High density optical neuroimaging predicts surgeons's subjective experience and skill levels

Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. These have disadvantages such as sporadic data, occasionally intrusive methodologies, subjective or misleading self-reporting. In addition, traditional approaches use subjective metrics that cannot distinguish between skill levels. Functional neuroimaging data was collected using a high density, wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using the NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However in the case of attending surgeons the opposite tendency was observed, namely higher activations in the lower v higher task loaded subjects. We found that response was greater in the left PFC of students particularly near the dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict the differences in skill and task load using machine learning while focussing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Our finding shows that there is sufficient information available in the optical signals to make accurate predictions about the surgeons’ subjective experiences and skill levels. The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods

Regulation of WNT Signaling by VSX2 During Optic Vesicle Patterning in Human Induced Pluripotent Stem Cells

Few gene targets of Visual System Homeobox 2 (VSX2) have been identified despite its broad and critical role in the maintenance of neural retina (NR) fate during early retinogenesis. We performed VSX2 ChIP-seq and ChIP-PCR assays on early stage optic vesicle-like structures (OVs) derived from human iPS cells (hiPSCs), which highlighted WNT pathway genes as direct regulatory targets of VSX2. Examination of early NR patterning in hiPSC-OVs from a patient with a functional null mutation in VSX2 revealed mis-expression and upregulation of WNT pathway components and retinal pigmented epithelium (RPE) markers in comparison to control hiPSCOVs. Furthermore, pharmacological inhibition of WNT signaling rescued the early mutant phenotype, whereas augmentation of WNT signaling in control hiPSC-OVs phenocopied the mutant. These findings reveal an important role for VSX2 as a regulator of WNT signaling and suggest that VSX2 may act to maintain NR identity at the expense of RPE in part by direct repression of WNT pathway constituents

Probing the atmosphere of HD189733b with the Na I and K I lines

High spectral resolution transmission spectroscopy is a powerful tool to characterize exoplanet atmospheres. Especially for hot Jupiters, this technique is highly relevant, due to their high altitude absorption e.g. from resonant sodium (Na I) and potassium (K I) lines. We resolve the atmospheric K I-absorption on HD189733b with the aim to compare the resolved K I -line and previously obtained high resolution Na I-D-line observations with synthetic transmission spectra. The line profiles suggest atmospheric processes leading to a line broadening of the order of 10 km/s for the Na I-D-lines, and only a few km/s for the K I-line. The investigation hints that either the atmosphere of HD189733b lacks a significant amount of K I or the alkali lines probe different atmospheric regions with different temperature, which could explain the differences we see in the resolved absorption lines

Ephemeris refinement of 21 Hot Jupiter exoplanets with high timing uncertainties

Transit events of extrasolar planets offer a wealth of information for planetary characterization. However, for many known targets, the uncertainty of their predicted transit windows prohibits an accurate scheduling of follow-up observations. In this work, we refine the ephemerides of 21 Hot Jupiter exoplanets with the largest timing uncertainty. We collected 120 professional and amateur transit light curves of the targets of interest, observed with 0.3m to 2.2m telescopes, and analyzed them including the timing information of the planets discovery papers. In the case of WASP-117b, we measured a timing deviation compared to the known ephemeris of about 3.5 hours, for HAT-P-29b and HAT-P-31b the deviation amounted to about 2 hours and more. For all targets, the new ephemeris predicts transit timings with uncertainties of less than 6 minutes in the year 2018 and less than 13 minutes until 2025. Thus, our results allow for an accurate scheduling of follow-up observations in the next decade

Prospective cohort study of radiotherapy with concomitant and adjuvant temozolomide chemotherapy for glioblastoma patients with no or minimal residual enhancing tumor load after surgery

Survival of glioblastoma patients has been linked to the completeness of surgical resection. Available data, however, were generated with adjuvant radiotherapy. Data confirming that extensive cytoreduction remains beneficial to patients treated with the current standard, concomitant temozolomide radiochemotherapy, are limited. We therefore analyzed the efficacy of radiochemotherapy for patients with little or no residual tumor after surgery. In this prospective, non-interventional multicenter cohort study, entry criteria were histological diagnosis of glioblastoma, small enhancing or no residual tumor on post-operative MRI, and intended temozolomide radiochemotherapy. The primary study objective was progression-free survival; secondary study objectives were survival and toxicity. Furthermore, the prognostic value of O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation was investigated in a subgroup of patients. One-hundred and eighty patients were enrolled. Fourteen were excluded by patient request or failure to initiate radiochemotherapy. Twenty-three patients had non-evaluable post-operative imaging. Thus, 143 patients qualified for analysis, with 107 patients having residual tumor diameters ≤1.5 cm. Median follow-up was 24.0 months. Median survival or patients without residual enhancing tumor exceeded the follow-up period. Median survival was 16.9 months for 32 patients with residual tumor diameters >0 to ≤1.5 cm (95% CI: 13.3–20.5, p = 0.039), and 13.9 months (10.3–17.5, overall p < 0.001) for 36 patients with residual tumor diameters >1.5 cm. Patient age at diagnosis and extent of resection were independently associated with survival. Patients with MGMT promoter methylated tumors and complete resection made the best prognosis. Completeness of resection acts synergistically with concomitant and adjuvant radiochemotherapy, especially in patients with MGMT promoter methylation

Cognitive Trajectories in Preclinical and Prodromal Alzheimer's Disease Related to Amyloid Status and Brain Atrophy:A Bayesian Approach

Background: Cognitive decline is a key outcome of clinical studies in Alzheimer’s disease (AD). Objective: To determine effects of global amyloid load as well as hippocampus and basal forebrain volumes on longitudinal rates and practice effects from repeated testing of domain specific cognitive change in the AD spectrum, considering non-linear effects and heterogeneity across cohorts. Methods: We included 1,514 cases from three cohorts, ADNI, AIBL, and DELCODE, spanning the range from cognitively normal people to people with subjective cognitive decline and mild cognitive impairment (MCI). We used generalized Bayesian mixed effects analysis of linear and polynomial models of amyloid and volume effects in time. Robustness of effects across cohorts was determined using Bayesian random effects meta-analysis. Results: We found a consistent effect of amyloid and hippocampus volume, but not of basal forebrain volume, on rates of memory change across the three cohorts in the meta-analysis. Effects for amyloid and volumetric markers on executive function were more heterogeneous. We found practice effects in memory and executive performance in amyloid negative cognitively normal controls and MCI cases, but only to a smaller degree in amyloid positive controls and not at all in amyloid positive MCI cases. Conclusions: We found heterogeneity between cohorts, particularly in effects on executive functions. Initial increases in cognitive performance in amyloid negative, but not in amyloid positive MCI cases and controls may reflect practice effects from repeated testing that are lost with higher levels of cerebral amyloid

Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)