Dispensed prescription medicine labels (prescription labels) are important information sources supporting safe and appropriate medicines use.To develop and user test patient‐centred prescription label formats.Five stages: developing 12 labels for four fictitious medicines of varying dosage forms; diagnostic user testing of labels (Round 1) with 40 consumers (each testing three labels); iterative label revision, and development of Round 2 labels (n = 7); user testing of labels (Round 2) with 20 consumers (each testing four labels); labelling recommendations. Evaluated labels stated the active ingredient and brand name, using various design features (eg upper case and bold). Dosing was expressed differently across labels: frequency of doses/day, approximate times of day (eg morning), explicit times (eg 7 to 9 AM), and/or explicit dosing interval. Participants’ ability to find and understand medicines information and plan a dosing schedule were assessed.Participants demonstrated satisfactory ability to find and understand the dosage for all label formats. Excluding active ingredient and dosing schedule, 14/19 labels  met industry standard on performance. Participants’ ability to correctly identify the active ingredient varied, with clear medicine name sign‐posting enabling all participants evaluating these labels to find and understand the active ingredient. When planning a dosing schedule, doses were correctly spaced if the label stated a dosing interval, or frequency of doses/day. Two‐thirds planned appropriate dosing schedules using a dosing table.Effective prescription label formatting and sign‐posting of active ingredient improved communication of information on labels, potentially supporting safe medicines use.Consumers actively contributed to the development of dispensed prescription medicine labels. Feedback from consumers following the first round was incorporated in revisions of the labels for the next round. Patient and public involvement in this study was critical to the development of readable and understandable dispensed prescription medicine labels. Functional health literacy is important in people's ability to find, understand and act on such information.Previous reviews have highlighted the evidence for information content and design practices which include strategies such as people‐centred structuring of labels, use of known good information design strategies , use of plain language and explicit directions for use.Actual labels and implementation of evidence‐based labelling strategies in practice can vary.Recommendations from an Australian national round table2‘User testing’ is a diagnostic process using individual interviews with small numbers of lay people.This study comprised two rounds of label development and consumer user testing.2.1User testing was employed to evaluate the usability of the study labels by determining people's ability to find the relevant information and understand the information found. All interviews were conducted face‐to‐face at The University of Sydney, in rooms commonly used for meetings or interviews, and participants were reimbursed AUD$40.Cohorts of 10 demographically similar consumers user tested each label in accordance with user testing protocol.Recruitment was conducted through online advertisements, recruitment flyer distribution and passive snowballing, according to set inclusion/exclusion criteria.Participants were excluded if they were a healthcare professional or employed in an occupation which dealt with medicines information, had self‐reported visual impairment, had significant cognitive impairment affecting participation or had participated in user testing in the previous six months.Participants were allocated to each cluster of labels to ensure an equal distribution of participant demographics  across the cohorts.Each participant in Round 1  evaluated three unique labels for different dosage forms in the following order: a tablet or capsule, suspension, and eye drops or cream. Round 2 participants  each user tested four labels  directions for use, including maximum dose information). For Round 2, the first three unique labels for different dosage forms in each cluster were evaluated in one of two orders: a cream, suspension and capsule; or a capsule, suspension and eye drops. The common tablet label was then evaluated as the last label in both Round 2 clusters. Round 2 participants were naïve to the user testing process in Round 1.User testing consisted of three steps. Each participant read the first label and responded to the structured core user testing questionnaire (UTQ) and dose application question(s) asked, as relevant to the label. The process was then repeated for subsequent labels. Finally, semi‐structured questions were asked, and the participant completed the demographic survey and questions on self‐perceived health literacy , medicine strength and dosage were generally well found and understood Table ; at leasLabels for non‐solid dosage forms demonstrated issues with participant understanding of the medicine's strength. Qualitative findings indicated that the medicine strength for the suspension was not clearly communicated when expressed in the format ‘500 mg/5 mL’. A statement explaining the medicine strength (‘Each 5 mL of the syrup contains 500 mg pentoampicillin’), included on Label 15 (Round 2), was well received overall.The strength of the cream proved problematic for 2/10 participants evaluating Label 5 (Round 1); their responses indicated confusion between the weight of the cream and medicine strength (ie 25 g or 50 g instead of 0.5%).A marked difference in the eye drops labels between Rounds 1 and 2 for the medicine strength was noted. In Round 1, of those who found the information, 2/18 participants did not understand the information about the strength of the eye drops; that is, what 1% meant. In Round 2, 4/10 participants could not find the strength of the eye drops, confusing volume (ie 10 mL) for the strength.3.2Participants’ ability to correctly identify the active ingredient varied between labels, influenced by its formatting and positioning  Table . HoweverEight of the 20 Round 2 participants planned inappropriate schedules. Of these, three demonstrated Vipparoll dosing issues where the time between the evening and bedtime doses was less than the 6‐hour dosing interval (Label 14A).3.43.4.1When participants were asked to explain how they would take the medicine  for the rest of the day if their pain started at 9 AM  did not specify an amount. How much cream to be applied was deemed to be dependent on the size of the rash, perceived appropriate amount, and/or observed effect of the cream with adjustment of the amount if necessary.In general, understanding of ‘1 fingertip amount’ was either to squeeze or dab an amount of cream onto the fingertip and ensure that there was enough to cover the area or squeeze a small amount onto the fingertip.4Good information writing and design principles, the expertise of the research team and the application of user testing supported the development of dispensed prescription medicine labels that performed well. The majority of labels met user testing benchmark standards for most of the core UTQ items.Changes in how ‘standard’ or administrative information  was formatted (eg single column versus two column) had little impact on how the patient name and expiry date on the label were found and understood. This indicates that either approach is acceptable, and emphasis should be placed on separating administrative information from medicine‐related information, as endorsed by the United States prescription labelling standard.Medicine‐related information communication was influenced by formatting. Determining the active ingredient proved problematic and dosage information expression influenced how well participants could apply the information to a dosing scenario. Previous studies have largely focused on evaluating understanding of the dosage and directions for use on labels.The worst performing formatting combination was seen on a label where the active ingredient was presented in bold font and first, and the brand name was included in brackets next to it. This order and formatting of active ingredient and brand name was the opposite to what participants regarded and understood as current labelling practice in Australia, based on their own experiences. This may explain why only a small number correctly identified the active ingredient on this label. Improved active ingredient identification was observed when using current practice of stating the brand name first, and then the active ingredient in brackets.Overall, the best performing labels were those that explicitly sign‐posted the active ingredient and brand name. Prior research informing best practice in formatting active ingredient/brand name information is lacking, despite the importance of ensuring the active ingredient name(s) is prominently placed on medication packaging.Across all study label formats, participants demonstrated satisfactory ability to find and understand the directions for use, despite differences in how they were expressed and formatted. This differs from previous findings where a higher proportion of participants understood labels that conveyed directions for use using approximate times per day (eg morning), compared to labels that provided explicit dosing intervals (eg every 12 hours) or frequency of doses per day (eg twice daily).(p16) The superior performance of a label depicting the UMS with fixed explicit dosing time‐frames for the scenario is self‐explanatory, considering it specified exactly how the medicine needed to be taken. This indicates that the label would promote the medicine user's functional health literacy through direct communication and simplifying cognitive load associated with processing the directions for use for actioning. However, rigidity in dosing times and the UMS’s limited ability to support three‐times‐a‐day dosing with appropriate intervals may ultimately cap the ability to improve the person's existing interactive health literacy in the face of ‘changing circumstances’Differences were observed in how participants said they would take the study medicine in the pain scenario between the different label variations of directions for use. Participant responses provided an indication of whether the labels supported people's interactive health literacy, defined as ‘more advanced literacy skills that enable individuals to extract information and derive meaning from different forms of communication; to apply new information to changing circumstances’.Other labelling strategies such as ‘take 1 capsule 4 times a day’ inherently promoted evenly spaced dosing intervals in the present study compared to other formats that ‘prescribed’ approximate times of day. The California State Board of Pharmacy Code of Regulations legislates the use of specified standard wordings oriented around approximate times of day, as appropriate for dosage.The study findings have been integrated into recommendations Box , 31 usedBox 14.1Participant self‐selection bias may be present due to voluntary study participation. Participants also had good health literacy levels. Medicines were fictitious and other dosage forms, such as inhalers, were not included. Although many formatting variations were explored in this study, there are unlimited possible further combinations to be explored. People who could not speak English, had a visual impairment or cognitive impairment were not included in the study, as the aim was to identify label characteristics that would be appropriate for the broader group, and the majority of people who would access labels, including carers of people with visual and cognitive impairment. As part of the research, it was critical to firstly identify the effective label characteristics for a patient‐centred label, which can then be modified to address the needs of people who cannot speak English, or have a visual or cognitive impairment. Thus, it was outside the scope of the study to develop and test labels for those who could not speak English, or had a visual or cognitive impairment.5The majority of the study‐developed labels met user testing industry standards criteria for most core UTQ items, and supported people in finding and understanding information on a prescription label. Round 2 labels collectively performed better than Round 1 labels, demonstrating the importance of the iterative revisions and user testing process.The information related to active ingredient and medicine strength was either difficult to find or difficult to understand. Label design formatting had a notable impact on active ingredient identification. Actively specifying the active ingredient and brand name through sign‐posting was the most effective labelling strategy to improve participants’ ability to identify the active ingredient. Use of a tabulated dosing schedule on the label was positively received by participants and may assist people with scheduling their medicines.The findings help to pre‐empt labelling strategies that may compromise medication safety and/or adherence. It provides further evidence for strategies that improve usability of labels and positively influence appropriate medication‐taking behaviours.www.lutoresearch.com) which develops, refines and tests health information materials. Diana Shipp is employed by the Australian Commission on Safety and Quality in Health Care.David K. Raynor is co‐founder and academic advisor to Luto Research . Daniel Lalor: Conceptualisation; Research Design and Methods; Writing – review and editing. Andrew Sobey: Research Design and Methods; Writing – review and editing. Alice Gilbert: Research Design and Methods; Writing – review and editing. Jackie Crofton: Research Design and Methods; Writing – review and editing. Joanne Young: Research Design and Methods; Writing – review and editing. Sophie Carter: Data collection; Data analysis; Writing – review and editing. Wing Poon: Data collection; Data analysis; Writing – review and editing. Shrada Chitlangia: Data collection; Data analysis; Writing – review and editing.Ethics approval for this study was granted by The University of Sydney Human Research Ethics Committee [2017/620].Supplementary MaterialClick here for additional data file.


Computer‐aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine‐learning (ML) techniques. In this review article, we describe applications of ML‐based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state‐of‐the‐art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast.22 BREAST CANCER is the most common cancer among women but has an encouraging cure rate if diagnosed at an early stage. Thus, early detection of breast cancer continues to be key for effective treatment. Magnetic resonance imaging (MRI) is an established essential tool in breast imaging for high‐risk screening, assessment, diagnosis, staging, and follow‐up of breast cancer.Inspired by computer‐aided diagnosis (CAD) systems to support diagnostic and screening activities in conventional X‐ray mammography, research initiatives nowadays focus on similar techniques to aid or even automatize the diagnosis in MRI of the breast. These efforts started as early as 2002 with the application of the multilayer perceptron (MLP) as a classifier of tumor‐extracted features describing dynamic, morphological, or combined characteristicsHowever, the delineation, detection, and diagnosis of nonmass enhancing (NME) lesions is clinically very challenging since the standard morphological and kinetic features that are relevant for masses fail to achieve equally good results for NME lesions.Application of ML and Morphology of Breast Tumors explains the equally important morphological criteria used in detection and diagnosis. Section To provide useful insights for ML techniques in connection with important CAD systems of NME lesions in MRI of the breast, this review article consists of seven sections. Section As mentioned in the previous section, morphologic, kinetic, or combined features represent important lesion characteristics for a computer‐assisted interpretation. For example, time–signal series, as measured during a DCE‐MRI examination for each image voxel, represents an important component in designing CAD systems for MRI of the breast. Early studies have demonstrated that the shape of the time–signal intensity curve provides an important biomarker for discriminating between benign and malignant enhancing lesions in DCE‐MRI and it is a key step of reporting MRI of the breast.k‐space acquisition strategies have been introduced for dynamic breast MRI such as time‐resolved angiography with stochastic trajectories (TWIST) and differential subsampling with Cartesian ordering (DISCO).k‐space in order to increase the spatial resolution for an improved diagnostic quality. Thus, it can be employed in clinical settings to acquire breast DCE‐MRI data with both high spatial resolution for accurate tumor morphology assessment and high temporal resolution for accurate representation of the contrast agent kinetics.Recently, new There is clinical evidence that novel enhancement curve parameters combined with morphological features are improving the diagnostic accuracy for the ultrafast protocol.Although signal characteristics represent an important biomarker for a radiologist to distinguish between different tissue states, their assessment is quite a time‐consuming task. This becomes challenging when the heterogeneity of lesion tissue is considered, which causes the spatial variation of signal characteristics. In addition, this variation reflects specific tissue properties that should be considered when assessing the state of lesions. Kinetic parameters extracted either from qualitative BI‐RADS or quantitative empirical mathematical models measures of kinetics have proven to be insufficient when it comes to the differential diagnosis of NME lesions.Morphological parameters describing either the shape or structure of the ROI are obtained from manual or semiautomatic detection. They are either qualitatively or quantitatively extracted from lesions and represent valuable diagnostic biomarkers.Based on morphology and type of enhancement, lesions are assigned according to risk assessment and a quality assurance tool, the BI‐RADS lexicon, to mass enhancement, nonmass, and focus.The correct detection of these lesions, in combination with many clinical applications ranging from diagnosis to therapeutic solutions, demand sophisticated image processing paradigms in connection with feature extraction. The response to these challenging processing tasks has guided the development of novel ML techniques, which will be described further below.Several ML techniques are incorporated as a classifier in CAD pipelines for breast cancer detection, prediction of neoadjuvant chemotherapy outcome, and diagnosis. A brief description of the most important techniques is given in this section. We start with “classical” ML approaches such as support vector machine and random forest and conclude with a brief discussion of deep learning.Artificial neural network (ANN) classifiers are an attempt to emulate the processing capabilities of biological neural systems. The architecture of the MLP is completely defined by an input layer, one or more hidden layers, and an output layer. Each layer consists of at least one neuron. The input vector is processed by the MLP in a forward direction, passing through each single layer. A neuron in a hidden layer is connected to every neuron in the layers above and below it. MLPs have been applied successfully to sophisticated classification problems. The training of the network is accomplished based on a supervised learning technique that requires given input–output data pairs. The training technique, known as the error backpropagation algorithm, is bidirectional, consisting of a forward and backward direction. During the forward direction a training vector is presented to the network and classified. The backward direction consists of a recursive updating of the weights in all layers based on the computed errors.The random forest represents a powerful statistical learning technique and is an ensemble methodThe support vector machine (SVM) represents a feedforward single layer classifier that can be employed either for linear or nonlinear separable datasets.The basic idea of the SVM algorithm is to design a hyperplane:n and its exact position in space or bias w0. The hyperplane separates the labeled input or training data into two classes by leaving the maximum‐margin from both classes. A given set of N labeled training examples {i}, i = 1,…, N, xi ∈ Rn assigned to two different classes yi ∈ {−1, 1}, is separated by a maximum‐margin hyperplane such that the distance between the hyperplane and the closest examples (the margin γ) is maximized. This hyperplane is fully specified by a subset of those training examples that lie closest to the decision surface and pose a challenge for a correct classification. The training samples that lie closest to the hyperplane represent the support vectors. To employ the SVM for nonlinearly classifiable data, we need to employ the so‐called “kernel trick.” This symmetric and nonlinear kernel function evaluates the inner product between two examples after their transformation by a nonlinear function by maintaining the original architecture of the linear SVM.characterized by its direction vector w ∈ RC1 or C2. The decision is made by determining its Euclidean distance d from all the trainings vectors belonging to various classes. We define two hyperspheres with the radius r1 and r2, respectively centered at x. Let V1 and V2 be the two hypersphere volumes corresponding to the two classes C1 and C2.K‐nearest neighbors (KNN) is a supervised classifier. This algorithm stores all available patterns and classifies new patterns based on a similarity measure . This procedure can be very easily elucidated based on a two‐class classification task: an unknown pattern x should be assigned to one of the two classes k‐nearest neighbor classification rule can be easily formulated in the case of two classes C1 and respectively C2 as:The Bayes decision theory represents a fundamental statistical approach in pattern classification assuming mutually exclusive and exhaustive classifications with known prior probabilities. Simplified formulated, the probability that a pattern belongs to a given class is determined.C1, C2. The a priori probabilities P (C1) and P (C2) are assumed to be known a priori since they can be easily determined from the available training dataset. Given are the pdfs p(xi|Ci), i = 1, 2. These pdfs p(xi|Ci) are known as likelihood functions of Ci with respect to x.A simple example represents the two‐class case with ω1, ω2 as:The Bayes classification rule can be easily stated for the two–class case Based on the above classification algorithm, a feature vector can be either assigned to one class or the other. This is equivalent to determining the maximum of the conditional pdfs evaluated at x.πi for each class Ci. This value describes the prior estimates about how probable a class is.As stated before, the Bayes classificationThis classification method assigns each new training sample to the class with the highest posterior probability.Thus, the classification rule becomes:μj represent the means of the classes and ∑j is the corresponding covariance matrix. The assignment to a certain class j for a certain input pattern is made based on the smallest computed value of Cj .where The covariance matrices can be either different for each class or identical. In the first case, we have a quadratic discriminant analysis (QDA) classifier, while in the latter case we have a linear discriminant analysis (LDA) classifier.f (x) = wT x + b that is used to discriminate multiclass data labels. The method employs adequate dimension reduction from the initial data space to discriminate between q classes. The optimization problem becomes finding w defined by q − 1 basis vectors.Fisher's linear discriminant analysis (FLDA) is both a projection and classification method. Similar to SVM, we are looking for a linear function This technique identifies the first discriminating component based on finding the vector w that maximizes the discrimination index, given as:B denoting the interclass sum‐of‐squares matrix and W the intraclass sum‐of‐squares matrix.with Decision trees represent a nonlinear multistage classifier in which classes are rejected over a sequence of decisions until a finally accepted class is reached. This means that the feature space is split sequentially in specific regions that correspond to the classes. Each feature vector traverses an existing tree based on a sequence of decisions and follows a path of nodes until it reaches the region where it belongs. In other words, the correct class corresponding to a feature vector is determined by searching a tree‐based decision system. This classification scheme is extremely beneficial when a large number of classes is given.xi ≤ α, with xi being a feature and α a threshold value, is made at each node for individual features.The most popular decision trees are binary decision trees. Binary decision trees separate the search space into hyperrectangles with sides parallel to the axis. The tree is searched in a sequential manner and a decision of the form This processing scheme is an essential part of many tree‐based vector quantization algorithms.c‐means algorithm, being an unsupervised classification algorithm applied in medical imaging mostly to pixel segmentation. In the beginning, a set of classes has to be determined and the centroid of each class is computed. Each pixel is then classified by its membership values of the classes according to its attributes. Membership value for a certain class indicates the probability of the pixel belonging to that class. The objective of the fuzzy c‐means algorithm is to compute membership values to minimize the within‐cluster distances and maximize the between‐cluster distances. The cluster centers are updated iteratively.In crisp classification the membership of each sample from the dataset to a given class is either zero or one. In fuzzy clustering, a sample can belong to more than one class and its membership takes any value between zero and one. A fuzzy classifier is described by the classical fuzzy IF‐THEN rules.Classical ML techniques cannot be applied to images directly, and hence it is required to define suitable features  to encode discriminative properties of the lesions of interest. The emergence of deep‐learning (DL) architectures allows working directly with images, and not with extracted or “engineered” features from these images, by learning the feature representation along with the classifiers.Inspired from the brain processing in the visual cortex, an architecture achieving several layers of abstraction based on a hierarchy of transformation appears as a well‐suited answer to the above problem. The most common architecture of this type is the convolutional neural network (CNN). Like the human brain, the first layer of this hierarchical network detects edges, then further layers primitive shapes and subsequently more complex visual shapes until a semantics concept is built. The number of layers determines the depth of the network and those networks with up to two hidden layers are considered shallow, while those with more than three are deep. Each layer can be viewed as creating a feature vector while the DL network can be viewed as a modality for learning a hierarchy of features. Thus, the higher layers implement a higher abstraction of the representation or mapping as reflected by the respective feature vector of that layer. With this novel “coding scheme,” the network is generating the features by itself without the need of human intervention. A typical deep CNN is shown in Fig. Due to the complex nature of DCE‐MRI, with both spatial (volumetric) and temporal variations, feature extraction either through conventional or DL‐based techniques is crucial to achieve good performance. Hundreds of features have been proposed in the literature to encode both morphological  and kinetics  properties of the tumor and its enhancement. CNNs were initially proposed to deal with 2D, low‐resolution, RGB images, and therefore need to be adapted in order to effectively process multiparametric inputs and encode both volumetric  and temporal changes.ML techniques have to be evaluated regarding their performance in the testing phase based on new, previously unseen data samples.The ability of a classifier to discriminate diseased malignant from benign cases is based on receiver operating characteristic (ROC) analysis. The most important evaluation metrics are accuracy, sensitivity, specificity, and area under the curve (AUC). Accuracy determines the ratio of the correct classified samples in relation to the total samples. The sensitivity (Sn) is the probability that a test result will be positive when the disease is present . The specificity (Sp) is the probability that a test result will be negative when the disease is not present . The AUC represents the degree of separability between the two classes and is a common figure of merit to compare the performance of different classifiers.NME lesions show a heterogeneous appearance in MRI, with high variations in kinetics and morphological characteristicsA search through the most important databases was performed to identify various studies related to the employed ML techniques. The primary aim was to categorize the studies according to the following research questionsIt is important to provide an improved differential diagnosis for these diagnostically challenging lesions based on a CAD system that ideally incorporates the spatiotemporal properties of these lesions and provides the radiologist with a fast and accurate computational diagnosis support. Available features describe the breast signal in the 4D space and may capture the temporal dynamics, the morphological characteristics, and also the spatial variations within the tumor. In this subsection, techniques that are rooted in quantitative feature extraction are reviewed. Table Based on the reviewed literature, it appears that ML techniques are a promising solution towards NME detection and characterization. There are, however, several challenges to be tackled.First, new techniques are needed for the simultaneous movement correction and segmentation considering spatial and temporal profiles: automatic motion correction represents an important prerequisite for a correct automated lesion evaluation.Another important point is the development of novel feature extraction for spatiotemporal modeling algorithms that can capture the subtle local variations in NME lesions. BI‐RADS‐based features proved to be insufficient to differentiate between malignant and benign for NME lesions, and therefore additional descriptors are needed to reduce the high proportion of false‐positive diagnosis and unnecessary biopsies.At the moment, the most known spatiotemporal feature descriptors are: Zernike velocity moments,The scaling index method is a technique that can capture both morphology and kinetics. Originating from the theory of complex systems, the scaling index extracts the local structure around a given point in an arbitrary dataset. This technique requires converting the image in point distribution, where each voxel corresponds to a point and its state is given by its coordinate and its gray scale intensity value. In the context of MRI of the breast, each point (or voxel) is thus described by its sagittal, coronal, and transverse positions along with the observed intensity value.In clinical practice, dynamic medical images  are often assessed qualitatively. However, there is a need to quantify the results from these images in order to provide an objective and effective method for the diagnosis for evaluation of treatment efficacy. Voxel‐based adaptive spatiotemporal modeling can accomplish this. Typically, images suffer from low signal‐to‐noise ratio, which makes quantitative voxelwise evaluation hard. One way to overcome this problem is to aggregate imaging data in an ROI. When using an ROI, however, one obviously loses the spatial information of the image.Kinetic parameters extracted either from qualitative BI‐RADS or quantitative empirical mathematical model measures of kinetics have proven not to be useful when it comes to the differential diagnosis of NME lesions.Table A simultaneous registration and segmentation can be achieved with independent component analysis (ICA). This technique facilitates the challenging segmentation of NME lesions and does not require a predefined ROI mandatory for manual analysis or an accurate threshold for semiautomated analysis. It incorporates spatial as well as temporal properties and provides an accurate motion correction and segmentation, even for noisy images and spatial smoothness compared with conventional level set methods or clustering‐based techniques. This method detects the interior contours automatically, filters out the noise, and is robust with respect to noise, and includes and evaluates tumor‐specific enhancement curves. ICA has proven to be an excellent method to separate motion artifacts in biomedical image processingTo overcome limitations in specificity, additional functional MRI techniques such as DWI and proton MR spectroscopy have been explored and have demonstrated improved diagnostic accuracy as well as response assessment.Few CAD systemsDevelopment of a novel image normalization framework for these multiparametric images. The normalization step represents a crucial step for the subsequent feature extraction and classification, since the images stem from heterogeneous sources. Usually, standard preprocessing step is followed by a novel joint segmentation and registration algorithm. A better solution is represented by novel joint segmentation and registration algorithm based on a variational model and level set approach that incorporates spatial as well as temporal contrast‐enhanced images. The multiparametric images are registered such that all segmented images will be in the same reference frame.The multiparametric MR images arise from heterogeneous sources and need to be regularized before relevant features for the CAD system can be extracted. This step includes a preprocessing stage and a joint tumor segmentation and registration stage such that all images are in the same reference frame. The preprocessing step includes noise filtering performed based on wavelet shrinkage,Due to the elasticity and heterogeneity of breast tissue, only nonrigid image registration methods are suitable. At the same time, accurate segmentation of the lesion is critical since the spatiotemporal features have to be extracted from the tumor region. Different solutions have been proposed to solve this problem: these range from purely image‐based statistical and geometrical models for regularization2‐MRI and advanced DWI methods such as intravoxel incoherent motion (IVIM) maps, restriction spectrum imaging, or multidimensional DWI. The apparent diffusion coefficient (ADC) is the most prevalent method for quantifying diffusion in clinical practice and is based on fitting a monoexponential model usually to two images acquired without diffusion‐weighting and with relatively high diffusion‐weighting. However, lesion heterogeneity is insufficiently described by a single ADC threshold and thus more detailed structural and functional image features have to be extracted from T2‐MRI and DWI. Novel descriptors should include additional information from multiparametric MRI and capture the structure of the breast tissue in a unique manner. Experimentally, the monoexponential fit provided by the ADC was in practice found to be only applicable to simple cysts, whereas malignant and benign lesions required a more complex biexponential model fitted from six or more images with varying diffusion‐weighting parameters.Identifying novel descriptors such as structure tensors and texture from TA few studies have exploited first‐order texture measurements statistics reflecting the lesion ADC heterogeneity,Identifying novel spatiotemporal descriptors from DCE‐MRI images as the most powerful discriminators. Some lesions exhibit a high variance in morphological and kinetic characteristics and the consequence is a high proportion of false‐positive diagnoses.The bottleneck that remains for providing an improved differential diagnosis, and thus contribute to advancing CAD systems beyond the current level, are determining the descriptors that incorporate the diagnostic information from multiparametric MR images for NME lesions. Important steps include:Dynamic texture features can be extracted based on the 2D+T curvelet transform.This novel technique is relevant for extracting nonlocal phenomena propagating temporally and operates based on a geometrical multiscale decomposition. As in the 2D case, a separable 3D convolution can be factored into 1D convolution along rows, columns, and image indexes of the MRI scans. As a result of this transform, a spatiotemporal segmentation algorithm is produced. The coefficients of the 2D+T curvelet transform contain discriminative information that can be employed for recognizing different dynamic textures. As in the case of 2D texture, the wavelet decomposition is employed to build feature vectors from detail subbands. The feature vector is composed by the average, standard deviation, energy, and entropy of the detail subbands. By adding a discrete cosine transformation to the 2D+T curvelet transform a morphological transformation can be implemented that considers also the local phenomena. Thus, the geometry of the dynamic texture can be additionally captured. This novel descriptorcould sufficiently represent the dynamical properties of the temporal texture characterizing the heterogeneous behavior of diagnostically challenging lesions.A possible CAD system for multiparametric breast MR images is shown in Fig. Neoadjuvant chemotherapy (NAC) is the standard of care and is widely used in patients with locally advanced breast cancer, offering several advantages, such as reduction of tumor and enabling breast‐conservation surgery instead of mastectomy as well as response‐guided NAC approaches. In patients undergoing NAC for breast cancer the achievement of a pathological complete response (pCR) is associated with a significantly improved disease‐free and overall survival. However, a pCR is achieved in only 30% of the patients after the completion of NAC and clinical studies have shown that the therapeutic outcome can be improved after treatment modifications during NAC. Predicting the pathological response after NAC in breast cancer patients is crucial and quantitative computerized methods represent an important step towards an accurate and effective breast cancer treatment. The first study assessing the role of an automatic CAD system in DCE‐MRI predicting the pathological response to NAC has been described.Tumor response is monitored in the latest clinical studies with PET/MRI. These techniques vary a lot in the performance of NAC response monitoring of different breast cancer types and the combined use of PET and MRI has been shown to have a complementary valueSeveral ML techniques were applied for NAC, mostly for breast masses. A CAD scheme based on a Bayesian classifierDL methods have been applied to automatically score HER2, a biomarker that determines the patients who are eligible for anti‐HER2 targeted therapies.In the past 3 years, a novel computational approach—radiomics—is emerging to represent oncological tissues based on quantitative descriptors.Radiomics represents a novel approach to achieve a detailed quantification of the tumor phenotypes by analyzing a large number of image descriptors. It has been hypothesized that a large number of radiomic features tremendously increase the diagnostic, prognostic, and predictive power. With the increasing importance of “personalized medicine,” new treatment strategies are being sought to respond to the specific characteristics of each patient and cancer phenotype. So far, personalized medicine is centered around molecular characteristics with genomics and proteomics data analysis. In Hoffman et al,The potential of radiomics as a training‐independent diagnostic decision tool has been shown.A comparison between DL and radiomics was performedAdvanced breast imaging techniques such as DCE‐MRI and DWI are complex and highly adjustable procedures. The difference in hardware and software implemented by different vendors can produce noticeable differences in image quality and appearance. In addition, acquisition protocols vary across and within studies, vendors, and acquisition centers, and may include different spatiotemporal resolutions, contrast agents, or imaging parameters . Postprocessing, including delineation and segmentation of the tumoral area, may further complicate this picture. ML models rely on quantitative features, either hand‐engineered or learned by CNNs, which may be heavily affected by such changes. Since collecting data for all possible acquisition protocols is unfeasible, these aspects need to be carefully considered in the design, training, and validation of ML models. The problem of how to design robust ML models that can generalize to multiple settings is still, in many ways, an open research question.There are two main approaches in order to build ML models robust to acquisition parameters: image standardization/harmonization and more robust feature extraction/selection. Besides working alongside vendors to standardize image acquisition, a laudable but notoriously difficult quest, image or feature harmonization may be more feasible. Feature harmonization was demonstrated to significantly improve benign vs. malignant lesion classification in an DCE‐MRI dataset acquired from multiple international institutions.Another line of research analyzes repeatability and reproducibility of individual features in order to select those features that guarantee a higher reproducibility.This systematic review aimed to give an overview of the currently available methodology and applications of ML‐based CAD systems for diagnostically challenging lesions in MRI of the breast. ML techniques have been successfully applied in medical image processing. Over the past decades, we have witnessed the transition of ML techniques from feature extraction from medical images to working directly with the raw images, as enabled by newer models such as CNNs.To date, applications of state‐of‐the‐art CAD systems are based on established feature engineering and enhancement curves extraction from DCE‐MRI; these techniques have proven to be valuable tools for the detection and diagnosis in clinical praxis. Radiologists can benefit from such ML‐based CAD systems, resulting in reduced interobserver variability and improved interpretation of breast imaging for the presence or absence of breast cancer.2‐weighted or DW sequences or hybrid (PET/MRI) techniques, as well as extracted quantitative radiomics features. Such advanced multiparametric ML‐based CAD systems are expected to further improve not only diagnostic accuracy for challenging lesions but also provide predictive and prognostic indicators for breast cancer. It has to be noted that, despite encouraging results, we are still at the dawn of a widespread implementation of ML‐based CAD systems in breast MRI. To date, studies have been mainly retrospective, single‐institution, using different equipment, scan protocols, sequence parameters, and postprocessing steps, and have included relatively small numbers of patients, which limits the statistical power of the studies and may compromise the generalizability of the results. Rigorous standardization of MRI hardware and software, quantitative MRI techniques, and multicenter large‐scale studies are needed to build and validate robust machine‐learning models that are applicable across patients and institutions to provide clinical value.Future directions for research and development aim to develop ML‐based CAD systems not only for diagnostic but also predictive and prognostic purposes, by including other MRI methods such as T


Bioinspiration is a promising lens for biology instruction as it allows the instructor to focus on current issues, such as the COVID‐19 pandemic. From social distancing to oxygen stress, organisms have been tackling pandemic‐related problems for millions of years. What can we learn from such diverse adaptations in our own applications? This review uses a seminar course on the COVID‐19 crisis to illustrate bioinspiration as an approach to teaching biology content. At the start of the class, students mind‐mapped the entire problem; this range of subproblems was used to structure the biology content throughout the entire class. Students came to individual classes with a brainstormed list of biological systems that could serve as inspiration for a particular problem . After exploration of relevant biology content, discussion returned to the focal problem. Students dug deeper into the literature in a group project on mask design and biological systems relevant to filtration and transparency. This class structure was an engaging way for students to learn principles from ecology, evolution, behavior, and physiology. Challenges with this course design revolved around the interdisciplinary and creative nature of the structure; for instance, the knowledge of the participants was often stretched by engineering details. While the present class was focused on the COVID‐19 crisis, a course structured through a bioinspired approach can be applied to other focal problems, or subject areas, giving instructors a powerful method to deliver interdisciplinary content in an integrated and inquiry‐driven way. From social distancing to oxygen stress, organisms have been tackling pandemic‐related problems for millions of years. What can we learn from such diverse adaptations in our own applications? We use a seminar course on the COVID‐19 crisis to illustrate bioinspiration as an approach to teaching biology content. In this review, we hope to not only illustrate our educational process but also provide some detail in problem areas where we feel we made promising insights. While we are not virologists or epidemiologists, we hope that some of these ideas could serve as inspiration for creative approaches to solving pandemic‐related problems during this or future outbreaks. We conclude this review by discussing some of the broader lessons and challenges in a bioinspired teaching format and areas for future work.2We started our class by “mind mapping” the COVID‐19 pandemic Davies, , with eaMind mapping the pandemic problem space revealed individual biases in how people explore problems. Each individual in the class mapped the COVID‐19 problem space separately and then compared notes  can be used as filters in human applications—in other words, filtration is a function in human applications rather than the evolved function of that trait . Filter‐feeding organisms vary in size from microscopic to the largest animals on earth . Animal filtration systems selectively exclude or include particles down to the micron level, meaning there are potential applications for blocking the aerosol particles relevant in COVID‐19 transmission, in addition to potentially the viral particles themselves. How are they filtering particles? Students divided their research efforts across species that used different filtration mechanisms. Many marine species that have been assumed to use sieve‐based filtration actually rely on a combination of filtration mechanisms, including crossflow filtration , we also identified other challenges associated with masks, such as transparency ; Resources (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Dimitri Smirnoff: Conceptualization ; Methodology (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Hunter Cantrell: Conceptualization (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Kaila Chapman: Conceptualization (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Elizabeth Kirscht: Conceptualization (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Elizabeth Stretch: Conceptualization ; Methodology (supporting).Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


How people with chronic obstructive pulmonary disease (COPD) engage with supportive social networks to enhance self‐care is not understood. The personal rationales for participation in socially directed support have not been addressed in the literature. To determine how people with COPD identify, engage and participate in socially supportive self‐care practices, we conducted a systematic review and meta‐ethnography of qualitative studies.A systematic literature search was conducted between June 2010 and June 2021. Of 3536 articles, 8 fulfilled the inclusion criteria. Using a meta‐ethnography approach to the qualitative synthesis, new concepts were derived from the data to identify aligning themes and develop a conceptual model.Interpretations from the papers yielded concepts of (1) accountability and personal responsibility in self‐care, (2) valued positive relationships with clinicians, (3) understanding of illness through shared and personal experiences and (4) acknowledging social networks in fostering self‐care engagement in people with COPD. The independence‐experience (Index) model of synthesized (third order) interpretations highlighted the processes of social networks and self‐care practices: (a) fear or avoidance of dependency, (b) learning from experiences of adaptive self‐care behaviours and (c) including valued practices in self‐care. Self‐care strategies are formed through illness experiences and relatable social encounters.The model derived from the third‐order interpretations is a framework to describe socially supported self‐care and can be used to direct future self‐care strategies and target interventions for people with COPD.The findings and model were presented to the long‐term conditions patient and public involvement group. The manuscript is coauthored by a public representative. The focus of the systematic review and qualitative synthesis was to develop an in‐depth understanding of the phenomenon of socially supportive self‐management among people with COPD. Search terms were designed to ensure that the qualitative studies selected examined how people with COPD engaged with socially supportive self‐management practices. Seven databases were searched: CINAHL, MEDLINE (OVID), APA PsycInfo, Web of Science, PubMed, Cochrane Library and EMBASE. This was supplemented by citation tracking and grey literature searches. The search was limited to English‐language papers between June 2010 and June 2021. The search terms used were COPD (and synonyms) AND Chronic Obstructive Pulmonary Disease OR COPD OR Emphysema AND Self‐management OR self‐care OR self‐management support OR social network support AND qualitative studi.Initial titles and abstracts of papers were read by one reviewer (L. W). Full articles of potentially eligible studies were reviewed by two researchers (L. W. and E. S). Inclusion criteria focused on qualitative studies that explored the enactment of socially supported self‐management in people with COPD. The quality of the studies included was assessed using a quality appraisal tool developed by the British Sociological Association.Selected studies aligned to the research question on the basis of detailed inclusion and exclusion criteria Table .In Step 3, selected studies were read and reread by two authors (L. W. and E. S). Study findings were entered into an extraction table Table . This inIn Step 4, we determined how the studies were related to each other by comparing individual study findings. The ontological stance of subjectivism, or interpretivism, was selected.In Step 5, studies were ‘translated into each other’, which involved examining the contribution of each study to a key theme or concept. Each theme or concept emerged from individual studies, but was also viewed as relevant to the studies included in the synthesis. The process of data extraction and linking of the concepts is presented in Table Step 6 involved generating third‐order interpretations from the main findings of the synthesis. These are new concepts directly derived from the interpretation of second‐order concepts Table . To demoFinally, Step 7 reports the synthesis. The Supporting Information Tables and the discussion present the stages of synthesis and findings, which were further depicted through the development of conceptual models to illustrate interactions of the phenomenon examined , focused on a specific medical treatment of COPD care delivery , perspectives of family carers or health professionals or were not reported in the English language.Eight studies were included in this review. Slevin et al.,Studies were conducted in Australia,Key characteristics of the included studies  were added to a data extraction table Tables  and 4. A3.1This meta‐ethnography generated four second‐order interpretations:Balancing social network participation with self‐care accountability and personal responsibility.The value of positive engagements with healthcare professionals where socially supported self‐care is relevant.Developing a personal understanding of illness through social participation, shared and personal experiences.Recognizing social network support to guide and validate personal choices for people with COPD.1.Balancing social network participation with self‐care accountability and personal responsibility.The progression of COPD and mobility limitations caused by increasing breathlessness creates the need to redistribute everyday work, heavy work and family responsibility.‘I know I have to take the responsibility; it's harmful to me if I don't. This is what the nurse said’ .Self‐care, therefore, is initiated and developed through behaviours based on personal and shared experiences rather than formal support.‘They're great. They lift me up on that thing (disability access ramp) because I've got the oxygen’ .DHT was also reported to have the potential to optimize the quality of lay professional consultation experiences. It was considered by most people with COPD to be an empowering experience to facilitate conversations and validate complex symptoms. This was related to having the ability to maintain a clear record of symptoms that could be shared real time with the health professional, and therefore, was not just a verbal report of retrospective symptoms, which participants often found difficult to recall. DHT levelled the balance of power in consultations by providing clear documentary evidence of symptoms, focusing discussions for people with COPD to their experiences of their condition.‘A lot of time is wasted in consultations talking about things I don't care about. If I was monitoring here at home then there would be plenty to talk about because the information's collected would be about my COPD, about my symptoms, so that wouldn't be a waste of time, it'd be actually something to talk about and try and figure out, say if it was bad at the time’(P127).3.Developing a personal understanding of illness through social participation, shared and personal experiencesThese concepts suggest that enactment of self‐care practices and social engagement is perceived as challenging due to the everyday uncertainty of the disease and previous negative illness experiences. Individuals living with COPD who were confident in the knowledge of their illness and interpretation of illness symptoms felt more able to engage with supportive social networks, compared to those who had not yet been able to comprehend their own illness experiences or the impact of their symptoms on their daily lives.The perceived physical and emotional struggle with breathlessness is a defining recurrent issue of living with COPD.1.household tasks,2.personal care,3.social activities,4.family responsibilities,5.medical appointments and6.exercise regimes.To be successful at managing COPD, patients must adapt their life to incorporate daily symptoms of breathlessness and fatigue. They are required to manage breathless, fatigue, cough and wheeze and flareups of these symptoms whilst continuing withPeople with COPD reported not associating many of these symptoms with COPD, and preferred a trial and error or learning by experience approach to adapting to their symptoms and self‐care practices.The shared validation of these symptoms was welcomed and supported through the validation of the fluctuation and worsening of symptoms in discussions with health professionals or DHT.‘Imagine after my diagnosis I'd be given a device to help me see the differences in a good day of breathing against a bad day of breathing? I know that would have eased the worry I had about every little change I was feeling. I'm sure plenty do panic at the slightest sign of being breathless’ (Patient 132).4.Recognizing social network support to guide and validate personal choices in people with COPDDHT, in this sense, can encourage positive help‐seeking and successful feedback from self‐initiated self‐care behaviours among individuals living with COPD. Even here, there are potential personal conflicts in engaging with the self‐care skills taught by healthcare professionals.People with COPD preferred to discuss their disease in the context of their social networks in terms of personal narratives related to their condition through stories and experiences.Social networks consisted of both peer and family members. Familial groups, extended close family networks ,‘People who receive good family support and care from their children get better answers [questionnaire study] are better off than those who live alone; not a hundred percent, but it is less likely. In addition, if [they] do not see or hear well it is not a good relationship with the outside world’ (Participant 16).The reassignment of emotional and family tasks can lead to social displacement or a loss of their usual social role. In turn, a sense of loss is felt for their social position in the family or wider society .‘All important, spiritual life, if we weren't at Church, I believe we won't be a happy family and be blessed with such obedient children… no one drinks alcohol, no one smokes cigarettes… they will have good futures’  or in acute fluctuations of disease  enables a process of self‐appraisal of disease phenomena. This appraisal draws from narratives or experiences of what has personally worked well in the past. Importantly, ‘working well’ is what has enabled independence, not necessarily what is deemed to be clinically correct disease management. Therefore, if a personal experience is of a peer (friend or relation) dying shortly after stopping smoking, then this is experiential knowledge.1.Experiential knowledge—Learning from the personal successes and failures of adaptive self‐care behaviours to inform future self‐care practices.2.Inclusion of valued practices in SSMS.3.Inevitability of dependence—The fear or avoidance of the inevitability of dependency.These third‐order interpretative accounts led to three broader overarching concepts that influence both positive and negative engagement along a continuum.The personal successes achieved from SSMS with adaptive self‐care behaviours can positively inform future self‐care practices and choices. Conversely, negative experiences of social self‐management can reduce self‐efficacy and experiential failures can leave people with a sense of powerlessness, which in turn increases feelings of dependency.independence experience (Index) continuum. Each person with COPD is either a novice to self‐care or has personal experience based on personal knowledge and previous experience of the disease. Individuals living with COPD move both between novice and experience and their own self‐care enactment, whilst simultaneously striving to remain as independent as they can for as long as they can.Figure The continuum can be influenced positively through peer support structures and positive social networks, but can be influenced negatively when isolated and a novice in COPD.3.3Public collaboration was sought to review the language and accessibility of the manuscript and the real‐world applicability of the findings. The NIHR ARC Wessex long‐term conditions (LTCs) patient and public involvement (PPI) group reviewed the second‐ and third‐order interpretations. The group affirmed the need to address SSMS as relevant aspects of person‐centred care. The group highlighted experiential learning in terms of understanding from their own health needs, in particular, valuing positive trusting relationships with healthcare professionals. A group member (A. A.) volunteered to review the manuscript, paying attention to the accessibility, language and jargon of the manuscript. Specific comments from the PPI group are included in Appendix 44.11.day‐to‐day social work‐based negotiations,2.healthcare interactions,3.social negotiations concerned with symptom interpretation and4.acceptance of the disease and its limitations by themselves and others.The aim and objectives of this qualitative meta‐ethnography were to focus on participation in social networks to enhance self‐care from the perspective of a person with COPD and to explore the process of adoption of socially supportive self‐care in people with COPD as targets for future self‐care interventions. The findings from this synthesis highlight the complexities involved in social participation linked to self‐care practices in COPD. People with COPD are required to understand and negotiate complex health circumstances and across a range of social contexts. These include the following:Therefore, engaging with COPD social networks to enhance self‐care requires multiple skills of negotiation, personal organisation and opportunities to engage in positive peer interactions to be successful.COPD has a complex, uncertain trajectory, meaning that the pursuit of clinical and social stability is challenging. The longer people live with COPD, the more opportunity there is to enhance the skills and knowledge of self‐care, but these enhanced skills are often against a backdrop of failing health and striving to maintain independence. This study advances COPD self‐care beyond taught healthcare professional interactions towards a more complex interplay with personal and peer‐acquired knowledge and a perceived fear of dependence.Through developing an understanding of personally valued practices and developing tailored healthcare encounters, one can improve engagement with socially supportive self‐care practices in people with COPD. However, practitioners often prioritize education and information provision over more psychosocial approaches to self‐care.Engaging in self‐management activities is positively correlated with the length of time living with the conditionThe conceptual model illustrates the independence and experience continuum of engagement in socially supportive self‐care behaviours among people living with COPD. Figure 4.2The conceptual model can be translated into a clinical practice tool. It can be used in clinical consultations to direct social prescribing  or social support decisions in partnership with healthcare professionals. The Index model could serve to provide a framework to structure patient–healthcare professional interactionsHealthcare professionals should consider integrating peer support and lay coaching into existing healthcare pathways to encourage purposefully selected peer encounters to enable supportive, positive self‐care practices. Current work to test the conceptual model with healthcare professionals is in progress.4.3This qualitative meta‐ethnography has strength in terms of the scrutiny of the methodology, drawn from several sources,5This study provides insights into how and why people engage with the principles and practices of socially supportive self‐care and the role of participation in social networks in supporting self‐care activities and practices from the perspective of people living with COPD. Through mapping the findings of the social continuum with the intensity of COPD symptoms, the Index model has been developed for use in clinical practice to measure the social capacity and direct social participation in people with COPD.The conceptual model can (i) explain how and why people with COPD understand and engage with social network support to manage their COPD and (ii) provide guidance for healthcare professionals to engage in person‐centred conversations relating to valued activities and personal care preferences.Socially supportive self‐care warrants embracing as a fundamental element an encouraging infrastructure of people to assistant in the lives of the person with COPD. In the long term, this can lead to the development of positive cognitive, emotional and physical self‐care practices to continually improve health outcomes in people with COPD.The authors declare that there are no conflict of interests.Lindsay Welch designed and delivered the qualitative meta‐ethnography, undertook quality appraisal, interpreted the findings, conceptualized the tool and drafted the manuscript. Euan Sadler read the papers, contributed to the analysis and synthesis and reviewed and edited the manuscript for publication. Anne Rogers conceptualized the work, advised on the design process, reviewed and quality‐appraised the selected studies and reviewed and edited the manuscript for publication. Anthony Austin reviewed and edited the manuscript from the perspective of a lay user. Anthony reviewed the language and the findings, and commented on the relevance of the findings to user experiences. All the listed authors have read and approved the manuscript.


II to SnIV. Most of reported strategies employ SnF2 in the perovskite precursor solution to prevent SnIV formation. Nevertheless, the working mechanism of this additive remains debated. To further elucidate it, we investigate the fluoride chemistry in tin halide perovskites by complementary analytical tools. NMR analysis of the precursor solution discloses a strong preferential affinity of fluoride anions for SnIV over SnII, selectively complexing it as SnF4. Hard X‐ray photoelectron spectroscopy on films shows the lower tendency of SnF4 than SnI4 to get included in the perovskite structure, hence preventing the inclusion of SnIV in the film. Finally, small‐angle X‐ray scattering reveals the strong influence of fluoride on the colloidal chemistry of precursor dispersions, directly affecting perovskite crystallization.Tin is the frontrunner for substituting toxic lead in perovskite solar cells. However, tin suffers the detrimental oxidation of Sn IV and affect the colloidal properties in solution. This study describes the working mechanism of SnF2 and highlights the importance of solution chemistry for controlling crystallization and SnII oxidation in tin halide perovskites.Fluoride chemistry in tin halide perovskites improves the crystallization process. Fluoride anions selectively coordinate and remove Sn II oxidizes into SnIV species, leading to the substantial decline in the performance through the undesirable formation of electron traps and p‐doping of the material.II. These include the use of new solvent systems to avoid the oxidation by dimethyl sulfoxide (DMSO),IV, such as metallic Sn powderIV, like the ever‐present SnF2.DMSO was used as a solvent for the NMR experiments, therefore agreeing with the previously reported experiences. For other species, no splitting occurs since chloride, bromide, and iodide do not have NMR‐active nuclei in significant amounts or with detectable line widths.The only appearance so far of this species can be found in Nakamura and co‐workers’ work. The authors use an SnFIV can have several important implications that may reduce SnIV content in the final film. For instance, the strong preference for SnIV means that fluorides could complex it as soon as it is generated, whether from O2 in the environment or DMSO‐driven oxidation.x2− units. The fact that better morphology films are obtained through the pale yellow, SnF2‐containing solution points out that, with solution color as an indication, the properties of the existing formations in solution critically influence the crystallization dynamics of tin halide perovskites. This feature is currently underexplored for these materials and proves to be much more complex and sensitive than for their lead analogues due to its quite restricted processing conditions.Regarding the change in solution color caused by SnF2  and their influence on perovskite properties are also frequently discussed in the literature.[3 precursor solutions according to SnX2 addition, given in Figure S9, to compare their respective functionality to the SnF2 addition. Similar to the scattering curves shown in Figure 2 in other solvents like DMSO and DMF. This confirms the need to investigate the strong dependence of additives and compositions used for tin halide perovskites. Finally, a scattering curve for the presence of SnIV is given in the inset window in Figure S9, for which we measured an aged sample of FASnI3. The effect of temperature‐induced degradation of DMSO solutions on its properties seems relevant, confirming that we did not influence unexpected SnIV content in FASnI3 with or without SnX2.Other SnXterature.17, 472 additives in DMSO solutions, they still caused a color change in a clear trend , although no notable difference was found except for the shielding effect from SnF2, already discussed in Figure 2 is the only SnX2 additive shifting the FASnI3 signal upfield, while SnCl2 and SnBr2 go slightly in the opposite direction. This effect correlates well with the chemical shifts in the different SnX2‐pure solutions in DMSO , except for the SnI2 case. Hence, the final SnII signal position might be an average value of all SnII species. After heating the solutions, we observed that the solution's darkening was negligible for chloride‐ and fluoride‐containing solutions, which could be due to both lower SnII oxidation and more efficient SnIV complexation by these anions, as explained in Figure 119Sn‐NMR  had limited solubility in common solvents. Therefore we had to saturate the FASnI3 solution below a 5 % molar ratio . Even though the concentration was lower than for SnF2, we observed the exact change in color from orange to pale yellow, potentially affecting perovskite subunits in solution in the same fashion. Similarly, these solutions experienced no darkening of solutions aged at 100 °C for 3 h in different conditions, proving the complexation of SnIV in the form of SnF4. Even though these particular additives may not be directly implementable due to the strong influence that Cs+ and Na+ cations can have in the perovskite solar cells processing and performance, these results confirm the universality of the working principle for fluoride‐based compounds. Furthermore, they suggest that SnF2 additive may be eventually replaceable by other fluoride‐based species if applied in the right conditions.To confirm that fluoride was responsible for these changes in solution, we prepared FASnI3 films and with 10 % of the excess of FAI, SnI2, SnBr2, SnCl2 and SnF2. Excess FAI was tried to study both stoichiometry sides of FASnI3. While there was no notable change in the X‐ray diffraction patterns , the scanning electron microscopy (SEM) results offered some differences among samples . The sample with 10 % SnI2 excess was the only one showing a high density of extensive pinholes. In contrast, the film with 10 % SnF2 was the most homogeneous one, free of pinholes and other minor irregularities that are present in the rest of the films, agreeing with previous papers that used SnF2 on its beneficial effect on morphology.[To complete the study, we wanted to investigate how these changes in perovskite solution properties affect the thin film formation and the corresponding solar cells performance. We fabricated pristine FASnIrphology.25, 283 perovskite strongly affects the pinhole density and the average grain size. The largest size was found for equimolar FASnI3 (695 nm), which went down when increasing or decreasing the SnI2 ratio . Moreover, SnF2 addition shows an impact on the size distribution itself, which is significantly narrowed to plain FASnI3 thin film. These observations agree with the results by SAXS, assuming that a uniformly nearly oriented attachment in solution leads to a more homogeneous distribution of the grain sizes in the film.There is an evident change in the resulting grain size with the changing halide element . Fluoride led to the smallest average grain size (568 nm) compared to chloride (629 nm) and bromide (623 nm). These results are orthogonal to those reported previously, where fluoride3 solutions seems beneficial for the device performance, showing some positive trend when moving to lower size halides . Though it appears that smaller halides—harder Lewis bases—work better by having a more decisive influence in the processing, chloride was the exception. Even though NMR and SAXS found SnCl2 to have very similar behavior to SnF2, the resulting devices yielded no efficiency, suggesting that chloride brings other factors into play. Previous works point out how chloride can be incorporated in the lattice and its tendency to form massive aggregates,[2 and requiring a more careful optimization. We suspect SnCl2 could mimic SnF2 to some extent in these solutions if the processing conditions are adjusted accordingly. It is also worth noting the slight improvement in efficiency produced just by using a 1,1:1 SnI2:FAI stoichiometry (i.e. 10 % SnI2 excess), despite the content of irregularly sized pores . This matches well with the results in previous works,[We then used these films for solar cells fabrication (more details in the Supporting Information) to investigate any possible trend between additives and performance. In this sense, adding a small portion of tin halides to FASnIgregates,16, 172 is a widely used additive for tin and lead/tin halide perovskites, systematically showing the same beneficial effects in all reported studies: perovskite films with lower SnIV content and improved morphology. We uncovered the different roles of fluoride in SnF2 on SnIV complexation and colloidal arrangement in the precursor solution. By studying the fluoride chemistry in perovskite solutions and films with different complementary techniques, we demonstrated that the fluoride in SnF2 has a critical role in reducing SnIV content in the precursor solution and the final perovskite film. We showed by NMR the selective complexation of SnIV in the form of SnF4, which HAXPES revealed to have a lower tendency to get introduced in the film than SnI4. Moreover, we showed how the introduction of SnF2 in perovskite solutions increases their stability against the oxidation caused by DMSO. This antioxidative character was also found for SnCl2, meaning that many other reported additives for tin halide perovskites may also block SnII oxidation by simple tuning of solution properties. Apart from reducing SnIV content in the thin film, SAXS measurements on the related precursor solutions evidenced that fluoride alters the essential formation of pre‐organized perovskite clusters. We identified an advanced colloidal arrangement in DMF compared to the DMSO solutions that are notably influenced by the addition of SnF2. We assigned this arrangement to an advanced nucleation process in DMF compared to DMSO. Finally, based on our findings, we proposed a nucleation mechanism that occurs in solution and is affected by the SnF2 addition resulting in improved overall crystal quality. In this sense, the effect of SnF2 on the film processing will be strongly determined by the environment in which it is applied . Consequently, there is an immediate need to fundamentally understand and optimize solution properties, their processing and studying the effect of additives. As we are doing in this study with the example of SnF2 as pioneering work and impulse for further research. Overall, we presented a complete comprehensive picture of the working mechanism of SnF2 in tin halide perovskites processing and provided the community with the guidelines for finding new additives with specific chemical properties to selectively complex SnIV species and regulate the crystallization.SnFThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Oncorhynchus spp., “PST”), and lampreys (Petromyzontiformes). Life history describes the phenotypic responses of organisms to environments and includes biological parameters that affect population growth or decline. Life‐history pathway(s) are the result of different organismal routes of development that can result in different life histories. These terms can be used to describe recognizable life‐history traits. Life history is generally used in organismal‐ and ecology‐based journals. The terms paired species/species pairs have been used to describe two different phenotypes, whereas in some species and situations a continuum of phenotypes may be expressed. Our review revealed overlapping definitions for race and subspecies, and subspecies and ecotypes. Ecotypes are genotypic adaptations to particular environments, and this term is often used in genetic‐ and evolution‐based journals. “Satellite species” is used for situations in which a parasitic lamprey yields two or more derived, nonparasitic lamprey species. Designatable Units, Evolutionary Significant Units (ESUs), and Distinct Population Segments (DPS) are used by some governments to classify ISD of vertebrate species within distinct and evolutionary significant criteria. In situations where the genetic or life‐history components of ISD are not well understood, a conservative approach would be to call them phenotypes.Understanding and preserving intraspecific diversity (ISD) is important for species conservation. However, ISD units do not have taxonomic standards and are not universally recognized. The terminology used to describe ISD is varied and often used ambiguously. We compared definitions of terms used to describe ISD with use in recent studies of three fish taxa: sticklebacks (Gasterosteidae), Pacific salmon and trout ( The terminology used to describe intraspecific diversity is varied and often used ambiguously. “Ecotype” was originally used to describe patterns in genes and ecology, and recent studies employing this term tend to report a genetic basis in ISD. By contrast, “life history” describes biological parameters that affect demography, and this term tends to be used in organismal‐ and ecology‐based journals. For objectives 2 and 3, we focused on three fish taxa, including sticklebacks (Gasterosteidae), Pacific salmon and trout  AND TI=(stickleback)). The same was done for “Oncorhynchus” and “lamprey.” These searches were executed between November 2020 and February 2021. Each article was reviewed to determine the focal species and phenotypes assessed; whether a genetic basis was identified for the diversity in phenotypes; and whether an article used both terms (ecotypes and life history) synonymously or both, but independently or used only one of the terms. Finally, the frequency of term use was calculated and compared among papers.We conducted three independent searches for the use of the terms, “ecotypes” and “life history” for sticklebacks, PST, and lampreys using the advanced search option in the Web of Science search engine for articles in English, over 30 years (for years 1990–2019). Each search included the words “stickleback” or “2.4We compared definitions (Objective 1) with the meta‐review (Objective 2) and trends in use of the terms ecotypes and life history (Objective 3). We addressed the questions: Are there clear patterns in how terms are used in particular contexts? Do redundancies or ambiguities exist in the use of some terms that suggest that some terms ought not to be used?33.1In our search for definitions of ecotype and life history, it became apparent that several terms are used more‐or‐less synonymously  are the result of different developmental routes by an organism that are contingent upon the physiological status and genetic thresholds of that organism. The different developmental routes can result in different life histories. The terms paired species/species pairs have been used to describe two different phenotypes such as benthic versus limnetic sticklebacks or freshwater resident kokanee versus anadromous sockeye salmon . Species pairs implies two phenotypes, whereas in some species and situations a continuum of phenotypes may be expressed. Our review revealed the ambiguity of the term, race, and the overlap in definitions of this term with subspecies. Classification of subspecies is controversial among taxonomists , and between phenotypes within these habitats  in question. This is because speciation within sticklebacks occurs along a continuum, from “continuous variation within panmictic populations” on one end to “complete and irreversible reproductive isolation” on the other, with factors affecting the divergence of populations along this continuum  and anadromy versus freshwater residency  and migratory behavior  and European brook lamprey , and nonparasitic northern brook lamprey  and the closely related western brook lamprey (L. richardsoni) and other Lampetra species along the west coast of North America , and maturation levels  revealed phenotypic differences in maturation timing, which were named “stream maturing” and “ocean maturing” Pacific lamprey , followed by lampreys (71.4% of the 14 papers), and then PST (61.7% of the 60 papers). The reviewers of this paper identified some key papers that were missed with our search  combinations in particular environments and hence Although we did not assess the use of derivative terms such as morphotypes, ecomorphotypes, ecophenotypes, these terms arguably do not offer insight into ISD, and we therefore recommend that authors either should not use these terms or use them sparingly. All attempts to describe ISD would benefit from clear definitions. Ecotypes must show genotypic adaptations—but not enough to warrant calling the genotypes distinct species. Life histories/life‐history pathways, by contrast, describe phenotypic responses in demographic parameters ; Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Resources (lead); Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Carl Schreck: Conceptualization (supporting); Resources ; Supervision (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting).


The first draft human mitochondrial DNA (mtDNA) sequence was published in 1981, paving the way for two decades of discovery linking mtDNA variation with human disease. Severe pathogenic mutations cause sporadic and inherited rare disorders that often involve the nervous system. However, some mutations cause mild organ‐specific phenotypes that have a reduced clinical penetrance, and polymorphic variation of mtDNA is associated with an altered risk of developing several late‐onset common human diseases including Parkinson’s disease. mtDNA mutations also accumulate during human life and are enriched in affected organs in a number of age‐related diseases. Thus, mtDNA contributes to a wide range of human pathologies. For many decades, it has generally been accepted that mtDNA is inherited exclusively down the maternal line in humans. Although recent evidence has challenged this dogma, whole‐genome sequencing has identified nuclear‐encoded mitochondrial sequences (NUMTs) that can give the false impression of paternally inherited mtDNA. This provides a more likely explanation for recent reports of ‘bi‐parental inheritance’, where the paternal alleles are actually transmitted through the nuclear genome. The presence of both mutated and wild‐type variant alleles within the same individual (heteroplasmy) and rapid shifts in allele frequency can lead to offspring with variable severity of disease. In addition, there is emerging evidence that selection can act for and against specific mtDNA variants within the developing germ line, and possibly within developing tissues. Thus, understanding how mtDNA is inherited has far‐reaching implications across medicine. There is emerging evidence that this highly dynamic system is amenable to therapeutic manipulation, raising the possibility that we can harness new understanding to prevent and treat rare and common human diseases where mtDNA mutations play a key role. Content List ‐ Read more articles from the symposium: “Mitochondria in human disease”. Mitochondria are essential intra‐cellular organelles that are the primary source of energy in the form of adenosine triphosphate (ATP). Over 1000 different proteins are required to synthesize mitochondria and originate from two distinct genomes: nuclear DNA and mitochondrial DNA (mtDNA) [Leu(UUR) gene mutation was described in Japanese families with mitochondrial encephalomyopathy with lactic acidosis and stroke‐like episodes (MELAS) [Lys mutation was described in families with myoclonic epilepsy with ragged‐red fibres (MERRF) [mtDNA mutations were first associated with disease in 1988 [MTND4, m.14484T>C in MTND6, and m.3460A>G in MTND1) [In parallel, maternally transmitted mtDNA mutations were also described in Leber hereditary optic neuropathy (LHON), with the three most common affecting respiratory chain complex I  [et al. observed three multi‐generational pedigrees where a paternal haplotype was transmitted down several generations [Cytoplasmic genomes, which include mtDNA, are all almost always exclusively inherited from one parent. In some species, it is the male, but in most species, it is the female. There are unusual examples in nature where mtDNA can be inherited bi‐parentally, and intriguingly, this can change in response to environmental constraint. Paternal transmission has been documented in sheep et al. One could speculate that in some families, there is an inherited predisposition to paternal mtDNA transmission, perhaps through the genetic disruption of the normal mechanisms that have evolved to prevent bi‐parental transmission . However, on theoretical grounds, some have argued it is a highly unlikely explanation [We recently looked for similar evidence of bi‐parental transmission of mtDNA in 11 035 trios . We idenBased on current evidence, it seems highly unlikely that paternal transmission occurs, and if it does, it must be exceptionally rare [Initial observations in Holstein cows showed that a new mtDNA variant could be heteroplasmic in one cow, but rapidly become fixed down the maternal line within a few generations [Jack Jenuth and Eric Shoubridge’s observations in the mid‐1990s showed that the variance in heteroplasmy levels seen amongst siblings was determined during the development of the mother’s oocytes [in vitro. These findings confirm that a similar genetic bottleneck comes into play in the developing human germ line [A similar reduction in mtDNA content has been seen in several other vertebrate species including the sheep, zebrafish and salmon [MT‐ATP6) usually present in sporadic cases, having segregated from very low to high levels over one generation [MT‐TK) show much more stable heteroplasmy levels with multiple generations being affected over time [Empirical observations in human pedigrees transmitting heteroplasmic mtDNA mutations imply that there are genetic bottlenecks of different sizes, with some mutations showing faster segregation than others. This would explain why some mutations , and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), the Evelyn Trust and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.No conflicts of interest were declared.