Homeopathy for Depression - DEP-HOM: study protocol for a randomized, partially double-blind, placebo controlled, four armed study

<p>Abstract</p> <p>Background</p> <p>Homeopathy is often sought by patients with depression. In classical homeopathy, the treatment consists of two main elements: the case history and the prescription of an individually selected homeopathic remedy. Previous data suggest that individualized homeopathic Q-potencies were not inferior to the antidepressant fluoxetine in a sample of patients with moderate to severe depression. However, the question remains whether individualized homeopathic Q-potencies and/or the type of the homeopathic case history have a specific therapeutical effect in acute depression as this has not yet been investigated. The study aims to assess the two components of individualized homeopathic treatment for acute depression, i.e., to investigate the specific effect of individualized Q-potencies versus placebo and to investigate the effect of different approaches to the homeopathic case history.</p> <p>Methods/Design</p> <p>A randomized, partially double-blind, placebo-controlled, four-armed trial using a 2 × 2 factorial design with a six-week study duration per patient will be performed. 228 patients diagnosed with major depression (moderate episode) by a psychiatrist will be included. The primary endpoint is the total score on the 17-item Hamilton Depression Rating Scale after six weeks. Secondary end points are: Hamilton Depression Rating Scale total score after two and four weeks; response and remission rates, Beck Depression inventory total score, quality of life and safety at two, four and six weeks. Statistical analyses will be by intention-to-treat. The main endpoint will be analysed by a two-factorial analysis of covariance. Within this model generalized estimation equations will be used to estimate differences between verum and placebo, and between both types of case history.</p> <p>Discussion</p> <p>For the first time this study evaluates both the specific effect of homeopathic medicines and of a homeopathic case taking in patients with depression. It is an attempt to deal with the challenges of homeopathic research and the results might be useful information in the current discussion about the evidence on homeopathy</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01178255">NCT01178255</a></p

Darolutamide and health-related quality of life in patients with non-metastatic castration-resistant prostate cancer : An analysis of the phase III ARAMIS trial

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.BACKGROUND: In the ARAMIS trial, darolutamide plus androgen deprivation therapy (ADT) versus placebo plus ADT significantly improved metastasis-free survival (MFS), overall survival (OS) and time to pain progression in patients with non-metastatic castration-resistant prostate cancer (nmCRPC). Herein, we present analyses of patient-reported health-related quality of life (HRQoL) outcomes. PATIENTS AND METHODS: This double-blind, placebo-controlled, phase III trial randomised patients with nmCRPC and prostate-specific antigen doubling time ≤10 months to darolutamide 600 mg (n = 955) twice daily or matched placebo (n = 554) while continuing ADT. The primary end-point was MFS; the secondary end-points included OS and time to pain progression. In this analysis, HRQoL was assessed by the time to deterioration using the Functional Assessment of Cancer Therapy-Prostate (FACT-P) prostate cancer subscale (PCS) and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Prostate Cancer Module (EORTC QLQ-PR25) subscales. RESULTS: Darolutamide significantly prolonged time to deterioration of FACT-P PCS versus placebo (hazard ratio [HR] 0.80, 95% confidence interval [CI] 0.70-0.91; P = 0.0005) at the primary analysis (cut-off date: 3rd September 2018). Time to deterioration of EORTC QLQ-PR25 outcomes showed statistically significant delays with darolutamide versus placebo for urinary (HR 0.64, 95% CI 0.54-0.76; P < 0.0001) and bowel (HR 0.78, 95% CI 0.66-0.92; P = 0.0027) symptoms. Time to worsening of hormonal treatment-related symptoms was similar between the two groups. CONCLUSION: In patients with nmCRPC who are generally asymptomatic, darolutamide maintained HRQoL by significantly delaying time to deterioration of prostate cancer-specific quality of life and disease-related symptoms versus placebo.publishersversionPeer reviewe

Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases

Purpose!#!A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images.!##!Methods!#!Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis.!##!Results!#!We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%.!##!Conclusions!#!An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences