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by Carmen Villagrasa, Giorgio Baiocco, Zine-El-Abidine Chaoui, Michael Dingfelder, Sébastien Incerti, Pavel Kundrát, Ioanna Kyriakou, Yusuke Matsuya, Takeshi Kai, Alessio Parisi, Yann Perrot, Marcin Pietrzak, Jan Schuemann, Hans Rabus
Biological effects induced by diverse types of ionizing radiation are known to show important variations. Nanodosimetry is suitable for studying the link between these variations and the patterns of radiation interactions within nanometer-scale volumes, using experimental techniques complemented by Monte Carlo track structure (MCTS) simulations. However, predicted nanodosimetric quantities differ among MCTS codes, primarily because each code employs distinct molecular-scale particle interaction models. This multi-code study examines these variations for low-energy electrons (20–10,000 eV), which play a critical role in energy deposition and biological effects by virtually all types of ionizing radiation. Specifically, the hypothesis tested in this work is that inter-code variability in nanodosimetry results is mainly caused by differences in assumptions regarding total interaction cross sections. Ionization cluster size distributions and derived nanodosimetric parameters were simulated with seven MCTS codes (PARTRAC, PHITS-TS, MCwater, PTra, and three Geant4-DNA options) in liquid water as a surrogate for biological tissue. Significant inter-code differences were observed, especially at the lowest energies. They were substantially reduced upon replacing the original cross sections in each code with a common, averaged dataset, created ad-hoc for this study and not based on theoretical assumptions. For example, for 50 eV electrons in 8 nm spheres, the variability in the predicted mean ionization numbers decreased from 23% to 5%, and in the probability of inducing two or more ionizations from 34% to 7% (relative standard deviations). This quantification demonstrates that total interaction cross sections are the primary source of uncertainty at low electron energies. A sensitivity test using DNA damage simulations with the PARTRAC code revealed that cross section variations notably affect biological outcome predictions. Replacing the code’s original cross sections with the averaged ones increased the predicted double-strand break yield by up to 15%. These findings underscore the urgent need for improved characterization of low-energy electron interaction cross sections to reduce uncertainties in MCTS simulations and enhance mechanistic understanding of radiation-induced biological effects.To develop prediction models for short-term outcomes following a first acute myocardial infarction (AMI) event (index) or for past AMI events (prevalent) in a national primary care cohort.
Retrospective cohort study using logistic regression models to estimate 1-year and 5-year risks of all-cause mortality and composite cardiovascular outcomes.
Primary care practices in England contributing data to the Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD databases between 2006 and 2019.
Patients with an incident (index) or prevalent AMI event. Models were trained on a random 80% sample of CPRD Aurum (n=1018 practices), internally validated on the remaining 20% (n=255) and externally validated using CPRD GOLD (n=248).
Discrimination assessed using sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Calibration assessed using calibration plots.
In the index (prevalent) cohorts, 94 241 (64 789) patients were included in the training and internal validation sets, and 16 832 (7479) in the external validation set. For the index cohort, AUCs for 1-year [5-year] all-cause mortality were 0.802 (95% CI 0.793 to 0.812) [0.847 (0.841 to 0.853)] internally and 0.800 (0.790 to 0.810) [0.841 (0.835 to 0.847)] externally. For the primary composite outcome (stroke, heart failure and all-cause death), AUCs were 0.763 (0.756 to 0.771) [0.824 (0.818 to 0.830)] internally and 0.748 (0.739 to 0.756) [0.808 (0.801 to 0.815)] externally. Discrimination was higher in the prevalent cohort, particularly for 1-year mortality (AUC: 0.896, 95% CI 0.887 to 0.904). Models excluding treatment variables showed slightly lower but comparable performance. Calibration was acceptable across models.
These models can support clinicians in identifying patients at increased risk of short-term adverse outcomes following AMI, whether newly diagnosed or with a prior history. This can inform monitoring strategies and secondary prevention and guide patient counselling on modifiable risk factors.
To examine the association between maternal and neonatal biochemical variables in babies born to mothers with hypertensive disorders of pregnancy (HDP) and admitted to the neonatal unit within 24 hours of delivery.
Retrospective chart review study.
Specialised antenatal hypertension clinic and neonatal unit in a tertiary unit referral hospital.
Pregnancies complicated with HDP (N=282) and their neonates if admitted to the neonatal unit within 24 hours of delivery.
We examined the association between maternal and neonatal biochemical variables, after controlling for maternal, neonatal and pregnancy characteristics.
There were strong associations and independent prediction of neonatal levels by maternal levels for urea, creatinine, sodium and calcium. The highest associations were between neonatal and maternal urea and creatinine, where the only predictor was the respective maternal variable (model R2= 0.61 and 0.60, respectively). Similarly, maternal sodium and calcium were the strongest predictors for neonatal sodium and calcium (model R2= 0.36 and 0.22, respectively). On the contrary, the strongest predictor for neonatal total protein, albumin and globulin was the gestational age (model R2= 0.43, 0.35 and 0.48, respectively) with no maternal contribution for total protein and albumin.
Maternal levels of urea, creatinine, sodium and calcium, in a pregnancy complicated by HDP, should be taken into consideration by both the obstetric and neonatal teams when deciding on timing of delivery and providing intensive monitoring.
Objetivo principal: El objetivo perseguido en esta investigación es estudiar la relevancia y características de la administración oral de fármacos, y sus implicaciones para los cuidados de enfermería, buscando aumentar la seguridad y efectividad de la misma. Metodología: Realizamos un estudio descriptivo en un servicio de medicina interna, concretamente de 194 episodios de ingreso correspondientes al año 2014. A continuación, se analizaron los principios activos y formas farmacéuticas más empleados por vía oral, revisando en la evidencia científica cuáles de ellos requerían recomendaciones especiales para garantizar la seguridad y eficacia en su administración. Resultados principales: La mayoría de los principios activos empleados poseen recomendaciones especiales, y se cometen errores e interacciones significativas como consecuencia de no aplicarlas. Conclusión principal: La administración de medicamentos oral entraña una serie de características, en la que los cuidados de enfermería, permiten al profesional garantizar que un proceso interdisciplinario como el tratamiento farmacológico, conduzca a una administración personalizada, segura y eficaz.
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