Differences in carbon metabolic capacity fuel co-existence and plasmid transfer between Salmonella strains in the mouse gut.

Antibiotic resistance plasmids can be disseminated between different Enterobacteriaceae in the gut. Here, we investigate how closely related Enterobacteriaceae populations with similar nutrient needs can co-bloom in the same gut and thereby facilitate plasmid transfer. Using different strains of Salmonella Typhimurium (S.Tm SL1344 and ATCC14028) and mouse models of Salmonellosis, we show that the bloom of one strain (i.e., recipient) from very low numbers in a gut pre-occupied by the other strain (i.e., donor) depends on strain-specific utilization of a distinct carbon source, galactitol or arabinose. Galactitol-dependent growth of the recipient S.Tm strain promotes plasmid transfer between non-isogenic strains and between E. coli and S.Tm. In mice stably colonized by a defined microbiota (OligoMM12), galactitol supplementation similarly facilitates co-existence of two S.Tm strains and promotes plasmid transfer. Our work reveals a metabolic strategy used by Enterobacteriaceae to expand in a pre-occupied gut and provides promising therapeutic targets for resistance plasmids spread.

Improving the efficiency of scale-up and deployment of community health workers in Mali: A geospatial analysis.

Optimising the scale and deployment of community health workers (CHWs) is important for maximizing geographical accessibility of integrated primary health care (PHC) services. Yet little is known about approaches for doing so. We used geospatial analysis to model optimised scale-up and deployment of CHWs in Mali, to inform strategic and operational planning by the Ministry of Health and Social Development. Accessibility catchments were modelled based on travel time, accounting for barriers to movement. We compared geographic coverage of the estimated population, under-five deaths, and plasmodium falciparum (Pf) malaria cases across different hypothetical optimised CHW networks and identified surpluses and deficits of CHWs compared to the existing CHW network. A network of 15 843 CHW, if optimally deployed, would ensure that 77.3% of the population beyond 5 km of the CSCom (community health centre) and CSRef (referral health facility) network would be within a 30-minute walk of a CHW. The same network would cover an estimated 59.5% of U5 deaths and 58.5% of Pf malaria cases. As an intermediary step, an optimised network of 4 500 CHW, primarily filling deficits of CHW in the regions of Kayes, Koulikoro, Sikasso, and Ségou would ensure geographic coverage for 31.3% of the estimated population. There were no important differences in geographic coverage percentage when prioritizing CHW scale-up and deployment based on the estimated population, U5 deaths, or Pf malaria cases. Our geospatial analysis provides useful information to policymakers and planners in Mali for optimising the scale-up and deployment of CHW and, in turn, for maximizing the value-for-money of resources of investment in CHWs in the context of the country's health sector reform. Countries with similar interests in optimising the scale and deployment of their CHW workforce may look to Mali as an exemplar model from which to learn.