Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Dietary fiber showed no preventive effect against colon and rectal cancers in Japanese with low fat intake: an analysis from the results of nutrition surveys from 23 Japanese prefectures

BACKGROUND: Since Fuchs' report in 1999, the reported protective effect of dietary fiber from colorectal carcinogenesis has led many researchers to question its real benefit. The aim of this study is to evaluate the association between diet, especially dietary fiber and fat and colorectal cancer in Japan. METHODS: A multiple regression analysis (using the stepwise variable selection method) was performed using the standardized mortality ratios (SMRs) of colon and rectal cancer in 23 Japanese prefectures as objective variables and dietary fiber, nutrients and food groups as explanatory variables. RESULTS: As for colon cancer, the standardized partial correlation coefficients were positively significant for fat (1,13, P = 0.000), seaweeds (0.41, P = 0.026) and beans (0.45, P = 0.017) and were negatively significant for vitamin A (-0.63, P = 0.003), vitamin C (-0.42, P = 0.019) and yellow-green vegetables (-0.37, P = 0.046). For rectal cancer, the standardized partial correlation coefficient in fat (0.60, P = 0.002) was positively significant. Dietary fiber was not found to have a significant relationship with either colon or rectal cancers. CONCLUSIONS: This study failed to show any protective effect of dietary fiber in subjects with a low fat intake (Japanese) in this analysis, which supports Fuchs' findings in subjects with a high fat intake (US Americans)

Probing High Reheating Temperature Scenarios at the LHC with Long-Lived Staus

We investigate the possibility of probing high reheating temperature scenarios at the LHC, in supersymmetric models where the gravitino is the lightest supersymmetric particle, and the stau is the next-to-lightest supersymmetric particle. In such scenarios, the big-bang nucleosynthesis and the gravitino abundance give a severe upper bound on the gluino mass. We find that, if the reheating temperature is \sim 10^8 GeV or higher, the scenarios can be tested at the LHC with an integrated luminosity of O(1 fb^{-1}) at \sqrt{s}=7 TeV in most of the parameter space.Comment: 17 pages, 5 figures, minor modification

Descriptive Strength and Range of Motion in Youth Baseball Players

# Background There are limited studies reporting descriptive strength and range of motion in youth baseball players 12 years of age or younger. # Purpose To establish normative data for external (ER) and internal (IR) rotation range of motion (ROM), total arc range of motion (TROM), and isometric rotator cuff strength in youth baseball players, and to compare between the dominant throwing arm (D) to the non-dominant arm (ND). # Study Design Cross-sectional # Methods Patient population included 50 (5 to 12-year-old) uninjured, healthy athletes. ROM measurements were performed preseason using a goniometer for IR and ER in the supine position with the shoulder in 90 degrees of abduction (abd) with scapular stabilization. Isometric strength measurements for IR and ER were collected in both neutral and 90 degrees (deg) of abduction with the use of a hand-held dynamometer and recorded in pounds (lbs) utilizing a “make” test. Descriptive statistics were obtained for all measures. # Results All data were analyzed as a single group (average age: 9.02). No significant difference in average total arc of PROM (ER+IR=Total Arc) on the D side compared to the ND side (136.7 ± 12.7 deg vs. 134.3 ± 12.3 deg). There were statistically significant differences between ER ROM (102.2 ± 7.7 deg vs. 96.8 ± 7.4 deg) and IR ROM (34.4 ± 9.0 deg vs. 37.5 ± 9.5 deg) between D versus ND arms (p= .000, .006 respectively). Mean ER strength in neutral (13.6 ± 3.4 and 12.8 ± 3.6 lbs) and 90 deg abduction (12.3 ± 3.4 and 12.5 ± 4.3 lbs) did were not significantly different between D and ND arms, respectively. Mean IR strength in neutral (18.0 ± 6.0 and 15.7 ± 4.7 lbs) and 90 deg abd (16.4 ± 5.6 and 15.0 ± 5.7 lbs) was significantly greater in the D arm vs ND arm, respectively (p=.000, .001). # Conclusion These data can provide descriptive information for clinicians who treat very young baseball players. These data show sport specific adaptations occur at very young ages (5-12) and are similar to prior reports on adolescent, high school and professional baseball players regarding upper extremity ROM and rotator cuff strength. # Level of Evidence

Extracellular microRNAs in blood differentiate between ischaemic and haemorrhagic stroke subtypes.

Rapid identification of patients suffering from cerebral ischaemia, while excluding intracerebral haemorrhage, can assist with patient triage and expand patient access to chemical and mechanical revascularization. We sought to identify blood-based, extracellular microRNAs 15 (ex-miRNAs) derived from extracellular vesicles associated with major stroke subtypes using clinical samples from subjects with spontaneous intraparenchymal haemorrhage (IPH), aneurysmal subarachnoid haemorrhage (SAH) and ischaemic stroke due to cerebral vessel occlusion. We collected blood from patients presenting with IPH (n = 19), SAH (n = 17) and ischaemic stroke (n = 21). We isolated extracellular vesicles from plasma, extracted RNA cargo, 20 sequenced the small RNAs and performed bioinformatic analyses to identify ex-miRNA biomarkers predictive of the stroke subtypes. Sixty-seven miRNAs were significantly variant across the stroke subtypes. A subset of exmiRNAs differed between haemorrhagic and ischaemic strokes, and LASSO analysis could distinguish SAH from the other subtypes with an accuracy of 0.972 ± 0.002. Further analyses predicted 25 miRNA classifiers that stratify IPH from ischaemic stroke with an accuracy of 0.811 ± 0.004 and distinguish haemorrhagic from ischaemic stroke with an accuracy of 0.813 ± 0.003. Blood-based, ex-miRNAs have predictive value, and could be capable of distinguishing between major stroke subtypes with refinement and validation. Such a biomarker could one day aid in the triage of patients to expand the pool eligible for effective treatment

Extracellular microRNAs in blood differentiate between ischaemic and haemorrhagic stroke subtypes

Rapid identification of patients suffering from cerebral ischaemia, while excluding intracerebral haemorrhage, can assist with patient triage and expand patient access to chemical and mechanical revascularization. We sought to identify blood-based, extracellular microRNAs 15 (ex-miRNAs) derived from extracellular vesicles associated with major stroke subtypes using clinical samples from subjects with spontaneous intraparenchymal haemorrhage (IPH), aneurysmal subarachnoid haemorrhage (SAH) and ischaemic stroke due to cerebral vessel occlusion. We collected blood from patients presenting with IPH (n = 19), SAH (n = 17) and ischaemic stroke (n = 21). We isolated extracellular vesicles from plasma, extracted RNA cargo, 20 sequenced the small RNAs and performed bioinformatic analyses to identify ex-miRNA biomarkers predictive of the stroke subtypes. Sixty-seven miRNAs were significantly variant across the stroke subtypes. A subset of exmiRNAs differed between haemorrhagic and ischaemic strokes, and LASSO analysis could distinguish SAH from the other subtypes with an accuracy of 0.972 +/- 0.002. Further analyses predicted 25 miRNA classifiers that stratify IPH from ischaemic stroke with an accuracy of 0.811 +/- 0.004 and distinguish haemorrhagic from ischaemic stroke with an accuracy of 0.813 +/- 0.003. Blood-based, ex-miRNAs have predictive value, and could be capable of distinguishing between major stroke subtypes with refinement and validation. Such a biomarker could one day aid in the triage of patients to expand the pool eligible for effective treatment.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

A Large Change in Temperature between Neighbouring Days Increases the Risk of Mortality

Background: Previous studies have found high temperatures increase the risk of mortality in summer. However, little is known about whether a sharp decrease or increase in temperature between neighbouring days has any effect on mortality. Method: Poisson regression models were used to estimate the association between temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and Los Angeles, United States during 1987–2000. The temperature change was calculated as the current day’s mean temperature minus the previous day’s mean. Results: In Brisbane, a drop of more than 3 °C in temperature between days was associated with relative risks (RRs) of 1.157 (95% confidence interval (CI): 1.024, 1.307) for total non external mortality (NEM), 1.186 (95%CI: 1.002, 1.405) for NEM in females, and 1.442 (95%CI: 1.099, 1.892) for people aged 65–74 years. An increase of more than 3 °C was associated with RRs of 1.353 (95%CI: 1.033, 1.772) for cardiovascular mortality and 1.667 (95%CI: 1.146, 2.425) for people aged < 65 years. In Los Angeles, only a drop of more than 3 °C was significantly associated with RRs of 1.133 (95%CI: 1.053, 1.219) for total NEM, 1.252 (95%CI: 1.131, 1.386) for cardiovascular mortality, and 1.254 (95%CI: 1.135, 1.385) for people aged ≥75 years. In both cities, there were joint effects of temperature change and mean temperature on NEM. Conclusion : A significant change in temperature of more than 3 °C, whether positive or negative, has an adverse impact on mortality even after controlling for the current temperature