Sociodemographic factors associated with acceptance of index case testing among newly diagnosed human immunodeficiency virus-positive clients in southern borno, Nigeria.

BACKGROUND: The study aimed to explore the sociodemographic factors associated with the acceptance of index case testing (ICT) among newly diagnosed people living with HIV (PLHIV) in Southern Borno, Nigeria. METHODS: The study involved four healthcare facilities providing antiretroviral therapy (ART) services in Southern Borno, Nigeria. Secondary data from the index case testing register were collected between April 2022 and April 2023. Categorical variables were analyzed using descriptive statistics, while bivariate and multiple logistic regression analyses was used to determine the factors associated with ICT acceptance, using a 95% confidence interval and significance level of p ≤ .05. RESULTS: Among the 302 index cases offered ICT, the acceptance rate was 69.5%. Individuals aged 25-49 years (AOR: 0.294, 95% CI: 0.098-0.775) and 50 years and above (AOR: 0.210, 95% CI: 0.046-0.862) were less likely to accept ICT compared to those aged 18-24 years. Married individuals were 38.86 times more likely to accept ICT than those who were single and never married (AOR: 38.856, 95% CI: 12.620-171.674). CONCLUSION: The study shows high ICT acceptance among newly diagnosed PLHIV in Southern Borno, with age and marital status as significant factors. These findings can guide targeted interventions to enhance ICT acceptance and enhance HIV care and prevention strategies in the region.

Exploring how ecological and epidemiological processes shape multi-host disease dynamics using global sensitivity analysis.

We use global sensitivity analysis (specifically, Partial Rank Correlation Coefficients) to explore the roles of ecological and epidemiological processes in shaping the temporal dynamics of a parameterized SIR-type model of two host species and an environmentally transmitted pathogen. We compute the sensitivities of disease prevalence in each host species to model parameters. Sensitivity rankings are calculated, interpreted biologically, and contrasted for cases where the pathogen is introduced into a disease-free community and cases where a second host species is introduced into an endemic single-host community. In some cases the magnitudes and dynamics of the sensitivities can be predicted only by knowing the host species' characteristics (i.e., their competitive abilities and disease competence) whereas in other cases they can be predicted by factors independent of the species' characteristics (specifically, intraspecific versus interspecific processes or a species' roles of invader versus resident). For example, when a pathogen is initially introduced into a disease-free community, disease prevalence in both hosts is more sensitive to the burst size of the first host than the second host. In comparison, disease prevalence in each host is more sensitive to its own infection rate than the infection rate of the other host species. In total, this study illustrates that global sensitivity analysis can provide useful insight into how ecological and epidemiological processes shape disease dynamics and how those effects vary across time and system conditions. Our results show that sensitivity analysis can provide quantification and direction when exploring biological hypotheses.

Probabilistic modelling of Escherichia coli concentration in raw milk under hot weather conditions.

Climate change is one of the threats to the dairy supply chain as it may affect the microbiological quality of raw milk. In this context, a probabilistic model was developed to quantify the concentration of Escherichia coli in raw milk and explore what may happen to France under climate change conditions. It included four modules: initial contamination, packaging, retailing, and consumer refrigeration. The model was built in R using the 2nd order Monte Carlo mc2d package to propagate the uncertainty and analysed its impact independently of the variability. The initial microbial counts were obtained from a dairy farm located in Saudi Arabia to reflect the impact of hot weather conditions. This country was taken as representative of what might happen in Europe and therefore in France in the future due to climate change. A large dataset containing 622 data points was analysed. They were fitted by a Normal probability distribution using the fitdistrplus package. The microbial growth was determined across various scenarios of time and temperature storage reflecting the raw milk supply-chain in France. Existing growth rate data from literature and ComBase were analysed by the Ratkowsky secondary model. Results were interpreted using the nlstools package. The mean E. coli initial concentration in raw milk was estimated to be 1.31 [1.27; 1.35] log CFU/ mL and was found to increase at the end of the supply chain as a function of various time and temperature conditions. The estimations varied from 1.73 [1.42; 2.28] log CFU/mL after 12 h, 2.11 [1.46; 3.22] log CFU/mL after 36 h, and 2.41 [1.69;3.86] log CFU/mL after 60 h of consumer storage. The number of milk packages exceeding the 2-log French hygiene criterion for E. coli increased from 10% [8;12%] to 53% [27;77%] during consumer storage. In addition, the most significant factors contributing to the uncertainty of the model outputs were identified by running a sensitivity analysis. The results showed that the uncertainty around the Ratkowsky model parameters contributed the most to the uncertainty of E. coli concentration estimates. Overall, the model and its outputs provide an insight on the possible microbial raw milk quality in the future in France due to higher temperatures conditions driven by climate change.

Ranking non-pharmaceutical interventions against Covid-19 global pandemic using global sensitivity analysis-Effect on number of deaths.

In this study, we use Global Sensitivity Analysis (GSA) to rank four non-pharmaceutical interventions (NPIs) in a deterministic compartmental model that might control Covid-19 related deaths in the United States. The NPIs are social distancing, isolation of infected individuals, identifying asymptomatically infected individuals through testing, and the use of face masks. The model uses a fear-based behavioral model that leads unmasked susceptible individuals to wear masks. The model parameters are estimated from the reported deaths for the United States of America from March 1, 2020 to November 26, 2020. Two GSA tools, the Sobol' sesntivity indices and Partial Rank Correlation Coefficient are used to obtain the rankings of the input parameters at different stages of the disease propagation. We found that social distancing and outward mask efficiency alone decreases the output uncertainty by 25-45%. Sobol' second order indices show that the combined effect of social distancing with increased mask usage and identifying and isolating asymptomatically infected individuals decreases uncertainty an additional 10%.

Tumor interferon signaling and suppressive myeloid cells are associated with CAR T-cell failure in large B-cell lymphoma.

Axicabtagene ciloleucel (axi-cel) is a chimeric antigen receptor (CAR) T-cell therapy for relapsed or refractory large B-cell lymphoma (LBCL). This study evaluated whether immune dysregulation, present before CAR T-cell therapy, was associated with treatment failure. Tumor expression of interferon (IFN) signaling, high blood levels of monocytic myeloid-derived suppressor cells (M-MDSCs), and high blood interleukin-6 and ferritin levels were each associated with a lack of durable response. Similar to other cancers, we found that in LBCL tumors, IFN signaling is associated with the expression of multiple checkpoint ligands, including programmed cell death-ligand 1, and these were higher in patients who lacked durable responses to CAR-T therapy. Moreover, tumor IFN signaling and blood M-MDSCs associated with decreased axi-cel expansion. Finally, patients with high tumor burden had higher immune dysregulation with increased serum inflammatory markers and tumor IFN signaling. These data support that immune dysregulation in LBCL promotes axi-cel resistance via multiple mechanistic programs: insufficient axi-cel expansion associated with both circulating M-MDSC and tumor IFN signaling, which also gives rise to expression of immune checkpoint ligands.

Application of the Geographic Information System (GIS) in immunisation service delivery; its use in the 2017/2018 measles vaccination campaign in Nigeria.

BACKGROUND: As global effort is made towards measles elimination, the use of innovative technology to enhance planning for the campaign has become critical. GIS technology has been applied to track polio vaccination activities in Nigeria with encouraging outcomes. Despite numerous measles vaccination campaigns after the first catch up campaign in 2005, sub-optimal outcomes of previous measles supplemental immunization activities necessitated the use of innovative ideas to achieve better outcomes especially when planning for the 2017/2018 measles vaccination campaign. This led to the application of the use of the GIS technology for the Northern states in 2017/2018 campaign. This study is a report of what was achieved with the use of the GIS in the 2017/2018 measles vaccination campaign in Nigeria. METHODS: GIS generated ward maps were used for the microplanning processes for the 2017/2018 measles vaccination campaign. These ward maps had estimates of the target population by settlements, the number and location of vaccination posts ensuring that a vaccination post is sited within one-kilometer radius of a settlement, and the number of teams needed to support the vaccination campaign as well as the catchment area and daily implementation plans. The ward microplans were verified by checking for accuracy and consistency of the target population, settlements, number of teams, vaccination posts and daily implementation work plans using a standard checklist. The ward maps were deployed into use for the measles vaccination campaign after the state team driven validation and verification by the National team (Government and Partners) RESULTS: The Northern states that applied the GIS technology had a closer operational target population to that on the verified microplan than those of the non-GIS technology states. Greater than 90% of the ward maps had all that is expected in the maps - i.e settlements, target populations, and vaccination posts captured, except Kaduna, Katsina and Adamawa states. Of all enumeration areas sampled during the post-campaign survey in states with GIS ward maps, none had a zero-vaccination coverage of the surveyed children, with the exception of one in Borno state that had security issues. In the post campaign coverage survey, the percentage of responses that gave vaccination post being too far as a reason for non-vaccination of children in the Northern zones that used GIS generated ward maps was less than half the rate seen in the southern zones where the GIS microplanning was not used. CONCLUSION: The use of GIS-generated wards maps improved the quality of ward micro plans and optimized the placement of vaccination posts, resulting in a significant reduction in zero-dose clusters found during the post campaign coverage survey.

Label-Free and Regenerative Electrochemical Microfluidic Biosensors for Continual Monitoring of Cell Secretomes.

Development of an efficient sensing platform capable of continual monitoring of biomarkers is needed to assess the functionality of the in vitro organoids and to evaluate their biological responses toward pharmaceutical compounds or chemical species over extended periods of time. Here, a novel label-free microfluidic electrochemical (EC) biosensor with a unique built-in on-chip regeneration capability for continual measurement of cell-secreted soluble biomarkers from an organoid culture in a fully automated manner without attenuating the sensor sensitivity is reported. The microfluidic EC biosensors are integrated with a human liver-on-a-chip platform for continual monitoring of the metabolic activity of the organoids by measuring the levels of secreted biomarkers for up to 7 d, where the metabolic activity of the organoids is altered by a systemically applied drug. The variations in the biomarker levels are successfully measured by the microfluidic regenerative EC biosensors and agree well with cellular viability and enzyme-linked immunosorbent assay analyses, validating the accuracy of the unique sensing platform. It is believed that this versatile and robust microfluidic EC biosensor that is capable of automated and continual detection of soluble biomarkers will find widespread use for long-term monitoring of human organoids during drug toxicity studies or efficacy assessments of in vitro platforms.