Communications medicine最新文献

Background: Neural recordings capture crucial pathophysiological processes along the dementia continuum. However, cross-center variability in recording techniques and paradigms limit their generalizability and diagnostic power, preventing clinical use. We here propose a computational approach enabling cross-center classification even in the presence of completely different clinical pipelines.

Methods: We leveraged a digital twin model to derive digital biomarkers linking neurodegeneration mechanisms to alterations in neural activity across multiple recording modalities. We tested the generalizability of digital biomarkers through cross-center classification of Mild Cognitive Impairment (MCI) and healthy subjects in two independent clinics. The two datasets presented different recording techniques (EEG and MEG), preprocessing modalities, recruitment criteria and diagnostic guidelines. Digital biomarkers derived from one clinic were tested for classifying patients in the other clinic and vice versa employing a transfer learning approach.

Results: Digital biomarkers outperform standard biomarkers in the MCI vs healthy classification in both separate datasets (83% vs 58% for EEG dataset and 75% vs 68% for MEG dataset). Moreover, they achieve accurate and consistent cross-center classification (77-78% accuracy), while standard biomarkers perform poorly in the generalization attempt (56-65%). Additionally, digital biomarkers reliably predict global cognitive status across clinics across both datasets ( p < 0.01), while standard biomarkers present no correlation.

Conclusions: Digital biomarkers generalize across recording techniques and datasets, enabling a cross-modal and cross-center classification of a patient's condition. These biomarkers offer a robust measure of patient-specific neurodegeneration, mapping neural recordings anomalies into a common framework of underlying structural alterations. The vast differences between the two datasets support the applicability of this approach also in the presence of high inter-center variability.

Background: Real-world studies based on electronic health records often require manual chart review to derive patients' clinical phenotypes, a labor-intensive task with limited scalability. Here, we developed and compared computable phenotyping based on rules using the spaCy framework and a Large Language Model (LLM), GPT-4, for sub-phenotyping of patients with Crohn's disease, considering age at diagnosis and disease behavior.

Methods: For our rule-based approach, we leveraged the spaCy framework and for the LLM-based approach, we used the GPT-4 model. The underlying data included 49,572 clinical notes and 2204 radiology reports from 584 Crohn's disease patients. A test set of 280 clinical texts was labeled at sentence-level, in addition to patient-level ground truth data. The algorithms were evaluated based on their recall, precision, specificity values, and F1 scores.

Results: Overall, we observe similar or better performance using GPT-4 compared to the rules. On a note-level, the F1 score is at least 0.90 for disease behavior and 0.82 for age at diagnosis, and on patient level at least 0.66 for disease behavior and 0.71 for age at diagnosis.

Conclusions: To our knowledge, this is the first study to explore computable phenotyping algorithms based on clinical narrative text for these complex tasks, where prior inter-annotator agreements ranged from 0.54 to 0.98. There is no statistical evidence for a difference to the performance of human experts on this task. Our findings underline the potential of LLMs for computable phenotyping and may support large-scale cohort analyses from electronic health records and streamline chart review processes in the future.

Background: Physical activity plays an important role in preventing chronic diseases, but most studies rely on self-reported or short-term data that fail to capture habitual behavior. This study utilizes Fitbit data to investigate the relationship between physical activity and various chronic diseases.

Methods: We analyzed data from 22,019 participants in the All of Us Research Program who shared at least six months of Fitbit activity data linked with electronic health records. Various physical activity patterns were evaluated using Cox proportional hazards and logistic regression models, adjusting for age, sex, and body mass index (BMI). To test robustness, sensitivity analyses were conducted using obesity defined by BMI, applying a two-year exclusion window for outcome diagnoses to mitigate potential reverse causation, and incorporating lifestyle covariates (smoking and alcohol use) under a simplified directed acyclic graph (DAG) framework to address residual confounding.

Results: Here, we show that higher physical activity levels are associated with lower risks of multiple chronic conditions. Higher daily step counts were negatively associated with obesity and type 2 diabetes, while greater elevation gains and longer vigorous activity are associated with lower risks of conditions such as morbid obesity, obstructive sleep apnea, and major depressive disorder. All sensitivity analyses yield consistent results, supporting the robustness of findings against reverse causation and lifestyle confounding.

Conclusions: Higher physical activity and lower sedentary time may help prevent diverse chronic diseases. These findings demonstrate the potential of large-scale wearable data to inform personalized prevention and population health strategies.

Background: Approximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in homologous recombination (HR) DNA repair. While there are genomic and transcriptomic methods, developed for other cancers, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR status in HGSC specifically. We have built a HGSC-specific model to predict HR status using gene expression.

Methods: We separated The Cancer Genome Atlas (TCGA) cohort of HGSCs into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were used to train a penalised logistic regression model.

Results: IdentifiHR uses the expression of 209 genes to predict HR status in HGSC. These genes preserve the genomic damage signal, capturing known regions of HR-specific copy number alteration which impact gene expression. IdentifiHR is 85% accurate in the TCGA test set and 86% accurate in an independent cohort of 99 samples, taken from primary tumours, ascites and normal fallopian tubes. Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD.

Conclusions: IdentifiHR is an accurate model to predict HR status in HGSC. It is available as an open source R package, empowering researchers to robustly classify HR status when only transcriptomic sequencing data is available.

Background: The relative efficacies of nalmefene versus naltrexone for alcohol use disorder is the subject of intense and ongoing debate. The two pan-opioid receptor ligands differ primarily in actions at the kappa opioid receptor, where naltrexone acts as an antagonist and nalmefene acts as a partial agonist. Parallel clinical trials for nalmefene or naltrexone have produced widely disparate outcomes and a marked lack of consensus regarding which of the compounds should be used for the treatment of alcohol use disorder.

Methods: Here we leveraged a mouse model (n = 56 male C57BL/6 J) to directly compare the efficacy of nalmefene and naltrexone within-subject. After acquiring operant responding for ethanol, each subject underwent four treatment block conditions: nalmefene (0.1 mg/kg i.p.), naltrexone (1.0 mg/kg i.p.), the selective kappa opioid receptor agonist U50,488 (1.0 mg/kg i.p.) and placebo (saline 10 ml/kg i.p.). Each treatment block consisted of an ethanol self-administration session followed by two subsequent sessions of punished (quinine adulterated) ethanol self-administration sessions with treatment given 30 min prior to each session.

Results: We show that nalmefene and naltrexone have similar efficacy in reducing ethanol consumption, whereas U50,488 increases ethanol consumption. Despite similar effects in aggregate analyses, nalmefene- and naltrexone-induced reductions in drinking are driven by fully separate subpopulations which do not show any beneficial response to the non-preferred compound and display markedly different behavioral phenotypes prior to treatment. A predictive model based on circulating biogenic amines allows for high accuracy classification of nalmefene- versus naltrexone-responders.

Conclusion: Together, these results provide a roadmap for improving alcohol use disorder treatment outcomes via precision application of existing compounds.

Background: Kidney volume, reflecting cumulative effects of many cysts, is an important prognostic biomarker for autosomal dominant polycystic kidney disease (ADPKD) but fails in many patients. Tracking individual cysts may more directly assess disease progression.

Methods: Individual cysts (n = 299) from 37 subjects were evaluated retrospectively over ≥ 8 years by serial MRI (mean follow-up = 11 years). Cysts were labeled on every available MRI scan, totaling 1654 contours (median timepoints per cyst = 5). Effects of cyst location, morphology, and growth pattern on kidney function decline were evaluated by univariate and multivariate analyses.

Results: Simple, T2-bright cysts follow logistic growth (median cyst growth rate = 11%/year). A subset (94/222, 42%) transitions over time to shrinking, to complex solid-fluid/fluid-fluid cysts, then to homogeneously T1-bright cysts and finally disappearing. By contrast, T1-bright complex cysts have no volume change (median cyst growth rate = 0%/year; p < 0.001). On multivariate analysis, faster kidney function decline is associated with simple cyst diameter > 2 cm on index scan (p = 0.007) and simple cyst transitions (p = 0.02). There is a trend towards faster kidney function decline with higher simple cyst growth rate (p = 0.16).

Conclusions: Profiling individual cysts on serial MRI to identify transitions as well as size and growth rate may improve predictions of ADPKD progression and treatment response.

Background: Gastric neuroendocrine carcinoma (G-NEC) presents with clinical and pathological features that closely resemble those of gastric adenocarcinoma (GC), often complicating differential diagnosis. However, G-NEC is markedly more aggressive and associated with a significantly poorer prognosis, necessitating accurate and timely identification to guide appropriate therapeutic interventions.

Methods: In response to this clinical need, we developed G-NECNet, a deep convolutional neural network tailored to detect G-NEC from histopathological whole-slide images.

Results: The model demonstrates excellent diagnostic performance, yielding an average area under the receiver operating curve (AUROC) of 0.993 in the internal validation cohort, 0.985 on an external single-institutional dataset, and 1.000 on an external multi-institutional consultation dataset. These consistently high AUROC values highlight the robustness, accuracy, and generalizability of G-NECNet across diverse clinical settings.

Conclusions: The integration of G-NECNet into routine diagnostic workflows may not only improve the precision of G-NEC classification but also reduce misdiagnosis-related healthcare costs, offering a practical and scalable solution for clinical application.

Background: Fumarate hydratase-deficient renal cell carcinoma (FHd-RCC) is a rare and aggressive renal cancer subtype characterised by increased fumarate accumulation and upregulated lactate production. Renal tumours demonstrate significant intratumoral metabolic heterogeneity, which may contribute to treatment failure. Emerging non-invasive metabolic imaging techniques have clinical potential to more accurately phenotype tumour metabolism and its heterogeneity.

Methods: In this case study we have used hyperpolarised ¹³C-pyruvate MRI (HP ¹³C-MRI) to assess ¹³C-lactate generation in a patient with an organ-confined FHd-RCC. Post-operative tissue samples were co-registered with imaging and underwent sequencing, IHC staining, and mass spectrometry imaging (MSI).

Results: HP ¹³C-MRI reveals two metabolically distinct tumour regions. The ¹³C-lactate-rich region shows a high lactate/pyruvate ratio and slightly lower fumarate on MSI compared to the other tumour region, as well as increased CD8 + T cell infiltration, and genetic dedifferentiation. Compared to the normal kidney, the vascularity in the tumour is decreased, while immune cell fraction is markedly higher.

Conclusions: This study shows the potential of metabolic HP ¹³C-MRI to characterise FHd-RCC and how targeting of biopsies to regions of metabolic dysregulation could be used to obtain the tumour samples of greatest clinical significance, which in turn can inform on early and successful response to treatment.

Background: Adequate antenatal care (ANC) is often unrealised in sub-Saharan Africa (SSA). This is exemplified in the Cameroonian Anglophone Crisis, an ongoing armed civil conflict. Conflict intensity varies nationally, causing differential impacts on healthcare access. This study aimed to quantify the impact of the conflict's intensity on ANC use and identify its geographical variations.

Methods: We analysed live births from the 2011 and 2018 Cameroonian Demographic and Health Surveys and the 2022 Cameroonian Malaria Indicator Survey. Conflict intensity was measured as the proportion of Anglophone Crisis-related deaths occurring in each division, using Armed Conflict Location & Event Data Project (ACLED) data. Associations between conflict intensity and the proportion of live births attending at least one (ANC1) and at least four (ANC4) ANC visits were assessed using multiple linear regression and geographically weighted regression.

Results: Between 2011 and 2022, North West and South West Cameroon experienced ANC4 compliance declines. North West also experienced an ANC1 decline, but South West experienced an ANC1 increase. There is no evidence for an association between ANC1 and conflict intensity (p = 0.403). There is strong evidence for a negative association between conflict intensity and ANC4 (p = 0.007). A 1% increase in conflict intensity is associated with a 1.14% (95% CI: 0.326, 1.963) decrease in ANC4. There is strong evidence for spatial variation of this relationship (p < 0.001).

Conclusions: There is strong evidence to suggest that the Anglophone Crisis has adversely impacted ANC use, with varying magnitudes nationwide. Targeted solutions are crucial to mitigate its impacts on sustained ANC use.

Background: Cardiac arrhythmias are a major cause of morbidity and mortality increasing the risk of stroke, heart failure, and sudden cardiac death. Imageless electrocardiographic Imaging has emerged as an accessible non-invasive alternative for cardiac electrical mapping from body surface potentials. However, conventional electrocardiographic imaging is restricted to epicardial reconstructions, reducing its reliability in accurately identifying arrhythmias arising from deeper myocardial structures. We aim to overcome this limitation by reconstructing three-dimensional cardiac activity.

Methods: We introduce a volumetric formulation, which extends beyond epicardial potential estimation by solving an inverse source problem using Green's functions. This technique enables three-dimensional reconstructions of cardiac activation, improving arrhythmia localization in anatomically complex regions. We evaluate the method on simulated premature ventricular beats and on four patients representing clinical challenges, including a premature ventricular contraction from the right ventricular outflow tract, a left bundle branch block, a ventricular tachycardia, and a Wolff-Parkinson-White. We also assess performance on an open-source dataset for myocardial infarction estimation.

Results: Our results indicate that volumetric electrocardiographic imaging reconstructs three-dimensional activation and enhances the localization of arrhythmia origins, yielding a 59.3% reduction in geodesic error between the estimated and simulated origins compared to surface-only approaches. In patient cases, the recovered activation patterns are consistent with the clinical diagnoses.

Conclusions: Imageless volumetric electrocardiographic imaging enables non-invasive, accessible, three-dimensional mapping of cardiac activation, addressing a fundamental limitation of surface-restricted methods. This capability may support more accurate pre-procedural planning, may help guide ablation targets, and could refine selection and optimization of cardiac resynchronization therapy candidates.