Journal of Translational Medicine

Background: Neuropathic pain is a burdensome, difficult-to-treat, and highly heterogeneous condition with limited therapeutic options, underscoring the need for robust and reproducible human disease models. Human induced pluripotent stem cell (iPSC)-derived sensory neurons provide a promising platform for patient specific disease modelling and drug screening; however, their translational use is hampered by variability in differentiation efficiency, cellular composition, and functional maturation across protocols and cell lines. Methods: Here, we present a standardized and potentially scalable framework for NGN1 driven differentiation of human iPSCs into sensory neurons. Building on a previously published two step protocol (1), we systematically deconstructed and optimized each stage of differentiation across a large panel of genetically diverse iPSC lines. Results: We identified robust parameters for neural crest like cell (NCLC) generation, established a flow cytometry-based quality control strategy for NCLCs, and defined optimal combinations of seeding density and lentiviral multiplicity of infection to maximize sensory neuron progenitor yield. To improve culture homogeneity, we compared antimitotic selection strategies and demonstrated that tightly timed Ara-C treatment combined with low progenitor seeding density yields consistently pure sensory neuron cultures. We further evaluated maturation under physiologically relevant glucose conditions and performed a systematic review of media compositions to derive two defined maturation media. Morphological, immunocytochemical, transcriptomic, and electrophysiological analyses revealed that time in culture is a major determinant of maturation, while specific supplements such as prostaglandin E2; (PGE2) selectively enhance transcriptional signatures associated with nociceptor identity without substantially altering global network activity. Bulk RNA sequencing demonstrated broad expression of sensory neuron and pain related markers and gene programs across conditions, with long term maturation and PGE2; treatment showing the highest similarity to human dorsal root ganglion reference data. Functional assessment using multi electrode arrays enabled the detection of donor specific electrophysiological phenotypes, including reproducible hyperexcitability in small fiber neuropathy patient derived lines. Conclusions: This study establishes a modular, reproducible NGN1 based differentiation workflow with integrated quality checkpoints that accommodates iPSC line to line variability. The framework provides a practical foundation for translational sensory neuron research, patient specific disease modelling, and scalable drug screening applications.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex multisystem disorder. Its hallmark feature, post-exertional malaise (PEM), is a delayed and prolonged worsening of symptoms following even minor exertion. As PEM commonly includes muscle pain, weakness, exercise intolerance and impaired recovery, skeletal muscle dysfunction may contribute to its pathophysiology, but the underlying molecular processes remain poorly understood. We generated induced pluripotent stem cell-derived skeletal muscle myotubes from individuals with ME/CFS (n = 6) and healthy controls (n = 7) to investigate disease-associated transcriptomic alterations and identify potential therapeutic candidates. Myogenic progenitor cells from both groups differentiated into elongated, multinucleated myotubes expressing titin and myosin heavy chain, while qPCR profiling of myogenic differentiation markers further supported successful differentiation. Gene-level analysis identified three differentially expressed genes at an FDR-adjusted p < 0.05: ERVH48-1, CDX4 and SLC2A14. Isoform-switching analysis identified 16 genes with significant differential isoform usage, including CACNA1H, MIEF2, SMAD2 and CHD4. Gene set enrichment analysis identified broader pathway-level alterations involving RNA processing, protein synthesis, proteostasis, mitochondrial quality control, metabolism and muscle-related processes. Library of Integrated Network-based Cellular Signatures Phase 2 (LINCS2) analysis further prioritised 14 compounds associated with mitochondrial metabolism, glucose and lipid regulation, PPAR signalling, calcium and excitatory signalling, neuroimmune activity and cellular stress responses. Overall, ME/CFS iPSC-derived myotubes showed modest gene-level differences alongside broader isoform- and pathway-level alterations, supporting their use for investigating skeletal muscle biology and prioritising candidates for functional validation.

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.

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

Background: Spatial organization is increasingly recognized as a key determinant of tumor-immune interactions in head and neck squamous cell carcinoma (HNSCC). The GSE300147 Xenium spatial transcriptomic resource generated by McCord and colleagues established a framework for mapping spatially coordinated T-cell states in HNSCC. However, how tumor-enriched epithelial immune states relate to metabolic, redox, and stress-adaptive transcript programs remains incompletely defined. Methods: A secondary, data-driven reanalysis of GSE300147 was performed, focusing on 17 confirmed HNSCC Xenium sections after exclusion of a non-HNSCC ameloblastoma specimen. A total of 1,148,244 cells were analyzed, including 558,867 EpCAM+ tumor-enriched epithelial cells. Tumor-enriched epithelial cells were classified into Hot, Intermediate, and Cold states using a Composite Hotness framework integrating T-cell inflammatory signature score, checkpoint-associated signaling, CD274 expression, IFN/antigen-presentation signature score (IFN/AP), and tumor-immune proximity. Six metabolic ecosystem states, neighborhood profiling, spatial permutation testing, and an integrated Immune-Metabolic-Redox Ecosystem Score (IMRES) were then applied. Results: Immune activation was spatially heterogeneous across HNSCC sections. Immune-hot tumor-enriched epithelial regions showed not only inflammatory, checkpoint-associated, and antigen-presentation signature scores, but also coordinated metabolic, oxidative-redox, and stress-response transcript programs. IMRES, derived from available immune, metabolic, redox, and stress-response transcript components represented in the Xenium panel, increased progressively from Cold to Intermediate to Hot tumor-enriched epithelial states and was associated with NFE2L2, GDF15, HLA-DRA, CD274, KEAP1, and MDM2. Integrating IMRES with Composite Hotness identified a distinct Hot+IMREShigh ecosystem comprising 106,874 tumor-enriched epithelial cells. This state showed the strongest immune-active and stress-adaptive features and was positioned closer to immune populations than expected by random assignment. An alternative rank-based robustness analysis reproduced the IMRES-associated ecosystem axis and correlated with the original module-based score (Spearman r = 0.597). Conclusions: This secondary reanalysis extends the original spatial T-cell framework by defining a complementary tumor-centered immune-metabolic-redox ecosystem in HNSCC. IMRES provides a transcript-derived framework for identifying Hot+IMREShigh neighborhoods where immune activation, checkpoint signaling, metabolic remodeling, and stress adaptation converge, providing a hypothesis-generating framework for studying immune resistance and therapeutic vulnerability.

BackgroundNeuropathic pain is a common and debilitating condition with persistently poor translation from preclinical research to effective clinical therapies. Preclinical systematic reviews and meta-analyses (SRMAs) can provide more robust estimates of intervention effects and help identify sources of bias and heterogeneity, but their use is limited by the time and effort required to synthesise rapidly expanding literatures. Automation may help overcome these barriers. MethodsWe developed Neuropathic Pain - Systematic Online Living Evidence Summaries (NP-SOLES), a machine-assisted approach used in this study to support systematic identification of animal studies relevant to neuropathic pain. Using NP-SOLES, we conducted a pilot systematic review and meta-analysis of controlled animal studies assessing the effects of citrus flavonoids on thermal and mechanical pain-related outcomes in streptozotocin-induced models of painful diabetic neuropathy. ResultsIn this pilot application, machine-assisted evidence synthesis supported reduced manual screening burden, illustrating the potential of such approaches to facilitate timely preclinical SRMAs. Fourteen studies met inclusion criteria for meta-analysis on thermal pain-related outcomes. Pooled estimates suggested a reduction in thermal pain-related behaviours following flavonoid treatment; however, substantial heterogeneity and widespread methodological limitations were observed. Nine studies were eligible for qualitative analysis on mechanical pain-related outcomes, which showed an overall reduction in pain-related behaviours following treatment but had similar methodological limitations. Reporting of key measures to reduce risk of bias, including randomisation and blinding, were consistently poor, and subgroup analyses were constrained by sparse data. ConclusionsAvailable evidence from preclinical animal studies suggests a potential analgesic effect of citrus flavonoids on thermal and mechanical pain-related outcomes in diabetic neuropathy models, but confidence in this estimate is limited by significant risks of bias. More rigorously designed and transparently reported preclinical studies are required before clinical translation can be justified.

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

Keloid disease (KD) is a fibroproliferative skin disorder resulting from abnormal scar formation that causes pain, itching, and decreased quality of life. While multiple KD transcriptomic studies exist, the influence of cell type composition on bulk tissue gene expression is unknown. We characterized fibroblast subtype and immune cell enrichment using bulk RNA-Seq of head and neck keloid and matched adjacent normal skin tissue (MANST) from 14 patients (10 African American and 4 European American). Cell type enrichment was calculated by single sample gene set enrichment analysis. Linear mixed-effects models were employed for 1) differential cell type enrichment across tissue, 2) tissue type-specific associations between fibroblast subtypes and immune cells, and 3) differentially expressed genes (DEGs) across tissue. Validation was conducted in an independent cohort of 8 African Americans. Three fibroblast subtypes and 14 immune cell types were differentially enriched across tissue type. Further, 17 tissue type-specific fibroblast subtype-immune cell enrichment associations were identified, with 14 exhibiting decreased association in keloid tissue relative to MANST. After adjustment for cell type enrichment, MIR31HG and NR4A2 were significant DEGs with the largest positive and negative fold-changes, respectively. By considering cell type enrichment, underlying keloid tissue-specific cell type and gene expression associations were revealed.

A large share of late-stage clinical trial failures reflects not the underlying biology of the target but the composition of the enrolled population: trials recruit patients in whom the drug cannot work. Methods that identify likely responders before treatment therefore address a failure mode that better target selection alone cannot. We applied interpretable machine learning to gene-expression data from a treatment-response cohort in lupus nephritis (GSE224705; 21,914 genes across 319 samples) covering four regimens: mycophenolate mofetil (MMF), azathioprine (AZA), hydroxychloroquine (HC) and standard of care (SOC). We independently reconstructed the expression matrix and metadata, rebuilt the treatment-specific cohorts, and derived compact multi-gene programs that separate responders from non-responders within each treated population. Two results follow. First, discriminative performance is strongly graded by regimen. Com pact programs of five to ten genes achieved patient-level AUROC of 0.847 (MMF) and 0.866 (AZA), but only 0.718 (HC) and 0.623 (SOC); the SOC programs performed close to chance (MCC 0.119, balanced accuracy 0.555). A regimen in which response is not transcriptionally discriminable is an actionable finding for trial design rather than a null result. Second, the programs proved considerably more stable than the differential-expression lists that generated them: reconstructed counts of significant genes differed markedly from the published analysis (222 vs. 46 for MMF; 4,455 vs. 157 for AZA; 6 vs. 24 for HC; 5 vs. 11 for SOC), yet the dominant biology and the predictive performance were preserved. Programs were also non-redundant: re moving a single gene (TUBB2A) from the MMF program reduced AUROC by approximately 0.17. At the pathway level, 13 cross-treatment enrichment relationships remained significant after adjustment, indicating that response landscapes are treatment-specific yet coupled. Patient-generalisable programs of this kind offer a concrete near-term route to enrichment style trial design, identifying before enrolment which patients a given therapy suits. Our results also caution that the number of differentially expressed genes is a poor proxy for the strength or stability of a response signal.

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.

Abstract Background: Immune-mediated skin diseases frequently co-occur, but the molecular architecture connecting clinically distinct disorders, and the extent to which molecular proximity predicts shared therapeutic opportunity, remain incompletely defined. We constructed a multilayer skin diseasome integrating transcriptomic overlap, pathway enrichment, protein-interactome topology, curated disease associations, real-world clinical co-occurrence, and drug-perturbation signatures across psoriasis (PS), atopic dermatitis (AD), hidradenitis suppurativa (HS), vitiligo (VL), and chronic spontaneous urticaria (CSU), using sodium lauryl sulfate-induced irritant contact dermatitis (SLS-ICD) as a mechanistically distinct barrier-injury comparator. Methods: Differentially expressed genes (DEGs) were derived from publicly available NCBI Gene Expression Omnibus datasets and compared using exclusive pairwise and higher-order intersections. Gene Ontology biological-process enrichment characterized shared and condition-specific programs. Interactome-based network separation (S_AB) quantified the relative topological proximity of disease modules. DisGeNET enrichment generated candidate disease +associations, a selected subset of which was examined descriptively for patient co-occurrence in TriNetX. Candidate compounds were ranked by reversal of disease-associated expression signatures against LINCS L1000 perturbation profiles. Results: The largest exclusive DEG intersection occurred between PS and HS (2,060 genes), followed by AD-PS-HS (490), VL-PS (464), AD-PS (402), and VL-PS-HS (379). Fourteen genes were shared across PS, AD, HS, VL, and CSU but not SLS-ICD. Neutrophil-associated processes recurred across the immune-mediated conditions, whereas SLS-ICD was dominated by keratinization and epidermal-differentiation programs. PS and HS had the lowest observed network-separation value (S_AB = -0.594); SLS-ICD had positive S_AB values in all five comparisons, although this separation did not persist once module sizes were harmonized. DisGeNET linked the index conditions to diverse immune, cardiovascular, neurologic, and neoplastic categories; 42 of these candidate diseases were observed to co-occur with an index condition in the queried TriNetX cohorts. Drug-signature overlap was greatest between PS and CSU, despite their marginal interactome separation, indicating that interactome proximity and predicted pharmacologic reversal capture different dimensions of disease relatedness. mTOR/PI3K-pathway inhibitors were prominent among the psoriasis reversal candidates, while methotrexate and mycophenolate mofetil emerged from both the CSU signature and the curated gene set associated with HIV infection. Conclusion: Multilayer integration identified a connected molecular core among five immune-mediated skin diseases and a distinct SLS-ICD injury module. The discordance between interactome proximity and drug-signature overlap suggests that no single layer adequately defines disease relatedness. These findings provide a hypothesis-generating framework for prioritizing disease links and repurposing candidates, but controlled epidemiologic analyses and experimental validation are required before clinical or therapeutic inference.

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.

Background Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, and emerging T cell engager (TCE) immunotherapies require predictive preclinical models that capture patient-specific tumour biology and tumour-immune interactions. Although patient-derived organoid (PDO)-immune co-culture systems show promise for evaluating immunotherapy responses, many rely on matrix-embedded cultures that limit scalability and reproducibility. We therefore developed and validated a low-matrix organoid-T cell co-culture platform for functional assessment of TCE activity in patient-derived CRC models. Methods Patient-derived CRC organoids representing diverse molecular and genetic backgrounds were co-cultured with activated allogeneic CD3+ T cells in a suspension low-matrix format. Matrix concentration, medium composition, T cell activation status, and assay duration were optimized. Tumour killing, T cells activation, cytokine secretion, apoptosis, and motility were assessed using flow cytometry, live-cell imaging, cytokine profiling, and immunofluorescence. The platform was evaluated using EGFR- and HER2-targeting bispecific T cell engagers across 21 CRC organoid models and multiple healthy donor-derived T cell populations. Results Optimization identified 1% matrix and a 1:1 organoid: T cell medium that preserved organoid integrity while maintaining T cell viability, activation, and motility. The final workflow enabled reproducible co-culture of 5-day mature organoids with 7-day activated T cells for 72 hours. EGFR-targeting TCEs induced tumour killing, T cell activation, and IFN-{gamma} secretion across donor-organoid combinations. Screening of 21 CRC organoid models revealed substantial inter-patient heterogeneity, with 12 models maintaining [&ge;]50% baseline viability and supporting functional TCE evaluation. EGFR- and HER2-targeting TCEs produced potent dose-dependent cytotoxicity, with IC50 values ranging from 0.0188 to 54.77 nM across responsive models and up to 75%-85% tumour killing in the most sensitive organoids. These responses were accompanied by increased effector cytokine secretion. Real-time imaging confirmed dynamic T cell engagement and apoptosis-driven organoid destruction following TCE treatment. Conclusions We establish a robust, scalable, human-relevant low-matrix organoid-T cell co-culture platform for functional screening of T cell engagers in patient-derived CRC models. By enabling integrated assessment of tumour killing, immune activation, cytokine responses, and tumour-immune interactions while preserving inter-patient heterogeneity, this system provides a translational framework for immunotherapy development. The platform may support candidate prioritisation, biomarker discovery, patient stratification, and preclinical evaluation of immune-engaging therapeutics.

Abstract Background: Cytolytic activity (CYT), a widely used transcriptomic surrogate of anti-tumor immune cytotoxicity derived from GZMA (granzyme A) and PRF1 (Perforin 1) expression, is associated with clinical outcomes across cancers. MicroRNAs (miRNAs) are key post-transcriptional regulators of tumor immunity, yet their pan-cancer roles in modulating cytolytic activity remain incompletely understood. Objective: This study aimed to identify conserved miRNA regulators of tumor cytolytic activity and their downstream gene-mediated networks across diverse cancer types, while evaluating their clinical and therapeutic relevance. Methods: Matched miRNA and mRNA expression profiles from 9,288 primary tumors across 31 TCGA cancer types were analyzed. A multi-stage framework was applied: per-cancer Spearman correlations (|{rho}| >= 0.30, FDR < 0.05) identified recurrent CYT-associated miRNAs (at least 3 cancer types); these were integrated with TargetScan-predicted targets and subjected to pan-cancer and cross-cancer triple filtering (miRNA-gene and gene-CYT associations). All associations underwent tumor purity adjustment using Consensus Purity Estimate (CPE), with LUMP (Leukocytes Unmethylation for Purity) as sensitivity analysis. Candidates were further prioritized by random forest modeling with bootstrap stability, cancer-type-adjusted Cox regression, mediation analysis, immune cell deconvolution, k-means molecular subtyping, pathway enrichment, and DGIdb-based drug-target prioritization. Results: The analysis converged on 38 high-confidence miRNA-gene-CYT regulatory triplets involving 9 conserved miRNAs and 31 target genes after stringent purity adjustment and multi-layer validation. All nine miRNAs exhibited complete bootstrap stability. Mediation analysis confirmed significant gene-level mediation in 37 of 38 triplets (FDR < 0.01), with mediated proportions up to 94%. The final miRNA signature defined two distinct pan-cancer immune subtypes (immune-hot vs. immune-cold) with significantly different cytolytic activity and overall survival (OS) (HR = 0.754, FDR = 1.12 x 10^-4). The network was enriched for T-cell activation and lymphocyte differentiation pathways and highlighted multiple druggable targets, including CTLA4 and CD274 (PD-L1), nominating 124 candidate compounds. Conclusions: In conclusion, this tumor purity-adjusted pan-cancer study defines a compact, reproducible, and clinically relevant miRNA network that regulates cytolytic activity across diverse malignancies. By linking miRNA biology to immune subtyping and actionable therapeutic targets, the present work provides a valuable foundation for advancing precision immuno-oncology.

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.

Background. Small fibre pathology has been proposed as a peripheral substrate for a subset of patients with fibromyalgia. A 2019 meta-analysis of 8 studies (222 patients) reported a pooled prevalence of 49% with moderate heterogeneity, and concluded that this supported a distinct fibromyalgia phenotype. The evidence base has since expanded substantially. Methods. We systematically searched PubMed for original human studies reporting the proportion of adults with fibromyalgia showing abnormal small fibre findings on an objective structural or functional test (skin biopsy with intraepidermal nerve fibre density, corneal confocal microscopy, sudomotor testing, microneurography, or laser-evoked potentials). Proportions were pooled with a random-effects model on the logit scale (REML). We pre-specified subgroup analyses by diagnostic method and assessment site, meta-regression on publication year and sample size, leave-one-out sensitivity analysis, and risk-of-bias appraisal with the JBI prevalence checklist. Where cohorts overlapped, the most complete report was retained. Search and initial screening were AI-assisted; eligibility, extraction and appraisal were performed independently by two reviewers with consensus. Results. Of 83 records, 34 studies met eligibility and 21 (1,268 patients) provided extractable proportions. Applying a uniform rule of abnormality at any assessed site, the pooled prevalence was 46.5% (95% CI 34.3-59.1), with I2=92.7% and a 95% prediction interval of 8.6% to 89.0%. Individual estimates ranged from 0% to 85.2%. Contrary to our pre-specified hypothesis, diagnostic method did not explain heterogeneity (QM p=0.89, R2=0). Assessment site was the dominant moderator: using each study's site-specific data, distal-leg assessment yielded 30.5% (22.4-40.1; 15 studies) and proximal-thigh assessment 73.9% (53.5-87.4; 4 studies). Three studies assessed both sites in the same patients and all three found more abnormality proximally (31.6% vs 46.2%; 12.3% vs 85.2%; 9.7% vs 83.9%). Publication year (p=0.50) and sample size (p=0.24) were not associated with prevalence. Leave-one-out estimates remained between 44.4% and 49.6%. Conclusions. More than tripling the evidence base did not stabilise the pooled prevalence estimate and more than doubled heterogeneity (I2 68% to 93%). The prediction interval now spans almost the entire possible range, and the method difference reported in 2019 is no longer detectable. Studies do not share a common definition of abnormality, and several do not report one at all. A pooled prevalence figure for small fibre pathology in fibromyalgia is therefore not currently interpretable; a standardised case definition is a prerequisite for meaningful synthesis. The anatomical site of assessment, not the technique, emerges as the dominant source of variation and should be treated as a primary moderator in future work.

Accurate classification of lung adenocarcinoma growth patterns from hematoxylin and eosin (H\&E)-stained whole-slide images is essential for treatment planning and prognosis. We develop and evaluate an exponentially weighted ensemble deep learning approach that combines five architectures (EfficientNet-B3, DeiT3, Swin Transformer, Vision Transformer, ConvNeXt) with weights determined by a softmax transformation of per-model Quadratic Weighted Kappa (QWK) scores. Using 25,545 image patches at 40x magnification from the Dartmouth-Hitchcock dataset, we evaluated performance with stratified 5-fold cross-validation. Individual model performance ranged from Cohen's kappa kappa = 0.5563 (ConvNeXt) to kappa = 0.9561 (EfficientNet-B3). The ensemble achieved 96.52% accuracy and kappa = 0.9648, exceeding the best individual model by delta kappa = +0.0087 with statistical significance (McNemar's test, chi^2 = 50.84, p < 10^-12; bootstrap 95% CI on delta kappa [+0.0056, +0.0117]. Pairwise disagreement rates of 16.6--43.2% between architectures indicate that the models capture different aspects of tissue morphology, and the framework automatically down-weights unreliable components, as illustrated by ConvNeXt receiving only 5.7% ensemble weight despite a single-fold training failure. The approach offers a clinically grounded method for ensemble aggregation in digital pathology.