SD00011C.

Correction for ‘High resolution voltammetric and field-effect transistor readout of carbon fiber microelectrode biosensors’ by Whirang Cho et al., Sens. Diagn., 2022, 1, 460–464, https://doi.org/10.1039/D2SD00023G.

Correction for ‘Turn-on fluorescent sensors for Cu-rich amyloid β peptide aggregates’ by Yiran Huang et al., Sens. Diagn., 2022, 1, 709–713, https://doi.org/10.1039/D2SD00028H.

Single-molecule (SM) analysis with surface-enhanced Raman scattering (SERS) usually requires sophisticated engineering on plasmonic nanostructures to provide extremely strong hot-spots to enhance the intrinsically weak signal. In this work, we report a cell-sized biosilica capsule based on the diatom Pinnularia sp. frustule decorated with high-density bimetallic nanostars consisting of gold and silver, capable of achieving reproducible single-molecule SERS analysis. Such a plasmonic nanostar-activated biological photonic crystal nanostructure synthesized by a simple self-assembly method enhances hot-spots universally, which is proved by finite difference time domain simulation. The cell-sized biosilica capsule also concentrates trace-levels of target molecules from ultra-small fluidic droplets through a drop-on-demand inkjet-printing technique. We experimentally demonstrated reproducible SM optofluidic-SERS sensing from 120 nL of 10⁻¹⁵ M rhodamine 6G solution containing only 72 molecules through statistical analysis of mapping data over the cell-sized biosilica capsule, achieving a 3× higher signal-to-noise ratio and 9× better SM detection possibility compared to that on the glass substrate.

Adenosine, a pivotal endogenous neuromodulator, plays a critical role in maintaining homeostasis related to sleep and emotion regulation. Mounting evidence indicates that dysregulated adenosine homeostasis is intricately involved in the pathological processes of brain disorders. Thus, the quantification of adenosine levels is crucial for evaluating disease states in the brain. Currently, numerous reviews have focused on adenosine detection technologies and their applications in tumor immunology and cardiovascular diseases. However, there have been few systematic reviews of adenosine monitoring in the central nervous system. Here, we first systematically summarize recent advances in adenosine detection technologies. Subsequently, we discuss the implications of adenosine detection in the regulation of central nervous system homeostasis. Finally, we highlight current challenges and future prospects, aiming to provide insights for the diagnosis, treatment, and prognosis of neurological disorders.

This work introduces a new electrochemical sensing approach, where the liquid sample containing nucleic acid targets can be blotted onto an electrode that is pre-functionalized with probe DNA. The post-hybridization signal and probe DNA signal (obtained by melting the hybrid) can be successively measured later, making the sensing scheme resilient to probe layer deterioration and circumventing the need to measure probe signal immediately before sample collection, ultimately mitigating the need for electrochemical sensing equipment at the sample collection site.

Unlike the polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP) lacks a consistent thermal cycle, making quantification particularly challenging. Previously, we demonstrated that LAMP can accurately diagnose Kaposi sarcoma (KS) from skin lesion biopsies at the point of care (receiver operating characteristic area under the curve (AUC) = 0.967). A common approach in LAMP analysis involves setting a minimum absorbance threshold and time cutoff for positivity, which can introduce bias. We present a less biased, automated signal processing approach involving the fitting of a signal curve to five, two-parameter algebraic function fits, and the training of an artificial intelligence (AI) model on those parameters and their variances. An extreme gradient boosting (XGB) model was trained and tested on a primary dataset consisting of 1317 LAMP curves (from 451 unique patient samples with replicates). Five-fold k-validation on the train/test set yielded an receiver operating curve (ROC) area under the curve (AUC) of 0.952 ± 0.029. Each of the five-fold models were then validated on a separate secondary dataset of 966 LAMP curves (from 414 unique patient samples with replicates) and achieved an AUC of 0.950 ± 0.005. While the traditional methodology (which did not implement k-validation or a test/train split) outperformed the AI model's train/test set performance, the AI model generalized better and achieved a higher accuracy on the validation set (0.950 ± 0.005 vs. 0.9347). It performed even better when the analysis was applied directly to the raw signal data without additional pre-processing steps such as artifact filtering. This suggests that the AI model is more generalizable to new data and is able to discriminate KS-present and KS-absent samples better than traditional methods.

The advancement of wearable electronics for personalized healthcare demands flexible, high-performance pressure sensors capable of detecting both subtle physiological signals and large mechanical motions. In this work, we present a highly sensitive piezoelectric pressure sensor based on a micro-textured PVDF–TrFE/BaTiO₃ composite nanofiber mat, fabricated via a facile electrospinning process. A commercial paper napkin was employed as a template collector to directly imprint a micro-structured architecture onto the nanofiber mat, eliminating the need for complex processes. The addition of BaTiO₃ (BTO) nanoparticles significantly enhanced the piezoelectric response. The optimized piezoelectric sensor demonstrated outstanding performance with a sensitivity of ∼197 mV kPa⁻¹, a fast response time of 3.5 ms, excellent frequency stability, and durability over 5000 loading cycles. Practical applicability was successfully verified through real-time monitoring of diverse physiological activities including arterial pulse, respiration, vocal vibrations, eye blinking, joint motion, and plantar pressure detection. These results underscore the potential of our sensor for use in wearable health monitoring, sports biomechanics, and human–machine interfaces, offering a scalable and cost-effective route to high-performance pressure sensing.