Journal of Translational Medicine

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.

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.

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.

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.

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.

Background: The C-reactive protein-albumin-lymphocyte (CALLY) index integrates systemic inflammation, nutritional status, and immune function. We evaluated its association with survival in primary lung malignancies. Methods: Embase, PubMed, Scopus, and Web of Science were searched from inception to 18 September 2026. Risk of bias was assessed using the Quality In Prognosis Studies tool. Hazard ratios (HRs) were synthesized using random-effects models with restricted maximum likelihood estimation and Hartung-Knapp adjustment. Sensitivity analyses, subgroup analyses, and meta-regression examined robustness and heterogeneity. Results: Eleven studies contributed 13 cohorts to the overall survival (OS) analysis and 8 cohorts from seven studies to a combined recurrence/progression-free survival (RFS/PFS) analysis. Lower CALLY was associated with poorer OS (HR 1.78, 95% CI 1.49-2.12; I2=71.6%) and poorer RFS/PFS (HR 1.60, 95% CI 1.40-1.84; I2=0%). Associations persisted in leave-one-out analyses. No statistically significant subgroup differences were detected. Funnel-plot asymmetry suggested small-study effects for OS and RFS/PFS. Seven studies had moderate and four had high overall risk of bias. The certainty of evidence was rated as low for both OS and RFS/PFS, primarily due to risk of bias, heterogeneity, and endpoint indirectness. Conclusion: Lower CALLY was associated with adverse survival outcomes in the current synthesis. Methodological limitations and variation in biomarker measurement and endpoint definitions restrict interpretation. Prospective validation using standardized measurements and clinically coherent outcomes is needed to establish incremental prognostic value and clinical utility. Keywords: CALLY index; lung cancer; prognosis; overall survival; systematic review; meta-analysis

Abstract Rationale: Vagal sensory neurons are molecularly and functionally diverse, and they play critical roles in visceral sensation and homeostasis. The vagal system is anatomically divided into jugular and nodose ganglia, which exhibit distinct peripheral innervation patterns and physiological functions. Although recent transcriptomic studies have characterized nodose neuronal subtypes in detail, the molecular profiles of jugular sensory neurons remain poorly defined. Objectives: To define the molecular profiles of genetically identified jugular sensory neurons and determine the transcriptional responses of jugular and nodose airway afferents to allergic airway inflammation. Methods: We performed deep transcriptomic profiling of two genetically defined jugular nociceptor populations, MrgprC11 and MrgprD neurons, and compared them with airway-innervating P2ry1 nodose neurons and Phox2b nodose neurons. We further examined transcriptional changes of MrgprC11 and P2ry1 neurons following OVA-induced allergic airway inflammation. Measurements and Main Results: Transcriptomic analyses revealed distinct molecular signatures of jugular nociceptive neurons and identified subtype-specific genes across diverse functional categories. Following OVA challenge, MrgprC11 and P2ry1 neurons exhibited strikingly divergent transcriptional responses. More than 400 genes that were differentially expressed in both neuronal populations were regulated in opposite directions. GO enrichment analysis suggested that MrgprC11 jugular neurons adopted a metabolically restrained state with reduced translation and bioenergetic activities, whereas P2ry1 nodose neurons exhibited metabolically active state with enhanced translation and bioenergetic activities. Conclusions: These findings provide new insight into vagal sensory neuron diversity and reveal distinct transcriptional adaptations of jugular and nodose airway afferents during allergic inflammation.

Background: Chronic kidney disease (CKD) may progress to stage 5 CKD (end-stage renal disease, ESRD), but stage-associated molecular alterations remain incompletely characterized. This study aimed to identify candidate biomarkers associated with CKD and ESRD. Methods: Two discovery datasets comparing CKD or ESRD with healthy controls were analyzed using differential expression analysis and weighted gene co-expression network analysis. Candidate genes were identified by intersecting differentially expressed genes with key co-expression modules, followed by protein-protein interaction network analysis using Degree, Closeness, and Maximal Clique Centrality algorithms. Candidate genes were evaluated in independent GEO validation datasets and further examined by quantitative real-time PCR and western blotting using clinical blood samples. Results: Ten hub genes were identified, among which EPB42, KLF1, and H3F3A were prioritized for further evaluation. EPB42 showed decreased expression in CKD but increased expression in ESRD, indicating an opposite-direction pattern between the two disease states. KLF1 showed no significant difference between healthy controls and CKD but was increased in ESRD, whereas H3F3A was increased in CKD and further increased in ESRD. Functional enrichment analysis highlighted processes related to oxygen transport, mitochondrial function, muscle-associated processes, and inflammation, suggesting possible involvement of hypoxia-associated oxidative injury and ferroptosis-related processes. Conclusions: Integrated transcriptomic and experimental analyses identified EPB42, KLF1, and H3F3A as candidate stage-associated biomarkers in CKD and ESRD. In particular, the opposite-direction expression pattern of EPB42 may provide additional molecular information for distinguishing CKD from ESRD.

Objective Based on the bioinformatics method of multi-database integration, this study systematically analyzes the expression characteristics, clinical pathological correlation, prognostic value, potential molecular mechanisms, and immune infiltration patterns of hepatocyte growth factor receptor (MET) in gastric cancer. Methods The UALCAN and GEPIA databases were employed to examine the differential expression of MET between gastric cancer and normal gastric mucosal tissues, as well as its associations with clinicopathological features. Kaplan-Meier Plotter was utilized to evaluate the impact of MET expression on overall survival (OS) and progression-free survival (PFS). Protein-protein interaction (PPI) network was constructed via LinkedOmics, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of co-expressed genes. Four algorithms, TIMER, CIBERSORT, EPIC, and MCPcounter, were used to cross evaluate the correlation between MET expression and immune cell infiltration; Further validate the cell type specific expression of MET using the gastric cancer single-cell sequencing queues (GSE134520, GSE167297) built into the TISCH database. Results MET expression was significantly elevated in gastric cancer tissues compared with normal gastric mucosa (P < .05), and its expression level was significantly correlated with tumor grade and TNM stage. Patients with high MET expression exhibited significantly poorer OS and PFS than those with low MET expression (P < .05). The PPI network revealed that MET could interact with 20 key proteins, including EGFR, ERBB2, HGF, STAT3, and GRB2 etc. GO enrichment analysis suggests that differentially expressed genes are significantly enriched in functions such as the ERBB signaling pathway, cadherin binding, and DNA repair complexes; KEGG enrichment analysis showed that MET related genes were significantly enriched in pathways such as homologous recombination, nuclear cytoplasmic transport, mismatch repair, and oxidative phosphorylation. Immune infiltration analysis showed that the negative association between MET and B cells infiltration has cross algorithm robustness, while the association with neutrophils, CD8+ T cells, CD4+ T cells, and macrophages exhibits algorithmic heterogeneity or insignificance; There is no significant correlation between MET and common immune checkpoint molecules such as PD-1, PD-L1, CTLA4, etc. Single cell validation further confirmed that MET is mainly enriched in malignant epithelial cells and endothelial cells, and is almost not expressed in immune cells. Conclusions Multidimensional bioinformatic analyses demonstrate that elevated MET expression serves as an independent risk factor for unfavorable prognosis in gastric cancer. MET may mediate dual drug resistance in gastric cancer via crosstalk with multiple signaling molecules (including EGFR, ERBB2, HGF, STAT3 and GRB2) and dysregulation of the homologous recombination repair pathway. Results from multiple-algorithm immune infiltration analysis, single-cell dataset analysis and immune checkpoint correlation analysis indicate that MET exerts only modest direct regulatory effects on the gastric cancer immune microenvironment. This exploratory study offers systematic bioinformatic evidence supporting MET as a candidate prognostic biomarker and potential therapeutic target for gastric cancer. Further functional experiments and prospective cohort studies are required to validate its molecular mechanisms and clinical utility.

Psoriasis is a chronic skin disease characterized by red, scaly plaques mainly driven by dysregulated T cells that induce keratinocytes (KCs) proliferation and inflammation. Despite significant advancements in disease management through biologics and topical therapies, these treatments offer only temporary relief and fail to prevent disease recurrence. Considering the current therapeutic needs, we described herein a novel small molecule TEM1657, derived from 3,5-dicaffeoylquinic acid with anti-inflammatory potency in imiquimod-induced psoriasis mouse model as well as in Th17-stimulated KCs. Mechanistically, its action is mediated through modulation of mitochondrial dynamics, specifically via OPA1, an inner mitochondrial membrane protein that alters mitochondrial calcium influx, and further impacts cellular bioenergetics. Moreover, TEM1657 rapidly suppresses transcription of NFKBIZ and various other psoriasis-associated genes. These observations highlighted a pathway that represents an innovative target in psoriasis and other NFKBIZ-related disorders.

Purpose Tarlatamab, a bispecific T-cell engager, has generated favorable response and survival outcomes in relapsed small cell lung cancer (SCLC). Factors influencing tarlatamab-associated treatment outcomes and toxicity have yet to be established. We performed a multivariate analysis to identify biomarkers associated with cytokine release syndrome (CRS) and immune effector cell associated neurotoxicity syndrome (ICANS) and integrated these variables into a novel risk stratification tool to identify patients at high risk of clinically significant toxicity. Patients and Methods Logistic and ordinal regression analyses evaluated the predictive value of variables contributing to the presence and severity, respectively, of CRS and ICANS in 115 patients with ES-SCLC receiving tarlatamab. Selected variables were then tuned using 5-fold cross-validation and incorporated into novel elastic net-based prediction models. Results Rates of observed CRS, ICANS, and dysgeusia were 46%, 28%, and 47%, respectively. ORR (overall response rate) and DCR (disease control rate) were 44% and 58.2%, while 6-month PFS (progression free survival) and OS (overall survival) rates were 30.4% and 54.9%, respectively. Excluding patients on tarlatamab <30 days, presence of dysgeusia was associated with a lower rate of disease progression (HR 0.44, p=0.00691) and death (HR 0.34, p=0.0145). The presence of, increased size and number of extracranial metastases as well as elevated baseline LDH were significantly associated with increased likelihood of CRS. Increased ECOG performance status, increased volume of brain metastases, development of CRS, and elevated baseline LDH were all associated with increased risk of developing ICANS. Our cross-validated risk model successfully predicted CRS grade [&ge;]2 and ICANS events with 90.9% and 75% sensitivity, respectively. Conclusions This exploratory analysis identified several predictive biomarkers of toxicity that successfully stratified low and high-risk populations as part of an internally validated novel risk scoring tool.

Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride (CaF2) and mirrored stainless-steel (SS) slides. Raw SS spectra were more intense than CaF2, and routine dewaxing left prominent paraffin peaks on CaF2 that were largely absent on SS. Extending CaF2 xylene immersion to 48 h reduced key paraffin peaks by 11 to 23%, aligning its biochemical profile with SS. Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65 - 0.66), while CaF2 improved from 0.41 - 0.57 to 0.65 - 0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.

Objective To systematically evaluate the predictability of drug chemical structures against the complete set of MedDRA Preferred Terms (PTs), construct a comprehensive structure adverse reaction landscape, validate the utility of molecular fingerprint representations in pan-screening tasks, and establish quantitative tools for drug safety pre-screening. Methods Drug adverse event association data were compiled from SIDER 4.1 and its derivative compilation Nsider, encompassing 2,005 approved drugs, of which 1,120 small-molecule drugs with successfully retrieved chemical structures were retained for analysis. For each Preferred Term (PT) meeting the minimum sample-size threshold ([&ge;]25 positive and [&ge;]30 negative drugs; n = 768), XGBoost classifiers were trained using MACCS 166-bit fingerprints, with model performance evaluated via 5-fold stratified cross-validation. A PT was designated as high-confidence if it achieved a validation AUC [&ge;] 0.75, a cross-validation AUC [&ge;] 0.70, and passed Benjamini Hochberg false discovery rate (FDR) correction (q < 0.05). The entire analytical pipeline was replicated using Morgan ECFP4 2048-bit fingerprints to assess the robustness of fingerprint selection. The robustness of significance screening was independently validated by permutation testing (500 random label permutations), and model generalizability was evaluated through Bemis-Murcko scaffold-based stratified split validation. SHAP (SHapley Additive exPlanations) analysis was employed to identify critical structural features, and mechanism-based case studies further demonstrated concordance between model-identified features and established pharmacophores. Results Among the 768 modelable PTs, 71 (9.2%) met the high-confidence significance criteria (FDR q < 0.05), predominantly comprising nervous system disorders (10 PTs) and psychiatric disorders (9 PTs). The toxicities exhibiting the strongest structural predictability included hyperprolactinaemia (AUC = 0.968), dermatitis acneiform (AUC = 0.966), tardive dyskinesia (AUC = 0.952), and breast tenderness (AUC = 0.979). Under a unified XGBoost hyperparameter configuration, the low-dimensional MACCS fingerprint (166-bit) demonstrated near-equivalent performance to the high-dimensional Morgan fingerprint (2048-bit) in pan-screening tasks ({Delta}AUC = -0.012, Cohen's d = 0.16), and achieved comparable performance at a substantially lower dimensionality across 18 of 22 organ systems. SHAP analysis identified key structural fragments corresponding to established pharmacophores (six-membered rings, tertiary amine cores, heteroatomic bonds, and bridged heteroatomic chains), and mechanism-based case studies further demonstrated that the model captured structural patterns concordant with known pharmacophores of D2 antagonists, EGFR inhibitors, and related agents. Bemis-Murcko scaffold-based stratified split validation revealed a median AUC decrease of merely 0.042, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions. Immune-mediated and multifactorial endpoints remained structurally opaque under the current data conditions. Conclusion This study constructed a comprehensive structure-adverse reaction predictability landscape encompassing 768 MedDRA PTs; seventy-one PTs achieved high-confidence criteria. The MACCS 166-bit fingerprint achieved near-equivalent performance to the Morgan 2048-bit fingerprint at a markedly lower dimensionality, and its chemically explicit features enable direct translation of model outputs into structural alerts, providing a computational tool for prospective screening of potential adverse reaction profiles for any novel compound. A tiered assessment strategy is proposed: machine learning ensemble models for high-confidence screening, substructure alert rules for high-specificity exclusion, and pharmacovigilance signal detection for complementary coverage of structurally unpredictable endpoints. Bemis-Murcko scaffold-based stratified split validation further demonstrated that the median generalization performance decrease was merely 0.042 AUC, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions, corroborating the existence of a genuine chemical basis underlying structure-adverse reaction mappings.

Background: Prediabetes is an intermediate state of increased glucose concentrations above the normal range but below the diagnostic threshold for type 2 diabetes (T2D). Adults with overweight and prediabetes are at increased risk of progression to T2D and metabolic imbalance. Feiolix -a whole-fruit feijoa powder rich in abscisic acid, polyphenols and dietary fibre which may support metabolic health with complementary pathways. Objective: To evaluate the effects of low and high-dose Feiolix compared with placebo, on HbA1C, glucagon like peptide (GLP-1) and other metabolic parameters. Methods: Adults aged 21-65 years with prediabetes i.e. glycosylated hemoglobulin (HbA1c) 5.7-6.4% and overweight with body mass index (BMI)- 25.0-29.9 kg/m2 were randomised to receive Feiolix-LD (300 mg/day) or Feiolix-HD (1,150 mg/day), or placebo (maltodextrin) for 12 weeks. Anthropometric measurements were obtained monthly, and fasting blood samples were collected at baseline and 12 weeks post intervention. Postprandial blood samples were collected at baseline and 12 weeks. Outcomes included HbA1c, GLP-1, fasting plasma glucose, insulin resistance lipid markers, body weight and BMI. Analyses were conducted using ANCOVA model with baseline values as covariates. Result: Of 41 individuals screened, 39 were randomised (13 per group); one participant withdrew from the Feiolix-LD. At week 12, HbA1c decreased by 0.5% in the Feiolix-HD and by 0.4% in the Feiolix-LD, with both reductions significantly greater than placebo (p < 0.05); Compared with placebo, Feiolix supplementation significantly lowered fasting glucose, insulin, HOMA-IR, total cholesterol and LDL-cholesterol. The Feiolix-HD significantly attenuated the increase in triglycerides observed in the placebo group and was associated with significant reductions in body weight and BMI versus placebo by month 2. However, no change in GLP-1 was observed between the treatments. No treatment-related adverse events were reported. Conclusion: This study supports the potential of Feiolix supplementation to improve metabolic health, including glycemic and lipid outcomes, with additional benefits for body weight and BMI at the higher dose. This study supports the potential of Feiolix supplementation to improve metabolic health, including glycemic and lipid outcomes, with additional benefits for body weight and BMI at the higher dose

Kaposi's sarcoma (KS) is an angioproliferative tumor associated with infection by human herpesvirus 8 (HHV-8), also known as Kaposi's sarcoma-associated herpesvirus (KSHV). Among the viral genes involved in KS pathogenesis, the viral G protein-coupled receptor (vGPCR) plays a key role in oncogenic transformation and angiogenesis, and its sustained activity contributes to tumor development and maintenance. Quercetin (QUE), a naturally occurring flavonoid with well-recognized anticancer properties, has been shown to reduce cell viability and proliferation and to induce apoptosis in vGPCR-transformed endothelial cells. This study further investigated the mechanisms underlying the antineoplastic effects of QUE in vGPCR cells, an in vitro model of Kaposi's sarcoma. Initially, QUE promoted the activation of MAPK signaling pathways in vGPCR cells, increasing the phosphorylation levels of ERK1/2, JNK1/2, and p38, without affecting AKT phosphorylation. In parallel, QUE increased intracellular oxidant levels, followed by lipid peroxidation and DNA damage, supporting a link between oxidative imbalance and genotoxicity. However, increased total glutathione content, and unaltered Gpx4 mRNA expression, suggest the induction of oxidative stress and compensatory antioxidant responses. Furthermore, increased intracellular oxidant levels were associated with MAPK signaling activation, particularly JNK1/2, whereas ERK1/2 and p38 activation may reflect a compensatory response that is insufficient to overcome QUE-induced cytotoxicity. Taken together, these findings indicate that QUE exerts antiproliferative effects in vGPCR cells through a mechanism involving oxidative stress, MAPK activation, and DNA damage, supporting its potential relevance in the study of therapeutic strategies for virus-associated malignancies.

Peripheral nerves are increasingly recognized as active components of the tumor microenvironment (TME). Yet, their role in non-muscle-invasive bladder cancer (NMIBC) and response to Bacillus Calmette-Guerin (BCG) remains poorly defined. Using multispectral imaging, we characterized autonomic and sensory nerve populations within the tumor-adjacent stroma of 241 treatment-naive high-risk NMIBC tumor regions from 141 patients. Stromal tumor-associated nerve (STaN) abundance varied substantially across patients and was associated with aggressive clinicopathological features, BCG failure, and disease progression. Unsupervised clustering resolved distinct STaN phenotypic groups with differential clinical and spatial associations. Noradrenergic and a neurochemically heterogeneous STaN populations showed the strongest associations with adverse outcomes. In contrast, a mixed population of nerves with high expression of the synaptic vesicle protein synaptophysin exhibited prominent spatial remodeling associated with BCG failure, despite limited prognostic value by abundance alone. Integration with an independent single-cell atlas nominated cancer-associated fibroblasts and mast cells as candidate cellular intermediaries of neural communication within the TME. Matched transcriptomic profiling revealed coordinated neuronal, extracellular matrix, lipid metabolism, and immune programs in STaN-high tumors, whereas STaN-low tumors preferentially enriched proliferative and hypoxic stress-response pathways. Leveraging these transcriptional features, we developed a transcriptomic classifier that distinguished STaN-high from STaN-low tumors in an independent test cohort (AUC = 0.87) and stratified clinical outcomes in additional patient cohorts. Together, these findings establish tumor innervation as a heterogeneous and clinically relevant feature of the NMIBC microenvironment, with neural abundance, phenotype, and spatial organization capturing distinct aspects of tumor behavior and clinical outcome.

Despite the advent of immunotherapy in the treatment of human-papilloma-virus (HPV)-negative head and neck squamous cell carcinoma, the overall prognosis still remains dismal. Novel therapeutic approaches are thus urgently needed. We recently reported that SET and MYND containing 3 (SMYD3), a protein methyltransferase that is known to activate the transcription of its target genes through H3K4me3, is overexpressed in ~60% of HPV-negative HNSCC tumors, it drives proliferation and invasion, and induces resistance to immunotherapy in HPV-negative HNSCC. These phenotypes are mediated partially through transcriptional regulation of cell cycle, EMT and immune-related target genes by SMYD3. Despite its enzymatic inhibitory potency in HPV-negative HNSC cell lines, the commercially available SMYD3 inhibitors EPZ031686 and BAY-6035 demonstrated no in vitro phenotypic efficacy in these cell lines, supporting that SMYD3 exerts its oncogenic functions predominantly through catalytically-independent mechanisms. Thus, to effectively target SMYD3, we designed a series of SMYD3 proteolysis targeting chimeras (PROTACs). Treatment of HPV-negative HNSCC cell lines with SMYD3 PROTACs at nanomolar concentrations degraded SMYD3 effectively and induced a drastic decrease in the proliferative and invasive potential of HPV-negative HNSCC cells. One of the designed compounds, IAP-08, showed a promising DC50 at ~177nM with Dmax of ~90%, and effectively induced the formation of ternary complexes with SMYD3 and the XIAP E3 ligase. Furthermore, IAP-08 showed a favorable pharmacokinetic profile in vivo, underscoring the feasibility for further clinical development of this compound. Mass spectrometry analysis of IAP-08 supported its high specificity towards SMYD3. SMYD3 depletion by IAP-08 in HNSCC cells revealed transcriptional regulation of cell cycle, EMT- and type I IFN response genes, in contrast to SMYD3 inhibition which had only a weak transcriptional effect. In summary, we show that inhibition of the enzymatic activity of SMYD3 is not sufficient to repress its oncogenic activity and provide evidence that depletion through PROTACs effectively targets the oncogenic functions of SMYD3 in HPV-negative HNSCC.

Importance: Dyslipidemia, hypertension, and diabetes are highly prevalent conditions for which medical nutrition therapy (MNT) is guideline-recommended, yet access remains limited, and real-world evidence on technology-enabled MNT models at scale is sparse. Objective: To characterize lipid, blood pressure, and glycemic outcomes among adults receiving virtual MNT (vMNT) across three cardiometabolic conditions. Design: Retrospective cohort study of adults with a first Nourish appointment before November 2025, followed for 6 months. Setting: National vMNT platform. Participants: Adults meeting enrollment criteria for dyslipidemia, hypertension, or diabetes and remaining engaged in care with [&ge;]1 follow-up measurement at 3 or 6 months. Final cohorts: dyslipidemia (n=6,501), hypertension (n=13,704), diabetes (n=2,444); 18,965 unique patients overall. Exposure: vMNT delivered by a registered dietitian via telehealth visits, with technology-enabled support between visits. Main Outcomes and Measures: Outcomes were LDL cholesterol, total cholesterol, HDL cholesterol, and triglycerides (dyslipidemia); systolic and diastolic blood pressure (SBP, DBP) (hypertension); and HbA1c (diabetes), assessed at enrollment, 3, and 6 months. Weight was a secondary outcome. The proportion achieving guideline-recommended targets (HbA1c <7.0%, SBP <130 mmHg) was also assessed. Results: Among 18,965 patients (mean [SD] age, 42.9 [11.6] years; 15,770 [83.2%] female), significant changes from baseline to 6 months included: LDL cholesterol (-14.0 mg/dL [SE, 0.56]); total cholesterol (-19.4 mg/dL [SE, 0.61]); HDL cholesterol (-0.5 mg/dL [SE, 0.15]); triglycerides (-28.6 mg/dL [SE, 1.06]); SBP (-5.5 mmHg [SE, 0.16]); DBP (-4.5 mmHg [SE, 0.11]); and HbA1c (-1.37 percentage points [SE, 0.04]) (all P<.001). Among those with elevated SBP, 57.3% achieved <130 mmHg by 6 months; among those with elevated HbA1c, 54.7% achieved <7.0%. Outcomes among patients with stable medication regimens were similar to the full cohort. Conclusions and Relevance: In this study of 18,965 patients, vMNT delivered through a technology-enabled care model was associated with clinically meaningful improvement across three prevalent cardiometabolic conditions. These findings support technology-enabled virtual care as a scalable approach to delivering effective MNT.

Introduction: Endothelial-mesenchymal transition (EndoMT) is a dynamic endothelial cell-state transition increasingly implicated in fibrosis, vascular dysfunction, inflammation and cancer. Despite expansion of EndoMT-focused transcriptomic research, EndoMT representation remains limited to broader epithelial-mesenchymal transition (EMT) frameworks in standard gene set repositories. Methods: To address the absence of a general-purpose EndoMT gene set for transcriptomic enrichment analysis, we developed an open EndoMT gene set resource and accompanying R package. A literature-informed EndoMT gene set was curated and directionally assigned to EndoMT UP and EndoMT DOWN signatures according to reported or hypothesised expression during partial EndoMT, defined here as an active transitional state rather than a complete mesenchymal endpoint, to improve capture of EndoMT-associated transcriptional changes across different stages of the process. Genes were annotated by marker subtype and implemented as Gene Matrix Transposed files distributed through the R package, together with exported R objects and gene-level metadata. Benchmarking was performed using bulk RNA-seq data sets GSE118446, comprising seven-day IL-1l{beta}/TGF-{beta}2-treated human umbilical vein endothelial cells (HUVECs) and human pulmonary arterial endothelial cells, and GSE342578, comprising three-day TGF-{beta}1/2/3-treated HUVECs; single-cell RNA-seq data set GSE237895, comprising seven-day TGF-{beta}1-treated and SNAI1-overexpressing HUVECs; and endothelial cell and cancer-associated fibroblast populations from human metastatic melanoma single-cell RNA-seq data set GSE72056. Results: In GSE118446, EndoMT UP was positively enriched and EndoMT DOWN negatively enriched in chronically IL-1{beta}/TGF-{beta}2-treated endothelial cells, concordant with experimentally validated EndoMT induction, while partial EndoMT-associated enrichment was detected in single ligand-treatment. In contrast, EndoMT UP was negatively enriched following acute TGF-{beta}1/2/3 treatment, suggesting temporal differences between acute TGF-{beta} stimulation and chronic EndoMT induction. In GSE237895 mesenchymal-like endothelial cells, EndoMT UP was positively enriched and EndoMT DOWN negatively enriched in both TGF-{beta}1-treated and SNAI1-overexpressing models. EndoMT gene sets also discriminated endothelial and mesenchymal/stromal transcriptional states in GSE72056 metastatic melanoma. Marker subtype and Gene Ontology Biological Process analyses provided additional exploratory resolution of EndoMT-associated components beyond MSigDB Hallmark EMT. Discussion: The EndoMT R package provides a dedicated, accessible and interoperable resource for EndoMT-focused transcriptomic enrichment analysis. Benchmarking demonstrates utility for detecting established, partial and divergent EndoMT-associated states, supporting broad applicability across EndoMT biology research.

CC10137 is a first-in-class peripherally restricted 2A-adrenergic receptor (2AAR) agonist with broad-spectrum analgesic efficacy and a favorable safety profile. Systematic exploration of the chemical space surrounding first-in-class leads is important for defining series boundaries and guiding continued optimization, but conventional analogue-by-analogue medicinal chemistry samples only a small fraction of the accessible structural space. Here, we used a scaffold-seeded enumeration strategy to expand the chemical space surrounding CC10137 from four SAR-informed seed compounds comprising CC10137 and three closely related structural variants. Application of predefined medicinal chemistry transformation rules in StarDrop generated a virtual library of 16,601,163 unique structures. Morgan fingerprint-based principal component analysis indicated that the library occupied a highly multidimensional structural space involving variation in scaffold substitution, peripheral functional groups, and side-chain composition. A retrospective comparison set of 43 compounds independently designed and experimentally characterized in the earlier CC10137 program represented only approximately 0.00026% of the 16.6-million-member library, yet all 43 were recovered as exact structural matches. Three compounds selected directly from the virtual library retained 2AAR binding affinity and agonist potency below 25 nM. Five representative compounds further showed significant anti-allodynic effects in the in vivo spared nerve injury model, with inhibition rates ranging from 39.3% to 55.7%. These findings support scaffold-seeded computational enumeration as a practical strategy for systematic chemical-space mapping around a first-in-class lead and for identifying additional pharmacologically active structural regions for further optimization.