Communications medicine最新文献

Background: Childhood cancer survivors have a high risk of chronic multi-organ disease that does not plateau over time. To date, there is a lack of sensitive diagnostic techniques that allow early detection of tissue damage before clinical symptoms occur, particularly regarding pulmonary function. Free-breathing phase-resolved functional lung (PREFUL) low-field magnetic resonance imaging (LF-MRI) may enable visualization and quantification of functional and structural lung damage without the need of specific contrast agents.

Methods: In this single-center, cross-sectional diagnostic study, we performed LF-MRI in a cohort of n = 27 children and adolescents (age range: 5 to 17 years) after treatment for acute lymphoblastic leukemia (ALL; n = 21) and Hodgkin's disease (HD; n = 6) to determine the frequency of morphologic and functional lung parenchymal changes.

Results: Here, we show that despite the absence of clinical symptoms, significant time-dependent pulmonary ventilation and perfusion defects are detected. A negative correlation between the time after the end of therapy and defect-free lung tissue in the cohort of patients treated for ALL (Spearman-coefficient = - 0.69, p = 0.0005) is observed.

Conclusions: Our results suggest an increase in pulmonary ventilation and perfusion defects preceding the increase in chronic disease that has already been reported in this patient population. Further research is needed to determine whether the functional abnormalities described in this study are an early morphological correlate of developing organ damage that may become clinically evident over time. PREFUL MRI may be an effective and highly sensitive tool for early detection of these changes in lung function, allowing longitudinal studies for risk stratification and potential future treatment adaptation.

Background: Speculative claims about COVID-19 vaccines affecting fertility and childbirth have circulated widely. We aimed to examine whether COVID-19 vaccination is causally associated with childbirth in Swedish women.

Methods: We conducted a cohort study using a representative population of 369,000 to emulate a randomized experiment, comparing childbirth rates between vaccinated and unvaccinated women. Cox proportional hazards models were applied, treating vaccination as a time-varying covariate. Causal modeling was used to adjust for potential bias. To capture vaccine effects on both conception and established pregnancies, the index event was set at an estimated conception date, 280 days prior to childbirth.

Results: We show that with an assumed average pregnancy length of 280 days, there are no statistically significant associations between COVID-19 vaccination and childbirth (unadjusted HR = 0.94 (95% CI 0.89-1.00); adjusted HR = 1.03 (95% CI 0.97-1.09). Assuming a shorter pregnancy length (266 days), the associations between vaccination and childbirth remain insignificant (unadjusted HR = 0.96 (95% CI 0.90-1.02); adjusted HR = 1.04 (95% CI 0.98-1.11)). Neither are there statistically significant associations between COVID-19 vaccination and recorded miscarriages (unadjusted HR = 0.84 (95% CI 0.69-1.03); adjusted HR = 0.86 (95% CI 0.70-1.05).

Conclusions: COVID-19 vaccination is not associated with a decrease in childbirth after adjusting for common confounding factors. These findings provide evidence to support vaccination policies for women of childbearing age.

Background: Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments.

Methods: We performed comprehensive RNA sequencing of skin and blood (n = 718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe chronic plaque psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy.

Results: Here we show, using two complementary dimensionality reduction methods, that co-expressed gene modules and factors within skin and blood are significantly associated with psoriasis phenotypes and disease severity. We identify a 14-gene signature negatively associated with BMI in nonlesional skin and with disease severity in lesional skin. Genotype integration reveals that HLA-DQA1*01 and HLA-DRB1*15 genotypes are positively associated with baseline psoriasis severity. Using explainable machine learning models, we define two disease severity-associated gene modules in lesional skin - one positive, one negatively-associated - and a 9-gene signature in lesional skin predictive of disease severity. Disease severity signatures in blood are only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status is independent of disease severity or drug.

Conclusions: These findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity.

Background: Ischemic heart disease is the leading cause of global mortality. Despite advances in clinical management, current diagnostic tools do not capture early metabolic disturbances associated with myocardial ischemia. Understanding these alterations may provide new insights into disease mechanisms.

Methods: A metabolomic approach based on magnetic resonance spectroscopy is used to characterize the metabolic profile of patients with ischemic heart disease compared with non-ischemic individuals. Plasma and pericardial fluid collected during cardiac surgery are analyzed to investigate both systemic and heart-proximal molecular changes. Small-molecule concentrations are quantified and statistically evaluated to identify metabolic differences associated with ischemia.

Results: Here we show that ischemic heart disease is associated with a distinct metabolic pattern. We observe increased concentrations of 3-hydroxybutyrate in both biological fluids, together with elevated succinate in pericardial fluid, indicating alterations in mitochondrial energy metabolism. Additional changes involve pathways linked to substrate utilization and redox balance.

Conclusions: These findings highlight a metabolic response to myocardial ischemia detectable in both systemic and locally collected fluids. The identified alterations offer a deeper understanding of the biochemical environment associated with ischemic heart disease.

Background: Early cancer detection has the potential to significantly improve treatment outcomes and survival rates. This study investigates the roles of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) as biomarkers for early-stage colorectal cancer (CRC) detection in cell-free DNA (cfDNA).

Methods: We analyzed cfDNA from 37 treatment-naive CRC patients and 32 healthy controls using duet evoC 6-base whole genome sequencing (A, C, T, G, 5mC, 5hmC). We then built CRC classifiers using differentially methylated regions identified in stage IV CRC tissue samples. We compared the performance of models using 5mC-only, 5hmC-only, modified cytosine, or 5mC and 5hmC features.

Results: Here we show that models combining measurements of 5mC and 5hmC significantly enhance diagnostic accuracy (AUC = 0.95) compared to traditional approaches that conflate these markers (modified C, AUC = 0.66). Notably, of all the DMRs considered, almost half show an increase in 5hmC at stage I and a corresponding decrease in 5mC at stage IV, suggesting that 5hmC can effectively track regions undergoing demethylation during tumor development.

Conclusions: These results support the hypothesis that distinguishing between 5mC and 5hmC improves the sensitivity of liquid biopsy tests for early-stage cancer detection.

Health system resilience requires a resilient and well workforce. During health crises, including pandemics and outbreaks of pathogens, frontline healthcare workers can face significant challenges, resulting in personal psychosocial costs for staff members, and organisational consequences for the health systems they work within. Here, we highlight that there is an urgent need to proactively incorporate organisational measures to protect and promote the wellbeing of frontline healthcare staff across all stages of response to health crises. Importantly, this not only involves specific emergency preparedness and planning efforts, but also supporting organisational-level everyday practices that foster staff wellbeing and health system resilience outside of crises.

Background: Cardiovascular disease screening faces significant challenges in resource-limited settings, where infrastructure and computational constraints preclude advanced assessment. These constraints are particularly acute for people living with human immunodeficiency virus (HIV), who experience elevated cardiovascular risk yet often receive care in clinics without specialist diagnostic capacity. Pretrained physiological foundation models offer potential for low-cost screening using wearable sensors, though their applicability in resource-constrained settings remains unclear.

Methods: We evaluate pretrained physiological embeddings from foundation models for cardiovascular disease detection using photoplethysmography signals from 80 people living with HIV in Ho Chi Minh City, Vietnam. Of 80 participants, 13 (16%) had cardiologist-confirmed cardiovascular disease. We compare strictly zero-shot deployment (NormWear without local training) with frozen PaPaGei embeddings plus locally trained classifier, alongside traditional approaches.

Results: Here we show that the PaPaGei-embedding approach achieves area under the receiver operating characteristic curve 0.769 (95% confidence interval: 0.70, 0.84) and average precision 0.489 (0.37, 0.61) in this pilot cohort, numerically higher than zero-shot NormWear (0.610; 0.226), principal component analysis features (0.651; 0.208), and supervised clinical models (0.744; 0.433). This approach requires local labels for classifier training but avoids computationally intensive foundation model fine-tuning. However, given the small positive class size (13 cases), these findings require validation in larger cohorts. PaPaGei embeddings capture clinically coherent structure: patients on dolutegravir-based regimens cluster in low-risk regions, while those with high cholesterol variability occupy high-risk areas.

Conclusions: These preliminary findings provide a potential methodological framework for deploying foundation models in resource-constrained settings, though adequately powered, multi-centre validation is essential before clinical implementation.

Background: Radiotherapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC). Recent pre-clinical studies highlight the potential of combining RT with immunotherapy due to RT-induced activation of the cyclic GMP-AMP-synthase (cGAS)/Stimulator of Interferon (IFN) Genes (STING) pathway, yet the clinical implications remain unclear.

Methods: In this study, we utilized a cGAS-mNeongreen knock-in tumor model, human HNSCC cell lines and serial tumor biopsies from HNSCC patients to investigate RT-induced cGAS activation and re-localization. cGAS positive micronuclei and IFN stimulated gene (ISG) induction were quantified. In HNSCC patient biopsies, cGAS positive micronuclei were identified in different cellular compartments and IFN/ISG and NF-κB serum levels were correlated to survival. Differences were tested using paired t-tests, Wilcoxon test, One-way and two-way ANOVA or Kaplan Meier curves.

Results: Here we show that cGAS positive foci present in micronuclei increase post-RT in both mouse tumor models (P = 0.0305; t-test) and HNSCC patient biopsies (P = 0.0012; Wilcoxon test), concurrently with higher ISG expression. Notably, cGAS re-localization in HNSCC patient biopsies is most prominent in immune cell-infiltrated tumor regions (P = 0.0061; Wilcoxon test). Enhanced systemic type I IFN inducing activity is observed post-RT (P = 0.0040; mixed-effects analysis) and correlates with improved survival (OS P = 0.0146; log-rank test; DFS P = 0.0256; Gehan-Breslow-Wilcoxon test), particularly in HPV positive patients (OS P = 0.0017; log-rank test; DFS P = 0.043; log-rank test).

Conclusions: These findings suggest that cGAS re-localization and systemic IFN responses may serve as biomarkers for RT efficacy, providing a basis for optimizing HNSCC treatment protocols, especially when integrated with immunotherapy.

Background: To accurately detect individuals' mental health issues using artificial intelligence and self-report scales, it is crucial to recognize how personal characteristics can affect the detection. This study focuses on the role of alexithymia-a condition where individuals struggle to recognize and articulate emotions and symptoms-in the detection of depression. We aimed to determine whether deep learning models could enhance the accuracy of depression detection in people with alexithymia compared to self-report scales.

Methods: We analyzed data from 194 patients with major depressive disorder and 105 community controls, employing eight large language models (LLMs) trained on transcript text from clinician-administered structured interviews using the Hamilton Depression Rating Scale (HAMD).

Results: Here we show that generalized logistic regression analysis indicates a positive relationship between alexithymia and depression. Using the HAMD as the gold standard, individuals with alexithymia show poorer performance on the self-reported Hospital Anxiety and Depression Scale-Depression Subscale (HADS-D) in identifying depression (b = -0.37, p = .002). Four of the eight LLMs (AUCs=0.87-0.89) significantly outperform the HADS-D (AUC = 0.79) in depression detection (p <0.05). Subgroup analysis demonstrates that while LLMs achieve AUCs ranging from 0.79 to 0.96, the HADS-D only reaches an AUC of 0.35 for individuals with alexithymia.

Conclusions: Our findings reveal that LLMs can potentially outperform self-report scales in detecting depression, particularly in those with alexithymia. These results highlight the importance of considering patient characteristics, such as alexithymia, when detecting depression. Deep learning analyses can enhance the accuracy of clinical assessments for depression and potentially for other mental health disorders.

Background: The skeletal muscle hypothesis refers to a vicious cycle of successive deterioration of left ventricular function, skeletal muscle remodeling, and functional capacity in patients with heart failure. Despite extensive research, the regulatory mechanisms and their associations with clinical status and prognosis are still largely unclear.

Methods: To identify mechanisms and characterize underlying processes involved in the disease pathophysiology, we performed RNA sequencing and network analysis using human skeletal muscle samples from 58 patients with severe symptomatic heart failure. A co-expression network with communities involved in established biological processes within human skeletal muscle was identified and validated in two independent cohorts.

Results: Here, we show network communities associated with mitochondrial beta-oxidation, extracellular matrix remodeling, oxidative phosphorylation, and contractile elements with lower expression in heart failure patients than in age-matched controls. Based on the strong correlation with clinical features and prognosis, extracellular matrix remodeling, mitochondrial beta-oxidation, and p53 signalling communities are identified as key underlying processes. The former two communities are highly enriched with genes regulated by physical (in)activity, i.e., bed rest and exercise, and associated weakly with prognosis. Community related to p53 signalling, with CDKN1A as a key regulator, is increased in heart failure patients relative to age-matched controls and associated with worse prognosis.

Conclusion: The current work differentiates previously proposed factors underlying heart failure-induced skeletal muscle dysfunction, emphasizing the p53 signalling community and importance of biological age in this process. The distinct association with clinical status and prognosis furthermore supports pathophysiological significance and clinical potential of this community.