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Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with limited treatment options and high resistance to chemotherapy. Doxorubicin is commonly used, but its efficacy is limited by variable sensitivity and resistance. Bacopaside II, a saponin compound, has shown anti-cancer potential. This study evaluates the effects of doxorubicin and bacopaside II, both individually and in combination, across TNBC subtypes to explore mechanisms of resistance and enhanced drug efficacy. Methods: The growth-inhibitory effects of doxorubicin and bacopaside II were assessed in four TNBC cell lines. IC50 values were determined using dose–response assays, and doxorubicin accumulation was measured via spectral flow cytometry. ATP-binding cassette (ABC) transporter expression (ABCB1, ABCC1, ABCC3, and ABCG2) was analyzed for correlations with drug sensitivity. In silico docking assessed the binding affinity of bacopaside II to ABC transporters. A 3D culture model simulated drug-resistant TNBC, and combination effects were evaluated with live-cell imaging. Results: Doxorubicin sensitivity varied across TNBC molecular subtypes, correlating to intracellular accumulation. Bacopaside II inhibited growth across subtypes, inducing apoptosis in sensitive cells and necrosis in resistant cells. Bacopaside II increased doxorubicin accumulation, independent of P-glycoprotein (ABCB1), possibly through interactions with other ABC transporters. In drug-resistant 3D cultures, bacopaside II maintained efficacy and enhanced doxorubicin accumulation, counteracting ABC transporter-mediated resistance. The doxorubicin and bacopaside II combination showed synergistic growth inhibition. Conclusions: Bacopaside II enhances doxorubicin efficacy in TNBC by increasing drug accumulation and overcoming ABC transporter-mediated resistance, suggesting its potential as an adjuvant in TNBC treatment. These findings support further investigation of bacopaside II, particularly for resistant TNBC subtypes. [ABSTRACT FROM AUTHOR]

Changes in biomechanical properties such as elasticity modulus, viscosity, and poroelastic features are linked to the health status of biological tissues. Ultrasound elastography is a non-invasive imaging tool that quantitatively maps these biomechanical characteristics for diagnostic and treatment monitoring purposes. Mathematical models are essential in ultrasound elastography as they convert the raw data obtained from tissue displacement caused by ultrasound waves into the images observed by clinicians. This article reviews the available mathematical frameworks of continuum mechanics for extracting the biomechanical characteristics of biological tissues in ultrasound elastography. Continuum-mechanics-based approaches such as classical viscoelasticity, elasticity, and poroelasticity models, as well as nonlocal continuum-based models, are described. The accuracy of ultrasound elastography can be increased with the recent advancements in continuum modelling techniques including hyperelasticity, biphasic theory, nonlocal viscoelasticity, inversion-based elasticity, and incorporating scale effects. However, the time taken to convert the data into clinical images increases with more complex models, and this is a major challenge for expanding the clinical utility of ultrasound elastography. As we strive to provide the most accurate imaging for patients, further research is needed to refine mathematical models for incorporation into the clinical workflow. [ABSTRACT FROM AUTHOR]

Biomechanical characteristics can be used to assist the early detection of many diseases, including breast cancer, thyroid nodules, prostate cancer, liver fibrosis, ovarian diseases, and tendon disorders. In this paper, a scale-dependent viscoelastic model is developed to assess the biomechanical behaviour of biological tissues subject to flexural waves. The nonlocal strain gradient theory, in conjunction with machine learning techniques such as extreme gradient boosting, k-nearest neighbours, support vector machines, and random forest, is utilised to develop a computational platform for biomechanical analysis. The coupled governing differential equations are derived using Hamilton's law. Transverse wave analysis is conducted to investigate different normal and pathological human conditions including ovarian cancer, breast cancer, and ovarian fibrosis. Viscoelastic, strain gradient, and nonlocal effects are used to describe the impact of fluid content, stiffness hardening caused by the gradients of strain components, and stiffness softening associated with the nonlocality of stress components within the biological tissues and cells. The integration of the scale-dependent biomechanical continuum model with machine learning facilitates the adoption of the developed model in practical applications by allowing for learning from clinical data, alongside the intrinsic mechanical laws that govern biomechanical responses. [ABSTRACT FROM AUTHOR]

Macrophages are prominent in the uterus and ovary at conception. Here we utilize the Cd11b-Dtr mouse model of acute macrophage depletion to define the essential role of macrophages in early [...]

Background: Mastitis is a common and distressing maternal postpartum condition, but the relationship between mastitis timing and antibiotic treatment and breastfeeding outcomes and postnatal mental health is unclear. Objectives: To describe the incidence of mastitis and treatment with antibiotics in first 6 months postpartum, and to investigate the impact of mastitis timing and antibiotic treatment on breastfeeding practices and postnatal mental health. Methods: This study is based on 79,985 mother‐infant dyads in the Norwegian Mother, Father and Child Cohort Study (MoBa). Women were classified according to self‐reported mastitis within first month ('early') or 1–6 months ('later') postpartum and antibiotic treatment. Breastfeeding outcomes included predominant or any breastfeeding and abrupt breastfeeding cessation until 6 months postpartum. Maternal mental health was assessed by self‐report at 6 months postpartum. Results: The incidence of mastitis was 18.8%, with 36.8% reporting treatment with antibiotics. Women reporting early mastitis were less likely to report predominant breastfeeding (adjustedd relative risk [aRR] 0.92, 95% confidence interval [CI] 0.86, 0.99) and any breastfeeding for 6 months (aRR 0.97, 95% CI 0.96, 0.98) than women who did not report mastitis, and more likely to report abrupt breastfeeding cessation (aRR 1.37, 95% CI 1.23, 1.53). Late‐onset mastitis was not associated with poorer breastfeeding outcomes. Among women reporting mastitis, the risk of abrupt breastfeeding cessation was higher in those also reporting antibiotic use. Mastitis was associated with an increased risk of mental health problems postpartum which was highest among those reporting no antibiotic use (aRR 1.29, 95% CI 1.18, 1.41), in contrast to those also reporting antibiotic use (aRR 1.08, 95% CI 0.96, 1.22). Conclusions: Lactational mastitis and its associated treatment with antibiotics are common. Early (<1 month postpartum) mastitis appears to be a modest risk factor for suboptimal breastfeeding outcomes. In addition, mastitis is associated with poorer mental health. [ABSTRACT FROM AUTHOR]