Concordance Correlation Research Articles

Comparative assessment of breast volume using a smartphone device versus MRI.

Assessment of breast volume has a relevance for aesthetic surgery and for the prevention and prediction of breast diseases. This study investigated breast volume measurements using a three-dimensional (3D) body surface scanner integrated in a smartphone device in comparison with magnetic resonance imaging (MRI) scans. Breast volume was assessed for 22 women who underwent routine MRI imaging. 3D surface images were acquired using a smartphone's digital texture camera (iPhone 11 Pro Max, Apple, California, USA, 2019). Breast volumes were manually outlined and calculated by two independent investigators using a 3D software tool (Meshmixer 3.5, Autodesk, Inc., 2018). Volume assessments from MRI images were performed by a radiologist using Syngo.via (Siemens Healthcare, Erlangen, Germany, VB50). The agreement between both methods and the inter-observer agreement was calculated with the concordance correlation coefficients and analysed with Bland-Altman plots. The mean breast volume as determined by MRI volumetry was 771.0ml on the left side and 763.9ml on the right side. Utilizing the 3D body surface volume assessment method, the mean breast volume was measured as 660.3ml (observer A) and 616.8ml (observer B) on the left side, and 701.9ml (observer A) and 638.6ml (observer B) on the right side. Although a high correlation was observed, differences in volume measurements appeared more pronounced in cases of larger breast volume. Smartphone-based 3D assessment of breast volume sufficiently agreed with MRI-based breast volume. This new technique could be used for cosmetic breast assessments in a surgical context and possibly in breast cancer risk studies assessing breast volume as outcome parameters.

The Motility Ratio method as a novel approach to qualify semen assessment.

Many scientific studies often assumed that the most reliable methods for assessing sperm motility are those that give the highest values, and this leads to misinterpretation of the results. This study aims to propose an objective method to validate sperm motility reliability. Bovine and porcine semen samples were split into two equal fractions. Fraction A was kept alive with a motile population considered at maximum proportion, while fraction B was killed with 0% motile population. A range of motile/non motile sperm was performed by mixing both fractions. The Motility Ratio method, comparing measured and theoretical motility, was validated using LEJA slide and IVOS II and applied to other slides. All slides showed strong Concordance Correlation Coefficient between measured and theoretical motility. However, with IVOS II, LEJA slide showed the lowest bias (< 1) while MAKLER or coverslip showed higher bias (> 2 and > 7 respectively) between measured and theoretical motility. This study shows that the best sperm motility is not always the true motility and highlights the importance of implementing a gold standard methodology for motility reliability such as The Motility Ratio method.

Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology. The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO3 and Sheather's sugar solution. Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO3 solution and 100.0% for samples prepared with Sheather's sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Lin's concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924-0.978 for strongyles and 0.944-0.955 for Parascaris spp., depending on the flotation solution. Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings.

The Agreement of Intraocular Pressure Measurement in Healthy Merinos Sheep Using Rebound Tonometer (Tonovet®) and Applanation Tonometer (Tono-Pen VetTM)

The assessment of intraocular pressure (IOP) holds significant importance in ophthalmology as a crucial diagnostic tool for various ocular disorders. In the realm of comparative ophthalmology research, investigating ocular examination in sheep, particularly Merinos, is of considerable interest. This study aimed to evaluate the agreement between rebound (TonoVet®, TV) and applanation (Tono-Pen Vet™, TPV) tonometers in measuring IOP in healthy Merino sheep. 155 healthy Merinos (80 males, 75 females) with a mean weight of 54.4 ± 8.7 kg, aged 24 ± 6 months, were included in the study. IOP was measured between 9:00 and 11:00 am using both the rebound and applanation tonometers. The rebound tonometer was utilized first, followed by the applanation tonometer. A total of 620 readings (310 readings per eye) were obtained from the two devices. No statistical differences were noted between the mean IOP measurements of the right and left eyes for both tonometers (P &gt; 0.05). However, there was a significant difference in the mean IOP measurements between the TV (11.8 ± 2.3 mmHg) and the TPV (13.9 ± 2.9 mmHg) tonometers (P &lt; 0.001). The concordance correlation coefficient indicated weak agreement strength (ρc = 0.319, CI 95% = -0.169 to 0.455) between the TV and TPV. The mean difference in bias and the 95% limits of agreement for the differences between TV and TPV were -2.1 mmHg (-9.0 to 3.5 mmHg). The regression equation derived from a Bland-Altman plot, describing the relationship between the two tonometers, was Y = 1.43 - 0.33X (Y = TV and X = TPV). In conclusion, the TPV measured higher IOP values compared to the TV, and due to the significant bias and limits of agreement, the two tonometers should not be used interchangeably for IOP measurement in Merino sheep.

Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson ARTEMIS trial (ClinicalTrials.gov, NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient (CCC), and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC). Results The study included 118 female patients (median age, 51 [range, 29-78] years). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the CCCs were 0.95 (95% CI: 0.90-0.98) and 0.94 (95% CI: 0.87-0.97), respectively. CNN-predicted TTC and TTV had an AUC of 0.72 (95% CI: 0.34-0.94) and 0.72 (95% CI: 0.40-0.95) for predicting tumor status at the time of surgery, respectively. Conclusion Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient's tumor during NAC using only pre-NAC MRI data. ©RSNA, 2024.

Interrater reliability of RheuMetric checklist scales for Physician Global Assessment, inflammation, damage and patient distress

Abstract Objective To analyze interrater reliability of four RheuMetric checklist 0–10 visual numerical scales (VNS) of physician global assessment (DOCGL), inflammation or reversible findings (DOCINF), organ damage or irreversible findings (DOCDAM), and patient distress or findings explained by fibromyalgia and/or depression (DOCDIS). Methods A retrospective study was performed of data from a rheumatology fellows’ continuity clinic at Rush University. Each rheumatology patient with any diagnosis seen in routine care completed a multidimensional health assessment questionnaire (MDHAQ). Both the rheumatology fellow and attending rheumatologist independently completed RheuMetric estimates at the same visit for DOCGL, DOCINF, DOCDAM, DOCDIS, and the proportion of DOCGL explained by each subglobal estimate (totaling 100%). Agreement between the two assessors was compared using paired t-tests, Spearman correlation coefficients, intraclass correlation coefficients (ICC), Lin’s concordance correlation coefficients (LCCC), and Bland-Altman plots. Results In 112 patients, mean levels of DOCINF were highest in inflammatory diseases, DOCDAM in osteoarthritis (OA), and DOCDIS in primary fibromyalgia (FM). However, mean DOCDAM was as high as DOCINF in inflammatory diseases. No statistically significant differences were seen between scores from attending rheumatologists and fellows. Agreement within 2/10 units ranged from 60% for DOCGL to 71% for DOICINF and DOCDAM. Spearman correlations were 0.49-0.65, ICCs were 0.46-0.63, LCCC were 0.46-0.62 between rheumatologist and fellow, indicating moderate agreement; reliability was slightly higher for each sub-global VNS than DOCGL. Conclusion RheuMetric 0–10DOCGL, DOCINF, DOCDAM, and DOCDIS have moderate interrater reliability and are feasible in routine care to estimate patient status beyond DOCGL for improved management decisions.

A standard area diagram for Fusarium yellows rating in sugar beet (Beta vulgaris)

AbstractMembers of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield and extractable sugar. Improving host plant resistance against Fusarium‐induced diseases, like Fusarium yellows, represents an important long‐term breeding target in sugar beet breeding programmes. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Version 1 and the improved version 2 SAD showed no statistical differences in Lin's concordance correlation coefficient (LCC) values, which was used to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine‐learning models that used pixel classification to determine disease score were tested for congruency to human estimates in SAD version 2. Root mean square error was lowest compared to the ‘true’ values for the unweighted model and a model where necrotic tissue was given a 2× weight (12.4 and 12.6, respectively). The creation of this SAD enables breeding programmes to make consistent, accurate disease ratings regardless of personnel's previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.

Accuracy of web-based automated versus digital manual cephalometric landmark identification.

The purpose of this study was to assess the accuracy of two web-based automated cephalometric landmark identification and analysis programs. Manual landmark identification using Dolphin Imaging software was used as reference. 105 cephalograms were selected and divided into three groups of 35 subjects each, Class I, II and III. Radiographs were traced using Dolphin imaging software. WebCeph™ (South Korea) and Cephio™ (Poland) were used for the automated cephalometric analysis. Bland-Altman limits of agreement and the concordance correlation coefficient (CCC) were calculated. Kruskal Wallis test was used to compare the accuracy of WebCeph™ and Cephio™ measurements between the three groups. Mann-Whitney U test was used to compare the absolute difference between cephalometric measurements obtained using WebCeph™ and Cephio™. The mean difference (MD) between AI and manually-derived measurements was less than 1mm/degree and ranged from 0.01 to 0.8 except for upper lip protrusion (MD 1.35°), nasolabial angle (MD 5.01°), SN-GoGn (MD 1.41°), Ramus height (MD 1.46°), and IMPA (MD 1.94°). The mean CCC was 0.91 (range 0.60 to 0.96). No statistically significant differences were found between the three malocclusion groups for most of the measurements (P > 0.05). For most of the measurements, automated cephalometric measurements were clinically acceptable. Few differences were found between Webceph™ and Cephio™ for most measurements. Measurements including SNA, SN-PP, IMPA as well as soft tissue measurements require extra consideration and manual adjustment of respective landmarks for higher precision and improved efficiency.

Exploring the impact of filament density on the responsiveness of 3D-Printed bolus materials for high-energy photon radiotherapy

Background3D-printed boluses in radiation therapy receive consideration for their ability to enhance treatment precision and patient comfort. Yet, thorough validation of 3D-printed boluses using various validation procedures and statistical analysis is missing. This study aims to determine the effectiveness of using 3D-printed boluses in radiation therapy. MethodThe CT Hounsfield Unit (HU) profiles of the 3D-printed materials were compared to those of the commercial bolus using the Eclipse Treatment Planning System (TPS) unit. Furthermore, absolute dose measurements were carried out to assess the efficacy of the 3D-printed samples by using concordance correlation coefficient to assess the agreement between 3D materials and the commercial bolus. ResultsThe average HU profiles of 3D-printed materials were: −144.53 (ABS), −124.40 (ASA), 9.55 (PLA-P), −140.79 (Polycarbonate), −68.58 (PLA-S), and −113.159 (PET-G), respectively. PDD scans showed that air gaps between the bolus and surface shifted the maximum dose depth. Whereas dosimetry has shown that ASA and Polycarbonate are different in attenuation from other tested filaments. This limitation could affect their performance in specific applications within radiation therapy. The final analysis, using the TPS-generated datasets to assess the area under the dose curve in the build-up zone of each 3D-bolus, excluded ABS. ConclusionsThe results from PDD scans and dose assessments offer compelling proof that 3D-printed boluses are effective for delivering surface dosage and are like commercially available boluses. Moreover, specific materials showed a statistically significant improvement in delivering the dose. The results highlight the capability of 3D-printed boluses to enhance the effectiveness of radiation therapy.

Monitoring cattle liveweight using a mobile, in-paddock weigh platform: Validation, attendance and utility

Cattle liveweight (LW) monitoring is essential for the effective management of animal productivity and welfare, particularly in decision-making on farms. Traditional static weigh (SW) systems require animals to be moved to fixed scales, posing challenges in extensive beef systems due to labour demands, costs, stress, and weight loss during muster. This study evaluated the relationship between LW measured by a SW system and a mobile in-field weighing system, Optiweigh (OW), in 65 weaners (Angus, Shorthorn, and Angus-Shorthorn cross) grazing forage oats on a commercial beef property in north-west NSW, Australia. Over 22 weeks, cattle were weighed fortnightly using SW scales in the cattle yards while OW continuously monitored LW in the paddock. Lin's concordance correlation coefficient showed a strong association between OW and SW liveweight (CCC = 0.97; P < 0.001), with no influence of breed or sex. However, OW slightly over-predicted LW for lighter cattle (≤ 382 kg) and under-predicted for heavier cattle (> 382 kg), prompting the development of a correction. Further research is needed to understand the reasons for these discrepancies, potentially related to diet. Additionally, cattle attendance at OW was affected by size, season, and individual variation (P < 0.001). Overall, the OW system simply and accurately monitored the temporal changes in cattle LW through voluntary animal attendance in remote systems.

Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as MtInhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit MtInhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R2)= 0.8789, Cross-validation leave one out correlation coefficient (Q2LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q2LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCCtr)=0.9355, CCCext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their MtInhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the MtInhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and MtInhA protein that may be necessary to develop new lead compounds against tuberculosis.

Bone Scintigraphy in Cardiac Transthyretin-Related Amyloidosis: A Novel Time-Saving Tool for Semiquantitative Analysis, with Good Potential for Predicting Different Etiologies

(1) Background: The visual and semiquantitative analysis of Technetium-99metastable-3,3-diphospono-1,2-propanodicarboxylic acid (99mTc-DPD) bone scintigraphy is promising for diagnosing cardiac amyloidosis but time-consuming. We validated a faster method, the geometric mean (GM) method with a semi-automated workflow, for heart–whole body (WB) ratio (H/WBr), heart retention (Hr), and WB retention (WBr) calculations compared to the classic method (CM) established in the literature. The capability of semiquantitative scintigraphy indexes to differentiate the etiology in transthyretin-related cardiac amyloidosis (cATTR) patients was investigated. (2) Methods: H/WBr, Hr, and WBr were calculated by extracting counts for WB, kidneys, bladder, and heart on early and late planar image scans and applying background, scan-time, and decay corrections, using CM and GM both on a referring workstation and on a semi-automated workflow in external software. The comparison between CM and GM was assessed with Pearson’s correlation, Lin’s Concordance Correlation Coefficient (CCC), and Bland–Altman analysis. H/WBr, Hr, and WBr and several clinical variables were used to implement LASSO, Random Forest (RF), and Neural Network (NN) models to predict mutated and wild-type ATTR etiologies. ROC curves and AUC were calculated. (3) Results: Hr, WBr, and H/WBr using CM and GM were highly correlated. Bland–Altman analysis between CM and GM showed biases of 0.12% [CI:0.04%;0.19%] for H/WBr, 0.07% [CI: 0.01%; 0.13%] for Hr, and -0.50% [CI: −1.22%; 0.22%] for WBr. LASSO and NN models had good performance in predicting etiologies with AUC values of 87.3% and 73.6%, respectively. The RF model showed a poorer AUC of 55.8%. (4) Conclusions: The GM in the assisted workflow was validated against the CM. LASSO and NN approaches allowed a good prediction performance to be obtained for patient etiology.

Relative Radiometric Normalization for the PlanetScope Nanosatellite Constellation Based on Sentinel-2 Images

Detecting and characterizing continuous changes on Earth’s surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites’ images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models’ performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin’s Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R2). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R2 and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets.

Opportunistic Bone Mineral Density Measurement Using Photon-Counting Detector CT Spectral Localizer Images: A Prospective Study.

Background: Spectral localizer images from photon-counting detector (PCD) CT can be used for bone mineral density (BMD) evaluation given their 2D-projectional nature and material decomposition capability. As all CT examinations include localizer images, this approach could allow opportunistic osteoporosis screening in patients undergoing clinically indicated imaging by PCD CT. Objective: To assess the utility of PCD-CT spectral localizer images for opportunistic derivation of area BMD (aBMD) values and T-scores, using dual-energy X-ray absorptiometry (DXA) as the reference standard. Methods: This prospective study included patients ≥18 years old scheduled for clinically indicated lumbar spine CT between October 2023 and February 2024 and who underwent DXA within the 13 prior months or were scheduled for DXA within the subsequent 13 months. Participants underwent lumbar spine CT by PCD CT including spectral localizer images. Lumbar spine aBMD was extracted from clinical DXA reports. ROIs were placed on lumbar vertebral bodies and background soft tissues to extract areal densities from spectral localizer images using material decomposition; areal densities were used to derive lumbar spine aBMD values. The aBMD values were used to derive T-scores, which were classified as representing normal (≥-1) or abnormal (<-1) bone mass. DXA-derived and PCD-CT derived measurements were compared. Results: The study included 51 participants (mean age: 62 years [range, 28-83 years]; 31 female, 20 male). Mean DXA-derived T-score was 0.39±1.64; mean PCD-CT derived T-score was 0.28±1.77 (p=.29). Lin's concordance correlation coefficient between DXA-derived and PCD-CT T-scores was 0.90. The difference between DXA-derived and PCD-CT derived T-scores showed a small correlation with patient age (r=-0.13), absolute interval between DXA and PCD CT (r=.15), and BMI (r=0.28); this difference in scores did not show a significant difference between male and female patients (0.08 vs 0.13, respectively; p=.81). PCD-CT T-scores had sensitivity of 97%, specificity of 71%, PPV of 90%, and NPV of 91% for detecting abnormal bone mass using DXA-derived T-scores as the reference standard. Conclusion: PCD-CT spectral localizers showed clinical utility for deriving aBMD values and, consequently, T-scores. Clinical Impact: The T-score derived from PCD-CT spectral localizers may serve as an opportunistic screening tool for low bone mass and osteoporosis.

QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition

Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥ 8 (IC50 values ≤ 10 nM). Our svm-based ensemble model predicted IC50 values &lt; 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions. We aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI-SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions. A total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI-SPECT images were included. Images were reconstructed using 15 combinations of filter cut-off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal-Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t-test, Wilcoxon rank-sum, and signed-rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features. Before applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC<0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first-order (FO) and gray-level co-occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray-tone difference matrix (NGTDM), GLCM, gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and gray-level dependence matrix (GLDM) under Cut-off, Filter, and Order batches. The Wilcoxon rank-sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups. The majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI-SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility.

Impact and consequences of the error of estimated GFR in patients with heart failure

Heart failure is a highly prevalent disease, which courses with frequent readmissions, mainly by Acute Heart Failure (AHF). Reduced renal function is associated with increased mortality in patients with HF. Therefore, an accurate and precise evaluation of renal function in patients with HF is crucial. The error of estimated GFR (eGFR) is wide and common, showing a ± 30% variability compared to measured GFR (mGFR). However, there is no evidence on the error of formulas in reflecting real renal function and particularly the consequences of this error in patients with AHF. This is a prospective study comparing the impact of mGFR versus eGFR in the onset of cardiovascular (CV) outcomes in patients with AHF. This was tested with cox survival analysis. Measured GFR was determined by the plasma clearance of iohexol-dbs and eGFR by Cockroft-Gould, MDRD, CKD-EPI creatinine, CKD-EPI cystatin-C and CKD-EPI creatinine + cystatin-C equations formulas. Also the agreement between mGFR and eGFR was analyzed. A total of 90 patients were included. Average age was 66 (± 12 years) and 52 (58%) were male. Of them 53 patients (59%) had a cardiovascular event during follow-up, 22 fatal (41%). The agreement between mGFR and eGFR indicated moderate precision and accuracy (concordance correlation coefficient of 0.77; CI = 0.73–0.82). In multiple cox survival analysis, mGFR was significantly associated with cardiovascular events together with NTproBNP, BMI, LVEF and previous coronary artery disease (p = 0.037; HR = 0.98, 95% CI = 0.95–0.99). Estimated GFR by formulas was not significant. In patients with AHF the error of formulas is large, frequent and random, also, mGFR and not eGFR predicted future CV events. The error of eGFR may have clinical consequences in specific subpopulations.

Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020

It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.

Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.

Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model. A QML model analyzed 52 CT exams from patients who underwent same-day conventional and PCD-CT for suspected ILD. Lin's concordance correlation coefficient (CCC) assessed agreement between conventional and PCD-CT QML results. A CCC >0.90 was regarded as excellent, 0.9 to 0.8 as good, and <0.80 as a poor concordance. Spearman rank correlation evaluated the association between pulmonary function test results (PFT) and QML features (reticulation [R], honeycombing [HC], ground glass [GG], interstitial lung disease [ILD], and vessel-related structures [VRS]). Correlations were statistically significant if the 95% CI did not include 0.00 and P value <0.05. Conventional and PCD-CT QML results had good to excellent concordance (CCC ≥0.8) except for total HC (CCC <0.8), likely related to better PCD-CT honeycombing delineation. Overall, compared with conventional CT, PCD-CT had consistently more statistically significant correlation with PFT for HC (9 PCD vs. 2 conventional of 28 total and regional associations), similar correlation for R (20 PCD vs. 18 conventional of 28 associations) and VRS (19 PCD vs. 23 conventional of 28 associations), and less correlation for GG extent (12 PCD vs. 20 conventional associations). There is strong agreement between conventional and PCD-CT QML ILD features except for HC. PCD-CT improved HC but decreased GG extent correlation with PFT. Therefore, even though most quantitative features were not impacted by the newer PCD-CT technology, model adjustment is necessary.

Comparing NIRS and Pulse Oximetry for Cerebral Oxygen Saturation During Hypoxia Testing

Objective: This study evaluates the suitability of cerebral oximetry using near-infrared spectroscopy (NIRS) compared to traditional pulse oximetry (SpO2) for measuring cerebral oxygen saturation during hypoxia testing, aiming to enhance safety during flight operations and training. Material and Methods: The study included 106 participants aged 18–60 years at the Aerospace Medicine Training Center in Königsbrück. Cerebral oxygen saturation (rSO2) and peripheral oxygen saturation (SpO2) were measured using the INVOS™ 5100C cerebral oximeter and Masimo™ MS5 pulse oximeter, respectively. Measurements were taken at baseline, during hypoxia at 25,000 feet, and post recovery. Data analysis included regression analysis, Bland–Altman plots, and concordance correlation coefficients (CCC). Ethical approval was obtained from the Hannover Medical School. Data from 100 participants were analyzed. Results: Baseline SpO2 was 97.5 ± 1.5%, and baseline rSO2 was 77.25 ± 6.4%. During hypoxia, SpO2 dropped significantly, while rSO2 showed higher values. SpO2 recovered faster than rSO2. Deviations in rSO2 between the right and left sides during hypoxia were minimal. Lin’s CCC indicated moderate to substantial concordance. NIRS measurements were more stable and less prone to disturbances, with 95 disruptions in pulse oximetry, 25 of which were potentially critical. Conclusions: NIRS is a reliable method for detecting cerebral oxygen saturation, offering significant advantages over traditional pulse oximetry in stability and reliability during hypoxia testing. NIRS is less error-prone, supporting its use for continuous monitoring in aviation settings and enhancing flight safety by providing more accurate hypoxia detection.