Protective neutralizing antibodies from human survivors of Crimean-Congo hemorrhagic fever.

Crimean-Congo hemorrhagic fever virus (CCHFV) is a World Health Organization priority pathogen. CCHFV infections cause a highly lethal hemorrhagic fever for which specific treatments and vaccines are urgently needed. Here, we characterize the human immune response to natural CCHFV infection to identify potent neutralizing monoclonal antibodies (nAbs) targeting the viral glycoprotein. Competition experiments showed that these nAbs bind six distinct antigenic sites in the Gc subunit. These sites were further delineated through mutagenesis and mapped onto a prefusion model of Gc. Pairwise screening identified combinations of non-competing nAbs that afford synergistic neutralization. Further enhancements in neutralization breadth and potency were attained by physically linking variable domains of synergistic nAb pairs through bispecific antibody (bsAb) engineering. Although multiple nAbs protected mice from lethal CCHFV challenge in pre- or post-exposure prophylactic settings, only a single bsAb, DVD-121-801, afforded therapeutic protection. DVD-121-801 is a promising candidate suitable for clinical development as a CCHFV therapeutic.

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes.

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

A High-Throughput Method To Measure Relative Quantum Yield of Lanthanide Complexes for Bioimaging.

Luminescent lanthanides provide a promising alternative to organic chromophores for cellular bioimaging and bioassay applications; efficacy is closely governed by their respective quantum yields. Conventionally utilized quantum-yield measurements for lanthanides are laborious and not amenable to rapid relative comparison of compound performance. Here, we introduce a high-throughput optical imaging method to determine and directly compare relative quantum yield using Cherenkov-radiation-mediated excitation of luminescent lanthanide complexes.