The bioelectric mechanisms of local calcium dynamics in cancer cell proliferation: an extension of the A549 in silico cell model.

Introduction: Advances in molecular targeting of ion channels may open up new avenues for therapeutic approaches in cancer based on the cells' bioelectric properties. In addition to in-vitro or in-vivo models, in silico models can provide deeper insight into the complex role of electrophysiology in cancer and reveal the impact of altered ion channel expression and the membrane potential on malignant processes. The A549 in silico model is the first computational cancer whole-cell ion current model that simulates the bioelectric mechanisms of the human non-small cell lung cancer cell line A549 during the different phases of the cell cycle. This work extends the existing model with a detailed mathematical description of the store-operated Ca2+ entry (SOCE) and the complex local intracellular calcium dynamics, which significantly affect the entire electrophysiological properties of the cell and regulate cell cycle progression. Methods: The initial model was extended by a multicompartmental approach, addressing the heterogenous calcium profile and dynamics in the ER-PM junction provoked by local calcium entry of store-operated calcium channels (SOCs) and uptake by SERCA pumps. Changes of cytosolic calcium levels due to diffusion from the ER-PM junction, release from the ER by RyR channels and IP3 receptors, as well as corresponding PM channels were simulated and the dynamics evaluated based on calcium imaging data. The model parameters were fitted to available data from two published experimental studies, showing the function of CRAC channels and indirectly of IP3R, RyR and PMCA via changes of the cytosolic calcium levels. Results: The proposed calcium description accurately reproduces the dynamics of calcium imaging data and simulates the SOCE mechanisms. In addition, simulations of the combined A549-SOCE model in distinct phases of the cell cycle demonstrate how Ca2+ - dynamics influence responding channels such as KCa, and consequently modulate the membrane potential accordingly. Discussion: Local calcium distribution and time evolution in microdomains of the cell significantly impact the overall electrophysiological properties and exert control over cell cycle progression. By providing a more profound description, the extended A549-SOCE model represents an important step on the route towards a valid model for oncological research and in silico supported development of novel therapeutic strategies.

Shedding Light on Cardiac Excitation: In Vitro and In Silico Analysis of Native Ca2+ Channel Activation in Guinea Pig Cardiomyocytes Using Organic Photovoltaic Devices.

OBJECTIVE: This study aims to explore the potential of organic electrolytic photocapacitors (OEPCs), an innovative photovoltaic device, in mediating the activation of native voltage-gated Cav1.2 channels (ICa,L) in Guinea pig ventricular cardiomyocytes. METHODS: Whole-cell patch-clamp recordings were employed to examine light-triggered OEPC mediated ICa,L activation, integrating the channel's kinetic properties into a multicompartment cell model to take intracellular ion concentrations into account. A multidomain model was additionally incorporated to evaluate effects of OEPC-mediated stimulation. The final model combines external stimulation, multicompartmental cell simulation, and a patch-clamp amplifier equivalent circuit to assess the impact on achievable intracellular voltage changes. RESULTS: Light pulses activated ICa,L, with amplitudes similar to voltage-clamp activation and high sensitivity to the L-type Ca2+ channel blocker, nifedipine. Light-triggered ICa,L inactivation exhibited kinetic parameters comparable to voltage-induced inactivation. CONCLUSION: OEPC-mediated activation of ICa,L demonstrates their potential for nongenetic optical modulation of cellular physiology potentially paving the way for the development of innovative therapies in cardiovascular health. The integrated model proves the light-mediated activation of ICa,L and advances the understanding of the interplay between the patch-clamp amplifier and external stimulation devices. SIGNIFICANCE: Treating cardiac conduction disorders by minimal-invasive means without genetic modifications could advance therapeutic approaches increasing patients' quality of life compared with conventional methods employing electronic devices.

A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients.

Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate. Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB. Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48 h estimation time and 8-16 h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0 L. Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.

Does Electrical Stimulation through Nerve Conduits Improve Peripheral Nerve Regeneration?-A Systematic Review.

BACKGROUND: Peripheral nerve injuries affect over 2% of trauma patients and can lead to severe functional impairment and permanent disability. Autologous nerve transplantation is still the gold standard in the reconstruction of nerve defects. For small defects, conduits can be considered for bridging. Lately, the combined use of conduits and electrical stimulation has gained attention in the treatment of peripheral nerve injury. This review aimed to present the currently available data on this topic. METHODS: PubMed, Embase, Medline and the Cochrane Library were searched for studies on electrical stimulation through nerve conduits for nerve defects in in vivo studies. RESULTS: Fifteen studies fit the inclusion criteria. All of them reported on the application of nerve conduits combined with stimulation for sciatic nerve gaps in rats. Functional, electrophysiological and histological evaluations showed improved nerve regeneration after electrical stimulation. High variation was observed in the treatment protocols. CONCLUSION: Electrically stimulated conduits could improve peripheral nerve regeneration in rat models. The combined application of nerve guidance conduits and electrical stimulation shows promising results and should be further evaluated under standardized conditions.

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1.

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin-Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.

Light Stimulation of Neurons on Organic Photocapacitors Induces Action Potentials with Millisecond Precision.

Nongenetic optical control of neurons is a powerful technique to study and manipulate the function of the nervous system. This research has benchmarked the performance of organic electrolytic photocapacitor (OEPC) optoelectronic stimulators at the level of single mammalian cells: human embryonic kidney (HEK) cells with heterologously expressed voltage-gated K+ channels and hippocampal primary neurons. OEPCs act as extracellular stimulation electrodes driven by deep red light. The electrophysiological recordings show that millisecond light stimulation of OEPC shifts conductance-voltage plots of voltage-gated K+ channels by ≈30 mV. Models are described both for understanding the experimental findings at the level of K+ channel kinetics in HEK cells, as well as elucidating interpretation of membrane electrophysiology obtained during stimulation with an electrically floating extracellular photoelectrode. A time-dependent increase in voltage-gated channel conductivity in response to OEPC stimulation is demonstrated. These findings are then carried on to cultured primary hippocampal neurons. It is found that millisecond time-scale optical stimuli trigger repetitive action potentials in these neurons. The findings demonstrate that OEPC devices enable the manipulation of neuronal signaling activities with millisecond precision. OEPCs can therefore be integrated into novel in vitro electrophysiology protocols, and the findings can inspire in vivo applications.

A549 in-silico 1.0: A first computational model to simulate cell cycle dependent ion current modulation in the human lung adenocarcinoma.

Lung cancer is still a leading cause of death worldwide. In recent years, knowledge has been obtained of the mechanisms modulating ion channel kinetics and thus of cell bioelectric properties, which is promising for oncological biomarkers and targets. The complex interplay of channel expression and its consequences on malignant processes, however, is still insufficiently understood. We here introduce the first approach of an in-silico whole-cell ion current model of a cancer cell, in particular of the A549 human lung adenocarcinoma, including the main functionally expressed ion channels in the plasma membrane as so far known. This hidden Markov-based model represents the electrophysiology behind proliferation of the A549 cell, describing its rhythmic oscillation of the membrane potential able to trigger the transition between cell cycle phases, and it predicts membrane potential changes over the cell cycle provoked by targeted ion channel modulation. This first A549 in-silico cell model opens up a deeper insight and understanding of possible ion channel interactions in tumor development and progression, and is a valuable tool for simulating altered ion channel function in lung cancer electrophysiology.

Human plasma proteomic profiles indicative of cardiorespiratory fitness.

Maximal oxygen uptake (VO2max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO2max, and of increases of VO2max in response to exercise training (ΔVO2max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO2max and ΔVO2max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual's ΔVO2max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans.

Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability.

The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a "clinical level" study. Almost 39% of the articles achieved a "robust candidate" and as many as 61% a "proof of concept" status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.

Modeling External Stimulation of Excitable Cells Using a Novel Light-Activated Organic Semiconductor Technology.

Optoelectronic neurostimulation is a promising, minimally invasive treatment modality for neuronal damage, in particular for patients with traumatic brain injury. In this work, a newly developed optoelectronic device, a so-called photocap, based on light-activated organic semiconductor structures with high spatial and temporal resolution is investigated. To prove and verify the feasibility of this new technology, a mathematical model was developed, simulating the electrical response of excitable cells to photocap stimulation. In the first step, a comprehensive technical review of the device concept was performed, building the basis for setting up the simulation model. The simulations demonstrate that photocaps may serve as a stimulation device, triggering action potentials in neural or cardiac cells. Our first results show that the model serves as a perfect tool for evaluating and further developing this new technology, showing high potential for introducing new and innovative therapy methods in the field of optoelectronic cell stimulation.

A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease.

Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine.

Standardizing the complication rate after breast reduction using the Clavien-Dindo classification.

BACKGROUND: Published complication rates for breast reduction surgery, also known as reduction mammaplasty, vary between 4% and 54%. This wide range of complication rates could be attributable to the lack of a standardized classification of complications in plastic surgery. The aim of this study was to analyze our single-center complication rates after reduction mammaplasty using the Clavien-Dindo classification. METHODS: We performed a retrospective chart review studying 804 patients between the ages of 18 and 81 years old who underwent breast reduction between 2005 and 2015 at our institution. Patients with a history of breast cancer, a previous breast operation, who did not undergo bilateral reduction mammaplasty, or who required systemic immunodeficiency/immunosuppressive drugs were excluded from our analysis. Complications were classified according to the Clavien-Dindo classification from Grades I to V. RESULTS: A total of 486 patients met the inclusion criteria for the analysis. Patients had an age (mean ± standard deviation) of 39 ± 13 years and a body mass index of 26 ± 4 kg/m2. Median follow-up was 274 days (interquartile range: 90.5-378). The overall rate of complications of reduction mammaplasty was 63%, with the majority of those being Grades I (48%) and II (9%), comprising 92% of all the complications. Operative revisions were required in 6% (1% Grade IIIA and 5% Grade IIIB). There were no complications graded in categories IV and V. CONCLUSION: Although complications occurred in more than half of the cases, the majority did not require operative reintervention. The Clavien-Dindo classification can classify the severity of complications and serve as a benchmark to compare complication rates between different practices. We believe that grading of complications should distinguish between those that do and do not require operative reinterventions.