Mutations in the KRAS gene in ovarian tumors.

RAS genes are the most frequently mutated oncogenes detected in human cancer. In this study we analyzed the presence of mutations at codon 12 of the KRAS gene in 78 women with ovarian tumor, including 64 invasive ovarian cancers and 14 borderline ovarian tumors, using an RFLP-PCR technique and we evaluated whether such alterations were associated with the selected clinicopathological parameters of the patients. KRAS codon 12 gene mutations were found in 6,2% of ovarian cancer tissue and in 14,3% of the borderline ovarian tumor. KRAS mutations were found with a significantly higher frequency in mucinous and borderline tumors compared to serous tumors (

Mutations of the KRAS oncogene in endometrial hyperplasia and carcinoma.

The aim of this study was to examine the prevalence and clinicopathological significance of KRAS point mutation in endometrial hyperplasia and carcinoma. We analysed KRAS in 11 cases of complex atypical hyperplasia and in 49 endometrial carcinomas using polymerase chain reaction associated with restriction fragment length polymorphism (PCR-RFPL). Point mutations at codon 12 of KRAS oncogene were identified in 7 of 49 (14,3%) tumor specimens and in 2 of 11 (18,2%) hyperplasias. No correlation was found between KRAS gene mutation and age at onset, histology, grade of differentiation and clinical stage. We conclude that KRAS mutation is a relatively common event in endometrial carcinogenesis, but with no prognostic value

Prognostic significance of smac/DIABLO in endometrioid endometrial cancer.

Apoptosis may occur via a death receptor-dependent or independent (mitochondrial) pathway. The mitochondrial pathway is regulated by small molecules, such as smac/Diablo, which activates caspase cascades. This study examined smac/DIABLO expression in 76 patients with endometrioid endometrial cancers. Presence of smac/DIABLO was quantified by Western blot analysis using nonfixed fresh frozen tissues. Its appearance was found in 55 (72%) of examined tumors. Smac/DIABLO expression significantly correlated with tumor grade (

Analysis of ibrutinib efficacy in a subgroup of chronic lymphocytic leukemia patients with 17p deletion: observational study of the Polish Adult Leukemia Group (PALG)

BackgroundThe 17p deletion is regarded as the strongest poor prognostic factor in chronic lymphocytic leukemia (CLL). Results of recently performed clinical trials have suggested that ibrutinib significantly improves the outcome in this patient group.AimThe study aimed at analyzing the efficacy and adverse events profile of ibrutinib monotherapy in CLL patients with 17p deletion treated in routine clinical practice outside clinical trials.Materials and MethodsClinical response and adverse events profile of ibrutinib monotherapy were assessed in thirty-five CLL patients with 17p deletion treated within the ibrutinib named patients program in Poland.ResultsOverall response rate was 80% (28/35 patients) with median observation time of 24.2 months (range 0,1 – 30,9). Complete remission was observed in 5 patients (14.3%), partial remission in 11 (31.4%), partial remission with lymphocytosis in 13 (37.1%), whereas stable disease and progression was noted in 4 (11.4%) and 1 (2.9%) respectively. Response was not assessed in 1 patient. Median progression-free survival was 29.5 months, whereas median overall survival was not reached. Eleven patients died (7 because of infection, 1 of CLL progression, 1 of sudden cardiac death, 1 of disseminated breast cancer and 1 of unknown causes). In 13 patients (37.1%) at least one 3 or 4 grade adverse event occurred. In 11 patients (31.4%) the treatment was temporary withheld or the dose reduced due to adverse events.ConclusionIbrutinib is characterized by high clinical efficacy and acceptable toxicity in CLL patients with 17p deletion in daily clinical practice

Factors associated with quality of life in systemic sclerosis: a cross-sectional study

© 2019, The Author(s). Introduction: Systemic sclerosis (SSc) is a connective tissue disease characterized by progressive fibrosis of the skin and internal organs, leading to their failure and disturbances in the morphology and function of blood vessels. The disease affects people in different ways, and identifying how the difficulties and limitations are related to quality of life may contribute to designing helpful interventions. The aim of this study was to identify factors associated with quality of life in people with SSc. Methods: This was a cross-sectional study conducted in 11 rheumatic centres in Poland. Patients diagnosed with SSc were included. Quality of life was measured using the SSc Quality of Life Questionnaire (SScQoL). The following candidate factors were entered in preliminary multivariable analysis: age, place of residence, marital status, occupational status, disease type, disease duration, pain, fatigue, intestinal problems, breathing problems, Raynaud’s symptoms, finger ulcerations, disease severity, functional disability, anxiety and depression. Factors that achieved statistical significance at the 10% level were then entered into a final multivariable model. Factors achieving statistical significance at the 5% level in the final model were considered to be associated with quality of life in SSc. Results: In total, 231 participants were included. Mean age (SD) was 55.82 (12.55) years, disease duration 8.39 (8.18) years and 198 (85.7%) were women. Factors associated with quality of life in SSc were functional disability (β = 2.854, p < 0.001) and anxiety (β = 0.404, p < 0.001). This model with two factors (functional disability and anxiety) explained 56.7% of the variance in patients with diffuse SSc and 73.2% in those with localized SSc. Conclusions: Functional disability and anxiety are significantly associated with quality of life in SSc. Interventions aimed at improving either of these factors may contribute towards improving the quality of life of people with SSc

An Acoustic Fault Detection and Isolation System for Multirotor UAV

With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault’s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model’s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well

An Acoustic Fault Detection and Isolation System for Multirotor UAV

With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault&rsquo;s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model&rsquo;s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well