Replantation and retention of ankylosed tooth after pubertal growth spurt enables stepwise replacement of both central incisors with implants. Partial extraction contributes to natural gingival contour. Replantation and retention of ankylosed tooth after pubertal growth spurt enable stepwise replacement of both central incisors with implants. Partial extraction contributes to natural gingival contour. Through a decade lasting carefully planned conservative, surgical and restorative stepwise approach, temporary retention and partial extraction of ankylosed tooth enabled preservation of tissue architecture and successful rehabilitation with two implants.Avulsion of permanent teeth is one of the most serious dental injuries and one of a few real emergencies in dentistry usually happening in childhood or adolescence.Negative aesthetic sequels of severe dental trauma in children and young adults that result in the loss of affected teeth or their ankylosis include not only vertical tissue defect due to the arrest in alveolar growth, alveolar ridge resorption, collapse of the gingival architecture, tilting, and infraocclusion of adjacent teeth but also psychological distress. When multiple teeth in the aesthetic region are lost or ankylosed, disturbances in alveolar process growth are even more pronounced.The main purpose of treatment in the reported case was to overcome such challenges with combined efforts and skills of an interdisciplinary team, representing pediatric dentist, periodontist and restorative dentist, working on a pre‐determined stepwise treatment plan in order to re‐create the aesthetic appearance and function of dentition after avulsion that resulted in loss of two central maxillary incisors in a young adult. The presence of an ankylotic tooth which was not causing serious complications was exploited to preserve tissue architecture. With a temporarily preserved ankylotic incisor, the first implant was inserted with a simultaneous guided bone regeneration procedure. In the second stage when the second incisor was removed, the modified socket‐shield technique was used to preserve the marginal tissue architecture at the second implant site. The objective of our treatment was to provide the patient with acceptable provisional prosthesis until the end of skeletal growth and to exploit all the possibilities of one ankylosed incisor to achieve aesthetically acceptable outcome after bone and soft tissue regeneration and rehabilitation with two implant‐supported crowns.22.1A 13‐year‐old girl with adolescence‐associated delicate psychological behavior and a gummy smile suffered dental trauma with avulsion of both upper central incisors after collision with another person while water sliding. After 30 min search in the pool water, only tooth #11 was found and held intraorally in the patient's saliva. First aid was provided to the patient 3 h later by a maxillofacial surgeon and included delayed replantation with rigid splinting of tooth #11. Tooth #21 was never found. Initial examination at Dental Clinic, UMC Ljubljana, 7 days after trauma demonstrated normal pulp sensitivity test results for adjacent teeth #13, #12, #22, #23 with no subjective nor clinical signs of inflammation. A comprehensive evaluation indicated that the collision resulted in avulsion of teeth #11, #21, subluxation of #12, and uncomplicated enamel‐dentin crown fracture of #11 and #12 Figure .6 Toot2.2Several surgical and prosthetic factors were assessed when replacement of maxillary central incisors with implants turned into a final strategic treatment plan. Soft tissue contour with inter‐implant papillary tissue configuration became a parameter that was difficult to obtain and required precise planning of conservative, surgical and restorative steps. All critical points and treatment alternatives were explained in detail to the patient and her parents. Consent for treatment was obtained from parents when the patient was under‐aged and from the patient before each surgical procedure.2.2.1It was inevitable to postpone implant placement by the end of skeletal growth. Meanwhile, tooth # 11 was treated endodontically with calcium hydroxide paste. Initially, tooth #21 was replaced with provisional acrylic tooth included into a flexible wire splint Figure . The spl2.2.2Seven years after trauma (at the age of 20), implant (Straumann BL NC Roxolid SLA active 12 mm)Figure  was plac2.3The primary goals of soft tissue contours re‐creation and preservation of central papillae after a decade‐long treatment was achieved and a pleasant smile was created for the patient. Clinical and radiographic follow‐ups are regularly performed in 6‐month intervals. Mesial and distal papillae complete interdental as well as inter‐implant spaces, there is also no soft tissue level nor texture discrepancy. Due to different reflection of light, the peri‐implant mucosa appears slightly gray. A critical assessment of peri‐implant soft tissue based on the pink aesthetic score (PES)3Treatment alternatives for replacement of one or more maxillary incisors that are lost or deemed to be lost due to avulsion or late replantation depend on the patient's age at the time of trauma as well as his/her social and aesthetic expectations, anticipated complications  and clinical and technical skills of the appointed dentists. Multiple solutions are available which include autotransplanted permanent teeth, orthodontic space closure, fixed or removable partial dentures, and implant‐borne prosthesis.Replacement of missing maxillary incisors with a suitable transplant (mainly premolar) is mostly considered optimal when the root development of the transplant has reached two‐thirds to three‐quarters of the expected final root length. Perfect candidates suited for autotransplantation of premolars to the maxillary anterior region are usually about 12 years of age, which corresponds with the period when most serious traumatic dental injuries occur in children.When implant‐supported crowns are selected, clinicians must bear in mind the utmost importance of postponing the implant placement by the end of skeletal growth. During the interim period which may last several years, the patient needs to wear either a fixed partial denture or removable appliance prone to fracture. The second challenge arises from the continuous reduction in both osseous and soft tissue support.Although single tooth replacement in the aesthetic zone is a frequent indication for implant therapy with long‐term success and high patient satisfaction,Thus, in our case, a naturally scalloped gingival appearance including interdental papillae putatively resulted from stepwise replacement of both missing central incisors with utilization of surgical guides to facilitate adequate 3D position and temporary preservation of hopeless ankylosed tooth. The patient was very sensitive in the early period of the treatment refusing any procedures involving extraction or decoronation of ankylotic tooth.Besides correct 3D implant positioning together with adequate quantity and quality of peri‐implant tissues after surgical procedures, a satisfying emergence profile and gingival architecture was finally achieved by repetitive composite reshaping of the temporary restorations. Such prosthetic management was crucial in maximizing the aesthetic result. Yet, application of pressure to the marginal gingiva must be performed with extreme caution to evade pain, anemia, or tissue necrosis.4To the best of our knowledge, this is the first case report on stepwise replacement of traumatized central maxillary incisors for two single implant‐supported crowns with temporary preservation of ankylosed tooth. It may represent a rare clinical indication in cases where one incisor is lost and the other ankylotic incisor does not cause severe infraposition or tilting of adjacent teeth in a patient who has passed pubertal growth spurt. Such solution requires interdisciplinary approach, with special efforts put into proper diagnosis and careful execution of all required clinical procedures to yield functional and aesthetically acceptable result.RG and RK planned the stepwise treatment approach, collected the clinical data and images. RK participated in initial examination and diagnosis; was in charge of conservative phase. RG took the lead in surgical part of the treatment; has been performing follow‐up care. ACK took the lead in material preparation and writing the first and final draft. KVG was involved in prosthodontic treatment of the patient. All authors reviewed and approved the final manuscript.Submitted and published with written consent of the patient.


Despite medical therapy for heart failure (HF) having proven benefits of improving quality of life and survival, many patients remain under‐treated. This may be due to a combination of under‐prescription by medical professionals and poor adherence from patients. In HF, as with many other chronic diseases, adherence to medication can deteriorate over time particularly when symptoms are well controlled. Therefore, detecting and addressing non‐adherence has a crucial role in the management of HF. Significant flaws and inaccuracies exist in the methods currently used to assess adherence such as patient reporting, pill counts, and pharmacy fill records. We aim to use high‐performance liquid chromatography–tandem mass spectrometry (HPLC‐MS) to detect metabolites of HF medications in the urine samples of chronic HF patients.Urine samples were collected from 35 patients in a specialist HF clinic. Patients were included if they had an ejection fraction <45% and were taking at least two disease‐modifying HF medications. They were excluded if they had been admitted to hospital for HF in the 3 months preceding clinic attendance. These samples were sent for HPLC‐MS and tested for all HF medications prescribed for that patient. A high rate of complete adherence of 89% was detected in these patients, with 94% being partially adherent (at least one HF medication detected) to therapy (at least one HF medication detected). This analysis also highlighted that mineralocorticoid antagonists represent both the most under‐prescribed (67%) and poorly adhered (75%) medication class.This analysis revealed a surprisingly high level of adherence to disease‐modifying therapy in chronic HF patients and highlights that most of our ‘total’ under‐treatment is likely to be from a failure to prescribe rather than a failure to adhere. Testing for metabolites of disease‐modifying HF drugs in urine using HPLC‐MS is feasible and is a useful adjunct to a specialist HF service. At present, the distinction between treatment failure and failure to take treatment is not always clear, which is important because the investigation and potential solutions are different. The former needs initiation of additional therapies and consideration of additional diagnoses, whereas the latter requires strategies to understand reasons underlying poor adherence and collaborative working to improve this: the wrong strategy will be ineffective. Medical therapy for heart failure (HF) is known to improve symptoms and prolong survival. However, as with other chronic diseases,Methods used to assess adherence at present, such as self‐reporting, pill counts, pharmacy refill records, and electronic medication monitoring, have potential pitfalls including inaccuracies in self‐reporting and collected but unused medication. This limits the ability to assess the true prevalence of non‐adherence to medical therapy; studies indicate that adherence ranges from 33%Given the importance of establishing and measuring adherence in chronic HF patients, we aimed to assess adherence in a real‐world group of chronic HF patients who did not have any recent admissions to hospital with HF.Patients with a diagnosis of HF attending a follow‐up appointment in a single specialist HF clinic were recruited over a period of 3 months. Patients were included in the study if they had an ejection fraction <45% and were prescribed with at least two disease‐modifying HF medications. Those with an HF admission in the preceding 3 months were excluded. All patients were fully informed on the day of clinic that drug metabolites were being measured and no patients declined to provide a sample when invited to take part.High‐performance liquid chromatography–tandem mass spectrometry (HPLC‐MS) was used to detect urinary drug metabolites for all disease‐modifying HF drugs prescribed to each patient. Metabolites for all drugs tested remain present in the urine for 24 h following ingestion,Continuous data are presented as mean and standard deviation where data are normally distributed and median and inter‐quartile range when the distribution is non‐normal. Categorical data are presented as number and percentage of the total population.TableThirty‐five patients who met the inclusion criteria were included in the analysis. The characteristics of those included in the study are summarized in All patients were prescribed with one of angiotensin‐converting enzyme inhibitor (63%), angiotensin receptor blocker (26%), or angiotensin receptor blocker–neprilysin inhibitor (11%). Beta‐blockers were prescribed to 34 patients (97%), and 15 patients (45%) had been prescribed with mineralocorticoid receptor antagonists (MRAs). The rates of prescription of the three main classes of HF medication in this study mirrored those found in the National Institute for Cardiovascular Outcomes Research HF audit 2019.FigureThirty‐one (89%) patients had urinary metabolite results indicating that they were taking all of their prescribed disease‐modifying HF medications. Thirty‐three patients (94%) were partially adherent to at least one HF medication with only two patients (6%) being completely non‐adherent to any HF medication. Figure1. Non‐These findings indicate that in the setting of a specialist multidisciplinary HF clinic, which includes an education programme delivered via specialist nurses, adherence to HF therapy can be maintained in chronic HF patients at levels equivalent to recently discharged patients.treatment failure compared with failure to treat or failure to take treatment allows bespoke solutions to be devised. Treatment failure may need initiation of additional therapies or consideration of additional diagnoses, whereas the failure to take treatment requires strategies to understand reasons underlying poor adherence and collaborative working to improve this: the wrong strategy will be ineffective.Accurately differentiating between The biggest limitation of this study is the small sample size of 35 patients; however, it provides an effective proof of concept. Additionally, in the real‐world setting of a specialist HF outpatient clinic, patients are already invested in their care and adherence is therefore likely to be higher than in other settings.In conclusion, in this group of patients who are managed in a contemporary multidisciplinary HF service, there were high levels of adherence to therapy. If true on a larger scale, the implications for prescribers are significant: most of our ‘total’ under‐treatment is likely to be from a failure to prescribe rather than a failure to adhere. Urinary drug metabolite testing may be a useful adjunct to an HF service and provides an effective strategy to assess adherence in patients who are not responding to treatment. It may also have a role in clinical trials, to evaluate the effect of interventions to improve adherence and to audit the effectiveness of HF services.None declared.This study was supported by a grant from the Nissan Cardiology Charitable Fund and the NIHR Imperial Biomedical Research Centre (BRC). M.S is supported by the Wellcome Trust (220573/Z/20/Z).


Whether females should prefer to mate with old males is controversial. Old males may sire offspring of low quality because of an aging germline, but their proven ability to reach an old age can also be an excellent indicator of superior genetic quality, especially in natural populations. These genetic effects are, however, hard to study in nature, because they are often confounded with direct benefits offered by old males to the female, such as experience and high territory quality. We, therefore, used naturally occurring extra‐pair young to disentangle different aspects of male age on female fitness in a natural population of collared flycatchers because any difference between within‐ and extra‐pair young within a nest should be caused by paternal genetic effects only. Based on 18 years of long‐term data, we found that females paired with older males as social partners experienced an overall reproductive advantage. However, offspring sired by old males were of lower quality as compared to their extra‐pair half‐siblings, whereas the opposite was found in nests attended by young males. These results imply a negative genetic effect of old paternal age, given that extra‐pair males are competitive middle‐age males. Thus, offspring may benefit from being sired by young males but raised by old males, to maximize both genetic and direct effects. Our results show that direct and genetic benefits from pairing with old males may act in opposing directions and that the quality of the germline may deteriorate before other signs of senescence become obvious. Male age may influence the number and quality of the offspring he produces. This is due to senescence, the gradual deterioration of organism function with age, which can affect the germline cells quality  and the ability to provide resources . However, proven ability to survive until old age may mean that older fathers instead will produce high‐genetic‐quality offspring, able to survive, and get old themselves. Additionally, old males may also be able to provide their offspring with better or more resources due to gained experience. These reproductive pros and cons of old male partners impose a dilemma for mate choosing females. Should they avoid or prefer old males to ensure direct and indirect benefits? This question has not yet been fully answered because direct and indirect effects of paternal age are hard to separate in the wild. We circumvented this problem by comparing nestlings sired by different males of different ages within the same brood. Therefore, we were able to distinguish the indirect (genetic) from direct  effects of biological fathers, as the environment and maternal genes were constant. We used 18 years of long‐term data on a collared flycatcher population of which 812 individuals were genotyped to assign paternity. We found that direct benefits increase with male age, but at the same time the germline quality decreases. Moreover, this deterioration happens before other signs of senescence become evident. These findings have profound consequences for the general understanding of the evolution of mate choice, which is rarely considered in the context of senescence. Our study is one of very few exploring the effects of male senescence in the wild and it is the first one to disentangle genetic from direct benefits in the context of mate choice.Ageing is the physiological deterioration of organism functionwith age and is widespreadin nature  as a consequence of senescence of the soma, but also genetically in terms of mutation accumulation in the male germline. Physiological senescence with increasing age is widespread across organisms , and/or sperm competition, perhaps even at the expense of their germline maintenance  to assess the effect of paternal age on offspring fledge number and offspring recruitment and (2) by using naturally occurring extra‐pair offspring to disentangle direct and genetic effects of paternal age on offspring quality while keeping maternal genetic effects constant. Collared flycatchers are small passerine birds that preferably breed in nest boxes which allows long term monitoring. The flycatcher population of Öland has been monitored over 18 years and pedigrees as well as age records are available since 2002  is a small migratory passerine of the family Muscicapidae; they overwinter in sub‐Saharan Africa and breed in Europe  of the offspring with their known mother and their social father using the software 2 due to expected age‐dependent solutions to life‐history trade‐offs  between 2002 and 2018 were visually explored with a population pyramid plot and age percentages of different age classes were calculated.n = 1094) were monitored between 2002 and 2018, and the total number of broods is 1527 of which 117 had 0 fledglings. All experimental (manipulated) broods in the study area were excluded for these analyses. We constructed separate linear mixed‐effect models with fledgling number or number of recruits as response variables. We fitted the age of the social father as explanatory variable and also age2, to investigate any nonlinear effect of age. For that purpose, age was mean centered before calculating age2. Male ID and year were included in the models as random effects on the intercept. Because both number of fledgling and number of recruits are count data that are generally underdispersed, we used both Gaussian distribution of the package lme4  and number of recruits , using both cross‐sectional data and longitudinal data of 1‐year‐old individuals to test for selection between age class 1 and 2. These males  was fit as response variable and age, ageTo determine whether mass at time of fledge is a good predictor for recruitment in our population, we constructed a generalized linear mixed‐effect model with binomial distribution having successful or unsuccessful recruitment as a response variable. Mass at day 12 (briefly before fledgling) and lay date were fitted as explanatory variables, whereas nest ID and year were fitted as random effects.2. Year and male ID were included as random effects on the intercept, male ID was then removed as it did not explain any variation in the model and caused singularity problems to the Gaussian model  and the number of nestlings in the nest, because there is evidence that this is an important factor influencing nestling weight  and 2 (25.5%) years old Fig. . Every at = 4.51, P < 0.001; Table t = 3.53, P < 0.001; Table 2, the linear effect of age is only borderline significant (depending upon model), and importantly the parameter estimate of age is much closer to zero in the longitudinal dataset  and indirect (genetic) benefits of pairing with old males. Thus, female collared flycatchers could maximize their fitness by having their offspring sired by young males but raised by old males, where the direct benefits have an overall stronger effect on female fitness. Below we discuss these findings in more detail together with possible constraints on optimal female mating strategies.The decision to mate with a male of a particular age reflects a balance of both direct and indirect benefits and costs associated with male age. Direct  benefits are associated with resource provisioning and nest defense, and older males can often provide more resources due to increased experience and access to higher quality territories  increased with male age, which agree with a number of studies finding improved reproductive performance with age in birds  2008 until (I) 2016.Table S1. A priori structure of the main models.Table S2. Age structure total known age individuals monitored between 2002 and 2018.Table S3. Linear mixed‐effect model (Gaussian) with fledgling number as response variable .Table S4. Generalized mixed‐effect model (genpois) with fledgling number as response variable .Table S5. Linear mixed‐effect model (Gaussian) with number of recruits as response variable .Table S6. Generalized mixed‐effect model (genpois) with number of recruits as response variable .Table S7. Linear mixed‐effect model with fledgling number as response variable .Table S8. Generalized mixed‐effect model (genpois) with fledgling number as response variable .Table S9. Linear mixed‐effect model (Gaussian) with number of recruits as response variable of longitudinal dataset (not including birds that died in year 1).Table S10. Generalized mixed‐effect model (genpois) with number of recruits as response variable of longitudinal dataset (not including birds that died in year 1).Table S11. Generalized mixed‐effect model  with recruitment as a response variable.Table S 12. Generalized mixed‐effect model with binomial distribution for the response variable Recruitment  with male's age and male's survival as explanatory variables.Table S 13. Generalized mixed‐effect model with binomial distribution for the response variable Recruitment  with male's age and male's survival as explanatory variables.Click here for additional data file.


This paper surveys the data‐driven dose prediction methods investigated for knowledge‐based planning (KBP) in the last decade. These methods were classified into two major categories—traditional KBP methods and deep‐learning (DL) methods—according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best‐matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data‐driven KBP methods to dose prediction. The traditional KBP methods include atlas‐based, statistical modeling, and machine learning (ML) methods. In general, traditional KBP methods utilize geometric features  and OAR overlap volume histogram (OVH)) either to find the best‐matched prior case(s) from a repository or to build dose prediction models . In this review, ML‐based methods are included in the traditional KBP category as ML follows a similar framework to traditional KBP methods in terms of inputs  and outputs (DVH metrics).To improve the treatment planning efficiency, data‐driven treatment planning approaches have been investigated that use knowledge from prior cases to predict the outcome of a new case. For example, this concept was utilized by researchers over a decade ago in the form of knowledge‐based planning (KBP) to predict dose in radiotherapy treatment planning.Prior to the investigations of DL for the dose prediction task, DL had been extensively studied for various imaging tasks including image registration, segmentation, etc., in which DL methods have gained considerable momentum, outperforming many of the existing state‐of‐art techniques.KBP research is expanding exponentially, and while there are excellent review papers in publication, this current review is needed to address recent advancements in dose prediction methods. Previous review papers are limited to only traditional KBP methods,22.1®, treatment planning automation, artificial neural network, convolutional neural network, and generative adversarial network. Only peer‐reviewed KBP research articles were included in this review. Each research article from the literature search was manually sorted based on the information presented in the abstract, which was followed by a further in‐depth review of the article. This review focuses on external beam therapies , while manuscripts on brachytherapy and patient‐specific quality assurance were excluded. Articles on dose prediction tasks were selected for this review. Dose prediction is a broad term that includes the prediction of the entire DVH curve, specific dose metrics , voxel dose, spatial dose distribution (either 2D or 3D), objective weights/constraints based on previous knowledge and the transfer of these metrics to a new case for planning.We queried papers from Elsevier Scopus®, Web of Science, PubMed, Google Scholar, and medical physics category of arXiv.org using logical statements that included the following keywords: knowledge‐based treatment planning, machine learning, deep learning, dose prediction, RapidPlan3Figure In the past few years, the number of publications on DL‐based image processing has increased exponentially. More recently, researchers have applied DL methods to dose prediction tasks. In the last 4 years, the number of publications on DL‐based dose predictions has increased from 1 in 2016 to 17 as of August 2020 as shown in Figure 3.1TM) estimates DVHs and generates optimization objectives for a new plan as shown in Table This section includes a review of over 92 articles only on traditional KBP‐based dose prediction methods. We separate these articles based on three dose prediction metrics: I) prediction of the entire DVH Table , II) preAtlas‐based and model‐based methods are the two most common traditional KBP techniques for dose prediction Tables , 2, 3. BFigure In Section 3.1.1A, represented by P number of features in a P‐dimensional vectors. If P is very large, then some characteristics are likely more valuable than others for the purpose of classification. The goal of PCA is to reduce the dimensionality of the original correlated dataset into a smaller set of uncorrelated variables.The high dimensional input feature space can be redundant, irrelevant, and correlated, which may result in inefficient training, model overfitting, reduced accuracy, and reduced generalizability of the model. Therefore, it is important to utilize the most discriminative features, rather than including all possible features. To address this during the implementation of a KBP‐based dose prediction model, dimensionality reduction methods are often used in KBP studies to decrease the number of variables through feature extraction and feature selection process. Feature extraction begins with an initial set of variables followed by redefinition that makes them more informative. Principal component analysis (PCA) is a widely used technique for reduced‐order modeling. PCA determines the features that vary the most among the data for representation in a smaller number of dimensions.3.1.2A common theme in the majority of traditional KBP methods is that the geometric relationship of the target with respect to nearby critical structures correlates with achievable plan quality metrics. Various geometric features have been correlated with dose. Commonly reported geometric features include OVH, distance to target histograms (DTH), and OAR distance‐to‐PTV. As an example, the influence of parotid size and proximity to the PTV was first studied by Hunt et al. and found to be predictive of the achievable parotid sparing.The OVH is a feature common to both atlas‐based and model‐based approaches for dose prediction, as summarized in Tables Several studies have combined historical data with the OVH methods to predict an entire DVH Table  or one oThe OVH has been investigated under a wide variety of clinical disease sites with highly variable PTV and OARs geometries. The OVH‐based model In earlier OVH‐based IMRT studies, large dose variations were reported at a given OVH distance for a specific fractional volume of an OAR.Projection‐based algorithms typically use the perspective of the beam's eye view (BEV) and statistical analysis to assess the similarity between a new case and prior cases. A best‐matched prior case may be identified based on the sum of mutual information values calculated from each BEV perspective across all of the beams in a plan. This method has been used for prostateTM in Eclipse® treatment planning software.The distance‐to‐target histogram (DTH) plots the fractional volume of an OAR within a certain distance of the PTV surface. In addition to volumes of the PTV and OARs, the DTH metric is also used as an input feature in ML approaches to KBP. ML techniques include multivariable nonlinear regression (MVNLR) and support vector regression (SVR).3.1.3TM‐based KBP; however, adding more than 20 outliers showed a moderate degradation of plan quality.Outlier detection is an important consideration when building a data‐driven dose prediction model that is generalizable to a variety of new cases. Outliers degrade the fit between geometry and dosimetry, which, in turn, can compromise model performance.d < errorc < errora < errorb.TM provides different statistical evaluation metrics for identifying the outliers as shown in Table Outlier detection has been studied for different disease sites and commercial software has metrics for identifying outliers. For pelvic RT, Sheng et al. assessed the effectiveness of outlier identification by studying the impact of both geometric and dosimetric outliers. This study suggested a greater impact of dosimetric outliers with a negative impact on both bladder and rectum model compared to geometric outliers with negative impact only on bladder model.TM in Eclipse®) and in‐house built KBP models. Wu et al. 2013 used the DVH objectives derived from previous IMRT plans as an optimization parameter for VMAT treatment planning in head/neck cancer, resulting in a similar dosimetric quality compared to IMRT plans.TM plans. The result was similar/increased bladder and rectum doses compared to the expert plan. Delaney et al. demonstrated that using a model‐based only on photon beam characteristics could make the DVH predictions for proton therapy. This predictor could be used as a patient selection tool for proton therapy.In KBP, the knowledge from prior cases can be used in different combinations . Here, we present a review of studies that investigated the applicability of traditional KBP methods with respect to variations in external parameters and their combinations . Studies of this nature were performed on both platforms: retrospectively on a commercial KBP module  can generate dose–volume objectives for plans with 6 and 10 MV photon beam energy; however, a RapidPlanTM model for flattened beams cannot optimize un‐flattened beams prior to adjusting the target objectives.TM module can generate high‐quality treatment plans compared to manually optimized plans for prostate cancer.KBP models have successfully navigated some plan differences but not others. Huang et al. demonstrated that the RapidPlanTM model as a quality control tool for multi‐institutional clinical trials to improve overall plan quality and provide decision support to determine the need for clinical trade‐offs between target coverage and OAR sparing.TM among five different institutions, Ueda et al. suggested that it is critical to ensure similarity of the registered DVH curves in the models to the institution's plan design before sharing the models. Good et al. applied the prostate model trained with a dataset from their institute to generate plans for patient datasets outside of their institution with the potential of homogenizing plan quality by transferring planning expertise from more to less experienced institutions.TM model was built as an offline quality assurance tool for which it improved the sparing of OARs in a large number of reoptimized plans submitted to the NRG‐HN001 clinical trial.KBP models have been investigated for standardizing plan quality across institutions. For patients enrolled in Radiation Therapy Oncology Group (RTOG) 0617, Kavanaugh et al. showed the feasibility of a single‐institution RapidPlanTM along with their treatment sites, training, and validation dataset size in Table Figure TM, it is indicated that the minimum number of plans required for model creation is 20; however, adding additional plans will usually help create a more robust plan.TM by high‐quality plans in training and found that 25–30 plans may produce a clinically acceptable plan for prostateThe size of the training set is an important consideration when building a KBP model. For RapidPlanThe requirement of sample size also partially depends on the robustness of the model used. Yuan et al. used 64 and 82 cases for prostate and head/neck case, respectively, in support vector regression (SVR) model for DVH predictions.TM module reduced the average planning time substantially—(295 minutes vs. 64 minutes)—compared to the average planning time of plans created without using RapidPlanTM module. For malignant pleural mesothelioma, the time taken with RapidPlan was 20 minutes compared to 4 hours for manually created plans by an experienced treatment planner.KBP methods may reduce the treatment planning time by minimizing the number of trial‐and‐error steps performed by a person in the treatment planning process. For the commercial KBP module, the total time including data collection, training time, generation of constraints and objectives, and optimization time can depend on the treatment type , design , site , and available GPU. For glioblastoma disease site, structure selection and generation of constraints and objectives took 2 minutes for both an IMRT and a 2‐arc VMAT plan, whereas the optimization and dose calculation step took 5 minutes for an IMRT plan and 11 minutes for a 2‐arc VMAT plan. Moreover, it took a planner about 4 hours to create a plan without any KBP module assistance.3.1.4TM models for multi‐institution adaptation.Overall, the review of traditional KBP dose prediction publications thus far suggests an improved efficiency compared to manual optimization, sufficient flexibility of traditional KBP methods in terms of their applicability , the need of these models for dynamic sites , the requirement of an automated approach for accounting for outliers to further enhance the treatment planning efficiency,3.2DL offers numerous advantages across the different multidisciplinary steps of radiotherapy treatment planning. In contrast to traditional KBP methods, DL methods can learn features directly from the raw dataset. Because DL methods are good at discovering intricate structures in high‐dimensional data, DL methods are able to solve a wide range of scientific problems.The convolutional layer consists of a set of convolutional kernels where each kernel acts as a filter. First, the image receptive fields are processed through a series of convolutional kernels that aid in extracting features. Kernels use a specific set of weights to convolve with corresponding elements of the receptive field. The weight sharing ability of convolutional operation allows the extraction of different sets of features within an image by sliding kernel with the same set of weights on the image. This makes CNN more efficient than the fully connected networks. This operation can be grouped based on the type and size of filters, direction of convolution, and type of padding.From the result of the convolution operation, the feature motifs can occur at different locations in the image. The goal is to preserve its approximate position relative to others rather than the exact location. The pooling or down‐sampling sums up similar information in the neighborhood of the receptive fields and outputs the dominant response within this local region, helping to extract the combination of features that are invariant to translational shifts.A nonlinear operation, also known as an activation function, helps in the learning of sophisticated patterns by serving as a decision function. Different activation functions reported in the literature are sigmoid, tanh, SWISH, ReLU,Batch normalization is applied to address the issue of internal covariance shifts, a change in the distribution of hidden unit values within the feature maps that can reduce the convergence speed. Batch normalization essentially unifies the distribution of feature map values by setting them to zero mean and unit variance, which, in turn, improves the generalization of the network by smoothening the flow of the gradient.Finally, overfitting occurs when the model is trained to closely or even exactly fit a set of training data at the expense of degraded model generality as it would fail to learn general underlying patterns within the data.Loss function, in its simplest form, is the difference between the predicted and the target output. A loss function is presented in the form of an objective function that is to be minimized during the backpropagation step to improve the network performance. Janocha et al. investigated how different choices of loss functions affect deep models and their learning abilities, as well as robustness to various effects.Backpropagation is a widely used technique in both traditional ML and recently emerging DL methods. Briefly, it systematically allows improvements in weights and biases to further improve the network's prediction in three steps: (1) feed‐forward network with learnable parameters; (2) network's performance is measured with a loss function; (3) the error is backpropagated through the network to alter the learnable parameters. This process is typically achieved through a gradient descent which allows for instantaneous rates of change between the parameter to the neural network's error. Deeper networks with many layers tend to train slowly due to gradient exploding/vanishing. For faster and efficient learning, He et al. proposed to utilize the residual functions instead of directly fitting a desired underlying mapping.3.2.1The simplest form of ANN consists of three layers: an input layer, the hidden layer, and the output layer. Neurons within each layer are nodes which are connected to subsequent nodes via links that correspond to biological axon‐synapse‐dendrite connections, analogous to the neural cell of human. The hidden layer is embedded between an initial input layer and the final output layer. The use of DL in dose prediction was initially utilized in the form of ANN.In layered format, each neuron receives information from the neurons in the previous layer and passes processed information to neurons of the next layer. Alternatively, residual connections can be added to connect neurons in non‐adjacent layers such as ResNet proposed by He et al.3.2.2CNNs, including fully convolutional neural network (FCN) and fully connected CNN (FCNN), have been applied so far for the dose prediction task Table . Figure 3.2.3Generative adversarial network (GAN) is a widely used supervised learning method in DL.3.2.4Reinforcement learning (RL) trains an agent, connected to its environment through perception and action, to make adjustments based on the interaction between the agent and the environment. The agent gets certain indications about the current knowledge of the environment at each step of its interaction. Based on this indication, the agent then chooses an action to generate an output. This action changes the state of the environment, the value of this state transition is communicated to the agent through a reward function. The agent's behavior can learn to do this over time through exploration and exploitation.3.2.5DL‐based dose prediction methods can be categorized according to DL properties such as network architectures , input image types , output types (2D or 3D dose distribution), and sample size . As shown in Figure 3.2.6As shown in Table Earlier work in DL‐based dose prediction methods involved predicting doses in a 2D manner.More recent works were focused on predicting 3D dose distributions using DL methods. To overcome increased computation load in 3D dose prediction, Nguyen et al. proposed Hierarchically Densely U‐Net (HD U‐Net), which not only was able to predict 3D dose distribution, but also outperformed dose predictions made by the standard U‐Net model.DL‐based methods have also been expanded to predict pareto optimal dose distributions so that physicians can learn the desired dosimetric trade‐offs in real‐time and learn the viability of different dosimetric goals. Ma et al. constructed the 3D U‐Net architecture to predict individualized dose distribution for different tradeoffs.U‐Net architecture has also been used for radiopharmaceutical dosimetry.He et al. proposed the residual network, known as ResNet, to mitigate the difficulty of training DNN caused by gradient vanishing.3.2.7GAN entails a pair of neural networks: a generator and a discriminator. From the treatment planning standpoint, the generator could be represented as the treatment planner who generates the plan, and the discriminative network could be represented by a radiation oncologist who evaluates the generated plan. Both the treatment planner and a radiation oncologist get better at performing their tasks as they become more experienced over time. Only a handful of studies have investigated the performance of GAN for dose prediction task as shown in Table Mahmood et al. demonstrated the first use of 2D GAN for predicting dose for each 2D slice independently for oropharyngeal cancer. Subsequently, Babier et al. proposed the first 3D GAN for the prediction of full 3D dose distributions, which outperformed the 2D GAN model proposed by Mahmood et al. presumably owing to its ability to learn the 3D features in contrast to 2D features by 2D GAN networks. Recently, Vasant et al. proposed a novel 3D attention‐gated generative adversarial network (DoseGAN) as a superior alternative to the current state of the art dose prediction networks.All four studies 3.2.8RL is a unique framework that resembles the workflow of treatment planning optimization. RL has been combined with DNN to accomplish human‐level performances for decision‐making tasks.3.2.9Three commonly used learning processes include unsupervised learning (USL), semi‐supervised learning (SSL), and supervised learning (SL). In this section, we briefly present a review of SL due to its wide adoption in dose prediction application. SL refers to a technique that utilizes labeled data with input‐output correspondence to train the model. The learning goal during training is defined by the paired input data and output target. While both, traditional KBP and DL‐based KBP, methods largely use the SL framework to train models, each presents a different framework with regards to labeled data with input. For instance, one subcategory of SL is regression analysis, which establishes a relationship among the variables by estimating how one set of variables affect their corresponding response variables . As can be seen in Tables 3.2.10In terms of the number of input parameters, Williems et al. studied the impact of four different inputs Table  for doseWhile many studies use only CT with anatomical information  as inputs to the CNNIn terms of losses, MSE is one of the most widely used cost functions in DL methods as it has many desirable properties from an optimization standpoint. Owing to its simplicity, well‐behaved gradient, and convexity, majority of previous studies including the ones shown in Table Lee et al. and Chen et al. utilized mean absolute error (MAE) cost function between the ground truth and dose rate map predicted by CNN.In general, the DL‐based methods require a large number of high‐quality data to be effective. Small datasets in DL can be challenging as it may result in overfitting. Data augmentation,As shown in Table Kandalan et al. studied the issue of generalizing DL‐based dose prediction models and make use of transfer learning to adapt a DL dose prediction model to different planning styles in the same institutions and planning practices at different institutions. A source model was adapted to four different planning styles only with 14–29 cases.4With the goals of improving plan quality and efficiency, researchers have investigated KBP to guide treatment planning for a new patient. In the last decade, there has been a rapid growth in the number of publications in traditional KBP dose prediction. More recently, the number of publications on DL has increased exponentially because of its flexibility and superior performances compared to many state‐of‐the‐art techniques. Over 90 articles have been published on traditional KBP dose prediction methods between 2011 and August 2020, whereas over 17 publications have already been published on DL‐based dose prediction as of August 2020.TM in Eclipse® treatment planning software in 2014, which allowed researchers from different centers to perform retrospective studies for investigating the influence of various parameters on the quality of plans generated through commercial KBP module. In addition to RapidPlanTM, RayStation allows a scripting option that allows user to implement a mathematical dose prediction model as demonstrated in.In general, most publications demonstrate improvements in comparison to manually optimized clinical plans in terms of both treatment planning quality and efficiency. A large number of manuscripts were published on traditional methods between 2015 and 2018, with the highest number of publications in 2017. This is presumably due to the commercialization of the RapidPlanIn terms of modality, both  methods were mostly applied to IMRT, VMAT, and other noncoplanar intensity‐modulated external beam radiation therapy treatments. Only a small number of studies were reported for the purpose of magnetic resonance imaging‐guided therapy (MRgRT).TM‐based KBP, several studies indicated the need to investigate the proper identification of outlier plans.TM involves statistics and regression plots for each structure, suggesting Cook's Distance >10.0, Studentized Residual >3.0, Areal Difference of Estimate >3, and Modified Z‐score >3.5 as potential outliers.In KBP, three commonly reported dose prediction metrics in the literature are the entire DVH curve Table , one or TM KBP module. This could inform readers of a range of training sample size used in the literature for each treatment site. Direct comparison of training sample size between the traditional and DL‐based KBP was not made as DL‐based dose prediction is a relatively new technique with a fewer number of investigations per site compared to traditional KBP methods.KBP research is growing rapidly. Past work has been surveyed before, though much has changed since then and even old work can be given a new context in light of more recent advancements. In contrast to a previous review that calculated an average number of training and testing dataset used in each year,There are key differences between traditional KBP and DL methods for dose prediction. In contrast to DL, an inherent limitation of traditional methods is that it is unable to extract important features and patterns hidden within the raw dataset. Both, similarity measures in atlas‐based methods and input features to model‐based methods require considerable effort to design handcrafted features  that can be processed either to identify the best‐matched case or into a representation from which patterns within the input can be classified through a classifier. In traditional KBP, PCA has been widely used in the literature for feature selection owing to its simplicity. However, a major limitation of PCA is that it learns a low dimensional representation of data only with a linear projection. Whereas DNNs can be used to address this issue and untangle non‐linear projections. For instance, an autoencoder is a type of neural network that is consisted of encoder, which encodes the input into a low dimensional latent space, and decoder, which restores the original input from the low dimensional latent space.Multiple DL networks have been investigated for dose prediction. The two most widely investigated networks, thus far, included CNN and GAN. From the results so far, it appeared that GANs may be a good choice for dose prediction tasks over conventional CNNs for several reasons. First, GAN has been proven to perform well in lesion detection and data augmentation tasks.4.1TM for prostate cancer.98 in PTV‐2 and V35 V50, V65 in the rectum. Given that features automatically extracted by DL methods can include both geometric/anatomic features and the mutual tradeoffs between the OARs, it gives an edge to DL methods in terms of dose prediction accuracy compared to traditional KBP methods that mainly rely on DVH and geometry‐based expected dose.KBP model performance varies as does the geometric and dosimetric complexity of treatment plans for different disease sites. For these reasons, KBP performance is presented here for specific disease sites. For head/neck cancer, the difference between the traditional KBP predicted and actual median doses for the parotids ranged from −17.7% to 15.3%,For oropharyngeal cancer, Mahmood et al. directly compared the GAN approach for generating predicted dose distribution with several traditional approaches including bagging query 120 between KBP‐like approach and actual plan was fourfold higher compared to that achieved by the attention gated GAN technique, demonstrating the ability of a DL‐based method to predict cold spots and hotspots that are prevalent in SBRT dose distributions. While both, traditional and DL‐based KBP methods used the data from previously treated patients to make dose prediction for a new patient, DL‐based methods have been shown to outperform several traditional KBP methods as demonstrated by some studies in the literature.For prostate SBRT, Vasant et al. compared the performance of the proposed attention gated GAN with an earlier approach that used relative distance map information of neighboring input structures.4.2TM, APP). Since DL‐based methods appear to be in their initial development stage, their possibilities are expected to be further explored through the development of new DL models and in different areas of dose prediction tasks in treatment planning workflow including adaptive radiotherapy in the near future.According to published articles on each KBP method in recent years, a bulk of research on further developments of models has shifted from traditional KBP methods to DL‐based methods. This is presumably due to the recent success of DL in various medical applications as well as its potential in dose prediction task. In terms of traditional KBP methods, future investigations are anticipated to be retrospective in nature by clinically available tools  involves adjusting dose distribution based on anatomical changes observed on intra‐procedural imaging such CBCT. The standard approach requires a physician to perform the recontouring of OAR and tumor regions followed by plan re‐optimization, which is difficult to implement with a patient on the table due to time constraints. To date, only one study has been reported to adopt DL methods for the purpose of ART of head/neck cancer.Post‐dose prediction, the main component of treatment planning workflow includes ensuring the achievability of the predicted dose plans, which often involves inverse treatment planning through manual intervention. Only a handful of studies extended such KBP in a fully automated pipeline that not only predict the dose distribution but also generates a complete treatment plan with minimum human interaction in traditional One of the challenges in data‐driven algorithms, including both ML and DL, is that it requires a large set of high‐quality data. Since the quality of data and radiotherapy practices vary from one center to the other, the heterogeneity in previously treated plans becomes a major obstacle in the deployment of data‐driven solutions in the field of radiation oncology. To address this issue, the concept of transfer learning for model adaptation to different learning styles at different centers may be investigated further in the future. A long‐term goal of this area of investigations would be to incorporate data‐driven predictive tools as a part of the clinical pathway. Pooled data from multiple institutions are the likely path forward for creating KBP models for rare disease sites.Finally, while the emergence of traditional ML and modern DL methods in the form of KBP has had a positive impact in radiotherapy clinics, adopting several practices from computer vision (CV) communities can further contribute to the growth of KBP on a larger scale. Data sharing is one category of CV that focuses on collaborative analysis and publishing on the web.5In the last decade, a tremendous amount of work has been done to improve treatment plan quality and efficiency through knowledge‐based planning research. We have reviewed over 120 articles covering two major KBP methods for dose prediction: traditional KBP methods and the more recent DL‐based KBP. Many traditional KBP methods are shown to be equivalent or superior to an experienced planner with greater efficiency. Recent developments in DL‐based KBP methods also hold a great potential to further improve the accuracy of the dose prediction task without compromising on efficiency. Both traditional and DL‐based KBP methods need further development for many other disease sites. Given the commercial availability of the traditional KBP module, more retrospectives studies are foreseen in the future. However, new DL‐based KBP methods are actively being introduced and trending in a steep upward direction and its commercialization may be anticipated in near future. There are various areas of future research, several of which have been highlighted in this review, required to achieve an ultimate goal of a fully automated treatment planning system.None.Shadab Momin: Conceptualization, Methodology, Investigation, Writing ‐ Original Draft, Visualization Yabo Fu: Methodology, Writing ‐ Review and Editing Yang Lei: Methodology, Writing ‐ Review and Editing Justin Roper: Writing ‐ Review and Editing Yang Lei: Writing ‐ Review and Editing Jeffrey D. Bradley: Writing ‐ Review and Editing Walter J. Curran: Writing ‐ Review and Editing, Supervision Tian Liu: Writing ‐ Review and Editing, Supervision Xiaofeng Yang: Conceptualization, Writing ‐ Review and Editing, Supervision, Project administration, Funding acquisition.


While many species are challenged by reduced‐pH levels, some species benefit from the altered environment and thrive. Here, we explore the molecular mechanisms of adaptation to ocean acidification in a population of a temperate fish species that experiences increased population sizes under elevated CO2. Fish from CO2 seeps exhibited an overall increased gene expression in gonad tissue compared with those from ambient CO2 sites. Up‐regulated genes at CO2 seeps are possible targets of adaptive selection as they can directly influence the physiological performance of fishes exposed to ocean acidification. Most of the up‐regulated genes at seeps were functionally involved in the maintenance of pH homeostasis and increased metabolism, and presented a deviation from neutral evolution expectations in their patterns of DNA polymorphisms, providing evidence for adaptive selection to ocean acidification. The targets of this adaptive selection are likely regulatory sequences responsible for the increased expression of these genes, which would allow a fine‐tuned physiological regulation to maintain homeostasis and thrive at CO2 seeps. Our findings reveal that standing genetic variation in DNA sequences regulating the expression of genes in response to a reduced‐pH environment could provide for adaptive potential to near‐future ocean acidification in fishes. Moreover, with this study we provide a forthright methodology combining transcriptomics and genomics, which can be applied to infer the adaptive potential to different environmental conditions in wild marine populations.Volcanic CO The effects of the uptake of anthropogenic atmospheric COTo survive in a reduced‐pH environment, marine organisms have to adjust their physiology, which, at the molecular level, is achieved by modifying the expression of genes. At the organismal level, we are learning that reduced‐pH environments alter the expression of many genes  that experiences doubling in its population size on temperate rocky reefs at natural CO2 vents at White Island, New Zealand, as a model species whose populations benefit from ocean acidification.Volcanic CO22.12 seeps at White Island  are located within temperate rocky reef habitats at 6 to 8 m of depth and have an average pH reduction of about −0.17 and −0.24 units, respectively, across years compared with ambient pH seawater  in January 2019. Ten individuals were caught at the underwater CO2 seeps with reduced pH  and ten in neighboring zones (~25 m away) with ambient pH . Individuals were measured for total body length. Their gonad tissue was dissected out immediately and placed in RNAlater (Invitrogen Inc.) and eventually frozen at −80 C for further processing.Two CO2.2Danio rerio) was downloaded from the NCBI SRA database  and was used as a control for the de novo assembly and annotation processes.RNA extractions were performed on each gonad tissue using a Qiagen RNeasy Micro Kit according to the manufacturer's instructions including DNAse I treatment. Quality checks were performed on a Qubit and an Agilent Bioanalyzer 2100, and all RNA had a RIN above 8. RNA‐seq libraries were built for the twenty Common triplefin individuals with Illumina TruSeq kits and randomized in barcoding to be sequenced at 150 bp paired‐end on an Illumina NovaSeq at the Center of Panoramic Sciences at the University of Hong Kong. After sequencing, the average number of paired‐end RNA‐seq reads per individual was 39.1 ± 2 M. Additionally, one RNA‐seq library from gonads of a wild‐type zebrafish  in the Bgee DataBase (https://bgee.org/). Then, we retrieved the protein sequences of the orthologous genes of these zp genes for four phylogenetically closest species to the triplefin with sequenced genomes , Cottoperca gobio (channel bull blenny), Gasterosteus aculeatus (stickleback), and Larimichthys crocea (large yellow croaker) from Ensembl orthologous gene database (https://www.ensembl.org). Then, RNA‐seq libraries of the Common triplefins were transformed to FASTA format using seqKit  and ten males (four from control sites and six from CO2 seeps).Given the small size of the Common triplefin gonads, the sex of the fish was determined by using the levels of expression of zona pellucida‐related genes (zp genes). We looked for highly expressed zp genes in female gonads of zebrafish  of the four phylogenetically closest species with sequenced genomes: Labrus bergylta, Cottoperca gobio, Gasterosteus aculeatus, and Larimichthys crocea. The same was performed for the control sample of zebrafish, but the primary assembly was blasted against the zebrafish Ensembl protein database.The longest protein sequences of the assembled transcripts for the four triplefin samples (min. 100 aa) were blasted against the Ensembl Release 99 protein databases (N = 4) was blasted (BLASTp) separately against each reference protein database (N = 4) with a cutoff of e‐value <10−6 and a percentage of sequence identity >70%.Each Common triplefin assembly  were merged grouping the results by a unique gene ID represented by the orthologous gene in stickleback (the best‐annotated genome of the four reference species), and keeping the ID of the assembled transcript whose blast alignment covered the highest length of a reference protein sequence. Merging different assemblies and using different reference genomes for annotation give a higher number and quality of assembled coding sequences than using single samples and only one reference species  and among pH environments without taking into account the variance in sex and body length . We used an ANCOVA test to investigate the effect of pH on the overall level of gene expression controlling for the variation in body size and sex. These gene expression levels represent the average of normalized read counts across all genes, as computed by DESeq2. Two‐way ANOVAs (with fixed factors pH and sex) were used to test differences in levels of gene expression for the whole dataset and by functional keywords categories . Statistical analyses were performed in R . The functional enrichment analysis was performed by counting the number of genes for each GO category in the up‐regulated gene dataset and comparing this with the number of annotated genes in each category in the stickleback genome. Tests for differences used Fisher's exact test where p‐values were adjusted via Bonferroni correction. Functional annotation for genes without GO annotation in stickleback Ensembl database (N = 8) was obtained from orthologous protein annotation in UniProt for stickleback or zebrafish. Functional keywords were extracted from the GO terms of each gene (Table https://www.genecards.org).Gene Ontology terms (GO) were obtained for the orthologous genes in stickleback genome from Ensembl Release 99 Database (ne Table . Informa2.82 seeps (up‐regulated genes) by comparing the fSFS of these genes with a set of neutral genes. We considered neutral genes the set of genes (N = 106) without evidence of different levels of expression between seeps and control, or between males and females , and not functionally related to up‐regulated genes  shows the frequency of the minor allele (MAF) in a dataset and is used to find deviations of the pattern of nucleotide diversity related to that expected under a neutral model. Thus, we evaluated the impact of natural selection on genes expressed at significantly higher levels in individuals from COE Edgar,  obtaininq‐value R package  in control (low expression) when compared to seeps (high‐expression) in candidate genes Table , indicat33.12 on the overall gene expression levels, taking into account individual traits that can influence this. The average level of gene expression was significantly higher in fish from seeps than from control , and this covaried negatively with fish body length , although the highest levels of expression were observed in males from seeps , and in a model not including the pH as a factor. For 107 genes, the model including environmental pH better explained the differential expression than the alternative model excluding this factor , translation , and structural component of ribosome (Molecular Function) . There was no evidence of enrichment in genes related to gonad‐specific functions, but we found five genes related to hormonal activity ; (2) energy production ; (3) protein production ; (4) FK: protein binding (N = 10); (5) FK: actin (N = 9); and (6) FK: signal transduction (N = 8). Overall, males from seeps displayed the highest values of gene expression for all six main functions and the gene expression was significantly different among sexes in four main functions . We tested this hypothesis by comparing the fSFS of the candidate genes to adaptive selection  against the fSFS obtained from a set of neutral genes . To minimize SNP miss‐calling (see Methods), we only obtained fSFS for genes in which full‐length transcripts could be assembled in all 20 samples . No significant difference was found between fSFSs from seep and control sites , energy production (N = 9), and protein production (N = 12)] and compared their fSFSs with that of neutral genes. These three functional categories showed significant deviations in comparison with the expected neutral fSFS , with the excess of intermediate‐frequency SNPs being more evident in the energy production‐related genes  would also present an excess of SNPs with intermediate frequencies, but the frequency of the alternative alleles would be different between control and seeps. We evaluated this possibility by testing differences in the allele frequencies of the SNPs detected in the candidate genes among seeps and control sites. The results indicated that there are no significant differences in the allele frequencies of the SNPs among the sites after correcting for multiple testing , suggesting no genetic differentiation between control and seeps adult fish for the up‐regulated genes in a reduced‐pH environment.If there is differential postlarval mortality among seep and control environments, or juveniles move preferentially to the environment to which they are better fitted, then the adults we analyzed do not necessarily represent the recruited larval population at the CO3.4aoc2, hsp90ab1, rhag, agmat, rasa3, and taldo1) have three or more intermediate‐frequency SNPs and were evaluated in more detail. The classification of these intermediate‐frequency SNPs in synonymous  and nonsynonymous , according to the related orthologous proteins in other species, showed that all but one are synonymous. This result suggests that the target mutations of adaptive selection to life at the reduced‐pH environment are not located within the coding sequences, but rather in neighboring linked functional regions such as regulatory sequences of gene expression.Nearly half of the evaluated candidate genes (47%) presented one or more SNPs with intermediate frequency (MAF ≥ 0.30) based on the two methods of SNP detection. Six of these genes (agmat (agmatinase) gene was the only one with a nonsynonymous SNP in intermediate frequency. This SNP changes the ancestral glutamine for an arginine in the second position of the 135th codon of the orthologous agmat gene from L. crocea, and it is surrounded by other synonymous intermediate‐frequency SNPs  with an excess of intermediate‐frequency SNPs are also functionally related to homeostasis maintenance  from COWe investigated the possibility of long‐term adaptation by searching for footprints of natural selection in the patterns of nucleotide polymorphism in the up‐regulated genes under reduced pH, as these genes are potential candidates to adaptive selection. We found a significant excess of intermediate‐frequency SNPs in these candidate genes when compared to neutral genes. Because neutral genes describe the expectations of genes without evidence of selective pressures for the reduced‐pH environment, this result is a clear indicator of an adaptive process for life in an acidified environment. The excess of intermediate‐frequency SNPs in the candidate genes is compatible with the expectations of balancing selection in its form of spatial heterogeneous selection Hedrick, . Because2 seeps. These mutated regulatory sequences would not impact the fitness of the individuals carrying them when living in an ambient pH environment, but these might allow fine‐tuned physiological regulation in a reduced‐pH environment. This idea of adaptation through changes in regulatory sequences of gene transcription is supported by the growing evidence indicating that such changes are highly important in species evolution  that in turn are responsible for the up‐regulation of these genes. Because mutations in DNA sequences are physically linked within chromosomic regions, natural selection favoring genetic variants in cis‐regulatory sequences at the acidified environment would increase the frequency of neutral linked SNPs in coding regions  leaving a pattern of an excess of intermediate‐frequency SNPs such as that we observed. The fitness effects of mutations in regulatory sequences that enhance the transcription of genes responding to the reduced pH are more likely to be smaller than those of mutations changing proteins. Reduced pH could trigger the expression of just a handful of genes with regulatory effects on the expression of many target genes, creating a cascade of increased transcription through mutated regulatory sequences (Yosef & Regev, Carroll,  and adapCarroll, . MoreoveCarroll, . Thus, o2 seeps (Nagelkerken et al., The mechanism of adaptation we observed for the Common triplefin suggests some differences with current evidence of adaptation to acidified environments in other marine species. Evidence of balancing selection that maintains adaptive genetic variation to ocean acidification has also been found in wild populations of purple sea urchins (Brennan et al., In summary, our results indicate that adaptive genetic variation to reduced pH could be located in regulatory sequences of the transcription of genes responding to this environmental change. Regulatory mutations could potentially change the regulatory circuits of gene expression allowing fine‐tuned physiological responses to ocean acidification. Our case‐study in the Common triplefin shows that these adaptive mutations are maintained within the whole population at a local scale. Evidence of population structuring at regional scales has been found for Common triplefins (Rabone et al., The authors declare no conflicts of interest.Figures S1–S5Click here for additional data file.Tables S1–S10Click here for additional data file.


Medicinal cannabis has been legalised for use for a range of specified medical conditions in Australia since 2016. However, the nature of the government regulations and the subsequent complexity of prescribing, as well as doctors’ safety uncertainties and the stigma of the plant, remain contributing barriers to patient access. Media representations can offer insights into the nature of the discourse about new medical products and therapies and how ideas and understandings about social phenomena become constructed. Focusing on professional medical publications, this study sought to investigate how medicinal cannabis is being represented in professional medical publications.Medical Journal of Australia, Australian Doctor, Medical Observer, Australian Journal of General Practice, Australian Family Physician, and Australian Medicine. Articles were coded according to article type, framings of cannabis, headline and article tone, and key sources used in the article. We also used manifest textual analysis to search for word frequencies, and specific conditions referred to in the articles retrieved.Using a content analysis approach, we investigated articles about medicinal cannabis from 2000 to the end of 2019 in the A total of 117 articles were retrieved for analysis, the majority of which were news stories for a physician audience. Across the longitudinal period, we found that most reports carried a positive tone towards medicinal cannabis. Cannabis is most frequently framed as a legitimate therapeutic option that is complex to prescribe and access, does not have a strong evidence base to support its use, and also carries safety concerns. At the same time, the outlook on cannabis research data is largely positive. Primary sources most frequently used in these reports are peer-reviewed journals or government reports, voices from medical associations or foundations, as well as government and university researchers. Chronic pain or pain were the conditions most frequently mentioned in articles about cannabis, followed by epilepsy, cancer or cancer pain, and nausea and chemotherapy.This analysis offers evidence that medicinal cannabis is being framed as a valid medicine advocated by the community, with potential for addressing a range of conditions despite the lack of evidence, and a medicine that is not free of risk. This article analyses media representations of medicinal cannabis in Australian medical publications. Legalisation occurred in Australia in 2016. Firstly, we contextualise cannabis in terms of its historical usage, its legal status, and research relating to knowledge, attitudes, and usage. Secondly, the media framing of medicinal cannabis is introduced in connection with Australian medical journals. As sources of medical information, they are relatively under-researched. Thirdly, our methods are outlined followed by the results of the framings, which show a gradual opening up to and receptivity by the medical profession towards medicinal cannabis. Finally, we discuss the findings and conclude that medicinal cannabis is on a journey towards becoming a legitimised medicine.The Cannabis sativa plant has an incredibly multi-layered, rich, and versatile history of human uses for food and fibre, as well as recreational, and religious and spiritual purposes throughout the world . Across the twentieth and twenty-first centuries, this has been a primary association and stigma for the plant, to such an extent that that its many other uses were erased from the memories and pharmacopoeias of those Western, industrialised countries that effectively banished it from holding any legitimate place in society, including as a medicine (Newhart and Dolphin The discovery of the endocannabinoid system shed light on how plant-derived cannabinoids like cannabidiol (CBD) and tetrahydrocannabinol (THC) interacted with human and animal systems. Research into the plant’s chemical compounds has also revealed a rich array of terpenes and flavonoids that may also account for the plant’s therapeutic effects  ODC , alongsiChemotherapy-induced nausea and vomitingRefractory paediatric epilepsyPalliative care indicationsCancer painNeuropathic painSpasticity from neurological conditionsAnorexia and wasting associated with chronic illness (such as cancer)The TGA  has apprThe TGA regulations permit applications for most cannabis products but nevertheless distinguish between cannabis products based on CBD and THC. The latter remains a dangerous drug and is considered higher risk for treatment than CBD TGA . AustralIn 2018, the Federal Minister for Health announced that the government wanted Australia to become the largest exporter of medicinal cannabis in the world Guardian :1-4 madeA recent study by Lintzeris et al.  measuredEvaluating the levels of knowledge about cannabis amongst Australian general practitioners (GP) is important because they are gatekeepers and prescribers for legal medicinal cannabis access. A 2018 study of 640 Australian GPs published in the British Medical Journal , peer-reviewed scholarly journal, The Medical Journal of Australia (MJA). The search terms used were as follows: “medicinal cannabis”; “cannabis”; “marijuana”; “medic* marijuana”; and “medic* cannabis”.A search was conducted from 2000 to the end of 2019 in the following publications for GPs: As GPs are the first port of call for patients who may seek medicinal cannabis, we selected GP publications with high readerships and followings, as well as the AMA’s own peer-reviewed scholarly journal. The publications were also chosen for their availability and accessibility. All items referring to medicinal cannabis were included. Articles referring to use or abuse of cannabis as a recreational drug without mentioning medicinal cannabis were excluded from the dataset.Articles were sourced from the start of 2000 to the end of 2019. The units of analysis for this content analysis were article type , framings of medicinal cannabis, headline and article tone,The coding method was a combination of deductive and inductive codes. These categories were based on a priori frames, drawing on earlier media representation research into mainstream news and biomedical representations of herbal medicine and complementary medicine in Australia Lewis ; 2019, aAustralian Family Physician, which yielded no results for references to medicinal use of cannabis. The vast majority of articles were news stories for a physician audience (n = 81). There were 8 original research or review papers, followed by editorial or opinion pieces (n = 14) and general information or guidelines for practitioners (n = 6). Three articles were audience polls about medicinal cannabis and three were sponsored content (e.g. upcoming seminars). One letter to the editor was retrieved from the MJA. There was one case study and one book review during this period.A total of 117 articles were retrieved for the analysis. We sourced articles from all publications, with the exception of The number of articles across the longitudinal period can be viewed in Fig. n = 34) ; spokespeople from medical associations or foundations (n = 25) such as the Australian medical Association (AMA) or the Royal Australian College of General Practitioners (RACGP); voices from government (n = 18); university researchers (n = 14); and medical personalities (n = 9). Less frequent sources included doctors who are registered cannabis prescribers (n = 6); TGA representatives (n = 3), laypeople (n = 1); and doctors who are represented as hospital-based practitioners (n = 1). Voices from private research and the corporate sector occurred in only one article each across the entire longitudinal period.The most prevalent sources drawn upon in these articles came from: journals or reports  than negative ones (n = 43), with a lower frequency of neutral media pieces (n = 15). Figure n = 55), with a substantially lower rate of mixed (n = 29) and negative (n = 26) intonation. Only seven articles carried a neutral tone towards medicinal cannabis. No negative articles appear in these medical publications until 2015. In contrast, 2017 is the only year in which negative and mixed articles outweigh positive ones. We do see the highest rate of negative articles in 2019 (n = 10), with 10 articles also carrying a positive tone towards cannabis, and five being mixed in tone.Overall, more articles carried positive headlines , framed cannabis as a medicine whose regulation is being driven by the community (n = 28), addressed the complexity of prescribing and patient access (n = 25) and the lack of evidence (n = 24), and conveyed safety concerns (n = 23). Cannabis was more frequently portrayed as a pharmaceutical medicine or drug (n = 11) rather than a plant product (n = 4). Concern about safety in relation to medicinal cannabis usage (n = 23) was more common than framings of medicinal cannabis as a more appropriate therapeutic option than some registered pharmaceutical products, such as opioids (n = 7).The most frequent framings see Fig.  occurrinn = 19) or those that focused on how evidence about it is poor or lacking (n = 24). In contrast, there were only two articles emphasising negative research findings about cannabis. Framings of the challenges for research in the cannabis field occurred in 15 articles.There was not much difference between numbers of articles that framed positive research about cannabis , Medical Journal of Australia (n = 5), Medical Observer (n = 3), and Australian Medicine (n = 3). The items were predominantly news stories (n = 20), followed by editorials (n = 3), original research or reviews (n = 2), one book review in the MJA, and one 2018 case study in Australian Doctor by the country’s first registered cannabis prescribing physician, Dr Vicki Kotsirilos. Reports framing cannabis as a legitimate therapeutic option were predominantly positive in tone (n = 21), with fewer (n = 6) carrying a mixed tone, and one neutral article.The articles positively characterise cannabis as a legitimate therapeutic option in the Australian healthcare landscape. In contrast, only two articles during the whole period framed cannabis as an illegitimate substance or approach to therapeutic care. Reports with legitimacy framings start in 2000 with a Medical Journal of Australia ‘Viewpoint’ article about cannabinoids and the endocannabinoid system, which had been discovered 8 years earlier. Legitimacy framings occur in at least three articles a year between 2013 and 2018 and are most predominant in 2014 and 2019 with a total of five articles in those years. These framings occurred most consistently in Australian Doctor Victorian Health Minister Jill Hennessy said children with severe epilepsy would be the first group to have access to the drug, beginning next year. “We’re starting with these children with severe epilepsy, whose lives have been shown to improve so significantly, because we know these children often don’t make it [into] adulthood,” Ms Hennessy told the ABC. “We want to improve their quality of life.” The following excerpts offer a glimpse into the legitimisation framings, drawing on notions of scientific evidence and safety, carrying a strong rhetoric of pathos:“About 70 years ago another natural medicine came into the medical arena,” writes Professor Nutt, of the Imperial College in London. “This was welcomed enthusiastically by UK doctors, even though there had been no placebo-controlled trials of its efficacy because it was seen to fulfil a major clinical need. “That drug was penicillin. If today’s medical profession could embrace cannabis in the same way as it did penicillin, then the true value of this plant medicine should rapidly be realised.” .This next quotation, from Australian Doctor (2019), invokes the history of penicillin as a ‘natural medicine’ that was accepted by the medical profession as a legitimate medicine some 70 years earlier:Legitimacy stories were most commonly associated with framings about the complexity of prescribing, concern about safety issues, the need for legalisation, alternative access issues, positive research findings, and the framing that cannabis is relatively safe. The validity of cannabis as a medicine is also reinforced by the high rate of references to specific conditions: in particular, chronic pain, epilepsy, cancer, and nausea and chemotherapy.It is rather novel to see a medicine like cannabis be embraced into the mainstream healthcare system in publications that are typically more circumspect towards botanical medicines. This finding contrasts with an earlier study by Lewis , 2011b oA group of Queensland mums are seeking to put medical cannabis on the state election agenda, claiming it could “save” their children.-Hoffman, Tessa ‘Mums lobby for legal cannabis for kids’ Australian Doctor 2015In the aftermath of this flurry of activity there has been widespread confusion and scepticism among doctors. Many question whether the cart has been put before the horse; whether legislative change has been driven by the passionate campaigns of patients and advocacy groups rather than evidence-based medicine.-Dunn, Emily ‘Your guide to the clamour for cannabis’ Australian Doctor 2017Medicinal cannabis certainly has had a very political and community driven introduction in this country.-AMA Vice President Dr Toney Bartone, ‘Medicinal cannabis – still a lot of misinformation’ Australian Medicine 2017Unlike many other medicines, cannabis has bypassed more orthodox methods of medicalisation, in terms of how its legalisation has evolved through an unusually bottom-up trend of patients and advocates seeking legitimisation of access to therapeutic use as opposed to a top-down trend where medical experts legitimise the practice  and derivatives of the word ‘prescribe’ (n = 303) generated by word mapping correlate with the frequency of framings about the complexity of prescribing and patient access. Practitioners can register with the TGA for special prescriber access in order to be able to prescribe for large numbers of patients without seeking TGA approval for each individual patient. Alternatively, for the Special Access Schemes (SAS) A and B,The high rate of references to ‘access’ (ibe’ n = 03 generan = 7) and 2018 (n = 4), with three items carrying such framings in 2016 and 2017. This indicates that the focus on cannabis risk is a more recent phenomenon articulated across these medical publications, and understandably coincides with the period following legislation, after which increasingly aware patients were requesting legal access to cannabis and doctors were starting to register and prescribe it.Concern about safety issues for medicinal cannabis use was a frame in the study with word mapping showing the words ‘risk’ or ‘risks’ occurring 152 times across articles, ‘safety’ or ‘safe’ were used 134 times, and ‘harm’ or ‘harms’ 46 times. It is important to note that the most concentrated periods for stories framing safety concerns about cannabis were in 2019  and more pharmaceutically-oriented terms, such as “cannabidiol” (n = 154) or “CBD” (n = 142), and “THC” (n = 106) which as discussed earlier refer to particular isolated active constituents (cannabinoids) contained in the whole plant. A purely pharmaceuticalised orientation towards medicinal cannabis discourse is, however, not so straightforward. Across the period, images of the whole plant and raw plant materials were far more common than photographs of the manufactured and bottled product or of clinical research or laboratory images. This could relate to the availability, access, and cost of using particular images for publishers, and undoubtedly the aesthetic appeal of the bright, green cannabis foliage. At the same time, it highlights a tension between cannabis as a pharmaceutical substance or a ‘drug’ and cannabis as a plant.This point about pharmaceuticalisation is reinforced by the word mapping, which revealed very substantial distinctions between the comparatively low frequency of the word “plant” , followed by adverse events (n = 15) and toxicity (n = 9), and drug interactions (n = 4).A previous study of representations of herbal medicines in the MJA across a 42-year period by Lewis , 2011b iImportantly, the framing of concerns about safety did not function to delegitimise the value of cannabis as a medicine. Whilst concerns are articulated about safety in 20% of reports, cannabis appears to be a trusted medical object in these doctors’ publications.Hypericum performatum (St John’s Wort), which has a solid evidence base for use in people with mild to moderate depression . We shall now look at some of the less common frames.Framings of research evidence in these articles demonstrate a pattern of acceptance of cannabis that is correlated with the level of evidence available for the particular condition it is treating. This is consistent with the findings from Karanges et al. . The fraIt is telling that a U.S. National Academies of Sciences, Engineering and Medicine report on therapeutic effects of cannabis and cannabinoids, and a later update,The scale of the opioid crisis and accompanying news coverage that depicts the risks and harms caused by the over-prescription of opioids and the influence of pharmaceutical companies in promulgating this culture of over-prescription has been huge  is not risk-free. These publications also acknowledge the role of the community in the legalisation of medicinal cannabis, which is largely framed as having a legitimate place in mainstream Australian healthcare. We conclude that developments in new therapies will only be successful if they are matched with concomitant progress in disseminating news and communication to both practitioners and patients.


Since the discovery and description of natural killer (NK) cells, approximately 45 years ago by Rolf KiesslingClinical & Translational Immunology (CTI) is a collaboration amongst leading scientists from the fields of NK cell biology, tumor immunity, clinical translation, inflammation and vaccinology to expound the recent discoveries of novel regulatory mechanisms for NK cell activity and how targeting these pathways could revolutionise the treatment of a variety of diseases. Collectively, this series of articles provides an updated overview of the emerging roles for NK cells and this fascinating cellular target for immunotherapy  also takes advantage of multiple inhibitory mechanisms that influence NK cell recruitment effector functions and tumoricidal function. The contribution by Riggan next wave of cellular therapies for cancer.Translating engineered NK cell therapies to humans, Rezvani and colleagues recently performed clinical trial infusions for the first time of NK cells from cord blood transduced for expressing a CD19‐CAR construct into B‐cell leukaemia patients.A new appreciation for vaccine development strategies in order to treat infectious diseases has emerged during the recent COVID‐19 pandemic events.Moving from vaccination to inflammation, an important aspect of NK cell biology is the additional pro‐inflammatory effector function of NK cells, which can be the first and major producers of pro‐inflammatory cytokines such as IFN‐γ and GM‐CSF.FSFG is a consultant and has a funded research agreement with Biotheus Inc.