The measurement of δ(2)H and δ(18)O in water samples by laser absorption spectroscopy (LAS) are adopted increasingly in hydrologic and environmental studies. Although LAS instrumentation is easy to use, its incorporation into laboratory operations is not as easy, owing to extensive offline data manipulation required for outlier detection, derivation and application of algorithms to correct for between-sample memory, correcting for linear and nonlinear instrumental drift, VSMOW-SLAP scale normalization, and in maintaining long-term QA/QC audits. Here we propose a series of standardized water-isotope LAS performance tests and routine sample analysis templates, recommended procedural guidelines, and new data processing software (LIMS for Lasers) that altogether enables new and current LAS users to achieve and sustain long-term δ(2)H and δ(18)O accuracy and precision for these important isotopic assays.
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http://dx.doi.org/10.1021/es403354n | DOI Listing |
United European Gastroenterol J
January 2025
"Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation.
View Article and Find Full Text PDFLiver Int
February 2025
Department of Epidemiology and Data Science, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
Background And Aims: The performance of non-invasive liver tests (NITs) is known to vary across settings and subgroups. We systematically evaluated whether the performance of three NITs in detecting advanced fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) varies with age, sex, body mass index (BMI), type 2 diabetes mellitus (T2DM) status or liver enzymes.
Methods: Data from 586 adult LITMUS Metacohort participants with histologically characterised MASLD were included.
Sci Rep
January 2025
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Department of Prosthetic Dentistry, LMU University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
Objective: Evaluation of the accuracy of direct digitization of maxillary scans depending on the scanning strategy.
Materials And Methods: A maxillary model with a metal bar as a reference structure fixed between the second molars was digitized using the CEREC Primescan AC scanner (N = 225 scans). Nine scanning strategies were selected (n = 25 scans per strategy), differing in scan area segmentation (F = full jaw, H = half jaw, S = sextant) and scan movement pattern (L = linear, Z = zig-zag, C = combined).
Int J Oral Sci
January 2025
School of Cyber Science and Engineering, Sichuan University, Chengdu, China.
The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis.
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