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During flexion/extension, the patients presented statistically significant higher activations on the convex side in the entire root-mean-square maps and synergy vector maps (p less then 0.05). During lateral bending and axial rotation, the patients exhibited less activated muscles on the dominant actuating side relative to the contralateral side and their synergy vector maps showed a less homogenous and more diffuse distribution of muscle contraction with statistically different centers of gravity. The findings suggest that a scoliotic spine might adopt an altered modular muscular coordination strategy to actuate different dominant muscles as adapted compensations for the deformation.

This paper presents a safe and effective keyhole neurosurgery intra-operative planning framework for flexible neurosurgical robots. The framework is intended to support neurosurgeons during the intraoperative procedure to react to a dynamic environment.

The proposed system integrates inverse reinforcement learning path planning algorithm combined with 1) a pre-operative path planning framework for fast and intuitive user interaction, 2) a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion and 3) a simulated robotic system.

Simulation results performed on a human brain dataset show that the inverse reinforcement learning intra-operative planning method can guide a steerable needle with bounded curvature to a predefined target pose with an average targeting error of 1.34 0.52 (25th=1.02, 75th=1.36) mm in position and 3.16 1.06 (25th=2, 75th=4.94) degrees in orientation under a deformable simulated environment, with a re-planning time of 0.02 sec and a success rate of 100%.

With this work, we demonstrate that the presented intra-operative steerable needle path planner is able to avoid anatomical obstacles while optimising surgical criteria.

The results demonstrate that the proposed method is fast and can securely steer flexible needles with high accuracy and robustness.

The results demonstrate that the proposed method is fast and can securely steer flexible needles with high accuracy and robustness.

Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions.

In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study.

Experimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region's contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings.

Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.

Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.Digital disruption and transformation of health care is occurring rapidly. Concurrently, a global syndemic of preventable chronic disease is crippling healthcare systems and accelerating the effect of the COVID-19 pandemic. Healthcare investment is paradoxical; it prioritises disease treatment over prevention. This is an inefficient break-fix model versus a person-centred predict-prevent model. It is easy to reward and invest in acute health systems because activity is easily measured and therefore funded. Social, environmental and behavioural health determinants explain ~70% of health variance; yet, we cannot measure these community data contemporaneously or at population scale. The dawn of digital health and the digital citizen can initiate a precision prevention era, where consumer-centred, real-time data enables a new ability to count and fund population health, making disease prevention 'matter'. Then, precision decision making, intervention and policy to target preventable chronic disease (e.g. obesity) can be realised. We argue for, identify barriers to, and propose three horizons for digital health transformation of population health towards precision prevention of chronic disease, demonstrating childhood obesity as a use case. Clinicians, researchers and policymakers can commence strategic planning and investment for precision prevention of chronic disease to advance a mature, value-based model that will ensure healthcare sustainability in Australia and globally.In early 2020, the COVID-19 pandemic emerged, posing multiple challenges to healthcare organisations and communities. The Darling Downs region in Queensland, Australia had its first positive case of COVID-19 confirmed in March 2020, which created understandable anxiety in the community. JNK-IN-8 mouse The Vulnerable Communities Group (VCG) was established to address this anxiety through open lines of communication to strengthen community resilience. This case study reports the evaluation of the VCG, plus lessons learned while establishing and running an intersectoral group, with stakeholders from more than 40 organisations, in response to the COVID-19 pandemic. An anonymous online survey with closed and open-ended questions was administered to participants. Data were subject to descriptive statistical tests and content analysis. Four categories were developed from the free text data for reporting 'Knowledge is power', 'Beating isolation through partnerships and linkages', 'Sharing is caring', and 'Ripple effects'. Whilst opractitioners? Practitioners can use a community of practice framework to establish and evaluate an intersectoral group, as described in our paper, to enhance community connectedness to reduce isolation and share information and resources to help negate the challenges caused by the COVID-19 pandemic.

To address the global diabetes epidemic, lifestyle counseling on diet, physical activity, and weight loss is essential. This study assessed the implementation of a diabetes self-management education and support (DSMES) intervention using a mixed-methods evaluation framework.

We implemented a culturally adapted, home-based DSMES intervention in rural Indigenous Maya towns in Guatemala from 2018 through 2020. We used a pretest-posttest design and a mixed-methods evaluation approach guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Quantitative data included baseline characteristics, implementation metrics, effectiveness outcomes, and costs. Qualitative data consisted of semistructured interviews with 3 groups of stakeholders.

Of 738 participants screened, 627 participants were enrolled, and 478 participants completed the study. Adjusted mean change in glycated hemoglobin A

was -0.4% (95% CI, -0.6% to -0.3%; P < .001), change in systolic blood pressure was - Guatemala and resulted in significant improvements in most clinical and psychometric outcomes. Scaling up sustainable DSMES in health systems in rural settings requires careful consideration of local barriers and facilitators.Most clinical and experimental studies have suggested that hepatitis C virus (HCV) is dominant over hepatitis B virus (HBV) during coinfection, although the mechanism remains unclear. Here, we found that HCV core protein inhibits HBV replication by downregulating HBx levels during coinfection in human hepatoma cells. For this effect, HCV core protein increased reactive oxygen species levels in the mitochondria and activated the ataxia telangiectasia mutated-checkpoint kinase two pathway in the nucleus, resulting in an upregulation of p53 levels. Accordingly, HCV core protein induced p53-dependent activation of seven in absentia homolog one expression, an E3 ligase of HBx, resulting in the ubiquitination and proteasomal degradation of HBx. The effect of the HCV core protein on HBx levels was accurately reproduced in both a 1.2-mer HBV replicon and in vitro HBV infection systems, providing evidence for the inhibition of HBV replication by HCV core protein. The present study may provide insights into the mechanism of HCV dominance in HBV- and HCV-coinfected patients.The matrix protein of many enveloped RNA viruses regulates multiple stages of viral life cycle and has the characteristics of nucleocytoplasmic shuttling. We have previously demonstrated that matrix protein 1 (M1) of an RNA virus, influenza virus, blocks host cell cycle progression by interacting with SLD5, a member of the GINS complex, which is required for normal cell cycle progression. In this study, we found that M protein of several other RNA viruses, including VSV, SeV and HIV, interacted with SLD5. Furthermore, VSV/SeV infection and M protein of VSV/SeV/HIV induced cell cycle arrest at G0/G1 phase. Importantly, overexpression of SLD5 partially rescued the cell cycle arrest by VSV/SeV infection and VSV M protein. In addition, SLD5 suppressed VSV replication in vitro and in vivo, and enhanced type Ⅰ interferon signalling. Taken together, our results suggest that targeting SLD5 by M protein might be a common strategy used by multiple enveloped RNA viruses to block host cell cycle. Our findings provide new mechanistic insights for virus to manipulate cell cycle progression by hijacking host replication factor SLD5 during infection.The shortcomings of current anti-human cytomegalovirus (HCMV) drugs has stimulated a search for anti-HCMV compounds with novel targets. We screened collections of bioactive compounds and identified a range of compounds with the potential to inhibit HCMV replication. Of these compounds, we selected bisbenzimide compound RO-90-7501 for further study. We generated analogues of RO-90-7501 and found that one compound, MRT00210423, had increased anti-HCMV activity compared to RO-90-7501. Using a combination of compound analogues, microscopy and biochemical assays we found RO-90-7501 and MRT00210423 interacted with DNA. In single molecule microscopy experiments we found RO-90-7501, but not MRT00210423, was able to compact DNA, suggesting that compaction of DNA was non-obligatory for anti-HCMV effects. Using bioinformatics analysis, we found that there were many putative bisbenzimide binding sites in the HCMV DNA genome. However, using western blotting, quantitative PCR and electron microscopy, we found that at a concentration able to inhibit HCMV replication our compounds had little or no effect on production of certain HCMV proteins or DNA synthesis, but did have a notable inhibitory effect on HCMV capsid production. We reasoned that these effects may have involved binding of our compounds to the HCMV genome and/or host cell chromatin. Therefore, our data expand our understanding of compounds with anti-HCMV activity and suggest targeting of DNA with bisbenzimide compounds may be a useful anti-HCMV strategy.