Video conference vs face-to-face group psychotherapy for distressed cancer survivors: A randomized controlled trial.

OBJECTIVE: This study assesses the effectiveness of face-to-face group positive psychotherapy for cancer survivors (PPC) compared to its online adaptation, online group positive psychotherapy for cancer survivors (OPPC), which is held via videoconference. A two-arm, pragmatic randomized controlled trial was conducted to examine the effects of both interventions on emotional distress, post-traumatic stress symptoms (PTSS), and post-traumatic growth (PTG) among cancer survivors and analyze attrition to treatment. METHODS: Adult women with a range of cancer diagnoses were invited to participate if they experienced emotional distress at the end of their primary oncological treatment. Emotional distress, PTSS, and PTG were assessed at baseline, immediately after treatment, and 3 months after treatment. Intention-to-treat analyses were carried out using general linear mixed models to test the effect of the interventions overtime. Logistic regressions were performed to test differential adherence to treatment and retention to follow-up. RESULTS: A total of 269 individuals participated. The observed treatment effect was significant in both modalities, PPC and OPPC. Emotional distress (b = -2.24, 95% confidence interval [CI] = -3.15 to -1.33) and PTSS (b = -3.25, 95% CI = -4.97 to -1.53) decreased significantly over time, and PTG (b = 3.08, 95% CI = 0.38-5.78) increased significantly. Treatment gains were sustained across outcomes and over time. Analyses revealed no significant differences between modalities of treatment, after adjusting for baseline differences, finding that OPPC is as effective and engaging as PPC. CONCLUSIONS: The OPPC treatment was found to be effective and engaging for female cancer early survivors. These results open the door for psycho-oncology interventions via videoconference, which are likely to lead to greater accessibility and availability of psychotherapy.

Nanonetworks in Biomedical Applications.

By interconnecting nanomachines and forming nanonetworks, the capacities of single nanomachines are expected to be enhanced, as the ensuing information exchange will allow them to cooperate towards a common goal. Nowadays, systems normally use electromagnetic signals to encode, send and receive information, however, in a novel communication paradigm, molecular transceivers, channel models or protocols use molecules. This article presents the current developments in nanomachines along with their future architecture to better understand nanonetwork scenarios in biomedical applications. Furthermore, to highlight the communication needs between nanomachines, two applications for nanonetworks are also presented: i) a new networking paradigm, called the Internet of NanoThings, that allows nanoscale devices to interconnect with existing communication networks, and ii) Molecular Communication, where the propagation of chemical compounds like drug particles, carry out the information exchange.

Robustness surfaces of complex networks.

Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforementioned problems by defining the R*-value and introducing the concept of robustness surface (Ω). The rationale of our proposal is to make use of Principal Component Analysis (PCA). We firstly adjust to 1 the initial robustness of a network. Secondly, we find the most informative robustness metric under a specific failure scenario. Then, we repeat the process for several percentage of failures and different realizations of the failure process. Lastly, we join these values to form the robustness surface, which allows the visual assessment of network robustness variability. Results show that a network presents different robustness surfaces (i.e., dissimilar shapes) depending on the failure scenario and the set of metrics. In addition, the robustness surface allows the robustness of different networks to be compared.