Developing a prediction model to estimate the true burden of respiratory syncytial virus (RSV) in hospitalised children in Western Australia

Respiratory Viral Testing Rate Patterns in Young Children Attending Tertiary Care Across Western Australia: A Population-Based Birth Cohort Study

BACKGROUND

An understanding of viral testing rates is crucial to accurately estimate the pathogen-specific hospitalisation burden. We aimed to estimate the patterns of testing for respiratory syncytial virus (RSV), influenza virus, parainfluenza virus (PIV) and human metapneumovirus (hMPV) by geographical location, age and time in children <5 years old in Western Australia.

METHODS

We conducted a population-based cohort study of children born between 1 January 2010 and 31 December 2021, utilising linked administrative data incorporating birth and death records, hospitalisations and respiratory viral surveillance testing records from state-wide public pathology data. We examined within-hospital testing rates using survival analysis techniques and identified independent predictors of testing using binary logistic regression.

RESULTS

Our dataset included 46,553 laboratory tests for RSV, influenza, PIV, or hMPV from 355,021 children (52.5% male). Testing rates declined in the metropolitan region over the study period (RSV testing in infants: from 242.11/1000 child-years in 2012 to 155.47/1000 child-years in 2018) and increased thereafter. Conversely, rates increased in non-metropolitan areas (e.g., RSV in Goldfields: from 364.92 in 2012 to 504.37/1000 child-years in 2021). The strongest predictors of testing were age <12 months (adjusted odds ratio [aOR] = 2.25, 95% CI 2.20-2.31), preterm birth (<32 weeks: aOR = 2.90, 95% CI 2.76-3.05) and remote residence (aOR = 0.77, 95% CI 0.73-0.81).

CONCLUSION

These current testing rates highlight the potential underestimation of respiratory virus hospitalisations by routine surveillance and the need for estimation of the true burden of respiratory virus admissions.

Satellite glial contact enhances differentiation and maturation of human iPSC-derived sensory neurons

Sensory neurons generated from induced pluripotent stem cells (iSNs) are used to model human peripheral neuropathies, however current differentiation protocols produce sensory neurons with an embryonic phenotype. Peripheral glial cells contact sensory neurons early in development and contribute to formation of the canonical pseudounipolar morphology, but these signals are not encompassed in current iSN differentiation protocols. Here, we show that terminal differentiation of iSNs in co-culture with rodent Dorsal Root Ganglion satellite glia (rSG) advances their differentiation and maturation. Co-cultured iSNs develop a pseudounipolar morphology through contact with rSGs. This transition depends on semaphorin-plexin guidance cues and on glial gap junction signaling. In addition to morphological changes, iSNs terminally differentiated in co-culture exhibit enhanced spontaneous action potential firing, more mature gene expression, and increased susceptibility to paclitaxel induced axonal degeneration. Thus, iSNs differentiated in coculture with rSGs provide a better model for investigating human peripheral neuropathies.

Plasma proteome profiling in giant cell arteritis

OBJECTIVES

This study aimed to identify plasma proteomic signatures that differentiate active and inactive giant cell arteritis (GCA) from non-disease controls. By comprehensively profiling the plasma proteome of both patients with GCA and controls, we aimed to identify plasma proteins that (1) distinguish patients from controls and (2) associate with disease activity in GCA.

METHODS

Plasma samples were obtained from 30 patients with GCA in a multi-institutional, prospective longitudinal study: one captured during active disease and another while in clinical remission. Samples from 30 age-matched/sex-matched/race-matched non-disease controls were also collected. A high-throughput, aptamer-based proteomics assay, which examines over 7000 protein features, was used to generate plasma proteome profiles from study participants.

RESULTS

After adjusting for potential confounders, we identified 537 proteins differentially abundant between active GCA and controls, and 781 between inactive GCA and controls. These proteins suggest distinct immune responses, metabolic pathways and potentially novel physiological processes involved in each disease state. Additionally, we found 16 proteins associated with disease activity in patients with active GCA. Random forest models trained on the plasma proteome profiles accurately differentiated active and inactive GCA groups from controls (95.0% and 98.3% in 10-fold cross-validation, respectively). However, plasma proteins alone provided limited ability to distinguish between active and inactive disease states within the same patients.

CONCLUSIONS

This comprehensive analysis of the plasma proteome in GCA suggests that blood protein signatures integrated with machine learning hold promise for discovering multiplex biomarkers for GCA.

Genome structural variants shape adaptive success of an invasive urban malaria vector Anopheles stephensi

Global changes are associated with the emergence of several invasive species. However, the genomic determinants of the adaptive success of an invasive species in a new environment remain poorly understood. Genomic structural variants (SVs), consisting of copy number variants, play an important role in adaptation. SVs often cause large adaptive shifts in ecologically important traits, which makes SVs compelling candidates for driving rapid adaptations to environmental changes, which is critical to invasive success. To address this problem, we investigated the role SVs play in the adaptive success of Anopheles stephensi , a primary vector of urban malaria in South Asia and an invasive malaria vector in several South Asian islands and Africa. We collected whole genome sequencing data from 115 mosquitoes from invasive island populations and four locations from mainland India, an ancestral range for the species. We identified 2,988 duplication copy number variants and 16,038 deletions in these strains, with ∼50% overlapping genes. SVs are enriched in genomic regions with signatures of selective sweeps in the mainland and invasive island populations, implying a putative adaptive role of SVs. Nearly all high-frequency SVs, including the candidate adaptive variants, in the invasive island populations are present on the mainland, suggesting a major contribution of existing variation to the success of the island populations. Among the candidate adaptive SVs, three duplications involving toxin-resistance genes evolved, likely due to the widespread application of insecticides in India since the 1950s. We also identify two SVs associated with the adaptation of An. stephensi larvae to brackish water in the island and two coastal mainland populations, where the mutations likely originated. Our results suggest that existing SVs play a vital role in the evolutionary success of An. stephensi in new environmental conditions.

SSR42 is a Novel Regulator of Cytolytic Activity in Staphylococcus aureus

SSR42 is the longest noncoding RNA in the S. aureus cell and the second-most abundant transcript in the stationary phase transcriptome, second only to RNAIII. It is highly conserved across strains and exhibits pronounced stability in stationary phase, however the mechanism behind its regulatory role has yet to be fully elucidated. Herein, we used transcriptomic and proteomic approaches to probe the role of SSR42, revealing that it is a powerful, novel activator of the primary leukocidin LukAB. SSR42 is required for cytotoxicity towards, and escape from within, human neutrophils, and also mediates survival within human blood. We show that SSR42 wields this role via derepression by the peroxide repressor PerR in response to the presence of human neutrophils and governs lukAB induction in this niche. Importantly, this regulation is driven by direct RNA-RNA interaction, as we show binding of the 5' UTR of the lukAB transcript with the 3' end of SSR42, which ultimately modulates transcript stability as well as translational activity. Finally, we demonstrate that this behavior is absolutely required for full virulence of S. aureus in murine models of both pneumonia and sepsis. Collectively, we present SSR42 as a pleiotropic regulatory RNA that acts as a nexus between environmental sensing and the regulation of pathogenesis, responding to environmental stimuli and host immune factors to bolster cytotoxic behavior and facilitate infection in S. aureus .

Importance

S. aureus is a master pathogen due to its formidable collection of virulence factors. These are tightly controlled by a diverse group of regulators that titrate their abundance to adapt to unique infectious niches. The role of regulatory RNAs in stress adaptation and pathogenesis is becoming increasingly more relevant in S. aureus . In this study, we provide the most comprehensive global analysis to date of just such a factor, SSR42. Specifically, we uncover that SSR42 is required for mediating cytotoxicity - one of the pillars of infection - in response to phagocytosis by human neutrophils. We find that SSR42 is induced by components of the host immune system and facilitates downstream activation of cytotoxic factors via RNA-RNA interactions. This illustrates that SSR42 forms a pivotal link between sensing the external environment and mediating resistance to oxidative stress while promoting virulence, solidifying it as a major global regulator in S. aureus .

Flexible decision-making is related to strategy learning, vicarious trial and error, and medial prefrontal rhythms during spatial set-shifting

Flexible decision-making requires a balance between exploring features of an environment and exploiting prior knowledge. Behavioral flexibility is typically measured by how long it takes subjects to consistently make accurate choices after reward contingencies switch or task rules change. This measure, however, only allows for tracking flexibility across multiple trials, and does not assess the degree of flexibility. Plus, although increases in decision-making accuracy are strong indicators of learning, other decision-making behaviors have also been suggested as markers of flexibility, such as the on-the-fly decision reversals known as vicarious trial and error (VTE) or switches to a different, but incorrect, strategy. We sought to relate flexibility, learning, and neural activity by comparing choice history-derived evaluation of strategy use with changes in decision-making accuracy and VTE behavior while recording from the medial prefrontal cortex (mPFC) in rats. Using a set-shifting task that required rats to repeatedly switch between spatial decision-making strategies, we show that a previously developed strategy likelihood estimation procedure could identify putative learning points based on decision history. We confirm the efficacy of learning point estimation by showing increases in decision-making accuracy aligned to the learning point. Additionally, we show increases in the rate of VTE behavior surrounding identified learning points. By calculating changes in strategy likelihoods across trials, we tracked flexibility on a trial-by-trial basis and show that flexibility scores also increased around learning points. Further, we demonstrate that VTE behaviors could be separated into indecisive and deliberative subtypes depending on whether they occurred during periods of high or low flexibility and whether they led to correct or incorrect choice outcomes. Field potential recordings from the mPFC during decisions exhibited increased beta band activity on trials with VTE compared to non-VTE trials, as well as increased gamma during periods when learned strategies could be exploited compared to prelearning, exploratory periods. This study demonstrates that increased behavioral flexibility and VTE rates are often aligned to task learning. These relationships can break down, however, suggesting that VTE is not always an indicator of deliberative decision-making. Additionally, we further implicate the mPFC in decision-making and learning by showing increased beta-based activity on VTE trials and increased gamma after learning.

Fine-mapping causal tissues and genes at disease-associated loci

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Decoding the Best Automated Segmentation Tools for Vascular White Matter Hyperintensities in the Aging Brain: A Clinician's Guide to Precision and Purpose

Cerebrovascular damage from small vessel disease (SVD) occurs in healthy and pathological aging. SVD markers, such as white matter hyperintensities (WMH), are commonly found in individuals over 60 and increase in prevalence with age. WMHs are detectable on standard MRI by adhering to the STRIVE criteria. Currently, visual assessment scales are used in clinical and research scenarios but is time-consuming and has rater variability, limiting its practicality. Addressing this issue, our study aimed to determine the most precise WMH segmentation software, offering insights into methodology and usability to balance clinical precision with practical application. This study employed a dataset comprising T1, FLAIR, and DWI images from 300 cognitively healthy older adults. WMHs in this cohort were evaluated using four automated neuroimaging tools: Lesion Prediction Algorithm (LPA) and Lesion Growth Algorithm (LGA) from Lesion Segmentation Tool (LST), Sequence Adaptive Multimodal Segmentation (SAMSEG), and Brain Intensity Abnormalities Classification Algorithm (BIANCA). Additionally, clinicians manually segmented WMHs in a subsample of 45 participants to establish a gold standard. The study assessed correlations with the Fazekas scale, algorithm performance, and the influence of WMH volume on reliability. Results indicated that supervised algorithms were superior, particularly in detecting small WMHs, and can improve their consistency when used in parallel with unsupervised tools. The research also proposed a biomarker for moderate vascular damage, derived from the top 95th percentile of WMH volume in healthy individuals aged 50 to 60. This biomarker effectively differentiated subgroups within the cohort, correlating with variations in brain structure and behavior.

Balancing the Senses: Electrophysiological Responses Reveal the Interplay between Somatosensory and Visual Processing During Body-Related Multisensory Conflict

In the study of bodily awareness, the predictive coding theory has revealed that our brain continuously modulates sensory experiences to integrate them into a unitary body representation. Indeed, during multisensory illusions (e.g., the rubber hand illusion, RHI), the synchronous stroking of the participant's concealed hand and a fake visible one creates a visuotactile conflict, generating a prediction error. Within the predictive coding framework, through sensory processing modulation, prediction errors are solved, inducing participants to feel as if touches originated from the fake hand, thus ascribing the fake hand to their own body. Here, we aimed to address sensory processing modulation under multisensory conflict, by disentangling somatosensory and visual stimuli processing that are intrinsically associated during the illusion induction. To this aim, we designed two EEG experiments, in which somatosensory- (SEPs; Experiment 1; N = 18; F = 10) and visual-evoked potentials (VEPs; Experiment 2; N = 18; F = 9) were recorded in human males and females following the RHI. Our results show that, in both experiments, ERP amplitude is significantly modulated in the illusion as compared with both control and baseline conditions, with a modality-dependent diametrical pattern showing decreased SEP amplitude and increased VEP amplitude. Importantly, both somatosensory and visual modulations occur in long-latency time windows previously associated with tactile and visual awareness, thus explaining the illusion of perceiving touch at the sight location. In conclusion, we describe a diametrical modulation of somatosensory and visual processing as the neural mechanism that allows maintaining a stable body representation, by restoring visuotactile congruency under the occurrence of multisensory conflicts.

Comparative efficacy and acceptability of different exercise patterns for reducing cardiovascular events in pre-diabetes: protocol for a systematic review and network meta-analysis of randomised controlled trials

INTRODUCTION

Exercise has been used to reverse dysglycaemic states in patients with pre-diabetes. Systematic reviews show that exercise is an effective way to reduce the incidence of diabetes, but there is conflicting evidence for reducing the occurrence of cardiovascular events. Therefore, we present a systematic review and network meta-analysis protocol designed to compare the effectiveness of different forms of exercise in reducing cardiovascular events and their tolerability in different populations.

METHODS AND ANALYSIS

We will include all randomised controlled trials and compare one exercise intervention to another. We will compare the following exercise patterns: standard endurance training, strength training, high-intensity interval training, mind-body exercise, and mixed strength and aerobic training. The primary outcomes are the occurrence of major cardiovascular events and the rate of patient attrition during the intervention. We will search major English and Chinese databases as well as trial registry websites for published and unpublished studies. All reference selection and data extraction will be conducted by at least two independent reviewers. We will conduct a random effects model to combine effect sizes and use the surface under the cumulative ranking curve and the mean ranks to rank the effectiveness of interventions. All data will be fitted at WinBUGS in a Bayesian framework and correlation graphs will be plotted using StataSE 14. We will also use the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework to evaluate the quality of evidence for the study results.

ETHICS AND DISSEMINATION

This study does not involve a population-based intervention, and therefore, does not require ethical approval. We will publish the findings of this systematic review in a peer-reviewed scientific journal, and the dataset will be made available free of charge. The completed review will be disseminated electronically in print and on social media, where appropriate.

PROSPERO REGISTRATION NUMBER

CRD42023422737.

Plasmodium knowlesi infection is associated with elevated circulating biomarkers of brain injury and endothelial activation

Introduction

Malaria remains a major public health concern with substantial morbidity and mortality worldwide. In Malaysia, the emergence of Plasmodium knowlesi has led to a surge in zoonotic malaria cases and deaths in recent years. Signs of cerebral involvement have been observed in a non-comatose, fatal case of severe knowlesi infection, but the potential impact of this malaria species on the brain remains underexplored. To address this gap, we investigated circulating levels of brain injury, inflammation, and vascular biomarkers in a cohort of knowlesi-infected patients and controls.

Methods

Archived plasma samples from 19 patients with confirmed symptomatic knowlesi infection and 19 healthy, age-matched controls from Peninsular Malaysia were analysed. A total of 52 plasma biomarkers of brain injury, inflammation, and vascular activation were measured using Luminex and SIMOA assays. Wilcoxon tests were used to examine group differences, and biomarker profiles were explored through hierarchical clustering heatmap analysis.

Results

Bonferroni-corrected analyses revealed significantly elevated brain injury biomarker levels in knowlesi-infected patients, including S100B (p<0.0001), Tau (p=0.0007), UCH-L1 (p<0.0001), αSyn (p<0.0001), Park7 (p=0.0006), NRGN (p=0.0022), and TDP-43 (p=0.005). Compared to controls, levels were lower in the infected group for BDNF (p<0.0001), CaBD (p<0.0001), CNTN1 (p<0.0001), NCAM-1 (p<0.0001), GFAP (p=0.0013), and KLK6 (p=0.0126). Hierarchical clustering revealed distinct group profiles for circulating levels of brain injury and vascular activation biomarkers.

Conclusions

Our findings highlight for the first time the impact of Plasmodium knowlesi infection on the brain, with distinct alterations in cerebral injury and endothelial activation biomarker profiles compared to healthy controls. Further studies are warranted to investigate the pathophysiology and clinical significance of these altered surrogate markers, through both neuroimaging and long-term neurocognitive assessments.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

Widespread impact of nucleosome remodelers on transcription at cis-regulatory elements

Nucleosome remodeling complexes and other regulatory factors work in concert to build a chromatin environment that directs the expression of a distinct set of genes in each cell using cis-regulatory elements (CREs), such as promoters and enhancers, that drive transcription of both mRNAs and CRE-associated non-coding RNAs (ncRNAs). Two classes of CRE-associated ncRNAs include upstream antisense RNAs (uaRNAs), which are transcribed divergently from a shared mRNA promoter, and enhancer RNAs (eRNAs), which are transcribed bidirectionally from active enhancers. The complicated network of CRE regulation by nucleosome remodelers remains only partially explored, with a focus on a select, limited number of remodelers. We endeavored to elucidate a remodeler-based regulatory network governing CRE-associated transcription (mRNA, eRNA, and uaRNA) in murine embryonic stem (ES) cells to test the hypothesis that many SNF2-family nucleosome remodelers collaborate to regulate the coding and non-coding transcriptome via alteration of underlying nucleosome architecture. Using depletion followed by transient transcriptome sequencing (TT-seq), we identified thousands of misregulated mRNAs and CRE-associated ncRNAs across the remodelers examined, identifying novel contributions by understudied remodelers in the regulation of coding and noncoding transcription. Our findings suggest that mRNA and eRNA transcription are coordinately co-regulated, while mRNA and uaRNAs sharing a common promoter are independently regulated. Subsequent mechanistic studies suggest that while remodelers SRCAP and CHD8 modulate transcription through classical mechanisms such as transcription factors and histone variants, a broad set of remodelers including SMARCAL1 indirectly contribute to transcriptional regulation through maintenance of genomic stability and proper Integrator complex localization. This study systematically examines the contribution of SNF2-remodelers to the CRE-associated transcriptome, identifying at least two classes for remodeler action.

GABAergic interneurons contribute to the fatal seizure phenotype of CLN2 disease mice

GABAergic interneuron deficits have been implicated in the epileptogenesis of multiple neurological diseases. While epileptic seizures are a key clinical hallmark of CLN2 disease, a childhood-onset neurodegenerative lysosomal storage disorder caused by a deficiency of tripeptidyl peptidase 1 (TPP1), the etiology of these seizures remains elusive. Given that Cln2 R207X/R207X mice display fatal spontaneous seizures and an early loss of several cortical interneuron populations, we hypothesized that those two events might be causally related. To address this hypothesis, we first generated an inducible transgenic mouse expressing lysosomal membrane-tethered TPP1 (TPP1LAMP1) on the Cln2 R207X/R207X genetic background to study the cell-autonomous effects of cell-type-specific TPP1 deficiency. We crossed the TPP1LAMP1 mice with Vgat-Cre mice to introduce interneuron-specific TPP1 deficiency. Vgat-Cre ; TPP1LAMP1 mice displayed storage material accumulation in several interneuron populations both in cortex and striatum, and increased susceptibility to die after PTZ-induced seizures. Secondly, to test the role of GABAergic interneuron activity in seizure progression, we selectively activated these cells in Cln2 R207X/R207X mice using Designer Receptor Exclusively Activated by Designer Drugs (DREADDs) in in Vgat-Cre : Cln2 R207X/R207X mice. EEG monitoring revealed that DREADD-mediated activation of interneurons via chronic deschloroclozapine administration accelerated the onset of spontaneous seizures and seizure-associated death in Vgat-Cre : Cln2 R207X/R207X mice, suggesting that modulating interneuron activity can exert influence over epileptiform abnormalities in CLN2 disease. Taken together, these results provide new mechanistic insights into the underlying etiology of seizures and premature death that characterize CLN2 disease.

Thermotherapy has Sexually Dimorphic Responses in APP/PS1 Mice

A thermoregulatory decline occurs with age due to changes in muscle mass, vasoconstriction, and metabolism that lowers core body temperature (Tc). Although lower Tc is a biomarker of successful aging, we have previously shown this worsens cognitive performance in the APP/PS1 mouse model of Alzheimer's disease (AD) [1]. We hypothesized that elevating Tc with thermotherapy would improve metabolism and cognition in APP/PS1 mice. From 6-12 months of age, male and female APP/PS1 and C57BL/6 mice were chronically housed at 23 or 30°C. At 12 months of age, mice were assayed for insulin sensitivity, glucose tolerance, and spatial cognition. Plasma, hippocampal, and peripheral (adipose, hepatic, and skeletal muscle) samples were procured postmortem and tissue-specific markers of amyloid accumulation, metabolism, and inflammation were assayed. Chronic 30°C exposure increased Tc in all groups except female APP/PS1 mice. All mice receiving thermotherapy had either improved glucose tolerance or insulin sensitivity, but the underlying processes responsible for these effects varied across sexes. In males, glucose regulation was influenced predominantly by hormonal signaling in plasma and skeletal muscle glucose transporter 4 expression, whereas in females, this was modulated at the tissue level. Thermotherapy improved spatial navigation in male C57BL/6 and APP/PS1 mice, with the later attributed to reduced hippocampal soluble amyloid-β (Aβ)42. Female APP/PS1 mice exhibited worse spatial memory recall after chronic thermotherapy. Together, the data highlights the metabolic benefits of passive thermotherapy with potential nonpharmacological management for some individuals with AD, and provides further evidence for the necessity of adopting personalized patient care.

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures

Background

Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels.

Methods

We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors.

Results

We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors.

Conclusions

Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Personalized Infant Risk Prediction for Severe Respiratory Syncytial Virus Lower Respiratory Tract Infection Requiring Intensive Care Unit Admission

Background

Currently, there are no available tools to identify infants at the highest risk of significant morbidity and mortality from respiratory syncytial virus (RSV) lower respiratory tract infection (LRTI) who would benefit most from RSV prevention products. The objective was to develop and internally validate a personalized risk prediction tool for use among all newborns that uses readily available birth/postnatal data to predict RSV LRTI requiring intensive care unit (ICU) admission.

Methods

We conducted a population-based birth cohort study of infants born from 1995 to 2007, insured by the Tennessee Medicaid Program, and who did not receive RSV immunoprophylaxis during the first year of life. The primary outcome was severe RSV LRTI requiring ICU admission during the first year of life. We built a multivariable logistic regression model including demographic and clinical variables available at or shortly after birth to predict the primary outcome.

Results

In a population-based sample of 429 365 infants, 713 (0.2%) had severe RSV LRTI requiring ICU admission. The median age of admission was 66 days (interquartile range, 37-120). Our tool, including 19 variables, demonstrated good predictive accuracy (area under the curve, 0.78; 95% confidence interval, 0.77-0.80) and identified infants who did not qualify for palivizumab, based on American Academy of Pediatrics guidelines, but had higher predicted risk levels than infants who qualified (27% of noneligible infants with >0.16% predicted probabilities [lower quartile for eligible infants]).

Conclusions

We developed a personalized tool that identified infants at increased risk for severe RSV LRTI requiring ICU admission, expected to benefit most from immunoprophylaxis.

Neural Trajectories of Conceptually Related Events

In a series of conceptually related episodes, meaning arises from the link between these events rather than from each event individually. How does the brain keep track of conceptually related sequences of events (i.e., conceptual trajectories)? In a particular kind of conceptual trajectory-a social relationship-meaning arises from a specific sequence of interactions. To test whether such abstract sequences are neurally tracked, we had participants complete a naturalistic narrative-based social interaction game, during functional magnetic resonance imaging. We modeled the simulated relationships as trajectories through an abstract affiliation and power space. In two independent samples, we found evidence of individual social relationships being tracked with unique sequences of hippocampal states. The neural states corresponded to the accumulated trial-to-trial affiliation and power relations between the participant and each character, such that each relationship's history was captured by its own neural trajectory. Each relationship had its own sequence of states, and all relationships were embedded within the same manifold. As such, we show that the hippocampus represents social relationships with ordered sequences of low-dimensional neural patterns. The number of distinct clusters of states on this manifold is also related to social function, as measured by the size of real-world social networks. These results suggest that our evolving relationships with others are represented in trajectory-like neural patterns.

Label-Free, Real-Time Monitoring of Cytochrome C Responses to Drugs in Microdissected Tumor Biopsies with a Multi-Well Aptasensor Platform

Functional assays on intact tumor biopsies can potentially complement and extend genomics-based approaches for precision oncology, drug testing, and organs-on-chips cancer disease models by capturing key determinants of therapeutic response, such as tissue architecture, tumor heterogeneity, and the tumor microenvironment. Currently, most of these assays rely on fluorescent labeling, a semi-quantitative method best suited to be a single-time-point terminal assay or labor-intensive terminal immunostaining analysis. Here, we report integrated aptamer electrochemical sensors for on-chip, real-time monitoring of increases of cytochrome C, a cell death indicator, from intact microdissected tissues with high affinity and specificity. The platform features a multi-well sensor layout and a multiplexed electronic setup. The aptasensors measure increases in cytochrome C in the supernatant of mouse or human microdissected tumors after exposure to various drug treatments. Since the aptamer probe can be easily exchanged to recognize different targets, the platform could be adapted for multiplexed monitoring of various biomarkers, providing critical information on the tumor and its microenvironment. This approach could not only help develop more advanced cancer disease models but also apply to other complex in vitro disease models, such as organs-on-chips and organoids.

Casein kinase 1 controls components of a TORC2 signaling network in budding yeast

Tor kinases play diverse and essential roles in control of nutrient signaling and cell growth. Tor kinases are assembled into two large multiprotein complexes referred to as Tor Complex 1 and Tor Complex 2 (TORC1 and TORC2). In budding yeast, TORC2 controls a signaling network that relays signals regarding carbon source that strongly influence growth rate and cell size. However, the mechanisms that control TORC2 signaling are poorly understood. Activation of TORC2 requires Mss4, a phosphoinositol kinase that initiates assembly of a multi-protein complex at the plasma membrane that recruits and activates downstream targets of TORC2. Localization of Mss4 to the plasma membrane is controlled by phosphorylation and previous work suggested that yeast homologs of casein kinase 1γ, referred to as Yck1 and Yck2, control phosphorylation of Mss4. Here, we generated a new analog-sensitive allele of YCK2 and used it to test whether Yck1/2 influence signaling in the TORC2 network. We found that multiple components of the TORC2 network are strongly influenced by Yck1/2 signaling.

CHOIR improves significance-based detection of cell types and states from single-cell data

Clustering is a critical step in the analysis of single-cell data, as it enables the discovery and characterization of putative cell types and states. However, most popular clustering tools do not subject clustering results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (clustering hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine which clusters represent distinct populations. We demonstrate the enhanced performance of CHOIR through extensive benchmarking against 14 existing clustering methods across 100 simulated and 4 real single-cell RNA-seq, ATAC-seq, spatial transcriptomic, and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable, and robust solution to the important challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.

Should vaccination status be a consideration during secondary triage?

The rapid development of widely available and effective vaccines has been integral to the international response to the COVID-19 pandemic. However, a significant minority of those offered vaccination have refused, often due to their adherence to 'anti-vax' beliefs. These beliefs include that vaccines are dangerous, render the recipient magnetic or contain government microchips.During the pandemic, numerous calls were made for those voluntarily refusing vaccination to be deprioritised when allocating scarce healthcare resources. While these calls were rejected, the likelihood of the same calls being made during future pandemics necessitates a thorough examination of the ethical implications entailed by such a policy.Here, I consider an intuitive argument for the use of vaccination status when allocating healthcare resources. This argument claims that, by avoiding vaccination, vaccine refusers are failing to fulfil a social obligation to protect those around them from harm by facilitating herd immunity. They are, therefore, less deserving of healthcare than their vaccinated peers.I explore three objections to this argument. While a first objection, asserting that no individual can be held responsible for a failure to develop herd immunity, fails, I find two further responses, respectively asserting the primacy of patient autonomy and highlighting the harms deprioritising vaccine refusers would cause to disadvantaged minorities, compelling. I, therefore, conclude that vaccination status should not be considered during healthcare resource allocation, as such discrimination would disproportionately harm marginalised communities.

Cortical Reorganization after Limb Loss: Bridging the Gap between Basic Science and Clinical Recovery

Despite the increasing incidence and prevalence of amputation across the globe, individuals with acquired limb loss continue to struggle with functional recovery and chronic pain. A more complete understanding of the motor and sensory remodeling of the peripheral and central nervous system that occurs postamputation may help advance clinical interventions to improve the quality of life for individuals with acquired limb loss. The purpose of this article is to first provide background clinical context on individuals with acquired limb loss and then to provide a comprehensive review of the known motor and sensory neural adaptations from both animal models and human clinical trials. Finally, the article bridges the gap between basic science researchers and clinicians that treat individuals with limb loss by explaining how current clinical treatments may restore function and modulate phantom limb pain using the underlying neural adaptations described above. This review should encourage the further development of novel treatments with known neurological targets to improve the recovery of individuals postamputation.Significance Statement In the United States, 1.6 million people live with limb loss; this number is expected to more than double by 2050. Improved surgical procedures enhance recovery, and new prosthetics and neural interfaces can replace missing limbs with those that communicate bidirectionally with the brain. These advances have been fairly successful, but still most patients experience persistent problems like phantom limb pain, and others discontinue prostheses instead of learning to use them daily. These problematic patient outcomes may be due in part to the lack of consensus among basic and clinical researchers regarding the plasticity mechanisms that occur in the brain after amputation injuries. Here we review results from clinical and animal model studies to bridge this clinical-basic science gap.

Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus

OBJECTIVES

Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons.

MATERIALS AND METHODS

This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features.

RESULTS

We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05.

CONCLUSION

Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

Respiratory syncytial virus disease morbidity in Australian infants aged 0 to 6 months: a systematic review with narrative synthesis

BACKGROUND

A significant proportion of the global respiratory syncytial virus (RSV) associated morbidity is accounted for by infants aged 0 to 6 months, who are particularly vulnerable to severe disease. In 2015, 44% of global hospitalisations in infants in this age group were secondary to RSV. The objective of this systematic review is to appraise and synthesise the local evidence of RSV infection morbidity among Australian infants aged 0 to 6 months and to assess the implications for future immunisation strategies.

METHODS

Electronic databases (Medline, Embase, Pubmed and Global Health) were searched for full-text articles published between 2000 and 2023 in English language. Studies that examined markers of RSV disease morbidity in infants aged 0 to 6 months in Australia who had laboratory confirmed RSV infection were eligible for inclusion. The outcomes of interest were incidence, prevalence, testing rate, positivity rate, mortality, emergency department visits, community health visits, hospitalisation, intensive care unit admission, supplementary oxygen use, mechanical ventilation, risk factors for disease severity and monoclonal antibody use.

RESULTS

The database search identified 469 studies. After removal of duplicates and full-text review, 17 articles were eligible for inclusion. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Synthesis without meta-analysis guidelines.

CONCLUSIONS

Qualitative analysis of the included studies showed that Australian infants aged 0 to 6 months have higher rates of RSV testing, positivity and incidence; and more likely to develop severe disease that requires hospitalisation, intensive care unit admission or respiratory support, compared to children and adults of all ages. Aboriginal and Torres Strait Islander infants aged 0 to 6 months demonstrated higher rates of RSV infection and hospitalisation, compared to non-Indigenous infants. Age-related trends persisted in geographic areas with varying seasonal transmission of RSV, and during the SARS-CoV-2 pandemic. Passive immunisation strategies targeting infants in their first 6 months of life, either via vaccination of pregnant women or administration of long-acting monoclonal antibody during infancy, could effectively reduce RSV disease burden in Australia.

Multimorbidity in African ancestry populations: a scoping review

OBJECTIVES

Multimorbidity (MM) is a growing concern linked to poor outcomes and higher healthcare costs. While most MM research targets European ancestry populations, the prevalence and patterns in African ancestry groups remain underexplored. This study aimed to identify and summarise the available literature on MM in populations with African ancestry, on the continent, and in the diaspora.

DESIGN

A scoping review was conducted in five databases (PubMed, Web of Science, Scopus, Science Direct and JSTOR) in July 2022. Studies were selected based on predefined criteria, with data extraction focusing on methodology and findings. Descriptive statistics summarised the data, and a narrative synthesis highlighted key themes.

RESULTS

Of the 232 publications on MM in African-ancestry groups from 2010 to June 2022-113 examined continental African populations, 100 the diaspora and 19 both. Findings revealed diverse MM patterns within and beyond continental Africa. Cardiovascular and metabolic diseases are predominant in both groups (80% continental and 70% diaspora). Infectious diseases featured more in continental studies (58% continental and 16% diaspora). Although many papers did not specifically address these features, as in previous studies, older age, being women and having a lower socioeconomic status were associated with a higher prevalence of MM, with important exceptions. Research gaps identified included limited data on African-ancestry individuals, inadequate representation, under-represented disease groups, non-standardised methodologies, the need for innovative data strategies, and insufficient translational research.

CONCLUSION

The growing global MM prevalence is mirrored in African-ancestry populations. Recognising the unique contexts of African-ancestry populations is essential when addressing the burden of MM. This review emphasises the need for additional research to guide and enhance healthcare approaches for African-ancestry populations, regardless of their geographic location.

Higher loss of livelihood and impoverishment in households affected by tuberculosis compared to non-tuberculosis affected households in Zimbabwe: a cross-sectional study

Introduction

Tuberculosis (TB) disproportionally affects poor people, leading to income and non-income losses. Measures of socioeconomic impact of TB, e.g. impoverishment and patient costs are inadequate to capture non-income losses. We applied impoverishment and a multidimensional measure on TB and non-TB affected households in Zimbabwe.

Methods

We conducted a cross-sectional study in 270 households: 90 non-TB; 90 drug-susceptible TB (DS-TB), 90 drug-resistant TB (DR-TB) during the COVID-19 pandemic (2020-2021). Household data included ownership of assets, number of household members, income and indicators on five capital assets: financial, human, social, natural and physical. We determined proportions of impoverished households for periods 12 months prior and at the time of the interview. Households with incomes below US$1.90/day were considered to be impoverished. We used principal component analysis on five capital asset indicators to create a binary outcome variable indicating loss of livelihood. Log-binomial regression was used to determine associations between loss of livelihood and type of household.

Results

TB-affected households reported higher previous episodes of TB and household members requiring care than non-TB households. Households that were impoverished 12 months prior to the study were: 21 non-TB (23%); 40 DS-TB (45%); 37 DR-TB (41%). The proportions increased to 81%, 88% and 94%, respectively by the time of interview. Overall, 56% (152/270) of households sold assets: 44% (40/90) non-TB, 58% (52/90) DS-TB and 67% (60/90) DR-TB. Children's education was affected in 31% (56/180) of TB-affected compared to 13% (12/90) non-TB households. Overall, 133(50%) households experienced loss of livelihood, with TB-affected households twice as likely to experience loss of livelihood; adjusted prevalence ratio (aPR=2.02 (95%CI:1.35-3.03)). The effect of TB on livelihood was most pronounced in poorest households (aPR=2.64, (95%CI:1.29-5.41)).

Conclusions

TB-affected households experienced greater socioeconomic losses compared to non-TB households. Multidimensional measures of TB are crucial to inform multisectoral approaches to mitigate impacts of TB and other shocks.

microRNAs in exhaled breath condensate for diagnosis of lung cancer in a resource-limited setting: a concise review

Lung cancer is one of the common cancers globally with high mortality and poor prognosis. Most cases of lung cancer are diagnosed at an advanced stage due to limited diagnostic resources. Screening modalities, such as sputum cytology and annual chest radiographs, have not proved sensitive enough to impact mortality. In recent years, annual low-dose computed tomography has emerged as a potential screening tool for early lung cancer detection, but it may not be a feasible option for developing countries. In this context, exhaled breath condensate (EBC) analysis has been evaluated recently as a noninvasive tool for lung cancer diagnosis. The breath biomarkers also have the advantage of differentiating various types and stages of lung cancer. Recent studies have focused more on microRNAs (miRNAs) as they play a key role in tumourigenesis by regulating the cell cycle, metastasis and angiogenesis. In this review, we have consolidated the current published literature suggesting the utility of miRNAs in EBC for the detection of lung cancer.

Using Vibrio natriegens for high-yield production of challenging expression targets and for protein deuteration

Production of soluble proteins is essential for structure/function studies, however, this usually requires milligram amounts of protein, which can be difficult to obtain with traditional expression systems. Recently, the Gram-negative bacterium Vibrio natriegens appeared as a novel and alternative host platform for production of proteins in high yields. Here, we used a commercial strain derived from V. natriegens (Vmax™ X2) to produce soluble bacterial and fungal proteins in milligram scale, which we struggled to achieve in Escherichia coli. These proteins include the cholera toxin (CT) and N-acetyl glucosamine binding protein A (GbpA) from Vibrio cholerae, the heat-labile enterotoxin (LT) from E. coli and the fungal nematotoxin CCTX2 from Coprinopsis cinerea. CT, GbpA and LT are secreted by the Type II secretion system in their natural hosts. When these three proteins were produced in Vmax, they were also secreted, and could be recovered from the growth media. This simplified the downstream purification procedure and resulted in considerably higher protein yields compared to production in E. coli (6- to 26-fold increase). We also tested Vmax for protein deuteration using deuterated minimal media with deuterium oxide as solvent, and achieved a 3-fold increase in yield compared to the equivalent protocol in E. coli. This is good news since isotopic labeling is expensive and often ineffective, but represents a necessary prerequisite for some structural techniques. Thus, Vmax represents a promising host for production of challenging expression targets and for protein deuteration in amounts suitable for structural biology studies.

Microglia are dispensable for experience-dependent refinement of visual circuitry

Microglia are proposed to be critical for the refinement of developing neural circuitry. However, evidence identifying specific roles for microglia has been limited and often indirect. Here we examined whether microglia are required for the experience-dependent refinement of visual circuitry and visual function during development. We ablated microglia by administering the colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622, and then examined the consequences for retinal function, receptive field tuning of neurons in primary visual cortex (V1), visual acuity, and experience-dependent plasticity in visual circuitry. Eradicating microglia by treating mice with PLX5622 beginning at postnatal day (P) 14 did not alter visual response properties of retinal ganglion cells examined three or more weeks later. Mice treated with PLX5622 from P14 lacked more than 95% of microglia in V1 by P18, prior to the opening of the critical period. Despite the absence of microglia, the receptive field tuning properties of neurons in V1 were normal at P32. Similarly, eradicating microglia did not affect the maturation of visual acuity. Mice treated with PLX5622 displayed typical ocular dominance plasticity in response to brief monocular deprivation. Thus, none of these principal measurements of visual circuit development and function detectibly differed in the absence of microglia. We conclude that microglia are dispensable for experience-dependent refinement of visual circuitry. These findings challenge the proposed critical role of microglia in refining neural circuitry.

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is ~50% to improve either luciferase activity or stability. These finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Factors Predicting Secondary Respiratory Morbidity Following Early-Life Respiratory Syncytial Virus Infections: Population-Based Cohort Study

Background

The association between early-life respiratory syncytial virus (RSV) infections and later respiratory morbidity is well established. However, there is limited evidence on factors that influence this risk. We examined sociodemographic and perinatal factors associated with later childhood respiratory morbidity requiring secondary care following exposure to a laboratory-confirmed RSV episode in the first 2 years.

Methods

We used a probabilistically linked whole-of-population-based birth cohort including 252 287 children born in Western Australia between 2000 and 2009 with follow-up to the end of 2012. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) of the association of various risk factors with the first respiratory episode for asthma, wheezing, and unspecified acute lower respiratory infection beyond the age of 2 years.

Results

The analytic cohort included 4151 children with a confirmed RSV test before age 2 years. The incidence of subsequent respiratory morbidity following early-life RSV infection decreased with child age at outcome (highest incidence in 2-<4-year-olds: 41.8 per 1000 child-years; 95% CI, 37.5-46.6), increased with age at RSV infection (6-<12-month-olds: 23.6/1000 child-years; 95% CI, 19.9-27.8; 12-<24-month-olds: 22.4/1000 child-years; 95% CI, 18.2-22.7) and decreasing gestational age (50.8/1000 child-years; 95% CI, 33.5-77.2 for children born extremely preterm, <28 weeks gestation). Risk factors included age at first RSV episode (6-<12 months: aHR, 1.42; 95% CI, 1.06-1.90), extreme prematurity (<28 weeks: aHR, 2.22; 95% CI, 1.40-3.53), maternal history of asthma (aHR, 1.33; 95% CI, 1.04-1.70), and low socioeconomic index (aHR, 1.76; 95% CI, 1.03-3.00).

Conclusions

Our results suggest that in addition to preterm and young infants, children aged 12-<24 months could also be potential target groups for RSV prevention to reduce the burden of later respiratory morbidities associated with RSV.

Developmentally distinct architectures in top-down circuits

The medial prefrontal cortex (mPFC) plays a key role in learning, mood and decision making, including in how individuals respond to threats 1-6 . mPFC undergoes a uniquely protracted development, with changes in synapse density, cortical thickness, long-range connectivity, and neuronal encoding properties continuing into early adulthood 7-21 . Models suggest that before adulthood, the slow-developing mPFC cannot adequately regulate activity in faster-developing subcortical centers 22,23 . They propose that during development, the enhanced influence of subcortical systems underlies distinctive behavioural strategies of juveniles and adolescents and that increasing mPFC control over subcortical structures eventually allows adult behaviours to emerge. Yet it has remained unclear how a progressive strengthening of top-down control can lead to nonlinear changes in behaviour as individuals mature 24,25 . To address this discrepancy, here we monitored and manipulated activity in the developing brain as animals responded to threats, establishing direct causal links between frontolimbic circuit activity and the behavioural strategies of juvenile, adolescent and adult mice. Rather than a linear strengthening of mPFC synaptic connectivity progressively regulating behaviour, we uncovered multiple developmental switches in the behavioural roles of mPFC circuits targeting the basolateral amygdala (BLA) and nucleus accumbens (NAc). We show these changes are accompanied by axonal pruning coinciding with functional strengthening of synaptic connectivity in the mPFC-BLA and mPFC-NAc pathways, which mature at different rates. Our results reveal how developing mPFC circuits pass through distinct architectures that may make them optimally adapted to the demands of age-specific challenges.

Assessing the transportability of clinical prediction models for cognitive impairment using causal models

BACKGROUND

Machine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a new setting without available data is challenging. We aimed to investigate the transportability by calibration and discrimination of prediction models for cognitive impairment in simulated external settings with different distributions of demographic and clinical characteristics.

METHODS

We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates were then used to generate datasets and evaluate prediction models with different sets of predictors. We measured transportability to external settings under guided interventions on age, APOE ε4, and tau-protein, using performance differences between internal and external settings measured by calibration metrics and area under the receiver operating curve (AUC).

RESULTS

Calibration differences indicated that models predicting with causes of the outcome were more transportable than those predicting with consequences. AUC differences indicated inconsistent trends of transportability between the different external settings. Models predicting with consequences tended to show higher AUC in the external settings compared to internal settings, while models predicting with parents or all variables showed similar AUC.

CONCLUSIONS

We demonstrated with a practical prediction task example that predicting with causes of the outcome results in better transportability compared to anti-causal predictions when considering calibration differences. We conclude that calibration performance is crucial when assessing model transportability to external settings.

Determining the true incidence of seasonal respiratory syncytial virus-confirmed hospitalizations in preterm and term infants in Western Australia

Respiratory syncytial virus contributes to significant global infant morbidity and mortality. We applied a previously developed statistical prediction model incorporating pre-pandemic RSV testing data and hospital admission data to estimate infant RSV-hospitalizations by birth month and prematurity, focused on infants aged <1 year. The overall predicted RSV-hospitalization incidence rates in infants <6 months were 32.7/1,000 child-years (95 % CI: 31.8, 33.5) and 3.1/1,000 child-years (95 % CI: 3.0, 3.1) in infants aged 6-<12 months. Predicted RSV-hospitalization rates for infants aged <6 months were highest for infants born in April/May. Predicted rates for preterm infants born 29-32 weeks gestation were highest in March-May, whereas infants born >33 weeks had peak RSV-hospitalization rates from May-June, similar to late preterm or term births. RSV-hospitalization rates in the pre-pandemic era were highly seasonal, and seasonality varied with degree of prematurity. Accurate estimates of RSV-hospitalization in high-risk sub-groups are essential to understand preventable burden of RSV especially given the current prevention landscape.

Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study)

INTRODUCTION

A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms.

METHODS AND ANALYSIS

This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another.

ETHICS AND DISSEMINATION

The study was approved by the Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences.

TRIAL REGISTRATION NUMBER

ISRCTN15022307.

Deep Learning-Enabled, Detection of Rare Circulating Tumor Cell Clusters in Whole Blood Using Label-free, Flow Cytometry

Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs representing an increased risk factor for metastasis. Current detection platforms are optimized for ex vivo detection of CTCs only. Microfluidic chips and size exclusion methods have been proposed for CTCC detection; however, they lack in vivo utility and real-time monitoring capability. Confocal backscatter and fluorescence flow cytometry (BSFC) has been used for label-free detection of CTCCs in whole blood based on machine learning (ML) enabled peak classification. Here, we expand to a deep-learning (DL) -based, peak detection and classification model to detect CTCCs in whole blood data. We demonstrate that DL-based BSFC has a low false alarm rate of 0.78 events/min with a high Pearson correlation coefficient of 0.943 between detected events and expected events. DL-based BSFC of whole blood maintains a detection purity of 72% and a sensitivity of 35.3% for both homotypic and heterotypic CTCCs starting at a minimum size of two cells. We also demonstrate through artificial spiking studies that DL-based BSFC is sensitive to changes in the number of CTCCs present in the samples and does not add variability in detection beyond the expected variability from Poisson statistics. The performance established by DL-based BSFC motivates its use for in vivo detection of CTCCs. Further developments of label-free BSFC to enhance throughput could lead to critical applications in the clinical detection of CTCCs and ex vivo isolation of CTCC from whole blood with minimal disruption and processing steps.

Tissue tension permits β-catenin phosphorylation to drive mesoderm specification in human embryonic stem cells

The role of morphogenetic forces in cell fate specification is an area of intense interest. Our prior studies suggested that the development of high cell-cell tension in human embryonic stem cells (hESC) colonies permits the Src-mediated phosphorylation of junctional β-catenin that accelerates its release to potentiate Wnt-dependent signaling critical for initiating mesoderm specification. Using an ectopically expressed nonphosphorylatable mutant of β-catenin (Y654F), we now provide direct evidence that impeding tension-dependent Src-mediated β-catenin phosphorylation impedes the expression of Brachyury (T) and the epithelial-to-mesenchymal transition (EMT) necessary for mesoderm specification. Addition of exogenous Wnt3a or inhibiting GSK3β activity rescued mesoderm expression, emphasizing the importance of force dependent Wnt signaling in regulating mechanomorphogenesis. Our work provides a framework for understanding tension-dependent β-catenin/Wnt signaling in the self-organization of tissues during developmental processes including gastrulation.

Assessing the performance of the Cell Painting assay across different imaging systems

Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.

Blind cavefish evolved food-searching behavior without changing sensory modality compared with sighted conspecies in the dark

In nature, animals must navigate to forage according to their sensory inputs. Different species use different sensory modalities to locate food efficiently. For teleosts, food emits visual, mechanical, chemical, and/or possibly weak-electrical signals, which can be detected by optic, auditory/lateral line, and olfactory/taste buds sensory systems. However, how fish respond to and use different sensory inputs when locating food, as well as the evolution of these sensory modalities, remain unclear. We examined the Mexican tetra, Astyanax mexicanus, which is composed of two different morphs: a sighted riverine (surface fish) and a blind cave morph (cavefish). Compared with surface fish, cavefish have enhanced non-visual sensory systems, including the mechanosensory lateral line system, chemical sensors comprising the olfactory system and taste buds, and the auditory system to help navigate toward food sources. We tested how visual, chemical, and mechanical stimuli evoke food-seeking behavior. In contrast to our expectations, both surface fish and cavefish did not follow a gradient of chemical stimulus (food extract) but used it as a cue for the ambient existence of food. Surface fish followed visual cues (red plastic beads and food pellets), but, in the dark, were likely to rely on mechanosensors-the lateral line and/or tactile sensor-as cavefish did. Our results indicate cavefish used similar sensory modality to surface fish in the dark, while adherence levels to stimuli were higher in cavefish. In addition, cavefish evolved an extended circling strategy to capture food, which may yield a higher chance to capture food by swimming-by the food multiple times instead of once through zigzag motion. In summary, we propose ancestors of cavefish similar to surface fish may have needed little modification in food-seeking strategy to adapt to the dark.

Transcriptional Profiling and Genetic Analysis of a Cystic Fibrosis Airway-Relevant Model Shows Asymmetric Responses to Growth in a Polymicrobial Community

Bacterial infections in the lungs of persons with cystic fibrosis are typically composed of multispecies biofilm-like communities, which modulate clinically relevant phenotypes that cannot be explained in the context of a single species culture. Most analyses to-date provide a picture of the transcriptional responses of individual pathogens, however, there is relatively little data describing the transcriptional landscape of clinically-relevant multispecies communities. Harnessing a previously described cystic fibrosis-relevant, polymicrobial community model consisting of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus sanguinis and Prevotella melaninogenica , we performed an RNA-Seq analysis to elucidate the transcriptional profiles of the community grown in artificial sputum medium (ASM) as compared to growth in monoculture, without mucin, and in fresh medium supplemented with tobramycin. We provide evidence that, although the transcriptional profile of P. aeruginosa is community agnostic, the transcriptomes of S. aureus and S. sanguinis are community aware. Furthermore, P. aeruginosa and P. melaninogenica are transcriptionally sensitive to the presence of mucin in ASM, whereas S. aureus and S. sanguinis largely do not alter their transcriptional profiles in the presence of mucin when grown in a community. Only P. aeruginosa shows a robust response to tobramycin. Genetic studies of mutants altered in community-specific growth provide complementary data regarding how these microbes adapt to a community context.

Importance

Polymicrobial infections constitute the majority of infections in the cystic fibrosis (CF) airway, but their study has largely been neglected in a laboratory setting. Our lab previously reported a polymicrobial community that can explain clinical outcomes in the lungs of persons with CF. Here we obtain transcriptional profiles of the community versus monocultures to provide transcriptional information about how this model community responds to CF-related growth conditions and perturbations. Genetic studies provide complementary functional outputs to assess how the microbes adapt to life in a community.

Multi-Scale Label-free Human Brain Imaging with Integrated Serial Sectioning Polarization Sensitive Optical Coherence Tomography and Two-Photon Microscopy

The study of neurodegenerative processes in the human brain requires a comprehensive understanding of cytoarchitectonic, myeloarchitectonic, and vascular structures. Recent computational advances have enabled volumetric reconstruction of the human brain using thousands of stained slices, however, tissue distortions and loss resulting from standard histological processing have hindered deformation-free reconstruction of the human brain. The development of a multi-scale and volumetric human brain imaging technique that can measure intact brain structure would be a major technical advance. Here, we describe the development of integrated serial sectioning Polarization Sensitive Optical Coherence Tomography (PSOCT) and Two Photon Microscopy (2PM) to provide label-free multi-contrast imaging, including scattering, birefringence and autofluorescence of human brain tissue. We demonstrate that high-throughput reconstruction of 4×4×2cm3 sample blocks and simple registration of PSOCT and 2PM images enable comprehensive analysis of myelin content, vascular structure, and cellular information. We show that 2μm in-plane resolution 2PM images provide microscopic validation and enrichment of the cellular information provided by the PSOCT optical property maps on the same sample, revealing the sophisticated capillary networks and lipofuscin filled cell bodies across the cortical layers. Our method is applicable to the study of a variety of pathological processes, including demyelination, cell loss, and microvascular changes in neurodegenerative diseases such as Alzheimer's disease (AD) and Chronic Traumatic Encephalopathy (CTE).

Role of CD14+ monocyte-derived oxidised mitochondrial DNA in the inflammatory interferon type 1 signature in juvenile dermatomyositis

OBJECTIVES

To define the host mechanisms contributing to the pathological interferon (IFN) type 1 signature in Juvenile dermatomyositis (JDM).

METHODS

RNA-sequencing was performed on CD4+, CD8+, CD14+ and CD19+ cells sorted from pretreatment and on-treatment JDM (pretreatment n=10, on-treatment n=11) and age/sex-matched child healthy-control (CHC n=4) peripheral blood mononuclear cell (PBMC). Mitochondrial morphology and superoxide were assessed by fluorescence microscopy, cellular metabolism by 13C glucose uptake assays, and oxidised mitochondrial DNA (oxmtDNA) content by dot-blot. Healthy-control PBMC and JDM pretreatment PBMC were cultured with IFN-α, oxmtDNA, cGAS-inhibitor, TLR-9 antagonist and/or n-acetyl cysteine (NAC). IFN-stimulated gene (ISGs) expression was measured by qPCR. Total numbers of patient and controls for functional experiments, JDM n=82, total CHC n=35.

RESULTS

Dysregulated mitochondrial-associated gene expression correlated with increased ISG expression in JDM CD14+ monocytes. Altered mitochondrial-associated gene expression was paralleled by altered mitochondrial biology, including 'megamitochondria', cellular metabolism and a decrease in gene expression of superoxide dismutase (SOD)1. This was associated with enhanced production of oxidised mitochondrial (oxmt)DNA. OxmtDNA induced ISG expression in healthy PBMC, which was blocked by targeting oxidative stress and intracellular nucleic acid sensing pathways. Complementary experiments showed that, under in vitro experimental conditions, targeting these pathways via the antioxidant drug NAC, TLR9 antagonist and to a lesser extent cGAS-inhibitor, suppressed ISG expression in pretreatment JDM PBMC.

CONCLUSIONS

These results describe a novel pathway where altered mitochondrial biology in JDM CD14+ monocytes lead to oxmtDNA production and stimulates ISG expression. Targeting this pathway has therapeutical potential in JDM and other IFN type 1-driven autoimmune diseases.

Defining the key intrahepatic gene networks in HCV infection driven by sex

OBJECTIVE

The transcriptional response in the liver during HCV infection is critical for determining clinical outcomes. This issue remains relatively unexplored as tissue access to address this at scale is usually limited. We aimed to profile the transcriptomics of HCV-infected livers to describe the expression networks involved and assess the effect on them of major predictors of clinical outcome such as IFNL4 (interferon lambda 4) host genotype and sex.

DESIGN

We took advantage of a large clinical study of HCV therapy accompanied by baseline liver biopsy to examine the drivers of transcription in tissue samples in 195 patients also genotyped genome-wide for host and viral single nucleotide polymorphisms. We addressed the role of host factors (disease status, sex, genotype, age) and viral factors (load, mutation) on transcriptional responses.

RESULTS

We observe key modules of transcription which can be impacted differentially by host and viral factors. Underlying cirrhotic state had the most substantial impact, even in a stable, compensated population. Notably, sex had a major impact on antiviral responses in concert with IL28B (interleukin 28B)/IFNL4 genotype, with stronger interferon and humoral responses in females. Males tended towards a dominant cellular immune response. In both sexes, there was a strong influence of the underlying host disease status and of specific viral mutations, and sex-specific expression quantitative trait loci were also observed.

CONCLUSION

These features help define the major influences on tissue responses in HCV infection, impacting on the response to treatment and with broader implications for responses in other sex-biased infections.

How do hospitals address health inequalities experienced by children and young people: a grey literature scoping review protocol

INTRODUCTION

Health inequalities are unfair, systematic differences in health between people. In the UK, the Health and Social Care Act 2012 recognised health inequalities as a responsibility of the National Health Service (NHS). Health inequalities were foregrounded in the publication of 2019 NHS Long Term Plan and during the SARS-CoV-2/COVID-19 pandemic. Hospitals are well placed to address health inequalities through their role as anchor institutions. While many hospitals have begun to address inequalities, children are often overlooked or assumed to have the same needs as adult populations. This grey literature scoping review aims to identify, collate and present approaches taken by hospitals to address health inequalities in children and young people.

METHODS AND ANALYSIS

This scoping review will follow Joanna Briggs Institute guidance. A four-step approach to identifying grey literature will be used. Literature will be examined to identify approaches that aim to address health inequalities. Literature must describe the health inequality they aim to address and be initiated by the hospital. It will exclude literature not available in English and published before 2010. Two reviewers will independently review the results of the searches using the inclusion and exclusion criteria. Data will be extracted using a data extraction tool. Study findings will be presented in tabular form detailing the interventions identified.

DISSEMINATION

The review will synthesise information on worldwide hospital approaches to addressing child health inequalities. The findings will be used to inform guidelines for children's hospitals in the UK and will be disseminated through national and international professional bodies, conferences and research papers.

Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review

OBJECTIVES

To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.

DESIGN

Systematic review.

DATA SOURCES

PubMed and Embase were searched from 1 January 1975 to 3 February 2022.

ELIGIBILITY CRITERIA

We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded.

DATA EXTRACTION AND SYNTHESIS

Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool).

RESULTS

Of 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment.

CONCLUSIONS

Diagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application.

PROSPERO REGISTRATION NUMBER

CRD42022308917.

First-in-human prospective trial of sonobiopsy in glioblastoma patients using neuronavigation-guided focused ultrasound

Sonobiopsy is an emerging technology that combines focused ultrasound (FUS) with microbubbles to enrich circulating brain disease-specific biomarkers for noninvasive molecular diagnosis of brain diseases. Here, we report the first-in-human prospective trial of sonobiopsy in glioblastoma patients to evaluate its feasibility and safety in enriching circulating tumor biomarkers. A nimble FUS device integrated with a clinical neuronavigation system was used to perform sonobiopsy following an established clinical workflow for neuronavigation. Analysis of blood samples collected before and after FUS sonication showed enhanced plasma circulating tumor biomarker levels. Histological analysis of surgically resected tumors confirmed the safety of the procedure. Transcriptome analysis of sonicated and unsonicated tumor tissues found that FUS sonication modulated cell physical structure-related genes but evoked minimal inflammatory response. These feasibility and safety data support the continued investigation of sonobiopsy for noninvasive molecular diagnosis of brain diseases.

Association Between Serum Lipids and Survival in Patients With Amyotrophic Lateral Sclerosis: A Meta-analysis and Population-Based Study

BACKGROUND AND OBJECTIVE

To explore the association between lipids, polygenic profile scores (PPS) for biomarkers of lipid metabolism, markers of disease severity, and survival in patients with amyotrophic lateral sclerosis (ALS).

METHODS

We meta-analyzed the current literature on the prognostic value of lipids in patients with ALS. Subsequently, we evaluated the relationship between lipid levels at diagnosis, clinical disease stage, and survival in all consecutive patients diagnosed in the Netherlands. We determined the hazard ratio (HR) of each lipid for overall survival, defined as death from any cause. A subset of patients was matched to a previous genome-wide association study; data were used to calculate PPS for biomarkers of lipid metabolism and to determine the association between observed lipid levels at diagnosis and survival.

RESULTS

Meta-analysis of 4 studies indicated that none of the biomarkers of the lipid metabolism were statistically significantly associated with overall survival; there was, however, considerable heterogeneity between study results. Using individual patient data (N = 1,324), we found that increased high-density lipoprotein (HDL) cholesterol was associated with poorer survival (HR of 1.33 (95% CI 1.14-1.55, p < 0.001)). The correlation between BMI and HDL cholesterol (Pearson r -0.26, 95% CI -0.32 to -0.20) was negative and between BMI and triglycerides (TG) positive (Pearson r 0.18, 95% CI 0.12-0.24). Serum concentrations of total cholesterol and LDL cholesterol were lower in more advanced clinical stages (both p < 0.001). PPS for biomarkers of lipid metabolism explained 1.2%-13.1% of their variance at diagnosis. None of the PPS was significantly associated with survival (all p > 0.50).

DISCUSSION

Lipids may contain valuable information about disease severity and prognosis, but their main value may be driven as a consequence of disease progression. Our results underscore that gaining further insight into lipid metabolism and longitudinal data on serum concentrations of the lipid profile could improve the monitoring of patients and potentially further disentangle ALS pathogenesis.