Drug Indication Classification and Encyclopedia (DICE) based on FDA approved human prescription drug labeling. A DICE scheme with 7,231 sentences categorized into five classes  was developed. To further elucidate the utility of the DICE, we developed nine different AI-based classifiers for the prediction of indications based on the developed DICE to comprehensively assess their performance. We found that the transformer-based language models yielded an average MCC of 0.887, outperforming the word embedding-based Bidirectional long short-term memory (BiLSTM) models (0.862) with a 2.82% improvement on the test set. The best classifiers were also used to extract drug indication information in DrugBank and achieved a high enrichment rate (>0.930) for this task. We found that domain-specific training could provide more explainable models without performance sacrifices and better generalization for external validation datasets. Altogether, the proposed DICE could be a standard resource for the development and evaluation of task-specific AI-powered, natural language processing (NLP) models.Drug labeling contains an ‘INDICATIONS AND USAGE’ that provides vital information to support clinical decision making and regulatory management. Effective extraction of drug indication information from free-text based resources could facilitate drug repositioning projects and help collect real-world evidence in support of secondary use of approved medicines. To enable AI-powered language models for the extraction of drug indication information, we used manual reading and curation to develop a  Thus, developing an effective approach to facilitate the mining of drug indication information from free text-based resources is an important task for biomedical natural language processing (NLP).Drug labeling contains an ‘INDICATIONS AND USAGE’ section that provides vital information to support clinical decision making and regulatory management. The primary role of drug indications is to enable health care practitioners to readily identify appropriate therapies for patients and support clinical decision making . The infSome attempts to extract drug indications from free text-based documents have been undertaken, mainly based on the combination of named entity recognition (NER) approaches with conventional machine learning algorithms . One exaBased on guidance for industry on the ‘INDICATIONS AND USAGE’ section of Labeling for Human Prescription Drug and Biological Products, content should be concise but unambiguous. The information in the ‘INDICATIONS AND USAGE’ section should readily allow the identification of approved indication(s) and reflect current scientific evidence. Furthermore, indication terminology should be standardized, clinically relevant, scientifically valid, and easily understandable. Also, this information should be consistent within/across drug and therapeutic classes to aid the indexing of indications in electronic drug databases and medical information systems. Drug indication information often comprises mixed information such as age group, subpopulations, classifications such as adjunctive or concomitant therapy, specific tests/diagnoses, and other disease conditions or clinical manifestations. Thus, drug labeling is a great resource for drug indication classification, facilitating the development of AI-based NLP models, and further improving drug indication information extraction.Drug Indication Classification and Encyclopedia (DICE) based on FDA approved human prescription drug labeling to facilitate the development of AI-based NLP approaches for enhanced drug indication extraction from free text-based document resources. The DICE scheme categorizes the >7,000 sentences in the ‘INDICATIONS AND USAGE’ section into five classes, including indication, contraindication, side effect, usage instruction, and clinical observations. To verify the utility of DICE, we developed nine different AI-based classifiers, including 4-word embeddings-based Bidirectional long short-term memory (BiLSTM) models and five transformer-based language models. The model performances were comprehensively assessed based on a test set and an independent validation set. Some critical questions such as the benefit of domain-specific training for AI-based NLP were also investigated. Furthermore, the model explainability was discussed for real-world applications.We developed a five-category https://www.fda.gov/media/114443/download). The ‘INDICATIONS AND USAGE’ section mainly contains information such as “1) The disease, condition, or manifestation of the disease or condition  being treated, prevented, mitigated, cured, or diagnosed;” and “2) When applicable, other information necessary to describe the approved indication .” Since the ‘INDICATIONS AND USAGE’ section contains such a variety of information, it is imperative to develop an indication classification corpus for automatic indication extraction.To curate an indication classification corpus, we employed US Food and Drug Administration (FDA)-approved drug labeling. Drug labeling, also known as the package insert or prescribing information, accompanies every FDA approved medicine as required under the US Code of Federal Regulations (21 CFR 201.56). Drug labeling is submitted by the manufacturer and approved by FDA and includes a rich source of information on safe and effective drug usages. There are more than 80 sections embedded in a drug labeling document . Among thttps://nctr-crs.fda.gov/fdalabel/ui/search) (https://www.fda.gov/industry/structured-product-labeling-resources/section-headings-loinc). The LOINC code for INDICATIONS AND USAGE section is “34067-9”  . Consequ34067-9” .a priori knowledge. Three experienced, expert pharmacologists carried out the manual annotations independently and a consensus assignment was selected for indication information classification.To manually annotate the information in the ‘INDICATIONS AND USAGE’ sections into different categories, we developed a five-class indication classification scheme . We spliFor the purposes of indication extraction, the extracted 7,231 sentences assigned with the category ‘indication’ were considered as positives, and sentences assigned to any of the other four categories were considered negative. The 7,231 curated sentences were divided into the training and test sets with an approximate ratio of 80:20. Consequently, we obtained 5,785 sentences and 1,446 sentences for the training and test sets, respectively. Two types of deep learning models were developed, including word embedding-based BiLSTM models, and transformer-based language models.We implemented the following procedure to preprocess the sentences: 1) the sentences were tokenized; with stripping of punctuation, digits, and words with less than two characters; 2) stop word removal; and 3) lemmatization.Word embedding is a set of language modeling and learning techniques in NLP to map words or phrases from a vocabulary to a numeric vector representation. In this study, we used two types of word embeddings including Word2vec  and Glovhttps://bio.nlplab.org/ and continuous skip-gram) used to estimate continuous vector representations of words from large text corpora . The genplab.org/. We devehttps://nlp.stanford.edu/projects/glove/.We also employed another well-known word embedding technique , which, when applied to aggregated global word-word co-occurrence statistics from a corpus, generate word vector representation . SpecifiTo better understand the framework and theory behind the BiLSTM, we provide a simple introduction on the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). An RNN is a set of artificial neural networks for sequential and time-series data. Unlike conventional neural networks, RNNs adopt recurrent hidden states to store previous inputs and leverage sequential information of the previous inputs to estimate the next element in the sequence. In theory, RNNs are able to leverage previous sequential information for arbitrarily long sequences. In practice, however, due to RNNs memory limitations called “vanishing gradients”, the length of the sequential information is limited to only a few steps back .Hochereiter and Schimdhuber  proposedTo further investigate the performance of advance AI-powered NLP approaches on indication classification, we employed the Bidirectional Encoder Representations from Transformers (BERT)  and its BERT is a transformer that learns contextual bidirectional representations from an unlabeled, large corpus of documents by using two training strategies: Masked Language Model (MLM) and Next Sentence Prediction (NSP) . In the Two condensed BERT models, DistillBERT and ALBERT, were proposed to overcome the obstacle of long training times. DistilBERT uses a technique called distillation, which approximates the BERT from the large neural network to a smaller one. By learning from the distilled version of BERT, DistilBERT retained about 97% performance while using only half as many parameters as the original BERT . One of RoBERTa is an updated version of BERT that improves the pretrained optimization process . First oIt was observed that generic pretrained transformer models may not work very well in conjunction with specific domain data. To fill this gap, BioBERT, a domain-specific BERT model, was proposed by training the BERTbase model on large biomedical corpus including PubMed abstracts and PMC full text . The BioBERT-like models are designed as pre-trained deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. They are then fine-tuned with an additional output layer to create models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. The fine-tuned base models of transformers were used in this study for the binary classification task for indication recognition. An important difference is that these models used their native tokenizers, which utilized sub-word tokenization  where larger words may be broken down to map to token(s), compared to the cruder tokenization implemented with the simpler model.To train the model and measure model performance, we employed area under the receiver operating characteristic (ROC) curve analysis, which demonstrates the performance of the classification model by plotting the true predictive rate (TPR) against the false positive rate (FDR). We calculated the area under the ROC curve (AUC) for each model described above. We also used seven other performance metrics including Matthews correlation coefficient (MCC), accuracy, sensitivity, specificity, precision, negative predictive value (NPV), and F1-score for further evaluation of model performance by using the following confusion matrix and formulashttps://go.drugbank.com/releases/latest. We developed an in-house script to extract the drug indication information from DrugBank XML file. Consequently, a list of 3,976 indication descriptions in DrugBank were extracted to further verify our developed model.To further investigate real-world applications of the developed indication classification model, we applied the best-performing models to indication descriptions in the DrugBank database. The indication information in DrugBank is a relatively concise description of the indication and usage of approved or investigational drugs . SpecifiTo investigate the discrimination powers of different word embeddings or sentence embeddings yielded from the transformers models used in this study, we employed t-distributed stochastic neighbor embedding (t-SNE) . t-SNE ihttps://github.com/arjun-bhatt/TransformersIndicationExtraction) to share the source code and curated drug indication corpus. Specifically, all the code script is developed under Python 3.6. The BiLSTM model is based on tensorflow version 1.12.3. The transformers models were based on Huggingface package version 3.02 and its backend is tensorflow version 2.3.0 and PyTorch version 1.5.1. t-SNE was implemented by using Python Scikit-learn package version 0.23.2.We developed a GitHub webpage  to get their top ten most similar words . Figure To further explore the possibility of improving the binary indication classification model performance, we implemented five different fine-turned BERT-like transformer models, including BERT, DistillBERT, ALBERT, RoBERTa, and BioBERT . First, We further employed a t-SNE analysis to visualize the contribution of hidden states of transformers on classification performance . We obseWorking towards a real-world application, we applied the top performance models to extract the indication-related sentences in DrugBank indication description notes. The Drugbank indication description notes are concise information for drug indications without other information such as contraindications, side effects, and specific population. We considered all drug indication notes as positives. Therefore, we could calculate the enrichment rate that measures the number of indication information sentences correctly recognized by the developed models. The enrichment rates were ranked as RoBERTa (0.952) > BioBERT (0.936) > BERT (0.930), which is consistent with previous results based on test sets . Based oDrug indications provide key medical information to support clinical decision making and promote the appropriate use of medicines. Furthermore, drug indication information is also considered a fundamental resource to assist in the standardization of medical coding and to potentially eliminate medical errors . AI-powehttps://www.fda.gov/regulatory-information/search-fda-guidance-documents/indications-and-usage-section-labeling-human-prescription-drug-and-biological-products-content-and). The potential utility of DICE can be divided into two aspects: 1) The DICE could serve as a standard biomedical classification corpus for deep learning-based NLP algorithm development; and 2) the DICE could also be utilized for indication information extraction model development towards real world applications such as off-label use and potential drug repurposing opportunities derived from free-text resources .There have been a few attempts to curate the standard corpus of drug indication information for NLP development. However, the sample size is limited  . Here, wThe benefits of the domain-specific training on different biomedical applications have been discussed elsewhere . The domAdvances in AI in NLP and increased computational power have allowed various transformer-based language models to be developed and successfully used in different downstream tasks . As prooThe current version of DICE and associated AI-based language models were based on the English language. Further evaluation of other languages will be a great addition to expand the utility of the developed DICE corpus. First, the proposed data curation process of the DICE corpus is reproducible and could be migrated to the documents in other languages. Accordingly, the associated AI-based language models could be developed for drug indication information extraction in other languages. Second, tremendous efforts have been made to language translation powered by AI in the biomedical domain . For exaIt is worthwhile to consider some additional studies to further investigate the utility of DICE in different medical applications. First, the model performance of different models was only evaluated based on one test set. Considering the lack of annotated data  in the other resources, we only employed DrugBank indication notes as positives to verify the proposed models for a real-world application. Some extra verifications are strongly recommended for expanding the utility of the developed DICE and accompanying models. Second, the developed DICE and classification models could serve as the first step to extract indication information. The other biomedical entity recognition approaches (e.g., UMLS MetaMap  or BioBEAutomatic drug indication extraction is of great importance for different biomedical applications. To fill this gap, we developed the DICE to facilitate AI-based algorithm development and verification. We hope our developed DICE will be considered as a standard drug indication classification corpus, providing the opportunity for other biomedical NLP researchers to promote AI-powered indication extraction in different real-world applications.


Coronavirus Disease 2019 (COVID-19) has been a pandemic challenge for the last year. Cardiovascular disease is the most described comorbidity in COVID-19 patients, and it is related to the disease severity and progression. COVID-19 induces direct damage on cardiovascular system, leading to arrhythmias and myocarditis, and indirect damage due to endothelial dysfunction and systemic inflammation with a high inflammatory burden. Indirect damage leads to myocarditis, coagulation abnormalities and venous thromboembolism, Takotsubo cardiomyopathy, Kawasaki-like disease and multisystem inflammatory syndrome in children. Imaging can support the management, assessment and prognostic evaluation of these patients. Ultrasound is the most reliable and easy to use in emergency setting and in the ICU as a first approach. The focused approach is useful in management of these patients due its ability to obtain quick and focused results. This tool is useful to evaluate cardiovascular disease and its interplay with lungs. However, a detailed echocardiography evaluation is necessary in a complete assessment of cardiovascular involvement. Computerized tomography is highly sensitive, but it might not always be available. Cardiovascular magnetic resonance and nuclear imaging may be helpful to evaluate COVID-19-related myocardial injury, but further studies are needed. This review deals with different modalities of imaging evaluation in the management of cardiovascular non-ischaemic manifestations of COVID-19, comparing their use in emergency and in intensive care. Coronavirus Disease 2019 (COVID-19) is a pandemic caused by SARS-CoV-2 and arose in 2019 in Wuhan, China . There aCOVID-19 presents severe lung involvement and hemodynamic alterations that therefore induce commonly cardiovascular complications showing a wide spectrum of severity . ClinicaThis review focuses on images that have proved to be very helpful for the evaluation of cardiovascular involvement in the COVID-19 patients.Cardiovascular disease is the most described comorbidity in COVID-19 patients, and it is related to the disease severity. It is likely related to lung failure and COVID-19 disease progression ,36. HoweThe high inflammatory burden due to cytokine release can significantly hit the patients’ cardiovascular systems ,45,46,47SARS-CoV-2 infection affects the myocardium in the form of myocarditis . As mentSevere myocarditis with reduction of systolic function were reported as consequence COVID-19 ,57. SomeGuo et al. reported an acute myocardial injury, defined as elevation of troponin T, in 28% of 187 hospitalized COVID-19 patients. Patients with high troponin T levels also presented higher inflammatory biomarkers, such as leukocytosis, lymphopenia, D-dimer, C-reactive protein and pro-calcitonin . Thus, mCOVID-19 patients are exposed to an elevated risk of arterial and venous thromboembolism (VTE) due to a state of endothelial dysfunction, vascular inflammation and hypercoagulability related to SARS-CoV-2 infection, and to its role in triggering innate immune response and inflammation, leading to immunothrombosis ,63. GąseRegarding microvascular thrombosis, many severe patients develop COVID-19-associated coagulopathy, which differs from classic thrombotic microangiopathy and disseminated intravascular coagulation (DIC) : the firIn these patients there are abnormal coagulation parameters, such as prothrombin time, activated partial thromboplastin time, fibrin degradation products and D-dimer. In particular, increased levels of fibrin degradation products (FDP) and D-dimer are closely associated with poor prognosis ,69. In aTang et al.  showed tHypercoagulability associate to a severe inflammatory state instead of acute DIC-like was described by Panigada et al. among 24 COVID-19 patients hospitalized in ICU . They deThus, current recommendations are to favour platelet inhibitors, which showed to be effective in improving hypoxemia and successful in weaning ventilator .Takotsubo cardiomyopathy (TCM), also known as stress-induced cardiomyopathy, may be associated with the SARS-CoV-2, considering its increased incidence in the COVID-19 patients  Figure . It is cTherefore, the association between TCM and COVID-19 may be explained by potential pathophysiological links between the two conditions. Though these direct connections are not fully understood, they may be attributable to three factors: the overactive immune response from cytokine storm, the sympathetic nervous system surge and the development of microvascular dysfunction noted in SARS-CoV-2 infection . SARS-CoV-2 is associated with sharp increase in the incidence of Kawasaki-like Disease (KL) ,83,84 F. The KawSkin biopsy from children presenting with chilblains showed endothelial cell damage, thrombosis and SARS-CoV-2 in endothelial cells . COVID-19 was initially reported as affecting children only mildly, as showed in an epidemiologic survey performed by Chinese Centre for Disease Control and Prevention including 2135 SARS-CoV-2-exposed children as well as in other studies ,91,92,93Arrhythmias are another example of common cardiovascular involvement in patients with COVID-19. In the setting of the coronavirus infection, high prevalence of arrhythmias in patients with or without a prior cardiovascular disease might be partly attributable to hypoxia, metabolic disarray, neurohormonal or inflammatory stress. Myocardial injury is associated with impairment of cardiac function . Atrial Heart failure (HF) can be induced by COVID-19 via different mechanisms, including virus-induced infiltration of inflammatory cells, pro-inflammatory cytokines that could cause necrosis and death of the myocardium, endothelial injury, acute respiratory distress syndrome (ARDS) and respiratory failure with severe hypoxia . In a syPatients with HF are at increased risk for poor outcomes such as hospitalization, and death from COVID-19. There is a significant difference in mortality between patients with and without HF: those with HF show higher mortality rates .Healthcare system evaluates a huge number of patients with suspected COVID-19 infection during the pandemic. It is essential to use a rapid execution diagnostic method that is free of detrimental effects and contraindications, and repeatable, in order to better manage resources and optimise therapy for patients in the right care setting. Ultrasound meets all these requirements, but other methods such as CT, may be evaluated when establishing the diagnosis and the clinical management. Point-of-care ultrasonography (POCUS) has demonstrated to be a ubiquitous and quick way to detect pulmonary changes . In partA possible useful algorithm for emergency evaluation is the A.B.C. algorithm. It is based on the three step process of cardiac resuscitation protocol: Airway (A), Breathing (B) and Circulation (C) . The airThe pleura is a hyperechoic structure. In a normal lung, it is detectable horizontal repetitions of the pleural line as a reverberation artifact, the A-lines. The association of the A profile with phlebothrombosis favours the diagnosis of pulmonary embolism (PE) . The maiIn the SARS-Cov-2 patients it has been also described the pleural line thickening showed as a thick hyperechoic pleural line and sub-pleural consolidations. The normal aerated lung tissue is replaced by tissue that mimics the aspect of other organs, for example liver; it is so called “the tissue-like sign” . ConsoliSARS-CoV-2 can determine cardiac events related to the infection ,116. ProTherefore, in patients with COVID-19 disease echocardiography should be performed on admission and should be repeated ,117 periIt has been explained how pulmonary thrombosis and arterial and venous thromboembolism affecting a sizeable proportion of patients in ICU, could cause DVT, PE, ischaemic stroke, myocardial infarction and systemic arterial embolism . ZotzmanThe evaluation of differential diagnosis for dyspnoea includes also assessment of echocardiography . TransthGlobal cardiac function might decline in patients with viral infections. A subcostal approach or an apical four-chamber view is useful for a global view of the heart mechanics is obtained through . ThroughIf inotropic drugs are administered, it should be done with caution and a clinically interpreted LV ejection fraction should be measured. Valvular  diseases should always be assessed, in particular at first echocardiogram. The presence of moderate-to-severe valvular disease may affect the course and severity of the lung disease and, obviously, therapies  .In a prospective international survey, cardiac abnormalities were described in COVID-19 patients . Out of In another single-centre study with TTE, 90 patients hospitalized for COVID-19 showed both severe and non-severe  echocardHowever, these alterations may not be permanent. In fact, in a short-term follow-up cross-sectional study, neither abnormality was identified in the heart of COVID-19 survivors, nor cardiac differences were detected between patients with different severity of illness, suggesting that patients who recover from COVID-19 do not have considerable cardiac sequelae .Reports on the use of echocardiography in ICU are increasing. The most common reliefs in patients with severe COVID-19 illness are: pulmonary artery hypertension, pericardial effusion, segmental wall motion abnormality and reduced ejection fraction . Other cA pragmatic FoCUS based strategy seems the most reasonable approach. Ultrasonography is an adjunct to the physical examination at the point of care to recognize typical ultrasonography signs that suggest a narrow list of potential diagnoses in specific clinical settings. Numerous data support the fact that non-cardiology trained users using small ultrasonography devices can assess heart change elevation more accurately than the physical examination. In addition, FoCUS-trained providers may have skills to perform ultrasonography imaging of body systems outside the heart to supplement their cardiac evaluation .As in emergency, and in ICU, LUS should be combined with FoCUS for a better evaluation of patients with respiratory failure. LUS helps clinical evaluation due its increased power in bedside detection of pulmonary pathologies, monitoring the progression of mechanical ventilation, and detecting complications such as pleural effusion, pneumothorax and atelectasis . MoreoveRight heart (RH) failure or dysfunction can occur in pre-existing pathologies such as obstructive sleep apnoea, chronic obstructive pulmonary disease, pulmonary hypertension or acute onset of new disease in the critically ill patients ,130,131.Cardiac ultrasound might explore RV enlargement. In such patients, the causes are PE, increased pulmonary vascular resistance due to hypoxia or to aggressive non-invasive ventilation (NIV) ,130,132.RV is directly and indirectly involved in COVID-19 disease course. Therefore, RV echocardiographic assessment has a pivotal role in the understanding of disease status and in monitoring the progression. The RV direct involvement often recurs. Interstitial pneumonia and pulmonary hypoxic vasoconstriction are two main causes of RV afterload increase. Mechanical ventilation (both non-invasive and invasive) influences heart–lung interaction and affects RV dimension .RV size can be easily assessed visually by the “eyeball” method in the short-axis and four chamber views. However, it is advisable to perform a quantitative evaluation the RV/LV ratio end-diastolic area . In 39% RV wall hypertrophy is defined by diastolic thickness >5 mm. Whenever this finding is detected in Emergency or ICU, it may indicate a chronic RV overload as a consequence of a previously unknown lung disease such as a chronic obstructive pulmonary disease. It has to be considered that RV hypertrophy may be caused by the mechanical ventilation itself in long standing ventilated patients, since the RV is able to thicken in response to increased intrathoracic pressure .Several factors may contribute to the development of increased systolic arterial pressures in COVID-19 patients. Hypoxic pulmonary vasoconstriction is probably the main factor . SecondlIn a retrospective study on 112 COVID-19 patients with mild disease, Deng et al.  reportedIn non-ventilated patients, Inferior vena cava (IVC) diameter and its collapse is a good estimation of RA pressure. IVC diameter <2.1 cm that collapses >50% during sniff has to be considered indicative for normal RA pressure (3 mmHg), while higher RA pressure (about 15 mmHg) is suggested by an IVC diameter >2.1 cm that collapses <50%. In patients with RV dysfunction, the IVC collapsibility should be interpreted with caution, considering also its trend and central venous pressure . An index of RV systolic function is tricuspid annular plane systolic excursion (TAPSE). . TAPSE <RV overload has also a prognostic role. In a prospective study on 94 consecutive patients, coupling RV function to the pulmonary circulation was evaluated as TAPSE to PAPs ratio . The autDirect cardiac involvement may present as different entities. The most described alterations are the detection of localized wall motion abnormalities, as a global ventricular depression in case of myocardial infarction with ST-elevation, or as dilated cardiomyopathy, as a severe decrease in LV systolic function and pericardial effusion in case of viral myocarditis . In COVIGarcía-Cruz et al.  confirmeCompressive ultrasound (CUS) for DVT  should bThe 4-point CUS has been widely used both in USA and Europe, as a non-invasive method of compression enabling emergency physician in screening for suspected DVT. Analysing the absence of proximal venous incompressibility at the four points   it is poIn a prospective observational study, consecutive COVID-19 patients with a diagnosis of PE were ultrasound screened for DVT in the lower extremities . DVT wasAccumulating evidence suggests that despite the use of anticoagulant therapy, there is a high incidence of thromboembolic events leading to the hyperinflammatory state especially in ICU patients . It is wMoreover, in patients requiring venovenous ECMO, the occurrence of VTE has been studied only using ultrasonography. Parzy et al.  through Computed tomography (CT) is highly sensitive for diagnosis of patients with COVID-19, but it might not always be available. Several studies underlined the importance of imaging for COVID-19 diagnosing . The typNext to GGO, the usually evaluated parameters in chest CT scans are: increased opacity of the lung, with preservation of bronchial and vascular margins, consolidation, crazy-paving pattern, cavity and bronchial dilatation ,151. VasThromboembolic complications with or without the presence of DVT are frequent and underestimated events. The diagnosis of PE mainly relies on CT scanning, but ultrasound is a diagnostic alternative for special clinical settings . PerformThe overall prevalence of acute PE diagnosed through CT ranged between 14–30% ,152,153.CTPA is becoming the standard of care for the evaluation of patients with suspected PE. In acute PE the diagnostic criteria include: possibly enlarged artery compared with adjacent patent vessels and/or arterial occlusion due to a large filling defect highlighted by failure in enhancing the entire lumen ,153 Fig.It has been demonstrated that acute PE is statistically significant associated to peripheral wedge-shaped areas of hyper-attenuation that may represent infarcts, along with linear bands. On the contrary, chronic PE can manifest as complete occlusive disease in vessels smaller than adjacent patent ones. Other CTPA findings in chronic PE include webs or flaps, recanalization and partial filling defects ,157,158.CT is currently deemed as the most sensitive imaging tool when COVID-19 is suspected, being capable to detect specific and highly suggestive signs: ground-glass opacities with or without consolidation in the lung periphery. Cardiac involvement is being frequently reported in chest CT. This procedure is useful to selected patients with elevated cardiac biomarkers, in case of inconclusive echocardiography and signs and symptoms of an acute coronary syndrome to rule out coronary artery disease. It is an important tool for diagnosing stenosis, valve dysfunction, coronary dissection and intra-cardiac device dysfunction. In various reports, pericardial effusion  and evidThe overlap of infectious symptoms and classic symptoms of cardiac syndromes presents a diagnostic challenge; therefore, awareness and vigilant surveillance for possible cardiovascular sequelae is critical. In this context, cardiovascular imaging offers an important instrument to facilitate the diagnosis of clinically suspected conditions. Various imaging modalities contribute to diagnostic evaluation, management and prognosis such as chest x-ray, echocardiography, chest computed tomography (CT), magnetic cardio-resonance and coronary angiography. Radiology is an important complement to clinical and epidemiological features, both in adult and paediatric population. Chest x-ray is frequently requested in patients with acute pulmonary symptoms admitted to the emergency department, as well as in the ICU, because it is inexpensive, suitable to be placed by the patient’s bed and has low radiation exposure. The most common findings refer to lung abnormalities while pleural effusion and altered cardiomediastinal contour are infrequent. Cardiac findings on chest x-ray are restricted to enlargement of cardiac silhouette and the presence of pulmonary oedema. Advanced cardiac imaging may play a role in discriminating the broad spectrum of differential diagnoses. The easiest tools are represented by the advanced imaging techniques in echocardiography. Among them, the most important one is the myocardial strain evaluation using the speckle-tracking analysis. Similarly, other useful tools are the cardiac magnetic resonance (CMR) and the positron-emission tomography (PET) that, however, are both more difficult to use in acute patients. Finally, there is no specific advanced imaging useful to differentiate or help in arrhythmias’ management. Nevertheless, being the latter issue an essential prospective, additional studies are needed  to furthTTE may be empowered by advanced imaging techniques. The longitudinal strain (LS) measured by two-dimensional speckle-tracking echocardiography (2D-STE). It is a recent method able to perform a more accurate and sensitive indicator of cardiac function in a variety of cardiovascular diseases ,162 and Xie et al.  investigFurthermore, RVLS is useful to evaluate patients who received ventilation. Abnormal LS was found in about 66% of patients ventilated in ICU, and it was associated with higher lung compliance, lower airway plateau pressures, lower tidal volume ventilation and reduced LV function . RV LS iComputerized tomography (CT) emerged as one of the primary imaging modalities in the COVID era. CT can be used to evaluate chest pain, LV dysfunction, new onset of heart failure or cardiomyopathy, and evaluation of patients with possible angina and new arrhythmias . CardiacIn COVID-19 patients is not clear the role of cardiovascular magnetic resonance (CMR). Advances in multi-parametric CMR now allow having a detailed tissue characterization including scar, diffuse fibrosis, oedema and quantitative ischaemia assessment. During the COVID-19 pandemic, CMR has not been widely considered, as for the necessary limitations due to the strain put upon the healthcare system . AccepteEsposito et al. reported a series of eight patients with elevated troponin and electrocardiography alterations whose CMR findings fulfilled the 2018 Lake Louise Criteria for the diagnosis of myocarditis . DespiteCMR may be helpful to evaluate the presence of a myocardial injury. In this setting, patients may present a decreased LV ejection fraction, increased LV volumes, and raised native T1 and T2. Wang et al.  confirmeCardiac evaluation of metabolic pattern should be assessed using [18F]-2-Fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) . It is aAn additional type of imaging is the coronary angiography (CAG): it is the gold standard to evaluate the coronary artery lumen, and it is recommended only in patients with ST-segment elevation on EKG or with new left bundle-branch block. In clinically suspected STEMI, coronary angiography should be performed whenever possible, since COVID-19 can trigger acute coronary syndromes. At the same time, alternative causes of troponin elevation must be investigated, if STEMI is excluded.Artificial intelligence (AI) is a brand new tool rising in helping the daily medical practice and clinical imaging . The useIn the COVID-19 era, the safety of healthcare providers and patients remains a universal priority regardless of the imaging modality used. Recommendations on imaging in COVID-19 emphasized the importance of proper PPE equipment for healthcare. They also recommend to limit testing to the ones which have an impact on patient health as well as clinical management ,186,187.During the COVID-19 pandemic, healthcare systems and health workers are making every effort to ensure the best treatments to each patient. In view of resources optimization, imaging can effectively support the assessment and prognostic evaluation of critically ill patients. The different techniques are able to provide different information. Ultrasound is the most reliable and easy to use in emergency settings and in the ICU as a first approach. It is also an amazing tool for frequent instrumental follow-ups. However, for many other diseases, there is the need for an increased diagnostic power, that only more complex machines are able to ensure. For this reason, a combination of different methods is the best possible way to perform adequate patient care and reduce operators’ exposure to infection. Additional prospective studies are needed to develop an effective method which combines the different tools.


Cationic nanomaterials are defined as nanoscale structures smaller than 100 nm bearing positive charges. They have been investigated to apply to many aspects including clinical diagnosis, gene delivery, drug delivery, and tissue engineering for years. Recently, a novel concept has been made to use cationic nanomaterials as cell-free nucleic acid scavengers and inhibits the inflammatory responses in autoimmune diseases. Here, we highlighted different types of cationic materials which have the potential for autoimmune disease treatment and reviewed the strategy for autoimmune diseases therapy based on cationic nanoparticles. This review will also demonstrate the challenges and possible solutions that are encountered during the development of cationic materials-based therapeutics for autoimmune diseases. Nanomaterials with particles size smaller than 100 nm and bearing positive charges or synthesized in the presence of novel cationic entities, incorporated on their backbone and/or as side chains, are considered as cationic nanomaterials . Cationitherapeutic method in autoimmune diseases, but the drugs used to suppress the immune response have numerous side effects with large doses and continuous therapy is not conducive to long-term host survival , PEGylation modification of PAMAM could increase the transfection efficiency and stabilization with lower cytotoxicity . A novelAs an important pH bioresponsive functional cationic nanomaterials, as well as the character of excellent stability and safety profile , PDMA F has beenResearchers anticipated that the properties of PDMA could be optimized by modifying different groups. Deshpande et al. utilized 2-(dimethylamino) ethyl methacrylate (DMAEMA) to construct three different polymers: DMAEMA-PEG (a diblock copolymer), DMAEMA-OEGMA 7 (a brush-type copolymer), and DMAEMA-stat-PEGMA (a comb-type copolymer). Compared with PDMAEMA, all of them exhibited more excellent binding ability with oligonucleotide while DMAEMA-stat-PEGMA showed the best. But DMAEMA-PEG and DMAEMA-OEGMA 7 own better long-term colloidal stability . PEO-PPOPolyphosphoramidate (PPA) , a biodePEGylation modification is also applied in PAA. PEG-b-PPA/DNA micelles with lower surface charge and smaller particle size ranging from 80 to 100 nm maintained similar transfection efficiency while showing lower cytokines and better biocompatibility compared with PPA/DNA . Moreovein vivo model of trauma hemorrhage, and the ability to inhibit inflammation and apoptotic cell death emerged (Hexadimethrine bromide (HDMBR)  has been emerged . But a c emerged .β-cyclodextrin-containing polycation (CDP) , a classin vivo is also a concern for CDP. Researchers have found that the introduction of PEG or Adamantane (AD) could enhance the stability of CDP  was first described in 1948  and the Due to the capacity of interacting with nucleic acids and forming electrostatic complexes, numerous cationic nanoparticles have been widely used for the non-viral transfection of cells with plasmid DNA, miRNA, and siRNA . RecentlFor the ability to scavenge nucleic acids, cationic nanomaterials were attempted for the treatment of autoimmune diseases.SLE is characterized by increased apoptosis and impaired clearance of apoptotic cells. Many factors can influence the clearance of the cell-free DNA in SLE patients, including the abnormalities of DNase activity to clear cf DNA, the combination of the cfDNA with the antibodies, proteins, and nucleosomes, and thus potentially activate inflammatory pathways . PAMAM-Gscavenge cfDNA and a more favorable biodistribution  cell activity, reduction of CD4+/CD8+ ratio, and inflammation cytokines  nanoparticles had been reported and the model demonstrated cationic nanoparticles suppressed the activity of ACE2 via binding with ACE2, resulting in an imbalance of the renin-angiotensin system .Although cationic nanomaterials have not yet entered the stage of clinical research in the treatment of autoimmune diseases, certain systemic toxicity has been found in clinical studies of cationic nanomaterials in tumors. Fatigue, chills, fever, and nausea were mostly described in clinical trials . CardiovTo minimize the toxicity, two strategies have been presented: designing and synthesizing biodegradable nanomaterials or masking of peripheral charge of nanomaterials by surface engineering .via nebulization and discovered that biodegradable PLGA mostly accumulated in lung and eliminated to half in 17.5 to 19.9 h without an elevated level of IL-6 and TNF-α in bronchoalveolar lavage (BAL) supernatant while non-biodegradable nanoparticle induced overexpression of pro-inflammation cytokines and the recruitment of polymorphonuclear to BAL  (PLGA), taking advantage of remarkable biocompatibility, biodegradability, solubility, and stability, plays a pivotal role as delivery systems for drug and gene, scaffold in tissue engineer and drug in treatment. Aragao-Santiago et al. compared the toxicity of biodegradable and non-biodegradable nanoparticles r to BAL . Sun et r to BAL . Biocompr to BAL . The toxZhang et al. synthesized a series of terpolymer with low charge density and high molecular weight, which possessed low toxicity and high conversion efficiency . ConsistKarabasz et al. confirmed that five-layer positively charged poly-l-lysine-terminated nanocapsules (NC5) with rapid hematotoxicity did not show cytotoxicity after being incorporated with PEG (l-asparagine), or poly-l-glutamic acid (l-lysine (PLL) and poly-L-glutamic (PGA) acid was coated with PGA(NC-PGA) and PEG (NC-PEG), respectively. The biochemical and histopathological evaluation suggested that neither of them showed acute or chronic hematotoxicity, hepatotoxicity, or nephrotoxicity. Compared with NC-PEG, NA-PGA didn’t provoke activation of immune system (Another strategy for the reduction of toxicity is the modification of the nanoparticle surface. In addition to this, modification of nanomaterial surface possesses extra properties such as prolongation of the retention time, improvement of biodistribution and efficiency, and so on . Polyethwith PEG . PEG couwith PEG . Howeverwith PEG . But Panwith PEG . Besidesmic acid . Toy et mic acid . A biodee system .in vivo, which enormous effort need to be devoted to. In addition, the structure construction, proportion distribution, dosage, usage, and pharmacokinetics of cationic compounds also need further exploration. The overall development prospect is considerable.In conclusion, extracellular nucleic acid is an important trigger mechanism in the development and progression of autoimmune diseases, and scavenging extracellular nucleic acid may be one of the candidates attempt to suppress the occurrence and severity of autoimmune diseases. However, the relevant research is still deficient. The use of cationic compounds, scavengers of extracellular nucleic acids, is only in its infancy as a novel treatment for autoimmune diseases. The applied cationic materials are concentrated on the several materials mentioned in the article, but more potential materials were not be studied. More research in the future can focus on other materials, including structural improvements and proportion optimization. At present, cationic materials have been thoroughly studied in various fields, and clinical studies have been carried out on some drugs and gene delivery. Despite the current challenges in cationic nanomaterials, continued improvements will likely yield achieving the new balance between low toxicity and high therapeutic efficacy


Background: Sex differences in clinical profiles and prognosis after acute myocardial infarction have been addressed for decades. However, the sex-based disparities among patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) remain largely unreported. Here, we investigated sex-specific characteristics and long-term outcomes in MINOCA population.Methods: A total of 1,179 MINOCA patients were enrolled, including 867 men and 312 women. The mean follow-up was 41.7 months. The primary endpoint was a composite of major adverse cardiovascular events (MACE), including all-cause death, non-fatal reinfarction, revascularization, non-fatal stroke, and hospitalization for unstable angina or heart failure. Baseline data and outcomes were compared. Kaplan-Meier curves and Cox regression analyses were used to identify association between sex and prognosis.Results: Female patients with MINOCA had more risk profiles with regard to older age and higher prevalence of hypertension and diabetes compared with men. The evidence-based medical treatment was similar in men and women. The incidence of MACE  did not differ significantly between the sexes. The Kaplan-Meier analysis also indicated that women had a similar incidence of MACE compared to men (log rank p = 0.385). After multivariate adjustment, female sex was not associated with the risk of MACE in overall  and in subgroups of MINOCA patients.Conclusion: The long-term outcomes were similar for men and women presenting with MINOCA despite older age and more comorbidities in women. Future research should aim to improve in-hospital and post-discharge care for both sexes with MINOCA. The sex differences in clinical presentation, treatment and outcomes of patients with acute myocardial infarction (AMI) have been investigated for decades –11. GeneAs previously reported, MINOCA occurs in 5–10% of AMIs and disproportionately affects the younger and female in comparison to those with obstructive coronary artery disease (CAD) –15. The n = 21,696); (2) prior revascularization (n = 312); (3) thrombolytic therapy for STEMI since the degree of prior stenosis may change after thrombolysis (n = 126); (4) alternate reasons for elevated troponin rather than coronary-related ischemia ; (5) lack of detailed baseline data (n = 33); (6) lost at follow up (n = 68). As a result, a total of 1,179 MINOCA patients were enrolled in final analysis  was tested with a liquid chromatography analyzer. Serum concentrations of creatinine, low density lipoprotein cholesterol (LDL-C), and high-sensitive C-reactive protein (hs-CRP) were measured with an automatic biochemistry analyzer. The N-terminal B-type natriuretic peptide (NT-proBNP) at admission and peak cardiac troponin I (TnI) values were recorded. The left ventricular ejection fraction (LVEF) was measured using the biplane Simpson method with echocardiography.Baseline characteristics regarding the demographic, clinical and laboratory data were obtained from in-person interviews and medical records. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Glycated hemoglobin  was defined as having a history of DM or newly diagnosed DM with fasting blood glucose ≥ 7.0 mmol/L or 2-h plasma glucose ≥ 11.1 mmol/L . HyperteThe primary endpoint of this study was major adverse cardiovascular events (MACE) defined as a composite of all-cause death, non-fatal MI, revascularization, non-fatal stroke, and hospitalization for unstable angina (UA) or heart failure (HF). The MACE was evaluated as time to first event. The secondary endpoints included each component of MACE and the composite “hard” endpoint of death, non-fatal MI, revascularization, or stroke. Of these, reinfarction was diagnosed based on the 4th universal definition of MI . Revascut-test or Mann-Whitney U test for continuous variables and Pearson's χ2 or Fisher's exact test for categorical variables. The survival curves indicating cumulative incidence of events among male and female groups were conducted using the Kaplan-Meier analysis and compared by log-rank test. Univariate and multivariate Cox proportional regression analyses were used to identify association between sex and event risk. Clinically relevant and prognosis-related variables among groups were enrolled into the multivariate model, including age, MI type (NSTEMI or STEMI), presence of hypertension, diabetes and dyslipidemia. The hazard ratio (HR) with 95% confidence interval (CI) were calculated. At subgroup analysis, patients were further stratified according to age, BMI, MI type, hypertension, diabetes, dyslipidemia, and LVEF level, and the female-to-male unadjusted risk of MACE was calculated. All tests were 2-tailed, and P < 0.05 was considered significant. The statistical analyses were performed using SPSS V.22.0 .The data were expressed as mean ± standard deviation or median with interquartile range for continuous variables and the number with percentage for categorical variables. Differences were assessed using Student's 1c and lower peak TnI values compared with men. There were no significant differences in vital signs at admission, Killip class, LVEF level, serum creatinine, LDL-C, hs-CRP, and NT-proBNP values between the sexes. Both men and women have similar rates to receive emergent angiography and the evidence-based medication at discharge.Patients with MINOCA were stratified by their sex, including 867 men and 312 women . As showp = 0.504). The incidence of the composite hard endpoint of death, MI, revascularization or stroke  and the other component event  also did not differ significantly between the two groups.During the mean follow-up time of 41.7 months, 168 MINOCA patients experienced MACE  . Female p = 0.385 and 0.392, respectively)   . The malr group) .p > 0.05) between sexes. After multivariate adjustment, female sex was not associated with the risk of MACE  and the composite endpoint of death, MI, revascularization or stroke . Further, the risk of MACE remained similar for men and women in a variety of subsets of MINOCA stratified by the age, BMI, MI type, hypertension, diabetes, dyslipidemia, and LVEF level   .In the present study, we described the sex-based clinical characteristics in MINOCA subpopulation and found that the long-term outcomes were similar for men and women presenting with MINOCA. Our data highlight the opportunities to improve healthcare for both sexes in this cohort who remain at considerable CV risks in the contemporary real-world management of MINOCA.It is increasingly recognized that a proportion of patients with AMI actually have no significant obstructive coronary artery lesions, and the term MINOCA has been coined to describe this distinct entity . The undIt may seem reasonable for MINOCA patients to have a better prognosis than those with MI and significant CAD , 24, howOver the past decade, studies have reported conflicting results on sex differences in clinical profiles and outcomes in patients with AMI. Generally, women are reported to be older, have more comorbidities and have lower rates of receiving PCI and evidence-based treatment than men –11. The Our study addressed this issue and consistently, we found that women with MINOCA were older, had lower presence of STEMI, and had more prevalence of hypertension and diabetes compared with men. Further, we did not observe significant sex disparities in crude and adjusted prognosis after MINOCA, which was in line with previous studies. A national registry found that no sex difference in mortality was observed in MINOCA . AnotherIn clinical practice, MINOCA is not uncommon and represents a distinct entity of all AMIs. The sex-related outcome differences in this population may be less related to the sex itself but instead should be largely explained by differences in age, comorbidities, and treatment utilization. These factors may serve as effect modifiers and further affect prognosis among men and women differently. In this regard, we should recognize them at early stages and take pre-emptive measures in order to find opportunities to improve care in women, especially those at high risks. On one hand, physicians should consider MINOCA as a heterogeneous working diagnosis that requires further evaluation with multi-modality imaging to find the underlying causes and thus tailor targeted treatment. On the other hand, the use of evidence-based treatment in women should be emphasized, and there is an enduring need to reduce or even eliminate the sex disparities in quality of care.Several limitations should be acknowledged. First, we enrolled more than a thousand subjects and this sample size was relatively considerable for MINOCA. Even through, the present data were derived from a single center, and our findings need to be verified by multicenter and larger cohort studies. Second, given the nature of sex, our analysis revealed critical associations but could not prove causation. Third, despite multivariate adjustment and subgroup analyses were performed, there might be other measured or unmeasured confounding variables that would have modified the relationship between sex and prognosis. Fourth, we did not capture the exact mechanism for every MINOCA patient. Future research are warranted to identify the etiology of MINOCA and find the sex-related outcomes in different phenotypes of MINOCA. Further, we did not record the percentage of menopausal women in all patients. The relation of menopause status with CV outcomes in MINOCA should also be addressed by future studies.Female patients with MINOCA tended to be older, less likely to present with STEMI, and had more prevalence of hypertension and diabetes compared with men. Both sexes received a similar rate of evidence-based medication. After multivariate adjustment, the female sex was not associated with the long-term risk of MACE after MINOCA. Future nationwide quality control programs are warranted to discover and possibly narrow the sex-related disparities in quality of care and outcomes in MINOCA population.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Ethics Committee of Fuwai hospital. The patients/participants provided their written informed consent to participate in this study.SG conceived and designed the study and drafted the manuscript. SG, WM, SH, and XL performed data analysis and interpretation. MY reviewed and gave final approval of the version to be published. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background: Following the spread of the coronavirus disease 2019 (COVID-19) pandemic a new disease entity emerged, defined as Pediatric Inflammatory Multisystem Syndrome temporally associated with COVID-19 (PIMS-TS), or Multisystem Inflammatory Syndrome in Children (MIS-C). In the absence of trials, evidence for treatment remains scarce.Purpose: To develop best practice recommendations for the diagnosis and treatment of children with PIMS-TS in Switzerland. It is acknowledged that the field is changing rapidly, and regular revisions in the coming months are pre-planned as evidence is increasing.Methods: Consensus guidelines for best practice were established by a multidisciplinary group of Swiss pediatric clinicians with expertise in intensive care, immunology/rheumatology, infectious diseases, hematology, and cardiology. Subsequent to literature review, four working groups established draft recommendations which were subsequently adapted in a modified Delphi process. Recommendations had to reach >80% agreement for acceptance.Results: The group achieved agreement on 26 recommendations, which specify diagnostic approaches and interventions across anti-inflammatory, anti-infectious, and support therapies, and follow-up for children with suspected PIMS-TS. A management algorithm was derived to guide treatment depending on the phenotype of presentation, categorized into PIMS-TS with (a) shock, (b) Kawasaki-disease like, and (c) undifferentiated inflammatory presentation.Conclusion: Available literature on PIMS-TS is limited to retrospective or prospective observational studies. Informed by these cohort studies and indirect evidence from other inflammatory conditions in children and adults, as well as guidelines from international health authorities, the Swiss PIMS-TS recommendations represent best practice guidelines based on currently available knowledge to standardize treatment of children with suspected PIMS-TS. Given the absence of high-grade evidence, regular updates of the recommendations will be warranted, and participation of patients in trials should be encouraged. Subsequent to the first wave of the coronavirus disease 2019 (COVID-19) pandemic . The cliWhile a number of studies have attempted to explain the underlying biological and genetic mechanisms of PIMS-TS, its pathophysiology and the observed variation in clinical presentation remains largely unknown –11. It iCardiac involvement includes a supposed post-infectious myocarditis or myocardial injury and the development of coronary artery abnormalities with a minority developing coronary artery abnormalities. Myocardial injury is thought to be attributed to an acute and dysregulated immune response related to cytokine storm, endothelial damage, microvascular dysfunction, and ischemic injury . The troIn order to standardize management in Switzerland, we aimed to develop best practice recommendations for the diagnosis and treatment of children with PIMS-TS.At present, professionals remain confronted with substantial uncertainties regarding clinical phenotypes, long-term outcomes, and optimal management . In the Subsequent to a call for Expressions of Interest, the Interest Group for Pediatric and Neonatal Intensive Care (IGPNI) of the Swiss Society of Intensive Care Medicine (SSICM) and the Pediatric Infectious Diseases Group Switzerland (PIGS) formed a working group on PIMS-TS. In total, 24 panelists across the fields of pediatric intensive care, infectious diseases, immunology and rheumatology, hematology, cardiology, and nursing composed the panel.Four subgroups focused on the domains of (i) disease criteria and diagnosis, (ii) anti-inflammatory therapies, (iii) anti-infective therapies, and (iv) additional support therapies including coagulation management. Each subgroup performed a focused literature review on publications since the description of PIMS-TS in early 2020 until December 2020. In addition, we searched for available pathways, diagnostic, therapeutic, and follow-up recommendations from different international institutions –27. OverFor PIMS-TS, the following case definition should be used:Adapted RCPCH—case definition , 28Patient aged <18 years of age. Persistent fever + inflammation  + single or multiorgan dysfunction  + additional features see . This maExclusion of any other probable cause, such as bacterial sepsis, staphylococcal/streptococcal shock syndromes, and viral infections associated with myocarditis. Waiting for these results should not delay seeking expert advice.Positive for current or recent SARS-CoV-2 infection by PCR, serology, or antigen test; or COVID-19 exposure within 4 weeks prior to the onset of symptoms. Waiting for these results should not delay seeking expert advice.If a child fulfills the diagnostic criteria for PIMS-TS we recommend classifying these patients according to the presenting phenotype due to the implications for diagnostic workup and management:Shock-like presentation: signs of shock as per the Goldstein 2005 definition of cardiovascular failure – Staphylococcal Scalded Skin Syndrome (SSSS)– Kawasaki disease (KD)– Viral myocarditis/infection – Serum sickness– Acute appendicitis/acute surgical abdomen– Gastroenteritis– Macrophage activation syndrome (MAS) and hemophagocytic lymphohistiocytosis (HLH) – Malignant diseases, e.g., acute leukemia.As pediatric Emergency Departments face large numbers of febrile children, we recommend a staged diagnostic approach, starting with standard investigations for all children where PIMS-TS is suspected , followePIMS-TS may manifest with features of classic or incomplete  KD with, or without cardiac involvement . AlthougEarly involvement of a pediatric multidisciplinary team (MDT) including intensive care, immunology, infectious diseases, rheumatology, cardiology, hematology, and others  should be considered. In addition to cardiac biomarkers (such as cTnT and NT-pro-BNP) the cardiac evaluation of a patient with PIMS-TS should include a 12-lead electrocardiogram and echocardiography . DiagnosA substantial proportion of children with PIMS-TS initially manifest significantly raised levels of NT-pro-BNP and modest elevations of cardiac troponin, which sometimes take days to weeks to normalize , 32. In ECG registration is recommended at the time of diagnosis and every 48 h while inpatient, daily in patients on PICU. Most ECG findings are non-specific and include ST-segment changes, QTc prolongation and premature atrial or ventricular beats. Another common finding is first-degree (rarely higher grade) atrioventricular block—which may progress after initial diagnosis , 36. SinCoronary artery aneurysms represent a serious complication and may place patients at risk for coronary artery thrombosis or stenosis, myocardial infarction and cardiac death. Coronary artery abnormalities have been reported to appear in 8–24% of patients with PIMS-TS irrespective of the phenotype , 32. Mos3. Therapeutic immune-modulation in PIMS-TS patients requires a multidisciplinary approach. Clinicians should re-evaluate the patient response and consider differential diagnoses at every step.We recommend a MDT approach to guide initiation, escalation, and tapering of empiric immunomodulatory therapy, particularly because available evidence for such treatment is currently only based on observational reports. The panel recommends using a management algorithm  to guide4. In patients with Kawasaki disease-like PIMS-TS, immunomodulation and management should follow established guidelines for Kawasaki disease.5. In patients with PIMS-TS shock, we recommend using immunoglobulins .6. In patients with PIMS-TS shock we recommend treatment with intravenous pulse high-dose methylprednisolone .7. In non-shocked patients with PIMS-TS undefined inflammatory presentation clinicians should consider administration of immunoglobulins .8. In non-shocked patients with PIMS-TS (Kawasaki-like presentation or undefined inflammatory presentation) clinicians should consider administration of prednisolone .9. In all patients with confirmed PIMS-TS treated with steroids, steroids should be tapered over a period of 2–6 weeks depending on the clinical course and considering the clinical and biochemical  response.For children with Kawasaki disease-like PIMS-TS and for children with coronary artery abnormalities irrespective of the PIMS-TS phenotype, the panel recommends that established institutional or international guidelines such as the 2020 American College of Rheumatology guidelines  on KawasWe recommend, based on currently available reports and in line with other recommendations, to use intravenous immunoglobulin (IVIG) in PIMS-TS patients presenting with shock, and to consider IVIG in PIMS-TS with undefined presentation. While IVIG should be usually administered as a single dose of 2 g/kg (max. 100 g/dose), clinicians should assess the cardiac and fluid status, particularly in patients in shock, as in some patients a slower administration become necessary.p-value 0.008)  . The panGiven potential side effects and the lack of data, we advise against the use of pulse steroids in PIMS-TS patients without shock but suggest considering a lower dose therapy given intravenously (up to 2 mg/kg/day) and, subsequently, orally. While the optimal duration of steroid therapy in PIMS-TS remains unknown, the joint view of the panel is that decisions on steroid treatment duration should be guided by the clinical response (resolution of signs and symptoms), as well as by laboratory evidence of decreasing inflammation and improving organ function. Most patients seem to recover well under a 14-day treatment schedule without subsequent signs of disease rebound. In patients with apparent rebound, alternative inflammatory diagnoses should be considered.A proportion of patients may not respond to initial treatment, and deterioration of the disease can be life-threatening. Refractory PIMS-TS is characterized by persistent fever and/or increase of inflammatory markers, worsening organ function, or increase in need of vasoactive drugs [measured by Vasoactive-Inotrope Score ] within Again, MDT assessment is recommended and needs to take differential diagnoses into account at every step before escalating treatment, and should carefully evaluate risk-vs.-benefit ratio of escalating immunomodulatory therapies. For example, PIMS-TS patients may develop secondary infections and immuno-suppressive treatment regimens may further impair the host defense against infection. The current list of biological drugs includes IL-1R , IL-6R  and tumor necrosis factor (TNF) (infliximab) blocking agents . In addi10. In patients with PIMS-TS refractory to initial treatment with IVIG and steroids, and after exclusion of alternative causes by the multidisciplinary team, we suggest consideration for anakinra. We recommend starting at 2–3 mg/kg q12 h s.c. . In case of clinical improvement, stopping of anakinra after 48–72 h should be considered in the multidisciplinary team.11. In patients with PIMS-TS where no clinical and biochemical improvement to anakinra treatment is observed within 24–48 h, the multidisciplinary team should consider other targeted immunomodulation therapy with either tocilizumab or infliximab.12. Clinical assessment and serial laboratory testing on measures of inflammation and organ dysfunction should guide escalation and duration of immunomodulation therapy treatment.Despite its limited licensed indication, anakinra is increasingly chosen for off-label use in PIMS-TS patients , 45–47. Tocilizumab is an IL-6R blocking agent currently approved for the treatment of both systemic and polyarticular juvenile idiopathic arthritis in children above 2 years of age. Tocilizumab has also been used in children with PIMS-TS as IL-6 has been described to be one of the main drivers of the inflammatory cytokine storm in this disease entity. However, safety concerns exist as significant side effects have been described such as a reversible elevation of liver enzymes and an increased risk for bacterial and fungal infections . TherefoThe TNF-alpha blocking agent infliximab, classically used in children with various autoimmune inflammatory diseases has also been successfully used in children with KD. However, since infliximab increases the risk for secondary infections, and based on its long half-life  for de-escalation of antimicrobial therapy in consultation with infectious diseases specialists. Stopping of antimicrobial therapy should be considered depending on the clinical course, microbiological findings, and the presence of PIMS-TS diagnostic criteria, including evidence of recent or current SARS-CoV-2 infection by serology or PCR.routinely used in children with PIMS-TS. However, PIMS-TS patients may be considered for the compassionate use of remdesivir on a case-by-case basis  after the first day should be considered, depending on renal function.20. In any other PIMS-TS patient requiring intensive care admission, we recommend prophylactic heparin (at a dose of 10 U/kg/h) or low molecular weight heparin  depending on renal function.21. For PIMS-TS patients not requiring PICU care, individual risk factors for thrombotic complications should be assessed to guide decisions on individualized anticoagulation therapy.22. In all patients with coronary artery abnormalities antiplatelet management should follow established guidelines for Kawasaki disease.23. As all patients with PIMS-TS irrespective of their phenotype are at risk for coronary artery aneurysms it is reasonable to administer low-dose acetyl salicylic acid  in all patients at least for 4–6 weeks until coronary abnormalities have been ruled out.While there are only scarce data on the use of anticoagulation in children with PIMS-TS , there sIn non-PICU patients, the overall assessment of the individual risk profile should include well-established risk factors such as a previous history of venous thromboembolism or a first-degree relative with venous thromboembolism, the presence of a central line, post-pubertal age or estrogen therapy amongst other. In addition, obesity may increase risk for thromboembolic events in patients with SARS-CoV-2 infection or PIMS-TS.Similar to classical KD patients, children with PIMS-TS irrespective of the phenotype may be at risk for the development of coronary artery aneurysms. Hence it appears to be reasonable to consider low dose aspirin  in addition to prophylactic anticoagulation in all patients with PIMS-TS.24. Patients who have been clinically well on regular wards with evidence of normal or recovering cardiac function, and who have been afebrile (<38 degrees) for 48 h should be considered for discharge home after MDT review.25. A multi-disciplinary follow up is recommended for all patients at least after 1–2 weeks, and 4–6 weeks, respectively, post discharge from hospital. Follow-up should include pediatric immunology/rheumatology and cardiology, and, depending on the presentation and severity, other disciplines.26. In case of cardiac involvement, cardiac follow up should be performed at least at 3, 6, and 12 months after discharge .Hospitals who do not have access to pediatric immunology, cardiology, or intensive care services should consult with the respective centers. In all children with PIMS-TS irrespective of the phenotype, consultation with cardiology is recommended before discharge to guide frequency of follow-up echocardiographic assessment, as cardiac function may not have fully normalized by the time of discharge and aneurysms may develop later in the course of the disease, even post-discharge. In general, we suggest performing a follow-up visit 1–2 weeks, and 4–6 weeks, respectively, after discharge which should encompass a multidisciplinary consultation. The visits should serve as well to critically review the indication, dosing, and duration of ongoing immunosuppressive therapy as appropriate. There is no evidence on duration and tapering schedules of steroids in PIMS-TS, nor on the recommended duration of hospitalization. Decisions on post-discharge treatment thus should be guided by ongoing laboratory or clinical evidence for persistent inflammation.In children with cardiac manifestations, extension of the follow-up is recommended, and specific cardiac follow up is dependent on the degree of initial cardiac involvement, persistent myocardial dysfunction, coronary artery abnormalities, or rhythm disturbances. For all patients with coronary artery abnormalities a cardiologic follow up according to the American Heart Association KD guidelines is recommended . Hence, Exercise restriction is recommended for 2 weeks from discharge if there is no cardiac involvement; for 3–6 months if cardiac involvement was documented. ECG, echocardiography, exercise stress test and Holter monitor should be performed before resuming sport activity and cTnT and NT-pro-BNP should be normalized .Within months of the COVID-19 pandemic spread, many countries across the globe have reported children presenting unwell with features of severe inflammation and multisystem disease , 65. UsiThese recommendation were established in exchange with experts from the U.K. and, accordingly, leverage from the RCPCH case definition. In comparison to another recently published institutional protocol on PIMS-TS from New York , our algA number of limitations need to be considered. First, while the expert group includes specialists from the relevant disciplines, numbers of children with PIMS-TS during the first wave of COVID-19 in Switzerland were low, limiting experience in managing the disease , 45. HowIn conclusion, it is imperative that children with PIMS-TS are enrolled in prospective trials where feasible –70, and LS and PR designed and coordinated the work, wrote the first draft, and take responsibility for the content of the work. MA, NS, SG, and NR led subgroups and wrote the first draft on their section. All other authors participated in literature review, voting, writing, and have seen and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Random-effects analyses showed a small but robust negative association between intelligence and religiosity r = -.14 . Effects were stronger for (i) psychometric intelligence tests than for proxy measures such as grade point averages and (ii) general population and college samples than pre-college samples. Moreover, we provide evidence from combinatorial, multiverse, and specification curve analyses that further corroborates the robustness of the investigated association. Out of 192 reasonable specifications all 135 (70.4%) significant summary effects were negative. In all, our results show small but robust negative associations between religiosity and intelligence that are differentiated in strength but generalize in terms of direction over moderating variables.Over the past century, a remarkable body of research about the relationship of intelligence and religiosity has accumulated. So far, the majority of studies that investigated this relationship showed a negative correlation, indicating lower cognitive abilities of individuals reporting stronger religious beliefs. Although the effect direction has been observed to be largely consistent across studies, the reported effect strength varied substantially across studies. Several potentially moderating variables such as different intelligence and religiosity assessment methods, educational status of samples, and participant sex have been proposed as likely candidates for explaining systematic differences in effect strengths. However, the effects of these moderators are to date unclear. Consequently, we focused in investigating effects of these moderating variables on the intelligence and religiosity link in an update of prior meta-analytical investigations in  Intelligence and religiosity associations have been investigated for more than 90 years by now. As early as in 1928, two studies . Inconsistencies in coding were resolved by discussion with an independent researcher [JP]. Inclusion of correlations of religiosity with psychometric intelligence tests was preferred over those with less salient assessments of intelligence .Coding of studies into categories  and recording of other relevant variables . In a series of subgroup analyses, effect sizes were grouped according to the religiosity assessment type (beliefs vs. behavior vs. mixed), sample type (pre-college vs. college vs. non-college), and status of publication (published vs. unpublished). Moreover, we investigated potential influences of intelligence assessment type (categorized into: IQ vs. GPA vs. mixed assessment) in an exploratory  analysis.k = 76). Finally, to test for a possible influence of primary study quality on reported estimates, meta-regressions of effect sizes on study quality were conducted.Continuous moderators were examined by means of linear precision-weighted meta-regressions. Because recent evidence suggests overproportional numbers of declining effect strengths over time in empirical research regardless of the investigated research question , we examWe used several standard and more modern methods to assess potentially confounding effects of dissemination bias. Only published studies were included for dissemination bias analyses. First, funnel plots were visually inspected for indications of asymmetry. Second, Begg and Mazumdar`s rank correlational method  was usedFourth, we used the Trim-and-Fill method  to estimp-curve, p-uniform, p-uniform*) that are based on similar ideas, but rely on different implementations. These methods allow the estimation of meta-analytical summary effects that are based on the p-value distributions and their associated degrees of freedom of significant published primary studies only. To this end, it is argued that selective reporting of studies is driven by statistical significance rather than the strength of effect sizes (as for instance assumed in the Trim-and-Fill procedure) . The absolute effect was significantly larger than the pre-college estimate , but did not differ from the general population summary effect .We corrected our effect estimations for range restriction of intelligence in college samples using Thorndike`s Case 2 formula  followinb = 0.22; R2 = .18, p = .002), indicating stronger links of intelligence and religiosity in men than in women. When conducting this meta-regression solely with studies using psychometric intelligence tests, the influence remained significant .A meta-regression of effect sizes on the proportion of men  revealedr = -.01; p = .97; 95% CI ) and of the two comprising exclusively women  contrasted our results from regression analyses. Mixed-effects subgroup analyses revealed significant differences , indicating larger effects for women than for men.Subgroup analyses between the six samples consisting solely of men . Again, results of studies that only used psychometric intelligence test scores were virtually identical, showing non-significantly larger absolute effects of published than unpublished studies . However, non-significant results of direct comparisons between published and unpublished effect sizes were to be expected, because of the  considerably smaller number of unpublished studies (83.81% published vs. 16.19% unpublished) and the corresponding large confidence interval of their summary effect.Differences in effect size estimates of published and unpublished studies conformed to the expected direction, showing smaller  effects for unpublished studies . Moreover, none of these variables had an effect in simple moderation analyses , thus further corroborating the remarkable generalizability of the intelligence and religiosity link across moderators.We tested the effects of sex, year, and publication status in a multiple moderation analysis. No significant influences of these variables on the intelligence and religiosity link were observed (proportion of men: k = 88). Visual inspection of the funnel plot , nor the rank correlation method  yielded any evidence for funnel plot asymmetry.Only published study effects were included in all dissemination bias analyses . Consequently, the number of expected significant studies in the hypothesis-conforming direction was 66. There were less studies with a significant outcome observed than what would have been expected based on our power calculations, thus indicating no evidence for bias.p-curve and p-uniform analyses were based on 55 published significant studies. We observed a right-skewed p-curve (p = .01) than large p-values , indicating that the present body of research holds empirical evidence and the extent of p-hacking is negligible. Broadly in line with our findings from our random-effects analysis, p-curve yielded an effect size estimate of r = -.16.Both  p-curve  with morp-uniform analyses the pp-value distribution of the fixed-effect estimate did not differ substantially from the uniform distribution (p = .67), indicating no evidence for dissemination bias. The p-uniform effect estimate was similar to the estimation based on our random-effects analysis and p-curve r = -.17 . The slightly larger effect estimates of p-curve and p-uniform compared to the random-effects analysis was to be expected and is most likely due to the typically observed effect overestimations of these two methods in presence of non-trivial between-studies heterogeneity [In our ogeneity .p-uniform*-based estimate was similar to the p-curve and p-uniform estimates, yielding r = -.16. Consequently, all our used dissemination bias detection and effect estimation methods convergently pointed towards negligible effects of dissemination bias in our present meta-analysis.The b < 0.001; Q = 0.54, R2 < .01, p = .46) or only published effect sizes . We repeated this analysis in a precision-weighted meta-regression and obtained virtually identical results , thus corroborating that study quality did not affect the observed associations.A simple regression of effect sizes on study quality showed no significant effect  that remained virtually identical in terms of strength when education was controlled for in partial correlations  was virtually reduced to zero and failed to reach nominal statistical significance when intelligence was controlled for . The direct path from intelligence to religiosity remained virtually identical when education was accounted for  and the direct effect of education on religiosity disappeared when accounting for intelligence, thus indicating a full mediation of this path by intelligence  indirect association between intelligence and religiosity did not yield significant results  was considerably reduced in strength, when cognitive style was controlled for , thus indicating a partial mediation effect. Similarly, the association of analytic style and religiosity was significant , but substantially reduced when intelligence was controlled for in partial correlations .In another mediation model, we examined potentially mediating effects of cognitive styles  on the intelligence and religiosity association. Here, our analytic approach was the same as described above . The significant intelligence and religiosity link  and both direct paths from intelligence and cognitive style on religiosity were partially mediated by cognitive style  being a poorer measure of an individual’s religiosity than self-reported beliefs, because behaviors may be motivated by the desire to conform to an in-group instead of religiosity  . These rThird, the educational status of samples moderated the strength of intelligence and religiosity associations. Correlations of non-college samples were substantially stronger than those of college samples, but differences disappeared when range restriction was accounted for. This means, that previously observed lower associations in better educated samples  are mostAlthough college and non-college samples appear to show virtually identical associations, effects of pre-college samples were substantially smaller, indicating only a trivial intelligence and religiosity association in children and adolescents. A possible explanation for this observation could be that religious worldviews become only fully developed in more mature ages . AlthougFourth, sex had been previously observed to be a potentially influential variable with women showing typically larger effects than men , althougFifth, we did not find significant changes in study effect strengths over time, thus contrasting findings of a previous systematic review . There wBased on evidence from longitudinal data, it has been hypothesized, that between-individual differences in education rather than intelligence are most likely the drivers of the intelligence and religiosity link ,60. We fAlthough the current meta-analytic data do not allow drawing conclusions about causal inferences, it should be acknowledged, that the available evidence indicates intelligence impacting religiosity rather than the other way around. Whilst cognitive ability can be measured quite reliably at an early age and is a good predictor of intelligence in adulthood , religioOn the whole, several plausible causes for a negative association of intelligence and religiosity have been suggested . In the According to the dual-process model of the mind ,67 thereHowever, recent studies promote single-process models where analytic and intuitive cognitive styles represent the extremes on a continuum, thus adopting a dimensional rather than a categorical view of cognitive styles . ArguabSome limitations of the present meta-analysis need to be acknowledged. It would have been desirable to investigate if belongingness to different religious denominations influences the intelligence and religiosity association. For instance, Catholicism or Judaism are characterized by a strong embeddedness of believers in their community. Common religious practices and rituals including other people are an inherent part of religious identity . AmericaMoreover, it was not possible to assess potential effects of conceptual differences between spirituality and religiosity in the present study. For example, as individuals age, religiosity seems to increase stronger in Catholics than in Protestants  which maBecause of sometimes uncertain dimensionality of assessment instruments and certain suboptimal methodical features of primary studies , correlThe results of the present meta-analysis demonstrate a clear–albeit small–and almost ubiquitous negative association of religiosity with intelligence. This negative link was most pronounced when results of psychometric intelligence tests were correlated with self-reported beliefs and seemed to be strongest in members of the adult general population. In all, the convergent evidence from more standard and several modern approaches to meta-analyses, such as combinatorial, multiverse, and specification-curve analyses, as well as results from a large number of bias assessment methods indicated a remarkable robustness of this effect that generalizes in direction and meaningfulness  across moderators.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(TXT)Click here for additional data file.S4 Appendix(DOCX)Click here for additional data file.S5 Appendix(PDF)Click here for additional data file.S6 Appendix(DOCX)Click here for additional data file.S7 Appendix(DOCX)Click here for additional data file.


This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative . Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients  and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM  and CNN . Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice. The diagnostic process of dementia is challenging and takes a substantial period of time after the first clinical symptoms arise: on average 2.8 years in late-onset and 4.4 years in young-onset dementia . The winMachine learning offers an approach for automatic classification by learning complex and subtle patterns from high-dimensional data. In AD research, such algorithms have been frequently developed to perform automatic diagnosis and predict the future clinical status at an individual level based on biomarkers. These algorithms aim to facilitate medical decision support by providing a potentially more objective diagnosis than that obtained by conventional clinical criteria . A largeDespite high performance of machine learning diagnosis and prediction methods for AD, it is largely unknown how these algorithms would perform in clinical practice. A next step would be to assess the generalizability of classification methods from a specific research population to another study population. There are however only very few studies assessing classification performance on an external data set . ResultsTherefore, this work aims to assess the generalizability of MRI-based classification performance to an external data set representing a tertiary memory clinic population for both diagnosis of AD and prediction of AD in individuals with MCI. To evaluate the value of neural networks and to determine their optimal MRI pre-processing approach, we compare a CNN with a conventional SVM classifier using two pre-processing approaches: minimal pre-processing using only rough spatial alignment and more extensive pre-processing into modulated GM maps. First, we optimize the methods using a large research cohort and assess classification performance using cross-validation. Subsequently, we validate AD prediction performance in MCI patients of the same cohort as well as AD diagnosis and prediction performance in the external data set.www.adni-info.org. We included all participants with a T1w MRI scan at baseline from the ADNI1/GO/2 cohorts: 336 AD patients, 520 control participants (CN), 231 mild cognitive impaired (MCI) patients who converted to AD within 3 years (MCIc) and 628 MCI patients who did not convert (MCInc). The CN group consisted of 414 cognitively normal participants and 106 participants with subjective cognitive decline (SCD). Demographics are shown in https://gitlab.com/radiology/neuro/bron-cross-cohortWe used data from two cohorts. The first group of 1715 participants was included from the Alzheimer’s Disease Neuroimaging Initiative . The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether clinical and neuropsychological assessment, serial magnetic resonance imaging (MRI), positron emission tomography (PET), and other biological markers can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). For up-to-date information, see  www.health-ri.nl/parelsnoer), a collaborative biobanking initiative of the eight university medical centers in the Netherlands  and a convolutional neural network (CNN).An SVM with a linear kernel was used as this approach previously showed good performance using voxel-based features for AD classification. . The c-pTo gain insight into the classifications, we calculated statistical significance maps (p-maps) that show which features contributed to the SVM decision. These maps were computed using an analytical expression that approximates permutation testing . Clusterive rate .An all convolutional neural network was used , which iThe network was compiled with a binary cross-entropy loss function and Adam optimizer . To facilitate a stable convergence, learning rate followed a step decay schedule, i.e. after each ten epochs the learning rate was divided by two. The dropout rate was set to To gain insight into the classifications, we made saliency maps that show which parts of the brain contributed the most to the prediction of the CNN, i.e. which voxels lead to increase/decrease of prediction score when changed. Saliency maps were made using guided backpropagation, changing the activation function of the output layers from softmax to linear activations . Maps weClassification performance was quantified by the area under the curve (AUC) and accuracy. For AD-CN classification, the data of the ADNI AD and CN groups were randomly split for 20 iterations preserving relative class sizes in each training and testing sample, using ependent .Subsequently, we retrained classifiers using all AD and CN participants from the ADNI as training set. These retrained classifiers were used for visualization and their performance was evaluated on three independent test sets: ADNI MCIc-MCInc, PND AD-SCD, and PND MCIc-MCInc. are test  (α<0.013https://gitlab.com/radiology/neuro/bron-cross-cohortTrained models, lists of included subjects and all code used in preparation of this article are available from Cross-validation performance for the ADNI AD-CN classification is shown in The performance of the classifiers trained on all ADNI AD and CN data to predict MCI conversion is shown in The performance of external validation, i.e. the application of the classifiers in the PND data set, is shown in For prediction of MCI conversion in PND, classification performance was also lower than that in ADNI. For the GM modulated maps, the AUC for CNN was 0.702 (Brains regions that contributed to the classifications are visualized using SVM p-maps in We performed a comparative study focusing on the generalizability of diagnostic and predictive performance of machine learning based on MRI data of the ADNI research cohort, to the PND multi-center data set representing a tertiary memory clinic population. Both cross-validation and external validation results for AD-CN diagnosis showed similar performance using the used deep learning classifier and conventional classifier. Both approaches significantly benefited from the use of modulated GM maps instead of raw T1w images. Application to MCI conversion prediction yielded higher performance for SVM than for CNN in ADNI, but this was not seen in PND. Performances were in line with the state-of-the-art . For MCIWhile in many medical imaging applications CNNs convincingly outperformed conventional classifiers , our resAlthough SVM and CNN classifiers yielded similar performance, their visualizations showed different brain regions to be involved in the classification. SVM significance maps showed a clear contribution of the hippocampus and medial temporal lobe as previously shown and expected based on prior knowledge . CNN salThis work is one of the few to address how AD classification performance of MRI-based machine learning generalizes to an independent cohort . On the A limitation of this study is that the diagnosis was based on clinical criteria rather than post-mortem histopathological examination. Although diagnosis was typically confirmed by follow-up, it is possible that some of the patients were misdiagnosed. An alternative could be to use amyloid data from PET imaging or cerebrospinal fluid to classify AD pathology instead of relying on the clinical diagnosis e.g., . In addiWhile the external validation performance was quite high, as expected some performance drop was observed. Therefore, research focusing on approaches to mitigate such performance drops, such as transfer learning, is highly relevant . In addiIn conclusion, classification performance of ADNI data generalized well to the multi-center PND biobank cohort representing tertiary memory clinic patients, with only a minor drop in performance. Conventional SVM classifiers and deep learning approaches using CNN showed comparable results, and both methods benefited from dedicated MRI processing using GM modulated maps. We hope that external validation results like those presented here will contribute to setting next steps towards the implementation of machine learning in clinical practice for aiding diagnosis and prediction.Esther E. Bron: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Supervision, Project administration, Funding acquisition. Stefan Klein: Conceptualization, Validation, Writing - review & editing. Janne M. Papma: Resources, Data curation, Writing - review & editing. Lize C. Jiskoot: Resources, Data curation, Writing - review & editing. Vikram Venkatraghavan: Conceptualization, Methodology, Writing - review & editing. Jara Linders: Methodology, Software, Validation, Writing - review & editing. Pauline Aalten: Resources, Data curation, Writing - review & editing. Peter Paul De Deyn: Resources, Writing - review & editing. Geert Jan Biessels: Resources, Writing - review & editing. Jurgen A.H.R. Claassen: Resources, Writing - review & editing. Huub A.M. Middelkoop: Resources, Writing - review & editing. Marion Smits: Conceptualization, Writing - review & editing. Wiro J. Niessen: Funding acquisition, Writing - review & editing. John C. van Swieten: Resources, Writing - review & editing. Wiesje M. van der Flier: Resources, Data curation, Writing - review & editing. Inez H.G.B. Ramakers: Resources, Data curation, Writing - review & editing. Aad van der Lugt: Conceptualization, Resources, Data curation, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.