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Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer.
J Transl Med
January 2025
Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Background: Drug resistance constitutes one of the principal causes of poor prognosis in breast cancer patients. Although cancer cells can maintain viability independently of mitochondrial energy metabolism, they remain reliant on mitochondrial functions for the synthesis of new DNA strands. This dependency underscores a potential link between mitochondrial energy metabolism and drug resistance.
View Article and Find Full Text PDFInsights into incompatible plasmids in multidrug-resistant Raoultella superbugs.
BMC Microbiol
January 2025
State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, China.
The emergence of multidrug-resistant (MDR) Raoultella isolates is linked to the acquisition of antibiotic resistance genes (ARGs) with plasmids playing a pivotal role in this process. While plasmid-mediated transmission of ARGs in Raoultella has been extensively reported, limited attention has been given to genetically dissecting the modular structures of plasmids. This study aims to elucidate the genomic features of novel incompatible plasmids in MDR Raoultella by presenting 13 complete plasmid sequences from four isolates, along with an analysis of 16 related plasmids from GenBank.
View Article and Find Full Text PDFSingle cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.
Mol Med
January 2025
Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.
Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment.
View Article and Find Full Text PDFTrauma-informed HIV prevention for forcibly displaced adolescents and young adults.
Lancet HIV
January 2025
New York State Psychiatric Institute, New York, NY, USA.
The number of people forcibly displaced due to conflict is rising rapidly each year. Previous studies have documented associations between mental ill health, HIV risk, and poor engagement with HIV care in conflict-affected populations. Most people forced to migrate are adolescents and young adults, who might already be affected by a high burden of mental ill health due to factors such as high trauma exposure during the developmental period.
View Article and Find Full Text PDFDiffusion transformer-augmented fMRI functional connectivity for enhanced autism spectrum disorder diagnosis.
J Neural Eng
January 2025
Department of Bioengineering, Lehigh University, Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, Bethlehem, 18015-3027, UNITED STATES.
Functional magnetic resonance imaging (fMRI) is often modeled as networks of Regions of Interest (ROIs) and their functional connectivity to study brain functions and mental disorders. Limited fMRI data due to high acquisition costs hampers recognition model performance. We aim to address this issue using generative diffusion models for data augmentation.
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