Journal of Translational Medicine

Keloids are fibroproliferative skin disorders arising following dermal injury that extend beyond the original wound margins. Their pathogenesis remains poorly understood, and current treatments are associated with high recurrence rates. Identifying transcriptomic biomarkers that distinguish keloids from other skin and scar phenotypes may provide insight into disease mechanisms and facilitate the development of targeted therapeutic approaches. However, previous transcriptomic studies have often been limited by small sample sizes, pairwise comparisons between tissue classes, heterogeneous data-integration strategies, and a reliance on conventional differential gene expression (DGE) analysis. Here, we employed a multi-stage machine learning (ML) workflow for robust keloid biomarker discovery using transcriptomic datasets derived from both bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq). We assembled and harmonized, to the best of our knowledge, the largest curated cross-study keloid transcriptomic cohort currently available, comprising 81 samples from 13 independent studies spanning four clinically relevant tissue classes: normal skin, normotrophic scar, hypertrophic scar, and keloid scar. Through study-aware cross-validation, feature selection, partition-stability analysis, and bootstrap validation across multiple ML classifiers, we identified a panel of eight highly consistent biomarkers capable of distinguishing keloid from non-keloid samples. These biomarkers were associated with dysregulation of extracellular matrix homeostasis, fibrosis-resolution pathways, vascular remodelling, and metabolic reprogramming. Comparison with conventional DGE analysis demonstrated substantial agreement while also highlighting important differences between the two approaches. In particular, FASN was consistently identified by the ML workflow as an upregulated discriminatory biomarker despite exhibiting weak, non-significant differential expression in the DGE analysis. Cell-type-specific analysis further supported this finding, revealing significant FASN upregulation in fibroblast and vascular endothelial populations. These results demonstrate that ML and DGE capture complementary aspects of transcriptomic variation. This study provides a robust strategy for cross-study transcriptomic biomarker discovery and identifies candidate genes and pathways for future mechanistic and therapeutic investigation in keloids. 1 Author SummaryKeloids are abnormal scars that continue to grow beyond the original wound and can be difficult to treat because they frequently recur after therapy. Although many studies have investigated the biology of keloids, the molecular mechanisms that distinguish them from other scar types remain incompletely understood. Identifying biomarkers involved in keloid formation may help inform improved treatment strategies. Previous transcriptomic studies have often been limited by small sample sizes and inconsistent analytical approaches. In this study, we combined gene-expression data from multiple independent studies to create, to the best of our knowledge, the largest cross-study transcriptomic collection available for keloid analysis. We then applied several machine learning approaches to identify genes that consistently distinguished keloids from other skin and scar phenotypes. The identified biomarkers were associated with extracellular matrix remodeling, fibrosis, vascular function, and cellular metabolism. One gene involved in fatty-acid synthesis, FASN, was repeatedly identified by the machine learning analyses despite being overlooked by conventional gene-expression methods. Additional single-cell analyses confirmed elevated FASN expression in specific cell populations within keloid tissue. More broadly, this work provides a strategy for discovering robust biomarkers from heterogeneous biological datasets and identifies molecular targets for future studies of keloid disease.

Background. Randomized controlled trials (RCTs) remain the reference standard for establishing clinical efficacy for candidate drugs, but their restrictive eligibility criteria limit generalizability, and for dietary supplements the body of well-powered RCTs is small relative to the number of proposed health claims. Self-reported real-world data (RWD) by patients in social media are increasingly recognized as a complementary evidence source, yet their value in the nutraceutical domain is largely uncharacterized. Objective. To test whether benefits that users positively report for natural ingredients on Reddit are statistically associated with benefits demonstrated for those ingredients in the clinical-trial literature, and to characterize what a community corpus can reveal about application areas and side effects. Methods. We assembled a cross-sectional corpus of 216,350 distinct comments from 86 supplement-related subreddits (January 2022 - April 2026), covering 329 canonical ingredients and a shared, MeSH/MedDRA-aligned vocabulary of 581 benefit terms. Comment-level directionality (benefit/no-effect/irrelevant) was assigned by a natural-language-inference model. The independence of clinical efficacy and Reddit endorsement was tested with 2x2 chi-squared and Fisher exact tests across a sweep of endorsement thresholds, plus per-ingredient tests with Bonferroni correction. Results. Of 31,359 unique (ingredient, benefit) pairs, 2,508 were efficacy-demonstrated under the primary definition. The two signals were statistically associated. The association strengthened monotonically with endorsement volume, reaching OR 3.06 at [≥]20 comments and 7.14 at [≥]100. Among heavily-endorsed benefits ([≥]100 comments) 38% were RCT-demonstrated, versus ~9% overall. Community attention concentrated on sleep, skin and anxiety, whereas the trial literature concentrated on cardiovascular, pain, metabolic and mood indications. The most discussed and clinically validated nutraceutical-health claim pairs include melatonin-sleep cycle and quality, ginger with gastrointestinal relief and zinc with skin conditions. The concordance analysis revealed that 81% (573/711) of clinically proved nutraceutical-health claim pairs were in agreement with the reported experience of the Reddit community. Conclusions. Community endorsement and clinical efficacy are independent at the single-comment noise floor but positively concordant once a benefit is endorsed by multiple users, with concordance rising with consensus. Reddit-derived RWD is a useful hypothesis-generating prior for nutraceutical efficacy and a complementary, though not confirmatory, signal for prioritizing ingredients and indications for formal clinical study taking into consideration personalized characteristics.

Generative AI tools such as ChatGPT are increasingly used by the public to seek guidance on diet and physical activity for type 2 diabetes (T2D) prevention and management. However, the consistency of model outputs across different users and disease-stage scenarios remains insufficiently characterized. This pilot study aims to evaluate the word-level and semantic-level consistency of GPT-4os diet and physical activity responses for type 2 diabetes prevention and management. We designed 12 prompts covering four categories: prediabetes, diagnosed type 2 diabetes (T2D), diagnosed T2D with complications, and general questions that did not specify dysglycemia stage. Word-level similarity was quantified with Term Frequency-Inverse Document Frequency (TF-IDF) cosine scores; sentence-level semantic similarity was measured using large language models (LLMs) - DeBERTa-v3 MNLI to calculate the entailment probabilities. The results showed that mean cosine similarity across users was moderate (0.44-0.66), whereas mean entailment similarity was higher (0.68-0.81). Across stages, word-level similarity was low to moderate (0.28-0.63) and entailment similarity remained moderate to high (0.63-0.80). Low similarity commonly referenced distinct food choices, operational details, safety warnings, and stage-specific suggestions. GPT-4o generated semantically consistent but variably detailed responses and the moderate semantic variation suggested limited differentiation of response content across diabetes-related stages in this pilot consistency assessment.

BackgroundPrevious exploratory studies identified recombinant human Neuregulin-1 {beta} (rhNRG{beta}1) as a promising therapeutic strategy for inhibiting the growth of Nf2-deficient schwannomas by promoting cellular differentiation. Because robust confirmation across independent laboratories is essential for advancing promising preclinical findings toward clinical translation, we conducted a multicenter, randomized, controlled confirmatory study under stringent preclinical standards. MethodsIn a pre-registered trial (DOI: 10.17590/asr.0000304), 216 mice (Nf2-flox;P0-Cre;Nefh-Cre) were randomized at three independent research sites. Following a standardized sciatic nerve crush, mice received systemic rhNRG{beta}1 (10 {micro}g/kg) or vehicle for 13 weeks. Rigorous quality measures included double-blinding, standardized surgery, centralized data management, and an automated Fiji macro for objective nerve thickness quantification (Primary Outcome). Secondary molecular outcomes included Western blot and in-depth, quantitative proteomics and phosphoproteomics. All methods were SOP-based for reproducible and comparable results across the three study centers ResultsThe primary confirmatory analysis revealed no reduction in nerve thickness in the rhNRG{beta}1 group (pbest case imputation = 0.076 and pworst case imputation = 0.533). Secondary analyses via quantitative Western blotting and DIA proteomics demonstrated that core biochemical markers of Schwann cell differentiation (MBP, ERBB2) remained unchanged across all centers. Based on the absence of macroscopic or primary biochemical effects, further histological analysis was omitted to avoid scientific redundancy. High-depth profiling of a predefined 60-protein functional marker panel confirmed a remarkably stable tumor proteome across all replication sites and both sexes, with no evidence of coordinated changes in key downstream oncogenic signaling pathways (Hippo/YAP, mTORC1, and RTK-Ras-MAPK) or metabolic signaling cascades. These findings indicate an absence of measurable target engagement under our tested dosing regimen, potentially reflecting pharmacokinetic or tissue-delivery limitations rather than an invalidation of the underlying biological pathway. ConclusionDespite high statistical power and rigorous methodology, this study could not confirm rhNRG{beta}1 as a robust therapeutic candidate for schwannoma growth arrest or shrinkage. These findings suggest that previously reported therapeutic effects were either highly context-dependent or could not be reproduced under adequately powered, rigorously controlled experimental conditions. As underpowered preclinical studies are more susceptible to random biological variation, our results highlight the importance of sufficient sample sizes alongside robust experimental design. Our study underscores the value of trial-like methodological standards in preclinical therapeutic evaluation to identify ineffective interventions (dead ends) early and strengthen translational decision-making. Although we could not confirm the previously reported efficacy of rhNRG{beta}1, the multicenter framework established here provides a methodological benchmark for robust preclinical testing in translational oncology, with the potential to improve reproducibility and the success of therapies progressing to early-phase clinical trials. From a translational perspective, these findings provide a robust foundation for optimizing future rhNRG{beta}1-based therapeutic approaches through improved dosing, delivery routes, and treatment schedules.

This systematic review and meta-analysis has several major aims, including evaluation of global and continent wise prevalence of diabetic peripheral neuropathies (DPNs), a detail assessment of DPN associated risk factors and most importantly to explore mechanistic basis of DPN by capturing its different pathophysiological hallmarks Diabetic peripheral neuropathy (DPN) imposes a substantial global burden, yet its mechanistic underpinnings and integrated risk architecture remain incompletely characterized. This systematic review and meta-analysis of 74 studies across 24 countries (83,560 participants) provides the most comprehensive quantitative synthesis of DPN prevalence, hallmark-based pathogenesis, and multi-covariate risk profiling to date. The global pooled DPN prevalence was 57.42% (95% CI: 48.56 to 66.05), with continent-specific gradients: Americas (74.52%), Europe (59.95%), and Asia (45.44%). Hallmark stratified subgroup analyses encompassing neuronal damage, metabolic dysregulation, neuroinflammation, and microvascular alteration identified microvascular alteration as the sole statistically significant mechanistic determinant (Q = 15.78, p = 0.0004), with prevalence escalating monotonically from 53% to 92% across increasing hallmark severity scores, constituting a compelling dose response relationship. Risk factor meta analysis of 27 covariates identified 12 significant determinants, including the novel meta analytic confirmation of peripheral vascular disease (OR: 3.70; highest effect size), male sex (OR: 1.52), and height (OR: 1.26) as independent DPN risk factors. Notably, HbA1c, BMI, and blood pressure were nonsignificant, challenging glycemia-centric paradigms. These findings collectively support a precision medicine framework grounded in hallmark stratified phenotyping and mechanism-targeted pharmacotherapy for DPN.

The therapeutic landscape for hepatocellular carcinoma (HCC) is evolving rapidly, necessitating scalable approaches to synthesize the expanding scientific literature. We characterized thematic shifts in HCC treatment and prognosis research by conducting a retrospective bibliometric analysis of influential publications from 2023 and 2024. Using the OpenAlex database, we identified the 50 most highly cited papers from each year based on eighteen-month post-publication citation counts. Large language models were deployed to extract, normalize, and classify concepts from unstructured text into canonical topics and parent themes, enabling quantitative year-over-year frequency comparisons. Analysis of these 100 papers revealed a distinct maturation in research focus. Although broad categories like general immunotherapy remained prevalent, their relative frequency declined in favor of specific dual immune checkpoint regimens, notably CTLA-4 inhibition and the durvalumab plus tremelimumab combination. Concurrently, parent themes related to radiomics, imaging, and health systems exhibited significant growth in the 2024 cohort. These findings demonstrate a thematic transition in high-impact HCC research from foundational immuno-oncology toward optimized combination therapies and precision diagnostics. Furthermore, this study highlights the utility of artificial intelligence-driven bibliometrics for objectively tracking dynamic conceptual shifts in oncology. A web interface for exploring the data is available at https://pri.pepkio.com/.

In idiopathic pulmonary fibrosis (IPF), the lung is remodeled through coordinated epithelial, stromal, and immune-associated programs, but individual transcriptomic cohorts are often too small to separate shared disease signals from demographic and study-level variation. To increase statistical power while preserving study-aware interpretation, we integrated raw bulk lung RNA sequencing (RNA-seq) data from five well-annotated studies and analyzed 223 samples in a common framework that modeled sex, age, library layout and repeated sampling. IPF showed a broad and reproducible expression shift, with 2,443 genes meeting the differential-expression threshold of false discovery rate (FDR) <0.05 and absolute log_2 fold change at least 1. The dominant program combined extracellular matrix remodeling, stromal and epithelial activation, complement and B-cell-related pathways, cilium-associated processes, and relative depletion of oxidative phosphorylation and proteasome pathways. Sex-stratified analyses recovered a shared fibrotic core with smaller sex-skewed components, whereas age-related disease effects were weaker and centered on immune activation. A leave-one-study-out elastic-net analysis using fixed disease-gene panels classified IPF across held-out studies, supporting cross-study portability of the core signature. This integrated reanalysis strengthens evidence for a stable matrix-immune IPF program and reinforces the view that core disease-associated transcriptional programs are reproducible across heterogeneous cohorts.

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d51629org.highwire.dtl.DTLVardef@a1f126org.highwire.dtl.DTLVardef@1df1b0aorg.highwire.dtl.DTLVardef@686f1c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Long COVID (LC) is a complex condition characterized by persistent, chronic multisystem manifestations, with a significant proportion of patients exhibiting neurological symptoms. Human ion channels (HICs), particularly potassium channels, are abundantly expressed in the nervous system and linked to key metabolic processes, making them potential candidates for understanding LC pathophysiology and drug repurposing. Meta-analysis of RNA-Seq datasets from COVID-19 recovered and LC patients was performed to identify altered HICs in LC. Differential gene expression analysis, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA) were performed to uncover key genes, pathways, and co-expression modules consisting of HICs, lipid metabolism-, and immune signaling-related genes. Drug-gene interaction analysis was performed to identify approved drugs targeting potential HICs. A total of 715 dysregulated genes, including eighteen HICs were identified, among which seven were potassium channels. Three significant modules containing HICs, lipid metabolism-, and immune signaling-related genes were identified and found to be associated with antigen processing and presentation, complement and coagulation cascades, and cytokine-related pathways. Approved drugs targeting KCNA6, KCNJ10, KCNN3, and KCNH4 were identified. With further experimental validation, these dysregulated potassium channels, supported by their co-expression networks and pathway associations, may act as potential candidates for drug repurposing in LC patients.

ObjectivesThe preferential phosphodiesterase 4B (PDE4B) inhibitor nerandomilast was recently approved for treatment of idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis. Its proposed immunomodulatory, anti-fibrotic, and endothelial-stabilising actions target all three cardinal features of SSc, yet PDE4B expression has not been systematically characterised in SSc tissue. We aimed to define PDE4B expression across fibrotic organs and cellular compartments in SSc. MethodsPDE4B expression was profiled in SSc lung, peripheral blood mononuclear cells (PBMCs) and skin on the transcript level using single-cell RNA sequencing data and on the protein level using immunohistochemistry, immunofluorescence and multiplexed immunofluorescent stainings. ResultsPDE4B was consistently dysregulated in immune cells across SSc tissue and PBMCs, with compartment-specific direction and distribution. In SSc-ILD lung, expression was increased in CD8 and CD4 memory T-cells. In PBMCs, expression was increased in B cells, monocytes, and CD8 T-cells, and stratified patients into three endotypes (PDE4B//hi) not distinguishable by clinical variables. In skin, bulk RNA-seq showed a significant global increase, which localized to myeloid cells in scRNA-seq data. Approximately 90% of FAP activated fibroblasts co-expressed PDE4B at the protein level in SSc skin, identifying the activated fibroblast compartment as a candidate target for PDE4B inhibition. No PDE4B dysregulation was detected in vascular cell types. ConclusionsThis first cell-type-resolved characterisation of PDE4B in SSc demonstrates consistent immune-cell dysregulation across tissues and protein-level enrichment in activated fibroblasts. This provides a human-tissue rationale for the immunomodulatory and anti-fibrotic effects of PDE4B inhibition and supporting PDE4B as a disease-relevant therapeutic target in SSc. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LINerandomilast (BI 1015550), a PDE4B-preferential inhibitor, was approved for idiopathic pulmonary fibrosis and progressive pulmonary fibrosis. C_LIO_LIPre-clinical studies indicate that PDE4B inhibition may act on all cardinal features of SSc. C_LI What this study addsO_LIFirst cell-type-resolved characterization of PDE4B expression across SSc-affected lung, PBMCs, and skin. C_LIO_LIPBMC PDE4B expression is heterogeneous, stratifying patients into PDE4B// endotypes independent of standard clinical variables. C_LIO_LIscRNA-seq shows increased myeloid PDE4B expression in SSc skin, while [~]90% of FAP activated fibroblasts in SSc skin express PDE4B protein. C_LI How this study might affect research, practice or policyO_LIThe study strengthens the human-level evidence underpinning the target rationale for PDE4B inhibition in SSc. C_LI

RNAGAN (version 2.0, https://github.com/ZhaozhengHou-HKU/RNAGAN-2.0.git) is a published foundation model that analyzes single-cell and bulk-level RNA sequencing samples and enables multiple applications that enhance medical insights. Here we applied this model to Nasopharyngeal Carcinoma (NPC) as in-context few-short format (i.e., the model was never trained with any NPC data). We conducted all four supported functions, which include sample stratification, vectorization, pseudo data generation, and marker identification. The results were then used for identifying metastatic NPC and to investigate mechanisms associated with NPC metastasis. Examination with stratification showed that the accuracy of RNAGAN results for evaluating the metastasis risk in NPC patients are comparable to or outcompeted recently published risk estimation linear prediction model. Vectorization results present consistency across multiple cohorts and RNAGAN model versions. In the task of identifying markers and mechanisms related to NPC metastasis, incorporating pseudo data substantially enhanced the representativeness of single-cohort-based differential expression (DE) analysis. Moreover, RNAGAN identified metastasis-related marker genes based on single cohort, were concordant with the ground truth obtained across multiple cohorts (p=1.05e-9). Regarding biomedical mechanisms, RNAGAN enabled second-order feature extraction, unveiling a remarkable domination of the protective function of adaptive immune responses (as indicated by IL21R levels) over the hazardous function of chronic, non-resolving innate inflammation (as indicated by S100A8 levels) against NPC metastasis after first-line treatment. This association demonstrates a high degree of consistency with the external cohort. This study demonstrates the utility of the foundation model RNAGAN in uncovering therapeutic insights for novel cancer types without extra training. We reveal a critical spatial mechanism preventing distant metastasis via humoral anti-tumor immunity in NPC. High S100A8 expression by innate antigen-presenting cells (APCs) triggers an inflammatory cascade promoting epithelial-mesenchymal transition (EMT) and metastasis. However, when germinal center IL21R+ B cells simultaneously colocalize with these innate signals, they override this suppressive tissue stress. Spatial analysis shows that a high S100A8/IL21R intersection within tumor regions strictly distinguishes treatment responders, whereas non-responders display spatial mismatch or S100A8+ hyper-infiltration. This coordinated innate-adaptive cross-talk sustains functional tertiary lymphoid structures (TLS) that mature IgG-secreting plasma cells, which opsonize and eliminate emerging EMT tumor cells before systemic escape. Consequently, while S100A8 alone is an unreliable prognosticator, its spatial colocalization with IL21R is a robust protective indicator overlooked by conventional bulk analysis methods.

Background: Inflammation and the tumor immune microenvironment contribute to lung adenocarcinoma (LUAD) progression, but the relationship among inflammation-linked transcriptional heterogeneity, patient survival, and immune-state variation remains incompletely defined. Objective: We aimed to identify inflammation-associated LUAD subtypes, derive a parsimonious survival-stratification signature, and characterize its immune and pathway context across public transcriptomic cohorts. Methods: Expression profiles and clinical data were obtained from TCGA-LUAD, GTEx normal lung, and GEO datasets GSE11969, GSE30219, GSE31210, and GSE40791. A curated set of 596 inflammation-related genes was used for consensus clustering. Differential-expression analysis, functional enrichment, univariate Cox regression, and LASSO-Cox modeling were integrated to construct a gene-expression risk score. The prognostic dataset comprised 730 cases and was randomly divided into training (n=502) and internal-validation (n=228) sets; 85 GSE30219 cases formed an external-validation cohort. Immune-cell enrichment, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and pan-cancer analyses were used for biological contextualization. Results: The LUAD-versus-control comparison identified 1,305 differentially expressed genes, including 498 upregulated and 807 downregulated genes. Consensus clustering resolved two inflammation-associated subtypes and 67 subtype-associated genes, of which 64 were higher and 3 were lower in Cluster 1 relative to Cluster 2. Thirty-three genes overlapped between the tumor-control and subtype contrasts. LASSO-Cox regression selected CHRDL1, FDCSP, CXCL13, CYP4B1, and S100P. The 1-, 3-, and 5-year areas under the time-dependent receiver operating characteristic curve were 0.6625, 0.6581, and 0.6658 in the training set; 0.7422, 0.6537, and 0.6761 in internal validation; and 0.6560, 0.6387, and 0.6753 in external validation. Risk groups differed across multiple T-cell, B-cell, natural-killer-cell, myeloid, dendritic-cell, macrophage, and granulocyte signatures. Positive GSEA signals included cell cycle (normalized enrichment score [NES]=2.67; adjusted P=1.42 x 10-), DNA replication (NES=2.52; adjusted P=2.52 x 10-), and mismatch repair (NES=2.20; adjusted P=1.77 x 10-). Conclusions: The five-gene expression score separated LUAD survival groups and captured coordinated proliferative and immune transcriptional states. Its moderate discrimination supports further biological and clinical validation rather than immediate clinical application.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common neurotoxicity of cancer treatment with limited diagnostic, monitoring, and treatment options. Neurofilament light chain (NfL) is an axonal cytoskeletal protein released during neuroaxonal injury and a promising biomarker of CIPN, but prospective evidence for NfL as a marker of CIPN from vincristine-containing lymphoma chemotherapy treatment remains limited. To fill this gap, we conducted a pragmatic single-center prospective observational cohort study of adults with non-Hodgkin lymphoma (NHL) receiving first-line vincristine-containing chemotherapy to evaluate NfL dynamics across multiple pre-cycle visits and assess 68 relationships with patient-reported and clinician-graded neuropathy measures. We followed 25 participants during 4-6 months of chemotherapy, and a small subset of those participants (n=6) for 24-42 months post-chemotherapy. Serial plasma NfL was measured and CIPN symptoms were assessed using patient- and clinician-reported measures. Longitudinal changes were analyzed using mixed-effects models. Plasma NfL increased relative to pre-cycle1 at all timepoints (all p<0.001), increasing more than threefold by pre-cycle4. Patient-reported CIPN scores and clinician-graded neuropathy also increased during treatment. Exploratory pooled visit-level analyses showed a modest NfL-CIPN association (Spearman {rho}=0.393, p=0.004), while timepoint-specific, lagged, and post hoc sensitivity analyses suggested potential to predict persistent CIPN symptoms from early NfL concentrations. To our knowledge, these findings provide the first prospective evidence that NfL is sensitive to vincristine exposure in adults with NHL and may complement patient-reported symptom assessment, clinician grading, and dose-modification context in future CIPN monitoring studies.

Objective and designWe tested whether a frozen branch-resolved IFN/STING-linked lesion-state architecture derived from paired psoriasis transcriptomes would transport to an untouched bulk RNA-sequencing cohort and remain detectable within broad cell compartments. Material or subjectsSeven paired bulk cohorts formed the derivation backbone. GSE121212 provided held-out replication using 27 matched lesional/nonlesional pairs; treatment-facing cohorts and GSE228421 provided pharmacodynamic and cellular-context analyses. TreatmentNo intervention was administered by the authors; public datasets included ustekinumab-, etanercept-, secukinumab- and risankizumab-exposed samples. MethodsFrozen modules and branches were scored after cohort-wide gene standardization. GSE121212 counts underwent trimmed mean of M-values normalization and log2 counts-per-million transformation. Paired effects, upper-quartile STING-high contrasts, lesional Spearman coupling and prespecified rank concordance were evaluated. ResultsGSE121212 lesional-minus-nonlesional effects were 1.288 for STING-core, 1.478 for IFN-responsive activity and 1.188 for proximal-only STING; all lower 95% confidence limits exceeded zero. Coupling-vector concordance was strong (rho = 0.893, P = 0.0068), whereas STING-high effect concordance was moderate (rho = 0.536), yielding partial replication. ConclusionsHeld-out replication was partial: the IFN/STING lesion anchor and proximal/IFN-dominant coupling transported, while broader ordering did not fully transport. Transcriptomic findings do not establish biochemical STING activation, causality or clinical thresholds.

Gastroesophageal reflux disease (GERD) is a prevalent chronic disorder where dietary modifications, particularly reducing spicy foods, are a primary management strategy. Salsa, a widely consumed condiment whose spiciness comes from capsaicin, lacks standardized heat labelling, potentially leading to inconsistent capsaicin exposure for consumers. To address this, our study aimed to develop and apply an LC-MS workflow for accurate capsaicin quantification in commercially available salsas. This approach seeks to provide objective "reflux-conscious" spice classification, supporting evidence-based dietary recommendations for individuals with GERD. In the eight commercial brands we examined, we found that products labelled "mild" had significantly lower capsaicin levels as compared to "medium" or "hot", but that there was an almost 15-fold range of capsaicin within this group. Surprisingly, there was no statistical difference in capsaicin content between those groups labelled "medium" or "hot" facilitating unambiguous assignment to either category, revealing that product labelling alone is insufficient to guide consumers seeking to control capsaicin exposure in their food. The results in this study enable improved brand-specific recommendations for GERD symptom management.

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

Abstract Background: Intraductal papillary mucinous neoplasms (IPMNs) are recognized as precursor lesions to pancreatic ductal adenocarcinoma (PDAC). However, the molecular programs underlying progression from low-grade dysplasia to advanced disease remain incompletely characterized. Herein, we performed an integrated plasma and tissue-proteomic analyses coupled with spatial and single-cell transcriptomics to identify biologically coherent remodeling programs reflected in circulation that distinguish IPMN by dysplasia grade and invasive disease. Methods: Using the O-link proximity extension assay platform, a panel of 1,104 proteins were quantified in plasma samples collected from patients with low-grade (LG) IPMN (n=30), high-grade (HG) IPMN with or without associated PDAC (IPMN/PDAC; n=40) and PDAC without IPMN (n=8). Predictive performance of individual biomarkers were assessed; likelihood ratio testing was performed to identify protein biomarkers that were complementarity with CA19-9 for risk of malignancy of IPMN. Findings were intersected with available spatial (N= 13) and single-cell (N= 6) transcriptomic datasets of IPMN tissues as well as mass spectrometry-based proteomic profiles of an independent set of resected human IPMN tissues (N= 9). Results: A total of 28, 43, and 35 circulating proteins were found to be differential in HG, IPMN/PDAC, and HG + IPMN/PDAC cases compared to LG IPMN. Among differential proteins were known PDAC-associated markers CEACAM5, CTRC, and REG3A as well as several biomarkers reflecting cytoskeletal and extracellular matrix remodeling and inflammatory processes. Focusing on cytoskeletal and ECM-related proteins and using likelihood ratio testing, an OR rule considering CA19-9, BGN, and ITGB1BP1 achieved overall sensitivity of 48.7% for HG + IPMN/PDAC, including 38.1% sensitivity for HG IPMN, at an overall specificity of 90%, which was improved compared to that of CA19-9 alone (overall sensitivity of 28.2%; McNemar Exact test 1-sided p-value: 0.011). Integrated proteomic and spatial transcriptomic datasets of IPMN tissues revealed coordinated alterations cytoskeletal and ECM remodeling and elevated matrix stiffness as prominent features associated with IPMN/PDAC, which paralleled concordant increases in BGN and ITGB1BP1. Cell-type of origin analyses based on spatial and single-cell data further revealed fibroblasts and myeloid cells as primary contributors to expression levels of BGN whereas ITGB1BP1 was primarily expressed in neoplastic epithelium. Conclusion: Advanced IPMN dysplasia and invasive disease are characterized by coordinated tissue remodeling programs that are systemically reflected in circulating proteomic profiles. Blood-based biomarkers identified through our study, such as BGN and ITB1BP1, have potential to improve upon CA19-9 for risk stratification of IPMN to better guide clinical management.

Drug repurposing -- the application of approved or shelved compounds to new therapeutic indications -- offers a cost- and time-efficient alternative to de novo drug discovery. However, the systematic identification of repurposing candidates from the rapidly expanding body of clinical trial data remains a significant challenge. Here I present a publicly accessible AI-powered tool that mines the ClinicalTrials.gov registry to identify approved drugs with under-explored therapeutic potential in high-value disease areas. The tool integrates natural language processing, mechanism-of-action pathway analysis, and trial density scoring to surface candidates where biological plausibility is high and clinical trial coverage is sparse. I demonstrate the tools utility across six cross-therapeutic case studies spanning oncology, cardiology, neurology, rare diseases, immunology, and infectious disease. Key findings include: the identification of Zonisamide as an under-explored combination candidate for obesity alongside GLP-1 receptor agonists; mechanistic validation of SGLT2 inhibitors in heart failure with preserved ejection fraction (HFpEF); and a novel cross-domain mapping of anti-TNF biologics to early-stage neurodegeneration via shared neuroinflammatory pathways. The tool is freely accessible and designed to lower the barrier for academic and industry researchers to systematically pursue repurposing opportunities.

Background: Serum proteomic signatures may reflect tumor- and host-related biology and serve as prognostic biomarkers in patients receiving immune checkpoint inhibitors (ICIs). We evaluated the association of the VeriStrat serum proteomic classification with survival outcomes and immune-related adverse events (irAEs) in patients with non-small cell lung cancer (NSCLC) treated with ICIs. Methods: We retrospectively reviewed patients with NSCLC who received ICI-containing therapy and underwent VeriStrat testing at Northwestern Memorial Hospital from October 2015 through June 2023. Patients were classified as proteomic signature Good (PS-Good) or Poor (PS-Poor). Progression-free survival (PFS) and overall survival (OS) were assessed among patients receiving palliative-intent ICI therapy. First any-grade and grade 3 or higher irAEs were evaluated in all ICI-treated patients using cumulative incidence functions and Fine-Gray competing-risk regression. Results: Among 162 ICI-treated patients included in the toxicity analysis, 129 received palliative-intent therapy and were included in the survival analysis; 91 (71%) were PS-Good and 38 (29%) were PS-Poor. PS-Good status was associated with longer PFS (median, 6 vs 3 months; hazard ratio [HR], 0.50; 95% CI, 0.33-0.77; P<0.01) and OS (median, 20 vs 8 months; HR, 0.59; 95% CI, 0.39-0.91; P=0.02). These associations remained significant after multivariable adjustment for PFS (adjusted HR, 0.46; 95% CI, 0.26-0.82; P<0.01) and OS (adjusted HR, 0.50; 95% CI, 0.28-0.87; P=0.01). Any-grade irAEs showed a nonsignificant trend toward a higher cumulative incidence in PS-Good patients. At 12 months, the cumulative incidence was 32.6% for PS-Good versus 22.5% for PS-Poor (subdistribution HR, 1.53; 95% CI, 0.77-3.02; P=0.23). The cumulative incidence of grade 3 or higher irAEs was similar between groups (16.3% vs 15.0%; subdistribution HR, 1.12; 95% CI, 0.48-2.62; P=0.79). Conclusions: PS-Good classification was independently associated with improved survival in patients with NSCLC receiving ICI-containing therapy. Although any-grade irAEs were numerically more frequent among PS-Good patients, proteomic classification was not significantly associated with any-grade or high-grade irAE risk. Prospective validation is warranted.

Plasma proteins are promising biomarkers and potential drug targets in psoriasis. We conducted a two-sample Mendelian randomisation analysis integrating protein quantitative trait loci from UK Biobank and deCODE genetics with a psoriasis GWAS meta-analysis of 36,466 cases. To strengthen causal inference, we performed colocalisation analyses to evaluate shared genetic signals and applied summary data-based MR (SMR) with HEIDI testing using expression quantitative trait loci to exclude linkage-driven associations. After correction for multiple testing, 78 circulating proteins showed genetically predicted associations with psoriasis, with 27 demonstrating strong colocalisation (PPH4>80%). Triangulation prioritised 12 Tier 1 proteins, STX4, FLT3, NFKB1, IL18, PRSS53, SPAG1, SGSH, PLAT, RALB, TNFSF11, SPHK2, and STAT3, supported by consistent effects and no heterogeneity. Network profiling and Genome for REPositioning analyses assessed biological connectivity and druggability, revealing enrichment in anatomical therapeutic chemical groups L and B. Single-cell RNA sequencing confirmed cell-type-specific expression and modulation following IL-23 blockade.