Publications by Year: 2026

BACKGROUND AND OBJECTIVE: Accurate prediction of recurrence and progression risk in non-muscle invasive bladder cancer (NMIBC) is essential for patient counseling and risk-adapted management. However, conventional models fail to account for the decrease in baseline risk over time. We therefore examined the conditional survival free of recurrence and progression in older adults with NMIBC to develop a dynamic risk prediction model.

METHODS: We identified patients 66 to 89 years with Ta/Tis/T1 cN0 cM0 urothelial bladder cancer treated with transurethral resection of bladder tumor (TURBT) between 2000 and 2017 in SEER-Medicare. Conditional recurrence-free (RFS) and progression-free (PFS) survival were estimated using the Kaplan-Meier method. The associations of baseline characteristics with RFS and PFS at prespecified landmark times were evaluated using Cox-proportional hazards models.

KEY FINDINGS AND LIMITATIONS: A total of 39,862 patients were included. Of these, 26,339 (66%) had Ta, 11,758 (29%) had T1, and 1,765 (4%) had Tis-disease. Median follow-up was 65 months. The 60-month RFS and PFS increased from 0.39 and 0.85 at baseline to 0.73 and 0.89 at 24-months event-free survival. Conditional RFS rapidly improved within the first 24 months before plateauing. Patients with T1-disease demonstrated the greatest improvement in conditional RFS. On multivariable analyses, T stage and tumor grade were less predictive of RFS with longer landmark times. Limitations include measurement error and risk heterogeneity within grade and stage subgroups.

CONCLUSIONS AND CLINICAL IMPLICATIONS: Among patients with NMIBC, recurrence and progression risks decrease with longer event-free intervals, particularly among patients at highest risk of each event as reflected by tumor stage and grade. A dynamic risk prediction model can improve patient counseling and support risk-adapted management during follow-up.

Raman spectroscopy is a powerful technique for probing molecular vibrations, yet the computational prediction of Raman spectra remains challenging due to the high cost of quantum chemical methods and the complexity of structure-spectrum relationships. Here, we introduce Mol2Raman, a deep-learning framework that predicts spontaneous Raman spectra directly from SMILES representations of molecules. The model leverages Graph Isomorphism Networks with edge features (GINE) to encode molecular topology and bond characteristics, enabling accurate prediction of both peak positions and intensities across diverse chemical structures. Trained on a novel dataset of over 31 000 molecules with state-of-the-art Density Functional Theory (DFT)-calculated Raman spectra, Mol2Raman outperforms both fingerprint-based similarity models and Chemprop-based neural networks. It achieves a high fidelity in reproducing spectral features, including for molecules with low structural similarity to the training set and for enantiomeric inversion. The model offers fast inference times (22 ms per molecule), making it suitable for high-throughput molecular screening. We further deploy Mol2Raman as an open-access web application, enabling real-time predictions without specialized hardware. This work establishes a scalable, accurate, and interpretable platform for Raman spectral prediction, opening new opportunities in molecular design, materials discovery, and spectroscopic diagnostics.

INTRODUCTION: Brown adipose tissue (BAT) contributes to thermogenesis and has been proposed as a therapeutic target for metabolic disease. Thyroid hormones (THs) regulate thermogenic activity, but the relationship between circulating and local TH concentrations and their associations with thermogenic gene and pathway expression in human adipose tissue remain unclear.

METHODS: We obtained paired deep neck and subcutaneous adipose tissue samples from adults undergoing thyroid surgery, which represent BAT and white adipose tissue, respectively. Serum and local adipose tissue TH concentrations (T3, T4, TSH) were measured. Bulk RNA-sequencing was performed on adipose tissue samples. Associations between hormone concentrations and thermogenic gene expression and pathway activation were analyzed, with false discovery rate correction for multiple testing.

RESULTS: Both serum and local T4 concentrations were positively associated with thermogenic pathway activation in deep neck adipose tissue. Although serum T3 was also positively associated, local T3 was inversely associated with thermogenic pathways in deep neck adipose tissue. However, circulating TH concentrations did not correlate with local tissue hormone levels. No significant associations were observed between serum or local TH concentrations and individual thermogenic gene expression after correction for different clinical covariates and multiple comparisons.

CONCLUSION: Local regulation of THs may play a role in human adipose tissue thermogenic activity. Pathway-level transcriptomic analysis may better capture these effects than single-gene approaches. Deep neck adipose tissue can serve as a practical model for studying BAT function and endocrine regulation in humans.

We present a 62-year-old male with end-stage kidney disease and a high-flow brachiocephalic arteriovenous fistula (3.3 L/min). Revascularization using distal inflow was performed with a naturally developed retrograde cephalic vein branch in the ipsilateral arm anastomosed to the distal radial artery. The original anastomosis was ligated. The patient could use the revised forearm portion of the autogenous moderate-flow fistula at 2-month follow-up. This case demonstrates the use of naturally augmented veins to facilitate revascularization using distal inflow rather than a prosthetic graft or saphenous vein conduit, offering a unique approach to flow reduction and creation of autogenous forearm conduits for cannulation.

Expression Atlas (https://www.ebi.ac.uk/gxa/home) is EMBL-EBI's comprehensive knowledgebase for gene and protein expression across tissues, cell types, conditions, and multiple species. Since our last update, Expression Atlas has expanded substantially in both content and functionality, now comprising >4500 studies from 67 species, with increased proteomics coverage and updated Genotype-Tissue Expression (GTEx) tissue profiles. The resource also includes hundreds of single-cell RNA-seq experiments spanning 21 species, among them externally analysed community datasets such as Tabula Sapiens and GTEx single-nucleus profiles, allowing exploration of curated atlases while maintaining their original analytical framework. Key methodological advances include a new marker gene analysis module for bulk baseline experiments, alongside workflow updates that improve reproducibility. Expression Atlas data are integrated into EMBL-EBI resources such as Ensembl, UniProt, and Europe PMC and disseminated through collaboration with model organism communities such as FlyBase and Gramene. The resource also supports translational research through the European Diagnostic Transcriptomic Library and integration with the Open Targets platform. Future directions include modernizing analysis pipelines, enhancing programmatic access, and delivering AI-ready data formats, strengthening Expression Atlas as a findable, accessible, interoperable, and reusable (FAIR) community-driven resource for both fundamental and translational discovery.

BACKGROUND: The aim of this study is to explore how neutrophil extracellular traps (NETs) and phosphatidylserine exposure contribute to hypercoagulability in periodontitis with type 2 diabetes (T2D).

METHODS: Ninety-six participants were divided into groups with periodontitis (CP), T2D, periodontitis with type 2 diabetes (DP), and a healthy control (CTR). Coagulation profiles were assessed using coagulation time and fibrin generation tests. Confocal microscopy and flow cytometry measured phosphatidylserine (PS)-exposed cells and NETs in blood samples. The impact of NETs on endothelial cells was evaluated through Western blot, confocal microscopy, and angiogenesis tests. We evaluated the NETs levels in patients before and after treatment through blood glucose control or periodontitis treatment.

RESULTS: DP patients showed shorter coagulation times, higher fibrinogen levels, and more blood cells (e.g., platelets and neutrophils) with PS exposure, along with increased NETs release. Activated platelets were found to stimulate NETs release more than microparticle-poor plasma. NETs damage vascular endothelial cells, leading to increased vascular cell adhesion molecule-1 (VCAM-1), decreased vascular endothelial cadherin (VE-cadherin), actin reorganization, reduced tube formation, and higher procoagulant activity in endothelial cells. After periodontal treatment or blood sugar control, the levels of NETs decreased significantly in patients with DP.

CONCLUSIONS: In patients with DP, activated platelets trigger neutrophils to release excess NETs, which create a pro-thrombotic state by damaging endothelial cells. Small-scale clinical trials underscore the value of controlling local infection and hyperglycemia as first-line "NET-modulating" strategies.

PLAIN LANGUAGE SUMMARY: The hypercoagulability of DP patient can be partially explained by the activated platelet-promoted excess NETs providing a scaffold for clotting factors, damaging endothelial cells intercellular connections, converting of endothelial cells to pro-coagulant phenotype, and impairing of endothelial cells tube formation capacity. Blood sugar control and periodontal treatment can regulate the levels of NETs which represents the generation of new promising DP treatment options. In the future, efforts should be made to develop therapeutic strategies targeting NETs or PS.

Large biobanks, including the Million Veteran Program (MVP), the UK Biobank, and FinnGen, provide genetic association results for more than 1 million individuals for hundreds of phenotypes. To select targets for pharmaceutical development, as well as to improve the understanding of existing targets, we harmonized these studies and performed two-sample Mendelian randomization (MR) on 2,003 phenotypes using genetic variants associated with gene expression (derived from GTEx and eQTLGen) and plasma protein levels (derived from ARIC, Fenland, and deCODE) as proxies of target modulation. We found 69,669 gene-trait pairs with evidence (p ≤ 1.6 × 10-9) for causal effects. From the selected gene-trait pairs, we observed 6,447 genes with strong causal evidence for at least one of 2,003 investigated traits. As expected, being identified as a gene-trait pair in our approach was significantly associated with higher odds of being an approved drug target and indication. We were able to rediscover 9% of approved drug targets in ChEMBL 34. Moreover, identified gene-traits were significantly associated with higher odds of being previously described as a gene-trait pair in OMIM, ClinVar, mouse knockout data, and rare variant burden studies. To enhance the translational potential of the resource, we developed a predictive ranking model trained using approved drug targets described in ChEMBL 34 as well as several different biological annotations. This model was able to accurately predict the odds of a particular significant MR result being developed into an approved drug and its clinical indication (precision-recall area under the receiver operating characteristic curve 0.79). We make our results publicly available in CIPHER.