The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.
Aim: We aimed to identify the characteristics of patients with diabetes who can derive cognitive benefits from intensive blood pressure (BP) treatment using machine learning methods.
Materials And Methods: Using data from the Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORD-MIND) study, 1349 patients with type 2 diabetes who underwent BP treatment (intensive treatment targeting a systolic BP <120 mmHg vs. standard treatment targeting <140 mmHg) were included in the machine learning analysis.
Background: The immune response against tumors relies on distinguishing between self and non-self, the basis of cancer immunotherapy. Neoantigens from somatic mutations are central to many immunotherapeutic strategies and understanding their landscape in breast cancer is crucial for targeted interventions. We aimed to profile neoantigens in Kenyan breast cancer patients using genomic DNA and total RNA from paired tumor and adjacent non-cancerous tissue samples of 23 patients.
Salivaomics has emerged as a ground-breaking field in the detection and management of oral cancer (OC), offering a non-invasive, efficient, and patient-friendly alternative to traditional diagnostic methods. This innovative approach leverages the comprehensive molecular insights provided by genomics, transcriptomics, proteomics, metabolomics, and microbiomics. The potential of salivaomics lies in its ability to enable early detection, predict malignant transformation, and monitor treatment outcomes and disease recurrence.
Unlabelled: Recently, there has been a debate regarding the association between polycystic ovary syndrome (PCOS) and pancreatic cancer (PC). In order to examine the causal relationship between PCOS and PC, we conducted a Mendelian randomization study, which utilized 12 single nucleotide polymorphisms (SNPs) identified from a genome-wide association study (GWAS) meta-analysis that included 10,074 PCOS cases and 103,164 controls of European ancestry as instrumental variables (IVs). The outcome data were obtained from the FinnGen database (including 605 cases and 218,187 controls).
Unlabelled: Lung cancer remains the leading cause of death among cancer patients, and the five-year survival rate is less than 25%. However, () polymorphism rs140693 predicts the prognosis of lung cancer patients still needs further verification. Primary lung cancer patients ( = 839) were collected from two hospitals, genomic DNA was extracted from blood, and genotyping was performed using SNPcan technology.
