Additional file 1: Supplementary figures 1 & 2. of FLIM-FRET analyzer: open source software for automation of lifetime-based FRET analysis

Figure S1. Comparison between FLIM-FRET analyzer and SPCImage software. Lifetime imaging measurement of the Cerulean Cyan Fluorescent Protein linked with Yellow Fluorescent Protein (CFP-YFP) chimera expressed in HEK-293 cells, using the microscope LEICA TCS SP2 combined with SPC-830 module (Becker & Hickl GmbH). A. Representative lifetime image was automatically segmented (red line) into four segmented cells which were independently analyzed by FLIM-FRET analyzer. B. Multi step process to analyze the fluorescence lifetime and distribution for each of the four cells using by SPCImage software. (The ROI in SPCImage software was manually selected.) C. The lifetime values calculated using FLIM-FRET analyzer shows high correlation (Pearson r > 0.99) with the values obtained with the SPCImage software. We additionally found the lifetime values of FLIM-FRET analyzer to be slightly longer than of the SPCImage, by a factor of 1.17±0.03. Figure S2. Validation of the FLIM-FRET analyzer using negative and positive FRET control probes expressed in cells. A. Lifetime images of CFP, CFP plus YFP, and CFP-YFP expressing HEK-293 cells processed with the FLIM-FRET analyzer. B. Comparative analysis of the fluorescence lifetime of single cells expressing CFP, CFP plus YFP, and CFP-YFP shows significant drop of the fluorescence lifetime for the C-Y chimera known to FRET. (PPTX 2044 kb

Additional file 2: of FLIM-FRET analyzer: open source software for automation of lifetime-based FRET analysis

User guide. (DOCX 4536 kb

Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems

<div><p>The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing composite scoring systems. DIC scores are simple and rapidly applicable. However, optimal fibrin-related markers and their cut-off values remain to be defined, requiring optimization for use. The aim of this study is to optimize the use of DIC-related parameters through machine learning (ML)-approach. Further, we evaluated whether this approach could provide a diagnostic value in DIC diagnosis. For this, 46 DIC-related parameters were investigated for both clinical findings and laboratory results. We retrospectively reviewed 656 DIC-suspected cases at an initial order for full DIC profile and labeled their evaluation results (Set 1; DIC, n = 228; non-DIC, n = 428). Several ML algorithms were tested, and an artificial neural network (ANN) model was established via independent training and testing using 32 selected parameters. This model was externally validated from a different hospital with 217 DIC-suspected cases (Set 2; DIC, n = 80; non-DIC, n = 137). The ANN model represented higher AUC values than the three scoring systems in both set 1 (ANN 0.981; ISTH 0.945; JMHW 0.943; and JAAM 0.928) and set 2 (AUC ANN 0.968; ISTH 0.946). Additionally, the relative importance of the 32 parameters was evaluated. Most parameters had contextual importance, however, their importance in ML-approach was different from the traditional scoring system. Our study demonstrates that ML could optimize the use of clinical parameters with robustness for DIC diagnosis. We believe that this approach could play a supportive role in physicians’ medical decision by integrated into electrical health record system. Further prospective validation is required to assess the clinical consequence of ML-approach and their clinical benefit.</p></div

Diagnostic scoring systems for disseminated intravascular coagulation (DIC) used in this study.

<p>Diagnostic scoring systems for disseminated intravascular coagulation (DIC) used in this study.</p

Patient demographics and clinical characteristics.

<p>Patient demographics and clinical characteristics.</p

Heat map presentation of the datasets used in this study.

<p>The x-axis denotes individual cases and the y-axis corresponds to the clinical variables. Each cell shows values of variables for each case. All cases are sorted horizontally by the labeled DIC status and predicted ANN model values. Rows 2–5 (ANN model, ISTH, JMHW, and JAAM criteria) show predictions of different DIC diagnostic classifiers based on the cut-off values (0.501 for ANN) or points (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195861#pone.0195861.t001" target="_blank">Table 1</a>).</p

Diagnostic performance of ANN model and scoring systems with receiver operating characteristic curve analysis and density plot.

<p>(A) Training (Set 1): ANN model shows the best performance among the four diagnostic classifiers. The area under curve (AUC) values: ANN (0.981), ISTH (0.945), JMHW (0.943), and JAAM (0.928). (B) External validation (Set 2): four variables were unavailable owing to the different hematologic analyzers, therefore the AUC value was compromised compared to the development set in the ANN model; ANN (0.968), ISTH (0.946). (C, D) Density plots of two represented diagnostic classifiers (ANN model, ISTH criteria) shows that the ANN model far obviously differentiates two groups (DIC and non-DIC). The cut-off value for the ANN model is determined at 0.501.</p

Schematic representation of patient enrollment and development of the artificial neural network (ANN) model.

<p>(A) Full DIC profile was defined as all laboratory results including complete blood count with differential counts, global coagulation tests (PT, PT % activity, international normalized ratio [INR], activated partial thromboplastin time [aPTT], and thrombin time), fibrinogen, D-dimer, FDP, and anti-thrombin III. The external validation hospital used different DIC profile: protein C was included instead of FDP, and RUO parameters were not provided. (B) ANN model for DIC diagnosis. In the training phase, the development set (n = 656) was randomly split into training and test sets in 80:20 ratio and hyper-parameters were determined for an optimal modeling. All layers have 32 nodes with an input-layer and two-hidden layers. The relative importance of input features was calculated based on the ‘Connection Weight’ approach, after the ANN model was established.</p