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Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL).
Front Biosci (Landmark Ed)
November 2024
Department of Hematology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 317000 Taizhou, Zhejiang, China.
In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) in refining the application of multi-omics and spatial multi-omics within the realm of diffuse large B-cell lymphoma (DLBCL) research. We scrutinized the current landscape of multi-omics and spatial multi-omics technologies, accentuating their combined potential with AI to provide unparalleled insights into the molecular intricacies and spatial heterogeneity inherent to DLBCL. Despite current progress, we acknowledge the hurdles that impede the full utilization of these technologies, such as the integration and sophisticated analysis of complex datasets, the necessity for standardized protocols, the reproducibility of findings, and the interpretation of their biological significance.
View Article and Find Full Text PDFUncertainty-aware automatic TNM staging classification for [F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.
BMC Med Inform Decis Mak
December 2024
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Background: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text.
View Article and Find Full Text PDFChanging the Inoculum Type From Preculture to Spore Suspension Markedly Alters the Production of Secondary Metabolites in Filamentous Microbial Coculture.
Curr Microbiol
December 2024
Department of Bioprocess Engineering, Faculty of Process and Environmental Engineering, Lodz University of Technology, ul. Wólczańska 213, 93-005, Łódź, Poland.
The shake flask cocultures of Aspergillus terreus and Streptomyces rimosus were investigated with regard to the production of mevinolinic acid (lovastatin), oxytetracycline, and other secondary metabolites (SMs). The aim of the study was to determine the effect of inoculum type (spore suspension or preculture) on the levels of SMs in the fermentation broth. Altogether, 17 SMs were detected, including 4 products with confirmed identities, 10 putatively annotated metabolites, and 3 unknown molecules.
View Article and Find Full Text PDFThe storage quality and transcriptome analysis of fresh-cut taro by L-ascorbic acid combined with ultrasonic treatment.
Ultrason Sonochem
November 2024
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Mycological Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address:
Fresh-cut taro, renowned for its high nutritional value and convenience, is prone to rapid browning post-cutting, which hinders its storage life. This study focused on the effects of L-ascorbic acid (AA) combined with ultrasound (US) treatment (AS) on the storage quality and transcriptome analysis of fresh-cut slices of Yongding June Red Taro. Compared to the control (CK) group, AS treatment effectively reduced the weight loss rate of taro slices, maintained higher hardness, delayed the increase of browning, and inhibited the accumulation of O and HO.
View Article and Find Full Text PDFDeep learning based binary classification of diabetic retinopathy images using transfer learning approach.
J Diabetes Metab Disord
December 2024
Dronacharya College of Engineering, Gurugram, Haryana 122001 India.
Objective: Diabetic retinopathy (DR) is a common problem of diabetes, and it is the cause of blindness worldwide. Detection of diabetic radiology disease in the early detection stage is crucial for preventing vision loss. In this work, a deep learning-based binary classification of DR images has been proposed to classify DR images into healthy and unhealthy.
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