Reduction in calcium excretion in women with breast cancer and bone metastases using the oral bisphosphonate pamidronate.

The bisphosphonate pamidronate (3 amino-1, 1-hydroxypropylidene bisphosphonate (APD), Ciba-Geigy) is a powerful inhibitor of osteoclast function and has been shown to significantly reduce osteolysis associated with bone metastases in breast cancer. Until recently, however, only an intravenous preparation has been readily available. We have evaluated the toxicity and effect on urinary calcium excretion of an enteric-coated oral preparation of pamidronate in a phase I/II trial in patients with bone metastases from breast cancer. Sixteen women with progressive disease and evidence of active bone resorption with an elevated calcium excretion (fasting urine calcium/creatinine ratio greater than 0.4 (mmol mmol-1) on two occasions prior to treatment) were studied. Four were given 150 mg daily; four 300 mg daily; four 450 mg daily and four 600 mg daily. Urinary calcium/creatinine (Ca2+/Cr) ratios were measured on all patients after an overnight fast. In patients on 150 mg daily the mean ratio fell from 0.65 (range 0.57-0.72) before treatment to 0.13 (0.02-0.19) after three weeks treatment. Mean values at entry for patients on 300, 450 and 600 mg were 1.18 (0.72-2.1), 0.76 (0.42-1.5) and 0.63 (0.52-0.82) respectively and after treatment these fell to 0.11 (0.05-0.18), 0.37 (0.14-0.68) and 0.17 (0.06-0.25). There were no significant differences in efficacy between treatment groups. Oral, enteric-coated disodium pamidronate is non-toxic and effectively reduces calcium excretion, raised in association with metastatic bone disease at doses of 150 mg or above. At the doses used to date it is as effective as weekly treatments with 30 mg of the intravenous preparation. Further studies are required in order to determine its value for preventing complications of bone disease and possibly as an adjuvant to surgery for breast cancer

Operational Variables for improving industrial wind turbine Yaw Misalignment early fault detection capabilities using data-driven techniques

This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recordOffshore wind turbines are complex pieces of engineering and are, generally, exposed to harsh environmental conditions that are making them to susceptible unexpected and potentially catastrophic damage. This results in significant down time, and high maintenance costs. Therefore, early detection of major failures is important to improve availability, boost power production and reduce maintenance costs. This paper proposes a SCADA data based Gaussian Process (GP) (a data-driven, machine learning approach) fault detection algorithm where additional model inputs, called operational variables (pitch angle and rotor speed) are used. Firstly, comparative studies of these operational variables are carried out to establish whether the parameter leads to improved early fault detection capability; it is then used to construct an improved GP fault detection algorithm. The developed model is then validated against existing methods in terms of capability to detect in advance (and by how much) signs of failure with a low false positive rate. Failure due to yaw misalignment results in significant down time and a reduction in power production was found to be a useful case study to demonstrate the effectiveness of the proposed algorithms. Historical SCADA 10-minute data obtained from pitch-regulated turbines were used for models training and validation purposes. Results show that (i) the additional model inputs were able to improve the accuracy of GP power curve models with rotor speed responsible for a significant improvement in performance; (ii) the inclusion of rotor speed enhanced early failure detection without any false positives, in contrast to the other methods investigated.U.S. Department of Commerc

High dose, dose-intensive chemotherapy with doxorubicin and cyclophosphamide for the treatment of advanced breast cancer.

Eighteen patients with advanced breast cancer were commenced on treatment with high dose doxorubicin (100 mg m-2) or doxorubicin (100 mg m-2) and cyclophosphamide (500 mg m-2) at 2 weekly intervals. Three cycles of treatment were planned. rG-CSF was given subcutaneously for 10 days, starting 24 h after each cycle of chemotherapy. Sixteen out of 18 patients responded (89%) of whom six (33%) achieved a complete remission. Twelve (67%) completed the three planned cycles, four (22%) received two cycles and two (11%) received one cycle only. The median time to progression was 5 1/2 months and the median survival was 18 1/2 months. Neutropenia occurred after 89% of courses and 65% of courses were accompanied by a significant (WHO grade III or IV) infection. The duration of neutropenia was short (mean 5.4 days) and mean time to absolute neutrophil count recovery (ANC > 1,000 x 10(6) litre) from the start of treatment was 11 days. Moderate to severe epithelial toxicity (WHO grade 3 or 4) accompanied 43% of courses and was dose limiting. Conclusion: High dose, dose intensive chemotherapy has an excellent initial therapeutic effect in advanced breast cancer but does not prolong duration of remission or overall survival beyond that of standard treatment. Although subcutaneous rG-CSF curtailed the expected duration of neutropenia substantially, the overall incidence of neutropenia and of infections requiring intravenous antibiotics was high. Furthermore, almost half of the courses were complicated by moderate to severe oral mucositis and/or mild to moderate palmar and plantar inflammation. The lack of survival benefit and excess toxicity seriously limits the wider application of this regime. It should not be used in place of standard dose palliative chemotherapy for metastatic breast cancer

Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy

This is the final version. Available on open access from Elsevier via the DOI in this recordThe dataset associated with this article is available in ORE at: http://hdl.handle.net/10871/125733Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel algorithmic approach which fuses Markov Chain Monte Carlo (MCMC) and Machine Learning methods to accelerate uncertainty quantification for groundwater flow models. We formulate the governing mathematical model as a Bayesian inverse problem, considering model parameters as a random process with an underlying probability distribution. MCMC allows us to sample from this distribution, but it comes with some limitations: it can be prohibitively expensive when dealing with costly likelihood functions, subsequent samples are often highly correlated, and the standard Metropolis-Hastings algorithm suffers from the curse of dimensionality. This paper designs a Metropolis-Hastings proposal which exploits a deep neural network (DNN) approximation of a groundwater flow model, to significantly accelerate MCMC sampling. We modify a delayed acceptance (DA) model hierarchy, whereby proposals are generated by running short subchains using an inexpensive DNN approximation, resulting in a decorrelation of subsequent fine model proposals. Using a simple adaptive error model, we estimate and correct the bias of the DNN approximation with respect to the posterior distribution on-the-fly. The approach is tested on two synthetic examples; a isotropic two-dimensional problem, and an anisotropic three-dimensional problem. The results show that the cost of uncertainty quantification can be reduced by up to 50% compared to single-level MCMC, depending on the precomputation cost and accuracy of the employed DNN.Engineering and Physical Sciences Research Council (EPSRC)Turing AI Fellowship, U

Zoledronic acid significantly improves pain scores and quality of life in breast cancer patients with bone metastases: a randomised, crossover study of community vs hospital bisphosphonate administration

Patients with bone metastases from breast cancer often experience substantial skeletal complications – including debilitating bone pain – which negatively affect quality of life. Zoledronic acid (4 mg) has been demonstrated to reduce significantly the risk of skeletal complications in these patients and is administered via a short, 15-min infusion every 3 weeks, allowing the possibility for home administration. This study compared the efficacy and safety of zoledronic acid administered in the community setting vs the hospital setting in breast cancer patients with ⩾1 bone metastasis receiving hormonal therapy. After a lead-in phase of three infusions of 4 mg zoledronic acid in the hospital setting, 101 patients were randomized to receive three open-label infusions in the community or hospital setting, followed by three infusions in the opposite venue (a total of nine infusions). The Brief Pain Inventory (BPI) and the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire 30 (EORTC QLQ-C30) were used to assess potential benefits of zoledronic acid therapy. At study end, analysis of the BPI showed significant reductions in worst pain (P=0.008) and average pain in the last 7 days (P=0.039), and interference with general activity (P=0.012). In each case, there were significantly greater improvements in pain scores after treatment in the community setting compared with the hospital crossover setting for worst pain (P=0.021), average pain (P=0.003), and interference with general activity (P=0.001). Overall global health status showed a significant median improvement of 8.3% (P=0.013) at study end. Physical, emotional, and social functioning also showed significant overall improvement (P=0.013, 0.005, and 0.043, respectively). Furthermore, physical, role, and social functioning showed significantly greater improvements after treatment in the community setting compared with the hospital crossover setting (P=0.018, 0.001, and 0.026, respectively). There was no difference between hospital and community administration in renal or other toxicity, with zoledronic acid being well tolerated in both treatment settings. These data confirm the safety and quality-of-life benefits of zoledronic acid in breast cancer patients with bone metastases, particularly when administered in the community setting

Shared understanding in psychiatrist–patient communication: Association with treatment adherence in schizophrenia

Objective Effective doctor–patient communication, including a shared understanding, is associated with treatment adherence across medicine. However, communication is affected by a diagnosis of schizophrenia and reaching a shared understanding can be challenging. During conversation, people detect and deal with possible misunderstanding using a conversational process called repair. This study tested the hypothesis that more frequent repair in psychiatrist–patient communication is associated with better treatment adherence in schizophrenia. Methods Routine psychiatric consultations involving patients with (DSM-IV) schizophrenia or schizoaffective disorder were audio-visually recorded. Consultations were coded for repair and patients’ symptoms and insight assessed. Adherence was assessed six months later. A principal components analysis reduced the repair data for further analysis. Random effects models examined the association between repair and adherence, adjusting for symptoms, consultation length and the amount patients spoke. Results 138 consultations were recorded, 118 were followed up. Patients requesting clarification of the psychiatrist's talk and the clarification provided by the psychiatrist was associated with adherence six months later (OR 5.82, 95% CI 1.31–25.82, p = 0.02). Conclusion The quality of doctor–patient communication also appears to influence adherence in schizophrenia. Practice implications Future research should investigate how patient clarification can be encouraged among patients and facilitated by psychiatrists’ communication