Endotracheal intubation to reduce aspiration events in acutely comatose patients: a systematic review

Background: It is customary to believe that a patient with a Glasgow Coma Scale (GCS) score less than or equal to 8 should be intubated to avoid aspiration. We conducted a systematic review to establish if patients with GCS 64 8 for trauma or non-traumatic emergencies and treated in the acute care setting (e.g., Emergency Department or Pre-hospital environment) should be intubated to avoid aspiration or aspiration pneumonia/pneumonitis, and consequently, reduce mortality. Methods: We searched six databases, Pubmed, Embase, Scopus, SpringerLink, Cochrane Library, and Ovid Emcare, from April 15th to October 14th, 2020, for studies involving low GCS score patients of whom the risk of aspiration and related complications was assessed. Results: Thirteen studies were included in the final analysis (7 on non-traumatic population, 4 on trauma population, 1 pediatric and 1 adult mixed case studies). For the non-traumatic cases, two prospective studies and one retrospective study found no difference in aspiration risk between intubated and non-intubated patients. Two retrospective studies reported a reduction in the risk of aspiration in the intubated patient group. For traumatic cases, the study that considered the risk of aspiration did not show any differences between the two groups. A study on adult mixed cases found no difference in the incidence of aspiration among intubated and non-intubated patients. A study on pediatric patients found increased mortality for intubated versus non-intubated non-traumatic patients with a low GCS score. Conclusion: Whether intubation results in a reduction in the incidence of aspiration events and whether these are more frequent in patients with low GCS scores are not yet established. The paucity of evidence on this topic makes clinical trials justifiable and necessary. Trial registration: Prospero registration number: CRD42020136987

Single-cell approaches to cell competition: high-throughput imaging, machine learning and simulations

Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition

Communication in the Gig Economy: Buying and Selling in Online Freelance Marketplaces

The proliferating gig economy relies on online freelance marketplaces, which support relatively anonymous interactions by text-based messages. Informational asymmetries thus arise that can lead to exchange uncertainties between buyers and freelancers. Conventional marketing thought recommends reducing such uncertainty. However, uncertainty reduction and uncertainty management theories indicate that buyers and freelancers might benefit more from balancing, rather than reducing, uncertainty, such as by strategically adhering to or deviating from common principles. With dyadic analyses of calls for bids and bids from a leading online freelance marketplace, this study reveals that buyers attract more bids from freelancers when they provide moderate degrees of task information and concreteness, avoid sharing personal information, and limit the affective intensity of their communication. Freelancers’ bid success and price premiums increase when they mimic the degree of task information and affective intensity exhibited by buyers. However, mimicking a lack of personal information and concreteness reduces freelancers’ success, so freelancers should always be more concrete and offer more personal information than buyers do. These contingent perspectives offer insights into buyer–seller communication in two-sided online marketplaces; they clarify that despite, or sometimes due to, communication uncertainty, both sides can achieve success in the online gig economy

A feasibility assessment of a retrofit Molten Carbonate Fuel Cell coal-fired plant for flue gas CO₂ segregation

This work considers the use of a Molten Carbonate Fuel Cell (MCFC) system as a power generation and CO2 concentrator unit downstream of the coal burner of an existing production plant. In this way, the capability of MCFCs for CO2 segregation, which today is studied primarily in reference to large-scale plants, is applied to an intermediate-size plant highlighting the potential for MCFC use as a low energy method of carbon capture. A technical feasibility analysis was performed using an MCFC system-integrated model capable of determining steady-state performance across varying feed composition. The MCFC user model was implemented in Aspen Custom Modeler and integrated into the reference plant in Aspen Plus. The model considers electrochemical, thermal, and mass balance effects to simulate cell electrical and CO2 segregation performance. Results obtained suggest a specific energy requirement of 1.41 MJ kg CO2 121 significantly lower than seen in conventional Monoethanolamine (MEA) capture processes

Minimal breast milk transfer of rituximab, a monoclonal antibody used in neurological conditions.

ObjectiveTo determine the transfer of rituximab, an anti-CD20 monoclonal antibody widely used for neurologic conditions, into mature breast milk.MethodsBreast milk samples were collected from 9 women with MS who received rituximab 500 or 1,000 mg intravenous once or twice while breastfeeding from November 2017 to April 2019. Serial breast milk samples were collected before infusion and at 8 hours, 24 hours, 7 days, and 18-21 days after rituximab infusion in 4 patients. Five additional patients provided 1-2 samples at various times after rituximab infusion.ResultsThe median average rituximab concentration in mature breast milk was low at 0.063 μg/mL (range 0.046-0.097) in the 4 patients with serial breast milk collection, with an estimated median absolute infant dose of 0.0094 mg/kg/d and a relative infant dose (RID) of 0.08% (range 0.06%-0.10%). Most patients had a maximum concentration at 1-7 days after infusion. The maximum concentration occurred in a woman with a single breast milk sample and was 0.29 μg/mL at 11 days postinfusion, which corresponds with an estimated RID of 0.33%. Rituximab concentration in milk was virtually undetectable by 90 days postinfusion.ConclusionsWe determined minimal transfer of rituximab into mature breast milk. The RID for rituximab was less than 0.4% and well below theoretically acceptable levels of less than 10%. Low oral bioavailability would probably also limit the absorption of rituximab by the newborn. In women with serious autoimmune neurologic conditions, monoclonal antibody therapy may afford an acceptable benefit to risk ratio, supporting both maternal treatment and breastfeeding

Cell-scale biophysical determinants of cell competition in epithelia

How cells with different genetic makeups compete in tissues is an outstanding question in developmental biology and cancer research. Studies in recent years have revealed that cell competition can either be driven by short-range biochemical signalling or by long-range mechanical stresses in the tissue. To date, cell competition has generally been characterised at the population scale, leaving the single-cell-level mechanisms of competition elusive. Here, we use high time-resolution experimental data to construct a multi-scale agent-based model for epithelial cell competition and use it to gain a conceptual understanding of the cellular factors that governs competition in cell populations within tissues. We find that a key determinant of mechanical competition is the difference in homeostatic density between winners and losers, while differences in growth rates and tissue organisation do not affect competition end result. In contrast, the outcome and kinetics of biochemical competition is strongly influenced by local tissue organisation. Indeed, when loser cells are homogenously mixed with winners at the onset of competition, they are eradicated; however, when they are spatially separated, winner and loser cells coexist for long times. These findings suggest distinct biophysical origins for mechanical and biochemical modes of cell competition