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Organ-specific proteomic aging and cognitive performance: implications for risk prediction of Alzheimer’s disease and related dementias in older adults
No full textBackground and objectives
Biological aging, characterized by cellular and molecular changes, may play a key role in neurodegenerative diseases. While recent proteomic advancements have introduced new aging clocks, widespread validation remains necessary. This study evaluated organ-specific and cognition-enriched proteomic clocks in relation to chronological age and cognitive change.
Methods
We analyzed plasma proteomic data from the CHARIOT PRO SubStudy (N = 409), measured using the SomaScan assay (version 4.1) at four time points over three years (months 0, 12, 24, and 36). Using published proteomic organ age weights, we calculated conventional, organ-specific, and cognition-enriched biological ages and compared them with chronological age. Adjusted multilevel regression analyses assessed associations between baseline proteomic AgeGaps (biological−chronological age differences) and cognitive performance over 54 months.
Results
The cohort (mean age: 71.8 ± 5.5 years; 50.1 % female) showed moderate to strong correlations between proteomic ages and chronological age (r = 0.37–0.80; MAE = 4.2–2.7). Over three years, AgeGaps increased across the conventional, organismal, muscle, liver, artery, and immune systems, ranging from 2.1 ± 1.9 to 1.0 ± 2.3 years. The artery AgeGap was most strongly associated with cognitive decline, with conventional and organismal AgeGaps showing similar patterns. Higher baseline AgeGap z-scores (i.e., greater biological age) in the artery and brain were associated with poorer cognition, as measured by the Repeatable Battery for the Assessment of Neuropsychological Status Total Scores (Coeff. −3.0, 95 % CI: −3.4, −2.5; and −1.1, 95 % CI: −1.5, −0.6) and the Preclinical Alzheimer's Cognitive Composite (Coeff. −0.5, 95 % CI: −0.6, −0.4; and −0.14, 95 % CI: −0.3, −0.03).
Conclusions
These findings highlight the interplay between neurological function and cardiovascular aging in cognitive decline. Organ-specific biological age assessments may aid in the early detection of age-related changes, informing personalized interventions. Our study underscores the importance of proteomic aging signatures in elucidating Alzheimer’s disease mechanisms and other neurodegenerative conditions, advocating for an integrated approach to brain and cardiovascular health
SoK: unified blockchain data structure
No full textOver the last 15 years, blockchain technologies have evolved into a heterogeneous ecosystem with numerous
practical applications. The diversity of blockchain data models – ranging from UTxO-based to account-based systems and evolving towards smart contract platforms – has led to fragmentation in how blockchain data is stored, accessed, and analysed. This heterogeneity hinders interoperability, complicates cross-chain analytics, and limits reproducibility in blockchain research. In this paper, we present a comprehensive study of blockchain data
unification, covering a wide range of academic and commercial approaches. We examine their underlying data structures and identify key limitations in existing solutions. Building on these insights, we propose a unified data model that abstracts and flattens token transfer data while capturing the semantics of both UTxO and account-based paradigms. We conclude by outlining future research directions to further validate and extend this model across diverse blockchain ecosystems
Evidence for circulation of high-virulence HIV-1 subtype B variants in the United Kingdom
No full textThe evolution of HIV-1 virulence has significant implications for epidemic control. Recent phylogenomic analyses identified low-prevalence HIV-1 variants exhibiting significant differences in disease progression. We analysed 40 888 partial HIV-1 pol sequences from the UK HIV Drug Resistance Database (UKRDB) across subtypes B, C, A1, and CRF02AG. We identified phylotypes with putative differences in transmission/phylogenetic patterns and assessed their virulence trends using pretreatment viral loads, CD4 cell counts, and four statistical methods. We classified three subtype B phylotypes—PT.B.40.UK, PT.B.69.UK, and PT.B.133.UK —as variants of interest (VOIs) due to significantly higher viral loads and/or accelerated CD4 decline. PT.B.40.UK and PT.B.69.UK exhibited higher viral loads, 4.93 log10 copies/ml (95% CI: 4.73–5.13) and 4.87 (4.65–5.10), representing 0.30–0.36 log10 copies/ml higher than the reference group (4.57; 4.55–4.59). Despite uncertainties in baseline CD4 counts, all three VOIs reached the clinically relevant threshold of 350 CD4 cells/mm3 significantly faster than the reference group (3.5 years, 3.1–3.9 years): 2.3 years (1.0–5.1) for PT.B.40.UK, 2.0 years (10.8 months–4.4 years) for PT.B.69.UK, and 1.8 years (10.8 months–3.6 years) for PT.B.133.UK. These VOIs and their closest relatives have been circulating in the UK for decades with limited international spread and did not exhibit unusually rapid growth rates. Although these findings suggest a heritable high-virulence HIV-1 phenotype, we did not find evidence that convergent genetic polymorphisms or switches in coreceptor usage explained these differences. The small fraction of HIV-1 subtype B variants in the UK evolving towards higher virulence is unlikely to pose a public health concern, given the ongoing decline in new HIV diagnoses following the widespread adoption of pre-exposure prophylaxis and targeted prevention campaigns. However, this study—alongside the detection of the VB variant in the Netherlands—demonstrates that more virulent variants are not rare and can emerge independently in multiple countries. Consequently, HIV-1 genomic surveillance remains crucial to monitor HIV-1 virulence and mitigate its healthcare impact
Rough differential equations in the flow approach
No full textWe show how the flow approach of Duch (2021), with elementary differentials as coordinates as in Chandra and Ferdinand (2024), can be used to prove well-posedness for rough stochastic differential equations driven by fractional Brownian motion with Hurst index > 1⁄4. A novelty appearing here is that we use coordinates for the flow that are indexed by trees rather than multi-indices
Modulation of cellular, molecular, and humoral responses by PQ Grass 27,600 SU for the treatment of seasonal allergic rhinitis: a randomised double blind placebo control exploratory field study
No full textBackground:
A short-course pre-seasonal subcutaneous injection of PQ Grass is clinically effective for the treatment of allergic rhinitis, though its mechanism remains unclear. The aim of the study was to interrogate immunological mechanisms induced by PQ Grass conventional and extended regimens.
Methods:
A RDBPC exploratory field study involving participants that either received injections of PQ Grass with a cumulative dose of 27,600 SU conventional (six once weekly injections) or extended regimen (three once weekly injections followed by three once monthly injections) or placebo containing microcrystalline tyrosine (MCT) (placebo + MCT) or saline (placebo) was performed. Humoral, cellular, and molecular responses were assessed at baseline (V1), end of treatment, prior to grass pollen season (V12) and end of pollen season (V15). Immunoglobulin analyses and cellular/gene microarray analyses were performed in the sub-study cohort consisting of PQ Grass Conventional (n = 25 and n = 10, respectively), PQ Grass Extended (n = 26 and n = 10, respectively), Placebo with MCT (n = 13 and n = 5, respectively), and Placebo (saline; n = 12 and n = 5, respectively).
Results:
Both PQ Grass regimens, conventional and extended, were associated with improvement in total combined scores (TCS) with a relative difference of −35.0% (p = 0.03) and −40.8% (p = 0.01) against placebo with MCT, respectively. Both PQ Grass treatment regimens were associated with increases in the sIgG4/sIgE ratio (all, p < 0.05) and induction of IgA1 (all, p < 0.05) and IgA2 (all, p < 0.01) compared to placebo groups. Nasal fluid (p < 0.01) and serum (p < 0.05) blocking antibodies are functional and have the capacity to inhibit allergen-IgE complex formation and binding to B cells in the PQ Grass groups. In vitro cellular and microarray gene analyses demonstrated that the extended PQ Grass regimen was more proficient in modulating the immune response towards a tolerogenic milieu by dampening pro-inflammatory type 2 immune response and the associated cytokines (p < 0.05), immune deviation towards a Th1 response (p < 0.05), and induction of FOXP3+ Treg cells (p < 0.05).
Conclusions:
For the first time, we highlight differential mechanisms of tolerance induction by PQ Grass, with the extended regimen being superior in modulating T cell compartments.
Trail Registration:
Trial number: PQGrass309, EudraCT number: 2020-000408-13, Clinicaltrials.gov identifier: NCT04687059, and NCT0554071
Exploring and analysing learning curves in robotic-assisted thoracoscopic anatomical lung resections: a systematic review and meta-analysis
No full textObjectives:
There has been a steady increase in the uptake of robotic-assisted thoracic surgery over recent years, with a necessary focus on training. The early learning curve has been extensively debated; however, a detailed understanding of how this extends as we gain experience has been poorly discussed. This study assesses the congruency and depth of the learning curve in robotic-assisted thoracic surgery of anatomical lung resection.
Methods:
All studies reporting a quantitative assessment of operator learning curve in robotic anatomical lung resection before March 1, 2024, were included. Two authors extracted data, and study quality was assessed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Meta-analysis was performed using random effects modeling.
Results:
Twenty-nine studies including 106 surgeons were identified. A triphasic learning curve was identified with “competency” achieved at 20 (interquartile range, 13) cases, and “proficiency” at 60 (interquartile range, 37.5) cases. Weighted mean difference operating time between novice (P1) and proficiency (P2) was 34.1 minutes and between “competency” (P2) and “proficiency” (P3) was 17.5 minutes. This decreased with newer generations of robotic technology (Da Vinci Xi, weighted mean difference P1 vs P3: 42.3 [22.0, 62.5] vs S/Si, 57.4 [45.6, 69.2] minutes).
Conclusions:
The learning curve in anatomical lung resection is triphasic, with a reproducible extension beyond the initial proficiency phase. As robotic lung resection becomes more widespread, we must better understand the translation of this learning pattern among trainee surgeons to maintain excellence in clinical outcomes while facilitating training of the next generation
Beyond the skin surface: melanocyte biology and the spectrum of health inequities
No full textA striking physiological variance between human beings is the color of their skin, which is evident across human populations. Skin color is determined by the amount of eumelanin, a subtype of the pigment melanin in the skin, with higher levels resulting in darker skin phototypes. An ongoing concern within the dermatological community is the lack of skin disease diagnosis algorithms for different skin tones, with many symptoms being defined as type I skin according to the Fitzpatrick skin type scale. There are racial disparities in participant inclusions, with an underrepresentation of darker skin phototypes in clinical trials. In addition, the lack of representation of skin diseases on darker skin tones in medical literature can result in insufficient education on the nuanced presentations of dermatoses across different skin types, leading to potential misdiagnosis. Recognising these variations is essential to improve early diagnosis and healthcare outcomes for underrepresented groups. This review examines the impact of skin pigmentation on disease presentation, clinical diagnosis, treatment, and susceptibility, highlighting the challenges faced by patients with darker skin tones. It also explores healthcare disparities and emphasises the need for inclusive research and personalized treatment approaches to improve dermatological outcomes for underrepresented groups
Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation
No full textPurpose: Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to the discontinuation of clinical trials and the withdrawal of drugs from the market. This study explores the application of graph neural networks (GNNs) for DILI prediction, using molecular graph representations as the primary input.
Methods: We evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. We introduce a novel approach that creates a custom graph dataset, driven by molecular optimisation, that incorporates detailed and realistic chemical features such as bond lengths and partial charges as input into the GNN models. We have named our model approach DILIGeNN.
Results: DILIGeNN achieved an AUC of 0.897 on the DILI dataset, surpassing current state-of-the-art model in the DILI prediction task. Furthermore, DILIGeNN outperformed the state-of-the-art in other graph-based molecular prediction tasks, achieving an AUC of 0.918 on the Clintox dataset and 0.993 on the BBBP dataset and 0.953 on the BACE dataset, indicating strong generalisation and performance across different datasets.
Conclusion: DILIGeNN, utilising a single graph representation as input, outperforms the state-of-the-art methods in DILI prediction that incorporate both molecular fingerprint and graph-structured data. These findings highlight the effectiveness of our molecular graph generation and the GNN training approach as a powerful tool for early-stage drug development and drug repurposing pipeline.
Scientific Contribution: DILIGeNN is a GNN framework that extracts graph features from 3D optimised molecular structures as is done in target-based drug discovery and molecular docking simulation. Our method is the first to encode spatial and electrostatic information into a single graph representation, as opposed to other work that require multiple graphs or additional chemical descriptors for feature representation. Our approach, using warm starts following repeated early stopping during training, outperforms the current state-of-the-art methods in liver toxicity (DILI), permeability (BBBP) and activity (BACE) prediction tasks
Metabolic signatures of adiposity indices in females and males associated with clinical biomarkers in the UK Biobank
No full textBackground: Excessive adiposity increases disease risk, however, the metabolic processes underlying these associations remain incompletely understood.
Methods: We compared metabolic signatures (MSs) of adiposity indices (non-allometric: body fat %, waist circumference, hip circumference, waist-to-hip ratio, body mass index; allometric: a body shape index
[ABSI], hip index [HI], waist-to-HI ratio) by sex and examined their cross-sectional associations with 29 clinical biomarkers in 151,526 UK Biobank participants. MSs performance was validated in an independent cohort.
Findings: In females, MSs mainly consisted of lipoprotein particle concentrations, apolipoproteins, fatty acids and inflammation-linked glycoprotein acetyls, whereas in males lipoproteins rich in cholesteryl esters and aromatic/branched-chain amino acids predominated. The highest percentages of common metabolites were observed between non-allometric adiposity indices (median: 42.4%; range: 9%–56%). MSs were
independently associated with over 25 biomarkers with differences observed by sex and adiposity index, and these associations were stronger compared to the respective phenotypic associations.
Interpretation: MSABSI was found to be more atherogenic, whereas MSHI was more favourable for health.
This study highlights i) that different regions of adipose tissue undergo distinct metabolic processes overall
and by sex, each having unique impact on health, and ii) the importance of considering metabolic factors
beyond simple adiposity indices in assessing health risk
Engineering Knowledge: the Institution of Civil Engineers and the UN Sustainable Development Goals
No full textInfrastructure systems have the potential to benefit all 17 of the United Nations Sustainable Development Goals (SDGs). In order to understand the role of civil engineers to contribute to the SDGs, the synergies and trade-offs between civil engineering projects, the 17 SDGs and their 169 targets were identified. Using a structured literature review of research published in key Institution of Civil Engineers journals, the priority targets for the SDGs that civil engineering is best placed to provide a contribution were identified. The highest synergies were noted in the sector of transport (Target 11.6), which highlights the need for a standalone transport goal for future SDGs beyond 2030. Across the transport, energy, water and waste management infrastructure, SDG 11 (Sustainable cities and communities) and SDG 12 (Responsible consumption and production) were found to have the highest synergies, but more work needs to be done to address the increasing uncertainty and threats of climate change (SDG 13). Finally, the civil engineering community needs to recognise the trade-offs between infrastructure projects with socio-economic SDG targets relating to health, education, gender equality and poverty reduction, which is best done by embedding the SDGs in projects from planning rather than at design or implementation stages