Reverse engineering of metabotropic glutamate receptor-dependent long-term depression in the hippocampus

status: publishe

Metabolite-based model predictive control of cell growth

Batch-to-batch variation causes significant challenges in the production of cell and gene therapies in terms of manufacturing planning and process comparability. Since the processes deal with living systems that are complex and time-varying, remediating process variability requires dynamic control over the Critical Process Parameters. Please click Additional Files below to see the full abstract

Interactive visualization of cell expansion process performance

As cell-based technologies are rapidly evolving beyond the laboratory scale, the demand for mass production of high quality cells is increasing. Unfortunately, only limited amounts of cells can be sourced from (human) donors. Therefore sequences of cell expansion steps are required to multiply the original number of cells taken from the donor biopsy to the amounts required for clinical application. Currently a large variety of expansion processes are used and described in literature. However, it is extremely difficult to compare them as many mesenchymal stem cell (MSC) subtypes are used in different culture vessels, with different medium compositions, etc. Moreover, adding to this variation in expansion strategies, within one process there can be significant fluctuations in outcome due to process variability and inherent donor-to-donor related variability. The aim of this work was to analyze the performance of a range of expansion processes for large-scale MSC expansion. Therefore a literature-based study was performed, currently resulting in a database of 73 individual cell expansion processes in 5 different types of culture vessels (microcarrier, (layered) flasks, hollow fiber-, multiplate-, and packed bed-bioreactor), 6 different types of MSCs and many different media compositions. The scale of the processes in terms of final cell numbers ranged between 7.5x106 and 1.1x1010 cells. An interactive process map was created where the scale, efficiency, cell type, culture method and load on downstream processing can be explored (see figure below). This interactive visualization tool provides an integrated perspective on the different culture processes and is able to increase the understanding on process comparability, attainable cell yield, scale-up strategies and the effect of certain critical process parameters on the expansion result. Please click Additional Files below to see the full abstract

Streamlining cell therapy manufacturing: Automated production and integrated data management

The manufacturing of cell therapy is a complex sequence of different unit operations. The substantial amount of manual and/or (semi-)automated manufacturing steps in a strictly regulated environment raise the need for scalable manufacturing and data management tools. Bioreactors have long been advocated as enabling technology for manufacturing of cell-based products. Automated bioreactors are not only able to substitute manual culture operations, but provide real-time monitoring and control of the culture environment, ultimately leading to improved process robustness. Additionally, they can be more easily integrated in an ‘Industry 4.0’ framework where data integration is required in real-time for process planning, regulatory compliance, etc. This work presents the integration of new a new bioreactor and a software platform, allowing more automation and efficient streamlining of the cell therapy manufacturing processes. On one hand, a closed benchtop perfusion bioreactor system with a reduced footprint was developed that is suitable for point-of-care use. Precise monitoring of the culture environment (pH, DO2 and T°) was achieved thanks to flexible sensor ports. The perfusion circuit was designed as a closed, single-use silicone tubing circuit linking a culture medium reservoir to a perfusion chamber via a peristaltic pump (cfr. Figure 1). A reusable cassette enclosed the disposable circuit and contained all the electronics required for bioreactor operation and environment control. A proof of concept study provided validation of the system on the perfusion culture of skeletal progenitor cells seeded onto 3D scaffolds. Regular sampling of the medium was performed during culture to measure glucose and lactate concentrations. Metabolic profiles, Live/Dead staining and DNA quantification of the constructs indicated similar cell growth kinetics compared to previously validated perfusion cultures of identical constructs. Please click Additional Files below to see the full abstract

Daily allergy burden and heart rate characteristics in adults with allergic rhinitis based on a wearable telemonitoring system

Background: Allergic rhinitis includes a certain degree of autonomic imbalance. However, no information is available on how daily changes in allergy burden affect autonomic imbalance. We aimed to estimate associations between daily allergy burden (allergy symptoms and mood) and daily heart rate characteristics (resting heart rate and sample entropy, both biomarkers of autonomic balance) of adults with allergic rhinitis, based on real-world measurements with a wearable telemonitoring system. Methods: Adults with a tree pollen allergy used a smartphone application to self-report daily allergy symptoms (score 0–44) and mood (score 0–4), and a Mio Alpha 2 wristwatch to collect heart rate characteristics during two pollen seasons of hazel, alder and birch in Belgium. Associations between daily allergy burden and heart rate characteristics were estimated using linear mixed effects distributed lag models with a random intercept for individuals and adjusted for potential confounders. Results: Analyses included 2497 participant-days of 72 participants. A one-point increase in allergy symptom score was associated with an increase in next-day resting heart rate of 0.08 (95% CI: 0.02–0.15) beats per minute. A one-point increase in mood score was associated with an increase in same-day sample entropy of 0.80 (95% CI: 0.34–1.26) × 10−2. No associations were found between allergy symptoms and heart rate sample entropy, nor between mood and resting heart rate. Conclusion: Daily repeated measurements with a wearable telemonitoring system revealed that the daily allergy burden of adults with allergic rhinitis has systemic effects beyond merely the respiratory system.</p

Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56%, sensitivity of 90.59%, precision of 86.52% and F-1-score of 88.50%. The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs

Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

<p>Abstract</p> <p>Background</p> <p>The intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique.</p> <p>Methods</p> <p>Non-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE).</p> <p>Results</p> <p>Median (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p < 0.001) and nurses (p = 0.044) but equivalent to physicians. GP had the lowest RMSRE (0.408) of all predictive models.</p> <p>Conclusions</p> <p>A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.</p