Metrological framework to support accurate, reliable, and reproducible nucleic acid measurements.
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ABSTRACT:
SUBMITTER: Milavec M
PROVIDER: S-EPMC8568362 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlthough pulmonary artery catheters (PACs) have been the reference standard for calculating cardiac output, echocardiographic estimation of cardiac output (CO) by cardiologists has shown high accuracy compared to PAC measurements. A few studies have assessed the accuracy of echocardiographic estimation of CO in critically ill patients by intensivists with basic training. The aim of this study was to evaluate the accuracy of CO measurements by intensivists with basic training using pulsed-wave Doppler ultrasound vs. PACs in critically ill patients.MethodsCritically ill patients who required hemodynamic monitoring with a PAC were eligible for the study. Three different intensivists with basic critical care echocardiography training obtained three measurements of CO on each patient. The maximum of three separate left-ventricular outflow tract diameter measurements and the mean of three LVOT velocity time integral measurements were used. The inter-observer reliability and correlation of CO measured by PACs vs. critical care echocardiography were assessed.ResultsA total of 20 patients were included. Data were analyzed comparing the measurements of CO by PAC vs. echocardiography. The inter-observer reliability for measuring CO by echocardiography was good based on a coefficient of intraclass correlation of 0.6 (95% CI 0.48-0.86, p < 0.001). Bias and limits of agreement between the two techniques were acceptable (0.64 ± 1.18 L/min, 95% limits of agreement of - 1.73 to 3.01 L/min). In patients with CO < 6.5 L/min, the agreement between CO measured by PAC vs. echocardiography improved (0.13 ± 0.89 L/min; 95% limits of agreement of - 1.64 to 2.22 L/min). The mean percentage of error between the two methods was 17%.ConclusionsCritical care echocardiography performed at the bedside by intensivists with basic critical care echocardiography training is an accurate and reproducible technique to measure cardiac output in critically ill patients.
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Project description:BACKGROUND:Ultrasonography is a fast and patient-friendly modality to assess cartilage thickness. However, inconsistent results regarding accuracy have been reported. Therefore, we asked what are (1) the accuracy, (2) reproducibility, and (3) reliability of ultrasonographic cartilage thickness measurement using contrast-enhanced micro-CT for validation? METHODS:A series of 50 cartilage-bone plugs were harvested from fresh bovine and porcine joints. Ultrasonic cartilage thickness was determined using an A-mode, 20-MHz hand-held ultrasonic probe with native (1580?m/s) and adjusted speed of sound (1696?m/s). All measurements were performed by two observers at two different occasions. Angle of insonation was controlled by tilting the device and recording minimal thickness. Retrieval of exact location for measurement was facilitated by aligning the circular design of both cartilage-bone plug and ultrasonic device. There was no soft tissue interference between cartilage surface and ultrasonic probe. Ground truth measurement was performed using micro-CT with iodine contrast agent and a voxel size of 16??m. The mean cartilage thickness was 1.383?±?0.402?mm (range, 0.588-2.460?mm). RESULTS:Mean accuracy was 0.074?±?0.061?mm (0.002-0.256?mm) for native and 0.093?±?0.098?mm (0.000-0.401?mm) for adjusted speed of sound. Bland-Altman analysis showed no systematic error. High correlation was found for native and adjusted speed of sound with contrast-enhanced micro-CT (both r?=?0.973; p?<?0.001). A perfect agreement for reproducibility (intraclass correlation coefficient [ICC] 0.992 and 0.994) and reliability (ICC 0.993, 95% confidence interval 0.990-0.995) was found. CONCLUSIONS:Ultrasonic cartilage thickness measurement could be shown to be highly accurate, reliable, and reproducible. The A-mode ultrasonic cartilage thickness measurement is a fast and patient-friendly modality which can detect early joint degeneration and facilitate decision making in joint preserving surgery.
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Project description:Protein-RNA and protein-DNA complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein-nucleic acid complexes without homology to known complexes is a largely unsolved problem. Here we extend the RoseTTAFold machine learning protein-structure-prediction approach to additionally predict nucleic acid and protein-nucleic acid complexes. We develop a single trained network, RoseTTAFoldNA, that rapidly produces three-dimensional structure models with confidence estimates for protein-DNA and protein-RNA complexes. Here we show that confident predictions have considerably higher accuracy than current state-of-the-art methods. RoseTTAFoldNA should be broadly useful for modeling the structure of naturally occurring protein-nucleic acid complexes, and for designing sequence-specific RNA and DNA-binding proteins.
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Project description:Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely accurate and, ideally, provide a measure of uncertainty for its predictions. Such accurate and reliable classifiers need enough labelled data for training, which requires time-consuming and costly manual annotation by pathologists. Thus, it is critical to minimise the amount of data needed to reach the desired accuracy by maximising the efficiency of training. We propose an accurate, reliable and active (ARA) image classification framework and introduce a new Bayesian Convolutional Neural Network (ARA-CNN) for classifying histopathological images of colorectal cancer. The model achieves exceptional classification accuracy, outperforming other models trained on the same dataset. The network outputs an uncertainty measurement for each tested image. We show that uncertainty measures can be used to detect mislabelled training samples and can be employed in an efficient active learning workflow. Using a variational dropout-based entropy measure of uncertainty in the workflow speeds up the learning process by roughly 45%. Finally, we utilise our model to segment whole-slide images of colorectal tissue and compute segmentation-based spatial statistics.
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Project description:Osteoarthritis, a disorder characterized by articular cartilage deterioration, varying degrees of inflammation, and chondrocyte apoptosis, is the most common chronic joint disease. To slow or reverse its progression, inflammation should be inhibited, and chondrocyte proliferation should be promoted. Tetrahedral framework nucleic acids can be internalized by chondrocytes (even inflammatory chondrocytes) and can enhance their proliferation and migration. Wogonin, a naturally occurring flavonoid, suppresses oxidative stress and inhibits inflammation. In this study, tetrahedral framework nucleic acids were successfully self-assembled and used to load wogonin. We confirmed the effective formation of tetrahedral framework nucleic acid/wogonin complexes by dynamic light scattering, zeta potential analysis, transmission electron microscopy, and fluorescence spectrophotometry. Tetrahedral framework nucleic acids, wogonin, and especially tetrahedral framework nucleic acid/wogonin complexes effectively alleviated inflammation in vitro and in vivo and prevented cartilage destruction. In addition, these materials remarkably downregulated the expression of inflammatory mediators and matrix metalloproteinases, upregulated chondrogenic markers, and promoted tissue inhibitor of metalloproteinase 1 and B-cell lymphoma 2 expression. In vivo, after treatment with tetrahedral framework nucleic acid/wogonin complexes, the bone mineral density in regenerated tissues was much higher than that found in the untreated groups. Histologically, the complexes enhanced new tissue regeneration, significantly suppressed chondrocyte apoptosis, and promoted chondrogenic marker expression. They also inhibited cell apoptosis, increased chondrogenic marker expression, and suppressed the expression of inflammatory mediators in osteoarthritis. Therefore, we believe that tetrahedral framework nucleic acid/wogonin complexes can be used as an injectable form of therapy for osteoarthritis.
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Project description:Water in its three ambient phases plays the central thermodynamic role in the terrestrial climate system. Clouds control Earth's radiation balance, atmospheric water vapour is the strongest "greenhouse" gas, and non-equilibrium relative humidity at the air-sea interface drives evaporation and latent heat export from the ocean. In this paper, we examine the climatologically relevant atmospheric relative humidity, noting fundamental deficiencies in the definition of this key observable. The metrological history of this quantity is reviewed, problems with its current definition and measurement practice are analysed, and options for future improvements are discussed in conjunction with the recent seawater standard TEOS-10. It is concluded that the International Bureau of Weights and Measures, (BIPM), in cooperation with the International Association for the Properties of Water and Steam, IAPWS, along with other international organisations and institutions, can make significant contributions by developing and recommending state-of-the-art solutions for this long standing metrological problem, such as are suggested here.
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Project description:In eukaryotic cells, miRNAs regulate a plethora of cellular functionalities ranging from cellular metabolisms, and development to the regulation of biological networks and pathways, both under homeostatic and pathological states like cancer.Despite their immense importance as key regulators of cellular processes, accurate and reliable estimation of miRNAs using Next Generation Sequencing is challenging, largely due to the limited availability of robust computational tools/methods/pipelines. Here, we introduce miRPipe, an end-to-end computational framework for the identification, characterization, and expression estimation of small RNAs, including the known and novel miRNAs and previously annotated pi-RNAs from small-RNA sequencing profiles. Our workflow detects unique novel miRNAs by incorporating the sequence information of seed and non-seed regions, concomitant with clustering analysis. This approach allows reliable and reproducible detection of unique novel miRNAs and functionally same miRNAs (paralogues). We validated the performance of miRPipe with the available state-of-the-art pipelines using both synthetic datasets generated using the newly developed miRSim tool and three cancer datasets (Chronic Lymphocytic Leukemia, Lung cancer, and breast cancer). In the experiment over the synthetic dataset, miRPipe is observed to outperform the existing state-of-the-art pipelines (accuracy: 95.23% and F 1-score: 94.17%). Analysis on all the three cancer datasets shows that miRPipe is able to extract more number of known dysregulated miRNAs or piRNAs from the datasets as compared to the existing pipelines.
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Project description: Not available
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