NEUTRON MEASUREMENTS AT HANFORD’S PLUTONIUM FINISHING PLANT

The Pacific Northwest National Laboratory (PNNL) conducted neutron measurements at Hanford’s Plutonium Finishing Plant (PFP). The measurements were performed to evaluate the performance of the Hanford Standard Dosimeter (HSD) and the 8816 TLD component of the Hanford Combination Neutron Dosimeter (HCND) in the neutron fields responsible for worker neutron exposures. For this study, TEPC detectors and multisphere spectrometers were used to measure neutron dose equivalent rate, and multispheres were used to measure average neutron energy. Water-filled phantoms holding Hanford dosimeters were positioned at each measurement location. The phantoms were positioned in the same location where a multisphere measurement was taken and TEPCs were also positioned there. Plant survey meters were also used to measure neutron dose rates at all locations. Three measurement locations were chose near the HC-9B glovebox in room 228A of Building 234-5. The multisphere spectrometers measured average neutron energies in the range of 337 to 555 keV at these locations. Personal dose equivalent, Hp(10)n, as measured by the multisphere and TEPC, ranged from 2.7 to 9.7 mrem/h in the three locations. Effective dose assuming a rotational geometry (EROT) was substantially lower than Hp(10), ranging from 1.3 to 3.6 mrem/h. These values were lower than the reported values from dosimeters exposed on a rotating phantom. Effective dose assuming an AP geometry (EAP) was also substantially lower than Hp(10), ranging from 2.3 to 6.5 mrem/h. These values were lower than the reported values from the dosimeters on slab phantoms. Since the effective dose values were lower than reported values from dosimeters, the dosimeters were shown to be conservative estimates of the protection quantities

CApecitabine plus Radium-223 (Xofigo™) in breast cancer patients with BONe metastases (CARBON): study protocol for a phase IB/IIA randomised controlled trial

Background: A substantial proportion of breast cancer patients develop metastatic disease, with over 450,000 deaths globally per year. Bone is the most common first site of metastatic disease accounting for 40% of all first recurrence and 70% of patients with advanced disease develop skeletal involvement. Treatment of bone metastases currently focusses on symptom relief and prevention and treatment of skeletal complications. However, there remains a need for further treatment options for patients with bone metastases. Combining systemic therapy with a bone-targeted agent, such as radium-223, may provide an effective treatment with minimal additional side effects. Methods/design: CARBON is a UK-based, open-label, multi-centre study which comprises an initial safety phase to establish the feasibility and safety of combining radium-223 given on a 6-weekly schedule in combination with orally administered capecitabine followed by a randomised extension phase to further characterise the safety profile and provide preliminary estimation of efficacy. Discussion: The CARBON study is important as the results will be the first to assess radium-223 with chemotherapy in advanced breast cancer. If the results find acceptable rates of toxicity with a decrease in bone turnover markers, further work will be necessary in a phase II/III setting to assess the efficacy and clinical benefit. Trial registration: ISRCTN, ISRCTN92755158, Registered on 17 February 2016

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

Vibrationally induced inversion of photoelectron forward-backward asymmetry in chiral molecule photoionization by circularly polarized light

Electron–nuclei coupling accompanying excitation and relaxation processes is a fascinating phenomenon in molecular dynamics. A striking and unexpected example of such coupling is presented here in the context of photoelectron circular dichroism measurements on randomly oriented, chiral methyloxirane molecules, unaffected by any continuum resonance. Here, we report that the forward-backward asymmetry in the electron angular distribution, with respect to the photon axis, which is associated with photoelectron circular dichroism can surprisingly reverse direction according to the ion vibrational mode excited. This vibrational dependence represents a clear breakdown of the usual Franck–Condon assumption, ascribed to the enhanced sensitivity of photoelectron circular dichroism (compared with other observables like cross-sections or the conventional anisotropy parameter-β) to the scattering phase off the chiral molecular potential, inducing a dependence on the nuclear geometry sampled in the photoionization process. Important consequences for the interpretation of such dichroism measurements within analytical contexts are discussed

Guanosine effect on cholesterol efflux and apolipoprotein E expression in astrocytes

The main source of cholesterol in the central nervous system (CNS) is represented by glial cells, mainly astrocytes, which also synthesise and secrete apolipoproteins, in particular apolipoprotein E (ApoE), the major apolipoprotein in the brain, thus generating cholesterol-rich high density lipoproteins (HDLs). This cholesterol trafficking, even though still poorly known, is considered to play a key role in different aspects of neuronal plasticity and in the stabilisation of synaptic transmission. Moreover, cell cholesterol depletion has recently been linked to a reduction in amyloid beta formation. Here we demonstrate that guanosine, which we previously reported to exert several neuroprotective effects, was able to increase cholesterol efflux from astrocytes and C6 rat glioma cells in the absence of exogenously added acceptors. In this effect the phosphoinositide 3 kinase/extracellular signal-regulated kinase 1/2 (PI3K/ERK1/2) pathway seems to play a pivotal role. Guanosine was also able to increase the expression of ApoE in astrocytes, whereas it did not modify the levels of ATP-binding cassette protein A1 (ABCA1), considered the main cholesterol transporter in the CNS. Given the emerging role of cholesterol balance in neuronal repair, these effects provide evidence for a role of guanosine as a potential pharmacological tool in the modulation of cholesterol homeostasis in the brain

Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. METHODS: We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross-validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). RESULTS: ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using the assigned inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. CONCLUSIONS: This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews with different inclusion prevalence levels, but represents a promising approach to integrating human decisions and automation in systematic review methodology

Guanosine reduces apoptosis and inflammation associated with restoration of function in rats with acute spinal cord injury

Spinal cord injury results in progressive waves of secondary injuries, cascades of noxious pathological mechanisms that substantially exacerbate the primary injury and the resultant permanent functional deficits. Secondary injuries are associated with inflammation, excessive cytokine release, and cell apoptosis. The purine nucleoside guanosine has significant trophic effects and is neuroprotective, antiapoptotic in vitro, and stimulates nerve regeneration. Therefore, we determined whether systemic administration of guanosine could protect rats from some of the secondary effects of spinal cord injury, thereby reducing neurological deficits. Systemic administration of guanosine (8 mg/kg per day, i.p.) for 14 consecutive days, starting 4 h after moderate spinal cord injury in rats, significantly improved not only motor and sensory functions, but also recovery of bladder function. These improvements were associated with reduction in the inflammatory response to injury, reduction of apoptotic cell death, increased sparing of axons, and preservation of myelin. Our data indicate that the therapeutic action of guanosine probably results from reducing inflammation resulting in the protection of axons, oligodendrocytes, and neurons and from inhibiting apoptotic cell death. These data raise the intriguing possibility that guanosine may also be able to reduce secondary pathological events and thus improve functional outcome after traumatic spinal cord injury in humans