Risk SNP-Mediated Promoter-Enhancer Switching Drives Prostate Cancer through lncRNA PCAT19.

The prostate cancer (PCa) risk-associated SNP rs11672691 is positively associated with aggressive disease at diagnosis. We showed that rs11672691 maps to the promoter of a short isoform of long noncoding RNA PCAT19 (PCAT19-short), which is in the third intron of the long isoform (PCAT19-long). The risk variant is associated with decreased and increased levels of PCAT19-short and PCAT19-long, respectively. Mechanistically, the risk SNP region is bifunctional with both promoter and enhancer activity. The risk variants of rs11672691 and its LD SNP rs887391 decrease binding of transcription factors NKX3.1 and YY1 to the promoter of PCAT19-short, resulting in weaker promoter but stronger enhancer activity that subsequently activates PCAT19-long. PCAT19-long interacts with HNRNPAB to activate a subset of cell-cycle genes associated with PCa progression, thereby promoting PCa tumor growth and metastasis. Taken together, these findings reveal a risk SNP-mediated promoter-enhancer switching mechanism underlying both initiation and progression of aggressive PCa.

Development and Validation of a Protein Risk Score for Mortality in Heart Failure : A Community Cohort Study.

BACKGROUND: Heart failure (HF) is a complex clinical syndrome with high mortality. Current risk stratification approaches lack precision. High-throughput proteomics could improve risk prediction. Its use in clinical practice to guide the management of patients with HF depends on validation and evidence of clinical benefit. OBJECTIVE: To develop and validate a protein risk score for mortality in patients with HF. DESIGN: Community-based cohort. SETTING: Southeast Minnesota. PARTICIPANTS: Patients with HF enrolled between 2003 and 2012 and followed through 2021. MEASUREMENTS: A total of 7289 plasma proteins in 1351 patients with HF were measured using the SomaScan Assay (SomaLogic). A protein risk score was derived using least absolute shrinkage and selection operator regression and temporal validation in patients enrolled between 2003 and 2007 (development cohort) and 2008 and 2012 (validation cohort). Multivariable Cox regression was used to examine the association between the protein risk score and mortality. The performance of the protein risk score to predict 5-year mortality risk was assessed using calibration plots, decision curves, and relative utility analyses and compared with a clinical model, including the Meta-Analysis Global Group in Chronic Heart Failure mortality risk score and N-terminal pro-B-type natriuretic peptide. RESULTS: The development (n = 855; median age, 78 years; 50% women; 29% with ejection fraction <40%) and validation cohorts (n = 496; median age, 76 years; 45% women; 33% with ejection fraction <40%) were mostly similar. In the development cohort, 38 unique proteins were selected for the protein risk score. Independent of ejection fraction, the protein risk score demonstrated good calibration, reclassified mortality risk particularly at the extremes of the risk distribution, and showed greater clinical utility compared with the clinical model. LIMITATION: Participants were predominantly of European ancestry, potentially limiting the generalizability of the findings to different patient populations. CONCLUSION: Validation of the protein risk score demonstrated good calibration and evidence of predicted benefits to stratify the risk for death in HF superior to that of clinical methods. Further studies are needed to prospectively evaluate the score's performance in diverse populations and determine risk thresholds for interventions. PRIMARY FUNDING SOURCE: Division of Intramural Research at the National Heart, Lung, and Blood Institute of the National Institutes of Health.

ACE2 and TMPRSS2 SARS-CoV-2 infectivity genes: deep mutational scanning and characterization of missense variants.

The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a 'landing pad' system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Mendelian randomization in hepatology: A review of principles, opportunities, and challenges.

Mendelian randomization has become a popular tool to assess causal relationships using existing observational data. While randomized controlled trials are considered the gold standard for establishing causality between exposures and outcomes, it is not always feasible to conduct a trial. Mendelian randomization is a causal inference method that uses observational data to infer causal relationships by using genetic variation as a surrogate for the exposure of interest. Publications using the approach have increased dramatically in recent years, including in the field of hepatology. In this concise review, we describe the concepts, assumptions, and interpretation of Mendelian randomization as related to studies in hepatology. We focus on the strengths and weaknesses of the approach for a non-statistical audience, using an illustrative example to assess the causal relationship between body mass index and NAFLD.

From genome-wide associations to candidate causal variants by statistical fine-mapping.

Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.

The clinical impact of estimating low-density lipoprotein cholesterol (LDL-C) using different equations in the general population.

BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is associated with atherosclerotic cardiovascular disease (ASCVD). Friedewald, Sampson, and Martin-Hopkins equations are used to calculate LDL-C. This study compares the impact of switching between these equations in a large geographically defined population. MATERIALS AND METHODS: Data for individuals who had a lipid panel ordered clinically between 2010 and 2019 were included. Comparisons were made across groups using the two-sample t-test or chi-square test as appropriate. Discordances between LDL measures based on clinically actionable thresholds were summarized using contingency tables. RESULTS: The cohort included 198,166 patients (mean age 54 years, 54% female). The equations perform similarly at the lower range of triglycerides but began to diverge at a triglyceride level of 125 mg/dL. However, at triglycerides of 175 mg/dL and higher, the Martin-Hopkins equation estimated higher LDL-C values than the Samson equation. This discordance was further exasperated at triglyceride values of 400 to 800 mg/dL. When comparing the Sampson and Friedewald equations, at triglycerides are below 175 mg/dL, 9% of patients were discordant at the 70 mg/dL cutpoint, whereas 42.4% were discordant when triglycerides are between 175 and 400 mg/dL. Discordance was observed at the clinically actionable LDL-C cutpoint of 190 mg/dL with the Friedewald equation estimating lower LDL-C than the other equations. In a high-risk subgroup (ASCVD risk score > 20%), 16.3% of patients were discordant at the clinical cutpoint of LDL-C < 70 mg/dL between the Sampson and Friedewald equations. CONCLUSIONS: Discordance at clinically significant LDL-C cutpoints in both the general population and high-risk subgroups were observed across the three equations. These results show that using different methods of LDL-C calculation or switching between different methods could have clinical implications for many patients.

Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach.

PURPOSE: Changes in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo. EXPERIMENTAL DESIGN: The proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology. RESULTS: Out of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%. CONCLUSIONS: The promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (P < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images (P < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022 Online supplemental material is available for this article.

Missense Genetic Variation of ICAM1 and Incident Heart Failure.

BACKGROUND: Intercellular adhesion molecule-1 (ICAM-1) is a cell surface protein that participates in endothelial activation and is hypothesized to play a central role in heart failure (HF). We evaluated associations of ICAM1 missense genetic variants with circulating ICAM-1 levels and with incident HF. METHODS AND RESULTS: We identified 3 missense variants within ICAM1 (rs5491, rs5498 and rs1799969) and evaluated their associations with ICAM-1 levels in the Coronary Artery Risk Development in Young Adults Study and the Multi-Ethnic Study of Atherosclerosis (MESA). We determined the association among these 3 variants and incident HF in MESA. We separately evaluated significant associations in the Atherosclerosis Risk in Communities (ARIC) study. Of the 3 missense variants, rs5491 was common in Black participants (minor allele frequency [MAF] > 20%) and rare in other race/ethnic groups (MAF < 5%). In Black participants, the presence of rs5491 was associated with higher levels of circulating ICAM-1 at 2 timepoints separated by 8 years. Among Black participants in MESA (n = 1600), the presence of rs5491 was associated with an increased risk of incident HF with preserved ejection fraction (HFpEF; HR = 2.30; [95% CI 1.25-4.21; P = 0.007]). The other ICAM1 missense variants (rs5498 and rs1799969) were associated with ICAM-1 levels, but there were no associations with HF. In ARIC, rs5491 was significantly associated with incident HF (HR = 1.24 [95% CI 1.02 - 1.51]; P = 0.03), with a similar direction of effect for HFpEF that was not statistically significant. CONCLUSIONS: A common ICAM1 missense variant among Black individuals may be associated with increased risk of HF, which may be HFpEF-specific.

Factors Associated With Ultrasound Color Doppler Twinkling by Breast Biopsy Markers: In Vitro and Ex Vivo Evaluation of 35 Commercially Available Markers.

BACKGROUND. Targeted axillary lymph node dissection after neoadjuvant systemic therapy (NST) for breast cancer depends on identifying marked metastatic lymph nodes. However, ultrasound visualization of biopsy markers is challenging. OBJECTIVE. The purpose of our study was to identify biopsy markers that show actionable twinkling in cadaveric breast and to assess the association of actionable twinkling with markers' surface roughness. METHODS. Commercial breast biopsy markers were evaluated for twinkling artifact in various experimental conditions relating to scanning medium (solid gel phantom, ultrasound coupling gel, cadaveric breast), transducer (ML6-15, 9L, C1-6), and embedding material (present vs absent). Markers were assigned twinkling scores from 0 (confident in no twinkling) to 4 (confident in exuberant twinkling); a score of 3 or greater represented actionable twinkling (sufficient confidence to rely solely on twinkling for target localization). Markers were hierarchically advanced to evaluation with increasingly complex media if showing at least minimal twinkling for a given medium. A 3D coherence optical profiler measured marker surface roughness. Mixed-effects proportional odds regression models assessed associations between twinkling scores and transducer and embedding material; Wilcoxon rank sum test evaluated associations between actionable twinkling and surface roughness. RESULTS. Thirty-five markers (21 with embedding material) were evaluated. Ten markers without embedding material advanced to evaluation in cadaveric breast. Higher twinkling scores were associated with presence of embedding material (odds ratio [OR] = 5.05 in solid gel phantom, 9.84 in coupling gel) and transducer (using the C1-6 transducer as reference; 9L transducer: OR = 0.36, 0.83, and 0.04 in solid gel phantom, ultrasound coupling gel, and cadaveric breast; ML6-15 transducer: OR = 0.07, 0.18, and 0.00 respectively; post hoc p between 9L and ML6-15: p < .001, p = .02, and p = .04). In cadaveric breast, three markers (Cork, Professional Q, MRI [Flex]) exhibited actionable twinkling for two or more transducers; surface roughness was significantly higher for markers with than without actionable twinkling for C1-6 (median values: 0.97 vs 0.35, p = .02) and 9L (1.75 vs 0.36; p = .002) transducers. CONCLUSION. Certain breast biopsy markers exhibited actionable twinkling in cadaveric breast. Twinkling was observed with greater confidence for the C1-6 and 9L transducers than the ML6-15 transducer. Actionable twinkling was associated with higher marker surface roughness. CLINICAL IMPACT. Use of twinkling for marker detection could impact preoperative or intraoperative localization after NST.

Pilot study to determine whether reduced-dose photon-counting detector chest computed tomography can reliably display Brody II score imaging findings for children with cystic fibrosis at radiation doses that approximate radiographs.

BACKGROUND: The Brody II score uses chest CT to guide therapeutic changes in children with cystic fibrosis; however, patients and providers are often reticent to undergo chest CT given concerns about radiation. OBJECTIVE: We sought to determine the ability of a reduced-dose photon-counting detector (PCD) chest CT protocol to reproducibly display pulmonary disease severity using the Brody II score for children with cystic fibrosis (CF) scanned at radiation doses similar to those of a chest radiograph. MATERIALS AND METHODS: Pediatric patients with CF underwent non-contrast reduced-dose chest PCD-CT. Volumetric inspiratory and expiratory scans were obtained without sedation or anesthesia. Three pediatric radiologists with Certificates of Added Qualification scored each scan on an ordinal scale and assigned a Brody II score to grade bronchiectasis, peribronchial thickening, parenchymal opacity, air trapping and mucus plugging. We report image-quality metrics using descriptive statistics. To calculate inter-rater agreement for Brody II scoring, we used the Krippendorff alpha and intraclass correlation coefficient (ICC). RESULTS: Fifteen children with CF underwent reduced-dose PCD chest CT in both inspiration and expiration (mean age 8.9 years, range, 2.5-17.5 years; 4 girls). Mean volumetric CT dose index (CTDIvol) was 0.07 ± 0.03 mGy per scan. Mean effective dose was 0.12 ± 0.04 mSv for the total examination. All three readers graded spatial resolution and noise as interpretable on lung windows. The average Brody II score was 12.5 (range 4-19), with moderate inter-reader reliability (ICC of 0.61 [95% CI=0.27, 0.84]). Inter-rater reliability was moderate to substantial for bronchiectasis (0.52), peribronchial thickening (0.55), presence of opacity (0.62) and air trapping (0.70) and poor for mucus plugging (0.09). CONCLUSION: Reduced-dose PCD-CT permits diagnostic image quality and reproducible identification of Brody II scoring imaging findings at radiation doses similar to those for chest radiography.

Nuclear Magnetic Resonance Metabolomic Profiling and Urine Chemistries in Incident Kidney Stone Formers Compared with Controls.

BACKGROUND: The urine metabolites and chemistries that contribute to kidney stone formation are not fully understood. This study examined differences between the urine metabolic and chemistries profiles of first-time stone formers and controls. METHODS: High-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was performed in 24-hour urine samples from a prospective cohort of 418 first-time symptomatic kidney stone formers and 440 controls. In total, 48 NMR-quantified metabolites in addition to 12 standard urine chemistries were assayed. Analysis of covariance was used to determine the association of stone former status with urine metabolites or chemistries after adjusting for age and sex and correcting for the false discovery rate. Gradient-boosted machine methods with nested cross-validation were applied to predict stone former status. RESULTS: Among the standard urine chemistries, stone formers had lower urine oxalate and potassium and higher urine calcium, phosphate, and creatinine. Among NMR urine metabolites, stone formers had lower hippuric acid, trigonelline, 2-furoylglycine, imidazole, and citrate and higher creatine and alanine. A cross-validated model using urine chemistries, age, and sex yielded a mean AUC of 0.76 (95% CI, 0.73 to 0.79). A cross-validated model using urine chemistries, NMR-quantified metabolites, age, and sex did not meaningfully improve the discrimination (mean AUC, 0.78; 95% CI, 0.75 to 0.81). In this combined model, among the top ten discriminating features, four were urine chemistries and six NMR-quantified metabolites. CONCLUSIONS: Although NMR-quantified metabolites did not improve discrimination, several urine metabolic profiles were identified that may improve understanding of kidney stone pathogenesis.

Vitrification versus slow freezing of human ovarian tissue: a systematic review and meta-analysis of histological outcomes.

A systematic review and meta-analysis of pertinent literature published from 2006 to January 2022 were conducted to study and compare vitrification and slow freezing, the two prominent methods of ovarian tissue cryopreservation. The primary outcome measures for this study were (1) proportion of intact primordial follicles, (2) proportion of intact stromal cells, (3) proportion of DNA fragmentation in primordial follicles, and (4) mean primordial follicle density. This meta-analysis of 19 studies revealed a significantly greater proportion of intact stromal cells in vitrified tissue versus slow-frozen tissue. No significant differences upon pooled analyses were observed between the two cryopreservation methods with respect to the proportion of intact primordial follicles, proportion of DNA fragmentation, or mean primordial follicle density. Due to differences seen in stromal cell viability, vitrification may be a preferred option to preserve histology of tissue. However, more work should be done to compare the two freezing techniques with less heterogeneity caused by patients, samples, and protocols.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

PURPOSE: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL). METHODOLOGY: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC. RESULTS: Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]). CONCLUSION: Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization.

BACKGROUND: Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation. METHODS: In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray's deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection. RESULTS: A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4-72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95-0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96-1.00). CONCLUSIONS: The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization.

Volumetric imaging and morphometric analysis of breast tumor angiogenesis using a new contrast-free ultrasound technique: a feasibility study.

BACKGROUND: There is a strong correlation between the morphological features of new tumor vessels and malignancy. However, angiogenic heterogeneity necessitates 3D microvascular data of tumor microvessels for more reliable quantification. To provide more accurate information regarding vessel morphological features and improve breast lesion characterization, we introduced a quantitative 3D high-definition microvasculature imaging (q3D-HDMI) as a new easily applicable and robust tool to morphologically characterize microvasculature networks in breast tumors using a contrast-free ultrasound-based imaging approach. METHODS: In this prospective study, from January 2020 through December 2021, a newly developed q3D-HDMI technique was evaluated on participants with ultrasound-identified suspicious breast lesions recommended for core needle biopsy. The morphological features of breast tumor microvessels were extracted from the q3D-HDMI. Leave-one-out cross-validation (LOOCV) was applied to test the combined diagnostic performance of multiple morphological parameters of breast tumor microvessels. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of the generated pooled model. RESULTS: Ninety-three participants (mean age 52 ± 17 years, 91 women) with 93 breast lesions were studied. The area under the ROC curve (AUC) generated with q3D-HDMI was 95.8% (95% CI 0.901-1.000), yielding a sensitivity of 91.7% and a specificity of 98.2%, that was significantly higher than the AUC generated with the q2D-HDMI (p = 0.02). When compared to q2D-HDMI, the tumor microvessel morphological parameters obtained from q3D-HDMI provides distinctive information that increases accuracy in differentiating breast tumors. CONCLUSIONS: The proposed quantitative volumetric imaging technique augments conventional breast ultrasound evaluation by increasing specificity in differentiating malignant from benign breast masses.

CpGtools: a python package for DNA methylation analysis.

MOTIVATION: DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data. RESULTS: We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) 'CpG position modules' focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) 'CpG signal modules' are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) 'differential CpG analysis modules' focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation. AVAILABILITY AND IMPLEMENTATION: CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of sex-specific genetic associations in response to opioid analgesics in a White, non-Hispanic cohort from Southeast Minnesota.

The study of sex-specific genetic associations with opioid response may improve the understanding of inter-individual variability in pain treatments. We investigated sex-specific associations between genetic variation and opioid response. We identified participants in the RIGHT Study prescribed codeine, tramadol, hydrocodone, and oxycodone between 01/01/2005 and 12/31/2017. Prescriptions were collapsed into codeine/tramadol and hydrocodone/oxycodone. Outcomes included poor pain control and adverse reactions within six weeks after prescription date. We performed gene-level and single-variant association analyses stratified by sex. We included 7169 non-Hispanic white participants and a total of 1940 common and low-frequency variants (MAF > 0.01). Common variants in MACROD2 (rs76026520), CYP1B1 (rs1056837, rs1056836), and CYP2D6 (rs35742686) were associated with outcomes. At the gene level, FAAH, SCN1A, and TYMS had associations for men and women, and NAT2, CYP3A4, CYP1A2, and SLC22A2 had associations for men only. Our findings highlight the importance of considering sex in association studies on opioid response.

Design, Synthesis, and Preliminary Evaluation of [68Ga]Ga-NOTA-Insulin as a PET Probe in an Alzheimer's Disease Mouse Model.

Aberrant insulin signaling has been considered one of the risk factors for the development of Alzheimer's disease (AD) and has drawn considerable attention from the research community to further study its role in AD pathophysiology. Herein, we describe the development of an insulin-based novel positron emission tomography (PET) probe, [68Ga]Ga-NOTA-insulin, to noninvasively study the role of insulin in AD. The developed PET probe [68Ga]Ga-NOTA-insulin showed a significantly higher uptake (0.396 ± 0.055 SUV) in the AD mouse brain compared to the normal (0.140 ± 0.027 SUV) mouse brain at 5 min post injection and also showed a similar trend at 10, 15, and 20 min post injection. In addition, [68Ga]Ga-NOTA-insulin was found to have a differential uptake in various brain regions at 30 min post injection. Among the brain regions, the cortex, thalamus, brain stem, and cerebellum showed a significantly higher standard uptake value (SUV) of [68Ga]Ga-NOTA-insulin in AD mice as compared to normal mice. The inhibition of the insulin receptor (IR) with an insulin receptor antagonist peptide (S961) in normal mice showed a similar brain uptake profile of [68Ga]Ga-NOTA-insulin as it was observed in the AD case, suggesting nonfunctional IR in AD and the presence of an alternative insulin uptake route in the absence of a functional IR. The Gjedde-Patlak graphical analysis was also performed to predict the input rate of [68Ga]Ga-NOTA-insulin into the brain using MicroPET imaging data and supported the in vivo results. The [68Ga]Ga-NOTA-insulin PET probe was successfully synthesized and evaluated in a mouse model of AD in comparison with [18F]AV1451 and [11C]PIB to noninvasively study the role of insulin in AD pathophysiology.