Acute Muscle Rigidity Secondary to Tetanus: A Toxicology Simulation Case for Fourth-Year Medical Students.

Introduction: Tetanus is uncommon in the United States secondary to vaccination. However, vaccination hesitancy is increasing. This case challenges medical students to consider tetanus in the differential and understand its complications. Methods: Fourth-year medical students took a pretest on the neurotransmitter glycine and associated disease states. They received two 10-minute lectures on glycine and acid-base abnormalities. Students then participated in a simulation featuring a 27-year-old man bitten by a dog, resulting in tetanus. Required equipment included a mannequin with monitor, a defibrillator, and personal protective equipment. Critical actions consisted of learners dividing up roles amongst each other, using closed-loop communication, placing the patient on a cardiac monitor, choosing to establish IV access and intubate the patient, starting IV fluids, and administering tetanus immunoglobulin. The case ended after 20 minutes. Outcome measurements encompassed performance on a posttest and critical actions. Results: Twenty students participated. Mean pretest and posttest scores were 69.5 and 92.5, respectively (p < .001). All groups completed the items on the critical actions checklist within a 20-minute time frame. Discussion: Rising vaccine hesitancy may increase the likelihood of physicians encountering new cases of tetanus and require them to perform lifesaving management of a patient presenting with muscle rigidity. This simulation provides learners with hands-on experience caring for a patient with tetanus and muscle rigidity. It can improve their knowledge of recognition, assessment, and decision-making toward lifesaving management of tetanus by allowing them to practice their skills in a safe environment.

Bayesian estimation of the measurement of interactions in epidemiological studies.

Background: Interaction identification is important in epidemiological studies and can be detected by including a product term in the model. However, as Rothman noted, a product term in exponential models may be regarded as multiplicative rather than additive to better reflect biological interactions. Currently, the additive interaction is largely measured by the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S), and confidence intervals are developed via frequentist approaches. However, few studies have focused on the same issue from a Bayesian perspective. The present study aims to provide a Bayesian view of the estimation and credible intervals of the additive interaction measures. Methods: Bayesian logistic regression was employed, and estimates and credible intervals were calculated from posterior samples of the RERI, AP and S. Since Bayesian inference depends only on posterior samples, it is very easy to apply this method to preventive factors. The validity of the proposed method was verified by comparing the Bayesian method with the delta and bootstrap approaches in simulation studies with example data. Results: In all the simulation studies, the Bayesian estimates were very close to the corresponding true values. Due to the skewness of the interaction measures, compared with the confidence intervals of the delta method, the credible intervals of the Bayesian approach were more balanced and matched the nominal 95% level. Compared with the bootstrap method, the Bayesian method appeared to be a competitive alternative and fared better when small sample sizes were used. Conclusions: The proposed Bayesian method is a competitive alternative to other methods. This approach can assist epidemiologists in detecting additive-scale interactions.

Deep partially linear cox model for current status data.

Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained by using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is $\sqrt{n}$-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to real-world data on news popularity.

Physical and Computational Modeling for Transcatheter Structural Heart Interventions.

Structural heart disease interventions rely heavily on preprocedural planning and simulation to improve procedural outcomes and predict and prevent potential procedural complications. Modeling technologies, namely 3-dimensional (3D) printing and computational modeling, are nowadays increasingly used to predict the interaction between cardiac anatomy and implantable devices. Such models play a role in patient education, operator training, procedural simulation, and appropriate device selection. However, current modeling is often limited by the replication of a single static configuration within a dynamic cardiac cycle. Recognizing that health systems may face technical and economic limitations to the creation of "in-house" 3D-printed models, structural heart teams are pivoting to the use of computational software for modeling purposes.

Ocean state rising: Storm simulation and vulnerability mapping to predict hurricane impacts for Rhode Island's critical infrastructure.

Predicting the consequences of a major coastal storm is increasingly difficult as the result of global climate change and growing societal dependence on critical infrastructure (CI). Past storms are no longer a reliable predictor of future weather events, and the traditional approach to vulnerability assessment presents accumulated loss in largely quantitative terms that lack the specificity local emergency managers need to develop effective plans and mitigation strategies. The Rhode Island Coastal Hazards Modeling and Prediction (RI-CHAMP) system is a geographic information system (GIS)-based modeling tool that combines high-resolution storm simulations with geolocated vulnerability data to predict specific consequences based on local concerns about impacts to CI. This case study discusses implementing RI-CHAMP for the State of Rhode Island to predict impacts of wind and inundation on its CI during a hurricane, tropical storm, or nor'easter. This paper addresses the collection and field verification of vulnerability data, along with RI-CHAMP's process for integrating those data with storm models. The project deeply engaged end-users (emergency managers, facility managers, and other stakeholders) in developing RI-CHAMP's ArcGIS Online dashboard to ensure it provides specific, actionable data. The results of real and synthetic storm models are presented along with discussion of how the data in these simulations are being used by state and local emergency managers, facility owners, and others.

From covalent transition states in chemistry to noncovalent in biology: from ß- to Φ-value analysis of protein folding.

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the ß-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of ß- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called BIC E 1 , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called BIC E 2 , in the presence of redundant random effects. We show that the derived formulae, BIC E 1 and BIC E 2 , adhere to empirical values via numerical demonstration and that BIC E ( E indicating either E 1 or E 2 ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of BIC E 1 is illustrated with a textbook example dataset.

A realistic aneurysm clipping simulation combining 3D-printed and placenta-based models-how I do it.

INTRODUCTION: Neurovascular surgery, particularly aneurysm clipping, is a critical skill for aspiring neurosurgeons. However, hands-on training opportunities are limited, especially with the growing popularity of endovascular techniques. To address this challenge, we present a novel neurovascular surgical training station that combines synthetic 3D-printed models with placental vascular structures to create a semi-realistic surgical field. METHODS: Our model consists of three components: a 3D-printed skull replica with anatomical landmarks, a malleable silicone parenchyma with a Sylvian fissure, and vascular layers (placenta). The placental vascular layer is catheterized and perfused to replicate pulsatile flow, offering a realistic aneurysm simulation. This innovative training station provides a cost-effective solution (approximately 200 USD once) without ethical constraints. Surgeons can practice essential skills such as Sylvian fissure dissection, managing anatomical constraints like bone, and achieving proximal vascular control. The model's realism allows for training in various scenarios, including clipping with different hand orientations and handling ruptures realistically. CONCLUSION: Our neurovascular surgical station bridges the gap between existing training models, offering affordability, ecological considerations, and minimal ethical concerns. It empowers neurosurgery residents to refine their skills in handling both emergencies and elective cases under close-to-real surgical conditions, with the potential for independent practice and senior supervision.

Water quality monitoring and modeling for an urban storm drainage channel in Thane, India.

The absence of a sewer system and inadequate wastewater treatment plants results in a discharge of untreated wastewater to the urban drainage channels and pollutes receiving waters. Field visits were carried out to observe water quality parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) in an urban drainage system (Kolshet drain) in Thane City, Mumbai Metropolitan Region, India. Dye-tracing studies using rhodamine WT dye were used for computing the velocity, discharge, and dispersion coefficient of the drain. The data analysis shows that the BOD and COD values in the drain are higher than the permissible limits (30 mg L-1 for BOD and 250 mg L-1 for COD), which is not suitable for disposal to any receiving water body. Also, the DO was less than the permissible limit of a minimum of 3 mg L-1 (for the survival of aquatic life). It is seen that the higher BOD load significantly reduced the DO throughout the drain. The Water Quality Analysis Simulation Program (WASP 8.32, 2019) developed by the US Environmental Protection Agency (USEPA) has been used for the simulation of the DO and BOD in the drainage channel. The model simulates an appropriate estimate of the expected variation of DO and BOD at points of interest. The modeling for the Kolshet drain is expected to enable better estimates of the wastewater parameters and the pollution transport in the drain for planning purposes.

Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation.

Accurate modeling of cerebral hemodynamics is crucial for better understanding the hemodynamics of stroke, for which computational fluid dynamics (CFD) modeling is a viable tool to obtain information. However, a comprehensive study on the accuracy of cerebrovascular CFD models including both transient arterial pressures and flows does not exist. This study systematically assessed the accuracy of different outlet boundary conditions (BCs) comparing CFD modeling and an in-vitro experiment. The experimental setup consisted of an anatomical cerebrovascular phantom and high-resolution flow and pressure data acquisition. The CFD model of the same cerebrovascular geometry comprised five sets of stationary and transient BCs including established techniques and a novel BC, the phase modulation approach. The experiment produced physiological hemodynamics consistent with reported clinical results for total cerebral blood flow, inlet pressure, flow distribution, and flow pulsatility indices (PI). The in-silico model instead yielded time-dependent deviations between 19-66% for flows and 6-26% for pressures. For cerebrovascular CFD modeling, it is recommended to avoid stationary outlet pressure BCs, which caused the highest deviations. The Windkessel and the phase modulation BCs provided realistic flow PI values and cerebrovascular pressures, respectively. However, this study shows that the accuracy of current cerebrovascular CFD models is limited.

Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review.

INTRODUCTION: An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. METHODS: Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. RESULTS: Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. CONCLUSION: Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

Optimizing laparoscopic and robotic skills through simulation in participants with limited or no prior experience: a systematic review and meta-analysis.

BACKGROUND: Simulation is an innovative tool for developing complex skills required for surgical training. The objective of this study was to determine the advancement of laparoscopic and robotic skills through simulation in participants with limited or no previous experience. METHODS: This is a systematic review and meta-analysis of randomized controlled trials (RCTs) in keeping with the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. We conducted searches using MEDLINE (PubMed), Web of Science, Google Scholar, and Cochrane Library. Variables analyzed were study characteristics, participant demographics, and characteristics of the learning program. Our main measures were effectiveness, surgical time, and errors. These were reported using standardized mean difference (SMD) with 95% CI (P < .05). Secondary measures included skill transfer and learning curve. RESULTS: A total of 17 RCTs were included and comprised 619 participants: 354 participants (57%) were in the simulation group and 265 (43%) in the control group. Results indicated that laparoscopic simulation effectively enhanced surgical skills (SMD, 0.59 [0.18-1]; P = .004) and was significantly associated with shorter surgical duration (SMD, -1.08 [-1.57 to -0.59]; P < .0001) and a fewer errors made (SMD, -1.91 [-3.13 to -0.70]; P = .002). In the robotic simulation, there was no difference in effectiveness (SMD, 0.17 [-0.19 to 0.52]; P = .36) or surgical time (SMD, 0.27 [-0.86 to 1.39]; P = .64). Furthermore, skills were found to be transferable from simulation to a real-life operating room (P < .05). CONCLUSION: Simulation is an effective tool for optimizing laparoscopic skills, even in participants with limited or no previous experience. This approach not only contributes to the reduction of surgical time and errors but also facilitates the transfer of skills to the surgical environment. In contrast, robotic simulation fails to maximize skill development, requiring previous experience in laparoscopy to achieve optimal levels of effectiveness.

Quantum simulation of Pauli channels and dynamical maps: Algorithm and implementation.

Pauli channels are fundamental in the context of quantum computing as they model the simplest kind of noise in quantum devices. We propose a quantum algorithm for simulating Pauli channels and extend it to encompass Pauli dynamical maps (parametrized Pauli channels). A parametrized quantum circuit is employed to accommodate for dynamical maps. We also establish the mathematical conditions for an N-qubit transformation to be achievable using a parametrized circuit where only one single-qubit operation depends on the parameter. The implementation of the proposed circuit is demonstrated using IBM's quantum computers for the case of one qubit, and the fidelity of this implementation is reported.

Catalyzing satellite communication: A 20W Ku-Band RF front-end power amplifier design and deployment.

This paper presents a groundbreaking Ku-band 20W RF front-end power amplifier (PA), designed to address numerous challenges encountered by satellite communication systems, including those pertaining to stability, linearity, cost, and size. The manuscript commences with an exhaustive discussion of system design and operational principles, emphasizing the intricacies of low-noise amplification, and incorporating key considerations such as noise factors, stability analysis, gain, and gain flatness. Subsequently, an in-depth study is conducted on various components of the RF chain, including the pre-amplification module, driver-amplification module, and final-stage amplification module. The holistic design extends to the inclusion of the display and control unit, featuring the power-control module, monitoring module, and overall layout design of the PA. It is meticulously tailored to meet the specific demands of satellite communication. Following this, a thorough exploration of electromagnetic simulation and measurement results ensues, providing validation for the precision and reliability of the proposed design. Finally, the feasibility of that design is substantiated through systematic system design, prototype production, and exhaustive experimental testing. It is noteworthy that, in the space-simulation environmental test, emphasis is placed on the excellent performance of the Star Ku-band PA within the 13.75GHz to 14.5GHz frequency range. Detailed power scan measurements reveal a P1dB of 43dBm, maintaining output power flatness < ± 0.5dBm across the entire frequency and temperature spectrum. Third-order intermodulation scan measurements indicate a third-order intermodulation of ≤ -23dBc. Detailed results of power monitoring demonstrate a range from +18dBm to +54dBm. Scans of spurious suppression and harmonic suppression, meanwhile, show that the PA evinces spurious suppression ≤ -65dBc and harmonic suppression ≤ -60dBc. Rigorous phase-scan measurements exhibit a phase-shift adjustment range of 0° to 360°, with a step of 5.625°, and a phase-shift accuracy of 0.5dB. Detailed data from gain-scan measurements show a gain-adjustment range of 0dB to 30dB, with a gain flatness of ± 0.5dB. Attenuation error is ≤ 1%. These test parameters perfectly align with the practical application requirements of the technical specifications. When compared to existing Ku-band PAs, our design reflects a deeper consideration of specific requirements in satellite communication, ensuring its outstanding performance and uniqueness. This PA features good stability, high linearity, low cost, and compact modularity, ensuring continuous and stable power output. These features position the proposed system as a leader within the market. Successful orbital deployment not only validates its operational stability; it also makes a significant contribution to the advancement of China's satellite PA technology, generating positive socio-economic benefits.

Learning agile soccer skills for a bipedal robot with deep reinforcement learning.

We investigated whether deep reinforcement learning (deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies. We used deep RL to train a humanoid robot to play a simplified one-versus-one soccer game. The resulting agent exhibits robust and dynamic movement skills, such as rapid fall recovery, walking, turning, and kicking, and it transitions between them in a smooth and efficient manner. It also learned to anticipate ball movements and block opponent shots. The agent's tactical behavior adapts to specific game contexts in a way that would be impractical to manually design. Our agent was trained in simulation and transferred to real robots zero-shot. A combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training enabled good-quality transfer. In experiments, the agent walked 181% faster, turned 302% faster, took 63% less time to get up, and kicked a ball 34% faster than a scripted baseline.

Collaborative virtual reality environment in disaster medicine: moving from single player to multiple learners.

BACKGROUND: The use of virtual reality (VR) in healthcare education is on the increase. In disaster medicine, it could be a solution to the cost and logistic constraints for a "full-scale" scenarios. However, VR is mainly designed for single players, which is not appropriate for the objectives pursued in disaster medicine. We decided to evaluate the educational value of using individual VR simulation in disaster medicine on a group of learners. METHODS: The VR scenario used was a reproduction of a major train crash, with 21 victims and whose objectives were START triage and first aid techniques. The sessions were carried out in multi-participant groups with different roles (active and immersed with headset, paper triage without headset, and active for communications not immersed in the headset). Their perceived self-efficacy was assessed before (T0), after (T1) and 2 months (T2) after the training. Satisfaction and confidence in learning were also measured. RESULTS: The median levels of satisfaction and confidence in learning were of 21/25 and 32/40 respectively. Their perceived self-efficacy increased significantly between T0 and T1 (p < 0.001), and remained stable until T2. The different roles of participant showed no difference in terms of satisfaction, confidence in learning or changes in perceived self-efficacy. One third of the participants agreed that the number of participants had interfered with their learning. A significant negative correlation (rS = -0.51, p = 0.002) was found between satisfaction and the fact of having been hindered by the number of participants. Around 90% of participants found the activity entertaining and found the new technologies appropriate for learning technical skills. CONCLUSIONS: This first experience of VR in a group setting is satisfactory and shows its positive effects. The limitations highlighted here will enable areas of improvement to be identified for the use of VR in disaster medicine, pending the development of multi-player tools. It would now be appropriate to analyse the impact of this type of simulation on learning and its retention over time.

spVC for the detection and interpretation of spatial gene expression variation.

Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.

Keyhole-aware laparoscopic augmented reality.

Augmented Reality (AR) from preoperative data is a promising approach to improve intraoperative tumour localisation in Laparoscopic Liver Resection (LLR). Existing systems register the preoperative tumour model with the laparoscopic images and render it by direct camera projection, as if the organ were transparent. However, a simple geometric reasoning shows that this may induce serious surgeon misguidance. This is because the tools enter in a different keyhole than the laparoscope. As AR is particularly important for deep tumours, this problem potentially hinders the whole interest of AR guidance. A remedy to this issue is to project the tumour from its internal position to the liver surface towards the tool keyhole, and only then to the camera. This raises the problem of estimating the tool keyhole position in laparoscope coordinates. We propose a keyhole-aware pipeline which resolves the problem by using the observed tool to probe the keyhole position and by showing a keyhole-aware visualisation of the tumour. We assess the benefits of our pipeline quantitatively on a geometric in silico model and on a liver phantom model, as well as qualitatively on three patient data.

Energy field-based lane changing behavior interaction model and risk evaluation in the weaving section of expressway.

OBJECTIVE: With the development of intelligent driving assistance systems, the evaluation of driving behavior risk has shifted from traditional single-vehicle studies to multi-vehicle studies. This study aimed to investigate the interaction mechanism between vehicles and to study the microscopic laws of traffic flow operation. METHODS: Firstly, the concept of "driving interaction field" was proposed. The virtual interaction quality and distance were used to define the driving interaction field. The interaction angle distinguished the vehicle interaction between different lanes. Then, the risk mechanism in the interaction process was analyzed by driving risk index. Corresponding thresholds of 50% and 85% quantile values were determined. Finally, the process of the lane-changing simulation experiments was divided into three phases (preparation, execution and adjustment). RESULTS: The driving risk index of the execution phase was larger than the other phases. Meanwhile, the comparison with the classical driving risk indexes revealed that the proposed index was more accurate and intuitive in describing the interaction risks. CONCLUSIONS: The driving interaction model proposed in this study quantified the overall environmental pressure on the vehicle. It overcomes the previous limitation of kinetic interaction parameters. The research provides a new idea for the ITS and autonomous driving systems, contributing to the enhancement of traffic safety and efficiency.