Biology subjects

Mikolajczyk, R.

Publications and source records attributed to Mikolajczyk, R..

3 recordsLinked to original sources

bioRxiv2026

Exposure to childhood adversity and adult alcohol, nicotine, and illicit drug use in Germany: a cross-sectional analysis of the German National Cohort (NAKO)

Rationale: Exposure to adversity in childhood and adolescence (i.e., "early-life stress"; ELS) is a well-established risk factor for substance use. Objectives: The aim of this study was to investigate associations between ELS exposure and substance use across subtypes of ELS, different substance classes, and behavioral profiles of substance use. Methods: In this cross-sectional study, we examined associations between ELS exposure and substance use in a population-based sample of 172,173 adults aged 19-74 years from the German National Cohort (NAKO). ELS exposure was assessed using the Childhood Trauma Screener. Substance use included nicotine, alcohol, and illicit drug use. Associations were examined using regression models evaluating overall ELS exposure, cumulative trauma load, and ELS subtypes. Latent class analysis (LCA) was conducted to identify substance use profiles. Results: ELS exposure was associated with higher odds of smoking, greater smoking severity, heavier alcohol consumption among drinkers, and higher odds of lifetime illicit drug use. Overall, the strongest associations were observed for opioid and stimulant use. Dose-response analyses revealed higher risk with increasing numbers of ELS subtypes. Abuse-related subtypes, particularly exposure to physical and sexual abuse, consistently showed strongest independent associations across substances, whereas neglect exhibited weaker, more variable associations. LCA identified four substance use profiles, with ELS exposure predicting membership in more severe polysubstance classes. Conclusions: ELS emerged as a robust risk factor for adult substance use, underscoring the need for trauma-informed approaches in clinical practice, public health, and policy to mitigate the long-term consequences of early adversity.

neuroscience↗

bioRxiv2022

Rapid hypermutation B cell trajectory recruits previously primed B cells upon third SARS-CoV-2 mRNA vaccination

High antibody affinity against the ancestral SARS-CoV-2 strain seems to be necessary (but not always sufficient) for the control of emerging immune-escape variants. Therefore, aiming at strong B cell somatic hypermutation - not only at high antibody titers - is a priority when utilizing vaccines that are not targeted at individual variants. Here, we developed a next-generation sequencing based SARS-CoV-2 B cell tracking protocol to rapidly determine the level of immunoglobulin somatic hypermutation at distinct points during the immunization period. The percentage of somatically hypermutated B cells in the SARS-CoV-2 specific repertoire was low after the primary vaccination series, evolved further over months and increased steeply after boosting. The third vaccination mobilized not only naive, but also antigen-experienced B cell clones into further rapid somatic hypermutation trajectories indicating increased affinity. Together, the strongly mutated post-booster repertoires and antibodies deriving from this may explain why the booster, but not the primary vaccination series, offers some protection against immune-escape variants such as Omicron B.1.1.529. Brief summaryPriming SARS-CoV-2 vaccinations generate antibodies from low-level matured B cells while the third vaccination strongly boosts somatic hypermutation potentially explaining different protection from immune-escape variants.

immunology↗

bioRxiv2017

Multiscale Modeling To Explore Ebola Vaccination Strategies

BackgroundTransmission in epidemics of infectious diseases is characterized by a high level of subject-specific elements. These include heterogeneous infection conditions, time-dependent transmission potential, and age-dependent contact structure. These insights are often lost in epidemic models using population data. Here we submit an approach that can capture these details, paving the way for studying epidemics in a more mechanistic and realistic way.\n\nMethodsUsing experimental data, we formulated mathematical models of a pathogen infection dynamics from which we can simulate its transmission potential mechanistically. The models were then embedded in our implement of an age-specific contact network structure that allows to express all elements relevant to the transmission process. This approach is illustrated here with an example of Ebola virus (EBOV).\n\nResultsThe results showed that within-host infection dynamics can capture EBOVs transmission parameters as good as approaches using population data. Population age-structure, contact distribution and patterns can also be captured with our network generating algorithm. This framework opens vast opportunities for the investigations of each element involved in the epidemic process. Here, estimating EBOVs reproduction number revealed a heterogeneous pattern among age-groups, prompting questions on current estimates which are not adjusted for this factor. Assessments of mass vaccination strategies showed that a time window from five months before to one week after the start of an epidemic appeared to be effective. Noticeably, compared to a non-intervention scenario, a low vaccination coverage of 33% could reduce number of cases by ten to hundred times as well as lessen the case-fatality rate.\n\nConclusionsThis is the first effort coupling directly within-host infection model into an age-structured epidemic network model, adding more realistic elements in simulating epidemic processes. Experimental data at the within-host infection are shown able to capture upfront key parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. Population of interest in epidemic assessments could be modeled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios are underway to explore multilevel aspects in infectious diseases epidemics.

epidemiology↗