CRISPR-Mediated Reactivation of DKK3 Expression Attenuates TGF-β Signaling in Prostate Cancer

The DKK3 gene encodes a secreted protein, Dkk-3, that inhibits prostate tumor growth and metastasis. DKK3 is downregulated by promoter methylation in many types of cancer, including prostate cancer. Gene silencing studies have shown that Dkk-3 maintains normal prostate epithelial cell homeostasis by limiting TGF-β/Smad signaling. While ectopic expression of Dkk-3 leads to prostate cancer cell apoptosis, it is unclear if Dkk-3 has a physiological role in cancer cells. Here, we show that treatment of PC3 prostate cancer cells with the DNA methyltransferase (DNMT) inhibitor decitabine demethylates the DKK3 promoter, induces DKK3 expression, and inhibits TGF-β/Smad-dependent transcriptional activity. Direct induction of DKK3 expression using CRISPR-dCas9-VPR also inhibited TGF-β/Smad-dependent transcription and attenuated PC3 cell migration and proliferation. These effects were not observed in C4-2B cells, which do not respond to TGF-β. TGF-β signals can regulate gene expression directly via SMAD proteins and indirectly by increasing DNMT expression, leading to promoter methylation. Analysis of genes downregulated by promoter methylation and predicted to be regulated by TGF-β found that DKK3 induction increased expression of PTGS2, which encodes cyclooxygenase-2. Together, these observations provide support for using CRISPR-mediated induction of DKK3 as a potential therapeutic approach for prostate cancer and highlight complexities in Dkk-3 regulation of TGF-β signaling

An inhaled sGC modulator can lower PH in COPD patients without deteriorating oxygenation

This study uses a highly fidelity computational simulator of pulmonary physiology to evaluate the impact of a soluble guanylate cyclase (sGC) modulator on gas exchange in patients with chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) as a complication. Three virtual COPD patients were configured in the simulator based on clinical data. In agreement with previous clinical studies, modeling systemic application of a soluble guanylate cyclase (sGC) modulator results in reduced partial pressure of oxygen (PaO2) and increased partial pressure of carbon dioxide (PaCO2) in arterial blood, if a drug-induced reduction of pulmonary vascular resistance (PVR) equal to that observed experimentally is assumed. In contrast, for administration via dry powder inhalation (DPI), our simulations suggest that the treatment results in no deterioration in oxygenation. For patients under exercise, DPI administration lowers PH while oxygenation is improved with respect to baseline values

Utility of Driving Pressure and Mechanical Power to Guide Protective Ventilator Settings in Two Cohorts of Adult and Pediatric Patients With Acute Respiratory Distress Syndrome: A Computational Investigation

Objectives: Mechanical power and driving pressure have been proposed as indicators, and possibly drivers, of ventilator-induced lung injury. We tested the utility of these different measures as targets to derive maximally protective ventilator settings.Design: A high-fidelity computational simulator was matched to individual patient data and used to identify strategies that minimize driving pressure, mechanical power, and a modified mechanical power that removes the direct linear, positive dependence between mechanical power and positive end-expiratory pressure.Setting: Interdisciplinary Collaboration in Systems Medicine Research Network.Subjects: Data were collected from a prospective observational cohort of pediatric acute respiratory distress syndrome from the Children’s Hospital of Philadelphia (n = 77) and from the low tidal volume arm of the Acute Respiratory Distress Syndrome Network tidal volume trial (n = 100).Interventions: Global optimization algorithms evaluated more than 26.7 million changes to ventilator settings (approximately 150,000 per patient) to identify strategies that minimize driving pressure, mechanical power, or modified mechanical power.Measurements and Main Results: Large average reductions in driving pressure (pediatric: 23%, adult: 23%), mechanical power (pediatric: 44%, adult: 66%), and modified mechanical power (pediatric: 61%, adult: 67%) were achievable in both cohorts when oxygenation and ventilation were allowed to vary within prespecified ranges. Reductions in driving pressure (pediatric: 12%, adult: 2%), mechanical power (pediatric: 24%, adult: 46%), and modified mechanical power (pediatric: 44%, adult: 46%) were achievable even when no deterioration in gas exchange was allowed. Minimization of mechanical power and modified mechanical power was achieved by increasing tidal volume and decreasing respiratory rate. In the pediatric cohort, minimum driving pressure was achieved by reducing tidal volume and increasing respiratory rate and positive end-expiratory pressure. The Acute Respiratory Distress Syndrome Network dataset had limited scope for further reducing tidal volume, but driving pressure was still significantly reduced by increasing positive end-expiratory pressure.Conclusions: Our analysis identified different strategies that minimized driving pressure or mechanical power consistently across pediatric and adult datasets. Minimizing standard and alternative formulations of mechanical power led to significant increases in tidal volume. Targeting driving pressure for minimization resulted in ventilator settings that also reduced mechanical power and modified mechanical power, but not vice versa

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. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants

Validation of at-the-bedside formulae for estimating ventilator driving pressure during airway pressure release ventilation using computer simulation

Background: Airway pressure release ventilation (APRV) is widely available on mechanical ventilators and has been proposed as an early intervention to prevent lung injury or as a rescue therapy in the management of refractory hypoxemia. Driving pressure (ΔP) has been identified in numerous studies as a key indicator of ventilator-induced-lung-injury that needs to be carefully controlled. ΔP delivered by the ventilator in APRV is not directly measurable in dynamic conditions, and there is no “gold standard” method for its estimation. Methods: We used a computational simulator matched to data from 90 patients with acute respiratory distress syndrome (ARDS) to evaluate the accuracy of three “at-the-bedside” methods for estimating ventilator ΔP during APRV. Results: Levels of ΔP delivered by the ventilator in APRV were generally within safe limits, but in some cases exceeded levels specified by protective ventilation strategies. A formula based on estimating the intrinsic positive end expiratory pressure present at the end of the APRV release provided the most accurate estimates of ΔP. A second formula based on assuming that expiratory flow, volume and pressure decay mono-exponentially, and a third method that requires temporarily switching to volume-controlled ventilation, also provided accurate estimates of true ΔP. Conclusions: Levels of ΔP delivered by the ventilator during APRV can potentially exceed levels specified by standard protective ventilation strategies, highlighting the need for careful monitoring. Our results show that ΔP delivered by the ventilator during APRV can be accurately estimated at the bedside using simple formulae that are based on readily available measurements

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

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). Using an established multi-compartmental cardiopulmonary simulator, we first modelled COT at a fixed FiO (0.6) with elevated respiratory effort for 30 min in 120 spontaneously breathing patients, before initiating HFNOT, CPAP, or NIV. Respiratory effort was then reduced progressively over 30-min intervals. Oxygenation, respiratory effort, and lung stress/strain were quantified. Lung-protective mechanical ventilation was also simulated in the same cohort. HFNOT, CPAP, and NIV improved oxygenation compared with conventional therapy, but also initially increased total lung stress and strain. Improved oxygenation with CPAP reduced respiratory effort but lung stress/strain remained elevated for CPAP >5 cm H O. With reduced respiratory effort, HFNOT maintained better oxygenation and reduced total lung stress, with no increase in total lung strain. Compared with 10 cm H O PEEP, 4 cm H O PEEP in NIV reduced total lung stress, but high total lung strain persisted even with less respiratory effort. Lung-protective mechanical ventilation improved oxygenation while minimising lung injury. The failure of noninvasive ventilatory support to reduce respiratory effort may exacerbate pulmonary injury in patients with early COVID-19 pneumonia. HFNOT reduces lung strain and achieves similar oxygenation to CPAP/NIV. Invasive mechanical ventilation may be less injurious than noninvasive support in patients with high respiratory effort

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

Background: There is ongoing 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. In accordance with recent data, the simulator parameters were manually adjusted to generate a population of 10 patients that recapitulate clinical features exhibited by certain COVID-19 patients, i.e. severe hypoxaemia combined with relatively well-preserved lung mechanics, being treated with supplemental oxygen. Results: Simulations were conducted at tidal volumes (VT) and respiratory rates (RR) of 7 ml/kg and 14 breaths/min (representing normal respiratory effort) and at VT/RR of 7/20, 7/30, 10/14, 10/20 and 10/30 ml/kg / breaths/min. While oxygenation improved with higher respiratory efforts, significant increases in multiple indicators of the potential for lung injury were observed at all higher VT/RR combinations tested. Pleural pressure swing increased from 12.0±0.3 cmH 2 O at baseline to 33.8±0.4 cmH 2 O at VT/RR of 7 ml/kg/30 breaths/min and to 46.2±0.5 cmH 2 O at 10 ml/kg/30 breaths/min. Transpulmonary pressure swing increased from 4.7±0.1 cmH 2 O at baseline to 17.9±0.3 cmH 2 O at VT/RR of 7 ml/kg/30 breaths/min and to 24.2±0.3 cmH 2 O at 10 ml/kg/30 breaths/min. Total lung strain increased from 0.29±0.006 at baseline to 0.65±0.016 at 10 ml/kg/30 breaths/min. Mechanical power increased from 1.6±0.1 J/min at baseline to 12.9±0.2 J/min at VT/RR of 7 ml/kg/30 breaths/min, and to 24.9±0.3 J/min at 10 ml/kg/30 breaths/min. Driving pressure increased from 7.7±0.2 cmH 2 O at baseline to 19.6±0.2 cmH 2 O at VT/RR of 7 ml/kg/30 breaths/min, and to 26.9±0.3 cmH 2 O at 10 ml/kg/30 breaths/min. Conclusions: Our results suggest that the forces generated by increased inspiratory effort commonly seen in COVID-19 acute hypoxaemic respiratory failure are comparable with those that have been associated with ventilator-induced lung injury during mechanical ventilation. Respiratory efforts in these patients should be carefully monitored and controlled to minimise the risk of lung injury

Computational simulation of novel treatment strategies for critical lung disease in adults, children and neonates

Critical lung diseases such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) are one of the leading causes of death worldwide. The most important component of treatment for patients with such conditions is mechanical ventilation. However, mechanical ventilation can also cause ventilator induced lung injuries (VILI). Consequently, clinicians have for many years been conducting extensive investigations using experimental studies in animal models and clinical trials in human patients in order to find safer and more effective ventilation strategies. To assist these efforts, engineering approaches such as computational modelling can also be employed to investigate and develop novel treatment strategies, with fewer ethical, practical and cost constraints than in vivo experiments. In this thesis, a state-of-the-art computational simulator of cardio-pulmonary physiology is used to investigate novel treatments including ventilatory strategies and drug interventions for critical lung disease in adults, paediatrics and neonates. First, the impact of a novel compound on gas exchange is evaluated in virtual patients with COPD and pulmonary hypertension (PH) as a complication, considering both systemic administration of the drug and dry powder inhalation. Next, the ability to simulate paediatric subjects is incorporated into the model, and a new dataset of paediatric ARDS patients is analysed to investigate whether, and how, more lung protective ventilation could be achieved in clinical practice. Subsequently, the utility of two recently proposed measures of VILI, mechanical power (MP) and driving pressure (ΔP), as targets to derive maximally protective ventilator settings is tested on two cohorts of virtual adult and paediatric ARDS patients. Finally, initial results on the adaptation of the computational simulator to investigate neonatal pathophysiology are presented

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 cmH 2 O more than halved the values of total lung stress, driving pressure, power and transpulmonary pressure swing. In the absence of significant reductions in inspiratory pressure, multiple indicators of lung injury increased after application of non-invasive ventilation. Clinical Relevance- We show using computer simulation that reduced inspiratory pressure after application of noninvasive ventilation translates directly into large reductions in multiple well-established indicators of lung injury, providing a potential physiological explanation for recent clinical findings