Neurogenesis in the postnatal mammalian brain is known to occur in the dentate gyrus of the hippocampus and the subventricular zone. These neurogenic niches serve as endogenous sources of neural precursor cells that could potentially replace neurons that have been lost or damaged throughout the brain. As an example, manipulation of the subventricular zone to augment neurogenesis has become a popular strategy for attempting to replace neurons that have been lost due to acute brain injury or neurodegenerative disease. In this review article, we describe current experimental strategies to enhance the regenerative potential of endogenous neural precursor cell sources by enhancing cell proliferation in neurogenic regions and/or redirecting migration, including pharmacological, biomaterial, and tissue engineering strategies. In particular, we discuss a novel replacement strategy based on exogenously biofabricated “living scaffolds” that could enhance and redirect endogenous neuroblast migration from the subventricular zone to specified regions throughout the brain. This approach utilizes the first implantable, biomimetic tissue-engineered rostral migratory stream, thereby leveraging the brain's natural mechanism for sustained neuronal replacement by replicating the structure and function of the native rostral migratory stream. Across all these strategies, we discuss several challenges that need to be overcome to successfully harness endogenous neural precursor cells to promote nervous system repair and functional restoration. With further development, the diverse and innovative tissue engineering and biomaterial strategies explored in this review have the potential to facilitate functional neuronal replacement to mitigate neurological and psychiatric symptoms caused by injury, developmental disorders, or neurodegenerative disease. Neurogenesis in the mammalian brain is a continuous process that occurs in multiple stages throughout the developing brain , 2 and pThere is controversial evidence regarding whether adult neurogenesis continues in the human subventricular zone (SVZ) surrounding the lateral ventricles and in the subgranular zone (SGZ) of the hippocampal dentate gyrus (DG) throughout life , 14–17. The current evidence indicating regionally-restricted locations of NPCs within the adult brain , 23 and There are currently three general strategies to replace lost neuronal populations throughout the brain: exogenous stem cell transplants –29, direThe strategies discussed in this review have the potential to enhance neuronal replacement in response to a wide range of brain disorders, including acquired brain trauma, developmental disorders, and neurodegenerative diseases. As such, the current article presents the technical details and findings to date for a range of regenerative therapies designed to facilitate endogenous neuronal replacement. Importantly, we discuss several challenges that need to be overcome in order to better utilize endogenous stem cells for repair purposes and discuss the diseases and disorders across the human lifespan for which these emerging technologies may be particularly applicable. Before we can reliably utilize these biomaterials and tissue engineering strategies as safe, effective clinical therapies to enhance neuronal regeneration, continued advancements must be made to ensure neuronal survival, reliable maturation and differentiation into appropriate cell types in target regions, proper integration, and network formation with resident neurons, and development of reliable ways to measure and modulate these factors. In particular, we recognize that the ability to influence cell fate determination is currently a major limitation of all of the strategies discussed herein. We attempt to answer what we can regarding the potential of these strategies with the understanding that success will be determined by future research indicating their ability to influence cell determination. Moreover, we consider the particular challenges in employing these new technologies as treatments to diseases and disorders that are not fully understood mechanistically. These challenges are critical to contemplate as we move toward utilization of these technologies in clinical trials. Overall, biomaterial and tissue engineering strategies that can reliably direct and enhance neurogenesis in a sustained fashion and to diverse brain regions may have the potential to successfully promote neuronal replacement in quantities large enough to effectively enhance functional recovery following a variety of injury, degeneration, and disease states that arise throughout human life.Neurogenic niches remain in some form within the mammalian brain post-development , 41, 42.There is ongoing discussion in the field of neuroscience regarding the exact definitions of neural stem and progenitor cells. Differentiation of stem cells exists along a continuum and multiple terms are currently used to define these different types of immature cells. In this review, we use the term neural precursor cells (NPCs), which is a broad term that encompasses both neural stem and progenitor cells. We recognize that by using this umbrella term we may lose some of the nuances of the particular cell types discussed in the literature reviewed herein. However, we choose to use this term for the sake of clarity and conciseness. With regards to the SVZ, we use the term NPCs when referring to immature cells from the time that they are activated type B1 cells in the SVZ to the time that they become type A cells (neuroblasts). We use the term neuroblast to refer to immature neurons that are migrating. We recognize that there is further detail and nuance surrounding these terms that will not be discussed in this review.In vitro research then demonstrated that cells isolated from the adult mouse brain are capable of dividing and differentiating into astrocytes and neurons  in adult rats caused increased proliferation of NPCs in the SVZ . These nIncreased production and altered migration patterns of SVZ-derived neuroblasts have also been observed in rodent models of disease other than stroke. For example, augmented SVZ NPC production has been observed in a mouse model of Huntington's disease (HD) . These SAltered NPC production and neuroblast migration has also been observed following acquired brain injury in adult humans. Augmented neurogenesis has been reported post-stroke in regions surrounding infarct cortical tissue . DamagedThese studies conducted across species highlight the potential of the SVZ to act as an endogenous source of replacement for lost or damaged neurons in response to diverse acquired brain injury and neurodegenerative disease arising in the adult brain. Observations of attempted endogenous neuroregeneration increased interest in the potential to harness endogenous stem cells as a tool to promote brain repair , 130. ThHere we will review the experimental approaches that currently exist to augment and redirect SVZ neuroblast migration, including various pharmacological approaches and acellular biomaterial scaffolds. We also discuss a tissue-engineered RMS, a newly developed technology that is predicted to facilitate sustained redirection of endogenous neuroblasts from the RMS and into neuron-deficient regions.Neurotrophic factors (NTFs) are proteins important for regulating the growth, survival, and plasticity of neurons . Increasin vitro, including interleukin (IL)-1, IL-6, IL-10, IL-15, Leukemia inhibitory factor, and TNF-alpha  into the cortex of uninjured mice caused an increase in BrdU-positive cells in the cortex , BDNF, insulin-like growth factor 1 (IGF-1), NGF, glial cell line-derived neurotrophic factor (GDNF), and neurturin (NRTN) have been delivered via subcutaneous, intrathecal, intraventricular, and IP routes of administration to patients with amyotrophic lateral sclerosis (ALS), PD, Alzheimer's Disease, and peripheral neuropathies . Howeverwithout the addition of NPCs into an area of injury may support the surrounding brain tissue and promote axon regeneration and neurite formation  control columns  regulations. Rigorous regulatory requirements will include quality assurance protocols, including health and potency following storage and distribution of biomanufactured products, particularly those that include live cells. There will be stringent preclinical and clinical testing requirements to ensure product safety and efficacy that will require significant time and expense to achieve.Finally, the development of reliable, non-invasive imaging methods would be useful to allow for visualization of scaffolds within the brain post-implantation. Once in the brain, it will be critical to visualize them at regular intervals to ensure they are degrading appropriately (if relevant) and not causing aberrant effects. Ideally, imaging methods should have the capacity to elucidate whether the scaffolds are working to appropriately redirect endogenous neuroblasts to neuron-deficient brain regions. As these challenges indicate, there are many milestones to overcome before tissue-engineered technologies will be safe for delivery into the human brain.realistically have the potential to redirect endogenous neuroblast migration in the human brain. If future research reveals that there is a very low quantity of endogenous NPCs that exist within the adult human SVZ, then biomaterial scaffolds designed to redirect endogenous NPCs are unlikely to promote neuronal repair on their own. Complementary strategies such as gene therapy or stem cell implants to rejuvenate the SVZ may be explored in parallel . Ischemic stroke can occur throughout the brain, leading to localized tissue damage in various regions containing diverse neuronal populations , 213. Thin vivo effects for each clinical application. There are significant challenges to overcome before these technologies can be used clinically.As discussed above, there remain several questions regarding how these tissue-engineered technologies would function in the aberrantly developing, degenerating, and injured brain. One concern would be that redirection of neuroblasts could lead to thinning of the brain regions from which these cells were diverted. This might be especially concerning in application to developmental disorders given the immature status of the brain. Perhaps the biggest challenge in understanding the effect that these technologies would have in the context of neural injuries and disorders is that we do not fully understand the mechanisms that regulate the injuries and disorders themselves. Our predictions regarding how these technologies will function could turn out to be accurate in the healthy brain but unreliable in certain instances of the diseased brain. It is possible that these engineered scaffolds could induce unique The presence of neurogenic niches within the adult brain provides endogenous NPCs that can potentially be harnessed to add neurons in cases of developmental disorders or replace neurons that have been lost due to injury or neurodegenerative disease. There have been substantial efforts to harness these cells for neuronal replacement purposes. The pharmacological, biomaterial, and tissue engineering strategies discussed herein that attempt to augment and/or redirect endogenous neuroblast migration from the SVZ have the potential to offset neuronal loss in diverse regions throughout the brain. Current research has provided evidence that these biomaterial and tissue engineering strategies will have the potential to promote neuronal repair following diverse afflictions including acquired brain injury, developmental disorders, and neurodegenerative diseases that arise throughout life. We predict that the most efficacious strategy to augment and redirect endogenous NPC replacement will build upon the brain's intrinsic mechanisms for neuronal replacement and will have the ability to promote sustained neuronal delivery over time. There are still many challenges to overcome—from ensuring biocompatibility of scaffolds to confirming that they can sufficiently promote neuroblast maturation and synaptic integration—before these newly developed technologies will be able to successfully redirect endogenous NPCs to promote neuronal repair.EP conducted literature review and drafted the manuscript. JO'D, HC, and DC provided significant guidance on manuscript content and critically reviewed the manuscript for important intellectual content. All authors contributed to manuscript revision, read, and approved the submitted version.DC is a co-founder of two University of Pennsylvania spin-out companies concentrating in applications of neuroregenerative medicine: INNERVACE, Inc and Axonova Medical, LLC. There are three patent applications related to the methods, composition, and use of micro-tissue engineered glial and neuronal networks, including U.S. Patent App. 15/534,934 titled “Methods of promoting nervous system regeneration” (DC), U.S. Patent App. 15/032,677 titled “Neuronal replacement and reestablishment of axonal connections” (DC) and U.S. Patent App. 16/093,036 titled “Implantable living electrodes and methods for the use thereof” (DC and HC). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mycobacterium tuberculosis (Mtb) set the stage for subsequent control or progression to active tuberculosis disease, early host–pathogen interactions following Mtb infection can be thought of as establishing a mycobacterial “set point,” which we define as the mycobacterial burden at the point of adaptive immune activation. This early immune response is impaired in the context of HIV coinfection, allowing for a higher mycobacterial set point and greater likelihood of progression to active disease with greater bacterial burden. Alveolar macrophages, as the first cells to encounter Mtb in the lungs, play a critical role in containing Mtb growth and establishing the mycobacterial set point. However, a number of key macrophage functions, ranging from pathogen recognition and uptake to phagocytosis and microbial killing, are blunted in HIV coinfection. To date, research evaluating the effects of HIV on the alveolar macrophage response to Mtb has been relatively limited, particularly with regard to the critical early events that help to dictate the mycobacterial set point. A greater understanding of alveolar macrophage functions impacted by HIV coinfection will improve our understanding of protective immunity to Mtb and may reveal novel pathways amenable to intervention to improve both early immune control of Mtb and clinical outcomes for the millions of people worldwide infected with HIV.As HIV has fueled a global resurgence of tuberculosis over the last several decades, there is a growing awareness that HIV-mediated impairments in both innate and adaptive immunity contribute to the heightened risk of tuberculosis in people with HIV. Since early immune responses to  HIV alswith HIV –105. WheHIV FDG) positron emission tomography combined with computed tomography (PET–CT), which identifies areas of increased cellular metabolic activity as would occur with an immune response to infection. Several macaque studies have leveraged PET-CT imaging to gain insight into early infection by following the progression of individual granulomas after aerosol infection [Mtb infection.The first such tool is 2-deoxy-2-[18F]fluoro-nfection , 132. PEnfection . Those wPRDX2, a member of the reactive oxygen species scavenger system. Further studies are warranted to explore the role of PRDX2 in early infection, and how that response may be skewed by HIV and other known tuberculosis risk factors. Similarly, characterization of Mtb-specific T cell responses may provide another means for understanding adaptive immune responses important in early infection. In a recent report of a whole blood assay of T cell responses to 60 Mtb antigens, immunogenic Mtb antigens were successfully identified across a diverse range of populations and infectious states [Mtb growth and dissemination.Blood-based immunologic signatures represent another potential avenue for studying early infection. Recent studies in macaques and humans have identified RNA signatures predictive of progression to active tuberculosis disease –136. Nots states . LongituMtb, and may be able to identify low levels even early in infection. Metabolomics has also been used to identify distinct signatures of the host immune response, whereby investigators were able to distinguish between risk-associated metabolites associated with a higher likelihood of progression to tuberculosis disease and disease-associated metabolites that increased in a time-dependent manner among those with progression to active tuberculosis disease [Yet another tool for identifying and studying early infection is plasma metabolomics, in which small molecules are identified using high-resolution mass spectrometry. This approach has been successfully employed to identify direct metabolites of  disease –140. LooMtb [Mtb, the mycobacterial set point provides a useful construct for understanding the impact of early immune control and clinical outcomes of tuberculosis. Given the disproportionate burden of tuberculosis disease and deaths among people with HIV despite widespread ART [Mtb infection and susceptibility. To date, research evaluating the effects of HIV on the alveolar macrophage response to Mtb has been relatively limited, particularly with regard to the critical early events that help to dictate the mycobacterial set point. While we believe that a higher mycobacterial set point is a key contributor to the increased morbidity among people with HIV, additional research is needed to better understand the mechanisms and biological pathways that may be involved in facilitating mycobacterial immune evasion. A greater understanding of the impact of HIV on these earliest responses to Mtb may lead to the development of novel interventions and host-directed therapies to reduce the persistent global burden of tuberculosis in this vulnerable population.Across the scientific community, there is overwhelming consensus over the need to understand protective immunity to Mtb . As a frread ART , it is i


Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed. Closely related to computational statistics, machine learning (ML) explores the study design and the construction of computer algorithms to learn from data and make data-driven predictions by employing complex mathematical optimization schemes.3,4 As more biophysical data become available before and during radiation treatment, the application of ML in radiation oncology will continue to experience tremendous growth, including treatment planning optimization,5,6 normal tissue toxicity prediction,7,8 tumor-response modeling,9,10 radiation physics quality assurance.11–13 In this paper, we focus on the application of ML approaches in radiation outcome modeling as a case study.Radiotherapy treatment in general and personalized adaptive radiotherapy (pART) in particular have a promising prospective to improve cancer patients’ therapeutic satisfaction.14 While supervised learning handles a set of data containing both the inputs and the labeled outputs, unsupervised learning deals with a set of data with only inputs. Reinforcement learning intends to identify the best actions in a dynamic system by maximizing the cumulative reward. As multilayer neural networks to extract “complex patterns” from large scale of data sets become popular in the era of Big Data, ML approaches can also be categorized into shallow learning and deep learning (DL), where the latter combines data representation with classification/regression tasks in the same framework. As DL has garnered remarkable attention for its capacity to achieve accurate prediction in various fields, there is a growing realization that better explanation of these ML methods is equally desired. While explainability and interpretability have been used interchangeably, we would like to distinguish between them to provide more accurate definition of different ML techniques in this review. Explainable models can be defined as those that are able to summarize the reasons for the behavior of ML algorithms, gain the trust of users, or allow the user to produce insights into the causes of the algorithm decisions.15 Essentially, one consensus among recent studies is that explainability based on human understanding is not a monolithic concept, but rather a complex construction. According to the description of Gilpin et al and Ribeiro et al,15,16 it can be decomposed into several human factors, such as trust, causality, transferability, and algorithm transparency. On the other hand, interpretability can be loosely defined as comprehending what a model did (or might have done) based on the inputs, with the capacity to defend its actions, provide relevant responses to questions, and be audited. Their relationship can be described as that explainable models are interpretable by default and the reverse is not always true. Although interpretability alone is insufficient for the explanation of different ML techniques, it is a necessary first step towards full explainability, and it is employed in this paper to classify existing literature in radiation oncology. In this context, ML approaches can be sorted into interpretable (IP) and non-interpretable (NIP) approaches. In addition to DL, some shallow learning approaches such as support vector machines (SVMs), random forests (RFs), and “shallow” neural network approaches belong to the NIP category. The rest of shallow learning approaches such as generalized linear models , linear discriminant analysis, decision trees, Bayesian networks are considered IP ML approaches.ML is typically classified into supervised learning, unsupervised learning and reinforcement learning methods.i.e. increase its credibility.17,18 However, no single IP or NIP approach is located at a Pareto optimum, where it enjoys both the highest accuracy and the highest interpretability, but it rather exists as a comprise between them. For example, while a decision tree has more interpretable capability than the RF approach, its accuracy is generally outperformed by the latter.For clinical applications such as radiation outcomes prediction, the accuracy and interpretability of the ML approaches are major concerns. As accurate prediction of the treatment outcomes provides direct guidance to tailor and adapt a treatment plan in cancer therapy, and it is highly essential to use interpretable results for clinical decision-making support. If the goal is to assist physicians and patients reach the best decision, then an ML approach with a good balance between interpretability of the results and accurate predictions is needed to gain trust of the treating clinician, 19 However, the selected ML approaches refer to “off the shelf” algorithms, where they have been implemented by someone else and are available in prepackaged libraries. In other words, there will be some room to improve their accuracy or interpretability performance. In fact, researchers in the field of medical physics have been struggling to improve accuracy and interpretability of the ML approaches for radiation outcomes prediction, and their efforts were based upon both the IP and NIP ML categories. This study intends to summarize current efforts and to provide a big picture of the current trends to develop more advanced ML approach for pART. The rest of the paper is organized as follows. Section 2 introduces the strategies that have been used to increase the accuracy of IP ML approaches, Section 3 summarizes the developed strategies or tactics to improve interpretability of NIP ML approaches, discussion and conclusions are given in Section 4.The relationship of IP and NIP ML approaches in terms of accuracy and interpretability has been studied.20 An objective statistical multivariate model was also developed to describe radiation pneumonitis risk by assessing continuous and nominal parameters to determine the optimal model order and its parameters.21 In order to predict pneumonia risk and hospital 30-day readmission, generalized additive models (GAMs) with low-dimensional terms were developed, and pairwise interactions were added to standard GAMs resulting in GA2Ms. Logistic regression with single and cross-terms not only improved accuracy compared to the GAMs, the pairwise interaction could also be visualized as a heat map22. It turns out adding pairwise cross-terms may improve the prediction accuracy of the logistic regression, although it might not be fully explainable.Logistic regression is most commonly used model to represent linear relationships with the assumption of uncorrelated features. For example, logistic regression has been employed to predict xerostomia after radiotherapy, and in some instances it can approximate the performance of neural networks when sigmoidal functions are used.2Ms could be very complicated by considering all the features and their interactions. While ridge regression (logistic regression +L2 norm regularization) intends to regularize the ill-posed problems caused by high dimensional data sets,22 least absolute shrinkage and selection operator , is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces.23 Elastic net is another regularized regression model that linearly combines the L1 and L2 penalties of the LASSO and ridge methods to handle correlated features and high-dimensional data set, and it was used for outcome prediction in chemoradiotherapy.24 The elastic net was reported to have similar prediction performance as RFs and yielded higher discriminative performance than decision tree, neural network, SVM and LogitBoost in chemoradiotherapy outcome and toxicity prediction, particularly, when the complexity of the input features is limited to basic clinical and dosimetric variables.24 However, with the increment of the accuracy, elastic net trades off a little interpretability compared to logistic regression.9For modern data sets with a high dimension of features, GAMs and GA25 The group at Memorial Sloan Kettering Cancer Center has developed several nomograms for varying cancer diagnostics.26 In addition, such nomograms have been used to predict response for treatment. For instance, a nomogram was devised for estimating treatment failure among males with clinically localized prostate cancer treated with radical prostatectomy27 and for predicting disease-specific survival after hepatic resection for metastatic colorectal cancer.28 Nomograms were also employed to predict recurrence-free survival for cervical cancer based on combining individual clinical information with imaging-based fludeoxyglucose/positron emission tomography prognostic factors.29In order to facilitate the interpretability of regression-based analyses, graphical calculating devices named “nomograms” were widely employed in clinical practice for oncology applications including radiation treatment outcomes prediction by conducting the approximate graphical computation of a regression function.24 More than three decades ago, a recursive algorithm (decision tree) was applied to arbitrary dose–volume histograms to estimate the complication probability for treatment planning optimization.30 Recently, a recursive partitioning analysis was constructed to stratify patients into risk groups for clinically significant radiation pneumonitis after chemoradiation therapy for lung cancer.31 Additionally, decision trees were employed to predict pneumonitis in Stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). Ensemble techniques based on the decision tree such as boosting with RUSBoost and bagging with RFs had been used to improve its accuracy, but at the expense of losing its interpretability.7Decision trees can model nonlinear effects and are obviously interpretable as long as the tree depth is shallow.32 It seems that tree structure has a good potential to interpret nonlinear relationships and to be integrated with other NIP ML approaches for prediction accuracy improvement. Gradient boosting machine (GBM) intends to produces a prediction model by combing weak prediction decision trees, and it has been employed to predict long-term meningioma,9 outcomes after radiosurgery for cerebral arteriovenous malformations with a high prediction performance and a less interpretability.33 As a tree-structured boosting, MediBoost is a new framework to construct decision trees that retain interpretability while having accuracy similar to ensemble methods.34 While it has the same structure as CART to build a single decision tree, it has the improved accuracy by considering weighted versions of all cases at each split.9In order to study weight loss in head and neck cancer patients treated with radiation therapy, a classification and regression tree (CART) prediction model was developed based on a knowledge-discovery approach. The CART not only does not require a specification of the function to model covariates, but also its prediction accuracy increases with additional treatment toxicity information.35 An advantage of the NBN is that it requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Since independent variables are assumed, only the variances of the variables for each class need to be determined instead of the entire covariance matrix.36 Hierarchical Bayesian networks (HBNs) are an extension of NBNs, which intends to improve inference and learning methods by using knowledge about the structure of the data. In order to predict 2-year survival in lung cancer patients treated with radiotherapy, HBN models were developed, and they were reported to outperform SVM models at handling missing data, and therefore are more suitable for the medical domain.37 In a study of modeling local failure in lung cancer, a graphical HBN framework was generated to demonstrate that combining physical and biological factors with a suitable framework can improve the overall prediction, which highlights the potential of the integrated approach to predict post-radiotherapy local failure in NSCLC patients.38 Additionally, a HBN was employed to estimate overall survival among colon cancer patients in a large population-based data set, resulting in a significant improvement upon existing AJCC stage-specific OS estimates.39Naïve Bayesian network (NBN) is a simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions, and it is interpretable but less accurate.4040 However, the confidence interval of the MO-HBN’s prediction performance is still relatively large.Moreover, a multiobjective HBN (MO-HBN) was developed to explore the biophysical relationships among treatment plans, patients’ personal characteristics and radiation outcomes so that appropriate treatment plans before and during the course of radiotherapy can be identified.41Although the BNs do not offer a significant improvement in outcome prediction over those resulting from less complex classifier algorithm as naïve Bayes, logistic regression or C4.5 decision trees, they still provide unique benefits to explore the relationship among features from large patient cohort data. The ability of carrying out causal inferences allows them to be utilized for answering complex clinical questions from unobserved evidence, and the probability distributions underlying the BN can be automatically updated with newly added patient information. Although it is hard to automatically learn a single graph that faithfully represents the casual structure of an application field, hybrid causal learning is an emerging field to show promise in obtaining causal structures with high prediction performance and causal patterns set out by domain experts .34 attempts to emulate the performance of RFs while maintaining the intuition of classical decision trees.42As previously stated, some shallow learning methods such as RFs and SVMs belong to the NIP ML approaches. RFs are an ensemble learning method which constructs a multitude of decision trees at training time and outputs the mean prediction of the individual trees. While variance can be controlled from the ensemble learning, the ensemble learning approach can sacrifice most of its interpretability at the same time, except that the frequency of feature appearance in the top layers of the ensemble decision tree may be used to explain their importance. However, the concept of RFs was integrated with other IP ML approaches to balance their accuracy and interpretability for radiation outcomes prediction. For example, formerly mentioned Mediboost approach24 A non-linear SVM was developed for prediction of local tumor control after Stereotactic Body Radiation Therapy for early-stage NSCLC, and the prediction performance of the SVM model was significantly larger than that of a logistic tumor control probability model.43 Interpreting SVM models is far from obvious, and the absence of a direct probabilistic interpretation also makes SVM inference difficult. However, work was done in providing methods to visualize SVM results as nomograms to support interpretability.42 A nonlinear kernel, called localized radial basis function kernel (SVM-LRBF) was developed with the assumptions of intrafeature nonlinearity and interfeature independence. In addition to capturing nonlinearity of the classification function, the LRBF kernel can be visualized via nomograms. The SVM-LRBF method together with other SVM methods with linear kernel and RBF kernel had been applied for breast cancer prediction, and the study showed that while all the three kernel methods were equal in performance in terms of the area under the curve in the ROC curve, LRBF kernel was less sensitive to noise features than an RBF kernel.44A SVM with a radial basis function kernel (SVM-RBF) transforms the original feature space into another space that can separate classes better. This transformation, however, can be much less intuitive than linear SVMs.45 The key property of DL is that it can automatically learn useful representations of the data without conducting feature selection, which is one important component of other ML techniques. The reason why DL has relatively high prediction performance is that an architecture with sufficient depth can produce a compact representation, whereas an insufficiently deep one may require an exponentially larger architecture to represent the same function.As a NIP ML approach, DL is mostly an extension of previously existing forms of artificial neural networks (ANNs) to larger number of hidden layers and artificial neurons in each layer for automatic discovery of useful features. Historically, ANNs lost popularity in favor of SVMs, RFs and gradient boosting trees due to the limited data set, computing resources and being prone to local minima. With the availability of larger data sets, graphical processing units (GPU) and stochastic gradient descent algorithms, DL became possible to explore faster the training of larger, deeper architectures.46 extract radiomics features from the image patterns in developing a radiomics-based predictive model,47 and extract deep features from preoperative multimodality MR images for survival prediction in Glioblastoma Multiforme.48 Also, a CNN was developed to analyze rectum dose distributions and to predict rectal toxicity. 28 Studies show that DL has better performance than shallow learning approaches in radiation outcomes prediction. As a methodological proof-of-principle, a deep neural network (DNN) was created to predict the complete response of advanced rectal cancer after neo-adjuvant chemoradiation, which is an accurate surrogate for long-term local control. The DNN outperformed a linear regression model and a SVM model.49 Also, CNNs and their variants were applied to the discovery of consistent patterns in three-dimensional dose plans associated with toxicities after liver stereotactic body radiotherapy. When the number of false negatives, i.e. missed toxicities, was minimized, DL produced almost two times fewer false‐positive toxicity predictions in comparison to dose–volume histogram‐based predictions.50Now, DL based on neural networks has broad applications in radiation response, and it is poised to dominate medical image analysis for radiation outcomes prediction. Convolution neural networks (CNNs) were used to extract features from non-medical images for computer-aided diagnosis tasks,49 Given that the field of medical physics has unique characteristics that differentiate it from those fields where these techniques have been applied successfully, the DL techniques have their limitations and nuances in radiation oncology with the limited sample sizes.51 Another important issue with the DL is that these black-box-like networks are very difficult to debug, isolate the reason behind certain outcomes, and predict when and where failures will happen,52 which are called interpretability challenges.53 As the limited sample size issue could be gradually released by further collaboration of the hospitals and data centers, solving the issue of DL’s interpretability becomes more important. Actually, the underlying mathematical principles of DLs are understandable. But they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures.54 Although the potential of DL to improve prediction accuracy may outweigh their interpretability challenges in many industries,32,54 professionals in the field of radiation oncology are working mostly with distributed heterogeneous and complex sources of data, and there must be a possibility to make the results re-traceable on demand.54 There has been increasing interest in radiation oncology to make DL transparent, interpretable, and explainable, and the efforts to improve its interpretability for radiation outcomes prediction are summarized in the next section.While the development of DL will transform the way we use imaging for diagnosis, treatment planning and decision making and will disrupt the way we practice medicine in a positive way, it may also affect clinical practice in negative ways.48 Similarly, a methodology was developed to extract and pool low- to middle-level features using a pretrained CNN and to fuse them with handcrafted radiomic features computed using conventional CADx methods. In comparison to existing methods, the fusion-based breast CADx method demonstrated statistically significant improvements in terms of area under the curve on three different imaging modalities, and can also be used to more effectively characterize breast lesions.55 Recently, the combination of traditional ML methods and DL variational autoencoders (VAE) techniques was developed to deal with limited datasets for radiation-induced lung damage prediction as shown in 56 It was demonstrated that a multilayer perceptron (MLP) method using weight pruning (WP) feature selection achieved the best performance among different hand-crafted feature selection methods, and the combination of handcrafted features and latent representation (Case D: latent Z + WP + MLP) yielded significant prediction performance improvement compared with handcrafted features only (Case A: WP + MLP), VAE-MLP disjoint (Case B) and VAE-MLP joint architectures (Case C).When a DNN is used as a feature extractor thousands of features are extracted. Unlike engineered handcrafted features, these features do not directly relate to something radiologists can easily interpret. Supplementing DL with information already known to be useful may improve the performance of these DL models and their interpretability. Previously, for survival prediction following glioblastoma multiforme, after deep features were extracted from preoperative multimodality MR images, a six-deep-feature signature was constructed by using the LASSO Cox regression model. The deep feature signature was combined with clinical risk factors to build a radiomics nomogram. The combined model not only achieved better performance for OS prediction, but also increased the interpretability of survival prediction through a nomogram construction.57 In a DL-based radiomics model for survival prediction in glioblastoma multiforme, a heat map was also used to show the Z-score difference of each radiomics feature between high risk and low risk group, and a consistency of radiomics feature Z-Score between the discovery data set and the validation data set.48 In a retrospective multicohort radiomics study for lung cancer prognostication, DL was used in medical image for automated quantification of radiographic characteristics to improve patient stratification. A gradient-weighted activation mapping technique was employed to generate activation maps by mapping important regions in an input image with respect to predictions made, and the heat maps indicate regions in the input image having the most impact on the final prediction layer as shown in 52 In a previous study to develop CNNs for individualized hepatobiliary toxicity prediction after liver stereotactic body proton therapy, saliency maps of the CNNs were used to estimate the toxicity risks associated with irradiation of anatomical regions of specific organs at risk, and the CNN saliency maps automatically estimated the toxicity risks for portal vein regions.50 In order to classify lung cancer using chest X-ray images, a 121-layer CNN was developed along with the transfer learning scheme. A class-activation map technique was employed to provide a heat map to identify the location of the lung nodule.58Another method to increase the interpretability of DL is to calculate the sensitivity of the prediction with respect to changes in the input. Heat maps are visualization techniques that represent the importance of each pixel for the prediction task, which could help further optimize a CNN training approach. In a study of developing survival CNNs to predict cancer outcomes from histology and genomics, a heat map was employed to investigate the visual pattern that SCNN methods associate with poor outcomes by displaying the risks predicted by the SCNN in different regions of whole-slide images. The transparent heat map overlays in the study enable pathologists to correlate the predictions of highly accurate survival models with the underlying histology over the expanse of a whole-slide image. 59 Additionally, a gated recurrent unit (GRU)-based recurrent neural network (RNN) with hierarchical attention (GRNN-HA) was developed for clinical outcomes prediction through handling the high dimensionality of medical codes, modeling the temporal dependencies of healthcare events and characterizing the hierarchical structure of healthcare data. It was reported to have a better prediction accuracy and improve the interpretability of predictive models compared to baseline models by using the diagnostic codes from the medical Information Mart for Intensive Care to evaluate the model. The interpretability of the model depends on attention weights assigned to individual diagnostic codes and hospital visits, which were determined from relative importance of diagnostic codes on prediction.60Attention mechanisms are optional components of sequential prediction systems that allow the system to sequentially focus on different subsets of the input, and the subset selection is typically conditioned on the state of the system which is itself a function of the previously attended subsets. In addition to reducing the computational burden of processing high dimensional inputs by selecting only process subsets of the input, attention mechanisms also allow the system to focus on distinct aspects of the input and thus improve the ability to extract the most relevant information. Especially, soft attention mechanisms avoid a hard selection of which subsets of the input to attend and use a soft weighting of the different subsets for each piece of the output, thus leading to improvements in the quality of the generated outputs. The advantage brought by the soft-weighting is that it is readily amenable to efficient learning via gradient backpropagation.61 An explanatory graph represents the knowledge hierarchy hidden in convolution-layers of a CNN. While each filter in a pre-trained CNN may be activated by different object parts, part patterns can be disentangled from each filter in an unsupervised manner to clarify the knowledge representation.62 A CNN was learned for object classification with disentangled representations in the top convolution-layer, where each filter represents a specific object part. Since the decision tree encodes various decision modes hidden inside fully connected layers of the CNN in a coarse-to-fine manner, given an input image, the decision tree infers a parse tree to quantitatively analyze rationales for the CNN prediction as shown in 63 The above methods focus on the understanding of a pre-trained network, but it is more challenging to learn networks with disentangled representations.64–66Training DNNs with disentangled hidden layer representations is an active area of research to improve the interpretability of DL, although they have not been used for radiation outcomes prediction. The disentanglement of “the mixture of patterns” encoded in each filter of CNNs mainly disentangle complex representation in convolution-layers and transform network representations into interpretable graphs.67 In medicine including radiation oncology, interpretability represents physician’s ability to accept, and interpret an algorithm decision in a scientifically sound manner without the need to explain algorithmic behaviors. As questions of accountability and transparency become more and more important, the interpretability of AI algorithms for radiotherapy outcomes prediction has improved in recent years, but is still far away from achieving full explainability. In this study, we focus on the trade-off of accuracy and interpretability in evaluating the prediction performance of common ML approaches, and summarize the balance of IP and NIP ML approaches for radiation outcomes prediction purposes from the current radiation oncology literature.Interpretability and explainability are different concepts although they have been used interchangeably. While the former is about being able to discern the predictions without necessarily knowing the underlying mechanics, the latter is being able to quite literally explain what are the mechanics that led to a particular behavior or decision by the algorithm.69 Due to its potential to enhance the interpretability of DL approaches, combining DL with LIME (DL-LIME) is considered as an improved DL approach as listed in 70 However, as previously stated, we only focus on accuracy and interpretability, an initial stage towards full explainability, in this paper. The efforts to balance them not only came from IP ML approaches but also from NIP ML aspects as illustrated in y- and x-axes of the figure represent the accuracy and interpretability of IP and NIP ML approaches for radiation outcomes prediction, respectively. Then the locations of these ML approaches were determined based on radiation oncology literatures as shown in Due to the limited data sizes in radiation treatment and the requirement of clinical decision-making for pART, developing unique ML approaches to achieve the Pareto optimum of accuracy and explainability is necessary and challenging at the same time. Explainable AI was proposed based on the trade-off between prediction accuracy and explainability by producing more explainable models and maintaining a high level of learning performance.59The rising of DL approaches is attributed to their potential for high accuracy performance when sufficient data and computational support are available. These black box models create nonlinear predictors and automatically take into consideration a large number of implicit variable interactions. However, what makes them accurate is what makes their predictions difficult to understand; they are too complex. The exact DL architecture does not seem to be the most important determinant in getting a good solution for both accuracy and interpretability. A key aspect that is often overlooked is that expert knowledge about the task to be solved can provide advantages that can go beyond adding more layers to a CNN.On the other hand, the main purpose of a predictive model’s interpretability is to conduct statistical inferences, which intends to use the model to learn about the data generation process. However, none of NIP ML methods are able to conduct inference. In contrast, linear regression models, which assume that the data follow a Gaussian distribution, determine the standard error of the coefficient estimates and output confidence intervals. Since they allow us to understand the probabilistic nature of the data generation process, they are suitable method for inference. Also, a decision tree is one of the most widely used and practical methods for inductive inference. Particularly, Bayesian networks are popular for causal inference, since these models can be arranged to incorporate many assumptions about the data generation process.44,71 and to combine a DL-based radiomics signature with clinical factors to improve the accuracy and interpretability of overall survival prediction.48 Moreover, other IP ML methods have also been used to improve the explanation of DL methods. For example, the problem of neural network structural learning was cast as a problem of Bayesian network structure learning, where a generative graph was learned, and its stochastic inverse was constructed resulting in a discriminative graph to simplify the neural network structure. Also it was proven that conditional-dependency relations among the latent variables in a generative graph are preserved in the class-conditional discriminative graph.72 In order to handle the exhaustive and empirical neural network parameterization process, a new deep Bayesian network architecture was proposed by adopting the principle of multilayer Bayesian network in order to make use of the edges’ significance, the causality, and the uncertainty of the Bayesian network for improving the meaningfulness of the hidden layers and the latent variable’s connections.73Although there is no unified framework for ML interpretability, in general, the interpretability of the NIP ML methods can be improved by integrating them with the IP ML approaches. In addition to combining handcrafted features with latent variables, employing decision trees to encode all potential decision modes of the CNN in a coarse-to-fine manner as previously stated in Interpretability Improvement for Deep Learning, nomograms were employed to visualize and interpret SVM resultsp(variables)>>n(samples) inference problem of statistical learning will become more challenging in the areas of personalized and adaptive radiation oncology. Therefore, more advanced data analytics will be deployed and the demand to integrate accuracy and interpretability will rise to cope with clinical practice needs in the field.74 Although different techniques are associated with distinct inherent limitations for radiation outcomes prediction, which include the independence assumption for features in logistic regression, the robustness in decision trees, the need for feature discretization in Bayesian networks, or the network configuration dependency in DL, our review shows that combining predictions among a handful of good, but different, IP and NIP models may result in better ML approaches to achieve higher accuracy and interpretability for radiation outcomes prediction.As more patient-specific information is becoming available due to advances in imaging and biotechnology, the classical


Early detection is the only way to achieve a high cure rate in women with ovarian cancer. Unfortunately, to date, there is no effective strategy for early detection, despite rapidly emerging biomarkers. The low prevalence of ovarian cancer, low specificity and high rates of false positives have been limitations of screening programs. In the hands of experts, transvaginal sonography and MRI are effective tools to characterise ovarian masses. Currently, ongoing efforts in standardization of technique and analysis are likely to improve diagnostic capabilities in clinical routine, as well as the introduction of predictive risk models of malignancy. Radiomics and radiogenomics potentially offer a broad spectrum of complementary information in ovarian cancer diagnosis and treatment.• Transvaginal sonography and MRI are effective tools to characterise ovarian masses.• Standardisation of imaging technique and implementation of predictive models of risk of malignancy contribute to early detection of ovarian cancer. From a clinical perspective, ovarian cancer remains a major challenge. Despite advances in therapy, only a marginal improvement in overall survival has been seen in the last decades. This is mainly attributed to the fact that ovarian cancer is mostly diagnosed late and subsequently will relapse. In contrast, borderline tumours and stage I invasive ovarian cancer have excellent prognoses. Unfortunately, early detection of ovarian cancer still remains one of the unmet needs in the management of this disease.Undoubtedly the concept of ovarian cancer has been completely revised. Ovarian cancer is now recognised as an umbrella term for different cancer types that differ widely not only on a morphological and genetic level but also in clinical behaviour. Furthermore, heterogeneity is a feature seen not only within the primary tumour but also among its metastases .Approximately 90% of ovarian cancers constitute of epithelial ovarian cancer types. Ovarian cancer has multiple cellular origins. The most common and aggressive type is high-grade serous ovarian cancer (HGSOC) which originates in the epithelium of the fallopian tube as a STIC lesion. HGSOC may manifest as an ovarian or fallopian tube mass or primary peritoneal cancer, and the term tubo-ovarian cancer is often used. In contrast, only the biologically more indolent type I cancers  derive from the ovaries. These two distinct cancer categories differ not only in origin and aggressiveness, but also in the presence of identified precursor lesions. In this context—although they comprise only the minority of ovarian cancers—it is pivotal for early detection that precursor lesions may precede ovarian cancer for several years. Serous and mucinous borderline tumours may arise within cystadenomas, and it seems that there is a continuum in their development to invasive cancers. This is supported by the fact that borderline tumours are diagnosed in women approximately 10 years younger than the average age for HGSOC and the coexistence of borderline and invasive cancer in the same histopathologic specimen  by the age of 70. The risk is low in high-risk women prior to the age of 40. This makes screening with transvaginal sonography (TVUS) an important tool for early diagnosis in this population and is the rationale for recommendations of semi-annual screening and risk reducing salpingo-oophorectomy as an efficient means for cancer reduction at ages of 35–40 years or after completion of childbearing [Why do guidelines recommend against screening for women at normal cancer risk? The data from large randomised screening trials do not support benefits of screening outweighing the harms related to false positive testing. Furthermore, the rate of detected ovarian cancer is low, the performance in detecting stage I disease is limited, and survival benefit was not evident in comparison with not-screened women .In the PLCO  cancer screening trial, no difference was found in the stage at diagnosis and the ovarian cancer death rate, but approximately 10% of participants had false positive results . This reIn the UKCTOCS trial with more than 50,000 postmenopausal women enrolled for annual TVUS, only 45 cancers were detected, and mortality reduction was not found in the screened women over a follow-up of 11 years. While concurrent Ca125 and TVUS screening was not effective, complementary TVUS performed in abnormal Ca-125 allowed diagnosing more early-stage cancers and borderline tumours [So early detection of ovarian cancer must overcome problems of false positives derived from screening tests or pelvic ultrasonography and allow detection of preclinical ovarian cancer and precursors. The International Ovarian Tumour Analysis (IOTA) group published various models to standardise analysis of TVUS. While approximately 25% of adnexal masses remain sonographically indeterminate even with sonographic expert level, in clinical practice, this rate will be substantially higher . Of noteThe complementary value of MRI lies in its ability to accurately characterise sonographic indeterminate masses. In an attempt to standardise ovarian mass assessment, the European Society of Radiology proposed an algorithmic pathway that allows for a specific diagnosis of most lesions and also serves as a guide for patient management . A 5-poiThe ability to detect ovarian cancer before it metastasises is crucial, as borderline tumours and most stage I invasive cancers have a 5-year survival rate of more than 90%. Furthermore, accurate preoperative assessment may allow a tailored patient-centred approach for adnexal masses when these are assessed by US or in indeterminate cases complemented by MRI , 4. PreoTo date, no epigenetic biomarkers are available for the early detection of ovarian cancer from tissues or fluids. However, research detecting cancer at a preclinical stage is driven by rapidly advancing techniques. Gene expression– and methylation-based arrays and other emerging techniques such as liquid biopsies or autoantibody serum biomarkers are under development and have yet to be validated for early cancer detection.The molecular classification of high-grade ovarian cancer (CLOVAR) allows distinction of four cancer subtypes that differ in genetic profiles and prognosis. Special imaging traits of peritoneal dissemination at the staging CT could be associated with these subtypes. Correlation of this subclassification using CT or MRI morphologic features or texture analysis rendered important prognostic information.Radiomics and radiogenomics from CT or MRI data have opened new insights in ovarian cancer tumour biology. New, rapidly evolving applications include identification of radiomic features and their correlation with phenotype, genetic features, and prediction of cancer progression or prognosis . The gre


The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients’ self-care and health. Recent years have witnessed the rapid growth of online health communities targeted at patients with long-term conditions -3. PatieOngoing initiatives such as the Values in Action Champions , the HeaAs suggested by previous work on social capital and health care access, there is still controversy over the definition and measurement of social capital and its association with various types of health-related outcomes . In thisWe broadly define social medical capital as the advantages that any user (patient or caregiver) can gain from participation in the social networks provided by online health communities, where communication takes place at virtually no cost and across spatial and temporal boundaries. Like other forms of capital , social medical capital enables the achievement of certain ends ; however, unlike other forms, it is based on the idea that social structure serves as a wellspring of advantages to users. That is, social medical capital is contingent on resources socially embedded in connections between users and accessible through these connections. This means not only that users can benefit from one another, but also that whether they can extract value from one another depends on how they interact ,17. ThisThe value of participating in a community lies in the users’ ability to gain prompt access to a range of people. As more users join a community, a catalyst of social medical capital is structural defragmentation; that is, social interaction becomes more valuable as more pairs of users become mutually reachable along some path. Recent work has suggested that communities undergo defragmentation by self-organizing into hub-dominated structures . The emeIn a socially cohesive closed network, links are forged locally, and pairs of connected individuals tend to be tied to at least one third party that they have in common A. PrevioAn open network is rich in structural gaps and opportunities of intermediation between individuals B. Open sIn cases where the community aims to provide both emotional and informational support, social medical capital lies at the interface between bonding and bridging ,22. In sEmpirically testing our theoretical framework will require the construction of large-scale longitudinal network data sets with information on users and their time-stamped messages. To comparatively assess the efficacy of network mechanisms of social capital, content analysis of messages would be needed so as to uncover how users’ phycological well-being and access to information vary as social connections also change over time.To help policy makers to realize our vision of social medical capital as a crucial enabler of large-scale health care interventions in resource-constrained systems, we propose four targets: 1) promote patients’ and caregivers’ participation in online health communities to ensure continuous provision of socially embedded resources  that users can access through their (direct and indirect) social connections ; (2) sup promote


No objective or reliable sperm quality indicators have been established and sperm selection is, to a great extent, based on subjective qualitative evaluation. The best method for sperm selection in IVF presents several challenges: intrinsic sperm qualities cannot be evaluated and the ideal endpoint for these studies is debatable. An ideal method for sperm selection in ART should be noninvasive and cost-effective, and allow the identification of high-quality spermatozoa and yield better outcomes in terms of pregnancy and live birth rates. This narrative review included 85 papers and focused on the new available methods and technologies that might shed some light on sperm selection in IVF. It discusses the available data on microfluidic devices, omics profiling, micronuclei studies, sperm plasma membrane markers, and other techniques, such as Magnetic Activated Cell Sorting (MACS), Raman micro-spectroscopy, and artificial intelligence systems. The new techniques herein reviewed offer fresh approaches to an old problem, for which a definite solution has yet to cross the bridge from bench to IVF clinics around the world, since clinical usefulness and application remain unproven.Spermatozoa wage battle to conquer fertilization but the traits needed to succeed remain elusive. The natural advantageous qualities that enable only a few selected sperm cells to reach the site of fertilization remain unknown. Although  In ancient Sparta, selection criteria were rather strict and male babies were inspected by the Gerousia, the council of elders, and those judged unfit were left at Mount Taygetus to die. Spartans believed there was no place for those unable to fight for themselves. Though cruel, such strategy gave birth to a formidable army. In vitro fertilization (IVF) apparently demands much less effort from spermatozoa. Nevertheless, decades of research have not found a universally accepted sperm selection procedure  and intracytoplasmic sperm injection (ICSI).We searched for papers available on PubMed up to April 2019 using the following keywords: sperm selection, sperm sorting, noninvasive sperm evaluation, No language or time restriction was applied, but only articles pertaining to humans were included. The first step in the selection process revolved around reading the titles of the papers found in the search and the removal of studies not relevant to the matter at hand. The abstracts of the remaining publications were read in the second step of the selection process. We focused on studies performed in humans in the area of ART that published outcomes in terms of fertilization, and pregnancy and/or live birth rates whenever possible.We left out Sperm DNA fragmentation, which has been associated with adverse outcomes in IVF including reduced fertilization and pregnancy rates, poor embryo quality, and increased miscarriage rates, as it has been extensively studied without leading to valid practical conclusions  in mammalian cells are produced during anaphase 1 in mitosis or meiosis as chromosomes separate, and the either whole chromosomes or fragments lagging behind possibly due to DNA damage can be observed in cytoplasm close to the cell nucleus as small nucleus-like particles. MN development may be due to various factors such as acquired or inherited genetic alterations, deficiency of micronutrients which act as co-factors in DNA metabolism, or exposure to genotoxicants .MN have been associated with serious genetic alterations in daughter cells and their development results in abnormal gene expression and diminished proliferative potential. Indeed, increased MN frequency is related to cytotoxicity, cell development arrest, and death . MN production takes place in reproductive tissues such as male germ cells, placenta, and the embryo, therefore resulting in adverse consequences on fertility and pregnancy .Some studies advocate the use of the MN assay to investigate the association between DNA damage and reproductive failure in humans . Trková Published studies report on MN assay in lymphocytes, since there is no technique developed to assess MN directly in human sperm. Sperm exhibits an extremely compacted DNA and harbors almost no cytoplasm where MN might be detected . The debOur literature search revealed that MN testing is a promising tool, although more research is needed before it is used in clinical practice. MN assaying, however, might turn into a practical way to assess sperm DNA damage once standard protocols have been developed and validated .cumulus-oophorus. Many other plasma membrane characteristics and markers have been evaluated and some are detailed below.Although the role of plasma membrane markers is not new, the subject is still understudied. As a matter of fact, the two most used tests nowadays are both based on selection of sperm according to membrane integrity. The first uses magnetic activated cell sorting (MACS) with colloidal super-paramagnetic microbeads conjugated with annexin V. The most widely evaluated sperm selection technique selects sperm cells that bind to hyaluronic acid (HA), the main component of the extracellular matrix of the Electrophoresis and electronegative charge were found to produce sperm populations enriched in DNA-intact spermatozoa.  and moreUbiquitin is a small chaperone molecule known mainly from post-translational modifications called ubiquitination. Ubiquitin as a quality control marker is secreted by the epididymal epithelium to eliminate defective spermatozoa by subsequent phagocytosis (Opsins are a family of G-protein coupled receptors (GPCRs) thought to act as thermosensors for sperm thermotaxis  has been described as a sperm selection technique in IVF. Results of a systematic review and meta-analysis of prospective randomized trials showed statistically significant differences in pregnancy rates when MACS was used to select sperm compared with conventional techniques (either density gradient centrifugation and swim-up), but implantation rates were unaffected. Live birth rates were reported  systems have been used to categorize oocytes and embryos and reliably identify perfect oocytes and embryos that lead to pregnancy. The use of such approaches might result in objective, automatic, non-invasive oocyte or embryo evaluation . Our seaAvailable published evidence does not show a clear advantage of using advanced techniques for sperm selection in IVF, since they have not been correlated with improved outcomes in terms of fertilization, implantation, or live birth. In addition, these techniques involve extensive sperm manipulation and possible exposure to chemicals and non-physiological environments that might adversely affect sperm DNA and result in disrupted embryo development . As the Sperm cells are rather complex and sophisticated. They must be as fit as Spartan soldiers in order to perform highly specialized functions and achieve fertilization. Unfortunately, despite decades of research, the currently available sperm function and selection tests have failed to offer the long expected improvement in ART success rates. Failure might be attributed to the complexity of spermatozoa and the battles these cells must wage in order to achieve fertilization and pregnancy. The strategies sperm cells use to win the battles they face while traveling the female reproductive system remain elusive. The new techniques reviewed herein offer fresh approaches to an old problem, for which a definite solution has yet to cross the bridge from bench to IVF clinics around the world. After all, in the wise words of Lennon, McCartney et al., this is a long and winding road.


Confirmed factors that lead to implantation failure involve unhealthy embryos, unreceptive endometrium, and asynchronous development and communication between the two. The quality of embryos is further dependent on sperm parameters, oocyte quality, and early embryo development after fertilization. The extensive involvement of such different factors contributes to the variability of implantation potential across different menstrual cycles. An ideal approach to predict the implantation outcome should not compromise embryo implantation. The use of clinical material, including follicular fluid, cumulus cells, sperm, seminal exosomes, spent blastocyst culture medium, blood, and uterine fluid, that can be collected relatively non-invasively without compromising embryo implantation in a transfer cycle opens new perspectives for the diagnosis of embryo implantation potential. Compositional comparison of these samples between fertile women and women or couples with implantation failure has identified both quantitative and qualitative differences in the expression of microRNAs (miRs) that hold diagnostic potential for implantation failure. Here, we review current findings of secreted miRs that have been identified to potentially be useful in predicting implantation outcome using material that can be collected relatively non-invasively. Developing non-invasive biomarkers of implantation potential would have a major impact on implantation failure and infertility.Embryo implantation failure is considered a leading cause of infertility and a significant bottleneck for  SecreteA standard IVF treatment broadly requires egg retrieval, sperm collection, IVF, and embryo culture before transfer. The collection of cumulus cells and follicular fluid is possible during egg retrieval without affecting IVF . AnalysiCumulus cells are implicated in oocyte development and competence . In addiFollicular fluid is also collected during oocyte retrieval; however, unlike cumulus cells, follicular fluid normally collected during IVF stimulation is a pool from several oocytes, rather than a single oocyte to avoid multiple vaginal punctures. Despite this limitation, one study using pooled follicular fluid from individual patients has identified differences in miR expression between groups with different pregnancy outcomes . This stDefective sperm function is widely acknowledged as a major contributor to infertility. Under physiological conditions, the sperm acquires functional competence during their transit through the epididymis and female reproductive tract. Both biophysical and biochemical changes occur along this journey, eventually culminating in the ability of sperm to undergo an acrosome reaction, recognize the oocyte, and contribute to embryo development . A numbein vitro for up to 5–6 days to the blastocyst stage before transfer. Spent culture media can be collected during media change without affecting embryo quality. It has been demonstrated that over 96% of miRs present in the spent culture media originate from the trophectoderm and can be consistently detected after blastulation under IVF culture conditions  and preclinical animal models (in vivo). Detailed functional studies have only covered a small proportion of miRs identified so far (The implanted and non-implanted embryos from which BCM was collected had an indistinguishable morphology based on currently available assessment of embryo quality. Thus, miRs in the BCM may serve as promising non-invasive biomarkers to improve the diagnostic accuracy of embryo quality and implantation potential. An obvious challenge to achieve this is to determine which cohorts of miRs are present in BCM samples that correlate with implantation outcome, in particular, regardless of embryo culture conditions. Although some miRs such as hsa-miR-19b-3p and hsa-miR-372 have been identified in at least two independent studies , the comd so far .MicroRNAs are also readily secreted into the blood. Circulating miRs are packaged in membrane-bound vesicles, attached to high-density lipoproteins or bound to RNA-binding proteins, which endow them with striking stability in the blood . The humThe obvious challenge to predict implantation outcome using miRs in the blood will be to distinguish the endometrial secreted miRs from miRs secreted by other tissues. Of note, the process of embryo implantation somewhat resembles that of cancer cell metastasis. Both processes share some of the cellular mechanisms in cell adhesion, invasion, and angiogenesis . miRs suAnother challenge of using miR levels in the blood for biomarker purposes has been identified in cancer diagnosis. A previous study selected 79 solid cancer-circulating miR biomarkers and determined their expression levels in blood cells. Forty-six of the 79 miRs were highly expressed in the blood cells . Plasma in vitro (in situ hybridization on endometrial sections, like what has been done for protein via immunostaining (The uterine fluid is secreted by the human endometrium as an indirect approach to communicate with an embryo for the preparation of implantation. Compared to other body fluids, uterine fluid is a more “local” secretion and, thus, may provide direct information when assessing biomarkers for implantation. Detailed compositional analysis has revealed that uterine fluid contains miRs and proteins with changed profiles across the menstrual cycle . Functioin vitro . Furtherin vitro . Of notestaining . Uterinestaining . To the staining . There aAlthough miRs are highly desirable as non-invasive biomarkers to predict implantation, this field of research is somewhat confounded by a general inconsistency of miR expression levels across different studies. It has been identified that a number of factors including RNA isolation and detection systems can contribute to this inconsistency. Recently published work from one laboratory, which used different commercial kits to isolate RNA, demonstrated that the recovery of RNA was variable between the commercial kits . In addiAdding to this challenge is the observation that inherent differences between women, together with different IVF protocols, may lead to differential expression patterns of miR in the human endometrium. A microarray study has identified that luteal support following controlled ovarian stimulation has a profound influence on the miR profile in the endometrium . SpecifiMeasurement of miRs in the samples that can be collected without compromising embryo transfer in the same menstrual cycle opens new perspectives for the diagnosis of embryo implantation potential. Unfortunately, our understanding of the mechanisms of how miR dysregulation impacts implantation and how this accordingly affects miR secretion remains far from complete. Interpretation of research findings is confounded by unstandardized assessment of miRs in a given body fluid. Resolving these questions would have a major impact on biomarker development and clinical practice for reproductive clinicians and scientists. This includes optimizing the selection of embryos for transfer during IVF, improvement of implantation success rates, and the minimization of multiple pregnancies. It is likely that a combination of samples that can be collected either non-invasively or relatively non-invasively, as summarized in this review, will be useful to assess implantation potential at different stages of conceptus establishment and development. This relies on research to find miR biomarkers related to implantation regulation and the development of new technologies to improve miR detection. A few microfluidic devices have been developed recently with a larger capacity to include more miRs and reduce analysis time. Improved diagnosis of embryo implantation could have a profound effect on psychological and financial well-being on women and couples undergoing IVF treatment.Both authors made substantial contributions to the conception of this review and the critical appraisal of the literature summarized herein, wrote the manuscript, and approved the final version of this article before submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Rare genetic diseases collectively affect around 300 million people worldwide and a staggering 74% of them are childhood-onset conditions with neurological manifestations  remains permeable. This entails establishing a diagnosis within this privileged time-window. Unfortunately, despite being early-onset diseases, many pediatric conditions manifest the first clinical signs when the BBB is no longer penetrable by viral vectors. Because of the limitations of our current toolbox, alternative strategies had to be designed in order to allow transgene delivery to the central nervous system (CNS) when the BBB has acquired its selective permeability. Methods to engineer the viral capsid such as peptide insertion, chemical modifications, shuffled genome libraries, directed evolution, or rationally targeted mutagenesis led to the generation of tenth of artificial variants among which some have been designed with the purpose to cross more efficiently the BBB when delivered into the bloodstream  vector variants. One of these, namely the AAV-PHP.B vector, induced a transgene expression in the CNS at least 40-fold greater than the parental AAV9 capsid when injected intravenously, and it retained a cellular tropism for peripheral organs , cytoskeleton dysfunction , Rett syndrome and lysosomal disease . In all of these animal models, reinstatement of gene expression in the CNS and peripheral organs afforded robust therapeutic effects. The broad correction of pathological signs resulted in behavioral improvements and increased lifespan . NotablyOne can anticipate that this first set of pre-clinical studies will pave the way for similar strategies in other models of severe inherited conditions for which no curative options are currently available. In addition, the positive outcomes observed in these models of fatal neurological disorders make the PHP variants attractive tools for other brain conditions with late-onset. It this line, other groups have already extended the therapeutic repertoire of these new viral vectors by successfully tested them in models of Parkinson's and Alzheimer's disease , a glycolsylphosphatidylinositol-anchored protein expressed by endothelial cells, as a key determinant for this differential efficacy , the first generation of PHP variants are unlikely to be used in clinic. This being said, Kumar and colleagues performed a multiplexed screening including a selection step in various mouse strains and they isolated a new capsid  that induced good brain transduction even in genetic backgrounds expressing low levels of Ly6a. Yet, this novel variant needs to be tested in other species as well as in disease models in order to determine its potential transferability to humans.In parallel to these gene therapy studies, other research groups examined the versatility of the PHP variants. Since these artificial serotypes were isolated after several rounds of This strategy of capsid engineering does not offer infinite possibilities and because of the shortage in AAV capsids, alternative approaches must be developed in order to overcome the limitations of these novel vectors in particular regarding their use in clinic. Capsid engineering also goes along with changes in the selectivity of tissue infection and cellular tropism. While the PHP.B serotype retains a transduction of peripheral tissues similar to that of the AAV9 parental capsid, the improved brain penetrance of the PHP.eB vector is at the expense of peripheral organs (Chan et al., Also, despite PHP vectors showed great efficacy in preclinical disease models, they are unlikely to be a solution for all brain disorders. Their region-dependent infectivity as well as their heterogenous tropism for neural cells represents a limitation for some diseases where specific cell types are affected (Deverman et al., In summary, recent efforts in designing brain-penetrant AAV-capsids have made an important step forward by generating several tools. Their valuable features have already been challenged in animal models of severe diseases and they demonstrated remarkable therapeutic effects. Regardless of their applicability in a clinical setting, these emerging tools offer a unique opportunity to provide the proof-of-concept that many genetic pediatric diseases with CNS pathology could be rescued by gene delivery when suitable viral vectors will be available. This important piece of information brings new hope for patients affected by these conditions.All authors contributed to the preparation and writing of this paper and agreed on its final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This is explained by the reduced incidence of stroke combined with improved treatment and management of strokes . Due to The original term ‘connectome’ refers to the network of anatomical connections between neural elements of the brain . The advBrain Communications, et al. did not find evidence for increases in structural connectivity that have been suggested to indicate structural plasticity after stroke. Although this was a prospective study, no pre-stroke imaging was available for these patients. Therefore, future studies would benefit from pre-stroke imaging data to allow the interplay of pre-existing white matter alterations and structural network changes post-stroke to be examined.In the paper in It has long been known that recovery post-stroke whether motor or cognitive function is facilitated at different levels ranging from single cells to entire brain networks . Variouset al. and Tscherpel et al., highlight long-term changes in the structural or functional connectome after stroke, confirming that even discrete stroke lesions lead to widespread alterations in connectivity. Collectively, studying these alterations to brain network structure and its function will help us understand why some patients make better recovery than others, and may hold utility as a target for new treatment approaches and stratification of treatments, with prognosis and treatment adapted to the individual needs of each patient (Both studies by Schlemm


Parkinson’s disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings. Parkinson's disease (PD) is the second most common of neurodegenerative disorders, and it is characterized by the symptoms bradykinesia, akinesia, resting tremor, impaired posture and balance, gait disturbance, and loss of automatic movements Jankovic . Almost Neuroimaging studies have shown the association between parkinsonian motor symptoms and various neurophysiological changes. For example, putamen dopaminergic degeneration   while impairing impulse control, which seeded the inverted-U shape hypothesis—dopamine therapy dose that is optimized for motor symptom amelioration may be overdosing the ventral striatum where the dopaminergic innervation is relatively spared in PD  in which electricity can be supplied directly by electrodes implanted in the subcortical area. DBS technique has been widely used for improving motor symptoms  and tDCS electrodes are secured on the scalp over the targeted brain region and on the control site . A very large expanse of brain regions in between the two sponges are therefore affected indiscriminately. As a result, more extensive literature reviews exist for the use of TMS in network modulation of Parkinson’s disease , to build spatially distributed brain networks on the basis of sources of variability coupled by principal component analysis (PCA) technique  and ranks them according to the variances accounted for (VAF). For instance, if VAF for a PC is less 5%, it may be neglected. In brief, PCs are eigenvectors resulted from singular value decomposition of a covariance matrix . And thus, if two distinct groups  are pooled and if the disease sufficiently affects the overall brain metabolic pattern, SSM/PCA can characterize the disease-related brain metabolic covariance pattern that differentiates the two groups . The well characterised PD-related metabolic covariance pattern (PDRP) is the first PC resulted from the SSM/PCA on pooled group of 33 PD patients and 33 age-matched healthy control  of tDCS is applied via sponge electrodes , with sizes varying from 15 to 80 cmIt should be noted that modeling studies suggest the effect of tDCS can vary according to the topography of cortical surface, i.e., the same anodal stimulation may depolarize or hyperpolarize depending on whether the target is in the gyri or sulci, which may explain the large inter-individual variability in tDCS responses    over DLPFC  improved patients’ cognitive function  as compared to sham stimulation  already makes it nearly impossible to empirically determine which protocol may result in the optimal outcome. Furthermore, optimal parameters may differ depending on what functions are targeted  and who is being stimulated . A discussion must be initiated to develop a consortium and produce a personalized optimization guideline based on published literature, as well as unpublished raw data, so that a computational model can be developed to simulate and predict the outcome of the proposed tDCS strategy.Out of 7 papers, three studies investigated the long-term impact (up to 3 months) of tDCS on cognitive deficits in PD (Biundo et al. The impact of medication use in tDCS effects is another important aspect which should be taken into consideration (McLaren et al. Abnormal brain network expression in PD is relatively well-characterized. Neuroimaging studies have shed light on the neural mechanisms of PD cognitive deficits, and provided a potential therapeutic target for non-invasive brain stimulation techniques. A handful of research groups have tested the efficacy of tDCS for cognitive improvement in PD. Although most studies showed some potential, stimulation parameters must be further understood and optimized to make it useful in clinical settings. The use of neuroimaging combined with tDCS is warranted and may aid both target identification and outcome assessment for future tDCS trials.


Over-the-counter (OTC) medication is a convenient, affordable way for older adults to treat a variety of conditions; older consumers use more OTCs per capita than other people for varied reasons. As with any medication, OTCs carry risks. Because the labels of OTCs are frequently the only information accessed by consumers, labels designed to communicate safety information are paramount for these products. Yet, available studies suggest that consumers do not regularly access the comprehensive information in the Drug Facts Label (DFL) when making purchase or use decisions, tending to rely on information found on the front of the package. Herein, we evaluate how four OTC label formats  affect how aging participants attend to critical information. Sixty-eight participants (65+) engaged a computer-based task answering yes/no questions that required use of labeling information about the warnings or active ingredients (AI). Results indicate that AI information is found accurately and relatively quickly compared to warning information and highlighting and placement on the front of the package had little effect. By contrast, warning information was found slowly and with low accuracy with the standard label, and highlighting and front of pack placement both significantly improved performance. These results suggest that novel labelling strategies could result in more effective, safer use of OTC medicines among older consumers and provide insights that could be used by regulators as they work to improve policy focused on improving mandates for OTC labeling.