Quantification Of Variation Research Articles

Interreader and Intermodality Variability in Macular Atrophy Quantification in Neovascular Age-related Macular Degeneration: Comparison of 6 Imaging Modalities

Macular atrophy is a common complication in neovascular age-related macular degeneration (AMD) and is associated with poorer visual outcomes. This study evaluated interreader and intermodality variability in measuring macular atrophy in previously treated neovascular AMD eyes without exudation using 6 imaging modalities. Prospective, cohort study. Thirty participants with previously treated neovascular AMD showing no signs of exudation at the time of enrollment and exhibiting macular atrophy. During the same clinic visit, patients were imaged using 6 different imaging modalities: color fundus photography (CFP; Clarus, Carl Zeiss Meditec), near-infrared imaging (NIR; Spectralis, Heidelberg Engineering), structural OCT (Spectralis, Heidelberg Engineering), green fundus autofluorescence (GAF; Clarus, Carl Zeiss Meditec), blue fundus autofluorescence (BAF; Spectralis, Heidelberg Engineering), and pseudocolor imaging (MultiColor; Spectralis, Heidelberg Engineering). Two readers independently measured the macular atrophy area. Interreader and intermodality agreement. The 95% coefficient of repeatability was 5.98 mm2 for CFP, 4.46 mm2 for MultiColor, 3.90 mm2 for BAF, 3.92 mm2 for GAF, 4.86 mm2 for NIR, and 3.55 mm2 for OCT. Similarly, the coefficient of variation was lowest for OCT-based grading at 0.08 and highest for NIR-based grading at 0.28. Accordingly, the intraclass correlation coefficient was 0.742 for CFP, 0.805 for MultiColor, 0.857 for BAF, 0.850 for GAF, 0.755 for NIR, and 0.917 for OCT. The 6 different imaging modalities presented measurements with different mean values, with only a limited number of comparisons not significantly different between the instruments, although measurements were correlated. The largest size of macular atrophy was measured with structural OCT-based grading (median= 4.65 mm2; interquartile range [IQR]= 4.78 mm2) and the smallest was with CFP-based grading (median= 3.86 mm2; IQR= 5.06 mm2). Inconsistencies arose from various factors. In patients with neovascular AMD, macular atrophy measurements vary significantly depending on the imaging technique used. Color fundus photography-based assessments yielded the smallest macular atrophy sizes, whereas structural OCT-based assessments produced the largest. These discrepancies stem from both the inherent limitations of each modality in assessing retinal pigment epithelial atrophy and factors related to neovascularization, such as the coexistence of fibrosis. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

Interlaboratory Comparison of Branched GDGT Temperature and pH Proxies Using Soils and Lipid Extracts

AbstractRatios of glycerol dialkyl glycerol tetraethers (GDGT), which are membrane lipids of bacteria and archaea, are at the base of several paleoenvironmental proxies. They are frequently applied to soils as well as lake‐ and marine sediments to generate records of past temperature and soil pH. To derive meaningful environmental information from these reconstructions, high analytical reproducibility is required. Based on submitted results by 39 laboratories from across the world, which employ a diverse range of analytical and quantification methods, we explored the reproducibility of brGDGT‐based proxies (MBT′5ME, IR, and #ringstetra) measured on four soil samples and four soil lipid extracts. Correct identification and integration of 5‐ and 6‐methyl brGDGTs is a prerequisite for the robust calculation of proxy values, but this can be challenging as indicated by the large inter‐interlaboratory variation. The exclusion of statistical outliers improves the reproducibility, where the remaining uncertainty translates into a temperature offset from median proxy values of 0.3–0.9°C and a pH offset of 0.05–0.3. There is no apparent systematic impact of the extraction method and sample preparation steps on the brGDGT ratios. Although reported GDGT concentrations are generally consistent within laboratories, they vary greatly between laboratories. This large variability in brGDGT quantification may relate to variations in ionization efficiency or specific mass spectrometer settings possibly impacting the response of brGDGTs masses relative to that of the internal standard used. While ratio values of GDGT are generally comparable, quantities can currently not be compared between laboratories.

An international phantom study of inter-site variability in Technetium-99m image quantification: analyses from the TARGET radioembolization study

BackgroundPersonalised multi-compartment dosimetry based on [99mTc]Tc-MAA is a valuable tool for planning 90Y radioembolization treatments. The establishment and effective application of dose–effect relationships in yttrium-90 (90Y) radioembolization requires [99mTc]Tc-MAA SPECT quantification ideally independent of clinical site. The purpose of this multi-centre phantom study was to evaluate inter-site variability of [99mTc]Tc-MAA imaging and evaluate a standardised imaging protocol. Data was obtained from the TARGET study, an international, retrospective multi-centre study including 14 sites across 8 countries. The impact of imaging related factors was estimated using a NEMA IQ phantom (representing the liver), and a uniformly filled cylindrical phantom (representing the lungs). Imaging was performed using site-specific protocols and a standardized protocol. In addition, the impact of implementing key image corrections (scatter and attenuation correction) in the site-specific protocols was investigated. Inter-site dosimetry accuracy was evaluated by comparing computed Lung Shunt Fraction (LSF) measured using planar imaging of the cylindrical and NEMA phantom, and contrast recovery coefficient (CRC) measured using SPECT imaging of the NEMA IQ phantom.ResultsRegarding the LSF, inter-site variation with planar site-specific protocols was minimal, as determined by comparing computed LSF between sites (interquartile range 9.6–10.1%). A standardised protocol did not improve variation (interquartile range 8.4–9.0%) but did improve mean accuracy compared to the site-specific protocols (5.0% error for standardised protocol vs 8.8% error for site-specific protocols). Regarding the CRC, inter-system variation was notable for site-specific SPECT protocols and could not be improved by the standardised protocol (CRC interquartile range for 37 mm sphere 0.5–0.7 and 0.6–0.8 respectively), however the standardised protocol did improve accuracy of sphere:background determination. Implementation of key image corrections did improve inter-site variation (CRC interquartile range for 37 mm sphere 0.6–0.7).ConclusionEliminating sources of variability in image corrections between imaging protocols reduces inter-site variation in quantification. A standardised protocol was not able to improve consistency of LSF or CRC but was able to improve accuracy.

Ensemble Smoother with Fully Convolutional VAE for seismic facies inversion

Seismic facies inversion is an important process in the oil and gas industry to estimate subsurface geological facies or rock types based on seismic data. Recently, the Ensemble Smoother with Multi Data Assimilation (ES-MDA) has shown great success in solving complex inverse problems by generating an ensemble of solutions of the model variables for uncertainty quantification. However, there are significant challenges related to the computational cost, Gaussian assumptions, and its application for categorical model variables. Recent developments have proposed to integrate ES-MDA with Variational Autoencoder (VAE) to reparametrize facies into a continuous feature space and reduce computational costs. Despite promising results, it has introduced new limitations concerning network convergence, spatial correlation loss, and manipulation of the latent space. In this paper, a Fully Convolutional Variational Autoencoder (FCVAE) was proposed to outperform the VAE in terms of preserving spatial information, requiring fewer network parameters, presenting higher dimension reduction and yielding better facies estimation. The objective of the application example is to draw sound conclusions based on a 2D synthetic application, where the results of both methods can be compared with reference facies for quality evaluation.

Comparative evaluation for the performance of environmental DNA and RNA analyses targeting mitochondrial and nuclear genes from ayu (Plecoglossus altivelis).

Environmental DNA and RNA (eDNA and eRNA; collectively eNA) analyses have the potential for non-invasive and cost-efficient biomonitoring compared with traditional capture-based surveys. Although various types of eNA particles, including not only mitochondrial eDNA but also nuclear eDNA and their transcripts, are present in the water, performances of eNA detection and quantification have not yet been evaluated sufficiently across multiple mitochondrial and nuclear genes. We conducted a tank experiment with ayu (Plecoglossus altivelis) to compare the detection sensitivity, yields per water sample, and quantification variability between replicates of each type of eNAs. The assay targeting the multi-copy nuclear gene exhibited a higher sensitivity than the assay targeting the mitochondrial gene, and both the target eDNA and eRNA concentrations per water sample were higher for the nuclear gene. On the contrary, variation in eRNA quantifications per sample does not necessarily correspond to that in eDNA, and the intra-sample quantification variability (represented as the CVs between PCR replicates) tended to be larger for eRNA than eDNA. Our results suggested that, even if suitable to the sensitive detection of species occurrence, the use of eRNA particularly derived from multi-copy nuclear gene may not be necessarily appropriate for the reliable assessment of species abundance. The findings in this study would help optimize eNA analyses for making biomonitoring and stock assessment in aquatic environments more efficient and reliable.

Location-Aware Encoding for Lesion Detection in 68Ga-DOTATATE Positron Emission Tomography Images.

Lesion detection with positron emission tomography (PET) imaging is critical for tumor staging, treatment planning, and advancing novel therapies to improve patient outcomes, especially for neuroendocrine tumors (NETs). Current lesion detection methods often require manual cropping of regions/volumes of interest (ROIs/VOIs) a priori, or rely on multi-stage, cascaded models, or use multi-modality imaging to detect lesions in PET images. This leads to significant inefficiency, high variability and/or potential accumulative errors in lesion quantification. To tackle this issue, we propose a novel single-stage lesion detection method using only PET images. We design and incorporate a new, plug-and-play codebook learning module into a U-Net-like neural network and promote lesion location-specific feature learning at multiple scales. We explicitly regularize the codebook learning with direct supervision at the network's multi-level hidden layers and enforce the network to learn multi-scale discriminative features with respect to predicting lesion positions. The network automatically combines the predictions from the codebook learning module and other layers via a learnable fusion layer. We evaluate the proposed method on a real-world clinical 68Ga-DOTATATE PET image dataset, and our method produces significantly better lesion detection performance than recent state-of-the-art approaches. We present a novel deep learning method for single-stage lesion detection in PET imaging data, with no ROI/VOI cropping in advance, no multi-stage modeling and no multi-modality data. This study provides a new perspective for effective and efficient lesion identification in PET, potentially accelerating novel therapeutic regimen development for NETs and ultimately improving patient outcomes including survival.

Standardization of flow cytometric detection of antigen expression.

Since response to antigen-based immunotherapy relies upon the level of tumor antigen expression we developed an antigen quantification assay using ABC values. Antigen quantification as a clinical assay requires methods for quality control and for interlaboratory and inter-cytometer platform standardization. A single lot of Cytotrol™ Lyophilized Control Cells (Beckman Coulter) used for all studies. The variability in antigen quantification across 4 different instrument platforms in 2 separate laboratories was evaluated. The effect of the antibody clone utilized, importance of custom 1:1 molar ratio (fluorophore to protein, F/P) verses off-the-shelf antibodies, and QuantiBrite PE calibration verses linearity calibration combined with a single point scale transformation with CD4 as reference were determined. Use of single lot control cells allowed validation of reproducibility between flow cytometer platforms and laboratories and allowed assessment of different antibody lots, cocktail preparation, and different antibody clones. Off the shelf antibody preparations provide reproducible estimates of antigen density, however custom 1:1 unimolar antibody preparations should be utilized for definitive measurement of antigen expression.Geometric Mean fluorescent Intensity (GeoMFI) was not comparable across instruments and inter-laboratory. The use of CD4 as the reference marker can minimize variability in ABC values. Comparable antigen quantification is vital in managing patients receiving antigen-based immunotherapy. If this assay is to be utilized in a clinical setting, quality control methods have to be instituted to assure reproducibility and allow validation across laboratories. We have demonstrated that use of a lyophilized cell control is highly valuable in achieveing these goals.

Aortic regurgitation in bicuspid aortic valve: know to decide

Abstract Funding Acknowledgements Type of funding sources: None. Chronic aortic regurgitation (AR) is the second cause of valve surgery in patients with bicuspid aortic valve (BAV) and has distinctive characteristics of trileaflet regurgitation. The quantification variables of aortic regurgitation do not take into account the unique characteristics of bicuspid regurgitation. Recognition of the mechanisms that lead to bicuspid AR is relevant as it allows correct treatment planning and valve repair in candidate patients. Objective a prospective study was designed to identify the mechanisms of AR and the factors (clinical and anatomical) associated with the development of significant AR (moderate/severe) in adult patients with BAV. Materials and methods We included consecutive adult patients with a confirmed diagnosis of BAV evaluated between 2014 and 2021 with at least 3 years of follow-up by the Structural Heart Disease group of our institution. Valve disease was evaluated by echocardiography and complementary methods. It was classified as mild, moderate (Mod), and severe (Sev) aortic regurgitation, according to current criteria (integral approach with quantitative and semi-quantitative criteria). The prevalence of regurgitation mechanisms, the progression of aortic regurgitation, and predictive variables of significant AR and major cardiovascular events (death, surgery) were analyzed using univariate and multivariate analyses. A p<0.05 was considered significant. Surgery was indicated following international recommendations (ESC guidelines): symptoms development or ventricular dysfunction. Results We included 300 patients (39.7 ± 14.9 years, 72.6% men). Regarding valvular phenotype 84% (257p) presented fused BAV (coronary leaflets) and the majority with raphe (87.5%). 71.7% of the patients had some degree of aortic regurgitation at the start of follow-up (24.1% Mod/Sev AR). Among the patients with significant AR, 50% had cusp prolapse (86% prolapse of the fused leaflet), 39.18% dilatation of the aortic root, 26.6% significant calcification of the valve, 1.3% valve perforation and 29.4% restriction of valve movement. 43% with Mod/Sev AR had mixed mechanisms of regurgitation and the combination, half represented by the combination of aortic root dilatation and cusp prolapse (Fig 2). During follow-up 18–6% of patients with Mod AR showed progression to Sev AR and 4.7% underwent surgery. On the other hand, 34.1% of patients with baseline Sev AR required aortic valve surgery (p<0.01). Conclusions In our cohort, AR was highly prevalent: 1 out of 4 patients had significant AR at the start of follow-up. Eccentric jets and the presence of valve prolapse were frequent in patients with significant AR. 43% of BAV patients with Mod/Sev AR exhibited mixed regurgitating mechanisms. Because mechanism affects the choice of surgical technique, comprehensive mechanism description is critical in patients with BAV and AR.

Variability in PET image quality and quantification measured with a permanently filled 68Ge-phantom: a multi-center study

BackgroundThis study evaluated, as a snapshot, the variability in quantification and image quality (IQ) of the clinically utilized PET [18F]FDG whole-body protocols in Finland using a NEMA/IEC IQ phantom permanently filled with 68Ge.MethodsThe phantom was imaged on 14 PET-CT scanners, including a variety of models from two major vendors. The variability of the recovery coefficients (RCmax, RCmean and RCpeak) of the hot spheres as well as percent background variability (PBV), coefficient of variation of the background (COVBG) and accuracy of corrections (AOC) were studied using images from clinical and standardized protocols with 20 repeated measurements. The ranges of the RCs were also compared to the limits of the EARL 18F standards 2 accreditation (EARL2). The impact of image noise on these parameters was studied using averaged images (AVIs).ResultsThe largest variability in RC values of the routine protocols was found for the RCmax with a range of 68% and with 10% intra-scanner variability, decreasing to 36% when excluding protocols with suspected cross-calibration failure or without point-spread-function (PSF) correction. The RC ranges of individual hot spheres in routine or standardized protocols or AVIs fulfilled the EARL2 ranges with two minor exceptions, but fulfilling the exact EARL2 limits for all hot spheres was variable. RCpeak was less dependent on averaging and reconstruction parameters than RCmax and RCmean. The PBV, COVBG and AOC varied between 2.3–11.8%, 9.6–17.8% and 4.8–32.0%, respectively, for the routine protocols. The RC ranges, PBV and COVBG were decreased when using AVIs. With AOC, when excluding routine protocols without PSF correction, the maximum value dropped to 15.5%.ConclusionThe maximum variability of the RC values for the [18F]FDG whole-body protocols was about 60%. The RC ranges of properly cross-calibrated scanners with PSF correction fitted to the EARL2 RC ranges for individual sphere sizes, but fulfilling the exact RC limits would have needed further optimization. RCpeak was the most robust RC measure. Besides COVBG, also RCs and PVB were sensitive to image noise.

Fetal Echocardiographic Z Score Pilot Project: Study Design and Impact of Gestational Age and Variable Type on Reproducibility of Measurements Within and Across Investigators

Fetal echocardiography is widely available, but normative data are not robust. In this pilot study, the authors evaluated (1) the feasibility of prespecified measurements in a normal fetal echocardiogram to inform study design and (2) measurement variability to assign thresholds of clinical significance and guide analyses in larger fetal echocardiographic Z score initiatives. Images from predefined gestational age groups (16-20, >20-24, >24-28, and >28-32weeks) were retrospectively analyzed. Fetal echocardiography expert raters attended online group training and then independently analyzed 73 fetal studies (18 per age group) in a fully crossed design of 53 variables; each observer repeated measures for 12 fetuses. Kruskal-Wallis tests were used to compare measurements across centers and age groups. Coefficients of variation (CoVs) were calculated at the subject level for each measurement as the ratio of SD to mean. Intraclass correlation coefficients were used to show inter- and intrarater reliabilities. Cohen's d>0.8 was used to define clinically important differences. Measurements were plotted against gestational age, biparietal diameter, and femur length. Expert raters completed each set of measurements in a mean of 23±9 min/fetus. Missingness ranged from 0% to 29%. CoVs were similar across age groups for all variables (P<.05) except ductus arteriosus mean velocity and left ventricular ejection time, which were both higher at older gestational age. CoVs were >15% for right ventricular systolic and diastolic widths despite fair to good repeatability (intraclass correlation coefficient>0.5); ductal velocities and two-dimensional measures, left ventricular short-axis dimensions, and isovolumic times all had high CoVs and high interobserver variability despite good to excellent intraobserver agreement (intraclass correlation coefficient>0.6). CoVs did not improve when ratios (e.g., tricuspid/mitral annulus) were used instead of linear measurements. Overall, 27 variables had acceptable inter- and intraobserver repeatability, while 14 had excessive variability between readers despite good intraobserver agreement. There is considerable variability in fetal echocardiographic quantification in clinical practice that may affect the design of multicenter fetal echocardiographic Z score studies, and not all measurements may be feasible for standard normalization. As missingness was substantial, a prospective design will be needed. Data from this pilot study may aid in the calculation of sample sizes and inform thresholds for distinguishing clinically significant from statistically significant effects.

Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface?

Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible–near-infrared–shortwave infrared (VNIR–SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR–SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSEp values of 7.82, 8.03 and 9.22 mg·kg−1, and for Ni: RMSEp values of 9.88, 10.32 and 11.02 mg·kg−1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR–SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, especially in heterogeneous soil environments.

Data-Driven Method to Quantify Correlated Uncertainties

Polynomial chaos (PC) has been proven to be an efficient method for uncertainty quantification, but its applicability is limited by two strong assumptions: the mutual independence of random variables and the requirement of exact knowledge about the distribution of the random variables. We describe a new data-driven method for dealing with correlated multivariate random variables for uncertainty quantification that requires only observed data of the random variables. It is based on the transformation of correlated random variables into independent random variables. We use singular value decomposition as a transformation strategy that does not require information about the probability distribution. For the transformed random variables, we can construct the PC basis to build a surrogate model. This approach provides an additional benefit of quantifying high-dimensional uncertainties by combining our method with the analysis-of-variance (ANOVA) method. We demonstrate in several numerical examples that our proposed approach leads to accurate solutions with a much smaller number of simulations compared to the Monte Carlo method.

Evaluation of intra- and inter-lab variability in quantifying SARS-CoV-2 in a state-wide wastewater monitoring network.

In December 2019, SARS-CoV-2, the virus that causes coronavirus disease 2019, was first reported and subsequently triggered a global pandemic. Wastewater monitoring, a strategy for quantifying viral gene concentrations from wastewater influents within a community, has served as an early warning and management tool for the spread of SARS-CoV-2 in a community. Ohio built a collaborative statewide wastewater monitoring network that is supported by eight labs (university, government, and commercial laboratories) with unique sample processing workflows. Consequently, we sought to characterize the variability in wastewater monitoring results for network labs. Across seven trials between October 2020 and November 2021, eight participating labs successfully quantified two SARS-CoV-2 RNA targets and human fecal indicator virus targets in wastewater sample aliquots with reproducible results, although recovery efficiencies of spiked surrogates ranged from 3 to 75%. When SARS-CoV-2 gene fragment concentrations were adjusted for recovery efficiency and flow, the proportion of variance between laboratories was minimized, serving as the best model to account for between-lab variance. Another adjustment factor (alone and in different combinations with the above factors) considered to account for sample and measurement variability includes fecal marker normalization. Genetic quantification variability can be attributed to many factors, including the methods, individual samples, and water quality parameters. In addition, statistically significant correlations were observed between SARS-CoV-2 RNA and COVID-19 case numbers, supporting the notion that wastewater surveillance continues to serve as an effective monitoring tool. This study serves as a real-time example of multi-laboratory collaboration for public health preparedness for infectious diseases.

Intra- and Inter-Observer Variability of Quantitative Parameters Used in Contrast-Enhanced Ultrasound of Kidneys of Healthy Cats.

Contrast-enhanced ultrasound (CEUS) is a non-invasive imaging technique which allows qualitative and quantitative assessment of tissue perfusion. Although CEUS offers numerous advantages, a major challenge remains the variability in tissue perfusion quantification. This study aimed to assess intra- and inter-observer variability for quantification of renal perfusion. Two observers with different levels of expertise performed a quantitative analysis of 36 renal CEUS studies, twice. The CEUS data were collected from 12 healthy cats at 3 different time points with a 7-day interval. The inter- and intra-observer agreement was assessed by the intraclass correlation coefficient. Within and between observers, a good agreement was demonstrated for intensity-related parameters in the cortex, medulla, and interlobular artery. For some parameters, ICCinter was considerably lower than ICCintra, mostly when the ROI encompassed the entire kidney or medulla. With the exception of time to peak (TTP) and mean transit time (mTTI), time-related and slope-related parameters showed poor agreement among observers. In conclusion, it may be advised against having the quantitative assessment of renal perfusion performed by different observers, especially if their experience levels differ. The cortical mTTI seemed to be the most appropriate parameter as it showed a favorable inter-observer agreement and inter-period agreement.

Characterization and genetic diversity in Paspalum notatum Flügge accessions: Morphological and geographical distance

The objective of this work was to describe morphology and grouping of Paspalum notatum accessions, based on multicategorical data which discards the redundant variables for quantification of genetic diversity. We also tested the hypothesis that geographical distance was correlated with morphological divergence. In our study, multivariate analyzes successfully demonstrated the geographic and morphological variability of the P. notatum accessions characterized. Many of these evaluated accessions can be included in future genetic improvement programs. Based on two methodologies for discarding [...]

Assessing the Variability of Cell-Associated HIV DNA Quantification through a Multicenter Collaborative Study.

ABSTRACTReliable and accurate quantification of cell-associated HIV DNA (CA HIV DNA) is critical for early infant diagnosis, clinical management of patients under therapy, and to inform new therapeutics efficacy. The present study assessed the variability of CA HIV DNA quantification obtained from various assays and the value of using reference materials to help harmonize the measurements. Using a common set of reagents, our multicenter collaborative study highlights significant variability of CA HIV DNA quantification and lower limit of quantification across assays. The quantification of CA HIV DNA from a panel of infected PBMCs can be harmonized through cross-subtype normalization but assay calibration with the commonly used 8E5 cell line failed to reduce quantification variability between assays, demonstrating the requirement to thoroughly evaluate reference material candidates to help improve the comparability of CA HIV DNA diagnostic assay performance.IMPORTANCE Despite a global effort, HIV remains a major public health burden with an estimated 1.5 million new infections occurring in 2020. HIV DNA is an important viral marker, and its monitoring plays a critical role in the fight against HIV: supporting diagnosis in infants and underpinning clinical management of patients under therapy. Our study demonstrates that HIV DNA measurement of the same samples can vary significantly from one laboratory to another, due to heterogeneity in the assay, protocol, and reagents used. We show that when carefully selected, reference materials can reduce measurement variability and harmonize HIV DNA quantification across laboratories, which will help contribute to improved diagnosis and clinical management of patients living with HIV.

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.

BackgroundComposition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists.ObjectiveThis study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks.MethodsA data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices.ResultsA poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality.ConclusionsThe interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale.

Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge.

To explore the relationship between novel, time-varying predictors for healthcare delivery strain (eg, counts of patient orders per hour) and imminent discharge and in-hospital mortality. We conducted a retrospective cohort study using data from adults hospitalized at 21 Kaiser Permanente Northern California hospitals between November 1, 2015 and October 31, 2020 and the nurses caring for them. Patient data extracted included demographics, diagnoses, severity measures, occupancy metrics, and process of care metrics (eg, counts of intravenous drip orders per hour). We linked these data to individual registered nurse records and created multiple dynamic, time-varying predictors (eg, mean acute severity of illness for all patients cared for by a nurse during a given hour). All analyses were stratified by patients' initial hospital unit (ward, stepdown unit, or intensive care unit). We used discrete-time hazard regression to assess the association between each novel time-varying predictor and the outcomes of discharge and mortality, separately. Our dataset consisted of 84162161 hourly records from 954477 hospitalizations. Many novel time-varying predictors had strong associations with the 2 study outcomes. However, most of the predictors did not merely track patients' severity of illness; instead, many of them only had weak correlations with severity, often with complex relationships over time. Increasing availability of process of care data from automated electronic health records will permit better quantification of healthcare delivery strain. This could result in enhanced prediction of adverse outcomes and service delays. New conceptual models will be needed to use these new data elements.

Intra-specific Variability in Anti-Diabetic Activity and UPLC Quantification of Oleanolic Acid from Two Morphotypes and Three Cytotypes of Achyranthes aspera

Achyranthes aspera is an economically important medicinal plant, exhibiting a wide range of cytomorphological diversity. Taking into account, its pharmacological activities, as well as variability, found within the species, the present attempt was made to study the detailed meiotic course in 13 plant samples collected from different areas of North India. The anti-diabetic activity of three different cytotypes (diploid, tetraploid, and hexaploid) and two different morphotypes (obovate-leaf morphotype and ovate-leaf morphotype) was investigated in alloxan-induced Swiss albino male rats. Additionally, phytochemical analysis of aerial parts of these cytotypes and morphotypes was performed using the UPLC technique. The meiotic analysis depicts the presence of three cytotypes with 2n=14, 28, and 42. The results indicated that the diploid cytotype of A. aspera has maximum activity against diabetes in alloxan-induced diabetic rats. Out of the two morphotypes, the obovate-leaf morphotype showed a potent anti-diabetic effect as compared to the ovate-leaf morphotype. UPLC results showed that the maximum percentage of active component oleanolic acid was present in diploid cytotype followed by tetraploid and hexaploid cytotypes. A comparative study of the two morphotypes within the tetraploid cytotype revealed that the obovate-leaf morphotype possessed a higher percentage of oleanolic acid as compared to the ovate-leaf morphotype.