Holmgaardluna6183

6% and 9.1%, respectively. There was no difference between the two cohorts in terms of the cumulative incidence of chronic GVHD and non-relapse mortality. Data from the multivariate analysis demonstrated that pro-DLI was an independent protective variable for LFS (P = 0.01, hazard ratio HR = 0.35), OS (P = 0.01, HR = 0.32), and relapse (P = 0.02, HR = 0.33). Taken together, we demonstrate that pro-DLI after ATG-F-based HSCT effectively decreases the risk of relapse and improves long-term survival of patients with high-risk acute leukemia without increasing treatment toxicity.

Observational.

To compare two methods for predicting segmental (arms, legs, trunk) lean tissue mass (LTM non-bone fat-free mass) from bioimpedance spectroscopy (BIS) against LTM measured from dual energy X-ray absorptiometry (DXA) in individuals with acute spinal cord injury (SCI).

Austin Health Victorian Spinal Cord Service, Victoria, Australia.

Fourteen participants (two female), within 8 weeks of traumatic SCI had BIS measured following an overnight fast and within 24 h of DXA scanning. Total body fat-free mass (FFM, body weight minus fat mass) and segmental LTM were predicted from BIS using manufacturer's proprietary software and a previously established SCI-specific prediction method. Appendicular LTM (ALM) was calculated from the sum of the LTM of the arms and legs. Agreement and strength of relationships with DXA for predicted LTM measures using both approaches were assessed using Lin's concordance coefficient and limits of agreementanalysis (LOA).

The BIS proprietary method performed better than the SCI-specific prediction method in predicting DXA LTM, demonstrating substantial concordance for total body FFM (rc = 0.80), ALM (rc = 0.78), arm (rc = 0.76) and leg LTM (rc = 0.65) and a smaller bias and LOA for ALM (+0.8 vs. -3.4 kg; LOA -4.9-6.4 vs. -11.9-5.1 kg), arm (+0.02 vs. -0.3 kg; LOA -1.1-1.1 kg vs. #link# -2.2-1.6 kg) and leg (+0.4 vs. -1.4 kg; LOA -2.0-2.8 vs. -5.6-2.8) LTM.

BIS can be used to accurately predict total body FFM, segmental LTM and ALM in individuals with acute SCI.

BIS can be used to accurately predict total body FFM, segmental LTM and ALM in individuals with acute SCI.

Within-subject, randomised cross-over trial.

To determine whether a commercially available 3D head-mounted (HMD) virtual reality (VR) device results in significant reductions in neuropathic pain compared to using a 2D screen device in people with spinal cord injury (SCI).

Greenwich Hospital, Sydney, Australia.

Sixteen men with established SCI and chronic neuropathic pain participated in a single-session randomised cross-over trial. We compared the effects of 3D HMD VR and a 2D screen application on SCI neuropathic pain intensity and levels of perceived presence.

Participants reported significantly lower pain intensity after 3D HMD VR compared to 2D screen application (1.9 ± SD 1.8 versus 3.4 ± SD 1.6, mean 95% CI 1.5, P < 0.0001). Participants reported significantly higher perceived levels of presence with the 3D HMD VR compared to 2D screen of (49.6 ± SD 8.9 versus 32.8 ± SD 11.1, mean 95% CI 16.6, P < 0.0001). Increased perceived presence was associated with significantly lower pain intensity regardless of randomised sequencing of the two conditions (mean 95% CI 0.06, P = 0.005). Effect size for pain reduction using 3D HMD VR was 0.80.

We suggest that 3D HMD VR may provide neuropathic pain relief for people with SCI. Given the lack of cybersickness and ease of access, we propose that immersive VR could be a helpful adjunct to current pharmacotherapy. Further research is required to show that VR can be effective for more long-term reductions in SCI pain.

We suggest that 3D HMD VR may provide neuropathic pain relief for people with SCI. Given the lack of cybersickness and ease of access, we propose that immersive VR could be a helpful adjunct to current pharmacotherapy. Further research is required to show that VR can be effective for more long-term reductions in SCI pain.Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the ongoing global outbreak of coronavirus disease (COVID-19) which is a significant threat to global public health. The rapid spread of COVID-19 necessitates the development of cost-effective technology platforms for the production of vaccines, drugs, and protein reagents for appropriate disease diagnosis and treatment. In this study, we explored the possibility of producing the receptor binding domain (RBD) of SARS-CoV-2 and an anti-SARS-CoV monoclonal antibody (mAb) CR3022 in Nicotiana benthamiana. Both RBD and mAb CR3022 were transiently produced with the highest expression level of 8 μg/g and 130 μg/g leaf fresh weight respectively at 3 days post-infiltration. The plant-produced RBD exhibited specific binding to the SARS-CoV-2 receptor, angiotensin-converting enzyme 2 (ACE2). Furthermore, the plant-produced mAb CR3022 binds to SARS-CoV-2, but fails to neutralize the virus in vitro. This is the first report showing the production of anti-SARS-CoV-2 RBD and mAb CR3022 in plants. Withaferin A provide a proof-of-concept for using plants as an expression system for the production of SARS-CoV-2 antigens and antibodies or similar other diagnostic reagents against SARS-CoV-2 rapidly, especially during epidemic or pandemic situation.Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.