Inhibition of poly(ADP-ribose) polymerase-1 attenuates the toxicity of carbon tetrachloride

Carbon tetrachloride (CCl4) is routinely used as a model compound for eliciting centrilobular hepatotoxicity. It can be bioactivated to the trichloromethyl radical, which causes extensive lipid peroxidation and ultimately cell death by necrosis. Overactivation of poly(ADP-ribose) polymerase-1 (PARP-1) can rapidly reduce the levels of (β-nicotinamide adenine dinucleotide and adenosine triphosphate and ultimately promote necrosis. The aim of this study was to determine whether inhibition of PARP-1 could decrease CCl4-induced hepatotoxicity, as measured by degree of poly(ADP-ribosyl)ation, serum levels of lactate dehydrogenase (LDH), lipid peroxidation,and oxidative DNA damage. For this purpose, male ICR mice were administered intraperitoneally a hepatotoxic dose of CCl4 with or without 6(5H)-phenanthridinone, a potent inhibitor of PARP-1. Animals treated with CCl4 exhibited extensive poly(ADP-ribosyl)ation in centrilobular hepatocytes, elevated serum levels of LDH, and increased lipid peroxidation. In contrast, animals treated concomitantly with CCl4 and 6(5H)-phenanthridinone showed significantly lower levels of poly(ADP-ribosyl) ation, serum LDH, and lipid peroxidation. No changes were observed in the levels of oxidative DNA damage regardless of treatment. These results demonstrated that the hepatotoxicity of CCl4is dependent on the overactivation of PARP-1 and that inhibition of this enzyme attenuates the hepatotoxicity of CCl4

Outcome of Endometrial Cancer Stage IIIA with Adnexa or Serosal Involvement Only

Objective. The aim of this study is to look at possible differences in outcome between serosa and adnexal involvement stage IIIA endometrial carcinoma. Methods. 67 patients with stage IIIA endometrial carcinoma were included, 46 with adnexal involvement and 21 with serosa. A central histopathological review was performed. Results. The 7-year locoregional failure rate was (LRFR) 2.2% for adnexal involvement and 16.0% for involvement of the serosa (P = .0522). The 7-year distant metastasis-free survival was 72.7% for adnexal involvement and 58.7% for serosa (P = .3994). The 7-year disease-specific survival (DSS) was 71.8% for patients with adnexal involvement and 75.4% for patients with serosa. Conclusion. Endometrial carcinoma stage IIIA with involvement of the adnexa or serosa showed to have a comparable disease-specific survival. Locoregional control was worse for serosa involvement compared to adnexa

Impact of COVID-19 Pandemic on Sleep Quality, Stress Level and Health-Related Quality of Life-A Large Prospective Cohort Study on Adult Danes

The everyday lives of Danish inhabitants have been affected by the COVID-19 pandemic, e.g., by social distancing, which was employed by the government in March 2020 to prevent the spread of SARS-CoV-2. Moreover, the pandemic has entailed economic consequences for many people. This study aims to assess changes in physical and mental health-related quality of life (MCS, PCS), in stress levels, and quality of sleep during the COVID-19 pandemic and to identify factors that impact such changes, using a prospective national cohort study including 26,453 participants from the Danish Blood Donor Study who answered a health questionnaire before the pandemic and during the pandemic. Descriptive statistics, multivariable linear and multinomial logistic regression analyses were applied. A worsening of MCS and quality of sleep was found, and an overall decrease in stress levels was observed. PCS was decreased in men and slightly increased in women. The extent of health changes was mainly affected by changes in job situation, type of job, previous use of anti-depressive medication and the participants’ level of personal stamina. Thus, living under the unusual circumstances that persisted during the COVID-19 pandemic has had a negative impact on the health of the general population. This may, in time, constitute a public health problem

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis