Lead ion (Pb), as a kind of heavy metal ion, is particularly harmful to human health and ecosystems due to its high toxicity and easy bioaccumulation. Fluorescent probes capable of selective and sensitive detection of Pb are crucial for enabling rapid and on-site monitoring and regulation, thereby mitigating its adverse health and environmental impacts. Additionally, the development of fluorescence probes for the detection of Pb in plant systems is rarely reported. Accordingly, the development of near-infrared (NIR) emission fluorescence probe for the detection of Pb in food, environment and in vivo is of great significance.

There have been some immunoassays reported for screening of the residues of estrogens in milk, but these methods can only determine one drug because of the limited recognition abilities of the used antibodies. Due to the broad specific recognition ability, receptor can be used as "special antibody" to develop pseudo immunoassay. However, there has been no article reporting the use of estrogen receptor for determination of estrogens in foods of animal origin so far. Furthermore, the recognition mechanism of estrogen receptor for estrogens have not been thoroughly studied.

Hydrogen sulfide (HS) is a physiological gaseous transmitter that plays a crucial role in maintaining the cellular redox state. Arthritis is usually accompanied by redness, swelling, pain, dysfunction and deformity of the joints, and in severe cases can lead to joint disability. Disorders of HS level are associated with the pathological process of arthritis. In this paper, a near-infrared fluorescent probe (TX-HS) was developed to detect the alterations in HS levels of arthritis. TX-HS has excellent response performance to HS such as near-infrared emission (725 nm), large Stokes shift (125 nm) and high fluorescence enhancement (72-fold). Owing to low cytotoxicity, the probe can be employed to observe the alterations of exogenous and endogenous HS level in HeLa and HepG2 cells. By making full use of near-infrared emission and good biocompatibility, the probe can be employed for exogenous HS imaging in mice, and is able to track the fluctuation of HS level during arthritis and its treatment. These make the probe have the potential to invent an efficient tool for the diagnosis of arthritic disease and its treatment.

As an emerging contaminant, microplastics(MPs) have been widely detected in the environment, the environmental risks it poses are also becoming more prominent. Among them, micron-sized MPs have relatively higher biotoxicity, necessitating a technique for processing and analysis to separate them by particle size and analyze their composition. The most commonly used MPs separation technology at present is the membrane filtration, which is easily to cause membrane blockage and set error accumulation. Centrifugal microfluidic technology received great attention as a high efficiency, low error and simple operation technology, has great potential for the separation of MPs.

Acylcarnitines (CARs) are metabolites of fatty acids that play crucial roles in various cellular energy metabolism pathways. The structural diversity of CAR species arises from several modifications localized on the fatty acyl chain and there is currently a lack of reports characterizing these detailed structures. High-performance liquid chromatography (HPLC)-electrospray mass spectrometry (ESI-MS) is the common tool for CARs analysis.

In this work, a novel biosensor based on aggregation-induced luminescence (AIE) of copper-silver alloy nanoclusters (CuAg NCs) has been developed for the detection of gastric cancer marker miRNA-142-3p. The alloy nanoclusters were synthesized by doping Ag ions into Cu NCs as nano-emitters. On the one hand, the doped Ag ions induced Ag-Cu metallophilic interactions, which promoted the relaxation of excited electrons through the radiative pathway. On the other hand, the doped Ag ions can reduce the band gap between the HOMO-LUMO orbitals of the nanoclusters, which decreased the energy consumed for electronic excitation. Therefore, the luminescent signal and stability of CuAg NCs were significantly enhanced. Furthermore, the modification of the permanently positively charged polysaccharide quaternary ammonium salt (Curdlan-g-PGTMAC) on CuAg NCs induced the aggregation-induced electrochemiluminescence (AIECL) effect. The introduction of Curdlan-g-PGTMAC also accelerated the reduction of the co-interaction reagent (SO), which significantly improved the ECL generation efficiency. The ECL response of the CuAg NC-based AIECL biosensor showed a good linear correlation with the concentration of miRNA-142-3p over a wide range from 1 fM to 100 nM with a detection limit as low as 8.8 fM. The ECL biosensor with high sensitivity and wide dynamic range was used for the clinical application of miRNA-142-3p in gastric cancer successfully.

On the one hand, for most of isothermal polymerization-based biosensing, the detection signals are uniformly originated from non-specific fluorescent staining that usually leads to high background or false positive, which limited their applications in molecular diagnostics. On the other hand, in virtue of characteristic advantages including but not limited to short preparation time (<5 min), large Stokes shift (>230 nm) and high template dependence, DNA-templated copper nanoparticles (CuNPs) enable low-cost and label-free signal transduction in low-background fluorescent sensing, which thus are ideal candidates for signal sources in promising molecular diagnostics.

The persistent global challenge of high incidence rates of foodborne bacterial pathogens continues to pose significant threats to both human health and public health security. Accurate determination of foodborne bacteria assumes paramount importance in effectively preventing and controlling related diseases. However, conventional bacterial detection methods often suffer limitations of cumbersome operation procedures, long time consumption, susceptibility to contamination or low sensitivity. The utilization of novel materials with direct self-reporting properties for developing ratiometric electrochemical biosensors offers a promising solution. Here, we present a ratiometric electrochemical aptasensor utilizing a magnetic 3D DNA walking machine and self-assembly of MOF for the sensitive detection of foodborne bacteria, with Staphylococcus aureus as the target model. A self-reporting MOF was employed as a signal probe, while [Fe(CN)] served as an inner reference probe. The introduction of target triggers the release of walker DNA from the walker/aptamer complexes, thereby initiating the DNA walking machine to produce numerous sticky sequences for further self-assembly of the linker probe-modified MOF on the magnetic beads surface. Quantification is accomplished by analyzing the ratio of the current signals. Attributed to the efficient magnetic separation, the self-reporting functionality of MOF, the cascade signal amplification strategy, and incorporation of a ratiometric signal output mode with self-calibration capability, this approach exhibits exceptional analytical performance and feasibility for testing complex samples. This study presents the first ratiometric aptasensor that integrates a DNA walking machine with a self-reporting MOF for the sensitive detection of bacteria. It offers a rapid, robust, and selective method for whole-cell detection without requiring complex sample preparation, demonstrating considerable potential in food safety monitoring and diagnosis of bacterial infections.

Titanium dioxide nanoparticles (TiO NPs) are commonly used in consumer products, leading to their release into the environment and raising concerns about their potential human health risks. Smaller TiO NPs penetrate cell membranes more easily and exhibit stronger bio-toxicity than larger particles. However, methods for analyzing small TiO NPs are limited.

The growing utilization of bismuth across diverse industries and pharmaceuticals has raised concerns about its environmental accumulation and the potential for neurotoxic and nephrotoxic effects in humans. Currently available techniques for its detection are often complex and costly, making CQDs an appealing alternative due to their low toxicity, cost-effectiveness, and ease of synthesis. Herein, a novel, environmentally sustainable one-pot microwave-assisted method for the synthesis of nitrogen-doped carbon quantum dots (N-CQDs) has been reported for the selective and sensitive detection of bismuth ions (Bi). The synthesized N-CQDs, with an impressive quantum yield of 47.5 %, exhibited remarkable stability and were applied as fluorescent sensors for detecting Bi ions, achieving highly selective detection through fluorescence quenching. The detection limit was calculated to be 0.365 μM within a linear concentration range of 0.95-61.5 μM, with the quenching mechanism identified as dynamic quenching via a photoinduced electron transfer (PET) process. The practical applicability of this sensing platform was demonstrated through the analysis of various real-world samples, including tap water, industrial wastewater and agricultural runoff, with recovery rates ranging from 98.7 % to 101.6 %. The applications of these N-CQDs as fluorescent ink and in anti-counterfeiting were demonstrated. Further, the N-CQDs were combined with an RGB analysis tool to detect Bi. This method-notable for its simplicity, cost-efficiency, and scalability-offers a sustainable and effective approach for detecting Bi ions in various environmental contexts, presenting a significant advancement in the field of metal ion sensing.

high complexity models, such as artificial neural networks (ANN), require large datasets for training to avoid overfitting and reproducibility issues. However, experimental datasets, especially those involving spectroscopic or other highly collinear data, often suffer from limited size due to practical constraints. Currently available data augmentation methods, either do not handle collinearity well, or require resource-intensive training. Thus, there is a pressing need for an efficient, scalable method for augmenting collinear datasets to enhance model performance in both regression and classification tasks.

Early diagnosis is vital for increasing the rates of curing diseases and patient survival in medicine. With the advancement of biotechnology, the types of bioomics data are increasing. The integration of multiomics data can provide more comprehensive biological information, thereby achieving more accurate diagnoses than single-omics data can. Nevertheless, current multiomics research is often limited to the intelligent diagnosis of a single disease or a few types of omics data and lacks a multiomics disease diagnosis model that can be widely applied to different diseases. Therefore, developing a model that can effectively utilize multiomics data and accurately diagnose diseases has become an important challenge in medical research.

Pharmaceutical residual such as antibiotics and disinfectants in aquaculture wastewater have significant potential risks for environment and human health. Surface enhanced Raman spectroscopy (SERS) has been widely used for the detection of pharmaceuticals due to its high sensitivity, low cost, and rapidity. However, it is remain a challenge for high-sensitivity SERS detection and accurate identification of complex pollutants.

Antibiotic residues in food and the environment pose significant risks to public health and safety, necessitating the development of rapid, portable, and efficient detection methods. Ofloxacin (OFL), a widely used antibiotic, is of particular concern due to its potential for contamination in milk and surface water. Current detection methods often require expensive instrumentation and complex procedures, limiting their applicability for on-site testing. This work addresses the critical need for a cost-effective, portable approach to reliably detect OFL residues. We developed a novel personal glucose meter (PGM)-based aptasensor utilizing a porous spherical cerium-based metal-organic framework (Ce-MOF) as a loading platform for glucose oxidase (GOx) and oligonucleotide sequences (Ce-MOF-GOx-cDNA). The hybrid probe, formed by conjugating Ce-MOF-GOx-cDNA with aptamer-modified magnetic beads, enabled specific recognition of OFL through nucleobase pairing. The sensor exhibited a detection range of 50 pg/mL to 500 ng/mL with a detection limit of 40 pg/mL under optimal conditions. The process showed excellent selectivity, stability, and reproducibility. Real-sample testing in spiked milk and surface water demonstrated recovery rates of 99.5% - 108%, with relative standard deviations of less than 4.7%. This study presents a portable and efficient strategy for detecting OFL residues using a PGM-based aptasensor. The method combines simplicity, rapid detection, and high sensitivity, offering significant potential for on-site applications in food and environmental safety monitoring.

Primer exchange reaction (PER) is a novel and simple nucleic acid-templated extension technique that has recently attracted much attention in the field of biosensing. However, current PER reactions have shown relatively slow rates and low amplification performances, resulting in long assay times and limited detection sensitivities. Here we report a nicking endonuclease-mediated PER reaction (named NEPER) that rapidly releases amplified DNA products by adding a nicking endonuclease to hydrolyze the hybridized double-stranded DNA (dsDNA), and consequently has a maximum speed that is thirty orders of magnitude greater than the maximum for conventional PER. We further combined a CRISPR/Cas12a signal readout technique and developed a cascade NEPER-CRISPR/Cas12a method that can detect miRNA-155 with a limit of detection (LOD) down to 3.1 fM. We also show that the NEPER-CRISPR/Cas12a can be used to detect targets in serum samples.

FTIR microspectroscopy is a popular non-destructive technique for chemical analysis and identification of microparticles, such as microplastics, pollen, spores, microplankton organisms, sediments and microfossils. Unfortunately, measured spectra of microparticles are usually distorted by Mie-type scattering interferents thus hindering the analysis of spectral data. To retrieve chemical absorbance spectra, two different approaches are regularly employed: analytical (application of scatter-correction preprocessing methods), and experimental (measurement in an embedding matrix). The comparative studies of preprocessing spectral strategies are needed to determine pros and cons of these approaches, and when they are most suitable for use.

Osteoarthritis (OA) is a major cause of disability worldwide, with symptoms like joint pain, limited functionality, and decreased quality of life, potentially leading to deformity and irreversible damage. Chemical changes in joint tissues precede imaging alterations, making early diagnosis challenging for conventional methods like X-rays. Although Raman imaging provides detailed chemical information, it is time-consuming. This paper aims to achieve rapid osteoarthritis diagnosis and grading using a self-developed Raman imaging system combined with deep learning denoising and acceleration algorithms. Our self-developed aberration-corrected line-scanning confocal Raman imaging device acquires a line of Raman spectra (hundreds of points) per scan using a galvanometer or displacement stage, achieving spatial and spectral resolutions of 2 μm and 0.2 nm, respectively. Deep learning algorithms enhance the imaging speed by over 4 times through effective spectrum denoising and signal-to-noise ratio (SNR) improvement. By leveraging the denoising capabilities of deep learning, we are able to acquire high-quality Raman spectral data with a reduced integration time, thereby accelerating the imaging process. Experiments on the tibial plateau of osteoarthritis patients compared three excitation wavelengths (532, 671, and 785 nm), with 671 nm chosen for optimal SNR and minimal fluorescence. Machine learning algorithms achieved a 98 % accuracy in distinguishing articular from calcified cartilage and a 97 % accuracy in differentiating osteoarthritis grades I to IV. Our fast Raman imaging system, combining an aberration-corrected line-scanning confocal Raman imager with deep learning denoising, offers improved imaging speed and enhanced spectral and spatial resolutions. It enables rapid, label-free detection of osteoarthritis severity and can identify early compositional changes before clinical imaging, allowing precise grading and tailored treatment, thus advancing orthopedic diagnostics and improving patient outcomes.

Mass spectra are common signatures used to discriminate between compounds. This often involves the use of a similarity score to classify and distinguish between spectra of different compounds. To separate spectra of structurally similar compounds, multiple authors have explored the use of statistical and probabilistic methods applied to replicate mass spectra. In this paper, we explore the use of various averaged versions of the Kolmogorov-Smirnov and t-test statistics to compare peak intensities for sets of replicate mass spectra.

Cement composition, including key oxides such as CaO, SiO, AlO, and FeO, plays a critical role in determining cement's strength and durability. Real-time monitoring of these components during cement production is essential for ensuring optimal raw material ratios. Spectroscopic techniques, such as Laser Induced Breakdown Spectroscopy (LIBS) and Near Infrared Spectroscopy (NIRS), offer significant potential for rapid and non-destructive cement analysis, but their individual limitations, such as matrix effects in LIBS and spectral overlap in NIRS, necessitate an integrated method to achieve accurate and stable results.

Lipid nanoparticles (LNPs) have become an important delivery system for nucleic acids, as applied in the first RNAi drug and two COVID-19 mRNA vaccines approved by the FDA. Despite advantages of their high cargo capacity, low immunogenicity allowing for redosing, scalability and low-cost manufacturing, challenges such as liver accumulation and difficulties in quality control persist for LNPs development. Conjugation of antibodies or antibody fragments onto LNPs holds promise in achieving precise targeting and higher stability of the targeting moieties on LNP surfaces. However, quality control of such multiple-component products poses additional challenges compared to LNP alone. LNP size and mass are critical quality attributes which play important roles in determining LNPs' nucleic acid cargo loading, antibody conjugation level, biodistribution, targeting capability, and overall efficacy.

In most water and biological samples, the sum of target or even non-target PFAS makes up only a small fraction of the extractable organofluorine (EOF). The methods used for EOF analysis in the methanol are combustion ion chromatography (CIC) and recently high-resolution graphite furnace molecular absorption spectrometry (HR GFMAS). Water samples show a bias towards higher concentrations measured with HR GFMAS than that of CIC. Whether the bias depends on the type of PFAS or on the sample matrix has not been known. Here we study the PFAS compound and the matrix effect using HR GFMAS and compare these with the CIC results.