Clinical and Translational Science

Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder characterized by persistent hyperglycemia, insulin resistance, and chronic low-grade inflammation. Despite the widespread use of established therapies such as metformin, long-term glycemic control remains suboptimal, and disease progression is often not adequately prevented. This highlights the need for novel therapeutic strategies that address both metabolic dysfunction and the underlying immunometabolic components of the disease. In this study, GLX10 (GLXM100) was evaluated as a novel immune modulator in a high-fat diet (HFD) and low-dose streptozotocin (STZ)-induced rat model of T2DM over a 91-day period. Glycemic outcomes were assessed using terminal random blood glucose and oral glucose tolerance testing (OGTT), with glucose exposure quantified by area under the curve (AUC 0-120). Complementary in vitro investigations were performed in hepatic and macrophage cell models to assess cytocompatibility, nitric oxide production, and modulation of pro-inflammatory cytokines, including IL-6 and TNF-. GLX10 treatment resulted in a significant reduction in random blood glucose levels and a marked improvement in glucose tolerance compared to diabetic control animals. Importantly, GLX10 demonstrated greater improvement in OGTT AUC compared to metformin under the same experimental conditions, indicating enhanced dynamic glucose regulation. In vitro, GLX10 maintained viability in normal hepatic cells while significantly suppressing nitric oxide production and inflammatory cytokine outputs in macrophages, supporting a favorable safety and immune profile. Collectively, these findings demonstrate that GLX10 exerts robust antidiabetic activity through a dual mechanism involving metabolic regulation and suppression of inflammatory signaling. The integration of in vivo efficacy with supportive in vitro safety and mechanistic data provides a strong preclinical foundation and supports the further development of GLX10 as a promising therapeutic candidate for T2DM.

BackgroundNon-adherence to lipid-lowering therapy (LLT) affects up to half of patients and contributes substantially to preventable cardiovascular morbidity and mortality. Existing measures, such as the proportion of days covered, provide cross-sectional summaries but fail to capture the dynamic patterns of adherence over time. Although group-based trajectory modelling identifies distinct longitudinal adherence patterns, no approach currently predicts trajectory membership prospectively while incorporating patient-reported barriers. We developed BRIDGE, a barrier-informed Bayesian model to predict adherence trajectories and identify their underlying drivers. MethodsBRIDGE incorporates patient-reported barriers as structured prior information within a Bayesian framework for adherence-trajectory prediction. The model was designed not only to estimate which patients are likely to follow different adherence trajectories, but also to generate clinically interpretable probability estimates that help explain why those trajectories may arise and what modifiable factors may be most relevant for intervention. ResultsBRIDGE achieved a macro AUROC of 0.809 (95% CI 0.806 to 0.813), comparable to random forest (0.815 (95% CI 0.812 to 0.819)) and XGBoost (0.821 (95% CI 0.818 to 0.824)), two widely used machine-learning benchmarks for structured clinical prediction. Calibration was superior to random forest (Brier score 0.530 vs 0.545; ), and performance was stable across six independent training runs (AUROC SD = 0.003). Incorporating barrier-informed priors improved accuracy by 3.5% and calibration by 5.5% compared to flat priors, showing that incorporation of patient-reported barriers added value beyond electronic medical record data alone. Four clinically distinct adherence trajectories were identified: gradual decline associated with treatment deprioritisation amid polypharmacy (10.4%), early discontinuation linked to asymptomatic risk dismissal (40.5%), rapid decline associated with intolerance (28.8%), and persistent adherence (20.2%). Counterfactual analysis identified trajectory-specific intervention levers. ConclusionsBRIDGE provides accurate and well-calibrated prediction of adherence trajectories while offering clinically actionable insights into their underlying drivers. By integrating patient-reported barriers with routine clinical data, the model supports targeted, mechanism-informed interventions at the point of prescribing to improve adherence to cardioprotective therapies. FundingMRFF CVD Mission Grant 2017451 Evidence before this studyWe searched PubMed and Scopus from database inception to December 2025 using the terms "medication adherence", "trajectory", "prediction model", "Bayesian", "lipid-lowering therapy", and "barriers", with no language restrictions. Group-based trajectory modelling has consistently identified three to five adherence patterns across cardiovascular cohorts; however, these applications have been descriptive rather than predictive. Machine-learning models for adherence prediction achieve moderate discrimination but treat adherence as a binary or continuous outcome, thereby overlooking the clinically meaningful heterogeneity captured by trajectory approaches. One prior study applied a Bayesian dynamic linear model to examine adherence-outcome associations, but it did not predict adherence trajectories or incorporate patient-reported barriers. To our knowledge, no published model integrates patient-reported barriers into trajectory prediction. Added value of this studyBRIDGE is, to our knowledge, the first model to incorporate patient-reported adherence barriers as hierarchical domain-informed priors within a Bayesian framework for trajectory prediction. Using 108 predictors derived from routine electronic medical records, the model achieves discrimination comparable to state-of-the-art machine-learning approaches while additionally providing uncertainty quantification, barrier-level interpretability, and counterfactual insights to inform intervention strategies. The identified trajectories differed not only in adherence level but also in switching behaviour, drug-class evolution, and medication burden, suggesting distinct underlying mechanisms of non-adherence that may require tailored clinical responses. Implications of all the available evidenceEach adherence trajectory implies a distinct intervention target: asymptomatic risk communication for early discontinuers (40.5% of patients), proactive tolerability management for rapid decliners, medication simplification for patients with gradual decline associated with polypharmacy, and maintenance support for persistent adherers. By integrating routinely collected clinical data with patient-reported barriers, BRIDGE can be deployed within existing primary care EMR infrastructure to generate actionable, trajectory and patient--specific recommendations at the point of prescribing, helping to bridge the gap between adherence measurement and targeted adherence management.

Retinitis Pigmentosa (RP) is a group of inherited retinal degenerations for which most subtypes lack effective drug treatments. This challenge is particularly critical for autosomal dominant (ad) RP, which is often unsuitable for gene replacement therapy. To address this challenge, we screened an FDA-approved compound library using a zebrafish adRP model expressing a human RHODOPSIN transgene with the Q344X mutation. The screen evaluated drug effects on larval visual behavior by assessing the visual-motor response (VMR). Four compounds significantly improved VMR in Q344X zebrafish: amitriptyline, difluprednate, maprotiline, and prednisolone. Further characterization revealed that these hits act through distinct mechanisms, including reducing rod death, promoting rod neogenesis, and enhancing the function of extraocular photoreceptors. Together, these findings demonstrate the potential to repurpose these drugs for adRP caused by the RHO Q344X mutation, providing preclinical candidates and revealing potential targets for future drug development.

PurposeAlanine transaminases (ALT), encoded by the GPT gene, catalyzes the reversible conversion of pyruvate and glutamate to alanine and alpha-ketoglutarate, thereby correlating carbohydrate and amino acid metabolism. However, its role in the human neural retina remains unclear. This study aimed to explore the expression, localization, and metabolic function of ALT in the human neural retina and its potential involvement in retinal diseases. MethodsALT1 and ALT2 expression and localization were examined in the retinas of healthy and diabetic retinopathy (DR) donors via immunoblotting and immunofluorescence. ALT function was assessed in ex vivo human retinal explants using pharmacological inhibition with beta-chloro-L-alanine (BCLA), followed by the analyses of enzyme activity, tissue injury, and transcriptomic responses. Stable-isotope tracing with 13C-and 15N-labelled substrates combined with GC-MS was used to define ALT-dependent carbon and nitrogen fluxes in macular and peripheral retinas. Redox level (NADPH/NADP+) was also evaluated under tert-butyl hydroperoxide-induced oxidative stress. ResultsALT1 and ALT2 were both expressed in the human neural retina, with prominent localization in Muller glia and photoreceptor inner segments. ALT1 displayed a diffuse cytoplasmic distribution, whereas ALT2 demonstrated a punctate pattern consistent with mitochondrial localization. In DR retinas, ALT1 expression was spatially disorganized and heterogeneous, while ALT2 remained comparatively preserved. Inhibition of ALT with BCLA markedly reduced ALT activity without causing overt cytotoxicity or major transcriptional changes. Isotope tracing demonstrated that retinal ALT predominantly channels pyruvate-derived carbon into alanine, whereas alanine was minimally contributed to pyruvate production under basal conditions. ALT inhibition suppressed alanine synthesis and release, redirected nitrogen flux towards glutamate, glutamine, and aspartate, and uncovered distinct metabolic adaptations in macular but not peripheral retinas. Under oxidative stress, ALT inhibition induced the decrease of NADP+/NADPH ratio and LDH release, indicating improved redox balance and reduced tissue injury. ConclusionsALT is previously unrecognized as a regulator of carbon and nitrogen partitioner in the human neural retina, contributing to redox homeostasis under stress. The altered distribution of ALT1 in DR retina and the protective metabolic effects of ALT inhibition suggest ALT as a potential contributor to retinal metabolic vulnerability and a candidate therapeutic target in retinal diseases.

BackgroundChemokine receptor type 5 (CCR5) is expressed on hepatic stellate cells (HSCs), which, together with fibroblasts, are major producers of extracellular matrix during liver fibrosis. Leronlimab is a humanized IgG4{kappa} monoclonal antibody that binds to CCR5. The objective of the present study was to evaluate the antifibrotic effects of leronlimab in three independent preclinical studies using two mouse models of liver fibrosis. MethodsIn STAM (Stelic Animal Model) model 1, leronlimab was administered at doses of 5 or 10 mg/kg/week for 3 weeks. STAM model 2 was conducted as a confirmatory study to validate the antifibrotic effect observed with the 10 mg/kg/week dose in STAM model 1. In a third study, a carbon tetrachloride (CCl)-induced liver fibrosis mouse model was used to evaluate leronlimab administered at 10 mg/kg/week for 3 weeks. An isotype-matched control antibody was included in all studies for comparison. Evaluations included liver enzymes and histological assessment of liver fibrosis. ResultsIn STAM model 1, leronlimab at 10 mg/kg/week significantly reduced fibrosis area compared with the isotype control (p = 0.0005). These findings were confirmed in STAM model 2 (p < 0.0001). Consistent antifibrotic effects were also observed in the CCl-induced liver fibrosis model (p = 0.0006). ConclusionsCollectively, these preclinical results demonstrate that CCR5 blockade by leronlimab is associated with a significant reduction of established liver fibrosis in multiple mouse models and support further evaluation of leronlimab as a potential therapeutic option, either as monotherapy or in combination regimens, for chronic liver diseases with fibrosis.

Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. Methods We performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[-&gt;]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions. Results Accuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[-&gt;]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions. Conclusions Reduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.

Background: Ageing is a systems level biological process underlying the onset and progression of multiple chronic disorders. Rather than arising from a single pathway, age related decline reflects interacting disturbances in metabolic regulation, inflammation, nutrient sensing, cellular stress responses, and tissue repair. Although GLP1 receptor agonists, sodium glucose cotransporter2 inhibitors, metformin, and rapamycin are usually evaluated against disease-specific endpoints. Objective: To develop an SBML compliant quantitative systems pharmacology model in which ageing is the primary pharmacological endpoint and to evaluate which combination therapy provides the greatest benefit for both metabolic and ageing related outcomes. Methods: We developed model comprising four layers: a metabolic/pharmacodynamic layer describing weight loss, HbA1c reduction, and nausea with tolerance; a drug layer capturing class-specific effects of GLP1 agonists, sodium glucose cotransporter2 inhibitors, metformin, and rapamycin; an ageing layer representing damage accumulation, repair capacity, frailty, and biological age gap; and a biomarker layer generating trajectories and estimated glucose disposal rate. Calibration was staged across semaglutide clinical endpoints. Bayesian hierarchical meta analysis, global sensitivity analysis, and practical identifiability analysis were used to assess robustness and interpretability. Results: The model reproduced semaglutide efficacy and tolerability dynamics and supported distinct drug-class profiles across metabolic and ageing axes. Rapamycin showed minimal glycaemic effect but emerged as a dominant driver of repair related ageing outcomes. Combination simulations predicted two distinct optima: one favouring metabolic improvement and one favouring ageing related benefit. Conclusion: The model supports the view that metabolic and ageing optimization are mechanistically distinct objectives and that weight loss and glycaemic improvement alone may be insufficient surrogates for health span benefit.

Serotonergic psychedelics such as N,N-dimethyltryptamine (DMT) and 4-phosphoryloxy-N,N-dimethyltryptamine (psilocybin) show therapeutic promise for psychiatric and neurodegenerative disorders but may be limited by liabilities from serotonin (5-HT)-2A mediated psychoactive effects and potential cardiotoxicity via 5-HT2B activation. To address these limitations, we designed and synthesized 2-halogenated derivatives of DMT and psilacetin to reduce 5-HT2A/5-HT2B activity while retaining engagement of therapeutically relevant targets, particularly 5-HT6, 5-HT2C, and 5-HT1B. This study demonstrated that 2-position halogenation decreased affinities, potencies, and efficacies at 5-HT2A and 5-HT1A receptors while preserving potent 5-HT6 agonism, especially for 2-Br-psilocin. The analogues exhibited reduced affinities at 5-HT2B and hERG ion channels, suggesting safer cardiac valve and cardiotoxic profiles. In C57BL/6J mice, 2-Br-psilacetin did not induce the head-twitch response and attenuated 2,5 dimethoxy-4-iodoamphetamine (DOI)-induced head-twitch behavior, suggesting a reduced potential for inducing psychedelic effects. Behavioral assays further revealed improvements in stress-induced affective measures and hippocampus-independent cued learning at intermediate doses. These findings identify 2-halogenated tryptamines as polypharmacological serotonergic ligands with reduced psychoactivity and cardiac valve and toxic liabilities, supporting their potential as next-generation psychedelic-inspired therapeutics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/718915v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@16aa5b2org.highwire.dtl.DTLVardef@a4813corg.highwire.dtl.DTLVardef@20c5f7org.highwire.dtl.DTLVardef@1a50a61_HPS_FORMAT_FIGEXP M_FIG C_FIG

Vaccine adjuvants are critical for enhancing immune responses and sustaining antibody production. Although their safety profiles are well established, assessments have largely focused on metabolic and excretory organs such as the liver and kidneys, with limited attention to the heart. Here, we systematically evaluated the cardiac effects of five representative adjuvants in mice: alum, MF59, AS03, Sigma Adjuvant Systems, and lipid A. None of the adjuvants impaired baseline cardiac contractile function. Notably, lipid A uniquely enhanced mitochondrial respiratory capacity in rat and human induced pluripotent stem cell-derived cardiomyocytes and promoted mitochondrial membrane hyperpolarization. We next examined its therapeutic potential in a doxorubicin (Dox)-induced heart failure model characterized by mitochondrial dysfunction. Co-administration of lipid A with influenza hemagglutinin (HA) antigen significantly ameliorated cardiac dysfunction. In parallel, lipid A prevented the Dox-induced decline in anti-HA antibody titers, an effect associated with preservation of splenic B cell populations. Collectively, these findings reveal a previously unappreciated cytoprotective dimension of lipid A, demonstrating that it not only potentiates immune responses but also counteracts chemotherapy-induced functional decline by enhancing mitochondrial activity.

Electronic health record (EHR) data are critical for clinical research but are challenging to share due to privacy and re-identification risks, particularly in sensitive domains such as opioid use disorder (OUD). Synthetic data generation offers a promising alternative; however, existing methods often struggle to preserve complex multivariate dependencies while maintaining a strong balance between data utility and privacy. The recently proposed MIIC-SDG framework leverages multivariate information theory and Bayesian network modeling to capture dependency structures and introduces Quality-Privacy Scores (QPS) to evaluate this trade-off, yet its capacity to model nonlinear relationships and support multi-task predictive settings remains limited. In this work, we propose a multi-task extension of TabGraphSyn, a graph-based generative framework for privacy-preserving EHR synthesis. The method constructs patient similarity graphs from high-dimensional tabular data and learns topology-aware embeddings via a graph convolutional network, which are then incorporated into a conditional variational autoencoder for synthetic data generation. Unlike prior approaches, our framework jointly models multiple clinically relevant OUD targets, including 180-day opioid abuse outcome, opioid concept group, and opioid source concept group, enabling preservation of label-dependent relationships across tasks. We evaluate TabGraphSyn against MIIC-SDG under a unified framework including multi-task predictive utility, distributional similarity, identifiability risk, membership inference risk, and QPS-based metrics. Results on the NIH All of Us dataset show that TabGraphSyn achieves a stronger overall utility-privacy balance, outperforming MIIC in most headline metrics, including higher synthetic multi-task ROC-AUC (0.5278 vs 0.4932), MetaQPS (AM: 0.0215 vs 0.0115; HM: 0.0391 vs 0.0223), while slightly underperforming in macro F1 (0.2321 vs 0.2840). These findings demonstrate improved modeling of nonlinear dependencies and more favorable quality-privacy trade-offs in multi-task settings, supporting its use for realistic and privacy-aware synthetic EHR data generation.

Drug overdose deaths in the United States reached record levels during the fentanyl era before recently declining. A plausible hypothesis is that a sudden drop in fentanyl purity beginning in 2023 caused the downturn in overdose mortality. We evaluated this hypothesis by replicating a published analysis with regional overdose data, using models that account for time trends and autocorrelation, and negative control indicators to test for spurious correlation. When fentanyl purity was rising, the national purity series did not track overdose increases in most regions and showed only a modest association in the West. When both purity and mortality later declined, the observed associations were also seen with unrelated macroeconomic indicators that shared the same time pattern. National fentanyl purity alone does not provide a sufficient explanation for recent overdose declines.

Objective The aim of this study was to develop and validate an automated, scalable framework to harmonise fragmented UK primary care prescription records into a research-ready dataset by mapping four diverse medical ontologies to a unified, historically comprehensive reference standard. Materials and Methods We used raw prescription records for consented participants in the UK Biobank, in which participants are uniquely characterized by multiple data modalities. Primary care data were preprocessed by selecting one drug code if multiple were recorded, cleaning codes to match reference presentations, expanding code granularity based on drug descriptions, and updating outdated codes to a single reference version. Harmonisation entailed mapping British National Formulary (BNF) and Read2 codes to dm+d, the universal NHS standard vocabulary for uniquely identifying and prescribing medicines. Harmonised dm+d records were then homogenised to a single concept granularity, the Virtual Medicinal Product (VMP). We validated our methods by creating medication profiles mapping contemporary drug prescribing patterns in 312 physical and mental health conditions. Results We preprocessed 57,659,844 records (100%) from 221,868 participants (100%). Of those, 48,950 records were dropped due to lack of drug code. 7,357,572 records (13%) used multiple ontologies. Most (76%) records were encoded in BNF and most had the code granularity expanded via the drug description (N=28,034,282; 49%). 41,244,315 records (72%) were harmonised to dm+d and 99.98% of these were converted to VMP as a homogeneous dataset. Across 312 diseases, we identified 23,352 disease-drug associations with 237 medications (represented as BNF subparagraphs) that survived statistical correction of which most resembled drug - indication pairs. Conclusion Our methodology converts highly fragmented and raw prescription records with inconsistent data quality into a streamlined, enriched dataset at a single reference, version, and granularity of information. Harmonised prescription records can be easily utilised by researchers to perform large-scale analyses in research.

Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness in working-age adults. In Chile, despite GES coverage since 2006, screening reaches only ~21% of the diabetic population under control. Chilean evidence shows that autonomous AI screening platforms have produced heterogeneous field results (sensitivity 40.8-100%, specificity 55.4%), while Ophthalmic Medical Technologists (TMOs) consistently achieve >97% sensitivity, suggesting AI is most effective as structured support for trained professionals rather than as an autonomous filter. Objective: We present DIRD+ (Diabetic Integrated Retinal Diagnosis), an open-source clinical platform that performs complete DR clinical workflows - patient management, AI-assisted lesion detection, clinical classification, annotation, and report generation - entirely within the web browser using WebAssembly-based inference, without transmitting patient data to any server. This work describes the system architecture and a preliminary technical validation. Methods: DIRD+ implements a six-stage inference pipeline using ONNX Runtime Web (v1.23) with SIMD and multi-thread optimizations, a pluggable clinical guideline engine (ICDR 2024, MINSAL Chile 2017), and a human-in-the-loop annotation workflow. A YOLOv26n detection model was trained on 500 pseudo-labeled APTOS 2019 images using the Annotix framework [11] and evaluated on the IDRiD test set (n=81 images). Results: Optic disc detection - the spatial calibration landmark - achieved AP=1.000 on IDRiD (IoU=0.1). Soft exudate detection achieved AP=0.243 (F1=0.364). Internal validation mAP50=0.578. Browser-based inference averaged 0.297 s/image (3.4 images/second) on CPU without GPU. Lesion detection performance reflects a first-generation model trained on 500 images; progressive improvement through collaborative annotation is ongoing. Conclusions: DIRD+ demonstrates that a complete offline-first DR clinical workflow can be deployed at zero cost within a standard web browser without server infrastructure or GPU. The pluggable guideline engine and human-in-the-loop architecture make DIRD+ a viable tool for TMO-assisted screening in connectivity-limited primary care settings.

Gingival inflammation is associated with dysbiotic oral biofilms characterized by reduced nitrate-reducing capacity and diminished nitric oxide (NO) bioavailability. While dietary nitrate has been shown to influence oral microbial activity, the effects of sustained, localized nitrate delivery on oral biofilm ecology and gingival inflammation remain incompletely defined. In this randomized, double-blind, placebo-controlled trial, 30 adults with gingival bleeding were assigned to receive localized prebiotic nitrate (~0.989 mmol per dose) or placebo for 21 days. The primary outcome was mean bleeding on probing (mBOP). Secondary outcomes included modified Gingival Index (mGI), Quigley-Hein plaque index (QHPI), salivary nitrite (as a proxy for NO bioavailability), oral pH, and microbiome composition assessed by 16S rRNA gene sequencing. Prebiotic nitrate supplementation formulation delivered in a slow-release chewing gum significantly reduced mBOP (25.7% to 15.3%; p = 0.0002) compared to placebo chewing gum. Salivary nitrite levels and oral pH increased, indicating enhanced nitrate metabolism. Microbiome analysis demonstrated enrichment of nitrate-reducing taxa, including Rothia mucilaginosa and Neisseria spp., and a relative reduction in inflammation-associated genera such as Prevotella and Porphyromonas. Localized prebiotic nitrate formula delivered in a functional chewing gum was associated with reduced gingival inflammation and shifts in oral microbiome composition consistent with enhanced nitrate-reducing capacity critical in nitric oxide formation. These findings support a role for biofilm-directed nutritional modulation as a non-antimicrobial approach for managing gingival inflammation and improving nitric oxide bioavailability.

BackgroundLasers have wide applications in medicine and dermatology, but are associated with pain and anxiety, particularly in younger patients. Pain mitigation is often limited to topical anesthetics in the outpatient setting. Distraction techniques are limited by the need for ocular protection, which can include adhesive eye patches that can completely occlude vision. Virtual reality is effective at managing procedural pain and anxiety under other short medical procedures and is a promising tool for this population. ObjectiveThis trial aims to assess the safety, feasibility, and efficacy of Virtual Reality Pain Alleviation Therapeutic (VR-PAT) for pain management during outpatient laser procedures. Methods40 patients requiring outpatient laser therapy for at least two sessions will be recruited from a pediatric hospital in the midwestern United States for this crossover randomized, two-arm clinical trial with a 1:1 allocation ratio. During the first laser visit, the participant will be randomly assigned to either play the VR-PAT game during their procedure or wear the headset with a dark screen. Participants will answer questions about their pain (Numeric Rating Scale (NRS) 0-10), anxiety (State Trait Anxiety Inventory for Children, NRS 0-10, Modified Yale Preoperative Anxiety Scale (mYPAS)), and pain medication usage. Those playing the VR-PAT will additionally report simulator sickness symptoms and their experience playing the game. At their second laser visit, participants will crossover to the opposite intervention from their first visit. The primary outcomes are the difference in self-reported pain and anxiety between the two interventions. Feasibility outcomes include the proportion of screened patients who are eligible, consent, and complete both visits and adverse events reported. To evaluate the efficacy of pain reduction, composite scores of pain score, pain medication will be calculated for each laser visit. To evaluate the efficacy of anxiety reduction, the change of mYPAS scores will be compared between control and VR groups at each visit using Wilcoxon rank sum tests. All statistical analyses will follow the intention-to-treat principle in regard to intervention assignment at each visit. ResultsThe study was funded in January 2023 and began enrollment at that time. A total of n=44 participants were recruited and data collection was completed in November 2025, with n=40 subjects completing both visits. The sample was balanced with n=40 subjects using the intervention and participating in the control condition. The age range of the complete sample was 6 to 21 years at recruitment and was 55% female sex. Data analysis is in progress with final results planned for June 2026. ConclusionsFindings from this innovative randomized clinical trial will provide early evidence on the efficacy of the VR-PAT for reducing self-reported pain and anxiety during outpatient laser procedures. The results from this trial will inform a large-scale, multisite study. Trial RegistrationClinicalTrials.gov: NCT05645224 [https://clinicaltrials.gov/study/NCT05645224]

The retinal topography of mammals reflects significant influences of the visual environment. In diurnal species, local specializations, such as the visual streak (VS) for panoramic vision and the area centralis or fovea for binocular vision, play a key role in optimizing visual perception and species viability. While the location of these sites is typically considered the retinal center, the definition of a "central retina" is less clear in nocturnal species. In mice, the most frequently used model in ophthalmologic research, the location of a central retina is hardly discernible in retinal images, neither in retinal structure (OCT sections) nor in vascular organization (SLO and angiography). In this study, we compare the murine retina with that of a diurnal rodent, the Mongolian gerbil (MG). We found that the S-opsin transitional zone (OTZ), a region characterized by the change from S-to M-opsin dominance along the dorsoventral opsin gradient in mice, has a similar relative position in the retina to the VS in the Mongolian gerbil, suggesting an evolutionary positional homology between these regions. Further, since the S-opsin-dominant region is optimized for visualizing the sky and the M-opsin-dominant region for visualizing the ground, the OTZ in between -much like the VS- naturally points toward the horizon. We therefore propose considering the OTZ as the position of a "central retinal area" in mice. Determining the anatomical-physiological center is particularly important to obtain meaningful relative measures such as averages across different retinal areas, as the common referencing to the optic nerve head (ONH) in mice does not take into account retinal organization and the eccentric position of the functional center.

ObjectiveOnly preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study DesignRetrospective video-based study for classification development and reliability validation. MethodsVideo recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. ResultsThe final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7/65) of cases. Of those incorrect, 57% (4/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. ConclusionTissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.

ObjectiveThis study evaluates the impact of systemic GLP-1 receptor agonist (GLP-1RA) use on surgical wound healing in high-risk surgical populations, including patients with diabetes, and implications for perioperative planning and healing outcomes. ApproachThis pilot retrospective cohort study compared adult surgery patients with non-healing postoperative wounds by their GLP-1RA use. Outcomes included healing status, time to wound closure, and number of surgical interventions. ResultsThe cohort included 35 non-GLP-1RA users and 16 GLP-1RA users with comparable baseline characteristics, except for significant higher prevalence of venous insufficiency among users. Though median time to closure was similar for all patients, users required fewer surgical interventions and their wounds reached closure in significant difference from non-users. Among patients with diabetes, all GLP-1RA users healed significantly compared to non-users. InnovationThe impact of GLP-1RA therapy on wound healing in high-risk reconstructive and soft-tissue surgery remains poorly defined. This pilot cohort addresses that gap, offering an early signal that GLP-1RA use is associated with improved wound healing and fewer postoperative interventions. These findings may inform perioperative practice by identifying a systemic pharmacologic factor that optimizes surgical outcomes in high-risk populations. ConclusionGLP-1RA use was associated with higher healing rates and fewer interventions, particularly among patients with diabetes. These findings support a beneficial role in surgical wound healing and warrant larger multi-site studies.

BackgroundPsoriasis is a chronic immune-mediated inflammatory disease (IMID) with systemic involvement. In mild-to-moderate disease, circulating cytokines may inadequately capture systemic inflammatory burden. Composite haematological indices derived from complete blood counts, such as the systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), have emerged as sensitive prognostic markers of systemic inflammation, including in psoriasis. This exploratory post hoc analysis investigated the effects of orally administered herring roe oil (HRO), a phospholipid-rich marine oil, on systemic inflammation in patients with mild-to-moderate psoriasis utilizing these biomarkers. MethodsData were analysed from a randomized, double-blind, placebo-controlled 26-week clinical study which investigated HRO supplementation in patients (N = 64) with mild-to-moderate psoriasis (NCT03359577). SII, SIRI, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) were calculated at baseline, week 12, and week 26 for patients where baseline complete blood counts (CBCs) were available (n = 60). Patients missing baseline CBCs were excluded from the analysis. Continuous changes were assessed using ANCOVA with baseline adjustment. Categorical responder analyses were performed with 25% and 30% reduction thresholds and stratification by baseline biomarker medians were performed to evaluate treatment responses and impact of baseline inflammation. ResultsCompared with placebo, HRO treatment resulted in significant mean reductions in SII, SIRI, and PLR at week 26, with supportive trends and responder effects observed as early as week 12 compared to placebo. Patients with elevated baseline inflammatory indices showed the greatest reductions in systemic inflammation. Stratification by baseline SII further revealed enhanced clinical benefit, with statistically significant PASI50 response rates in the HRO arm at week 26 among patients with lower baseline SII. ConclusionHRO supplementation was associated with a time{square}dependent reduction in systemic inflammatory biomarkers in mild{square}to{square}moderate psoriasis patients. These findings support the utility of composite inflammatory indices for monitoring systemic inflammation and suggest that baseline SII may have utility in predicting treatment response and may be a useful tool for stratification in clinical trials in mild to moderate psoriasis patients. These results could also suggest platform-potential of HRO for resolution{square}oriented interventions across several inflammatory conditions.

BackgroundDNA replication timing (RT) is a fundamental feature of genome organization that is regulated in a cell-type-specific manner and frequently altered in disease. Repli-seq is the standard approach for genome-wide RT profiling; however, its analysis typically requires multiple independent tools and custom scripts, limiting reproducibility, portability, and accessibility, particularly for users without computational expertise. In addition, existing workflows often lack standardization and require substantial user intervention. ResultsWe developed REPLAY, a fully automated, reproducible, and user-friendly application for replication timing analysis. REPLAY is distributed as a standalone executable that enables end-to-end processing from compressed FASTQ files to genome-wide RT profiles without requiring software installation or programming experience. Through an intuitive graphical interface, users can configure analysis parameters, including input and output directories, reference genome, normalization strategy (quantile, median, or interquartile range), and smoothing. The application integrates all processing steps--quality control, trimming, alignment, binning, RT log2 calculation, normalization, smoothing, and visualization-- within a single automated workflow. Application of REPLAY to publicly available datasets demonstrate accurate reconstruction of RT profiles and high reproducibility across samples. ConclusionsREPLAY offers a portable, reproducible, and accessible solution for the analysis of RT data. By eliminating the need for command-line tools and complex installations, it lowers the entry barrier enabling standardized analysis across diverse research settings.