Sox5 and Sox6 are needed to develop and maintain source, columnar, and hypertrophic chondrocytes in the cartilage growth plate

Sox5 and Sox6 encode Sry-related transcription factors that redundantly promote early chondroblast differentiation. Using mouse embryos with three or four null alleles of Sox5 and Sox6, we show that they are also essential and redundant in major steps of growth plate chondrocyte differentiation. Sox5 and Sox6 promote the development of a highly proliferating pool of chondroblasts between the epiphyses and metaphyses of future long bones. This pool is the likely cellular source of growth plates. Sox5 and Sox6 permit formation of growth plate columnar zones by keeping chondroblasts proliferating and by delaying chondrocyte prehypertrophy. They allow induction of chondrocyte hypertrophy and permit formation of prehypertrophic and hypertrophic zones by delaying chondrocyte terminal differentiation induced by ossification fronts. They act, at least in part, by down-regulating Ihh signaling, Fgfr3, and Runx2 and by up-regulating Bmp6. In conclusion, Sox5 and Sox6 are needed for the establishment of multilayered growth plates, and thereby for proper and timely development of endochondral bones

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

The breadth of primary care: a systematic literature review of its core dimensions

Background: Even though there is general agreement that primary care is the linchpin of effective health care delivery, to date no efforts have been made to systematically review the scientific evidence supporting this supposition. The aim of this study was to examine the breadth of primary care by identifying its core dimensions and to assess the evidence for their interrelations and their relevance to outcomes at (primary) health system level. Methods: A systematic review of the primary care literature was carried out, restricted to English language journals reporting original research or systematic reviews. Studies published between 2003 and July 2008 were searched in MEDLINE, Embase, Cochrane Library, CINAHL, King's Fund Database, IDEAS Database, and EconLit. Results: Eighty-five studies were identified. This review was able to provide insight in the complexity of primary care as a multidimensional system, by identifying ten core dimensions that constitute a primary care system. The structure of a primary care system consists of three dimensions: 1. governance; 2. economic conditions; and 3. workforce development. The primary care process is determined by four dimensions: 4. access; 5. continuity of care; 6. coordination of care; and 7. comprehensiveness of care. The outcome of a primary care system includes three dimensions: 8. quality of care; 9. efficiency care; and 10. equity in health. There is a considerable evidence base showing that primary care contributes through its dimensions to overall health system performance and health. Conclusions: A primary care system can be defined and approached as a multidimensional system contributing to overall health system performance and health

A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies

Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions

Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning

Retinopathy of prematurity (ROP) is a disease that affects premature infants, where abnormal growth of the retinal blood vessels can lead to blindness unless treated accordingly. Infants considered at risk of severe ROP are monitored for symptoms of plus disease, characterized by arterial tortuosity and venous dilation at the posterior pole, with a standard photographic definition. Disagreement among ROP experts in diagnosing plus disease has driven the development of computer-based methods that classify images based on hand-crafted features extracted from the vasculature. However, most of these approaches are semi-automated, which are time-consuming and subject to variability. In contrast, deep learning is a fully automated approach that has shown great promise in a wide variety of domains, including medical genetics, informatics and imaging. Convolutional neural networks (CNNs) are deep networks which learn rich representations of disease features that are highly robust to variations in acquisition and image quality. In this study, we utilized a U-Net architecture to perform vessel segmentation and then a GoogLeNet to perform disease classification. The classifier was trained on 3,000 retinal images and validated on an independent test set of patients with different observed progressions and treatments. We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts’ consensus diagnosis. Future work will aim to further validate the method on larger cohorts of patients to assess its applicability within the clinic as a treatment monitoring tool

A microcosting study of microsurgery, LINAC radiosurgery, and gamma knife radiosurgery in meningioma patients

The aim of the present study is to determine and compare initial treatment costs of microsurgery, linear accelerator (LINAC) radiosurgery, and gamma knife radiosurgery in meningioma patients. Additionally, the follow-up costs in the first year after initial treatment were assessed. Cost analyses were performed at two neurosurgical departments in The Netherlands from the healthcare providers’ perspective. A total of 59 patients were included, of whom 18 underwent microsurgery, 15 underwent LINAC radiosurgery, and 26 underwent gamma knife radiosurgery. A standardized microcosting methodology was employed to ensure that the identified cost differences would reflect only actual cost differences. Initial treatment costs, using equipment costs per fraction, were €12,288 for microsurgery, €1,547 for LINAC radiosurgery, and €2,412 for gamma knife radiosurgery. Higher initial treatment costs for microsurgery were predominantly due to inpatient stay (€5,321) and indirect costs (€4,350). LINAC and gamma knife radiosurgery were equally expensive when equipment was valued per treatment (€2,198 and €2,412, respectively). Follow-up costs were slightly, but not significantly, higher for microsurgery compared with LINAC and gamma knife radiosurgery. Even though initial treatment costs were over five times higher for microsurgery compared with both radiosurgical treatments, our study gives indications that the relative cost difference may decrease when follow-up costs occurring during the first year after initial treatment are incorporated. This reinforces the need to consider follow-up costs after initial treatment when examining the relative costs of alternative treatments