Purpose: Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-relevant toxicity during the course of treatment.
Methods: Clinical records were retrieved from a single-center, consecutive cohort of patients who underwent neoadjuvant treatment for early breast cancer.
T cell activation is the final key event (KE4) in the adverse outcome pathway (AOP) of skin sensitization. However, validated new approach methodologies (NAMs) for evaluating this step are missing. Accordingly, chemicals that activate an unusually high frequency of T cells, as does the most prevalent metal allergen nickel, are not yet identified in a regulatory context.
View Article and Find Full Text PDFWe report the sudden onset of dyspnea and loss of consciousness and fetal bradycardia in a middle-aged obese nulliparous woman at 39 weeks of gestation during first stage of labor leading to the decision for emergency cesarean section. Still during surgery, the mother underwent cardiac arrest. Transesophageal echocardiography during resuscitation showed right ventricular failure leading to the diagnosis of pulmonary embolism.
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