Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or 'modules' enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man.Medicinal Chemistr

Psychology and aggression

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68264/2/10.1177_002200275900300301.pd

Recovery of dialysis patients with COVID-19 : health outcomes 3 months after diagnosis in ERACODA

Background. Coronavirus disease 2019 (COVID-19)-related short-term mortality is high in dialysis patients, but longer-term outcomes are largely unknown. We therefore assessed patient recovery in a large cohort of dialysis patients 3 months after their COVID-19 diagnosis. Methods. We analyzed data on dialysis patients diagnosed with COVID-19 from 1 February 2020 to 31 March 2021 from the European Renal Association COVID-19 Database (ERACODA). The outcomes studied were patient survival, residence and functional and mental health status (estimated by their treating physician) 3 months after COVID-19 diagnosis. Complete follow-up data were available for 854 surviving patients. Patient characteristics associated with recovery were analyzed using logistic regression. Results. In 2449 hemodialysis patients (mean ± SD age 67.5 ± 14.4 years, 62% male), survival probabilities at 3 months after COVID-19 diagnosis were 90% for nonhospitalized patients (n = 1087), 73% for patients admitted to the hospital but not to an intensive care unit (ICU) (n = 1165) and 40% for those admitted to an ICU (n = 197). Patient survival hardly decreased between 28 days and 3 months after COVID-19 diagnosis. At 3 months, 87% functioned at their pre-existent functional and 94% at their pre-existent mental level. Only few of the surviving patients were still admitted to the hospital (0.8-6.3%) or a nursing home (∼5%). A higher age and frailty score at presentation and ICU admission were associated with worse functional outcome. Conclusions. Mortality between 28 days and 3 months after COVID-19 diagnosis was low and the majority of patients who survived COVID-19 recovered to their pre-existent functional and mental health level at 3 months after diagnosis

Non-AIDS defining cancers in the D:A:D Study-time trends and predictors of survival : a cohort study

BACKGROUND:Non-AIDS defining cancers (NADC) are an important cause of morbidity and mortality in HIV-positive individuals. Using data from a large international cohort of HIV-positive individuals, we described the incidence of NADC from 2004-2010, and described subsequent mortality and predictors of these.METHODS:Individuals were followed from 1st January 2004/enrolment in study, until the earliest of a new NADC, 1st February 2010, death or six months after the patient's last visit. Incidence rates were estimated for each year of follow-up, overall and stratified by gender, age and mode of HIV acquisition. Cumulative risk of mortality following NADC diagnosis was summarised using Kaplan-Meier methods, with follow-up for these analyses from the date of NADC diagnosis until the patient's death, 1st February 2010 or 6 months after the patient's last visit. Factors associated with mortality following NADC diagnosis were identified using multivariable Cox proportional hazards regression.RESULTS:Over 176,775 person-years (PY), 880 (2.1%) patients developed a new NADC (incidence: 4.98/1000PY [95% confidence interval 4.65, 5.31]). Over a third of these patients (327, 37.2%) had died by 1st February 2010. Time trends for lung cancer, anal cancer and Hodgkin's lymphoma were broadly consistent. Kaplan-Meier cumulative mortality estimates at 1, 3 and 5 years after NADC diagnosis were 28.2% [95% CI 25.1-31.2], 42.0% [38.2-45.8] and 47.3% [42.4-52.2], respectively. Significant predictors of poorer survival after diagnosis of NADC were lung cancer (compared to other cancer types), male gender, non-white ethnicity, and smoking status. Later year of diagnosis and higher CD4 count at NADC diagnosis were associated with improved survival. The incidence of NADC remained stable over the period 2004-2010 in this large observational cohort.CONCLUSIONS:The prognosis after diagnosis of NADC, in particular lung cancer and disseminated cancer, is poor but has improved somewhat over time. Modifiable risk factors, such as smoking and low CD4 counts, were associated with mortality following a diagnosis of NADC

Eucalypt species drive rhizosphere bacterial and fungal community assembly but soil phosphorus availability rearranges the microbiome

Soil phosphorus (P) availability may limit plant growth and alter root-soil interactions and rhizosphere microbial community composition. The composition of the rhizosphere microbial community can also be shaped by plant genotype. In this study, we examined the rhizosphere microbial communities of young plants of 24 species of eucalypts (22 Eucalyptus and two Corymbia species) under low or sufficient soil P availability. The taxonomic diversity of the rhizosphere bacterial and fungal communities was assessed by 16S and 18S rRNA gene amplicon sequencing. The taxonomic modifications in response to low P availability were evaluated by principal component analysis, and co-inertia analysis was performed to identify associations between bacterial and fungal community structures and parameters related to plant growth and nutritional status under low and sufficient soil P availability. The sequencing results showed that while both soil P availability and eucalypt species influenced the microbial community assembly, eucalypt species was the stronger determinant. However, when the plants are subjected to low P-availability, the rhizosphere selection became strongest. In response to low P, the bacterial and fungal communities in the rhizosphere of some species showed significant changes, whereas in others remained relatively constant under low and sufficient P. Co-inertia analyses revealed a significant co-dependence between plant nutrient contents and bacterial and fungal community composition only under sufficient P. By contrast, under low P, bacterial community composition was related to plant biomass production. In conclusion, our study shows that eucalypt species identity was the main factor modulating rhizosphere microbial community composition; significant shifts due to P availability were observed only for some eucalypt species

Lexical entailment for information retrieval

Abstract. Textual Entailment has recently been proposed as an application independent task of recognising whether the meaning of one text may be inferred from another. This is potentially a key task in many NLP applications. In this contribution, we investigate the use of various lexical entailment models in Information Retrieval, using the language modelling framework. We show that lexical entailment potentially provides a significant boost in performance, similar to pseudo-relevance feedback, but at a lower computational cost. In addition, we show that the performance is relatively stable with respect to the corpus the lexical entailment measure is estimated on.

Increasing returns to liquidity in futures markets

A simple model, based on the binomial theorem, is employed to predict that the probability of matching buyers and sellers increases with the number of transactions. The ask-bid spread, interpreted as a measure of liquidity, is assumed to vary negatively with the probability of matching buyers and sellers. The hypothesis addressed in this paper is that the ask-bid spread varies negatively with volume. This hypothesis is investigated for six contracts traded on the Sydney Futures Exchange from 1980 to 1991. The results support the hypothesis for the majority of contracts studied. The implication of these results is that futures trading can be expected to become concentrated geographically in a few key locations, and within exchanges in a few key contracts.