Postoperative bleeding after dentoalveolar surgery in patients with thrombocytopenia-are prophylactic platelet transfusions necessary?

PURPOSE: Benefits of prophylactic platelet (PLT) transfusion before dentoalveolar surgery are unclear. This study investigated the effect of prophylactic PLT transfusions on the incidence of postoperative bleeding (POB) in patients with thrombocytopenia and a PLT count ≤ 75*109/L. METHODS: The cohort in this retrospective study comprised 83 patients with thrombocytopenia ≤ 75*109/L who had undergone dentoalveolar surgery. Exclusion criteria were other coagulation deficiencies or medications that would affect hemostasis. In all, 144 teeth had been removed. POB events were extracted and compared between the group that had received prophylactic PLT transfusion before dentoalveolar surgery and the group that had not. RESULTS: POB events were observed in 5 of 83 patients (6.0%) who had a median PLT count of 35*109/L before any transfusion. The group with no postoperative bleeding (NPOB) had a median PLT count of 34*109/L. Two (4.2%) of the 48 patients who had received prophylactic PLT transfusions before dentoalveolar surgery developed POB. Three (8.6%) of the 35 patients who had not received a transfusion experienced POB. The difference between these two groups was not significant (p = 0.646). When two or more teeth were removed in the same session, a significantly higher incidence of POB was observed (p = 0.042). CONCLUSIONS: Our data indicate that prophylactic PLT transfusions in thrombocytopenic patients with PLT counts ≤ 75*109/L do not reduce the incidence of POB after dentoalveolar surgery. However, caution is warranted when extracting multiple teeth in the same surgical session since we found this to be significantly associated with an increased risk of POB.

Scalable and flexible inference framework for stochastic dynamic single-cell models.

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.