Detection of Viral Hemorrhagic Septicemia virus (VHSV) from the leech Myzobdella lugubris Leidy, 1851

The leech Myzobdella lugubris is widespread in the Lake Erie Watershed, especially Lake St. Clair. However, its role in pathogen transmission is not fully understood. In this same watershed, several widespread fish mortalities associated with the Viral Hemorrhagic Septicemia virus (VHSV) were recorded. Viral Hemorrhagic Septicemia is an emerging disease in the Great Lakes Basin that is deadly to the fish population, yet little is known about its mode of transmission. To assess the potential role of M. lugubris in VHSV transmission, leeches were collected from Lake St. Clair and Lake Erie and pooled into samples of five. Cell culture and reverse transcriptase polymerase chain reaction (RT-PCR) were used to determine the presence of the virus and its identity. Results showed that 57 of the 91 pooled leech samples were positive by cell culture for VHSV and 66 of the 91 pooled leech samples were positive by RT-PCR for the VHSV. Two representative virus isolates were sequenced for further genetic confirmation and genotype classification. VHSV detected within M. lugubris was homologous to the Great Lakes strain of VHSV genotype IVb. This is the first record of the VHSV being detected from within a leech, specifically M. lugubris, and suggests the potential of M. lugubris being involved in VHSV transmission

Development and Phenotype of Heart Failure in Long-Term Survivors of Childhood Cancer:The CVSS Study

BACKGROUND: The CVSS (Cardiac and Vascular Late Sequelae in Long-Term Survivors of Childhood Cancer) study aimed to investigate the prevalence of different stages of heart failure (HF) in childhood cancer survivors (CCSs) compared with the general population. METHODS AND RESULTS: A total of 1002 CCSs (age range, 23– 48 years) diagnosed with neoplasia before an age of 15 years underwent a comprehensive cardiovascular screening. An age-and sex-matched sample from the population-based GHS (Gutenberg Health Study) served as a comparison group. Although prevalence of HF was significantly higher in CCSs, prevalence of different HF stages varied strongly by specific tumor history. Compared with the population, the prevalence ratio was 2.6 (95% CI, 2.4– 2.8) for HF stage A and 4.6 (95% CI, 4.1– 5.1) for the composite of HF stage B to D in an age-and sex-adjusted Poisson regression model. Multivariable linear regression, adjusting for tumor entities, age, sex, and cardiovascular risk factors, revealed a lower left ventricular ejection fraction in patients with history of bone tumors (β, −4.30 [95% CI, −5.70 to −2.80]), soft tissue sarcoma (β, −1.60 [95% CI, −2.90 to −0.30]), and renal tumors (β, −1.60 [95% CI, −2.80 to −0.29]) compared with the population. The same model for the diastolic marker, ratio of the peak early diastolic filling velocity/lateral mitral annular early diastolic velocity, showed an association only with cardiovascular risk factors but not with tumor entities. CONCLUSIONS: The prevalence of HF stage A to D was significantly higher among long-term CCSs compared with the population and varied strongly by tumor entity. Systolic dysfunction was primarily associated with tumor entities, whereas diastolic dysfunction was associated with a higher burden of cardiovascular risk factors in CCSs.</p

Development and Phenotype of Heart Failure in Long-Term Survivors of Childhood Cancer:The CVSS Study

BACKGROUND: The CVSS (Cardiac and Vascular Late Sequelae in Long-Term Survivors of Childhood Cancer) study aimed to investigate the prevalence of different stages of heart failure (HF) in childhood cancer survivors (CCSs) compared with the general population. METHODS AND RESULTS: A total of 1002 CCSs (age range, 23– 48 years) diagnosed with neoplasia before an age of 15 years underwent a comprehensive cardiovascular screening. An age-and sex-matched sample from the population-based GHS (Gutenberg Health Study) served as a comparison group. Although prevalence of HF was significantly higher in CCSs, prevalence of different HF stages varied strongly by specific tumor history. Compared with the population, the prevalence ratio was 2.6 (95% CI, 2.4– 2.8) for HF stage A and 4.6 (95% CI, 4.1– 5.1) for the composite of HF stage B to D in an age-and sex-adjusted Poisson regression model. Multivariable linear regression, adjusting for tumor entities, age, sex, and cardiovascular risk factors, revealed a lower left ventricular ejection fraction in patients with history of bone tumors (β, −4.30 [95% CI, −5.70 to −2.80]), soft tissue sarcoma (β, −1.60 [95% CI, −2.90 to −0.30]), and renal tumors (β, −1.60 [95% CI, −2.80 to −0.29]) compared with the population. The same model for the diastolic marker, ratio of the peak early diastolic filling velocity/lateral mitral annular early diastolic velocity, showed an association only with cardiovascular risk factors but not with tumor entities. CONCLUSIONS: The prevalence of HF stage A to D was significantly higher among long-term CCSs compared with the population and varied strongly by tumor entity. Systolic dysfunction was primarily associated with tumor entities, whereas diastolic dysfunction was associated with a higher burden of cardiovascular risk factors in CCSs.</p

Development and Phenotype of Heart Failure in Long-Term Survivors of Childhood Cancer:The CVSS Study

BACKGROUND: The CVSS (Cardiac and Vascular Late Sequelae in Long-Term Survivors of Childhood Cancer) study aimed to investigate the prevalence of different stages of heart failure (HF) in childhood cancer survivors (CCSs) compared with the general population. METHODS AND RESULTS: A total of 1002 CCSs (age range, 23– 48 years) diagnosed with neoplasia before an age of 15 years underwent a comprehensive cardiovascular screening. An age-and sex-matched sample from the population-based GHS (Gutenberg Health Study) served as a comparison group. Although prevalence of HF was significantly higher in CCSs, prevalence of different HF stages varied strongly by specific tumor history. Compared with the population, the prevalence ratio was 2.6 (95% CI, 2.4– 2.8) for HF stage A and 4.6 (95% CI, 4.1– 5.1) for the composite of HF stage B to D in an age-and sex-adjusted Poisson regression model. Multivariable linear regression, adjusting for tumor entities, age, sex, and cardiovascular risk factors, revealed a lower left ventricular ejection fraction in patients with history of bone tumors (β, −4.30 [95% CI, −5.70 to −2.80]), soft tissue sarcoma (β, −1.60 [95% CI, −2.90 to −0.30]), and renal tumors (β, −1.60 [95% CI, −2.80 to −0.29]) compared with the population. The same model for the diastolic marker, ratio of the peak early diastolic filling velocity/lateral mitral annular early diastolic velocity, showed an association only with cardiovascular risk factors but not with tumor entities. CONCLUSIONS: The prevalence of HF stage A to D was significantly higher among long-term CCSs compared with the population and varied strongly by tumor entity. Systolic dysfunction was primarily associated with tumor entities, whereas diastolic dysfunction was associated with a higher burden of cardiovascular risk factors in CCSs.</p

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility

RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility