Department of Neurology, UC Davis School of Medicine

Introduction: The objective of this study was to compare long-term neurological outcomes in low-risk patients with traumatic intracranial hemorrhage (tICH) admitted to the ICU (intensive care unit) versus patients admitted to the floor.

Methods: This retrospective study was conducted at a Level 1 trauma center from October 1, 2008, to February 1, 2013. We defined low-risk patients as age less than 65 years, isolated head injury, normal admission mental status, and no shift or swelling on initial head CT (computed tomography). Clinical data were abstracted from a trauma registry and linked to a brain injury database. We compared the Extended Glasgow Outcome Scale (GOS-E) score at six months between patients admitted to the ICU and patients admitted to the floor. We did a risk-adjusted analysis of the influence of floor admission on a normal GOS-E.

Results: We identified 151 patients; 45 (30%) were admitted to the floor and 106 (70%) to the ICU. Twenty-three (51%; 95% CI [36-66%]) patients admitted to the floor and 55 (52%; 95% CI [42-62%]) patients admitted to the ICU had a normal GOS-E. On adjusted analysis; the odds ratio for floor admission was 0.77 (95% CI [0.36-1.64]) for a normal GOS-E at six months.

Conclusion: Long-term neurological outcomes in low-risk patients with tICH were not markedly different between patients admitted to the ICU and those admitted to the floor. However, we were unable to demonstrate non-inferiority on adjusted analysis. Future work aimed at a larger, prospective cohort may better evaluate the relative impacts of admission type on outcomes. [West J Emerg Med. 2015;16(2):284–290.]

Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In this study, we propose a novel application of machine learning in the automated screening of microinfarcts and microhemorrhages. Utilizing whole slide images (WSIs) from postmortem human brain samples, we adapted a patch-based pipeline with convolutional neural networks. Our cohort consisted of 22 cases from the University of California Davis Alzheimer's Disease Research Center brain bank with hematoxylin and eosin-stained formalin-fixed, paraffin-embedded sections across 3 anatomical areas: frontal, parietal, and occipital lobes (40 WSIs with microinfarcts and/or microhemorrhages, 26 without). We propose a multiple field-of-view prediction step to mitigate false positives. We report screening performance (ie, the ability to distinguish microinfarct/microhemorrhage-positive from microinfarct/microhemorrhage-negative WSIs), and detection performance (ie, the ability to localize the affected regions within a WSI). Our proposed approach improved detection precision and screening accuracy by reducing false positives thereby achieving 100% screening accuracy. Although this sample size is small, this pipeline provides a proof-of-concept for high efficacy in screening for characteristic brain changes of cerebrovascular disease to aid in screening of microinfarcts/microhemorrhages at the WSI level.

Short-term motor adaptation to novel movement dynamics has been shown to involve at least two concurrent learning processes: a slow process that responds weakly to error but retains information well and a fast process that responds strongly to error but has poor retention. This modeling framework can explain several properties of motion-dependent motor adaptation (e.g., 24 h retention). An important assumption of this computational framework is that learning is only based on the experienced movement error, and the effect of noise (either internally generated or externally applied) is not considered. We examined the respective error sensitivity by quantifying adaptation in three subject groups distinguished by the noise added to the motion-dependent perturbation. We assessed the feedforward adaptive changes in motor output and examined the adaptation rate, retention, and decay of learning. Applying a two-state modeling framework showed that the applied noise during training mainly affected the fast learning process, with the slow process largely unaffected; participants in the higher noise groups demonstrated a reduced force profile following training, but the decay rate across groups was similar, suggesting that the slow process was unimpaired across conditions. Collectively, our results provide evidence that noise significantly decreases motor adaptation, but this reduction may be due to its influence over specific learning mechanisms. Importantly, this may have implications for how the motor system compensates for random fluctuations, especially when affected by brain disorders that result in movement tremor (e.g., essential tremor).

This scientific commentary refers to Cerebrovascular reactivity and response times describe recent ischemic symptomatology in patients with moyamoya, by Han et al.  (https://doi.org/10.1093/braincomms/fcae381).

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

INTRODUCTION: The Alzheimers Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core was launched to advance Alzheimers disease (AD) and AD-related dementia (ADRD) health equity research in underrepresented populations (URPs). We describe our evidence-based, scalable culturally informed, community-engaged research (CI-CER) model and demonstrate its preliminary success in increasing URP enrollment. METHODS: URPs include ethnoculturally minoritized, lower education (≤ 12 years), and rural populations. The CI-CER model includes: (1) culturally informed methodology (e.g., less restrictive inclusion/exclusion criteria, sociocultural measures, financial compensation, results disclosure, Spanish Language Capacity Workgroup) and (2) inclusive engagement methods (e.g., the Engagement Core team; Hub Sites; Community-Science Partnership Board). RESULTS: As of April 2024, 60% of ADNI-4 new in-clinic enrollees were from ethnoculturally or educationally URPs. This exceeds ADNI-4s ≥ 50% URP representation goal for new enrollees but may not represent final enrollment. DISCUSSION: Findings show a CI-CER model increases URP enrollment in AD/ADRD clinical research and has important implications for clinical trials to advance health equity. HIGHLIGHTS: The Alzheimers Disease Neuroimaging Initiative-4 (ADNI-4) uses a culturally informed, community-engaged research (CI-CER) approach. The CI-CER approach is scalable and sustainable for broad, multisite implementation. ADNI-4 is currently exceeding its inclusion goals for underrepresented populations.

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Ferroptosis initiation is often utilized for synergistic immunotherapy. While, current immunotherapy is limited by an immunosuppressive tumor microenvironment (TME), and ferroptosis is limited by insufficient reactive oxygen species (ROS) and ferroptotic lipids in tumor cells. Here, an arachidonic acid (AA) loaded nanosystem (CTFAP) is developed to mutually reinforce ferroptosis and cancer immunotherapy by augmenting ROS generation and modulating ferroptotic lipids. CTFAP is composed of acid-responsive core calcium peroxide (CaO2) nanoparticles, ferroptotic lipids sponsor AA, tetracarboxylic porphyrin (TCPP) and Fe3+ based metal-organic framework structure, and biocompatible mPEG-DSPE for improved stability. Once endocytosed by tumor cells, CTFAP can release oxygen (O2) and hydrogen peroxide (H2O2) in the acidic TME, facilitating TCPP-based sonodynamic therapy and Fe3+-mediated Fenton-like reactions to generate substantial ROS for cell ferroptosis initiation. The immunogenic cell death (ICD) after ferroptosis promotes interferon γ (IFN-γ) secretion to up-regulate the expression of long-chain family member 4 (ACSL4), cooperating with the released AA from CTFAP to accelerate the accumulation of lipid peroxidation (LPO) and thereby promoting ferroptosis in cancer cells.CTFAP with ultrasound treatment efficiently suppresses tumor growth, has great potential to challenges in cancer immunotherapy.

As of 2023, it is estimated that 6.7 million individuals in the United States live with Alzheimer's disease (AD). Prior research indicates that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research shows that hippocampal functional connectivity differs by sex and may be related to the observed sex differences in AD, and apolipoprotein E (ApoE) ε4 carriers have reduced hippocampal functional connectivity. The purpose of this study was to determine if the ApoE genotype plays a role in the observed sex differences in hippocampal functional connectivity in Alzheimer's disease. The resting state fMRI and T2 MRI of individuals with AD (n = 30, female = 15) and cognitively normal individuals (n = 30, female = 15) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the functional connectivity toolbox (CONN). Our results demonstrated intrahippocampal functional connectivity differed between those without an ε4 allele and those with at least one ε4 allele in each group. Additionally, intrahippocampal functional connectivity differed only by sex when Alzheimer's participants had at least one ε4 allele. These results improve our current understanding of the role of the interacting relationship between sex, ApoE genotype, and hippocampal function in AD. Understanding these biomarkers may aid in the development of sex-specific interventions for improved AD treatment.