UNASSIGNED: Underlying causes of vaccine hesitancy could significantly affect successful uptake of the SARS-CoV2 vaccine booster doses during new waves of COVID-19. Booster rates among US adults are far below what is needed for immunity, but little is known about booster hesitancy among fully vaccinated adults and whether medical mistrust exacerbates barriers to uptake.
UNASSIGNED: A cross-sectional survey was completed among 119 adults in Philadelphia, PA who reported having received the primary SARS-CoV2 vaccine series but not a booster dose. Using the LaVeist Medical Mistrust (MM) Index, a k-means cluster analysis showed two clusters (Low MM, High MM) and differences in attitudes and perceptions about COVID-19 booster vaccines were assessed using F-tests.
UNASSIGNED: Respondents were 62% Black and female; mean age was 41; 46% reported earning less than $25,000 and 53% had a high school education or less. Overall intention to get boosted was low (mean 3.3 on 0-10 scale). Differences in COVID-19 booster perceptions between those with High (n = 56) vs. Low (n = 59) MM were found, independent of any demographic differences. Most statements (7/10) related to reasons to not be boosted were significant, with those with High MM indicating more concern about feeling sick from the vaccine (F=-3.91, p≤ .001), beliefs that boosters are ineffective for vaccinated people (F= -3.46, p≤ .001), and long-term side effect worries (F=-4.34, p≤ .001). Those with High MM were also more concerned about the adverse effects of the vaccine (F=-2.48, p=.02), but were more likely to trust getting information from doctors or healthcare providers (F= -2.25, p=.03).
UNASSIGNED: Results indicate that medical mistrust is an important independent construct when understanding current COVID-19 booster hesitancy. While much work has looked at demographic differences to explain vaccine hesitancy, these results suggest that further research into understanding and addressing medical mistrust could be important for implementing interventions to increase booster rates.

Preclinical mouse models are critical for understanding the pathophysiological response to infections and developing treatment strategies for sepsis. In keeping with ethical values, researchers follow guidelines to minimize the suffering of the mice. Weight loss is a criteria used as a humane end point, but there is no official recommendation for a maximum weight loss leading to euthanasia. To evaluate whether the thresholds used in daily practice are optimal, we performed a comprehensive retrospective analysis of data generated over 10 years with > 2300 mice used in models of infection with Listeria monocytogenes, Streptococcus pneumoniae, Candida albicans and H1N1 influenza virus. Weight loss segregated mice that survived from those that did not. Statistical analyses revealed that lowering the weight loss thresholds used (none, 30% or 20%) would have increased mortality rates due to the sacrifice of mice that survived infections (p < 0.01-0.001). Power calculations showed high variability and reduction of power as weight loss thresholds approached 20% for S. pneumoniae and L. monocytogenes models. Hence, weight loss thresholds need to be adapted to each model of infection used in a laboratory. Overall, weight loss is a valuable predictor of mortality that contributes to the robustness of composite scores. To our knowledge, this is the most extensive study exploring the relationship between weight loss threshold and sepsis outcome. It underscores the importance of the infection-model-specific evaluation of weight loss for use in clinical scores defining humane endpoints to minimize mouse suffering without compromising statistical power and scientific objectives.

A leading cause of mortality after influenza infection is the development of a secondary bacterial pneumonia. In the absence of a bacterial superinfection, prescribing antibacterial therapies is not indicated but has become a common clinical practice for those presenting with a respiratory viral illness. In a murine model, we found that antibiotic use during influenza infection impaired the lung innate immunologic defenses toward a secondary challenge with methicillin-resistant Staphylococcus aureus (MRSA). Antibiotics augment lung eosinophils, which have inhibitory effects on macrophage function through the release of major basic protein. Moreover, we demonstrated antibiotic treatment during influenza infection causes a fungal dysbiosis that drive lung eosinophilia and impair MRSA clearance. Finally, we evaluated three cohorts of hospitalized patients and found eosinophils positively correlated with antibiotic use, systemic inflammation, and worsened outcomes. Altogether, our work demonstrates a detrimental effect of antibiotic treatment during influenza infection that has harmful immunologic consequences via recruitment of eosinophils to the lungs thereby increasing the risk of developing a secondary bacterial infection.

In humans, lymph nodes are the primary site of measles virus (MeV) replication. To understand the immunological events that occur at this site, we infected human lymphoid tissue explants using a pathogenic strain of MeV that expresses GFP. We found that MeV infected 5%-15% of cells across donors. Using single-cell RNA-Seq and flow cytometry, we found that while most of the 29 cell populations identified in the lymphoid culture were susceptible to MeV, there was a broad preferential infection of B cells and reduced infection of T cells. Further subsetting of T cells revealed that this reduction may be driven by the decreased infection of naive T cells. Transcriptional changes in infected B cells were dominated by an interferon-stimulated gene (ISG) signature. To determine which of these ISGs were most substantial, we evaluated the proteome of MeV-infected Raji cells by mass spectrometry. We found that IFIT1, IFIT2, IFIT3, ISG15, CXCL10, MX2, and XAF1 proteins were the most highly induced and positively correlated with their expression in the transcriptome. These data provide insight into the immunological events that occur in lymph nodes during infection and may lead to the development of therapeutic interventions.

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical-demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical-demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

Antibiotic tolerance in Mycobacterium tuberculosis reduces bacterial killing, worsens treatment outcomes, and contributes to resistance. We studied rifampicin tolerance in isolates with or without isoniazid resistance (IR). Using a minimum duration of killing assay, we measured rifampicin survival in isoniazid-susceptible (IS, n=119) and resistant (IR, n=84) isolates, correlating tolerance with bacterial growth, rifampicin minimum inhibitory concentrations (MICs), and isoniazid-resistant mutations. Longitudinal IR isolates were analyzed for changes in rifampicin tolerance and genetic variant emergence. The median time for rifampicin to reduce the bacterial population by 90% (MDK90) increased from 1.23 days (IS) and 1.31 days (IR) to 2.55 days (IS) and 1.98 days (IR) over 15-60 days of incubation, indicating fast and slow-growing tolerant sub-populations. A 6 log10-fold survival fraction classified tolerance as low, medium, or high, showing that IR is linked to increased tolerance and faster growth (OR = 2.68 for low vs. medium, OR = 4.42 for low vs. high, p-trend = 0.0003). High tolerance in IR isolates was associated with rifampicin treatment in patients and genetic microvariants. These findings suggest that IR tuberculosis should be assessed for high rifampicin tolerance to optimize treatment and prevent the development of multi-drug-resistant tuberculosis.

Tuberculosis remains a leading cause of death by infectious disease. The long treatment regimen and the spread of drug-resistant strains of the causative agent Mycobacterium tuberculosis (Mtb) necessitates the development of new treatment options. In a phenotypic screen, a nitrofuran-resorufin conjugate 1 was identified as a potent sub-micromolar inhibitor of whole cell Mtb. Complete loss of activity was observed for this compound in Mtb mutants affected in enzyme cofactor F420 biosynthesis (fbiC), suggesting that 1 undergoes prodrug activation in a manner similar to anti-tuberculosis prodrug pretomanid. Exploration of the structure-activity relationship led to the discovery of novel resorufin analogues that do not rely on the deazaflavin-dependent nitroreductase (Ddn) bioactivation pathway for their antimycobacterial activity. These analogues are of interest as they work through an alternative, currently unknown mechanism that may expand our chemical arsenal towards the treatment of this devastating disease.

BACKGROUND: In sub-Saharan African countries, preventable and manageable diseases such as diarrhea and acute respiratory infections still claim the lives of children. Hence, this study aims to estimate the rate of change in the log expected number of days a child suffers from Diarrhea (NOD) and flu/common cold (NOF) among children aged 6 to 11 months at the baseline of the study.
METHODS: This study used secondary data which exhibit a longitudinal and multilevel structure. Based on the results of exploratory analysis, a multilevel zero-inflated Poisson regression model with a rate of change in the log expected NOD and NOF described by a quadratic trend was proposed to efficiently analyze both outcomes accounting for correlation between observations and individuals through random effects. Furthermore, residual plots were used to assess the goodness of fit of the model.
RESULTS: Considering subject and cluster-specific random effects, the results revealed a quadratic trend in the rate of change of the log expected NOD. Initially, low dose iron Micronutrient Powder (MNP) users exhibited a higher rate of change compared to non-users, but this trend reversed over time. Similarly, the log expected NOF decreased for children who used MNP and exclusively breastfed for six months, in comparison to their counterparts. In addition, the odds of not having flu decreased with each two-week increment for MNP users, as compared to non-MNP users. Furthermore, an increase in NOD resulted in an increase in the log expected NOF. Region and exclusive breastfeeding also have a significant relationships with both NOD and NOF.
CONCLUSIONS: The findings of this study underscore the importance of commencing analysis of data generated from a study with exploratory analysis. The study highlights the critical role of promoting EBF for the first six months and supporting children with additional food after six months to reduce the burden of infectious diseases.

Infectious diseases in livestock not only cause significant economic losses but also affect food security. Although wildlife may be involved in these infectious diseases by serving as reservoirs, research has primarily focused on livestock and related species. Moreover, while these species represent a potential threat in wildlife-borne infectious diseases, comprehensive surveys of the presence of various species are limited. In this study, we aimed to quantitatively investigate the occurrence of various mammals and birds at three pig farms in Japan. We conducted camera trap surveys from October 15, 2020, to March 24, 2022, and determined the relative abundance index inside and outside the sanitary control zone. Wild boar (Sus scrofa), sika deer (Cervus nippon), and Japanese serow (Capricornis crispus) were photographed only outside the sanitary control zone. In contrast, small and medium-sized mammals, such as feral cats (Felis catus), raccoons (Procyon lotor), and rodent species (Muridae), and birds, such as the rufous oriental turtle dove (Streptopelia orientalis) and crows of the genus Corvus, were photographed both inside and outside the sanitary control zone. This comprehensive quantitative evidence suggests that various mammals and birds may be in indirect contact with livestock, highlighting the need to improve biosecurity at livestock farms. Moreover, while fences are effective against large mammals, they are inefficient against small to medium-sized mammals and birds. Therefore, the results of this study provide important insights into enhancing biosecurity on livestock farms by strengthening physical fencing, disinfecting clothing and equipment, and complying with standards of hygiene management.

The United Nations Inter-Agency Group for Child Mortality Estimation (UNIGME) estimates that every year 2.5 million neonates die in their first month of life, accounting for nearly one-half of deaths in children under 5 years of age. Neonatal sepsis is the third leading cause of neonatal mortality. The worldwide burden of bacterial sepsis is expected to increase in the next decades due to the lack of effective molecular therapies to replace the administration of antibiotics whose efficacy is compromised by the emergence of resistant strains. In addition, prolonged exposure to antibiotics can have negative effects by increasing the risk of infection by other organisms. With the global burden of sepsis increasing and no vaccine nor other therapeutic approaches proved efficient, the World Health Organization (WHO) stresses the need for new therapeutic targets for sepsis treatment and infection prevention (WHO, A73/32). In response to this unresolved clinical issue, the P2X7 receptor (P2X7R), a key component of the inflammatory cascade, has emerged as a potential target for treating inflammatory/infection diseases. Indeed numerous studies have demonstrated the relevance of the purinergic system as a pharmacological target in addressing immune-mediated inflammatory diseases by regulating immunity, inflammation, and organ function. In this review, we analyze key features of sepsis immunopathophysiology focusing in neonatal sepsis and on how the immunomodulatory role of P2X7R could be a potential pharmacological target for reducing the burden of neonatal sepsis.