High-fidelity simulation versus case-based discussion for training undergraduate medical students in pediatric emergencies: a quasi-experimental study

Abstract Objective: To evaluate the effect of high-fidelity simulation of pediatric emergencies compared to case-based discussion on the development of self-confidence, theoretical knowledge, clinical reasoning, communication, attitude, and leadership in undergraduate medical students. Methods: 33 medical students were allocated to two teaching methods: high-fidelity simulation (HFS, n = 18) or case-based discussion (CBD, n = 15). Self-confidence and knowledge tests were applied before and after the interventions and the effect of HFS on both outcomes was estimated with mixed-effect models. An Objective Structured Clinical Examination activity was conducted after the interventions, while two independent raters used specific simulation checklists to assess clinical reasoning, communication, attitude, and leadership. The effect of HFS on these outcomes was estimated with linear and logistic regressions. The effect size was estimated with the Hedge'sg. Results: Both groups had an increase in self-confidence (HFS 59.1 × 93.6, p < 0.001; CDB 50.5 × 88.2, p < 0.001) and knowledge scores over time (HFS 45.1 × 63.2, p = 0.001; CDB 43.5 × 56.7, p-value < 0.01), but no difference was observed between groups (group*time effect in the mixed effect models adjusted for the student ranking) for both tests (p = 0.6565 and p = 0.3331, respectively). The simulation checklist scores of the HFS group were higher than those of the CBD group, with large effect sizes in all domains (Hedges g 1.15 to 2.20). Conclusion: HFS performed better than CBD in developing clinical reasoning, communication, attitude, and leadership in undergraduate medical students in pediatric emergency care, but no significant difference was observed in self-confidence and theoretical knowledge.

Failure mechanism and control technology of high-stress red shale roadway in Kaiyang phosphate mining of China.

To solve the supporting problem of high-stress red shale roadway in Kaiyang phosphate mining area, the mechanical properties and microstructure of red shale are studied. The results show that the compressive strength of the red shale is related to the bedding angle, and the strength of the 0° samples is the highest, and the strength of the 60° and 30° samples decreases gradually. With comprehensive consideration, the composite supporting method of cantilever piles and grid arch is adopted. Combining the numerical simulation and theoretical calculation, the parameters of cantilever pile with interval distance of 5 m and rock-socketed depth of 500 m are more reasonable. The monitoring results show that the roof subsidence was controlled within 250 mm, and the floor heave was within 100 mm, which could effectively control the severe deformation of the roadway and also is of great significance to the safe mining of phosphate resources.

Virtual reality versus low level simulation in newborn care teaching in Turkey.

The aim of this study is a virtual reality versus low level simulation in newborn care teaching in Turkey. Data were collected in 2019-2020 academic year in midwifery students in Turkey. In the first stage, virtual reality simulation software was developed. In the second stage, newborn care was provided with a virtual reality simulator to the experimental group and a low-fidelity simulator to the control group. Students' practice, self-confidence and satisfaction levels were compared using two different simulators. There was a difference between the two groups in terms of their skills, satisfaction and self-confidence. The simulator, which was developed and evaluated in the research, increased the students' satisfaction, self-confidence and skills. It was also found to be more effective than the classical method.

Le but de cette étude est une réalité virtuelle versus simulation de bas niveau dans l'enseignement des soins aux nouveau-nés en Turquie. Les données ont été collectées au cours de l'année universitaire 2019-2020 auprès d'étudiantes sages-femmes en Turquie. Dans un premier temps, un logiciel de simulation de réalité virtuelle a été développé. Dans la deuxième étape, les soins aux nouveau-nés ont été fournis avec un simulateur de réalité virtuelle pour le groupe expérimental et un simulateur basse fidélité pour le groupe témoin. Les niveaux de pratique, de confiance en soi et de satisfaction des étudiants ont été comparés à l'aide de deux simulateurs différents. Il existe une différence entre les deux groupes en termes de compétences, de satisfaction et de confiance en soi. Le simulateur développé et évalué dans le cadre de la recherche a accru la satisfaction, la confiance en soi et les compétences des étudiants. Elle s'est également révélée plus efficace que la méthode classique.

Investigation of mechanical behavior of porous carbon-based matrix by molecular dynamics simulation: Effects of Si doping.

Understanding the mechanical properties of porous carbon-based materials can lead to advancements in various applications, including energy storage, filtration, and lightweight structural components. Also, investigating how silicon doping affects these materials can help optimize their mechanical properties, potentially improving strength, durability, and other performance metrics. This research investigated the effects of atomic doping (Si particle up to 10 %) on the mechanical properties of the porous carbon matrix using molecular dynamics methods. Young's modulus, ultimate strength, radial distribution function, interaction energy, mean square displacement and potential energy of designed samples were reported. MD outputs predict the Si doping process improved the mechanical performance of porous structures. Numerically, Young's modulus of the C-based porous matrix increased from 234.33 GPa to 363.82 GPa by 5 % Si inserted into a pristine porous sample. Also, the ultimate strength increases from 48.54 to 115.93 GPa with increasing Si doping from 1 % to 5 %. Silicon doping enhances the bonding strength and reduces defects in the carbon matrix, leading to improved stiffness and load-bearing capacity. This results in significant increases in mechanical performance. However, excess Si may disrupt the optimal bonding network, leading to weaker connections within the matrix. Also, considering the negative value of potential energy in different doping percentages, it can be concluded that the amount of doping added up to 10 % does not disturb the initial structure and stability of the system, and the structure still has structural stability. So, we expected our introduced atomic samples to be used in actual applications.

The effect of constant electric field on the crack growth process of aluminum nanosheet using molecular dynamics simulation.

Aluminum nanosheets are a form of Al nanoparticle that have been recently manufactured on an industrial scale and have a variety of uses. Al nanoparticles are extensively used in a variety of sectors, including aerospace, construction, medical, chemistry, and marine industries. Crack propagation in various constructions must be investigated thoroughly for structural design purposes. Cracks in nanoparticles may occur during the production of nanosheets (NSs) or when different mechanical or thermal pressures were applied. In this work, the effect of a continuous electric field on the fracture formation process of aluminum nanosheets was investigated. For this study, molecular dynamics simulation and LAMMPS software were used. The effects of various electric fields on several parameters, including as stress, velocity (Velo), and fracture length, were explored, and numerical data were retrieved using software. The results show that the amplitude of the electric field parameter affected the atomic development of modeled Al nanosheets throughout the fracture operation. This effect resulted in atomic resonance (amplitude) fluctuations, which affected the mean interatomic forces and led the temporal evolution of atoms to converge to certain specified initial conditions. The crack length in our modeled samples ranged from 22.88 to 32.63 Å, depending on the electric field parameter (0.1-1 V/Å). Finally, it was determined that the crack growth of modeled Al nanosheets may be controlled using CEF parameters in real-world situations.

Impact of a simulator-based training program on the success rate of external cephalic version.

OBJECTIVE: To compare the success and complication rates of external cephalic version before and after the implementation of a simulator-based training program at a tertiary care university centre with a dedicated external cephalic version team. STUDY DESIGN: In this single-center intervention study, the success rate and the complication rates of external cephalic version in the two years before the implementation of a simulation-based training program for all specialists and residents, were compared with the two years following the event. T- student, Mann-Whitney, and Chi-square tests were used. All data were extracted from the hospital's electronic patient records. RESULTS: A total of 96 external cephalic versions were performed in the 2 years before the training program, and 74 after the training program. The overall success rates were similar between the two groups: 44.8 % before training and 43.2 % after training (p = 0.824). No major complications occurred, and no emergency cesarean deliveries were performed in either period. CONCLUSION: In a tertiary care university training center with a dedicated team in external cephalic version, a structured simulation-based training program did not impact the success rate or the complication rates of the procedure.

The DairyPrint Model: A Decision-Support Model to Help Dairy Farmers and Other Stakeholders Towards Improved Sustainability.

Dairy farmers face increasing pressure to reduce greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)], but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate those emissions. However, current models tend to be not user-friendly, difficult to access or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal related variables (i.e., dry matter intake, milk yield, manure excretion, and enteric CH4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard free-stall system with 1000 dairy cows averaging 40 kg/day of milk production. These factors included 2 levels of NDF-ADF in the diet (28-22.8% and 24-19.5%), the presence or absence of 3-NOP dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying [once (only Fall) or twice a year (Fall and Spring)], and the utilization or non-utilization of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kgCO2eq/kg of milk corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kgCO2eq/kg) involved a combination of factors, including a lower level of NDF-ADF in the diet in addition to incorporation of 3-NOP, utilization of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both Fall and Spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kgCO2eq/kg) involved a combination of higher level of NDF-ADF in the diet and excluded the incorporation of 3-NOP, utilization of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in Fall. All these scenarios can be easily simulated in the DairyPrint model and results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.

Obstetrics and Gynecology Hospitalists as Champions of Drills and Simulation.

Obstetrics and gynecology Hospitalists are not only skilled providers of emergency obstetric and gynecologic care but also safety officers who advocate for and maintain safety and quality in the hospital setting. In these areas and others, they play an essential role in championing and establishing simulation-based education in the hospital setting. The use of Simulations and Drills in maintaining quality and safety in patient care is nationally recognized by leading obstetric and gynecologic organizations.

OpenSim Moco tracking simulations efficiently replicate predictive simulation results across morphologically diverse shoulder models.

OpenSim Moco enables solving for an optimal motion using Predictive and Tracking simulations. However, Predictive simulations are computationally prohibitive, and the efficacy of Tracking in deviating from its reference is unclear. This study compares Tracking and Predictive approaches applied to the generation of morphology-specific motion in statistically-derived musculoskeletal shoulder models. The signal analysis software, CORA, determined mean correlation ratings between Tracking and Predictive solutions of 0.91 ± 0.06 and 0.91 ± 0.07 for lateral and forward-reaching tasks. Additionally, Tracking provided computational speed-up of 6-8 times. Therefore, Tracking is an efficient approach that yields results equivalent to Predictive, facilitating future large-scale modelling studies.

Generation of a network slicing dataset: The foundations for AI-based B5G resource management.

This paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the Radio Access Network (RAN) infrastructure. Each sample consists of pairs of a network scenario and the associated performance metrics: the network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of scenarios in multiple situations of over and underprovisioning. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.

Suppressing Mycobacterium tuberculosis virulence and drug resistance by targeting Eis protein through computational drug discovery.

Tuberculosis (TB) remains a critical health threat, particularly with the emergence of multidrug-resistant strains. This demands attention from scientific communities and healthcare professionals worldwide to develop effective treatments. The enhanced intracellular survival (Eis) protein is an acetyltransferase enzyme of Mycobacterium tuberculosis that functions by adding acetyl groups to aminoglycoside antibiotics, which interferes with their ability to bind to the bacterial ribosome, thereby preventing them from inhibiting protein synthesis and killing the bacterium. Therefore, targeting this protein accelerates the chance of restoring the aminoglycoside drug activity, thereby reducing the emergence of drug-resistant TB. For this, we have screened 406,747 natural compounds from the Coconut database against Eis protein. Based on MM/GBSA rescoring binding energy, the top 5 most prominent natural compounds, viz. CNP0187003 (- 96.14 kcal/mol), CNP0176690 (- 93.79 kcal/mol), CNP0136537 (- 92.31 kcal/mol), CNP0398701 (- 91.96 kcal/mol), and CNP0043390 (- 91.60 kcal/mol) were selected. These compounds exhibited the presence of a substantial number of hydrogen bonds and other significant interactions confirming their strong binding affinity with the Eis protein during the docking process. Subsequently, the MD simulation of these compounds exhibited that the Eis-CNP0043390 complex was the most stable, followed by Eis-CNP0187003 and Eis-CNP0176690 complex, further verified by binding free energy calculation, principal component analysis (PCA), and Free energy landscape analysis. These compounds demonstrated the most favourable results in all parameters utilised for this investigation and may have the potential to inhibit the Eis protein. There these findings will leverage computational techniques to identify and develop a natural compound inhibitor as an alternative for drug-resistant TB.

Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization.

The operation of modern wastewater treatment facilities is a balancing act in which a multitude of variables are controlled to achieve a wide range of objectives, many of which are conflicting. This is especially true within secondary activated sludge systems, where significant research and industry effort has been devoted to advance control optimization strategies, both domain-driven and data-driven. Among data-driven control strategies, reinforcement learning (RL) stands out for its ability to achieve better than human performance in complex environments. While RL has been applied to activated sludge process optimization in existing literature, these applications are typically limited in scope, and never for the control of more than three actions. Expanding the scope of RL control has the potential to increase the optimization potential while concurrently reducing the number of control systems that must be tuned and maintained by operations staff. This study examined several facets of the implementation of multi-action, multi-objective RL agents, namely how many actions a single agent could successfully control and what extent of environment data was necessary to train such agents. This study observed improved control optimization with increasing action scope, though control of waste activated sludge remains a challenge. Furthermore, agents were able to maintain a high level of performance under decreased observation scope, up to a point. When compared to baseline control of the Benchmark Simulation Model No. 1 (BSM1), an RL agent controlling seven individual actions improved the average BSM1 performance metric by 8.3 %, equivalent to an annual cost savings of $40,200 after accounting for the cost of additional sensors.

Regioselective photocyclodimerization of 2-anthracenecarboxylic acid through ATP hydrolysis-driven conformational change using simulation prediction-designed GroEL mutant.

GroEL, a chaperone protein responsible for peptide and denatured protein folding, undergoes substantial conformational changes driven by ATP binding and hydrolysis during folding. Utilizing these conformational changes, we demonstrated the GroEL-mediated regioselective photocyclodimerization of 2-anthracenecarboxylic acid (AC) using ATP hydrolysis as an external stimulus. We designed and prepared an optimal GroEL mutant to employ in a docking simulation that has been actively used in recent years. Based on the large difference in the motif of hydrogen bonds between AC and GroEL mutant compared with the wild-type, we predicted that GroELMEL, in which the 307‒309th amino acid residues were mutated to Ala, could alter the orientation of bound AC in GroEL. The GroELMEL-mediated photocyclodimerization of AC can be used for regioselective inversion upon ATP addition to a moderate extent.

Nephrology Partnership for Advancing Technology in Healthcare (N-PATH) Erasmus+ project: The Renal Expert in Vascular Access (REVAC) module.

The Renal Expert in Vascular Access (REVAC) is one of the four modules of the Nephrology Partnership for Advancing Technology in Healthcare (N-PATH) project, the first European-wide advanced training course in diagnostics and interventional nephrology, funded by Erasmus+ Knowledge Alliance, a European Commission program. The N-PATH primary goal was to train 40 young European nephrologists in both theoretical knowledge and practical skills related to interventional nephrology. The REVAC module focused on the crucial aspects of vascular access (VA) care in nephrology practice, as a complementary training path to the actual residency program. The aim was to provide nephrology fellows with comprehensive knowledge and skills related to VA management. The methodology was based on face-to-face meetings and online learning, modern facilities, experienced tutors, cutting edge simulators, augmented reality tools by means of a multidisciplinary international faculty and hands-on-courses. A feedback survey reported the experience of fellows who attended the REVAC module, confirming the positive impact on their ongoing nephrology training. We are confident that this project will revitalize their nephrology careers and will help training the next generation of nephrologists; they will be able to manage VA needs with the help of multi-disciplinary teams to safely optimize the care of hemodialysis patients.

The Effect of Time Pressure on Surgical Skill Retention in Novices: A Randomized Controlled Trial.

BACKGROUND: There are limited opportunities to practice surgical skills and techniques in residency. Therefore, it is important to explore strategies which optimize surgical simulation experiences to enhance learning outcomes and skill retention. METHODS: Novice medical students (n = 29) were recruited to participate in a Fundamentals of Laparoscopic Surgery (FLS) peg transfer task training. Participants were randomly assigned to a control group, practicing the peg transfer task independently, or an experimental group, practicing with time pressure. Participant skill assessments were completed before the training, after the training, and 8-weeks after the training. Subjective and objective stress measurements were taken in the form of self-report surveys and heart rate variability data, respectively. RESULTS: For all the skill assessment measurements, there was no difference between groups in performance on the FLS task. Both groups showed improvement in performance after the training compared to before. The experimental group reported higher stress during and after the training period compared to the control group; however, there was no difference between groups on heart rate variability metrics. CONCLUSION: Time pressure while practicing an FLS task did not significantly impact learning acquisition or retention. However, the experimental group reported higher levels of stress. This preliminary study suggests time pressure does not confer an enhanced surgical skill learning experience for novices.

Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations.

Estimating sample size and statistical power is an essential part of a good epidemiological study design. Closed-form formulas exist for simple hypothesis tests but not for advanced statistical methods designed for exposure mixture studies. Estimating power with Monte Carlo simulations is flexible and applicable to these methods. However, it is not straightforward to code a simulation for non-experienced programmers and is often hard for a researcher to manually specify multivariate associations among exposure mixtures to set up a simulation. To simplify this process, we present the R package mpower for power analysis of observational studies of environmental exposure mixtures involving recently-developed mixtures analysis methods. The components within mpower are also versatile enough to accommodate any mixtures methods that will developed in the future. The package allows users to simulate realistic exposure data and mixed-typed covariates based on public data set such as the National Health and Nutrition Examination Survey or other existing data set from prior studies. Users can generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This paper presents tutorials and examples of power analysis using mpower.

Comparing Male and Female Resident Physicians in Central Venous Catheter Insertion Self-confidence and Competency: A Retrospective Cohort Study.

BACKGROUND: Female physicians often report lower self-confidence in their procedural and clinical competency compared to male physicians. There is limited data regarding self-reported confidence of female versus male trainees and any relation to objective competency in central venous catheter insertion. OBJECTIVE: To analyze differences between male and female trainees in self-confidence and skill-based outcomes in placing central venous catheters. DESIGN: Using data from a central venous catheter simulation training program at a large tertiary medical center, we performed linear regressions to analyze confidence difference pre- and post-training, number of restarts, and number of cannulation attempts while controlling for baseline demographic characteristics of the sample. PARTICIPANTS: PGY-1 physician residents in all residency specialties who insert central venous catheters in the clinical setting at a tertiary academic center with a sample size of 281 residents. MAIN MEASURES: Confidence difference pre- and post-training measured on a Likert scale 1-5, number of restarts (novel global assessment variable), and number of cannulation attempts during the competency evaluation. KEY RESULTS: Female trainees had both lower pre-program confidence (1.35 versus 1.74 out of 5, p < 0.001) and lower post-program confidence (3.77 versus 4.12 out of 5, p = 0.0021) as compared to male trainees. There was no statistically significant difference in number of restarts (95% CI - 0.073 to 0.368, p = 0.185) or cannulation attempts (95% CI - 0.039 to 0.342, p = 0.117) between sexes in linear regressions controlled for age, specialty designation, prior central venous catheter training, prior ultrasound guided vessel cannulation training, and pre-training confidence level. CONCLUSIONS: Female trainees rated their confidence significantly lower than their male counterparts both before and after the training program, despite no significant difference in skill-based outcomes. We discuss potential implications for trainees acquiring procedural skills during residency and for physician educators as they design training programs and delegate procedural opportunities.

Student nurses' utilisation of self-directed simulation learning at a University in Gauteng.

BACKGROUND:  Self-directed simulation learning (SSL) is a globally accepted teaching and learning strategy wherein student nurses take the initiative in diagnosing their learning needs, formulate learning goals, identify resources for learning, and implement relevant strategies in response to their learning needs. This autonomous learning strategy will assist student nurses in taking ownership of their learning. Consequently, student nurses exit the training programme to become lifelong learners, safe and competent professional nurses. OBJECTIVES:  This study aimed to explore and describe the experiences of student nurses' utilisation of SSL at a University in Gauteng and to make recommendation(s) to enhance the use of SSL. METHOD:  A qualitative, exploratory, descriptive, and contextual research design was used to uncover the student nurses' experiences with the use of SSL at a University. Nineteen participants were purposively sampled. Data collection was conducted through focus group interviews. Tesch's method of data analysis was used to analyse, organise and interpret data. RESULTS:  Theme: student nurses experience time constraints, which hinder their utilisation of SSL. Subthemes: (1) a compacted academic timetable, and (2) limited access to the clinical simulation laboratory for self-directed learning. CONCLUSION:  Time constraints hinder the utilisation of SSL, and this challenge threatens the acquisition of clinical skills and knowledge during the training of student nurses.Contribution: Evidence-based recommendations to enhance the utilisation of SSL at a University.

Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.

IMPORTANCE: Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable. OBJECTIVES: Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response. MATERIALS AND METHODS: Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time. RESULTS: All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively. DISCUSSION AND CONCLUSION: The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.

Redesigning amino/carboxyl ends of DARPin G3 for high thermostability and production in tobacco transplastomic plants.

KEY MESSAGE: Redesigning the N- and C-capping repeats of the native DARPin G3 significantly improved its stability, and may facilitate its purification from the total soluble proteins of high-temperature dried leaf materials of transplastomic plants. Designed ankyrin repeat proteins (DARPins) constitute a promising class of binding molecules that can overcome the limitations of monoclonal antibodies and enable the development of novel therapeutic approaches. Despite their inherent stability, detailed studies have revealed that the original capping repeats derived from natural ankyrin repeat proteins impair the stability of the initial DARPin design. Consequently, the development of thermodynamically stabilized antibody mimetics may facilitate the development of innovative drugs in the future. In this study, we replaced the original N- and C-capping repeats with improved caps to enhance the thermostability of native DARPin G3. Computational analyses suggested that the redesigned thermostable DARPin G3 structure possessed optimal quality and stability. Molecular dynamics simulations verified the stability of the redesigned thermostable DARPin G3 at high temperatures. The redesigned thermostable DARPin G3 was expressed at high levels in tobacco transplastomic plants and subsequently purified from high-temperature dried leaf materials. Thermal denaturation results revealed that the redesigned thermostable DARPin G3 had a higher Tm value than the native DARPin G3, with a Tm of 35.51 °C greater than that of native DARPin G3. The results of the in vitro bioassays confirmed that the purified thermostable DARPin G3 from high-temperature dried leaf materials maintained its binding activity without any loss of affinity and specifically bound to the HER2 receptor on the cell surface. These findings demonstrate the successful improvement in the thermostability of DARPin G3 without compromising its biological activity.