Reliable, robust, large-scale molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for monitoring the ongoing coronavirus disease 2019 (COVID-19) pandemic. We have developed a scalable analytical approach to detect viral proteins based on peptide immuno-affinity enrichment combined with liquid chromatography-mass spectrometry (LC-MS). This is a multiplexed strategy, based on targeted proteomics analysis and read-out by LC-MS, capable of precisely quantifying and confirming the presence of SARS-CoV-2 in phosphate-buffered saline (PBS) swab media from combined throat/nasopharynx/saliva samples. The results reveal that the levels of SARS-CoV-2 measured by LC-MS correlate well with their correspondingreal-time polymerase chain reaction (RT-PCR) read-out (r = 0.79). The analytical workflow shows similar turnaround times as regular RT-PCR instrumentation with a quantitative read-out of viral proteins corresponding to cycle thresholds (Ct) equivalents ranging from 21 to 34. Using RT-PCR as a reference, we demonstrate that the LC-MS-based method has 100% negative percent agreement (estimated specificity) and 95% positive percent agreement (estimated sensitivity) when analyzing clinical samples collected from asymptomatic individuals with a Ct within the limit of detection of the mass spectrometer (Ct ≤ 30). These results suggest that a scalable analytical method based on LC-MS has a place in future pandemic preparedness centers to complement current virus detection technologies.
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http://dx.doi.org/10.7554/eLife.70843 | DOI Listing |
Anal Chem
December 2024
Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1, Canada.
Mass spectrometry (MS)-based metabolomics often rely on separation techniques when analyzing complex biological specimens to improve method resolution, metabolome coverage, quantitative performance, and/or unknown identification. However, low sample throughput and complicated data preprocessing procedures remain major barriers to affordable metabolomic studies that are scalable to large populations. Herein, we introduce PeakMeister as a new software tool in the R statistical environment to enable standardized processing of serum metabolomic data acquired by multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), a high-throughput separation platform (<4 min/sample) which takes advantage of a serial injection format of 13 samples within a single analytical run.
View Article and Find Full Text PDFMembranes (Basel)
November 2024
Institute of Environmental and Chemical Engineering, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10 Pardubice, Czech Republic.
The widespread use of antimicrobial drugs has contributed to the increasing trace levels of contaminants in the environment, posing an environmental problem and a challenge to modern-day medicine seeking advanced solutions. Nanofiltration is one such breakthrough solution for the selective removal of antibiotics from wastewater due to their high efficiency, scalability, and versatility. This study examines the separation of antibiotics (sulfamethoxazole (SMX), trimethoprim (TMP), and metformin (MET), respectively) using commercially available membranes with an emphasis on AFC membranes (AFC 30 and AFC 80).
View Article and Find Full Text PDFFront Nutr
December 2024
Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
Introduction: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.
Aim: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).
SLAS Technol
December 2024
School of science, engineering and environment, University of Salford, United Kingdom. Electronic address:
This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations.
View Article and Find Full Text PDFHealth Res Policy Syst
December 2024
Health Information System, World Health Organization (WHO) Regional Office for Africa, Brazzaville, Republic of Congo.
Background: There is widespread enthusiasm for scaling interventions to strengthen health systems. However, little is known about the scalability of such interventions in Africa. In this study, we seek to assess the scalability of interventions for improving the functionality of health systems in Africa, as a key to large-scale implementation strategy of interventions with potential for impact.
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