Talanta最新文献

Medical image processing has transformed the Complete Blood Count (CBC) analysis to enhance diagnostic accuracy and to detect diseases such as neurodegenerative diseases, infections, and anemia through non-invasive blood cell analysis. Nonetheless, the Multispectral Imaging (MSI) datasets have class imbalance, which severely impairs the existing deep learning models. To overcome this, we introduce a new Dual Path Sliding Window Attention (DP-SWA) model with a convolutional block to extract the initial feature, dual-path processing with local and global sliding window attention, and a fusion compression block. This architectural design is complemented with a hybrid SMOTE-Tomek Links data balancing strategy to eliminate the class imbalance. Our proposed model with 0.937 million parameters and 1.05 GFLOPs is able to achieve an inference time of 4.3 ms and a state-of-the-art accuracy of 99.02 % in the multi-wavelength MSI dataset, which is better than the best-performing baseline, YOLOv8-N (93.01 %), and other current models. DP-SWA is an effective method that addresses the issue of class imbalance and provides better accuracy with an impressive level of computational efficiency, which has great potential to increase the accuracy of clinical diagnosis.

Pogostemon cablin (Blanco) Benth (P.cablin), known for its unique aroma and rich chemical components, occupies an important position in the field of cosmetics, food and medicine. Identifying its origin is crucial for quality control and preventing adulteration. Traditional identification methods are time-consuming and labor-intensive, and usually require complex chemical analysis. In this study, a rapid and universal method was proposed to identify P.cablin from three major origins based on hyperspectral image (HSI) and deep learning, named DeepHSI. Furthermore, metabolomics and transcriptomics analyses were performed to validate the feasibility of HSI analysis for origins identification of P.cablin. HSI data collected under three experimental conditions (batches) were used for model training and transfer learning, which demonstrate the generality of DeepHSI. The simplified multi-origins identification model fusion mechanism ensures scalability for practical research applications and provides a paradigm for multi-classification research. These advantages provide a promising solution for rapid and nondestructive origin identification, quality control, and authenticity verification.

Nucleotides, carbohydrates, amino acids, and lipids have long been considered homochiral within mammalian systems. However, an increasing number of studies have reported a variety of chiral metabolites across various living organisms, some biologically active and others identified as potential disease biomarkers. Enantiomers of the same compound may have distinct biological activities, chemical reactivities, and metabolism, highlighting the increasing attention to molecular chirality in biomedical research. Like peptides, amino acids, and organic acids, lipids also possess chirality and are essential components of biological membranes, influencing both structure and functionality. Studies using simple model systems, like liposomes and vesicles, challenge the assumption that only homochiral membranes are stable, demonstrating comparable stability in racemic heterochiral membranes. Nevertheless, chirality within eukaryotic cells remains largely overlooked, resulting in limited understanding of its impact on lipid membrane organization, lipid-lipid and lipid-protein interactions, and the overall lipid metabolism. This gap primarily reflects the lack of robust experimental methods for chiral lipidomics profiling. This review provides a comprehensive overview of analytical techniques used for the separation and analysis of chiral lipids in complex biological samples, emphasizing advances in chromatographic and mass spectrometric techniques, and their application in disease biomarker discovery. We also discuss the structural and functional impact of chirality on phospholipid membranes and highlight future directions in chiral lipidomics research.

The advancement of film thickness metrology with integrated accuracy, repeatability, traceability and convenience is a critical foundation for innovating thin-film fabrication technologies and promoting industrial applications. In this study, leveraging the high-sensitivity elemental analysis capability of the pulsed glow discharge mass spectrometer (GDMS), the sputtering time of the film was determined by monitoring elemental composition variations. Simultaneously, white-light interferometry (WLI) provides superior spatial resolution for three-dimensional topographical reconstruction of GDMS ablation craters, enabling precise depth profiling of the sputtered regions. After optimizing sputtering conditions including discharge voltage, pulse duration, and gas flow rate, a correlation between the sputtering rate and sputtering time was established based on statistical data analysis, achieving standard-free quantitative measurement of film thickness. This method demonstrates broad applicability to conductor/semiconductor coatings and free-standing films ranging from nano-meter to micro-meter scales. Validation experiments on silicon (Si)-supported copper (Cu), aluminum (Al), and tungsten (W) films revealed thickness measurement consistency with scanning electron microscopy (SEM). The minimum detectable thickness change was determined to be 6 nm, 21 nm, 12 nm, and 9 nm for Si, Cu, Al, and W films, respectively. Notably, the thickness measurement for a 500 nm film exhibited a relative standard deviation (RSD) of less than 3 %. This research overcame the difficulties of uneven sputtering pits and different sputtering rates of different materials. The main sources of uncertainty in measurement include: time, depth, accuracy of substrate concentration measurement, start-up measurement time, blank of substrate element in the film, etc. This paper established a thin film thickness measurement method combining erosion and three-dimensional structure based on the principle of matching the exposed area with the substrate concentration. This novel thickness quantification method demonstrates potential as a traceable metrological technology for emerging material films.

Ayahuasca, a traditional Amazonian hallucinogenic plant brew once used in healing rituals, is now globally popular, raising safety concerns outside ceremonial contexts. Its compounds, alkaloids, are stimulating scientific interest for their potential antidepressant and anxiolytic effects. In this study, we employed a more sustainable analytical extraction method for the determination of N,N-dimethyltryptamine (DMT), harmine (HRN), harmaline (HRL), and tetrahydroharmine (THH) in human hair using LC-MS/MS. We incorporating dispersive liquid-liquid microextraction (DLLME) in line with the principles of Green Analytical Toxicology (GAT). The limit of quantification (LoQ) was 3 pg/mg for HRN and 8 pg/mg for DMT, HRL, and THH. The method was linear in the LoQ range up to 1000 pg/mg (r² ≥ 0.99). Intra- and inter-day precision and accuracy met the acceptance criteria at three quality control (QC) levels. Matrix effect (EM) showed both ionization enhancement and suppression, values ranged from 80.20 % (DMT, CQB) to 121.85 % (HRL, CQA). Recovery (RE) was low with recovery values ranging from 36.28 % to 57.91 %. Selectivity studies revealed no interference. Application to six authentic samples confirmed the viability of the method. Measured concentrations were DMT (21.5-204.4 pg/mg) and β-carbolines: THH (55.5 - >LOQ pg/mg); HRL (42.0-988.2 pg/mg) and HRN (163.0 - >LOQ pg/mg). Notably, β-carbolines were generally detected at higher concentrations than DMT. The proposed method uses smaller amounts of organic solvents compared to conventional hair extraction methods, representing a significant methodological advancement in the analysis of psychoactive alkaloids in biological matrices.