The Circle Always Grew: Folklore and Gay Identity, 1945-1960

It has become a common place in Gay studies that the rise of Gay culture as we know it today has its roots in the years immediately following World War II. Using life history field techniques as a means of doing field research, the folklore of Gay men of this era is examined. Interviews were conducted with men who were out in the Gay world during the fifteen years after 1945. Biographies of the men are provided. Specific kinds of folkloric behavior are explicated including bar customs, nicknaming, parties, festival events and popular means by which men were able to identify one another as Gay and become part of the Gay community. The role folklore plays in the process of Gay identification is also examined. Historical context is provided for the era as it impacts the ways in which Gays were seen and the influence the Gay presence reflects the tenor of the times. Underlying concepts of Gay identity and community are given priority as a theoretical underpinning furthering understanding of the ways in which folklore is a necessary ingredient for both identity and community. It is demonstrated that any understanding of Gay men of that era must attend to their creative abilities in using folklore to carve a place for themselves in the cultural arena

Postposed Articles and DP Structures in Torlak

This article sheds light on postposed articles and DP structures in Torlak, a non-standardised Balkan Slavic variety. Torlak and specifically Trgoviste-Torlak, unlike Bulgarian and Macedonian, does not exhibit MD. We argue that this scenery is due to a partial grammaticalization of the determiner, which is arguably an inflectional affix and maintains the demonstrative feature. In addition, we verify the nature of the Torlak DP and we make some considerations on the intermediate nature of this element with respect to the grammaticalization path, followed by the other Balkan Slavic varieties

Substance P induces TNF-α and IL-6 production through NFκB in peritoneal mast cells

AbstractThe neuropeptide Substance P (SP) is an important mediator of neuroimmunomodulatory activity. The aim of this study is to elucidate the mechanism used by SP to promote increased production of pro-inflammatory cytokines in fresh isolated rat peritoneal mast cells (rPMC). We have demonstrated that SP induces production of interleukin-6 (IL-6) in rPMC through the PI-3K, p42/44 and p38 MAP kinase pathways. SP-stimulated rPMC also exhibited an enhanced nuclear translocation of the nuclear factor κ B (NFκB). The tumour necrosis factor-α (TNF-α) and IL-6 production was completely inhibited by using (E)-4-hydroxynonenal (HNE) as an inhibitor of IκB-α and -β phosphorylation. Further, TNF-α and IL-6 expression was significantly inhibited by the oligonucleotides (ODNs) containing the NFκB element (NFκB decoy ODNs) but not by the scrambled control ODNs. These findings indicate that the NFκB pathway is involved in the transcriptional regulation of the TNF-α and IL-6 overexpression in primary SP-stimulated mast cells

Adherence in Rheumatoid Arthritis patients assessed with a validated Italian version of the 5-item compliance questionnaire for rheumatology

OBJECTIVES: The 5-item Compliance Questionnaire for Rheumatology (CQR5) proved reliability and validity in respect of identification of patients likely to be high adherers (HAs) to anti-rheumatic treatment, or low adherers (LAs), i.e. taking<80% of their medications correctly. The objective of the study was to validate an Italian version of CQR5 (I-CQR5) in rheumatoid arthritis (RA) patients and to investigate factors associated with high adherence. METHODS: RA patients, undergoing treatment with ≥1 self-administered conventional synthetic disease-modifying anti-rheumatic drug (csDMARD) or biological DMARD (bDMARD), were enrolled. The cross-cultural adaptation and validation of I-CQR5 followed standardised guidelines. I-CQR5 was completed by patients on one occasion. Data were subjected to factor analysis and Partial Credit model Parametrisation (PCM) to assess construct validity of I-CQR5. Analysis of factors associated with high adherence included demographic, social, clinical and treatment information. Factors achieving a p<0.10 in univariate analysis were included in multivariable analysis. RESULTS: Among 604 RA patients, 274 patients were included in the validation and 328 in the analysis of factors associated with adherence. Factor analysis and PCM confirmed the construct validity and consistency of I-CQR5. HAs were found to be 109 (35.2%) of the patients. bDMARD treatment and employment were found to be independently associated with high adherence: OR 2.88 (1.36-6.1), p=0.006 and OR 2.36 (1.21-4.62), p=0.012, respectively. CONCLUSIONS: Only one-third of RA patients were HAs according to I-CQR5. bDMARDs and employment status increased by almost 3-fold the likelihood of being highly adherent to the anti-rheumatic treatment.Peer reviewe

Comorbidity-adjusted relative survival in newly hospitalized heart failure patients: A population-based study

Background This study aims to identify comorbidities through various sources and assess their short-term impact on relative survival in a cohort of heart failure (HF) patients. Methods Newly hospitalized HF patients were identified from hospital discharge abstracts (HDA) of Lombardy Region, Italy, from 2008 to 2010. Charlson comorbidities were assessed using the HDA and supplemented with drug prescriptions and disease-specific exemptions. A Cox model was fit for the one-year relative survival from HF. Results The cohort consisted of 51,061 HF patients (53% women; median age 80\uc2\ua0years). After integrating information from all sources, the prevalence rates of diabetes, chronic pulmonary disease and renal disease were 27.6%, 26.2% and 14.2%, respectively. The prevalence of comorbidity increased to 78%. Survival in the HF cohort was worse with increasing number of comorbidities and was inferior to that in the reference population. Notably, the overall performance of the relative survival models was similar regardless of the strategy used to ascertain comorbidity. Conclusions Comorbidities cluster in hospitalized HF patients, and increasing comorbidity burden is associated with worse survival. Integration of a comprehensive search of electronic records to supplement HDA improves the prevalence estimates of comorbidities, although it does not improve discrimination of the risk prediction model

A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Background: Randomized Clinical Trials (RCT) represent the gold standard among scientific evidence. RCTs are tailored to control selection bias and the confounding effect of baseline characteristics on the effect of treatment. However, trial conduction and enrolment procedures could be challenging, especially for rare diseases and paediatric research. In these research frameworks, the treatment effect estimation could be compromised. A potential countermeasure is to develop predictive models on the probability of the baseline disease based on previously collected observational data. Machine learning (ML) algorithms have recently become attractive in clinical research because of their flexibility and improved performance compared to standard statistical methods in developing predictive models. Objective: This manuscript proposes an ML-enforced treatment effect estimation procedure based on an ensemble SuperLearner (SL) approach, trained on historical observational data, to control the confounding effect. Methods: The REnal SCarring Urinary infEction trial served as a motivating example. Historical observational study data have been simulated through 10,000 Monte Carlo (MC) runs. Hypothetical RCTs have been also simulated, for each MC run, assuming different treatment effects of antibiotics combined with steroids. For each MC simulation, the SL tool has been applied to the simulated observational data. Furthermore, the average treatment effect (ATE), has been estimated on the trial data and adjusted for the SL predicted probability of renal scar. Results: The simulation results revealed an increased power in ATE estimation for the SL-enforced estimation compared to the unadjusted estimates for all the algorithms composing the ensemble SL

Handling missing continuous outcome data in a Bayesian network meta-analysis

Background: A Bayesian network meta-analysis (NMA) model is a statistical method aimed at estimating the relative effects of multiple interventions against the same disease. The method has recently gained prominence, leading to the synthesis of the evidence regarding rank probabilities for each treatment. In several cases, an NMA is performed excluding incomplete data of studies retrieved through a systematic review, resulting in a loss of precision and power.&nbsp; Methods: There are several methods for handling missing or incomplete data in an NMA framework, especially for continuous outcomes. In certain cases, only baseline and follow-up measurements are available; in this framework, to obtain data regarding mean changes, it is necessary to consider the pre-post study correlation. In this context, in a Bayesian setting, several authors suggest imputation strategies for pre-post correlation. In other cases, a variability measure associated with a mean change score might be unavailable. Different imputation methods have been suggested, such as those based on maximum standard deviation imputation. The purpose of this study is to verify the robustness of Bayesian NMA models concerning different imputation strategies through simulations.&nbsp; Results: Simulation results show that the bias is notably small for every scenario, confirming that rankings provided by models are robust concerning different imputation methods in several heterogeneity-correlation settings.&nbsp; Conclusions: This NMA method seems to be more robust to missing data imputation when data reported in different studies are generated in a low-heterogeneity scenario. The NMA method seems to be more robust to missing value imputation if the expectation of the prior distribution, defined on the heterogeneity parameter, approaches the true value of the variability across studies.&nbsp

A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines