Simulation training for medical emergencies in the dental setting using an inexpensive software application

INTRODUCTION

Every dental provider needs to be educated about medical emergencies to provide safe dental care. Simulation training is available with simulators such as advanced life support manikins and robot patients. However, the purchase and development costs of these simulators are high. We have developed a simulation training course on medical emergencies using an inexpensive software application. The purpose of this study was to evaluate the educational effectiveness of this course.

MATERIALS AND METHODS

Fifty-one dental providers participated in this study from December 2014 to March 2015. Medical simulation software was used to simulate a patient's vital signs. We evaluated participants' ability to diagnose and treat vasovagal syncope or anaphylaxis with an evaluation sheet and conducted a questionnaire before and after the scenario-based simulation training.

RESULTS

The median evaluation sheet score for vasovagal syncope increased significantly from 7/9 before to 9/9 after simulation training. The median score for anaphylaxis also increased significantly from 8/12 to 12/12 (P < .01). For the item "I can treat vasovagal syncope/anaphylaxis adequately," the percentage responding "Strongly agree" or "Agree" increased from 14% to 56% for vasovagal syncope and from 6% to 42% for anaphylaxis with simulation training.

CONCLUSIONS

This simulation course improved participants' ability to diagnose and treat medical emergencies and improved their confidence. This course can be offered inexpensively using a software application.

Detecting directional couplings from multivariate flows by the joint distance distribution

The identification of directional couplings (or drive-response relationships) in the analysis of interacting nonlinear systems is an important piece of information to understand their dynamics. This task is especially challenging when the analyst's knowledge of the systems reduces virtually to time series of observations. Spurred by the success of Granger causality in econometrics, the study of cause-effect relationships (not to be confounded with statistical correlations) was extended to other fields, thus favoring the introduction of further tools such as transfer entropy. Currently, the research on old and new causality tools along with their pitfalls and applications in ever more general situations is going through a time of much activity. In this paper, we re-examine the method of the joint distance distribution to detect directional couplings between two multivariate flows. This method is based on the forced Takens theorem, and, more specifically, it exploits the existence of a continuous mapping from the reconstructed attractor of the response system to the reconstructed attractor of the driving system, an approach that is increasingly drawing the attention of the data analysts. The numerical results with Lorenz and Rössler oscillators in three different interaction networks (including hidden common drivers) are quite satisfactory, except when phase synchronization sets in. They also show that the method of the joint distance distribution outperforms the lowest dimensional transfer entropy in the cases considered. The robustness of the results to the sampling interval, time series length, observational noise, and metric is analyzed too.

Electronic Structure of Ce-Doped and -Undoped Nd_{2}CuO_{4} Superconducting Thin Films Studied by Hard X-Ray Photoemission and Soft X-Ray Absorption Spectroscopy

In order to realize superconductivity in cuprates with the T^{'}-type structure, not only chemical substitution (Ce doping) but also postgrowth reduction annealing is necessary. In the case of thin films, however, well-designed reduction annealing alone without Ce doping can induce superconductivity in the T^{'}-type cuprates. In order to unveil the origin of superconductivity in the Ce-undoped T^{'}-type cuprates, we have performed bulk-sensitive hard x-ray photoemission and soft x-ray absorption spectroscopy on superconducting and nonsuperconducting Nd_{2-x}Ce_{x}CuO_{4} (x=0, 0.15, and 0.19) thin films. By postgrowth annealing, core-level spectra exhibited dramatic changes, which we attributed to the enhancement of core-hole screening in the CuO_{2} plane and the shift of chemical potential along with changes in the band filling. The result suggests that the superconducting Nd_{2}CuO_{4} film is doped with electrons despite the absence of the Ce substitution.

Element Selectivity in Second-Harmonic Generation of GaFeO_{3} by a Soft-X-Ray Free-Electron Laser

Nonlinear optical frequency conversion has been challenged to move down to the extreme ultraviolet and x-ray region. However, the extremely low signals have allowed researchers to only perform transmission experiments of the gas phase or ultrathin films. Here, we report second harmonic generation (SHG) of the reflected beam of a soft x-ray free-electron laser from a solid, which is enhanced by the resonant effect. The observation revealed that the double resonance condition can be met by absorption edges for transition metal oxides in the soft x-ray range, and this suggests that the resonant SHG technique can be applicable to a wide range of materials. We discuss the possibility of element-selective SHG spectroscopy measurements in the soft x-ray range.

Tensile-Strain-Dependent Spin States in Epitaxial LaCoO_{3} Thin Films

The spin states of Co^{3+} ions in perovskite-type LaCoO_{3}, governed by the complex interplay between the electron-lattice interactions and the strong electron correlations, still remain controversial due to the lack of experimental techniques which can directly detect them. In this Letter, we revealed the tensile-strain dependence of spin states, i.e., the ratio of the high- and low-spin states, in epitaxial thin films and a bulk crystal of LaCoO_{3} via resonant inelastic soft x-ray scattering. A tensile strain as small as 1.0% was found to realize different spin states from that in the bulk.

Endotoxin Metabolism Reflects Hepatic Functional Reserve in End-Stage Liver Disease

BACKGROUND

The hepatic clearance of endotoxin (Et) may reflect hepatic functional reserve and ischemic injury to hepatocytes. Therefore, we examined the relationships between Et activity (EA) and the metrics Pediatric End-Stage Liver Disease (PELD)/Model of End-Stage Liver Disease (MELD) score and alanine transaminase (ALT) levels in the postoperative period.

METHODS

We performed 8 living-donor liver transplantations (LDLTs) for biliary atresia at our center from April 2012 to December 2012. EA was measured by means of an Et activity assay (EAA) in samples collected from a vein 1 day before LDLT, from the portal vein during the intraoperative anhepatic phase, from an artery 1 hour after reperfusion, from an artery on postoperative day (POD) 1, and from an artery or vein at PODs 7 and 14.

RESULTS

EAs generally remained at low levels. EA at the reperfusion period was significantly lowest. The correlation coefficient for the preoperative MELD/PELD score and the EAA was 0.837, and the corresponding P value was .009; thus, there was a significant relationship between the preoperative MELD/PELD score and the EAA. The correlation coefficients for ALT at POD 1 and EA during the anhepatic phase, at 1 hour after reperfusion, and at POD 1 were 0.64, 0.43, and 0.38, respectively, and the P values for these correlations were .08, .67, and .34. Thus, we observed that ALT and EA generally tended to be somewhat directly correlated, but no significant relationships between these 2 metrics were observed.

CONCLUSIONS

Endotoxin metabolism reflects the hepatic functional reserve capacity of end-stage liver disease.

Reconstructing latent dynamical noise for better forecasting observables

I propose a method for reconstructing multi-dimensional dynamical noise inspired by the embedding theorem of Muldoon et al. [Dyn. Stab. Syst. 13, 175 (1998)] by regarding multiple predictions as different observables. Then, applying the embedding theorem by Stark et al. [J. Nonlinear Sci. 13, 519 (2003)] for a forced system, I produce time series forecast by supplying the reconstructed past dynamical noise as auxiliary information. I demonstrate the proposed method on toy models driven by auto-regressive models or independent Gaussian noise.

Personalizing Androgen Suppression for Prostate Cancer Using Mathematical Modeling

Using a dataset of 150 patients treated with intermittent androgen suppression (IAS) through a fixed treatment schedule, we retrospectively designed a personalized treatment schedule mathematically for each patient. We estimated 100 sets of parameter values for each patient by randomly resampling each patient’s time points to take into account the uncertainty for observations of prostate specific antigen (PSA). Then, we identified 3 types and classified patients accordingly: in type (i), the relapse, namely the divergence of PSA, can be prevented by IAS; in type (ii), the relapse can be delayed by IAS later than by continuous androgen suppression (CAS); in type (iii) IAS was not beneficial and therefore CAS would have been more appropriate in the long run. Moreover, we obtained a treatment schedule of hormone therapy by minimizing the PSA of 3 years later in the worst case scenario among the 100 parameter sets by searching exhaustively all over the possible treatment schedules. If the most frequent type among 100 sets was type (i), the maximal PSA tended to be kept less than 100 ng/ml longer in IAS than in CAS, while there was no statistical difference for the other cases. Thus, mathematically personalized IAS should be studied prospectively.

Antibody Drug Treatment for Steroid-Resistant Rejection After Pediatric Living Donor Liver Transplantation: A Single-Center Experience

BACKGROUND

Antibody drugs have been used to treat steroid-resistant rejection (SRR) after liver transplantation. Although anti-thymocyte globulin has been used for SRR after liver transplantation in place of muromonab-CD3 since 2011 in Japan, the effectiveness of anti-thymocyte globulin after pediatric living-donor liver transplantation (LDLT) has not yet been reported. The aim of this study was to evaluate the effectiveness of antibody drug treatment for SRR after pediatric LDLT in our single center.

METHODS

Between May 2001 and December 2013, 220 pediatric LDLTs were performed. Initial immunosuppression after LDLT included tacrolimus and methylprednisolone therapy. Acute rejection was diagnosed by use of a liver biopsy and the administration of steroid pulse treatment, and SRR was defined as acute rejection refractory to the steroid pulse treatment.

RESULTS

Acute rejection and SRR occurred in 74 (33.6%) and 16 patients (7.3%), respectively. The graft survival rates of non-SRR and SRR were 92.4% and 87.5%, respectively (P = .464). The median concentration of alanine aminotransferase before and after the administration of antibody drug was 193.5 mU/mL (range, 8-508) and 78 mU/mL (range, 9-655), respectively (P = .012). The median rejection activity index before and after the administration of antibody drugs was 5 (range, 2-9) and 1 (range, 0-9), respectively (P = .004). After antibody drug treatment, 12 patients had cytomegalovirus infections, 2 patients had Epstein-Barr virus infections, 3 patients had respiratory infections, and 1 patient had encephalitis. The cause of death in 1 patient with SRR was recurrence of infant fulminant hepatic failure.

CONCLUSIONS

Antibody drug treatment for SRR after pediatric LDLT is safe and effective.

Prediction of flow dynamics using point processes

Describing a time series parsimoniously is the first step to study the underlying dynamics. For a time-discrete system, a generating partition provides a compact description such that a time series and a symbolic sequence are one-to-one. But, for a time-continuous system, such a compact description does not have a solid basis. Here, we propose to describe a time-continuous time series using a local cross section and the times when the orbit crosses the local cross section. We show that if such a series of crossing times and some past observations are given, we can predict the system's dynamics with fine accuracy. This reconstructability neither depends strongly on the size nor the placement of the local cross section if we have a sufficiently long database. We demonstrate the proposed method using the Lorenz model as well as the actual measurement of wind speed.

Dimensionless embedding for nonlinear time series analysis

Recently, infinite-dimensional delay coordinates (InDDeCs) have been proposed for predicting high-dimensional dynamics instead of conventional delay coordinates. Although InDDeCs can realize faster computation and more accurate short-term prediction, it is still not well-known whether InDDeCs can be used in other applications of nonlinear time series analysis in which reconstruction is needed for the underlying dynamics from a scalar time series generated from a dynamical system. Here, we give theoretical support for justifying the use of InDDeCs and provide numerical examples to show that InDDeCs can be used for various applications for obtaining the recurrence plots, correlation dimensions, and maximal Lyapunov exponents, as well as testing directional couplings and extracting slow-driving forces. We demonstrate performance of the InDDeCs using the weather data. Thus, InDDeCs can eventually realize "dimensionless embedding" while we enjoy faster and more reliable computations.

On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy

In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm.

Hawkes process model with a time-dependent background rate and its application to high-frequency financial data

A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method. Our model can capture not only the slow time variation, such as in the intraday seasonality, but also the rapid one, which follows a macroeconomic news announcement. By analyzing the tick data of the Nikkei 225 mini, we find that (i) our model is better fitted to the data than the Hawkes models with a constant background rate or a slowly varying background rate, which have been commonly used in the field of quantitative finance; (ii) the improvement in the goodness-of-fit to the data by our model is significant especially for sessions where considerable fluctuation of the background rate is present; and (iii) our model is statistically consistent with the data. The branching ratio, which quantifies the level of the endogeneity of markets, estimated by our model is 0.41, suggesting the relative importance of exogenous factors in the market dynamics. We also demonstrate that it is critically important to appropriately model the time-dependent background rate for the branching ratio estimation.

Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory

Atopic dermatitis (AD) is a common chronic skin disease characterized by recurrent skin inflammation and a weak skin barrier, and is known to be a precursor to other allergic diseases such as asthma. AD affects up to 25% of children worldwide and the incidence continues to rise. There is still uncertainty about the optimal treatment strategy in terms of choice of treatment, potency, duration and frequency. This study aims to develop a computational method to design optimal treatment strategies for the clinically recommended 'proactive therapy' for AD. Proactive therapy aims to prevent recurrent flares once the disease has been brought under initial control. Typically, this is done by using an anti-inflammatory treatment such as a potent topical corticosteroid intensively for a few weeks to 'get control', followed by intermittent weekly treatment to suppress subclinical inflammation to 'keep control'. Using a hybrid mathematical model of AD pathogenesis that we recently proposed, we computationally derived the optimal treatment strategies for individual virtual patient cohorts, by recursively solving optimal control problems using a differential evolution algorithm. Our simulation results suggest that such an approach can inform the design of optimal individualized treatment schedules that include application of topical corticosteroids and emollients, based on the disease status of patients observed on their weekly hospital visits. We demonstrate the potential and the gaps of our approach to be applied to clinical settings.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.