Biology subjects

Petersen, K.

Publications and source records attributed to Petersen, K..

3 recordsLinked to original sources

bioRxiv2026

Unraveling Tissue-Specific Molecular Signatures and Convergent Pathway Enrichments in Suicidal Behavior

Suicide is a leading cause of death worldwide, yet the biological mechanisms underlying suicide remain poorly understood. A clearer understanding at the molecular level is essential for developing objective biomarkers and targeted interventions. In this study, we used transcriptomic profiling to investigate gene expression patterns associated with suicidal thoughts and behaviors across peripheral blood (n=264) and postmortem brain tissue from two prefrontal regions (dorsolateral prefrontal cortex, DLPFC; subgenual anterior cingulate cortex, sgACC) of individuals with and without psychiatric illness (n=249). Peripheral analyses revealed broad transcriptional changes associated with suicidal thoughts and behaviors, marked by dysregulated immune-related and inflammatory processes. Longitudinal modeling further revealed gene co-expression modules that predicted future suicide attempts over a 12-month follow-up, highlighting processes related to apoptosis, mitochondrial function, and immune regulation. By contrast, transcriptomic analyses of postmortem tissue derived from the DLPFC and sgACC revealed largely suppressed neuroimmune activity. Gene co-expression analyses in the brain identified suicide-associated modules enriched for synaptic plasticity, oxidative stress, and neuroimmune function, some of which displayed regional specificity. Cross-tissue comparison showed minimal gene-level overlap between brain and blood, although shared pathway-level themes emerged in immune, sensory, and cellular stress processes. Taken together, these findings suggest that suicide is associated with distinct but functionally convergent transcriptional alterations across brain and blood. By integrating tissue-specific and systems-level molecular signatures, this work provides insight into the biological architecture of suicide and lays the groundwork for developing novel biomarkers and therapeutic targets to improve prevention and treatment outcomes.

neuroscience↗

bioRxiv2025

Development of a PEGylated Parylene Nanopocket Membrane for the Capture and Release of Lipid Vesicles

Membranes are commonly used for the separation and purification of a variety of biological species. In this study, we developed a nanopocket membrane that can capture nanoparticles and extracellular vesicles in tangential flow filtration by pulling the species of interest into a nanopocket, while tangential flow washes away particles too large to be captured. We developed the pores using a four-step lithography process. By optimizing photolithography parameters, particularly exposure dose, we achieved well-defined pore shapes with the desired wall tilt, forming nanopocket geometries. We validated membrane performance using bead mixtures of varying diameters (50 nm -10 {micro}m), demonstrating its ability to pass small particles, capture mid-sized particles, and exclude larger ones. Surface modification with poly(l-lysine)- grafted poly(ethylene glycol) enhanced this performance by reducing pore size and improving particle release. Additionally, we demonstrated selective capture and release of liposomes as well as EVs in the presence of fluorescently labeled BSA (f-BSA) protein. During this process, EVs were retained and later released, while f-BSA passed through, confirming selective capture and reduced protein contamination. These PEGylated nanopocket membranes constitute a low-pressure platform for gentle, size-selective isolation of nanoscale bioparticles, laying the groundwork for improved extracellular-vesicle analytics and point-of-care diagnostic applications.

bioengineering↗

bioRxiv2021

Development of a Bayesian network for probabilistic risk assessment of pesticides

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency, by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network (BN) modelling is explored as an alternative to traditional risk calculation. BNs can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a BN has been developed and parameterised for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterisation using azoxystrobin as an example. We also demonstrate seasonal risk calculation for the three pesticides.

scientific communication and education↗