Journal of Translational Medicine

BackgroundPrimary and secondary resistance to immune checkpoint blockade (ICB) remains a critical challenge in advanced melanoma. Oncolytic Viruses (OV) selectively lyse tumor cells while generating systemic anti-tumor immune responses with minimal side effects. Yet their clinical use is limited to refractory melanoma patients and are only given in combination with second-line ICB regimens. ICB can both help and hinder OV efficacy depending on the source of checkpoint interactions across the tumor-immune microenvironment (TiME). However, functional checkpoint interactions are typically inferred from gene or protein expression and rarely contextualized within myeloid- and antigen presenting cell-associated immune niches during OV therapy, despite these populations dominating melanoma TiMEs and serving as key regulators of anti-viral immunity. MethodsAn integrated multi-omics framework combining Nanostring GeoMx digital spatial profiling (DSP), COMET sequential immunofluorescence (seqIF) and functional oncology mapping (FuncOmap) was applied to melanoma patient tissues collected pre- and post-neoadjuvant Talimogene Laherparepvec (T-VEC) to characterize immune remodeling and directly quantify checkpoint interaction dynamics associated with pathologic responses to OV therapy. ResultsT-VEC induced broad lymphocyte- and myeloid-associated immune transcriptional activation across melanoma TiMEs; however, pathologic responses could not be defined by bulk transcriptomics or cellular deconvolution alone. COMET seqIF analysis identified that HSV-associated M1/APC-like tumor-associated macrophages (TAMs) were enriched in complete pathologic response (CR) tissues and were a major source of PD-1/PD-L1 interaction niches. While partial (PR) and non-pathologic response (NR) tissues retained melanoma-centered PD-1/PD-L1 interaction niches and were enriched for B cell and M2-like TAM populations. FuncOmap analysis indicated that post-T-VEC PD-1/PD-L1 interaction states were consistently elevated in tumor bed, but not in lymph node tissues, across all pathologic response groups. Suggesting that immune checkpoint interactions may benefit T-VEC therapeutic responses depending on their spatial and immune context relative to OV infection. ConclusionsThese findings highlight the importance of integrated transcriptomic and functional proteomic analyses for resolving the spatial distribution and functional status of immune niches during OV therapy. Resolving PD-1/PD-L1 interaction states to specific M1/APC-like TAM and B cell niches may define mechanisms of responses and resistance to OV therapy.

Liver sinusoidal endothelial cells (LSECs) are increasingly recognized as a critical yet underexplored cell type in anti-fibrotic drug development. This study presents a computational drug screening platform integrating LSEC-specific transcriptomic analysis across simple steatosis, fibrotic nonalcoholic steatohepatitis (NASH), and cirrhosis, with tiered gene signature selection combining machine learning, large language model-assisted curation, gene safety assessment, and Connectivity Map-based screening using human endothelial perturbational profiles. The platform identifies 6 clinical-stage and 8 preclinical candidates with LSEC-protective potential. Among these, vorinostat (SAHA), a clinically approved histone deacetylase (HDAC) inhibitor, is selected for experimental validation. In hepatocyte-specific Asah1-deficient mice fed a Paigen diet, SAHA attenuates hepatic inflammation, fibrosis, LSEC dysfunction, and portal hemodynamic abnormalities, with effects confirmed in a hepatotoxin (CCl4)-induced fibrosis model. High mobility group box 1 (HMGB1) is identified as a key hepatocyte-derived paracrine mediator of LSEC injury through Transwell co-culture and glycyrrhizin rescue. Vorinostat dose-dependently reverses HMGB1-induced LSEC dysfunction across inflammation, capillarization, fibrogenesis, and vasoconstriction, associated with endothelial transcription factor reprogramming including KLF2 upregulation, validated in primary LSECs and in vivo. SAHA also protected LSECs from TNF--induced inflammation and reduced monocyte adhesion. These findings establish an LSEC-focused drug repurposing framework and identify candidates for LSEC-protective anti-fibrotic therapy. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/727430v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@a71eb3org.highwire.dtl.DTLVardef@1d090acorg.highwire.dtl.DTLVardef@1ca1450org.highwire.dtl.DTLVardef@1fbb3fb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Black patients and individuals with low socioeconomic status (SES) face significant disparities in accessing curative therapies for hepatocellular carcinoma (HCC), including liver transplantation. This study aimed to develop provider-co-created intervention prototypes in response to patient-identified barriers and recommendations. Methods: A human-centered design session with hepatology and transplant providers at a large academic medical center was conducted. Prior to the session, participants were presented with barriers and preliminary solutions identified through an earlier human-centered design session with Black and low-SES patients. Using structured ideation methods, including brainwriting, challenge mapping, and concept voting, providers co-created intervention prototypes. Final concepts were synthesized from patient insights, provider input, and design methods using affinity diagramming and concept modeling. Results: Nine providers participated in the session. They focused on three key areas for intervention: inefficiencies in transplant pre-evaluation, inadequate social support, and information overload. Solutions included: (1) a structured triage pathway to standardize referrals and reduce delays; (2) a peer navigator model to guide patients through the transplant process; and (3) a multimodal transplant education roadmap to improve comprehension and engagement. These prototypes addressed both patient- and system-level barriers. Conclusions: Protypes developed through provider-led design, grounded in patient-identified barriers and co-created ideas, can yield actionable, scalable strategies to advance equity in HCC care. Future work will refine these prototypes through patient feedback and pilot them in clinical settings.

Introduction: Myalgic Encephalomyelitis (ME)/Chronic Fatigue Syndrome (CFS) is a debilitating condition characterised by severe fatigue and post-exertional malaise (PEM). Reported neuropsychophysiological abnormalities suggest ME/CFS is multifactorial, but current knowledge remains fragmented. This study protocol outlines a multimodal investigation designed to (1) compare neuropsychophysiological mechanisms between ME/CFS patients and healthy participants, (2) test an integrative model of ME/CFS, (3) identify neuropsychophysiological subgroups within the patient population, and (4) identify predictors of symptom response during rehabilitation. Methods and analysis: This study will enroll 115 ME/CFS patients and 55 healthy participants. Groups will be comparable in age, sex, and education level, with a larger patient sample enabling subgroup and longitudinal analyses. A cross-sectional assessment at baseline will be carried out in both groups. Patients will then be evaluated longitudinally throughout a standardized cognitive-behavioral therapy rehabilitation program delivered as routine care. Baseline measures include systemic inflammation and general health biomarkers, measures of autonomic and central nervous system function, neuroinflammation (magnetic resonance spectroscopy, [18F]DPA714 PET in a subsample), serum short-chain fatty acid levels, gut microbiota composition and function, and neuroendocrine and self-reported responses to psychosocial stress. Fatigue severity (physical and cognitive) and PEM will be assessed through validated questionnaires, ecological momentary assessment, and laboratory tasks. These will be re-evaluated during therapy, and all non-neuroimaging measures will be repeated after the rehabilitation program. Statistical analyses will comprise multivariate analysis of variance, general linear models, classification algorithms, structural equation models, least absolute shrinkage selection operator principal component regression (LASSO-PCR), cluster analysis and latent class growth analysis (LCGA).

BackgroundTumor-associated macrophages (TAMs) are key drivers of the immunosuppressive tumor microenvironment (TME), supporting tumor progression through diverse functions. However, mechanistic studies of TAM polarization remain limited by the lack of physiologically relevant human model systems that capture stromal-immune interactions and macrophage heterogeneity. MethodsWe established advanced human co-culture systems that integrate healthy donor-derived macrophages with patient-derived organoids and tumoroids (PDOs and PDTs), as well as matched normal fibroblasts (NFs) and cancer-associated fibroblasts (CAFs). These multicellular models enabled the investigation of interactions among stromal, epithelial, and immune cells within tumor and adjacent normal tissue environments. ResultsThe co-culture systems recapitulated distinct macrophage states associated with tumor and adjacent normal environments and identified fibroblasts as major regulators of macrophage phenotypes. CAFs promoted macrophage metabolic remodeling characterized by altered lipid handling and enrichment of TAM-like signatures. Mechanistically, we identified thrombospondin 1 (TSP1) as a CAF-secreted factor linked to metabolic priming. Recombinant TSP1 induced transient lipid accumulation followed by mitochondrial remodeling. In tumor co-culture conditions, CD36 inhibition reduced lipid accumulation in macrophages, supporting a role for TSP1-linked lipid crosstalk in stromal-immune interactions. ConclusionOur study establishes advanced patient-derived co-culture models as a platform to investigate human TAM biology and stromal-immune interactions in CRC. Using these systems, we identify a fibroblast-associated TSP1-lipid axis linked to macrophage metabolic remodeling and TAM-like polarization, highlighting stromal metabolic communication as a potential targetable feature of the CRC microenvironment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/728363v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@16d60aborg.highwire.dtl.DTLVardef@1e593e7org.highwire.dtl.DTLVardef@15124b0org.highwire.dtl.DTLVardef@3fca92_HPS_FORMAT_FIGEXP M_FIG C_FIG

Currently, the genetic architecture of Middle Eastern populations is underrepresented in global genomic databases. This gap increases the rate of Variants of Uncertain Significance (VUSs) and clinical misinterpretations of genomic data especially in Middle Eastern populations. Whole exome sequencing was conducted on 90 healthy individuals from Jordan and the data were analysed using Principal Component Analysis (PCA) and multi-computational filtering. PCA revealed a double ancestry (EUR-AFR) admixture rather than a triple admixture (EUR-AFR-AMR). More than 3,500 populations-specific variants (PSVs) were identified, of which 72% were singletons. Additionally, 19 variants were significantly enriched compared to the maximum allele frequencies in public global databases (Fisher's exact test with Benjamini-Hochberg false discovery rate correction, p-value < 0.05). Consequently, the results suggest the reclassification of variants of Uncertain Significance (VUS) which reside in the ECE2 gene to likely benign and the variants of Conflicting Classification of Pathogenicity in the genes IL1RN and THPO to benign based on the significant allele frequency (AF=0.0389, p-value < 0.05). Furthermore, a pathogenic ClinVar variant was identified in a healthy individual, warranting careful interpretation. The findings underscore the importance of identifying PSVs in order to minimize or even prevent clinical misdiagnosis and highlight the unique genetic signature in Jordan. The study serves as a foundational resource for precision medicine in the region.

Background: Immune checkpoint inhibitors have transformed cancer treatment, yet a large number of patients fail to respond. Identifying molecular characteristics that predict response before treatment initiation remains an unmet need. Towards that end, this study presents a large-scale integrative analysis of existing single-cell and bulk tissue datasets, aimed at identifying predictive features while providing insights into their cellular origin and potential function within the tumor microenvironment. Methods: A stepwise analysis was performed using single-cell RNA-sequencing data from 60 melanoma patients at baseline, separated into discovery (n=41) and validation (n=19) sets. An integrated bulk transcriptomics dataset (n=128) from melanoma patients and a bladder cancer dataset (n=298) were used for further validation. Results: Integrative analysis of melanoma single-cell datasets revealed that responders exhibit distinct molecular profiles across multiple cell types compared to non-responders. Notably, these included downregulation of the TNFR superfamily and other immunosuppressive genes (TNFRSF18, TNFRSF9, TNFRSF4, LGALS1, BATF, IL12RB2, LINGO1, DUSP4, SDC4, VCAM1) in T-cells. By investigating the findings from the immune cell populations in the bulk tumor context, 13 transcripts were found to be consistently associated with response across all cohorts. These were differentially expressed in T-cells (SELL, EPB41, CD96, UHFR2, LINGO1, LGALS1), B-cells (ALDH5A1), NK cells (PLEC, PDGFRB) and Monocytes (TLR10, ST6GAL1, IKZF1, MPRIP). A predictive model based on these features effectively discriminated responders from non-responders in melanoma (AUC=0.73). The model maintained significant predictive power in an independent bladder cancer dataset (IMvigor210; AUC=0.64). Of high clinical relevance, it demonstrated enhanced performance in identifying responders among patients with low tumor mutational burden (AUC=0.75). Conclusion: Our study reveals pre-treatment molecular features related to immune-cancer crosstalk that are associated with response to immunotherapy. A 13-gene model demonstrates potential added clinical value in stratifying responders, particularly in patients with low tumor mutational burden, meriting further validation.

Background: Head and neck cancer (HNC) patients experience clinically significant anxiety and depression in 65-85% of cases during active treatment. Current supportive care lacks personalized, real-time non-pharmacological interventions. Skitii is a novel HRV-adaptive music therapy system that uses continuous RMSSD (root mean square of successive differences) monitoring via a Polar H10 chest sensor to select music in real-time, targeting parasympathetic recovery (RMSSD >=30ms). Methods: This is a prospective, open-label, randomized controlled trial (1:1 allocation) at Yenepoya Medical College Hospital, Mangalore, India. Adults aged 18-75 years with confirmed head and neck cancer (any subsite, Stage I-IV) undergoing radiotherapy and/or chemotherapy with baseline distress (HADS >=8 or NCCN Distress Thermometer >=4) will be enrolled. Participants are randomized to Skitii adaptive music therapy (20-minute sessions, 3 times daily, 3 weeks) or static music therapy control. Skitii uses a two-phase algorithm: Phase 1 (0-2.5 minutes) uses heart rate as a stress proxy for immediate music selection; Phase 2 (2.5-20 minutes) uses RMSSD to adapt music every 2.5 minutes when physiological state changes by >=20%. Primary endpoints are HADS-Anxiety score and resting RMSSD at Week 3. Sample size is 70 (35 per arm), powered at 80% to detect a 2.5-point HADS difference (SD=3.8, alpha=0.05, 15% dropout). Analysis is ANCOVA, intent-to-treat. Discussion: This is the first randomized controlled trial evaluating RMSSD-based adaptive music therapy in cancer patients. The active control design isolates the effect of the adaptive algorithm from music exposure alone. If positive, results will support a scalable, cost-effective supportive care intervention with objective physiological monitoring, and provide the clinical evidence base for CDSCO Class B medical device approval for Skitii in India, with future CE Mark and FDA applications planned. Trial Registration: Clinical Trials Registry - India CTRI CTRI/2025/11/116732

Background & AimsColorectal cancer liver metastasis is the leading cause of mortality in affected patients, with liver sinusoidal endothelial cells playing a pivotal role in metastatic niche formation. Proprotein convertase subtilisin/kexin type 9 has emerged as a regulator of tumor biology, but its function in the hepatic microenvironment remains poorly defined. This study aimed to characterize the role and subcellular localization of PCSK9 in liver sinusoidal endothelial cells and to evaluate the therapeutic potential of its endothelial-specific inhibition in colorectal liver metastasis. MethodsIn vitro studies were performed using human and murine liver sinusoidal endothelial cells stimulated with conditioned media from metastatic colorectal cancer cells and cancer stem cells. Subcellular localization was assessed by immunofluorescence, immunogold electron microscopy, and biochemical fractionation. Protein interactions were investigated using co-immunoprecipitation and proteomic analyses. For in vivo validation, a murine model of colorectal liver metastasis was generated by intrasplenic injection of tumor cells, followed by systemic administration of chondroitin sulfate-targeted nanoparticles delivering PCSK9 siRNA every 5 days for 18 days. ResultsPCSK9 was consistently expressed in liver sinusoidal endothelial cells and displayed a predominant nuclear localization, which increased upon tumor-induced activation. Proteomic integration identified multiple candidate interacting proteins involved in metabolic and tumor-related pathways. Targeted nanoparticle-mediated delivery achieved efficient PCSK9 silencing in vitro. In vivo, endothelial-specific PCSK9 inhibition significantly reduced liver metastatic tumor burden compared with control groups, whereas free siRNA showed no significant effect. ConclusionsPCSK9 exhibits a novel nuclear localization in liver sinusoidal endothelial cells and potentially interacts with proteins implicated in tumor mediated pathways. Selective inhibition of endothelial PCSK9 using targeted nanoparticles significantly reduces colorectal liver metastasis, highlighting a novel therapeutic strategy focused on the hepatic microenvironment. Impact and ImplicationsThis study provides mechanistic insight into how PCSK9 contributes to colorectal liver metastasis by identifying its novel nuclear localization and potential function in liver sinusoidal endothelial cells. These findings are important for researchers and clinicians seeking to understand microenvironment-driven metastasis and resistance to current therapies. The demonstration that endothelial-specific targeting of PCSK9 reduces metastatic burden suggests a new avenue for therapeutic development beyond systemic inhibition. Such strategies could be translated into precision nanomedicine approaches to improve outcomes in patients with metastatic colorectal cancer while minimizing off-target effects.

Background: Alpha-1 antitrypsin deficiency (AATD) caused by the PI*ZZ mutation (Glu342Lys) results in hepatic accumulation of misfolded AAT-Z protein and reduced circulating AAT levels, leading to progressive liver disease and emphysema. Gene correction therapy represents a potentially curative approach by directly correcting the underlying genetic defect. We report the first case of successful hepatic gene correction with early histological and functional assessment. Methods/Case presentation: We report the case of a 66-year-old male patient with PI*ZZ AATD who underwent gene correction therapy within the YOLT-202 phase I/Ia clinical trial (clinical trial.gov ID NCT07193615). Ten weeks post treatment a liver biopsy was performed to re-evaluate pre-existing F2 liver fibrosis as measured by elastography before entering the study. Serum samples allowed functional assessment of the AAT-mediated elastase inhibition. Results: Liver biopsy did not show signs of hepatic inflammation and demonstrated 54% (Sanger) and 57% (Illumina) gene correction rate of the PI*ZZ variant on the DNA level with no bystander edits or off-target effects. Following a transient elevation of transaminases during the early post-treatment period, liver enzymes normalized. Monthly serum AAT measurements demonstrated biologically active and stable therapeutic levels throughout follow-up. Conclusions: This case demonstrates efficient and precise hepatic gene correction without concerning histological alterations and with substantial improvement of functional parameters, supporting the feasibility and safety of gene editing approaches for AATD.

Melanoma remains one of the most treatment-refractory malignancies due to immune evasion, high mutational burden, and profound tumor heterogeneity. Although immune checkpoint inhibitors have transformed frontline management, a substantial proportion of patients develop resistance or experience relapse, underscoring the need for alternative and complementary immunotherapeutic strategies. Tumor-infiltrating lymphocyte (TIL) therapy and engineered viral vector-based immunotherapies represent mechanistically distinct yet clinically promising approaches for advanced melanoma. This systematic review and Bayesian meta-analysis evaluated the comparative efficacy of TIL therapy and engineered viral vector immunotherapies in advanced melanoma. A structured search of PubMed, Embase, Scopus, and Web of Science (2015-2025) identified 13 eligible studies, including four randomized controlled trials and nine prospective single-arm studies, reporting objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse events. Eight studies met criteria for inclusion in the Bayesian quantitative synthesis of ORR outcomes. Risk of bias and certainty of evidence were assessed using Cochrane and GRADE frameworks. TIL therapy demonstrated substantial standalone efficacy, particularly in PD-1-refractory populations, with reported ORRs reaching 49%, median PFS of 7.2 months, and OS extending to 25.8 months. Viral vector-based therapies, including talimogene laherparepvec (T-VEC) and RP1, showed more modest monotherapy activity but demonstrated improved responses when combined with immune checkpoint inhibitors. Among the studies included in the Bayesian quantitative synthesis, the pooled ORR estimate was 37.8% (95% highest density interval [HDI]: 30.6%-45.3%). Sensitivity analysis excluding the small-sample Cui et al. (2022) study yielded a similar pooled estimate of 38.3% (95% HDI: 30.4%-46.2%). Exploratory meta-regression supported the overall robustness of the findings. Certainty of evidence for ORR was moderate, whereas survival and safety outcomes were downgraded due to heterogeneity, sparse reporting, and inconsistent endpoint definitions. Collectively, these findings support complementary rather than competing roles for TIL and engineered viral vector immunotherapies within evolving melanoma treatment paradigms. The results further highlight the potential importance of biomarker-guided sequencing strategies, including viral immune priming followed by adoptive cellular therapy, as a framework for optimizing personalized immunotherapy in both refractory and earlier-line melanoma settings.

BackgroundmTOR signaling promotes cell growth and anabolic processes in all eukaryotes. Hyperactivation of mTOR signaling is associated with various cancers along with hepatocellular carcinoma (HCC). HCC is a highly lethal malignancy with multiple aetiologies such as viral infection, alcohol abuse, and metabolic dysfunction. Therapeutic options for HCC remain limited due to an incomplete understanding of oncogenic drivers and poorly characterized mechanisms of disease progression. MethodsVarious regimens of DEN and CCl4 carcinogen dosage were investigated on C57BL/6J mice to induce HCC. The histological analysis for fibrosis and serum markers for liver function were performed. Molecular analyses of oncogenic drivers were performed in the HCC tissues obtained from mice and HCC patients. The impact of inhibition of mTOR signaling was assessed on HCC progression. ResultsWe established a rapid DEN+CCl4 induced (DCI) HCC model in C57BL/6 mice to study disease progression longitudinally. The molecular analysis revealed upregulation of MAPK and downregulation of mTORC1-S6K-S6 signaling in HCC. However, other branches of mTOR such as mTORC1-ULK1, mTORC1-4EBP1, and mTORC2-PKC were upregulated due to the increased expression. Similar observations were found in tissues derived from HCC patients. Furthermore, inhibition of mTORC1 alone by Rapamycin did not reduce HCC progression but Torin 1 mediated inhibition of both mTORC1 and mTORC2 significantly reduced HCC progression. ConclusionsWe propose this biased mTOR signaling modulates mTOR activity towards specific downstream processes that are crucial for cancer cell growth and targeting both the mTOR complexes has better therapeutic potential in HCC. Impact and ImplicationsThis study provides a rapid pre-clinical model for understanding HCC progression and to explore various intervention strategies. The study reports a novel phenomenon of biased mTOR signaling where deregulation of downstream substrate levels modulates the mTOR activity towards the specific branches, the master regulator of cell growth and metabolism. Furthermore, the study suggests that the clinical investigations exploring the rapalogs against HCC should be cautiously considered depending on the aetiology and signaling status of HCC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/727188v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@58834forg.highwire.dtl.DTLVardef@114f43corg.highwire.dtl.DTLVardef@aea643org.highwire.dtl.DTLVardef@2596ce_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDEN and CCl4 treatment generated a well-established HCC within 4 months. C_LIO_LILiver fibrosis and serum markers correlated with HCC progression. C_LIO_LIUpregulation of mTOR pathway substrates create biased mTOR signaling. C_LIO_LIDual inhibition of mTORC1 and mTORC2 reduced HCC progression significantly. C_LI

ObjectiveTo 1) determine the expression and distribution of all PDE4 isozymes (A-D) along the length of the anterior urethra, 2) culture fibroblasts and epithelial cells from healthy and strictured urethras, 3) investigate an in vitro model of anterior urethral stricture disease (aUSD), and 4) assess the therapeutic potential of phosphodiesterase-4 (PDE4) inhibitors and testosterone compared to paclitaxel. MethodsThe presence and relative abundance of PDE4 isozymes (A-D) was confirmed using immunohistochemistry on 5 male cadaveric urethras. Human urethral fibroblasts (FBs) were cultured from healthy control urethras of patients undergoing vaginoplasty (n=3) and from idiopathic bulbar urethral strictures (L2S1E2) of patients undergoing urethroplasty (n=3). Epithelial cells (ECs) were cultured from a healthy control urethra and two urethral strictures. To investigate a model of aUSD, Control FBs were stimulated with TGF{beta}1 and compared to Stricture FBs on assays of cell proliferation and expression of genes relevant to aUSD pathophysiology. To test therapeutics, Stricture FBs were treated with the PDE4 inhibitor, roflumilast, testosterone (T), or paclitaxel and compared to Control FBs on the previously mentioned assays and cell viability. ResultsPDE4- A, B, and D were detected along the length of the urethra. Expression levels did not differ between urethral regions. TGF{beta}1 altered proliferation and gene expression in a dose-dependent manner. Roflumilast and T preserved cell viability and proliferation and decreased expression of genes positively associated with auSD. ConclusionUrethral FBs and ECs can be cultured from healthy and strictured surgical specimens, enabling in vitro research. PDE4 inhibitors and T may be non-cytotoxic alternatives or additions to paclitaxel for aUSD. HighlightsO_LIPDE4 isozymes A, B, and D are expressed in adult anterior urethras C_LIO_LIPDE4 is expressed equally from proximal bulbar to meatal urethra C_LIO_LIEpithelial cells and fibroblasts can be cultured from healthy and stricture urethra C_LIO_LITGF{beta}1 may not be an optimal method to model aUSD in vitro C_LIO_LIUnlike paclitaxel, roflumilast and testosterone are not toxic to urethral cells C_LI

Rationale: Air trapping in functional areas of the lung is common in chronic obstructive pulmonary disease (COPD). We developed a novel music-based intervention, Engagement of Music for Pulmonary Obstruction With Expiratory Restoration (EMPOWER) aimed at reducing air trapping and functional small airways disease (fSAD) in patients with COPD. Objectives: We conducted a pilot study to assess if air trapping and fSAD in COPD patients are reduced by our targeted EMPOWER music-based singing intervention. Methods: Participants completed four weeks of singing and vocalizing with a board-certified music therapist. Pre- and post-intervention assessments of standard pulmonary function tests (PFTs), and quantitative computed tomography (qCT) lung imaging documented changes in air trapping. Pre- and post-intervention change in psychological and patient-reported outcomes of hope, emotional wellbeing, agency and COPD symptom burden were also obtained. Main Results: All five adult participants with COPD who enrolled completed the study and reported strong interest in continuing with a similar program. Additionally, we observed trends toward improvement in qCT-measured fSAD, six-minute walk distance, and patient-reported symptoms on the COPD Assessment Test. Conclusion: Results of this preliminary study showed improvements in both patient-reported and imaging-indicated respiratory outcomes, suggesting that targeted singing components in music-based interventions such as the EMPOWER intervention may support physiological lung function changes in COPD patients.

BackgroundPrevious machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such as DeepGlioma, have achieved high performance (91.5% accuracy) on curated datasets. However, when used intraoperatively, DeepGlioma (162M parameters) runs slowly on current SRH hardware and underperforms due to its lack of an image rejection mechanism and its validation on curated images. Here, we introduce SRH-Informed Glioma classificatioN with Attention Learning (SIGNAL) (27M parameters), a lighter model with a built-in attention-based rejection mechanism that outperforms DeepGlioma on uncurated clinical datasets. MethodsSIGNAL was developed using 1.56 million SRH fields-of-view from 967 adult diffuse glioma patients collected between December 2017 and July 2025. We used 412 patients from NYU for training and internal validation and a multi-institutional, international cohort of 555 patients for testing. SIGNAL uses a ResNet50 backbone pretrained using a hierarchical contrastive loss function followed by a multi-head multi-layer perceptron (MLP). Using a patch-based attention threshold of 0.6, a final MLP was trained to predict glioma subtypes: glioblastoma, oligodendroglioma, or astrocytoma. ResultsSIGNAL outperformed DeepGlioma, achieving greater overall accuracy (90.10% vs. 72.59%) while running faster (16.0 vs. 6.7 patches/s). SIGNAL also outperformed DeepGlioma on all three molecular classification tasks, including IDH mutation (accuracy: 93.51% vs. 79.22%), 1p19q codeletion (93.51% vs. 88.31%), and ATRX loss (89.61% vs. 83.98%). SIGNALs attention mechanism had a strong positive linear correlation with mean patch cellularity (r=0.96, p<0.001) and a strong negative correlation with patch blood coverage (r=-0.99,p<0.001). Finally, subtype and molecular accuracy between tumor core and margin samples were equivalent despite significantly lower patch retention in tumor margins (44.5% vs 60.2%, p<0.0001). ConclusionSIGNAL is a lightweight model for intraoperative molecular classification of gliomas using SRH imaging. Its attention-based image quality filter allows for excellent performance, quick processing, and highly interpretable outputs critical for reliable use in intraoperative workflows. Brief 1-2 Sentence DescriptionWe present SIGNAL, a lightweight machine learning model for intraoperative molecular classification of diffuse gliomas using stimulated Raman histology, whose core innovation is a learned attention mechanism that filters diagnostically uninformative tissue, such as blood and acellular regions, before classification, enabling robust real-world generalizability. Validated on 555 patients across four international centers, SIGNAL outperforms the previous state-of-the-art model DeepGlioma on glioma subtype classification (90.10% vs. 72.59% accuracy) while running 2.4 times faster on intraoperative hardware.

Background Liver fibrosis (LF) represents a pivotal pathological phase in the advancement of chronic liver disorders toward cirrhosis. Amino acid metabolism reprogramming plays a pivotal role in its pathogenesis, yet the underlying molecular mechanisms remain incompletely understood. Methods Integrating three public datasets (GSE14323, GSE84044, and GSE136103) with amino acid metabolism-related gene sets, we performed consensus clustering, machine learning algorithms, functional enrichment analysis, immune microenvironment composition, regulatory network construction, and drug prediction. Results Fibrotic samples were classified into two amino acid metabolism-related subtypes with distinct immune landscapes and functional phenotypes. Through integrated analysis of differentially expressed genes (DEGs) common to both subtypes, fibrotic versus control comparisons, and amino acid metabolism-related gene sets, four biomarkers, GSTP1, LDHB, OXCT1, and PTGDS, were identified. These biomarkers were enriched in pathways related to epithelial-mesenchymal transition, interferon responses, and TNF/NF-{kappa}B signaling. Notably, GSTP1 and LDHB positively correlated with M1 macrophage infiltration and negatively with regulatory T cell abundance. Single-cell transcriptomic analysis revealed that cholangiocytes expressed all four biomarkers with elevated levels in fibrosis and interacted with macrophages/mesenchymal cells via MIF-CD74/CXCR4. Regulatory network analysis highlighted key modulators, including MALAT1, hsa-miR-3163, OXCT1, SMAD4, and RELA. Furthermore, 5-fluorouracil was predicted as a multi-target compound, with the strongest predicted binding affinity for OXCT1. In vitro validation confirmed the upregulation of GSTP1 and LDHB, aligning with the bioinformatics findings. Conclusion This study identified four amino acid metabolism-related biomarkers, revealing immune heterogeneity and cholangiocyte-centered intercellular communication in LF. These findings establish a foundation for biomarker-based diagnosis, subtype-guided patient stratification, and the development of cell-type-specific therapeutic strategies in LF.

Introduction: Adenoviral vectors such as chimpanzee ChAdOx1 were selected for COVID-19 vaccines due to their low seroprevalence in humans, minimizing the impact of neutralising anti-vector immunity that could attenuate vaccine responses. However, the influence of pre-existing adenoviral immunity on vaccine response remains incompletely understood. We have previously shown that SARS-CoV-2 spike-specific T cells were enhanced in ChAdOx1 nCoV-19 vaccinated immunodeficient patients compared to mRNA-based BNT162b2. Here, we assess immune cross-reactivity between ChAdOx1 and human adenovirus 5 (HuAd5), and test the hypothesis that in antibody-deficient individuals, cross-neutralisation may be impaired, allowing bystander enhancement of SARS-CoV-2 spike-specific T cell responses following ChAdOx1 nCoV-19 vaccination. Methods: We studied healthy healthcare workers (HCWs) and immunodeficient patients (IDPs) who received homologous ChAdOx1 nCoV-19 or BNT162b2 vaccines. HCWs samples were collected pre-vaccination and 4-6 weeks after the second dose, while IDP samples were obtained 4-6 weeks after the second dose. Serum anti-HuAd5 hexon IgG was quantified using a Luminex multiplex assay, and neutralizing antibodies were assessed using a replication-deficient HuAd5-GFP virus neutralization assay with flow cytometry readout. Ex vivo ELISpot and flow cytometry assays were used to measure T cell responses to HuAd5 hexon. These data were compared with previously published ChAdOx1 nCoV-19 vaccine responses in the same cohorts. Results: HuAd5 hexon-binding IgG titres were significantly higher in ChAdOx1 nCoV-19 compared to BNT162b2 vaccine recipients in both HCWs (p = 0.0043) and IDPs (p = 0.0328). Within ChAdOx1 nCoV-19 vaccine group, titres were lower in IDPs than HCWs (p = 0.0015) but not within the BNT162b2 group (p = 0.1261). HuAd5 neutralisation titres did not differ between cohorts or vaccine groups. In ChAdOx1 nCoV-19 vaccinated IDPs and HCWs, there was a significant negative correlation between HuAd5 hexon IgG titres and SARS-CoV-2 spike-specific T cell responses. Similarly, HuAd5 neutralisation titres showed an inverse correlation with spike-specific T cell responses in ChAdOx1 nCoV-19 vaccinated IDPs and HCWs. ChAdOx1 nCoV-19 vaccination induced significantly higher frequencies of HuAd5 hexon-reactive T cells compared with BNT162b2 vaccination in IDPs (p < 0.0001), consistent with cross-reactive adenoviral T cell responses. In IDPs, HuAd5 hexon-specific T cell frequencies positively correlated with SARS-CoV-2 spike-specific T cell responses following ChAdOx1 nCoV-19 vaccination but not following BNT162b2 vaccination. Functional profiling in ChAdOx1 nCoV-19 vaccinated IDPs demonstrated expansion of HuAd5 hexon-specific CD4IFN-{gamma}TNF T cells in high SARS-CoV-2 spike responders (p = 0.0002) compared to low responders, and the frequency of these cells strongly correlated with spike-specific T cell response. Discussion: ChAdOx1 nCoV-19 has been associated with stronger T cell responses than BNT162b2 in certain populations, including immunodeficient and elderly individuals. While this has been attributed to antigen persistence and innate adjuvant effects, our findings support a mechanism whereby heterologous pre-existing adenovirus immunity modulates vaccine-induced responses. Specifically, cross-reactive HuAd5-specific T cells may enhance spike-specific T cell responses via bystander enhancement, while cross-reactive binding antibodies may exert opposing effects. An implication of this study is that vaccine protocols could incorporate therapies that suppress vector-specific or cross-reactive antibodies while preserving T cell responses especially in cases where T cell-specific responses are most desirable. Also, safe vector-based vaccines can be developed for patient groups with predominant antibody deficiency. Targeted vaccination strategy could be implemented for clinical cohorts based on immune competence.

Precision medicine aims to advance our ability from a "one-size-fits-all" approach to personalized and predictive healthcare across diverse populations. It promotes integration of multi-omics and phenotypic data to understand disease mechanisms and discover novel biomarkers and risk factors, which could be used to predict and prevent critical diseases in individual patients across diverse populations. The potential implications of precision medicine approach can accelerate our ability to classify patients at higher risk of developing critical diseases, improve diagnostic capabilities, develop deeper understanding of individual risk, investigate racial differences and demographic characteristics, and find relationships between genetic variants, expressions, and diseases. This study focuses on implementing an innovative and data driven framework of translational bioinformatics and Machine Learning (ML) techniques to analyze multi-omics, including RNA-seq and Whole-Genome Sequencing (WGS) data, generated using blood samples of randomly consented patients. First, we utilized bioinformatics pipelines to identify differentially expressed genes and their pathogenic and likely pathogenic variants for the downstream data analysis, annotation, and visualization. Then, applied a nexus of ML models for multi-omics biomarker discovery, disease prediction, density-based clustering, single-patient profiling, and pathogenicity classification. WGS data analysis supported the exploration of genetic variation and diversity among patients to identify known and novel biomarkers, whereas RNA-seq data analysis improved our understanding of functional and biological pathways that underlying disease states. We classified and clustered pathogenic variants and expressions across various genes and discovered numerous diseases leading risk factors. Our results include gene-disease associations and captured common pathways across the broader population, demonstrating a level of sensitivity and accuracy that has broad clinical implications. We validated our results through clinical records, and state of the science literature. This study delves into the strengths of multi-omics data integration and capabilities of ML application in genetically diverse and complex patient cohorts. Our approach has the potential to elucidate complex gene-disease interactions for genetically diverse populations, which can support earlier diagnoses for patients in many disease realms.

IntroductionVagus nerve stimulation modulates laryngeal, cardiac, pulmonary, and gastrointestinal functions. Knowledge of where along the vagal trunk organ-specific branches emerge may support alternative surgical placements of stimulation devices to engage targeted functions while avoiding off-target effects. However, no quantified map of how vagal branches emerge and how they relate to surgically relevant anatomical landmarks exists in humans. MethodsFifty-eight vagus nerves (29 left, 29 right) from 29 embalmed donor bodies (15 females) were dissected from the jugular foramen through the thoracic cavity. Branches were traced to end organs and allocated to seven groups -- sympathetic, muscular, vascular, cardiac, pulmonary, esophageal, and multiple targets -- and several sub-groups. Distances between branch emergence and the jugular foramen (JF) were normalized to three anatomical landmarks: carotid bifurcation, laryngeal prominence, and superior border of clavicle. ResultsBranch emergence follows a proximal-to-distal order: sympathetic (5.28 cm from JF), muscular (9.59 cm), vascular (10.70 cm), cardiac (19.65 cm), pulmonary (25.36 cm), and esophageal (26.57 cm). Vagal branches emerge into two embryological domains separated near the clavicle: pharyngeal arch-targeting branches cluster proximally (9.34 cm) and primitive mediastinum-targeting branches cluster distally (23.74 cm), with sympathetic, muscular, and vascular sub-groups occupying distinct zones within the proximal domain. The largest branch-free intervals occur above the left clavicle (2.33 {+/-} 2.80 cm) and below the left carotid bifurcation (2.58 {+/-} 3.17 cm). Alternate placement regions separating targeted organs from off-targets: sympathetic vs. cervical visceral at 6/8 cm (L/R), cardiac vs. carotid sinus/bifurcation at 14/10 cm, and recurrent laryngeal vs. other cervical visceral at 18/13 cm from JF. Overall, no differences were found between male and female donors. ConclusionsThis study provides a quantified, landmark-registered map of cervical and thoracic vagal branch emergence, offering a standardized anatomical template that may inform strategies for more function-selective vagal neuromodulation.

Background and AimsA major goal of personalised liver oncology is the ability to make targeted predictions about cancer-specific toxicity, however there are limited methods available. To address this, we validated the performance of our bioinformatics framework, TARGET-SL, through ex vivo drug screening. MethodsUsing TARGET-SL we predicted gain of function (GOF), loss of function (LOF) and synthetic lethal (SL) genetic events, and corresponding drug candidates. We validated drug predictions across hepatocellular carcinoma (HCC) cell lines, and a cohort of HCC and cholangiocarcinoma (CCA) patient-derived organoids (PDOs). ResultsFor HCC cells and PDOs we found 37.5% and 25% of the respective selected compounds induced unique target-specific growth inhibition based on genetic biomarkers, suggesting novel biomarker-driven drug sensitivities. ConclusionsOur analyses demonstrate TARGET-SLs potential to enhance personalized drug screening for liver cancer, by focusing on genetically informed targets. This will reduce experimental costs and accelerate the pace of therapeutic discovery. Impact and ImplicationsPrimary liver cancer (PLC) is a cancer with poor prognosis, and current therapies increase survival only for a minority of patients. Through the application of TARGET-SL we can predict, for each patient, the essential genes and corresponding small molecule inhibitors. These data support further investigation in larger patient cohorts and offer the possibility to specify new small molecule inhibitors and to repurpose current drugs for PLC treatment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/725819v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@10cb252org.highwire.dtl.DTLVardef@8f3049org.highwire.dtl.DTLVardef@ab4467org.highwire.dtl.DTLVardef@17f9d3_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITARGET-SL can predict gene and drug sensitivities for cell lines and patient-derived organoids C_LIO_LIThis may reduce drug screening costs and accelerate the pace of therapeutic discovery. C_LIO_LITARGET-SL may assist in the repurposing of current drugs and their rapid translation for primary liver cancer C_LIO_LITARGET-SL is tumour-type agnostic, and therefore may have application in other cancers with poor prognosis C_LI