Biology subjects

Johansen, A.

Publications and source records attributed to Johansen, A..

5 recordsLinked to original sources

bioRxiv2023

"Tranq-Dope" Overdose and Mortality: Lethality Induced by Fentanyl and Xylazine

The recreational use of fentanyl in combination with xylazine (i.e., "tranq-dope") represents a rapidly emerging public health threat characterized by significant toxicity and mortality. This study quantified the interactions between these drugs on lethality and examined the effectiveness of potential rescue medications to prevent a lethal overdose. Male and female mice were administered acute doses of fentanyl, xylazine, or their combination via intraperitoneal injection, and lethality was determined 30, 60, 90, 120, and 1440 min (24 hr) after administration. Both fentanyl and xylazine produced dose-dependent increases in lethality when administered alone. A nonlethal dose of fentanyl (56 mg/kg) produced an approximately 5-fold decrease in the estimated LD50 for xylazine (i.e., the dose estimated to produce lethality in 50% of the population). Notably, a nonlethal dose of xylazine (100 mg/kg) produced an approximately 100-fold decrease in the estimated LD50 for fentanyl. The opioid receptor antagonist, naloxone (3 mg/kg), but not the alpha-2 adrenergic receptor antagonist, yohimbine (3 mg/kg), significantly decreased the lethality of a fentanyl-xylazine combination. Lethality was rapid, with death occurring within 10 min after a high dose combination and generally within 30 min at lower dose combinations. Males were more sensitive to the lethal effects of fentanyl-xylazine combinations under some conditions, suggesting biologically relevant sex differences in sensitivity to fentanyl-xylazine lethality. These data provide the first quantification of the lethal effects of "tranq-dope" and suggest that rapid administration of naloxone may be effective at preventing death following overdose.

pharmacology and toxicology↗

bioRxiv2023

The association between brain serotonin 2A receptor binding and neuroticism in healthy individuals: A Cimbi database independent replication study

BackgroundUsing the [18F]altanserin tracer to image serotonin 2A receptors (5-HT2AR), we previously showed that there exists a positive association between cortical 5-HT2AR binding and the inward facets of neuroticism, namely depression, anxiety, self-consciousness, and vulnerability. Fairly recently, the [11C]Cimbi-36 tracer was also shown to be a suitable radioligand for imaging 5-HT2A receptors in the human brain. In the present study, we examined whether our previously reported finding of the association between 5-HT2AR binding and the inward facets of neuroticism can be replicated in an independent sample of healthy individuals scanned using the newer [11C]Cimbi-36 tracer. Furthermore, to determine whether this association of 5-HT2AR binding with neuroticism merely reflects its known relation to stress-coping related indices such as cortisol dynamics. The present study also investigated the potential role of cortisol awakening response on the association between 5-HT2AR binding and the inward facets of neuroticism. MethodsSixty-nine healthy volunteers underwent a [11C]CIMBI-36 scan for the assessment of 5-HT2AR binding, completed the standardized NEO-PI-R personality questionnaire, and provided salivary samples for the determination of cortisol awakening response. A linear latent variable model (LVM) was used to examine the association between 5-HT2AR binding and the inward facets of neuroticism with adjustment for age, sex, cortisol awakening response, and MR scanner. A second latent variable model examined the potential moderating effect of cortisol awakening response on the association between 5-HT2AR binding and the inward facets of neuroticism. ResultsWe replicated a positive association between 5-HT2AR binding and the inward facets of neuroticism (r=0.37, p=0.015). We saw no moderating effect of the cortisol awakening response on this association (p=0.98). ConclusionsIn an independent cohort of healthy individuals imaged with the [11C]CIMBI-36 tracer, we confirm the link between serotonin 2A receptor binding and the inward-directed facets of neuroticism that is independent of cortisol dynamics.

neuroscience↗

bioRxiv2023

Non-target Analysis of Wastewater Treatment Plant Effluents: Chemical Fingerprinting as a Monitoring Tool.

This study aims at discovering and characterizing the plethora of xenobiotic substances released into the environment with wastewater effluents. We present a novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a new data-processing pipeline. This approach was applied to effluent samples from two state-of-the-art urban, and one small rural wastewater treatment facility. In total, 785 structures were obtained, of these 38 were identified as single compounds, while 480 structures were identified at a putative level. The vast majority of these were therapeutics and drugs, present as parent compounds and metabolites. Using the R packages Phyloseq and MetacodeR, we here present a novel way of visualizing LCMS data while showing significant difference in xenobiotic presence in the wastewater effluents between the three sites. 1. SignificanceWe characterized a wide spectrum of xenobiotic substances using ultra-high performance liquid chromatography, and analysed the data with a new data-processing pipeline using microbial ecological tools to visualize and perform statistical testing of the chemical data to reveal trends in compound composition at the three WWTPs. This approach was applied to obtain and analyse data from effluent samples collected at three wastewater treatment facilities. In total, 785 chemical structures were achieved, with a majority identified as therapeutics and drugs. Several of the compounds are suspected endocrine disruptors. The data reveal a significant difference in compound diversity persisting in the wastewater effluents at the three sites. Our findings reveal the presence of undesirable compounds in effluent released into waterways, and address the greatest challenge in environmental chemistry - pinpointing single compounds of interest from masses of data produced.

ecology↗

bioRxiv2022

Kinetic models for PET displacement studies

The traditional design of PET target engagement studies is based on a baseline scan and one or more scans after drug administration. We here evaluate an alternative design in which the drug is administered during an on-going scan (i.e., a displacement study). This approach results both in lower radiation exposure and lower costs. Existing kinetic models assume steady state. This condition is not present during a drug displacement and consequently, our aim here was to develop kinetic models for analysing PET displacement data. We modified existing compartment models to accommodate a time-variant increase in occupancy following the pharmacological in-scan intervention. Since this implies the use of differential equations that cannot be solved analytically, we developed instead one approximate and one numerical solution. Through simulations, we show that if the occupancy is relatively high, it can be estimated without bias and with good accuracy. The models were applied to PET data from six pigs where [11C]UCB-J was displaced by intravenous brivaracetam. The dose-occupancy relationship estimated from these scans showed good agreement with occupancies calculated with Lassen plot applied to baseline-block scans of two pigs. In summary, the proposed models provide a framework to determine target occupancy from a single displacement scan.

neuroscience↗

bioRxiv2021

NetSolP: predicting protein solubility in E. coli using language models

Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased. In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence. Our model NetSolP is based on deep learning protein language models called transformers and we show that it achieves state-of-the-art performance and improves extrapolation across datasets. As we find current methods are built on biased datasets, we curate existing datasets by using strict sequence-identity partitioning and ensure that there is minimal bias in the sequences. The predictor is available at https://services.healthtech.dtu.dk/service.php?NetSolP-1.0

bioinformatics↗