Food & Function

Abstract Background and Objective: To investigate the efficacy of a new, proprietary high-resistance potato starch as a prebiotic in comparison to inulin and a control. Methods: In this prospective study, an intervention of 9 g of resistant potato starch (RPS, Potatodaat), inulin, or accessible corn starch was given to participants with mild to moderate indigestion for 30 days. Short chain fatty acid (SCFA) levels, changes to the gut microbiome, and changes in clinical symptoms of indigestion were assessed as primary outcomes. Results: Subjects in the RPS (n = 22), inulin (n = 23), and accessible corn starch (n = 22) groups demonstrated similarity in age, sex, and baseline parameters. At 30 days, the groups experienced 21.1%, 6.29%, and 9.15% increases in stool butyrate, respectively. Clinical symptoms like indigestion and flatulence showed greater improvement with the use of RPS compared to the other two groups. Conclusion: Consumption of high resistance potato starch helps improve stool SCFA levels along with clinical symptoms related to digestion, showing its better prebiotic potential as compared to inulin and accessible corn starch. The new high-resistance potato starch can be considered an effective replacement for inulin.

Animal cell-cultured ingredients are entering the EU and UK pet food markets under frameworks that do not require pre-market, ingredient-level safety assessments, creating an ethical need for transparent safety disclosure. We present the first public safety dossier for this sector, describing the proprietary mouse embryonic stem cell line PE25 and its derived, non-viable cellular and conditioned media ingredient produced in food and feed-grade media. PE25 characterization confirmed Mus musculus identity, sterility, absence of mycoplasma and replication-competent retroviruses, and stable growth. Doxorubicin-induced p53 stress testing, CD44/BMI1 profiling, and soft agar assays showed no cancer-like traits and a non-tumorigenic profile; the final ingredient contains no viable cells. Independent OECD TG 471 and 487 assays confirmed non-genotoxicity. Heavy metals, biogenic amines, solvents, and chemical residues were below regulatory limits. Given process variability, we recommend case-by-case safety evaluation and propose this dossier as a model for responsible commercialization.

Background: Short-chain fatty acids (SCFAs) are microbial metabolites with immunoregulatory properties. Human milk contains SCFAs which have been proposed as potential modulators of infant immune development. We aimed to examine associations between human milk SCFA concentrations and infant allergic disease outcomes in a high-risk cohort of infants of atopic mothers. Methods: SCFAs were measured by targeted liquid chromatography-mass spectrometry in human milk samples collected at 3 and 6 months postpartum from atopic mothers enrolled in the Infant Fish Oil Supplementation (IFOS) Study (n=147). Associations between milk SCFA concentrations and early childhood allergic disease outcomes (atopic dermatitis, food allergy, allergic rhinitis, and allergen sensitisation at 1 and 2-3 years) were examined using logistic regression. Results: Human milk SCFA concentrations were broadly stable between 3 and 6 months postpartum, except for acetate which was significantly elevated at 6 months. No significant associations were observed between human milk SCFA concentrations and any allergic disease outcome after correction for multiple comparisons (all p>0.05). Conclusions: Human milk SCFA concentrations are not associated with allergic disease outcomes up to 4 years of age. These findings suggest that oral SCFA exposure via human milk is insufficient to reduce infant allergy risk, and that gut SCFA production may be a more relevant target for future allergy prevention research.

This review evaluates the biological pathways linking soft drink consumption with the risk of several cancers within the framework of the Global Cancer Update Programme (CUP Global). Soft drink consumption has been associated with increased risk of multiple cancers, and glucose or insulin dysregulation has been proposed as a potential underlying mechanism. We applied a three-stage framework. In the first stage, we identified insulin sensitivity as the key biological process potentially linking soft drink consumption (sugar-sweetened or artificially sweetened) to cancer risk, with glucose-related and insulin-related biomarkers as potential intermediate phenotypes, using a combination of expert knowledge and a web-based text mining tool. In the second stage, we conducted targeted PubMed searches to identify studies examining associations between consumption of soft drinks and these intermediate phenotypes (IPs) and between these IPs and the risk of several cancers in adult humans. In the third stage, the evidence was evaluated by the Expert Committee on Cancer Mechanisms (MEC), who assessed the strength of the evidence for these associations. The MEC concluded that there was weak evidence supporting a role of glucose or insulin-related processes as a potential mechanistic pathway linking the consumption of sugar-sweetened or artificially sweetened beverages to the risk of various cancers evaluated.

Efficiently summarizing dietary records at scale remains a persistent bottleneck in nutritional epidemiology. We present FoodScribe, which translates free-text meal descriptions into quantitative nutrient profiles by combining ingredient parsing with nutrient retrieval by querying the USDA FoodData Central (FDC) database. Benchmarked using three LLM providers using Nutribench dataset, FoodScribe completed annotation of 3,807 meal descriptions in 2.5 hours, a task otherwise requiring substantial manual effort from trained nutritionists. FoodScribe achieved accuracy across macronutrient estimation (F1=0.79-0.89), with models performing better for protein than fat estimation. Application to a Mediterranean diet intervention cohort indicated dietary shifts consistent with the intervention pattern based on model-derived estimates. Integration with metabolomics data suggested that fiber and vegetable intake were positively associated with a fecal metabolite cluster.

Glycation, a non-enzymatic reaction occurring between sugars and biological macromolecules, plays a critical role in ageing and disease pathogenesis. Methylglyoxal (MG) is a highly reactive -oxoaldehyde that leads to the formation of endogenous advanced glycation end products (AGEs). These AGEs are associated with diabetes and many other diseases, including neurodegeneration and cancer. This is often through interactions with the receptor for advanced glycation end products (RAGE). Inhibition of glycation/AGEs formation using natural products to target cancer is an area of recent interest. In vitro AGEs formation was observed by browning of samples, increased fluorescence, and carbonyl stress. MG induced changes in the structure of BSA were analysed using electrophoresis, spectroscopy, TEM, AFM, DLS, and CD spectroscopy. Our results show that AGEs form random structures, oligomeric aggregates, and {beta}-sheets. Thioflavin T and Congo red staining further validated these findings. Galangin and Caffeic acid demonstrated significant antiglycation activity, suppressing AGEs formation in vitro. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/737425v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@113b391org.highwire.dtl.DTLVardef@7208a1org.highwire.dtl.DTLVardef@94c2e1org.highwire.dtl.DTLVardef@867b85_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMethylglyoxal-induced Advanced Glycation End Products were prepared in vitro C_LIO_LIMethylglyoxal -induced structural modifications in BSA C_LIO_LIAGEs were characterised using various parameters C_LIO_LIBoth fluorescent and non-fluorescent AGEs were formed. C_LIO_LIPhytochemical treatment induced inhibition of AGEs formation C_LI

Consumer-grade bioelectrical impedance analyzers have become widely used for body composition assessment, yet their accuracy varies considerably across devices. The Hume Pod is a popular consumer-grade analyzer marketed as being highly accurate, but independent validation is lacking. The purpose of this study was to evaluate the reliability and validity of the Hume Pod relative to both a four-compartment (4C) model and dual-energy X-ray absorptiometry (DXA). Sixty-seven adults (42 females, 25 males; age 37.2 +/- 13.5 years, body mass index: 24.6 +/- 4.9 kg/m2, body fat percentage [BF%]: 26.4 +/- 10.2%) completed duplicate Hume Pod assessments alongside DXA and 4C evaluations. Reliability was evaluated using the technical error of measurement (TEM) and intraclass correlation coefficients (ICC). Validity was assessed using equivalence testing, Lin's concordance correlation coefficient (CCC), standard error of the estimate (SEE), Bland-Altman analysis, and additional tests. The Hume Pod demonstrated strong reliability, with ICCs >/= 0.993 and TEMs of 0.8% for BF% and 0.6 kg for fat mass (FM) and fat-free mass (FFM). Relative to the 4C model, BF%, FM, and FFM estimates were statistically equivalent (all p<0.05), with strong agreement (CCC=0.95-0.98), low SEE values (3.1%, 2.3 kg, and 2.2 kg, respectively), moderate limits of agreement (+/-6.1%, +/-4.5 kg, and +/-4.5 kg), and no proportional bias. Compared with DXA, generally strong agreement was also observed. These findings indicate that the Hume Pod demonstrates strong reliability and validity compared with laboratory reference methods for body composition estimation, supporting its potential use as a consumer body composition assessment.

N-glycolylneuraminic acid (Neu5Gc) has been repeatedly associated with human cancer, but reliable detection has remained elusive, generating controversy regarding its presence in human samples. To address this, matrix-matched calibration curves, which have been pioneered in proteomics and metabolomics for assessing changes in complex mixtures, were measured of released N-glycans at four orders of magnitude dynamic range in defined mixtures, systematically benchmarking Neu5Gc-containing N-glycan detection across multiple LC-MS platforms and sites. Orthogonally, the gold-standard analytical method, consisting of fluorescence detection of labelled monosaccharides separated by LC, was applied to the same samples, yielding absolute concentrations of Neu5Gc. LC-MS demonstrated an extended detection range of three or more orders of magnitude while retaining intact N-glycan measurement, improving assay specificity and enabling detection of the variety of Neu5Gc-bearing N-glycans. By combining orthogonal dimensions of evidence, including chromatographic separation, isotopic distribution matching, and composition-confirming MS/MS, LC-MS confidently resolved Neu5Gc signals from noise, even at low abundance. In comparison, DMB-LC-FLR was limited to two orders of magnitude dynamic range, insufficient for detection of Neu5Gc in commercially available pooled human sera. These findings strongly support that DMB-LC-FLR assay specificity and sensitivity are insufficient for Neu5Gc detection in human samples due to noise overwhelming the Neu5Gc signal. By establishing a reusable benchmarking framework for future glycomic studies, we aim to use LC-MS to improve the measurement of Neu5Gc in clinical samples.

Chronic kidney disease and heart failure disproportionately burden populations of African ancestry, yet Mendelian randomisation (MR) studies of the causal relationship between kidney function and heart failure subtypes have been conducted exclusively in European ancestry populations. We performed a forward two-sample MR analysis to evaluate the causal effect of genetically predicted estimated glomerular filtration rate (eGFR) on heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) in individuals of African ancestry. Genetic instruments were selected from an African ancestry eGFR genome-wide association study (N = 67,943) at genome-wide significance, with linkage disequilibrium clumping using an African ancestry reference panel. Heart failure subtype summary statistics were obtained from the Million Veteran Program (HFpEF: 5,379 cases / 113,041 controls; HFrEF: 9,104 cases / 109,632 controls). Six independent SNPs (F-statistics 30.5 &#8211 107.3; R&#178 = 0.62%) were retained as instruments. The primary inverse-variance weighted analysis provided no evidence of a causal effect of eGFR on HFpEF (OR 0.92, 95% CI 0.80 &#8211 1.06, p = 0.248) or HFrEF (OR 0.98, 95% CI 0.78 &#8211 1.23, p = 0.878). Sensitivity analyses were directionally consistent. There was no evidence of heterogeneity or directional pleiotropy. Minimum detectable effects at 80% power were OR 1.28 for HFpEF and OR 1.22 for HFrEF. These null findings should be interpreted as inconclusive given current power constraints; larger ancestry-matched studies are needed.

Background with objective: Detecting EGFR mutations is critical for treating lung adenocarcinoma with highly effective targeted therapies. However, standard genetic testing is expensive, complex, and often unavailable in resource-limited settings like Bangladesh. Because elevated serum CEA has been linked to these genetic alterations, it could serve as an accessible screening tool. This study aims to evaluate the association between serum CEA levels and EGFR mutation status to determine if routine CEA testing can reliably predict these mutations and guide treatment. Methodology: In this cross-sectional analytical study, we recruited 58 patients with histologically confirmed treatment naive lung adenocarcinoma. The presence of EGFR mutations in the ctDNA was determined via ARMS (Amplification Refractory Mutation System) PCR. Patient data was statistically analyzed to assess the diagnostic correlation between serum CEA levels and the presence of EGFR mutations. Result: The overall EGFR mutation rate was 43.1% with exon 19 deletion (48%) and exon 21 mutations (44%) were the predominant types. Median serum CEA levels were significantly higher in patients with EGFR mutations compared to wild-type cases (14.6 ng/ml vs 2.8 ng/ml, p<0.001). A multivariate analysis revealed a 14% increased likelihood of an EGFR mutation for 1 ng/ml rise in serum CEA. Furthermore, serum CEA showed strong diagnostic accuracy for ctDNA samples at a 6.39 ng/ml cut-off (AUC 0.82, sensitivity 68.0%, specificity 84.8%). Conclusion: Serum CEA is a valuable, cost-effective, and non-invasive biomarker demonstrating significantly higher levels and strong diagnostic accuracy in EGFR-mutated lung adenocarcinoma compared to wild-type cases.

Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settings. Nurses increasingly make autonomous clinical decisions using telephone, video, and online/digital systems, often with limited sensory information and under conditions of uncertainty. However, empirical understanding of how nurses make clinical decisions via remote reviews remains limited. Aim: To explore and understand how registered nurses (RNs) make clinical decisions about patient care via remote reviews. Methods: A convergent mixed-methods design was employed. Quantitative data (analytic quantitative sample N=53) were collected using validated questionnaires that measured decision-making processes, physician-nurse collaboration, decision-making stress, and perceived decision-making ability. Qualitative data (N=23) were generated through semi-structured interviews. Data collection took place between October 2024 and April 2025. Quantitative data were analysed using descriptive statistics, correlation, and multiple regression. Qualitative data were analysed using framework analysis. Integration was achieved through pillar-building and theory-driven synthesis and illustrated by joint display tables. Results: Most nurses demonstrated a flexible decision-making style, integrating analytical and intuitive reasoning. Both analytical and intuitive processes were positively associated with perceived decision-making ability. Physician-nurse collaboration emerged as a strong predictor of decision-making confidence, while decision-related stress was not a significant predictor. Qualitative findings identified three themes: characteristics of remote review; making adaptive decisions shaped by both internal and external constraints and enablers; and external influencing factors. The integrated findings informed a theory-informed ICE framework to illustrate how nurses make clinical decisions via remote reviews. Conclusion: Remote clinical decision-making is a dynamic cognitive-environmental process rather than a purely individual cognitive act. The ICE framework conceptualises this interaction, extending existing decision-making theories to digitally mediated care. Impact: Understanding remote decision-making supports training design, clinical governance, and the development of Artificial Intelligence-enhanced decision-support tools grounded in ecological bounded rationality. Patient or Public Contribution: Patient and public representatives contributed to stakeholder discussions that informed the development of the interview topic guide and the theoretical model. Patients or members of the public were not involved in recruitment, data collection, analysis, interpretation of findings, or preparation of the manuscript. Keywords: clinical decision-making, remote reviews, telehealth, nursing, mixed methods, ecological bounded rationality

Background: Post-stroke dysphagia (PSD) contributes to aspiration, pneumonia, malnutrition, prolonged hospitalization and mortality. We evaluated the discrimination, validity and readiness of machine learning and data-driven prediction models for PSD-related outcomes. Methods: Following a prospectively registered protocol (PROSPERO CRD420261419259), we searched PubMed/MEDLINE, Embase, Web of Science Core Collection, CINAHL and CENTRAL from inception through June 7, 2026. Eligible studies developed or validated multivariable prediction models for PSD-related outcomes in adults with stroke. We used PROBAST and PROBAST+AI to assess risk of bias and applicability and TRIPOD+AI to evaluate reporting. Area under the curve (AUC) estimates were pooled on the logit scale with random-effects models. Results: Twenty-four studies were included and ten contributed to meta-analysis. Four studies predicting early or incident PSD yielded a pooled AUC of 0.94 (95% CI 0.60-0.99; I2 = 95.6%). Pooled AUCs were 0.84 (95% CI 0.71-0.92) for aspiration or penetration-aspiration and 0.89 (95% CI 0.24-1.00) for severe dysphagia. The exploratory analysis of all ten risk-prediction models produced an AUC of 0.90 (95% CI 0.80-0.95), but heterogeneity was substantial (I2 = 90.3%) and the prediction interval was 0.51-0.99. Every study had high risk of bias because of analysis-domain concerns; calibration and external validation were uncommon. Conclusions: Reported discrimination was often high, but the evidence does not establish reliable performance in care. Independent validation, calibration, complete model reporting and clinical-impact studies are needed before these models guide post-stroke swallowing care. Keywords: Post-stroke dysphagia; Stroke; Deglutition disorders; Machine learning; Clinical prediction model; Area under the curve; Meta-analysis

Introduction Parkinson's disease (PD) is a multifactorial disorder, affecting multiple neurotransmitter systems, including the cholinergic system. Cholinergic denervation is heterogeneous across patients and difficult to predict based on clinical presentation. In this study, we assessed the sensitivity of structural MRI (sMRI) and functional MRI (fMRI) to cholinergic degeneration related to PD and to cognitive functioning in PD. We compared our results to results from previously reported [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) PET imaging, which is considered the gold standard for cholinergic imaging. Methods 34 PD patients and 10 healthy controls underwent structural T1-weighted MRI. A subset of 14 patients and 9 controls also underwent resting-state fMRI. We extracted the bilateral volumes of the nucleus basalis of Meynert (NBM) from the sMRI images. Functional connectivity (FC) from the NBM to the cortex (NBM-FC) was determined using fMRI data. Principal component analysis (PCA) was applied to reduce the dimensionality of the NBM-FC images. We assessed performances for NBM-FC in distinguishing patients from controls using stepwise logistic regression. Similarly, NBM volume was used using logistic regression. Furthermore, the relation between these measures and cognitive function in several domains was investigated with (stepwise) linear regression. Leave-one-out cross validation (LOOCV) and bootstrapping was performed to assess robustness of the results. Results NBM-FC was well able to discriminate patients from controls with an AUC of 0.84 (95% CI: 0.62-1). NBM volume showed lower performance, but was still better than chance: AUC: 0.75 (95% CI: 0.57-0.93). Significant correlations were found between 1) cognition in the attentional domain and NBM-FC (r=0.63; p=.015) and 2) global cognition and NBM volume (r=0.55, p=.001). These results were inferior to those previously reported using [18F]FEOBV tracer uptake (see Chapter 6). Bootstrapping revealed that NBM volume of only the left hemisphere was stably related to PD diagnosis and global cognition in PD patients. We found that a lower NBM-FC in specific brain areas, including the fusiform gyrus, supramarginal gyrus and dorsolateral prefrontal cortex, was related to PD diagnosis. Bootstrapping revealed no stable NBM-FC pattern related to attention. Conclusion Although MRI results were slightly inferior to [18F]FEOBV PET data, MRI may provide a cheaper and more widely available alternative for cholinergic imaging. We recommend testing the utility of MRI as predictor and monitor of cholinergic treatment effect in a longitudinal study.

Cortisol, our stress hormone, exerts widespread influence on neural activity. However, its influence on the aperiodic component of the electroencephalography power spectrum remains to be investigated. Given individual differences in the capacity to cope with stress and adversity, it also remains unclear whether trait resilience moderates this relationship. Hence, the present study examined whether individual differences in trait resilience moderates the association between resting cortisol and aperiodic activity. Participants (N=145) completed various self-report questionnaires (e.g., trait resilience). Electroencephalography was recorded over a 20-minute baseline period, followed by salivary cortisol collection. The results revealed a significant moderating effect of trait resilience in the occipital scalp region. Specifically, higher cortisol concentration was associated with flatter 1/f slopes amongst individuals with low trait resilience, whereas this association was reversed amongst those with high trait resilience. Overall, our findings highlight the role of individual differences in trait resilience in shaping hypothalamic-pituitary-adrenal axis-related neural dynamics.

White matter lesion (WML) identification, assessment, and characterization using magnetic resonance imaging (MRI) are fundamental for diagnosis and monitoring of multiple sclerosis (MS). Portable ultra-low field (pULF) MRI at 64 millitesla (mT) has been shown to visualize WML with at least one dimension greater than 4 mm. An automated WML segmentation tool catered to pULF-MRI can provide standardized and accurate quantitative measurements of WML volume. In this study, we sought to investigate and compare the accuracy of machine-learning (ML) and deep-learning (DL) pULF MRI segmentation tools. Same-day paired pULF (64mT) and high-field (HF, 3T) MRI scans from 84 adults with MS or suspected-MS (mean age {+/-} SD: 48 {+/-} 13, 62 females) included T2-FLAIR and T1w images. Reference WML segmentations were manually annotated on pULF T2-FLAIR for all scans, with WML confirmed with registered HF T2-FLAIR. HF reference WML segmentations were created. Four automated segmentation methods were applied to pULF scans: Method for Inter-Modal Segmentation Analysis (MIMoSA), an ML algorithm trained on HF WML masks; WMH-SynthSeg, a convolutional neural network model with flexible segmentation capabilities across field strengths and resolution; nnU-Net, a DL algorithm trained on pULF reference WML masks; and Pseudo-Label Assisted nnU-Net (PLAn), a DL algorithm pre-trained on HF reference WML masks and refined with 64mT reference WML masks. Two models were trained with nnU-Net, one using T2-FLAIR images only (nnU-Net-FL) and one using T1w and T2-FLAIR images (nnU-Net-FL/T1). The same was done with PLAn, creating PLAn-FL and PLAn-FL/T1. The six automated WML segmentation outputs were compared to the manual segmentations to determine Dice Similarity Coefficient (DSC) scores. Associations of WML volume estimates with clinical measures were investigated. DSC scores with pULF reference WML masks from PLAn-FL (DSC mean {+/-} SD: 0.50 {+/-} 0.24) outperformed MIMoSA (0.24 {+/-} 0.20, p < 0.0001), WMH-SynthSeg (0.30 {+/-} 0.18, p < 0.0001), nnU-Net-FL (0.41 {+/-} 0.24, p < 0.0001), and nnU-Net-FL/T1 (0.41 {+/-} 0.26, p = 0.0004). Worse Expanded Disability Status Scale (EDSS) and Scripps Neurologic Rating Scale (SNRS) scores were correlated with higher WML volumes in the pULF and HF reference masks. They were also correlated with WML volumes derived from WHM-SynthSeg, nnU-Net-FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1, but not MIMoSA. After adjusting for age, WHM-SynthSeg, nnU-Net FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1 had significant associations with EDSS and SNRS scores. nnU-Net and PLAn performed best in segmenting WML on pULF-MRI at 64 mT, providing accurate quantitative estimates of WML burden. Moreover, WML volumes estimated by these algorithms were associated with clinical measures of disability, underscoring their utility for reflecting clinical and radiological disease severity. Given pULF-MRI's mobility and lower cost, these findings highlight its relevance in clinical trials, particularly in involving more participants who face logistical constraints and barriers.

Abstract Objective: To synthesize the available evidence on the association between SARS-CoV-2-related microvascular thrombosis and acute kidney injury (AKI), with emphasis on renal outcomes, mortality, and renal replacement therapy requirements. Methods: This systematic review followed the PRISMA 2020 statement and was prospectively registered in PROSPERO (CRD420251132701). PubMed/MEDLINE, Scopus, and Embase were searched for systematic reviews, including meta-analyses, and umbrella reviews investigating the association between SARS-CoV-2-related microvascular thrombosis and acute kidney injury. Two reviewers independently performed study selection, data extraction, and methodological quality assessment using AMSTAR-2 and ROBIS. Evidence was synthesized through a structured narrative synthesis supported by quantitative data extracted from the included reviews. Results: Six evidence syntheses evaluating kidney involvement, thrombotic events, and microvascular mechanisms in COVID-19 were included. AKI incidence was 9.2% (95%CI 4.6-13.9) among hospitalized patients and 32.6% (95%CI 8.5-56.6) among critically ill patients. In children with multisystem inflammatory syndrome associated with SARS-CoV-2, AKI incidence was 20% (95%CI 14-28). Microvascular or thrombotic events were associated with adverse renal outcomes (OR 2.14; 95%CI 1.32-3.48). AKI was associated with increased mortality (OR 4.68; 95%CI 1.06-20.70) and greater likelihood of renal replacement therapy requirement (OR 2.87; 95%CI 1.45-5.68). The certainty of evidence ranged from moderate to high for the principal outcomes. Conclusion: Current evidence supports an important association between microvascular thrombotic injury and COVID-19-associated AKI. These findings reinforce the relevance of endothelial dysfunction and thromboinflammatory pathways in kidney involvement during COVID-19 and highlight the need for early renal monitoring, risk stratification, and kidney-protective strategies in high-risk patients. Keywords: COVID-19; Acute Kidney Injury; Microvascular Thrombosis; SARS-CoV-2; Renal Replacement Therapy; Systematic Review

Magnetic Resonance Imaging (MRI) derived brain age varies substantially between individuals, but it remains unclear whether early deviations from normal brain ageing precede future cognitive decline and whether they provide predictive value beyond conventional MRI measures. Here, we investigated whether MRI-derived brain age gap estimation (BrainAGE) identifies early structural brain ageing differences among cognitively normal individuals who later develop mild cognitive impairment (MCI) or dementia. We analysed longitudinal structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and replicated the main findings in the population-based Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE). Individuals who later converted to MCI or dementia had higher BrainAGE values several years before diagnosis and, in ADNI, showed steeper longitudinal increases than stable individuals. Elevated BrainAGE values were also associated with increased risk of future conversion to MCI in cognitively healthy individuals and faster subsequent memory decline. Cross-sectional differences and the association between BrainAGE and risk of future conversion were replicated in OSTPRE. Importantly, adding BrainAGE to models including demographic, APOE4, cognitive, and MRI-derived measures consistently improved prediction of future cognitive outcomes, with the greatest benefit observed for individuals who converted after longer follow-up. These findings show that structural brain ageing begins to diverge years before the onset of MCI. BrainAGE captures this early divergence, providing complementary information beyond conventional structural MRI measures that may improve the early identification of cognitively normal individuals at increased risk of future cognitive decline when integrated with other biomarkers.

Introduction Hospitalised community-acquired pneumonia (CAP) is heterogeneous in aetiology, severity, and outcome. Phenotyping and endotyping approaches offer potential to stratify patients biologically and guide targeted therapy, but require well-characterised cohorts with linked biosamples. We describe the PARIS (Pneumonia: Acute Respiratory Infection +/- Sepsis) study: a prospective observational cohort of hospitalised patients with pneumonia, designed to characterise functional outcomes and to provide a biobank for translational immunological research. Methods Adults admitted with CAP to a single NHS district general hospital were enrolled within 24 hours of admission between December 2020 and March 2022. Clinical, functional, and physiological data were collected at enrolment, hospital discharge, and 6-8 week follow-up. Serial blood samples were collected for flow cytometry, transcriptomics, pathogen DNA detection, and plasma biobanking. Results Forty-seven patients were enrolled (15 without and 32 with sepsis [SOFA >=2] at enrolment); 87% met sepsis criteria by 24 hours post enrolment. Most patients (30/47, 64%) were managed as COVID-19, microbiologically confirmed in 27. Mean age was 57 years (SD 16), 70% were male, and baseline comorbidity burden was low. Severity was moderate (median NEWS2 4 at enrolment, rising to 6 by 24 hours post enrolment; p<0.001). Mortality was 4/47 (8.5%), with 44/47 (94%) alive at hospital discharge. Median length of stay was 8 days (IQR 5.5-11). Translational samples were collected from the majority: fresh flow cytometry (44/47, 94%), transcriptomics from the sepsis subgroup (31/32, 97%), pathogen DNA sampling (35 samples received across study timepoints; see Table 5), and stored plasma (29/47, 62%). The primary outcome of functional decline (Barthel score decrease >=1.85) occurred in only 1/29 patients with paired assessments (3.4%). Persistent CRP elevation (>3 mg/L) at 6-8 week follow-up was present in 16/31 (52%) survivors with available data. Conclusions The PARIS cohort provides a well-characterised clinical platform and linked biobank to support translational studies of pneumonia and sepsis. The low rate of functional decline reflects the younger, lower-comorbidity, COVID-predominant population recruited. Primary protocol endpoints were not achieved owing to pandemic-related disruption. Data and samples underpin a programme of linked translational studies.

Background: Actigraphy-derived rest-activity rhythm (RAR) features are widely used to characterize clinical states in bipolar disorder (BD). Both mean levels and temporal variability of these features have been associated with mood episodes; however, variability measures are often statistically coupled with the mean, particularly in skewed distributions. This raises a question as to whether variability reflects a separate characteristic of the data or whether the observed association arises from statistical properties of the data. Objective: In this study, we aim to determine whether temporal variability of actigraphy-derived RAR features provides standalone information on mood episodes in BD beyond mean activity levels after accounting for mean-variance dependence. Methods: We analyzed actigraphy data from a subset of 72 participants with BD drawn from a larger longitudinal study, extracting 22 daily RAR features aggregated weekly as sample mean (MEAN) and within-week temporal variability computed as sample standard deviation (VAR). Variance-stabilizing transformations (Box-Cox or Yeo-Johnson) were applied to the entire study cohort to reduce mean-variance dependence. Associations with mood episodes and remission (mania: n=34; depression: n=58 annotated participants) were evaluated using generalized linear mixed-effects models with a logistic link function, including univariate (MEAN or VAR) and multivariate (MEAN+VAR) specifications, assessed by likelihood-based metrics and the area under the receiver operating characteristic curve (AUC). Results: Transformations reduced mean-absolute correlations from 0.43 to below 0.06. Temporal variability remained significantly associated with clinical state for 11/22 RAR features in mania and 16/22 features in depression, with all significant associations remaining after false discovery rate correction (p<0.05). Joint models showed modest incremental gains (AUC 3%-4% overall; up to 12% in mania, 7% in depression), with absolute performance remaining limited (AUC 0.50-0.66). In both mania and depression, nearly all significant variability-based regressors contributed incremental information beyond mean-based models. Only sleep duration and activity changes around wake time (+-1 hour), did not improve discrimination between mania and remission. Conclusions: Temporal variability in RAR features can be considered a standalone state marker of mood episodes not captured by mean activity. We found it to be more consistently associated with depression than mania. Its incremental discriminative contribution is modest, suggesting greater utility within multivariate or multimodal frameworks.

Objective: Photobiomodulation therapy has emerged as a promising modality to facilitate scar healing and pain management in dermatology and plastic surgery. However, its role in postoperative care following spine surgeries remains understudied. This double-blinded, placebo-controlled study aimed to investigate the effects of photobiomodulation in patients with chronic lower back pain undergoing lumbar decompression, with postoperative wound healing as the primary outcome and pain reduction and functional recovery as secondary outcomes. Methods: Patients were randomized to receive either active photobiomodulation braces (N=13) or placebo braces (N=12). Follow-up assessments were performed at 2, 4, 6, 8, and 12 weeks postoperatively. Outcomes included wound healing (Stony Brook Scar Evaluation Scale), back and leg pain (Visual Analog Scale), quality of life (EuroQol 5D), and functional status (Oswestry Disability Index). Results: Compared to the placebo group, the photobiomodulation treatment group had a 4.12-fold cumulative improvement in final scar scores, with significant between-group differences at postoperative weeks 6, 8, and 12 (p = 0.0062, 0.010, 0.042). Among patients with severe preoperative disability, treatment resulted in a 1.89-fold faster improvement in back pain (p=0.025) and a 1.80-fold faster improvement in ODI scores (p=0.025); and superior treatment effect on wound healing were again observed at weeks 6, 8, and 12. Among patients with poor initial scars, treatment led to a significantly better scar outcome than placebo at week 6 and a 1.94-fold faster EQ5D improvement (p=0.052), with significant gains observed as early as two weeks after surgery. There were no adverse events associated with photobiomodulation treatment. Conclusions: Photobiomodulation significantly promoted postoperative wound healing following lumbar decompression surgery, with therapeutic benefits preserved even in patients with poor baseline scar scores and functional impairment. This indicates that the efficacy of photobiomodulation is not limited by the initial scar condition or disability, supporting its broad clinical applicability. Additionally, patients with severe preoperative disability experienced greater benefits from photobiomodulation than placebo, including faster reduction in back pain and more rapid improvement in functional capacity, highlighting its role in postoperative pain management and rehabilitation. These therapeutic effects are likely mediated by photobiomodulation-induced reduction of inflammation and enhancement of tissue repair. Together, this study suggests that photobiomodulation can be a promising adjunct therapy to facilitate postoperative recovery in patients undergoing spine surgery.