eBioMedicine

BackgroundDetermining the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. MethodsWe developed the classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N=705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. ResultsWe selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N=97) and retrospectively (N=100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. ConclusionsWith further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

BackgroundAcute mountain sickness (AMS) represents a considerable issue for individuals sojourning to high altitudes with systemic hypoxemia known to be intimately involved in its development. Based on recent evidence that ketone ester (KE) intake attenuates hypoxemia, we sought to investigate whether exogenous ketosis might mitigate AMS development and improve hypoxic tolerance. MethodsFourteen healthy, male participants were enrolled in two 29h protocols (simulated altitude of 4,000-4,500m) receiving either KE or a placebo (CON) at regular timepoints throughout the protocol in a randomized, crossover manner. Select physiological responses were characterized after 15min and 4h in hypoxia, and the protocol was terminated prematurely upon development of severe AMS. ResultsAll participants tolerated the protocol equally long (n=6, of which n=5 completed the protocol in both conditions) or longer (n=8) in KE. Overall protocol duration increased by 32% on average with KE, and doubled for AMS-developing participants. KE consistently induced diurnal ketosis, a mild metabolic acidosis, hyperventilation, and relative sympathetic dominance. KE also fully negated the progressive hypoxemia that was observed between 15min and 4h in hypoxia in CON, while concomitantly increasing cerebral oxygenation and capillary pO2 within this timeframe. This coincided with a KE-induced reduction in cerebral oxygen supply, suggesting that KE reduced cerebral oxygen consumption under hypoxic conditions. ConclusionsThese data indicate that exogenous ketosis improves hypoxic tolerance in humans and attenuates AMS development. The key underlying mechanisms include improved arterial and cerebral oxygenation, in combination with lowered cerebral blood flow and oxygen demand, and increased sympathetic dominance. SummaryKetone ester intake attenuated the development of acute mountain sickness at a simulated altitude of 4,000-4,500m. This likely resulted from a mitigation of arterial and cerebral hypoxemia, reduced cerebral blood flow and increased sympathetic drive.

Tuberculosis (TB) diagnosis remains challenging, particularly for extrapulmonary TB (EPTB), where invasive sampling, low bacillary burden, and suboptimal sensitivity of nucleic acid-based tests in peripheral specimens hinder timely detection. Here, we report an immunology-driven strategy for biomarker discovery and development of a peptide-based serological assay targeting Mycobacterium tuberculosis zinc metalloprotease-1 (Zmp1). Leveraging fundamental principles of adaptive immunity that antigenic regions containing overlapping B-cell and CD4 T-helper cell epitopes would preferentially generate high antibody titers through linked recognition and cognate T-cell help, we used an immunoinformatics pipeline to identify two nested immunodominant peptide regions within Zmp1 (Mtb-Zp-NT and Mtb-Zp-CT) enriched for overlapping B- and T-cell epitopes. The diagnostic potential of these peptides was evaluated through ELISA-based serological assays. A blinded pilot study (N=137) demonstrated a clear discrimination between active TB and TB-recovered individuals. The assay was subsequently validated in an expanded cohort (N=875) by screening 6,086 individuals, which identified 457 TB-positive cases. The cohort included pulmonary TB (PTB), EPTB, TB-recovered individuals, household contacts, non-specific infections, and healthy controls. Receiver operating characteristic analyses, supported by DeLong and bootstrap comparisons, revealed superior diagnostic performance of the peptide-based assays relative to full-length Zmp1. Mtb-Zp-CT exhibited the highest accuracy (AUC=0.93; specificity >90%), while Mtb-Zp-NT also demonstrated strong discriminatory power (AUC{approx}0.89). These findings establish that the immunologically optimized Zmp1 peptides are highly promising serological biomarkers for TB and EPTB. More broadly, they demonstrate how mechanistically informed epitope selection can accelerate translation of pathogen-specific immune signatures into sensitive, minimally invasive, and potentially point-of-care diagnostic platforms for resource-limited settings.

Background. Plasma and serum metabolomic studies of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) have repeatedly implicated hypometabolic, lipid, mitochondrial, redox and tryptophan-kynurenine pathways, but prior cohorts have been modest in size and have used heterogeneous case definitions. Whether similar pathway-level signals are detectable at scale in dried blood spots (DBS), across questionnaire-derived fatigue constructs and across orthogonal LC gradients in the same individuals remains unresolved. Methods. We profiled DBS extracts from 1,784 community-cohort adults by reverse-phase LC-MS using paired 5 min and 15 min gradients. Six questionnaire-derived endpoints captured a pragmatic self-reported PEM-like phenotype, a DSQ-derived PEM-like construct, high or review clinical status, temporal fatigue state, comorbid fatigue and self-reported chronic fatigue. The locked primary endpoint for Phase 1 was pragmatic_fatigue_pem with 226 cases and 914 controls after excluding major metabolic comorbidity. We tested a biology-first panel comprising 22 literature-curated metabolites represented by four participant-level descriptors each, and evaluated three discovery extensions: a targeted m/z search of additional literature candidates, a hypothesis-free univariate screen across 4,553 5 min and 5,625 15 min consensus features, and pairwise z-difference ratios. Endpoint-specific Ridge classifiers were evaluated by five-fold out-of-fold AUC with bootstrap stability filtering. Cross-gradient agreement was assessed by per-metabolite AUC concordance between paired 5 min and 15 min profiles. Severity was modelled as an ordinal grade derived from the number of fatigue criteria met and chronic-fatigue-form status. Results. The biology-first DBS panel achieved out-of-fold AUC 0.81 for the pragmatic self-reported PEM-like endpoint (226 cases / 914 controls). The DSQ-derived PEM-like construct reached AUC 0.60 (57 cases / 201 controls) on the un-filtered set and AUC 0.778 (SD 0.013, twenty seeds) in a post-hoc signature-decomposition follow-up restricted to participants without a self-declared major-metabolic-history tag (29 cases / 230 controls); both are treated as construct-validity anchors rather than as provoked or clinically adjudicated PEM. An optimised operationalisation of the same construct (panel-self normalisation, restriction to non-comorbid participants and demographic covariates) reached AUC 0.71 (95 % CI 0.55 to 0.76), and an exploratory age-stratified signature decomposition suggested age-dependent pathway composition that requires confirmation given small per-stratum case counts. Stable contributors mapped to carnitine-shuttle, TCA-cycle, redox-thiol and tryptophan-kynurenine pathways. Cross-gradient analysis of 22 matched metabolites yielded Pearson r = 0.62 for signed univariate effects (p = 0.002; 68 % directional agreement). The metabolomic score increased with severity grade (Spearman rho = 0.45, p = 4 x 10^-91; median scores 0.24, 0.51 and 0.75 across grades 0, 1 and 2). Sensitivity analyses on the covariate-complete subset (n = 565; 138 cases / 427 controls) showed that the DBS signal was robust to adjustment for age, sex, BMI and medication burden (DBS-only AUC 0.76, DBS plus covariates 0.78, covariates only 0.64), and produced a metabolomic-specific lift of approximately 0.13 AUC over the strongest anti-leak declarative cross-form questionnaire baseline (AUC 0.63). DBS-only AUC was stable across sex, age and BMI subgroups, and a 1:4 nearest-neighbour matched analysis on age, sex and BMI yielded AUC 0.72 (95 % CI 0.67 to 0.77). The observed pattern supported pathway-level convergence with prior ME/CFS metabolomics literature, including carnitine shuttle, fatty-acid beta-oxidation, TCA cycle, redox-thiol, urea cycle, glycerophospholipid and tryptophan-kynurenine axes. In contrast, the hypothesis-free 15 min screen produced high-AUC features that mapped predominantly to environmental or technical signals, including pesticide, industrial-amine and mobile-phase artifact annotations; only one of eight top leads, a truncated oxidised phospholipid, was biologically plausible, and none had tandem-MS support. Conclusions. In this large community cohort, a literature-curated DBS metabolomic panel captured pathway-level biology associated with a questionnaire-derived PEM-like fatigue phenotype, showed directional concordance across LC gradients, scaled with symptom severity and remained robust to key demographic, anthropometric and anti-leak questionnaire baselines. The findings converge with several metabolic axes previously reported in ME/CFS plasma and serum studies, including carnitine-shuttle, TCA-cycle, redox-thiol, urea-cycle, glycerophospholipid and tryptophan-kynurenine pathways. They should not be interpreted as clinical validation of a diagnostic test, screening tool or objective provoked-PEM biomarker. Rather, they support at-home-compatible DBS metabolomics as a biologically grounded platform for future clinically adjudicated validation, decision-support development and longitudinal monitoring in fatigue and PEM-like syndromes. Because DBS contains cellular and plasma-derived components, matrix effects must be considered when comparing individual metabolites with venous plasma or serum studies, and hypothesis-free screening at this scale can preferentially surface exposome or technical variance unless molecular identification is enforced before biological interpretation.

Reduced nicotinamide mononucleotide (NMNH) is a reduced NAD precursor with reported NAD- augmenting activity in preclinical models; however, controlled human data remain limited. This was a randomized, double-blind, placebo-controlled, parallel-group phase I trial evaluating oral NMNH-Ca in healthy adults aged 40-65 years. Eighty participants received placebo or NMNH-Ca 125, 250, or 500 mg once daily for 90 days. The primary objective was safety and tolerability. Whole-blood NAD was assessed as the key pharmacodynamic endpoint, including a 24-hour post-dose substudy, with biomarker-derived blood phenotypic age, treadmill-based six-minute walk distance, body mass index, and SF-36 domains analyzed as exploratory outcomes. NMNH-Ca was well tolerated at all doses, with no serious adverse events, treatment-related adverse events, or discontinuations. In the acute substudy, whole-blood NAD increased after single-dose NMNH-Ca, with peak mean concentrations at 12 hours. Over 90 days, NAD increased in a dose-related pattern; Day 90 mean changes from baseline were 2.33 {+/-} 18.53 M with placebo and 8.22 {+/-} 10.25, 15.85 {+/-} 11.16, and 39.90 {+/-} 14.11 M with NMNH-Ca 125, 250, and 500 mg, respectively. Exploratory analyses showed hypothesis-generating favorable signals in blood phenotypic age, treadmill-based six-minute walk distance, and health-related quality of life, most consistently at 500 mg. Oral NMNH-Ca was safe and pharmacodynamically active over 90 days, supporting larger and longer confirmatory trials with prespecified geroscience endpoints and tissue-relevant NAD metabolomics.

Background and ObjectivesAmyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease with an increasing global burden. Available treatments for ALS present marginal efficacy. To identify novel candidate therapeutic targets for ALS, we conducted a proteome-wide Mendelian randomization (MR) study. MethodsWe leveraged data from the largest summary statistics for ALS to date (27,205 patients with ALS and 110,881 controls). Genetic instruments of more than 4,000 proteins defined by cis-protein quantitative loci (pQTL) genetic instruments on plasma and cerebrospinal fluid (CSF) were obtained from Fenland (discovery, n=10,709), deCODE (replication, n=35,559), and a recently published dataset (replication, n=971). To investigate the causal ALS-associated proteins, proteome-wide Mendelian randomization based on summary-data-based MR (SMR and multi-SNP-based SMR) were performed. Then, two-sample MR analyses using five additional methods were conducted as sensitivity analyses. To further address the linkage disequilibrium bias, heterogeneity in dependent instruments test and colocalization analyses were performed. Steiger filtering and bi-directional MR analyses were conducted to address the potential reverse causality. Four drug target datasets were searched to extract druggability profiles for candidate target proteins. In addition, we carried out a case-control study involving up to 21 patients with ALS and 21 matched controls to assess the protein levels difference in CSF for evidence triangulation. ResultsGenetically predicted levels of six circulating proteins were associated with incident ALS in primary SMR analysis. After removing proteins with any linkage disequilibrium bias, SHBG, SIGLEC7, and SIGLEC9 presented consistent associations with ALS risk, supported by medium-to-high colocalization across both plasma pQTL datasets. In CSF, higher level of SHBG was also causally associated with the risk of ALS. There was no reverse causality detected. The case-control study using CSF proteomics conducted in our center observed consistent alteration in the levels of SHBG and SIGLEC7 with MR prediction, further suggesting their functionally relevant to ALS as potential druggable targets. DiscussionCombined with the findings from MR and our observational study, we prioritize SHBG, SIGLEC7, and SIGLEC9 as drug candidate proteins for ALS, and further studies are needed to verify our findings and elucidate the underlying mechanism.

Structured AbstractO_ST_ABSBackgroundC_ST_ABSBilirubin is a potent antioxidant with a protective role in many diseases. We examined the relationships between serum bilirubin (SB) levels, tobacco smoking (a known cause of low SB), and aerodigestive cancers, grouped as lung (LC) and head and neck (HNC). MethodsWe examined the associations between SB, LC and HNC using data from 393,210 participants from UCLA Health, employing regression models, propensity score matching, and polygenic scores. ResultsCurrent tobacco smokers showed lower SB (-0.04mg/dL, 95% CI: [-0.04, -0.03]), compared to never-smokers. Lower SB levels were observed in HNC and LC cases (-0.10 mg/dL, [-0.13, -0.09] and -0.09 mg/dL, CI [-0.1, -0.07] respectively) compared to cancer-free controls with the effect persisting after adjusting for smoking. SB levels were inversely associated with HNC and LC risk (ORs per SD change in SB: 0.64, CI [0.59,0.69] and 0.57, CI [0.43,0.75], respectively). Lastly, a polygenic score (PGS) for SB was associated with LC (OR per SD change of SB-PGS: 0.71, CI [0.67, 0.76]). ConclusionsLow SB levels are associated with an increased risk of both HNC and LC, independent of the effect of tobacco smoking with tobacco smoking demonstrating a strong interaction with SB on LC risk. Additionally, genetically predicted low SB (from polygenic scores) is negatively associated with LC. ImpactThese findings suggest that SB could serve as a potential early biomarker for LC and HNC.

Corona virus disease 2019 (COVID-19) has been associated with a wide range of divergent pathologies, and risk of severe disease is reported to be increased by a similarly broad range of co-morbidities. The present study investigated blood metabolites in order to elucidate how infection with severe acute respiratory syndrome coronavirus 2 can lead to such a variety of pathologies and what common ground they share. COVID-19 patient blood samples were taken at hospital admission in two Belgian patient cohorts, and a third cohort that included longitudinal samples was used for additional validation (total n=581). A total of 251 blood metabolite measures and ratios were assessed using nuclear magnetic resonance spectroscopy and tested for association to disease severity. In line with the varied effects of severe COVID-19, the range of severity-associated biomarkers was equally broad and included increased inflammatory markers (glycoprotein acetylation), amino acid concentrations (increased leucine and phenylalanine), increased lipoprotein particle concentrations (except those of very low density lipoprotein, VLDL), decreased cholesterol levels (except in large HDL and VLDL), increased triglyceride levels (only in IDL and LDL), fatty acid levels (decreased poly-unsaturated fatty acid, increased mono-unsaturated fatty acid) and decreased choline concentration, with association sizes comparable to those of routine clinical chemistry metrics of acute inflammation. Our results point to systemic metabolic biomarkers for COVID-19 severity that make strong targets for further fundamental research into its pathology (e.g. phenylalanine and omega-6 fatty acids).

Tuberculosis (TB) continues to pose a significant global public health challenge with substantial patient morbidity and mortality. Current TB patient biomarkers lack sufficient resolution to inform treatment response and patient stratification. This necessitates the development of sensitive and reliable host biomarkers. We previously demonstrated the efficacy of TruCulture whole blood stimulation for differentiating asymptomatic TB from active pulmonary TB disease patients in endemic regions. Our systems immunology study expands upon this previous work by evaluating the potential of TruCulture to monitor longitudinal responses to TB treatment in patients from the Predict-TB trial before, during, and after 6 months of antibiotic therapy. We stimulated whole blood from TB patients (n=40) using TruCulture under four conditions (Null, Mycobacterium tuberculosis-antigen, LPS, and IL-1{beta}) at baseline (week 0), during treatment (weeks 16 and 24), and one-year follow-up post- treatment (week 72). 20/25 measured cytokines exhibited significant changes throughout treatment, with several continuing to evolve during post-therapy follow-up. Machine learning based analysis identified Mtb-Ag-induced IL-1RA (AUC = 0.90, 0.92, 0.95 at weeks 16, 24, 72) and LPS-induced NLRP3 (AUC = 0.94 at week 16) as the best protein and transcriptional biomarkers for distinguishing treated from untreated patients, strongly implicating the inflammasome response. Combining these results with the extent of lung disease assessed by FDG PET/CT scans, we showed direct disease relevance for these blood-based biomarkers. The identified biomarker profiles hold promise for improving TB patient care through early prediction of treatment responses, real-time therapy monitoring, and informed development of host-directed therapeutic strategies for clinical decision-making. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/723467v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@14a32eforg.highwire.dtl.DTLVardef@55f3d4org.highwire.dtl.DTLVardef@fb0137org.highwire.dtl.DTLVardef@10cf39e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Predict-TB clinical study overview and summary of TB-specific biomarkers identified from TruCulture whole blood stimulation system. C_FIG

BackgroundAdverse gastric symptoms persist in up to 20% of fundoplication surgeries completed for gastroesophageal reflux disease, causing significant morbidity, and driving the need for revisional procedures. Non-invasive techniques to assess the mechanisms of persistent postoperative symptoms are lacking. We aimed to investigate gastric myoelectrical abnormalities and symptoms in patients after fundoplication using a novel non-invasive body surface gastric mapping (BSGM) device. MethodsPatients with previous fundoplication surgery and ongoing significant gastroduodenal symptoms, and matched controls were included. BSGM using Gastric Alimetry (Alimetry, New Zealand) was employed, consisting of a high resolution 64-channel array, validated symptom-logging App, and wearable reader. Results16 patients with significant chronic symptoms post-fundoplication were recruited, with 16 matched controls. Overall, 6/16 (37.5%) patients showed significant spectral abnormalities defined by unstable gastric myoelectrical activity (n = 2), abnormally high gastric frequencies (n = 3) or high gastric amplitudes (n = 1). Those with spectral abnormalities had higher Patient Assessment of Upper Gastrointestinal Disorders-Symptom Severity Index scores (3.2 [2.8 to 3.6] vs 2.3 [2.2 to 2.8]; p =0.024). 7/16 patients (43.8%) had Gastric Alimetry tests suggestive of gut-brain axis contributions, and without myoelectrical dysfunction. Increasing Principal Gastric Frequency deviation, and decreasing Rhythm Index were associated with symptom severity (r>0.40, p<0.05). ConclusionA significant number of patients with persistent post-fundoplication symptoms display abnormal gastric function on Gastric Alimetry testing, which correlates with symptom severity. These findings advance the pathophysiological understanding of post-fundoplication disorders which may inform diagnosis and patient selection for medical therapy and revisional surgery.

Type 2 Diabetes Mellitus (T2DM) is increasingly prevalent and significantly impacts patients lives. However, the phenotypic and genetic heterogeneity of the preclinical stage of T2DM, along with the subsequent effects on various clinical outcomes, remain unclear, impeding progress in disease screening and prevention. To address this gap, we employed a robust machine learning algorithm (Subtype and Stage Inference, SuStaIn) with cross-sectional clinical data from the UK Biobank (20,305 preclinical-T2DM participants and 20,305 controls) to identify underlying subtypes and their progression trajectories for preclinical-T2DM. Our analysis revealed one subtype distinguished by elevated circulating leptin levels and decreased leptin receptor levels, coupled with increased BMI, diminished lipid metabolism, and heightened susceptibility to psychiatric conditions such as anxiety disorder, depression disorder, and bipolar disorder. Conversely, individuals in the second subtype manifested typical abnormalities in glucose metabolism, including rising glucose and HbA1c levels, with observed correlations with neurodegenerative disorders. Over ten-year follow-up observations of these individuals reveal differential deterioration in brain and heart organs, and statistically significant difference in disease risk and clinical outcomes between the two subtypes. Our findings indicate a heterogenous pathobiological basis underlying the progression of preclinical-T2DM, with clinical implications for understanding human health from a multiorgan perspective, and improving disease risk screening, prediction, and prevention efforts.

Necrotizing enterocolitis (NEC), frequently resulting in sepsis, is among the leading causes of morbidity and mortality of pre-term newborns. However, diagnostic and therapeutic strategies for NEC and sepsis are still limited and controversial. In this context, there are ongoing debates regarding the application of human milk-based fortifiers (HMF) versus bovine milk-based fortifiers (BMF), but robust evidence is lacking. Systematic reviews and meta-analyses are expected to provide the highest level of evidence, but they are time-consuming and resource-intensive and are at risk of potential bias and subjectivity. The rapidly progressing large language model (LLM) artificial intelligence (AI) tools thus emerge as a promising complementary methodology for systematic review and meta-analysis. We conceptualized a cross-LLM AI platform meta-research and evidence synthesis workflow, leveraging 3 representative state-of-the-art platforms, ChatGPT, Claude and Manus AI. We analyzed 3371 PubMed-indexed publications. 3 platforms reported highly concordant findings. We found that prior systematic reviews and meta-analyses generally reported mixed findings comparing HMF versus BMF. Our LLM AI-assisted meta-research and evidence synthesis found non-inferiority of BMF to HMF for NEC and sepsis outcomes. Here, we present an unbiased direct head-to-head comparison between HMF and BMF in the context of NEC and sepsis. Our analyses also represent a proof-of-concept example for LLM AI-assisted meta-research and evidence synthesis, supporting the integration of LLM AI methodologies into evidence-based medicine and digital health.

Influenza remains a significant threat to human and animal health. Assessing serological protection against influenza has relied upon haemagglutinin inhibition assays, which are used to gauge existing immune landscapes, seasonal vaccine decisions and in systems vaccinology studies. Here, we adapt our high-throughput live virus microneutralisation assay for SARS-CoV-2, benchmark against haemagglutinin inhibition assays, and report serological vaccine responsiveness in a cohort of older (>65yo) community dwelling adults (n=73), after the adjuvanted 2021-22 Northern Hemisphere quadrivalent vaccine. We performed both assays against all four viruses represented in the vaccine (A/Cambodia/H3N2/2020, A/H1pdm/Victoria/2570/2019, B/Yamagata/Phuket/2013, BVIC/Washington/02/201), using sera drawn on days 0 [range: d-28 to d0], 7 [d6-10] and 182 [d161-196] with respect to vaccination. We found population-level concordance between the two assays (Spearmans correlation coefficient range 0.48-0.88; all P[&le;]1.4 x 10-5). The improved granularity of microneutralisation was better able to estimate fold-changes of responses, and quantify the inhibitory effect of pre-existing antibody. Our high-throughput method offers an alternative approach to assess influenza-specific serological responses with improved resolution.

BackgroundLimited data are available on the diagnostic accuracy of blood RNA biomarker signatures for extrapulmonary TB (EPTB). We addressed this question among people investigated for TB lymphadenitis and TB pericarditis, in Cape Town, South Africa. MethodsWe enrolled 440 consecutive adults referred to a hospital for invasive sampling for presumptive TB lymphadenitis (n=300) or presumptive TB pericarditis (n=140). Samples from the site of disease underwent culture and/or molecular testing for Mycobacterium tuberculosis complex (Mtb). Discrimination of patients with and without TB defined by microbiology or cytology reference standards was evaluated using seven previously reported blood RNA signatures by area under the receiver-operating characteristic curve (AUROC) and sensitivity/specificity at predefined thresholds, benchmarked against blood C-reactive protein (CRP) and the World Health Organization (WHO) target product profile (TPP) for a TB triage test. Decision curve analysis (DCA) was used to evaluate the clinical utility of the best performing blood RNA signature and CRP. ResultsData from 374 patients for whom results were available from at least one microbiological test from the site of disease, and blood CRP and RNA measurements, were included. Using microbiological results as the reference standard in the primary analysis (N=204 with TB), performance was similar across lymphadenitis and pericarditis patients. In the pooled analysis of both cohorts, all RNA signatures had comparable discrimination with AUROC point estimates ranging 0.77-0.82, superior to that of CRP (0.61, 95% confidence interval 0.56-0.67). The best performing signature (Roe3) achieved an AUROC of 0.82 (0.77-0.86). At a predefined threshold of 2 standard deviations (Z2) above the mean of a healthy reference control group, this signature achieved 78% (72-83%) sensitivity and 69% (62-75%) specificity. In this setting, DCA revealed that Roe3 offered greater net benefit than other approaches for services aiming to reduce the number needed to investigate with confirmatory testing to <4 to identify each case of TB. InterpretationRNA biomarkers show better accuracy and clinical utility than CRP to trigger confirmatory TB testing in patients with TB lymphadenitis and TB pericarditis, but still fall short of the WHO TPP for TB triage tests. FundingSouth African MRC, EDCTP2, NIH/NIAID, Wellcome Trust, NIHR, Royal College of Physicians London. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSBlood RNA biomarker signatures and CRP measurements have emerged as potential triage tests for TB, but evidence is mostly limited to their performance in pulmonary TB. Microbiological diagnosis of extrapulmonary TB (EPTB) is made challenging by the need for invasive sampling to obtain tissue from the site of disease. This is compounded by lower sensitivity of confirmatory molecular tests for EPTB compared to their performance in pulmonary disease. We performed a systematic review of diagnostic accuracy studies of blood RNA biomarkers or CRP measurements for EPTB, which could mitigate the need for site-of-disease sampling for the diagnosis of TB. We searched PubMed up to 1st August 2023, using the following criteria: "extrapulmonary [title/abstract] AND tuberculosis [title/abstract] AND biomarker [title/abstract]". Although extrapulmonary TB was included in several studies, none focused specifically on EPTB or included an adequate number of EPTB cases to provide precise estimates of test accuracy. Added value of this studyTo the best of our knowledge, we report the first diagnostic accuracy study of blood RNA biomarkers and CRP for TB among people with EPTB syndromes. We examined the performance of seven previously identified blood RNA biomarkers as triage tests for TB lymphadenitis and TB pericarditis compared to a microbiology reference standard among people referred to hospital for invasive sampling in a high TB and HIV prevalence setting. Multiple blood RNA biomarkers showed comparable diagnostic accuracy to that previously reported for pulmonary TB in both EPTB disease cohorts, irrespective of HIV status. All seven blood RNA biomarkers showed superior diagnostic accuracy to CRP for both lymphadenitis and pericarditis, but failed to meet the combined >90% sensitivity and >70% specificity recommended for a blood-based diagnostic triage test by WHO. Nonetheless, in decision curve analysis, an approach of using the best performing blood RNA biomarker to trigger confirmatory microbiological testing showed superior clinical utility in clinical services seeking to reduce the number needed to test (using invasive confirmatory testing) to less than 4 for each EPTB case detected. If acceptable to undertake invasive testing in more than 4 people for each true case detected, then a test-all approach will provide greater net benefit in this TB/HIV hyperendemic setting. Implications of all the available evidenceBlood RNA biomarkers show some potential as diagnostic triage tests for TB lymphadenitis and TB pericarditis, but do not provide the level of accuracy for blood-based triage tests recommended by WHO for community-based tests. CRP has inferior diagnostic accuracy to blood RNA biomarkers and cannot be recommended for diagnostic triage among people with EPTB syndromes referred for invasive sampling.

Prolonged fasting may benefit metabolic health, but data in healthy individuals remain limited. We performed a randomized, waitlist-controlled study (LEANER study), with 38 healthy participants completing a 5-day-fasting intervention with 12-week follow-up. Fasting acutely lowered body mass index (BMI), via fat mass loss. These changes partially persisted at follow-up. Fasting altered the gut microbiome composition and induced metabolite shifts in plasma and feces. Changes to gut microbiome alpha diversity after fasting correlated with baseline microbiome diversity. Long-term BMI response at follow-up could be predicted through machine learning (ML) using baseline microbiome and clinical data, highlighting an unknown Faecalibacterium sp., Oscillibacter sp. 50_27, LDL cholesterol, and systolic blood pressure as key predictors. This ML model was validated in independent patient cohorts with metabolic syndrome and multiple sclerosis. These findings support prolonged fasting as an effective metabolic intervention and demonstrate that individual responses to fasting interventions can be predicted using pre-intervention features. Trial registration: ClinicalTrials.gov, NCT04452916. Registered on June 26, 2020

Self-repairs, in-utterance revisions in which a speaker abandons and reformulates their speech, are a promising interpretable marker for speech-based cognitive screening. Detecting them automatically is difficult because a self-repair is defined by its relationship to surrounding speech rather than by fixed lexical cues. On the DementiaBank ADReSS corpus, we compared the capability of generative LLMs under a five-condition prompt ablation against a fine-tuned DistilBERT token classifier at detecting self-repairs. GPT-5 performed best (test F1 = 0.73) and was largely insensitive to prompt design, whereas the LLaMA (open-weight alternative) was both weaker and far more prompt-sensitive (test F1 = 0.47). DistilBERT, nearly 100 times smaller, matched the open-weight LLM at a fraction of the computational cost. These results suggest that a locally deployable encoder, given sufficient in-domain annotation, is a more plausible route to clinical self-repair detection than scaling model size or prompt complexity.

BackgroundNeutralising antibody titres are widely used as key immunogenicity endpoints in Ebola virus (EBOV) vaccine and monoclonal antibody clinical trials. However, direct comparison of results across studies remains challenging due to the use of heterogeneous neutralisation platforms, ranging from pseudotyped viruses to live EBOV assays. These limitations restrict assay standardisation, validation, scalability, and compliance with good clinical laboratory practice (GCLP), particularly in outbreak-prone and resource-limited settings. There is an unmet need for neutralisation assays that combine biological authenticity with clinical-trial compatibility. MethodsWe developed and optimised a fluorescence-based microneutralisation assay using a biologically contained EBOV lacking the essential VP30 gene (EBOV{Delta}VP30), enabling multi-cycle viral replication under containment level 2 conditions. Using a defined panel of serum samples from Ebola virus disease survivors and EBOV-negative controls, we benchmarked EBOV{Delta}VP30 neutralisation titres against previously generated data obtained with wild-type EBOV and pseudotyped virus platforms. Assay performance was evaluated in terms of sensitivity, reproducibility, discrimination between positive and negative samples, and correlation with live virus neutralisation. Calibration was performed using the WHO International Standard for anti-EBOV immunoglobulin. FindingsThe EBOV{Delta}VP30 microneutralisation assay robustly distinguished EBOV survivor sera from negative controls (p < 0{middle dot}0001) and demonstrated a strong correlation with live EBOV neutralisation titres (Spearman {rho} = 0{middle dot}8725). This correlation exceeded that observed for HIV-1-based pseudotyped assays and for the vesicular stomatitis virus-based platforms. The fluorescence-based read-out showed comparable sensitivity to conventional immunostaining, supporting its suitability for high-throughput and standardised implementation. Importantly, assay conditions were compatible with BSL-2 laboratories and GCLP-aligned workflows. InterpretationBiologically contained EBOV{Delta}VP30 provides a clinically relevant and scalable alternative to existing neutralisation platforms, bridging the gap between pseudotyped assays and wild-type virus testing. By improving biological relevance while maintaining accessibility and standardisation, this assay has the potential to enhance comparability of immunogenicity data across EBOV vaccine and therapeutic antibody (pre-)clinical trials, aligning with global outbreak preparedness and trial harmonisation objectives. FundingStated in acknowledgement section of manuscript. Research in contextO_ST_ABSEvidence before the studyC_ST_ABSBefore starting this study, we reviewed published work on how neutralising antibodies against Ebola virus are measured in vaccine and monoclonal antibody research. We searched PubMed, Web of Science, and reference lists of key review papers for studies published up to mid-2025, without restricting by language. Search terms included "Ebola virus", "neutralising antibodies", "neutralisation assay", "pseudovirus", "live virus", and "clinical trials". We focused on studies describing neutralisation tests using wild-type Ebola virus as well as commonly used pseudotyped virus systems. From this body of evidence, neutralisation assays using wild-type Ebola virus are considered the most biologically relevant but can only be performed in biosafety level 4 laboratories. This limits their availability, scalability, and use in clinical trials. Pseudotyped virus assays can be performed under lower biosafety conditions and are widely used, but multiple studies have reported variable performance and inconsistent agreement with live virus results. Although biologically contained Ebola viruses have been developed and used in laboratory research, their application as neutralisation assays and their direct comparison with both live virus and pseudotyped systems using the same human serum samples had not been systematically studied. As a result, it remained unclear whether such systems could support reliable immunogenicity assessment in clinical trials. Added value of this studyThis study shows that a biologically contained Ebola virus lacking the VP30 gene can be used to measure neutralising antibodies in a robust and scalable way under biosafety level 2 conditions. By directly comparing this system with wild-type Ebola virus and widely used pseudotyped assays using the same set of human serum samples, we demonstrate that neutralisation results obtained with the biologically contained virus closely align with those of the wild-type virus reference assay. The assay reliably distinguishes samples from Ebola survivors and uninfected individuals and can be read using different detection methods, making it compatible with GCLP-aligned workflows and suitable for further qualification and validation in support of clinical development. This work provides clear evidence that biologically contained Ebola virus can combine biological relevance with practical usability. Implications of all the available evidenceTogether with existing evidence, our findings indicate that biologically contained Ebola virus offers a valuable new option for measuring neutralising antibodies in vaccine and monoclonal antibody clinical trials. By reducing reliance on high-containment laboratories while preserving key features of authentic virus infection, this approach can improve the consistency and comparability of immunogenicity data across studies and sites. Broader use of such assays could support better decision-making during clinical development and strengthen outbreak preparedness. More generally, this work highlights how biologically contained viruses can help advance research licensure of medical countermeasures for high-consequence pathogens in ways that are directly relevant to human health.

We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease. GAGs were analyzed using the MIRAM(R) Free Glycosaminoglycan Kit with ultra-high-performance liquid chromatography and electrospray ionization triple-quadrupole mass spectrometry. We detected two GAGome features, 0S chondroitin sulfate (CS) and 4S CS, with cancer-specific changes. Based on the observed GAGome changes, we devised a model to predict lung cancer. The model, named the GAGome score, could detect lung cancer with 41.2% sensitivity (95% CI: 9.2-54.2%) at 96.4% specificity (CI: 95.2-100.0%, n=113). Furthermore, we found that the GAGome score, when combined with a cfDNA test, could increase the sensitivity for lung cancer from 42.6% (95% CI: 31.7-60.6%, cfDNA alone) to 70.5% (CI: 57.4 - 81.5%) at 95% specificity (CI: 75.1-100%, n=74). Notably, the combined GAGome and cfDNA testing improved the sensitivity, especially in early stages, relative to the cfDNA alone. Our findings show that plasma GAGome profiles can enhance cfDNA testing performance, highlighting the applicability of a multiomics approach in lung cancer diagnostics.

The rising threat of pandemic viruses, such as SARS-CoV-2, requires development of new preclinical discovery platforms that can more rapidly identify therapeutics that are active in vitro and also translate in vivo. Here we show that human organ-on-a-chip (Organ Chip) microfluidic culture devices lined by highly differentiated human primary lung airway epithelium and endothelium can be used to model virus entry, replication, strain-dependent virulence, host cytokine production, and recruitment of circulating immune cells in response to infection by respiratory viruses with great pandemic potential. We provide a first demonstration of drug repurposing by using oseltamivir in influenza A virus-infected organ chip cultures and show that co-administration of the approved anticoagulant drug, nafamostat, can double oseltamivirs therapeutic time window. With the emergence of the COVID-19 pandemic, the Airway Chips were used to assess the inhibitory activities of approved drugs that showed inhibition in traditional cell culture assays only to find that most failed when tested in the Organ Chip platform. When administered in human Airway Chips under flow at a clinically relevant dose, one drug - amodiaquine - significantly inhibited infection by a pseudotyped SARS-CoV-2 virus. Proof of concept was provided by showing that amodiaquine and its active metabolite (desethylamodiaquine) also significantly reduce viral load in both direct infection and animal-to-animal transmission models of native SARS-CoV-2 infection in hamsters. These data highlight the value of Organ Chip technology as a more stringent and physiologically relevant platform for drug repurposing, and suggest that amodiaquine should be considered for future clinical testing.

Brain disorders, including neurodegenerative diseases and mental illnesses, are often difficult to diagnose and study due to clinical and pathological heterogeneity, overlap in clinical manifestations between disorders, and frequent comorbidities, hampering drug development and fundamental research. Hence, there is a clear need for data-driven approaches to disentangle these complex disorders. Here, we established a computational pipeline to process clinical summaries from donors with a wide range of brain disorders that were neuropathologically diagnosed by the Netherlands Brain Bank. First, we identified and defined 90 cross-disorder signs and symptoms within cognitive, motor, sensory, psychiatric, and general domains. Second, we trained and optimized natural language processing (NLP) models to identify these signs and symptoms in individual sentences of the extensive clinical summaries from donors of the NBB, resulting in temporal disease trajectories. Third, we studied the temporal manifestation and survival profiles across rare and complex dementias, alpha-synucleinopathies, frontotemporal dementia subtypes, and mental illnesses, giving new insight into how symptomatology differs in manifestation and temporal profiles across brain disorders. Lastly, we trained a recurrent neural network to predict the Neuropathological Diagnosis. Taken together, this integrated approach resulted in a highly unique resource that can facilitate research into cross-disorder symptomatology.