Phase II trial of Oxaliplatin and 5-Fluorouracil/Leucovorin combination in epithelial ovarian carcinoma relapsing within 2 years of platinum-based therapy

Objective To evaluate the efficacy and toxicity of Oxaliplatin and 5-Fluorouracil (5-FU)/Leucovorin (LV) combination in ovarian cancer relapsing within 2 years of prior platinum-based chemotherapy in a phase II trial. Methods Eligible patients had at least one prior platinum-based chemotherapy regimen, elevated CA-125 ≥ 60 IU/l, radiological evidence of disease progression and adequate hepatic, renal and bone marrow function. Patients with raised CA-125 levels alone as marker of disease relapse were not eligible. Oxaliplatin (85 mg/m 2) was given on day 1, and 5-Fluorouracil (370 mg/m 2) and Leucovorin (30 mg) was given on days 1 and 8 of a 14-day cycle. Results Twenty-seven patients were enrolled. The median age was 57 years (range 42-74 years). The median platinum-free interval (PFI) was 5 months (range 0-17 months) with only 30% of patients being platinum sensitive (PFI > 6 months). Six patients (22%) had two prior regimens of chemotherapy. A total of 191 cycles were administered (median 7; range 2-12). All patients were evaluable for toxicity. The following grade 3/4 toxicities were noted: anemia 4%; neutropenia 15%; thrombocytopenia 11%; neurotoxicity 8%; lethargy 4%; diarrhea 4%; hypokalemia 11%; hypomagnesemia 11%. Among 27 enrolled patients, 20 patients were evaluable for response by WHO criteria and 25 patients were evaluable by Rustin's CA-125 criteria. The overall response rate (RR) by WHO criteria was 30% (95% CI: 15- 52) [three complete responses (CRs) and three partial responses (PRs)]. The CA-125 response rate was 56% (95% CI: 37-73). Significantly, a 25% (95% CI: 9-53) radiological and a 50% (95% CI: 28-72) CA-125 response rate were noted in platinum resistant patients (PFI < 6 months). The median response duration was 4 months (range 3-12) and the median overall survival was 10 months. Conclusion Oxaliplatin and 5-Fluorouracil/ Leucovorin combination has a good safety profile and is active in platinum-pretreated advanced epithelial ovarian cancer. © 2004 Elsevier Inc. All rights reserved.

Nausea e vomito em laparoscopia ginecologica: Efeitos do oxido nitroso e da metoclopramida

Background and Objectives - Gynecological laparoscopy causes high postoperative morbidity, mainly due to occurrences such as nausea and vomiting. They result from a great multiplicity of etiologies and drugs used in anesthesia may function as contributing factors. Both the emetic properties of nitrous oxide and the efficacy of metoclopramide as antiemetic agent are controversial. This study was undertaken to determine the effects of both drugs, when used alone or in combination. Methods - Eighty three physical status ASA I and II women were studied. They were premedicated with midazolam before induction of anesthesia with alfentanil and propofol. Anesthesia was maintained with isoflurane with or without nitrous oxide in oxygen. Muscle relaxation was achieved with atracurium. There were 4 groups of patients: GI: midazolam, alfentanil, propofol, atracurium, isoflurane/oxygen; GII: midazolam, alfentanil, propofol, atracurium, isoflurane/nitrous oxide/oxygen; GIII: metoclopramide, midazolam, alfentanil, propofol, atracurium, isoflurane/oxygen; GIV: metoclopramide, midazolam, alfentanil, propofol, atracurium, isoflurane/nitrous oxide/oxygen. The incidence of nausea and vomiting was assessed both in the recovery room (RR) and in the ward. Results - There were no significant differences as regards age, weight and height of the patients and duration of anesthesia and surgery. Nausea and vomiting were more frequent in patients who received N2O (GII, 50%; GIV, 33%), as compared to those who didn't receive this agent (GI and GII, 9.5% and 14.35%, respectively). Metoclopramide decreased the incidence of nausea and vomiting in the recovery room, in patients who didn't receive N2O (GII). These patients remained in the recovery room for 90 minutes. Conclusions - N2O increases the incidence of nausea and vomiting and metoclopramide is effective in reducing these complications only in the recovery room.

Lyprinol : is it a useful anti-inflammatory agent?

The New Zealand green lipped mussel preparation Lyprinol is available without a prescription from a supermarket, pharmacy or Web. The Food and Drug Administration have recently warned Lyprinol USA about their extravagant anti-inflammatory claims for Lyprinol appearing on the web. These claims are put to thorough review. Lyprinol does have anti-inflammatory mechanisms, and has anti-inflammatory effects in some animal models of inflammation. Lyprinol may have benefits in dogs with arthritis. There are design problems with the clinical trials of Lyprinol in humans as an anti-inflammatory agent in osteoarthritis and rheumatoid arthritis, making it difficult to give a definite answer to how effective Lyprinol is in these conditions, but any benefit is small. Lyprinol also has a small benefit in atopic allergy. As anti-inflammatory agents, there is little to choose between Lyprinol and fish oil. No adverse effects have been reported with Lyprinol. Thus, although it is difficult to conclude whether Lyprinol does much good, it can be concluded that Lyprinol probably does no major harm.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.