Comprehensive assessment of the fitness of older people for chemotherapy

Purpose: To present the results of a mixed-method study comparing the level of agreement of a two-phased, nurse-administered Comprehensive Geriatric Assessment (CGA) with current methods that assess the fitness for chemotherapy of older cancer patients. A nurse-led model of multidisciplinary cancer care based on the results is also described. Methods: The two phases comprised initial screening by a nurse with the Vulnerable Elders Survey-13 [VES-13], followed by nurse administration of a detailed CGA. Both phases were linked to a computerised algorithm categorising the patient as ‘fit’, ‘vulnerable’ or ‘frail’. The study determined the level of agreement between VES-13- and CGA-determined categories; and between the CGA and the physicians’ assessments. It also compared the CGA’s predictive abilities in terms of subsequent treatment toxicity; while interviews determined the acceptability of the nurse-led procedure from key stakeholders' perspectives. Results: Data collection was completed in December 2011. The results will be presented at the conference. A consecutive-series n=170 will be enrolled, 33% of whom are ‘fit’; 33% ‘vulnerable’; and 33% ‘too frail’ for treatment. This sample can detect, with 90% power, kappa coefficients of agreement of ≥ 0.70 or higher (“substantial agreement”). Fitness sub-group comparisons of agreement between the medical oncologist and the nurse assessments can detect kappa estimates of Κ ≥ 0.80 with the same power. Conclusion: The results have informed a nurse-led model of cancer care. It meets a clear need to develop, implement and test a nurse-led, robust, evidence-based, clinically-justifiable and economically-feasible CGA process that has relevance in national and international contexts.

Preparation and characterization of TiO2/acid leached serpentinite tailings composites and their photocatalytic reduction of Chromium(VI)

Composite TiO2/acid leached serpentine tailings (AST) were synthesized through the hydrolysis–deposition method and characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energydispersive X-ray spectrometry (EDS), Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and surface area measurement (BET). The XRD analysis showed that TiO2 coated on the surface of acid leached serpentine tailings was mixed crystal phases of rutile and anatase, the grain size of which is 10–30 nm. SEM, TEM, and EDS analysis exhibited that nano-TiO2 particles were deposited on the surface and internal cavities of acid leaching serpentine tailings. The XPS and FT-IR analysis demonstrated that the coating process of TiO2 on AST was a physical adsorption process. The large specific surface area, porous structure, and plentiful surface hydroxyl group of TiO2/AST composite resulted in the high adsorption capacity of Cr(VI). The experimental results demonstrated that initial concentration of Cr(VI), the amount of the catalyst, and pH greatly influenced the removal efficiency of Cr(VI). The removal kinetics of Cr(VI) at a relative low initial concentration was fitted well with Langmuir–Hinshelwood kinetics model with R2 value of about unity. The asprepared composites exhibited strong adsorption and photocatalytic capacity for the removal of Cr(VI), and the possible photocatalytic reduction mechanism was studied. The photodecomposition of Cr(VI) was as high as 95% within 2 h, and the reusability of the photocatalysis was proven.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.