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Introduction: For people living with HIV, the first antiretroviral treatment (ART) regimen offers the best chance for a good virological response. Early identification of those unlikely to respond to first‐line ART could enable timely intervention and increase chances of a good initial treatment response. In this study we assess the extent to which the HIV RNA viral load (VL) at 1 and 3 months is predictive of first‐line treatment outcome at 6 months. Methods: All previously ART‐naive individuals starting ART at two London centres since 2000 with baseline (−180 to 3 days) VL >500 c/mL had a VL measurement between 6 and 12 months after starting ART, and at least one at month 1 (4–60 days) or month 3 (61–120 days) were included. Lack of treatment response was defined as (i) VL >200 copies/mL at 6 months or (ii) VL >200 copies/mL at 6 months or simultaneous switch in drugs from at least two different drug classes before 6 months. The association with VL measurements at 1 and 3 months post‐ART; change from pre‐ART in these values; and CD4 count measurements at 1 and 3 months were assessed using logistic regression models. The relative fit of the models was compared using the Akaike information criterion (AIC). Results: A total of 198 out of 3258 individuals (6%) experienced lack of treatment response at 6 months (definition i), increasing to 511 (16%) for definition (ii). Those with a 1‐month (day 4–60 window) VL of <1000, 1000–9999, 10,000–99,999 and >100,000 copies/ml had a 4%, 8%, 23% and 24% chance, respectively, of subsequently experiencing treatment non‐response at 6 months (definition (i)). When considering the 3‐month (day 61–120 window) VL, the chances of subsequently experiencing treatment non‐response were, respectively, 3%, 25%, 67% and 75%. Results were similar for definition (ii). Conclusions: Whilst 3‐month VL provides good discrimination between low and high risk of treatment failure, 1‐month VL does not. Presence of a VL >10,000 copies/ml after 3 months of ART is a cutoff above which individuals are at a sufficiently higher risk of non‐response that they may be considered for intervention.

Background The widespread availability of antiretroviral therapy (ART) has dramatically reduced mortality and improved life expectancy for people living with HIV (PLWH). However, even with HIV-1 suppression, chronic immune activation and elevated inflammation persist and have been linked to a pro-inflammatory gut microbiome composition and compromised intestinal barrier integrity. PLWH in urban versus rural areas of sub-Saharan Africa experience differences in environmental factors that may impact the gut microbiome and immune system, in response to ART, yet this has not previously been investigated in these groups. To address this, we measured T cell activation/exhaustion/trafficking markers, plasma inflammatory markers, and fecal microbiome composition in PLWH and healthy participants recruited from an urban clinic in the city of Harare, Zimbabwe, and a district hospital that services surrounding rural villages. PLWH were either ART naïve at baseline and sampled again after 24 weeks of first-line ART and the antibiotic cotrimoxazole or were ART-experienced at both timepoints. Results Although expected reductions in the inflammatory marker IL-6, T-cell activation, and exhaustion were observed with ART-induced viral suppression, these changes were much more pronounced in the urban versus the rural area. Gut microbiome composition was the most highly altered from healthy controls in ART experienced PLWH, and characterized by both reduced alpha diversity and altered composition. However, gut microbiome composition showed a pronounced relationship with T cell activation and exhaustion in ART-naïve PLWH, suggesting a particularly significant role for the gut microbiome in disease progression in uncontrolled infection. Elevated immune exhaustion after 24 weeks of ART did correlate with both living in the rural location and a more Prevotella-rich/Bacteroides-poor microbiome type, suggesting a potential role for rural-associated microbiome differences or their co-variates in the muted improvements in immune exhaustion in the rural area. Conclusion Successful ART was less effective at reducing gut microbiome-associated inflammation and T cell activation in PLWH in rural versus urban Zimbabwe, suggesting that individuals on ART in rural areas of Zimbabwe may be more vulnerable to co-morbidity related to sustained immune dysfunction in treated infection. CoV3FsV7YLEhEop2PZ1D47 Video Abstract

This art-based dissertation explored professionals' reactivity toward problematic-client interactions across the art therapy, expressive arts therapy, hairstyling, and tattooing disciplines. The concept was explored with a participant from each discipline in a four-phase sculpting process and through semi-structured interviews, response sculptures, and video footage and aggregates. Research questions examined (1) what happens to the participants' professional understanding of problematic clients and (2) self-understanding through engaging in a sculpting process focused on problematic clients, (3) similarities in participants' experiences across disciplines, and (4) what happens when the researcher creates and presents response art to the research participants. The two main outcomes were needs for transformation and supervision. The overarching transformation outcome encompassed the study's findings, which were the participants' (a) embodiment of sculptures to represent emotions and (b) negativity and discomfort in witnessing. Additionally, the participants identified the need for life adjustments, including (c) overcoming feelings of incompetence by (d) regaining control and empathy, as well as (e) improving self-regulation and (f) managing wellness.

This study assessed the safety and performance of ARTFit, a new tool embedded in MAESTRO, the cochlear implant (CI) system software by MED-EL GmbH (Innsbruck, Austria). ARTFit automatically measures thresholds of the electrically evoked compound action potential (ECAP) to produce initial ‘maps’ (ECAPMAPs), i.e., configuration settings of the audio processor that the audiologist switches to live mode and adjusts for comfortable loudness (LiveECAPMAPs). Twenty-three adult and ten pediatric users of MED-EL CIs participated. The LiveECAPMAPs were compared to behavioral maps (LiveBurstMAPs) and to the participants’ everyday clinical maps (ClinMAPs). Four evaluation measures were considered: average deviations of the maximum comfortable loudness (MCL) levels of the LiveECAPMAPs and the LiveBurstMAPs from the MCLs of the ClinMAPs; correlations between the MCLs of the LiveECAPMAPs (MCLecap) and the LiveBurstMAPs (MCLburst) with the MCLs of the ClinMAPs (MCLclin); fitting durations; and speech reception thresholds (SRTs). All evaluation measures were analyzed separately in the adult and pediatric subgroups. For all evaluation measures, the deviations of the LiveECAPMAPs from the ClinMAPs were not larger than those of the LiveBurstMAPs from the ClinMAPs. The Pearson correlation between the MCLecap and the MCLclin across all channels was r2 = 0.732 (p < 0.001) in the adult and r2 = 0.616 (p < 0.001) in the pediatric subgroups. The mean fitting duration in minutes for the LiveECAPMAPs was significantly shorter than for that of the LiveBurstMAPs in both subgroups: adults took 5.70 (range 1.90–11.98) vs. 9.27 (6.83–14.72) min; children took 3.03 (1.97–4.22) vs. 7.35 (3.95–12.77). SRTs measured with the LiveECAPMAPs were non-inferior to those measured with the ClinMAPs and not statistically different to the SRTs measured with the LiveBurstMAPs. ARTFit is a safe, quick, and reliable tool for audiologists to produce ECAP-based initial fitting maps in adults and young children who are not able to provide subjective feedback.

Diffuse brain injuries are assessed with deformation-based criteria that utilize metrics based on rotational head kinematics to estimate brain injury severity. Although numerous metrics have been proposed, many are based on empirically-derived models that use peak kinematics, which often limit their applicability to a narrow range of head impact conditions. However, over a broad range of impact conditions, brain deformation response to rotational head motion behaves similarly to a second-order mechanical system, which utilizes the full kinematic time history of a head impact. This study describes a new brain injury metric called Diffuse Axonal Multi-Axis General Evaluation (DAMAGE). DAMAGE is based on the equations of motion of a three-degree-of-freedom, coupled 2nd-order system, and predicts maximum brain strain using the directionally dependent angular acceleration time-histories from a head impact. Parameters for the effective mass, stiffness, and damping were determined using simplified rotational pulses which were applied multiaxially to a 50th percentile adult human male finite element model. DAMAGE was then validated with a separate database of 1747 head impacts including helmet, crash, and sled tests and human volunteer responses. Relative to existing rotational brain injury metrics that were evaluated in this study, DAMAGE was found to be the best predictor of maximum brain strain.

Cerebral atrophy in response to traumatic brain injury is a well-documented phenomenon in both primary investigations and review articles. Recent atrophy studies focus on exploring the region-specific patterns of cerebral atrophy; yet, there is no study that analyzes and synthesizes the emerging atrophy patterns in a single comprehensive review. Here we attempt to fill this gap in our current knowledge by integrating the current literature into a cohesive theory of preferential brain tissue loss and by identifying common risk factors for accelerated atrophy progression. Our review reveals that observations for mild traumatic brain injury remain inconclusive, whereas observations for moderate-to-severe traumatic brain injury converge towards robust patterns: brain tissue loss is on the order of 5% per year, and occurs in the form of generalized atrophy, across the entire brain, or focal atrophy, in specific brain regions. The most common regions of focal atrophy are the thalamus, hippocampus, and cerebellum in gray matter and the corpus callosum, corona radiata, and brainstem in white matter. We illustrate the differences of generalized and focal gray and white matter atrophy on emerging deformation and stress profiles across the whole brain using computational simulation. The characteristic features of our atrophy simulations—a widening of the cortical sulci, a gradual enlargement of the ventricles, and a pronounced cortical thinning—agree well with clinical observations. Understanding region-specific atrophy patterns in response to traumatic brain injury has significant implications in modeling, simulating, and predicting injury outcomes. Computational modeling of brain atrophy could open new strategies for physicians to make informed decisions for whom, how, and when to administer pharmaceutical treatment to manage the chronic loss of brain structure and function.

Head impacts leading to traumatic brain injury (TBI) present a major health risk today, projected to become the third leading cause of death by 2020. While finite element (FE) models of the human brain are important tools to understand and mitigate TBI, many unresolved issues remain that need to be addressed to improve these models. This work aims to provide readers with background information regarding the current state of research in this field as well as to present recent advancements made possible by improvements to computational resources. Specifically, this has manifested as a drive to introduce more details in FE models in the form of increased spatial resolution and improved material models such as nonlinear and anisotropic constitutive models. The need to work with high-resolution FE meshes is underlined by the dominant wavelengths involved in transient pressure and shear wave propagation and the ability to model the brain surface. We also discuss improvements to experimental validation techniques which allow for better calibrated models. We review these recent developments in detail, highlighting their contributions to the field as well as identifying open issues where more research is needed.

Dozens of finite element models of the human brain have been developed for providing insight into the mechanical response of the brain during impact. Many models used in traumatic brain injury research are based on different computational techniques and approaches. In this study, a comprehensive review of the numerical methods implemented in 16 brain models was performed. Differences in element type, mesh size, element formulation, hourglass control, and solver were found. A parametric study using the SIMon FE brain model was performed to quantify the sensitivity of model outputs to differences in numerical implementation. Model outputs investigated in this study included nodal displacement (commonly used for validation) and maximum principal strain (commonly used for injury assessment), and these results were demonstrated using the loading characteristics of a reconstructed football concussion event. Order-of-magnitude differences in brain response were found when only changing the characteristics of the numerical method. Mesh type and mesh size had the largest effect on model response. These differences have important implications on the interpretation of results among different models simulating the same impacts, and of the results between model and in vitro experiments. Additionally, future studies need to better report the numerical methods used in the models.

Many human brain finite element (FE) models lack mesoscopic (~ 1 mm) white matter structures, which may limit their capability in predicting TBI and assessing tissue-based injury metrics such as axonal strain. This study investigated an embedded method to explicitly incorporate white matter axonal fibers into an existing 50th percentile male brain model. The white matter was decomposed into myelinated axon tracts and an isotropic ground substance that had similar material properties to gray matter. The axon tract bundles were derived from a population-based tractography template explicitly modeled using 1-D cable elements. The axonal fibers and ground substance material were implemented using hyper-viscoelastic constitutive models, which were calibrated using white and gray matter brain tissue material testing data available in the literature. Finally, the new axon-based model was extensively validated for brain-skull relative deformation under various loading conditions (n = 17) and showed good biofidelity compared to other brain models. Through these analyses, we demonstrated the applicability of this method for incorporating axonal fiber tracts into an existing FE brain model. The axon-based model will be a useful tool for understanding the mechanisms of TBI, evaluating tissue-based injury metrics, and developing injury mitigation systems.

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