Course of depression,mental health service utilization and treatment preferences in women receiving chemotherapy for breast cancer

Objective

This prospective study aimed to estimate the prevalence and course of depression during chemotherapy in women with Stage I–III breast cancer, identify potential risk factors for depression and determine which treatments for depression were being used and which were most preferred.

Method

Thirty-two women were followed over consecutive chemotherapy infusions, with 289 assessments conducted altogether (mean, 9.0 assessments/subject). Current depression, anxiety, physical symptoms and mental health service use were recorded during each assessment. A linear mixed effects model was used to identify factors associated with depression. Patients also ranked depression treatment preferences. We referred patients with more severe depression for treatment.

Results

Clinically significant depression was identified in 37.5% of patients. Depression severity tended to peak at 12–14 weeks and 32 or more weeks of chemotherapy. Depression severity was associated with anxiety severity, physical symptom burden, non-White race, receiving one's first chemotherapy regimen, Adriamycin-Cytoxan chemotherapy and chemotherapy duration. Most (65.5%) patients preferred evidence-based treatments for depression, and 66.7% of depressed patients were using such treatments.

Conclusions

Depression is common in women receiving chemotherapy for breast cancer. Most patients prefer evidence-based depression treatments. We recommend regular screening for depression during chemotherapy to ensure adequate detection and patient-centered treatment.     

Elastic Characteristics of Digital Cores from Longmaxi Shale Using Lattice Spring Models

Effective medium methods for the attribution of micro-structures to macro elastic properties of shales are important for the prediction of sweet spots in the shale-gas production. With X-ray micro-computed tomography (XMCT), the micro-structures of shale core samples from Longmaxi Formation are visualized and characterized by 3D digital images. As an efficient alternative to conventional effective medium methods for estimating elastic properties, we propose a consistent workflow of lattice spring modeling (LSM) to emulate the digital cores using three types of lattices. Particular attention is paid to investigate the effective Young's moduli, Poisson's ratios, and preferred orientations, by uniaxial compression tests along two directions. Within elastic deformation, the impact of lattice arrangements on the anisotropy is even more than those of stress disturbances and micro-structural features. Compared with analytical approximations and theoretical predictions, the LSM numerical scheme shows general applicability for heterogeneous porous rocks.     

A Stable Q Compensated Reverse Time Migration Method Based on Excitation Amplitude Imaging Condition

The stability and efficiency, especially the stability, are generally concerned issues in Q compensated reverse time migration (Q-RTM). The instability occurs because of the exponentially boosted high frequency ambient noise during the forward or backward seismic wavefield propagation. The regularization and low-pass filtering methods are two effective strategies to control the instability of the wave propagation in Q-RTM. However, the regularization parameters are determined experimentally, and the wavefield cannot be recovered accurately. The low-pass filtering method cannot balance the selection of cutoff frequency for varying Q values, and may damage the effective signals, especially when the signal-to-noise ratio (SNR) of the seismic data is low, the Q-RTM will be a highly unstable process. In order to achieve the purpose of stability, the selection of cutoff frequency will be small enough, which can cause great damage to the effective high frequency signals. In this paper, we present a stable Q-RTM algorithm based on the excitation amplitude imaging condition, which can compensate both the amplitude attenuation and phase dispersion. Unlike the existing Q-RTM algorithms enlarging the amplitude, the exponentially attenuated seismic wavefield will be used during both the forward and backward wavefield propagation of Q-RTM. Therefore, the new Q-RTM algorithm is relative stable, even for the low SNR seismic data. In order to show the accuracy and stability of our stable Q-RTM algorithm clearly, an example based on Graben model will be illustrated. Then, a realistic BP gas chimney model further demonstrates that the proposed method enjoys good stability and anti-noise performance compared with the traditional Q-RTM with amplitude amplification. Compare the Q-RTM images of these two models to the reference images obtained by the acoustic RTM with acoustic seismic data, the new Q-RTM results match the reference images quite well. The proposed method is also tested using a field seismic data, the result shows the effectiveness of our proposed method.     

Pathogenesis of acute stroke and the role of inflammasomes

Inflammation is an innate immune response to infection or tissue damage that is designed to limit harm to the host, but contributes significantly to ischemic brain injury following stroke. The inflammatory response is initiated by the detection of acute damage via extracellular and intracellular pattern recognition receptors, which respond to conserved microbial structures, termed pathogen-associated molecular patterns or host-derived danger signals termed damage-associated molecular patterns. Multi-protein complexes known as inflammasomes (e.g. containing NLRP1, NLRP2, NLRP3, NLRP6, NLRP7, NLRP12, NLRC4, AIM2 and/or Pyrin), then process these signals to trigger an effector response. Briefly, signaling through NLRP1 and NLRP3 inflammasomes produces cleaved caspase-1, which cleaves both pro-IL-1β and pro-IL-18 into their biologically active mature pro-inflammatory cytokines that are released into the extracellular environment. This review will describe the molecular structure, cellular signaling pathways and current evidence for inflammasome activation following cerebral ischemia, and the potential for future treatments for stroke that may involve targeting inflammasome formation or its products in the ischemic brain.     

Development and testing of the pain opioid analgesics beliefs scale in Taiwanese cancer patients

The purpose of the study was to develop and preliminarily test the feasibility, validity, reliability, and factor structures of the Pain Opioid Analgesics Beliefs Scale-Cancer (POABS-CA) in hospitalized adults diagnosed with cancer in Taiwan. This scale was developed in three phases. In Phase I, item development was based on qualitative analysis as well as a review of the literature. Face validity, content validity, and feasibility were also evaluated. In Phase II, internal consistency reliability was further tested in 42 subjects with pain. In Phase III, test-retest reliability, internal consistency, and essential construct validity were further assessed in a sample of 361 hospitalized cancer patients with pain. The POABS-CA evolved from testing as a 10-item 5-point Likert-type instrument. Higher scores indicated more negative beliefs regarding opioids and their use in managing pain. Satisfactory face validity and content validity were found. The POABS-CA was also shown to be a reliable and stable pain belief scale, with Cronbach's alpha and test-retest reliability of 0.70 and 0.94, respectively. Two factors, namely pain endurance beliefs and negative effect beliefs, were extracted from the principal component factor analysis to support the construct validity. In conclusion, preliminary evidence indicates the POABS-CA is a reliable, stable, valid and easily applied scale for assessing beliefs regarding opioid use for cancer pain. Further studies should test this scale in different populations to increase its applications in cancer pain management.