Classification of secondary explosives with a 1D convolutional neural network technique using terahertz time-domain spectroscopy in reflection geometry.

SERS signal unification strategy: Preparation of AuNPs@4-MBA and its quantitative detection in mixed lactic acid bacteria.

Overuse of carbapenems and other broad-spectrum antibiotics increases both costs and the risk of antimicrobial resistance (AMR). This study assessed whether optimizing antimicrobial susceptibility testing (AST) reports could improve clinical and economic outcomes for hospitalized patients with bloodstream infections (BSIs). From June 2022 to May 2023, a series of microbiology laboratory interventions were implemented at the Affiliated Hospital of North Sichuan Medical College, including the use of BacT/Alert Virtuo system (bioMérieux, France) for rapid loading, VITEK MS (bioMérieux, France) for rapid microbiology identification (ID), 24-hour laboratory service, and dual AST cards (N335 + XN04) replacing the single card (GN13). Previous simultaneously reported ID and AST results were successfully replaced by a separate reporting process. Data from BSI patients before (June 2021-May 2022) and after (June 2023-May 2024) interventions were retrospectively analyzed for ID and AST reporting time, antibiotic use, length of stay (LOS), and costs. A total of 256 BSI patients were analyzed (125 pre-intervention; 131 post-intervention). The post-intervention group had significantly shorter times to ID (median 41.26 vs. 74.35 h) and AST reports (56.02 vs. 74.35 h), with earlier opportunities for targeted antibiotic adjustments. Antibiotic modifications occurred ~ 36 h sooner within the first 72 h. Among all agents, carbapenems underwent the most MIC-based changes (20 additions and 10 discontinuations), reflecting improved carbapenem stewardship driven by expanded AST coverage. Post-intervention patients also showed shorter LOS, reduced antibiotic and testing costs, and a potential improvement in survival, especially in ICU cases. Optimized microbiology workflows, including dual AST cards and faster AST report, significantly accelerated reporting and facilitated timely, precise antimicrobial therapy, particularly carbapenem stewardship, while reducing clinical and economic burdens.

Rationale: Existing data processing applications for SP-ICP-ToF-MS, while valuable, have some limitations including restricted multi-sample analysis capabilities, vendor-specific constraints and the lack of efficient visualization approaches. Researchers need flexible, interactive visualization tools that can reveal compositional patterns and enable side-by-side sample comparisons for rapid trend identification and comprehensive data exploration across large datasets. Methodology: we developed IsotopeTrack, an open-source Python-based platform designed specifically for SP-ICP-ToF-MS data processing with cross-platform compatibility (Windows and MacOS). The application implements complete calibration methodologies including transport rate and sensitivity calibrations, three distinct peak detection algorithms (Currie method, Formula C, and compound Poisson log-normal) and element specific parameter optimization. Performance was validated using diverse engineered nanoparticles including TiO₂, CeO₂, metallic alloys (Ni-Fe-Co, Ni-Fe-Cr-Mn, Ni-Fe-Mo), and Au/Ag core-shell particles. The platform features an interactive results canvas with drag and drop capabilities for constructing customized analysis pipelines and it supports multiple file formats. Results: IsotopeTrack successfully analyzed multi-element alloy compositions with a high accuracy. For Ni-Fe-Co alloys, measured mass compositions of ⁶⁰Ni (29.3%), ⁵⁷Fe (55.1%), and ⁵⁹Co (17.7%) closely matched known values of 28% Ni, 55% Fe, and 17% Co. Ultra-uniform gold nanoparticles yielded mean diameters of 52.0±5.0 nm, 29.5±4.4 nm, and 21.1±4.3 nm for nominal 50, 30, and 20 nm particles. The platform generated comprehensive visualizations including elemental correlations, isotopic ratio distributions, ternary diagrams, and composition heatmaps. Processing time was reduced from hours to minutes through parallel processing. The comparison of environmental samples consisting of hundreds of thousands of particles was greatly facilitated. Discussion: IsotopeTrack addressed critical limitations in SP-ICP-ToF-MS data analysis by providing batch processing and interactive visualization tools. Element specific optimization ensured analytical rigor, while dramatically reducing processing time. This open-source framework represents a significant advance in single particle analysis, enabling efficient processing of large datasets essential for nanoparticle characterization in complex systems.

The dissemination of multidrug-resistant Enterobacterales, particularly those resistant to third-generation cephalosporins (3GC), poses a significant public health challenge. This study evaluated the performance of the Beta-Lactamase Rapid Electrochemical Detection (BL-RED) test (CORIS BioConcept, Belgium) alongside the β-LACTA test (Bio-Rad, France), another routinely used technique in our laboratory, for the rapid detection of 3GC-resistant Enterobacterales from 4 h subcultures of positive blood cultures. A total of 178 isolates were included: 55 clinical isolates with characterized resistance mechanism and 123 isolates from positive blood cultures prospectively collected between April and July 2023. Parallel testing with the BL-RED and β-LACTA tests was performed, and the results were compared to those of conventional antibiotic susceptibility testing by disk diffusion, following the 2024 EUCAST recommendations. The sensitivity, the specificity, the observed positive predictive value, and the observed negative predictive value for 3GC resistance detection in the prospective cohort were 74%, 100%, 100%, and 95%, respectively, for both the BL-RED and β-LACTA tests. Both tests demonstrated good performance in detecting 3GC-resistant Enterobacterales, particularly those expressing common Ambler class A beta-lactamases. However, they were less effective in detecting 3GC resistance due to the presence of other Ambler class families, notably overproduced chromosomal and plasmid-mediated Ambler class C beta-lactamases. The ease of use of these tests makes them suitable for routine microbiology laboratory testing. We propose a diagnostic algorithm combining the use of either the BL-RED and β-LACTA tests with that of MALDI-TOF MS in order to initiate targeted antibiotic therapy early.IMPORTANCEAntimicrobial resistance, especially among Enterobacterales resistant to third-generation cephalosporins (3GC), represents a major public health threat. Delayed detection of these resistant pathogens can lead to inappropriate empirical antibiotic therapy, with risk of treatment failures and further spread of resistance. This study underscores the value of rapid diagnostic tools, specifically the BL-RED and β-LACTA tests, for the rapid identification of 3GC-resistant Enterobacterales directly from positive blood cultures. By enabling quicker therapeutic decision-making, these assays provide a practical solution for microbiology laboratories to enhance patient management and contribute to global efforts in mitigating the impact of antibiotic-resistant infections.

The COVID-19 pandemic and recurring outbreaks of infectious diseases underscore the urgent demand for multiplex diagnostics capable of rapid and accurate pathogen identification. Although multiplex nucleic acid amplification tests (NAATs) are widely used for diagnosing diverse infectious diseases, their inherent amplification bias and long turnaround times highlight the demand for faster and reliable alternatives. Here, we present multicolor SATORI (mSATORI), an amplification-free single-molecule genetic test that leverages the complementary activities of CRISPR-Cas13a and Cas13b to achieve simultaneous detection of dual RNA targets. mSATORI identified Influenza A and SARS-CoV-2 RNAs within ∼10 min, with analytical limits of detection (LoD) of 86 aM and 52 aM, respectively. Validation using clinical specimens demonstrated robust diagnostic performance, achieving femtomolar limits of detection (550 aM for Influenza A and 640 aM for SARS-CoV-2), along with sensitivities exceeding 80% and specificities of 100%. Collectively, these results establish mSATORI as a platform for next-generation molecular diagnostics, with broad implications for clinical implementation, outbreak preparedness, and global infectious disease surveillance.

Evaluation of organism inclusion on five rapid blood pathogen identification systems and clinical significance of off-panel organisms.

Deep learning-based cross-device standardization of surface-enhanced Raman spectroscopy for enhanced bacterial recognition.

RPA-CRISPR-Cas12a/RPA-CRISPR-Cas12a-LFD: Two detection platforms for the rapid identification of five forensic body fluids.

Non-spectroscopic multiplex molecular diagnosis for simultaneous detection of virulence and antibiotic resistance genes from pathogenic bacteria.

Rapid identification and quantification of residual daratumumab in multiple myeloma patients by MALDI - TOF mass spectrometry.

Machine learning-assisted sensing platform for simultaneous and visual detection of tetracycline hydrochloride and ofloxacin.

The Baijiu market is complex and vast, rapid and effective identification of Baijiu quality will be beneficial to Baijiu production and market management. Here, tribological methods were employed to distinguish Baijiu from varying quality levels based on their physicochemical properties. The nano Zetasizer, atomic force microscope (AFM), rheometer, and contact angle meter were used to characterize the particle size, viscosity, and wettability of Baijiu with different qualities. Results reveal distinct physicochemical properties among different grade Baijiu, affecting their tribological responses. They also significantly depend on the friction pairs and applied loads. These findings demonstrate that tribology offers a rapid and reliable approach for Baijiu quality assessment. Additionally, differences in metal element composition, polarization curves, and electrochemical impedance spectra (EIS) across samples were explored. The comparison between two identification methods further proves the effectiveness and simplicity of the tribology test. This research helps to identify Baijiu quality quickly and simply.

Targeting emerging viruses with phage display-driven engineered antibodies: Bridging molecular design and clinical application.

Rapid, non-destructive and comprehensive quantitative analysis of honey by combined use of conventional and broadband-WET NMR spectra.

Division approach of high-risk heavy metal(loid)s areas in metallurgical industrial regions: A practical inversion approach.

A high-throughout PCR test strip method for the rapid identification of four placentas.

Deep-learning-driven spectral image analysis for intelligent monitoring of multiple pesticides and antibiotics.

Rapid identification of chemical components in Polygonatum kingianum Collett & Hemsl. based on UHPLC-Q-Exactive orbitrap MS coupled with molecular networking.

Abstract: Monkeypox, a re-emerging zoonotic viral disease, is often misdiagnosed due to its clinical resemblance to other skin conditions such as chickenpox and measles. Traditional diagnostic methods like clinical observation and PCR testing, while accurate, are resource-intensive, time- consuming, and inaccessible in under-resourced regions. To address these limitations, this paper proposes a Monkeypox Detection System—a lightweight, web-based, AI-powered diagnostic tool that leverages deep learning for rapid identification. The system integrates a fine-tuned VGG19 convolutional neural network to classify skin lesion images into monkeypox or non-monkeypox categories with high accuracy. Implemented using Flask, the platform supports real-time prediction, voice-command navigation, PDF report generation, and interactive data visualization. User authentication and prediction history enhance reliability, while visual analytics aid medical interpretation. The modular architecture ensures scalability for integration with healthcare systems and expansion to multiple skin diseases. Experimental results demonstrate that the system achieves reliable performance in real-world scenarios, offering a cost-effective and accessible solution for early diagnosis. This work contributes to AI-enabled preventive healthcare, particularly in outbreak-prone and resource-limited settings. Keywords Monkeypox, Deep Learning, Convolutional Neural Network (CNN), VGG19, Artificial Intelligence, Medical Imaging, Web Application, Decision Support System.