A decision tree model for predicting live birth in FMR1 premutation carriers undergoing preimplantation genetic testing for monogenic/single gene defects.

RESEARCH QUESTION: Can patient selection for successful preimplantation genetic testing for women who are fragile X (FMR1) premutation carriers be optimized using a decision tree analysis? This decision support tool enables a comprehensive study of a set of clinical parameters and the expected outcomes. DESIGN: A retrospective case-control study analysing the results of 264 fresh and 21 frozen preimplantation genetic testing for monogenic disorders/single gene defects (PGT-M) cycles in 64 FMR1 premutation carriers. Primary outcome was live birth per cycle start. Live birth rate was calculated for the start of the ovarian stimulation cycle. Fresh and frozen embryo transfers from the same cycle were included. RESULTS: The decision tree model showed that the number of cytosine guanine (CGG) repeats was only a moderate predictor for live birth, whereas an age younger than 36 years was the best predictor for live birth, followed by a collection of 14 or more oocytes. These findings were supported by the results of the logistic regression, which found that only age and oocyte number were significantly associated with live birth (P = 0.005 and 0.017, respectively). CONCLUSIONS: The number of CGG repeats is a relatively poor predictor for live birth in PGT-M cycles. FMR1 premutation carriers are no different from non-carriers. Age is the best identifier of live birth, followed by the number of retrieved oocytes.

An immune-protein signature combining TRAIL, IP-10 and CRP for accurate prediction of severe COVID-19 outcome

BACKGROUNDAccurately identifying COVID-19 patients at-risk to deteriorate remains challenging. Tools integrating host-protein expression have proven useful in determining infection etiology and hold potential for prognosticating disease severity. METHODSAdults with COVID-19 were recruited at medical centers in Israel, Germany, and the United States. Severe outcome was defined as intensive care unit admission, non-invasive or invasive ventilation, or death. Tumor necrosis factor related apoptosis inducing ligand (TRAIL) and interferon gamma inducible protein-10 (IP-10; also known as CXCL10) and C-reactive protein (CRP) were measured using an analyzer providing values within 15 minutes. A signature indicating the likelihood of severe outcome was derived generating a score (0-100). Patients were assigned to 4 score bins. RESULTSBetween March and November 2020, 518 COVID-19 patients were enrolled, of whom 394 were eligible, 29% meeting a severe outcome. The signatures area under the receiver operating characteristic curve (AUC) was 0.86 (95% confidence interval: 0.81-0.91). Performance was not confounded by age, sex, or comorbidities and superior to IL-6 (AUC 0.77; p = 0.033) and CRP (AUC 0.78; p < 0.001). Likelihood of severe outcome increased significantly (p < 0.001) with higher scores. The signature differentiated patients who further deteriorated after meeting a severe outcome from those who improved (p = 0.004) and projected 14-day survival probabilities (p < 0.001). CONCLUSIONThe derived immune-protein signature combined with a rapid measurement platform is an accurate predictive tool for early detection of COVID-19 patients at-risk for severe outcome, facilitating timely care escalation and de-escalation and appropriate resource allocation. FUNDINGMeMed funded the study

BNT162b2 COVID-19 mRNA vaccine elicits a rapid and synchronized antibody response in blood and milk of breastfeeding women

We describe the dynamics of the vaccine-specific antibody response in the breastmilk and serum in a prospective cohort of ten lactating women who received two doses of the Pfizer-BioNTech BNT162b2 COVID-19 mRNA vaccine. The antibody response was rapid and highly synchronized between breastmilk and serum, reaching stabilization 14 days after the second dose. The predominant serum antibody was IgG. The response in the breastmilk included both IgG and IgA with neutralizing capacity.

Real-time IP-10 measurements as a new tool for inflammation regulation within a clinical decision support protocol for managing severe COVID-19 patients

The challenge of treating severely ill COVID-19 patients is particularly great due to the need to simultaneously manage oxygenation and the inflammatory state without compromising viral clearance. Currently, there are many tools to aid in oxygen management and in monitoring viral replication. However, predictive biomarkers for monitoring the host immune response across COVID-19 disease stages and specifically, for titrating immunomodulatory therapy are lacking. We utilized a recently cleared platform (MeMed Key) that enables rapid and easy serial measurement of IP-10, a host protein implicated in lung injury due to viral-induced hyperinflammation. A dynamic clinical decision support protocol was employed for managing SARS-CoV-2 positive patients admitted to a COVID-19 dedicated medical center run by Clalit Health Services. This is the first protocol to include real-time measurements of IP-10 as a potential aid for regulating inflammation. Overall, 502 serial real-time IP-10 measurements were performed on 52 patients recruited between 7th April 2020 to 10th May 2020, with 12 patients admitted to the intensive care unit (ICU). IP-10 levels correlated with increased COVID-19 severity score and ICU admission. Within the ICU admitted patients, the number of days with IP-10 measurements >1,000 pg/ml was associated with mortality. Upon administration of corticosteroid immunomodulatory therapy, a significant decrease in IP-10 levels was observed. Real-time IP-10 monitoring represents a new tool to aid in management and therapeutic decisions relating to the inflammatory status of COVID-19 patients.