SUMMARY Efficient chemotaxis requires rapid coordination between different parts of the cell in response to changing directional cues. Here we investigate the mechanism of front-rear coordination in chemotactic neutrophils. We find that changes in the protrusion rate at the cell front are instantaneously coupled to changes in retraction at the cell rear, while myosin II accumulation at the rear exhibits a reproducible 9-15 sec lag. In turning cells, myosin II exhibits dynamic side-to-side relocalization at the cell rear in response to turning of the leading edge, and facilitates efficient turning by rapidly re-orienting the rear. These manifestations of front-rear coupling can be explained by a simple quantitative model incorporating reversible actin-myosin interactions with a rearward-flowing actin network. Finally, the system can be tuned by the degree of myosin regulatory light chain (MRLC) phosphorylation, which appears to be set in an optimal range to balance persistence of movement and turning ability.

Summary Dynamic actin networks are excitable. In migrating cells, feedback loops can amplify stochastic fluctuations in actin dynamics, often resulting in traveling waves of protrusion. The precise contributions of various molecular and mechanical interactions to wave generation have been difficult to disentangle, in part due to complex cellular morphodynamics. Here we use a relatively simple cell type – the fish epithelial keratocyte – to define a set of mechanochemical feedback loops underlying actin network excitability and wave generation. Although keratocytes are normally characterized by the persistent protrusion of a broad leading edge, increasing cell-substrate adhesion strength results in waving protrusion of a short leading edge. We show that protrusion waves are due to fluctuations in actin polymerization rates, and that overexpression of VASP, an actin anti-capping protein that promotes actin polymerization, switches highly-adherent keratocytes from waving to persistent protrusion. Moreover, VASP localizes both to adhesion complexes and the leading edge. Based on these results, we developed a mathematical model for protrusion waves in which local depletion of VASP from the leading edge by adhesions, along with lateral propagation of protrusion due to the branched architecture of the actin network, and negative mechanical feedback from the cell membrane, results in regular protrusion waves. Consistent with our model simulations, we show that VASP localization at the leading edge oscillates, with VASP leading edge enrichment greatest just prior to protrusion initiation. We propose that the mechanochemical feedbacks underlying wave generation in keratocytes may constitute a general module for establishing excitable actin dynamics in other cellular contexts.

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Use of embryonic zebrafish keratocytes as a model system shows that increased myosin light chain kinase (MLCK) activity promotes the formation of multiple protrusions independently of ROCK by increasing myosin accumulation in lamellipodia.

Background The COVID-19 pandemic resulted in a transformation of clinical care practices to protect both patients and providers. These changes led to a decrease in patient volume, impacting physician trainee education due to lost clinical and didactic opportunities. We measured the prevalence of trainee concern over missed educational opportunities and investigated the risk factors leading to such concerns. Methods All residents and fellows at a large academic medical center were invited to participate in a web-based survey in May of 2020. Participants responded to questions regarding demographic characteristics, specialty, primary assigned responsibility during the previous 2 weeks (clinical, education, or research), perceived concern over missed educational opportunities, and burnout. Multivariable logistic regression was used to assess the relationship between missed educational opportunities and the measured variables. Results 22% (301 of 1375) of the trainees completed the survey. 47% of the participants were concerned about missed educational opportunities. Trainees assigned to education at home had 2.85 [95%CI 1.33–6.45] greater odds of being concerned over missed educational opportunities as compared with trainees performing clinical work. Trainees performing research were not similarly affected [aOR = 0.96, 95%CI (0.47–1.93)]. Trainees in pathology or radiology had 2.51 [95%CI 1.16–5.68] greater odds of concern for missed educational opportunities as compared with medicine. Trainees with greater concern over missed opportunities were more likely to be experiencing burnout (p = 0.038). Conclusions Trainees in radiology or pathology and those assigned to education at home were more likely to be concerned about their missed educational opportunities. Residency programs should consider providing trainees with research or at home clinical opportunities as an alternative to self-study should future need for reduced clinical hours arise.

Electronic health records (EHR) use is often considered a significant contributor to clinician burnout. Informatics researchers often measure clinical workload using EHR-derived audit logs and use it for quantifying the contribution of EHR use to clinician burnout. However, translating clinician workload measured using EHR-based audit logs into a meaningful burnout metric requires an alignment with the conceptual and theoretical principles of burnout. In this perspective, we describe a systems-oriented conceptual framework to achieve such an alignment and describe the pragmatic realization of this conceptual framework using 3 key dimensions: standardizing the measurement of EHR-based clinical work activities, implementing complementary measurements, and using appropriate instruments to assess burnout and its downstream outcomes. We discuss how careful considerations of such dimensions can help in augmenting EHR-based audit logs to measure factors that contribute to burnout and for meaningfully assessing downstream patient safety outcomes.

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Background Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders. Methods The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020. Results Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit. Conclusions A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New