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C-reactive protein (CRP), a sensitive inflammatory biomarker, is predominantly synthesised by the liver. Its concentration rises rapidly in response to infection, inflammation, or tissue injury. Persistent or excessive elevation of CRP is associated with serious health risks, including cardiovascular disease and acute or chronic kidney injury. Owing to its high sensitivity to inflammation, infection, and cardiovascular risk, CRP is widely regarded as a key biomarker for assessing these conditions. Conventional detection methods, such as immunoturbidimetry (ITM), enzyme-linked immunosorbent assay (ELISA), and lateral flow immunoassay (LFIA), are often constrained by high cost, complex operation, and cumbersome processes. Recent advances integrating biosensing, microfluidics, and nanomaterials have led to the development of multi-dimensional, synergistic detection platforms, substantially enhancing CRP detection performance. This review comprehensively summarises recent progress in CRP detection technologies, focusing on three major approaches: immunoassays, electrochemical detection, and optical sensing. The core principles and performance optimisation strategies of these techniques are elucidated, with particular attention given to emerging sweat-based detection methods. An in-depth analysis of their sensor design, analytical challenges, and point-of-care application potential is provided. Furthermore, the review systematically outlines future prospects for point-of-care testing (POCT) of CRP using non-invasive biofluids like sweat. This review aims to systematically summarise detection methods for CRP, thereby offering diagnostic researchers a reference guide that integrates cutting-edge insights with practical value.

Coxsackievirus A6 (CVA6) has emerged as a major cause of hand–foot–mouth disease (HFMD), yet no standardized detection method for it is currently available. Developing a simple, sensitive, and specific CVA6 test is crucial for HFMD control and safeguarding the health of at-risk children. Herein, a photonic crystal (PC) sensing array based on a tandem CRISPR/Cas13a system has been proposed for highly specific and ultra-sensitive analysis of CVA6 RNA, without the need for reverse transcription and amplification procedures. In this strategy, two crRNAs targeting CVA 6 RNA were designed and screened, and the fluorescence signal of the tandem CRISPR/Cas13a system was found to be up to 4.2 times higher than that of the non-tandem CRISPR system. The PC array with periodic nanostructures was prepared through self-deposition and further enhanced the fluorescent signal output from the tandem CRISPR system, owing to the match of the emission wavelength of the fluorescent dyes and the photonic band gap (PBG) of the PC. Benefitting from the synergistic effect of the tandem CRISPR system and PC array, as well as the high trans-cleavage activity of Cas13a protein, this engineered sensing array enables ultra-sensitive detection with a limit of detection (LOD) as low as 24.9 fM for CVA6. Meanwhile, this sensing strategy also achieved high-throughput and rapid analysis with a detection frequency of about 96 samples every 3.4 minutes. Therefore, the proposed strategy offers a simple workflow without reverse transcription or amplification, along with high sensitivity and high throughput, demonstrating strong potential for applications in biometrics and clinical diagnostics.

A conformation-dependent bifunctional sensor is developed by capitalizing on a vibration-induced emission chromophore for viscosity sensing and an acid-triggered oxazolidine switch for pH sensing. This design yields a large Stokes shift (>150 nm), enabling dual-channel and crosstalk-free monitoring of viscosity and pH in oxidative stress models.

Traditional tunable diode laser absorption spectroscopy (TDLAS) techniques primarily rely on single-point or sparse-point measurements, making it difficult to fully capture the two-dimensional spatial structure of combustion fields. Additionally, existing combustion diagnostic methods suffer from dynamic response delays. This paper proposes a spatio-temporal predictive diagnostic method integrating two-dimensional array TDLAS direct imaging with deep learning. Leveraging the absorption characteristics of O₂ molecules, a 64-pixel array detector replaces conventional single-point sensors to achieve parallel direct imaging of the two-dimensional temperature field within the flame, effectively capturing the spatial distribution information of the combustion zone. A prediction model centered on the SwinLSTM deep network is constructed. Its sliding window attention mechanism effectively learns the spatial global dependencies of the temperature field, while the Long Short-Term Memory (LSTM) unit captures its temporal dynamic characteristics, enabling forward prediction from historical sequences to future time points. The experiment employed a “point-surface integration” strategy combining standard Type B thermocouples with an infrared thermal imager for multidimensional validation. Results demonstrated that the maximum relative error in single-point quantitative inversion was on was merely 3.75%, whilst accurately reflecting the flame's macroscopic topological structure. In prediction tasks, the SwinLSTM-D model achieves an SSIM value of 0.961 and a PSNR value of 38.625 dB, significantly outperforming traditional methods such as ConvLSTM and PredRNN. Research indicates that the method proposed in this paper can accurately reconstruct the two-dimensional temperature field of flames. Furthermore, in short-term prediction tasks, the model can precisely capture the spatiotemporal evolution patterns of flame temperature fields and perform accurate predictions. This provides new research approaches and methodologies for current combustion measurement and diagnostic technologies.

Functional nucleic acids (FNAs) have emerged as a cutting-edge tool in environmental pollutant detection, attributed to their exceptional stability, robust specificity, and remarkable capacity for signal transduction and amplification. This review elaborates comprehensively on four pivotal categories of FNAs—aptamers, RNA-cleaving DNAzymes, G-quadruplex/hemin DNAzymes, and gRNAs—alongside their applications in monitoring a spectrum of pollutants. These encompass organic contaminants (e.g., pesticides and bisphenols), heavy metals (such as Pb²⁺ and Hg²⁺), biotoxins, and pathogenic microorganisms. It also underscores the integration of FNAs with sophisticated technologies like nanomaterials and CRISPR/Cas systems to augment detection sensitivity and efficacy. Despite prevailing challenges, including susceptibility to environmental variables (pH and temperature) and intricate synthesis procedures, FNAs hold immense potential for advancing environmental monitoring and pollution control.