Molecular cloning, expression analysis and assignment of the porcine tumor necrosis factor superfamily member 10 gene (TNFSF10) to SSC13q34-->q36 by fluorescence in situ hybridization and radiation hybrid mapping

We have cloned the complete coding region of the porcine TNFSF10 gene. The porcine TNFSF10 cDNA has an ORF of 870 nucleotides and shares 85% identity with human TNFSF10, and 75% and 72% identity with rat and mouse Tnfsf10 coding sequences, respectively. The deduced porcine TNFSF10 protein consists of 289 amino acids with the calculated molecular mass of 33.5 kDa and a predicted pI of 8.15. The amino acid sequence similarities correspond to 86, 72 and 70% when compared with human, rat and mouse sequences, respectively. Northern blot analysis detected TNFSF10-specific transcripts (approximately 1.7 kb) in various organs of a 10-week-old pig, suggesting ubiquitous expression. Real-time RT-PCR studies of various organs from fetal (days 73 and 98) and postnatal stages (two weeks, eight months) demonstrated developmental and tissue-specific regulation of TNFSF10 mRNA abundance. The chromosomal location of the porcine TNFSF10 gene was determined by FISH of a specific BAC clone to metaphase chromosomes. This TNFSF10 BAC clone has been assigned to SSC13q34-->q36. Additionally, the localization of the TNFSF10 gene was verified by RH mapping on the porcine IMpRH panel.

Thrombophilia and risk of VTE recurrence according to the age at the time of first VTE manifestation

BACKGROUND Whether screening for thrombophilia is useful for patients after a first episode of venous thromboembolism (VTE) is a controversial issue. However, the impact of thrombophilia on the risk of recurrence may vary depending on the patient's age at the time of the first VTE. PATIENTS AND METHODS Of 1221 VTE patients (42 % males) registered in the MAISTHRO (MAin-ISar-THROmbosis) registry, 261 experienced VTE recurrence during a 5-year follow-up after the discontinuation of anticoagulant therapy. RESULTS Thrombophilia was more common among patients with VTE recurrence than those without (58.6 % vs. 50.3 %; p = 0.017). Stratifying patients by the age at the time of their initial VTE, Cox proportional hazards analyses adjusted for age, sex and the presence or absence of established risk factors revealed a heterozygous prothrombin (PT) G20210A mutation (hazard ratio (HR) 2.65; 95 %-confidence interval (CI) 1.71 - 4.12; p < 0.001), homozygosity/double heterozygosity for the factor V Leiden and/or PT mutation (HR 2.35; 95 %-CI 1.09 - 5.07, p = 0.030), and an antithrombin deficiency (HR 2.12; 95 %-CI 1.12 - 4.10; p = 0.021) to predict recurrent VTE in patients aged 40 years or older, whereas lupus anticoagulants (HR 3.05; 95%-CI 1.40 - 6.66; p = 0.005) increased the risk of recurrence in younger patients. Subgroup analyses revealed an increased risk of recurrence for a heterozygous factor V Leiden mutation only in young females without hormonal treatment whereas the predictive value of a heterozygous PT mutation was restricted to males over the age of 40 years. CONCLUSIONS Our data do not support a preference of younger patients for thrombophilia testing after a first venous thromboembolic event.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example are the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example is the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.