The role of SOCS3/pSTAT3 pathway in the development of diabetic retinopathy

Purpose: Suppressor of cytokine signalling (SOCS) proteins are feedback inhibitors of the JAK/STAT pathway. SOCS3 critically controls STAT3 activation, cytokine signalling and inflammatory gene expression in macrophages and microglia. In this study, we investigated the role of SOCS3/STAT3 in myeloid cells in the initiation and progression of diabetic retinopathy (DR). 
Methods: Mice with a conditional deletion of SOCS3 in myeloid cells (LysMCre-SOCS3 fl/fl) and C57BL/6J (as control) were rendered diabetic by a low-dose multiple intraperitoneal injections of Stroptozocine. Diabetes related retinal changes, including leukostasis, acellular capilliaries, and microglial activation were assessed at different stages of disease. Bone marrow derived macrophages (BMDMs) from LysMCreSOCS3 fl/fl and C57BL/6J mice were cultured in high glucose (HG) medium, and cell activation was evaluated by real-time RT-PCR.
Results: In C57BL/6J diabetic mice the expression of phosphorylated STAT3 (pSTAT3) was increased and SOCS3 was decreased in the retina. Interleukin 6 (IL-6), the main cytokine that stimulates STAT3 activation, was increased in the plamsa in diabetic mice. Although blood glucose levels and Hbac-1 were comparable between LysMCre-SOCS3fl/fl and WT mice after STZ injection, the LysMCreSOCS3 fl/fl diabetic mice developed severe retinal vasculopathy, including increased leukostasis and microglial activation at one month and enhanced acellular capillary formation at 6 months after diabetes induction. 
Conclusions: our study suggests that the JAK/STAT3 pathway is involved in the initiation and progression of DR, and uncontrolled STAT3 activation results in accelerated DR progression. Targeting the STAT3 pathway may be a novel approach for the management of DR.

A belief revision framework for revising epistemic states with partial epistemic states

Belief revision performs belief change on an agent’s beliefs when new evidence (either of the form of a propositional formula or of the form of a total pre-order on a set of interpretations) is received. Jeffrey’s rule is commonly used for revising probabilistic epistemic states when new information is probabilistically uncertain. In this paper, we propose a general epistemic revision framework where new evidence is of the form of a partial epistemic state. Our framework extends Jeffrey’s rule with uncertain inputs and covers well-known existing frameworks such as ordinal conditional function (OCF) or possibility theory. We then define a set of postulates that such revision operators shall satisfy and establish representation theorems to characterize those postulates. We show that these postulates reveal common characteristics of various existing revision strategies and are satisfied by OCF conditionalization, Jeffrey’s rule of conditioning and possibility conditionalization. Furthermore, when reducing to the belief revision situation, our postulates can induce Darwiche and Pearl’s postulates C1 and C2.

β-Blocker usage and colorectal cancer mortality:a nested case-control study in the UK Clinical Practice Research Datalink cohort

BACKGROUND: Epidemiological and laboratory studies suggest that β-blockers may reduce cancer progression in various cancer sites. The aim of this study was to conduct the first epidemiological investigation of the effect of post-diagnostic β-blocker usage on colorectal cancer-specific mortality in a large population-based colorectal cancer patient cohort.

PATIENTS AND METHODS: A nested case-control analysis was conducted within a cohort of 4794 colorectal cancer patients diagnosed between 1998 and 2007. Patients were identified from the UK Clinical Practice Research Datalink and confirmed using cancer registry data. Patients with a colorectal cancer- specific death (data from the Office of National Statistics death registration system) were matched to five controls. Conditional logistic regression was applied to calculate odds ratios (OR) and 95% confidence intervals (95% CIs) according to β-blocker usage (data from GP-prescribing records).

RESULTS: Post-diagnostic β-blocker use was identified in 21.4% of 1559 colorectal cancer-specific deaths and 23.7% of their 7531 matched controls, with little evidence of an association (OR = 0.89 95% CI 0.78-1.02). Similar associations were found when analysing drug frequency, β-blocker type or specific drugs such as propranolol. There was some evidence of a weak reduction in all-cause mortality in β-blocker users (adjusted OR = 0.88; 95% CI 0.77-1.00; P = 0.04) which was in part due to the marked effect of atenolol on cardiovascular mortality (adjusted OR = 0.62; 95% CI 0.40-0.97; P = 0.04).

CONCLUSIONS: In this novel, large UK population-based cohort of colorectal cancer patients, there was no evidence of an association between post-diagnostic β-blocker use and colorectal cancer-specific mortality.

CLINICAL TRIALS NUMBER: NCT00888797.

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

Explaining variation in cancer survival between 11 jurisdictions in the International Cancer Benchmarking Partnership: a primary care vignette survey

OBJECTIVES: The International Cancer Benchmarking Partnership (ICBP) is a collaboration between 6 countries and 12 jurisdictions with similar primary care-led health services. This study investigates primary care physician (PCP) behaviour and systems that may contribute to the timeliness of investigating for cancer and subsequently, international survival differences.

DESIGN: A validated survey administered to PCPs via the internet set out in two parts: direct questions on primary care structure and practice relating to cancer diagnosis, and clinical vignettes, assessing management of scenarios relating to the diagnosis of lung, colorectal or ovarian cancer.

PARTICIPANTS: 2795 PCPs in 11 jurisdictions: New South Wales and Victoria (Australia), British Columbia, Manitoba, Ontario (Canada), England, Northern Ireland, Wales (UK), Denmark, Norway and Sweden.

PRIMARY AND SECONDARY OUTCOME MEASURES: Analysis compared the cumulative proportion of PCPs in each jurisdiction opting to investigate or refer at each phase for each vignette with 1-year survival, and conditional 5-year survival rates for the relevant cancer and jurisdiction. Logistic regression was used to explore whether PCP characteristics or system differences in each jurisdiction affected the readiness to investigate.

RESULTS: 4 of 5 vignettes showed a statistically significant correlation (p<0.05 or better) between readiness to investigate or refer to secondary care at the first phase of each vignette and cancer survival rates for that jurisdiction. No consistent associations were found between readiness to investigate and selected PCP demographics, practice or health system variables.

CONCLUSIONS: We demonstrate a correlation between the readiness of PCPs to investigate symptoms indicative of cancer and cancer survival rates, one of the first possible explanations for the variation in cancer survival between ICBP countries. No specific health system features consistently explained these findings. Some jurisdictions may consider lowering thresholds for PCPs to investigate for cancer-either directly, or by specialist referral, to improve outcomes.

, Lexicographic Preferences in Discrete Choice Experiments: Consequences on Individual-Specific Willingness to Pay Estimates

In discrete choice experiments respondents are generally assumed to consider all of the attributes across each of the alternatives, and to choose their most preferred. However, results in this paper indicate that many respondents employ simplified lexicographic decision-making rules, whereby they have a ranking of the attributes, but their choice of an alternative is based solely on the level of their most important attribute(s). Not accounting for these simple decision-making heuristics introduces systemic errors and leads to biased point estimates, as they are a violation of the continuity axiom and a departure from the use of compensatory decision-making. In this paper the implications of lexicographic preferences are examined. In particular, using a mixed logit specification this paper investigates the sensitivity of individual-specific willingness to pay (WTP) estimates conditional on whether lexicographic decision-making rules are accounted for in the modelling of discrete choice responses. Empirical results are obtained from a discrete choice experiment that was carried out to address the value of a number of rural landscape attributes in Ireland

A chemically unlocked binary molecular switch

A highly luminescent and sensitive terbium complex of a ligand comprising of a phthalimide group appended to a DO3A moiety is an active pH sensor that is conditional on its previous pH. © 2012 The Royal Society of Chemistry.

Shared common variants in prostate cancer and blood lipids

BACKGROUND: Epidemiological and clinical studies suggest comorbidity between prostate cancer (PCA) and cardiovascular disease (CVD) risk factors. However, the relationship between these two phenotypes is still not well understood. Here we sought to identify shared genetic loci between PCA and CVD risk factors.

METHODS: We applied a genetic epidemiology method based on conjunction false discovery rate (FDR) that combines summary statistics from different genome-wide association studies (GWAS), and allows identification of genetic overlap between two phenotypes. We evaluated summary statistics from large, multi-centre GWA studies of PCA (n=50 000) and CVD risk factors (n=200 000) [triglycerides (TG), low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol, systolic blood pressure, body mass index, waist-hip ratio and type 2 diabetes (T2D)]. Enrichment of single nucleotide polymorphisms (SNPs) associated with PCA and CVD risk factors was assessed with conditional quantile-quantile plots and the Anderson-Darling test. Moreover, we pinpointed shared loci using conjunction FDR.

RESULTS: We found the strongest enrichment of P-values in PCA was conditional on LDL and conditional on TG. In contrast, we found only weak enrichment conditional on HDL or conditional on the other traits investigated. Conjunction FDR identified altogether 17 loci; 10 loci were associated with PCA and LDL, 3 loci were associated with PCA and TG and additionally 4 loci were associated with PCA, LDL and TG jointly (conjunction FDR <0.01). For T2D, we detected one locus adjacent to HNF1B.

CONCLUSIONS: We found polygenic overlap between PCA predisposition and blood lipids, in particular LDL and TG, and identified 17 pleiotropic gene loci between PCA and LDL, and PCA and TG, respectively. These findings provide novel pathobiological insights and may have implications for trials using targeting lipid-lowering agents in a prevention or cancer setting.

Survival for oesophageal, stomach and small intestine cancers in Europe 1999-2007: Results from EUROCARE-5

Background: European regional variation in cancer survival was reported in the EUROCARE-4 study for patients diagnosed in 1995-1999. Relative survival (RS) estimates are here updated for patients diagnosed with cancer of the oesophagus, stomach and small intestine from 2000 to 2007. Trends in RS from 1999-2001 to 2005-2007 are presented to monitor and discuss improvements in patient survival in Europe. Materials and methods: EUROCARE-5 data from 29 countries (87 cancer registries) were used to investigate 1- and 5-year RS. Using registry-specific life-tables stratified by age, gender and calendar year, age-standardised 'complete analysis' RS estimates by country and region were calculated for Northern, Southern, Eastern and Central Europe, and for Ireland and United Kingdom (UK). Survival trends of patients in periods 1999-2001, 2002-2004 and 2005-2007 were investigated using the 'period' RS approach. We computed the 5-year RS conditional on surviving the first year (5-year conditional survival), as the ratio of age-standardised 5-year RS to 1-year RS. Results Oesophageal cancer 1- and 5-year RS (40% and 12%, respectively) remained poor in Europe. Patient survival was worst in Eastern (8%), Northern (11%) and Southern Europe (10%). Europe-wide, there was a 3% improvement in oesophageal cancer 5-year survival by 2005-2007, with Ireland and the UK (3%), and Central Europe (4%) showing large improvements. Europe-wide, stomach cancer 5-year RS was 25%. Ireland and UK (17%) and Eastern Europe (19%) had the poorest 5-year patient survival. Southern Europe had the best 5-year survival (30%), though only showing an improvement of 2% by 2005-2007. Small intestine cancer 5-year RS for Europe was 48%, with Central Europe having the best (54%), and Ireland and UK the poorest (37%). Five-year patient survival improvement for Europe was 8% by 2005-2007, with Central, Southern and Eastern Europe showing the greatest increases (≥9%). Conclusions Survival for these cancer sites, particularly oesophageal cancer, remains poor in Europe with wide variation. Further investigation into the wide variation, including analysis by histology and anatomical sub-site, will yield insights to better monitor and explain the improvements in survival observed over time.

Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Oral Bisphosphonate Exposure and the Risk of Upper Gastrointestinal Cancers

The association between oral bisphosphonate use and upper gastrointestinal cancer has been controversial. Therefore, we examined the association with esophageal and gastric cancer within the Kaiser Permanente, Northern California population. A total of 1,011 cases of esophageal (squamous cell carcinoma and adenocarcinoma) and 1,923 cases of gastric adenocarcinoma (cardia, non-cardia and other) diagnosed between 1997 and 2011 from the Kaiser Permanente, Northern California cancer registry were matched to 49,886 and 93,747 controls, respectively. Oral bisphosphonate prescription fills at least one year prior to the index date were extracted. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (95% CI) for the associations between prospectively evaluated oral bisphosphonate use with incident esophageal and gastric cancer diagnoses with adjustment for potential confounders. After adjustment for potential confounders, no significant associations were found for esophageal squamous cell carcinoma (OR 0.88; 95% CI: 0.51, 1.52), esophageal adenocarcinoma (OR 0.68; 95% CI: 0.37, 1.24), or gastric non-cardia adenocarcinoma (OR 0.83, 95% CI: 0.59, 1.18), but we observed an adverse association with gastric cardia adenocarcinoma (OR 1.64; 95% CI: 1.07, 2.50). In conclusion, we observed no association between oral bisphosphonate use and esophageal cancer risk within a large community-based population. A significant association was detected with gastric cardia and other adenocarcinoma risk, although this needs to be replicated.

Low-Dose Aspirin Use After Diagnosis of Colorectal Cancer Does Not Increase Survival: A Case–Control Analysis of a Population-Based Cohort

BACKGROUND & AIMS: Individuals who began taking low-dose aspirin before they were diagnosed with colorectal cancer were reported to have longer survival times than patients who did not take this drug. We investigated survival times of patients who begin taking low-dose aspirin after a diagnosis of colorectal cancer in a large population-based cohort study.

METHODS: We performed a nested case-control analysis using a cohort of 4794 patients diagnosed with colorectal cancer from 1998 through 2007, identified from the UK Clinical Practice Research Datalink and confirmed by cancer registries. There were 1559 colorectal cancer-specific deaths, recorded by the Office of National Statistics; these were each matched with up to 5 risk-set controls. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI), based on practitioner-recorded aspirin usage.

RESULTS: Overall, low-dose aspirin use after a diagnosis of colorectal cancer was not associated with colorectal cancer-specific mortality (adjusted OR = 1.06; 95% CI: 0.92-1.24) or all-cause mortality (adjusted OR = 1.06; 95% CI: 0.94-1.19). A dose-response association was not apparent; for example, low-dose aspirin use for more than 1 year after diagnosis was not associated with colorectal cancer-specific mortality (adjusted OR = 0.98; 95% CI: 0.82-1.19). There was also no association between low-dose aspirin usage and colon cancer-specific mortality (adjusted OR = 1.02; 95% CI: 0.83-1.25) or rectal cancer-specific mortality (adjusted OR = 1.10; 95% CI: 0.88-1.38).

CONCLUSIONS: In a large population-based cohort, low-dose aspirin usage after diagnosis of colorectal cancer did not increase survival time.