Nicotine and Tobacco Research

Background: Guidelines on pre-pregnancy counselling are primarily clinical, and although recommendations and policy documents on preconception care exist in Australia, they place little or no emphasis on the preconception health of adolescents and young adults. Objective: To identify and prioritise unanswered questions and evidence uncertainties concerning preconception health needs of adolescents and young adults residing in Australia. Design: Research priority exercise Setting and Participants: Participants included young interest-holders (18-24 years) and professional interest-holders from the academics, healthcare, policy, community and government sectors residing in Australia. Methods: We followed the James Lind Alliance (JLA) methodology to identify research priorities for preconception health of adolescents and young adults. The process was led by a multidisciplinary steering committee comprising young interest-holders and professional interest-holders (including academics and clinicians). A rapid literature review was conducted from which 80 research questions were developed across ten domains, which were refined through consultation and prioritised via two rounds of online surveys on Qualtrics using a 9-point Likert scale. Results: The participants included 14 young interest-holders in each survey round, with 22 professional interest-holders in the first round and 33 in the second. Participants from across Australia participated in the survey, but most were from South Australia. In the first survey round, 28 questions across seven domains were prioritised by both professional and young interest-holders. This was followed by a reprioritisation exercise, resulting in the final top 10 research questions spanning five domains. The highest-priority research questions identified by the interest-holders concentrated in the domains of violence and mental health; early intervention and prevention; smoking, tobacco, alcohol, and substance use; access to preconception care and the healthcare system; and priority populations. Conclusion: The study identified the top 10 priority research questions informed by professional and young interest-holders. It promotes new research and collaboration while offering guidance on future research investments and on designing preconception interventions for adolescents and young adults in Australia. Turning these priorities into research could improve the health outcomes for adolescents and their future generations.

Introduction: Conventional substance-level disproportionality analysis may miss safety patterns specific to a dose form, route, or intended site. More granular analyses are hindered by incomplete, inconsistent reporting of product information. The Pharmaceutical Product Identifier (PhPID), representing products by substance, strength, and dose form, may support more granular analyses. Objective: To explore the use of PhPID-like dose form information for site-specific disproportionality analysis in dexamethasone. Methods: We evaluated VigiBase reports (January 1, 2001 - December 31, 2024) for completeness of dose form and route data. We standardized dexamethasone entries to PhPID Level 3 standards, representing substance and administrable dose form. Through disproportionality analysis (Information Component, IC) we compared substance-level and site-specific results. Results: Among 56.4 million suspected/interacting drugs, dose form was reported in 47.7%, route in 69.4%. Among 109,248 dexamethasone entries, 703 dose form and 80 route variations were mapped to 53 and 44 standard codes respectively; about half could be mapped unambiguously. Site-specific analyses revealed biologically plausible patterns not apparent in substance-level analyses. Ocular use showed higher ICs for glaucoma and cataract, while systemic use showed higher IC for psychiatric and endocrine events (e.g., depression, agitation, Cushing's syndrome). IC time-trends suggested that some signals (e.g., cataract with Ocular use) could emerge earlier in site-specific analyses. Conclusion: More granular product information, aligned with PhPID, may improve signal detection and characterization of site-specific safety issues. These findings support granular identifiers in pharmacovigilance while highlighting the need for better capture and standardization of dose form and route of administration data.

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: Diabetes mellitus is an increasing public health challenge in Sierra Leone, where access to diagnosis, treatment, and long-term care remains limited. Traditional medicine continues to play a significant role in disease management; however, ethnobotanical knowledge related to diabetes remains insufficiently documented. Methods: A cross-sectional ethnobotanical survey was conducted among 40 informants, including traditional healers, herbalists, and knowledgeable community members in Waterloo, Pendembu, and Bo. Data were collected using structured questionnaires administered via Kobo Toolbox and paper-based tools. Information on medicinal plants, plant parts used, preparation methods, routes of administration, and knowledge transmission pathways was obtained. Quantitative ethnobotanical indices, including Frequency of Citation (FC), Relative Frequency of Citation (RFC), and Informant Consensus Factor (ICF), were calculated. Results: A total of 21 medicinal plant species were documented. The most frequently cited species were Moringa oleifera (FC = 9; RFC = 0.225), Vernonia amygdalina (FC = 7; RFC = 0.175), and both Cassia siberiana and Telfairia occidentalis (FC = 6; RFC = 0.150). Leaves were the most commonly utilized plant part (40.9%), and decoction was the predominant preparation method (76.2%), with oral administration accounting for 95.2% of use. The Informant Consensus Factor (ICF = 0.69) indicated a relatively high level of agreement among informants. Knowledge was primarily transmitted through apprenticeship and inherited family practices. Conclusion: Traditional medicinal plants remain an important component of diabetes management in Sierra Leone. The high level of consensus among informants and the repeated citation of specific plant species suggest structured and culturally validated therapeutic practices. The findings provide a foundation for future phytochemical and pharmacological investigations and highlight the need for documentation, preservation, and sustainable utilization of ethnobotanical knowledge.

Background. Compounded tirzepatide is prescribed at scale as a cheaper substitute for branded Mounjaro and Zepbound, yet the cost case is almost always built by setting one compounded price against one branded list price. That framing ignores the question that actually decides the answer: cheaper than which branded price the patient can reach. Branded tirzepatide is now sold at sharply different tiers, namely insurance copay (often $25-$150/month), LillyDirect Self Pay ($299-$449/month), and retail cash price ($1,000-$1,200/month). Whether compounded saves money turns entirely on which of these a given patient faces. A second open question is whether the two formulations even produce comparable effectiveness, since observed differences may reflect selection on insurance, baseline characteristics, and adherence rather than the drug. Methods. We conducted a retrospective cohort study of tirzepatide users in the Mochi Health telehealth program, classified by formulation from their refills as branded-only (Mounjaro/Zepbound; 6,238), compounded-only (71,683), or switchers (4,996); switchers were excluded from the formulation contrast. Among single-formulation patients with a documented six-month weight observation, the analytic cohort was 7,271 (869 branded, 6,402 compounded). The primary outcome was six-month percent body weight loss; the secondary outcome was >=10% response. We used 1:1 nearest-neighbor propensity-score matching (0.25 SD caliper) on baseline covariates only - age, sex, baseline BMI, baseline weight, comorbid diabetes, hypertension, dyslipidemia, prior bariatric surgery, and self-reported insurance coverage - deliberately excluding post-treatment variables such as adherence and time in program, which are mediators of the formulation effect. We pre-specified an equivalence margin of +/-2 percentage points on mean loss and tested equivalence with two one-sided tests (TOST). A directed acyclic graph (DAG) makes the identifying assumptions explicit; metformin use could not be reliably ascertained and is treated as an unmeasured confounder. The cost comparison reports the savings or premium of compounded versus branded under five branded price scenarios: retail list, LillyDirect Self Pay (two dose tiers), and insurance copay (typical and low end). It is a cost comparison (cost-minimization under demonstrated similar effectiveness), not a formal cost-effectiveness analysis: we computed no ICER, QALY, or discounting. Results. Branded and compounded patients had similar outcomes even before adjustment (mean loss 11.7% vs 11.5%; >=10% response 60.9% vs 58.8%). The largest baseline difference between the groups was insurance coverage (branded patients far more likely insured; standardized mean difference 0.67), which matching balanced to 0.01. After 1:1 matching (718 pairs, all |SMD| < 0.04), mean loss was 11.4% vs 11.4% (difference +0.08 pp, 95% CI -0.70 to +0.80) and >=10% response 59.3% vs 57.2% (difference +2.1 pp, 95% CI -3.1 to +7.1). The two formulations were statistically equivalent within the pre-specified +/-2 pp margin (TOST p < 0.001). Cost depends on the branded scenario: compounded saves $6,000 over six months versus retail list price, $1,494 versus LillyDirect maintenance-dose (5-15 mg) Self Pay, and $594 over a low-dose (2.5 mg) LillyDirect prescription, while it costs $300 more than branded under a typical insurance copay ($150/month) and is more expensive still at lower copays (savings turn negative below $200/month). Conclusions. Branded and compounded tirzepatide were statistically equivalent in six-month effectiveness within a pre-specified +/-2 pp margin, so the choice between them is essentially a cost decision - and that cost advantage is real but conditional on the branded price the patient can access. It is large against retail list price and shrinks to zero or reverses against LillyDirect Self Pay or a low insurance copay. Whether compounded is the lower-cost choice for an individual patient is, therefore, a question about which price tier that patient faces.

Purpose This study aimed to examine the association between screen time and depressive symptoms among Chinese college students, and to investigate the mediating roles of sleep quality and emotion regulation in this relationship. Furthermore, a serial mediation model was constructed to elucidate the underlying psychological mechanisms linking screen exposure to depression. Methods A stratified cluster sampling method was employed to recruit 10,999 college students for a cross-sectional questionnaire survey. Data were collected on screen time, sleep quality, emotion regulation ability, and depressive symptoms. Descriptive statistics, correlation analyses, and regression analyses were conducted using SPSS 26.0 A serial mediation model was tested using the PROCESS macro (Model 6), and bootstrapping procedures were applied to estimate the significance of indirect effects. Results Correlation analyses indicated that screen time was significantly positively associated with depressive symptoms (r = 0.16, p < 0.01) and sleep quality (r = 0.15, p < 0.01), and significantly negatively associated with emotion regulation (r = -0.13, p < 0.01). Sleep quality was positively correlated with depressive symptoms (r = 0.31, p < 0.01), whereas emotion regulation was negatively correlated with depressive symptoms (r = -0.42, p < 0.01). Regression analyses further showed that screen time significantly positively predicted depressive symptoms ({beta} = 0.712, p < 0.001), positively predicted sleep quality ({beta} = 0.217, p < 0.001), and negatively predicted emotion regulation ({beta} = -0.085, p < 0.001). In addition, both sleep quality ({beta} = 1.318, p < 0.001) and emotion regulation ({beta} = -0.424, p < 0.001) were significant predictors of depressive symptoms. Mediation analyses demonstrated that sleep quality significantly mediated the association between screen time and depressive symptoms (95% CI [0.239, 0.332]), as did emotion regulation (95% CI [0.269, 0.416]). Moreover, a significant serial mediation effect of sleep quality and emotion regulation was observed in the relationship between screen time and depressive symptoms (95% CI [0.082, 0.117]). Conclusion Screen time is significantly associated with depressive symptoms among college students, with sleep quality and emotion regulation serving as important mediating mechanisms. Extended screen exposure may be linked to higher levels of depressive symptoms by impairing sleep quality and weakening emotion regulation capacity.

Background. GLP-1 receptor agonist trials are tightly controlled: standardized titration, intensive dietary counseling, frequent in-person follow-up, and rigorous exclusion criteria. The real world is none of those things. In a U.S. telehealth GLP-1 program, diet engagement, exercise, medication choice, dose timing, and out-of-pocket cost vary substantially from patient to patient. Whether trial-level efficacy translates into the outcomes a patient and clinician will actually see is an open question, and the answer matters, because telehealth is now where most GLP-1 prescribing happens. Methods. We conducted a retrospective cohort study of 13,507 adults who used a single GLP-1 agent (tirzepatide or semaglutide) through the Mochi Health telehealth obesity program and had a documented six-month weight observation. The primary outcome was achievement of >=10% total body weight loss at six months. To address selection bias in the tirzepatide-semaglutide comparison, we used 1:1 nearest-neighbor propensity-score matching on age, sex, baseline BMI, baseline weight, and comorbid diabetes, hypertension, dyslipidemia, and prior bariatric surgery (recorded at intake), with a 0.25 SD caliper on the propensity logit. We drew a directed acyclic graph (DAG) with a clinical co-author to make the identifying assumptions explicit and to mark where unobserved variables (insurance, socioeconomic status, concomitant medications such as metformin) limit causal interpretation. We report multivariable predictors via logistic regression, compute an E-value for the matched contrast, and benchmark our point estimates against landmark RCT outcomes. Results. Overall, 59.1% of patients achieved >=10% loss at six months, with mean loss of 11.5% (median 11.3%). Threshold attainment was 86.6% at >=5%, 59.1% at >=10%, 27.5% at >=15%, and 9.1% at >=20%. The unadjusted tirzepatide-semaglutide response gap was +16.0 percentage points (68.8% vs 52.8%); after 1:1 propensity-score matching (3,480 pairs, all post-match |SMD| < 0.05) the gap was +18.1 percentage points (69.6% vs 51.6%, 95% CI +15.9 to +20.3). Matching on the measured covariates did not attenuate the advantage, indicating that selection on those characteristics does not explain it; the matched risk ratio was 1.35 (E-value 2.04). The gap was unchanged when a self-reported insurance indicator was added to the matching (+18.4 pp) and remained large (+14.2 pp) within patients who reached a therapeutic dose. Multivariable predictors of response were tirzepatide (OR 2.10, 1.95-2.26), female sex (OR 1.37, 1.20-1.56), and prior bariatric surgery (OR 1.36, 1.18-1.57); response was lower with comorbid diabetes (OR 0.84, 0.77-0.92) and, modestly, with higher baseline BMI per unit (OR 0.98, 0.97-0.99). Response varied by baseline BMI, from 58.0% in overweight patients (BMI <30) and a peak of 63.5% in Obese I to 52.4% in Obese III. Conclusions. Real-world response to GLP-1 therapy in a telehealth setting is meaningfully attenuated from RCT benchmarks but remains clinically substantial: roughly three in five patients reach the 10% threshold. The tirzepatide advantage over semaglutide is large and, notably, does not shrink under propensity-score matching on measured confounders, so it is not an artifact of the observed selection variables; an unmeasured confounder would need a risk-ratio association of about 2.0 with both drug choice and response to explain it away (E-value 2.04). The findings are observational, conditional on the DAG's identifying assumptions, and unmeasured confounders (insurance, socioeconomic status, concomitant medications) remain possible.

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 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.

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.

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

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.

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.

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict suicide or determine who is offered treatment. Underpinning this position is the premise that routinely collected health data contain no useful predictive signal, which has received little direct scrutiny. Objective. To test whether routinely collected electronic health record data can distinguish groups at higher and lower risk of severe outcomes following paracetamol overdose. Methods. We analysed 4,095 adults presenting to NHS Lothian emergency departments with paracetamol overdose (2017-2023). Elastic-net logistic regression was fitted to 37 routinely collected electronic health record features to predict a composite of death or mental health inpatient admission at 0-7, 8-30 and 31-365 days following attendance, evaluated on a held-out 20% test set with bootstrapping. Findings. Events occurred in 5.5% of patients at 0-7 days, 2.0% at 8-30 days and 7.9% at 31-365 days, dominated by mental health admission. Bootstrap AUROC 95% confidence intervals lay above 0.5 in every window (0.65-0.82, 0.63-0.90, 0.71-0.85): models ranked patients better than chance. Calibration slopes (1.04, 1.14, 1.07) were close to one. Ranking drew primarily on mental health-related features. Conclusions. Routinely collected health data carried predictive signal for severe outcomes after paracetamol overdose, although discrimination fell short of what is needed for individual-level clinical use. Clinical implications. These models are not proposed for clinical deployment; however, treating risk prediction as a settled question will redirect research efforts, potentially excluding this patient population from machine learning advances driving improvements in care in other medical specialties.

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.

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.

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.