Objective.To assess the performance of various sonographic elemental entheseal lesions in distinguishing between psoriatic arthritis (PsA) and controls to inform the development of a novel sonographic enthesitis score for PsA.Methods.A total of 100 age- and sex-matched individuals (50 PsA and 50 controls) were evaluated. Eleven entheseal sites were scanned bilaterally according to a standardized protocol by 2 sonographers. Based on the Outcome Measures in Rheumatology (OMERACT) definition of sonographic enthesitis, the following lesions were assessed: structural entheseal changes (hypoechogenicity), thickening, bone erosion, enthesophytes, calcification, and Doppler signal, in addition to bursitis and bone irregularities. The images were read by 2 readers blinded to the clinical information. A series of logistic regression models were used to find the optimal combination of entheseal sites and elementary lesions that distinguished PsA from controls.Results.Mean age was 55 ± 10 years (59% males). The optimal model that distinguished PsA from controls included 5 elementary lesions (enthesophytes, Doppler signal, erosions, thickening, and hypoechogenicity) and 6 entheseal sites (patellar ligament insertions into the distal patella and tibial tuberosity, Achilles tendon and plantar fascia insertions into the calcaneus, common extensor tendon insertion into lateral epicondyle, and supraspinatus insertion into the superior facet of the humerus). The area under the receiver-operating characteristic curve for this model was 0.93 (95% CI 0.88–0.98).Conclusion.We identified potential elemental ultrasonographic abnormalities and entheseal sites that could distinguish PsA and controls. This information will contribute to the development of a new sonographic score for assessment of enthesitis in patients with PsA.

The invasion and metastasis of malignant tumor cells lead to normal tissue destruction and are major prognostic factors for many malignant cancers. Long non-coding RNA (LncRNA) is associated with occurrence, development and prognoses of non-small cell lung cancer (NSCLC), but its mechanisms of action involved in tumor invasion and metastasis are not clear. In this study, we screened and detected the expression of LncRNA in two NSCLC lines 95D and 95C by using high throughput LncRNA chip. We found that TATDN1 (Homo sapiens TatD DNase domain containing 1, TATDN1), one of LncRNAs, was highly expressed in 95D cells and NSCLC tumor tissues compared to 95C cells. Knockdown of TATDN1–1 by shRNA significantly inhibited cell proliferation, adhesion, migration and invasion in 95D cells. Further mechanism study showed that TATDN1 knockdown suppressed the expression of E-cadherin, HER2, β-catenin and Ezrin. Moreover, knockdown TATDN1 also inhibited tumor growth and metastasis in a 95D mouse model in vivo by inhibiting β-catenin and Ezrin. These data indicate that TATDN1 expression is associated with 95D cells' higher potential of invasion and metastasis, and suggest that TATDN1 may be a potential prognostic factor and therapeutic target for NSCLCs.

SummaryThe heritability of chronic diseases can be effectively studied by examining the nature and extent of within-family associations in disease onset times. Families are typically accrued through a biased sampling scheme in which affected individuals are identified and sampled along with their relatives who may provide right-censored or current status data on their disease onset times. We develop likelihood and composite likelihood methods for modeling the within-family association in these times through copula models in which dependencies are characterized by Kendall's τ . Auxiliary data from independent individuals are exploited by augmentating composite likelihoods to increase precision of marginal parameter estimates and consequently increase efficiency in dependence parameter estimation. An application to a motivating family study in psoriatic arthritis illustrates the method and provides some evidence of excessive paternal transmission of risk.

Background Circular RNAs (circRNAs) are a new type of extensive non-coding RNAs that regulate the activation and progression of different human diseases, including cancer. However, information on the underlying mechanisms and clinical significance of circRNAs in lung squamous cell carcinoma (LUSC) remains scant. Methods The expression profile of RNAs in 8 LUSC tissues, and 9 healthy lung tissues were assayed using RNA sequencing (RNA-seq) techniques. Real-time quantitative polymerase chain reaction (qRT-PCR) was used to profile the expression of circPVT1 and its relationship with the prognosis of LUSC, i.e., survival analysis. Moreover, in vitro and in vivo experiments were performed to evaluate the impacts of circPVT1 on the growth of tumors. RNA pull-down tests, mass spectrometry, dual-luciferase reporter assessment, and RNA immune-precipitation tests were further conducted to interrogate the cross-talk between circPVT1, HuR, or miR-30d/e in LUSC. Results Our data showed that circPVT1 was upregulated in LUSC tissues, serum, and cell lines. LUSC patients with higher circPVT1 expression exhibited shorter survival rates. The in vivo and in vitro data revealed that circPVT1 promotes the proliferation of LUSC cells. Additionally, mechanistic analysis showed that HuR regulated circPVT1. On the other hand, circPVT1 acted as a competing endogenous RNA (ceRNA) of miR-30d and miR-30e in alleviating the suppressive influences of miR-30d and miR-30e on its target cyclin F (CCNF). Conclusion CircPVT1 promotes LUSC progression via HuR/circPVT1/miR-30d and miR-30e/CCNF cascade. Also, it acts as a novel diagnostic biomarker or treatment target of individuals diagnosed with LUSC.

SummaryIn cluster-randomized trials, intervention effects are often formulated by specifying marginal models, fitting them under a working independence assumption, and using robust variance estimates to address the association in the responses within clusters. We develop sample size criteria within this framework, with analyses based on semiparametric Cox regression models fitted with event times subject to right censoring. At the design stage, copula models are specified to enable derivation of the asymptotic variance of estimators from a marginal Cox regression model and to compute the number of clusters necessary to satisfy power requirements. Simulation studies demonstrate the validity of the sample size formula in finite samples for a range of cluster sizes, censoring rates and degrees of within-cluster association among event times. The power and relative efficiency implications of copula misspecification is studied, as well as the effect of within-cluster dependence in the censoring times. Sample size criteria and other design issues are also addressed for the setting where the event status is only ascertained at periodic assessments and times are interval censored.Keywords: censored data; cluster-randomized trials; copula models; robust inference; sample size This is the peer reviewed version of the following article: Zhong Yujie, and Cook Richard J. (2015), Sample size and robust marginal methods for cluster-randomized trials with censored event times, Statist. Med., 34, pages 901-923.