Publications by authors named "J G Hecker"

Purpose: The management of soft tissue sarcoma (STS) at reference centers with specialized multidisciplinary tumor boards (MTB) improves patient survival. The German Cancer Society (DKG) certifies sarcoma centers in German-speaking countries, promoting high standards of care. This study investigated the variability in treatment recommendations for localized STS across different German-speaking tertiary sarcoma centers.

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Background: There are important inter-relationships between miRNAs and metabolites: alterations in miRNA expression can be induced by various metabolic stimuli, and miRNAs play a regulatory role in numerous cellular processes, impacting metabolism. While both specific miRNAs and metabolites have been identified for their role in childhood asthma, there has been no global assessment of the combined effect of miRNAs and the metabolome in childhood asthma.

Methods: We performed miRNAome-metabolome-wide association studies ('miR-metabo-WAS') in two childhood cohorts of asthma to evaluate the contemporaneous and persistent miRNA-metabolite associations: 1) Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (N = 1121); 2) the Childhood Asthma Management Program (CAMP) (N = 312 and N = 454).

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Objectives: For individuals living with rare neurodevelopmental disorders, especially those who are at the most severe end of the spectrum, standardized outcome measures may lack the sensitivity to capture small but meaningful changes. Personalized endpoints such as Goal Attainment Scaling (GAS) allow the assessment of treatment response across variable baseline states and disease manifestations and thus provide a highly sensitive measure of efficacy. The current study tested the feasibility of using GAS in rare SCN2A-associated developmental and epileptic encephalopathy (SCN2A-DEE).

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As the burden of Alzheimer's disease (AD) escalates with an ageing population, the demand for early and accessible diagnostic methods becomes increasingly urgent. Saliva, with its non-invasive and cost-effective nature, presents a promising alternative to cerebrospinal fluid and plasma for biomarker discovery. : In this study, we conducted a comprehensive multi-omics analysis of saliva samples ( = 20 mild cognitive impairment (MCI), = 20 Alzheimer's disease and age- and = 40 gender-matched cognitively normal individuals), from the South Australian Neurodegenerative Disease (SAND) cohort, integrating proteomics, metabolomics, and microbiome data with plasma measurements, including pTau181.

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Article Synopsis
  • Alzheimer's disease is a leading form of dementia in older adults, and early detection is crucial for effective intervention since its effects can begin decades before symptoms show.
  • The study utilized advanced machine learning techniques to create models predicting Alzheimer's status and onset age, analyzing a range of biological and medical data from UK Biobank, with significant emphasis on the importance of specific proteins.
  • Notable findings include that GFAP and CXCL17 proteins are strong predictors of Alzheimer's, while genomics and proteomics provided the most valuable information in predicting disease status, although expanding the dataset with "AD-by-proxy" cases didn't enhance prediction accuracy.
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