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Serverless computing is gaining popularity for machine learning (ML) serving workload due to its autonomous resource scaling, easy to use and pay-per-use cost model. Existing serverless platforms work well for image-based ML inference, where requests are homogeneous in service demands. That said, recent advances in natural language processing could not fully benefit from existing serverless platforms as their requests are intrinsically heterogeneous. Batching requests for processing can significantly increase ML serving efficiency while reducing monetary cost, thanks to the pay-per-use pricing model adopted by serverless platforms. Yet, batching heterogeneous ML requests leads to additional computation overhead as small requests need to be "padded" to the same size as large requests within the same batch. Reaching effective batching decisions (i.e., which requests should be batched together and why) is non-trivial: the padding overhead coupled with the serverless auto-scaling forms a complex optimization problem. To address this, we develop Multi-Buffer Serving (MBS), a framework that optimizes the batching of heterogeneous ML inference serving requests to minimize their monetary cost while meeting their service level objectives (SLOs). The core of MBS is a performance and cost estimator driven by analytical models supercharged by a Bayesian optimizer. MBS is prototyped and evaluated on AWS using bursty workloads. Experimental results show that MBS preserves SLOs while outperforming the state-of-the-art by up to 8 x in terms of cost savings while minimizing the padding overhead by up to 37 x with 3 x less number of serverless function invocations.

Background. Acute respiratory failure from COVID-19 pneumonia is a major cause of death after SARS-CoV-2 infection. We investigated whether PaO2/FiO2, oxygenation index (OI), SpO2/FiO2, and oxygen saturation index (OSI), commonly used to assess the severity of acute respiratory distress syndrome (ARDS), can predict mortality in mechanically ventilated COVID-19 patients. Methods. In this single-centered retrospective pilot study, we enrolled 68 critically ill mechanically ventilated adult patients with confirmed COVID-19. Physiological variables were recorded on the day of intubation (day 0) and postintubation days 3 and 7. The association between physiological parameters, PaO2/FiO2, OI, SpO2/FiO2, and OSI with mortality was assessed using multiple variable logistic regression analysis. Receiver operating characteristic analysis was conducted to evaluate the performance of the predictive models. Results. The ARDS severity indices were not statistically different on the day of intubation, suggesting similar baseline conditions in nonsurviving and surviving patients. However, these indices were significantly worse in the nonsurviving as compared to surviving patients on postintubation days 3 and 7. On intubation day 3, PaO2/FiO2 was 101.0 (61.4) in nonsurviving patients vs. 140.2 (109.6) in surviving patients, p = 0.004 , and on day 7 106.3 (94.2) vs. 178.0 (69.3), p < 0.001 . OI was 135.0 (129.7) in nonsurviving vs. 84.8 (86.1) in surviving patients ( p = 0.003 ) on day 3 and 150.0 (118.4) vs. 61.5 (46.7) ( p < 0.001 ) on day 7. OSI was 12.0 (11.7) vs. 8.0 (10.0) ( p = 0.006 ) on day 3 and 14.7 (13.2) vs. 6.5 (5.4) ( p < 0.001 ) on day 7. Similarly, SpO2/FiO2 was 130 (90) vs. 210 (90) ( p = 0.003 ) on day 3 and 130 (90) vs. 230 (50) ( p < 0.001 ) on day 7, while OSI was 12.0 (11.7) vs. 8.0 (10.0) ( p = 0.006 ) on day 3 and 14.7 (13.2) vs. 6.5 (5.4) ( p < 0.001 ) on day 7 in the nonsurviving and surviving patients, respectively. All measures were independently associated with hospital mortality, with significantly greater odds ratios observed on day 7. The area under the receiver operating characteristic curve (AUC) for mortality prediction was greatest on intubation day 7 (AUC = 0.775, 0.808, and 0.828 for PaO2/FiO2, OI, SpO2/FiO2, and OSI, respectively). Conclusions. Decline in oxygenation indices after intubation is predictive of mortality in COVID-19 patients. This time window is critical to the outcome of these patients and a possible target for future interventions. Future large-scale studies to confirm the prognostic value of the indices in COVID-19 patients are warranted.

Background Sigh is a cyclic brief recruitment maneuver: previous physiologic studies showed that its use could be an interesting addition to pressure support ventilation to improve lung elastance, decrease regional heterogeneity, and increase release of surfactant. Research Question Is the clinical application of sigh during pressure support ventilation (PSV) feasible? Study Design and Methods We conducted a multicenter noninferiority randomized clinical trial on adult intubated patients with acute hypoxemic respiratory failure or ARDS undergoing PSV. Patients were randomized to the no-sigh group and treated by PSV alone, or to the sigh group, treated by PSV plus sigh (increase in airway pressure to 30 cm H2O for 3 s once per minute) until day 28 or death or successful spontaneous breathing trial. The primary end point of the study was feasibility, assessed as noninferiority (5% tolerance) in the proportion of patients failing assisted ventilation. Secondary outcomes included safety, physiologic parameters in the first week from randomization, 28-day mortality, and ventilator-free days. Results Two-hundred and fifty-eight patients (31% women; median age, 65 [54-75] years) were enrolled. In the sigh group, 23% of patients failed to remain on assisted ventilation vs 30% in the no-sigh group (absolute difference, –7%; 95% CI, –18% to 4%; P = .015 for noninferiority). Adverse events occurred in 12% vs 13% in the sigh vs no-sigh group (P = .852). Oxygenation was improved whereas tidal volume, respiratory rate, and corrected minute ventilation were lower over the first 7 days from randomization in the sigh vs no-sigh group. There was no significant difference in terms of mortality (16% vs 21%; P = .337) and ventilator-free days (22 [7-26] vs 22 [3-25] days; P = .300) for the sigh vs no-sigh group. Interpretation Among hypoxemic intubated ICU patients, application of sigh was feasible and without increased risk. Trial Registry ClinicalTrials.gov ; No.: NCT03201263 ; URL: www.clinicaltrials.gov

Rationale: Obesity is characterized by elevated pleural pressure (Ppl) and worsening atelectasis during mechanical ventilation in patients with acute respiratory distress syndrome (ARDS). Objectives: To determine the effects of a lung recruitment maneuver (LRM) in the presence of elevated Ppl on hemodynamics, left and right ventricular pressure, and pulmonary vascular resistance. We hypothesized that elevated Ppl protects the cardiovascular system against high airway pressure and prevents lung overdistension. Methods: First, an interventional crossover trial in adult subjects with ARDS and a body mass index ≥ 35 kg/m2 (n = 21) was performed to explore the hemodynamic consequences of the LRM. Second, cardiovascular function was studied during low and high positive end-expiratory pressure (PEEP) in a model of swine with ARDS and high Ppl (n = 9) versus healthy swine with normal Ppl (n = 6). Measurements and Main Results: Subjects with ARDS and obesity (body mass index = 57 ± 12 kg/m2) after LRM required an increase in PEEP of 8 (95% confidence interval [95% CI], 7–10) cm H2O above traditional ARDS Network settings to improve lung function, oxygenation and V./Q. matching, without impairment of hemodynamics or right heart function. ARDS swine with high Ppl demonstrated unchanged transmural left ventricular pressure and systemic blood pressure after the LRM protocol. Pulmonary arterial hypertension decreased (8 [95% CI, 13–4] mm Hg), as did vascular resistance (1.5 [95% CI, 2.2–0.9] Wood units) and transmural right ventricular pressure (10 [95% CI, 15–6] mm Hg) during exhalation. LRM and PEEP decreased pulmonary vascular resistance and normalized the V./Q. ratio. Conclusions: High airway pressure is required to recruit lung atelectasis in patients with ARDS and class III obesity but causes minimal overdistension. In addition, patients with ARDS and class III obesity hemodynamically tolerate LRM with high airway pressure. Clinical trial registered with www.clinicaltrials.gov (NCT 02503241).

Nearly all principal cloud providers now provide burstable instances in their offerings. The main attraction of this type of instance is that it can boost its performance for a limited time to cope with workload variations. Although burstable instances are widely adopted, it is not clear how to efficiently manage them to avoid waste of resources. In this article, we use predictive data analytics to optimize the management of burstable instances. We design CEDULE+, a data-driven framework that enables efficient resource management for burstable cloud instances by analyzing the system workload and latency data. CEDULE+ selects the most profitable instance type to process incoming requests and controls CPU, I/O, and network usage to minimize the resource waste without violating Service Level Objectives (SLOs). CEDULE+ uses lightweight profiling and quantile regression to build a data-driven prediction model that estimates system performance for all combinations of instance type, resource type, and system workload. CEDULE+ is evaluated on Amazon EC2, and its efficiency and high accuracy are assessed through real-case scenarios. CEDULE+ predicts application latency with errors less than 10%, extends the maximum performance period of a burstable instance up to 2.4 times, and decreases deployment costs by more than 50%.

Electronic cigarettes (e-cigarettes) have been used widely as an alternative to conventional cigarettes and have become particularly popular among young adults. A growing body of evidence has shown that e-cigarettes are associated with acute lung injury and adverse effects in multiple other organs. Previous studies showed that high emissions of aldehydes (formaldehyde and acetaldehyde) in aerosols were associated with increased usage of the same e-cigarette coils. However, the impact on lung function of using aged coils has not been reported. We investigated the relationship between coil age and acute lung injury in mice exposed to experimental vaping for 1 h (2 puffs/min, 100 ml/puff). The e-liquid contains propylene glycol and vegetable glycerin (50:50, vol) only. The concentrations of formaldehyde and acetaldehyde in the vaping aerosols increased with age of the nichrome coils starting at 1200 puffs. Mice exposed to e-cigarette aerosols produced from 1800, but not 0 or 900, puff-aged coils caused acute lung injury, increased lung wet/dry weight ratio, and induced lung inflammation (IL-6, TNF-α, IL-1β, MIP-2). Exposure to vaping aerosols from 1800 puff-aged coils decreased heart rate, respiratory rate, and oxygen saturation in mice compared to mice exposed to air or aerosols from new coils. In conclusion, we observed that the concentration of aldehydes (formaldehyde and acetaldehyde) increased with repeated and prolonged usage of e-cigarette coils. Exposure to high levels of aldehyde in vaping aerosol was associated with acute lung injury in mice. These findings show significant risk of lung injury associated with prolonged use of e-cigarette devices.

High-dose nitric oxide is a novel treatment associated with improved oxygenation and decreased tachypnea in pregnant patients with severe coronavirus disease 2019 (COVID-19)., BACKGROUND: Rescue therapies to treat or prevent progression of coronavirus disease 2019 (COVID-19) hypoxic respiratory failure in pregnant patients are lacking. METHOD: To treat pregnant patients meeting criteria for severe or critical COVID-19 with high-dose (160–200 ppm) nitric oxide by mask twice daily and report on their clinical response. EXPERIENCE: Six pregnant patients were admitted with severe or critical COVID-19 at Massachusetts General Hospital from April to June 2020 and received inhalational nitric oxide therapy. All patients tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. A total of 39 treatments was administered. An improvement in cardiopulmonary function was observed after commencing nitric oxide gas, as evidenced by an increase in systemic oxygenation in each administration session among those with evidence of baseline hypoxemia and reduction of tachypnea in all patients in each session. Three patients delivered a total of four neonates during hospitalization. At 28-day follow-up, all three patients were home and their newborns were in good condition. Three of the six patients remain pregnant after hospital discharge. Five patients had two negative test results on nasopharyngeal swab for SARS-CoV-2 within 28 days from admission. CONCLUSION: Nitric oxide at 160–200 ppm is easy to use, appears to be well tolerated, and might be of benefit in pregnant patients with COVID-19 with hypoxic respiratory failure.

To evaluate whether the use of inhaled nitric oxide (iNO)200 improves respiratory function.This retrospective cohort study used data from pregnant patients hospitalized with severe bilateral coronavirus disease 2019 (COVID-19) pneumonia at four teaching hospitals between March 2020 and December 2021. Two cohorts were identified: 1) those receiving standard of care alone (SoC cohort) and 2) those receiving iNO200 for 30 minutes twice daily in addition to standard of care alone (iNO200 cohort). Inhaled nitric oxide, as a novel therapy, was offered only at one hospital. The prespecified primary outcome was days free from any oxygen supplementation at 28 days postadmission. Secondary outcomes were hospital length of stay, rate of intubation, and intensive care unit (ICU) length of stay. The multivariable-adjusted regression analyses accounted for age, body mass index, gestational age, use of steroids, remdesivir, and the study center.Seventy-one pregnant patients were hospitalized for severe bilateral COVID-19 pneumonia: 51 in the SoC cohort and 20 in the iNO200 cohort. Patients receiving iNO200 had more oxygen supplementation-free days (iNO200: median [interquartile range], 24 [23-26] days vs standard of care alone: 22 [14-24] days, P=.01) compared with patients in the SoC cohort. In the multivariable-adjusted analyses, iNO200 was associated with 63.2% (95% CI 36.2-95.4%; P.001) more days free from oxygen supplementation, 59.7% (95% CI 56.0-63.2%; P.001) shorter ICU length of stay, and 63.6% (95% CI 55.1-70.8%; P.001) shorter hospital length of stay. No iNO200-related adverse events were reported.In pregnant patients with severe bilateral COVID-19 pneumonia, iNO200 was associated with a reduced need for oxygen supplementation and shorter hospital stay.

Summary: Acute kidney injury is a common complication after cardiac surgery that is associated with high postoperative morbidity and mortality. Levels of hemolysis are associated closely with the incidence and severity of kidney injury after cardiac surgery. Hemolysis is caused by prolonged surgical procedures and blood transfusions from cell-saver devices and is associated with the use of cardiopulmonary bypass. Plasma oxyhemoglobin is released into the circulation by damaged red blood cells that, via a dioxygenation reaction, depletes vascular nitric oxide (NO), a potent vasodilator molecule responsible for modulating organ perfusion and vascular homeostasis. Depleted plasma NO and increased levels of plasma oxyhemoglobin in the bloodstream lead to impairment of organ perfusion, inflammation, oxidative stress, and direct tubular injury, which, together, contribute to the development of renal injury after cardiac surgery. The administration of NO, a gas originally approved to treat pulmonary hypertension, maintains organ perfusion by preventing vascular NO depletion. In addition, this treatment improves cardiac output by reducing pulmonary vascular resistance and right heart workload. The clinical evidence of renal protection of NO gas therapy is supported by preclinical animal studies exploring the extrapulmonary protective effects of NO. Recent clinical trials showed a significant reduction of postoperative acute kidney injury when NO gas was administered during and after cardiac surgery.

Nitric Oxide (NO) is administered as gas for inhalation to induce selective pulmonary vasodilation. It is a safe therapy, with few potential risks even if administered at high concentration. Inhaled NO gas is routinely used to increase systemic oxygenation in different disease conditions. The administration of high concentrations of NO also exerts a virucidal effect in vitro. Owing to its favorable pharmacodynamic and safety profiles, the familiarity in its use by critical care providers, and the potential for a direct virucidal effect, NO is clinically used in patients with coronavirus disease-2019 (COVID-19). Nevertheless, no device is currently available to easily administer inhaled NO at concentrations higher than 80 parts per million (ppm) at various inspired oxygen fractions, without the need for dedicated, heavy, and costly equipment. The development of a reliable, safe, inexpensive, lightweight, and ventilator-free solution is crucial, particularly for the early treatment of non-intubated patients outside of the intensive care unit (ICU) and in a limited-resource scenario. To overcome such a barrier, a simple system for the non-invasive NO gas administration up to 250 ppm was developed using standard consumables and a scavenging chamber. The method has been proven safe and reliable in delivering a specified NO concentration while limiting nitrogen dioxide levels. This paper aims to provide clinicians and researchers with the necessary information on how to assemble or adapt such a system for research purposes or clinical use in COVID-19 or other diseases in which NO administration might be beneficial.

Background Inhaled nitric oxide (NO) is a selective pulmonary vasodilator. In-vitro studies report that NO donors can inhibit replication of SARS-CoV-2. This multicenter study evaluated the feasibility and effects of high-dose inhaled NO in non-intubated spontaneously breathing patients with Coronavirus disease-2019 (COVID-19). Methods This is an interventional study to determine whether NO at 160 parts-per-million (ppm) inhaled for 30 min twice daily might be beneficial and safe in non-intubated COVID-19 patients. Results Twenty-nine COVID-19 patients received a total of 217 intermittent inhaled NO treatments for 30 min at 160 ppm between March and June 2020. Breathing NO acutely decreased the respiratory rate of tachypneic patients and improved oxygenation in hypoxemic patients. The maximum level of nitrogen dioxide delivered was 1.5 ppm. The maximum level of methemoglobin (MetHb) during the treatments was 4.7%. MetHb decreased in all patients 5 min after discontinuing NO administration. No adverse events during treatment, such as hypoxemia, hypotension, or acute kidney injury during hospitalization occurred. In our NO treated patients, one patient of 29 underwent intubation and mechanical ventilation, and none died. The median hospital length of stay was 6 days [interquartile range 4–8]. No discharged patients required hospital readmission nor developed COVID-19 related long-term sequelae within 28 days of follow-up. Conclusions In spontaneous breathing patients with COVID-19, the administration of inhaled NO at 160 ppm for 30 min twice daily promptly improved the respiratory rate of tachypneic patients and systemic oxygenation of hypoxemic patients. No adverse events were observed. None of the subjects was readmitted or had long-term COVID-19 sequelae.

Identifying performance problems in modern software systems is nontrivial, even more so when looking at specific application domains, such as cyber-physical systems. The heterogeneity of software and hardware components makes the process of performance evaluation more challenging, and traditional software performance engineering techniques may fail while dealing with interacting and heterogeneous components. The goal of this paper is to introduce a model-based approach to understand software performance problems in cyber-physical systems. In our previous work, we listed some common bad practices, namely software performance antipatterns, that may occur. Here we are interested in shedding light on these antipatterns by means of performance models, i.e., queuing network models, that provide evidence of how antipatterns may affect the overall system performance. Starting from the specification of three software performance antipatterns tailored for cyber-physical systems, we provide the queuing network models capturing the corresponding bad practices. The analysis of these models demonstrates their usefulness in recognizing performance problems early in the software development process. This way, performance engineers are supported in the task of detecting and fixing the performance criticalities.

Architecting Cyber-Physical Systems is not trivial since their intrinsic nature of mixing software and hardware components poses several challenges, especially when the physical space is subject to dynamic changes, e.g., paths of robots suddenly not feasible due to objects occupying transit areas or doors being closed with a high probability. This paper provides a quantitative evaluation of different architectural patterns that can be used for cyber-physical systems to understand which patterns are more suitable under some peculiar characteristics of dynamic spaces, e.g., frequency of obstacles in paths. We use stochastic performance models to evaluate architectural patterns, and we specify the dynamic aspects of the physical space as probability values. This way, we aim to support software architects with quantitative results indicating how different design patterns affect some metrics of interest, e.g., the system response time. Experiments show that there is no unique architectural pattern suitable to cope with all the dynamic characteristics of physical spaces. Each architecture differently contributes when varying the physical space, and it is indeed beneficial to switch among multiple patterns for an optimal solution.

Background Acute respiratory distress syndrome (ARDS) secondary to coronavirus disease-2019 (COVID-19) is characterized by substantial heterogeneity in clinical, biochemical, and physiological characteristics. However, the pathophysiology of severe COVID-19 infection is poorly understood. Previous studies established clinical and biological phenotypes among classical ARDS cohorts, with important therapeutic implications. The phenotypic profile of COVID-19 associated ARDS remains unknown. Methods We used latent class modeling via a multivariate mixture model to identify phenotypes from clinical and biochemical data collected from 263 patients admitted to Massachusetts General Hospital intensive care unit with COVID-19-associated ARDS between March 13 and August 2, 2020. Findings We identified two distinct phenotypes of COVID-19-associated ARDS, with substantial differences in biochemical profiles despite minimal differences in respiratory dynamics. The minority phenotype (class 2, n = 70, 26·6%) demonstrated increased markers of coagulopathy, with mild relative hyper-inflammation and dramatically increased markers of end-organ dysfunction (e.g., creatinine, troponin). The odds of 28-day mortality among the class 2 phenotype was more than double that of the class 1 phenotype (40·0% vs.· 23·3%, OR = 2·2, 95% CI [1·2, 3·9]). Interpretation We identified distinct phenotypic profiles in COVID-19 associated ARDS, with little variation according to respiratory physiology but with important variation according to systemic and extra-pulmonary markers. Phenotypic identity was highly associated with short-term mortality. The class 2 phenotype exhibited prominent signatures of coagulopathy, suggesting that vascular dysfunction may play an important role in the clinical progression of severe COVID-19-related disease.

As the COVID-19 outbreak evolves around the world, the World Health Organization (WHO) and its Member States have been heavily relying on staying at home and lock down measures to control the spread of the virus. In last months, various signs showed that the COVID-19 curve was flattening, but the premature lifting of some containment measures (e.g., school closures and telecommuting) are favouring a second wave of the disease. The accurate evaluation of possible countermeasures and their well-timed revocation are therefore crucial to avoid future waves or reduce their duration. In this paper, we analyze patient and route data collected by the Korea Centers for Disease Control & Prevention (KCDC). We extract information from real-world data sets and use them to parameterize simulations and evaluate different what-if scenarios.

Serverless computing is a new pay-per-use cloud service paradigm that automates resource scaling for stateless functions and can potentially facilitate bursty machine learning serving. Batching is critical for latency performance and cost-effectiveness of machine learning inference, but unfortunately it is not supported by existing serverless platforms due to their stateless design. Our experiments show that without batching, machine learning serving cannot reap the benefits of serverless computing. In this paper, we present BATCH, a framework for supporting efficient machine learning serving on serverless platforms. BATCH uses an optimizer to provide inference tail latency guarantees and cost optimization and to enable adaptive batching support. We prototype BATCH atop of AWS Lambda and popular machine learning inference systems. The evaluation verifies the accuracy of the analytic optimizer and demonstrates performance and cost advantages over the state-of-the-art method MArk and the state-of-the-practice tool SageMaker.

International audience; Purpose : Conflict of interest (COI) may compromise, or have the appearance of compromising, a researcher’s judgment or integrity in conducting or reporting research. We sought to assess time trends of COI and funding statement reporting in the critical care literature.Methods : PubMed was searched by using Medical Subject Headings and the appropriate corresponding keywords: “INTENSIVE CARE UNIT” or “ICU” as a major topic. Four years in a 15-year time period (2001–2016) were arbitrarily chosen and one study month was randomly selected for each study period. Studies published during the selected months were included in the analysis.Results : Three hundred and seventy-four studies were evaluated, including five reviews (1.3%) and ten randomized clinical trials (RCTs) (2.7%). COI statements were available in 65% of the studies and 8% had declared COI. COI statement rate, declared COI and funding statements increased over time, while the number of authors affiliated with industry and the discordance between the lack of COI statement and affiliation with industry decreased. Declared COI were more frequent in 2011–2016 as compared to 2001–2010 (OR 4.06; 95% CI 1.15–25.79) and in the higher quartile of a journal’s impact factor (OR of 16.73; 95% CI 3.28–306.20). Surprisingly, focus of the study, country of the first author and/or endorsement of the study by a trial group were not associated with COI statements.Conclusion : Our study suggests COI reporting to have been unintuitive to most investigators and unreliable before ICMJE statements, and that strong incentives are needed to implement adequate reporting of COI.

Mobile Crowdsensing (MCS) is a contribution-based paradigm involving mobiles in pervasive application deployment and operation, pushed by the evergrowing and widespread dissemination of personal devices. Nevertheless, MCS is still lacking of some key features to become a disruptive paradigm. Among others, control on performance and reliability, mainly due to the contribution churning. For mitigating the impact of churning, several policies such as redundancy, over-provisioning and checkpointing can be adopted but, to properly design and evaluate such policies, specific techniques and tools are required. This paper attempts to fill this gap by proposing a new technique for the evaluation of relevant performance and energy figures of merit for MCS systems. It allows to get insightson them from three different perspectives: end users, contributors and service providers. Based on queuing networks (QN), the proposed technique relaxes the assumptions of existing solutions allowing a stochastic characterization of underlying phenomena through general, non exponential distributions. To cope with the contribution churning it extends the QN semantics of a service station with variablenumber of servers, implementing proper mechanisms to manage the memory issues thus arising in the underlying process. This way, a preliminary validation of the proposed QN model against an analytic one and an in depth investigation also considering checkpointing have been performed through a case study.

Background Limited data exist regarding ventilation in patients with class III obesity [body mass index (BMI) > 40 kg/m2] and acute respiratory distress syndrome (ARDS). The aim of the present study was to determine whether an individualized titration of mechanical ventilation according to cardiopulmonary physiology reduces the mortality in patients with class III obesity and ARDS. Methods In this retrospective study, we enrolled adults admitted to the ICU from 2012 to 2017 who had class III obesity and ARDS and received mechanical ventilation for > 48 h. Enrolled patients were divided in two cohorts: one cohort (2012–2014) had ventilator settings determined by the ARDSnet table for lower positive end-expiratory pressure/higher inspiratory fraction of oxygen (standard protocol-based cohort); the other cohort (2015–2017) had ventilator settings determined by an individualized protocol established by a lung rescue team (lung rescue team cohort). The lung rescue team used lung recruitment maneuvers, esophageal manometry, and hemodynamic monitoring. Results The standard protocol-based cohort included 70 patients (BMI = 49 ± 9 kg/m2), and the lung rescue team cohort included 50 patients (BMI = 54 ± 13 kg/m2). Patients in the standard protocol-based cohort compared to lung rescue team cohort had almost double the risk of dying at 28 days [31% versus 16%, P = 0.012; hazard ratio (HR) 0.32; 95% confidence interval (CI95%) 0.13–0.78] and 3 months (41% versus 22%, P = 0.006; HR 0.35; CI95% 0.16–0.74), and this effect persisted at 6 months and 1 year (incidence of death unchanged 41% versus 22%, P = 0.006; HR 0.35; CI95% 0.16–0.74). Conclusion Individualized titration of mechanical ventilation by a lung rescue team was associated with decreased mortality compared to use of an ARDSnet table.

During the past few years, all leading cloud providers introduced burstable instances that can sprint their performance for a limited period to address sudden workload variations. Despite the availability of burstable instances, there is no clear understanding of how to minimize the waste of resources by regulating their burst capacity to the workload requirements. This is especially true when it comes to non-CPU-intensive applications. In this paper, we investigate how to limit network and I/O usage to optimize the efficiency of the bursting process. We also study which resource shall be controlled to benefit both cloud providers and end-users. We design MRburst (Multi-Resource burstable performance scheduler) to automatically limit multiple resources (i.e., network, I/O, and CPU) and make the application comply with a user-defined service level objective (SLO) while minimizing wasted resources. MRburst is evaluated on Amazon EC2 using two multi-resource applications: an FTP server and a Ceph system. Experimental results show that MRburst outperforms state-of-the-art approaches by allowing instances to speed up their performance for up to 2.4 times longer period while meeting SLO.

Lipopolysaccharide, also known as endotoxin, is a fundamental component of gram-negative bacteria and plays a crucial role in the development of sepsis and septic shock. The early identification of an infectious process that is rapidly evolving to a critical illness might prompt a quicker and more intensive treatment, thereby potentially leading to better patient outcomes. The Endotoxin Activity (EA) assay can be used at the bedside as a reliable biomarker of systemic endotoxemia. The detection of elevated endotoxin activity levels has been repeatedly shown to be associated with an increased disease severity in patients with sepsis and septic shock. The assay is quick and easy to perform. Briefly, after sampling, an aliquot of whole blood is mixed with an anti-endotoxin antibody and with added LPS. Endotoxin activity is measured as the relative oxidative burst of primed neutrophils as detected by chemioluminescence. The assay's output is expressed on a scale from 0 (absent) to 1 (maximal) and categorized as “low” (

Purpose of review: Lower respiratory tract infections remain one of the leading causes of death in the world. Recently, the introduction of molecular methods based on DNA sequencing and microarrays for the identification of nonculturable microorganisms and subspecies variations has challenged the previous 'one bug - one disease' paradigm, providing us with a broader view on human microbial communities and their role in the development of infectious diseases. The purpose of this review is to describe recent understanding of the role of microbiome and bacterial biofilm in the development of lung infections, and, at the same time, to present new areas of research opportunities. Recent findings: The review describes recent literature in cystic fibrosis patients, chronic obstructive pulmonary disease patients, and literature in mechanically ventilated patients that helped to elucidate the role of microbiome and biofilm formation in the development of pneumonia. Summary: The characterization of the human microbiome and biofilms has changed our understanding of lower respiratory tract infections. More comprehensive, sensitive, and fast methods for bacterial, fungal, and viral detection are warranted to establish the colonization of the lower respiratory tract in healthy individuals and sick patients. Future research might explore the global bacterial, fungal, and viral pulmonary ecosystems and their interdependence to target novel preventive approaches and therapeutic strategies in chronic and acute lung infections.

Acute respiratory failure, either hypoxemic or hypercapnic, arises as the consequence of an inadequate response to the respiratory workload demanded by the organism. Several, often interlaced, factors contribute to the pathophysiology of this acute syndrome, whose treatment is essentially based on the resolution of the underlying critical illness. However, the use of ventilatory support might sustain or completely replace lung function and allow the patient more time to be treated. The pursuit of a noninvasive ventilatory strategy can be considered as a first-line option. Nevertheless, endotracheal intubation and invasive mechanical ventilation should be initiated in case of a persisting or progressive condition. The aim of mechanical ventilation is to maintain an adequate alveolar ventilation and oxygen delivery, thus restoring acid-base balance and reducing the patient’s work of breathing. Among others, the well-known risk of ventilator-induced lung injury must be taken into account. Accordingly, a protective ventilatory approach must be adopted. In case of a severe refractory disease, extracorporeal membrane oxygenation (ECMO) might be the only possibility to rescue patients with an otherwise fatal respiratory failure. Again, bypassing lung function through the use of an artificial lung must be weighed against the challenges and hazards of such an advanced life support treatment.

Trauma care is a time-sensitive emergency. Prehospital and in-hospital early care are the two necessarily interlaced crucial phases in the management of severe traumatic injuries, with potential impact on the survival of patients. While the main goals of an effective prehospital trauma system are clearly recognized, the relative impact on patient’s outcome of different approaches has yet to be established. The diversity among healthcare systems of different countries and the scarcity of high-quality evidence make it difficult to reach a consensus on essentially every aspect of prehospital trauma management. Once arrived at the selected trauma center, within dedicated areas of the emergency department, trauma victims should be assessed and treated by a specifically trained multidisciplinary trauma team, directed by a team leader, according to pre-specified institutional algorithms. Following a classic ABCDE method, thorough stepwise evaluations of the patient, and simultaneous emergency damage control maneuvers, are performed, prior to directing the patient to a definitive care plan. The management of trauma patients involves extremely complex logistics, across several levels of any healthcare system. Multiple providers with different training, both pre- and in-hospital, are called to face technical, organizational, clinical, and communication challenges, in a significantly time-constrained situation.

The evolution of digital technologies and software applications has introduced a new computational paradigm that involves the concurrent processing of jobs taken from a large pool in systems with limited computational capacity. Pool Depletion Systems is a framework proposed to analyze this paradigm where an optimal admission policy for jobs allocation is adopted to improve the performance of the system. Markov analysis and discrete event simulation, two techniques adopted to study Pool Depletion Systems framework, may require a long time before providing results, especially when dealing with complex systems. For this reason, a fluid approximation technique is presented in this chapter; in fact, it can provide results in a very short time, slightly decreasing their accuracy.

Cloud service providers use the concept of "burstable performance instance" that can temporally ramp up its performance to handle bursty workloads by utilizing spare resources. The state-of-the-practice to using the available burst capacity is independent of the workload, which results in squandering spare resources. In this work, we quantify and optimize the efficiency of using burst capacity so that it benefits both cloud service providers and end users. More specifically, we use a throttling mechanism as a control knob to continuously adapt the amount of spare resources based on workload characteristics such as traffic intensity. To identify optimal throttling, we integrate lightweight profiling and quantile regression in a synergistic way and build a prediction model that accurately predicts tail latency. We build an autonomic scheduling framework called CEDULE that can make adaptive scheduling decisions to maximize the efficiency of spare resources while achieving user defined SLOs. We conduct extensive experimental evaluations of the proposed scheduling framework on Amazon EC2 using popular benchmark applications, such as Sysbench, YCSB, and TPC-W. Experimental results demonstrate the high accuracy of the prediction model, i.e., average errors range from 1% to 15%. The effectiveness of CEDULE is verified as it can triple the efficiency of spare resources while meeting stringent SLOs.

Fog Computing (FC) systems represent a novel and promising generation of computing systems aiming at moving storage and computation close to end-devices so as to reduce latency, bandwidth and energy-efficiency. Despite their gaining importance, the literature about capacity planning studies for FC systems is very limited only considering very simplified technological cases. This paper considers a model for the capacity planning of a FC system for smart monitoring applications. More specifically, this paper considers a FC-based rock collapse forecasting system based on a hybrid wired-wireless architecture deployed in the Swiss-Italian Alps. The system is composed by sensing units deployed on rock faces to gather environmental data and FC-units providing high-performance computing for smart monitoring purposes.

Ventilator-associated pneumonia (VAP) is one of the most frequent hospital-acquired infections occurring in intubated patients. Because VAP is associated with higher mortality, morbidity, and costs, there is a need to solicit further research for effective preventive measures. VAP has been proposed as an indicator of quality of care. Clinical diagnosis has been criticized to have poor accuracy and reliability. Thus, the Centers for Disease Control and Prevention has introduced a new definition based upon objective and recordable data. Institutions are nowadays reporting a VAP zero rate in surveillance programs, which is in discrepancy with clinical data. This reduction has been highlighted in epidemiological studies, but it can only be attributed to a difference in patient selection, since no additional intervention has been taken to modify pathogenic mechanisms in these studies. The principal determinant of VAP development is the presence of the endotracheal tube (ETT). Contaminated oropharyngeal secretions pool over the ETT cuff and subsequently leak down to the lungs through a hydrostatic gradient. Impairment of mucociliary motility and cough reflex cannot counterbalance with a proper clearance of secretions. Lastly, biofilm develops on the inner ETT surface and acts as a reservoir for microorganism inoculum to the lungs. New preventive strategies are focused on the improvement of secretions drainage and prevention of bacterial colonization. The influence of gravity on mucus flow and body positioning can facilitate the clearance of distal airways, with decreased colonization of the respiratory tract. A different approach proposes ETT modifications to limit the leakage of oropharyngeal secretions: subglottic secretion drainage and cuffs innovations have been addressed to reduce VAP incidence. Moreover, coated-ETTs have been shown to prevent biofilm formation, although there is evidence that ETT clearance devices (Mucus Shaver) are required to preserve the antimicrobial properties over time. Here, after reviewing the most noteworthy issues in VAP definition and pathophysiology, we will present the more interesting proposals for VAP prevention.

In this review we have summarized the known virulent factors of P. aeruginosa in the development of ventilator associated pneumonia (VAP). This gram negative rod bacterium colonizes through several pathogenic mechanisms that can be summarized as follows: 1) cell surface virulence factors; 2) secreted virulence factors; 3) type secretion system; 4) quorum sensing. Another additional aspect that is covered is the capacity of P. aeruginosa of organized growth as a biofilm, which is a particular growth pattern in which bacteria gain high levels of antimicrobial resistance. The development of new antimicrobial strategies may use this understanding of the bacterium behavior for possible prevention, or eradication of the infective agent, in case of severe infections. © 2013 Bentham Science Publishers.

The volume of data, one of the five “V” characteristics of Big Data, grows at a rate that is much higher than the increase of ability of the existing systems to manage it within an acceptable time. Several technologies have been developed to approach this scalability issue. For instance, MapReduce has been introduced to cope with the problem of processing a huge amount of data, by splitting the computation into a set of tasks that are concurrently executed. The savings of even a marginal time in the processing of all the tasks of a set can bring valuable benefits to the execution of the whole application and to the management costs of the entire data center. To this end, we propose a technique to minimize the global processing time of a set of tasks, having different service requirements, concurrently executed on two or more heterogeneous systems. The validity of the proposed technique is demonstrated using a multiformalism model that consists of a combination of Queueing Networks and Petri Nets. Application of this technique to an Apache Hive case-study shows that the described allocation policy can lead to performance gains on both total execution time and energy consumption.

INTRODUCTION: Intubation compromises mucus clearance, allowing secretions to accumulate inside the endotracheal tube (ETT). The purpose of this trial was to evaluate a novel device for ETT cleaning. We hypothesized that its routine use would reduce tube occlusion due to mucus accumulation, while decreasing airway bacterial colonization. METHODS: Subjects were randomized to either the use of the device every 8 h, or the institutional standard of care (blind tracheal suction) only. ETTs were collected at extubation and analyzed with high-resolution computed tomography (HRCT) for quantification of mucus volume. Microbiological testing was performed on biofilm samples. Vital signs and ventilatory settings were collected at the bedside. In-hospital follow-up was conducted, and a final evaluation survey was completed by respiratory therapists. RESULTS: Seventy-four subjects expected to remain intubated for longer than 48 h were enrolled (77 ETTs, 37 treatment vs 40 controls). Treated tubes showed reduced mucus accumulation (0.56 ± 0.12 vs 0.71 ± 0.28 mL; P = .004) and reduced occlusion (6.3 ± 1.7 vs 8.9 ± 7.6%; P = .039). The HRCT slice showing the narrowest lumen within each ETT exhibited less occlusion in cleaned tubes (10.6 ± 8.0 vs 17.7 ± 13.4%, 95% CI: 2–12.1; P = .007). Data on microbial colonization showed a trend in the treatment group toward a reduced ETT-based biomass of bacteria known to cause ventilator-associated pneumonia. No adverse events were reported. The staff was satisfied by the overall safety and feasibility of the device. CONCLUSION: The endOclear is a safe and effective device. It prevents luminal occlusion, thereby better preserving ETT nominal function.

Endotracheal tubes (ETT) of intubated patients are constantly challenged with abundant bacteria-laden secretions. These bacteria may rapidly form a well-organized structure, referred to as biofilm, on the inner surface of the ETT. Secretions become very tenacious and are difficult to clear. However, bacteria and secretions can detach spontaneously or become dislodged by suction catheters and enter the lungs, providing a source of infection. Recently, several strategies have been developed to prevent accumulation of mucus and bacterial colonization of the respiratory circuit. In this review, we summarize published studies on antimicrobial ETT-coated and clearance devices. Numerous antimicrobial-impregnated ETTs have been designed with bactericidal/static properties to prevent adhesion of bacteria on ETT surfaces. Several in vitro experiments and animal-studies have shown success in the prevention of bacterial colonization through the use of these specialized ETTs. At present, only two ETT coatings, silver-hydrogel and silver-sulfadiazine in polyurethane, have been tested in clinical trials. Both coatings have been shown to prevent/lower bacterial colonization of the ETT, while only the silver-hydrogel coating decreased bacterial colonization of lungs in a large multicenter study. Another innovative approach is to reduce contaminated-secretions in the ETT-lumen with novel medical devices designed to retrieve accumulated-mucus from the ETT. The mucus shaver and the mucus slurper are two devices intended to reduce loaded-bacteria secretions from within the ETT. While experimental and preliminary clinical trials are promising, there are no large clinical trials showing outcome benefits. © 2012 Bentham Science Publishers.

The evolutions of digital technologies and software applications have introduced a new computational paradigm that involves initially the creation of a large pool of jobs followed by a phase in which all the jobs are executed in systems with limited capacity. For example, a number of libraries have started digitizing their old books, or video content providers, such as YouTube or Netflix, need to transcode their contents to improve playback performances. Such applications are characterized by a huge number of jobs with different requests of computational resources, like CPU and GPU. Due to the very long computation time required by the execution of all the jobs, strategies to reduce the total energy consumption are very important.

Background: We report the peculiar case of a patient with a retained large epidural catheter fragment, incidentally found 12 years after its placement. Our primary aim is to emphasize how the breakage and retention of even exceptionally large portions of this device can go undetected. The patient can be completely asymptomatic and, with no clue that such a foreign body exists, the presentation of its potential complications can be subtle and misleading. To our knowledge, this is the first reported case of the incidental discovery of such a large fragment so many years after its placement. No consensus exists about how to handle this complication, therefore our report adds to the amount of available evidence. Case Presentation: A 53-year-old caucasian female with a history of diverticulitis requiring multiple hospitalizations underwent laparoscopic sigmoidectomy. The early postoperative period was complicated by peritonitis, demanding an urgent "second-look" exploratory laparoscopy. Nine days post-operatively, a filiform metallic object in the upper-quadrant was noted on x-ray. No epidural had been placed for either one of her recent surgeries. Given the patient's history, the object was initially thought to be a retained surgical sponge. Previous studies, however, showed that the same image was already present preoperatively. Upon further questioning, the patient reported an epidural being placed twelve years before, at the time of her pregnancy. No mention of breakage had been made to her at that time, nor a retained foreign body was ever reported afterwards, despite her many imaging exams. She also never experienced any symptoms. A 15 cm fragment of a wire-reinforced catheter was surgically retrieved under local anesthesia and fluoroscopic guidance. Conclusion: Breakage of the epidural catheter with fragment retention is a known complication of this device, possibly leading to devastating sequelae. The fragment can go undetected for years. In this case our finding was incidental and the patient was asymptomatic. However, in the event a neurologic complication arose, the identification of the unknowingly retained epidural as the causative agent could have been difficult and delayed, with potential harm to the patient.