Search

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

Abstract Background Globally, good health system performance has resulted from continuous reform, including adaptation of Decentralisation by Devolution policies, for example, the Direct Health Facility Financing (DHFF). Generally, the role of decentralisation in the health sector is to improve efficiency, to foster innovations and to improve quality, patient experience and accountability. However, such improvements have not been well realised in most low- and middle-income countries, with the main reason cited being the poor mechanism for disbursement of funds, which remain largely centralised. The introduction of the DHFF programme in Tanzania is expected to help improve the quality of health service delivery and increase service utilisation resulting in improved health system performance. This paper describes the protocol, which aims to evaluate the effects of DHFF on health system performance in Tanzania. Methods An evaluation of the effect of the DHFF programme will be carried out as part of a nationwide programme rollout. A before and after non-controlled concurrent mixed methods design study will be employed to examine the effect of the DHFF programme implementation on the structural quality of maternal health, health facility governing committee governance and accountability, and health system responsiveness as perceived by the patients’ experiences. Data will be collected from a nationally representative sample involving 42 health facilities, 422 patient consultations, 54 health workers, and 42 health facility governing committees in seven regions from the seven zones of the Tanzanian mainland. The study is grounded in a conceptual framework centered on the Theory of Change and the Implementation Fidelity Framework. The study will utilise a mixture of quantitative and qualitative data collection tools (questionnaires, focus group discussions, in-depth interviews and documentary review). The study will collect information related to knowledge, acceptability and practice of the programme, fidelity of implementation, structural qualities of maternal and child health services, accountability, governance, and patient perception of health system responsiveness. Discussion This evaluation study will generate evidence on both the process and impact of the DHFF programme implementation, and help to inform policy improvement. The study is expected to inform policy on the implementation of DHFF within decentralised health system government machinery, with particular regard to health system strengthening through quality healthcare delivery. Health system responsiveness assessment, accountability and governance of Health Facility Government Committee should bring autonomy to lower levels and improve patient experiences. A major strength of the proposed study is the use of a mixed methods approach to obtain a more in-depth understanding of factors that may influence the implementation of the DHFF programme. This evaluation has the potential to generate robust data for evidence-based policy decisions in a low-income setting.

The Kilombero Malaria Project (KMP) attemps to define opperationally useful indicators of levels of transmission and disease and health system relevant monitoring indicators to evaluate the impact of disease control at the community or health facility level. The KMP is longitudinal community based study (N = 1024) in rural Southern Tanzania, investigating risk factors for malarial morbidity and developing household based malaria control strategies. Biweekly morbidity and bimonthly serological, parasitological and drug consumption surveys are carried out in all study households. Mosquito densities are measured biweekly in 50 sentinel houses by timed light traps. Determinants of transmission and indicators of exposure were not strongly aggregated within households. Subjective morbidity (recalled fever), objective morbidity (elevated body temperature and high parasitaemia) and chloroquine consumption were strongly aggregated within a few households. Nested analysis of anti-NANP40 antibody suggest that only approximately 30% of the titer variance can explained by household clustering and that the largest proportion of antibody titer variability must be explained by non-measured behavioral determinants relating to an individual's level of exposure within a household. Indicators for evaluation and monitoring and outcome measures are described within the context of health service management to describe control measure output in terms of community effectiveness.