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Ammonia (NH) and alkylamines are ubiquitous in the atmosphere and have been suggested to play important global roles through new particle formation and aerosol growth. In this work, we optimized an ion-chromatographic (IC) method to separate and quantify the ten most abundant atmospheric alkylamines with high selectivity and separation efficiency, using 4 μm packed columns and resin-based suppressors, alongside stabilizing amine standards. Modern resin suppressors operating on a gradient elution program affected the linear response of this IC technique. Calibration statistical analyses found a loss of analytes in these cation-exchange devices. Suppressor operational longevity was optimized by using a stepped current and an external water supply, which improved precision, accuracy, and LODs compared to other suppression modes. When this new method was applied to real samples, amines were found ubiquitously in size-resolved marine aerosol samples; monopropylamine, isomonopropylamine, and monobutylamine were detected and quantified, which have not been reported before. The molar ratio of the sum of aminium to ammonium ranged from 0.02 to 0.2, showcasing the application of the developed method towards studying the diversity and importance of alkylamines in coastal marine particle composition. The new analytical method also found NH present in a suite of new homes with a mean mixing ratio of 25 ± 15 ppbv; a common level reached between homes across the study during the first year of occupation which can then be transported outdoors.
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http://dx.doi.org/10.1039/d3ay01158e | DOI Listing |
Biosystems
September 2025
Laboratory of Future Nanomedicines and Theoretical Chronopharmaceutics, Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA.
Quantifying the dynamic interplay between p53 and Mdm2 is critical for uncovering their roles in cancer suppression and therapeutic targeting. Experimental studies have shown that p53-Mdm2 interactions exhibit oscillatory behavior in response to DNA damage. However, several mathematical models fail to sustain these oscillations or do not fit well with the experimental data, instead converging to constant steady-state values of p53 and Mdm2, which is unrealistic.
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July 2025
Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, South Yorkshire, UK.
Assessing MGMT promoter methylation is crucial for determining appropriate glioblastoma therapy. Previous studies have focused on intratumoral regions, overlooking the peritumoral area. This study aimed to develop a radiomic model using MRI-derived features from both regions.
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May 2025
Laboratory Medicine Center, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China.
Background: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Available serum biomarkers lack specificity, making it difficult to accurately identify early non-metastatic GC cases. Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis.
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May 2025
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Effective prediction of molecular features is crucial for the prognostic assessment of glioma patients. This study aims to develop a nomogram model using fractal analysis and Visually AcceSAble Rembrandt Images (VASARI) features to predict the molecular characteristics of WHO Grade 3-4 diffuse gliomas. Retrospective analysis of clinical data and VASARI features of patients with WHO grade 3-4 diffuse gliomas confirmed by pathology between January 2020 and December 2023 at our institution.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed.