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Cancer is a leading cause of death worldwide, and the development of new diagnostic and treatment methods is crucial. Manganese-based nanomaterials (MnNMs) have emerged as a focal point in the field of cancer diagnosis and treatment due to their multifunctional properties. These nanomaterials have been extensively explored as contrast agents for various imaging technologies such as magnetic resonance imaging (MRI), photoacoustic imaging (PAI), and near-infrared fluorescence imaging (NIR-FL). The use of these nanomaterials has significantly enhanced the contrast for precise tumor detection and localization. Moreover, MnNMs have shown responsiveness to the tumor microenvironment (TME), enabling innovative approaches to cancer treatment. This review provides an overview of the latest developments of MnNMs and their potential applications in tumor diagnosis and therapy. Finally, potential challenges and prospects of MnNMs in clinical applications are discussed. We believe that this review would serve as a valuable resource for guiding further research on the application of manganese nanomaterials in cancer diagnosis and treatment, addressing the current limitations, and proposing future research directions.
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http://dx.doi.org/10.3389/fbioe.2024.1363569 | DOI Listing |
Pediatr Blood Cancer
September 2025
Department of Pediatrics, Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta and Emory University, Atlanta, Georgia, USA.
Moyamoya syndrome (MMS) is a chronic vasculopathy characterized by progressive stenosis of intracerebral arteries, leading to an increased risk of stroke. Children with Down syndrome (DS) are at an increased risk of co-occurring medical conditions, including MMS and leukemia. We report four patients with the triad of DS, MMS, and acute lymphoblastic leukemia (ALL).
View Article and Find Full Text PDFJ Cancer Surviv
September 2025
Department of Otolaryngology - Head and Neck Surgery, University of Pittsburgh Medical Center, 203 Lothrop St # 500, Pittsburgh, PA, 15213, USA.
Purpose: Despite its importance, little is known about the patterns and predictors of Survivorship Clinic attendance in head and neck cancer (HNC). We sought to determine the cumulative incidence of Survivorship Clinic attendance stratified by demographic, clinical, and socioeconomic factors, and to identify factors independently associated with attendance.
Methods: Our analysis population consisted of 2,252 patients diagnosed with primary HNC and seen at our institution's HNC Survivorship Clinic after completing treatment from 2016-2021.
J Egypt Natl Canc Inst
September 2025
National Cancer Institute of Cairo University, Giza, Egypt.
Objectives: To balance the extended functional urinary voiding and morbidity outcomes amid Ileal W and Y-shaped contrasted to spherical ileocoecal (IC) orthotopic bladders subsequent prostate-sparing radical cystectomy (PRC) versus standard radical cystoprostatectomy (RC).
Material And Methods: Two hundred eight male bladder cancer patients were grouped into 98 RC followed by 43-W, 31-Y, and 23-IC in comparison to 110 PRC followed by 35-W, 37-Y, and 38-IC. The functional voiding outcomes were determined by detailed patients' interview and urodynamic studies (UDS).
Virchows Arch
September 2025
Department of Pathology & Laboratory Medicine, Cleveland Clinic Florida, Weston, FL, USA.
Langerhans cell sarcoma (LCS) is an aggressive malignant neoplasm with a Langerhans cell immunophenotype and high-grade cytological features. Occasionally, it can coexist with other hematopoietic neoplasms with proven clonal relationship. Most of these neoplasms were found to be of lymphoid origin.
View Article and Find Full Text PDFNihon Hoshasen Gijutsu Gakkai Zasshi
September 2025
Department of Radiological Technology, Faculty of Health Sciences, Gifu University of Medical Science.
Purpose: We aimed to develop an AI-based system to score the positioning in mammography (MG), with the goal of establishing a foundation for future technical support.
Methods: Using 800 mediolateral oblique (MLO) images, we developed an AI model (Mask Generation Model) for automatic extraction of three regions: the pectoralis major muscle, the mammary gland region, and the nipple. Using this model, we extracted three regions from 1544 MLO images and generated mask images.