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C‐reactive protein (CRP) and growth differentiation factor 15 (GDF15) are important markers of inflammation associated with brain health. Compared to plasma, DNA methylation (DNAm) measures of CRP and GDF15 may provide stable epigenetic measures of chronic exposure to inflammation and could therefore be robustly predictive of inflammation‐related brain aging and neurodegeneration. We leveraged a subsample of Baltimore Longitudinal Study of Aging (BLSA) participants with DNAm/plasma data and longitudinal neuroimaging/cognition data (n = 430–1100). We used a proteome‐wide analysis to characterize the biology of DNAm CRP and GDF15, and latent growth curve models to explore the associations with longitudinal trajectories of 19 brain region volumes and five cognitive domains. Finally, we related DNAm/plasma CRP and GDF15 to dementia risk in two external cohorts. DNAm CRP and GDF15 showed a proteomic signature consistent with systemic immune activation. We identified several brain regions with significant associations between elevated DNAm CRP And GDF15 and (a) lower brain volume level (at age 75) and (b) greater rate of atrophy. Compared to plasma CRP, DNAm CRP was more strongly associated with brain volume, cognitive trajectories, and dementia risk. DNAm and plasma GDF15 were similarly associated with several total lobar, total lobar white matter, and AD‐relevant region trajectories and dementia risk, but DNAm measures outperformed plasma measures in relation to cognitive trajectories. Epigenetic signatures of CRP and GDF15 reflect immune and inflammation‐related pathway activation. These signatures, especially DNAm CRP, were associated with accelerated brain atrophy, cognitive decline, as well as long‐term dementia risk. The current study provides evidence that epigenetic proxies of CRP and GDF15 exposure reflect the activation of immune/inflammation‐related pathways and are associated with longitudinal brain atrophy and cognitive decline as well as long‐term dementia risk.

Six refugee screening sites collaborated to estimate the prevalence of hepatitis C virus (HCV) antibodies among newly arrived refugees in the United States from 2010 to 2017, identify demographic characteristics associated with HCV antibody positivity, and estimate missed HCV antibody-positive adults among unscreened refugees. We utilized a cross-sectional study to examine HCV prevalence among refugees (N = 144,752). A predictive logistic regression model was constructed to determine the effectiveness of current screening practices at identifying cases. The prevalence of HCV antibodies among the 64,703 refugees screened was 1.6%. Refugees from Burundi (5.4%), Moldova (3.8%), Democratic Republic of Congo (3.2%), Burma (2.8%), and Ukraine (2.0%) had the highest positivity among refugee arrivals. An estimated 498 (0.7%) cases of HCV antibody positivity were missed among 67,787 unscreened adults. The domestic medical examination represents an opportunity to screen all adult refugees for HCV to ensure timely diagnosis and treatment.

We investigated the association between traumatic brain injury (TBI) and incidence of early-onset dementia (EOD) versus late-onset dementia (LOD). We included 501,710 UK Biobank participants (average age 56.5). Over a median 13.6 years of follow-up, 836 developed EOD (before 65) and 8947 developed LOD (after 65). We estimated hazard ratios (HRs) for TBI's association with EOD and LOD using Cox proportional hazard models with TBI as a time-varying exposure and coefficient. TBI was associated with higher risk of both EOD (HR: 4.06 [95% confidence interval: 3.13, 5.26]) and LOD (HR: 2.51 [2.31, 2.72]. Among participants included in both EOD and LOD analyses, the effect estimate was higher for EOD (3.41 vs. 2.80), but the difference was not statistically significant. The association of TBI with EOD was stronger in those with more severe TBIs. TBI is associated with an increased risk of EOD, with a higher risk observed in more severe injuries. Traumatic brain injury (TBI) is a significant risk factor for early-onset dementia (EOD). TBI was analyzed as a time-varying exposure and time-varying coefficient. Moderate/severe/penetrating TBIs have a heightened risk of developing EOD. TBI is more strongly associated with an earlier age cut-off for dementia.

INTRODUCTION Dementia prevalence is associated with modifiable factors. We quantified the contribution of dementia risk factors in midlife (45–64 years) and late life (≥ 65 years) in the United States. METHODS Data from six community‐based cohorts in the Dementia Risk Prediction Project (DRPP) were used. We estimated risk factor prevalence using nationally representative data. Cohort‐specific Cox regression models were used to estimate the association between modifiable risk factors and incident dementia in midlife and late life. Hazard ratios were pooled using meta‐analysis then used to calculate population attributable fractions (PAFs) and potential impact fractions. RESULTS Midlife and late‐life risk factors contributed to 22.7% and 16.5% of total dementia cases, respectively. Midlife obesity (PAF: 7.7%; 95% confidence interval [CI]: 4.9%–10.5%), lower education (PAF: 8.1%; 95% CI: 5.2%–11.1%), and late‐life physical inactivity (PAF: 10.4%; 95% CI: 6.2%–14.5%) were the greatest contributors. DISCUSSION Midlife and late‐life modifiable risk factors contribute to dementia risk, highlighting a need for interventions across the life course. Highlights Our sample included 37,931 participants across six pooled, longitudinal US cohorts. We observed midlife and late‐life risk factors contributed to 22.7% and 16.5% of dementia cases, respectively. Midlife obesity, late‐life physical inactivity, and lower education appear to be the greatest contributors to dementia risk.

The majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most over-expressed in MYCN -amplified, compared with MYCN -non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targets. MYCN amplification is common in neuroblastomas. Here, the authors identify a long noncoding RNA, lncNB1 in these cancers and show that it promotes tumorigenesis by binding to ribosomal protein, RPL35 to enhance E2F1 and DEPDC1B protein synthesis, which phosphorylates ERK to stabilise N-Myc.

Technologies to treat wastewater in decentralized systems are critical for sustainable development. Bioreactors are suitable for low-energy removal of inorganic and organic compounds, particularly for non-potable applications where a small footprint is required. One of the main problems associated with bioreactor use is sporadic spikes of chemical toxins, including nanoparticles. Here, we describe the development of DIYBOT (Digital Proxy of a Bio-Reactor), which enables remote monitoring of bioreactors and uses the data to inform decisions related to systems management. To test DIYBOT, a household-scale membrane aerated bioreactor with real-time water quality sensors was used to treat household greywater simulant. After reaching steady-state, silver nanoparticles (AgNP) representative of the mixture found in laundry wastewater were injected into the system to represent a chemical contamination. Measurements of carbon metabolism, effluent water quality, biofilm sloughing rate, and microbial diversity were characterized after nanoparticle exposure. Real-time sensor data were analyzed to reconstruct phase-space dynamics and extrapolate a phenomenological digital proxy to evaluate system performance. The management implication of the stable-focus dynamics, reconstructed from observed data, is that the bioreactor self-corrects in response to contamination spikes at AgNP levels below 2.0 mg/L. DIYBOT may help reduce the frequency of human-in-the-loop corrective management actions for wastewater processing.

This article addresses the evolving challenges in evaluating insecticide-based tools for vector control. In response to the emergence of insecticide resistance in major malaria vectors, novel chemistries and products are coming to market, and there is a need to review the available testing methodologies. Commonly used methods for evaluating insecticides, such as the World Health Organization (WHO) cone bioassay, are inadequate for the diverse range of tools now available. Innovation to Impact (I2I) has studied the variability in laboratory methods, with the aim of identifying key factors that contribute to variation and providing recommendations to tighten up protocols. The I2I Methods Landscape is a living document which presents a review of existing methods for evaluating vector control tools, with the scope currently extending to insecticide-treated nets (ITNs) and indoor residual sprays (IRS). The review reveals a lack of validation for many commonly used vector control methods, highlighting the need for improved protocols to enhance reliability and robustness of the data that is generated to make decisions in product development, evaluation, and implementation. A critical aspect highlighted by this work is the need for tailored methods to measure endpoints relevant to the diverse modes of action of novel insecticides. I2I envisage that the Methods Landscape will serve as a decision-making tool for researchers and product manufacturers in selecting appropriate methods, and a means to prioritise research and development. We call for collective efforts in the pro-active development, validation, and consistent implementation of suitable methods in vector control to produce the data needed to make robust decisions.

Activated B-cell-like diffuse large B-cell lymphomas (ABC-DLBCLs) are characterized by constitutive activation of nuclear factor κB driven by the B-cell receptor (BCR) and Toll-like receptor (TLR) pathways. However, BCR-pathway-targeted therapies have limited impact on DLBCLs. Here we used >1,100 DLBCL patient samples to determine immune and extracellular matrix cues in the lymphoid tumour microenvironment (Ly-TME) and built representative synthetic-hydrogel-based B-cell-lymphoma organoids accordingly. We demonstrate that Ly-TME cellular and biophysical factors amplify the BCR–MYD88–TLR9 multiprotein supercomplex and induce cooperative signalling pathways in ABC-DLBCL cells, which reduce the efficacy of compounds targeting the BCR pathway members Bruton tyrosine kinase and mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1). Combinatorial inhibition of multiple aberrant signalling pathways induced higher antitumour efficacy in lymphoid organoids and implanted ABC-DLBCL patient tumours in vivo. Our studies define the complex crosstalk between malignant ABC-DLBCL cells and Ly-TME, and provide rational combinatorial therapies that rescue Ly-TME-mediated attenuation of treatment response to MALT1 inhibitors.A hydrogel-based modular lymphoma organoid identifies how lymphoid microenvironment cues dampen the effect of MALT1 inhibitors and informs effective combination therapies to rescue the treatment response

Purpose Prognostication of surgical complexity is crucial for optimizing decision-making and patient counseling in pituitary surgery. This study aimed to develop a clinical score to predict gross-total resection (GTR) in non-functioning pituitary adenomas (NFPAs) using externally validated machine-learning (ML) models. Methods Clinical and radiological data were collected from two tertiary medical centers. Patients had pre- and postoperative structural T1-weighted MRI with gadolinium and T2-weighted preoperative scans. Three ML classifiers were trained on the National Hospital for Neurology and Neurosurgery dataset and tested on the Foundation IRCCS Ca’ Granda Polyclinic of Milan dataset. Feature importance analyses and hierarchical-tree inspection identified predictors of surgical complexity, which were used to create the grading score. The prognostic performance of the proposed score was compared to that of the state-of-the art TRANSSPHER grade in the external dataset. Surgical morbidity was also analyzed. Results All ML models accurately predicted GTR, with the random forest classifier achieving the best performance (weighted-F1 score of 0.87; CIs: 0.71, 0.97). Key predictors—Knosp grade, tumor maximum diameter, consistency, and supra-sellar nodular extension—were included in the modified (m)-TRANSSPHER grade. The ROC analysis showed superior performance of the m-TRANSSPHER grade over the TRANSSPHER grade for predicting GTR in NFPAs (AUC 0.85 vs. 0.79). Conclusions This international multi-center study used validated ML algorithms to refine predictors of surgical complexity in NFPAs, yielding the m-TRANSSPHER grade, which demonstrated enhanced prognostic accuracy for surgical complexity prediction compared to existing scales.