Role of transferrin, transferrin receptors, and iron in macrophage listericidal activity.

It is not yet known what properties distinguish macrophages which can kill facultative intracellular bacteria, such as Listeria monocytogenes, from those which cannot. Listeria is an organism which requires iron for growth, yet macrophage listericidal mechanisms are also likely to be iron dependent. We show here that resident peritoneal macrophages and thioglycollate-elicited macrophages cannot kill listeria, but proteose peptone-elicited and FCS-elicited macrophages can. All these cell populations phagocytose listeria. Transferrin receptor expression is low on resident cells, intermediate on peptone- and FCS-elicited cells, and high on thioglycollate-elicited cells. Transferrin transports iron into cells via the transferrin receptor: thus, iron content of resident cells is low, of peptone- and FCS-elicited cells is intermediate, and of thioglycollate-elicited cells is high. Moreover, antibody to transferrin, which prevents it binding its receptor, inhibits listericidal macrophages from killing this bacterium. Finally, nonlistericidal cells with high transferrin receptor expression and high intracellular iron become listericidal if they are incubated with apotransferrin, an iron-free ligand which prevents iron uptake by cells. These data suggest that macrophages must have enough available intracellular iron to support listericidal mechanisms, but too much iron favors growth of the bacterium, which no longer can be killed by the macrophage

The potential application of artificial intelligence for diagnosis and management of glaucoma in adults

BACKGROUND: Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma. SOURCES OF DATA: This literature review is based on articles published in peer-reviewed journals. AREAS OF AGREEMENT: There have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior. AREAS OF CONTROVERSY: Concerns that the increased reliance on AI may lead to deskilling of clinicians. GROWING POINTS: AI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable. AREAS TIMELY FOR DEVELOPING RESEARCH: There is a need to determine the external validity of deep learning algorithms and to better understand how the 'black box' paradigm reaches results

Applications of interpretability in deep learning models for ophthalmology

PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT FINDINGS: The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' 'interpretability' and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice

Electron excitation and energy transfer rates for H2O in the upper atmosphere

Recent measurements of the cross sections for electronic state excitations in H2O have made it possible to calculate rates applicable to these excitation processes. We thus present here calculations of electron energy transfer rates for electronic and vibrational state excitations in H2O, as well as rates for excitation of some of these states by atmospheric thermal and auroral secondary electrons. The calculation of these latter rates is an important first step towards our aim of including water into a statistical equilibrium model of the atmosphere under auroral conditions.Comment: 15 pages, 8 figure

A risk profile for identifying community-dwelling elderly with a highrisk of recurrent falling: results of a 3-year prospective study

Introduction: The aim of the prospective study reported here was to develop a risk profile that can be used to identify community-dwelling elderly at a high risk of recurrent falling. Materials and methods: The study was designed as a 3-year prospective cohort study. A total of 1365 community-dwelling persons, aged 65 years and older, of the population-based Longitudinal Aging Study Amsterdam participated in the study. During an interview in 1995/1996, physical, cognitive, emotional and social aspects of functioning were assessed. A follow-up on the number of falls and fractures was conducted during a 3-year period using fall calendars that participants filled out weekly. Recurrent fallers were identified as those who fell at least twice within a 6-month period during the 3-year follow-up. Results: The incidence of recurrent falls at the 3-year follow-up point was 24.9% in women and 24.4% in men. Of the respondents, 5.5% reported a total of 87 fractures that resulted from a fall, including 20 hip fractures, 21 wrist fractures and seven humerus fractures. Recurrent fallers were more prone to have a fall-related fracture than those who were not defined as recurrent fallers (11.9% vs. 3.4%; OR: 3.8; 95% CI: 2.3-6.1). Backward logistic regression analysis identified the following predictors in the risk profile for recurrent falling: two or more previous falls, dizziness, functional limitations, weak grip strength, low body weight, fear of falling, the presence of dogs/cats in the household, a high educational level, drinking 18 or more alcoholic consumptions per week and two interaction terms (high educationx18 or more alcohol consumptions per week and two or more previous falls x fear of falling) (AUC=0.71). Discussion: At a cut-off point of 5 on the total risk score (range 0-30), the model predicted recurrent falling with a sensitivity of 59% and a specificity of 71%. At a cut-off point of 10, the sensitivity and specificity were 31% and 92%, respectively. A risk profile including nine predictors that can easily be assessed seems to be a useful tool for the identification of community-dwelling elderly with a high risk of recurrent falling. © International Osteoporosis Foundation and National Osteoporosis Foundation 2006

Why do people fail to turn good intentions into action? : The role of executive control processes in the translation of healthy eating intentions into action in young Scottish adults

Non peer reviewedPublisher PD