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Biomass-Based Microbial Protein Production: A Review of Processing and Properties.
Front Biosci (Elite Ed)
December 2024
Environmental Sciences, Faculty of Environmental and Conservation Sciences, North Dakota State University, Fargo, ND 58102, USA.
A rise in population and societal changes have increased pressure on resources required to meet the growing demand for food and changing dietary preferences. The increasing demand for animal protein is concerning and raises questions regarding sustainability due to its environmental impact. Subsequently, scientists seek alternative proteins, such as microbial proteins (MPs), as an environmentally friendly choice.
View Article and Find Full Text PDFDefNEtTrp: An Iron Dual Chelator Approach for Anticancer Application.
JACS Au
December 2024
Department of Chemistry, University of Puerto Rico, Río Piedras Campus, Río Piedras, Puerto Rico 00931, United States.
Targeting iron metabolism has emerged as a novel therapeutic strategy for the treatment of cancer. As such, iron chelator drugs are repurposed or specifically designed as anticancer agents. Two important chelators, deferasirox (Def) and triapine (Trp), attack the intracellular supply of iron (Fe) and inhibit Fe-dependent pathways responsible for cellular proliferation and metastasis.
View Article and Find Full Text PDFMacrophage Infiltration Correlated with IFI16, EGR1 and MX1 Expression in Renal Tubular Epithelial Cells Within Lupus Nephritis-Associated Tubulointerstitial Injury via Bioinformatics Analysis.
J Inflamm Res
December 2024
Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
Objective: A comprehensive bioinformatics analysis was conducted to investigate potential new diagnostic biomarkers and immune infiltration characteristics associated with tubulointerstitial injury in lupus nephritis (LN), and to examine possible correlations between key genes and infiltrating immune cells.
Methods: The GSE32591, GSE113342, and GSE200306 datasets were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) were identified in the pooled dataset. Support vector machine-recursive feature elimination analysis and the least absolute shrinkage and selection operator regression model were used to screen for possible markers, and the compositional patterns of the 22 types of immune cell fractions in LN were determined using CIBERSORT.
TDERS, an exosome RNA-derived signature predicts prognosis and immunotherapeutic response in clear cell renal cell cancer: a multicohort study.
J Natl Cancer Cent
December 2024
Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China.
Background: Tumor-derived exosomes are involved in tumor progression and immune invasion and might function as promising noninvasive approaches for clinical management. However, there are few reports on exosom-based markers for predicting the progression and adjuvant therapy response rate among patients with clear cell renal cell carcinoma (ccRCC).
Methods: The signatures differentially expressed in exosomes from tumor and normal tissues from ccRCC patients were correspondingly deregulated in ccRCC tissues.
MODALITY-AGNOSTIC LEARNING FOR MEDICAL IMAGE SEGMENTATION USING MULTI-MODALITY SELF-DISTILLATION.
Proc IEEE Int Symp Biomed Imaging
May 2024
Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA.
In medical image segmentation, although multi-modality training is possible, clinical translation is challenged by the limited availability of all image types for a given patient. Different from typical segmentation models, modality-agnostic (MAG) learning trains a single model based on all available modalities but remains input-agnostic, allowing a single model to produce accurate segmentation given any modality combinations. In this paper, we propose a novel frame-work, MAG learning through Multi-modality Self-distillation (MAG-MS), for medical image segmentation.
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