Drug-induced vascular injury (DIVI) is a common preclinical toxicity usually characterized by hemorrhage, vascular endothelial and smooth muscle damage, and inflammation. DIVI findings can cause delays or termination of drug candidates due to low safety margins. The situation is complicated by the absence of sensitive, noninvasive biomarkers for monitoring vascular injury and the uncertain relevance to humans. The Safer And Faster Evidence-based Translation (SAFE-T) consortium is a public-private partnership funded within the European Commission's Innovative Medicines Initiative (IMI) aiming to accelerate drug development by qualifying biomarkers for drug-induced organ injuries, including DIVI. The group is using patients with vascular diseases that have key histomorphologic features (endothelial damage, smooth muscle damage, and inflammation) in common with those observed in DIVI, and has selected candidate biomarkers associated with these features. Studied populations include healthy volunteers, patients with spontaneous vasculitides and other vascular disorders. Initial results from studies with healthy volunteers and patients with vasculitides show that a panel of biomarkers can successfully discriminate the population groups. The SAFE-T group plans to seek endorsement from health authorities (European Medicines Agency and Food and Drug Administration) to qualify the biomarkers for use in regulatory decision-making processes.

PET-CT FDG uptake may, together with TTF-1 expression, help diagnosis in lung adenocarcinoma cases when evaluating for EGFR mutation status.

A few previous studies have reported that the patients with chronic obstructive pulmonary disease (COPD) have a 29.1% to 36.8% frequency of restless legs syndrome (RLS). In this study, we observed RLS symptoms in patients experiencing COPD exacerbation to better understand the relationship between the many clinical parameters of COPD and the presence of RLS and to attract the attention of specialists on the association between the two conditions. Twenty-two male patients in COPD exacerbation; 17 healthy individuals were evaluated in this study. The patients were evaluated using the 2003 RLS symptom criteria outlined by the International Restless Legs Syndrome Study Groups (IRLSSG). The Pittsburgh Sleep Quality Index and Epworth daytime sleepiness scale were used to assess the sleep quality of patients. The RLS symptoms were correlated with blood levels of laboratory and clinical parameters. Statistical analyses were performed using SPSS 17.0 statistical software packet. The Pittsburgh Sleep Quality Index and Epworth daytime sleepiness scale scores were increased in COPD patients and correlated significantly with RLS symptoms. It was found that 54.5% of COPD patients with acute exacerbations were observed to have RLS symptoms. The Pittsburgh Sleep Quality Index was significantly higher in COPD patients with RLS symptoms compared to COPD patients without RLS symptoms (p < 0.05). We did not observe any significant difference in the previously reported metabolic and clinical parameters associated with RLS in COPD patients with and without RLS. RLS symptoms increase during COPD exacerbation and lead to decreased sleep quality.

Introduction: A machine learning technique that imitates neural system and brain can provide better than traditional methods like logistic regression for survival prediction and create an algorithm by determining influential factors. Aim: To determine the influential factors on survival time of palliative care cancer patients and to compare two statistical methods for better prediction of survival. Methods: One-year data is gathered from the patients that we followed in the palliative care clinic of our hospital (2017-2018) (n = 189). All data were retrospectively evaluated. After descriptive statistics, we used Pearson and Spearman correlations for parametric and non-parametric variables. The Artificial Neural Networks (ANN) and logistic regression model were applied to parameters which have a significant correlation with short survival. Results: Significantly correlated variables with short survival were Palliative Performance Scale (PPS), Edmonton Symptom Assessment System (ESAS), Karnofsky Performance Scale (KPS), brain, liver, and distant metastasis, hemogram parameters, cero-reactive protein (CRP) and albumin (ALB). ANN model showed 89.3% prediction accuracy while the logistic regression model showed 73.0%. ANN model achieved a better AUC value of 0.86 than logistic regression model (0.76). Discussion: There are several prognostic evaluation tools such as PPS, KPS, CRP, albumin, leukocytes, neutrophil were reported several studies as survival-related parameters in logistic regression models, also. Many studies compare ANN with logistic regression. When we evaluated these parameters totally, we observed the same relations with survival then we used the same parameters in the ANN model. The effectivity of the survival prediction models can be improved with the use of ANN. Conclusion: ANN provides a more accurate estimation than logistic regression. ANN model is an important statistical method for survival prediction of cancer patients.

Background Obstructive sleep apnea (OSA) is often reported in connection with interstitial lung disease. As yet, there is insufficient data on the association of OSA severity parameters with lung involvement. We purposed to assess the frequency of OSA in our study group and to investigate the relationship between radiological involvement and OSA severity parameters. Material/Methods The study included 79 patients with interstitial lung disease who underwent spirometry, a carbon monoxide diffusion test (DLCO), high-resolution computed tomography, and polysomnography. The data were analyzed using SPSS 22 software. Results Of the 79 patients, 53 patients (67.1%) had OSA, and there was a negative correlation between DLCO and the mean time spent with oxygen saturation levels below 90% (r=−0.686, P =0.001). The Warrick score was used as an indicator of the extent and severity of pulmonary involvement and was positively correlated with the apnea-hypopnea index, oxygen desaturation index, and the mean time spent with oxygen saturation below 90% (r=0.275, P =0.014; r=0.264 P =0.019; r=0.235, P =0.038). Conclusions In our study, a significant relationship was found between the Warrick score and the OSA severity parameters, as determined by polysomnography. Polysomnographic examinations might be useful, especially in patients with a Warrick score greater than 15, to avoid possible complications.