Survivability of freeze-dried probiotic Pediococcus pentosaceus strains GS4,GS17 and Lactobacillus gasseri(ATCC 19992)during storage with commonly used pharmaceutical excipients within a period of 120 days

Objective: To examine the survivability and stability of probiotic strains in presence and absence of pharmaceutical excipients for a long period of time at(4 ± 1)℃.Methods: The survival rates of probiotic strains, Pediococcus pentosaceus GS4(MTCC12683)(NCBI HM044322), GS17(NCBI KJ608061) and Lactobacillus gasseri(ATCC 19992), were evaluated. Probiotic strains were lyophilized individually and in combination of excipients(sorbitol, ascorbic acid, fructose and skim milk). The preparation was monitored for 120 d storing at(4 ± 1)℃. During storage, all the preparations were evaluated for viability and stability of probiotic properties like lactic acid production, antimicrobial effect, water activity, and adherence to epithelial cells.Results: Sorbitol, ascorbic acid and skim milk favoured the viability of freeze-dried cells and sustained probiotic properties during storage. Without excipients(control group),strains showed percentage of survivability not more than 70% while strains with excipients survived for 73%–93% for a long period of time.Conclusions: Commonly used excipients can be considered as a vehicle for delivering active principle in probiotic formulation and for sustaining the viability and stability of probiotic strains for a period of 120 d.

Pair formation models for sexually transmitted infections:A primer

Formodelling sexually transmitted infections,duration of partnerships can strongly influence the transmission dynamics of the infection.If partnerships are monogamous,pairs of susceptible individuals are protected from becoming infected,while pairs of infected individuals delay onward transmission of the infection as long as they persist.In addition,for curable infections re-infection froman infected partnermay occur.Furthermore,interventions based on contact tracing rely on the possibility of identifying and treating partners of infected individuals.To reflect these features in a mathematical model,pair formation models were introduced tomathematical epidemiology in the 1980's.They have since been developed into a widely used tool in modelling sexually transmitted infections and the impact of interventions.Here we give a basic introduction to the concepts of pair formation models for a susceptibleinfected-susceptible(SIS)epidemic.We review some results and applications of pair formation models mainly in the context of chlamydia infection.

Modelling the dynamics of population viral load measures under HIV treatment as prevention

In 2011 the Centers for Disease Control and Prevention(CDC)published guidelines for the use of population viral load(PVL),community viral load(CVL)and monitored viral load(MVL),defined as the average viral load(VL)of all HIV infected individuals in a population,of all diagnosed individuals,and of all individuals on antiretroviral treatment(ART),respectively.Since then,CVL has been used to assess the effectiveness of ART on HIV transmission and as a proxy for HIV incidence.The first objective of this study was to investigate how aggregate VL measures change with the HIV epidemic phase and the drivers behind these changes using a mathematical transmission model.Secondly,we aimed to give some insight into how well CVL correlates with HIV incidence during the course of the epidemic and roll out of ART.We developed a compartmental model for disease progression and HIV transmission with disease stages that differ in viral loads for epidemiological scenarios relevant to a concentrated epidemic in a population of men who have sex with men(MSM)in Western Europe(WE)and to a generalized epidemic in a heterosexual population in Sub-Saharan Africa(SSA).The model predicts that PVL and CVL change with the epidemic phase,while MVL stays constant.These dynamics are linked to the dynamics of infected subgroups(undiagnosed,diagnosed untreated and treated)in different disease stages(primary,chronic and AIDS).In particular,CVL decreases through all epidemic stages:before ART,since chronic population builds up faster than AIDS population and after ART,due to the build-up of treated population with low VL.The trends in CVL and incidence can be both opposing and coinciding depending on the epidemic phase.Before ART is scaled up to sufficiently high levels,incidence increases while CVL decreases.After this point,CVL is a useful indicator of changes in HIV incidence.The model predicts that during the ART scale-up HIV transmission is driven by undiagnosed and diagnosed untreated individuals,and that new infections decline due to the increase in the number of treated.Although CVL is not able to capture the contribution of undiagnosed population to HIV transmission,it declines due to the increase of people on ART too.In the scenarios described by our model,the present epidemic phase corresponds to declining trends in CVL and incidence.