Prediagnostic adult body mass index change and esophageal adenocarcinoma survival

Background: We examined whether body mass index (BMI) changes in adulthood, prior to disease onset, are associated with overall survival among esophageal adenocarcinoma patients. Methods: We included 285 histologically confirmed patients with a complete baseline BMI questionnaire. Using extended Cox regression models, we obtained adjusted hazard ratios (HRs) for the associations between overall survival and BMI at diagnosis, BMI 6 months before diagnosis, self-reported average adult BMI, and ΔBMI (BMI 6 months before diagnosis minus average adult BMI), categorized into tertiles 25 and <35 kg/m2 was associated with better overall survival. Compared to patients with stable BMI in adulthood, patients who gained BMI throughout adulthood had 1.68 times the all-cause hazard of death (95% CI: 1.17-2.43; P <.01), independent of diagnosis BMI and percent weight loss 6 months before diagnosis. Compared to patients with average adult BMI < 27.5 who maintained stable adult BMI, patients with average adult BMI ≥ 27.5 kg/m2 who gained BMI had the worst survival (HR = 3.05; 95% CI 1.62-5.72; P <.01). Conclusion: Body mass index gain in adulthood is associated with poor overall survival, and maintaining a normal body weight throughout adulthood is associated with the best overall survival among esophageal adenocarcinoma patients, independent of BMI at diagnosis

Amygdala subnuclei response and connectivity during emotional processing

The involvement of the human amygdala in emotion-related processing has been studied using functional magnetic resonance imaging (fMRI) for many years. However, despite the amygdala being comprised of several subnuclei, most studies investigated the role of the entire amygdala in processing of emotions. Here we combined a novel anatomical tracing protocol with event-related high-resolution fMRI acquisition to study the responsiveness of the amygdala subnuclei to negative emotional stimuli and to examine intra-amygdala functional connectivity. The greatest sensitivity to the negative emotional stimuli was observed in the centromedial amygdala, where the hemodynamic response amplitude elicited by the negative emotional stimuli was greater and peaked later than for neutral stimuli. Connectivity patterns converge with extant findings in animals, such that the centromedial amygdala was more connected with the nuclei of the basal amygdala than with the lateral amygdala. Current findings provide evidence of functional specialization within the human amygdala

Disease burden affects aging brain function

BACKGROUND: Most older adults live with multiple chronic disease conditions, yet the effect of multiple diseases on brain function remains unclear. METHODS: We examine the relationship between disease multimorbidity and brain activity using regional cerebral blood flow (rCBF) 15O-water PET scans from 97 cognitively normal participants (mean baseline age 76.5) in the Baltimore Longitudinal Study of Aging (BLSA). Multimorbidity index scores, generated from the presence of 13 health conditions, were correlated with PET data at baseline and in longitudinal change (n = 74) over 5.05 (2.74 SD) years. RESULTS: At baseline, voxel-based analysis showed that higher multimorbidity scores were associated with lower relative activity in orbitofrontal, superior frontal, temporal pole and parahippocampal regions, and greater activity in lateral temporal, occipital, and cerebellar regions. Examination of the individual health conditions comprising the index score showed hypertension and chronic kidney disease individually contributed to the overall multimorbidity pattern of altered activity. Longitudinally, both increases and decreases in activity were seen in relation to increasing multimorbidity over time. These associations were identified in orbitofrontal, lateral temporal, brainstem, and cerebellar areas. CONCLUSION: Together, these results show that greater multimorbidity is associated with widespread areas of altered brain activity, supporting a link between health and changes in aging brain function

Knowledge reuse integrating the collaboration from experts in industrial maintenance management

Distributed environments, technological evolution, outsourcing market and information technology (IT) are factors that considerably influence current and future industrial maintenance management. Repairing and maintaining the plants and installations requires a better and more sophisticated skill set and continuously updated knowledge. Today, maintenance solutions involve increasing the collaboration of several experts to solve complex problems. These solutions imply changing the requirements and practices for maintenance; thus, conceptual models to support multidisciplinary expert collaboration in decision making are indispensable. The objectives of this work are as follows: (i) knowledge formalization of domain vocabulary to improve the communication and knowledge sharing among a number of experts and technical actors with Conceptual Graphs (CGs) formalism, (ii) multi-expert knowledge management with the Transferable Belief Model (TBM) to support collaborative decision making, and (iii) maintenance problem solving with a variant of the Case-Based Reasoning (CBR) mechanism with a process of solving new problems based on the solutions of similar past problems and integrating the experts’ beliefs. The proposed approach is applied for the maintenance management of the illustrative case study

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis

Smoking cessation prolongs survival and decreases mortality of patients with non‐small‐cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome‐wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two‐stage study design to identify DNA methylation-smoking cessation interactions that affect overall survival for early‐stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology‐stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation-smoking cessation interaction terms. Interactions with false discovery rate‐q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology‐specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510SIPA1L3) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)interaction = 1.12; 95% confidence interval (CI): 1.07-1.16; P = 4.30 × 10-7]. Further, the effect of smoking cessation on early‐stage LUAD survival varied across patients with different methylation levels of cg02268510SIPA1L3. Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34-0.82; P = 4.61 × 10-3) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86-1.70; P = 0.266) of cg02268510SIPA1L3. Moreover, there was an antagonistic interaction between elevated methylation of cg02268510SIPA1L3 and smoking cessation (HRinteraction = 2.1835; 95% CI: 1.27-3.74; P = 4.46 × 10−3). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510SIPA1L3. The results have implications for not only smoking cessation after diagnosis, but also possible methylation‐specific drug targeting

Trends and correlates of HIV-1 resistance among subjects failing an antiretroviral treatment over the 2003-2012 decade in Italy

BACKGROUND: Despite a substantial reduction in virological failures following introduction of new potent antiretroviral therapies in the latest years, drug resistance remains a limitation for the control of HIV-1 infection. We evaluated trends and correlates of resistance in treatment failing patients in a comprehensive database over a time period of relevant changes in prescription attitudes and treatment guidelines. METHODS: We analyzed 6,796 HIV-1 pol sequences from 49 centres stored in the Italian ARCA database during the 2003-2012 period. Patients (n = 5,246) with viremia > 200 copies/mL received a genotypic test while on treatment. Mutations were identified from IAS-USA 2013 tables. Class resistance was evaluated according to antiretroviral regimens in use at failure. Time trends and correlates of resistance were analyzed by Cochran-Armitage test and logistic regression models. RESULTS: The use of NRTI backbone regimens slightly decreased from 99.7% in 2003-2004 to 97.4% in 2010-2012. NNRTI-based combinations dropped from 46.7% to 24.1%. PI-containing regimens rose from 56.6% to 81.7%, with an increase of boosted PI from 36.5% to 68.9% overtime. In the same reference periods, Resistance to NRTIs, NNRTIs and PIs declined from 79.1% to 40.8%, from 77.8% to 53.8% and from 59.8% to 18.9%, respectively (p < .0001 for all comparisons). Dual NRTI + NNRTI and NRTI + PI resistance decreased from 56.4% to 33.3% and from 36.1% to 10.5%, respectively. Reduced risk of resistance over time periods was confirmed by a multivariate analysis. CONCLUSIONS: Mutations associated with NRTIs, NNRTIs and PIs at treatment failure declined overtime regardless of specific class combinations and epidemiological characteristics of treated population. This is likely due to the improvement of HIV treatment, including both last generation drug combinations and prescription guidelines

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure