Open Access

The Born cross sections of the e+e− → D*+D*− and e+e− → D*+D− processes are measured using e+e− collision data collected with the BESIII experiment at center-of-mass energies from 4.085 to 4.600 GeV, corresponding to an integrated luminosity of 15.7 fb−1. The results are consistent with and more precise than the previous measurements by the Belle, Babar and CLEO collaborations. The measurements are essential for understanding the nature of vector charmonium and charmonium-like states.

By analyzing 6.32 fb − 1 of e+ e− annihilation data collected at the center-of-mass energies between 4.178 and 4.226 GeV with the BESIII detector, we determine the branching fraction of the leptonic decay D + s → τ + ντ, with τ+ → π + π0¯ντ, to be B D + s → τ + ν τ = (5.29 ± 0.25 stat ± 0.20 syst) %. We estimate the product of the Cabibbo-Kobayashi-Maskawa matrix element |Vcs|and the D + s decay constant f D + s to be f D + s|Vcs| = (244.8 ± 5.8 stat ± 4.8syst) MeV, using the known values of the τ + and D + s masses as well as the D + s lifetime, together with our branching fraction measurement. Combining the value of |Vcs| obtained from a global fit in the standard model and f D + s from lattice quantum chromodynamics, we obtain f D + s = (251.6 ± 5.9 stat ± 4.9syst) MeV and |Vcs| = 0.980 ± 0.023 stat ± 0.019 syst. Using the branching fraction of B D + s → μ + νμ = (5.35±0.21)×10−3, we obtain the ratio of the branching fractions B D + s → τ + ντ/B D +s → μ+νμ = 9.89±0.71, which is consistent with the standard model prediction of lepton flavor universality.

We report a measurement of the observed cross sections of e+ e− → J/ψX based on 3.21 fb − 1 of data accumulated at energies from 3.645 to 3.891 GeV with the BESIII detector operated at the BEPCII collider. In analysis of the cross sections, we measured the decay branching fractions of B(ψ(3686) → J/ψX) = (64.4 ± 0.6 ± 1.6)% and B(ψ(3770) → J/ψX) = (0.5 ± 0.2 ± 0.1)% for the first time. The energy-dependent line shape of these cross sections cannot be well described by two Breit-Wigner (BW) amplitudes of the expected decays ψ (3686) → J/ψX and ψ(3770) → J/ψX. Instead, it can be better described with one more BW amplitude of the decay R(3760)→ J/ψX. Under this assumption, we extracted the R (3760) mass M R (3760 ) = 3766.2 ± 3.8 ± 0.4 MeV/c2, total width Γ tot R ( 3760 ) = 22.2 ± 5.9 ± 1.4 MeV, and product of leptonic width and decay branching fraction ΓeeR(3760) B[R(3760) → J/ψX] = (79.4 ± 85.5 ± 11.7) eV. The significance of the R(3760) is 5.3σ. The first uncertainties of these measured quantities are from fits to the cross sections and second systematic.

Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.