Who has responsibility for access to essential medical drugs in the developing world?

L’accès aux traitements de base est un enjeu crucial pour la santé, la pauvreté et le développement. La responsabilité en matière d’accès est alors une question essentielle. Le huitième Objectif du Millénaire pour le Développement postule qu’en coopération avec les firmes pharmaceutiques, l’accès aux traitements essentiels doit être assuré. Les principales parties prenantes qui doivent engager leur responsabilité pour l’accès aux médicaments sont (1) l’industrie pharmaceutique, (2) les gouvernements, (3) la société au sens large, et (4) les individus (qu’ils soient ou non malades). Quatre approches permettent d’appréhender la responsabilité: (a) l’approche déontologique; (b) l’utilitarisme; (c) l’égalitarisme; (b) l’approche basée sur les droits de l’homme. Ces quatre arguments peuvent être utilisés pour assigner une responsabilité aux gouvernements dans l’accès aux médicaments. Le papier conclut qu’il est parfois difficile de distinguer entre ces quatre approches et qu’un « glissement-d’échelle » de la responsabilité est une voie utile pour appréhender les rôles des quatre principales parties prenantes dans l’accès aux médicaments, dépendant du pays ou de la région et de son environnement interne.Access to basic medical treatments emerges as cause and effect of health, poverty and development. Where the responsibility for improving access to essential medicines lies is, therefore, a crucial question. Millennium Development Goal (MDG) number 8, states, "In cooperation with pharmaceutical companies, provide access to affordable essential drugs in developing countries" (UN 1). The key stakeholders who may take responsibility for access to drugs are (1) the pharmaceutical industry, (2) governments, (3) society at large, and (4) individuals (both with and without disease). Four lenses through which responsibility can be viewed are: (a) deontological; (b) utilitarian; (c) egalitarian; and (d) human rights-based approaches. All four arguments can be used to assign responsibility for improving access to drugs to the governments, especially utilitarian and human-rights approaches. The paper concludes that it is sometimes difficult to distinguish between the four ethical approaches and that a “sliding-scale” of responsibility is the most useful way to view the roles of the four main players in access to essential drugs, depending on the country or region and its internal environment. Mots-clefs : enfants des rues, ville, travail, Cameroun, Madagasca

Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations

In this paper, we describe a new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM), a popular model for reservoir computing. Compared to the parallel perceptron architecture trained by the p-delta algorithm, which is the state of the art in terms of performance of readout stages, our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation. Inspired by the nonlinear properties of dendrites in biological neurons, our readout stage incorporates neurons having multiple dendrites with a lumped nonlinearity. The number of synaptic connections on each branch is significantly lower than the total number of connections from the liquid neurons and the learning algorithm tries to find the best 'combination' of input connections on each branch to reduce the error. Hence, the learning involves network rewiring (NRW) of the readout network similar to structural plasticity observed in its biological counterparts. We show that compared to a single perceptron using analog weights, this architecture for the readout can attain, even by using the same number of binary valued synapses, up to 3.3 times less error for a two-class spike train classification problem and 2.4 times less error for an input rate approximation task. Even with 60 times larger synapses, a group of 60 parallel perceptrons cannot attain the performance of the proposed dendritically enhanced readout. An additional advantage of this method for hardware implementations is that the 'choice' of connectivity can be easily implemented exploiting address event representation (AER) protocols commonly used in current neuromorphic systems where the connection matrix is stored in memory. Also, due to the use of binary synapses, our proposed method is more robust against statistical variations.Comment: 14 pages, 19 figures, Journa

The developing world in The New England Journal of Medicine

BACKGROUND: Rampant disease in poor countries impedes development and contributes to growing North-South disparities; however, leading international medical journals underreport on health research priorities for developing countries. METHODS: We examined 416 weekly issues of the New England Journal of Medicine (NEJM) over an eight-year period, January 1997 to December 2004. A total of 8857 articles were reviewed by both authors. The content of each issue was evaluated in six categories: research, review articles, editorial, correspondence, book reviews and miscellaneous. If the title or abstract concerned a topic pertinent to any health issue in the developing world, the article was reviewed. RESULTS: Over the eight years covered in this study, 1997–2004, in the three essential categories of original research articles, review articles and editorials, less than 3.0 percent of these addressed health issues in the developing world. Publications relevant to DC were largely concerned with HIV and communicable diseases and constituted 135 of the 202 articles of which 63 were devoted to HIV. Only 23 articles addressed non-communicable disease in the DC and only a single article – a book review – discussed heart disease. CONCLUSION: The medical information gap between rich and poor countries as judged by publications in the NEJM appears to be larger than the gap in the funding for research. Under-representation of developing world health issues in the medical literature is a global phenomenon. International medical journals cannot rectify global inequities, but they have an important role in educating their constituencies about the global divide

Long COVID and cardiovascular disease: a learning health system approach

Cardiovascular disease is both a risk factor and potential outcome of the direct, indirect and long-term effects of COVID-19. A recent analysis in >150,000 survivors of COVID-19 demonstrates an increased 1-year risk of numerous cardiovascular diseases. Preventing and managing this new disease burden presents challenges to health systems and requires a learning health system approach

DEVELOPING MACHINE LEARNING TECHNIQUES FOR NETWORK CONNECTIVITY INFERENCE FROM TIME-SERIES DATA

Inference of the connectivity structure of a network from the observed dynamics of the states of its nodes is a key issue in science, with wide-ranging applications such as determination of the synapses in nervous systems, mapping of interactions between genes and proteins in biochemical networks, distinguishing ecological relationships between different species in their habitats etc. In this thesis, we show that certain machine learning models, trained for the forecasting of experimental and synthetic time-series data from complex systems, can automatically learn the causal networks underlying such complex systems. Based on this observation, we develop new machine learning techniques for inference of causal interaction network connectivity structures underlying large, networked, noisy, complex dynamical systems, solely from the time-series of their nodal states. In particular, our approach is to first train a type of machine learning architecture, known as the ‘reservoir computer’, to mimic the measured dynamics of an unknown network. We then use the trained reservoir computer system as an in silico computational model of the unknown network to estimate how small changes in nodal states propagate in time across that network. Since small perturbations of network nodal states are expected to spread along the links of the network, the estimated propagation of nodal state perturbations reveal the connections of the unknown network. Our technique is noninvasive, but is motivated by the widely used invasive network inference method, whereby the temporal propagation of active perturbations applied to the network nodes are observed and employed to infer the network links (e.g., tracing the effects of knocking down multiple genes, one at a time, can be used infer gene regulatory networks). We discuss how we can further apply this methodology to infer causal network structures underlying different time-series datasets and compare the inferred network with the ground truth whenever available. We shall demonstrate three practical applications of this network inference procedure in (1) inference of network link strengths from time-series data of coupled, noisy Lorenz oscillators, (2) inference of time-delayed feedback couplings in opto-electronic oscillator circuit networks designed the laboratory, and, (3) inference of the synaptic network from publicly-available calcium fluorescence time-series data of C. elegans neurons. In all examples, we also explain how experimental factors like noise level, sampling time, and measurement duration systematically affect causal inference from experimental data. The results show that synchronization and strong correlation among the dynamics of different nodal states are, in general, detrimental for causal network inference. Features that break synchrony among the nodal states, e.g., coupling strength, network topology, dynamical noise, and heterogeneity of the parameters of individual nodes, help the network inference. In fact, we show in this thesis that, for parameter regimes where the network nodal states are not synchronized, we can often achieve perfect causal network inference from simulated and experimental time-series data, using machine learning techniques, in a wide variety of physical systems. In cases where effects like observational noise, large sampling time, or small sampling duration hinder such perfect network inference, we show that it is possible to utilize specially-designed surrogate time-series data for assigning statistical confidence to individual inferred network links. Given the general applicability of our machine learning methodology in time-series prediction and network inference, we anticipate that such techniques can be used for better model-building, forecasting, and control of complex systems in nature and in the lab