Background and purpose: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion:The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.
Introducing wrinkling or rough features into substrates is of great practical significance to construct various functional surfaces. Due to the sensitivity of assembled units towards environmental stimuli, the internals of layer-by-layer films can be readily adjusted to generate various micro- and nanostructures. We previously described a self-roughening polyelectrolyte multilayer (PEM) to facilitate the introduction of surface microstructures. In the present work, the growth process of PEI/PAA multilayer films was investigated and the mean size of the surface microstructures was found to increase linearly with the film thickness. The spontaneous formation of these surface features can be attributed to swelling-induced film deformation during the assembling process, which is similar to the surface wrinkling of hydrogels undergoing a volume phase transition. When exposed to saturated humidity, the internal stress as well as the surface microstructures can be diminished spontaneously, leading to a flat surface over the substrates. Given the effect of the underlying film thickness on the characteristic wavelength of the surface wrinkles, multiscale surface microstructures can be readily realized by means of spatially presetting the distribution of the film thickness.
BackgroundThe aim of this study was to develop and validate reliable nomograms to predict individual overall survival (OS) and cancer-specific survival (CSS) for patients with primary tracheal tumors and further estimate the role of postoperative radiotherapy (PORT) for these entities.Patients and methodsA total of 405 eligible patients diagnosed between 1988 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. All of them were randomly divided into training (n=303) and validation (n=102) sets. For the purpose of establishing nomograms, the Akaike information criterion was employed to select significant prognostic factors in multivariate Cox regression models. Both internal and external validations of the nomograms were evaluated by Harrell’s concordance index (C-index) and calibration plots. Propensity score matching (PSM) method was performed to reduce the influence of selection bias between the PORT group and the non-PORT group.ResultsTwo nomograms shared common variables including age at diagnosis, histology, N and M stages, tumor size, and treatment types, while gender was only incorporated in the CSS nomogram. The C-indices of OS and CSS nomograms were 0.817 and 0.813, displaying considerable predictive accuracy. The calibration curves indicated consistency between the nomograms and the actual observations. When the nomograms were applied to the validation set, the results remained reconcilable. Moreover, the nomograms showed superiority over the Bhattacharyya’s staging system with regard to the C-indices. After PSM, PORT was not associated with significantly better OS or CSS. Only squamous cell carcinoma (SCC) patients in the PORT group had improved OS compared to non-PORT group.ConclusionThe first two nomograms for predicting survival in patients with primary tracheal tumors were proposed in the present study. PORT seems to improve the prognosis of SCC patients, which needs further exploration.
Phosphorylation of G-protein-coupled receptors (GPCRs) is a required step in signal deactivation. Rhodopsin, a prototypical GPCR, exhibits high gain phosphorylation in vitro whereby a hundred-fold molar excess of phosphates are incorporated into the rhodopsin pool per molecule of activated rhodopsin. The extent by which high gain phosphorylation occurs in the intact mammalian photoreceptor cell, and the molecular mechanism underlying this reaction in vivo, is not known. Trans-phosphorylation is a mechanism proposed for high gain phosphorylation, whereby rhodopsin kinase, upon phosphorylating the activated receptor, continues to phosphorylate nearby nonactivated rhodopsin. We used two different transgenic mouse models to test whether trans-phosphorylation occurs in the intact photoreceptor cell. The first transgenic model expressed a murine cone pigment, S-opsin, together with the endogenous rhodopsin in the rod cell. We showed that selective stimulation of rhodopsin also led to phosphorylation of S-opsin. The second mouse model expressed the constitutively active human opsin mutant K296E. K296E, in the arrestin؊/؊ background, also led to phosphorylation of endogenous mouse rhodopsin in the darkadapted retina. Both mouse models provide strong support of transphosphorylation as an underlying mechanism of high gain phosphorylation, and provide evidence that a substantial fraction of nonactivated visual pigments becomes phosphorylated through this mechanism. Because activated, phosphorylated receptors exhibit decreased catalytic activity, our results suggest that dephosphorylation would be an important step in the full recovery of visual sensitivity during dark adaptation. These results may also have implications for other GPCR signaling pathways.Visual pigments, such as rhodopsin and cone opsins, belong to a family of G-protein-coupled receptors (GPCRs) 2 that contain a cluster of Ser/Thr sites at their carboxyl termini. Visual pigments initiate G-protein signaling upon photon absorption. As with other GPCRs, phosphorylation of the carboxyl-terminal Ser/Thr sites, followed by arrestin binding, are required steps in signal deactivation (1). Rhodopsin phosphorylation is catalyzed by rhodopsin kinase (GRK1 or RK), which is activated upon association with light-activated rhodopsin (R*) in the MII conformation (2, 3). In vitro and in vivo evidence shows that phosphorylated rhodopsin exhibits diminished catalytic activity (4 -7) and that arrestin binding is required to fully terminate R* signaling (5,8).Since the discovery of light-activated rhodopsin phosphorylation, a number of studies have reported that, in isolated rod outer segments, several hundred-fold molar excess of phosphates are incorporated into the rhodopsin pool per mol of R* (9 -12). Given that each rhodopsin has been observed to incorporate only up to nine phosphates (13), the straightforward interpretation is that nonactivated rhodopsin molecules, which we designate here as R, are phosphorylated as well as R*. This phenomenon has been termed high gain ...
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