Resultados de investigación

A) Schematics of the tamoxifen (TAM) and cardiotoxin (CTX) injection regimens in TA muscles of two-month-old mice. B) Sections showing tdTomato (TOM, red) expression with WGA (green) and DAPI (blue) after single or multiple CTX injuries as shown in A. C) TOM signal in TA muscles of i-cKO and control mice after one round of injury (i in A). Arrows in the Control closeup mark single TOM+ cells at the periphery of repaired myofibers. n = 4 per condition., Peer reviewed

A) Sections of uninjured TA muscles showing TOM (red), WGA (green) and DAPI (blue) after single or multiple TAM inductions as described in Fig 5A. TOM+ single cells are indicated by arrowheads. Intensity of TOM signal in myofibers is scored as follows: (+++) strong, (++) moderate, and (+/–) minimal to no expression. B) Quantification of TOM+ single cells per mm2 of uninjured TA sections after single and multiple rounds of TAM induction. C) Quantification of percentage of TOM+ myofibers with various TOM intensities after single and multiple TAM inductions. The graph shows strong (dark brown), moderate (light brown), and minimal to no (blue) expression, with corresponding statistics in the same colors labeled on top. D) Immunostaining for PAX7 (green) in uninjured TA sections of control and i-cKO animals after one round of TAM induction. Arrows point to cells double-positive for PAX7 and TOM (red). E) Quantification of the percentage of TOM+ cells co-expressing PAX7 in control and i-cKO TA sections after one round of TAM. F) Immunostaining for MYOD (green) and Ki67 (magenta) in uninjured TA sections of control and i-cKO animals after one round of TAM. G) Quantification of the percentage of TOM+ cells expressing MYOD, Ki67, both or none. Each dot represents an animal in B, E, G. Mean±SD; two-way ANOVA with Šidák’s (B) or Tukey’s (C) multiple comparisons test and unpaired t test (E, G). ns: not significant, *P < 0.05, ***P < 0.001 and ****P < 0.0001., Peer reviewed

A) Western blot image using anti-V1744 antibody shows the expression level of cleaved (active) NOTCH1 intracellular domain (N1-ICD) in satellite cells isolated from muscles of control versus Poglut1-cKO mice with GAPDH as loading control. B) Poglut1 and Pax7 mRNA levels measured by qRT-PCR in C2C12 cells stably expressing an shRNA against Poglut1 or a non-target (NT) control shRNA. Unpaired t test. **P < 0.01, ****P < 0.0001. C) Cell-based signaling assays using the above-mentioned C2C12 cells cultured on plates coated with JAG1, DLL1 or IgG as control to induce signaling from endogenous (empty vector) or overexpressed Notch receptors. Relative luciferase expression levels normalized for transfection efficiency are shown. Three-way ANOVA with Tukey’s multiple comparisons test. Each circle represents a cell culture well. Each graph is representative of at least 2 independent experiments. Note that for each dataset, DLL1- and JAG1-induced signaling upon NOTCH receptor overexpression was significantly higher than that induced by the same ligand in the control (Empty vector) samples (##). Mean±SD, ns: not significant, *P < 0.05, **P < 0.01, and ****P < 0.0001. D) Schematic of the EGF repeats of mouse NOTCH3 decorated with glycan structures based on mass spectrometry data. For each EGF repeat, the most prevalent glycoform is shown. Mass spectrometry traces and glycoform quantifications are shown in S5 Fig. EGF repeats labeled with a question mark were not detected in mass spectrometry data., Peer reviewed

Anti-CRISPR (Acr) and Acr-LOV2 fusion injected in Drosophila for this study, Peer reviewed

a) classification results using three different feature engineering models (Random Forests, Gradient Boosted Trees, and Multi-Layer Perceptron) without topological attention. b) Integration of extracted features using topological attention. c) Comparison of different approaches and their evaluations, including Multi-Layer Perceptron, which outperformed all other examined classification algorithms. d) Model architecture upon evaluation metrics of the different approaches, including accuracy, precision, recall, and F1-score. e) Training curves for learning on the patient dataset using different training hyperparameters. f) an ECDF plot of the distance values for two groups of samples, Y1 survivors, and Y1 non-survivors. g) Evaluation curves re-calibrated based on the previously described distance, including a subplot showing the performance of the machine learning model in terms of precision and recall, as well as the corresponding F1-score, and a subplot showing the precision-recall curve displayed differently using a step function. h) The trade-off between precision and recall for a binary classification model., Peer reviewed

a) Trajectory plots of ECMO variables in survivor and non-survivor individuals.The left panel shows the trajectories for survivors, with a linear pattern indicating a simpler course of ECMO therapy. The right panel shows trajectories for non-survivors, with greater variability and complexity in the trajectory. b) Variability analysis between survivor and non-survivor individuals. Subplot 1 shows survival probability curves for survivor and non-survivor individuals, with the survivor curve surpassing the non-survivor curve at the point of their maxima. Subplot 2 shows a curve of standard deviation as a function of cluster labels, with three normal mixture distributions plotted on the curve. Subplot 3 shows the relationship between standard deviation, mean, and time in the dataset, with vertical lines at time points indicate important events or changes in the dataset., Peer reviewed

a) SHAP bar plot showing the impact of each feature on the model output, in terms of its contribution to the prediction.Red bars indicate features that increase the prediction, while blue bars indicate features that decrease it. The top 5 features with the largest impact on the predictions are presented, in order of importance. b) SHAP dependence plot illustrating one representative local feature interaction for ICU stay duration. c) SHAP partial dependence plot for ICU stay duration, showing a complex relationship with interacting features., Peer reviewed

a) Impact of different features on the model output.The red values indicate positive feature values that have a positive impact on the model output, while the remaining values are more centered around 0 and have a less significant impact. The top 5 features with the highest SHAP values are ARF, MV duration, SAPS2 Day 0, ICU_LOS, and AMR. b) Comparison of swarm plot, Shap bar plot, and Cohen’s distance effect bar plot to identify important features for the model output. The consistent important features are identified, and their effect on the model output is explored. c) Partial dependence plots for ARF, MV duration, SAPS2 Day 0, ICU_LOS, and ECMO duration. The shaded regions represent the level of confidence in the model’s predictions, with a wider shaded region indicating greater variability in the model’s predictions. d) Partial dependence plots for Age and the recently discovered important COPD variable, along with three other feature variables. The subplots suggest the presence of non-linear relationships between some features and the model output., Peer reviewed

a) ROC curves with confidence intervals showing the performance of the four models.The topo transformers model outperforms the classic models with an AUC of 0.87 (std = 0.06) for survivor, non-survivor classes, and mean ROC. b) table comparing the AUC and accuracy values for all four models. c) risk scores between the survivor and non-survivor groups., Peer reviewed

Alternative learning models (LMs) that were compared to our MLP, along with their corresponding performances and topological features., Peer reviewed