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Ovarian cancer continues to be one of the most lethal gynecological malignancies, with earlier symptoms that are frequently subtle, resulting in detection at late stages. Although there are several traditional treatments, patients do not respond well to them owing to serious side effects. Alginate, a polysaccharide extracted from brown seaweed (a natural polymer), has gained significant attention as an ideal biopolymer for developing drug delivery systems because of its nontoxicity, biodegradability, and ease of manipulation. Alginate-based NPs (ABNPs) represent a new strategy for the targeted treatment of ovarian cancer, increasing the efficacy of chemotherapeutic agents in tumor cells while reducing systemic toxicity. Current strategies to exploit ABNPs relate to their capability to encapsulate different types of payloads, including small-molecule drugs, proteins, and genetic materials. Functionalization with targeting peptides, antibodies, or FA imparts selective affinity for ovarian cancer cells, and hence, a targeted chemotherapeutic approach. Alginate NPs are a versatile and potent platform for the targeted treatment of ovarian cancer, integrating drug delivery into diagnostics, as well as gene therapy. This review presents the latest research trends and an understanding of the characteristic features and functions of ABNPs in targeted delivery against ovarian cancer.
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http://dx.doi.org/10.1016/j.ijbiomac.2025.145365 | DOI Listing |
Int J Surg
September 2025
Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, People's Republic of China.
Int J Surg
September 2025
Department of Gynecology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.
Background: Ovarian cancer remains the most lethal gynecological cancer, with fewer than 50% of patients surviving more than five years after diagnosis. This study aimed to analyze the global epidemiological trends of ovarian cancer from 1990 to 2021 and also project its prevalence to 2050, providing insights into these evolving patterns and helping health policymakers use healthcare resources more effectively.
Methods: This study comprehensively analyzes the original data related to ovarian cancer from the GBD 2021 database, employing a variety of methods including descriptive analysis, correlation analysis, age-period-cohort (APC) analysis, decomposition analysis, predictive analysis, frontier analysis, and health inequality analysis.
Arch Gynecol Obstet
September 2025
Department of Obstetrics and Gynecology, University Medical Center Freiburg, Freiburg, Germany.
Objective: To investigate the clinical utility of diagnostic laparoscopy in guiding treatment strategy and surgical outcomes for patients with advanced-stage ovarian cancer, specifically regarding operability assessment and the likelihood of complete cytoreduction.
Methods: This retrospective cohort study analyzed 183 patients with histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) stage III-IV ovarian cancer treated with curative intent between January 2018 and December 2023 at a tertiary referral center. Patients were divided into two groups: those who underwent diagnostic laparoscopy prior to primary treatment (n = 80) and those managed without laparoscopy (n = 103).
Int J Surg
September 2025
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, China.
Introduction: Recent advancements in surgical techniques and perioperative care have improved cancer survival rates, yet postoperative comorbidity and mortality remain a critical concern. Despite progress in cancer control, systematic analyses of long-term mortality trends and competing risks in surgery-intervened cancer populations are lacking. This study aimed to quantify temporal patterns of postoperative mortality causes across 21 solid cancers and identify dominant non-cancer risk factors to inform survivorship care strategies.
View Article and Find Full Text PDFCancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
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