Treatment of hyperphosphatemia in hemodialysis patients: The Calcium Acetate Renagel Evaluation (CARE Study)

Treatment of hyperphosphatemia in hemodialysis patients: The Calcium Acetate Renagel Evaluation (CARE Study).BackgroundHyperphosphatemia underlies development of hyperparathyroidism, osteodystrophy, extraosseous calcification, and is associated with increased mortality in hemodialysis patients.MethodsTo determine whether calcium acetate or sevelamer hydrochloride best achieves recently recommended treatment goals of phosphorus ≤5.5mg/dL and Ca × P product ≤55mg2/dL2, we conducted an 8-week randomized, double-blind study in 100 hemodialysis patients.ResultsComparisons of time-averaged concentrations (weeks 1 to 8) demonstrated that calcium acetate recipients had lower serum phosphorus (1.08mg/dL difference, P = 0.0006), higher serum calcium (0.63mg/dL difference, P < 0.0001), and lower Ca × P (6.1mg2/dL2 difference, P = 0.022) than sevelamer recipients. At each week, calcium acetate recipients were 20% to 24% more likely to attain goal phosphorus [odds ratio (OR) 2.37, 95% CI 1.28–4.37, P = 0.0058], and 15% to 20% more likely to attain goal Ca × P (OR 2.16, 95% CI 1.20–3.86, P = 0.0097). Transient hypercalcemia occurred in 8 of 48 (16.7%) calcium acetate recipients, all of whom received concomitant intravenous vitamin D. By regression analysis hypercalcemia was more likely with calcium acetate (OR 6.1, 95% CI 2.8–13.3, P < 0.0001). Week 8 intact PTH levels were not significantly different. Serum bicarbonate levels were significantly lower with sevelamer hydrochloride treatment (P < 0.0001).ConclusionCalcium acetate controls serum phosphorus and calcium-phosphate product more effectively than sevelamer hydrochloride. Cost-benefit analysis indicates that in the absence of hypercalcemia, calcium acetate should remain the treatment of choice for hyperphosphatemia in hemodialysis patients

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

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

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

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

BCOR and BCORL1 Mutations Drive Epigenetic Reprogramming and Oncogenic Signaling by Unlinking PRC1.1 from Target Genes

Polycomb repressive epigenetic complexes are recurrently dysregulated in cancer. Unlike polycomb repressive complex 2 (PRC2), the role of PRC1 in oncogenesis and therapy resistance is not well-defined. Here, we demonstrate that highly recurrent mutations of the PRC1 subunits BCOR and BCORL1 in leukemia disrupt assembly of a noncanonical PRC1.1 complex, thereby selectively unlinking the RING-PCGF enzymatic core from the chromatin-targeting auxiliary subcomplex. As a result, BCOR-mutated PRC1.1 is localized to chromatin but lacks repressive activity, leading to epigenetic reprogramming and transcriptional activation at target loci. We define a set of functional targets that drive aberrant oncogenic signaling programs in PRC1.1-mutated cells and primary patient samples. Activation of these PRC1.1 targets in BCOR-mutated cells confers acquired resistance to treatment while sensitizing to targeted kinase inhibition. Our study thus reveals a novel epigenetic mechanism that explains PRC1.1 tumor-suppressive activity and identifies a therapeutic strategy in PRC1.1-mutated cancer. We demonstrate that BCOR and BCORL1 mutations in leukemia unlink PRC1.1 repressive function from target genes, resulting in epigenetic reprogramming and activation of aberrant cell signaling programs that mediate treatment resistance. Our study provides mechanistic insights into the pathogenesis of PRC1.1-mutated leukemia that inform novel therapeutic approaches. This article is highlighted in the In This Issue feature, p. 85