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Novel metabolic prognostic score for predicting survival in patients with cancer.
Sci Rep
January 2025
Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
Cancer is a fatal disease with a high global prevalence and is associated with an increased incidence of metabolic disorders. This study aimed to develop a novel metabolic prognostic system to evaluate the overall metabolic disorder burden in cancer patients and its relationship with their prognosis. The patients in this study were enrolled from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) project.
View Article and Find Full Text PDFPre-breeding in alfalfa germplasm develops highly differentiated populations, as revealed by genome-wide microhaplotype markers.
Sci Rep
January 2025
Plant Science Research Unit, USDA-ARS, St. Paul, MN, USA.
Plant genebanks contain large numbers of germplasm accessions that likely harbor useful alleles or genes absent in commercial plant breeding programs. Broadening the genetic base of commercial alfalfa germplasm with these valuable genetic variations can be achieved by screening the extensive genetic diversity in germplasm collections and enabling maximal recombination among selected genotypes. In this study, we assessed the genetic diversity and differentiation of germplasm pools selected in northern U.
View Article and Find Full Text PDFEnhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.
Plant Genome
March 2025
USDA-ARS Southeast Area, Plant Science Research, Raleigh, North Carolina, USA.
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions.
View Article and Find Full Text PDFImprovement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.
Plant Genome
March 2025
INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France.
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization.
View Article and Find Full Text PDFLong-Term Effect of Tafamidis on Clinical Parameters and Prognostic Predictors in Patients With Transthyretin Amyloid Cardiomyopathy.
Circ J
January 2025
Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University.
Background: Accurate prediction of prognosis in transthyretin amyloid cardiomyopathy (ATTR-CM) is crucial for optimal treatment selection, including tafamidis, the only approved therapy for ATTR-CM. Although tafamidis has been proven to improve prognosis, the long-term serial changes in comprehensive parameters related to ATTR-CM, including cardiac biomarkers and imaging parameters, under tafamidis remain unknown.
Methods And Results: In this study, we used Cox regression analysis on data from 258 consecutive patients diagnosed with ATTR-CM at Kumamoto University to determine prognostic factors.
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