To this date, there are no recommendations for personalized stress ulcer prophylaxis (SUP) in critical care that would take the patient's individual genetic predispositions into account. Of drugs used for this purpose, proton pump inhibitors (PPIs) are the first-choice drugs in intensive care unit patients. The degradation of proton pump inhibitors is mediated by cytochrome P450 (CYP) enzymes; in particular, CYP2C19 and, to a lesser extent, CYP3A4 are involved. Expression and metabolic activity of, namely in, CYP2C19 is significantly affected by single nucleotide polymorphisms, the drug metabolization rate varies greatly from ultrarapid to poor and likely influences the optimal dosage. As these CYP2C19 predictive phenotypes via CYP2C19 haplogenotypes (rs12248560/rs4244285) can be relatively easily determined using the current standard equipment of hospital laboratories, we prepared a set of recommendations for personalized PPI-based stress ulcer prophylaxis taking into account the patient's CYP2C19 predictive phenotype determined in this way. These recommendations are valid, in particular, for European, American and African populations, because these populations have the high representations of the CYP2C19*17 allele associated with the overexpression of the CYP2C19 gene and ultrarapid degradation of PPIs. We propose the CYP2C19 gene profiling as a tool for personalized SUP with PPI in critically ill patients.
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Background: Vagal nerve stimulation (VNS) can be indicated in patients with drug-resistant epilepsy, who are not eligible for resective epilepsy surgery. In VNS therapy, the responder rate (i.e., percentage of subjects experiencing ≥50% seizure reduction) is ~50%. At the moment, there is no widely-accepted possibility to predict VNS efficacy in a particular patient based on pre-implantation data, which can lead to unnecessary surgery and improper allocation of financial resources. The principal aim of PRediction of vagal nerve stimulation EfficaCy In drug-reSistant Epilepsy (PRECISE) study is to verify the predictability of VNS efficacy by analysis of pre-implantation routine electroencephalogram (EEG). Methods: PRECISE is designed as a prospective multicentric study in which patients indicated to VNS therapy will be recruited. Patients will be classified as predicted responders vs. predicted non-responders using pre-implantation EEG analyses. After the first and second year of the study, the real-life outcome (responder vs. non-responder) will be determined. The real-life outcome and predicted outcome will be compared in terms of accuracy, specificity, and sensitivity. In the meantime, the patients will be managed according to the best clinical practice to obtain the best therapeutic response. The primary endpoint will be the accuracy of the statistical model for prediction of response to VNS therapy in terms of responders and non-responders. The secondary endpoint will be the quantification of differences in EEG power spectra (Relative Mean Power, %) between real-life responders and real-life non-responders to VNS therapy in drug-resistant epilepsy and the sensitivity and specificity of the model. Discussion: PRECISE relies on the results of our previous work, through which we developed a statistical classifier for VNS response (responders vs. non-responders) based on differences in EEG power spectra dynamics (Pre-X-Stim). Trial Registration: www.ClinicalTrials.gov, identifier: NCT04935567.
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Idiopathic pulmonary fibrosis (IPF) is a rare lung disease with poor prognosis. The diagnosis and treatment possibilities are dependent on the health systems of countries. Hence, comparison among countries is difficult due to data heterogeneity. Our aim was to analyse patients with IPF in Central and Eastern Europe using the uniform data from the European Multipartner IPF registry (EMPIRE), which at the time of analysis involved 10 countries. Newly diagnosed IPF patients (N = 2,492, between March 6, 2012 and May 12, 2020) from Czech Republic (N = 971, 39.0%), Turkey (N = 505, 20.3%), Poland (N = 285, 11.4%), Hungary (N = 216, 8.7%), Slovakia (N = 149, 6.0%), Israel (N = 120, 4.8%), Serbia (N = 95, 3.8%), Croatia (N = 87, 3.5%), Austria (N = 55, 2.2%), and Bulgaria (N = 9, 0.4%) were included, and Macedonia, while a member of the registry, was excluded from this analysis due to low number of cases (N = 5) at this timepoint. Baseline characteristics, smoking habit, comorbidities, lung function values, CO diffusion capacity, high-resolution CT (HRCT) pattern, and treatment data were analysed. Patients were significantly older in Austria than in the Czech Republic, Turkey, Hungary, Slovakia, Israel, and Serbia. Ever smokers were most common in Croatia (84.1%) and least frequent in Serbia (39.2%) and Slovakia (42.6%). The baseline forced vital capacity (FVC) was >80% in 44.6% of the patients, between 50 and 80% in 49.3%, and <50% in 6.1%. Most IPF patients with FVC >80% were registered in Poland (63%), while the least in Israel (25%). A typical usual interstitial pneumonia (UIP) pattern was present in 67.6% of all patients, ranging from 43.5% (Austria) to 77.2% (Poland). The majority of patients received antifibrotic therapy (64.5%); 37.4% used pirfenidone (range 7.4-39.8% between countries); and 34.9% nintedanib (range 12.6-56.0% between countries) treatment. In 6.8% of the cases, a therapy switch was initiated between the 2 antifibrotic agents. Significant differences in IPF patient characteristics and access to antifibrotic therapies exist in EMPIRE countries, which needs further investigation and strategies to improve and harmonize patient care and therapy availability in this region.
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Background: The research of primary progressive multiple sclerosis (PPMS) has not been able to capitalize on recent progresses in advanced magnetic resonance imaging (MRI) protocols. Objective: The presented cross-sectional study evaluated the utility of four different MRI relaxation metrics and diffusion-weighted imaging in PPMS. Methods: Conventional free precession T1 and T2, and rotating frame adiabatic T1ρ and T2ρ in combination with diffusion-weighted parameters were acquired in 13 PPMS patients and 13 age- and sex-matched controls. Results: T1ρ, a marker of crucial relevance for PPMS due to its sensitivity to neuronal loss, revealed large-scale changes in mesiotemporal structures, the sensorimotor cortex, and the cingulate, in combination with diffuse alterations in the white matter and cerebellum. T2ρ, particularly sensitive to local tissue background gradients and thus an indicator of iron accumulation, concurred with similar topography of damage, but of lower extent. Moreover, these adiabatic protocols outperformed both conventional T1 and T2 maps and diffusion tensor/kurtosis approaches, methods previously used in the MRI research of PPMS. Conclusion: This study introduces adiabatic T1ρ and T2ρ as elegant markers confirming large-scale cortical gray matter, cerebellar, and white matter alterations in PPMS invisible to other in vivo biomarkers.
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Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low- and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment. Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method). Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER (p = 0.024), PR (0.048), and higher levels of L1CAM (p = 0.001) were observed; in cervical involvement a higher expression of L1CAM (p = 0.001), lower PR (p = 0.014); in tumors with positive LVSI, higher L1CAM (p = 0.014); in cases with positive LN, lower expression of ER/PR (p < 0.001), higher L1CAM (p = 0.002) and frequent mutation of p53 (p = 0.008). Cut-offs for determination of high-risk tumors were established: ER <78% (p = 0.001), PR <88% (p = 0.008), and L1CAM ≥4% (p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8-96.5%), 63% (52.1-72.8%), 83% (70.5-90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5-63.4%), 65% (55.6-74.0%), and 77% (67.3-84.2%). Mutation of p53 revealed PPV 94% (67.4-99.1%) and NPV 61% (56.1-66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001). Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.
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