Psychopathy is a personality construct characterized by emotional detachment, impulsivity, and antisocial tendencies. Despite a clinical prevalence of 1%, the biological underpinnings of psychopathy remain poorly understood. Emerging evidence suggests a role for the microbiota-brain axis in personality and behavior, yet its relevance to psychopathy remains unexplored. This study investigated the relationship between gut and oral microbiota composition and psychopathic traits, while assessing metabolomic signatures that may contribute to microbiota-brain interactions. A cohort of 200 participants completed the Self-Report Psychopathy Scale Short Form, alongside measures of empathy (Affective and Cognitive Empathy Questionnaire; Interpersonal Reactivity Index) and anxiety (State-Trait Inventory for Cognitive and Somatic Anxiety). Microbiota composition was analyzed in fecal and oral samples, and targeted and untargeted metabolomics was performed in plasma and fecal samples. Distinct psychopathy-related profiles were identified via K-means clustering in an untargeted approach and exploratory analyses were done to assess associations with psychopathy scores. While gut and oral alpha and beta-diversity did not differ between psychopathy clusters, beta-diversity analyses revealed significant associations with psychopathy scores. Gut taxa Allisonella, Prevotella, Ruminococcaceae DTU089 and Cloacibacillus evryensis, as well as oral taxa Treponema vincentii exhibited differences in relative abundance between clusters. Allisonella, Prevotella, and C. evryensis showed significant positive associations with psychopathy scores, while T. vincentii was negatively associated. Fecal glucose and taurine levels differed between clusters, with both metabolites showing positive associations with psychopathy scores. These findings provide novel evidence linking gut and oral microbiota composition to subclinical psychopathic traits, highlighting potential pathways for understanding the biological basis of psychopathy.

Changes in the peripheral metabolome, particularly in the blood, may provide biomarkers for assessing lesion severity and predicting outcomes after spinal cord injury (SCI). Using principal component analysis (PCA) and Orthogonal Partial Least Squares Discriminatory Analysis (OPLS-DA), we sought to discover how SCI severity and location acutely affect the nuclear magnetic resonance-acquired metabolome of the blood, spinal cord, and liver at 6 h post-SCI in mice. Unsupervised PCA of the spinal cord metabolome separated mild (30 kdyne) and severe (70 kdyne) contusion injury groups but did not distinguish between lesion level. However, OPLS-DA could discriminate thoracic level T2 from T9 lesions in both blood plasma (accuracy 86 ± 6%) and liver (accuracy 89 ± 5%) samples. These differences were dependent on alterations in energy metabolites (lactate and glucose), lipoproteins, and lipids. Lactate was the most discriminatory between mild and severe injury at T2, whereas overlapping valine/proline resonances were most discriminatory between injury severities at T9. Plasma lactate correlated with blood-spinal cord barrier breakdown and plasma glucose with microglial density. We propose that peripheral biofluid metabolites can serve as biomarkers of SCI severity and associated pathology at the lesion site; their predictive value is most accurate when the injury level is also considered.

BACKGROUND:Reliable biomarkers for predicting disease progression in multiple sclerosis (MS) are crucial for advancing precision medicine and optimising treatment strategies. This study evaluates the predictive potential of serum nuclear magnetic resonance (NMR)-based metabolomics, individually and in combination with well-established biomarkers of neuroinflammation (serum glial fibrillary acidic protein, sGFAP) and axonal damage (neurofilament light chain, sNfL), in an extreme-phenotype subset of the Swiss Multiple Sclerosis Cohort (SMSC). METHODS:Serum samples were analysed using NMR-based metabolomics, along with quantification of sNfL and sGFAP. Supervised multivariate analysis was performed to differentiate MS phenotypes and identify future progressors. Multivariable receiver operating characteristic (ROC) analysis evaluated predictive performance, with key metabolite findings validated in an independent Oxford MS cohort. RESULTS:NMR-based metabolomics reliably distinguishes relapsing-remitting MS (RRMS) from secondary-progressive MS (SPMS) and predicts individual transitions. The identified predictive metabolites (lipoproteins, glutamine, alanine, valine, glucose) are also associated with progression independent of relapse activity (PIRA), a clinically relevant marker of sustained disability worsening. This demonstrates that the approach can both stage disease and forecast progression irrespective of stage. ROC analysis shows strong predictive performance (AUC = 0.81, p = 0.001), with external validation confirming robustness. Integration of NMR-metabolomics with sGFAP and sNfL further improves accuracy, yielding AUCs of 0.91 (p < 0.0001) and 0.87 (p = 0.0002), respectively, supported by independent validation. CONCLUSIONS:The integration of metabolic and protein biomarkers enables both accurate staging of RRMS versus SPMS and, critically, early prediction of progression irrespective of stage. This dual capability provides a clinically actionable, serum-based tool that can refine monitoring, improve therapeutic decision-making, and support a shift towards stage-agnostic, progression-focused care in MS.

Background: In the COVID-19 pandemic, several phase II and III randomized trials were launched to evaluate the effectiveness of camostat, an orally administered TMPRSS2 inhibitor previously approved for other indications, for treating SARS-CoV-2 infections. Owing to the rapidly changing landscape during the pandemic, many of these trials were unable to reach completion. Further, methods for synthesizing trials that were launched and not completed were critical.Methods: This systematic review aimed to consolidate global evidence by identifying placebo controlled, randomized trials of camostat and analyzing their collective clinical and virologic impact on SARS-CoV-2 through an individual patient data meta-analysis (IPDMA). We harmonized data from the studies and utilized Bayesian statistical models to assess virologic outcomes (measured by the rate of change in viral shedding) and clinical outcomes (based on the time to the first of two consecutive symptom-free days), adjusting for age and sex.Results: The IPDMA incorporated data from six countries, totaling 431 patients across the studies; 118 patients contributed data for the primary virologic outcome and 240 for the clinical symptom outcome. Camostat did not improve the rate of change in viral load (difference in rate of change = 0.11 Ct value/day higher, 95% credible interval 2.04 lower to 2.23 higher) or time to symptom resolution (hazard ratio = 0.87, 95% credible interval 0.51, 1.55) when compared to placebo.Conclusions: Despite its theoretically promising mode of action, camostat did not demonstrate a statistically significant virologic or clinical benefit in treating COVID-19, highlighting the complexity of drug repurposing in emergency health situations.

Granulocytes play a well-established role in the pathogenesis of brain tissue damage in neuromyelitis optica spectrum disorder (NMOSD). The release of granulocyte activation markers (GAM) into CSF has recently been shown to distinguish NMOSD from multiple sclerosis (MS) with high accuracy. However, their pathogenetic role in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is less clear, and their usefulness for diagnostic differentiation is unknown. This observational cohort study by eight tertiary centres in Europe and Japan included 244 CSF samples from patients with MOGAD (n = 71), NMOSD (n = 48), MS (n = 125) and control persons (n = 19). CSF levels of GAM [neutrophil elastase, myeloperoxidase, neutrophil gelatinase-associated lipocalin (NGAL), matrix metalloproteinase-8 and 9 (MMP-8, MMP-9)], astrocyte damage markers [ADM: glial fibrillary acidic protein (GFAP), S100B], and complement factors C5 and C5a were analysed by capillary ELISA (Ella™) or Luminex®. The primary outcome was the capacity of these markers to differentiate MOGAD, NMOSD and MS in the acute (≤21 days post-exacerbation) stage, and the correlation of GAM with C5 and C5a. Secondary analyses included the correlations of these markers with disability severity, measured by the Expanded Disability Status Scale (EDSS). GAM (except for MMP-9), ADM and C5/C5a levels peaked at onset of disease exacerbation of MOGAD and NMOSD (regardless of aquaporin-4 antibody status), and were significantly higher than in MS. MMP-9 levels were continuously increased in MS over MOGAD and NMOSD, both in acute and subacute/chronic stages. C5 and C5a were equally increased over MS in acute stages of MOGAD and NMOSD. A logistic model and receiver operating characteristics analyses incorporating GAM and C5 displayed high discriminatory power between MOGAD/NMOSD versus MS [area under the curve (AUC) = 0.880], NMOSD versus MS (AUC = 0.837) and MOGAD versus MS (AUC = 0.925) in acute stages. Accordingly, increased ADM levels in NMOSD differentiated NMOSD from MS and MOGAD (AUC = 0.897 and 0.843, respectively). GAM levels correlated with EDSS scores in MOGAD and NMOSD, but not in MS, while those of ADM correlated with disability in NMOSD, but not in MOGAD and MS. Determining CSF levels of GAM and C5/C5a, and of ADM provide a biology-driven approach to differentiate MOGAD, NMOSD and MS. Their measurement can be processed faster and with similar accuracy than with most autoantibody assays, enabling timely initiation of appropriate therapy in acute presentations. The correlation between GAM and C5/C5a levels with neurological impairment in MOGAD and NMOSD corroborates their role as effectors of neural damage, supporting the acute stage use of inhibitors of C5 activation.

Aging is characterized by measurable reductions in tissue repair, immune balance, and metabolic regulation. Increasing evidence suggests that these changes may arise, in part, from an insufficiency or altered quality of endogenous extracellular vesicle (EV) signaling. EVs, including exosomes, carry regenerative and immunoregulatory cues, and age-related alterations in their abundance, cargo, and bioactivity have been linked to impaired cellular communication across organ systems. This has fueled growing interest in stem cell-derived EVs, which provide biologically more youthful vesicles that reproduce key paracrine functions of their parent cells while avoiding the limitations of cell transplantation. By transferring defined protein, lipid, and RNA cargoes, these vesicles influence pathways central to aging biology, including mitochondrial function, inflammatory control, and maintenance of stem cell niches. Preclinical studies support their efficacy in models of neurodegeneration, wound healing, musculoskeletal decline, and systemic inflammation. However, their function depends on stem cell origin, donor age, and environmental conditioning, variables that complicate standardization and clinical scalability. As interest expands across therapeutic and cosmetic domains, a comparative understanding of EV sources and their mechanistic actions is required. In this review, we examine stem cell-derived EVs across biological sources, outline how aging and environmental factors shape their regenerative potency, and evaluate current progress in clinical translation. The field has reached a point where future advances depend less on further demonstrations of efficacy and more on resolving challenges related to manufacturing, quality control, and regulatory alignment. Addressing these constraints will determine whether stem cell-derived EVs can progress from experimental promise to practical interventions for aging and regenerative medicine.

Emerging evidence suggests that bile acids, traditionally recognized for their role in digestion, also influence brain function and memory. This study examined the effects of two microbiota-derived secondary bile acids, deoxycholic acid (DCA) and glycodeoxycholic acid (GDCA), on memory in mice and the associated molecular mechanisms. Male and female mice received daily oral administration of DCA, GDCA, or vehicle, and spatial working and reference memory (Y-maze) and recognition memory (novel object recognition task) were assessed. After testing, gene expression and signaling activity were measured in the frontal cortex and hippocampus. Administration of GDCA after 10 d disrupted recognition memory, whereas DCA intake for 12 d impaired spatial reference memory. Neither bile acid administered for 5 d affected spatial working memory. GDCA reduced NMDA receptor subunit (GluN1, GluN2A) mRNAs and encoded protein and brain-derived neurotrophic factor (BDNF) mRNA expression and attenuated CREB signaling in the frontal cortex, which is consistent with the observed recognition memory deficit. GDCA did not alter the abundance of transcripts encoding bile acid receptors (FXR or TGR5) or their corresponding protein levels. In contrast, DCA modified the FXR and TGR5 mRNAs and proteins in a region-specific manner and decreased CREB signaling in the hippocampus, likely contributing to spatial memory deficits. In the frontal cortex, DCA increased GluA1 phosphorylation and reduced IL-1β and IL-6 expression, which may have helped preserve recognition memory. Exploratory metagenomic analysis of fecal samples showed no significant microbial differences, though subtle, non-significant functional gene changes suggested early adaptations. These findings reveal that DCA and GDCA exert distinct, receptor- and region-specific effects on cognition, identifying bile acids as modulators of microbiome-gut-brain communication.

Astrocytes play essential roles in neuropathology. Human astrocytes exhibit unique properties, highlighting the importance of studying astrocytic responses in human models. Varicose projection astrocytes, previously considered exclusive to hominoids and a physiological type of astrocytes, were suggested to reflect pathological burden, albeit direct evidence linking them to neurological diseases has been lacking. Here, we demonstrate that varicose projection astrocytes also appear in other mammals and show, from four distinct human-based disease models, that varicose projection astrocytes are induced by neuroinflammation and characterized by distinctive subcellular features, indicating involvement in cellular stress responses, and their density is increased in aging and human neuropathology. Our findings establish varicose projection astrocytes as a reactive phenotype associated with neuropathology.

BackgroundBlood-based metabolomics is increasingly recognised as a powerful tool for disease detection in human medicine. However, its application in veterinary science remains limited.ObjectiveTo evaluate the ability of an NMR-based metabolomics platform combined with machine learning to screen dogs for cancer, cardiovascular disease (CVD), and overall health status.AnimalsClient-owned dogs were recruited from two sites. Of 156 animals enrolled, 139 remained after exclusions and were used for training and cross-validation of classification models.MethodsBlood samples were obtained from clinically healthy dogs and dogs with a range of diseases. Full blood count was performed, and serum metabolomic and lipoprotein profiling data were generated using NMR spectroscopy. Machine learning classifiers were trained to distinguish healthy from non-healthy dogs, and to further identify cancer and CVD cases. Model performance was evaluated by cross-validation and against null models with permuted class labels.ResultsModels showed high discriminative performance for separating healthy from non-healthy animals (ROC AUC 0.916 ± 0.012; accuracy 86.5 ± 3.8%; sensitivity 81.7 ± 6.9%; specificity 87.5 ± 6.0%) and identifying pets with cancer (ROC AUC 0.911 ± 0.008; accuracy 83.5 ± 3.4%; sensitivity 86.5 ± 6.7%; specificity 82.4 ± 6.6%) or CVD (ROC AUC 0.924 ± 0.010; accuracy 90.0 ± 5.8%; sensitivity 85.6 ± 5.1%; specificity 90.6 ± 7.2%) from pets without the disease. Key predictive features included glutamine and creatine concentrations, lymphocyte count and percentage, platelet count and mean platelet volume (MPV), as well as lipoprotein cholesterol levels.ConclusionThis study provides the first evidence that NMR metabolomics combined with machine learning enables accurate, non-invasive, multi-disease screening in dogs, highlighting its potential for translation into routine veterinary practice for diagnosis and health monitoring.

Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1 H-NMR-based metabolomics can identify cancer with high accuracy. SCAN2 tested whether integrating metabolomics with glycomics improves discrimination in a clinically complex, real-world population. Serum from 369 SCAN patients (59 cancers) was analysed using AXINON®lipoFIT® -derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. Integration of glycomics with metabolomics improved discrimination, achieving an AUC of 0.88 in a refined cohort excluding dominant comorbidities. Cancer-associated bi- and tri-antennary glycans, including FA2G2S1, FA2BG1, and M5A1G1S1, differentiated cancer cases. A classifier targeting metastatic disease achieved an AUC of 0.80. Joint probability analysis preserved cancer-associated metabolic signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.8%. These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and demonstrate that integrating metabolomics with glycomics enhances cancer detection in patients with non-specific symptoms. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing.

Understanding the earliest pathological changes in Alzheimer disease (AD) is critical for improving early intervention strategies. However, the relationship between amyloid plaque characteristics and early behavioral and molecular alterations remains unclear. We used 6-month-old female APPswe/PS1dE9 mice, a model of early amyloid-dominant pathology, to assess cognition and emotionality across a battery of behavioral tests. Amyloid plaques were quantified using Congo red staining; RT-qPCR and GFAP immunoreactivity were used to assess molecular and glial changes. APPswe/PS1dE9 mice exhibited increased anxiety-like behavior without significant changes in overall locomotor activity. Small plaques (<100 μm2) predominated across all regions; however, behavioral measures of hyperactivity and anxiety correlated specifically with the density and size of large (>200 μm2) plaques. Gene expression changes, including altered SYP, IGF1, TNF, and IL6 expression, were observed primarily in the midbrain and did not correlate with amyloid plaque characteristics. These findings demonstrate that large amyloid plaques, rather than total plaque burden, are selectively associated with early behavioral alterations in APPswe/PS1dE9 mice. Moreover, the midbrain emerges as an early site of molecular dysregulation despite limited plaque deposition. Together, these results support the use of 6-month-old APPswe/PS1dE9 mice as a model of early, amyloid-dominant stages of AD.

Abstract Background Osteoarthritis is a leading cause of pain and disability, yet the biological processes linking peripheral joint pathology with central pain mechanisms and wider symptom burden remain poorly defined. Methods We performed an integrated metabolomic and inflammatory analysis of cerebrospinal fluid and serum obtained from patients with osteoarthritis ( n = 81) and healthy pain-free controls ( n = 70). Proton nuclear magnetic resonance spectroscopy was used for metabolomic profiling, alongside targeted protein assays for inflammatory mediators. Orthogonal partial least squares discriminant analysis was applied to assess separation between groups and to determine diagnostic accuracy. Associations between metabolites and clinical outcomes, including pain intensity, disability and sleep disturbance, were examined, with adjustment for age and BMI. Results Clear separation between osteoarthritis and healthy pain-free control participants was observed in both biofluids, with classification accuracies of 87% for serum and 89% for cerebrospinal fluid. Reduced serum histidine, glutamine, albumin (lysyl) and lysine distinguished osteoarthritis from healthy controls. In cerebrospinal fluid, osteoarthritis was characterised by higher lactate and glutamate and lower glucose and glutamine compared to controls. Combining metabolomic data with inflammatory proteins increased diagnostic accuracy to 90% and remained significant after matching for age and BMI. Reductions in serum histidine and glutamine were consistent across subgroups, including stratification by pain severity. These metabolites correlated inversely with pain intensity, disability, sleep disturbance and overall symptom impact, and were more markedly altered in women, who also reported greater symptom burden. Conclusions Osteoarthritis is associated with a distinct pattern of peripheral and central metabolic disturbance. Histidine and glutamine emerge as promising biomarkers related to pain and clinical severity, highlighting metabolic pathways as potential targets for improved stratification and intervention in osteoarthritis pain.

Background: Mild cognitive impairment (MCI) is a heterogeneous state between normal ageing and dementia, often considered prodromal to Alzheimer's disease (AD). Progression is variable, and distinguishing stable from progressive MCI remains difficult, particularly in the presence of mixed neuropathology. Blood biomarkers such as phosphorylated tau181 (pTau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) demonstrate prognostic value in established AD, but limited performance for prognosticating progression from MCI. Methods: Blood protein biomarkers (pTau181, GFAP, NfL) were integrated with NMR- and LC-MS-derived metabolomic features. In a deeply phenotyped MCI cohort (VITACOG; n=68) with two-year MRI follow-up, cross-validated logistic regression identified discriminative multi-analyte panels to distinguish stable from progressive MCI. Disease progression was defined by worsening cortical atrophy, measured via annualised brain volume loss. Generalisability was tested in a larger community-based cohort from UK Biobank (n=223) and two Oxford Project to Investigate Memory and Ageing (OPTIMA) subsets with histopathological diagnosis (n=61, n=37). Results: Integration of pTau181 with six metabolite features yielded the highest prognostic performance (AUC 0.91; accuracy 80%), with metabolomic findings independently validated in the OPTIMA cohort. A complementary GFAP-NMR panel also performed strongly (AUC 0.80; accuracy 75%). In contrast, individual metabolites, including the atrophy marker homocysteine, and standalone protein biomarkers performed poorly (AUC ≤0.66), as well their combination (AUC 0.68), highlighting the added value of multi-omic integration. In an asymptomatic ageing population (UK Biobank), the models served as a population-level stress test, confirming that multi-omic integration improved specificity for MRI-derived atrophy measures and captured atrophy-related risk in community cohorts. Conclusion: Multi-omic integration of protein and metabolic features markedly improved prognostication of MCI progression by capturing early neurodegenerative signatures, yielding translational panels suitable for scalable risk stratification and early therapeutic intervention in clinical practice. ### Competing Interest Statement A.D.S. and H.R. are named as inventors on two patents held by the University of Oxford on the use of B-vitamins to treat cognitive disorders (US9364497 and US10966947). F.J. is named as an inventor on US10966947. These patents have been licensed to Elysium Health, NY. J.S.O.M. has a research contract and equipment loan from ThermoFisher Scientific, which manufactures IC-MS systems. S. de J., Q. G. and E. S. are employees of Numares AG (Am Biopark 9, 93053 Regensburg-Grass, Germany). All other authors report no conflicts of interest. N.J.A. received consultancy or speaker fees from BioArtic, Biogen, Lilly, Quanterix and Alamar Biosciences. H.Z. has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZpath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Enigma, LabCorp, Merck Sharp & Dohme, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Quanterix, Red Abbey Labs, reMYND, Roche, Samumed, ScandiBio Therapeutics AB, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures sponsored by Alzecure, BioArctic, Biogen, Cellectricon, Fujirebio, LabCorp, Lilly, Novo Nordisk, Oy Medix Biochemica AB, Roche, and WebMD, is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, and is a shareholder of CERimmune Therapeutics (outside submitted work). S. M. S. is co-founder and part-owner of SBGneuro. ### Funding Statement T.K. is funded by an EPSRC Postdoctoral Pathway Scheme (EP/Z534870/1) and EPSRC talent and skills funding and Numares AG (Am Biopark 9, 93053 Regensburg-Grass, Germany). This research was supported in part by the Aqua-Synapse EU framework (2022-2026) to D.A., funded by the European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 101086453. The authors are solely responsible for the content of this publication, which does not necessarily represent the official views of the European Union or the European Research Executive Agency. H.Z. is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397), the European Union's Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). The original VITACOG trial was supported by grants from Charles Wolfson Charitable Trust, Medical Research Council, Alzheimer's Research Trust, Henry Smith Charity, John Coates Charitable Trust, Thames Valley Dementias and Neurodegenerative Diseases Research Network of the National Institute for Health Research, UK, and the Sidney and Elizabeth Corob Charitable Trust. The original OPTIMA cohort was supported by grants from Bristol-Myers Squibb, Medical Research Council and the Charles Wolfson Charitable Trust. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The VITACOG trial was approved by a local NHS Research Ethics Committee (COREC 04/Q1604/100). For the OPTIMA cohort, ethical approval was obtained from the Frenchay Research Ethics Committee (REC Ref 09/H0107/9). All participants provided written informed consent in accordance with the Declaration of Helsinki. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Anonymised data not published within this article will be made available by request from any qualified investigator.

Traumatic brain injury (TBI) significantly contributes to morbidity and mortality worldwide, often leading to cognitive decline. Although there is a recognised link between TBI and the acceleration of Alzheimer's disease (AD), the precise biological mechanisms driving this relationship are not fully understood. While several studies have investigated TBI in AD mouse models, none have examined the role of systemic inflammation in this context. In this study, we investigated the inflammatory responses, both centrally and peripherally, in 1-year-old wild-type (WT) and J20 mice (Tg:PDGFB-APPSwInd), overexpressing human amyloid precursor protein with the Swedish and Indiana mutations. Following controlled cortical impact (CCI) at 0.5 mm depth to the left somatosensory cortex, we examined outcomes at 1 and 7 days post-injury. The J20 mice exhibited a persistent sensorimotor impairment post-TBI, as determined by the adhesive removal test. Although amyloid-β42 deposition progressively increased post-injury, this behavioural deficit was not associated with greater neuronal loss compared to WT mice. Using qPCR, it was revealed that the level of proinflammatory cytokine and chemokine expression in the brain was largely conserved between WT and J20 mice, though brain Cxcl10 expression increased by 28.6 % in J20 mice at 7d-post injury compared to WT. However, J20 mice exhibited an exaggerated acute phase response (APR) to the TBI in the liver and spleen at 7d. Accompanying the potentiated APR, 1H NMR revealed that plasma glucose was decreased in J20 mice compared to WT at 7d. Taken together, this suggests that the sustained sensorimotor deficit in J20 mice is associated with increased amyloid-β pathology, and a dysregulated and prolonged systemic inflammatory response, accompanied by hypoglycaemia. In general, TBI in the presence of AD pathology, results in extended systemic inflammatory and metabolic responses that are likely to underpin the extended cognitive impairment, and our findings emphasise the need for customised interventions that address central and systemic inflammation after TBI in individuals with neurodegenerative disease.

Untargeted metabolic profiling of plasma and serum by liquid chromatography-mass spectrometry (LC-MS) is becoming increasingly important in clinical and translational research; however, sample preparation protocols can have a significant impact on study outcomes, and there is currently a lack of standardized approaches. In this study we demonstrate that pretreatment of serum and plasma samples with 1% formic acid (FA, v/v) prior to acetonitrile (MeCN)-induced protein precipitation significantly enhances analytical performance in untargeted metabolomics using reversed-phase liquid chromatography (RPLC)-MS. We show an increase in sample preparation reproducibility and signal intensity across both positive and negative ionization modes. In two independent serum cohorts (OPTIMA and VITACOG), FA-based extraction improved multivariate modeling (orthogonal partial least-squares discriminant analysis, OPLS-DA), with consistently higher classification accuracy, sensitivity, and specificity, alongside reduced variability and increased fold-changes in discriminatory compound-features. We investigated factors potentially involved in the enhanced performance and observed outcomes consistent with the disruption of noncovalent protein-metabolite interactions and the stabilization of labile species. We found no correlation with either protein depletion or differential adduct formation. The results were also not attributable to lowering pH after metabolite extraction. In summary, we demonstrate that FA pretreatment of plasma and serum, prior to protein precipitation, significantly improves sample reproducibility and detection sensitivity in untargeted RPLC-MS metabolomics. This optimized sample preparation strategy offers clear advantages for clinical and translational metabolomics, with the potential to enhance biomarker discovery and metabolic phenotyping.

Toxoplasma gondii, a ubiquitous neurotropic parasite, infects roughly one-third of the global population. In immunocompetent individuals, infection is typically asymptomatic, yet recent evidence suggests that latent T. gondii infection can subtly impair brain function and increase vulnerability to neurological disorders. This commentary, prompted by recent findings by Baker et al., highlights how chronic infection may exacerbate seizure susceptibility and neuroinflammation, particularly under a 'second hit' model. The implications of such latent infections in public health and the importance of considering infection history in neurological disease models are discussed.

Study schematic showing patient recruitment into the study, exclusions, biofluid collection, and confirmed diagnoses.

A, Fold changes in key metabolites identified by multivariate analysis concentrations in unwell patients with solid tumors relative to the mean metabolite concentrations in unwell patients without cancer. B, Fold changes in key metabolite concentrations in patients with metastatic cancer, relative to the mean metabolite concentration in patients with nonmetastatic cancer. C, Venn diagram illustrating direction of metabolite concentration changes in metastatic and nonmetastatic cancers, relative to unwell patients without cancer. HDL, high-density lipoprotein. Note that “/” represents that the two metabolites overlap in the NMR data, and not a ratio of the two metabolite concentrations. Individual plots of metabolite concentrations are given in Supplementary Fig. S4.

Neuroinflammation is a key feature of Alzheimer’s disease (AD), and stem cell therapies have emerged as promising candidates due to their immunomodulatory properties. Neuro-Cells (NC), a combination of unmodified mesenchymal stem cells (MSCs) and hematopoietic stem cells (HSCs), have demonstrated therapeutic potential in models of central nervous system (CNS) injury and neurodegeneration. Here, we studied the effects of NC in APPswe/PS1dE9 mice, an AD mouse model. Twelve-month-old APPswe/PS1dE9 mice or their wild-type littermates were injected with NC or vehicle into the cisterna magna. Five to six weeks post-injection, cognitive, locomotor, and emotional behaviors were assessed. The brain was stained for amyloid plaque density using Congo red, and for astrogliosis using DAPI and GFAP staining. Gene expression of immune activation markers (Il-1β, Il-6, Cd45, Tnf) and plasticity markers (Tubβ3, Bace1, Trem2, Stat3) was examined in the prefrontal cortex. IL-6 secretion was measured in cultured human monocytes following endotoxin challenge and NC treatment. Untreated APPswe/PS1dE9 mice displayed impaired learning in the conditioned taste aversion test, reduced object exploration, and anxiety-like behavior, which were improved in the NC-treated mutants. NC treatment normalized the expression of several immune and plasticity markers and reduced the density of GFAP-positive cells in the hippocampus and thalamus. NC treatment decreased amyloid plaque density in the hippocampus and thalamus, targeting plaques of <100 μm2. Additionally, NC treatment suppressed IL-6 secretion by human monocytes. Thus, NC treatment alleviated behavioral deficits and reduced amyloid plaque formation in APPswe/PS1dE9 mice, likely via anti-inflammatory mechanisms. The reduction in IL-6 production in human monocytes further supports the potential of NC therapy for the treatment of AD.