Background And Aims: NAFLD is a major disease burden and a foremost cause of chronic liver disease. Presently, nearly 300 trials evaluate the therapeutic efficacy of > 20 drugs. Remarkably, the majority of drugs fail. To better comprehend drug failures, we investigated the reproducibility of fatty liver genomic data across 418 liver biopsies and evaluated the interpatient variability of 18 drug targets.
Approach And Results: Apart from our own data, we retrieved NAFLD biopsy genomic data sets from public repositories and considered patient demographics. We divided the data into test and validation sets, assessed the reproducibility of differentially expressed genes and performed gene enrichment analysis. Patients were stratified by disease activity score, fibrosis grades and sex, and we investigated the regulation of 18 drug targets across 418 NAFLD biopsies of which 278 are NASH cases. We observed poor reproducibility of differentially expressed genes across 9 independent studies. On average, only 4% of differentially expressed genes are commonly regulated based on identical sex and 2% based on identical NAS disease score and fibrosis grade. Furthermore, we observed sex-specific gene regulations, and for females, we noticed induced expression of genes coding for inflammatory response, Ag presentation, and processing. Conversely, extracellular matrix receptor interactions are upregulated in males, and the data agree with clinical findings. Strikingly, and with the exception of stearoyl-CoA desaturase, most drug targets are not regulated in > 80% of patients.
Conclusions: Lack of data reproducibility, high interpatient variability, and the absence of disease-dependent drug target regulations are likely causes of NASH drug failures in clinical trials.
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http://dx.doi.org/10.1097/HEP.0000000000000780 | DOI Listing |
Cogn Neurodyn
December 2024
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027 Anhui China.
Domain adaptation (DA) has been frequently used to solve the inter-patient variability problem in EEG-based seizure prediction. However, existing DA methods require access to the existing patients' data when adapting the model, which leads to privacy concerns. Besides, most of them treat the whole existing patients' data as one single source and attempt to minimize the discrepancy with the target patient.
View Article and Find Full Text PDFBiosens Bioelectron
December 2024
Juniata College, Department of Information Technology and Computer Science, Huntingdon, United States. Electronic address:
The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation has traditionally relied on cardiologists' expertise. Deep learning has revolutionized medical data analysis, especially within ECG diagnostics. However, the challenge of inter-patient variability limits the generalizability of ECG-AI models trained on population datasets, often reducing accuracy for specific patients or groups.
View Article and Find Full Text PDFAnal Bioanal Chem
December 2024
Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China.
Bruton's tyrosine kinase inhibitors (BTKis) exhibit significant interindividual pharmacokinetics, making therapeutic drug monitoring (TDM) a promising approach for personalized therapy. However, simultaneous quantification of multiple BTKis poses technical challenges. A unified protocol for BTKis detection would be clinically desirable.
View Article and Find Full Text PDFImmunooncol Technol
December 2024
Department of Dermatology and Netherlands Institute for Pigment Disorders, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam Institute for Immunology and Infectious Diseases, Amsterdam, The Netherlands.
Background: Tumor heterogeneity is a hurdle to effective therapy, as illustrated by the 'mixed responses' frequently seen in immunotherapy-treated patients. Previously, AXL+ tumor cells were identified to be highly resistant to targeted therapy, whereas more differentiated MITF+ tumor cells do respond to RAF and MEK inhibitors.
Patients And Methods: In this study, we analyzed tumor heterogeneity and explored the presence of the previously described AXL+ or MITF+ melanoma subpopulations in metastatic tissues by NanoString gene expression analysis, single-cell RNA sequencing and multiplex immunofluorescence.
Cancers (Basel)
December 2024
Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting.
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