A working memory intervention for children with prenatal alcohol exposure: Pilot study.

Cogmed© is a computerized working memory training program designed to improve attention and working memory. We examined the short- and long-term impacts of a 25-session Cogmed© intervention on working memory and other cognitive and learning domains in children with prenatal alcohol exposure (PAE) and typically developing children. Participants included 38 children (4 - 13 years old) from Alberta, Canada in two groups: PAE (n = 20) and typically developing (n = 18). Significant improvements in areas of working memory and attentional control for both the PAE and the typically developing group were reported immediately after intervention completion (short-term impact). The gains on some measures were retained at five-week follow up (long-term impact). Preliminary findings indicate that computerized interventions may positively impact WM and attention control and that these changes may be maintained after a delay period.

Developing high-affinity decoy receptors to treat multiple myeloma and diffuse large B cell lymphoma.

Disease relapse and treatment-induced immunotoxicity pose significant clinical challenges for patients with hematological cancers. Here, we reveal distinctive requirements for neutralizing TNF receptor ligands APRIL and BAFF and their receptor activity in MM and DLBCL, impacting protein translation and production in MM cells and modulating the translation efficiency of the ATM interactor (ATMIN/ACSIZ). Therapeutically, we investigated the use of BCMA decoy receptor (sBCMA-Fc) as an inhibitor of APRIL and BAFF. While wild-type sBCMA-Fc effectively blocked APRIL signaling in MM, it lacked activity in DLBCL due to its weak BAFF binding. To expand the therapeutic utility of sBCMA-Fc, we engineered an affinity-enhanced mutant sBCMA-Fc fusion molecule (sBCMA-Fc V3) 4- and 500-fold stronger in binding to APRIL and BAFF, respectively. The mutant sBCMA-Fc V3 clone significantly enhanced antitumor activity against both MM and DLBCL. Importantly, we also demonstrated an adequate toxicity profile and on-target mechanism of action in nonhuman primate studies.

Detecting structural variations with precise breakpoints using low-depth WGS data from a single oxford nanopore MinION flowcell.

Structural variation (SV) is a major cause of genetic disorders. In this paper, we show that low-depth (specifically, 4×) whole-genome sequencing using a single Oxford Nanopore MinION flow cell suffices to support sensitive detection of SV, particularly pathogenic SV for supporting clinical diagnosis. When using 4× ONT WGS data, existing SV calling software often fails to detect pathogenic SV, especially in the form of long deletion, terminal deletion, duplication, and unbalanced translocation. Our new SV calling software SENSV can achieve high sensitivity for all types of SV and a breakpoint precision typically ± 100 bp; both features are important for clinical concerns. The improvement achieved by SENSV stems from several new algorithms. We evaluated SENSV and other software using both real and simulated data. The former was based on 24 patient samples, each diagnosed with a genetic disorder. SENSV found the pathogenic SV in 22 out of 24 cases (all heterozygous, size from hundreds of kbp to a few Mbp), reporting breakpoints within 100 bp of the true answers. On the other hand, no existing software can detect the pathogenic SV in more than 10 out of 24 cases, even when the breakpoint requirement is relaxed to ± 2000 bp.

Student Preparation for Nursing Leadership: Lessons from an Undergraduate Programs Review.

Facilitating student achievement of nationally recognized entry-level-competencies in nursing leadership is a critical step in building capacity to promote patient safety, quality of care, and healthy work environments. Data for this substudy were drawn from a larger multi-phased, multi-method, cross-sectional, descriptive study conducted to inform comprehensive undergraduate curricular revision. The research question for this substudy was: what are the perceptions about undergraduate program preparation for nursing leadership? Frequencies and percentages summarized relevant quantitative data from the surveys and deductive content analysis was used to further explore the category of leadership which emerged from content analysis of qualitative data in the larger study. Key findings illustrate need for additional attention to learning experiences in conflict management, delegation and supervision of clinical teams, and advocacy. Greater collaboration between educational and clinical agencies is needed to find mutually beneficial strategies to support nursing leadership development for nursing students and new graduates.

Filtering of false positive microRNA candidates by a clustering-based approach.

BACKGROUND: MicroRNAs are small non-coding RNA gene products that play diversified roles from species to species. The explosive growth of microRNA researches in recent years proves the importance of microRNAs in the biological system and it is believed that microRNAs have valuable therapeutic potentials in human diseases. Continual efforts are therefore required to locate and verify the unknown microRNAs in various genomes. As many miRNAs are found to be arranged in clusters, meaning that they are in close proximity with their neighboring miRNAs, we are interested in utilizing the concept of microRNA clustering and applying it in microRNA computational prediction. RESULTS: We first validate the microRNA clustering phenomenon in the human, mouse and rat genomes. There are 45.45%, 51.86% and 48.67% of the total miRNAs that are clustered in the three genomes, respectively. We then conduct sequence and secondary structure similarity analyses among clustered miRNAs, non-clustered miRNAs, neighboring sequences of clustered miRNAs and random sequences, and find that clustered miRNAs are structurally more similar to one another, and the RNAdistance score can be used to assess the structural similarity between two sequences. We therefore design a clustering-based approach which utilizes this observation to filter false positives from a list of candidates generated by a selected microRNA prediction program, and successfully raise the positive predictive value by a considerable amount ranging from 15.23% to 23.19% in the human, mouse and rat genomes, while keeping a reasonably high sensitivity. CONCLUSION: Our clustering-based approach is able to increase the effectiveness of currently available microRNA prediction program by raising the positive predictive value while maintaining a high sensitivity, and hence can serve as a filtering step. We believe that it is worthwhile to carry out further experiments and tests with our approach using data from other genomes and other prediction software tools. Better results may be achieved with fine-tuning of parameters.