Umbilical cord mesenchymal stem cells for COVID-19 acute respiratory distress syndrome: A double-blind, phase 1/2a, randomized controlled trial

Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with high mortality. Mesenchymal stem cells are known to exert immunomodulatory and anti-inflammatory effects and could yield beneficial effects in COVID-19 ARDS. The objective of this study was to determine safety and explore efficacy of umbilical cord mesenchymal stem cell (UC-MSC) infusions in subjects with COVID-19 ARDS. A double-blind, phase 1/2a, randomized, controlled trial was performed. Randomization and stratification by ARDS severity was used to foster balance among groups. All subjects were analyzed under intention to treat design. Twenty-four subjects were randomized 1:1 to either UC-MSC treatment (n = 12) or the control group (n = 12). Subjects in the UC-MSC treatment group received two intravenous infusions (at day 0 and 3) of 100 ± 20 × 106 UC-MSCs; controls received two infusions of vehicle solution. Both groups received best standard of care. Primary endpoint was safety (adverse events [AEs]) within 6 hours; cardiac arrest or death within 24 hours postinfusion). Secondary endpoints included patient survival at 31 days after the first infusion and time to recovery. No difference was observed between groups in infusion-associated AEs. No serious adverse events (SAEs) were observed related to UC-MSC infusions. UC-MSC infusions in COVID-19 ARDS were found to be safe. Inflammatory cytokines were significantly decreased in UC-MSC-treated subjects at day 6. Treatment was associated with significantly improved patient survival (91% vs 42%, P =.015), SAE-free survival (P =.008), and time to recovery (P =.03). UC-MSC infusions are safe and could be beneficial in treating subjects with COVID-19 ARDS

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Conditional uncertainty: Misinterpretations of “significant” p values

Misapplication of p values can lead to very misleading conclusions. The frequent insignificance of a “significant” p value 1provides examples illustrating situations where p values are misused in interpretation and describes alternatives to p values to consider. The concern about p values, and their potential shortcomings and misinterpretations, has been widely discussed and has received a lot of debate from the statistical community. While we agree with much of the content presented in the article, the issue fundamentally represents a misinterpretation of conditional probability, misapplication of measures regarding diagnostic accuracy, and consequently violations of assumptions inherent in experimental design and the validity of many approaches to statistical analysis and inference. One way this can be remedied is through team science, and collaboration between clinical and biostatistical scientists in research

Nonparametric paired tests for censored survival data incorporating prognostic covariate information.

In clinical trials, complications in the data structure can arise by design, as when treatment groups are dependent, or by happenstance, as when selection bias is present. In this dissertation, we develop nonparametric paired tests based on weighted integrated survival that incorporate prognostic covariate information, and adjust for selection bias as well as informative censoring. In the absence of these sources of bias, these new methods can improve power over current nonparametric tests in two ways. First, by extending existing methods that take advantage of covariate information to the paired setting, we gain power over methods that either ignore the covariates or the dependence between the treatment groups. Second, the methods we use to adjust for selection bias can produce additional efficiency gains when treatment groups are comparable with respect to baseline covariates. When information collected post-baseline is incorporated, additional efficiency gains are made as long as censoring is uninformative. Additionally, we develop paired stratified tests using both weighted rank-based and weighted integrated survival based methods. In addition to adjusting for potential bias from baseline covariate imbalances and weakening assumptions concerning uninformative censoring, stratification may improve power when treatment alternatives are more clearly detectable within each covariate stratum. We investigate the strengths and limitations of the different methods presented using a number of simulation scenarios. Data from the Early Treatment Diabetic Retinopathy Study are used throughout to illustrate methodology in comparing time to severe vision loss between treatment groups receiving either early or deferred photocoagulation therapy.Ph.D.Biological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/131477/2/3057914.pd

Baseline Assessment of Circulating MicroRNAs Near Diagnosis of Type 1 Diabetes Predicts Future Stimulated Insulin Secretion

Type 1 diabetes is an autoimmune disease resulting in severely impaired insulin secretion. We investigated whether circulating microRNAs (miRNAs) are associated with residual insulin secretion at diagnosis and predict the severity of its future decline. We studied 53 newly diagnosed subjects enrolled in placebo groups of TrialNet clinical trials. We measured serum levels of 2,083 miRNAs, using RNA sequencing technology, in fasting samples from the baseline visit (<100 days from diagnosis), during which residual insulin secretion was measured with a mixed meal tolerance test (MMTT). Area under the curve (AUC) C-peptide and peak C-peptide were stratified by quartiles of expression of 31 miRNAs. After adjustment for baseline C-peptide, age, BMI, and sex, baseline levels of miR-3187-3p, miR-4302, and the miRNA combination of miR-3187-3p/miR-103a-3p predicted differences in MMTT C-peptide AUC/peak levels at the 12-month visit; the combination miR-3187-3p/miR-4723-5p predicted proportions of subjects above/below the 200 pmol/L clinical trial eligibility threshold at the 12-month visit. Thus, miRNA assessment at baseline identifies associations with C-peptide and stratifies subjects for future severity of C-peptide loss after 1 year. We suggest that miRNAs may be useful in predicting future C-peptide decline for improved subject stratification in clinical trials

Association of serum microRNAs with islet autoimmunity, disease progression and metabolic impairment in relatives at risk of type 1 diabetes

MicroRNAs (miRNAs) are key regulators of gene expression and novel biomarkers for many diseases. We investigated the hypothesis that serum levels of some miRNAs would be associated with islet autoimmunity and/or progression to type 1 diabetes. We measured levels of 93 miRNAs most commonly detected in serum. This retrospective cohort study included 150 autoantibody-positive and 150 autoantibody-negative family-matched siblings enrolled in the TrialNet Pathway to Prevention Study. This was a young cohort (mean age = 11 years), and most autoantibody-positive relatives were at high risk because they had multiple autoantibodies, with 39/150 (26%, progressors) developing type 1 diabetes within an average 8.7 months of follow-up. We analysed miRNA levels in relation to autoantibody status, future development of diabetes and OGTT C-peptide and glucose indices of disease progression. Fifteen miRNAs were differentially expressed when comparing autoantibody-positive/negative siblings (range -2.5 to 1.3-fold). But receiver operating characteristic (ROC) analysis indicated low specificity and sensitivity. Seven additional miRNAs were differentially expressed among autoantibody-positive relatives according to disease progression; ROC returned significant AUC values and identified miRNA cut-off levels associated with an increased risk of disease in both cross-sectional and survival analyses. Levels of several miRNAs showed significant correlations (r values range 0.22-0.55) with OGTT outcomes. miR-21-3p, miR-29a-3p and miR-424-5p had the most robust associations. Serum levels of selected miRNAs are associated with disease progression and confer additional risk of the development of type 1 diabetes in young autoantibody-positive relatives. Further studies, including longitudinal assessments, are warranted to further define miRNA biomarkers for prediction of disease risk and progression