The purpose of this research is to investigate the correlation between coagulation parameters and survival on patients with breast cancer. A total of 244 patients with breast cancer and 84 patients with metastatic advanced breast cancer admitted to the Second Affiliated Hospital of Chongqing Medical University from January 2017 to February 2019 were selected as the research objects. Their age, tumor size, status of estrogen receptor and progesterone receptor status, status of Her-2 receptor and lymph node were evaluated. Additional 99 cases of benign breast tumors were selected as the control group. All patients were newly diagnosed. PTA, PT ratio, PT, APTT, APTT ratio, TT, TT ratio, PT-INR, FIB, and FDP were detected on all patients. There were significant differences in APTT, APTT ratio, FIB, TT, TT ratio, and FDP between breast cancer and benign tumor control group (P < 0.05). The changes of FIB were correlated with age (P = 0.007), PR status (P = 0.017), and Her-2 status (P = 0.008). The older the age, the higher the FIB level. The FIB level of Er negative state is higher than that of positive state, and the FIB level of PR negative state is also higher than that of positive state. Survival analysis showed that PR negative status, the elevated level of APTT, APTT ratio, TT increased, and TT ratio were the indicators affecting 3-year DFS of patients (P < 0.05). Multivariate COX regression analysis showed that APTT and TT were independent prognostic factors affecting 3 years DFS (P < 0.05). Blood hypercoagulable state in patients with breast cancer, abnormal coagulation system plays an important role in the progress of breast cancer.
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http://dx.doi.org/10.1097/MBC.0000000000001084 | DOI Listing |
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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