Distinguishing tumors from normal brain cells is important but challenging in glioma surgery due to the lack of clear interfaces between the two. The ability of label‐free third harmonic generation (THG) microscopy in combination with automated image analysis to quantitatively detect glioma infiltration in fresh, unprocessed tissue in real time is assessed. The THG images reveal increased cellularity in grades II–IV glioma samples from 23 patients, as confirmed by subsequent hematoxylin and eosin histology. An automated image quantification workflow is presented for quantitative assessment of the imaged cellularity as a reflection of the degree of glioma invasion. The cellularity is validated in three ways: 1) Quantitative comparison of THG imaging with fluorescence microscopy of nucleus‐stained samples demonstrates that THG reflects the true tissue cellularity. 2) Thresholding of THG cellularity differentiates normal brain from glioma infiltration, with 96.6% sensitivity and 95.5% specificity, in nearly perfect (93%) agreement with pathologists. 3) In one patient, a good correlation between THG cellularity and preoperative magnetic resonance and positron emission tomography imaging is demonstrated. In conclusion, quantitative real‐time THG microscopy accurately assesses glioma infiltration in ex vivo human brain samples, and therefore holds strong potential for improving the accuracy of surgical resection. Shortcomings of clinical imaging techniques such as magnetic resonance imaging (MRI)/intraoperative MRI, intraoperative computed tomography (CT), ultrasound, and bright field neurosurgical microscopes include too low resolution for detection of invasive tumor cells at the microscopic level, quality of staining, and/or lack of real‐time imaging.2 was reported for Raman spectroscopy.The availability of a tool allowing for intraoperative detection of not only the highly cellular central glioma mass, but also the (gradient of) more peripherally located invasive tumor cells can be expected to contribute to improved surgical resection of these tumors. Indeed, great effort has recently been put into detection of invasive glioma cells at (sub)cellular resolution. Fluorescence‐guided surgery has been shown to improve extent of resection in high‐grade glioma resection but it appears to be difficult to detect low‐grade/low‐cellularity glioma infiltration.Third and second harmonic generation (THG/SHG) microscopy differs from OCT and Raman spectroscopy in the sense that it directly visualizes cellular morphology in brain tissue, and in comparison to Raman microscopies, it is technically much simpler.Here, we test the feasibility of THG microscopy to detect brain tumor infiltration indicated by cellularity, in ex vivo fresh, unprocessed tissue samples from 23 patients undergoing surgical resection for a diffuse glioma or epilepsy. We demonstrate automated image analysis of the THG images to quantitate the contained information and to provide a fast, THG‐based diagnosis. We provide both qualitative and quantitative evidence demonstrating that the automated analysis of the THG images reflects the true cellularity of histologically normal brain and glioma tissue, and thereby demonstrate that THG microscopy holds great potential as a tool for ex vivo and, ultimately, in situ assessment of diffuse glioma infiltration in human brain.22.1Cellular morphology is a cornerstone for discriminating normal brain tissue from glioma tissue. We first extended our previous study on THG imaging of glioma tissue samples,FigureTen histologically normal tissue samples were obtained from seven patients undergoing temporal lobe resection for intractable epilepsy. Lipid‐rich components of brain tissue, e.g., the lipid‐rich myelinated axons in the neuropil , are a major source of contrast for THG,We next investigated the appearance of cellular morphology and density in THG images of grades II–IV gliomas. According to the 2016 WHO Classification of Tumors of the Central Nervous System, the vast majority of diffuse gliomas in adults are diffuse astrocytic tumors of different molecular classes (IDH‐wildtype or IDH‐mutant) and different malignancy grades (WHO grades II–IV), and low‐grade and anaplastic (WHO grades II and III) oligodendrogliomas.THG imaging was also able to reveal microvascular changes, one of the main histopathological hallmarks of diffuse gliomas of high grade malignancy (WHO grades III or IV).2.2FigureBased on these findings, demonstrating a very good “histology‐grade” (and even “cytology‐grade”) correlation between the morphology of diffuse gliomas in H&E and THG images, we proceeded with the development of an automated workflow for THG images to quantitatively assess the differences in cell density between histologically normal brain and glioma tissue. We have reported previously that the quantification of THG images is hampered by the information complexity.We then used this automated workflow to quantitative differences between THG images of normal brain and glioma tissue based on the differences in the measured density of brain cells, nuclei, and neuropil. We performed statistical analysis of the density of these features in 800 THG images obtained from a first group of patients, patients 1–10.2.3Figurep‐value 0.01 for the shown example) with the nucleus density quantified in fluorescence images  from patients 11–23, accompanied by six “training” examples of THG images of normal brain and gliomas, was presented to neuropathologists with ample expertise in the histological diagnosis of gliomas. They were asked to classify each THG image as normal or tumor. The classification by the algorithm and that by pathologists were double‐blind for each side. Comparison showed a nearly perfect agreement (93%) between the automated classification and the consensus of neuropathologists, and on average, the agreement between either two pathologists was 84%  brain, tumor periphery, and tumor core Figure5. The 2 s−1) and sample 4 (0.0013200 mm2 s−1) was on an average ≈40% higher than sample 1 (0.0012290 mm2 s−1) and sample 2 (0.0008282 mm2 s−1), which quantitatively agreed with THG imaging. 18F‐fluoro‐ethyl‐tyrosine (18F‐FET) positron emission tomography (PET) data suggested that cell metabolism increased from sample 1 to sample 4.18F‐FET PET imaging. However, the subcellular resolution of THG shows heterogeneity of the tissue samples with areas of different cell densities. If used intraoperatively next to MRI, THG imaging thus has great potential for enabling more refined removal of areas with abnormal cell density and morphology.After THG imaging of these samples, the tissue state was also analyzed by performing microscopic evaluation of H&E stained histological slides. Large blood vessels were used as “landmarks” for identification of the same tissue area in the THG and H&E images Figure F. The ap3Diffuse gliomas are highly invasive and they invade beyond the visible MRI borders.This study demonstrates how THG microscopy can be used to quantitatively detect glioma infiltration in human brain tissue in ex vivo fresh, unprocessed surgical tissue samples. THG imaging of such samples from 23 patients revealed histology‐grade characteristics of normal brain and of WHO grades II–IV diffuse gliomas. Qualitative and quantitative comparison of THG with H&E and fluorescence data demonstrates the great potential of THG microscopy for detection of the presence of glioma infiltration by cellularity. The evaluation by the neuropathologists suggests that THG images can indeed be efficiently interpreted by neuropathologists.In these kind of studies, the ultimate ground truth for tumor‐infiltrated versus noninfiltrated areas in a tissue sample is difficult to acquire. Preoperative imaging techniques such as MRI suffer from brain shift and brain surface deformations after craniotomy. One‐to‐one comparison of THG with H&E images is difficult because of the fixation and slicing process. Fluorescence imaging suffers from uneven staining that also makes it difficult to do one‐to‐one comparison between THG and fluorescence imaging in a large image scale. Here, we choose the strategy to colocalize and/or correlate THG imaging with all these imperfect ground truths and show the ability of THG microscopy to directly visualize the decreasing gradient of invasive tumor cells, spreading from the main tumor mass into normal tissue.Correlating THG imaging with MRI, PET, and H&E analysis not only illustrates the potential of THG microscopy as an auxiliary tool for fast diagnosis of tissue to be excised, but also suggests THG as a potential tool for tumor preliminary typing during the biopsy procedure. We have shown the substantial morphological differences in THG images of different glioma types. THG can also reveal diagnostic features of extrinsic brain tumors, e.g., meningioma . THG microscopy thus is promising for the selection of representative tumor tissue that can be used for more detailed and more accurate (molecular) diagnosis of brain tumors. Meanwhile, we are fully aware that substantially more patient data are needed to demonstrate the validity of tumor typing with THG.Although other label‐free imaging techniques such as optical coherence tomography and Raman techniques have also been shown to be able to differentiate tumor infiltration,3, but also the laser power needed for efficient generation of THG/SHG signals can be reduced to 14 mW.2 and with ≈600 × 600 pixels.Currently, compact and transportable THG microscopes using low laser power and with high scanning rates that can be used for real‐time ex vivo tissue assessment in the operation room are appearing in the market. Employing fiber lasers operating at lower (1–30 MHz) repetition rates instead of the optical parametric oscillators that currently are used for most THG imaging, not only THG microscopes can be minimized to a table size of ≈1 × 1 × 1 mIn summary, we demonstrate the power of ex vivo quantitative THG microscopy in detecting and quantifying diffuse glioma infiltration based on the assessment of tissue cellularity. Correlation with multiple clinical imaging modalities demonstrates the feasibility and efficiency of using THG microscopy as a potential imaging tool next to MRI, either ex vivo or in situ. Thereby, quantitative THG microscopy has the potential to improve the accuracy of brain tumor surgery.4Study Design: The objective of this study was to evaluate the potential of THG microscopy to quantitatively distinguish glioma infiltration from normal brain tissue. 56  tissue samples were removed from 23 patients with epilepsy (n = 7) or grades II–IV diffuse gliomas (n = 16). Both male and female adult patients with ages from 20 to 70 were included in the study to reduce the dependence of data on gender and age. All patients gave a written informed consent for tissue biopsy collection and signed a declaration permitting the use of their biopsy specimens in scientific research. All procedures on human tissue were performed with the approval of the Medical Ethical Committee of the VU University Medical Center (VUmc) and in accordance with Dutch license procedures and the declaration of Helsinki. The preoperative diagnosis was based on MRI imaging, the diagnosis of normal brain tissue or of glioma was based on histological analysis, and in case of glioma with additional molecular testing. The tissue sample size was ≈1 cm × 1 cm × 0.5 cm and was chosen based on the design of previous studies comparing THG and H&E imaging modalities. THG imaging was performed at the laboratory of Vrije Universiteit Amsterdam located within 500 m from VUmc. After THG imaging, the tissue samples were fixated in 4% formaldehyde and returned to the Pathology department of VUmc for standard H&E analysis. A trained neuropathologist (P.W.) histologically classified the samples as normal or tumor.To quantify THG images, an automatic quantification workflow was implemented in Visual Studio C++2010. The automatic classification results were compared with the classification from three neuropathologists , through a double‐blinded survey. The survey consisted of a “training” dataset with six examples to briefly introduce the cell morphology present in THG images of normal brain and gliomas, and a “testing” dataset with 100 THG images to be classified as normal or tumor.THG Microscopy: THG is a nonlinear optical process that depends on the third‐order susceptibility χ(3) of the tissue and certain phase‐matching conditions.2 with ≈1000 × 1000 pixels, was ≈8 s. The mosaic imaging of the sample was performed by transverse (xy) scanning of the motorized translation stage. Images were recorded and stored using TriMScope I software (“Imspector Pro”), and the adjacent THG/SHG images had 20% overlap. According to this fixed overlapping rate, the stitching algorithm provided by ImageJ software  was used to stitch adjacent THG/SHG image tiles into mosaic images.An average laser power of 100 mW was used on the tissue sample to generate THG/SHG signals. The image acquisition time for each pair of THG and SHG images, having a field of view ≈300 × 300 µmTissue Preparation: After tissue resection, brain tissue samples were placed within 30 s in ice‐cold artificial cerebrospinal fluid (ACSF) at 4 °C, and transported to the laboratory within 15 min. There, the samples were cut with a surgical scalpel to generate flat surfaces, rinsed with ACSF to remove blood contamination, embedded in agar blocks, and flattened with a 0.17 mm thick glass cover slip for THG imaging. For the fluorescence staining experiments, three drops of Hoechst‐33342 dye were put into 1 mL of ACSF and the tissue slices were incubated for 15–20 min to label nuclear DNA of brain cells.Quantification Workflow and Statistical Analysis: Here, an integrated workflow , low‐grade (n = 414), and high‐grade (n = 246) glioma tissue samples obtained from patients 1–10. Mosaic THG images with the presence of both normal brain and glioma tissue obtained from patients 1–10 but excluded from the statistical analysis were used to generate the density thresholds to differentiate normal brain from glioma areas. An independent THG dataset containing 839 THG images obtained from patients 11–23 was used to test the accuracy of classifying THG images with the two thresholds, in terms of sensitivity and specificity.The statistical analysis of the densities of brain cells, nuclei, and neuropil Figure D–G were The authors declare no conflict of interest.SupplementaryClick here for additional data file.


To evaluate the impact of  data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints.Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation , and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI.Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25.Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling. Brain–blood barrier dysfunction with abnormal permeability has been shown to occur in multiple pathologies, including Alzheimer's disease, multiple sclerosis, acute ischemic stroke, and brain tumors.1‐weighted MRI, functional changes that can be monitored with DCE MRI happen on a shorter time scale. This makes DCE MRI a candidate to monitor early response to therapy and to potentially alter treatment to mend side‐effects or to opt for a more aggressive treatment and potentially enhance chances of survival.Dynamic contrast enhanced MRI (DCE‐MRI) of the brain provides a powerful tool to non‐invasively assess neurovascular parameters, such as permeability of the brain–blood barrier  parameters have to be accurate, precise, and reproducible.Numerous  sampling strategies have been proposed for dynamic MRI in general . For Cartesian sampling, these strategies could broadly be classified into zone‐based,Inherent to DCE‐MRI is a very high data redundancy in the mapping from multi‐coil, multi‐frame k‐space data to a few static TK parameter maps. This data redundancy can be exploited with a maximum‐likelihood‐like estimatorIn this work, we compare  sampling patterns in a framework that assumes TK model consistency without making additional assumptions about the object . We use pathologically and anatomically realistic digital reference objects and use both Cramér‐Rao bound (CRB) analysis and Monte‐Carlo simulation to evaluate TK parameter variances. We compare 8 commonly used  data sampling patterns over a range of undersampling factors and SNR levels relevant to high‐resolution whole‐brain DCE‐MRI.22.1yk‐zk plane, and the impact of sampling can be adequately studied with 2D axial DROs in the y‐z plane. All brain slices were cropped to contain as little space around the head as possible and had 106 × 88 pixels which corresponds to an approximate voxel size of 4 mm × 4 mm × 5 mm. To cover a broader range of tumors that are likely to be found in clinical exams, 3 brain DROs with one large tumor, one small tumor, and one metastasized tumor were used. Tumor data originated from high grade glioma patients. The DROs are shown in Figure The optimal sampling strategy is expected to depend on the image characteristics and the specific details of the forward model. As a result, we conducted our analysis using realistic digital references objects (DROs) that are typical of the protocol used at our imaging center. Pathologically and anatomically realistic DROsT10, plasma volume vp, interstitial volume fraction ve, and volume‐transfer constant Ktrans listed in Supporting Information Table Each tissue was then simulated at 3T using the proton density ρ, precontrast spin‐lattice relaxation time 2.2kt denotes samples in  space, U the undersampling operator, F the Fourier operator, S coil sensitivity encoding, and Φt incorporates Bloch simulation and TK modeling to generate the signal intensity at time t for a given set of tracer‐kinetic parameters θ. nt is additive complex, circularly symmetric Gaussian noise.DCE MRI monitors bolus arrival, accumulation, and wash‐out over time in a given field‐of‐view (FOV). Specifically, the DCE MRI experiment can be modelled mathematically as2.3R × R in the 2D phase‐encoding plane for a given sampling reduction factor R. The CAIPIRINHA‐type reduction factor is specified byRy and Rz as reduction factors in the two phase‐encoding directions, and Δ the circular shift between two consecutive rows of the lattice elementary cell. Breuer et alRCAIPI=1×RR to yield among the lowest g‐factor for 3D (static) brain imaging. Following this notation, the elementary cells of the 3D phase‐encoding lattice (yk × zk × t) in this study are chosen according toEight different sampling patterns were compared and are shown in Figure yk, zk, and time t before cropping the lattice to the desired k‐space dimensions. Analogously, random sampling was implemented with uniform and variable density in  space. For each of these Cartesian sampling patterns, the number of samples per frame and region of sampling density are approximately identical for every frame. We further included pseudo radial sampling with golden angle increments (GAR) and its randomized derivative in our analysis (RGAR).The lattice structure is built from the elementary cells by replication of the elementary cells in linear order along the two phase‐encodes 2.4We used Monte‐Carlo simulations to quantify the precision of TK parameter fitting. We evaluated efficiency of direct reconstructionC is chosen to be the Cholesky decomposition of the noise covariance matrix. In the ordinary least squares (OLS) approach C is set to identity. In both cases the optimization problem is solved using nonlinear conjugate gradient descent.In the weighted least squares (WLS) variant, We did not apply any spatial or temporal constraint during SENSE reconstruction. Monte‐Carlo simulations were performed over a pre‐contrast white matter SNR range from 10–100. The goal was to determine the operational minimum SNR for each method. This range covers best case and clinically realistic scenarios down to noise levels that make TK parameter estimation practically infeasible. For each SNR level, we used 100 noise realizations that were found to be sufficient for the variance estimates to stabilize.2.5vp and Ktrans) for a broad range of undersampling factors. The CRB gives a lower bound on the covariance of any unbiased estimator for the desired parameter vector,We compared the Cramér‐Rao bound (CRB) for the common TK parameters of the extended Tofts and the Patlak model  is the Fisher information matrix (FIM). The corresponding unbiased estimator that meets this bound tightly is called efficient. If the FIM is singular, this indicates that the CRB is near‐infinite for certain parameters , which is undesirable. Hence, from a measurement point of view these settings should be avoided if possible.The CRB values were computed for the entire image but were analyzed within regions‐of‐interest (ROIs) tightly drawn around all tumors. We also compared spatial maps of the CRB at an undersampling factor of 16. To be able to compare all sampling patterns at the same undersampling factor, the figures of merit for lattice sampling with variable density were linearly interpolated to match the nominal undersampling factors.3Figure vp and Ktrans for the extended Tofts model across the three tumor ROIs. For all sampling patterns except Keyhole sampling, the averaged SDs for both TK parameters scale approximately with the square root of the undersampling factor, which indicates the loss in measurement precision to be dominated by fewer measurements and shorter acquisition time.vp whereas they are all approximately equal for Ktrans. At all higher undersampling rates , lattice sampling with uniform density outperforms all other sampling strategies in this comparison. Although Keyhole sampling achieves SDs comparable to those of the other seven sampling strategies in this comparison, the SDs of Keyhole sampling lie well above the all other sampling patterns at R ≥ 9.Figure vp than for Ktrans. There is an increase in average SDs of up to 19% for vp and 10% for Ktrans when random instead of lattice sampling is applied. For both lattice and random sampling, there is little difference among their respective uniform and variable density layouts. For pseudo radial sampling with GAR, randomization leads to an increase of SD of up to 14% for vp and up to 7% for Ktrans. With regard to the maximum coefficient of variation, the relative ranking between different sampling patterns remains approximately identical for vp up to R = 25 compared to the ranking seen for the averaged SDs. For Ktrans, the maximum coefficients of variation obtained by TRICKS, random sampling with uniform and variable density, lattice sampling with uniform and variable density, and Golden Angle radial are roughly identical across all undersampling rates. At lower undersampling rates, i.e., R < 25, randomized Golden Angle pseudo‐radial sampling achieves lower maximum coefficient of variation compared to all other sampling patterns.Figure vp and Ktrans of the extended Tofts model across all tumor ROIs for the DROs in Figure Ktrans, the plots in Figure Figures Figures Ktrans of the extended Tofts model zoomed into the tumor region of the first DRO in Figure Figure vp are approximately on the same order of magnitude irrespective of whether the Patlak or extended Tofts model is used. Average SDs for Ktrans, however, are about one order of magnitude lower for the Patlak model compared to the extended Tofts estimates across all sampling patterns. Although the exact values for SDs of TK parameters estimated according to the Patlak and extended Tofts model differ, the trends and relative rankings are identical for higher undersampling rates . For the Patlak model, the relative rankings of sampling patterns are identical irrespective of the TK parameter and whether low SD or maximum coefficient is of concern. Lattice sampling with uniform density outperformed all other sampling patterns in this comparison at R ≥ 9. This is in contrast to estimation of Ktrans of the extended Tofts model where randomized Golden Angle pseudo‐radial sampling achieves lowest maximum coefficient of variation.Figures 4vp and 10% for Ktrans at undersampling factors of up to R = 25. TRICKS as well as pseudo radial sampling with Golden Angle and its randomized derivative exhibited substantially higher SDs at R > 5 that incurred a loss relative to uniform lattice sampling of no more than 10% in maximum coefficient of variation for Ktrans up to R = 25. At R = 4, randomized Golden Angle radial sampling achieved the lowest average SDs and maximum coefficient of variation for extended Tofts Ktrans in the tumor ROI.This study compared eight different Cartesian sampling strategies for high‐resolution whole brain DCE‐MRI with tracer‐kinetic model constraints. Uniform density, lattice‐based sampling provided the best performance overall. Lattice‐based sampling with higher density at the k‐space center led to a loss in precision of up to 5%. Differences in averaged SD to random sampling with uniform and variable density were up to 19% for We observed several effects for the different sampling patterns in the spatial maps of CRB‐predicted SDs: For the simulated 8‐channel head array all spatial maps showed central enhancement. As a consequence, measurements of tumors located in the center of the brain  will be associated with higher measurement uncertainty because of noise rather than tumors at the brain boundary . TRICKS sampling displayed swirl‐like pattern in the CRB‐predicted SD maps whereas lattice‐based sampling resulted in stripes of enhanced SD. Similar to g‐factor maps, CRBs do no provide a way to directly explain the sources of noise amplification arising from correlations in  space and arbitrary undersampling.Ktrans obtained with the Patlak model to be about one order of magnitude lower compared to those of the extended Tofts model. This suggests a substantial performance boost if the simpler Patlak model is used when appropriate. We anticipate that this could be taken advantage of by incorporating model selection criteria, such as the Akaike Information Criterion, the Bayesian Information Criterion, or F‐tests,We found the SDs for 1 and the arterial or vascular input functions that propagate into the TK parameter estimates. Bane et al1 maps of about 20% on 3T scanners using the commonly deployed Variable Flip‐Angle (VFA) Method. Applying first order error propagation analysis, such error would propagate (super‐) proportionally into the CV of TK parameters.Ktrans in prostate DCE‐MRI because of AIF detection, whereas Klawer et alKtrans because of inaccuracy in AIF estimation can be reduced to about 12% with the proposed AIF estimation in the complex plane. Accurate and precise estimation of the peak contrast during the first pass of the arterial input function remains an open and challenging problemOther prominent sources of error in DCE‐MRI are estimation errors in the auxiliary input parameters such as pre‐contrast TKtrans on the order of approximately 10−4 to 10−3 min−1.Although the main focus of this study is DCE‐MRI for brain tumors, this study could potentially inform DCE‐MRI protocols for age‐related neurological diseases such as Alzheimer's disease. These DCE‐MRI exams typically observe very slow leakage with This work assumed a known AIF, but as mentioned previously, an evaluation of different sampling strategies depends critically on the imaging assumptions. In practical settings, the AIF is not always considered to be known. Although population‐based AIFs can be used to reduce measurement uncertainty,In a similar vein, DCE MRI may suffer from motion corruption . Previous work in dynamic MRI has addressed this issue by design of sampling patterns that enable motion to be estimated from the acquired data in  space for subsequent correction.This work focused on Cartesian sampling patterns only. These types of sampling pattern do not require any form of gridding which is common in radial, spiral, kooshball, or cone sampling for example. Gridding introduces noise correlations between different k‐space locations. Because such noise correlations were not part of the current study, these non‐Cartesian sampling strategies require further investigation to assess their performance.vp and Ktrans under no assumptions beyond knowledge of the AIF and the nonlinear 3‐parameter extended Tofts tracer kinetic model. Implementation and testing of all varieties of compressed sensing and constrained reconstruction approaches for parameter estimation in DCE MRI is beyond the scope of this study. This present methodology could, however, be used to test the statistical variance of additional acquisition and reconstruction combinations, potentially including constrained reconstruction algorithms not included in this work.The framework in this study consists of a digital reference object and Cramér‐Rao analysis to compare various  sampling strategies. We further use Monte Carlo simulations to demonstrate that direct fitting of TK parameters to k‐space data remain within 2.5 times the CRB predicted SDs for 5vp of the Patlak and extended Tofts model and Ktrans of the Patlak model. For Ktrans of the extended Tofts model, randomized golden angle pseudo‐radial sampling achieved lowest maximum coefficient of variation. Interestingly, we found no substantial benefit of variable over uniform density for both lattice and pseudo‐random sampling. Furthermore, lattice and random sampling were nearly equivalent; with random sampling leading to at most 19% higher TK parameter SDs, which is smaller than many other known sources of variation in vivo.We have carried out an evaluation of the impact of  sampling scheme on the SDs of TK parameters in sparse brain tumor DCE‐MRI using digital reference objects. This approach is applicable when a realistic DRO is available, when an appropriate kinetic model is known, and when the AIF, coil sensitivities, and noise properties are known. We found that lattice sampling produced the lowest SDs overall for the estimation of TK parameters with the Patlak and extended Tofts model at undersampling rates above 9, and zone‐based sampling produced the highest SDs overall. This trend was consistent for the maximum coefficient of variation in the estimation of FIGURE S1 Estimator performance as a function of SNR for both vp (left column) and Ktrans (right column) of the Patlak model: comparison of SDs predicted by CRB with SDs computed by Monte‐Carlo simulation with conventional (conv) and direct reconstruction with (WLS) and without (OLS) accounting for correlated, non‐isotropic noise for uniform random sampling with R = 4 and uniform density undersamplingFIGURE S2 Comparison of sampling patterns as a function of undersampling factor for the estimation of vp (left column) and Ktrans (right column) with the Patlak model. CRB‐predicted SDs averaged over brain tumor regions are shown in the top row and maximum coefficient of variation, i.e., SD divided by true parameter, in the bottom row. For all sampling patterns except Keyhole sampling, the averaged SDs for both TK parameters scale approximately with the square root of the undersampling factor at low undersampling factors. As the undersampling factor is increased, the scaling behavior deviates from this rule indicating an increased interaction between the undersampling and the coil geometryFIGURE S3 Relative comparison of sampling patterns as a function of undersampling factor for the estimation of vp (left column) and Ktrans (right column) with the Patlak model. Relative increase in SD bounds averaged across the tumor ROI (top row) and maximum coefficients of variation across the tumor ROI (bottom row) are shown with respect to figures of lattice with UD. Although random sampling with uniform and variable density perform best at R = 4, lattice sampling leads to lowest average SD for both vp and Ktrans compared to uniform sampling at higher undersampling factors. For both lattice and random sampling, the uniform density variants perform comparable to their variable density counterparts.FIGURE S4 Patlak vp SD bounds within brain tumor regions of interest, as a function of sampling strategy and undersampling factor. Fully sampled data is chosen as reference. Top row left to right: Keyhole sampling, TRICKS, Golden Angle radial and randomized Golden Angle radial. Bottom row left to right: lattice with uniform (UD) and variable density (VD), random sampling with uniform (UD) and variable density (VD). Different undersampling factors are indicated by color. Bounds are shown for SNR = 30FIGURE S5 Patlak Ktrans SD bounds within brain tumor regions of interest, as a function of sampling strategy and undersampling factor. Fully sampled data is chosen as reference. Top row left to right: Keyhole sampling, TRICKS, Golden Angle radial and randomized Golden Angle radial. Bottom row left to right: lattice with uniform (UD) and variable density (VD), random sampling with uniform (UD) and variable density (VD). Different undersampling factors are indicated by color. Bounds are shown for SNR = 30FIGURE S6 Visualization of Patlak vp parameter SDs predicted by CRB for different sampling patterns at undersampling factor 16 for the first DRO in Figure 1. Top row left to right: Keyhole sampling, TRICKS, Golden Angle radial and randomized Golden Angle radial. Bottom row left to right: lattice with uniform (UD) and variable density (VD), random sampling with uniform (UD) and variable density (VD). All sampling patterns show central enhancement of SD bounds for both TK parametersFIGURE S7 Visualization of Patlak Ktrans parameter SDs predicted by CRB for different sampling patterns at undersampling factor 16 for the first DRO in Figure 1. Top row left to right: Keyhole sampling, TRICKS, Golden Angle radial and randomized Golden Angle radial. Bottom row left to right: lattice with uniform (UD) and variable density (VD), random sampling with uniform (UD) and variable density (VD). All sampling patterns show central enhancement of SD bounds for both TK parametersFIGURE S8 Visualization of Patlak Ktrans SDs predicted by CRB for different sampling patterns at undersampling factor 16 for the tumor region of the first DRO in Figure 1. CRB accounts for measured coil sensitivities from an 8‐channel head arrayFIGURE S9 The SNR map for the simulated 8‐channel head array with measured coil sensitivities and noise covariance matrix. The SNR map is computed for a hypothetical object with uniform signal across the whole field‐of‐view.TABLE S1 Simulation parameters for each tissue type in the Digital Reference Objects. Parameters for tissue types are taken from literature valuesClick here for additional data file.VIDEO S1 Illustration of data sampling strategies for 9‐fold undersampling. Shown are the yk, zk phase encoding planes for different time frames. White dots indicate phase encodes that are acquired. Black indicate phase encodes that are not acquired. Left‐to‐right: Keyhole, TRICKS, lattice with uniform density, lattice with variable density, random with uniform density, and random with variable density, pseudo‐radial with Golden Angle increments (GAR) and randomized pseudo‐radial with Golden Angle increments (RGAR). Each sampling pattern had a fully sampled first time frame, and varied for all subsequent time framesClick here for additional data file.


Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine‐learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain.Based on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine‐learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase.Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research.The present computational functional genomics‐based approach provided a computational systems‐biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine‐learned methods provide innovative approaches to knowledge discovery from previous evidence.We show that knowledge discovery in genetic databases and contemporary machine‐learned techniques can identify relevant biological processes involved in Persitent pain. The GO knowledge base is searchable for three major categories, consisting of biological process, cellular component and molecular function. As the most suitable representation of processes that are involved in the chronification of central sensitization to pain, the GO category biological process was selected as previously ∣xi∈Nd,i=1,…,n} comprising the d biological process to which the n genes in the analysed set were annotated. This feature space was searched for a cluster structure. Among several methods available for clustering, a method of unsupervised machine‐learning shown recently to provide a viable unbiased clustering of high‐dimensional biomedical data, outperforming classical clustering algorithms  of the Kohonen type =∣{datapointsxi∣d<=r}∣ estimated as the number of data points in a sphere with radius r around x at each grid point on the ESOM's output grid. The calculations were performed using our R library ‘Umatrix’ , the information was transformed into a more intelligible form using the method of ‘functional abstraction’ , given the p‐value threshold of 1 × 10−6 and the α‐correction according to Bonferroni. One gene was neglected in this analysis, COL6A4P1, the collagen type VI alpha 4 pseudogene 1, because it was not referenced in the GO.In a first analytical approach, functional subgroups were sought in the set of human genes, variants of which have been associated with modulation of the clinical phenotype of persisting pain. A filtered representation of the particularly important processes in which the analysed genes were involved while disregarding processes that would have been found by chance in any similarly sized gene set, was obtained by means of overrepresentation analysis (ORA) of the biological processes to which the genes were annotated in the GO knowledgebase. This identified n = 58). However, this cluster was still composed of functionally very different genes, pointing towards a large heterogeneity of the genes chosen as candidate modulators of persisting pain in the different clinical studies, or was found with genomewide association studies (GWAS) without a focus on a particular gene. In addition, six smaller clusters were suggested, but their distinct separation was occasionally incomplete or, according to the U‐matrix landscape analogy, they were not clearly presented as ‘valleys’ but consisted merely of ‘mountain’ zones separated by slightly higher ridges  and ‘positive regulation of lymphocyte mediated immunity’ . A second functional area centred on ‘reactive oxygen species metabolic process’  and comprised mainly nitric oxide signalling related processes  and its regulation.A first functional area to emerge was ‘immune system process’, represented in this particular gene set as an important common biological function , comprising the reaction of the body to several challenges such as ‘response to chemical’  and ‘defense response’ . Further subordinate areas included ‘inflammatory response’  and ‘response to other organism’  to which ‘response to bacterium’ . In addition, the associated functional area ‘transport’, mainly comprised subordinate processes related to ion or transmitter transport such as ‘regulation of ion transport’  or ‘regulation of secretion by cell’ . Finally, a functional area ‘neurological system process’ , as a more specific subordinate term to ‘system process’, reflected the expected involvement of nervous system related processes with pain.Further functional areas, however, mainly reflected processes known from previous research to contribute to pain  addressing genetic modulators in the context of the underlying disease and (2) including a selection of genetic markers that mimic other successful reports of comparable studies.The situation became clearer when the analytical focus was narrowed to genetic modulations consistently observed across several clinical conditions with potential underlying painful diseases. This reduced the analytical bias generated by genetic modulations responsible for a specific pain‐causing disease and is in keeping with the contemporary approach to persisting pain as a distinct condition of central sensitization to pain and not merely a symptom of another underlying chronic disease. Consequently, it would be expected that the trait is modulated by specific genes which should be reflected by observations on its modulation in clinical research. The mathematically precise calculation provided by the ABC analysis    genes  of the immune system and of (2) nitric oxide signalling in persisting pain. Involvement of both processes is biologically highly plausible; however, their emergence as major process groups from a functional genomics analysis of data from clinical genetic research on persisting pain was not anticipated. Specific roles for the present subset of 22 genes, with repeated evidence for involvement in persisting pain, were exhibited by the 12 genes annotated as ‘immune system process’ . After performing a similar ORA , ‘nitrogen compound metabolic process’ and again ‘response to stimulus’, the most significant term being ‘nitric oxide biosynthetic process’ .The role of cytokines was further highlighted by further restricting the gene subset most consistently related to persisting pain by identifying the appearance of the relevant gene in an independently created list of 535 so‐called pain genes . Hence, although the evidence was generated in separate studies, the combination of positive findings of a genetic modulation of persisting pain allows a limited yet valid genomewide analysis of the trait, providing a more comprehensive picture of functional background of persisting pain from genomics perspective than associations of single genotypes.With the caution advised by the implicit research bias regarding the gene selection, the results of the present analysis were (1) unexpected considering that hypotheses about the involvement of immune system processes or of nitric oxide signalling were not preformulated for the present analysis and (2) biologically plausible and completely compatible with current research activities on persisting pain in the light of increasingly acknowledgement of an involvement of immune processes that has attracted concerted research activities (Kringel and Lötsch, 5While many studies have focused on particular genes, the present analysis pursued the question whether their combined results may provide more complex insights into the pathophysiology of persisting pain in humans. In keeping with the contemporary trend towards ‘big data’ analysis in biomedical research, the current empirical data on modulation of persisting pain via human genetic polymorphisms have been subjected here to a computational functional genomics analysis. This evidence was then analysed for emergent, principal pathophysiological processes that characterize persisting sensitization to pain. Analysis of 110 unique genes, with variants that have been reported to modulate the clinical phenotype of persisting pain, led to the selection of functionally heterogeneous genes. By focusing on genes that have been repeatedly associated with modulation of persisting pain phenotypes in several clinical settings, a clearer picture emerged of the main processes identified by current human genetics research on persisting pain. A mathematically supported, precise selection of a subset of genes was possible using a computational functional genomics approach. On this basis, including the research bias of current clinical genetic association studies, the evidence gathered so far points to the view of persisting pain as a trait resulting from alterations in the immune system and/or in nitric oxide signalling, a concept that is biologically highly plausible and agrees with other lines of pain research. While analysing existing evidence and therefore limited to previously shown functional pathways, the present computational functional genomics‐based approach provides a computational systems‐biology perspective on chronic sensitization to pain by summarizing the empirical evidence gathered in many separate studies. Moreover, human genetic research on persisting pain emphasizes the immune system as a potential source of important future targets for analgesic drugs directed against persisting pain and demonstrates that contemporary machine‐learned methods offer innovative approaches to knowledge discovery from previous evidence.JL, DK and EK conceived and designed the analysis. JL, CL and DK analysed the data. JL, DK, EK and MJP wrote the article. JL, AU, DK and EK involved in discussion of methods and results.


Although an association between gender equality and contraceptive use has been confirmed among adult samples, few studies have explored this relationship among adolescents. An examination of whether adolescents’ contraceptive use is more prevalent in countries with higher levels of gender equality is needed to fill this gap.Nationally representative data from 33 countries that participated in the 2013–2014 Health Behaviour in School‐Aged Children study and country‐level measures of gender equality—using the 2014 Global Gender Gap Index—were analyzed. Multilevel multinomial logistic regression analyses were employed to assess associations between gender equality and contraceptive use  at last intercourse as reported by 4,071 females and 4,110 males aged 14–16.Increasing gender equality was positively associated with contraceptive use among both males and females. For every 0.1‐point increase on the equality scale, the likelihood of condom use at last intercourse rose , as did the likelihood of pill use  and dual method use . Associations with pill use and dual use remained significant after national wealth and income inequality were controlled for. Overall, associations were stronger for females than for males.More research is needed to identify potential causal pathways and mechanisms through which gender equality and adolescents’ contraceptive use may influence one another. Encouraging young people to practice safer sex is a major public health challengeAlthough a number of studies have speculated that macro‐level gender equality may at least partly explain the cross‐national variation in adolescents’ contraceptive use,A full understanding of the patterns of contraceptive use among adolescents requires an ecological approach, involving an examination of individual and contextual variables, at different levels, and their interaction.Gender equality—here defined as the extent to which women and men have an equal share of paid work, money and decision‐making power in society—varies considerably across European and North American countries; Scandinavian countries typically have the highest rates of gender equality and eastern European countries the lowest.Although the link between gender equality and contraceptive use among adults has been confirmed, much less is known about how gender equality relates to contraceptive use among adolescents. Yet, there are reasons to assume that gender equality may be associated with cross‐national variability in contraceptive use among youth in particular. First, adolescents tend to be vulnerable to the influence of gender norms, as they are at a life stage in which consciousness of the need to “fit in” is highly salient and gender identities are actively being formed (or reformed).Second, countries with high levels of gender equality tend to invest more in comprehensive sex educationBuilding on research showing that macro‐level gender equality is associated with more egalitarian gender norms,Data for the present study come from the 2013–2014 Health Behaviour in School‐Aged Children (HBSC) study. This study was conducted in 42 nations, primarily in Europe, in collaboration with the World Health Organization's Regional Office for Europe. It was designed to examine the health and health behaviors of adolescents across country contexts.Data were collected through school‐based, anonymous surveys using a standard methodology.We used the Global Gender Gap Index for 2014 as a measure of country‐level gender equality.Respondents were asked if they had ever had sexual intercourse. In parentheses, other terms, such as “making love,” “having sex” and “going all the way,” were added to make sure respondents understood what was meant by “sexual intercourse”; these terms varied by country. Those who indicated that they had had intercourse were asked about their contraceptive use: “The last time you had sexual intercourse, did you or your partner use a condom?” and “The last time you had sexual intercourse, did you or your partner use birth control pills?” Response options were “yes,” “no” and “don't know.” On the basis of these two variables, a third variable was created, indicating the use of dual methods. Students who reported using both a condom and pills were classified as having used dual methods.All individual‐level variables relevant to adolescents’ contraceptive use that were available in the international HBSC data set were included in the analyses.Age was included as a continuous variable. The HBSC Family Affluence ScaleLiving arrangement was specified using four categories: with both biological parents, with a step family, with a single parent or in another situation. Classmate support was measured as the mean score on three items: “Students in my class(es) enjoy being together,” “Most of the students in my class(es) are kind and helpful” and “Other students accept me as I am.” Participants indicated to what extent they agreed with the items using five‐point Likert scales . Possible scores ranged from 0 to 4; higher scores reflect higher perceived classmate support.Substance use was measured with three items: the frequency of alcohol use in the last 30 days , frequency of current tobacco use  and frequency of getting drunk in the last 30 days .The measure of easy parent‐child communication was based on separate questions asking about respondents’ perceptions of communication with their mother and father about things that really bothered them . Each item was dichotomized (easy vs. difficult), and the more positive rating was used.In total, 12,907 respondents in our sample indicated that they had had sexual intercourse. We excluded students who did not know if they had used contraceptives at last intercourseAnalyses were conducted using MLwiN 2.36. First, we examined the individual‐level sample characteristics. Second, we made descriptive comparisons of the prevalence of the contraceptive methods used and the levels of gender equality across countries. The third step entailed assessing whether the variation in contraceptive use across countries was statistically significant and, if so, testing whether it was related to countries’ level of gender equality. This was done by conducting multilevel multinomial logistic regression analyses stratified by gender, in which condom use, pill use and dual method use were compared with no contraceptive use. By using multilevel models rather than standard multinomial models, we accounted for the fact that respondents were clustered within countries; ignoring this clustering would have led to an overestimation of the associations between gender equality and contraceptive use.. In model 2, we added gender equality to assess its relationship with contraceptive use while controlling for the individual‐level variables.Our first model was an empty, or null, model, which assessed whether the variation in contraceptive use across countries was statistically significant (as indicated by the country intercept). The intraclass correlation coefficient indicated the amount of variance in contraceptive use that was due to differences across country contexts. The higher the coefficient, the more important the country level is in explaining contraceptive use. If the variation in use across countries is statistically significant and the intraclass correlation coefficient is considerable, variables can be added to the model. In model 1, we included individual‐level controls to assess associations between contraceptive use and these variablesBecause most countries with high levels of gender equality are relatively wealthy,For conceptual reasons, analyses were conducted for males and females separately. To test whether the association between gender equality and adolescents’ contraceptive use differed significantly between young men and young women, an interaction analysis of “adolescent sex x gender equality” was performed on the combined data.Our sample was evenly split between males and females Table , and theThe contraceptive methods used at last intercourse varied significantly across countries Figure . While cGender equality also varied significantly across countries Figure . Global In our null model, the country intercepts were all significant (p < .001), indicating that there was significant variation across countries in young women's contraceptive use (not shown). The intraclass correlation coefficients indicated that 7% of the variability in females’ condom use, 22% in their pill use and 19% in their use of dual methods were due to differences across country contexts. This suggests that variables at the national level play a considerable role in contraceptive use among young women.In model 1, we included individual‐level covariates Table . Young wIn model 2, we added the country‐level measure of gender equality. An increasing equality score was positively associated with condom use , pill use (9.6) and dual method use (5.6) at last intercourse. Thus, sexually experienced young women living in relatively gender‐equal countries were more likely to report the use of all three contraceptive methods than their counterparts living in relatively gender‐unequal countries. For example, for every 0.1‐point increase on the gender equality scale, the odds of pill use increased by 860%. Thus, the odds of pill use among young women in Iceland, where the Global Gender Gap Index score is 0.86, were almost nine times those of young women in France, where the score is 0.76 (not shown). Most of the associations between contraceptive use and individual‐level variables became more pronounced after gender equality was added to the model.In the null model, the country intercepts were all significant (p < .001), demonstrating significant variation across countries in young men's contraceptive use (not shown). The intraclass correlation coefficients indicated that 8% of the variability in condom use, 21% in pill use and 13% in use of dual methods were due to differences across country contexts.In model 1, young men's use of each contraceptive method at last intercourse was positively associated with increasing age  and high family affluence .Inclusion of national‐level measures for wealth and income inequality in the models did not change the results for pill use or dual method use, but did for condom use; the association lost significance for both genders. This suggests that condom use may be higher in gender‐equal societies because these countries are wealthier; young people may have better access to condoms in these countries. Excluding countries with relatively small samples  did not affect the main results or conclusions.The current study revealed that societal gender equality is positively associated with contraceptive use among adolescents, especially pill use. Even when national wealth and income inequality were taken into account, the association between gender equality and pill use remained significant. Thus, in countries with generally equitable wealth and income inequality levels, societal gender equality may ameliorate young women's risk for unwanted pregnancy. Such equality may be related to pill use in particular because the method is under the control of young women, and they may feel more empowered in relatively gender‐equal societies.Our finding that the association between gender equality and pill use was stronger among young women than among young men may reflect the fact that young men may not always be aware of their partner's pill use. Even in relatively gender‐equal countries, such as Sweden, it is generally perceived that young women have a greater responsibility in avoiding pregnancy than do young men, who often put blind trust in women's use of hormonal contraceptives or emergency contraception.Associations with the use of condoms only and dual methods were not as strong as those for use of pills only, and the association with condom use lost significance when national wealth and income inequality were considered. This finding may be explained in different ways. First, condoms are readily accessible through commercial establishments, and compared with the pill, there may be less stigma about obtaining them. Young women often have to visit a health care provider to get the pill; access difficulties present barriers that may be diminished in more gender‐egalitarian environments. Second, condom use is traditionally regarded as a male‐dominated method—one that men should take care of. This belief may prevail even in countries with high levels of gender equality, and may limit young women's confidence in getting actively involved in the decision‐making process regarding condom use. While young women can use the pill without having to negotiate with their partner, condom use is dependent on two decision makers.The observed association between macro‐level gender equality and adolescents’ contraceptive use is consistent with findings from empirical studies that examined adolescents’ contraceptive use and gender equality at an individual or micro level.Interestingly, we found that when controlling for gender equality, associations between individual‐level variables and contraceptive use became more pronounced, especially for young women. This suggests that suppressor effects are at play. Gender equality may make parent‐child communication about contraceptives easier , and may lessen socioeconomic differences in contraceptive use . This explains why variables such as family affluence and parent‐child communication appear to be more strongly related to contraceptive use once gender equality is taken into account.This study has multiple strengths, such as the use of large, nationally representative data sets, the inclusion of many European countries and the use of multilevel analyses to quantitatively estimate associations between gender equality and adolescents’ contraceptive use. However, the study's findings should be interpreted with awareness of its limitations. First, our study was cross‐sectional, and therefore no causal inferences can be drawn on the basis of our results. Yet, given that gender equality was measured as a country‐level index reflecting objective measures in different societal domains (and was not based on the perceptions of adolescents in our sample), we avoided some potential problems of endogeneity .Second, contraceptive use was self‐reported and subject to reporting biases potentially related to cultural norms. If use was underreported in countries with lower gender equality, then our results may overestimate the association between equality and use.Third, the use of a complete case analysis may result in selectivity biases. In particular, our comparison between the students in the final sample and those who were excluded because of missing data suggested that, compared with young women, sexually experienced young men may be less likely to report on their home environment, contraceptive use and risk behavior. As a result, we need to be cautious in drawing conclusions about differences between young men and young women in the relationship between gender equality and contraceptive use.Fourth, we did not have concrete data on adolescents’ use of reliable methods other than the condom and the pill. In total, 16% of respondents reported having used a method other than these two. It is, however, unclear how many of these adolescents used reliable methods, because unreliable methods such as withdrawal also fall into the category “other methods.” From studies that have assessed IUD and implant use among 15‐year‐old females, the use of these methods seems to be very low. For instance, in a nationally representative study in the United States, 3% of 15–19‐year‐old women reported ever having used an IUD, and 3% an implant.Finally, we were unable to control for some potentially important individual confounders, or mediators, such as knowledge of and attitudes toward contraceptive methods and the quality of communication with sexual partners, in relation to contraceptive use because of the limited availability of such measures within the HBSC data set. If future data sets become available that include such measures, replications of our analyses including these variables would be warranted.Having taken individual‐level and national economic variables into account, this study demonstrates that compared with their peers living in relatively gender‐unequal countries, sexually experienced adolescents living in relatively gender‐equal countries are more likely to report contraceptive use, especially pill use. These findings underscore that increasing safer sex practices among adolescents may require more than implementing individually oriented programs aimed solely at increasing contraceptive use. Rather, public health policy may need to adopt a society‐level perspective to address gender norms and equality. However, more research is needed to identify potential causal pathways and mechanisms through which gender equality and adolescents’ contraceptive use are linked.


However, the original STOPP/START criteria developed in the West, may not be directly applicable to resource limited healthcare settings like Sri Lanka. Hence, we aimed to modify STOPP/START criteria (Version 2) to suit Sri Lanka.Two investigators  reviewed and flagged criteria that were unfeasible to Sri Lanka based on their previous research experiences on using STOPP/START version 1. A Delphi consensus methodology was conducted among six experts, including geriatricians, clinical pharmacologists, physicians and a pharmacist, to review and assess each criterion (including the ones flagged by the researchers) for suitability to Sri Lanka.n = 11), re-wording (n = 25), splitting (n = 1) of original criteria and adding a new criterion (n = 1). Main reasons for modifications were unavailability of some medicines in the country, unavailability or inaccessibility of specific clinical information required for assessment of criteria, and adherence to treatment guidelines commonly used in the country.Two Delphi validation rounds were conducted. A final meeting was held with the participation of all experts to resolve disagreements and to establish 100% consensus. The expert panel agreed on a list of 105 criteria, including 70 STOPP and 35 START criteria, indicating an 8% reduction in criteria compared to the original version. Modifications included complete removal (A list of ‘Modified STOPP/START criteria for Sri Lanka’ was developed. These criteria are currently being validated through a multi-centre study. Older adults are more prone to drug related problems as most are taking a number of medicines for multiple comorbidities , 2. TheiMedicines of older adults need to be managed on an individual basis as complications of one may be vastly different from another. While individualized care is a must in older adults to reduce drug related problems, screening for potential inappropriate prescribing would be of help in identifying those who need special care. Beers’ and Screening Tool of Older Persons’ Prescriptions (STOPP) and Screening Tool to Alert Doctors to Right Treatment (START) are simple, quick to apply, and objective explicit criteria used to assess appropriateness of medicines, but Beers’ criteria have not been validated outside North America . STOPP/SSTOPP/START will help to identify those with potential problems in their prescriptions for the purpose of referring to appropriate specialist advice . In factExplicit criteria used to assess medication appropriateness in a country needs to suit its healthcare system. Most criteria are developed in the West and may not be completely transferable to resource limited settings such as the healthcare system in Sri Lanka. The prevalence of older adults in Sri Lanka is expected to rise above 30% of the population by the end of 2030 and is expected to contribute to the increase in the prevalence of non-communicable disease burden in Sri Lanka . This inGiven these limitations, we identified the need for modification and re-validation of the existing STOPP/START criteria for low resource settings like Sri Lanka. This paper aims to discuss the process of modifying the STOPP/START criteria for Sri Lanka through a Delphi consensus methodology.N = 2), clinical pharmacologists (N = 2), a specialist in internal medicine (N = 1) and a pharmacist (N = 1) to assess the suitability of the STOPP/START criteria  for Sri Lanka. The expert group represented clinical pharmacologists from academia, and clinicians from tertiary healthcare hospitals (both state and private owned). The STOPP/START criteria  was used as the working document with permission from the original authors. Two independent investigators who facilitated the Delphi process  had previous experience on using STOPP/START criteria , among 468 community dwelling older patients in Sri Lanka [A Delphi consensus methodology was used among a panel of six experts , includiri Lanka . Based oThe Modified STOPP/START criteria for Sri Lanka consisted of 105 criteria, including 70 STOPP criteria and 35 START criteria after two Delphi validation rounds and a final meeting held with the participation of all experts to resolve disagreements and to establish 100% consensus. Modifications included complete removal of 10 STOPP criteria and one START criterion, splitting of one START criterion in to two, adding one new criterion to the START section, and re-wording of 25 criteria (17 STOPP and 8 START). A summary of the types of modifications and main reasons for modifications of these criteria are shown in Table n = 2), presence of unnecessary words or phrases (n = 1), possible difficulty in assessing as an explicit criteria (n = 2), medicines or medicine strengths not registered in Sri Lanka (n = 6), less clarity (n = 5), medicines use justified in special cases (n = 1), unavailability of better treatment options in state healthcare sector in Sri Lanka (n = 2), matching current treatment guidelines (n = 3) [n = 1), duplication with other criteria (n = 3), and better suitability as a START criteria (n = 1)  –17, infrNine criteria were flagged by the investigators for complete removal before the Delphi Round 1, of which only two were rejected after the Delphi validation process. Four criteria were rejected in Round 1 and were endorsed as rejected by experts at the final meeting. Five other criteria were rejected at the final meeting after iterative discussions.STOPP/START criteria version 2 was modified to suit the needs of a healthcare system operated in a resource limited setting such as Sri Lanka. The modified criteria consisted of 105 criteria including 70 STOPP and 35 START criteria, indicating a 12.5% reduction in STOPP criteria and a 3% increase in START criteria compared to the original version. Among many reasons for modification, removal of medicines or medicine strengths not registered in Sri Lanka, and re-wording of criteria to improve clarity were the most common.STOPP/START criteria are useful to screen potential medicine related problems among older adults attending healthcare institutions of any level. It is especially useful in resource limited healthcare settings like Sri Lanka , 11, 18 There are some limitations in this study that must be acknowledged. The STOPP/START criteria were modified based on characteristics of the healthcare system of Sri Lanka and hence may not be directly applicable to other resource limited healthcare settings. If the modified tool is to be used in similar resource limited countries, it should be validated in the respective countries in a similar manner. The expert panel was limited to only six experts which is a small group but evidence supports that this is an acceptable number for a scientifically valid Delphi process . AlthougIn conclusion, the STOPP/START criteria were modified to overcome common issues encountered when applying the tool in low resource healthcare settings like Sri Lanka. The modified criteria included 105 criteria; 70 STOPP and 35 START criteria, indicating an 8% reduction in criteria compared to the original version. The simplified and more relevant set of criteria is expected to yield a more realistic outcome when assessing for inappropriate medication among older adults in Sri Lanka. It is expected that the modified tool will be used by medical officers or pharmacists to screen out older patients with potentially inappropriate medicines to refer for specialist care. Once validated, we hope to make the Modified STOPP/START criteria for Sri Lanka available online and accessible without a fee for healthcare professionals in Sri Lanka. We also hope to introduce this tool at undergraduate level in a relevant section of the Bachelor of Pharmacy curriculum. The modified tool could also be largely applicable to other countries with similar healthcare systems but should always be validated to the respective country before application in large scale.Additional file 1: Table S1. Details of modifications in the ‘Modified STOPP/START criteria’ for Sri Lanka.


Cell–substrate interactions can modulate cellular behaviors in a variety of biological contexts, including development and disease. Light‐responsive materials have been recently proposed to engineer active substrates with programmable topographies directing cell adhesion, migration, and differentiation. However, current approaches are affected by either fabrication complexity, limitations in the extent of mechanical stimuli, lack of full spatio‐temporal control, or ease of use. Here, a platform exploiting light to plastically deform micropatterned polymeric substrates is presented. Topographic changes with remarkable relief depths in the micron range are induced in parallel, by illuminating the sample at once, without using raster scanners. In few tens of seconds, complex topographies are instructed on demand, with arbitrary spatial distributions over a wide range of spatial and temporal scales. Proof‐of‐concept data on breast cancer cells and normal kidney epithelial cells are presented. Both cell types adhere and proliferate on substrates without appreciable cell damage upon light‐induced substrate deformations. User‐provided mechanical stimulation aligns and guides cancer cells along the local deformation direction and constrains epithelial colony growth by biasing cell division orientation. This approach is easy to implement on general‐purpose optical microscopy systems and suitable for use in cell biology in a wide variety of applications. Mechanotransduction, i.e., the transformation of mechanical stimuli into biochemical signaling, has been the subject of numerous studies in the last two decades, due to its importance in a wide variety of contexts ranging from embryonic development and regeneration to homeostasis and cancer progression.Cells are mechanically influenced by the substrate both as a consequence of its molecular composition and of its larger scale structure, such as the presence of long filamentous structure, the presence of curvature, or the variation in density and rigidity.Different geometries have been shown to promote cell orientation and migration along ridges The substantial inability of conventional substrates to be mechanically tuned on demand can be overcome by using stimuli‐responsive polymers.In this framework, light represents a very attractive external trigger, as it can be noninvasively provided according to arbitrary spatio‐temporal patterns at different wavelengths and polarization states.When dealing with 2D light‐responsive substrates, topographies can be instructed by a direct laser writing exposure of azopolymeric films previously spun on planar glass substrates.Here, we present an approach to spatio‐temporally control substrate topographies starting from a prepatterned azopolymeric structure on a glass‐bottom Petri dish. Topography modifications are performed nonsequentially in a modified inverted microscope, with a single exposure of the target area with a properly controlled laser distribution. A short (few tens of seconds) illumination of the as‐fabricated structure results in stable (months) and reproducible deformation patterns which can be customized according to the biological need. More specifically, depending on the local polarization orientation and intensity of the laser, micropillars get elongated along a direction parallel to the laser polarization, thus resulting in a generally both anisotropic and inhomogenous microtextured surface.The topography modifications that can be obtained with the presented approach lie in the micrometer range, therefore allowing a particularly effective dynamical stimulation for biological purposes.Importantly, due to the short irradiation times, micropillar deformation can be performed directly on substrates hosting living cells on‐board. We present an experimental validation of our technical approach by showing orientation and directional migration of cancer cells and growth confinement of epithelial colonies on deformed topographies. These two case studies were chosen as representative of classes of problems on single cell and multicellular scale that can be tackled by our technical approach. A discussion of several possible extensions of the proposed method is presented.The deformation capabilities of azopolymeric microstructures under irradiation by moderate local laser intensity are known from several previously published works.FigureIn Representative deformation patterns are shown in Figure The capability to elongate micropillars along any desired direction within specific regions of the sample is exploited here in an adaptive fashion, with living cells attached on the substrate. To illustrate this we first seed MDA‐MB‐231 cells on poly‐DR1M micropillars . Once attached to the pillar arrays, we identify one or more proper target cells to focus on. In the procedure illustrated as a flow in Figure As a first application of the proposed approach, we decided to test the ability of cancer cells to respond to substrate mechanical stimulations. Cancer cells can acquire different capabilities to better survive in hostile environments . The degree of sensitivity of cancer cells to external mechanical cues and their ability to fit a nonnative microenvironment are related to the malignancy of the cells. With this idea in mind, we selected the highly aggressive breast cancer cell line MDA‐MB‐231, originating from a triple‐negative breast tumor, as a good candidate to test our approach.We seeded MDA‐MB‐231 on patterned substrates and we observed the expected degree of cell adhesion and proliferation, comparable to tissue culture plasticware. We therefore assessed whether such cells would be sensitive to substrate deformation, despite their extremely aggressive phenotype.FigureAfter at least 24 h from seeding, substrates were laser‐illuminated and correspondingly deformed in selected regions with various patterns and after other 24 h cells were fixed and decorated in order to see actin and nuclei as shown in To further understand the effect of the instructed topography on cells, we assessed the degree of cell polarization, i.e., cell elongation, on a deformed substrate against that on undeformed substrates. Cell polarization is measured here as a major to minor axis lengths ratio. It is worth noting that cell elongation on nondeformed substrates is the relevant control for our experiment as opposed to cells seeded on flat nondeformed substrates. This is due to the fact that a flat substrate offers a continuous surface while a pillar‐based substrate only has discretely distributed adhesion points. We compared the distribution of cell polarization  and found only minor variations of cell polarization distribution in the two samples. This has four consequences: i) cells are able to deform and adhere on the substrate irrespective of the deformation; ii) cell orientation upon substrate deformation is not caused by a mere increase in cell polarization ; iii) to observe a statistically significant mean elongation difference one can design pillars with different pitch, fill factor, and depth, while seeding cells at lower densities in order to have isolated cells; and iv) curvilinear deformations with curvature radii comparable to cell lengths are not effective at inducing maximum elongations (as expected).Next, we investigated the induction and persistence of alignment overtime, by measuring cell migration along the direction of the deformation. To this aim, we performed time‐lapse microscopy on cells infected with viruses allowing visualization of nuclei by fluorescent proteins tagging. We seeded cells on undeformed substrates, waited for the cells to adhere and then deformed according to hyperbolic, azimuthal, and linear profiles. 24 h postdeformation, we monitored cells moving for about 3 days as shown in Movies M7–M10 in the Supporting Information. By performing cell tracking, we were able to record several single cell trajectories, and to correlate them with the deformation pattern instructed on the substrates. The results of this analysis are shown in Figure Our analysis demonstrates that mesenchymal‐like cancer cells adhere and elongate on azopolymeric microtextured patterns, regardless of the imposed deformation, indicating that neither patterning nor deformation alters the adhesion process appreciably. However, the local anisotropy induced on the deformed pattern is effective in orienting the cell bodies along the deformation direction. Moreover, time‐lapse analysis revealed that cells are viable and actively migrating on the substrate along a stable orientation as instructed by the laser polarization.Epithelial cells, as opposed to typical cancer cells frequently showing mesenchymal features, tend to develop tight cell–cell junctions which introduce yet another mechanical input to cell behavior, i.e., that of intercellular interactions. Indeed, cultured epithelial cells grow attached to each other by means of specialized junctions strongly biasing cell movement and form colonies. In the bulk of large colonies, cells tend to move very little and to control cell proliferation tightly, a phenomenon commonly called contact inhibition,To verify whether we could influence epithelial growth by spatio‐temporally controlling substrate topography, we cultured a commonly used kidney epithelial cell line  on patterned substrates to be exposed with proper laser illumination. Differently from the previous set of experiments on cancer cells, in this case, colony‐wide regions exhibiting a homogenous anisotropy are required to be induced.FigureMDCK was able to attach and form colonies on all undeformed patterns as shown in After inducing micropillars deformation along specific directions on regions surrounding growing cell colonies at an early stage, we measured the colony outlines as a function of time . The outline of the colony was in most cases oriented along the direction of deformation of the patterns, showing that topography can indeed influence epithelial cell growth, despite the presence of cells in the bulk moving apparently in all directions  deformations occur at once and do not require laser scanning and ii) substrate stimulation and biological observation can be performed simultaneously. Such features allow to employ our setup to study biological phenomena that occur on the scale of a few tens of seconds and on the spatial scale of a cell, such as calcium signaling, e.g., which has been shown to be influenced by topographies.Large and slow scales can also be explored thanks to the possibility of obtaining stable  topography modifications over large areas, by means of irradiation with low‐magnification lenses. Such is the case of some of the phenomena explored here, like epithelial growth. Of note, another extremely interesting scenario can be faced: that of temporally fast and spatially large scales. This situation is the most extreme and gives access to a number of collective phenomena in biology, that occur on temporal scales of a few minutes, or even less than a minute, and on spatial scale of several hundreds of microns. Phenomena like these have been shown, e.g., in protein activation wavesSome aspects of the proposed technique can be modified or expanded in order to be more suitably adapted to specific biological needs. For example, alternative compositions of the light‐responsive polymeric substrate can be used, wherein mechanical properties such as elasticity or deformation reversibility are enhanced , pDR1M (Sigma‐Aldrich) azopolymer was employed to fabricate light‐responsive 2D pillar arrays by soft‐printing technique. Poly‐DR1M was one of the most well‐known and largely used azopolymers in SR fabrication and easily available in the market. pDR1M was first dissolved at 2 wt% in N,N‐dimethylformamide and then drop‐casted onto a Petri dish . A PDMS stamp previously obtained from a laser‐lithographed and etched silicon master was pressed onto the casted pDR1M and then put on a hot plate (55 °C temperature) for 2 h. Once the solvent was completely evaporated, the PDMS stamp was carefully removed from the Petri dish, thus leaving a pDR1M complementary structure on the glass substrate. After each use, the PDMS stamp was washed in ethanol and placed for 20 min into ultrasound bath to remove residual traces of pDR1M. Each stamp was employed three to ten times. Azopolymeric pillars had a 4 µm × 4 µm squared cross‐section and a total height of 1.3 µm. Four different pillar spacings were used: 5, 7, 9, and 11 µm, resulting in four different arrays over an area 2.5 mm × 2.5 mm each . In the results shown here, arrays corresponding to spatial periods 5 and 7 µm were used, which were found to induce a more significant cell response. Atomic force microscopy characterization of the patterned substrates is shown in Figure S2 in the Supporting Information.Optical Setup: The setup was based on an inverted microscope  equipped with a home‐made incubator for living cells. Bright‐field imaging was performed by means of a monochromatic 16‐bit CCD camera (Apogee Ascent) placed within the eyepiece housing. From a lateral port of the microscope, a laser pattern was input and then projected onto the bottom surface of the Petri dish through the microscope objective . The laser pattern was generated by using an SLM illuminated by a collimated, Gaussian laser beam at 532 nm wavelength. The linearly polarized light from a doubled frequency Nd:YAG source  was expanded to a beam diameter of about 2 cm. The beam was directed toward the SLM  where a proper phase mask was displayed. The phase mask  was calculated by applying an iterative Fourier transformation algorithm on the desired intensity pattern to project. A spatial filtering stage blocked the light component corresponding to the zeroth diffracted order, while transmitting the first diffracted order from the SLM. A lens system focused the transmitted light on the intermediate image plane, corresponding to the microscope lateral port, where the intended intensity pattern was formed. A motorized stage mounting a zero‐order half‐wave plate (Thorlabs Inc.) was used to control the orientation of the linear polarized laser pattern before projection onto the sample. By synchronizing the orientation of the half‐wave plate with the SLM, multiple patterns or pattern portions with different polarization orientations could be sequentially projected onto the sample, thus implementing complex deformed pattern with arbitrary anisotropy and inhomogeneity. In addition or in alternative, other polarization elements could be employed. For example, a polarizing device including a θ‐cell (ARCoptix S.A.)Cell Culture and Manipulation: Cell lines were cultured in Dulbecco's modified Eagle medium  or Eagle's minimal essential medium  supplemented with 10% fetal bovine serum, penicillin/streptomycin, and L‐glutamine. Cells were kept at 37°C under 5% CO2 humidified air. Cells were seeded on the patterned glass‐bottom Petri dish  at a density between 2.5 × 104 and 105 per dish. For actin and nuclear staining, Phalloidin‐488 (Invitrogen) and either 4',6‐diamidino‐2‐phenylindole or DRAQ5 (ThermoFisher) were used. Cells were rinsed twice in phosphate‐buffered saline (PBS), fixed in 4%paraformaldehyde, rinsed again in PBS and incubated 10 min with the appropriate conjugated reagent for 10 min, rinsed and kept in PBS in a cold room prior to imaging. pLenti.PGK.LifeAct‐GFP.W and pLenti.PGK.LifeAct‐Ruby.W were gifts from R. Lansford . LV‐GFP, RFP and CFP were a gift from Elaine Fuchs .Microscopy: Cells were infected with two lentiviruses carrying the transgenes LifeAct‐Ruby and H2B‐GFP which stain for polymerized actin and nuclei, respectively. Time‐lapse video‐microscopy experiments were conducted either on a Nikon Ti‐E inverted microscope (bright‐field only), or on a Leica SP8 inverted confocal microscope equipped with a motorized stage and an incubator to keep the plate stably at 37° and 5% CO2. Imaging on fixed cells was performed either with the confocal microscope or on an inverted widefield (Leica AF6000).Image Processing and Analysis: Segmentation of nuclear and cytoplasmic signals was performed by means of Ilastik.Tracking of MDA‐MB‐231 was performed by segmenting nuclear signal and by implementing a tracking algorithm based on the minimum distance between objects in different frames. Segments of tracks corresponding to 1 h were used to calculate alignment onto the local direction of the deformation.Colony growth was measured by segmenting cytoplasmic signal to obtain the outline of the colony and then calculating area and orientation of the colony at each time as the major axis of the segmented colony. Cell division orientation was measured by projecting the direction defined by the line joining the two daughter cells at each mitosis onto the local direction of deformation of the pattern.Data Processing and Analysis: Violin plots were used to represent statistical distribution of the different measurements performed.The authors declare no conflict of interest.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.


A new and diverse range of somatic mutation signatures are observed in late‐stage cancers, but the underlying reasons are not fully understood. We advance a “combinatorial association model” for deaminase binding domain (DBD) diversification to explain the generation of previously observed cancer‐progression associated mutation signatures. We also propose that changes in the polarization of tumour‐associated macrophages (TAMs) are accompanied by the expression of deaminases with a new and diverse range of DBDs, and thus accounting for the generation of new somatic mutation signatures. The mechanism proposed is molecularly reminiscent of combinatorial association of heavy (H) and light (L) protein chains following V(D)J recombination of immunoglobulin molecules (and similarly for protein chains in heterodimers α/β and γ/δ of V(D)Js of T Cell Receptors) required for pathogen antigen recognition by B cells and T cells, respectively. We also discuss whether extracellular vesicles (EVs) emanating from tumour enhancing M2‐polarized macrophages represent a likely source of the de novo deaminase DBDs. We conclude that M2‐polarized macrophages extruding EVs loaded with deaminase proteins or deaminase‐specific transcription/translation regulatory factors and like information may directly trigger deaminase diversification within cancer cells, and thus account for the many new somatic mutation signatures that are indicative of cancer progression. This hypothesis now has a plausible evidentiary base, and it is worth direct testing in future investigations. A long‐term objective would be to identify molecular biomarkers predicting cancer progression (or metastatic disease) and to support the development of new drug targets before metastatic pathways are activated. Ultimately, the accumulation of deaminase mutations during transcription may lead to tumorigenesis.In 2010, we showed that many non‐lymphoid cancers display the same strand‐biased spectrum of point mutations at G:C and A:T base pairs in non‐Ig genes as observed in normal physiological Ig somatic hypermutation (SHM) of B cells, indicative of AID and ADAR1 action during transcription.As further support for the TSM‐profiling approach briefly described here, the analytical methods involved have also been applied to the analysis of the dbSNP database of curated clinically significant single nucleotide polymorphisms (SNPs). It was shown that a significant number of human SNPs appear to have arisen by AID/APOBEC and ADAR deaminations leading to the germline incorporation of these SNPs over evolutionary time‐scales.Thus, there is mounting evidence supporting the view that TSM signatures observed in DNA are highly targeted, and manifest as somatic mutations that accumulate in the host cell genome as collateral damage during an innate immune response to a pathogenic insult. The resulting de novo somatic mutation patterns are now widely known to be associated with cancer progression.2The rapid maturation of AID/APOBEC and ADAR deamination mutation signatures associated with a prediction of cancer progression was first shown by analyzing changes in the Inf‐DBDs of high‐grade serous ovarian (HGsOv) carcinoma. A number of new deaminase‐linked TSM signatures and their association with disease progression were identified.In addition to the above genomic analyses, there are other plausible molecular explanations for generating the functional maturation of C‐to‐U(T) and A‐to‐I(G) deaminase DBD diversity in late‐stage cancer. Additional diversity has previously been contributed by ADARs self‐editing their active DBDs, and thereby altering their function,With these genomic deaminase mutator mechanisms firmly in mind, we now discuss a general model for cancer progression based on deaminase‐mediated Innate Immunity via Functional Combinatorial Association  formation as a result of the molecular mechanisms described in the previous section and reviewed in Lindley et al.Table For APOBEC1, APOBEC2, APOBEC4 and the APOBEC3 family series of deaminases,There are also logically fewer potential ADAR protein heterodimers3The activated macrophage cell type, and its associated functions, is the first line of defence against pathogen infections in all eukaryotic and prokaryotic organisms.Candida albicans stimulation of human THP‐1 macrophages produces EVs containing APOBEC3C. It should also be noted that APOBEC proteins were not found in EVs secreted by TAMS in two other recent studies.Activated macrophages are the main cell types responsible for effective first line cell‐mediated immunity against intracellular infectionIt is also known that in some specific cases, transfer of Innate Immune regulatory states can occur via EVs. EVs derived from neural stem cells have been shown to transfer IFN‐γ, activating the ISG pathway via Stat1 signalling in target cells.There is therefore great potential for M1/M2 TAMs to secrete various stimulators of ISG pathways and thus directly affect both activity and gene expression of the APOBEC and ADAR family proteins in target cancer cells.Of special relevance here, the “inhibit and kill” functions of the macrophage through the induction by the nitrous oxide (NO) metabolic programme and the arginine/ornithine “healing” or wound repair pathways have been identified with the two alternative polarized states of “M1” and “M2” macrophage phenotypes.Here we interrogate the relationship between the events triggering the formation of new deaminase heterodimers and the changes in the polarized states of M1 and M2 as cancer progresses. To address this, we will also need to consider the role of lymphocyte activation in the tumour microenvironment.4The basic model of lymphocyte activation is also fundamental to our current understanding of immunity and the host‐tumour relationship as cancer progresses. Antigen‐specific adaptive immunity and the corollary development of our understanding of T cell‐mediated regulation of the tumour‐host relationship led by Bretscher5Communication between cells is necessary to co‐ordinate and regulate deaminase and macrophage polarization changes in the TME. It is known that cytosolic proteins, lipids, carbohydrates, small regulators and RNAs (including mRNAs and miRNAs) typical of the functional and regulatory cytosolic state of a cell do communicate with neighbouring cells, or more distant cells throughout the vascular and lymphatic systems. This occurs via their EVs.6Thus, in oncogenesis, the collective processes involve an ongoing and classical “armed stand‐off” between host immunity, tumour mutagenesis and adaptive survival. This involves an orchestrated set of molecular processes that appear to occur in two stages: The first involves pre‐ or early‐stage cancer development where the M1/M2 ratio is high, and most of the TSM signatures are identified as the main homodimers for key deaminases. The second stage is characterized by a low M1/M2 ratio, and C‐PAS can be identified and are indicative of deaminase heterodimers. The main features of this model for cancer progression are shown in Figure We propose that M1 and M2 TAMs secrete deaminase proteins  that are loaded into extracellular vesicles (EVs) within the pro‐ (M1) and anti‐ (M2) inflammatory tumour microenvironment. Further, we propose that ADAR1 and ADAR2 play essential roles in physiological regulation in situ and are not for promiscuous export, for example the control of inappropriate Innate Immunity cascades to self dsRNA structures, as described by Liddicoat et al.Thus, in the proposed model Figure , a celluBased on this model, we therefore need to view the TME as a combination of aberrant tumour cell death, and a complex mix of macrophage plasticity and activation states M1↔M2) that are interlinked with endothelial cell activation and deaminase activity. Together, these create an “onco‐regenerative niche” where EVs from dying tumour cells convey information about the tumour cell physiological state to other cells in the microenvironment.↔M2 that 7The available evidence is consistent with a model in which tumour enhancing M2‐polarized macrophages extrude EVs loaded with deaminase proteins and deaminase‐specific transcription/translation regulatory factors. We believe this directly triggers deaminase DBD diversification within cancer cells, and thus accounts for the observed C‐PAS. This hypothesized deaminase‐mediated cancer progression model thus predicts the presence of an elevated expression of M2 polarized macrophages that is accompanied by the identification of new functionally active APOBEC/ADAR family heterodimers. Future directions include establishing the deaminase content of EVs extruded from M2 TAMSThe authors declare no conflict of interest.Robyn A. Lindley conceived of the proposed model and contributed to the writing of the paper. Edward J. Steele, Siddharth Balachandran and Jared Mamrot provided additional insights and also contributed to the writing of the paper.