Acute hepatitis associated with Q fever in a man in Greece: a case report

Coxiella burnetii is the causative agent of Q fever. Q fever is a worldwide zoonosis that is responsible for various clinical manifestations. However, in Greece hepatitis due to Coxiella is rarely encountered. A case of Q fever associated with hepatitis is reported here. Diagnosis was made by specific serological investigation (enzyme-linked immunosorbent and indirect immunofluorescene assays) for Coxiella burnetii

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

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

Application of polymerase chain reaction for detection of Legionella pneumophila in serum samples

ObjectiveTo apply the polymerase chain reaction (PCR) to serum samples for the rapid diagnosis of Legionnaire's disease using the L5SL9 and L5SR93 primers designed to generate a 104-base-pair (bp) fragment from the 5S RNA gene of Legionella spp. The amplified product was detected by electrophoresis and by hybridization with the L5S-1-specific probe.MethodsSingle specimens of serum obtained from 24 patients with confirmed legionellosis, at different stages of their disease, were tested by PCR. Additionally, 10 serum samples from patients with no clinical symptoms of pneumonia and 10 samples from patients suffering from pneumonia caused by Mycoplasma pneumoniae, Coxiella bumetii or Chlamydia psittaci were also tested as controls in order to determine the specificity of the method.ResultsOf the 24 examined serum samples, the amplified products from 12 hybridized with the L5S-1 probe (sensitivity 50%). None of the negative controls was positive after PCR. No correlation was found between the day of illness and the positivity in the test.ConclusionsThe PCR technique could be applied as a diagnostic tool for the rapid diagnosis of legionellosis in serum samples after modification, mainly to improve its sensitivity