| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Lentendu, G., et al.
(författare)
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Protist Biodiversity and Biogeography in Lakes From Four Brazilian River-Floodplain Systems
- 2019
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Ingår i: Journal of Eukaryotic Microbiology. - : Wiley. - 1066-5234 .- 1550-7408. ; 66:4, s. 592-599
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Tidskriftsartikel (refereegranskat)abstract
- The biodiversity and biogeography of protists inhabiting many ecosystems have been intensely studied using different sequencing approaches, but tropical ecosystems are relatively under-studied. Here, we sampled planktonic waters from 32 lakes associated with four different river-floodplains systems in Brazil, and sequenced the DNA using a metabarcoding approach with general eukaryotic primers. The lakes were dominated by the largely free-living Discoba (mostly the Euglenida), Ciliophora, and Ochrophyta. There was low community similarity between lakes even within the same river-floodplain. The protists inhabiting these floodplain systems comprise part of the large and relatively undiscovered diversity in the tropics.
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| 3. |
- Lima, Emilly M., et al.
(författare)
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Deep neural network-estimated electrocardiographic age as a mortality predictor
- 2021
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information. The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient's age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients). On a 15% hold-out split, patients with ECG-age more than 8 years greater than the chronological age have a higher mortality rate (hazard ratio (HR) 1.79, p < 0.001), whereas those with ECG-age more than 8 years smaller, have a lower mortality rate (HR 0.78, p < 0.001). Similar results are obtained in the external cohorts ELSA-Brasil (n = 14,236) and SaMi-Trop (n = 1,631). Moreover, even for apparent normal ECGs, the predicted ECG-age gap from the chronological age remains a statistically significant risk predictor. These results show that the AI-enabled analysis of the ECG can add prognostic information.
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| 4. |
- Meira, M N C, et al.
(författare)
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Atelectasis observed by computerized tomography after Caesarean section
- 2010
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Ingår i: British Journal of Anaesthesia. - : Elsevier BV. - 0007-0912 .- 1471-6771. ; 104:6, s. 746-750
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Atelectasis after either vaginal or Caesarean delivery has not been adequately quantified. This study addresses the hypothesis that atelectasis may be worse in women who undergo Caesarean section when compared with vaginal delivery under regional anaesthesia. METHODS: Twenty healthy non-smoking women submitted to a chest computed tomography (CT) 2 h after delivery in a University Hospital, who had experienced vaginal delivery (n=10) under combined spinal-epidural analgesia or a Caesarean section (n=10) under spinal anaesthesia, were evaluated. The percentage cross-sectional area of atelectasis in dependent lung regions were measured from the CT images obtained at cross-section of the xiphoid process and the top of the diaphragm. RESULTS: The percentage cross-sectional area of atelectasis was 3.95% in the vaginal delivery group and 14.1% in the Caesarean group (P<0.001, Mann-Whitney rank sum test). CONCLUSIONS: These results suggested that pulmonary atelectasis is greater after Caesarean section delivery under spinal anaesthesia than after vaginal delivery with combined spinal-epidural analgesia.
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| 8. |
- Ribeiro, Antônio H., et al.
(författare)
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Automatic diagnosis of the 12-lead ECG using a deep neural network
- 2020
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Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice. The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.
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