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Background: Physiological changes in pregnancy may affect drug safety and efficacy, sometimes requiring dose adjustments. Pregnancy-adjusted doses, however, are missing for most medications. Increasingly, pharmacokinetic models can be used for antenatal dose finding. Given the novelty of this technique and questions regarding dose credibility, the acceptability of model-informed antenatal doses should be explored.Objective: We aimed to assess the willingness-to-use and preferred features for model-informed antenatal doses among healthcare practitioners (HCPs) and pregnant women in European countries.Methods: A cross-sectional, web-based study drawing on two open surveys was performed between 8 September and 30 November 2022. Each survey comprised statements drawn from prior focus groups, associated with Likert-scales. Themes included respondents' information needs, search behaviours along with their willingness-to-use and preferred features for model-informed antenatal doses. The surveys were disseminated through professional societies, pregnancy websites and social media. A descriptive analysis was performed.Results: In total, 608 HCPs from different specialties and 794 pregnant women across 15 countries participated, with 81% of respondents across both groups in the Netherlands or Belgium. Among pregnant women, 31% were medical professionals and 85% used medication during pregnancy. Eighty-three percent of HCPs found current antenatal pharmacotherapy suboptimal and 97% believed that model-informed antenatal doses would enhance the quality of antenatal care. Most HCPs (93%) and pregnant women (75%) would be willing to follow model-informed antenatal doses. Most HCPs desired access to the evidence (88%), including from pharmacokinetic modelling (62%). Most pregnant women (96%) wanted to understand antenatal dosing rationales and to be involved in dosing decisions (97%).Conclusion: The willingness-to-use model-informed antenatal doses is high among HCPs and pregnant

RIVM rapport:Het RIVM heeft het rijtijdenmodel voor de spoedeisende ambulancezorg geactualiseerd. Dit gebeurt elke vier jaar. Dit model schat de rijtijd die een ambulance gemiddeld nodig heeft om met spoed naar een locatie te rijden. Hiervoor gebruiken we feitelijk gemeten snelheden van ambulances van juli 2019 tot en met juni 2020. Voor de meeste wegtypen (denk aan snel- en hoofdwegen en voetgangersgebieden) zijn de gemiddelde gemeten snelheden lager dan in de vorige meetperiode (2015-2016). Gemiddeld gezien is de rijtijd van ambulances in heel Nederland met 0,8 minuten langer geworden, zowel binnen als buiten de bebouwde kom. Daarom geeft het nieuwe rijtijdenmodel voor veel trajecten langere rijtijden dan het oude rijtijdenmodel uit 2016. Onderzoek naar de oorzaak van de lagere snelheden viel buiten de vraagstelling. Wel is van twee bijzondere gebeurtenissen vanaf maart 2020 onderzocht wat de invloed is op de gemiddelde snelheden van ambulances: de gedeeltelijke lockdown in verband met de corona-pandemie en de begrenzing van de maximum snelheid op snelwegen overdag tot 100 km per uur. Er werd voor beide gebeurtenissen geen effect gevonden. Bij elke actualisatie wordt gekeken of de met het model geschatte rijtijden nog beter kunnen aansluiten bij de werkelijke rijtijden. Dit keer is gekeken of een andere regio-indeling voor ambulances en minder tijdsblokken (alleen binnen of buiten de spitsuren) beter zijn. Deze varianten blijken de werkelijke rijtijden net zo goed te voorspellen als het model uit 2016. Het RIVM heeft daarom besloten het model niet te veranderen en alleen de gemiddelde snelheden te actualiseren. Het RIVM gebruikt het rijtijdenmodel voor de spoedeisende ambulancezorg voor verschillende bereikbaarheidsanalyses in de spoedeisende zorg., RIVM has updated the driving times model for urgent ambulance services. It does this every four years. This model is used to estimate the driving time an ambulance needs on average to reach a location urgently. It is based on actual ambulance speeds measured between July 2019 and June 2020. For most road types (such as motorways, main roads and pedestrian areas), the average speeds were lower than in the previous measurement period (2015–2016). We did not study the reasons for this. Throughout the Netherlands, ambulance driving times have increased by an average of 0.8 minutes. This has happened both within and outside built-up areas. This is why the new driving times model uses longer driving times for many routes. We investigated the influence on ambulance speeds of two events that both came up since March 2020: partial lockdown due to the COVID pandemic and the reduced maximum speed on motorways of 100 km/h between 6:00 and 19:00. Both events had no effect. With each update, we investigate how we can reduce the difference between estimated driving times and actual driving times. This time, we looked at the effect of using different regions for ambulances and fewer time blocks (only two were used: during rush hours and outside of rush hours). We found that these variants predicted actual driving times just as well as the 2016 model. For this reason, RIVM decided not to change the model, but only to update the average speeds. RIVM uses the driving times model for urgent ambulance services to analyse the availability of various types of urgent care.

Secondary cell walls (SCWs) in stem xylem vessel and fibre cells enable plants to withstand the enormous compressive forces associated with upright growth. It remains unclear if xylem vessel and fibre cells can directly sense mechanical stimuli and modify their SCW during development. We provide evidence that Arabidopsis SCW-specific Fasciclin-Like Arabinogalactan-proteins 11 (FLA11) and 12 (FLA12) are possible cell surface sensors regulating SCW development in response to mechanical stimuli. Plants overexpressing FLA11 (OE-FLA11) showed earlier SCW development compared to the wild-type (WT) and altered SCW properties that phenocopy WT plants under compression stress. By contrast, OE-FLA12 stems showed higher cellulose content compared to WT plants, similar to plants experiencing tensile stress. fla11, OE-FLA11, fla12, and OE-FLA12 plants showed altered SCW responses to mechanical stress compared to the WT. Quantitative polymerase chain reaction (qPCR) and RNA-seq analysis revealed the up-regulation of genes and pathways involved in stress responses and SCW synthesis and regulation. Analysis of OE-FLA11 nst1 nst3 plants suggests that FLA11 regulation of SCWs is reliant on classical transcriptional networks. Our data support the involvement of FLA11 and FLA12 in SCW sensing complexes to fine-tune both the initiation of SCW development and the balance of lignin and cellulose synthesis/deposition in SCWs during development and in response to mechanical stimuli.