Communications Medicine

PurposeTo spatially characterize the single-cell tumor microenvironment (TME) of salivary gland carcinomas (SGC) and identify prognostic biomarkers. Experimental DesignSGC, including salivary duct carcinomas (SDC), acinic cell, mucoepidermoid, and secretory carcinomas, were analyzed using a 13-marker imaging mass cytometry panel. Multichannel image data from 54 primary cases and nodal metastases were processed to generate single-cell datasets. Cell phenotypes (tumor cells, cancer-associated fibroblasts (CAFs), endothelia, immune cells) were classified using a validated CAF algorithm, followed by spatial analysis and clinicopathological correlation. ResultsAmong 509,364 cells, SDC exhibited the highest fractions of Collagen-and matrix-CAFs (mCAFs). Acinic cell carcinomas (ACC) showed enriched CD4+/CD8+ T cells and antigen-presenting CAFs (apCAFs), indicating strong immune infiltration. A spatially defined cellular neighborhood (CN8) of mCAFs and endothelia was elevated in SDC, with higher CAF infiltration in androgen receptor (AR)high versus ARlow SDC. Elevated mCAF frequency and CN8 were significantly associated with reduced recurrence-free probability (RFP) and distant control rates (DCR). Additionally, higher mCAF frequencies were an independent prognostic factor for decreased RFP and DCR in Cox regression analysis. ConclusionSDC are characterized by Collagen-/mCAF-rich microenvironments and mCAF-endothelial spatial interactions that are linked to metastasis. ACC display pronounced immune infiltration, suggesting its potential for immunotherapy. mCAFs in SDC emerge as prognostic biomarkers and therapeutic targets, highlighting the importance of targeting CAF-driven metastasis in future treatments. This study provides insights into the biology of SGC and identifies novel prognostic markers.

Human papillomavirus-associated oropharyngeal squamous cell carcinomas (HPV+OPSCC) release circulating tumor HPV DNA (ctHPVDNA) into the blood which we, and others, have shown is an accurate real-time biomarker of disease status. In a prior prospective observational trial of 34 patients with AJCC 8 stage I-II HPV+OPSCC treated with surgery, we reported that ctHPVDNA was rapidly cleared within hours of surgery in patients who underwent complete cancer extirpation, yet remained elevated in those with macroscopic residual disease. The primary outcomes of this study were to assess 2-year OS and RFS between patients with and without molecular residual disease (MRD) following completion of treatment in this prospective cohort. MRD was defined as persistent elevation of ctHPVDNA at two consecutive time points, without clinical evidence of disease. The secondary outcomes were 2-year OS and RFS between patients with and without detectable MRD after surgery. We observed that patients with MRD after treatment completion were more likely to recur compared to patients without MRD, while there was no difference in recurrence rates between patients with MRD and without MRD on postoperative day 1. OS did not significantly differ between patients with MRD after surgery or treatment completion compared to patients without MRD; however, time to death was significantly different between the groups in both settings, suggesting that with a larger sample size OS would differ significantly between the groups or that the impact of MRD detection on survival is time dependent.

IntroductionThyroid nodules with indeterminate cytology (Bethesda III and IV) present a diagnostic challenge, as conventional cytology offers limited predictive value and can lead to unnecessary surgeries. Recently, validated molecular classifiers have been developed with the aim of improving the stratification of the risk of malignancy in these nodules and optimizing clinical decision-making. Objectives To evaluate and compare the diagnostic yield of validated commercial molecular systems, including ThyroSeq and Afirma, versus conventional cytology in Bethesda III and IV thyroid nodules, using the result of postsurgical histopathology as a reference. MethodA structured review of prospective studies, randomized controlled trials, retrospective cohorts, and meta-analyses that analyzed the performance of commercial molecular classifiers in Bethesda III and IV nodules was conducted. We included studies that reported sensitivity, specificity, positive and negative predictive value, and that used postoperative histopathology as a reference standard. The sample volume of individual studies ranges from several hundred to more than six thousand nodules using pooled analyses. ResultsThe selected studies show that molecular classifiers such as ThyroSeq v3 and Afirma GSC achieve a high sensitivity and negative predictive value ([≥]94% and [≥]96%, respectively), outperforming conventional cytology. Specificity and positive predictive value show greater variability between studies and clinical settings. The use of these classifiers has made it possible to reduce the number of unnecessary surgeries on benign nodules. ConclusionsThe available evidence supports that validated molecular classifiers increase diagnostic accuracy in thyroid nodules with indeterminate cytology, reduce unnecessary surgical interventions, and improve clinical decision-making compared to conventional cytology, using histopathology as a standard reference.

There is limited information describing features and outcomes of patients requiring hospitalization for COVID19 disease and still no treatments have clearly demonstrated efficacy. Demographics and clinical variables on admission, as well as laboratory markers and therapeutic interventions were extracted from electronic Clinical Records (eCR) in 4712 SARS-CoV2 infected patients attending 4 public Hospitals in Madrid. Patients were stratified according to age and stage of severity. Using multivariate logistic regression analysis, cut-off points that best discriminated mortality were obtained for each of the studied variables. Principal components analysis and a neural network (NN) algorithm were applied. A high mortality incidence associated to age >70, comorbidities (hypertension, neurological disorders and diabetes), altered vitals such as fever, heart rhythm disturbances or elevated systolic blood pressure, and alterations in several laboratory tests. Remarkably, analysis of therapeutic options either taken individually or in combination drew a universal relationship between the use of Cyclosporine A and better outcomes as also a benefit of tocilizumab and/or corticosteroids in critically ill patients. We present a large Spanish population-based study addressing factors influencing survival in current SARS CoV2 pandemic, with particular emphasis on the effectivity of treatments. In addition, we have generated an NN capable of identifying severity predictors of SARS CoV2. A rapid extraction and management of data protocol from eCR and artificial intelligence in-house implementations allowed us to perform almost real time monitoring of the outbreak evolution.

Tinnitus is associated with a variety of aetiologies, phenotypes, and underlying pathophysiological mechanisms, and available treatments have limited efficacy. A combination of treatments, addressing various aspects of tinnitus, might provide a viable and superior treatment strategy. In this international multicentre, parallel-arm, superiority, randomised controlled trial, patients with chronic subjective tinnitus were recruited from five clinical sites across the EU as part of the interdisciplinary collaborative UNITI project. Patients were randomly assigned using a web-based system, stratified by their hearing and distress level, to single or combination treatment of 12 weeks. Cognitive-behavioural therapy, hearing aids, structured counselling, and sound therapy were administered either alone or as a combination of two treatments resulting in ten treatment arms. The primary outcome was the difference in the change from baseline to week 12 in the total score of the Tinnitus Handicap Inventory (THI) between single and combination treatments in the intention-to-treat population. All statistical analysis were performed blinded to treatment allocation. 674 patients of both sexes aged between 18 and 80 years were screened for eligibility. 461 participants (190 females) with chronic subjective tinnitus and at least mild tinnitus handicap were enrolled, 230 of which were randomly assigned to single and 231 to combination treatment. Least-squares mean changes from baseline to week 12 were -11.7 for single treatment (95% confidence interval [CI], -14.4 to -9.0) and -14.9 for combination treatments (95% CI, -17.7 to -12.1), with a statistically significant group difference (p=0.034). Cognitive-behavioural therapy and hearing aids alone had large effect sizes, which could not be further increased by combination treatment. No serious adverse events occurred. In this trial involving patients with chronic tinnitus, all treatment arms showed improvement in THI scores from baseline to week 12. Combination treatments showed a stronger clinical effect than single treatment, however, no clear synergistic effect was observed when combining treatments. We observed rather a compensatory effect, where a more effective treatment offsets the clinical effects of a less effective treatment. ClinicalTrials.gov Identifier: NCT04663828.

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, resulting in classifications that do not fully capture the diseases complexity. Here, we bridge this gap by introducing a subtyping pipeline that integrates clinical and gene expression data with variational autoencoders. We apply this methodology to the COPDGene study, a large study of current and former smoking individuals with and without COPD. Our approach generates a set of vector embeddings, called Personalized Integrated Profiles (PIPs), that recapitulate the joint clinical and molecular state of the subjects in the study. Prediction experiments show that the PIPs have a predictive accuracy comparable to or better than other embedding approaches. Using trajectory learning approaches, we analyze the main trajectories of variation in the PIP space and identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and disease outcomes. Notably, these subtypes are more robust to data resampling compared to those identified using traditional clustering approaches. Overall, our findings provide new avenues to establish fine-grained associations between the clinical characteristics, molecular processes, and disease outcomes of COPD.

The co-administration of drugs known to interact has a high impact on morbidity, mortality, and health economics. We study the drug-drug interaction (DDI) phenomenon by analyzing drug administrations from population-wide Electronic Health Records (EHR) in Blumenau (Brazil), Catalonia (Spain), and Indianapolis (USA). Despite very different health care systems and drug availability, we find a common large risk of DDI administration that affected 13 to 20% of patients in these populations. In addition, the increasing risk of DDI as patients age is very similar across all three populations but is not explained solely by higher co-administration rates in the elderly. We also find that women are at higher risk of DDI overall-- except for men over 50 years old in Indianapolis. Finally, we show that PPI alternatives to Omeprazole can reduce the number of patients affected by known DDIs by up to 21% in both Blumenau and Catalonia, and 2% in Indianapolis, exemplifying how analysis of EHR data can lead to a significant reduction of DDI and its associated human and economic costs. Although the risk of DDIs increases with age, administration patterns point to a complex phenomenon that cannot be solely explained by polypharmacy and multimorbidity. The lack of safer drug alternatives, particularly for chronic conditions, further overburdens health systems, thus highlighting the need for disruptive drug research.

BackgroundSinonasal malignancies frequently present with symptoms overlapping chronic inflammatory conditions such as chronic rhinosinusitis (CRS), complicating early detection and delaying treatment. A fast, scalable, non-invasive approach capable of resolving immune and epithelial cell states across inflammatory and malignant disease from routine nasal swabs could substantially improve clinical screening, leading to the initiation of appropriate treatment. MethodsWe developed a deep learning-enabled single-cell morpholomic framework using the REM-I platform to generate a reference atlas of >641K cell brightfield images from purified immune cell populations. This reference atlas was applied to >2.5 million images obtained from nasal swabs spanning a clinical spectrum of health, CRS, and sinonasal carcinoma. Embeddings were integrated using dimensionality reduction for differential feature testing and comparative feature enrichment across disease states. FindingsAcross the disease continuum, sinonasal carcinoma samples exhibited distinct immune remodeling, including increased myeloid-like cell abundance and elevated small dark pixel intensity consistent with enhanced granulocyte activity. Basophil/NK-enriched clusters contained tumor-associated cells with deep learning-derived morphologic signatures not observed in CRS or healthy samples. Tumor-associated epithelial cells were significantly smaller and displayed disease-specific morpholomic patterns distinct from chronic inflammation. ConclusionsThis study establishes a deep learning-enabled single-cell morpholomic atlas of nasal swabs spanning healthy epithelium, chronic inflammation and sinonasal malignancies. Morpholomic cytology reveals reproducible immune and epithelial states associated with inflammatory and malignant disease and provides a scalable, non-invasive framework for cellular stratification in sinonasal pathology, supporting future applications in early point-of-care diagnostics.

Sinonasal squamous cell carcinoma (SNSCC) is an aggressive head and neck cancer of the sinonasal cavity which has not benefitted from therapeutic advances over decades1. Though historically attributed to inhaled carcinogens such as hardwood dust and tobacco smoking2, SNSCC is incidentally associated with human papillomavirus (HPV)3,4. Importantly, HPV is the primary oncogenic driver of >80% of anatomically adjacent oropharyngeal cancers5. While viral status drives clinical staging and treatment guidelines in these malignancies6,7, the potentially oncogenic consequences and prognostic value of host-virus interactions in SNSCC remain incompletely defined. Here, through paired host and viral whole-genome sequencing (WGS), we map the genomic footprint of HPV in SNSCC. Strikingly, lesser studied strains such as HPV45, 51, and 39 constitute driver infections in this rare but clinically credentialed cancer, where extrachromosomal DNA (ecDNA)-associated viral integration and APOBEC mutagenesis are shown to underpin somatic tumor evolution. Statement of SignificancePaired host viral and whole-genome sequencing of SNSCC nominates HPV as a primary oncogenic driver of SNSCC. HPV-human ecDNA amplicons harboring noncanonical strains such as HPV45, 51 mediate viral carcinogenesis. Routine clinical diagnostic HPV panels should be expanded to capture the activity of lesser studied strains.

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early identification of individuals at risk of developing type 2 diabetes (T2D). Electronic health records (EHRs) provide longitudinal clinical trajectories, yet many predictive frameworks fail to capture short-, intermediate-, and long-term temporal patterns or incorporate clinically validated metabolic biomarkers. This study introduces a hybrid deep learning framework that integrates hierarchical temporal modeling with clinically engineered predictors for early T2D risk estimation. The approach includes data preprocessing, temporal sequencing, and the incorporation of derived biomarkers such as triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C), low-density lipoprotein to high-density lipoprotein cholesterol ratio (LDL/HDL-C), total cholesterol to high-density lipoprotein cholesterol ratio (TC/HDL-C), very low-density lipoprotein (VLDL), obesity status, and prediabetes indicators. A multilevel convolutional neural network (CNN) extracts low-, mid-, and high-level temporal features, which are processed in parallel by long short-term memory (LSTM) modules to capture multi-scale temporal dependencies. The fused temporal and biochemical representations form a unified CNN-LSTM architecture that is evaluated using standard classification metrics. Experiments conducted on 19,218 patients and 368,790 clinical visits from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) achieved 93.2% accuracy, 75.7% sensitivity, 98.8% specificity, and an 84.4% F1 score, outperforming bidirectional long short-term memory (Bi-LSTM), support vector machine (SVM), k-nearest neighbor (KNN), and baseline CNN-LSTM models. Feature importance analysis identified fasting blood sugar (FBS), glycated hemoglobin (HbA1c), and lipid ratios as the strongest predictors. By combining temporal representation learning with clinically grounded biomarkers, the proposed framework provides an interpretable, scalable, and robust foundation for early diabetes risk prediction and can be extended to other chronic diseases characterized by longitudinal EHR data. Author SummaryIn this study, we focus on the growing challenge of type 2 diabetes, a condition that develops gradually and often remains undetected until significant health damage has occurred. Our goal was to create an approach that identifies individuals at increased risk much earlier by examining how their clinical measurements change over time. To achieve this, we analyzed routine health information collected during repeated medical visits and combined it with key biological markers known to reflect metabolic health, such as blood sugar levels, long-term glucose measures, and cholesterol-related indicators. We developed a computational model that learns how these factors evolve and how they relate to the future onset of diabetes. When tested on a large population dataset, our model detected risk patterns more accurately than several widely used prediction methods. We also found that variations in blood sugar, long-term glucose, and lipid measures played a particularly important role in identifying individuals likely to develop the disease. By offering earlier and more reliable risk assessment, our work supports more proactive and personalized preventive care. Ultimately, this approach has the potential to help clinicians intervene sooner and reduce the burden of diabetes-related complications.

Electronic health records can be used to track diagnoses and drug prescriptions in large heterogeneous populations over time. Coupled with recent advances in causal inference from observational data, these records offer new opportunities to emulate clinical trials and identify potential targets for drug repositioning. Here, we run a hypothesis generating cohort study of Danes aged 50 to 80 years from 2001 to 2015 (n = 2,512,380), covering a total of 23,371,354 years of observations. We examine prescription drugs at ATC level-4 and their effect on 9 major disease outcomes. Using Bayesian time-varying Cox regression and longitudinal minimum loss estimation, our analysis successfully reproduces known drug-disease associations from clinical trials, such as the reduction in the 3-year absolute risk of death associated with Statins (ATC:C10AA) -0.8% (95% CI =[-1.2%, -0.5%]) and -0.8% (95% CI =[-1.3%, -0.2%]) for females and males, respectively. Additionally, we discovered novel associations that suggest potential repositioning opportunities. For instance, Statins were associated with a reduction in the 3-year absolute risk of dementia by -0.3% (95% CI =[-0.5%, -0.1%]) for females and -0.2% (95% CI =[-0.4%, 0.1%]) for males. Furthermore, Biguanides (ATC:P01BB) stands out as a particularly interesting candidate with absolute risk reductions across various outcomes. In total, we identified 76 potential drug-disease pairs for further investigation. However, it should be stressed that the emulation of clinical trials here is solely of hypothesis generating nature and identified effects need to be corroborated with additional evidence, preferably from RTCs, as the risk of confounding by indication in this study is substantial. In summary, this study provides a large-scale screen of prescribed drugs and their effect on major debilitating disease in the Danish health registries. This provides an additional source of information that can be used in the search for possible repositioning candidates.

IMPORTANCEDifferentiated thyroid cancer (DTC) is commonly diagnosed in women of child-bearing age, but whether pregnancy influences the prognosis of DTC remained controversial. OBJECTIVEThis systematic review and meta-analysis aimed to summarize and appraise the existing evidence of the impact of pregnancy on the prognosis of patients previously treated for DTC. DATA SOURCESWe searched PubMed, Embase, Web of Science, Cochrane, and Scopus until February 2023. STUDY SELECTIONStudies of patients diagnosed and treated for DTC before pregnancy reporting the recurrence/progression condition of DTC were included. Case reports and studies failing to identify the time of diagnosis or initial treatment were excluded. DATA EXTRACTION AND SYNTHESISMeta-analyses were conducted according to MOOSE guideline. Data extraction was conducted by two independent investigators with a standard form. Pooled effect estimates were calculated in a random-effects model. MAIN OUTCOMES AND MEASURESDTC recurrence/progression and the type of recurrence/progression (structural or biochemical). RESULTSAmong the 10 included studies (n = 625), 4 (n = 143) of them compared the pregnancy group with the non-pregnancy group while the remaining 6 (n = 482) only included the pregnant patients. The pooled proportion of recurrence/progression in all pregnant patients was 13% (95% CI, 6%, 25%). Compared with the non-pregnancy group, the pooled odds ratio of recurrence/progression in the pregnancy group was 0.75 (95% CI, 0.45, 1.23). Two included studies focused on patients with distant metastasis and also did not observe difference in disease recurrence/progression between the pregnancy group and the non-pregnancy group [OR, 0.51 (95% CI, 0.14-1.87)]. Six included studies also reported response to therapy status prior to pregnancy, and the pooled proportion for recurrence/progression in pregnant DTC patients with excellent response (n=287), indeterminate response (n=44), biochemical incomplete response (n=41) and structural incomplete response (n=70) was 0.00 (95% CI, 0.00-0.86), 0.09 (95% CI, 0.00-0.99), 0.20 (95% CI, 0.06-0.46) and 0.45 (95% CI, 0.17-0.76), respectively. There was a trend for an increasingly higher risk of recurrence/progression from excellent, indeterminate, biochemical incomplete to structural incomplete response to therapy (P<0.05). CONCLUSIONS AND RELEVANCEPregnancy appears to have a minimal impact on the prognosis of DTC with initial treatment. Clinicians may pay more attention to the progression of DTC among pregnant women with biochemical and/or structural persistence. Key PointsO_ST_ABSQuestionC_ST_ABSDoes subsequent pregnancy has an impact on the prognosis of patients previously treated for differentiated thyroid cancer (DTC)? FindingsIn this systematic review and meta-analysis of 10 studies including 625 patients previously treated for DTC and underwent pregnancy subsequently, pregnancy might have a minimal impact on DTC recurrence/progression. Patients with biochemical and/or structural incomplete response to DTC treatment prior to pregnancy appears to have a higher risk of DTC recurrence/progression compared to those with excellent or indeterminate response. MeaningThough pregnancy appears to have little influence on the prognosis of patients previously treated for DTC, patients with biochemical and/or structural persistence should be more carefully monitored during pregnancy.

Background and AimsHeart Failure is a common and serious condition that often remains undetected until a major cardio-vascular event leads to diagnosis is secondary care. Here we propose a portable artificial intelligence tool that integrates clinical guidelines with phenotypic markers to identify high-risk patients who may benefit from formal diagnosis evaluation and timely initiation of treatment. MethodsDiagnosis guidelines are first encoded using a rule-based model, which is then used to train a neural network. Relying on de-identified real-world evidence from UK primary care, transfer learning is used to train on 91,346 historical records and forecast the 6.2% patients who received a diagnosis within 3 years. Tested for portability in an independent sample consisting of 56,308 validation records, predictions are interpreted using Shapley values and individually assessed for statistical significance by comparison with matched digital twin cohorts. A Kaplan-Meier survival analysis links positive predictions to the observed excess mortality. ResultsCompared with the prevailing challenge of under-diagnosis, model predictions in the validation set (0.7% TP, 2.7% FP) demonstrate strong statistical support, with fewer than 1.5% failing to reject a null hypothesis at p=0.05. Among the TP, the likelihood of receiving a future diagnosis is over 7.6 times higher than the baseline prevalence in the validation cohort. In both TP and FP cohorts, patients aged 60-70 years exhibited mortality rates more than fivefold higher than the control population. Furthermore, variables derived from the Complete Blood Count (CBC) including white blood cell count (WBC) and red cell distribution width (RDW), contribute significant predictive value beyond established diagnosis criteria. ConclusionsWhen implemented within a clinical decision support system, predictive AI has the potential to improve patients outcomes by leveraging routinely collected phenotypic markers, which are challenging for clinicians to interpret in the context of complex decision-making pathways.1

Suspected colorectal cancer (CRC) referrals based on non-specific symptoms currently lead to large numbers of patients being referred for invasive investigations and poor yield in cancer detection. Secondary care diagnostics, particularly endoscopy, struggle to meet the ever-increasing demand and patients face lengthy waits from the point of referral. Here we propose a blood test utilising high-throughput Raman spectroscopy and machine learning as an accurate triage tool. We present results from the first mixed methods clinical validation study of its kind, evaluating the ability of the test to perform in its target population of primary care patients, and its acceptability to those administering and receiving the test. The test was able to accurately rule out cancer with a negative predictive value of 98.0%. This performance could reduce the number of invasive diagnostic procedures in the cohort by at least 47%. Collectively, our findings promote a novel, non-invasive solution to triage CRC referrals with potential to reduce patient anxiety, accelerate access to treatment and improve outcomes.

Mammography plays a crucial role in breast cancer (BC) risk assessment. Recent breakthroughs show that deep learning (DL) in mammography is expanding from diagnosis to effective risk prediction. Moreover, the brightest mammographic breast density (MBD), termed "cirrocumulus," signifies an authentic risk. Addressing the challenges in quantifying above recent measures, we present MIDAS: a DL-derived system for multi-level MBD and risk feature score (FS). Using >260,000 multicenter images from South Korea and the US, FS consistently outperforms conventional MBD metrics in risk stratification. Only within the high FS, cirrocumulus further enriches assessment, pinpointing "double-higher" subgroup. Their risk profiles are notable: women in double upper-tertile showed OR=10.20 for Koreans and 5.67 for US, and OR=7.09 for scree-detected cases (US only). We also reveals the "black-box" nature of FS that it predominantly captures complex patterns of higher-intensity MBD. Our research enhances the potential of digital mammography in identifying individuals at elevated BC risks.

Cochlear implants (CIs) enable hearing with the deafened ear, via direct, electrical stimulation of the spiral ganglion neurons (SGN). Thus, the outcome depends on the number and excitability of the SGNs. We recently established the electrically-evoked compound action potential (eCAP)-derived Failure Index (FI) as cochlear-health marker in the animal model. The FI informs about the presence, site, and size of a SGN lesion. Here, we translated the FI to clinical recordings of MED-EL CI users. For the retrospective study, we selected patient data from the database of the German Hearing Center Hannover recorded 2017 to 2024. We included 199 post-lingually and 79 pre-lingually deafened ears. Averaged FIs over all contacts of a CI were stable within the analysis period (3rd month to 1st year postoperatively). The FI increased with age and was elevated for etiologies associated with higher SGN loss. Utilizing 3D information from cone beam-computed tomography scans, we confirmed that the FI was independent of distance (0.1-2.5 mm) to the modiolus. The FI showed individual patterns along the array with maxima usually at basal contacts, corresponding to elevated SGN loss at high frequencies. In a selected group of post-lingually deaf ears, we confirmed the correlation of the FI with speech perception in quiet and in noise (n=28, r2=0.12-0.55). Thus, we propose the FI as promising clinical tool to identify CI-implanted ears with reduced neural health and contacts close to areas of SGN loss. Thereby, it can serve to guide speech-processor fitting to optimize CI outcomes.

Background Chronic rhinosinusitis (CRS) and nasal polyps (NP) are closely related inflammatory airway diseases, and their co-occurrence is often associated with more persistent symptoms, frequent recurrence, and substantial respiratory morbidity. However, the extent to which CRS without and with NP (CRSsNP and CRSwNP) share genetic susceptibility-and which genetic mechanisms are disease-specific-remains poorly characterized. Methods We conducted cross-population genome-wide association meta-analyses of overall CRS (including both CRSwNP and CRSsNP) and NP (a proxy for CRSwNP) using data from six biobanks. We estimated genome-wide genetic correlations between overall CRS, CRSwNP, and a spectrum of respiratory diseases. We applied five complementary gene-prioritization strategies to nominate CRS- and CRSwNP-associated genes and performed pathway enrichment analyses to infer implicated biological processes. For CRSwNP, we integrated single-cell transcriptomic data to characterize cell-type-specific expression of prioritized genes and used stratified LD score regression to quantify heritability enrichment across immune and epithelial annotations. To delineate shared versus disease-specific genetic signals, we performed three comparative analyses-local genetic correlation, CRSwNP-CRS colocalization, and genomic structural equation modeling. Finally, we performed proteome-wide Mendelian randomization to identify circulating proteins with putative causal effects on CRS and CRSwNP. Results This GWAS meta-analysis identified 96 genome-wide significant loci for CRSwNP and 41 for overall CRS, prioritizing 92 and 39 candidate genes, respectively. CRSwNP and overall CRS showed shared genetic susceptibility (rg = 0.59; P = 6.8e-16), while CRS exhibited broader genetic correlations across multiple respiratory disorders. Pathway analyses consistently implicated immune signaling albeit with disease-specific emphases and lipid-metabolism networks. Single-cell analyses demonstrated distinct expression of CRSwNP-prioritized genes across nasal epithelial and immune cell clusters, and immune annotations explained more CRSwNP heritability (enrichment score = 4.1; P = 0.010) than epithelial annotations (2.5; P = 0.072). Comparative genetic analyses highlighted multiple shared loci-including BACH2, CD247, FADS2, FOXP1, FUT2, GPX4, IL7R, NDFIP1, RAB5B, RORA, SMAD3, TSLP - as well as 3 CRSwNP-specific and 6 CRS-specific loci. Proteome-wide MR identified 10 and 8 putatively causal circulating proteins for CRSwNP and overall CRS, respectively, with protein TNFSF11, IL2RB, and STX4 associated with both conditions. Conclusions This multi-population GWAS meta-analysis expanded genetic discovery for CRS and CRSwNP and showed substantial shared liability with distinct disease-specific components. Immune components explained a larger proportion of CRSwNP heritability than epithelial annotations, reinforcing the primacy of immune-driven mechanisms in polyp disease.

BackgroundCYFRA 21-1, a cytokeratin-19 fragment, is a validated serum biomarker for non-small cell lung cancer (NSCLC). However, most studies rely on single time-point measurements, limiting its specificity in differentiating malignancy from benign pulmonary conditions. Inspired by the clinical utility of serial PSA measurements in prostate cancer, we investigated whether longitudinal trends in CYFRA 21-1 could enhance diagnostic and monitoring capabilities in patients with pulmonary nodules Methods and FindingsWe analyzed 132 patients with pulmonary nodules, including 41 with lung cancer and 91 with benign diagnoses. CYFRA 21-1 levels were measured serially using electrochemiluminescence assays. Longitudinal trends were assessed using linear mixed-effects models to estimate biomarker trajectories. Subgroup analyses examined differences between benign, untreated cancer, and post-treatment cancer groups, as well as within-patient changes in a subset of 16 cancer patients with both pre- and post-surgical measurements. Log-transformed data were used for the analysis. At baseline, CYFRA 21-1 levels were significantly higher in malignant versus benign nodules. Over time, CYFRA trajectories diverged: benign cases showed slight increases, whereas cancer patients exhibited greater biomarker volatility. In treated cancer patients, trend of CYFRA levels on the natural log scale decline from -0.00137 pre-surgery to - 0.00263 to post-surgery, and both cancer groups showed significantly higher absolute slopes than the benign group (p < 0.05). While pre- vs post-treatment slope differences did not reach significance (p = 0.211), the general pattern indicated that CYFRA 21-1 is a dynamic marker responsive to tumor presence and removal. ConclusionsCYFRA 21-1 exhibits substantial within-patient variability over time, with trajectories that reflect disease state and treatment. These findings suggest that longitudinal monitoring of CYFRA 21-1--analogous to PSA velocity in prostate cancer-- may offer improved diagnostic and prognostic insight in the evaluation of pulmonary nodules. Further studies in larger cohorts are warranted to validate these findings and explore clinical implementation of CYFRA trajectory analysis.

Early identification and treatment of hearing impairments are essential for childrens development. International guidelines recommend a stepwise approach for conductive hearing screening in newborns. However, while the majority of countries around the world implemented universal newborn hearing screening, inconsistencies remain in terms of procedures and data management. In particular, Level 3 of the screening pathway--comprising diagnostic confirmation and therapeutic management--has received limited attention in the literature, despite its central role in determining program effectiveness and patient outcomes. This study investigates the clinical and organizational aspects of Level 3 within the neonatal hearing screening program of the Friuli-Venezia Giulia Region in Italy, analyzing data from 106 children enrolled between 2019 and 2023. The analysis considers the regional protocol, the roles of birthing centers, pediatricians, hospitals, and the Regional Center for Pediatric Hearing Loss Care, and subdivides Level 3 into four Phases (A-D) reflecting both organizational and diagnostic functions. By examining patient flow, false positives, loss to follow-up/documentation, and management practices, the study highlights how organizational factors--particularly the coordination between local and specialized facilities-- produce "cascade" outcomes directly affecting diagnostic timelines and treatment initiation. Findings provide critical insights into weaknesses of the current system and propose directions for improving program efficiency, accuracy, and overall quality of care.

BackgroundElectronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking) will facilitate studies of the health effects of vaping. To identify putative biomarkers of vaping and dual use, we performed association analysis in an observational cohort of 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data and self-reported current vaping and smoking behavior. MethodsBiomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. ResultsWe identified three transcriptomic and three proteomic associations with vaping, and 90 transcriptomic and 100 proteomic associations to dual use. Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (three transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (two transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality, and respiratory exacerbations. ConclusionsIn summary, vaping and dual use are associated with RNA and protein blood-based biomarkers that are also associated with adverse health outcomes.