The spine is one of the organs that is most affected by metastasis in cancer patients. Since the control of primary tumor is continuously improving, treatment of metastases is becoming one of the major challenges to prevent cancer-related death. Due to the anatomical proximity to the spinal cord, local spread of metastasis can directly cause neurological deficits, severely limiting the patient’s quality of life. To investigate the underlying mechanisms and to develop new therapies, preclinical models are required which represent the complexity of the multistep cascade of metastasis. Current research of metastasis focuses on the formation of the premetastatic niche, tumor cell dormancy and the influence and regulating function of the immune system. To unveil whether these influence the organotropism to the spine, spinal models are irreplaceable. Mouse models are one of the most suitable models in oncologic research. Therefore, this review provides an overview of currently used mouse models of spinal metastasis. Furthermore, it discusses technical aspects clarifying to what extend these models can picture key steps of the metastatic process. Finally, it addresses proposals to develop better mouse models in the future and could serve as both basis and stimulus for researchers and clinicians working in this field. The spine is one of the most frequently affected systems in the context of metastatic processes . Up to 7respective entity”. Studies were screened and considered if they had a clear focus on spinal metastases. These studies are included in Literature research was performed with defined terms to look for spinal mouse models of entities that metastasize most frequently into spinal structures. These includes metastasis originating from prostate, breast, lung and kidney cancer as well as melanoma ,7,17,18.Various techniques have been developed over the years that are used to induce and model spinal and bone metastases, respectively. In order to gain insight into the technical aspects, special features of the individual inoculation methods are discussed in the following paragraph and summarized in The direct implantation was carried out in four different ways. Tumor cells were implanted through an anterior transabdominal approach into the vertebral body L3 , throughIntravascular inoculation methods differ from direct implantation methods as they include the steps of the metastatic cascade related to the tumor cell homing via the blood stream as well as the extravasation at distant sites. For intravenous inoculation, the lateral tail vein was chosen, which is an easy and well establish procedure in animal research in general ,20,21. T3. Then, the solid tumor is resected, and the animals are kept until they develop their metastasis spontaneously. For orthotopic implantation, the method depends on the targeted organ for primary tumor inoculation. To induce breast tumor metastases, Withana et al. injected tumor cells mixed with Matrigel into the mammary gland [Spontaneous metastasis models which focus on spinal metastasis are rare; only two studies have been published so far ,38. In fry gland . AnotherAll models listed in this review are cell line-derived models, as the development of spontaneous tumors in small animals with corresponding metastasis is extremely rare and not suitable for metastasis research . More thThe success rate of the establishment of spinal lesions is highly different between the listed studies . Intrava3 or worse [There is a wide range of analysis methods that have been used to study metastatic processes and endpoints. One of the most challenging parts is to track tumor cells during the colonization process before they form a growing metastatic tumor. To investigate the development of metastasis longitudinally, Bioluminescence (BLI), Computer tomography (CT), Magnet Resonance Imaging (MRI) as well as Positron emission tomography (PET) have been used, with different advantages and disadvantages elsewhere described in detail ,43. To tor worse . A promior worse .Another important feature of experimental models of spinal metastasis is paying attention to the clinical assessment in particular the occurrence of limb paresis and plegia. This was most reproducible in models of direct tumor cell inoculation into vertebral structures ,36. CossIn vivo selection is a well-known principle to enhance site specific metastasis by harvesting tumor cells from a specific site and re-implanting them into the next generation of animals after culturing ,25. ThisIt is of particular interest to determine to what extent the different steps of the metastatic process are mimicked in the currently used mouse models. The metastatic process consists of multiple steps which are necessary to form metastasis. These multiple steps were defined previously and summarized as followed. 1. Selection of tumor cells with metastatic potential from the primary tumor, 2. conditioning of the premetastatic niche and pre-colonization, 3. local invasion and intravasation, 4. circulation and survival, 5. adherence and arrest in a new organ, 6. extravasation into the surrounding tissue and 7. initiation and maintenance of growth ,11,46,47One of the most exciting questions is whether the process of metastasizing into the spine is spine specific, and which molecular mechanisms contribute to it. Only a few groups have focused on the metastatic ZIP code of the spine. One group from Shanghai identified CX3CL1 as a chemokine which promotes spine specific metastasis ,33,45,48Other studies, in contrast, questioned the concept of spine organotropism. First, the idea that cells preferentially enter the spine via the so-called Batson venous plexus circumventing most of caval and portal circulation was disproved . OriginaIf the metastatic process is taken as not spine-specific, but rather bone-specific, mechanisms of bone homing processes can be applied equally on spinal metastasis. There are several mechanisms which have already been identified to serve cancer cell homing to the bone ,47,55. IIt is undisputed that spinal bone structures or, bone in general, provide a tumor microenvironment that enables optimal conditions for the establishment and growth of metastases. Since most publications focus on investigations of the bone in general, this chapter also takes their findings into account. Systems that are involved in the formation of bone metastases are the bone structure itself with its different types of stromal cells and stem cell niches, the vascular system and the immune system ,58. DiffCurrent issues in the research of the mechanisms of metastasis relate to the formation of the premetastatic niche, tumor cell dormancy and the involvement of immunological interactions ,13,42. CHowever, the introduction of appropriate mouse models comes with major challenges as well. One finding of Takahashi et al. underpins the challenge of creating reproducible metastases at a high frequency in spontaneous metastatic models . They shIt is also important to examine spinal metastases separately from bone metastases. Although both types of metastasis have a lot in common due to the bone structure, they can be influenced by their proximity to special structures, especially by the dura. In this context, Szerlip et al. showed the crucial role of dura as a biological active tissue which contributes to forming an appropriate niche and metastatic outgrowth by releasing factors to prepare an immunosuppressive environment by stimulation of bone marrow myeloid cells and promotes invasion and proliferation of metastatic cells .Besides the need of investigating the involved mechanisms in early steps of metastatic process, it is also important to pay attention to treatments that are currently used in end stage metastatic disease but are not represented in preclinical spinal mouse models. This is mainly true for radiation therapy. Radiation therapy is already established in multidisciplinary treatment schedules, and advances have been made over the last years in optimizing techniques to achieve better outcomes ,67. RadiThis review provides an overview of currently used mouse models of spinal metastasis and discusses to what extend the complexity of metastatic process is represented. Although spine metastasis is one of the most frequent sites of overall metastasis, specific models are rare. The discussion remains as to whether spinal metastasis is organ specific or whether metastasis occurs more frequently in the spine due to favorable conditions. The studies observed generally mimic only a few steps of the metastatic cascade in spinal metastasis and need to be improved. One reason is that mainly intravascular inoculation methods were used, which especially exclude the early steps of metastasis. Preclinical models must therefore be adapted and further developed in line with current research efforts relating to the formation of the premetastatic niche, the influence of the immune system and dormancy mechanisms of metastatic cells. Spontaneous syngeneic mouse models utilized to simulate bone metastasis are a good basis and can be transferred. Importantly, they include primary tumor formation as well as resection, therefore optimally mimicking clinical reality and should be established in preclinical mouse models of spine metastasis more frequently.


In older adults, it is important to maintain awareness of memory as well as memory performance. However, it is not clear whether the awareness of episodic and lexical memory changes with age and is related to self-evaluation of memory and executive function. Here age-related changes and the relationship between metamemory, executive function, and metamemory scale were investigated. Healthy old (n=40) and young (n=34) groups participated in this study. In the episodic memory task, participants were asked to memorize ten Kanji words and to estimate the number of words they could recall after ten minutes. In the lexical memory task, they rated the likelihood that they could write a target Kanji word written in hiragana and then wrote them down. They were also asked to complete the metamemory in adulthood(MIA) and the position stroop task. In the episodic and lexical memory and the position stroop task and MIA subscales, the performances of the younger group were significantly better than those of the older group. In the episodic memory task, there were correlations between the metamemory and MIA subscales in both groups, but in the lexical memory task, only in the old group. No correlation was found between the results of both memory tasks and the stroop test. These results suggest that older people overestimate memory performances in the episodic and lexical memory tasks and metamemory performances may be associated with self-evaluation of memory. In addition, metamemory might not be related to frontal lobe function as shown in executive function tasks.


Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results , paving the way for new data-centric and knowledge-driven approaches. Skin cancer incidence has been increasing over recent decades, and according to the World Health Organization, almost three million cases occur globally each year, corresponding to one-third of all diagnosed cancers . The preMoreover, major advances in the automatic classification of skin lesions through computer-processed imaging have been recently reported ,4,5. StiGiven this, we can conclude that there is a shortage of systems that convert these different acquired knowledge’s into an effective artificial intelligence-based tool designed to support the referral dermatological process . FurtherThe present work integrates a larger project, DermAI, that aims to improve the existing Teledermatology processes between Primary Care Units (PCU) and Dermatology Services in the Portuguese National Health Service (NHS) for skin lesion referral. Through the usage of Artificial Intelligence (AI) and Computer Vision, we envision two major goals: (a) to support doctors in Primary Care Units through the development of a mobile application that fosters image acquisition standardization  and (b) This research addresses the second goal of the DermAI project regarding case prioritization. Besides its major relevance in the clinical context, the automatic prioritization of dermatological clinical cases is a complex task, usually downstream from the more studied skin lesion classification. To the best of the authors’ knowledge, this work is the first attempt to tackle it with an AI-based solution. In this context, we propose a Deep Learning-based framework to predict the priority level given a clinical image of a skin lesion . The dataset comprises retrospective data from the Portuguese NHS related to referral requests from PCU for the first Dermatology Hospital consultation. Its images are not standardized, and the priority (target) labels add to the complexity since each healthcare site and individual physicians have different requirements for higher priority cases . Inspired by how physicians proceed in case prioritization, a skin lesion classification task is included as an intermediate step in the framework. Although extensive work has been carried out in the last decades regarding this goal, it is still an open problem, especially in real-world, uncontrolled data. Accordingly, this study investigates the broader potential of including domain knowledge into the prioritization framework. This knowledge can be used to inspire data-centric preprocessing methods and more robust learning schemes. Moreover, explicit knowledge from clinical experts is used in the form of a novel knowledge map representing the relationship between differential diagnosis and the expected priority distribution, which can then be explored in different ways to guide the final predictions.an innovative framework for the unexplored and yet-relevant task of dermatological case prioritization;a study on how to include implicit and explicit forms of domain knowledge; anda novel, explicit knowledge map and its integration to guide model predictions.The main contributions of this work are summarized as follows:The skin lesion diagnosis classification paradigm is challenging mainly due to the large amounts of intra-class variability and complex textures and geometric structures. Furthermore, the discrepancy in the number of images of some classes can lead to highly biased Deep Learning models. Over the last few years, there has been an attempt to use transfer learning to mitigate some of the obstacles. The main consensus is that fine-tuning a well-established Convolutional Neural Network (CNN) with high performance in a large dataset, such as ImageNet , in a smOther work used pre-trained networks coupled with linear classifiers ,15 in orThe aggregation of information from different types of dermatological images and data modalities has also been attempted, inspired by the fact that, in clinical practice, dermatologists also combine visual inspection (dermoscopic and macroscopic view) with additional clinical information about the patient. Mahbod et al.  used twoDespite the great successes demonstrated by Deep Neural Networks, some of them presented above, it is still challenging, not to say impossible, to guide the learning process towards specific decision paths. Feature engineering is not required for such models and when data are available in sufficient amounts and quality. While this is a clear advantage for the efficiency of the modeling process, we may miss essential inputs that would benefit the final solution. On the other hand, data are often limited and of poor quality, especially in the medical field. In this context, there are interesting attempts at incorporating knowledge from the domain into the learning process, hoping to achieve better suited and robust models.There are three major approaches to achieving the integration of expert knowledge into Deep Learning processes: relational, logical, and scientific . The firA survey by Xie et al.  summarizArtifacts such as hairs, veins, ruler markings and air bubbles, or non-target lesions in dermoscopic images can potentially deceive a model’s classifier. Skin lesion segmentation and subsequent selection of an area of interest have been considered to help discriminate between lesions without the interference of such background noises . HoweverSubsequently, the correct selection of an area of interest for use during the diagnostic classification after the segmentation was also analyzed. Yu et al.  proposedTraditional CNN-based classification systems assume that all classes are equally hard to distinguish, compromising the classification of visually similar categories. Therefore, to enhance CNN’s performance, hierarchical systems have been considered in recent years, having already proved their effectiveness over flat classification approaches. These systems decompose the global classification problem into multiple sub-problems in a hierarchical manner, aiming to preserve some relationships that exist across various categories. Yan et al.  introducAs previously mentioned, another approach that intends to mitigate the differences verified in the classes’ classification difficulty consists of mimicking the human gradual learning process, starting from the easiest tasks and gradually progressing to the hardest ones. This learning procedure is called curriculum learning  and has Recalling the main objective of this project—improving case referral from general practitioners to dermatology specialists—the primary goal of this work is to create a predictive model to classify the priority of a case into one of three levels: normal, priority, or high priority. The authors had access to anonymized retrospective data from the Portuguese National Health System related to the referral requests from Local Health Care Units for the first Dermatology Hospital consultation. The referral requests that occurred between 2014 to 2020 (before COVID-19 pandemic) and only cases corresponding to single lesions with differential diagnosis available were considered. The authors counted with the support of a group of dermatologists to select the clinically relevant list of differential diagnoses to be considered when building a prioritization model for dermatological referrals. The DermAI dataset used here was first presented in  and consObserving the partition distributions in Finally, bearing in mind that the priority level in different hospitals can change depending on the available resources, the authors worked closely with a group of three experienced dermatologists in order to build a knowledge map of the priority level for each differential diagnosis. We asked the dermatologists to individually provide their priority level distribution depending on the differential diagnosis. In Preprocessing transformations such as lesion segmentation may enhance the performance of CNN models, as proven by the positive results of the works presented in In order to obtain segmentation masks for the DermAI dataset, a procedure based on two methods was implemented. First, an attention-guided methodology using the Gradient-weighted Class Activation Mapping (GradCAM) technique  was applThe overlap between the segmentation masks obtained from both methods was found. In cases where the overlapping area was above 50% of the GradCAM mask’s area, this mask was accepted; if this condition was not verified but the overlapping area was more than 60% of the DeepLab mask’s area, then the latter was taken. Otherwise, an empty mask was considered, which means that the original image was used.The location of the lesion was inferred from the mask. A square image patch containing the whole skin lesion was defined by finding the difference between the largest and smallest dimensions of the bounding box and by adding half of this value to both sides of the latter, except in cases where this was not possible due to the total dimensions of the image. Furthermore, a tolerance based on a percentage of the approximate radius of the lesion was studied, as neighboring pixels may include contextually relevant information. Each square area of interest was used to crop the original images for both training and testing of the differential diagnosis models explained in The EfficientNet is a group of networks developed based on the network scaling , achieving state-of-the-art results while being multiple times smaller and faster . We chosTo accommodate an EfficientNetB3 network for skin lesion diagnosis, a fully connected layer was applied on top of the extracted feature map, generating a number of channels related to the number of classes to predict (13). For dimensionality reduction, the Global Average Pooling method was applied, as it is known to reduce overfitting . The finThe role of a lesion segmentation step in skin lesion diagnosis was investigated by training models with each of the following inputs: original images of the DermAI dataset and cropped images with 0%, 10%, 30%, and 50% tolerance . The inpThe network was trained using the weights pre-trained on ImageNet. The frozen block approach was adopted for better results : each EfThe input images resulting in the best performing flat model were adopted for the learning schemes described below. As further discussed later, the choice of these learning approaches was inspired by clinical and intuitive procedures, intending for the introduction of domain knowledge to improve the final priority assessment.To address the challenge of some types of skin lesions having similar clinical manifestations, dermatologists usually consider the intrinsic hierarchical organization of these lesions when performing their diagnosis. Therefore, to mimic the medical procedure, the dataset was hierarchically organized according to some of the methodologies used by clinicians, with the aim to facilitate the classification of the lesions. This organization was made in collaboration with the three experienced dermatologists, resulting in a two-level and a three-level hierarchy, as illustrated in Although the process of dividing lesions into different coarse categories can make it easier for dermatologists to diagnose skin lesions, due to their complexity, this is not a straightforward process, and for this reason, some exceptions may arise . Therefore, the learning started with the easiest classes , and throughout the process, the hardest classes were introduced .Similar to the previously described approaches , the EffThe previously described strategies can be further developed to incorporate clinical information as additional input, since the combination of complementary information from different data modalities has been shown to slightly improve performance ,27,28. OThe prioritization branch is an end-to-end approach to ascertain the priority level of a skin lesion, without considering its diagnostic. Therefore, the EfficientNetB3 network, with weights trained on ImageNet, is trained with the priority labels described in the last line of Last, the role of lesion segmentation and the incorporation of clinical information in the prioritization branch was also tested. For the former, two types of cropping were considered: the original images and the best tolerance of cropping identified on the Flat differential diagnostic approach.The incorporation of explicit domain knowledge in the final priority prediction given by the proposed framework can be performed in a number of ways. The naive method of including information from the experts is to combine the results of the differential diagnostic class and the knowledge map shown in Nonetheless, the naive approach does not consider any learning regarding the prioritization task, as it only considers the learned diagnostic branch and the knowledge map. A simple way to include the baseline priority is to fuse the outputs of the baseline priority model and the naive approach. To achieve this, a weighted addition on the outputs was performed, as in The last approach tested (Combined Approach) includes the learning step during the training of the prioritization task. For this, an auxiliary branch can be added to the differential diagnosis model to compute the prioritization. This ensures that the model learns the intricate relationship between the class and the priority of the lesions. Thus, the Combined Approach has two branches: the first computes the diagnosis and the second computes the prioritization. These branches are then joined to learn the final priority. The diagnostic output is combined with the knowledge map and added, with the expression used in the previous approach, to the prioritization branch output. This addition is made during training, and the loss function takes into consideration both diagnostic and prioritization, making sure that the diagnosis is not prejudiced for the priority.The overall metrics score results for the different experiments on skin lesion segmentation for diagnosis improvement are summarized in Analyzing sensitivity, precision, and F1 per class for the models with best overall results (trained on 30% and 50% tolerance images), the first presented the best scores for the majority of the malignant classes , thus being considered the top-performing approach. The metrics for the best model (cropped images with 30%) are illustrated in Taking into account the previous results achieved with the flat classification approach, the cropped images with 30% tolerance were adopted as input images both in the case of the hierarchical classification and curriculum learning strategies. In Analyzing the results concerning the hierarchical classification (B-CNN), it is demonstrated that the results of the flat classification approach, which was used as the baseline, were outperformed with both hierarchies (two and three levels). In the case of the three-level hierarchy, an absolute difference of almost 3% is verified in terms of accuracy, 2% in weighted F1, and with respect to macro F1, the improvement corresponded to around 2.5%. Moreover, comparing the two hierarchies, although the three-level hierarchy achieved slightly higher results in terms of accuracy and weighted F1, this difference was about 1.5% at most, demonstrating a similar performance between them.With respect to the curriculum learning strategy, the gradual learning of the different classes benefited the differential diagnosis, achieving a considerable improvement in the overall metrics, when compared with both flat and hierarchical strategies. Taking the flat classification as the baseline, an absolute difference of 10% was verified in accuracy, 8% in weighted F1, and regarding macro F1, a difference of 6% was observed.The corresponding scores for the best learning scheme (curriculum learning) for each diagnostic class are represented in It is also worth mentioning that, although the structure of the DermAI dataset used in our previous work is not exactly the same as the one considered in this study, as here the data were stratified considering the priority and differential diagnosis, the curriculum learning experiment allowed us to improve the results obtained before . In termTherefore, considering the results achieved with the various experiments, the curriculum learning approach was the learning scheme that demonstrated better performance for skin lesion differential diagnosis.Confronting the experimental results presented in Considering those results, the curriculum learning strategy without metadata (using only images) was the learning scheme adopted for differential diagnosis classification in the following experiments.Nevertheless, as dermatologists also take clinical information into account to perform skin lesion diagnosis, these were not the expected results. We expected that the introduction of metadata could improve the outcomes as previously stated in ,27,28. IAs the dataset employed in this work is private, the proposed skin lesion diagnostic model is not directly comparable with other studies in the literature, except for the work of Nedelcu et al. , which aRegarding the organization of the diagnostic classes, there is the differentiation between benign vs. basal cell carcinoma vs. melanoma , seborrhMoreover, as the employed datasets were not always the same, the images’ difficulty could also differ in the various works, which may have had an influence on the algorithms’ outcomes. For instance, images from the Dermofit database were collected under standardized conditions, whereas images from the DermAI dataset were acquired under different lighting and capture conditions, which increased their variability. Although Esteva et al.  also useFurthermore, concerning the modalities of the images explored in the various studies, some works only considered dermoscopic images ,39, whicIt is, then, possible to infer the complexity of the DermAI dataset, which comprises few examples relating to some skin lesion categories, and high image variability caused not only by the various image acquisition conditions but also from the different explored modalities. Nonetheless, as previously mentioned, compared with the other study conducted on the DermAI dataset , the modThe overall results from the trained prioritization models are summarized in For the top-performing model, the metrics and confusion matrix for each prioritization class, shown in These results are a product of the intrinsic variability of the gathered data, meaning that, depending on the hospital resources (facility and experts availability), the priority level attributed might change.The results from the combination of the trained models with the knowledge map to obtain the final prioritization are summarized in The result of the ground truth of the diagnostic and the knowledge map (KM) , it is possible to affirm that the concern with the high priority and mid priority classes is still unresolved and that the performance for the normal priority worsened. Although the baseline priority model achieves higher sensitivity in priority classes and the diagnostic curriculum with summing domain knowledge (Naive Sum) achieves good F1-score in normal class, the Simple Approach that combines both information does not obtain better results, against our expectations. This suggests that the inclusion of a direct prioritization route actually hinders the overall performance, indicating that features learned in this branch are not so priority-discriminant as the ones that account for the diagnostic categories, especially considering the inclusion of explicit domain knowledge. Interestingly, the last approach tested (Combined), despite not obtaining better global metrics, achieves the best results for the mid-priority class and the predictions on high priority are more acceptable than the previous model (Simple), since the misclassifications of high priority are on the priority class instead of normal. However, this comes at the cost of having worse results for the normal class.To the best of our knowledge, there are no reports of the prioritization task of skin lesions, so the final prioritization results were further confirmed in test set 2, as shown in Regarding the approaches where differential diagnosis and knowledge map are included, we see a drop in macro F1 , which may be indeed a more reliable result for real-world performance, considering the validation of its labels. This comparison highlights the importance of building a sound validation strategy for any learning model, especially when dealing with a highly variable target such as dermatological case prioritization. Different hospitals and clinicians assign different levels according to available resources, previous experience, and other factors. In this context, data curation is critical, even if relevant experts cannot validate all samples. Nonetheless, data-centric methods and the inclusion of domain knowledge (either implicitly or explicitly) may help to circumvent these limitations, as shown here, improving the priority baseline by incorporating diagnostic classification  and exploring direct relationships between differential diagnosis and expected priority distributions in the form of a knowledge map provided by experts.This paper proposes different strategies for the relevant and yet-unexplored task of automatic dermatological case prioritization based on skin lesion images and metadata. Retrospective data from the Portuguese National Health System was used, consisting of dermatological images and metadata (age and sex) from 13 different differential diagnostic classes and three priority levels  attributed in the referral process for dermatology specialty consultation in public hospitals. Besides the natural diversity from different acquisition settings, the dataset includes the variability regarding the prioritization levels, which are dependent on the hospital resources to book the consultations, besides the individual clinician’s experience.This work proposes an innovative framework for prioritization, leveraging different forms of implicit and explicit domain knowledge. The range from preprocessing operations and methodologies for training a Deep Neural Network . These Considering the real-world dataset used and the diversity of dermatological images obtained, the first experiment concerned the preprocessing operations needed in terms of lesion segmentation to improve the diagnosis classification. In this study, the best results were obtained when using cropped images with 30% tolerance , when compared with the original images and with cropped images with 0%, 10%, and 50% tolerance.In terms of learning schemes for differential diagnosis classification, the cumulative strategy of curriculum learning provided the best results, achieving 48.75% accuracy, and 49.64% and 33.55% weighted and macro F1 for test set 1, respectively, when compared with flat the classification approach and both hierarchical approaches (two and three levels). Moreover, the inclusion of patients’ metadata in the differential diagnosis classification models was also studied, showing that it was not beneficial for the experiments tested, against our expectations.Regarding the prioritization branch experiments, the priority baseline was established using the images cropped with 30% tolerance and no metadata, similar to what was found to return the best results for the differential diagnosis branch . This baseline was highly surpassed when considering only the diagnostic branch with the knowledge map (Naive Sum), reaching in test set 2 an accuracy of 50.52%, weighted F1 of 44.39%, and macro F1 of 43.61%. Against our expectations, the Simple Approach global results, which incorporate both diagnostics with the knowledge map, and prioritization models, although superior to the baseline , did not improve in comparison with Naive (Sum). Nevertheless, the Combined Approach achieved the best general results regarding weighted (44.93%) and macro F1 (43.93%) and the priority class achieved better sensitivity results. However, this result comes at the cost of having worse results for the normal class. From the clinical point of view, it might be preferable to choose the Combined Approach, as it gives more importance to the results for priority classes.In what concerns future work, we identify different open research lines, namely regarding data acquisition. The DermAI project was able to design a new mobile acquisition software to support even non-specialists in capturing higher quality and more standardized lesion images. We believe that a new iteration of the proposed framework using newly acquired data in real-world settings will overcome some of the limitations shown by the current dataset, especially regarding image focus and variability. Moreover, following the potential demonstrated by the experiments with image cropping based on automatic lesion segmentation, we believe there is still margin for improvement in this important task, resorting to specific image processing techniques that could not be explored in this work. Regarding the learning schemes, although curriculum learning returned the overall best results, the shared clinical features of some diagnostic categories lead us to believe that hierarchical classification deserves to be further explored. In this context, we think an iterative approach where we use the misclassifications at a specific iteration to group fine-grained categories into higher levels could yield interesting results. Finally, concerning the inclusion of explicit domain knowledge, the standard deviation information should be also considered in the model.In conclusion, we firmly believe that existing pre-trained models, especially for image classification, encompass more-than-enough complexity for most tasks, even the most complex ones such as dermatological case prioritization. On the other hand, research should focus on data-centric approaches to extract the most value from existing data or to improve the acquisition process and on strategies to use essential domain knowledge, be it to inspire new learning schemes or to include it explicitly to guide the predictions.


Recent literature has addressed initial coin offering (ICO) projects, which are an innovative form of venture financing through cryptocurrencies using blockchain technology. Many features of ICOs remain unexplored, leaving much room for additional research, including the success factors of ICO projects. We investigate the success of ICO projects, with our main purpose being to identify factors that influence a project’s outcome. Following a literature review, from which several potential variables were collected, we used a database comprising 428 ICO projects in the banking/financial sector to regress several econometric models. We confirmed the impacts of several variables and obtained particularly valuable results concerning project and campaign variables. We confirmed the importance of a well-structured and informative whitepaper. The proximity to certain markets with high availability of financial and human capital is also an important determinant of the success of an ICO. We also confirm the strong dependency on cryptocurrency and the impact of cryptocurrency valuations on the success of a project. Furthermore, we confirm the importance of social media in ICO projects, as well as the importance of human capital characteristics. Our research contributes to the ICO literature by capturing most of the success factors previously identified and testing their impacts based on a large database. The current research contributes to the building of systems theory and signaling theory by adapting their frameworks to the ICO environment. Our results are also important for regulators, as ICOs are mainly unregulated and have vast future potential, and for investors, who can benefit from our analysis and use it in their due diligence. Initial coin offerings (ICOs) are a novel concept that first appeared in 2013 with the MasterCoin project proposed by J. R. Willett. These projects allow the financing of innovative ideas at a global level, which contributes to the democratization of financial investments and allows an entire new reach hardly achieved through conventional means Brochado . Highly The characteristics of the project are compiled in a whitepaper, which is unregulated but tends to follow certain characteristics and can be compared to a regulated prospectus. The whitepaper is also a measure of the project’s credibility, as it contains technical information, business information, and information regarding the team.As in crowdfunding, the success of an ICO project may depend on the amount of capital raised. An ICO project may have none to several thresholds defining the capital to be raised  and transparent nature, which allows completely secure transactions  social components, which represent the people, and (ii) technical components, which represent the technology and machines  channels among market participants : (i) be observable to the receiver; (ii) be costly to realize and imitate, since if no costs are involved, the signals will be easy to replicate and, thus, have no value  the size of the problem that the business is attempting to solve, (ii) the elegance of the solution, (iii) the entrepreneurial team, (iv) financial statements, and (v) legal aspects Yadav . As ICO As ICOs are highly technological, signals they might use to affirm high-quality projects to investors are: (i) patents ; (ii) technical whitepaper ; (iii) high-quality source code  Fisch . SignalsSignals are considered to be success factors of ICO projects because they reduce information asymmetries and the projects are more easily perceived as high-quality projects  international efforts for the creation of international legislation concerning ICOs; (ii) EU regulatory actions to make ICOs comply with regulatory guidelines concerning the disclosure of crucial information . The common argument is that a longer campaign negatively affects the performance of a project, and shorter campaigns are more likely to see better outcomes  the signals provided by the project promoters to investors in the open systems relationships contribute positively to the outcome of a project; (H2) projects that provide higher levels of quality information are more successful.Four econometric models were built containing several variables identified as signals of project quality. The four models differ in terms of the dependent variable used: (i) logarithmic version of the total capital raised by a project, (ii) binary variable measuring the achievement of the soft-cap threshold, (iii) binary variable measuring the achievement of the hard-cap threshold, and (iv) binary variable measuring the existence of a secondary market. The reasoning behind the creation of several models was to ensure that the results remain consistent among them and to understand which dependent variable best captures a project’s success. The variables were clustered according to their characteristics and subsequently added to the model. We expect that projects that provide more information are also the most successful, measured in terms of capital raised. This measure is a direct result of good quality signaling and also functions as feedback in the dynamics of open systems.The database was built through an API accessible with a premium subscription to the ICO Bench website and comprises 556 projects in the banking/financial sector. This sector was selected due to the impacts it faces from the rise of Fintech companies . The second most represented location is the Asia–Pacific region with 106 projects, followed by North America with 44 projects. The other locations combined represent 59 projects. Most of the projects have low (180 projects) or average (179 projects) ratings attributed by external parties, and very few projects obtain exceptional classifications (69 projects). Most of the entrepreneurs have participated in up to three projects (3073 profiles), and only a few promoters have participated in more than three projects (85 profiles). The majority of the teams comprised between 11 and 20 members. Complementing the database, we collected information using the Twitter and LinkedIn social network platforms. We were able to collect information on Twitter activity, such as the number of followers and activity during the ICO campaign, and on LinkedIn networks, such as the team members’ number of connections from team members.The dependent variable that primarily captures projects’ success is the total capital raised by each project in U.S. dollars, as used in previous studies. We used its logarithmic form in our econometric model. We also regressed three extra models using different dependent variables: (i) a binary variable measuring the achievement of the soft-cap threshold, (ii) a binary variable measuring the achievement of the hard-cap threshold, and (iii) a binary variable measuring the existence of a secondary market. The independent variables can be divided into four main groups: (i) project variables (related to the project’s characteristics), (ii) campaign variables (related to ICO campaign characteristics), (iii) social network variables , and (iv) team variables .Concerning the project variables, we captured variables related to the project itself and obtained the following: (i) whitepaper  restricted countries ; (iii) region .The variables capturing the campaign characteristics are focused on aspects relevant to the ICO campaign as follows: (i) pre-sales ; (ii) bonus scheme (captures the existence of a bonus to investors); (iii) token price (captures the price at which the token was sold); (iv) cryptocurrencies’ average prices (captures the yearly average price of Bitcoin and Ethereum); (v) currencies accepted (captures the number of currencies the project accepts as investment) Table .Table 1VSocial networks have become an essential part of the promotion of new ventures, and we captured their characteristics as follows: (i) Twitter activity ; (ii) GitHub activity (captures the existence of a GitHub account and the existence of publicly available code before the ICO campaign); (iii) active website (captures the existence of an available website in May 2020).Concerning the team variables, we were able to capture the following aspects: (i) team members (number of members in the team); (ii) advisors (number of advisors in the project); (iii) LinkedIn connections (the sum of team members’ LinkedIn connections).There were several more variables that we were able to capture, but that we decided not to include in the model due to multicollinearity issues  method and performed a test to detect skewness and kurtosis, which we verified to be present. Therefore, we applied a Shapiro–Wilk test to confirm that the residuals did not have a normal distribution STATA . There wrreg” in STATA  and between the last two (hard-cap achievement and secondary market existence), but with substantial differences among them. In the last two models, the percentage of unsuccessful projects is high, which makes the analysis much more difficult. The measures of success used in the first two models allowed us to obtain a much more balanced percentage between success and failure, maintaining a clear tendency for more unsuccessful projects. The last two variables seem to incompletely capture the success of a project for several reasons: (i) the achievement of the hard cap assumes that the projects have a maximum financing threshold, which is not always the case (the same issue exists for the soft-cap achievement variable); (ii) the achievement of the hard-cap threshold is not a necessary condition for the project to be successful because a project has the necessary conditions to work once it achieves the soft-cap threshold; (iii) although it is expected, several projects may not achieve the stage of tradability in a secondary market, but that does not necessarily mean that they did not achieve the financing thresholds needed for the project’s funding.Focusing on the first two models, which allow for clearer conclusions regarding descriptive statistics, we confirm that disclosing the team and having a technical and longer whitepaper are associated with success, accounting for the highest percentage of success. In terms of country restrictions and project region, there is no clear division between successful and unsuccessful projects, as the percentages are similarly divided and do not lead to a clear conclusion. Concerning the campaign variables, we cannot reach a clear conclusion with descriptive statistics on the variables related to the existence of pre-sales, the number of currencies accepted by the project, or the token price. This is due to a similar allocation of successful and unsuccessful projects, regardless of the value of this variable.The prices of cryptocurrencies, namely, Bitcoin and Ethereum, seem to be inversely related to the project’s success. Whereas a cheaper price for Bitcoin is related to more successful projects, the opposite occurs with Ethereum, which tends to have more successful projects when its price is higher. The variables related to the use of social networks tended to influence project success. Having an active Twitter account during the ICO campaign is associated with successful projects (32%), in contrast to not having an active Twitter campaign (15%), and having more followers is linked to successful projects (32%), in contrast to having smaller networks (16%). The number of tweets does not allow for a clear analysis because of the percentages obtained. Although at a much smaller scale, we also confirm that having an active GitHub account is linked to greater success (28%) than not having one (19%). Having code publicly available before the start of the ICO campaign is not confirmed as having a strong relationship with success, contrary to the existence of an active website, because more projects are considered successful when they have one (30%) than when they do not (17%). Finally, the team variables considered relevant to a project’s success are the number of team elements and LinkedIn networks. Larger teams and networks have higher percentages of successful projects (29% for both) than the opposite .As shown in Table To normalize the data, we rescaled several variables using log transformation. We also developed a frequency table for binary variables, as shown in Table Although the majority of the projects included pre-sales of tokens (54%), most of them had no bonus scheme (55%). In terms of variables related to the use of social networks, we verified that most projects had an active campaign on Twitter (59%). The projects with a GitHub account represent 51% of the sample, and only 36% of them had publicly available code prior to the ICO campaign. Currently, 56% of projects have active websites.After performing a correlation and VIF analysis,We started by regressing the econometric model with the dependent logarithmic variable  using the standard OLS methodIn the first two models, the results are consistent in terms of the statistical significance of the variables and coefficients. We obtain statistical significance for the following project variables: (i) whitepaper (team disclosed) and (ii) whitepaper . The campaign variables considered significant are as follows: (i) bonus scheme, (ii) token price, (iii) Bitcoin price, and (iv) Ethereum price. Concerning the social network variables, the ones considered significant are: (i) Twitter active during the ICO campaign and (ii) Twitter number of followers. The team variable considered significant was the number of team members. Although statistically insignificant, we would like to highlight the importance of Twitter activity measured by the number of Tweets, since, as in the literature, we found that extremely active Twitter accounts, which put pressure on investors, may contribute negatively to project success.Even though there are some discrepancies between the results of Models 3 and 4, there are variables that retain their strong statistical significance in all the models: (i) whitepaper technical, (ii) bitcoin price, and (iii) number of Twitter followers (except for Model 4). Model 2 provides an interesting conclusion regarding the length of the whitepaper, revealing that it contributes to the project’s success, mainly due to the amount of information included, which is important for reducing information asymmetries. Model 4 contributes to the discussion by revealing the importance of locations concerning projects located in Europe. Models 3 and 4 prove to be statistically significant for the variable measuring the existence of pre-sales and reveal its negative impact on the success of a project. It is important to highlight that the coefficients are consistent among the models, but it was not possible to maintain similar results regarding the statistical significance.We built on systems theory and signaling theory as a theoretical background for the current research. The ICO projects were interpreted in light of the theoretical framework proposed by both theories. It was found that the ICO projects operate as a system, particularly as an open system  and keep them engaged (measured by the number of followers) are also more capable of achieving positive results.The findings of our study contribute to the literature on ICO projects. We were able to assemble a database and test most of the variables concerning the success factors of ICOs mentioned in the literature. We confirmed several aspects present in the literature, but also obtained different results, thereby contributing to a wider discussion. Our research reported here, based on a considerably large database of blockchain projects and the use of theory, makes our contribution unique in the literature.Furthermore, this research has elaborated on systems theory and signaling theory. These theoretical backgrounds were adapted to the blockchain-based projects, and we were able to confirm that ICOs are open systems operating in an environment with several other systems that interact among them. The importance of feedback and the exchange of information is crucial in systems with these characteristics. Nevertheless, the ICO markets are characterized by information asymmetries between project promoters and investors, which can lead to flawed markets. This is why providing quality signals, here described as success factors, created by the project and sent to investors, are so important.We were able to prove that the two theories we used are relevant in the context of ICOs and that further analysis on them should account for the theoretical foundations analyzed here. Thus, the findings of this study uniquely contribute to the literature by using and interconnecting the two theories and adapting them to innovative blockchain projects. This provides ICO projects with an appropriate theoretical framework for their analysis.Our research is also important for regulators, mainly regarding our conclusions concerning whitepapers. Initial coin offering projects are highly unregulated, but because of the amount of money involved and the importance they have acquired, regulators need to pay closer attention to them. As the current risks of blockchain projects involve security and fraud, regulators should focus on measures supporting transparency and accountability to avoid fraud and crime (Frizzo-Barker et al. For the promoters of ICO projects, the implications of this research are mainly related to investors’ perceptions of the project and the aspects promoters should pay particular attention to when promoting their ideas. We reinforce some of the conclusions found in the literature and add new insights that can be important in determining a project’s outcome. Indeed, a project promoter should, for instance, focus on having a detailed whitepaper, in which the team is disclosed and some technical details are highlighted. Furthermore, the project’s timing should be selected to take advantage of cryptocurrency prices, the campaign should be as short as possible, and social networks should be well-managed. In summary, the ICO project’s promoters should take advantage of the messages presented in the literature to control all the factors that can obtain the best outcome.The database used for this research originally comprised 558 ICO projects, but some had to be discarded due to lack of information, leaving 428 projects for analysis. The database included only projects in the banking/financial sectors and may, therefore, not be generalizable to other industries. We also removed some of the variables collected because of their collinearity levels. It would be valuable to keep these variables in future studies, in order to clearly observe their behavior. Concerning Twitter and GitHub activity, we faced an issue with inactive or blocked accounts. Although the number was not enough to compromise the research, we point out the fact that, in some cases, we were not able to collect information on social platform activity, due to the current unavailability of the account. The same issue occurred with some projects’ whitepapers, which we could not find, even by searching several platforms dedicated to ICOs and on the project’s official website.We collected information on the ratings attributed to the project, namely, project rating, vision rating, and team rating. We were not able to use all of these factors because of their degree of collinearity. We decided to remove all these variables because we could not be certain of the time when they were attributed. The ratings are usually attributed before the ICO starts and are updated throughout the campaign and again after it is concluded. Although we are confident of their correct usage in the model, we could not ensure that they were completely unbiased and we, therefore, decided to exclude them. In our preliminary analysis, we concluded that the ratings for the team, project, and product had a positive impact on project success, and the rating for the vision had a negative impact on the project. This is due to the fact that visionary projects tend to be more difficult to achieve and harder to understand, creating a barrier for investment (Gompers and Lerner A final limitation identified in this research concerns the inclusion of variables measuring the achievement of soft-cap and hard-cap thresholds. To keep the same sample used for the remaining models, we considered the value 0 for the projects that did not establish fundraising thresholds. Their exclusion would reduce the sample, as several projects do not define financing thresholds, and after regressing the model with a reduced sample, we concluded that the results remain consistent with those obtained when using the entire sample collected.There are many avenues for future research in ICO projects because of the novelty of the topic and several unexplored features. After performing the current research, we found that some variables have a strong impact on project success, for instance, the whitepaper. For future studies, it would be interesting to isolate these variables and focus on their effects. Regarding the whitepaper, it would be interesting to analyze its smallest aspects to understand what is most important. Furthermore, we confirmed that the secondary market is of great importance. Therefore, it is important to analyze the behavior of tokens in the secondary market in terms of volatility, tradability, returns for investors, and many other factors. Concerning the database, it would be useful to construct a wider database and cluster it by the project’s industry before determining the most successful industry and confirming the success factors that are transversal to industries or those that apply to only one specific industry.Building on OST and signaling theory, future studies might focus on the sub-system relationships, that is, those between projects, to determine if there is any degree of cooperation and the extent to which cooperation is useful. It would also be interesting to analyze the relationships within the project, such as human relationships, aspects of corporate governance, or the way feedback is processed for the correct adaptation of the system (Mele et al.


Despite the recent surge of COVID-19 infections in Ethiopia, we are observing a profound ignorance of preventive measures by the general public and leaders at different levels. This is presenting considerable challenges in the effort to contain and control the pandemic. We believe that the current health communication approach implemented by the health authorities and media outlets need to be redesigned to bring a sustainable COVID-19 preventive behavior. The purpose of this perspective paper, therefore, is to stimulate discussions on effective health communication strategy to help the public persistently practice COVID-19 preventive measures over the long term. We undertook a series of discussions amongst the authors in order to synthesize individual viewpoints into ‘experts' perspective’ driven by our daily observations and our expertise in the health service research. In light of this, we suggested that an effective health communication strategy need to address context specific situations to avoid temptation to ignore the ramifications of this very serious pandemic. This strategy includes trying to make sense of daily reported COVID-19 cases, being highly selective regarding sources of information, and being sensitive and responsive to religious and cultural factors. The media, health professionals, and leaders need to teach us how to live with the pandemic informed by robust scientific sources. As of 31 October 2020, COVID-19 cases are increasing in Ethiopia steadily with 96,169 confirmed cases, 1,469 deaths and 52,517 recoveries . It is aPrior to the first confirmed case of the novel coronavirus in the African region, Ethiopia was among the 13 African countries that the World Health Organization (WHO) considered as a top priority for COVID-19 preparedness due to direct links or a high volume of travel to China . Since tth of March 2020, the government has put measures in place including declaring a state of emergency, implementing mass communication around social distancing, hand hygiene and wearing masks. The government implemented measures including banning public gatherings, closing schools and universities, and announcing the postponement of highly anticipated national elections previously scheduled for August 2020  poses concerns for the public to distinguish fact from fiction, and for government agencies to conduct evidence-based policy-making (y-making . The misPromote resilience: Finally, a health information communication strategy intended to promote resilience and adaptation to the ‘new normal’ will have an indispensable role in enhancing adherence to COVID-19 prevention, and also in educating the public on how to live with the pandemic in the long term. The progressive decline in people's motivation to perform COVID-19 preventive behaviors might be partly attributed to the fact that there is a need to consistently apply these measures for several months, if not years, until the pandemic is eradicated or effective vaccine or treatment options are available. It is necessary for individuals and families to change their way of life, even though it is particularly challenging for interdependent societies. Our daily life needs to incorporate wearing face masks, physical distancing, proper handwashing, applying hand sanitizers, and other infection prevention measures. As such, developing our communication strategy to advocate sustainably of these behaviors will be important to contain the pandemic. In light of this, effective health communication needs to address context specific situations to avoid temptation to ignore the ramifications of this very serious pandemic. The information we get from the media, health authorities, and leaders should help us to carefully appraise the seriousness of the problem without panicing.In conclusion, the coronavirus infection is surging at an alarming rate in Ethiopia. This has given rise to several challenges on an unprecedented scale ranging from public health emergencies to political and societal problems, and a significant disruption of the economy. However, more alarming than the current surge of positive cases is the progressive ignorance of the public to perform preventive behaviors. Effective and innovative COVID-19 health information communication strategies are imperative to encourage and sustain adherence to preventive behaviors, in efforts to flatten the case and death curves and mitigate the impact of the COVID-19 pandemic in Ethiopia.


Evidence-based practice for stroke prevention in high-income countries involves screening for abnormal transcranial Doppler (TCD) velocity and initiating regular blood transfusions for at least 1 year, followed by treatment with hydroxyurea. This practice has not been transferred to low-resource settings like Nigeria, the country with the highest global population density of SCD. Following a multi-center randomized controlled trial among children with SCA in northern Nigeria, screening for stroke and initiation of hydroxyurea was established as standard of care at the clinical trial sites and other locations. We aim to describe the critical steps we took in translating research into practice for stroke prevention in SCA in Nigeria. Guided by the PRISM framework, we describe how we translated results from a randomized controlled trial for primary prevention of stroke in children with sickle cell anemia into usual care for children with SCA in Kaduna, Nigeria.Findings from this study demonstrate the importance of organizational support and stakeholder involvement from the onset of a clinical trial. Having the dual objective of conducting an efficacy trial while simultaneously focusing on strategies for future implementation can significantly decrease the lag time between discovery and routine practice. Sickle cell anemia (SCA), an inherited blood disorder, is the leading cause of stroke in children. An estimated 11% of unscreened and untreated children with SCA are at increased risk of stroke and will have at least one stroke by 17 years of age . This EBThis paper presents a case study, highlighting the critical steps taken in translating research into practice for stroke prevention in SCA in Nigeria. Few publications share experiences in planning for implementation while conducting effectiveness sickle cell research in LMICs, and we argue that sharing these details is critical for investigators in similar countries to learn from these efforts. When implementing EBP in low resource settings, much happens in terms of establishing the infrastructure so a trial can be successful and not a “Trojan Horse” for the community . In otheUnderstanding the contextual factors is necessary to integrate EBP into usual practice . Our worDespite the high stroke burden within the region, there were no stroke prevention strategies for children with SCA in Kaduna. Barriers to stroke prevention strategies included a lack of TCD services , primariBefore our randomized controlled trial, families had little knowledge regarding SCA and the associated complications. There was little organizational infrastructure for stroke prevention in local health facilities, including the academic hospital. To run the trial, we secured buy-in from hospital leadership and assembled a team. While the site awaited approval from the national and local institutional review boards, the team was certified in research governance, including ethical conduct of research, good clinical practice, and NIH training in research on human participants and how to conduct audits.organizational level, securing the commitment of top management of the hospital for the trial was essential. The hospital leadership appropriated clinic space, personnel, and other hospital facilities for the study. All team members, including the research coordinators, research assistants, phlebotomists, pharmacists, nurses, medical officers, and medical monitors, participated in regular weekly meetings with the Principal Investigator and other members of the wider research team to address any problems encountered during the week. The stroke prevention program was incorporated into the existing SCA clinic, which ran weekly.At the To address the lack of TCD screening services, the research leadership provided TCD machines and organized trainings on the conduct of TCD and stroke detection. Given the shortage of radiologists in the hospital, we trained non-specialist medical officer alongside a radiologist to conduct TCD screenings, a more sustainable option than training a radiologist alone. Conducting TCD screening on clinic days and those with abnormal TCD to be evaluated by the pediatrician on the same day helped reduce the burden of having to return at a later date for parents and patients, including the cost of transportation, hours spent out of work for parents, and missed school days for the children.patient characteristics to develop general education materials on SCA, emphasizing stroke signs and symptoms, including videos and pamphlets in Hausa language, the region’s indigenous language. With the support of the hospital leadership and the State’s Ministry of Health, radio jingles were aired on local radio stations describing stroke as a complication of SCA, how it can be prevented, and the availability of TCD screening at the academic hospital. Additionally, patients were able to call the clinic directly to book appointments for TCD screenings.Having recognized the knowledge gap regarding SCA and stroke among families of children with SCA, we considered the Working closely with Kaduna’s state leadership was imperative for implementing the SCA guidelines. Their endorsement of the stroke screening programs was essential to disseminate information and raise awareness about the screenings. Other health care facilities within the state were informed of these guidelines and the stroke prevention program at the academic hospital through the leadership of the hospitals, and radio announcements was used to create awareness to the public.After the completion of our SPRING trial, TCD screening became standard of care, and children with abnormal TCD values were identified and prescribed hydroxyurea. Unfortunately, the team realized that most of the families could not afford hydroxyurea; therefore, we developed stop-gap strategies to provide hydroxyurea for these children, which included; (a) raising money from within the teams’ personal resources and from local philanthropists and (b) the leadership of the academic hospital taking responsibility for providing free hydroxyurea and some laboratory investigations for the children with abnormal TCD values for 1 year.We met with officials at the Health Ministry to deliberate on the best strategy for ensuring the availability and sustainability of hydroxyurea for the children after completion of enrollment for the trial. After significant advocacy, the governor of Kaduna State approved the provision of free hydroxyurea for children with abnormal TCD values for 5 years. More recently, the leadership of the agency responsible for the procurement, warehousing, and distribution of health commodities, has recognized the role of hydroxyurea in the prevention of stroke among children with SCA. As such, the agency has included the provision of hydroxyurea into the state’s free maternal and child drug package, thereby making it accessible at no cost to families. To ensure transparency and accountability of the government’s free hydroxyurea, we established an e-prescription system, which provides a platform for periodic audits.Given that our initial study was conducted in an academic hospital, we have secured funding from the NIH to initiate a stroke prevention program in a community hospital by task shifting of TCD screening to nurses. Currently, we plan to scale up the stroke prevention program to other parts of the state.no TCD was done in Kaduna. Capacity building was incorporated at the beginning of the trial, therefore, immediately after enrolment was completed, for the trial, TCD screening became standard of care and children with abnormal TCD values were identified and prescribed hydroxyurea. From 2017 to October, 2021, a total of 2005 TCD examinations were conducted, out of which 123 were found to be abnormal (> 200 cm/s) and 98 of these children have been receiving hydroxyurea free of charge  is 10% , which is quite low. Given that our initial study was conducted in an academic hospital, we were funded by Fogarty International Center of the NIH (K43TW011583) to replicate a similar program as the one at the academic center at a community hospital in a densely populated part of Kaduna town where majority of the patients with SCD seek care. To sustain this effort, we plan on scaling up the stroke prevention program to involve other parts of Kaduna State and eventually, the whole of Nigeria.Before the SPRING trial, We have described how the PRISM model was used to identify contextual factors in the planning, implementation, and sustainability of our research-oriented program on stroke prevention in children with SCD in Kaduna, Nigeria, and its integration into the usual care of this patient population at an academic hospital , one of the sites for the SPRING trials. Research findings are generated in controlled settings and often fail to be fully utilized in real-world settings because contextual factors that enhance the intervention’s uptake are not appropriately addressed . PreviouOne of the significant challenges we faced in these trials included frequent changes in the leadership of the Ministry of Health. Some of the senior policymakers had no knowledge of stroke in children, particularly those with SCA, and were skeptical about the intervention. To address these challenges, we engaged with leadership and policymakers whenever there was a change in the administrative direction by conducting advocacy visits and briefs. Additionally, we partnered with a ‘champion’ of our work at the Ministry of Health, who would provide updates and follow up on previous discussions. A summary of the challenges we faced and how they were addressed is presented in Fig. Applying implementation science methods to improve the uptake EBPs in LMICs allows researchers to create resources, change behavior, use creative and innovative methods, build research capacity, and push for policy changes to support the science. With an increase in funding for SCA-related research, it is our hope that, by utilizing the tools of implementation science at the onset of the randomized controlled trials with ongoing weekly assessment and critical evaluations, we can promote uptake of evidence-based care for children with SCA.


It is considered a “social stigma”, as it can determine a number of psychological and relationship consequences that affect people’s lives. The purpose of this review is to examine the role of psychological factors in the condition of self-perceived halitosis in adolescent subjects and adulthood. (2) Type of studies reviewed: We conducted, by the PRISMA  guidelines, systematic research of the literature on PubMed and Scholar. The key terms used were halitosis, halitosis self-perception, psychological factors, breath odor and two terms related to socio-relational consequences . Initial research identified 3008 articles. As a result of the inclusion and exclusion criteria, the number of publications was reduced to 38. (3) Results: According to the literature examined, halitosis is a condition that is rarely self-perceived. In general, women have a greater ability to recognize it than men. Several factors can affect the perception of the dental condition, such as socioeconomic status, emotional state and body image. (4) Conclusion and practical implication: Self-perceived halitosis could have a significant impact on the patient’s quality of life. Among the most frequent consequences are found anxiety, reduced levels of self-esteem, misinterpretation of other people’s attitudes and embarrassment and relational discomfort that often result in social isolation. Halitosis, commonly called bad breath, is a problem that can affect both the external, relational, social communication and internal, psychological sphere, with implications on perceived quality of life . People A review strategy has been conducted to summarize the available results of experimental studies. This review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  Figure . To identify the studies, we performed a systematic literature search on the PubMed, Scholar and ScienceDirect databases. The search was limited to studies written in English. We identified the literature from January 2010 to January 2020 using four key terms related to self-perceived halitosis, namely halitosis, self-perception halitosis, psychological factors and breath odor, and two terms related to sociorelational consequences . The articles of 1994 and 2001 were only cited to introduce the body image theory and to link it to the perception that each individual has of his or her own smell. The electronic research strategy used is described in Articles were selected online in relation to the title and abstracts; articles were read in full when titles and abstracts were consistent with the objective of our study. Following this procedure, we found 889 articles on the PubMed database, 1950 articles on Scholar and 169 articles on ScienceDirect; after applying the inclusion and exclusion criteria, the total number of relevant publications was reduced to 38.The articles were included in the review according to the following inclusion criteria: English language, publication in peer-reviewed scientific journals and quantitative information about self-perceived halitosis and oral hygiene. Articles were excluded based on title and abstract screen; review articles, editorial comments and case reports were also excluded. The quality of included studies was appraised by separate methods for qualitative and quantitative studies; features of study design, methodology and analysis were assessed. Qualitative studies were appraised using Critical Appraisal Skills Programme tool (CASP), while the Effective Public Health Practice Project (EPHPP) tool was used for quantitative studies. The search in the PubMed, Scholar and ScienceDirect databases provided a total of 3008 articles; no further studies meeting the inclusion criteria were identified. After eliminating duplicates, further studies were excluded according to the inclusion and exclusion criteria. After screening, 38 studies were selected as appropriate for the present review.In all studies included in this review, a potential bias of the database should be considered. Only articles in English have been used, which could have compromised access to articles published in other languages.According to the literature examined, halitosis is a condition that is rarely self-perceived. In particular, 10 articles focus on the self-perception of halitosis, 10 deal with halitosis by defining it and analyzing the epidemiology, 1 study focuses on body image related to the perception of one’s smell, 7 studies analyze the different psychological factors related to halitosis and 10 studies address the sociorelational problems caused by halitosis. Quality assessment of the selected works was performed using CASP and EPHPP tools; the results of quality assessment are reported in This study has identified the importance of the consequences that the perception of bad breath has on the psychorelational side and, in general, on patients’ quality of life.The studies analyzed focus on the importance of self-perception of halitosis and the consequences it has on the life of the individual. It is an oral health problem that could be prevented through appropriate oral hygiene practices and to which attention should be paid, given the influence it has on patients’ quality of life. The subject’s awareness of his or her condition leads to such anxiety that he or she obsessively uses appropriate instruments to eclipse the bad smell, such as sprays, chewing gum, pills or mouthwashes . AlthougThis study suffers from several limitations: indeed, due to the relatively low number of studies on the topic and their great methodological heterogeneity, a proper formal quality assessment of each work was precluded. Therefore, selection bias was not properly addressed, which can impact the general interpretability of results. In general, care is required when interpreting the results of the cited studies, and further investigations are needed to clarify the relationship between self-perceived and objective halitosis.This study has identified the importance of the consequences that the perception of bad breath has on the psychorelational side and, in general, on patients’ quality of life. Literature studies have shown that women have, as a percentage, a greater awareness of their dental condition than men. Socioeconomic status is one of the factors that can influence self-perceived halitosis. Research has also led to an understanding of how body image and emotional state may be a variable that can influence perception. Oral hygiene measures can significantly reduce the bad smell. Oral health education must also be provided in many places, including private dental and medical clinics, and it is important that healthcare professionals, including general practitioners and health professionals, understand the etiology and critical factors in order to diagnose and treat patients appropriately. This review has reviewed the studies in the literature. The limitation of our study concerns the exclusive treatment of psychological factors implicated in halitosis at the expense of possible treatments.


Insufficient government management and supervision at clinic-level dental institutions lead to limited scope for regulating the installation of convenience facilities. Better laws, with continued support and supervision of central and local governments, are needed to resolve this issue.To maintain the oral health and ability to smoothly receive dental treatment of people with disabilities, their accessibility to dental institutions must be enforced. Hence, this study aimed to assess the status of installation of convenience facilities for people with disabilities in dental institutions in Chungbuk Province, South Korea. According to the checklist based on installation items for convenience facilities for people with disabilities, 245 dental institutions in Chungbuk Province were visited and investigated to examine whether they had installed internal, intermediary, sanitary, and information-related facilities for people with disabilities. Inputs were analyzed using SPSS 25.0 . Frequency analysis by item was conducted to determine the rate of installation, and Fisher’s exact test was performed to analyze the actual state of installation according to the type of institution. The results revealed a relatively higher rate of appropriate installation and access routes to the main entrance and parking spaces for people with disabilities in terms of intermediary and internal facilities, but at times they were installed at inappropriate locations or lacked proper signboards. The state of installation of convenience facilities by institution type was 0–1.3% per item at the clinic level but 75–87.5% per item in general and dental hospitals ( The standards for sanitary facilities include the installation of a washbasin, specifically for people with disabilities, and at least one toilet and one urinal for both males and females. For information facilities, braille blocks should be installed on the sidewalk connecting the building’s main entrance and the road, and braille information boards and alarm systems must be installed near the main entrance.Convenience facilities for persons with disabilities refer to facilities and equipment that enable convenient—and equal—access to services and information for all people, whether with disabilities or fully fit . These fAlthough convenience facilities for individuals with disabilities have been progressively improving over the years, they lack quality and are plagued by the persistent problem of limited accessibility ,4,5. ThiAccording to the 2018 Survey Report on Convenience Facilities for People with Disabilities , ChungbuIn this context, this study investigated the current state of installation of convenience facilities for people with disabilities at dental institutions in Chungbuk Province, South Korea, to collect basic data for suggesting appropriate improvements for access and expansion of convenience facilities at such institutions.All dental institutions in Chungbuk registered with the National Health Insurance Service were included as research subjects. A total of 245 dental institutions, registered in Chungbuk as of January 2020, were studied via a complete enumeration survey. To investigate the installation status and standard of convenience facilities for people with disabilities, we visited the dental institutions to record measurements and make observations. Two researchers with previous training in measuring standards prepared a checklist  regardinThe items related to convenience facilities for people with disabilities at dental institutions, as stipulated in Article 4(2) of the Enforcement Decree of the Act on Convenience Promotion of people with disabilities, the Elderly, and Pregnant Women, were analyzed by dividing the area into intermediary facilities, internal facilities, sanitary facilities, and information facilities. From the checklist, under intermediary facilities, the accessibility of the routes to the main entrance, parking zone designated for people with disabilities, and height differences to main entrance  were investigated; under internal facilities, main entrance (door), corridors, stairs, and elevators installations were examined; under sanitary facilities, toilet seats, urinals, and washbasins for people with disabilities were checked; and for information facilities, the braille block, guide and information system, and warning and evacuation system were examined . For eacThe input of the convenience facility checklist was analyzed using SPSS 25.0 . Frequency analysis by item, according to the type of convenience facility, was conducted to determine the rate of installation, and Fisher’s exact test was performed to analyze the actual state of installation according to the type of medical institution. The statistical significance level was set at 0.05.When grouped according to types of medical institutions examined in this study, dental clinics, which fall under the category of primary medical institutions, formed the majority, with 96.7%, of which 68.6% had been established after 2000 .p < 0.001). Regarding intermediary facilities, the majority of dental clinics surveyed either did not have them installed or they were installed inappropriately; however, hospital-level dentistry showed a high installation rate of convenience facilities. In medical institutions above the hospital level, internal facilities and information facilities were mostly installed, but they were not installed in any of the dental clinics (p < 0.001). This could be because medical institutions at the hospital level or higher are required to create amenities for people with disabilities, while those at clinic level were not required to do so until recently  . South Kp < 0.001). As evident from the results, most of the dental clinics had either no, or inappropriately installed, convenience facilities. However, dental institutions at the hospital level or above were found to have a relatively high rate of installation, which is consistent with the reports of previous studies [2 or more. We assume that this is the reason for the lower installation rate of convenience facilities at clinic-level institutions compared with hospital-level institutions. The findings of this study reveal that the installation rate of convenience facilities for such people at dental institutions in Chungbuk was 2.4%, and the inappropriate installation rate was 97.6%. Therefore, most of the dental institutions investigated in this study were identified as having inappropriate installation of convenience facilities. In a study by Bae  on dentaMany domestic studies that examined the installation state of convenience facilities for persons with disabilities in public facilities or organizations reported a state of general insufficiency ,29,30,31Owing to poor access to dental service, people with disabilities are likely to postpone or abandon treatment ,11,12. TThis study is significant because it researched the installation status of convenience facilities for people with disabilities through on-site investigations. These investigations were based on specific criteria to identify the problems that people with disabilities encounter when visiting dental institutions. It also provided the basic data to improve convenience facilities. However, there are limitations in generalizing these research results, as the study was conducted exclusively on dental institutions in one particular region of Korea. Follow-up studies are required to systemically understand the current situation of welfare support and the needs of people with disabilities, to formulate welfare policies for them.In conducting this study on 245 dental institutions in Chungbuk Province, South Korea, actual measurements and on-site observations were used to assess the state of installation of convenience facilities for people with disabilities in these institutions. The results of this study and recommendations for improvement can be summarized as follows. First, intermediary and internal facilities showed a relatively higher rate of appropriate installation. The rate of installation of access routes to the main entrance and parking spaces for people with disabilities was high, but many were installed at inappropriate locations or lacked proper signboards. Regarding internal facilities, the installation rate for effective width of corridors and elevators for people with disabilities was found to be inadequate.Second, for sanitary and information facilities, the appropriate installation rate was significantly low. In most of the clinic-level medical institutions—which account for the majority of the medical institutions investigated—toilets for persons with disabilities were either not installed or installed without any distinction between toilets for men and women. Additionally, since the rate of installation of braille blocks and guidance and information facilities for the visually impaired was low, there is an urgent need to improve sanitary and information facilities.p < 0.001).Third, examination of the state of installation of convenience facilities by institution type revealed that the appropriate installation rate at the clinic level was only 0–1.3% per item but was 75–87.5% per item for general and dental hospitals (Currently, owing to insufficient management and supervision by the Government, the scope to regulate the installation of convenience facilities for people with disabilities at clinic-level dental institutions is limited. Revisions in the laws are required not only for newly established facilities but also for existing facilities with inappropriately installed—or missing—convenience facilities, which requires social and economic support from the central and local governments.