Binary Local Fractal Dimension: a Precise Structure Parameter for 3D High Resolution Computed Tomography Images of the Human Spongiosa

We present the Binary Local Fractal Dimension (LFD) to analyze osteoporosis induced fracture risk with clinical 3D high resolution quantitative computed tomographic (HRCT) images of human vertebrae. We test if LFD parameters provide precise additional information besides bone mineral density (BMD) and standard descriptors of bone quality, for example bone surface ratio (BS/BV). We define a weighted LFD (wLFD) using the ¯R2 of the H¨older exponents. We compare the LFD with standard methods (distance transform, direct secant method and run-length method) on 5 vertebrae × 8 volumes of interest and 5 repeated scans. The wLFD contains the highest direct and BMD-independent precision (R2 = 0.985 and R2 = 0.949), followed by BS/BV (R2 = 0.977 and R2 = 0.920) including low correlation with BMD (wLFD: R2 = 0.704, BS/BV: R2 = 0.814). LFD improves the translation from reference μCT- to clinical HRCT-resolution. In conclusion, LFD provides a strong diagnostic tool to characterize bone quality to predict osteoporosis induced fracture risk.Sociedad Argentina de Informática e Investigación Operativ

Binary Local Fractal Dimension: a Precise Structure Parameter for 3D High Resolution Computed Tomography Images of the Human Spongiosa

We present the Binary Local Fractal Dimension (LFD) to analyze osteoporosis induced fracture risk with clinical 3D high resolution quantitative computed tomographic (HRCT) images of human vertebrae. We test if LFD parameters provide precise additional information besides bone mineral density (BMD) and standard descriptors of bone quality, for example bone surface ratio (BS/BV). We define a weighted LFD (wLFD) using the ¯R2 of the H¨older exponents. We compare the LFD with standard methods (distance transform, direct secant method and run-length method) on 5 vertebrae × 8 volumes of interest and 5 repeated scans. The wLFD contains the highest direct and BMD-independent precision (R2 = 0.985 and R2 = 0.949), followed by BS/BV (R2 = 0.977 and R2 = 0.920) including low correlation with BMD (wLFD: R2 = 0.704, BS/BV: R2 = 0.814). LFD improves the translation from reference μCT- to clinical HRCT-resolution. In conclusion, LFD provides a strong diagnostic tool to characterize bone quality to predict osteoporosis induced fracture risk.Sociedad Argentina de Informática e Investigación Operativ

Binary Local Fractal Dimension: a Precise Structure Parameter for 3D High Resolution Computed Tomography Images of the Human Spongiosa

We present the Binary Local Fractal Dimension (LFD) to analyze osteoporosis induced fracture risk with clinical 3D high resolution quantitative computed tomographic (HRCT) images of human vertebrae. We test if LFD parameters provide precise additional information besides bone mineral density (BMD) and standard descriptors of bone quality, for example bone surface ratio (BS/BV). We define a weighted LFD (wLFD) using the ¯R2 of the H¨older exponents. We compare the LFD with standard methods (distance transform, direct secant method and run-length method) on 5 vertebrae × 8 volumes of interest and 5 repeated scans. The wLFD contains the highest direct and BMD-independent precision (R2 = 0.985 and R2 = 0.949), followed by BS/BV (R2 = 0.977 and R2 = 0.920) including low correlation with BMD (wLFD: R2 = 0.704, BS/BV: R2 = 0.814). LFD improves the translation from reference μCT- to clinical HRCT-resolution. In conclusion, LFD provides a strong diagnostic tool to characterize bone quality to predict osteoporosis induced fracture risk.Sociedad Argentina de Informática e Investigación Operativ

The Suppressor of AAC2 Lethality SAL1 Modulates Sensitivity of Heterologously Expressed Artemia ADP/ATP Carrier to Bongkrekate in Yeast

The ADP/ATP carrier protein (AAC) expressed in Artemia franciscana is refractory to bongkrekate. We generated two strains of Saccharomyces cerevisiae where AAC1 and AAC3 were inactivated and the AAC2 isoform was replaced with Artemia AAC containing a hemagglutinin tag (ArAAC-HA). In one of the strains the suppressor of ΔAAC2 lethality, SAL1, was also inactivated but a plasmid coding for yeast AAC2 was included, because the ArAACΔsal1Δ strain was lethal. In both strains ArAAC-HA was expressed and correctly localized to the mitochondria. Peptide sequencing of ArAAC expressed in Artemia and that expressed in the modified yeasts revealed identical amino acid sequences. The isolated mitochondria from both modified strains developed 85% of the membrane potential attained by mitochondria of control strains, and addition of ADP yielded bongkrekate-sensitive depolarizations implying acquired sensitivity of ArAAC-mediated adenine nucleotide exchange to this poison, independent from SAL1. However, growth of ArAAC-expressing yeasts in glycerol-containing media was arrested by bongkrekate only in the presence of SAL1. We conclude that the mitochondrial environment of yeasts relying on respiratory growth conferred sensitivity of ArAAC to bongkrekate in a SAL1-dependent manner. © 2013 Wysocka-Kapcinska et al

Ten years of external quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in Europe elucidate high reliability of data

BACKGROUND: Confidence in any diagnostic and antimicrobial susceptibility testing data is provided by appropriate and regular quality assurance (QA) procedures. In Europe, the European Gonococcal Antimicrobial Susceptibility Programme (Euro-GASP) has been monitoring the antimicrobial susceptibility in Neisseria gonorrhoeae since 2004. Euro-GASP includes an external quality assessment (EQA) scheme as an essential component for a quality-assured laboratory-based surveillance programme. Participation in the EQA scheme enables any problems with the performed antimicrobial susceptibility testing to be identified and addressed, feeds into the curricula of laboratory training organised by the Euro-GASP network, and assesses the capacity of individual laboratories to detect emerging new, rare and increasing antimicrobial resistance phenotypes. Participant performance in the Euro-GASP EQA scheme over a 10 year period (2007 to 2016, no EQA in 2013) was evaluated. METHODS: Antimicrobial susceptibility category and MIC results from the first 5 years (2007-2011) of the Euro-GASP EQA were compared with the latter 5 years (2012-2016). These time periods were selected to assess the impact of the 2012 European Union case definitions for the reporting of antimicrobial susceptibility. RESULTS: Antimicrobial susceptibility category agreement in each year was ≥91%. Discrepancies in susceptibility categories were generally because the MICs for EQA panel isolates were on or very close to the susceptibility or resistance breakpoints. A high proportion of isolates tested over the 10 years were within one (≥90%) or two (≥97%) MIC log2 dilutions of the modal MIC, respectively. The most common method used was Etest on GC agar base. There was a shift to using breakpoints published by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in the latter 5 years, however overall impact on the validity of results was limited, as the percentage categorical agreement and MIC concordance changed very little between the two five-year periods. CONCLUSIONS: The high level of comparability of results in this EQA scheme indicates that high quality data are produced by the Euro-GASP participants and gives confidence in susceptibility and resistance data generated by laboratories performing decentralised testing.The study was funded by the European Centre for Disease Prevention and Control (Framework Contract No. ECDC/2013/015). The funding body contributed to the design of the study, the interpretation of the data and to the writing of the manuscript.S

Assessing machine learning for diagnostic classification of hypertension types identified by ambulatory blood pressure monitoring

Background: Inaccurate blood pressure classification results in inappropriate treatment. We tested if machine learning (ML), using routine clinical data, can serve as a reliable alternative to Ambulatory Blood Pressure Monitoring (ABPM) in classifying blood pressure status. Methods: This study employed a multi-centre approach involving three derivation cohorts from Glasgow, Gdańsk, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into five groups: Normal/Target, Hypertension-Masked, Normal/Target-White-Coat, Hypertension-White-Coat, and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model. Results: Overall XGBoost showed the highest AUROC of 0.85-0.88 across derivation cohorts, Glasgow (n=923; 43% females; age 50.7±16.3 years), Gdańsk (n=709; 46% females; age 54.4±13 years), and Birmingham (n=1,222; 56% females; age 55.7±14 years). But accuracy (0·57-0·72) and F1 scores (0·57-0·69) were low across the three patient cohorts. The evaluation cohort (n=6213, 51% females; age 51.2±10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-White-Coat and Hypertension-White-Coat groups, with heightened 27-year all-cause mortality observed in all groups except Hypertension-Masked, compared to the Normal/Target group. Conclusions: Machine learning has limited potential in accurate blood pressure classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted