Effects of a Virtual Reality-Based Meditation Intervention on Anxiety and Depression Among Patients With Acute Leukemia During Induction Chemotherapy: A Randomized Controlled Trial.

BACKGROUND: Emerging evidence supports that virtual reality (VR)-based meditation interventions may improve anxiety and depression among patients with cancer. However, empirical studies involving patients with acute leukemia during induction chemotherapy are limited. OBJECTIVE: This study aimed to examine the effects of VR-based meditation intervention on alleviating anxiety and depression and improving the quality of life among patients with acute leukemia during induction chemotherapy. METHODS: This randomized controlled trial recruited 63 patients newly diagnosed with acute leukemia. Participants were randomly assigned to an intervention group (received VR-based meditation for 20 min daily for 14 days) and a control group. Anxiety, depression, and quality of life were measured using the State Anxiety Inventory, the Center for Epidemiological Studies Depression Scale, and the Functional Assessment of Cancer Therapy-Leukemia Questionnaire, respectively. All outcomes were measured at baseline and post-intervention. RESULTS: Compared with patients in the control group, those in the intervention group demonstrated a significantly greater reduction in anxiety (P = .04) and improvement in quality of life (P = .04). However, no significant difference was noted in depression levels between groups (P = .09), although a decreasing trend was observed in the intervention group. CONCLUSION: Virtual reality-based meditation intervention effectively alleviated anxiety and improved the quality of life among acute leukemia patients during induction chemotherapy. Future randomized controlled trials with larger sample sizes and longer follow-up periods are warranted. IMPLICATION FOR PRACTICE: Virtual reality-based meditation can be applied in clinical practice virtually anytime and anywhere to provide a convenient intervention for anxiety reduction for acute leukemia patients during induction chemotherapy.

The novel HLA-DRB1*14:07:03 allele, identified by Sanger dideoxy nucleotide sequencing in a Chinese individual.

HLA-DRB1*14:07:03 differs from HLA-DRB1*14:07:01 by one nucleotide in exon 2.

[Blocking Two Signals of Immuno-Activated T Cells from Antigen Presenting Cells Can Prevent Chronic GVHD of Murine].

OBJECTIVE: To explore the effects of blocking TCR-CD3 and B7-CD28 signals on immune function of mice with chronic GVHD by using TJU103 and CTLA4-Ig. METHODS: On the basis of foregoing murine model of chronic GVHD, according to interference modes after infusion 6×107 spleen cells of donor mice, the recipients were divided into 5 groups: blank control, cGVHD, TJU103 interference, CTLA4-Ig interference and TJU103+CTLA4-Ig interference groups. The score of clinical manifestation and tissue histopathology were used to evaluate the effects of all the interferences on chronic GVHD. RESULTS: TJU103 and CTLA4-Ig could not influence the formation of the mouse chimera. The analysis of Kaplan survival curve of mice with chronic GVHD showed that the CTLA4-Ig and TJU103+CTLA4-Ig reduced the incidence of chronic GVHD, the TJU103 could delay the occurrence of chronic GVHD, but all the interference factors could not change the severity of chronic GVHD. CONCLUSION: TJU103 can delay the onset time of chronic GVHD, and the CTLA4-Ig can reduce the incidences of cGVHD, the combining use of TJU103 and CTLA4-Ig can significantly reduce the incidence of chronic GVHD, but can not change the severity of chronic GVHD.

Exploring High-D Spaces with Multiform Matrices and Small Multiples.

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.