BackgroundThe propensity of compounds to produce adverse health effects in humans is generally evaluated using animal-based test methods. Such methods can be relatively expensive, low-throughput, and associated with pain suffered by the treated animals. In addition, differences in species biology may confound extrapolation to human health effects.ObjectiveThe National Toxicology Program and the National Institutes of Health Chemical Genomics Center are collaborating to identify a battery of cell-based screens to prioritize compounds for further toxicologic evaluation.MethodsA collection of 1,408 compounds previously tested in one or more traditional toxicologic assays were profiled for cytotoxicity using quantitative high-throughput screening (qHTS) in 13 human and rodent cell types derived from six common targets of xenobiotic toxicity (liver, blood, kidney, nerve, lung, skin). Selected cytotoxicants were further tested to define response kinetics.ResultsqHTS of these compounds produced robust and reproducible results, which allowed cross-compound, cross-cell type, and cross-species comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited species- or cell type–specific cytotoxicity. Closely related cell types and analogous cell types in human and rodent frequently showed different patterns of cytotoxicity. Some compounds inducing similar levels of cytotoxicity showed distinct time dependence in kinetic studies, consistent with known mechanisms of toxicity.ConclusionsThe generation of high-quality cytotoxicity data on this large library of known compounds using qHTS demonstrates the potential of this methodology to profile a much broader array of assays and compounds, which, in aggregate, may be valuable for prioritizing compounds for further toxicologic evaluation, identifying compounds with particular mechanisms of action, and potentially predicting in vivo biological response.

To evaluate MRI features associated with pathologically defined extraprostatic extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic EPE. Materials and Methods: In this prospective study, consecutive male study participants underwent preoperative 3.0-T MRI from June 2007 to March 2017 followed by robotic-assisted laparoscopic radical prostatectomy. An MRI-based EPE grading system was defined as follows: curvilinear contact length of 1.5 cm or capsular bulge and irregularity were grade 1, both features were grade 2, and frank capsular breach were grade 3. Multivariable logistic regression and decision curve analyses were performed to compare the MRI grade model and clinical parameters (prostate-specific antigen, Gleason score) for pathologic EPE prediction by using the area under the receiver operating characteristic curve (AUC) value. Results: Among 553 study participants, the mean age was 60 years 6 8 (standard deviation); the median prostate-specific antigen value was 6.3 ng/mL. A total of 125 of 553 (22%) participants had pathologic EPE at radical prostatectomy. Detection of pathologic EPE, defined as number of pathologic EPEs divided by number of participants with individual MRI features, was as follows: curvilinear contact length, 88 of 208 (42%); capsular bulge and irregularity, 78 of 175 (45%); and EPE visible at MRI, 37 of 56 (66%). For MRI, grades 1, 2, and 3 for detection of pathologic EPE were 18 of 74 (24%), 39 of 102 (38%), and 37 of 56 (66%), respectively. Clinical features plus the MRI-based EPE grading system (prostate-specific antigen, International Society of Urological Pathology stage, MRI grade) predicted pathologic EPE better than did MRI grade alone (AUC, 0.81 vs 0.77, respectively; P , .001). Conclusion: Higher MRI-based extraprostatic extension (EPE) grading categories were associated with a greater risk of pathologic EPE. Clinical features plus MRI grading had the highest diagnostic performance for prediction of pathologic EPE.

The major constituents in grape seed and pine bark extracts are proanthocyanidins. To evaluate material available to consumers, select lots were analyzed using high-performance liquid chromatography, gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS), gel permeation chromatography (GPC), and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Atmospheric pressure chemical ionization (APCI) LC/MS was used to identify monomers, dimers, and trimers present. GC/MS analyses led to the identification of ethyl esters of hexadecanoic acid, linoleic acid, and oleic acid, as well as smaller phenolic and terpene components. The GPC molecular weight (MW) distribution indicated components ranging from approximately 162 to approximately 5500 MW (pine bark less than 1180 MW and grape seed approximately 1180 to approximately 5000 MW). MALDI-TOF MS analyses showed that pine bark did not contain oligomers with odd numbers of gallate units and grape seed contained oligomers with both odd and even numbers of gallate. Reflectron MALDI-TOF MS identified oligomers up to a pentamer and heptamer, and linear MALDI-TOF MS showed a mass range nearly double that of reflectron analyses.

Background The Prostate Imaging Reporting and Data System version 2 (PI‐RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies investigating the intrareader reproducibility of PI‐RADSv2. Purpose To evaluate both intra‐ and interreader reproducibility of PI‐RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI). Study Type Retrospective. Population/Subjects In all, 102 consecutive biopsy‐naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)‐guided biopsy. Field Strength/Sequences Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI). Assessment Previously detected and biopsied lesions were scored by four readers from four different institutions using PI‐RADSv2. Readers scored lesions during two readout rounds with a 4‐week washout period. Statistical Tests Kappa (κ) statistics and specific agreement (Po) were calculated to quantify intra‐ and interreader reproducibility of PI‐RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC). Results Overall intrareader reproducibility was moderate to substantial (κ = 0.43–0.67, Po = 0.60–0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence‐specific interreader agreement for all readers was similar to the overall PI‐RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po = 0.47, 0.44, and 0.54 in T2‐weighted, diffusion‐weighted imaging (DWI), and dynamic contrast‐enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively. Data Conclusion PI‐RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI. Level of Evidence: 2 Technical Efficacy: Stage 2

Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD). We optimized the holistically nested edge detection (HED) deep convolutional neural network. Our dataset contains T2, apparent diffusion coefficient, and high b-value images from 186 patients across six institutions worldwide: 92 with an endorectal coil (ERC) and 94 without. Ground-truth was based on tumor segmentations manually drawn by expert radiologists based on histologic evidence of cancer.The training set consisted of 120 patients and the validation set and test set included 19 and 47, respectively. Slice-level probability maps are evaluated at the lesion level of analysis. The best model: HED using 5 × 5 convolutional kernels, batch normalization, and optimized using Adam. This CAD performed significantly better (p < 0.001) in the peripheral zone (AUC ¼ 0.94 AE 0.01) than the transition zone. It outperforms a previous CAD from our group in a head-to-head comparison on the same ERC-only test cases (AUC ¼ 0.97 AE 0.01; p < 0.001). Our CAD establishes a state-of-the-art performance for predicting prostate cancer lesions on mpMRIs.

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The characterization of herbal materials is a significant challenge to analytical chemists. Goldenseal (Hydrastis canadensis L.), which has been chosen for toxicity evaluation by NIEHS, is among the top 15 herbal supplements currently on the market and contains a complex mixture of indigenous components ranging from carbohydrates and amino acids to isoquinoline alkaloids. One key component of herbal supplement production is botanical authentication, which is also recommended prior to initiation of efficacy or toxicological studies. To evaluate material available to consumers, goldenseal root powder was obtained from three commercial suppliers and a strategy was developed for characterization and comparison that included Soxhlet extraction, HPLC, GC-MS, and LC-MS analyses. HPLC was used to determine the weight percentages of the goldenseal alkaloids berberine, hydrastine, and canadine in the various extract residues. Palmatine, an isoquinoline alkaloid native to Coptis spp. and other common goldenseal adulterants, was also quantitated using HPLC. GC-MS was used to identify non-alkaloid constituents in goldenseal root powder, whereas LC-MS was used to identify alkaloid components. After review of the characterization data, it was determined that alkaloid content was the best biomarker for goldenseal. A 20-min ambient extraction method for the determination of alkaloid content was also developed and used to analyze the commercial material. All three lots of purchased material contained goldenseal alkaloids hydrastinine, berberastine, tetrahydroberberastine, canadaline, berberine, hydrastine, and canadine. Material from a single supplier also contained palmatine, coptisine, and jatrorrhizine, thus indicating that the material was not pure goldenseal. Comparative data for three commercial sources of goldenseal root powder are presented.