Ruthenium(0) nanoparticles stabilized by metal-organic framework (ZIF-8): Highly efficient catalyst for the dehydrogenation of dimethylamine-borane and transfer hydrogenation of unsaturated hydrocarbons using dimethylamine-borane as hydrogen source

Ruthenium(0) nanoparticles supported on zeolitic imidazolate framework (ZIF-8), RuNPs/ZIF-8, were reproducibly prepared by borohydride reduction of RuCl3/ZIF-(8) precatalyst in water at room temperature. The characterization of the dehydrated RuNPs/ZIF-8 was done by a combination of complimentary techniques, which reveals that the formation of well-dispersed ruthenium(0) nanoparticles (1.9 +/- 0.6 nm) on the surface of ZIF-8 by keeping the host framework intact. The catalytic activity of RuNPs/ZIF-8 was firstly tested in the dehydrogenation of dimethylamine-borane ((CH3)(2)NHBH3) in toluene. We found that ruthenium(0) nanoparticles supported on ZIF-8 can catalyze the dehydrogenation of dimethylamineborane with an initial TOF value of 59 min(-1) at 40 degrees C. Additionally, RuNPs/ZIF-8 catalyze the transfer hydrogenation of various unsaturated substrates in the presence of dimethylamine borane as hydrogen source even at low catalyst loadings. More importantly, they show high durability against leaching and sintering throughout the catalytic runs, which make them reusable catalyst in these important catalytic transformations. (C) 2014 Elsevier B.V. All rights reserved

Breast Cancer in Women Aged 75 Years and Older

Objective: Breast cancer is the most common cancer in women, with incidence and mortality increasing dramatically with age. Applying data ofyounger patients to the geriatric age group indicates ‘‘evidence biased medicine’’. Therefore, this study aimed to present the clinical and pathologicalfeatures of breast cancer and treatment choices in older patients.Materials and Methods: This study included 72 patients aged 75 years and older with breast cancer who were admitted to our medical oncologyclinic between 2005 and 2013. Clinicopathological and demographic features, progression-free survival and overall survival and adjuvant andpalliative treatments were recorded retrospectively. Categorical variables were presented as number (n) and percentage (%) and continuous variablesas median and minimum-maximum. Survival curves were drawn using the Kaplan-Meier method. P<0.05 was considered as statistically significant.Results: The study population consisted of 72 patients, with a median age of 78 (minimum-maximum: 75-88). The most common pathologicaltype of breast cancer was invasive ductal carcinoma, followed by infiltrative lobular carcinoma. Steroid receptor positivity rates were high, and thecerbB2 status was mostly negative; older patients had favourable tumours. Endocrine therapy was the most preferred option in this geriatric patientgroup, and aromatase inhibitors were the most commonly chosen hormonotherapy. Endocrine therapy is the first choice in palliative treatment;however, chemotherapy was preferred in second- and third-line treatment in metastatic diseases.Conclusion: According to available literature, geriatric patients show similarities in histologic and intrinsic subtypes with postmenopausal women,except for frailty and comorbidities. However, in geriatric patients, endocrine therapy is preferred as adjuvant and/or metastatic treatment becausethey are more susceptible to chemotherapeutic agents. Oncologists should consulate every older patient to geriatric medicine, and comprehensivegeriatric assessment should be done to decide and continue treatment. Age should not be the only factor in decision-making

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

ObjectivesThe aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).IntroductionNAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.MethodsThis article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.ResultsPathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.ConclusionIt was concluded that deep learning methods can predict breast cancer treatment