A signature of platelet reactivity in CBC scattergrams reveals genetic predictors of thrombotic disease risk.

Genetic studies of platelet reactivity (PR) phenotypes may identify novel antiplatelet drug targets. However, such studies have been limited by small sample sizes (n < 5000) because of the complexity of measuring PR. We trained a model to predict PR from complete blood count (CBC) scattergrams. A genome-wide association study of this phenotype in 29 806 blood donors identified 21 distinct associations implicating 20 genes, of which 6 have been identified previously. The effect size estimates were significantly correlated with estimates from a study of flow cytometry-measured PR and a study of a phenotype of in vitro thrombus formation. A genetic score of PR built from the 21 variants was associated with the incidence rates of myocardial infarction and pulmonary embolism. Mendelian randomization analyses showed that PR was causally associated with the risks of coronary artery disease, stroke, and venous thromboembolism. Our approach provides a blueprint for using phenotype imputation to study the determinants of hard-to-measure but biologically important hematological traits.

The cacao gene atlas: a transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L.

BACKGROUND: Theobroma cacao, the cocoa tree, is a tropical crop grown for its highly valuable cocoa solids and fat which are the basis of a 200-billion-dollar annual chocolate industry. However, the long generation time and difficulties associated with breeding a tropical tree crop have limited the progress of breeders to develop high-yielding disease-resistant varieties. Development of marker-assisted breeding methods for cacao requires discovery of genomic regions and specific alleles of genes encoding important traits of interest. To accelerate gene discovery, we developed a gene atlas composed of a large dataset of replicated transcriptomes with the long-term goal of progressing breeding towards developing high-yielding elite varieties of cacao. RESULTS: We describe the creation of the Cacao Transcriptome Atlas, its global characterization and define sets of genes co-regulated in highly organ- and temporally-specific manners. RNAs were extracted and transcriptomes sequenced from 123 different tissues and stages of development representing major organs and developmental stages of the cacao lifecycle. In addition, several experimental treatments and time courses were performed to measure gene expression in tissues responding to biotic and abiotic stressors. Samples were collected in replicates (3-5) to enable statistical analysis of gene expression levels for a total of 390 transcriptomes. To promote wide use of these data, all raw sequencing data, expression read mapping matrices, scripts, and other information used to create the resource are freely available online. We verified our atlas by analyzing the expression of genes with known functions and expression patterns in Arabidopsis (ACT7, LEA19, AGL16, TIP13, LHY, MYB2) and found their expression profiles to be generally similar between both species. We also successfully identified tissue-specific genes at two thresholds in many tissue types represented and a set of genes highly conserved across all tissues. CONCLUSION: The Cacao Gene Atlas consists of a gene expression browser with graphical user interface and open access to raw sequencing data files as well as the unnormalized and CPM normalized read count data mapped to several cacao genomes. The gene atlas is a publicly available resource to allow rapid mining of cacao gene expression profiles. We hope this resource will be used to help accelerate the discovery of important genes for key cacao traits such as disease resistance and contribute to the breeding of elite varieties to help farmers increase yields.

Graphormer-IR: Graph Transformers Predict Experimental IR Spectra Using Highly Specialized Attention.

Infrared (IR) spectroscopy is an important analytical tool in various chemical and forensic domains and a great deal of effort has gone into developing in silico methods for predicting experimental spectra. A key challenge in this regard is generating highly accurate spectra quickly to enable real-time feedback between computation and experiment. Here, we employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only simplified molecular-input line-entry system (SMILES) strings. Our data set includes 53,528 high-quality spectra, measured in five different experimental media (i.e., phases), for molecules containing the elements H, C, N, O, F, Si, S, P, Cl, Br, and I. When using only atomic numbers for node encodings, Graphormer-IR achieved a mean test spectral information similarity (SISµ) value of 0.8449 ± 0.0012 (n = 5), which surpasses that the current state-of-the-art model Chemprop-IR (SISµ = 0.8409 ± 0.0014, n = 5) with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multilayer perceptron improves scores to SISµ = 0.8523 ± 0.0006, a total improvement of 19.7σ (t = 19). These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for phase encoding, learned node feature embeddings, and a one-dimensional (1D) smoothing convolutional neural network (CNN). Graphormer-IR's innovations underscore its value over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intramolecular relationships.

hSSB1 (NABP2/OBFC2B) modulates the DNA damage and androgen-induced transcriptional response in prostate cancer.

BACKGROUND: Activation and regulation of androgen receptor (AR) signaling and the DNA damage response impact the prostate cancer (PCa) treatment modalities of androgen deprivation therapy (ADT) and radiotherapy. Here, we have evaluated a role for human single-strand binding protein 1 (hSSB1/NABP2) in modulation of the cellular response to androgens and ionizing radiation (IR). hSSB1 has defined roles in transcription and maintenance of genome stability, yet little is known about this protein in PCa. METHODS: We correlated hSSB1 with measures of genomic instability across available PCa cases from The Cancer Genome Atlas (TCGA). Microarray and subsequent pathway and transcription factor enrichment analysis were performed on LNCaP and DU145 prostate cancer cells. RESULTS: Our data demonstrate that hSSB1 expression in PCa correlates with measures of genomic instability including multigene signatures and genomic scars that are reflective of defects in the repair of DNA double-strand breaks via homologous recombination. In response to IR-induced DNA damage, we demonstrate that hSSB1 regulates cellular pathways that control cell cycle progression and the associated checkpoints. In keeping with a role for hSSB1 in transcription, our analysis revealed that hSSB1 negatively modulates p53 and RNA polymerase II transcription in PCa. Of relevance to PCa pathology, our findings highlight a transcriptional role for hSSB1 in regulating the androgen response. We identified that AR function is predicted to be impacted by hSSB1 depletion, whereby this protein is required to modulate AR gene activity in PCa. CONCLUSIONS: Our findings point to a key role for hSSB1 in mediating the cellular response to androgen and DNA damage via modulation of transcription. Exploiting hSSB1 in PCa might yield benefits as a strategy to ensure a durable response to ADT and/or radiotherapy and improved patient outcomes.

Argentination: A Silver Bullet for Cannabinoid Separation by Differential Mobility Spectrometry.

As the legality of cannabis continues to evolve globally, there is a growing demand for methods that can accurately quantitate cannabinoids found in commercial products. However, the isobaric nature of many cannabinoids, along with variations in extraction methods and product formulations, makes cannabinoid quantitation by mass spectrometry (MS) challenging. Here, we demonstrate that differential mobility spectrometry (DMS) and tandem-MS can distinguish a set of seven cannabinoids, five of which are isobaric: Δ9-tetrahydrocannabinol (Δ9-THC), Δ8-THC, exo-THC, cannabidiol, cannabichromene, cannabinol, and cannabigerol. Analytes were detected as argentinated species ([M + Ag]+), which, when subjected to collision-induced dissociation, led to the unexpected discovery that argentination promotes distinct fragmentation patterns for each cannabinoid. The unique fragment ions formed were rationalized by discerning fragmentation mechanisms that follow each cannabinoid's MS3 behavior. The differing fragmentation behaviors between species suggest that argentination can distinguish cannabinoids by tandem-MS, although not quantitatively as some cannabinoids produce small amounts of a fragment ion that is isobaric with the major fragment generated by another cannabinoid. By adding DMS to the tandem-MS workflow, it becomes possible to resolve each cannabinoid in a pure N2 environment by deconvoluting the contribution of each cannabinoid to a specific fragmentation channel. To this end, we used DMS in conjunction with a multiple reaction monitoring workflow to assess cannabinoid levels in two cannabis extracts. Our methodology exhibited excellent accuracy, limits of detection (10-20 ppb depending on the cannabinoid), and linearity during quantitation by standard addition (R2 > 0.99).

Novel 3D printable PEEK-HA-Mg2SiO4 composite material for spine implants: biocompatibility and imaging compatibility assessments.

PURPOSE: To develop a novel 3D printable polyether ether ketone (PEEK)-hydroxyapatite (HA)-magnesium orthosilicate (Mg2SiO4) composite material with enhanced properties for potential use in tumour, osteoporosis and other spinal conditions. We aim to evaluate biocompatibility and imaging compatibility of the material. METHODS: Materials were prepared in three different compositions, namely composite A: 75 weight % PEEK, 20 weight % HA, 5 weight % Mg2SiO4; composite B: 70 weight% PEEK, 25 weight % HA, 5 weight % Mg2SiO4; and composite C: 65 weight % PEEK, 30 weight % HA, 5 weight % Mg2SiO4. The materials were processed to obtain 3D printable filament. Biomechanical properties were analysed as per ASTM standards and biocompatibility of the novel material was evaluated using indirect and direct cell cytotoxicity tests. Cell viability of the novel material was compared to PEEK and PEEK-HA materials. The novel material was used to 3D print a standard spine cage. Furthermore, the CT and MR imaging compatibility of the novel material cage vs PEEK and PEEK-HA cages were evaluated using a phantom setup. RESULTS: Composite A resulted in optimal material processing to obtain a 3D printable filament, while composite B and C resulted in non-optimal processing. Composite A enhanced cell viability up to ~ 20% compared to PEEK and PEEK-HA materials. Composite A cage generated minimal/no artefacts on CT and MR imaging and the images were comparable to that of PEEK and PEEK-HA cages. CONCLUSION: Composite A demonstrated superior bioactivity vs PEEK and PEEK-HA materials and comparable imaging compatibility vs PEEK and PEEK-HA. Therefore, our material displays an excellent potential to manufacture spine implants with enhanced mechanical and bioactive property.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

PURPOSE: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS: We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS: Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION: A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Elemental and biochemical nutrient limitation of zooplankton: A meta-analysis.

Primary consumers in aquatic ecosystems are frequently limited by the quality of their food, often expressed as phytoplankton elemental and biochemical composition. However, the effects of these food quality indicators vary across studies, and we lack an integrated understanding of how elemental (e.g. nitrogen, phosphorus) and biochemical (e.g. fatty acid, sterol) limitations interactively influence aquatic food webs. Here, we present the results of a meta-analysis using >100 experimental studies, confirming that limitation by N, P, fatty acids, and sterols all have significant negative effects on zooplankton performance. However, effects varied by grazer response (growth vs. reproduction), specific manipulation, and across taxa. While P limitation had greater effects on zooplankton growth than fatty acids overall, P and fatty acid limitation had equal effects on reproduction. Furthermore, we show that: nutrient co-limitation in zooplankton is strong; effects of essential fatty acid limitation depend on P availability; indirect effects induced by P limitation exceed direct effects of mineral P limitation; and effects of nutrient amendments using laboratory phytoplankton isolates exceed those using natural field communities. Our meta-analysis reconciles contrasting views about the role of various food quality indicators, and their interactions, for zooplankton performance, and provides a mechanistic understanding of trophic transfer in aquatic environments.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Whole blood viscosity is associated with baseline cerebral perfusion in acute ischemic stroke.

Whole blood viscosity (WBV) is the intrinsic resistance to flow developed due to the frictional force between adjacent layers of flowing blood. Elevated WBV is an independent risk factor for stroke. Poor microcirculation due to elevated WBV can prevent adequate perfusion of the brain and might act as an important secondary factor for hypoperfusion in acute ischaemic stroke. In the present study, we examined the association of WBV with basal cerebral perfusion assessed by CT perfusion in acute ischaemic stroke. Confirmed acute ischemic stroke patients (n = 82) presenting in hours were recruited from the single centre. Patients underwent baseline multimodal CT (non-contrast CT, CT angiography and CT perfusion). Where clinically warranted, patients also underwent follow-up DWI. WBV was measured in duplicate within 2 h after sampling from 5-mL EDTA blood sample. WBV was significantly correlated with CT perfusion parameters such as perfusion lesion volume, ischemic core volume and mismatch ratio; DWI volume and baseline NIHSS. In a multivariate linear regression model, WBV significantly predicted acute perfusion lesion volume, core volume and mismatch ratio after adjusting for the effect of occlusion site and collateral status. Association of WBV with hypoperfusion (increased perfusion lesion volume, ischaemic core volume and mismatch ratio) suggest the role of erythrocyte rheology in cerebral haemodynamic of acute ischemic stroke. The present findings open new possibilities for therapeutic strategies targeting erythrocyte rheology to improve cerebral microcirculation in stroke.

Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.

Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work investigates the effect of training dataset size on the performance of deep learning classifiers, focusing on chest radiograph pneumothorax detection as a proxy visual task in the radiology domain. Two open-source datasets (ChestX-ray14 and CheXpert) comprising 291,454 images were merged and convolutional neural networks trained with stepwise increase in training dataset sizes. Model iterations at each dataset volume were evaluated on an external test set of 525 emergency department chest radiographs. Learning curve analysis was performed to fit the observed AUCs for all models generated. For all three network architectures tested, model AUCs and accuracy increased rapidly from 2 × 103 to 20 × 103 training samples, with more gradual increase until the maximum training dataset size of 291 × 103 images. AUCs for models trained with the maximum tested dataset size of 291 × 103 images were significantly higher than models trained with 20 × 103 images: ResNet-50: AUC20k = 0.86, AUC291k = 0.95, p < 0.001; DenseNet-121 AUC20k = 0.85, AUC291k = 0.93, p < 0.001; EfficientNet AUC20k = 0.92, AUC 291 k = 0.98, p < 0.001. Our study established learning curves describing the relationship between dataset training size and model performance of deep learning convolutional neural networks applied to a typical radiology binary classification task. These curves suggest a point of diminishing performance returns for increasing training data volumes, which algorithm developers should consider given the high costs of obtaining and labelling radiology data.

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.

Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumbar central canal, lateral recess, and neural -foraminal stenosis. Materials and Methods In this retrospective study, lumbar spine MRI scans obtained from September 2015 to September 2018 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality, as well as postgadolinium studies and studies of patients with scoliosis, were excluded. Axial T2-weighted and sagittal T1-weighted images were used. Studies were split into an internal training set (80%), validation set (9%), and test set (11%). Training data were labeled by four radiologists using predefined gradings (normal, mild, moderate, and severe). A two-component DL model was developed. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second CNN for classification. An internal test set was labeled by a musculoskeletal radiologist with 31 years of experience (reference standard) and two subspecialist radiologists (radiologist 1: A.M., 5 years of experience; radiologist 2: J.T.P.D.H., 9 years of experience). DL model performance on an external test set was evaluated. Detection recall (in percentage), interrater agreement (Gwet κ), sensitivity, and specificity were calculated. Results Overall, 446 MRI lumbar spine studies were analyzed (446 patients; mean age ± standard deviation, 52 years ± 19; 240 women), with 396 patients in the training (80%) and validation (9%) sets and 50 (11%) in the internal test set. For internal testing, DL model and radiologist central canal recall were greater than 99%, with reduced neural foramina recall for the DL model (84.5%) and radiologist 1 (83.9%) compared with radiologist 2 (97.1%) (P < .001). For internal testing, dichotomous classification (normal or mild vs moderate or severe) showed almost-perfect agreement for both radiologists and the DL model, with respective κ values of 0.98, 0.98, and 0.96 for the central canal; 0.92, 0.95, and 0.92 for lateral recesses; and 0.94, 0.95, and 0.89 for neural foramina (P < .001). External testing with 100 MRI scans of lumbar spines showed almost perfect agreement for the DL model for dichotomous classification of all ROIs (κ, 0.95-0.96; P < .001). Conclusion A deep learning model showed comparable agreement with subspecialist radiologists for detection and classification of central canal and lateral recess stenosis, with slightly lower agreement for neural foraminal stenosis at lumbar spine MRI. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Effect of pH on the activity of ice-binding protein from Marinomonas primoryensis.

The ability of an ice-binding protein (IBP) from Marinomonas primoryensis (MpIBP) to influence ice crystal growth and structure in nonphysiological pH environments was investigated in this work. The ability for MpIBP to retain ice interactivity under stressed environmental conditions was determined via (1) a modified splat assay to determine ice recrystallization inhibition (IRI) of polycrystalline ice and (2) nanoliter osmometry to evaluate the ability of MpIBP to dynamically shape the morphology of a single ice crystal. Circular dichroism (CD) was used to relate the IRI and DIS activity of MpIBP to secondary structure. The results illustrate that MpIBP secondary structure was stable between pH 6 and pH 10. It was found that MpIBP did not interact with ice at pH ≤ 4 or pH ≥ 13. At 6 ≤ pH ≥ 12 MpIBP exhibited a reduction in grain size of ice crystals as compared to control solutions and demonstrated dynamic ice shaping at 6 ≤ pH ≥ 10. The results substantiate that MpIBP retains some secondary structure and function in non-neutral pH environments; thereby, enabling its potential utility in nonphysiological materials science and engineering applications.

SARS-COV-2 INFECTION AND LONGITUDINAL FECAL SCREENING IN MALAYAN TIGERS (PANTHERA TIGRIS JACKSONI), AMUR TIGERS (PANTHERA TIGRIS ALTAICA ), AND AFRICAN LIONS (PANTHERA LEO KRUGERI) AT THE BRONX ZOO, NEW YORK, USA.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged as the cause of a global pandemic in 2019-2020. In March 2020, New York City became the epicenter in the United States for the pandemic. On 27 March 2020, a Malayan tiger (Panthera tigris jacksoni) at the Bronx Zoo in New York City developed a cough and wheezing with subsequent inappetence. Over the next week, an additional Malayan tiger and two Amur tigers (Panthera tigris altaica) in the same building and three lions (Panthera leo krugeri) in a separate building also became ill. The index case was anesthetized for diagnostic workup. Physical examination and bloodwork results were unremarkable. Thoracic radiography and ultrasonography revealed a bronchial pattern with peribronchial cuffing and mild lung consolidation with alveolar-interstitial syndrome, respectively. SARS-CoV-2 RNA was identified by real-time, reverse transcriptase PCR (rRT-PCR) on oropharyngeal and nasal swabs and tracheal wash fluid. Cytologic examination of tracheal wash fluid revealed necrosis, and viral RNA was detected in necrotic cells by in situ hybridization, confirming virus-associated tissue damage. SARS-CoV-2 was isolated from the tracheal wash fluid of the index case, as well as the feces from one Amur tiger and one lion. Fecal viral RNA shedding was confirmed in all seven clinical cases and an asymptomatic Amur tiger. Respiratory signs abated within 1-5 days for most animals, although they persisted intermittently for 16 days in the index case. Fecal RNA shedding persisted for as long as 35 days beyond cessation of respiratory signs. This case series describes the clinical presentation, diagnostic evaluation, and management of tigers and lions infected with SARS-CoV-2 and describes the duration of viral RNA fecal shedding in these cases. This report documents the first known natural transmission of SARS-CoV-2 from humans to nondomestic felids.

Reproducibility of whole-brain temperature mapping and metabolite quantification using proton magnetic resonance spectroscopy.

Assessing brain temperature can provide important information about disease processes (e.g., stroke, trauma) and therapeutic effects (e.g., cerebral hypothermia treatment). Whole-brain magnetic resonance spectroscopic imaging (WB-MRSI) is increasingly used to quantify brain metabolites across the entire brain. However, its feasibility and reliability for estimating brain temperature needs further validation. Therefore, the present study evaluates the reproducibility of WB-MRSI for temperature mapping as well as metabolite quantification across the whole brain in healthy volunteers. Ten healthy adults were scanned on three occasions 1 week apart. Brain temperature, along with four commonly assessed brain metabolites-total N-acetyl-aspartate (tNAA), total creatine (tCr), total choline (tCho) and myo-inositol (mI)-were measured from WB-MRSI data. Reproducibility was evaluated using the coefficient of variation (CV). The measured mean (range) of the intra-subject CVs was 0.9% (0.6%-1.6%) for brain temperature mapping, and 4.7% (2.5%-15.7%), 6.4% (2.4%-18.9%) and 14.2% (4.4%-52.6%) for tNAA, tCho and mI, respectively, with reference to tCr. Consistently larger variability was found when using H2 O as the reference for metabolite quantifications: 7.8% (3.3%-17.8%), 7.8% (3.1%-18.0%), 9.8% (3.7%-31.0%) and 17.0% (5.9%-54.0%) for tNAA, tCr, tCho and mI, respectively. Further, the larger the brain region (indicated by a greater number of voxels within that region), the better the reproducibility for both temperature and metabolite estimates. Our results demonstrate good reproducibility of whole-brain temperature and metabolite measurements using the WB-MRSI technique.

Pyrvinium Pamoate Use in a B cell Acute Lymphoblastic Leukemia Model of the Bone Tumor Microenvironment.

PURPOSE: Pyrvinium pamoate (PP) is an anthelmintic drug that has been found to have anti-cancer activity in several cancer types. In the present study, we evaluated PP for potential anti-leukemic activity in B cell acute lymphoblastic leukemia (ALL) cell lines, in an effort to evaluate the repurposing potential of this drug in leukemia. METHODS: ALL cells were treated with PP at various concentrations to determine its effect on cell proliferation. Metabolic function was tested by evaluating Extracellular Acidification Rate (ECAR) and Oxygen Consumption Rate (OCR). Lastly, 3D spheroids were grown, and PP was reformulated into nanoparticles to evaluate distribution effectiveness. RESULTS: PP was found to inhibit ALL proliferation, with varied selectivity to different ALL cell subtypes. We also found that PP's cell death activity was specific for leukemic cells, as primary normal immune cells were resistant to PP-mediated cell death. Metabolic studies indicated that PP, in part, inhibits mitochondrial oxidative phosphorylation. To increase the targeting of PP to a hypoxic bone tumor microenvironment (BTME) niche, we successfully encapsulated PP in a nanoparticle drug delivery system and demonstrated that it retained its anti-leukemic activity in a hemosphere assay. CONCLUSION: We have demonstrated that PP is a novel therapeutic lead compound that counteracts the respiratory reprogramming found in refractory ALL cells and can be effectively formulated into a nanoparticle delivery system to target the BTME.

Hypoalbuminemia in children with cancer treated with chemotherapy.

BACKGROUND: Hypoalbuminemia is a well-recognized finding associated with cancer, but its prevalence and prognostic significance have not been well studied in children with cancer. OBJECTIVE: To determine the prevalence of hypoalbuminemia prior to starting chemotherapy in children with cancer and its association with relapse-free survival (RFS) and overall survival (OS). DESIGN/METHOD: We performed a single institution, IRB-approved, retrospective review of pediatric oncology patients diagnosed between 1998 and 2012. Five-year survival was estimated using the Kaplan-Meier method; groups were compared using Cox regression. RESULTS: We identified 659 pediatric patients with a first diagnosis of cancer and a serum albumin level prior to starting chemotherapy. Mean age was 8.6 years (SD = 5.8); 62% were male and 92% were non-Hispanic. Hypoalbuminemia prior to starting chemotherapy was present at least once in 302 (45.8%). The five-year RFS and OS of those with hypoalbuminemia and without hypoalbuminemia were not significantly different. However, patients with severe hypoalbuminemia (defined as a value 10% or more below the lower limit of normal) had inferior RFS and OS for patients with hematologic/lymphatic malignancies, and inferior RFS for patients with metstatic Ewing sarcoma. CONCLUSION: Hypoalbuminemia prior to starting chemotherapy in pediatric oncology patients is common (nearly half in this cohort). Severe hypoalbuminemia was associated with inferior 5-year RFS in some subgroups.

Mid-infrared long-pass filter for high-power applications based on grating diffraction.

A gold-coated silicon grating has been developed, enabling efficient spatial separation of broadband mid-infrared (MIR) beams with wavelengths >5 µm from collinearly propagating, broadband, high-power light in the near-infrared (NIR) spectral range (centered at 2 µm). The optic provides spectral filtering at high powers in a thermally robust and chromatic-dispersion-free manner such as that necessary for coherent MIR radiation sources based on parametric frequency downconversion of femtosecond NIR lasers. The suppression of a >20 W average-power, 2 µm driving pulse train by three orders of magnitude, while retaining high reflectivity of the broadband MIR beam, is presented.

Impact of cyclophosphamide and etoposide on outcome of clear cell sarcoma of the kidney treated on the National Wilms Tumor Study-5 (NWTS-5).

PURPOSE: To improve the event-free survival (EFS) and overall survival (OS) for patients with clear cell sarcoma of the kidney (CCSK) by incorporating cyclophosphamide and etoposide into treatment on National Wilms Tumor Study (NWTS)-5. PATIENTS AND METHODS: Patients less than 16 years of age with a centrally confirmed pathological diagnosis of CCSK were eligible for treatment on this prospective single-arm study conducted between August 1995 and June 2002. Staging consisted of CT scans of chest, abdomen, pelvis, bone scan, skeletal survey, and CT or MRI of the head. Treatment consisted of vincristine/doxorubicin/cyclophosphamide alternating with cyclophosphamide/etoposide for 24 weeks and radiation to sites of disease. RESULTS: One hundred eight eligible patients were enrolled on study (69% males, 63% Caucasian), with a median age of 22 months. Stage distribution was as follows: stage I, 12; II, 44; III, 45; IV, 7. Median follow-up was 9.7 years. Five-year EFS and OS were 79% (95% CI: 71%-88%) and 90% (95% CI: 84%-96%). Five-year EFS for stage I-IV was 100%, 88%, 73%, and 29%, respectively. Twenty of the 23 disease-related events occurred within three years of initial treatment. The most common site of recurrence was brain (12/23). CONCLUSION: The outcome for patients with CCSK treated on NWTS-5 was similar to NWTS-4 and accomplished over a shorter treatment duration. Stage was highly predictive of outcome. Brain metastases occurred more frequently than on NWTS-4. Regimen I showed more benefit for patients with stage I and II disease as compared with higher stages of disease where new therapies are needed.

Exposure to phthalate-containing prescription drugs and the risk of colorectal adenocarcinoma: A Danish nationwide case-control study.

PURPOSE: Some drug products contain phthalates as excipients, and in vitro studies have demonstrated that phthalates interfere with cellular mechanisms involved in colorectal cancer development. We therefore examined the association between cumulative phthalate exposure from drug products and risk of colorectal adenocarcinomas. METHODS: We used the Danish Cancer Registry to identify all patients with incident colorectal adenocarcinoma from 2008 to 2015 (n = 25 814). Each cancer case was matched to ten population controls. Linking information from Danish registers, we quantified cumulative phthalate exposure to the ortho-phthalates diethyl phthalate (DEP) and dibutyl phthalate (DBP) as well as enteric phthalate polymers from orally administered drugs. The association between cumulative phthalate exposure and colorectal cancer was estimated using conditional logistic regression. RESULTS: Cumulative exposure to ortho-phthalates exceeding 500 mg was associated with lower odds of colorectal cancer diagnosis (ORadj  = 0.89; 95% CI, 0.81-0.96). Similar associations were observed for all DEP exposure exceeding 500 mg. Subgroup analysis excluding NSAID users, demonstrated that ortho-phthalate exposure was positively associated with colorectal cancer (ORadj  = 1.26; 95% CI, 1.05-1.51). CONCLUSION: We found an apparent overall protective effect of cumulative phthalate exposure from drug excipients for colorectal adenocarcinoma. Omitting NSAID users reversed the signal and suggested a slightly increased risk associated with high cumulative ortho-phthalate exposure.