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. 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. 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. 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. Parker B. Francis and pilot grant from 2R25-HL126140. Funding agencies had no involvement in the study

Studies summarizing the clinical picture of COVID-19 in children are lacking. This review characterizes clinical symptoms, laboratory, and imaging findings, as well as therapies provided to confirmed pediatric cases of COVID-19. Adhering to PRISMA guidelines, we searched four medical databases (PubMed, LitCovid, Scopus, WHO COVID-19 database) between December 1, 2019 to May 14, 2020 using the keywords “novel coronavirus”, “COVID-19” or “SARS-CoV-2”. We included published or in press peer-reviewed cross-sectional, case series, and case reports providing clinical signs, imaging findings, and/or laboratory results of pediatric patients who were positive for COVID-19. Risk of bias was appraised through the quality assessment tool published by the National Institutes of Health. PROSPERO registration # CRD42020182261. We identified 131 studies across 26 countries comprising 7780 pediatric patients. Although fever (59·1%) and cough (55·9%) were the most frequent symptoms 19·3% of children were asymptomatic. Patchy lesions (21·0%) and ground-glass opacities (32·9%) depicted lung radiograph and computed tomography findings, respectively. Immunocompromised children or those with respiratory/cardiac disease comprised the largest subset of COVID-19 children with underlying medical conditions (152 of 233 individuals). Coinfections were observed in 5.6% of children and abnormal laboratory markers included serum D-dimer, procalcitonin, creatine kinase, and interleukin-6. Seven deaths were reported (0·09%) and 11 children (0·14%) met inclusion for multisystem inflammatory syndrome in children. This review provides evidence that children diagnosed with COVID-19 have an overall excellent prognosis. Future longitudinal studies are needed to confirm our findings and better understand which patients are at increased risk for developing severe inflammation and multiorgan failure. Parker B. Francis and pilot grant from 2R25-HL126140. Funding agencies had no involvement in the study.

Tracheotomy is a commonly performed procedure for patients requiring prolonged mechanical ventilation.¹ Benefits from a tracheotomy include patient comfort and less exposure to sedatives.^2,3 Evidence also suggests that tracheotomies improve pulmonary recruitment and decrease the length of hospitalization in severely ill patients.^4,5 Accordingly, earlier tracheostomy placement may provide benefit in adults requiring prolonged assisted ventilation.

Introduction: 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. Methods: 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 (n < 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. Results: 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 (n = 17). Discussion/Conclusion: 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.

Mesenchymal stem cells (MSCs) are self-renewing multipotent cells that have the capacity to secrete multiple biologic factors that can restore and repair injured tissues. Preclinical and clinical evidence have substantiated the therapeutic benefit of MSCs in various medical conditions. Currently, MSCs are the most commonly used cell-based therapy in clinical trials because of their regenerative effects, ease of isolation and low immunogenicity. Experimental and clinical studies have provided promising results using MSCs to treat diabetes. This review will summarize the role of MSCs on tissue repair, provide emerging strategies to improve MSC function and describe how these processes translate to clinical treatments for diabetes.