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The widespread distribution of herbicides in the environment poses a significant risk to human health and wildlife. Surface-enhanced Raman scattering (SERS) has emerged as a powerful technique for detecting and analyzing herbicides. However, developing a low-cost, highly sensitive, reproducible, stable, and Raman-active nanostructured substrate for herbicide detection remains a particular challenge. In this research, a nanohybrid substrate consisting of gold nanoparticles@carbon nanodots (AuNPs@CNDs) was synthesized by reducing HAuCl₄ in the presence of CNDs at 100 °C. The optical, chemical, and physical properties of CNDs, AuNPs, and the hybrid AuNPs@CND substrates were thoroughly investigated using various techniques including UV-vis spectrometry, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and CytoViva darkfield and hyperspectral imaging. The SERS effect of the substrates was evaluated using rhodamine 6G (Rh6G), a Raman-active probe, and two groups of herbicides containing mesotrione or S-metolachlor. The results demonstrated a significant signal amplification in the SERS spectra of Rh6G and herbicide molecule detection using the AuNPs@CND substrate compared to bare CNDs and AuNPs alone. This suggests that the nanohybrid AuNPs@CND SERS substrate holds promise for the detection of herbicides and other organic compounds in environmental applications.

Considering the importance of accurate information of full-length (FL) transcripts in functional analysis, researchers prefer to develop new sequencing methods based on third-generation sequencing (TGS) rather than short-read sequencing. Several FL circRNA sequencing strategies have been developed. However, the current methods are inapplicable to low-biomass samples, since a large amount of total RNAs are acquired for circRNA enrichment before library preparation. In this work, we developed an effective method to detect FL circRNAs from a nanogram level (1–100 ng) of total RNAs based on a nanopore platform. Additionally, prior to the library preparation process, we added a series of 24 nt barcodes for each sample to reduce the cost and operating time. Using this method, we profiled circRNA expression in the striatum, hippocampus and cerebral cortex of a Parkinson's disease (PD) mouse model. Over 6% of reads were effective for FL circRNA identification in most datasets. Notably, a reduction in the RNA initial input resulted in a lower correlation between replicates and the detection efficiency for longer circRNA, but the lowest input (1 ng) was able to detect numerous FL circRNAs. Next, we systematically identified over 263 934 circRNAs in PD and healthy mice using the lower-input FL sequencing method, some of which came from 50.52% of PD-associated genes. Moreover, significant changes were observed in the circRNA expression pattern at an isoform level, and high-confidence protein translation evidence was predicted. Overall, we developed an effective method to characterize FL circRNAs from low-input samples and provide a comprehensive insight into the biological function of circRNAs in PD at an isoform level.

Plant health, which affects the nutritional quality and safety of derivative food products, is influenced by symbiotic interactions with microorganisms. These interactions influence the local molecular profile at the tissue level. Therefore, studying the distribution of molecules within plants, microbes, and plant-based food is crucial to assess plant health, ensure the safety and quality of the agricultural products that become part of our food supply, and plan agricultural management practices. Within this framework, the molecular distribution within plant-based samples can be visualized with mass spectrometry imaging (MSI). This review describes key MSI methodologies, highlighting the role they play in unraveling the localization of metabolites, lipids, proteins, pigments, and elemental components across plants, microbes, and food products. Furthermore, investigations that involve multimodal molecular imaging approaches combining MSI with other imaging techniques are described. The advantages and limitations of the different MSI techniques that influence their applicability in diverse agro-food studies are described to enable informed choices for tailored analyses. For example, some MSI technologies involve meticulous sample preparation while others compromise spatial resolution to gain throughput. Key parameters such as sensitivity, ionization bias and fragmentation, reference database and compound class specificity are described and discussed in this review. With the ongoing refinements in instrumentation, data analysis, and integration of complementary techniques, MSI deepens our insight into the molecular biology of the agricultural ecosystem. This in turn empowers the quest for sustainable and productive agricultural practices.

Modern criminal investigations heavily rely on trace bodily fluid evidence as a rich source of DNA. DNA profiling of such evidence can result in the identification of an individual if a matching DNA profile is available. Alternatively, phenotypic profiling based on the analysis of body fluid traces can significantly narrow down the pool of suspects in a criminal investigation. Urine stain is a frequently encountered specimen at the scene of crime. Raman spectroscopy offers great potential as a universal confirmatory method for the identification of all main body fluids, including urine. In this proof-of-concept study, Raman spectroscopy combined with advanced statistics was used for race differentiation based on the analysis of urine stains. Specifically, a Random Forest (RF) model was built, which allowed for differentiating Caucasian (CA) and African American (AA) descent donors with 90% accuracy based on Raman spectra of dried urine samples. Raman spectra were collected from samples of 28 donors varying in age and sex. This novel technology offers great potential as a universal forensic tool for phenotypic profiling of a potential suspect immediately at the scene of a crime, providing invaluable information for a criminal investigation.

DNA conformations, which encompass the three-dimensional structures of the DNA strand, play a crucial role in genome regulation. During DNA translocation in a nanopore, various conformations occur due to interactions among force fields, the fluidic environment, and polymer features. The most common conformation is folding, where DNA moves through the nanopore in a two-strand or multi-strand manner, influencing the current signature. Factors such as hydrodynamic drag, ionic environments, and DNA length significantly affect these conformations. Notably, conical nanopores, with their asymmetrical geometry, impose unique constraints on DNA translocation. Our findings reveal that during forward translocation, from the narrow (cis) end to the wide (trans) end, DNA experiences less resistance, resulting in shorter translocation times and higher blockade currents. Conversely, backward translocation, from the wide (trans) end to the narrow (cis) end, leads to longer translocation times and more complex conformations due to increased hydrodynamic drag and geometric constraints. This study employs molecular ping-pong methods to confine DNA, further highlighting the intricate dynamics of DNA folding within nanopores. These insights enhance the understanding of DNA behavior in confined environments, contributing to advancements in nanopore-based sensing and sequencing technologies, with implications for genome regulation and biomedical applications.

Cationic surfactants are widely used as corrosion inhibitors for industrial tubings and pipelines. They protect the surface of steel pipes through a film-forming mechanism, providing both anodic and cathodic inhibition. To improve the efficiency of the corrosion protection, it is essential to understand the interactions between the surfactants and metal surfaces. To achieve this, surface enhanced Raman spectroscopy (SERS) can serve as a powerful tool due to its surface sensitivity and potential to detect trace amounts of analytes in complex media. In this contribution, we have investigated the behaviour of in situ prepared AgNPs in the presence of benzalkonium chloride as a model corrosion inhibitor using SERS coupled to visible spectroscopy and combined with light scattering methods. By combining these experimental methods, we were able to correlate the aggregation of silver particles with the concentration of added surfactant in the resulting mixture. Using this insight, we also established a SERS method for the detection of benzalkonium chloride traces in water. For this, we utilised the quenching of the SERS response of methylene blue by competitive adsorption of methylene blue and the surfactant on SERS active AgNPs. We believe that our approach can serve a variety of applications to improve the industrial water treatment. For example, the modelling of the interaction of different surfactants with SERS can be used for process intensification, and ultimately, to move towards the digital twinning of corrosion processes for more efficient corrosion inhibition. Furthermore, the ability to adapt our sensing protocol for on-line corrosion inhibitor monitoring allows a fast response to process changes, hence, enabling resource-efficient, continuous process control.

Typing of bacterial subspecies is urgently needed for the diagnosis and efficient treatment during disease outbreaks. Physicochemical spectroscopy can provide a rapid analysis but its identification accuracy is still far from satisfactory. Herein, a novel feature-extractor-based fusion-assisted machine learning strategy has been developed for high accuracy and rapid strain differentiation using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and Raman spectroscopy. Based on this fusion approach, rapid and reliable identification and analysis can be performed within 24 hours. Validation on a panel of important pathogens comprising Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii showed that the identification accuracies of k-nearest neighbors (KNNs), support vector machines (SVMs) and artificial neural networks (ANNs) were 100%. In particular, when benchmarked against a MALDI-TOF MS spectral dataset, the new approach improved the identification accuracy of Acinetobacter baumannii from 87.67% to 100%. This work demonstrates the effectiveness of combining MALDI-TOF MS and Raman spectroscopy fusion data in pathogenic bacterial subtyping.

Glyphosate (GLY) is widely applied in agriculture and horticulture as a herbicide. The development of genetically modified plants has caused abuse of GLY, with excessive residues potentially causing harm to human health. Consequently, a novel method needs to be built to detect GLY in soybeans and corn. Computer simulation was used to design an excellent hapten which was used to produce an anti-GLY monoclonal antibody (mAb) with outstanding sensitivity and affinity, and its 50%-inhibitory concentration (IC₅₀) was 128.59 ng mL⁻¹. Afterwards, an immunochromatographic assay strip was developed based on the mAb. In soybeans and corn, the visual detection limits were 1 mg kg⁻¹ and 0.2 mg kg⁻¹, while the cut-off values were 50 mg kg⁻¹ and 5 mg kg⁻¹, respectively. The reliability of the strips was proved by the existing methods. Thus, a rapid method to detect GLY residues on-site in soybeans and corn was established.

Detecting multiple targets in complex cellular and biological environments yields more reliable results than single-label assays. Here, we introduced an electrochemical biosensor equipped with computing functions, acting as a smart automaton to enable computing-based detection. By defining the logic combinations of miR-21 and miR-122 as detection patterns, we proposed the corresponding AND and OR detection automata. In both logic gate modes, miR-21 and miR-122 could be replaced with single-stranded F_O or F_A, modified with Fc, binding to the S chain on the electrode surface. This process led to a significant decrease in the square wave voltammetry (SWV) of Fc on the same sensing platform, as numerous ferrocene (Fc)-tagged DNA fragments escaped from the electrode surface. Experimental results indicated that both automata efficiently and sensitively detected the presence of the two targets. This strategy highlighted how a small amount of target could generate a large current signal decrease in the logic automata, significantly reducing the detection limit for monitoring low-abundance targets. Moreover, the short-stranded DNA components of the detection automata exhibited a simple composition and easy programmability of probe sequences, offering an innovative detection mode. This simplified the complex process of detection, data collection, computation, and evaluation. The direct detection result (“0” or “1”) was exported according to the embedded computation code. This approach could be expanded into a detection system for identifying other sets of biomarkers, enhancing its potential for clinical applications.

Correction for ‘Polydopamine-capped AgNPs as a novel matrix overcoming the ion suppression of phosphatidylcholine for MALDI MS comprehensive imaging of glycerophospholipids and sphingolipids in impact-induced injured brain’ by Chao Han et al., Analyst, 2019, 144, 6304–6312, https://doi.org/10.1039/C9AN01361J.