Background Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. Main body This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman’s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. Conclusions We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.

In non industrialized countries the incidence of heavy menstrual bleeding (HMB) appears to be similar to that of industrialized countries, although data is scanty. In low-resource settings, women with abnormal uterine bleeding (AUB) often delay seeking medical care because of cultural beliefs that a heavy red menstrual bleed is healthy. Efforts to modify cultural issues are being considered. A detailed history and a meticulous examination are the important foundations of a definitive diagnosis and management in low-resource settings but are subject to time constraints and skill levels of the small numbers of health professionals. Women's subjective assessment of blood loss should be combined, if possible, with a colorimetric hemoglobin assessment, if full blood count is not possible. Outpatient endometrial sampling, transvaginal sonography, and hysteroscopy are available in some non industrialized countries but not in the lowest resource settings. After exclusion of serious underlying pathology, hematinics should be commenced and antifibrinolytic or nonsteroidal anti-inflammatory drugs considered during menses to control the bleeding. Intrauterine or oral progestogens or the combined oral contraceptive are often the most cost-effective long-term medical treatments. When medical treatment is inappropriate or has failed, the surgical options available most often are myomectomy or hysterectomy. Hysteroscopic endometrial resection or newer endometrial ablation procedures are available in some centers. If hysterectomy is indicated the vaginal route is the most appropriate in most low-resource settings. In low-resource settings, lack of resources of all types can lead to empirical treatments or reliance on the unproven therapies of traditional healers. The shortage of human resources is often compounded by a limited availability of operative time. Governments and specialist medical organizations have rarely included attention to AUB and HMB in their health programs. Local guidelines and attention to training of doctors, midwives, and traditional health workers are critical for prevention and improvement in management of HMB and its consequences for iron deficiency anemia and postpartum hemorrhage, the major killer of young women in developing countries.

Introduction: Group-based models for well-child care have been shown to positively affect patient experience. One promising group well-child care model is CenteringParenting. However, clinician self-efficacy with delivery of the model is unknown and clinician satisfaction with the model has been understudied. Objectives: To investigate sense of self-efficacy, degree of satisfaction, and comfort with trauma-informed care (TIC) among diverse clinical providers implementing the CenteringParenting curriculum. We also examined the relationship between self-efficacy, satisfaction, and comfort with TIC, and delivery of the model. Methods: Electronic surveys were sent to CenteringParenting providers (N = 98) from 49 clinics. Providers (N = 41) from 24 clinical sites completed the survey, corresponding to a 42% individual and 49% site response rate. Surveys explored provider: satisfaction with the curriculum, perceived self-efficacy, and perspective on competency with TIC. Results: Providers indicated that the CenteringParenting model achieves each of its four objectives (means ranged from 4.10 to 4.52 for each objective, with 5 being the highest possible response). Providers rated their level of satisfaction (scale of 1 [unsatisfied] to 5 [very satisfied]) with their ability to address patient concerns higher with CenteringParenting in the group care setting (mean = 4.10) than in the individual care setting (mean = 3.55). Respondents demonstrated a high mean average Self-Efficacy in Group Care score of 93.63 (out of 110). Unadjusted logistical regression analyses demonstrated that higher provider Self-Efficacy in Group Care score (odds ratio [OR] = 1.08) and higher comfort with TIC (OR = 22.16) is associated with curriculum content being discussed with a facilitative approach. Conclusions: Providers from diverse clinical sites report high satisfaction with and self-efficacy in implementing the CenteringParenting model.

The killing of Mycobacterium leprae by resting and gamma interferon (IFN--y)-activated macrophages in normal subjects and leprosy patients was assessed. Resting macrophages from normal individuals demonstrated the ability to kill M. Ieprae. For macrophages from tuberculoid patients, killing of M. leprae was only achieved in the presence of IFN--y, suggesting that initial T-cell activation occurs prior to the killing of M.