Ethical challenges in designing and conducting medicine quality surveys.

OBJECTIVES: In this paper we discuss the main ethical challenges related to the conduct of medicine quality surveys and make suggestions on how to address them. METHOD: Most evidence-based information regarding medicine quality derives from surveys. However, existing research ethical guidelines do not provide specific guidance for medicine quality surveys. Hence, those conducting surveys are often left wondering how to judge what counts as best practice. A list of the main ethical challenges in the design and conduct of surveys is presented. RESULTS AND CONCLUSIONS: It is vital that the design and conduct of medicine quality surveys uphold moral and ethical obligations and analyse the ethical implications and consequences of such work. These aspects include the impact on the local availability of and access to medicines; the confidentiality and privacy of the surveyors and the surveyed; questions as to whether outlet staff personnel should be told they are part of a survey; the need of ethical and regulatory approvals; and how the findings should be disseminated. Medicine quality surveys should ideally be conducted in partnership with the relevant national Medicine Regulatory Authorities. An international, but contextually sensitive, model of good ethical practice for such surveys is needed

Manslaughter by Fake Artesunate in Asia—Will Africa Be Next?

Fake artesunate could compromise the hope that artemisinin-based combination therapy offers for malaria control in Africa and Asia

Guidelines for Field Surveys of the Quality of Medicines: A Proposal

Paul Newton and colleagues propose guidelines for conducting and reporting field surveys of the quality of medicines

A Collaborative Epidemiological Investigation into the Criminal Fake Artesunate Trade in South East Asia

Paul Newton and colleagues' international, collaborative study found evidence that counterfeit artesunate was being manufactured in China, which prompted a criminal investigation

Deep Learning Human Mind For Automated Visual Classification

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories in a reading the mind effort. Afterward, we transfer the learned capabilities to machines by training a Convolutional Neural Network (CNN)-based regressor to project images onto the learned manifold, thus allowing machines to employ human brain-based features for automated visual classification. We use a 128-channel EEG with active electrodes to record brain activity of several subjects while looking at images of 40 ImageNet object classes. The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain-driven approach obtains competitive performance, comparable to those achieved by powerful CNN models and it is also able to generalize over different visual datasets

Genuine (Left) and Counterfeit (Right) Cotexcin (Dihydroartemisinin) from Tanzania (Photograph by Manuela Sunjio)

<p>Genuine (Left) and Counterfeit (Right) Cotexcin (Dihydroartemisinin) from Tanzania (Photograph by Manuela Sunjio)</p

Genuine (Right) and Counterfeit (Left) Arsumax (Artesunate) from Cameroon (Photograph by Manuela Sunjio)

<p>Note that the genuine Arsumax bears a hologram from Guilin Pharmaceutical, which manufactures the tablets (see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0030197#sg001" target="_blank">Figure S1</a>). </p