Rapid Decline of Serum Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) in Non-Cirrhotic Patients with Chronic Hepatitis C Infection Receiving Direct-Acting Antiviral Therapy.

Direct-acting antivirals (DAAs) efficiently eradicate the hepatitis C virus (HCV). Low-density lipoprotein (LDL) levels increase rapidly upon DAA treatment. Proprotein convertase subtilisin/kexin 9 (PCSK9) induces degradation of the hepatic LDL receptor and thereby elevates serum LDL. The aim of this study was to determine serum PCSK9 concentrations during and after DAA therapy to identify associations with LDL levels. Serum PCSK9 was increased in 82 chronic HCV-infected patients compared to 55 patients not infected with HCV. Serum PCSK9 was low in HCV patients with liver cirrhosis, but patients with HCV-induced liver cirrhosis still exhibited higher serum PCSK9 than patients with non-viral liver cirrhosis. Serum PCSK9 correlated with measures of liver injury and inflammation in cirrhotic HCV patients. In patients without liver cirrhosis, a positive association of serum PCSK9 with viral load existed. Serum PCSK9 was not different between viral genotypes. Serum PCSK9 did not correlate with LDL levels in HCV patients irrespective of cirrhotic status. Serum PCSK9 was reduced, and LDL was increased at four weeks after DAA therapy start in non-cirrhotic HCV patients. Serum PCSK9 and LDL did not change upon DAA treatment in the cirrhotic group. The rapid decline of PCSK9 after the start of DAA therapy in conjunction with raised LDL levels in non-cirrhotic HCV patients shows that these changes are not functionally related

Future Directions for Postdoctoral Training in Cancer Prevention: Insights from a Panel of Experts

Cancer prevention postdoctoral fellowships have existed since the 1970s. The National Cancer Institute facilitated a meeting by a panel of experts in April 2013 to consider four important topics for future directions for cancer prevention postdoctoral training programs: 1) future research needs; 2) underrepresented disciplines; 3) curriculum; and 4) career preparation. Panelists proffered several areas needing more research or emphasis, ranging from computational science to culture. Health care providers, along with persons from non-traditional disciplines such as engineers and lawyers, were among disciplines recognized as being underrepresented in training programs. Curriculum suggestions were that fellows receive training in topics such as leadership and human relations, in addition to learning the principles of epidemiology, cancer biological mechanisms, and behavioral science. For career preparation, there was a clear recognition of the diversity of employment options available besides academic positions, and that program leaders should do more to help fellows identify and prepare for different career paths. The major topics and strategies covered at this meeting can help form the basis for cancer prevention training program leaders to consider modifications or new directions, and keep them current with the changing scientific and employment climate for doctoral degree recipients and postdoctoral fellows

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study.

peer reviewed[en] BACKGROUND: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated. OBJECTIVE: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group. METHODS: Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. RESULTS: Overall, people with PD had a regionally smaller posterior lobe (dmax  = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax  = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax  = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17). CONCLUSIONS: We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011