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[Obstetrical butt sphincter injury : How to manage another delivery ?]
Objectives The study aimed to assess the effect of demographic and clinical features of epilepsy, anxiety, depressed mood, sleep, and quality of life on the prediction of cognitive decline in patients with epilepsy. Methods Two hundred and six consecutive patients with epilepsy (age 41.8 ± 15.6 years, mean, SD) out of 279, were included in this cross-sectional study. We used simple linear regression to calculate the results. Results Objective cognitive status was predicted by anxiety and depression mood changes (Beck Anxiety Inventory (BAI), p = 0.03, Beck Depression Inventory (BDI), p = 0.005), language subdomain of Quality of Life Inventory in Epilepsy-89 (QOLIE-89) (p = 0.003), and total QOLIE-89 (p = 0.001). No significance was shown in demographic and clinical features of epilepsy (gender, age at onset, epilepsy duration, type, etiology of epilepsy, and antiepileptic treatment), except frequency of generalized epileptic seizures (p = 0.03), which also served as an independent predictor of anxiety (BAI) and depression (BDI). Conclusions Our findings point at the role of mood changes in the cognitive status of patients with epilepsy, which should be used as an essential therapeutic target apart of seizure control.Immune checkpoint inhibitor (ICI) therapy induces an immune response against cancer cells. Immune checkpoint inhibitor therapy has tremendously improved the prognosis for a large number of cancers, but is associated with considerable immune-related adverse events (irAEs). Cardiovascular complications from ICI therapy occur in a modest proportion of patients, but show the highest lethality rates of all ICI-related complications. While ICI-related myocarditis is the most dangerous complication, its clinical manifestation varies, e.g., asymptomatic reduction of left ventricular function, isolated increase in cardiac troponins, and arrhythmias. This review delineates current data on cardiovascular complications of ICI therapy. The effects of ICI therapy on the cardiovascular system are classified in the context of preclinical data on the biochemical and immunological function of the immune checkpoint signaling pathways in the heart and the vascular system. Incidence, suspected pathomechanisms, typical symptoms, as well as recommended diagnostics are summarized. Current therapy recommendations for ICI-related cardiotoxicity are outlined and innovative new approaches with high potential for improving outcome in ICI-related myocarditis are delineated. A better understanding of cardiovascular complications is essential for the best possible oncocardiology care of the growing number of patients undergoing ICI therapy.Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid and accurate prediction of drug- and chemical-induced hepatocyte injury in humans. HepaRG cells were employed for high-throughput imaging in combination with phenotypic profiling. A reference set of 69 drugs and chemicals was screened at a range of 7 concentrations, and the cellular response values were used for training a supervised classifier and for determining assay performance by using tenfold cross-validation. The results showed that the best performing phenotypic features were related to nuclear translocation of RELA (RELA proto-oncogene, NF-kB subunit; also known as NF-kappa B p65), DNA organization, and the F-actin cytoskeleton. Using a subset of 30 phenotypic features, direct hepatocyte toxicity in humans could be predicted with a test sensitivity, specificity and balanced accuracy of 73%, 92%, and 83%, respectively. The method was applied to another set of 26 drugs and chemicals with unclear annotation and their hepatocyte toxicity in humans was predicted. The results also revealed that the identified discriminative phenotypic changes were related to cell death and cellular senescence. Whereas cell death-related endpoints are widely applied in in vitro toxicology, cellular senescence-related endpoints are not, although cellular senescence can be induced by various drugs and other small molecule compounds and plays an important role in liver injury and disease. These findings show how phenotypic profiling can reveal unexpected chemical-induced mechanisms in toxicology.Purpose Managing the pharmacokinetic variability of immunosuppressive drugs after pediatric hematopoietic stem cell transplantation (HSCT) is a clinical challenge. Thus, the aim of our study was to design and validate a decision support tool predicting the best first cyclosporine oral dose to give when switching from intravenous route. Methods We used 10-years pediatric HSCT patients' dataset from 2008 to 2018. A tree-augmented naïve Bayesian network model (method belonging to artificial intelligence) was built with data from the first eight-years, and validated with data from the last two. Results The Bayesian network model obtained showed good prediction performances, both after a 10-fold cross-validation and external validation, with respectively an AUC-ROC of 0.89 and 0.86, a percentage of misclassified patients of 28.7% and 35.2%, a true positive rate of 0.71 and 0.65, and a false positive rate of 0.12 and 0.14 respectively. Conclusion The final model allows the prediction of the most likely cyclosporine oral dose to reach the therapeutic target specified by the clinician. The clinical impact of using this model needs to be prospectively warranted. Respecting the decision support tool terms of use is necessary as well as remaining critical about the prediction by confronting it with the clinical context.Background Current classifications for periprosthetic joint infections (PJIs) often lack a detailed description of the overall underlying situation of a patient. The PJI-TNM classification uses the principles of the TNM classification from oncology for the description of critical parameters in PJIs affected joint, type of implant and implant stability, soft tissue conditions, maturity of biofilm formation, causative microorganism, comorbidities of the patient and recurrence of infection. The aim of the current work is to evaluate the feasibility of this new PJI-TNM classification in clinical practice. selleck compound Methods The PJI-TNM classification was used in 20 patients with hip, knee and shoulder PJIs. Based on a retrospective chart review, the respective parameters T (tissue and implants), N (non-eukaryotic cells and fungi), M (morbidity) and r (reinfection) were classified for each case. Results All 20 cases (12 male, 8 female, average age 72.2 (40-88 years)) with 13 hip, 6 knee and 1 shoulder PJIs were to be classified with the new TNM-PJI classification system.
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