Assessing Barriers Encountered by Women in Cervical Cancer Screening and Follow-Up Care in Urban Bolivia, Cochabamba.

BACKGROUND: Timely detection of cervical cells infected with high-risk human papillomavirus (HPV) improves cervical cancer prevention. In Bolivia, actual screening coverage only reaches 33.3% of the target population aged between 25 and 64 years despite free cytology screening. Furthermore, 50% to 80% screened women are lost during follow-up. This study aimed at identifying factors explaining this lack of follow-up care. METHOD: During the first phase, face-to-face semi-structured interviews were conducted with HPV-positive women. Secondly, we explored the reasons for the non-adherence to the follow-up care: knowledge, perceptions and beliefs about HPV, as well as barriers to healthcare access, using a structured survey on Cochabamba women and healthcare professionals. RESULTS: Barriers to effective follow-up of the targeted populations were associated with health system shortcomings, including poor service delivery at the front- and second-line, health providers shortage, inadequate training, waiting time, high direct and indirect costs of care seeking and care, complex procedures to obtain HPV screening results and poor patient-provider communication. The follow-up was perceived as extremely stressful by the participants. CONCLUSION: Improved communication on HPV and HPV-related cancers in terms of representation in the general population and among the health professional's population is vital to improve access for HPV infection follow-up care.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks.

Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.