The organisation of an educational program for specialists in clinical chemistry by the Greek Society of Clinical Chemistry-Clinical Biochemistry

In Greece, there is no officially organized training in clinical chemistry for scientists. The Greek Society of Clinical Chemistry-Clinical Biochemistry decided to organize an intensive educational prog-ram of 18 seminars on clinical chemistry content as it is described in the EC4 Syllabus. The duration of each seminar was about 6 hours and consisted of 6 to 9 lectures. At the end of each seminar there was a voluntary written examination, comprised of 24 multiple choice questions. Suc-cessful completion of the Educational program was leading to a Certificate of Competence. Two cycles of the 18 seminars were performed: 1st cycle from October 2003 to December 2005 and 2nd cycle from March 2005 to October 2007. One hundred eighty nine colleagues was the mean at-tendance per seminar for the seminars of the 1st cycle and 38 colleagues for the seminars of the 2nd cycle. The mean participation to the examination for each seminar was almost 80% for the 1st cycle and 68% for the 2nd cycle. More than 80% of the participants performed Good or Very good in the examination in both cycles. It is estimated that more than 40% of the scientists who practice Clinical Chemistry in Greece, partici-pated to this educational activity. This program is now provided as an e-learning application, and it is open for all scientists who want to follow the discipline of clinical chemistry

The organisation of an educational program for specialists in clinical chemistry by the Greek Society of Clinical Chemistry-Clinical Biochemistry

In Greece, there is no officially organized training in clinical chemistry for scientists. The Greek Society of Clinical Chemistry-Clinical Biochemistry decided to organize an intensive educational prog-ram of 18 seminars on clinical chemistry content as it is described in the EC4 Syllabus. The duration of each seminar was about 6 hours and consisted of 6 to 9 lectures. At the end of each seminar there was a voluntary written examination, comprised of 24 multiple choice questions. Suc-cessful completion of the Educational program was leading to a Certificate of Competence. Two cycles of the 18 seminars were performed: 1st cycle from October 2003 to December 2005 and 2nd cycle from March 2005 to October 2007. One hundred eighty nine colleagues was the mean at-tendance per seminar for the seminars of the 1st cycle and 38 colleagues for the seminars of the 2nd cycle. The mean participation to the examination for each seminar was almost 80% for the 1st cycle and 68% for the 2nd cycle. More than 80% of the participants performed Good or Very good in the examination in both cycles. It is estimated that more than 40% of the scientists who practice Clinical Chemistry in Greece, partici-pated to this educational activity. This program is now provided as an e-learning application, and it is open for all scientists who want to follow the discipline of clinical chemistry

Connection between Telomerase Activity in PBMC and Markers of Inflammation and Endothelial Dysfunction in Patients with Metabolic Syndrome

Metabolic syndrome (MS) is a constellation of metabolic derangements associated with vascular endothelial dysfunction and oxidative stress and is widely regarded as an inflammatory condition, accompanied by an increased risk for cardiovascular disease. The present study tried to investigate the implications of telomerase activity with inflammation and impaired endothelial function in patients with metabolic syndrome. Telomerase activity in circulating peripheral blood mononuclear cells (PBMC), TNF-α, IL-6 and ADMA were monitored in 39 patients with MS and 20 age and sex-matched healthy volunteers. Telomerase activity in PBMC, TNF-α, IL-6 and ADMA were all significantly elevated in patients with MS compared to healthy volunteers. PBMC telomerase was negatively correlated with HDL and positively correlated with ADMA, while no association between TNF-α and IL-6 was observed. IL-6 was increasing with increasing systolic pressure both in the patients with MS and in the healthy volunteers, while smoking and diabetes were positively correlated with IL-6 only in the patients' group. In conclusion, in patients with MS characterised by a strong dyslipidemic profile and low diabetes prevalence, significant telomerase activity was detected in circulating PBMC, along with elevated markers of inflammation and endothelial dysfunction. These findings suggest a prolonged activity of inflammatory cells in the studied state of this metabolic disorder that could represent a contributory pathway in the pathogenesis of atherosclerosis

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

Initial Public Offerings and the Firm Location

The firm geographic location matters in IPOs because investors have a strong preference for newly issued local stocks and provide abnormal demand in local offerings. Using equity holdings data for more than 53,000 households, we show the probability to participate to the stock market and the proportion of the equity wealth is abnormally increasing with the volume of the IPOs inside the investor region. Upon nearly the universe of the 167,515 going public and private domestic manufacturing firms, we provide consistent evidence that the isolated private firms have higher probability to go public, larger IPO underpricing cross-sectional average and volatility, and less pronounced long-run under-performance. Similar but opposite evidence holds for the local concentration of the investor wealth. These effects are economically relevant and robust to local delistings, IPO market timing, agglomeration economies, firm location endogeneity, self-selection bias, and information asymmetries, among others. Findings suggest IPO waves have a strong geographic component, highlight that underwriters significantly under-estimate the local demand component thus leaving unexpected money on the table, and support state-contingent but constant investor propensity for risk

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