922017 research outputs found
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Palliative radiotherapy in cancers of female genital tract: Outcomes and prognostic factors
No full textBackground and purposeMetastatic and incurable cancers of the gynaecological tract (FGTC) represent a major global health burden. Systemic treatment has modest efficacy and radiotherapy is often used for local symptoms. This study combines experience from two large UK centres in palliative radiotherapy for gynaecological cancers. Materials and methodsPooled data from two major centres was analysed. Advanced FGTC patients who received at least one fraction of palliative radiotherapy to the pelvis between 2013 to 2018 were included. Data collected included demographic and tumour details, radiotherapy dose fractionation and details of previous and subsequent treatment. Response was defined in terms of toxicity, symptomatic response and survival. Comorbidities were recorded using a modified ACE 27 score which is adjusted for the presence of uncontrolled FGTC in all the patients.ResultsA total of 184 patients were included for treatment response and toxicity; survival data was available for 165 patients. Subjective response in pre-radiotherapy symptoms was documented in 80.4%. Grade 3 or worse gastrointestinal, urinary and other(vomiting, fatigue, pain ) toxicity incidence was 2.2%, 3.8%, and 2.7% respectively. No statistically significant correlation between the prescribed EQD210 and symptom control or toxicity was seen. 1 year overall survival was 25.1% (median 5.9 months). Absent distant metastases, completion of the intended course of radiotherapy, response to radiotherapy, and receipt of further lines of treatment were independent prognostic factors. Conclusion.Palliative radiotherapy is effective for symptoms of advanced FGTC with low toxicity. The absence of a dose response argues for short low dose palliative radiotherapy schedules to be used.<br/
Understanding the Effects of Zn Injection and OLNC-Treatment on 316 Stainless Steel Oxide under Simulated BWR Conditions
No full textThis study investigates the mechanism of incorporation of metal cations (Zn and Co) into the 316 stainless steel (SS) oxide under hydrogenated water chemistry (HWC) and On-Line NobleChemTM (OLNC). Coupons of 316 SS were exposed to simulated boiling water reactor (BWR) conditions (pure water, H:O molar ratio ~8) at 288 °C and then analysed via surface characterisation techniques. Specifically, coupons were initially exposed under HWC conditions for 500 h, then subjected for 200 h to the OLNCtreatment, and then a final exposure under HWC conditions for 500 h, all with simultaneous monitoring of the electrochemical corrosion potential. These exposures were performed both with and without Zn and or Co injection. It was found that Zn decreased the oxide thickness of the inner oxide layer and decreased the size of the crystallites on the outer layer. Addition of 0.2 ppb of Co in the water chemistry containing 10 ppb Zn did not appear to influence the oxide morphology nor its composition. Hard X-ray XPS analysis showed that Zn did not completely suppress Co incorporation in the outer oxide layer which was still found in concentrations up to 0.6 at. % on the surface of the oxide under Zn addition conditions
Effects of Sensor Design on the Performance of Wearable Sweat Monitors
No full textWearable sweat sensors are rapidly emerging for continuous and noninvasive monitoring of ‘wet’ parameters not included in current commercial wearables. This paper investigates the optimization of screen printed sweat sensors, intended to ultimately be compatible with large scale, roll-to-roll, fabrication. Sweat sensors containing Na+, K+ ion selective electrodes and pH sensing elements were screen printed on polyethylene naphthalate films with different electrode array designs. For sensing Na+ ions, a PEDOT:PSS transducer layer was found to effectively improve the sensor performance, showing an up to 32% sensitivity enhancement of up to 113.3mV/decade. Large working electrode sensing area was found to play an important role in achieving high electrical sensitivity, and the position of the electrodes influenced the individual sensor performance. These findings provide a solid foundation for an optimized development on wearable sweat sensors
Effect of aging treatment on phase evolution and mechanical properties of selective laser melted Al-Mg-Er-Zr alloy
No full textEr and Zr modified Al-Mg alloy was manufactured by selective laser melting. The effect of aging treatment on phase evolution and mechanical properties of alloy has been studied. The results show that bimodal grain structures (2.8±1.1 μm) could be obtained, thanks to the Al3Zr primary phases promoting the formation of equiaxed grains (0.8±0.3 μm) at the boundary of molten pool. During the aging of 375 ℃, Al3(Er,Zr) particles with the size of 2-5 nm were produced via synergistic precipitation of Er and Zr, which would greatly improve the strength and reach 510±9 MPa. At the same time, the Mg-rich phase was dissolved, the Mn-rich phase was precipitated, and dislocations could accelerate the diffusion of solute atoms during the evolution of the phases
Time restricted feeding – a free and effective method of managing hypertension by ameliorating inflammation?
No full textAuthor response to letter regarding 'Skin and respiratory ill-health attributed to occupational face mask use'
No full textAffective Human-Robot Interaction with Multimodal Explanations
No full textFacial expressions are one of the most practical and straightforward ways to communicate emotions. Facial Expression Recognition has been used in lots of fields such as human behaviour understanding and health monitoring. Deep learning models can achieve excellent performance in facial expression recognition tasks. As these deep neural networks have very complex nonlinear structures, when the model makes a prediction, it is not easy for human users to understand what is the basis for the model’s prediction. Specifically, we do not know which facial units contribute to the classification more or less. Developing affective computing models with more explainable and transparent feedback for human interactors is essential for a trustworthy human-robot interaction. Comparing to “white-box” approaches, “black-box” approaches using deep neural networks, which have advantages in terms of overall accuracy but lack reliability and explainability. In this work, we introduce a multimodal affective human-robot interaction framework, with visualbased and verbal-based explanation, by Layer Wise Relevance Propagation (LRP) and Local Intepretable Mode-Agnostic Explanation (LIME). The proposed framework has been tested on the KDEF dataset, and in human-robot interaction experiments with the Pepper robot. This experimental evaluation shows the benefits of linking deep learning emotion recognition systems with explainable strategies