Background: Arterial blood gas analysis (ABGA) is a useful test that estimates the acid-base status of patients. However, numerically reported test results make rapid interpretation difficult. To overcome this problem, we have developed an algorithm that automatically interprets ABGA results, and assessed the validity of this algorithm for applications in clinical laboratory services.
Methods: The algorithm was developed based on well-established guidelines using three test results (pH, PaCO₂ and [HCO₃⁻]) as variables. Ninety-nine ABGA test results were analysed by the algorithm. The algorithm's interpretations and the interpretations of two representative web-based ABGA interpretation programs were compared with those of two experienced clinicians.
Results: The concordance rates between the interpretations of each of the two clinicians and the algorithm were 91.9% and 97.0%, respectively. The web-based programs could not issue definitive interpretations in 15.2% and 25.3% of cases, respectively, but the algorithm issued definitive interpretations in all cases. Of the 10 cases that invoked disagreement among interpretations by the algorithm and the two clinicians, half were interpreted as compensated acid-base disorders by the algorithm but were assessed as normal by at least one of the two clinicians. In no case did the algorithm indicate a normal condition that the clinicians assessed as an abnormal condition.
Conclusions: The interpretations of the algorithm showed a higher concordance rate with those of experienced clinicians than did two web-based programs. The algorithm sensitively detected acid-base disorders. The algorithm may be adopted by the clinical laboratory services to provide rapid and definitive interpretations of test results.
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http://dx.doi.org/10.1258/acb.2010.010180 | DOI Listing |
Brief Bioinform
November 2024
School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFBrief Bioinform
November 2024
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.
View Article and Find Full Text PDFAnal Methods
January 2025
Department of Food Science and Postharvest Technology, School of Applied Sciences and Technology, Cape Coast Technical University, Cape Coast, Ghana.
This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms.
View Article and Find Full Text PDFCirc Genom Precis Med
January 2025
Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Los Angeles. (W.F., N.D.W.).
Background: Lp(a; Lipoprotein[a]) is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).
Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies.
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