Rapid detection and classification of pathogenic microbes for food hygiene, healthcare, environmental contamination, and chemical and biological exposures remain a major challenge due to nonavailability of fast and accurate detection methods. The delay in clinical diagnosis of the most frequent bacterial infections, particularly urinary tract infections (UTIs), which affect about half of the population at least once in their lifetime, can be fatal if not detected and treated appropriately. In this work, we have fabricated aluminum (Al) foil integrated pegylated gold nanoparticles (AuNPs) as a potential surface-enhanced Raman scattering (SERS) substrate, which is used for the detection and classification of uropathogens, namely, , , and directly from the culture without any pretreatment. The substrate is first drop cast with bacterial pellets and then pegylated AuNPs, and the interaction of two on Al foil base gives identifiable characteristic Raman peaks with good reproducibility. With the use of chemometric methods such as principal component analysis (PCA), the Al foil-based SERS substrate offers a quick, effective detection and classification of three strains of UTI bacteria with the least bacterial concentration (10 cells mL) necessary for clinical diagnosis. In addition, this substrate was able to detect positive clinical samples by giving SERS fingerprint information directly from centrifuged urine samples within minutes. The stability of pegylated AuNPs provides for its application at the point of care with rapid and easy detection of uropathogens as well as the possibility of advancement in healthcare applications.
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http://dx.doi.org/10.1021/acsabm.4c00722 | DOI Listing |
Eur Radiol
January 2025
Department of Urological Surgical, JiangNan University Medical Center, Wuxi, China.
Objective: To conduct a meta-analysis assessing the diagnostic performance of the node reporting and data system (Node-RADS) for detecting lymph node (LN) invasion.
Method: We performed a systematic literature search of online scientific publication databases from inception up to July 31, 2024. We used the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) to assess the study quality, and heterogeneity was determined by the Q-test and measured with I statistics.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
<b>Background and Objective:</b> It is well documented that Whole Genome Sequencing (WGS) has recently used to explore new resistance patterns and track the dissemination of extensive and pan drug-resistant microbes in healthcare settings. This article explores the link between traumatic infections caused by road traffic accidents (RTAs) leading to coma and the development of chest infections caused by extensively drug-resistant (XDR) <i>Klebsiella pneumoniae</i> and <i>Pseudomonas aeruginosa</i>. <b>Materials and Methods:</b> The study was carried out from March to December 2022 which included a 45-year-old male patient admitted to the ICU of Al Ramadi Teaching Hospitals following a severe RTA that resulted in a TBI and subsequent coma.
View Article and Find Full Text PDFInt J Gynecol Cancer
January 2025
Department of Gynecology, European Institute of Oncology, IEO, IRCCS, Milan, Italy. Electronic address:
Objective: No biomarkers are available to predict treatment response in patients with endometrial cancers who undergo fertility-sparing treatment. Therefore, we aimed to evaluate the prognostic role of molecular classification.
Methods: Patients with endometrial cancer who underwent fertility-sparing treatment with progestins between 2005 and 2021 were retrospectively identified.
ACS Electrochem
January 2025
Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.
In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment.
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