Investigating the applicability domain of the hiPSC-based PluriLum assay: an embryotoxicity assessment of chemicals and drugs.

To meet the growing demand for developmental toxicity assessment of chemicals, New Approach Methodologies (NAMs) are needed. Previously, we developed two 3D in vitro assays based on human-induced pluripotent stem cells (hiPSC) and cardiomyocyte differentiation: the PluriBeat assay, based on assessment of beating differentiated embryoid bodies, and the PluriLum assay, a reporter gene assay based on the expression of the early cardiac marker NKX2.5; both promising assays for predicting embryotoxic effects of chemicals and drugs. In this work, we aimed to further describe the predictive power of the PluriLum assay and compare its sensitivity with PluriBeat and similar human stem cell-based assays developed by others. For this purpose, we assessed the toxicity of a panel of ten chemicals from different chemical classes, consisting of the known developmental toxicants 5-fluorouracil, all-trans retinoic acid and valproic acid, as well as the negative control compounds ascorbic acid and folic acid. In addition, the fungicides epoxiconazole and prochloraz, and three perfluoroalkyl substances (PFAS), PFOS, PFOA and GenX were tested. Generally, the PluriLum assay displayed higher sensitivity when compared to the PluriBeat assay. For several compounds the luminescence readout of the PluriLum assay showed effects not detected by the PluriBeat assay, including two PFAS compounds and the two fungicides. Overall, we find that the PluriLum assay has the potential to provide a fast and objective detection of developmental toxicants and has a level of sensitivity that is comparable to or higher than other in vitro assays also based on human stem cells and cardiomyocyte differentiation for assessment of developmental toxicity.

Optimization of the organizational structure in hospitals to account for patients with multiple diseases.

Many hospitals operate with a structure that separates inpatients according to their primary disease. Conversely, these hospitals may reduce the cases of bed shortage and provide a better care for patients with multiple diseases by striving for a setup containing fewer nursing wards. We present a method for optimizing the organizational structure in a hospital where the medical specialties are consolidated into fewer wards. The patient diagnoses are the basis of our approach as we derive an improved organizational structure by using a heuristic optimization algorithm. In this algorithm, we evaluate the solution by simulating the patient flow and penalize the objective value for every patient with a diagnosis that does not match the specialties in the ward. Through numerical experimentation, and data from a Danish hospital, we validate the applicability of our approach. The proposed algorithm converged to the optimal solution in all smaller problem instances. Further, tests with the hospital data indicate that consolidating medical specialties into fewer wards is beneficial for patients with diagnoses stemming from various medical specialties.

Strategic room type allocation for nursing wards through Markov chain modeling.

Providing patients with the best possible care is the most essential function of any hospital. In an increasing number of countries, hospitals are governed by the number of patients they are able to attract and the corresponding services they provide for patients. One such service, which is often of significant importance for patients, is the option to choose their room type. Hospital decision makers would benefit from a strategic method for optimizing the configuration of room types among nursing wards by distinguishing between patients who prefer private rooms and those who have no preference concerning whether they are assigned to a private or shared room. Such a decision support method is currently non-existent, therefore the goal of this study is to provide a methodology for hospital management. Specifically, a mixed modeling approach is proposed which evaluates the patient flow behavior by applying a Continuous-Time Markov Chain within a heuristic search procedure. This procedure recursively improves a configuration of rooms among the wards by sampling from a gradually improved interpolation of an objective function. Based on patient data obtained from both a Danish and Belgian hospital, the performance and robustness of the proposed approach is validated through various numerical experiments, demonstrating that solutions within a relative gap of 1% from the optimum are attained in most cases.