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Invasive meningococcal disease (IMD) is a life-threatening yet vaccine-preventable disease, with vaccines approved and recommended in the United States by the Centers for Disease Control and Prevention. This study assessed complications, mortality, healthcare resource utilization (HCRU), and healthcare costs among a sample of commercially-insured individuals living in the United States who experienced IMD. We used retrospective data from large claims databases limited to individuals with IMD covered by commercial health plans between January 2010–March 2022. Health outcomes, HCRU, and healthcare costs were analyzed during the acute (index date to 30 days post-hospital discharge) and post-acute (end of acute phase to end of follow-up period) phases. Among 618 IMD cases identified, the most common acute phase complications were severe brain damage, renal failure, and autoimmune disease. The most common post-acute phase complications were autoimmune disease, arthralgia, and renal failure. Acute phase HCRU ranged from 72.7 (95% confidence interval [CI] 19.81–186.12) intensive care unit (ICU) stays to 12,102.9 (95% CI 11,201.07–13,058.00) surgeries per 1,000 patient-years. Post-acute phase HCRU ranged from 3.6 (95% CI 1.18–8.50) ICU stays to 7,808.3 (95% CI 7,661.22–7,957.54) specialty physician visits per 1,000 patient-years. Patients with IMD incurred average healthcare costs of $60,866.23 and $145,883.65 during the acute and post-acute phases, respectively. Negative health outcomes and high HCRU and costs were observed among commercially-insured patients with IMD. Our findings suggest that IMD immunization efforts and healthcare interventions targeting education on vaccine recommendations to healthcare providers and patients could help prevent IMD and reduce disease burden.

Objective: Normative values of Rhinoplasty Outcome Evaluation (ROE) and Functional Rhinoplasty Outcome Inventory 17 (FROI-17) allow the monitoring of surgery outcomes. The objective of our study was to determine the reference norms of these disease-specific questionnaires in the age group that most often seeks rhinoplasty. Methods: The normative values of the ROE and FROI-17 questionnaires were calculated for 570 (459 women and 111 men) young adults at the mean age of 19.3 ± 1.3 years, range 18–25 years. Each participant underwent an ENT examination. All those who obtained a positive result were asked to complete two questionnaires: ROE and FROI-17. Results: The mean total ROE score was 13.4 ± 2.3, with a median of 13 and a range from 7 to 24. The mean overall FROI-17 score was 9.1 ± 13.3, with a median of 4 and a range from 4 to 72. For nasal symptoms, the mean was 4.0 ± 6.0, with a median of 1 and a range from 0 to 29. We observed a statistically significant difference between men and women only for the normative values of nasal symptoms (mean 4.0 ± 5.9 and median 2 (0–27) vs. mean 3.7 ± 6.5 and median 0 (0–29)). Additionally, there was a statistically significant correlation between the normative values of the ROE and FROI-17 scores (ρs = −0.413 for all participants, ρs = −0.314 for women, and ρs = −0.437 for men). Conclusions: The established normative values for the ROE and FROI-17 questionnaires among young, healthy individuals without nasal abnormalities can assist in the initial assessment of individuals seeking rhinoplasty. Deviations from these normative values in the ROE and FROI-17 questionnaires results may serve as indicators of potential concerns, such as body dysmorphic disorder (BDD).

Abstract Background Ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) represent a novel approach for the assessment and delivery of psychological support to depressed patients in daily life. Beyond the classical paper-and-pencil daily diaries, the more recent progresses in Information and Communication Technologies (ICT) enabled researchers to bring all the needed processes together in only one device, i.e., response signaling, repeated symptom collection, information storage, secure data transfer, and psychological support delivery. Despite evidence showing the feasibility and acceptability of these techniques, EMAs are only beginning to be applied in real clinical practice, whether the development of EMIs for clinically depressed patients is still very limited. The objective of this systematic review is to provide the state of the art of technology-based EMAs and EMIs for major depressive disorder (MDD), with the aim of leading the way to possible future directions for the clinical practice. Methods We will conduct a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Data sources will include two bibliographic databases, PubMed and Web of Science (Web of Knowledge), supplemented by searches for unpublished or ongoing studies. Eligible studies will report data for adult (≥ 18 years old) with a primary (both current and past) diagnosis of MDD, defined by a valid criterion standard. We will consider studies adopting technology-based EMAs and EMIs for the investigation and/or assessment of depression and for the delivery of a psychological intervention. We will exclude studies adopting paper-and-pencil tools. Discussion The proposed systematic review will provide new insights on the advantages and benefits of adopting technology-based EMAs and EMIs for MDD in the traditional clinical practice, taking into consideration both clinical and technological issues. The potential of using sensors and biosensors along with machine learning for affective modeling will also be discussed.

Automation is becoming ubiquitous in all laboratory activities, moving towards precisely defined and codified laboratory protocols. However, the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, while protocols define the steps carried out during an experiment: neither cover the domain of the other, although they both attempt to characterize the same phenomena. We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection. To this end, we present a language to model and optimize experimental biochemical protocols that facilitates such an integrated description, and that can be combined with experimental data. We provide probabilistic semantics for our language in terms of Gaussian processes (GPs) based on the linear noise approximation (LNA) that formally characterizes the uncertainties in the data collection, the underlying model, and the protocol operations. In a set of case studies, we illustrate how the resulting framework allows for automated analysis and optimization of experimental protocols, including Gibson assembly protocols.

Glyphosate (N-(phosphonomethyl)glycine) is an active ingredient of the most widely used herbicide formulations in protecting agricultural and horticultural crops. Numerous results (mostly published in the years 2010-2013) concerning the action of glyphosate and its formulations in the recent decade were analyzed. Initial reports about alleged biodegradability of glyphosate in the environment turned out to be wrong. It has been shown that glyphosate remains in the soil and can reach people by spreading along with groundwater. Recent publications have shown that glyphosate is detected at low concentrations in the human blood. Publications cited in this article, which indicate a possible induction of neoplastic changes by glyphosate formulation, have raised great concern and controversy in the scientific world. Presenting adverse effects of glyphosate and its formulations we focused on the role of glyphosate formulations in hormonal disorders by impeding the expression of steroidogenic acute regulatory protein and the inhibition of aromatase activity. The impact of glyphosate on oxygen reactive species formation, changes in redox system and the effect on necrosis and apoptosis in various types of cells was shown. We also revealed that glyphosate as a phosphonate herbicide does not inhibit directly the activity of acetylcholinesterase. Based on numerous studies it was noted that commercial formulations of glyphosate exhibit higher toxicity than that of the active substance itself. The discussed problems clearly show the need to evaluate the toxicity of glyphosate and its formulations and related potential threat to humans. Med Pr 2013;64(5):717–729

Modal automata are a classic formal model for component-based systems that comes equipped with a rich specification theory supporting abstraction, refinement and compositional reasoning. In recent years, quantitative variants of modal automata were introduced for specifying and reasoning about component-based designs for embedded and mobile systems. These respectively generalize modal specification theories for timed and probabilistic systems. In this paper, we define a modal specification language for combined probabilistic timed systems, called abstract probabilistic timed automata, which generalizes existing formalisms. We introduce appropriate syntactic and semantic refinement notions and discuss consistency of our specification language, also with respect to time-divergence. We identify a subclass of our models for which we define the fundamental operations for abstraction, conjunction and parallel composition, and show several compositionality results.

We present a strategic analysis of a trust model that has recently been proposed for promoting cooperative behaviour in user-centric networks. The mechanism for cooperation is based on a combination of reputation and virtual currency schemes in which service providers reward paying customers and punish non-paying ones by adjusting their reputation, and hence the price they pay for services. We model and analyse this system using PRISM-games, a tool that performs automated verification and strategy synthesis for stochastic multi-player games using the probabilistic alternating-time temporal logic with rewards (rPATL). We construct optimal strategies for both service users and providers, which expose potential risks of the cooperation mechanism and which we use to devise improvements that counteract these risks.

The toxicity of herbicides to animals and human is an issue of worldwide concern. The present study has been undertaken to assess toxic effect of widely used pesticide-glyphosate, its metabolites: aminomethylphosphonic acid (AMPA) and methylphosphonic acid and its impurities: N-(phosphonomethyl)iminodiacetic acid (PMIDA), N-methylglyphosate, hydroxymethylphosphonic acid and bis-(phosphonomethyl)amine on human peripheral blood mononuclear cells (PBMCs). We have evaluated the effect of those compounds on viability, ATP level, size (FSC-A parameter) and granulation (SSC-A parameter) of the cells studied. Human peripheral blood mononuclear cells were exposed to different concentrations of glyphosate, its metabolites and impurities (0.01-10 mM) for 4 and 24 h. It was found that investigated compounds caused statistically significant decrease in viability and ATP level of PBMCs. The strongest changes in cell viability and ATP level were observed after 24 h incubation of PBMCs with bis-(phosphonomethyl)amine, and particularly PMIDA. Moreover, all studied compounds changed cell granularity, while PMIDA and bis-(phosphonomethyl)amine altered PBMCs size. It may be concluded that bis-(phosphonomethyl)amine, and PMIDA caused a slightly stronger damage to PBMCs than did glyphosate. Changes in the parameters studied in PBMCs were observed only at high concentrations of the compounds examined, which clearly shows that they may occur in this cell type only as a result of acute poisoning of human organism with these substances.

We propose novel controller synthesis techniques for probabilistic systems modelled using stochastic two-player games: one player acts as a controller, the second represents its environment, and probability is used to capture uncertainty arising due to, for example, unreliable sensors or faulty system components. Our aim is to generate robust controllers that are resilient to unexpected system changes at runtime, and flexible enough to be adapted if additional constraints need to be imposed. We develop a permissive controller synthesis framework, which generates multi-strategies for the controller, offering a choice of control actions to take at each time step. We formalise the notion of permissivity using penalties, which are incurred each time a possible control action is disallowed by a multi-strategy. Permissive controller synthesis aims to generate a multi-strategy that minimises these penalties, whilst guaranteeing the satisfaction of a specified system property. We establish several key results about the optimality of multi-strategies and the complexity of synthesising them. Then, we develop methods to perform permissive controller synthesis using mixed integer linear programming and illustrate their effectiveness on a selection of case studies.

We study and provide efficient algorithms for multi-objective model checking problems for Markov Decision Processes (MDPs). Given an MDP, M, and given multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a strategy \sigma for the controller such that, for all i, the probability that a trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We provide an algorithm that decides whether there exists such a strategy and if so produces it, and which runs in time polynomial in the size of the MDP. Such a strategy may require the use of both randomization and memory. We also consider more general multi-objective \omega -regular queries, which we motivate with an application to assume-guarantee compositional reasoning for probabilistic systems. Note that there can be trade-offs between different properties: satisfying property \varphi\_1 with high probability may necessitate satisfying \varphi\_2 with low probability. Viewing this as a multi-objective optimization problem, we want information about the "trade-off curve" or Pareto curve for maximizing the probabilities of different properties. We show that one can compute an approximate Pareto curve with respect to a set of \omega -regular properties in time polynomial in the size of the MDP. Our quantitative upper bounds use LP methods. We also study qualitative multi-objective model checking problems, and we show that these can be analysed by purely graph-theoretic methods, even though the strategies may still require both randomization and memory.