Background Multisystem inflammatory syndrome in children (MIS-C), also known as pediatric inflammatory multisystem syndrome, is a new dangerous childhood disease that is temporally associated with coronavirus disease 2019 (COVID-19). We aimed to describe the typical presentation and outcomes of children diagnosed with this hyperinflammatory condition. Methods We conducted a systematic review to communicate the clinical signs and symptoms, laboratory findings, imaging results, and outcomes of individuals with MIS-C. We searched four medical databases to encompass studies characterizing MIS-C from January 1st, 2020 to July 25th, 2020. Two independent authors screened articles, extracted data, and assessed risk of bias. This review was registered with PROSPERO CRD42020191515. Findings Our search yielded 39 observational studies ( n = 662 patients). While 71·0% of children ( n = 470) were admitted to the intensive care unit, only 11 deaths (1·7%) were reported. Average length of hospital stay was 7·9 ± 0·6 days. Fever (100%, n = 662), abdominal pain or diarrhea (73·7%, n = 488), and vomiting (68·3%, n = 452) were the most common clinical presentation. Serum inflammatory, coagulative, and cardiac markers were considerably abnormal. Mechanical ventilation and extracorporeal membrane oxygenation were necessary in 22·2% ( n = 147) and 4·4% ( n = 29) of patients, respectively. An abnormal echocardiograph was observed in 314 of 581 individuals (54·0%) with depressed ejection fraction (45·1%, n = 262 of 581) comprising the most common aberrancy. Interpretation Multisystem inflammatory syndrome is a new pediatric disease associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is dangerous and potentially lethal. With prompt recognition and medical attention, most children will survive but the long-term outcomes from this condition are presently unknown. Funding Parker B. Francis and pilot grant from 2R25-HL126140. Funding agencies had no involvement in the study

<b><i>Introduction:</i></b> Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. <b><i>Methods:</i></b> A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (<i>n</i> &#x3c; 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. <b><i>Results:</i></b> Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (<i>n</i> = 17). <b><i>Discussion/Conclusion:</i></b> ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.

Background Lung disease is a leading cause of morbidity and mortality. A breach in the lung alveolar-epithelial barrier and impairment in lung function are hallmarks of acute and chronic pulmonary illness. This review is part two of our previous work. In part 1, we demonstrated that CdM is as effective as MSCs in modulating inflammation. Herein, we investigated the effects of mesenchymal stromal cell (MSC)-conditioned media (CdM) on (i) lung architecture/function in animal models mimicking human lung disease, and (ii) performed a head-to-head comparison of CdM to MSCs. Methods Adhering to the animal Systematic Review Centre for Laboratory animal Experimentation protocol, we conducted a search of English articles in five medical databases. Two independent investigators collected information regarding lung: alveolarization, vasculogenesis, permeability, histologic injury, compliance, and measures of right ventricular hypertrophy and right pulmonary pressure. Meta-analysis was performed to generate random effect size using standardized mean difference with 95% confidence interval. Results A total of 29 studies met inclusion. Lung diseases included bronchopulmonary dysplasia, asthma, pulmonary hypertension, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and pulmonary fibrosis. CdM improved all measures of lung structure and function. Moreover, no statistical difference was observed in any of the lung measures between MSCs and CdM. Conclusions In this meta-analysis of animal models recapitulating human lung disease, CdM improved lung structure and function and had an effect size comparable to MSCs.

Background Adult clinical trials have reported safety and the therapeutic potential of stem cells for cardiac disease. These observations have now translated to the pediatric arena. We conducted a meta-analysis to assess safety and efficacy of cell-based therapies in animal and human studies of pediatric heart disease. Methods and results A literature search was conducted to examine the effects of cell-based therapies on: (i) safety and (ii) cardiac function. In total, 18 pre-clinical and 13 human studies were included. Pre-clinical: right ventricular dysfunction was the most common animal model (80%). Cardiac-derived (28%) and umbilical cord blood (24%) cells were delivered intravenously (36%) or intramyocardially (35%). Mortality was similar between cell-based and control groups (OR 0.94; 95% CI 0.05, 17.41). Cell-based treatments preserved ejection fraction by 6.9% ( p < 0.01), while intramyocardial at a dose of 1–10 M cells/kg optimized ejection fraction. Clinical: single ventricle physiology was the most common cardiac disease ( n = 9). Cardiac tissue was a frequent cell source, dosed from 3.0 × 10 5 to 2.4 × 10 7 cells/kg. A decrease in adverse events occurred in the cell-based cohort (OR 0.17, p < 0.01). Administration of cell-based therapies improved ejection fraction (MD 4.84; 95% CI 1.62, 8.07; p < 0.01). Conclusions In this meta-analysis, cell-based therapies were safe and improved specific measures of cardiac function. Implications from this review may provide methodologic recommendations (source, dose, route, timing) for future clinical trials. Of note, many of the results described in this study pattern those seen in adult stem cell reviews and meta-analyses.