Search

Artificial intelligence (AI) is increasingly being used in bone-age (BA) assessment due to its complicated and lengthy nature. We aimed to evaluate the clinical performance of a commercially available deep learning (DL)–based software for BA assessment using a real-world data. From Nov. 2018 to Feb. 2019, 474 children (35 boys, 439 girls, age 4–17 years) were enrolled. We compared the BA estimated by DL software (DL-BA) with that independently estimated by 3 reviewers (R1: Musculoskeletal radiologist, R2: Radiology resident, R3: Pediatric endocrinologist) using the traditional Greulich–Pyle atlas, then to his/her chronological age (CA). A paired t-test, Pearson’s correlation coefficient, Bland–Altman plot, mean absolute error (MAE) and root mean square error (RMSE) were used for the statistical analysis. The intraclass correlation coefficient (ICC) was used for inter-rater variation. There were significant differences between DL-BA and each reviewer’s BA (P P

We have implemented a linear ion trap (LIT)-based SIM-stitching method for ultra-high-resolution Fourier transform mass spectrometry (FTMS) that increases the S/N over a wide m/z range compared to non-segmented wide full-scan (WFS) spectra. Here we described an improved segmented spectral scan stitching method that was based on quadrupole mass filter (QMF)-SIM, which overcame previous limitations of ion signal loss in LIT. This allowed for accurate representation of isotopologue distributions, both at natural abundance and in stable isotope-resolved metabolomics (SIRM)-based experiments. We also introduced a new spectral binning method that provided more precise and resolution-independent bins for irreversibly noise-suppressed FTMS spectra. We demonstrated a substantial improvement in S/N and sensitivity (typically > 10-fold) for 13C labeled lipid extracts of human macrophages grown as three-dimensional (3D) cell culture, with detection of an increased number of 13C isotopologue ions. The method also enabled analysis of extracts from very limited biological samples.

Objectives To assess the diagnostic performance of dual-energy CT (DECT) with electron-density (ED) image reconstruction compared with standard CT (SC) and virtual non-calcium (VNCa) image CT reconstruction for detecting cervical disc herniation. Methods This cross-sectional study was approved by the IRB. We enrolled 64 patients (336 intervertebral discs from C2/3 to C7/T1; mean age, 55 years; 17 women and 47 men) who underwent DECT with spectral reconstruction and 3-T MRI within 2 weeks between January 2018 and June 2020. Four radiologists independently evaluated the first image set of randomized SC, VNCa, and ED images to detect cervical disc herniation. After 8 weeks, the readers re-evaluated the second and the last image sets with an 8-week interval. MRI evaluations performed by two other experienced served as the reference standard. Comparing diagnostic performance between each images set was evaluated by a generalized estimating equation. Results A total of 233 cervical disc herniations were noted on MRI. For detecting cervical disc herniation, electron-density images showed higher sensitivity (94% [219/233; 95% CI, 90–97] vs. 76% [177/233; 70–81] vs. 69% [160/233; 62–76]) (p 0.05) as SC and VNCa images, respectively. Inter-reader agreement for cervical disc herniation calculated among the four readers was moderate for all image sets (κ = 0.558 for ED, κ = 0.422 for SC, and κ = 0.449 for VNCa). Conclusion DECT with ED reconstruction can improve cervical disc herniation detection and diagnostic confidence compared with SC and VNCa images. Key Points • Intervertebral discs with high material density are well visualized on electron-density images obtained from dual-energy CT. • Electron-density images showed much higher sensitivity and diagnostic accuracy than standard CT and virtual non-calcium images for the detection of cervical disc herniation. • Electron-density images can have false-negative results, especially for disc herniation with high signal intensity on T2W images and can show pseudo-disc extrusion at the lower cervical spine. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08374-y.

Copyright © 2021 The Author(s). This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber-attacks. Significant dynamic linkages (interdependence) between the three cryptocurrencies under investigation are found in most cases when cyber-attacks are taken into account, Bitcoin appearing to be the dominant cryptocurrency. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber-attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber-attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.

In the pubertal period, bone age advances rapidly in conjunction with growth spurts. Precise bone-age assessments in this period are important, but results from the hand and elbow can be different. We aimed to compare the bone age between the hand and elbow around puberty onset and to elucidate the chronological age confirming puberty onset according to elbow-based bone age. A total of 211 peripubertal subjects (127 boys and 84 girls) who underwent hand and elbow radiographs within 2 months was enrolled. Two radiologists and a pediatric orthopedic surgeon assessed bone age. Hand bone age was graded using the Greulich–Pyle (GP) method, and elbow bone age was determined using the Sauvegrain method. The correlation of 2 methods was evaluated by Demining regression analysis, and the mean absolute difference (MAD) with chronological age was compared between pre-pubertal and pubertal subjects. Receiver-operating characteristic curve analysis was performed to determine the chronological age confirming puberty onset. There was a statistically significant difference in bone age revealed by the GP and Sauvegrain methods in the pubertal group. In the pubertal group, the MAD was 1.26 ± 0.90 years with the GP method and 0.61 ± 0.47 years with the Sauvegrain method in boys (P

The aim of this study was to assess the concordance between magnetic resonance imaging (MRI) grading for cervical neural foraminal stenosis (CNFS) based on axial and oblique sagittal images and evaluate the reliability of each grading plane. CNFS was graded at C2-3 to C7-T1 levels based on axial and oblique sagittal images separately by three radiologists. The concordance between CNFS grading based on axial and oblique sagittal images was strong for all three observers (Kendall's W=0.80, 0.79, and 0.82), despite the tendency of higher grading with oblique sagittal images. Both imaging planes supported strong interobserver reliability.

We introduce a new model for optimal internal capital allocation, which would allow banks to maximize their Return on Risk-Adjusted Capital (RORAC) under regulatory and capital constraints. We extend the single period model of Buch et al. (2011) to a multi-period model and improve its forecasting accuracy by including the debt effect and Bayesian learning innovations. The empirical application shows that our model significantly improves the RORAC of a sample of banks listed in the S&P 500 index.

This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015 - 28/2/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets.

BackgroundBoth multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) are used for assessment of lumbar foraminal stenosis (LFS). Therefore, it is relevant to assess agreement between these imaging modalities.PurposeTo determine intermodality, inter-, and intra-observer agreement for assessment of LFS on MDCT and MRI.Material and MethodsA total of 120 foramina in 20 patients who visited our institution in January and February 2014 were evaluated by six radiologists with different levels of experience. Radiologists evaluated presence and severity of LFS on sagittal CT and MR images according to a previously published LFS grading system. Intermodality agreement was analyzed by using weighted kappa statistics, while inter- and intra-observer agreement were analyzed by using intraclass correlation coefficients (ICCs) and kappa statistics.ResultsOverall intermodality agreement was moderate to good (kappa, 0.478–0.765). In particular, two professors and one fellow tended to overestimate the degree of LFS on CT compared with MRI. For inter-observer agreement of all six observers, ICCs indicated excellent agreement for both CT (0.774) and MRI (0.771), while Fleiss’ kappa values showed moderate agreement for CT (0.482) and MRI (0.575). There was better agreement between professors and fellows compared with residents. For intra-observer agreement, ICCs indicated excellent agreement, while kappa values showed good to excellent agreement for both CT and MRI.ConclusionMDCT was comparable to MRI for diagnosis and assessment of LFS, especially for experienced observers. However, there was a tendency to overestimate the degree of LFS on MDCT compared with MRI.

BackgroundAlthough both 1.5-Tesla (T) and 3-T magnetic resonance imaging (MRI) are widely used for the diagnosis of cervical spondylotic myelopathy (CSM), there have been no comparative studies.Pur...

Additional file 3 A proposition to show that for the case of two-group comparison in presence of detection limit, our test is also a valid test (in terms of preserving the type i error rate) to assess the null hypothesis that the distribution of underlying abundance level without censoring by the detection limit is the same between groups.

To assess the efficacy of fluoroscopy-guided facet joint injection for symptomatic spondylolysis and to analyze the outcome predictors. This study included 108 patients who underwent fluoroscopy-guided facet joint steroid injection for symptomatic spondylolysis with follow-up medical records from January 2013 to December 2016. Among them, 47 patients underwent concomitant epidural steroid injection. Response to injection was assessed at the initial follow-up. The symptom-free interval was analyzed using the Kaplan–Meier method. Outcome predictors were statistically analyzed using independent t test and Chi-square test. The facet joint injection was effective in 52 of 108 (48.1%) patients at initial follow-up. Recurrence was reported in 21 of these 52 (40.4%) patients. For the 52 patients in whom facet joint injection was effective, the median symptom-free interval was 298 days (95% confidence interval, 29–567 days). No significant difference was found in the response between the group with facet joint injection only (n = 61) and the group with facet joint injection and additional ESI (n = 47). Additionally, no significant outcome predictors were detected. Fluoroscopy-guided facet joint injection may potentially be an effective therapy for providing prolonged pain relief and avoiding unnecessary surgery for symptomatic spondylolysis without significant outcome predictors.

Lung cancer is the leading cause of cancer deaths in the United States. Patients with early stage lung cancer have the best prognosis with surgical removal of the tumor, but the disease is often asymptomatic until advanced disease develops, and there are no effective blood-based screening methods for early detection of lung cancer in at-risk populations. We have explored the lipid profiles of blood plasma exosomes using ultra high-resolution Fourier transform mass spectrometry (UHR-FTMS) for early detection of the prevalent non-small cell lung cancers (NSCLC). Exosomes are nanovehicles released by various cells and tumor tissues to elicit important biofunctions such as immune modulation and tumor development. Plasma exosomal lipid profiles were acquired from 39 normal and 91 NSCLC subjects (44 early stage and 47 late stage). We have applied two multivariate statistical methods, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) to classify the data. For the RF method, the Gini importance of the assigned lipids was calculated to select 16 lipids with top importance. Using the LASSO method, 7 features were selected based on a grouped LASSO penalty. The Area Under the Receiver Operating Characteristic curve for early and late stage cancer versus normal subjects using the selected lipid features was 0.85 and 0.88 for RF and 0.79 and 0.77 for LASSO, respectively. These results show the value of RF and LASSO for metabolomics data-based biomarker development, which provide robust an independent classifiers with sparse data sets. Application of LASSO and Random Forests identifies lipid features that successfully distinguish early stage lung cancer patient from healthy individuals.

Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolites. Observed spectral features (peaks) that are not isotopologues do not directly correspond to known compounds and cannot be placed into existing metabolic networks. Spectral artifacts account for many of these unidentified peaks, and misassignments made to these artifact peaks can create large interpretative errors. Without accurate identification of artifactual features and correct assignment of real features, discerning their roles within living systems is effectively impossible.We have observed three types of artifacts unique to FT-MS that often result in regions of abnormally high peak density (HPD), which we collectively refer to as HPD artifacts: i) fuzzy sites representing small regions of m/z space with a ‘fuzzy’ appearance due to the extremely high number of peaks present; ii) ringing due to a very intense peak producing side bands of decreasing intensity that are symmetrically distributed around the main peak; and iii) partial ringing where only a subset of the side bands are observed for an intense peak. Fuzzy sites and partial ringing appear to be novel artifacts previously unreported in the literature and we hypothesize that all three artifact types derive from Fourier transformation-based issues. In some spectra, these artifacts account for roughly a third of the peaks present in the given spectrum. We have developed a set of tools to detect these artifacts and approaches to mitigate their effects on downstream analyses.

Many studies on yeast metabolism are focused on its response to specific stress conditions because the results can be extended to the human medical issues. Most of those works have been accomplished through functional genomics studies. However, these changes may not show a linear correlation with protein or metabolite levels. For many organisms including yeast, the number of metabolites is far fewer than that of genes or gene products. Thus, metabolic profiling can provide a simpler yet efficient snapshot of the system's physiology. Metabolites of Saccharomyces cerevisiae under various stresses were analyzed and compared with those under the normal, unstressed growth conditions by two-dimensional NMR spectroscopy. At least 31 metabolites were identified for most of the samples. The levels of many identified metabolites showed significant increase or decrease depending on the nature of the stress. The statistical analysis produced a holistic view: different stresses were clustered and isolated from one another with the exception of high pH, heat, and oxidative stresses. This work could provide a link between the metabolite profiles and mRNA or protein profiles under representative and well-studied stress conditions.

The crosslink density of carbon black-reinforced natural rubber (NR) was correlated with 1H chemical shift by liquid-state 1H NMR spectroscopy. Instead of observing the rubber directly with the solid-state NMR technique, we used toluene molecules in the liquid state as probes to explore the inner cavities of the rubber sample. Four NR composites with different carbon black contents of 20, 40, 60, and 80 phr were tested. The toluene signals were split into two: one from the bulk solvent and the other from inside the rubber. The chemical shift difference between those two split signals was observed to be in linear relationship with the carbon black content. The samples thermally aged at 70 °C for 100 days showed larger chemical shift differences than the unaged sample. Furthermore, the chemical shift difference was directly proportional to the apparent crosslink density obtained independently by the conventional swelling method. This result offers by far the simplest method to measure the apparent crosslink density in rubber.

Metabolites of urine samples from 6 colorectal cancer patients were analyzed by two-dimensional NMR spectroscopy, where the samples were collected before and after the surgical treatments per patient. NMR data were analyzed with the help of the metabolome database and the statistics software. Urine samples before and after the treatments showed significantly different metabolic profiles from each other. We were able to compare 10 different metabolites. Most of the assigned metabolites of every patient showed a tendency of increase after the surgery except for a few cases. The amount of changes in individual metabolites varied significantly from patient to patient, but the combination of such changes could be used to distinguish the condition before the surgery from after, which could be done by PCA analysis. The analysis via HSQC spectra proved to be applicable in assessing and classifying global metabolic profiles of the urines from colorectal cancer patients.

Metabolites of colorectal cancer tissues from 12 patients were analyzed and compared with those of the normal tissues by two-dimensional NMR spectroscopy. NMR data were analyzed with the help of the metabolome database and the statistics software. Cancerous tissues showed significantly altered metabolic profiles as compared to the normal tissues. Among such metabolites, the concentrations of taurine, glutamate, choline were notably increased in the cancerous tissues of most patients, and those of glucose, malate, and glycerol were decreased. Changes in individual metabolites varied significantly from patient to patient, but the combination of such changes could be used to distinguish cancerous tissues from normal ones, which could be done by PCA analysis. The traditional chemometric analysis was also per- formed using AMIX software. By comparing those two results, the analysis via 1 H- 13 C HSQC spectra proved to be more robust and effective in assessing and classifying global metabolic profiles of the colorectal tissues.

To retrospectively evaluate the diagnostic role of the contrast-enhanced MRI (CE-MRI) for differentiation between benign VCFs and malignant VCFs focusing on the internal transparent trabecular bone on CE-MRI (the “see-through sign”). The institutional review board approved this study and informed consent was waived due to the retrospective nature of the study. From January 2012 to December 2013, all 149 consecutive benign or malignant VCF patients were enrolled for consideration in this study from a CE-MRI database. In the first analysis, four radiologists independently evaluated the presence or absence of the see-through sign. The see-through sign was defined as internal transparent trabecular bone morphology on CE-MRI. The intraclass correlation coefficient (ICC), percentage agreement, and Fleiss’s kappa statistics were obtained. Fifty-seven patients (M:F = 27:30; mean age, 63 years; age range, 20–88 years) who diagnosed as acute benign (n = 24) and malignant (n = 33) VCFs were finally included for the analysis. The results of all readers showed that the see-through sign was associated with acute benign VCFs (p