Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels

Article number 701778Research in bioprinting is booming due to its potential in addressing several manufacturing challenges in regenerative medicine. However, there are still many hurdles to overcome to guarantee cell survival and good printability. For the 3D extrusion-based bioprinting, cell viability is amongst one of the lowest of all the bioprinting techniques and is strongly influenced by various factors including the shear stress in the print nozzle. The goal of this study is to quantify, by means of in silico modeling, the mechanical environment experienced by the bioink during the printing process. Two ubiquitous nozzle shapes, conical and blunted, were considered, as well as three common hydrogels with material properties spanning from almost Newtonian to highly shear-thinning materials following the power-law behavior: Alginate-Gelatin, Alginate and PF127. Comprehensive in silico testing of all combinations of nozzle geometry variations and hydrogels was achieved by combining a design of experiments approach (DoE) with a computational fluid dynamics (CFD) of the printing process, analyzed through a machine learning approach named Gaussian Process. Available experimental results were used to validate the CFD model and justify the use of shear stress as a surrogate for cell survival in this study. The lower and middle nozzle radius, lower nozzle length and the material properties, alone and combined, were identified as the major influencing factors affecting shear stress, and therefore cell viability, during printing. These results were successfully compared with those of reported experiments testing viability for different nozzle geometry parameters under constant flow rate or constant pressure. The in silico 3D bioprinting platform developed in this study offers the potential to assist and accelerate further development of 3D bioprinting.Horizonte 2020 RIA(Unión Europea) 874837Horizonte 2020 (Unión Europea) INSITE 772418Fondo de Investigaciones Científicas (FNRS) T.0256.16Beca José Castillejo CAS17 /0017

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

IntroductionUrinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as “black-box” has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. MethodsWe used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. ResultsAll models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. ConclusionThe outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model’s simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model’s predictions is essential

Rasch analysis of the Patient and Observer Scar Assessment Scale (POSAS) in burn scars

The Patient and Observer Scar Assessment Scale (POSAS) is a questionnaire that was developed to assess scar quality. It consists of two separate six-item scales (Observer Scale and Patient Scale), both of which are scored on a 10-point rating scale. After many years of experience with this scale in burn scar assessment, it is appropriate to examine its psychometric properties using Rasch analysis. Cross-sectional data collection from seven clinical trials resulted in a data set of 1,629 observer scores and 1,427 patient scores of burn scars. We examined the person-item map, item fit statistics, reliability, response category ordering, and dimensionality of the POSAS. The POSAS showed an adequate fit to the Rasch model, except for the item surface area. Person reliability of the Observer Scale and Patient Scale was 0.82 and 0.77, respectively. Dimensionality analysis revealed that the unexplained variance by the first contrast of both scales was 1.7 units. Spearman correlation between the Observer Scale Rasch measure and the overall opinion of the clinician was 0.75. The Rasch model demonstrated that the POSAS is a reliable and valid scale that measures the single-construct scar qualit

Infographic LIME-Personal Health Train

Het principe van de Personal Health Train, dat als doel heeft het uitwisselen van medische gegevens ten behoeve van de burger, zorgverlener en onderzoek, in beeld gebracht

Factsheet data uitwisseling in de zorg; technieken voor standaardisatie en uitwisselen van data

<p>Factsheet data-uitwisseling in de zorg; beschrijving van technieken met betrekking tot standaardisatie en initiatieven rondom het uitwisselen van medische gegevens. </p

Doelen LIME-Personal Health Train

Doelen van de Personal Health Train, het uitwisselen van medische gegevens ten behoeve van de burger, zorgverlener en onderzoek, schematisch weergegeven

Factsheet inzage medische gegevens

Overzicht van de verschillende informatiesystemen voor het digitaal inzien, beheren en delen van medische gegevens

Factsheet data uitwisseling in de zorg; overzicht van ICT-standaarden naar techniek en inhoud.

<p>Factsheet over data uitwisseling in de zorg; overzicht van ICT-standaarden naar techniek en inhoud</p

Film PHT-LIME

Kort filmpje over het principe van de Personal Health Train, met als doel uitwisseling van medische gegevens voor zowel burger, zorgverlener als onderzoek