Digital twins suggest a mechanistic basis for differing responses to increased flow rates during high-flow nasal cannula therapy.

Background: Inconsistent responses to increased flow rates have been observed in patients with acute hypoxemic respiratory failure (AHRF) treated with high-flow nasal cannula (HFNC) therapy, with a significant minority in two recent studies exhibiting increased respiratory effort at higher flow rates. Digital twins of patients receiving HFNC could help understand the physiological basis for differing responses.

Methods: Patient data were collated from previous studies in AHRF patients who were continuously monitored with electrical impedance tomography and oesophageal manometry and received HFNC at flow rates of 30, 40 or 45 L/min.

Gene Expression Depends on the Interplay Among Growth, Resource Biogenesis, and Nutrient Quality.

The gene expression capacity of bacteria depends on the interplay between growth and the availability of the transcriptional and translational machinery. Growth rate is accepted as the physiological parameter controlling the allocation of cellular resources. Understanding the relationship between growth and resources is key for the efficient design of genetic constructs, but it is obscured by the mutual dependence between growth and gene expression.

Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure.

Background: Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed to establish whether machine learning (ML) models could predict HFNC outcome, early in the course of treatment, with greater accuracy than currently used clinical indices.

Methods: We developed ML models trained using measurements made within the first 2 h of treatment from 184 AHRF patients (37% HFNC failures) treated at the respiratory ICU of the University Hospital of Modena between 2018 and 2023.

Airway pressures generated by high flow nasal cannula in patients with acute hypoxemic respiratory failure: a computational study.

Introduction And Objectives: High flow nasal cannula (HFNC) therapy is an increasingly popular mode of non-invasive respiratory support for the treatment of patients with acute hypoxemic respiratory failure (AHRF). Previous experimental studies in healthy subjects have established that HFNC generates flow-dependent positive airway pressures, but no data is available on the levels of mean airway pressure (mP) or positive end-expiratory pressure (PEEP) generated by HFNC therapy in AHRF patients. We aimed to estimate the airway pressures generated by HFNC at different flow rates in patients with AHRF, whose functional lung volume may be significantly reduced compared to healthy subjects due to alveolar consolidation and/or collapse.

Design principles for engineering bacteria to maximise chemical production from batch cultures.

Bacteria can be engineered to manufacture chemicals, but it is unclear how to optimally engineer a single cell to maximise production performance from batch cultures. Moreover, the performance of engineered production pathways is affected by competition for the host's native resources. Here, using a 'host-aware' computational framework which captures competition for both metabolic and gene expression resources, we uncover design principles for engineering the expression of host and production enzymes at the cell level which maximise volumetric productivity and yield from batch cultures.

Digital Twins of Acute Hypoxemic Respiratory Failure Patients Suggest a Mechanistic Basis for Success and Failure of Noninvasive Ventilation.

Objectives: To clarify the mechanistic basis for the success or failure of noninvasive ventilation (NIV) in acute hypoxemic respiratory failure (AHRF).

Design: We created digital twins based on mechanistic computational models of individual patients with AHRF.

Setting: Interdisciplinary Collaboration in Systems Medicine Research Network.

A Theoretical Framework for Implementable Nucleic Acids Feedback Systems.

Chemical reaction networks can be utilised as basic components for nucleic acid feedback control systems' design for Synthetic Biology application. DNA hybridisation and programmed strand-displacement reactions are effective primitives for implementation. However, the experimental validation and scale-up of nucleic acid control systems are still considerably falling behind their theoretical designs.

Evaluating current guidelines for cardiopulmonary resuscitation using an integrated computational model of the cardiopulmonary system.

Objective: We aimed to use a high-fidelity computational model that captures key interactions between the cardiovascular and pulmonary systems to investigate whether current CPR protocols could potentially be improved.

Methods: We developed and validated the computational model against available human data. We used a global optimisation algorithm to find CPR protocol parameters that optimise the outputs associated with return of spontaneous circulation in a cohort of 10 virtual subjects.

Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.

Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their unproven performance in real-world settings, remain significant constraints to their widespread adoption in clinical practice. One approach to tackle this issue is to base learning on large multi-center datasets.

Why Reduced Inspiratory Pressure Could Determine Success of Non-Invasive Ventilation in Acute Hypoxic Respiratory Failure.

The magnitude of inspiratory effort relief within the first 2 hours of non-invasive ventilation for hypoxic respiratory failure was shown in a recent exploratory clinical study to be an early and accurate predictor of outcome at 24 hours. We simulated the application of non-invasive ventilation to three patients whose physiological and clinical characteristics match the data in that study. Reductions in inspiratory effort corresponding to reductions of esophageal pressure swing greater than 10 cmHO more than halved the values of total lung stress, driving pressure, power and transpulmonary pressure swing.

A computational cardiopulmonary physiology simulator accurately predicts individual patient responses to changes in mechanical ventilator settings.

We present new results validating the capability of a high-fidelity computational simulator to accurately predict the responses of individual patients with acute respiratory distress syndrome to changes in mechanical ventilator settings. 26 pairs of data-points comprising arterial blood gasses collected before and after changes in inspiratory pressure, PEEP, FiO, and I:E ratio from six mechanically ventilated patients were used for this study. Parallelized global optimization algorithms running on a high-performance computing cluster were used to match the simulator to each initial data point.

gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs.

Motivation: A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates.

Application and potential of artificial intelligence in neonatal medicine.

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies.

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls.

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates.

Optimising respiratory support for early COVID-19 pneumonia: a computational modelling study.

Background: Optimal respiratory support in early COVID-19 pneumonia is controversial and remains unclear. Using computational modelling, we examined whether lung injury might be exacerbated in early COVID-19 by assessing the impact of conventional oxygen therapy (COT), high-flow nasal oxygen therapy (HFNOT), continuous positive airway pressure (CPAP), and noninvasive ventilation (NIV).

Methods: Using an established multi-compartmental cardiopulmonary simulator, we first modelled COT at a fixed FiO (0.

Restoring circadian gene profiles in clock networks using synthetic feedback control.

The circadian system-an organism's built-in biological clock-is responsible for orchestrating biological processes to adapt to diurnal and seasonal variations. Perturbations to the circadian system (e.g.

Pre-hospital continuous positive airway pressure after blast lung injury and hypovolaemic shock: a modelling study.

Background: In non-traumatic respiratory failure, pre-hospital application of CPAP reduces the need for intubation. Primary blast lung injury (PBLI) accompanied by haemorrhagic shock is common after mass casualty incidents. We hypothesised that pre-hospital CPAP is also beneficial after PBLI accompanied by haemorrhagic shock.

High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study.

Background: There is on-going controversy regarding the potential for increased respiratory effort to generate patient self-inflicted lung injury (P-SILI) in spontaneously breathing patients with COVID-19 acute hypoxaemic respiratory failure. However, direct clinical evidence linking increased inspiratory effort to lung injury is scarce. We adapted a computational simulator of cardiopulmonary pathophysiology to quantify the mechanical forces that could lead to P-SILI at different levels of respiratory effort.

Designing an irreversible metabolic switch for scalable induction of microbial chemical production.

Bacteria can be harnessed to synthesise high-value chemicals. A promising strategy for increasing productivity uses inducible control systems to switch metabolism from growth to chemical synthesis once a large population of cell factories are generated. However, use of expensive chemical inducers limits scalability of this approach for biotechnological applications.

Ventilation strategies for front of neck airway rescue: an in silico study.

Background: During induction of general anaesthesia a 'cannot intubate, cannot oxygenate' (CICO) situation can arise, leading to severe hypoxaemia. Evidence is scarce to guide ventilation strategies for small-bore emergency front of neck airways that ensure effective oxygenation without risking lung damage and cardiovascular depression.

Methods: Fifty virtual subjects were configured using a high-fidelity computational model of the cardiovascular and pulmonary systems.

A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits.

Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision.