Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians' standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5-7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5-10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5-7 cm H2O and 53.6% more frequently PEEP levels of 7-9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50-55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients

Microphytobenthos of Arctic Kongsfjorden (Svalbard, Norway): biomass and potential primary production along the shore line

During summer 2007, Arctic microphytobenthic potential primary production was measured at several stations around the coastline of Kongsfjorden (Svalbard, Norway) at ?5 m water depth and at two stations at five different water depths (5, 10, 15, 20, 30 m). Oxygen planar optode sensor spots were used ex situ to determine oxygen exchange in the overlying water of intact sediment cores under controlled light (ca. 100 ?mol photons m?2 s?1) and temperature (2–4°C) conditions. Patches of microalgae (mainly diatoms) covering sandy sediments at water depths down to 30 m showed high biomass of up to 317 mg chl a m?2. In spite of increasing water depth, no significant trend in “photoautotrophic active biomass” (chl a, ratio living/dead cells, cell sizes) and, thus, in primary production was measured at both stations. All sites from ?5 to 30 m water depth exhibited variable rates of net production from ?19 to +40 mg O2 m?2 h?1 (?168 to +360 mg C m?2 day?1) and gross production of about 2–62 mg O2 m?2 h?1 (17–554 mg C m?2 day?1), which is comparable to other polar as well as temperate regions. No relation between photoautotrophic biomass and gross/net production values was found. Microphytobenthos demonstrated significant rates of primary production that is comparable to pelagic production of Kongsfjorden and, hence, emphasised the importance as C source for the zoobenthos

Natural Killer Cell Education Is Associated With a Distinct Glycolytic Profile

NK cells expressing self-inhibitory receptors display increased functionality compared to NK cells lacking those receptors. The acquisition of functional competence in these particular NK-cell subsets is termed education. Little is known about the underlying mechanisms that lead to the functional differences between educated and uneducated NK cells. An increasing number of studies suggest that cellular metabolism is a determinant of immune cell functions. Thus, alterations in cellular metabolic pathways may play a role in the process of NK-cell education. Here, we compared the glycolytic profile of educated and uneducated primary human NK cells. KIR-educated NK cells showed significantly increased expression levels of the glucose transporter Glut1 in comparison to NKG2A-educated or uneducated NK cells with and without exposure to target cells. Subsequently, the metabolic profile of NK-cell subsets was determined using a Seahorse XF Analyzer. Educated NK cells displayed significantly higher rates of cellular glycolysis than uneducated NK cells even in a resting state. Our results indicate that educated and uneducated NK cells reside in different metabolic states prior to activation. These differences in the ability to utilize glucose may represent an underlying mechanism for the superior functionality of educated NK cells expressing self-inhibitory receptors

Different osteosyntheses for Colles' fracture: A mechanical study in 42 cadaver bones

Background and purpose In recent years several different plate designs for internal fixation of fractures of the distal radius have been developed. However, few biomechanical studies have been performed to compare these new implants. The purpose of this study was to compare the mechanical properties of 5 different commercially available plates (3 volar and 2 dorsal) with standard K-wire fixation using a distal radial cadaver model

Addressing the Black Box of AI – A Model and Research Agenda on the Co-Constitution of Aging and Artificial Intelligence

Algorithmic technologies and (large) data infrastructures, often referred to as Artificial Intelligence (AI), have received increasing attention from gerontological research in the last decade. While there is much literature that dissects and explores the development, application, and evaluation of AI relevant for gerontology, this article makes a novel contribution by critically engaging with the theorizing in this growing field of research. We observe that gerontology’s engagement with AI is shaped by an interventionist logic that situates AI as a black box for gerontological research. We demonstrate how this black box logic has neglected many aspects of AI as a research topic for gerontology and discuss three classical concepts in gerontology to show how they can be used to open various black boxes of aging and AI in the areas: a) the datafication of aging, b) the political economy of AI and aging, and c) everyday engagements and embodiments of AI in later life. In the final chapter, we propose a model of the co-constitution of aging and AI that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond an interventionist logic.Vera Gallistl’s work on this paper has been funded by the Vienna Science and Technology Fund (WWTF) and by the State of Lower Austria through project ICT20-055 (Grant-ID: 10.47379/ICT20055). Barbara Marshall’s work on this paper has been funded by the Social Science and Humanities Research Council of Canada (435-2017-1343) and the Canadian Institute for Health Research (155188). We acknowledge support by Open Access Publishing Fund of Karl Landsteiner University of Health Sciences, Krems, Austria

Circulating extracellular DNA is an independent predictor of mortality in elderly patients with venous thromboembolism.

Venous thromboembolism (VTE) is a major cause of morbidity and mortality in elderly patients. Extracellular DNA is a pro-inflammatory and pro-thrombotic mediator in vitro and in animal models. Levels of circulating extracellular DNA (ceDNA) are increased in VTE patients, but the association of ceDNA with VTE extent and clinical outcome is poorly understood. We analyzed the association of ceDNA with the extent of VTE, categorized as distal and proximal deep vein thrombosis and pulmonary embolism, and with the clinical outcomes VTE recurrence and mortality. We quantified ceDNA by a fluorescent probe, as well as circulating nucleosomes and neutrophil extracellular traps (NETs) by ELISA in plasma from 611 patients aged ≥ 65 years with acute VTE of a prospective cohort study (SWITCO65+). Levels of ceDNA and nucleosomes, but not NETs, correlated with VTE extent. Infectious comorbidities independently increased ceDNA levels in VTE. CeDNA strongly correlated with C-reactive protein and leukocytosis, suggesting an association of ceDNA with inflammation in VTE patients. CeDNA furthermore predicted PE-related and all-cause mortality, but not VTE recurrence, during a 3-year follow-up. Our study suggests that ceDNA levels in VTE patients reflect the degree of inflammation and may serve as a biomarker to stratify VTE patients at risk for mortality

Front Immunol

NK cells are phenotypically and functionally diverse lymphocytes due to variegated expression of a large array of receptors. NK-cell activity is tightly regulated through integration of receptor-derived inhibitory and activating signals. Thus, the receptor profile of each NK cell ultimately determines its ability to sense aberrant cells and subsequently mediate anti-viral or anti-tumor responses. However, an in-depth understanding of how different receptor repertoires enable distinct immune functions of NK cells is lacking. Therefore, we investigated the phenotypic diversity of primary human NK cells by performing extensive phenotypic characterization of 338 surface molecules using flow cytometry (n = 18). Our results showed that NK cells express at least 146 receptors on their surface. Of those, 136 (>90%) exhibited considerable inter-donor variability. Moreover, comparative analysis of CD56bright and CD56dim NK cells identified 70 molecules with differential expression between the two major NK-cell subsets and allowed discrimination of these subsets via unsupervised hierarchical clustering. These receptors were associated with a broad range of NK-cell functions and multiple molecules were not previously associated with predominant expression on either subset (e.g. CD82 and CD147). Altogether, our study contributes to an improved understanding of the phenotypic diversity of NK cells and its potential functional implications on a cellular and population level. While the identified distinct signatures in the receptor repertoires provide a molecular basis for the differential immune functions exerted by CD56bright and CD56dim NK cells, the observed inter-individual differences in the receptor repertoire of NK cells may contribute to a diverging ability to control certain diseases

Social Entrepreneurship and Social Innovation in Aging

Social entrepreneurship is usually understood as an economic activity which focuses at social values, goals, and investments that generates surpluses for social entrepreneurs as individuals, groups, and startups who are working for the benefit of communities, instead of strictly focusing mainly at the financial profit, economic values, and the benefit generated for shareholders or owners. Social entrepreneurship combines the production of goods, services, and knowledge in order to achieve both social and economic goals and allow for solidarity building. From a broader perspective, entities that are focused on social entrepreneurship are identified as parts of the social and solidarity economy. These are, for example, social enterprises, cooperatives, mutual organizations, self-help groups, charities, unions, fair trade companies, community enterprises, and time banks. Social innovation is a key element of social entrepreneurship. Social innovation is usually understood as new strategies, concepts, products, services, and organizational forms that allow for the satisfaction of needs. Such innovations are created in particular in the contact areas of various sectors of the social system. For example, these are spaces between the public sector, the private sector, and civil society. These innovations not only allow the solving of problems but also extend possibilities for public action