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Current drug detection methods, such as blood and urine analysis, are often invasive and raise ethical and privacy concerns. This study demonstrates that breathing through typical polypropylene-based meltblown cloth face masks is an efficient and user-friendly method for collecting drugs from exhaled breath for analysis. By using codeine, ephedrine, guaifenesin, and chlorpheniramine found in cough syrup as model compounds, we found that these face masks achieved a collection efficiency exceeding 92% for the tested drugs. The analysis yielded pharmacokinetic parameters─such as half-life (), time to maximum concentration (), and detection window─that were comparable to those obtained through parallel urine analysis. Given the increasing demand for noninvasive drug detection methods due to the rising abuse of substances like marijuana and fentanyl, this method is expected to have broad applications in forensic analysis and drug development.
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http://dx.doi.org/10.1021/acs.analchem.5c01129 | DOI Listing |
BMC Res Notes
September 2025
Center for Molecular Medicine Cologne, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
Objectives: Small cell lung cancer (SCLC) accounts for approximately 15% of lung tumors and is marked by aggressive growth and early metastatic spread. In this study, we used two SCLC mouse models with differing tumor mutation burdens (TMB). To investigate tumor composition, spatial architecture, and interactions with the surrounding microenvironment, we acquired multiplexed images of mouse lung tumors using imaging mass cytometry (IMC).
View Article and Find Full Text PDFSupport Care Cancer
September 2025
Department of Psycho-Oncology, HCG Cancer Center, Khasra No. 50, 51, Mouja Wanjri, Bande Nawaz Nagar, Near Automotive Square, Kalamna Ring Road, Nagpur, 440026, Maharashtra, India.
Purpose: Head and neck cancer (HNC) patients undergoing radiation therapy (RT) often experience mask anxiety due to the use of thermoplastic masks for immobilization. This study evaluated the effectiveness of a combined music therapy and relaxation-visualization intervention in reducing mask anxiety among HNC patients receiving RT at a tertiary care super-specialty oncology hospital in central India.
Methods: A total of 216 HNC patients scheduled for RT were randomized into either the intervention group (N = 108) or the control group (N = 108).
Am J Audiol
September 2025
Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC.
Purpose: This exploratory study examined if hearing handicap in older adults affected listening-related fatigue during health care interactions and explored whether different face mask types worn during the coronavirus disease 2019 (COVID-19) pandemic influenced this association.
Method: A cross-sectional observational study among community-dwelling adults aged 60 years and older receiving care at an academic health care system outpatient audiology or otolaryngology clinics was conducted. Eligible participants completed and returned a mail-in self-reported packet including the Hearing Handicap Inventory for the Elderly (Screener Version; HHIE-S) and the 10-item Vanderbilt Fatigue Scale for Adults (VFS-A-10).
IEEE Trans Pattern Anal Mach Intell
September 2025
Generalized visual grounding tasks, including Generalized Referring Expression Comprehension (GREC) and Segmentation (GRES), extend the classical visual grounding paradigm by accommodating multi-target and non-target scenarios. Specifically, GREC focuses on accurately identifying all referential objects at the coarse bounding box level, while GRES aims for achieve fine-grained pixel-level perception. However, existing approaches typically treat these tasks independently, overlooking the benefits of jointly training GREC and GRES to ensure consistent multi-granularity predictions and streamline the overall process.
View Article and Find Full Text PDFJ Ultrasound Med
September 2025
Department of Clinical Analysis, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil.
Objectives: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.
Methods: This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks.