A metabolite-sensitive, thermodynamically-constrained model of\ud cardiac cross-bridge cycling: Implications for force development during ischemia

We present a metabolically regulated model of cardiac active force generation with which we investigate the effects of ischemia on maximum forceproduction. Our model, based on the Rice et al. (2008) model of cross-bridge kinetics, reproduces many of the observed effects of MgATP, MgADP, Pi and H+ on force development while still retaining the force/length/Ca2+ properties of the original model. We introduce three new parameters to account for the competitive binding of H+ to the Ca2+ binding site on troponin C and the binding of MgADP within the cross-bridge cycle. These parameters along with the Pi and H+ regulatory steps within the cross-bridge cycle were constrained using data from the literature and validated using a range of metabolic and sinusoidal length perturbation protocols. The placement of the MgADP binding step between two strongly-bound and force-generating states leads to the emergence of an unexpected effect on the force-MgADP curve, where the trend of the relationship (positive or negative) depends on the concentrations of the other metabolites and [H+]. The model is used to investigate the sensitivity of maximum force production to changes in metabolite concentrations during the development of ischemia

A thermodynamic framework for modelling membrane transporters

Membrane transporters contribute to the regulation of the internal environment of cells by translocating substrates across cell membranes. Like all physical systems, the behaviour of membrane transporters is constrained by the laws of thermodynamics. However, many mathematical models of transporters, especially those incorporated into whole-cell models, are not thermodynamically consistent, leading to unrealistic behaviour. In this paper we use a physics-based modelling framework, in which the transfer of energy is explicitly accounted for, to develop thermodynamically consistent models of transporters. We then apply this methodology to model two specific transporters: the cardiac sarcoplasmic/endoplasmic Ca$^{2+}$ ATPase (SERCA) and the cardiac Na$^+$/K$^+$ ATPase

Differentiable Physics-based Greenhouse Simulation

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.Comment: Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022. 7 pages, 2 figure

Artificial Intelligence and Medical Trainees - Valuable Tool or Learning Impediment?

Background ● Artificial intelligence (AI) assisted clinical documentation tools are becoming increasingly available across outpatient clinical settings ● Voice to text recognition programs collect audio from patient-provider interactions and use AI to automatically generate notes documenting the encounter ● These notes can then be made available in the electronic health record within minutes ● Though these tools are available to attending physicians, there has been no research on attitudes regarding when such tools should be made available to medical trainees, such as medical students and residents ● We surveyed US medical students, residents, and attending physicians regarding when such tools should be introduced to learners, if at all, and concerns around such tools Study Design ● Cross-sectional survey ● Setting: Single institution in the Pacific region ● Study population: Medical students, resident physicians, fellows, and attending physicians (179 respondents) ● Study timeline: Survey available 5/9/24-6/10/24 ● Data collection: ○ Administered 15 question RedCap survey ○ Assessed respondents demographic and professional characteristics, including gender, highest level of training, percent of time spent in outpatient practice, time spent on patient interactions, and time spent on clinical documentation ● Study outcomes: ○ Relationship between respondents’ demographic and professional characteristics and what level of training they felt that AI-assisted clinical documentation tools should be available to trainees, should enter the medical record, and specific concerns with regard to their use (not meeting documentation milestones, detriment to patient-provider experience, accuracy, bias, violation of patient privacy, detriment to forming differential diagnoses, detriment to forming plans, and concerns about using personal devices) ● Statistical analysis: ○ Chi-squared analysis to determine association between demographic/professional characteristics and outcomes with significance level set at p < 0.05 ○ Cramer’s values calculated to determine strength of association ○ Descriptive characterisation of respondents Results 40% of female attending physicians believed that AI tools should not be available to trainees at all, compared to 25% of attending physicians overall and 17% of respondents in general Female respondents were far more likely to agree with the statement “I am concerned about AI-assisted clinical documentation violating patient privacy” and far less likely to agree with running the software on a personal device Summary ● Attending physicians, particularly female attending physicians, felt that AI assisted clinical documentation tools should be introduced to trainees later on and were more likely to believe that trainees should not be able to generate notes using such tools ● Female respondents were far more likely to have concerns about these tools adversely affecting trainees in achieving clinical documentation milestones and were far more likely to have concerns about privacy and use of personal devices surrounding these tools ● Respondents who spent more time with patients (P = 0.01, Cramer’s value = 0.19) and more time writing notes (P = 0.03, Cramer’s value = 0.20) were less likely to believe that that AI-assisted clinical documentation tools should be available to trainees Limitations ● Single institution survey ● Responses restricted to provided survey options ● Data not collected on experience with ambient clinical documentation tools within cohort Discussion ● Previous research has shown that physicians and medical students have positive attitudes and a willingness to learn about AI tools in healthcare ● However, our data suggests that attending physicians and female respondents in general have more concerns about privacy with regard to AI for clinical documentation tools and favor later introduction of such tools to trainees ● While trainees are amenable to learning about AI tools, faculty may recommended delaying the introduction of these tools to residency or later ● More research is needed to better understand why female physicians are more concerned about privacy with regard to AI tools and how these tools should be introduced to medical trainees ● Next steps: conducting interviews with respondents for more nuanced recommendations/understanding of concerns References AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? DIGITAL HEALTH. 2023;9. doi:10.1177/20552076231152167 Giavina-Bianchi M, Amaro Jr E, Machado BS, Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study, JMIRx Med 2024;5:e50803, doi: 10.2196/50803 Waheed MA, Liu L, Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization, JMIR AI 2024;3:e40781, doi: 10.2196/4078