Analytical Chemistry最新文献

Fired cartridge cases are often found at crime scenes connected with a shooting, and their prompt analysis can be very useful for the police investigation. In addition to dactyloscopy (fingerprints) that tends to be more or less damaged on the cartridges and often are not adequate for individual identification, there are also scent traces on the fired cartridges that are not fully destroyed by the gun's being fired. In this pilot study, we compare the human scent remaining on cartridge cases after firing with scent samples from different volunteers to find out who loaded the gun before the gun was shot. In this experiment, a simulated crime scene was prepared, and one of our volunteers loaded the weapon. Analysis of the scent remaining on cartridge cases was carried out using two different methods, namely, olfactronics and olfactorics.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a crucial analytical tool in the field of oncology, particularly presenting significant challenges for the diagnosis and treatment of head and neck cancer. This Review provides an overview of the current status and prospects of SERS applications, highlighting their profound impact on molecular biology-level diagnosis, tissue-level identification, HNC therapeutic monitoring, and integration with emerging technologies. The application of SERS for single-molecule assays such as epidermal growth factor receptors and PD-1/PD-L1, gene expression analysis, and tumor microenvironment characterization is also explored. This Review showcases the innovative applications of SERS in liquid biopsies such as high-throughput lateral flow analysis for ctDNA quantification and salivary diagnostics, which can offer rapid and highly sensitive assays suitable for immediate detection. At the tissue level, SERS enables cancer cell visualization and intraoperative tumor margin identification, enhancing surgical precision and decision-making. The role of SERS in radiotherapy, chemotherapy, and targeted therapy is examined along with its use in real-time pharmacokinetic studies to monitor treatment response. Furthermore, this Review delves into the synergistic relationship between SERS and artificial intelligence, encompassing machine learning and deep learning algorithms, marking the dawn of a new era in precision oncology. The integration of SERS with genomics, metabolomics, transcriptomics, proteomics, and single-cell omics at the multiomics level will revolutionize our comprehension and management of HNC. This Review offers an overview of the transformative impacts of SERS and examines future directions as well as challenges in this dynamic research field.

There is a widely accepted material characterization paradigm in the success of synthesis of luminescent metal nanoclusters (NCs) in the aqueous phase: new emission, metal reduction, and ultrasmall particles (size < 3 nm). Herein, we falsified well-known fluorescent histidine (His)-directed Au NCs and a new model of metastable His-Au(I) complexes with emissive His oxidation products has been established. The redox reaction of His and Au(III) yields His oligomers with blue-green fluorescence and reducible Au(I) self-assemblies, which can form ultrasmall particles at electron bombardment. The resultant Au(I) complexes can be further reduced by d-penicillamine (DPA) via forming anisotropic Au nanoparticles with distinct local surface plasmon resonance absorption. The emerging absorption can quench the fluorescence of the His oxidation products through the inner filter effect pathway. A facile dual-model analytical approach is thus proposed to directly detect DPA fluorometrically and colorimetrically without interference from common biothiols, including cysteine and glutathione. Thus, with the help of a smartphone app, a highly sensitive and selective point-of-care testing for DPA direct detection can be realized. Our study warrants the importance of thinking twice about characterization results and supports corrective models for finding new reactions and possible applications.

mRNA, a critical biomarker for various diseases and a promising target for cancer therapy, is central to biological and medical research. However, the development of multiplexed approaches for in situ monitoring of mRNA in live cells are limited by their reliance on enzyme-based signal amplification, challenges with in situ signal diffusion, and the complexity of nucleic acid design. In this study, we introduce a nonenzymatic catalytic DNA assembly (NEDA) technique to address these limitations. NEDA facilitates the precise in situ imaging of intracellular mRNA by assembling three free hairpin DNA amplifiers into a low-mobility, three-dimensional DNA spherical structure. This approach also enables the simultaneous detection of four distinct targets via the combination of fluorescent signals, with a detection limit as low as 141.2 pM for target mRNA. To enhance the efficiency of nucleic acid design, we employed computer-aided design (CAD) to rapidly generate feasible sequences for highly multiplexed detection. By integrating various machine learning algorithms, we achieved impressive accuracy of nearly 96.66% in distinguishing multiple cell types and 87.80% in identifying the same cell type under different drug stimulation conditions. Notably, our platform can also identify drug stimuli with similar mechanisms of action, highlighting its potential in drug development. This multiplexed 3D assembly sensing strategy with CAD not only enhances the ability to image nucleic acid sequences in situ simultaneously but also provides a novel platform for efficient molecular diagnostics and personalized therapy.

Detecting and quantifying mycotoxins using LFIA are challenging due to the need for high sensitivity and accuracy. To address this, a dual-mode colorimetric-SERS LFIA was developed for detecting deoxynivalenol (DON). Rhodium nanocores provided strong plasmonic properties as the SERS substrate, while silver nanoparticles created electromagnetic "hotspots" to enhance signal sensitivity. Finite element modeling optimized the electromagnetic field intensity, and Prussian blue generated a distinct signal at 2156 cm^-1, effectively reducing background interference. This dual-mode LFIA achieved a detection limit of 4.21 pg/mL, 37 times lower than that of colloidal gold-based LFIA (0.156 ng/mL). Machine learning algorithms, including ANN and KNN, enabled precise classification and quantification of contamination, achieving 98.8% classification accuracy and an MSE of 0.57. These results underscore the platform's potential for analyzing harmful substances in complex matrices and demonstrate the important role of machine learning-enhanced nanosensors in advancing detection technologies.

Affinity selection-mass spectrometry (AS-MS) is a ligand discovery platform that relies upon mass spectrometry to identify molecules bound to a biomolecular target. When utilized with large peptide libraries (10⁸ members), AS-MS sample complexity can surpass the sequencing capacity of modern mass spectrometers, resulting in incomplete data, identification of few target-specific ligands, and/or incomplete sequencing. To address this challenge, we introduce pyBinder to perform quantitation on AS-MS data to process primary MS¹ data and develop two scores to rank the peptides from the integration of their peak area: target selectivity and concentration-dependent enrichment. We benchmark pyBinder utilizing AS-MS data developed against antihemagglutinin antibody 12ca5, revealing that peptides that contain a motif known for target-specific high-affinity binding are well characterized by these two scores. AS-MS data from a second protein target, WD Repeat Domain 5 (WDR5), is analyzed to confirm the two pyBinder scores reliably capture the target-specific motif-containing peptides. From the results delivered by pyBinder, a list of target-selective features is developed and fed back into subsequent MS experiments to facilitate expanded data generation and the targeted discovery of selective ligands. pyBinder analysis resulted in a 4-fold increase in motif-containing sequence identification for WDR5 (from 3 to 14 ligands discovered), showing the utility of the two scores. This work establishes an improved approach for AS-MS to enable discovery outcomes (i.e., more ligands identified), but also a way to compare AS-MS data across samples, protocols, and conditions broadly.

Microfluidic-based in vitro physiological barrier models are capable of simulating crucial environmental factors during barrier formation, including fluid shear and geometric-level cellular cocultures, thus offering enhanced physiological fidelity relative to conventional platforms. However, the sealed structure of microfluidic barrier chips faces challenges in characterizing and monitoring the barrier performance, especially in measuring transendothelial/epithelial electrical resistance (TEER). Here, we developed a microfluidic barrier chip that can be easily adapted to commercial TEER detectors. During the barrier construction phase, continuous perfusion culture was utilized to maintain a constant fluid shear stress; for barrier characterization, commercial resistance meters were employed to measure TEER directly. Using this chip, we successfully constructed an in vitro blood-brain barrier model with a TEER of approximately 220 Ω·cm², indicating high physiological relevance. This scenario-adaptive microfluidic chip demonstrates extensive potential for developing organ-on-a-chip models across various barrier systems, with significant implications for barrier characteristic monitoring and in situ cell sampling within the chip.