Identification of hypoxia-related diagnostic biomarkers and immune signatures in diminished ovarian reserve.

Background: Diminished ovarian reserve (DOR) becomes more common with age, and hypoxia is a key cause of apoptosis in ovarian granulosa cells. This study investigated the genetic links between hypoxia and DOR.

Methods: The GSE87201 dataset for DOR was sourced from Gene Expression Omnibus database, normalized for common differentially expressed genes (Co-DEGs), and identified Hypoxia-related differentially expressed genes (HRDEGs) GeneCards; Receiver Operating Characteristic (ROC) curves evaluated HRDEGs' diagnostic value, and protein-protein interaction networks were visualized with STRING and Cytoscape.

Wavelet-domain frequency-mixing transformer unfolding network for low-dose computed tomography image denoising.

Background: Low-dose computed tomography (LDCT) has become clinically essential for reducing radiation risks, but image noise remains a major challenge. Although deep learning methods have shown promise in denoising, they often fail to preserve fine structures while suppressing noise, particularly in orthopedic imaging where subtle bone textures are diagnostically critical. This study aimed to develop a wavelet-domain frequency-mixing transformer (FMT) network that simultaneously addresses noise suppression and structural preservation in orthopedic LDCT images, overcoming the limitations of current spatial-domain methods.

Whole‑exome evolutionary profiling of osteosarcoma uncovers metastasis‑related driver mutations and generates an independently validated predictive classifier.

Background: Osteosarcoma is the most common primary malignant bone tumor, with high invasiveness and metastatic potential and a poor prognosis in patients with metastatic cancer. Despite the rapid advancements in genomics in recent years that provided new perspectives for studying the molecular mechanisms of osteosarcoma, the understanding of its tumor heterogeneity and evolutionary mutation process remains limited.

Methods: In this study, whole-exome evolutionary profiling was performed on data from the TARGET database representing 61 osteosarcoma cases.

A novel perspective on bone tumors: advances in organoid research.

Bone tumor organoids are three-dimensional cell culture models derived from patient tissues or cells, capable of highly replicating the growth patterns and cell interactions of bone tumors . Current treatments for bone tumors are hindered by challenges such as drug resistance, recurrence, and metastasis. Organoids enhance the physiological relevance of bone tumor models, thereby improving treatment precision and overcoming the limitations of current therapeutic approaches.

Network pharmacology and molecular docking to explore the treatment potential and molecular mechanism of Si-Miao decoction against gouty arthritis.

Gouty arthritis (GA) is a common metabolic rheumatological disease. Si-Miao decoction has therapeutic effects on GA. In our study, we investigated the mechanism of Si-Miao decoction against GA using network pharmacology and molecular docking analytical methods.

Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular Dynamics.

Due to the increased concern about energy and environmental issues, significant attention has been paid to the development of large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flow battery (RFB) is one of the most promising systems. Recently, the high cost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redox-active organic molecules.