Vaccines and Myocardial Injury in Patients Hospitalized for COVID-19 Infection: the CardioCOVID-Gemelli Study

BACKGROUND

Myocardial injury is prevalent among patients hospitalized for COVID-19. However, the role of COVID-19 vaccines in modifying the risk of myocardial injury is unknown.

OBJECTIVES

To assess the role of vaccines in modifying the risk of myocardial injury in COVID-19.

METHODS

We enrolled COVID-19 patients admitted from March 2021 to February 2022 with known vaccination status and ≥1 assessment of hs-cTnI within 30 days from the admission. The primary endpoint was the occurrence of myocardial injury (hs-cTnI levels >99th percentile upper reference limit).

RESULTS

1019 patients were included (mean age 67.7±14.8 years, 60.8% male, 34.5% vaccinated against COVID-19). Myocardial injury occurred in 145 (14.2%) patients. At multivariate logistic regression analysis, advanced age, chronic kidney disease and hypertension, but not vaccination status, were independent predictors of myocardial injury. In the analysis according to age tertiles distribution, myocardial injury occurred more frequently in the III tertile (≥76 years) compared to other tertiles (I tertile:≤60 years;II tertile:61-75 years) (p<0.001). Moreover, in the III tertile, vaccination was protective against myocardial injury (OR 0.57, CI 95% 0.34-0.94; p=0.03), while a previous history of coronary artery disease was an independent positive predictor. In contrast, in the I tertile, chronic kidney disease (OR 6.94, 95% CI 1.31-36.79, p=0.02) and vaccination (OR 4.44, 95% CI 1.28-15.34, p=0.02) were independent positive predictors of myocardial injury.

CONCLUSIONS

In patients ≥76 years, COVID-19 vaccines were protective for the occurrence of myocardial injury, while in patients ≤60 years, myocardial injury was associated with previous COVID-19 vaccination. Further studies are warranted to clarify the underlying mechanisms.

A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells

Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains underexplored. In this paper, we propose a novel deep learning model for cancer detection from urinary cytopathology screening images. We leverage recent ideas from the field of multioutput neural networks to provide a model that can efficiently train even on small-scale datasets, such as those typically found in real-world scenarios. Additionally, we argue that calibration (i.e., providing confidence levels that are aligned with the ground truth probability of an event) has been a major shortcoming of prior works, and we experiment a number of techniques to provide a well-calibrated model. We evaluate the proposed algorithm on a novel dataset, and we show that the combination of focal loss, multiple outputs, and temperature scaling provides a model that is significantly more accurate and calibrated than a baseline deep convolutional network.

Expression of luteal estrogen receptor, interleukin-1, and apoptosis-associated genes after PGF2alpha administration in rabbits at different stages of pseudopregnancy

The dynamic expression for estrogen receptor subtype-1 (ESR1), interleukin-1beta (IL1B), and apoptosis-associated genes, as well as nitric oxide synthase activity, were examined in corpora lutea (CL) of rabbits after prostaglandin F(2alpha) (PGF(2alpha)) administration on either day 4 or day 9 of pseudopregnancy. By reverse transcriptase polymerase chain reaction, the steady-state level of ESR1 transcript was lower (P < 0.01) and that of anti-apoptotic B-cell CLL/lymphoma 2 (BCL2) -like 1 (BCL2L1) was greater in day 4 (P < 0.01) than in day 9 CL. Western blot analysis revealed that BCL2-associated X protein (BAX) abundance was greater in day 4 (P < 0.01) than in day 9 CL, whereas BCL2L1 protein was undetectable at both luteal stages. After PGF(2alpha), ESR1 transcript decreased (P < 0.01) in day 9 CL, whereas IL1B mRNA showed a transitory increase (P < 0.01) at both stages. The pro-apoptotic tumor protein p53 (TP53) gene had diminished (P < 0.01) on day 4 and on day 9 after a transitory increase (P < 0.01), whereas the BAX/BCL2L1 expression ratio increased (P < 0.01) in day 9 CL 24 h after treatment. Following PGF(2alpha), TP53 protein increased (P < 0.01) at both luteal stages, and BAX decreased (P < 0.01) in day 4 CL but increased (P < 0.01) 24 h later in day 9 CL; BCL2L1 became detectable 6 h later in day 4 CL. Nitric oxide synthase activity temporarily increased (P < 0.01) following PGF(2alpha). These findings suggest that PGF(2alpha) regulates luteolysis by ESR1 mRNA down-regulation and modulation of pro- and anti-apoptotic pathways in CL that have acquired a luteolytic capacity.