FACS purification and transcriptome analysis of drosophila neural stem cells reveals a role for Klumpfuss in self-renewal

Drosophila neuroblasts (NBs) have emerged as a model for stem cell biology that is ideal for genetic analysis but is limited by the lack of cell-type-specific gene expression data. Here, we describe a method for isolating large numbers of pure NBs and differentiating neurons that retain both cell-cycle and lineage characteristics. We determine transcriptional profiles by mRNA sequencing and identify 28 predicted NB-specific transcription factors that can be arranged in a network containing hubs for Notch signaling, growth control, and chromatin regulation. Overexpression and RNA interference for these factors identify Klumpfuss as a regulator of self-renewal. We show that loss of Klumpfuss function causes premature differentiation and that overexpression results in the formation of transplantable brain tumors. Our data represent a valuable resource for investigating Drosophila developmental neurobiology, and the described method can be applied to other invertebrate stem cell lineages as well

A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers

This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability

A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers

This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability

For which decisions is Shared Decision Making considered appropriate? – A systematic review

Objective:To identify decision characteristics for which SDM authors deem SDM appropriate or not, and what arguments are used.Methods:We applied two search strategies: we included SDM models from an earlier review (strategy 1) and conducted a new search in eight databases to include papers other than describing an SDM model, such as original research, opinion papers and reviews (strategy 2).Results:From the 92 included papers, we identified 18 decision characteristics for which authors deemed SDM appropriate, including preference-sensitive, equipoise and decisions where patient commitment is needed in implementing the decision. SDM authors indicated limits to SDM, especially when there are immediate life-saving measures needed. We identified four decision characteristics on which authors of different papers disagreed on whether or not SDM is appropriate.Conclusion:The findings of this review show the broad range of decision characteristics for which authors deem SDM appropriate, the ambiguity of some, and potential limits of SDM.Practice implications:The findings can stimulate clinicians to (re)consider pursuing SDM in situations in which they did not before. Additionally, it can inform SDM campaigns and educational programs as it shows for which decision situations SDM might be more or less challenging to practice

Shared Decision Making in Health Care Visits for CKD:Patients’ Decisional Role Preferences and Experiences

Rationale &amp; Objective: Research on shared decision making (SDM) in chronic kidney disease (CKD) has focused almost exclusively on the modality of kidney replacement treatment. We explored what other CKD decisions are recognized by patients, what their preferences and experiences are regarding these decisions, and how decisions are made during their interactions with medical care professionals. Study Design: Cross-sectional study. Setting &amp; Participants: Patients with CKD receiving (outpatient) care in 1 of 2 Dutch hospitals. Exposure: Patients’ preferred decisional roles for treatment decisions were measured using the Control Preferences Scale survey administered after a health care visit with medical professionals. Outcome: Number of decisions for which patients experienced a decisional role that did or did not match their preferred role. Observed levels of SDM and motivational interviewing in audio recordings of health care visits, measured using the 4-step SDM instrument (4SDM) and Motivational Interviewing Treatment Integrity coding tools.Analytical Approach: The results were characterized using descriptive statistics, including differences in scores between the patients’ experienced and preferred decisional roles. Results: According to the survey (n = 122) patients with CKD frequently reported decisions regarding planning (112 of 122), medication changes (82 of 122), or lifestyle changes (59 of 122). Of the 357 reported decisions in total, patients preferred that clinicians mostly (125 of 357) or fully (101 of 357) make the decisions. For 116 decisions, they preferred a shared decisional role. For 151 of 357 decisions, the patients’ preferences did not match their experiences. Decisions were experienced as “less shared/patient-directed” (76 of 357) or “more shared/patient-directed” (75 of 357) than preferred. Observed SDM in 118 coded decisions was low (median 4; range, 0 – 22). Motivational interviewing techniques were rarely used. Limitations: Potential recall and selection bias, and limited generalizability. Conclusions: We identified multiple discrepancies between preferred, experienced, and observed SDM in health care visits for CKD. Although patients varied in their preferred decisional role, a large minority of patients expressed a preference for shared decision making for many decisions. However, SDM behavior during the health care visits was observed infrequently. Plain-Language Summary: Shared decision making (SDM) may be a valuable approach for common chronic kidney disease (CKD) decisions, but our knowledge is limited. We collected patient surveys after health care visits for CKD. Patients most frequently experienced decisions regarding planning, medication, and lifestyle. Three decisional roles were preferred by comparable numbers of patients: let the clinician alone decide, let the clinician decide for the most part, or “equally share” the decision. Patients’ experiences of who made the decision did not always match their preferences. In audio recordings of the health care visits, we observed low levels of SDM behavior. These findings suggest that the preference for “sharing decisions” is often unmet for a large number of patients.</p

Predicting outcomes in chronic kidney disease:needs and preferences of patients and nephrologists

Introduction: Guidelines on chronic kidney disease (CKD) recommend that nephrologists use clinical prediction models (CPMs). However, the actual use of CPMs seems limited in clinical practice. We conducted a national survey study to evaluate: 1) to what extent CPMs are used in Dutch CKD practice, 2) patients’ and nephrologists’ needs and preferences regarding predictions in CKD, and 3) determinants that may affect the adoption of CPMs in clinical practice. Methods: We conducted semi-structured interviews with CKD patients to inform the development of two online surveys; one for CKD patients and one for nephrologists. Survey participants were recruited through the Dutch Kidney Patient Association and the Dutch Federation of Nephrology. Results: A total of 126 patients and 50 nephrologists responded to the surveys. Most patients (89%) reported they had discussed predictions with their nephrologists. They most frequently discussed predictions regarded CKD progression: when they were expected to need kidney replacement therapy (KRT) (n = 81), and how rapidly their kidney function was expected to decline (n = 68). Half of the nephrologists (52%) reported to use CPMs in clinical practice, in particular CPMs predicting the risk of cardiovascular disease. Almost all nephrologists (98%) reported discussing expected CKD trajectories with their patients; even those that did not use CPMs (42%). The majority of patients (61%) and nephrologists (84%) chose a CPM predicting when patients would need KRT in the future as the most important prediction. However, a small portion of patients indicated they did not want to be informed on predictions regarding CKD progression at all (10–15%). Nephrologists not using CPMs (42%) reported they did not know CPMs they could use or felt that they had insufficient knowledge regarding CPMs. According to the nephrologists, the most important determinants for the adoption of CPMs in clinical practice were: 1) understandability for patients, 2) integration as standard of care, 3) the clinical relevance. Conclusion: Even though the majority of patients in Dutch CKD practice reported discussing predictions with their nephrologists, CPMs are infrequently used for this purpose. Both patients and nephrologists considered a CPM predicting CKD progression most important to discuss. Increasing awareness about existing CPMs that predict CKD progression may result in increased adoption in clinical practice. When using CPMs regarding CKD progression, nephrologists should ask whether patients want to hear predictions beforehand, since individual patients’ preferences vary.</p

Cerebral Autoregulation Assessment Using the Near Infrared Spectroscopy 'NIRS-Only' High Frequency Methodology in Critically Ill Patients:A Prospective Cross-Sectional Study

Impairments in cerebral autoregulation (CA) are related to poor clinical outcome. Near infrared spectroscopy (NIRS) is a non-invasive technique applied to estimate CA. Our general purpose was to study the clinical feasibility of a previously published 'NIRS-only' CA methodology in a critically ill intensive care unit (ICU) population and determine its relationship with clinical outcome. Bilateral NIRS measurements were performed for 1-2 h. Data segments of ten-minutes were used to calculate transfer function analyses (TFA) CA estimates between high frequency oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) signals. The phase shift was corrected for serial time shifts. Criteria were defined to select TFA phase plot segments (segments) with 'high-pass filter' characteristics. In 54 patients, 490 out of 729 segments were automatically selected (67%). In 34 primary neurology patients the median (q1-q3) low frequency (LF) phase shift was higher in 19 survivors compared to 15 non-survivors (13° (6.3-35) versus 0.83° (-2.8-13), p = 0.0167). CA estimation using the NIRS-only methodology seems feasible in an ICU population using segment selection for more robust and consistent CA estimations. The 'NIRS-only' methodology needs further validation, but has the advantage of being non-invasive without the need for arterial blood pressure monitoring

Variables associated with in-hospital and postdischarge outcomes after postcardiotomy extracorporeal membrane oxygenation:Netherlands Heart Registration Cohort

Objectives: Extracorporeal membrane oxygenation (ECMO) for postcardiotomy cardiogenic shock has been increasingly used without concomitant mortality reduction. This study aims to investigate determinants of in-hospital and postdischarge mortality in patients requiring postcardiotomy ECMO in the Netherlands. Methods: The Netherlands Heart Registration collects nationwide prospective data from cardiac surgery units. Adults receiving intraoperative or postoperative ECMO included in the register from January 2013 to December 2019 were studied. Survival status was established through the national Personal Records Database. Multivariable logistic regression analyses were used to investigate determinants of in-hospital (3 models) and 12-month postdischarge mortality (4 models). Each model was developed to target specific time points during a patient's clinical course. Results: Overall, 406 patients (67.2% men, median age, 66.0 years [interquartile range, 55.0-72.0 years]) were included. In-hospital mortality was 51.7%, with death occurring in a median of 5 days (interquartile range, 2-14 days) after surgery. Hospital survivors (n = 196) experienced considerable rates of pulmonary infections, respiratory failure, arrhythmias, and deep sternal wound infections during a hospitalization of median 29 days (interquartile range, 17-51 days). Older age (odds ratio [OR], 1.02; 95% CI, 1.0-1.04) and preoperative higher body mass index (OR, 1.08; 95% CI, 1.02-1.14) were associated with in-hospital death. Within 12 months after discharge, 35.1% of hospital survivors (n = 63) died. Postoperative renal failure (OR, 2.3; 95% CI, 1.6-4.9), respiratory failure (OR, 3.6; 95% CI, 1.3-9.9), and re-thoracotomy (OR, 2.9; 95% CI, 1.3-6.5) were associated with 12-month postdischarge mortality. Conclusions: In-hospital and postdischarge mortality after postcardiotomy ECMO in adults remains high in the Netherlands. ECMO support in patients with higher age and body mass index, which drive associations with higher in-hospital mortality, should be carefully considered. Further observations suggest that prevention of re-thoracotomies, renal failure, and respiratory failure are targets that may improve postdischarge outcomes