Simulation Trials Research Articles

Designing ultra-broadband terahertz polarization converters based on the transformer model

Polarization, a fundamental property of terahertz (THz) waves, can be controlled using metasurfaces for signal transmission and sensitive measurements. However, conventional methods for designing polarization-conversion metasurfaces require numerous numerical simulation iterations and trial and error attempts that are computationally intensive and time-consuming, necessitating a more rapid and efficient approach. This study proposes a transformer-based method for the inverse design of terahertz polarization converters; the approach can accurately design the corresponding metasurface based on the target spectrum. The model also comprises a forward network that can precisely and intuitively predict the meta-atoms spectra with high simulation speed and reduced computational cost. Using this approach, two ultra-broadband polarization conversion devices were designed in approximately 0.006 s within a target frequency range of 0.5–4 THz, with polarization conversion ratios greater than 90% within 1.23–3.19 THz and 1.18–3.24 THz. Additionally, the average cross-polarization ratio was greater than 50%. This study provides insights into designing terahertz polarization converters, which are applicable in terahertz communications, polarization imaging, and biomedical procedures.

Demonstrating Bioequivalence for Two Dose Strengths of Niraparib andAbiraterone AcetateDual-Action Tablets Versus Single Agents: Utility of Clinical Study Data Supplemented with Modeling and Simulation.

The combination of niraparib and abiraterone acetate (AA) plus prednisone is under investigation for the treatment of patients with metastatic castration-resistant prostate cancer (mCRPC) and metastatic castration-sensitive prostate cancer (mCSPC). Regular-strength (RS) and lower-strength (LS) dual-action tablets (DATs), comprising niraparib 100 mg/AA 500 mg and niraparib 50 mg/AA 500 mg, respectively, were developed to reduce pill burden and improve patient experience. A bioequivalence (BE)/bioavailability (BA) study was conducted under modified fasting conditions in patients with mCRPC to support approval of the DATs. This open-label randomized BA/BE study (NCT04577833) was conducted at 14 sites in the USA and Europe. The study had a sequential design, including a 21-day screening phase, a pharmacokinetic (PK) assessment phase comprising three periods [namely (1) single-dose with up to 1-week run-in, (2) daily doseon days 1-11, and (3) daily dose ondays 12-22], an extension where both niraparib and AA as single-agent combination (SAC; reference) or AA alone was continued from day 23 until discontinuation, and a 30-day follow-up phase. Patients were randomly assigned in a parallel-group design (four-sequence randomization) to receive a single oral dose of niraparib 100 mg/AA 1000 mg as a LS-DAT or SAC in period 1, and patients continued as randomized into a two-way crossover design during periods 2 and 3 where they received niraparib 200 mg/AA 1000 mg once daily as a RS-DAT or SAC. The design was powered on the basis of crossover assessment of RS-DAT versus SAC. During repeated dosing (periods 2 and 3, and extension phase), all patients also received prednisone/prednisolone 5 mg twice daily. Plasma samples were collected for measurement of niraparib and abiraterone plasma concentrations. Statistical assessment of the RS-DAT and LS-DAT versus SAC was performed on log-transformed pharmacokinetic parameters data from periods 2 and 3 (crossover) and from period 1 (parallel), respectively. Additional paired analyses and model-based bioequivalence assessments were conducted to evaluate the similarity between the LS-DAT and SAC. For the RS-DAT versus SAC, the 90% confidence intervals (CI) of geometric mean ratios (GMR) for maximum concentration at a steady state (Cmax,ss) and area under the plasma concentration-time curve from 0-24 h at a steady state (AUC 0-24h,ss) were respectively 99.18-106.12% and 97.91-104.31% for niraparib and 87.59-106.69 and 86.91-100.23% for abiraterone. For the LS-DAT vs SAC, the 90% CI of GMR for AUC0-72h of niraparib was 80.31-101.12% in primary analysis, the 90% CI of GMR for Cmax,ss and AUC 0-24h,ss of abiraterone was 85.41-118.34% and 86.51-121.64% respectively, and 96.4% of simulated LS-DAT versus SAC BE trials met the BE criteria for both niraparib and abiraterone. The RS-DAT met BE criteria (range 80%-125%) versus SAC based on 90% CI of GMR for Cmax,ss and AUC 0-24h,ss. The LS-DAT was considered BE to SAC on the basis of the niraparib component meeting the BE criteria in the primary analysis for AUC 0-72h; abiraterone meeting the BE criteria in additional paired analyses based on Cmax,ss and AUC 0-24h,ss; and the percentage of simulated LS-DAT versus SAC BE trials meeting the BE criteria for both. NCT04577833.

Probability distribution of write failure in a memory cell array consisting of magnetic tunnel junction elements with distributed write error rates

Write failure (WF) is a major reliability issue for applications of magnetoresistive random access memory (MRAM), and much effort has been devoted to reducing the write error rate (WER), which is the probability of write failures of a memory cell. Recently, it was shown that the WER of MRAM obeys a skewed probability distribution even though the variation in material parameters obeys a normal distribution. However, little is known about the effect of WER distribution on WF in a memory cell array. Here, we study WF in a memory cell array consisting of magnetic tunnel junction elements with distributed WERs based on numerical simulations. We simulated Bernoulli trials of writing, assuming that the WER obeys a beta distribution. The results show that for typical writing patterns, WF in a memory cell array obeys a binomial distribution, with the mean of the WER as the probability of success. The statistical properties of WF in a memory cell array are not affected by the variance and skewness of the WER. The results provide a basic understanding of the statistical properties of WF in a memory cell array and will be useful for the development of computing systems that exploit erroneous memories.

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

A method based on multi-agent reinforcement learning is proposed to tackle the challenges to capture escaping Target by Unmanned Ground Vehicles (UGVs). Initially, this study introduces environment and motion models tailored for cooperative UGV capture, along with clearly defined success criteria for direct capture. An attention mechanism integrated into the Soft Actor-Critic (SAC) is leveraged, directing focus towards pivotal state features pertinent to the task while effectively managing less relevant aspects. This allows capturing agents to concentrate on the whereabouts and activities of the target agent, thereby enhancing coordination and collaboration during pursuit. This focus on the target agent aids in refining the capture process and ensures precise estimation of value functions. The reduction in superfluous activities and unproductive scenarios amplifies efficiency and robustness. Furthermore, the attention weights dynamically adapt to environmental shifts. To address constrained incentives arising in scenarios with multiple vehicles capturing targets, the study introduces a revamped reward system. It divides the reward function into individual and cooperative components, thereby optimizing both global and localized incentives. By facilitating cooperative collaboration among capturing UGVs, this approach curtails the action space of the target UGV, leading to successful capture outcomes. The proposed technique demonstrates enhanced capture success compared to previous SAC algorithms. Simulation trials and comparisons with alternative learning methodologies validate the effectiveness of the algorithm and the design approach of the reward function.

Progression analysis versus traditional methods to quantify slowing of disease progression in Alzheimer’s disease

BackgroundThe clinical meaningfulness of the effects of recently approved disease-modifying treatments (DMT) in Alzheimer’s disease is under debate. Available evidence is limited to short-term effects on clinical rating scales which may be difficult to interpret and have limited intrinsic meaning to patients. The main value of DMTs accrues over the long term as they are expected to cause a delay or slowing of disease progression. While awaiting such evidence, the translation of short-term effects to time delays or slowing of progression could offer a powerful and readily interpretable representation of clinical outcomes.MethodsWe simulated disease progression trajectories representing two arms, active and placebo, of a hypothetical clinical trial of a DMT. The placebo arm was simulated based on estimated mean trajectories of clinical dementia rating scale–sum of boxes (CDR-SB) recordings from amyloid-positive subjects with mild cognitive impairment (MCI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The active arm was simulated to show an average slowing of disease progression versus placebo of 20% at each visit. The treatment effects in the simulated trials were estimated with a progression model for repeated measures (PMRM) and a mixed model for repeated measures (MMRM) for comparison. For PMRM, the treatment effect is expressed in units of time (e.g., days) and for MMRM in units of the outcome (e.g., CDR-SB points). PMRM results were implemented in a health economics Markov model extrapolating disease progression and death over 15 years.ResultsThe PMRM model estimated a 19% delay in disease progression at 18 months and 20% (~ 7 months delay) at 36 months, while the MMRM model estimated a 25% reduction in CDR-SB (~ 0.5 points) at 36 months. The PMRM model had slightly greater power compared to MMRM. The health economic model based on the estimated time delay suggested an increase in life expectancy (10 months) without extending time in severe stages of disease.ConclusionPMRM methods can be used to estimate treatment effects in terms of slowing of progression which translates to time metrics that can be readily interpreted and appreciated as meaningful outcomes for patients, care partners, and health care practitioners.

Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.

Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery. This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes. In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes. Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.

Implementation and evaluation of a decentralized medical data exchange system based on the IOTA Tangle and approximate algorithms for optimal data placement


 
 
 
 
 
 
 
 © Voronin V., Rybalchenko A., Shmatko O., Kolomiitsev O., Tretiak V., Kliuchka Ya., 2023
 
 
 
 
 
 
 
 
 Facilitating the transfer of patient medical information across healthcare providers is crucial for ensuring high-quality care. However, this process encounters obstacles pertaining to privacy, security, and centralised control. This article outlines the creation of a decentralised system for exchanging medical data. The system utilises Distributed Ledger Technology, specifically the IOTA Tangle (open protocol (and network) for data and value transfer). This document outlines the structure and fundamental elements of a system designed for the secure and unalterable storage and transfer of medical records. The system employs masking and encryption methodologies to protect patient confidentiality while enabling healthcare practitioners to access complete information with patient authorization. The IOTA Tangle facilitates transactions without any fees and ensures the verification of data integrity through its Directed Acyclic Graph (DAG) topology. Simulation trials validate the system’s capability to securely communicate medical data on a large scale, while incurring lower resource costs than typical blockchain systems. The system demonstrates the feasibility of a decentralised and self-governing method for efficient and confidential sharing of medical data, utilising Distributed Ledger Technology (DLT). The secure data sharing platform facilitates the implementation of novel care and research frameworks, all the while upholding patient confidentiality and adhering to healthcare ethical standards. Also, in the development of billing Online Transaction Processing (OLTP) systems, which are designed for input, structured storage and processing of information in real time, the use of cloud technology is proposed. The problems that arise and their relevance to the solution of an integer linear programming problem with Boolean variables are shown. Approximate algorithms for optimal data placement and mathematical models for optimizing the structure of a distributed database of a cloud system are proposed, taking into account the limitations on the amount of node memory, the available costs of renting cloud resources, and the number of replicas of fragments of the distributed database.

Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.

Cost-effectiveness of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing and isolation strategies in nursing homes.

Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory coronavirus virus 2 (SARS-CoV-2) evolves. We developed an agent-based model representing a typical nursing home, COVID-19 spread, and its health and economic outcomes to determine the clinical and economic value of various screening and isolation strategies and how it may change under various circumstances. Under winter 2023-2024 SARS-CoV-2 omicron variant conditions, symptom-based antigen testing averted 4.5 COVID-19 cases compared to no testing, saving $191 in direct medical costs. Testing implementation costs far outweighed these savings, resulting in net costs of $990 from the Centers for Medicare & Medicaid Services perspective, $1,545 from the third-party payer perspective, and $57,155 from the societal perspective. Testing did not return sufficient positive health effects to make it cost-effective [$50,000 per quality-adjusted life-year (QALY) threshold], but it exceeded this threshold in ≥59% of simulation trials. Testing remained cost-ineffective when routinely testing staff and varying facemask compliance, vaccine efficacy, and booster coverage. However, all antigen testing strategies became cost-effective (≤$31,906 per QALY) or cost saving (saving ≤$18,372) when the severe outcome risk was ≥3 times higher than that of current omicron variants. SARS-CoV-2 testing costs outweighed benefits under winter 2023-2024 conditions; however, testing became cost-effective with increasingly severe clinical outcomes. Cost-effectiveness can change as the epidemic evolves because it depends on clinical severity and other intervention use. Thus, nursing home administrators and policy makers should monitor and evaluate viral virulence and other interventions over time.

Multi-Parameter Analysis of Textile Materials

Advanced constructions in the automotive, military, and aircraft sectors often employ textile materials. This is due to its advantageous mechanical characteristics, appealing supporting components, affordable manufacture, and simple handling. For architectural layout, textile composite analysis becomes crucial. It is costly and time-consuming to experimentally determine the rigidity and durability of textile materials. Due to the impossibility of disintegration, experimental testing can only provide the characteristics of a single textile layer. The disadvantage of a textile layer made up of various fibre orientations is that it is likely to display anisotropic physical performance. Based on the physical reaction of glass and epoxy resin materials, also the design of the textile materials, a multi-parameter (MP) analysis is used to identify the substance behaviour of textile materials via virtual testing. An estimate for just one layer or a whole textile prototype may be created using these simulated trials. Pressure-dependent and nonlinear responses of materials in the multi-parameter analysis are material models created especially for this purpose and are used to simulate. The simulations are contrasted with the outcomes of experimental tests, which reveal the properties of the textile materials. The findings stated that the proposed model has provided rigidity-dislocation of 0.30 in the simulation results

Time-course Changes of Field- and Laboratory-based Performance Indicators in Junior Cyclists Through a Season.

This study aimed to assess the seasonal evolution of field-based and laboratory-based performance indicators in cyclists. Thirteen Junior male road cyclists (age 17.4±0.5 years) were followed up during a season, which was divided in three phases: early season (involving mainly training sessions), mid-season (including the first competitions), and late season (including the major competitions of the season). During each phase, field-based power output data were registered for the assessment of maximum mean power values, and laboratory-based endurance (ramp test and simulated 8-minute time trial), muscle strength/power (squat, lunge, hip thrust) and body composition indicators (dual-energy X-ray absorptiometry) were also assessed. A progressive (p<0.01) increase in maximum mean power values (e.g., 3.8±0.3 and 4.5±0.4 watts/kg in early and late season, respectively, for 60-minute efforts) and on 8-minute time trial performance (i.e., 5.3±0.3 and 5.6±0.4 watts/kg, respectively) was observed through the season. Yet, more "traditional" endurance indicators (i.e., ventilatory threshold, respiratory compensation point, or maximum oxygen uptake) seemed to show a ceiling effect beyond the mid-season. In addition, neither peak power output, body composition, nor muscle strength indicators followed a similar pattern to the aforementioned field-based indicators. In summary, in Junior cyclists field-based indicators seem more sensitive to monitor endurance cyclists' changes in actual fitness and performance capacity than more "traditional" laboratory-based markers in Junior cyclists.

A quasi-experimental study in sibling dyads: differential provocation-aggression patterns in the interactive taylor aggression paradigm.

The Taylor Aggression Paradigm (TAP) is a well-established tool for assessing provocation-induced reactive aggression. We introduce an interactive version, the iTAP, with real-time opponents across 60 trials, including five simulated provocation trials in the middle. In this quasi-experimental study, we evaluate the effectiveness of the paradigm to investigate reactive aggression in interacting participants. The design allows us to employ the TAP in settings of high familiarity dyads, addressing an existing gap. Twenty-eight healthy same-sex adult sibling pairs (N = 56) competed against each other in the iTAP, exemplifying high familiarity through their social and emotional co-development, and mutual knowledge. Additionally, we explore naturally arising aggression types in terms of sibling pairs' reciprocal aggression trajectories across trials. Lastly, we investigate situational and personal variables influencing reactive aggression on the iTAP within high familiarity dyads. In line with non-interactive TAP versions, siblings employed a global "tit-for-tat" strategy in response to heightened provocation: Aggression increased during manipulated trials of increasing provocation, persisted during real interaction and declined in the final block, suggesting sibling co-regulation which was underscored by the convergence in within-pair aggression level. We found no gender differences in these dynamics but a trend for higher initial aggression levels within brother pairs and higher responsiveness to increased provocation in sister pairs. Overall aggression levels were related to situational variables including trial outcome (lost, won, and tie), Further, siblings' state anger correlated positively with aggression scores on the iTAP. Aggression was not reliably related to personal variables predicting aggression. We identified subgroups of sibling pairs with distinct provocation-aggression patterns related to differences in reported behavioral motivations and emotional states. The results highlight situational over personal variables in determining aggressive behavior on the task in this sample of healthy adults. While no direct link between sibling relationship quality and aggression was found, the overall behavior was likely influenced by the familiarity between siblings and the specific context of their relationship. The iTAP demonstrates promise as a tool for studying reciprocal aggressive behavior. The emergence of different interaction patterns underscores the ecological validity introduced by the interactive context, which complements the standard versions of the TAP.

An Empirical Investigation of Manufacturers’ Operations Innovations in New Product Development Enabled by E-Commerce Platforms

E-commerce platforms are playing an increasingly important role in influencing manufacturers’ supply chain and product decisions. An emerging supply chain innovation, known as the platform-based consumer-to-manufacturer (PC2M) model, has been initiated by several large e-commerce platforms based on established digital links between consumers and manufacturers. These links enable consumer inputs into manufacturers’ operations, indirectly by capturing consumer preferences from platform-accumulated big data and directly by enabling consumer interaction with manufacturers through the e-commerce platform. Although manufacturers are increasingly integrating PC2M into new product development (NPD), there is little research on operations innovations in connection with the PC2M model and its impact on manufacturers’ new product success. To fill this research gap, we investigate the PC2M model of JD.com, a leading e-commerce platform in China that launched the PC2M model in 2018. We first identify two uses of PC2M by manufacturers to facilitate product development—platform-enabled big data analytics (PBA) and platform-enabled simulated product trials (PST)—and explore how PC2M enables operations innovations in NPD. Next, drawing on the knowledge-based view, we develop research hypotheses and empirically examine whether PC2M adoption improves new product performance using a large-scale, transactional dataset from JD.com. Through a series of carefully executed analyses, our study consistently finds that use of either PBA or PST in manufacturers’ NPD processes improves new product performance. We also explore how these effects vary across product types and markets with varying new product introduction rates. The findings offer important managerial insights for improving new product success in today's data-rich environment.

Clinical performance and utility: A microsimulation model to inform the design of screening trials for a multi-cancer early detection test.

Designing cancer screening trials for multi-cancer early detection (MCED) tests presents a significant methodology challenge, as natural histories of cell-free DNA-shedding cancers are not yet known. A microsimulation model was developed to project the performance and utility of an MCED test in cancer screening trials. Individual natural history of preclinical progression through cancer stages for 23 cancer classes was simulated by a stage-transition model under a broad range of cancer latency parameters. Cancer incidences and stage distributions at clinical presentation in simulated trials were set to match the data from Surveillance, Epidemiology, and End Results program. One or multiple rounds of annual screening using a targeted methylation-based MCED test (GalleriⓇ) was conducted to detect preclinical cancers. Mortality benefit of early detection was simulated by a stage-shift model. In simulated trials, accounting for healthy volunteer effect and varying test sensitivity, positive predictive value in the prevalence screening round reached 48% to 61% in 6 natural history scenarios. After 3 rounds of annual screening, the cumulative proportions of stage I/II cancers increased by approximately 9% to 14%, the incidence of stage IV cancers was reduced by 37% to 46%, the reduction of stages III and IV cancer incidences was 9% to 24%, and the reduction of mortality reached 13% to 16%. Greater reductions of late-stage cancers and cancer mortality were achieved by five rounds of MCED screening. Simulation results guide trial design and suggest that adding this MCED test to routine screening in the United States may shift cancer detection to earlier stages, and potentially save lives.

Exact-matching algorithms using administrative health claims database equivalence factors for real-world data analysis based on the target trial emulation framework

AbstractReal-world data have become increasingly important in medical science and healthcare. A new, effective, and practically feasible statistical design is needed to unlock the potential of real-world data that decision-makers and practitioners can use to meet people’s healthcare needs. In the first half of the study, we validated our proposed new method by simulation, and in the second half, we conducted a clinical study on actual real-world data. We proposed the “Exact Matching Algorithm Using Administrative Health Claims Database Equivalence Factors (AHCDEFs)” using a target trial emulation framework. The simulation trials were conducted 500 times independently, considering the misclassification and chance errors of all variables and competing events of outcome. Two conventional methods, multivariate and propensity score analyses, were compared. Next, we estimated the effect of specific health guidance provided in Japan on the prevention of diabetes onset and medical expenditures. Our proposed novel method for real-world data returns improved estimates and fewer type I errors (the probability of erroneously determining that there is a difference when, in fact, there is no difference) than conventional methods. We quantitatively demonstrated the effectiveness of specific health guidance in Japan in preventing the onset of diabetes and reducing medical expenditures during five years. We proposed a new method for analyzing real-world data and an exact-matching algorithm using AHCDEFs. The larger the number of patients available for analysis, the more the AHCDEFs that can be matched, thereby removing the influence of confounding factors. This method will generate significant evidence when applied to real-world data.

525 - Reducing adverse delivery outcomes through teleneonatology: a randomized simulation trial

525 - Reducing adverse delivery outcomes through teleneonatology: a randomized simulation trial

Exploring the Benefits of a Simulator-Based Emergency Braking Exercise with Novice Teen Drivers

This exploratory study investigated whether using the Pedals Emergency Stop© interactive driving simulator exercise improved the understanding and performance of emergency braking among novice teen drivers. Seventy-one high school driver education students (aged 15–19) participated. All of the teens completed the Pedals Emergency Stop© interactive exercise driving simulator task and then an on-road ABS exercise in a driver’s education vehicle; there was no control group. Students’ ability to complete the simulator-based emergency braking task increased from an initial passing rate of only 18.3% to a maximum of 81.7% by the end of the simulation exercise. A positive trend was observed over successive simulator trials, with the linear effect explaining 51.1% of the variance in emergency stopping “pass” rates using the simulator task. In addition, participants who passed more trials during the Pedals Emergency Stop© simulator exercise were 12.3% more likely to fully activate the ABS during the on-road emergency stop activity using the driver’s education vehicle. Post-study surveys revealed that 95% of the participants improved their understanding of ABS as a result of the simulation-based training, and 98% felt there was a positive impact from the driving simulation exercise on their real-world emergency braking capabilities. Participants highly endorsed the Pedals Emergency Stop© exercise for ABS education and refresher training, with a rating of 4.7 out of 5. This study emphasizes the potential benefits of incorporating simulator-based exercises into driver education and training, with the long-term goal of promoting safe driving behaviors and outcomes.

Early intranasal medication administration in out-of-hospital cardiac arrest: Two randomized simulation trials.

Intranasal medications have been proposed as adjuncts to out-of-hospital cardiac arrest (OHCA) care. We sought to quantify the effects of intranasal medication administration (INMA) in OHCA workflows. We conducted separate randomized OHCA simulation trials with lay rescuers (LRs) and first responders (FRs). Participants were randomized to groups performing hands-only cardiopulmonary resuscitation (CPR)/automated external defibrillator with or without INMA during the second analysis phase. Time to compression following the second shock (CPR2) was the primary outcome and compression quality (chest compression rate (CCR) and fraction (CCF)) was the secondary outcome. We fit linear regression models adjusted for CPR training in the LR group and service years in the FR group. Among LRs, INMA was associated with a significant increase in CPR2 (mean diff. 44.1s, 95% CI: 14.9, 73.3), which persisted after adjustment (p=0.005). We observed a significant decrease in CCR (INMA 95.1 compressions per min (cpm) vs control 104.2cpm, mean diff. -9.1cpm, 95% CI -16.6, -1.6) and CCF (INMA 62.4%vs control 69.8%, mean diff. -7.5%, 95% CI -12.0, -2.9). Among FRs, we found no significant CPR2 delays (mean diff. -2.1s, 95% CI -15.9, 11.7), which persisted after adjustment (p=0.704), or difference in quality (CCR INMA 115.5cpm vs control 120.8cpm, mean diff. -5.3cpm, 95% CI -12.6, 2.0; CCF INMA 79.6%vs control 81.2% mean diff. -1.6%, 95% CI -7.4, 4.3%). INMA in LR resuscitation was associated with diminished resuscitation performance. INMA by FR did not impede key times or quality.

A novel ventriculoperitoneal shunt flow sensor based on electrically induced spatial variation in cerebrospinal fluid charge density.

Introduction: Ventriculoperitoneal (VP) shunts divert cerebrospinal fluid (CSF) out of cerebral ventricles in patients with hydrocephalus or elevated intracranial pressure (ICP). Despite high failure rates, there exist limited clinically viable solutions for long-term and continuous outpatient monitoring of CSF flow rate through VP shunts. We present a novel, low-power method for sensing analog CSF flow rate through a VP shunt premised on induced spatial electrical charge variation. Methods: Two geometric variants of the proposed sensing mechanism were prototyped: linear wire (P1) and cylindrical (P2) electrodes. Normal saline was gravity-driven through P1 and a commercially available shunt system in series. True flow rates were measured using a high-precision analytical balance. Subsequently, artificial CSF was driven by a programmable syringe pump through P2. Flow rate prediction models were empirically derived and tested. Sensor response was also assessed during simulated obstruction trials. Finally, power consumption per flow measurement was measured. Results: P1 (17mm long) and P2 (22mm long) averaged 7.2% and 4.2% error, respectively, in flow rate measurement from 0.01 to 0.90mL/min. Response curves exhibited an appreciably flattened profile during obstruction trials compared to non-obstructed states. P2 consumed 37.5 µJoules per flow measurement. Conclusion: We propose a novel method for accurately sensing CSF flow rate through a VP shunt and validate this method at the benchtop with normal saline and artificial CSF over a board range of flows (0.01-0.90mL/min). The sensing element is highly power efficient, compact, insertable into existing shunt and valve assemblies, and does not alter CSF flow mechanics.

Laboratory and simulated semi-field larvicidal efficacy of Aframomum angustifolium (Sonn.) K. Schum and Tagetes patula essential oils against Anopheles gambiae

IntroductionMalaria is a vector-borne, important public health problem spread by mosquitoes in tropical regions. The use of synthetic insecticides for mosquito control has been linked to insecticide resistance and environmental consequences. Thus, there is an urgent need to search for new highly selective and biodegradable insecticides lacking long-term toxicity to humans and mammals which are botanical-based compounds from indigenous plant sources available in local communities. Therefore, this study focused on evaluating the larvicidal activity of Aframomum angustifolium and Tagetes patula essential oils against Anopheles gambiae larvae in laboratory and simulated fields. MethodsThe Eos was extracted by the hydro distillation method, and the chemical composition of the Eos was determined using gas chromatography-mass spectrometry (GC-MS). The WHO protocol was followed for the laboratory and the simulated bioassays. Both laboratory-reared and field-collected larvae, as well as non-targeted organisms (Gambusia affinis), were used in simulated field conditions. The collected data were analyzed using probit analysis, and their means were compared in one-way analysis of variance (ANOVA) using IBM SPSS Statistic version 26. ResultsThe main active ingredients in T. patula Eo were terpinolene (20.75 %) and (Z)-ocimene (17.10 %) and A. angustifolium were cis-pinen-3-ol (58.48 %) β-pinene (31.03 %). The EO of T. patula was the most efficacious in the bioassay. The larvicidal bioassay findings demonstrated that the mortality rate was dose- and exposure-dependent. In the laboratory, the EOs of A. angustifolium and T. patula have larvicidal activity against An. gambiae larvae, with LC50 values of 1.71 and 0.71 ppm after 48 h, respectively. Both binary combinations of the EOs showed synergistic interactions at 24 h but antagonistic interactions at 48 h. In the simulated field trial using laboratory-reared larvae, the mortality rate was higher in the simulated setting compared with the laboratory setting for all the tested concentrations for both EOs. Only A. angustifolium EO was toxic against Gambusia affins at 100 ppm. ConclusionThe findings of this study have shown that T. patula and A. angustifolium oils have good larvicidal activities for An. gambiae. The two plants are potential sources of larvicidal compounds that could be used to control the malaria vector.