Preparation of magnetic activated carbon fibers@Fe3O4 by electrostatic self-assembly method and adsorption properties for methylene blue.

Nano-Fe3O4 was loaded onto coconut-based activated carbon fibres (CACF) using an electrostatic self-assembly method. The effects of the mass ratio of CACF to nano-Fe3O4, loading time, pH and temperature on the loading effect were investigated and ideal loading conditions were determined. To study the adsorption performance of MACF@Fe3O4 for methylene blue, the effects of the initial concentration, pH and time on the adsorption were investigated and the working conditions of adsorption were established. MACF@Fe3O4 was systematically characterized. Adsorption kinetics were investigated under ideal conditions. The ideal loading conditions for MACF@Fe3O4 were as follows: mass ratio of 1:1, 20 min, pH 9.36, 22.5°C. The saturation magnetization of MACF@Fe3O4 was 48.2263 emu·g-1, which could be quickly separated under an external magnetic field. When the dosage was 0.010 g, the adsorption rate reached 97.29% and the maximum adsorption capacity was 12.1616 mg·g-1. The adsorption process conformed to pseudo-first-order kinetics during the first 15 min and pseudo-second-order kinetics during 20-120 min. The equations were ln( Q e - Q t )=2.2394-0.0689t and t Q t =0.0774 + 0.5295t , respectively. The isothermal adsorption model showed that MACF@Fe3O4 was more in line with the Langmuir model, indicating that the adsorption process was mainly monolayer adsorption. The thermodynamic analysis results showed that the adsorption process of MB by MACF@Fe3O4 was an endothermic process. In this study, MACF@Fe3O4 with high adsorption capacity and easy separation from coconut palm fibres has good application prospects in the field of adsorption, which can promote the high-value utilization of coconut palms.

Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis.

PURPOSE: Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. RESULTS: A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CONCLUSION: CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research.

Breast cancer is a multifactorial heterogeneous disease and the leading cause of cancer-related deaths in women; its diagnosis and treatment require clinical sensitivity and a comprehensive disciplinary research approach. The expression of different receptors on tumor cells not only provides the basis for molecular typing of breast cancer but also has a decisive role in the diagnosis, treatment, and prognosis of breast cancer. To date, immunohistochemistry (IHC), which uses invasive histological sampling, has been extensively used in clinical practice to analyze the status of receptors and to make an accurate diagnosis of breast cancer. As an invasive assay, IHC can provide important biological information on tumors at a single point in time, but cannot predict future changes (due to treatment or tumor mutations) without additional invasive procedures. These issues highlight the need to develop a non-invasive method for predicting receptor status. The emerging field of radiomics may offer a non-invasive approach to identification of receptor status without requiring biopsy. In this paper, we present a review of the latest research results in radiomics for predicting the status of breast cancer receptors, with potential important clinical applications.

Diagnostic value of mammography density of breast masses by using deep learning.

Objective: In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density. Methods: This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity. Results: In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001). Conclusions: Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.