Epigenetic therapy inhibits metastases by disrupting premetastatic niches.

Cancer recurrence after surgery remains an unresolved clinical problem1-3. Myeloid cells derived from bone marrow contribute to the formation of the premetastatic microenvironment, which is required for disseminating tumour cells to engraft distant sites4-6. There are currently no effective interventions that prevent the formation of the premetastatic microenvironment6,7. Here we show that, after surgical removal of primary lung, breast and oesophageal cancers, low-dose adjuvant epigenetic therapy disrupts the premetastatic microenvironment and inhibits both the formation and growth of lung metastases through its selective effect on myeloid-derived suppressor cells (MDSCs). In mouse models of pulmonary metastases, MDSCs are key factors in the formation of the premetastatic microenvironment after resection of primary tumours. Adjuvant epigenetic therapy that uses low-dose DNA methyltransferase and histone deacetylase inhibitors, 5-azacytidine and entinostat, disrupts the premetastatic niche by inhibiting the trafficking of MDSCs through the downregulation of CCR2 and CXCR2, and by promoting MDSC differentiation into a more-interstitial macrophage-like phenotype. A decreased accumulation of MDSCs in the premetastatic lung produces longer periods of disease-free survival and increased overall survival, compared with chemotherapy. Our data demonstrate that, even after removal of the primary tumour, MDSCs contribute to the development of premetastatic niches and settlement of residual tumour cells. A combination of low-dose adjuvant epigenetic modifiers that disrupts this premetastatic microenvironment and inhibits metastases may permit an adjuvant approach to cancer therapy.

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling.

Oxylipins Other Than Jasmonic Acid Are Xylem-Resident Signals Regulating Systemic Resistance Induced by Trichoderma virens in Maize.

Multiple long-distance signals have been identified for pathogen-induced systemic acquired resistance, but mobile signals for symbiont-induced systemic resistance (ISR) are less well understood. We used ISR-positive and -negative mutants of maize (Zea mays) and the beneficial fungus Trichoderma virens and identified 12-oxo-phytodienoic acid (12-OPDA) and α-ketol of octadecadienoic acid (KODA) as important ISR signals. We show that a maize 13-lipoxygenase mutant, lox10, colonized by the wild-type T. virens (TvWT) lacked ISR response against Colletotrichum graminicola but instead displayed induced systemic susceptibility. Oxylipin profiling of xylem sap from T. virens-treated plants revealed that 12-OPDA and KODA levels correlated with ISR. Transfusing sap supplemented with 12-OPDA or KODA increased receiver plant resistance in a dose-dependent manner, with 12-OPDA restoring ISR of lox10 plants treated with TvWT or T. virens Δsm1, a mutant unable to induce ISR. Unexpectedly, jasmonic acid (JA) was not involved, as the JA-deficient opr7 opr8 mutant plants retained the capacity for T. virens-induced ISR. Transcriptome analysis of TvWT-treated maize B73 revealed upregulation of 12-OPDA biosynthesis and OPDA-responsive genes but downregulation of JA biosynthesis and JA response genes. We propose a model that differential regulation of 12-OPDA and JA in response to T. virens colonization results in ISR induction.

Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors.

Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.

PKPD and cardiac single cell modeling of a DDI study with a CYP3A4 substrate and itraconazole to quantify the effects on QT interval duration.

Plasma drug concentration and electrocardiogram (ECG) data from a drug-drug interaction (DDI) study employing the metabolic inhibitor itraconazole have been used as part of a prospectively defined pharmacokinetic/pharmacodynamic modelling strategy to quantify the potential for QT interval prolongation from basmisanil, an investigational compound. ECG data were collected on multiple days during repeat dosing treatment regimens, thereby allowing the capture of QT data across a wide range of drug concentrations in each study participant and encompassing both "therapeutic" and "supra-therapeutic" exposures. The data were used to develop a non-linear mixed effect concentration-QT (C-QT) model that differentiated drug-induced QT prolongation from other factors altering QT interval duration. Food effects were accounted by quantitating their influences on the parameters describing the diurnal variation of QT. The final model demonstrated that itraconazole does not cause QT prolongation, while for basmisanil, the 1-sided upper 95% CI of the QT interval at 240 mg (the highest dose tested in ongoing phase 2 studies) with DDI, was below the 10 ms threshold considered to be of clinical significance by regulatory authorities. The empirical modelling was complemented with a human mechanistic cardiac single cell model that was used to simulate the change in action potential duration as a function of drug concentration. The results of the two approaches were in agreement, suggesting that the effect of basmisanil on QT interval duration can be attributed to the effect on hERG alone. The C-QT model for basmisanil can be used to derive the QT interval corrected changes in heart rate (QTc) and thus inform cardiac safety strategy in later development without the need for a separate, dedicated study.

Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System.

Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug development and requires a deep understanding of a compound's action on ion channels. In vitro hERG channel current recordings are an important step in evaluating the proarrhythmic potential of small molecules and are now routinely performed using automated high-throughput patch-clamp platforms. These machines can execute traditional voltage-clamp protocols aimed at specific gating processes, but the array of protocols needed to fully characterize a current is typically too long to be applied in a single cell. Shorter high-information protocols have recently been introduced that have this capability, but they are not typically compatible with high-throughput platforms. We present a new 15 second protocol to characterize hERG (Kv11.1) kinetics, suitable for both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, by applying it to Chinese hamster ovary cells stably expressing hERG1a. From these recordings, we construct 124 cell-specific variants/parameterizations of a hERG model at 25°C. A further eight independent protocols are run in each cell and are used to validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we use to quantify the uncertainty in the model parameters, and their variability from cell-to-cell; we use this model to suggest reasons for the variability. This study demonstrates a robust method to measure and quantify uncertainty and shows that it is possible and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly.

Rapid Characterization of hERG Channel Kinetics II: Temperature Dependence.

Ion channel behavior can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel kinetics (rates of opening, closing, inactivating, and recovery) are commonly represented with Q10 coefficients or an Eyring relationship. In this article, we assess the validity of these representations by characterizing channel kinetics at multiple temperatures. We focus on the human Ether-à-go-go-Related Gene (hERG) channel, which is important in drug safety assessment and commonly screened at room temperature so that results require extrapolation to physiological temperature. In Part I of this study, we established a reliable method for high-throughput characterization of hERG1a (Kv11.1) kinetics, using a 15-second information-rich optimized protocol. In this Part II, we use this protocol to study the temperature dependence of hERG kinetics using Chinese hamster ovary cells overexpressing hERG1a on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, with temperature control. We characterize the temperature dependence of hERG gating by fitting the parameters of a mathematical model of hERG kinetics to data obtained at five distinct temperatures between 25 and 37°C and validate the models using different protocols. Our models reveal that activation is far more temperature sensitive than inactivation, and we observe that the temperature dependency of the kinetic parameters is not represented well by Q10 coefficients; it broadly follows a generalized, but not the standardly-used, Eyring relationship. We also demonstrate that experimental estimations of Q10 coefficients are protocol dependent. Our results show that a direct fit using our 15-s protocol best represents hERG kinetics at any given temperature and suggests that using the Generalized Eyring theory is preferable if no experimental data are available to derive model parameters at a given temperature.

Differential expression analysis of Trichoderma virens RNA reveals a dynamic transcriptome during colonization of Zea mays roots.

BACKGROUND: Trichoderma spp. are majorly composed of plant-beneficial symbionts widely used in agriculture as bio-control agents. Studying the mechanisms behind Trichoderma-derived plant benefits has yielded tangible bio-industrial products. To better take advantage of this fungal-plant symbiosis it is necessary to obtain detailed knowledge of which genes Trichoderma utilizes during interaction with its plant host. In this study, we explored the transcriptional activity undergone by T. virens during two phases of symbiosis with maize; recognition of roots and after ingress into the root cortex. RESULTS: We present a model of T. virens - maize interaction wherein T. virens experiences global repression of transcription upon recognition of maize roots and then induces expression of a broad spectrum of genes during colonization of maize roots. The genes expressed indicate that, during colonization of maize roots, T. virens modulates biosynthesis of phytohormone-like compounds, secretes a plant-environment specific array of cell wall degrading enzymes and secondary metabolites, remodels both actin-based and cell membrane structures, and shifts metabolic activity. We also highlight transcription factors and signal transduction genes important in future research seeking to unravel the molecular mechanisms of T. virens activity in maize roots. CONCLUSIONS: T. virens displays distinctly different transcriptional profiles between recognizing the presence of maize roots and active colonization of these roots. A though understanding of these processes will allow development of T. virens as a bio-control agent. Further, the publication of these datasets will target future research endeavors specifically to genes of interest when considering T. virens - maize symbiosis.

Time-reversal symmetry in temporal coupled-mode theory and nonreciprocal device applications.

We present a fundamental result on the role of time-reversal symmetry in the temporal coupled-mode theory (TCMT). The TCMT is a phenomenological theory that describes resonant wave scattering in photonics, acoustics, and other fields. Modifications to the canonical formulation of the TCMT are required for nonreciprocal devices where time-reversal symmetry is usually absent. We discover that previous results on reciprocity for the TCMT are incomplete, and we provide a mathematical proof to clarify the roles of time-reversal symmetry and reciprocity in the TCMT. The new result leads to a general treatment of nonreciprocity in the TCMT. The theoretical result has many practical applications, including the design of nonreciprocal devices such as optical circulators and isolators.

Cardiac tissue slices: preparation, handling, and successful optical mapping.

Cardiac tissue slices are becoming increasingly popular as a model system for cardiac electrophysiology and pharmacology research and development. Here, we describe in detail the preparation, handling, and optical mapping of transmembrane potential and intracellular free calcium concentration transients (CaT) in ventricular tissue slices from guinea pigs and rabbits. Slices cut in the epicardium-tangential plane contained well-aligned in-slice myocardial cell strands ("fibers") in subepicardial and midmyocardial sections. Cut with a high-precision slow-advancing microtome at a thickness of 350 to 400 µm, tissue slices preserved essential action potential (AP) properties of the precutting Langendorff-perfused heart. We identified the need for a postcutting recovery period of 36 min (guinea pig) and 63 min (rabbit) to reach 97.5% of final steady-state values for AP duration (APD) (identified by exponential fitting). There was no significant difference between the postcutting recovery dynamics in slices obtained using 2,3-butanedione 2-monoxime or blebistatin as electromechanical uncouplers during the cutting process. A rapid increase in APD, seen after cutting, was caused by exposure to ice-cold solution during the slicing procedure, not by tissue injury, differences in uncouplers, or pH-buffers (bicarbonate; HEPES). To characterize intrinsic patterns of CaT, AP, and conduction, a combination of multipoint and field stimulation should be used to avoid misinterpretation based on source-sink effects. In summary, we describe in detail the preparation, mapping, and data analysis approaches for reproducible cardiac tissue slice-based investigations into AP and CaT dynamics.

Alignment of multiradiation isocenters for megavoltage photon beam.

The accurate measurement of the linear accelerator (linac) radiation isocenter is critical, especially for stereotactic treatment. Traditional quality assurance (QA) procedure focuses on the measurement of single radiation isocenter, usually of 6 megavoltage (MV) photon beams. Single radiation isocenter is also commonly assumed in treatment planning systems (TPS). Due to different flattening filters and bending magnet and steering parameters, the radiation isocenter of one energy mode can deviate from another if no special effort was devoted. We present the first experience of the multiradiation isocenters alignment on an Elekta linac, as well as its corresponding QA procedure and clinical impact. An 8 mm ball-bearing (BB) phantom was placed at the 6 MV radiation isocenter using an Elekta isocenter search algorithm, based on portal images. The 3D radiation isocenter shifts of other photon energy modes relative to the 6 MV were determined. Beam profile scanning for different field sizes was used as an independent method to determine the 2D multiradiation isocenters alignment. To quantify the impact of radiation isocenter offset on targeting accuracy, the 10 MV radiation isocenter was manually offset from that for 6 MV by adjusting the bending magnet current. Because our table isocenter was mechanically aligned to the 6 MV radiation isocenter, the deviation of the table isocentric rotation from the "shifted" 10 MV radiation isocenter after bending magnet adjustment was assessed. Winston-Lutz test was also performed to confirm the overall radiation isocenter positioning accuracy for all photon energies. The portal image method showed the radiation isocenter of the 10 MV flattening filter-free mode deviated from others before beam parameter adjustment. After the adjustment, the deviation was greatly improved from 0.96 to 0.35 mm relative to the 6 MV radiation isocenter. The same finding was confirmed by the profile-scanning method. The maximum deviation of the table isocentric rotation from the 10 MV radiation isocenter was observed to linearly increase with the offset between 6 and 10 MV radiation isocenter; 1 mm radiation isocenter offset can translate to almost 2 mm maximum deviation of the table isocentric rotation from the 10 MV radiation isocenter. The alignment of the multiradiation isocenters is particularly important for high-precision radiotherapy. Our study provides the medical physics community with a quantitative measure of the multiradiation isocenters alignment. A routine QA method should be considered, to examine the radiation isocenters alignment during the linac acceptance.

Optical impedance transformer for transparent conducting electrodes.

A fundamental limitation of transparent conducting electrode design is thought to be the trade-off between photonic and electronic performances. The photonic transmission property of a transparent conducting electrode, however, is not intrinsic but depends critically on the electromagnetic environment where the electrode is located. We develop the concept of optical impedance transformation, and use this concept to design nanophotonic structures that provide broadband and omnidirectional reduction of optical loss in an ultrathin transparent conducting electrode, without compromising its electrical performance.

Large-area free-standing ultrathin single-crystal silicon as processable materials.

Silicon has been driving the great success of semiconductor industry, and emerging forms of silicon have generated new opportunities in electronics, biotechnology, and energy applications. Here we demonstrate large-area free-standing ultrathin single-crystalline Si at the wafer scale as new Si materials with processability. We fabricated them by KOH etching of the Si wafer and show their uniform thickness from 10 to sub-2 µm. These ultrathin Si exhibits excellent mechanical flexibility and bendability more than those with 20-30 µm thickness in previous study. Unexpectedly, these ultrathin Si materials can be cut with scissors like a piece of paper, and they are robust during various regular fabrication processings including tweezer handling, spin coating, patterning, doping, wet and dry etching, annealing, and metal deposition. We demonstrate the fabrication of planar and double-sided nanocone solar cells and highlight that the processability on both sides of surface together with the interesting property of these free-standing ultrathin Si materials opens up exciting opportunities to generate novel functional devices different from the existing approaches.

Two-dimensional chalcogenide nanoplates as tunable metamaterials via chemical intercalation.

New plasmonic materials with tunable properties are in great need for nanophotonics and metamaterials applications. Here we present two-dimensional layered, metal chalcogenides as tunable metamaterials that feature both dielectric photonic and plasmonic modes across a wide spectral range from the infrared to ultraviolet. The anisotropic layered structure allows intercalation of organic molecules and metal atoms at the van der Waals gap of the host chalcogenide, presenting a chemical route to create heterostructures with molecular and atomic precision for photonic and plasmonic applications. This marks a departure from a lithographic method to create metamaterials. Monochromated electron energy-loss spectroscopy in a scanning transmission electron microscope was used to first establish the presence of the dielectric photonic and plasmonic modes in M2E3 (M = Bi, Sb; E = Se, Te) nanoplates and to observe marked changes in these modes after chemical intercalation. We show that these modal properties can also be tuned effectively by more conventional methods such as thickness control and alloy composition of the nanoplates.

Fundamental bounds on decay rates in asymmetric single-mode optical resonators.

We derive tight upper and lower bounds of the ratio between decay rates to two ports from a single resonance exhibiting Fano interference, based on a general temporal coupled-mode theory formalism. The photon transport between these two ports involves both direct and resonance-assisted contributions, and the bounds depend only on the direct process. The bounds imply that, in a lossless system, full reflection is always achievable at Fano resonance, even for structures lacking mirror symmetries, while full transmission can only be seen in a symmetric configuration where the two decay rates are equal. The analytic predictions are verified against full-field electromagnetic simulations.

Avoiding miscommunication in acute musculoskeletal trauma cases: use of standardized reporting and classification schemes.

Classification schemes can be a key element of a structured radiology report, providing succinct guidance for clinical decision making. Classification systems delineate the location and morphological characteristics of fractures (diagnosis), may provide a graded measure of severity (prognosis), and ideally guide treatment options. Reports structured in this fashion optimize communication between the physician interpreting the examination and the physician directing the patient's treatment. This article reviews the concept and utility of standardized structured radiologic reporting based on templates or checklists to avoid miscommunication in the context of acute musculoskeletal trauma.

Absorption enhancement in ultrathin crystalline silicon solar cells with antireflection and light-trapping nanocone gratings.

Enhancing the light absorption in ultrathin-film silicon solar cells is important for improving efficiency and reducing cost. We introduce a double-sided grating design, where the front and back surfaces of the cell are separately optimized for antireflection and light trapping, respectively. The optimized structure yields a photocurrent of 34.6 mA/cm(2) at an equivalent thickness of 2 µm, close to the Yablonovitch limit. This approach is applicable to various thicknesses and is robust against metallic loss in the back reflector.

Machine-learning-based adverse drug event prediction from observational health data: A review.

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.

Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case.

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

Perspectives of data science in preclinical safety assessment.

The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.