Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Subcellular pharmacokinetic measurements have informed the study of central nervous system (CNS)-acting drug mechanisms. Recent investigations have been enhanced by the use of genetically encoded fluorescent biosensors for drugs of interest at the plasma membrane and in organelles. We describe screening and validation protocols for identifying hit pairs comprising a drug and biosensor, with each screen including 13-18 candidate biosensors and 44-84 candidate drugs. After a favorable hit pair is identified and validated via these protocols, the biosensor is then optimized, as described in other papers, for sensitivity and selectivity to the drug. We also show sample hit pair data that may lead to future intensity-based drug-sensing fluorescent reporters (iDrugSnFRs). These protocols will assist scientists to use fluorescence responses as criteria in identifying favorable fluorescent biosensor variants for CNS-acting drugs that presently have no corresponding biosensor partner. eLife (2022), DOI: 10.7554/eLife.74648 Graphical abstract.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724018 | PMC |
| http://dx.doi.org/10.21769/BioProtoc.4551 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Antiplatelet therapy and central nervous system hematomas: a cohort study using real-world data from the FAERS and vigiaccess databases.
Int J Surg
September 2025
Department of Pharmacy, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, School of Clinical Medicine, Henan University, Zhengzhou, Henan, China.
Background: Antiplatelet therapy is a cornerstone in the management of atherosclerotic cardiovascular disease. However, the risk profile of central nervous system (CNS) hematomas associated with antiplatelet agents remains incompletely characterized.
Methods: We analyzed CNS-related hematoma adverse event (hAE) reports across the four antiplatelet drugs, using data from the U.
Do psycho-behavioural interventions improve mental and physical health in chronic kidney disease? A systematic review of randomised controlled trials.
J Nephrol
September 2025
Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, Health Psychology Section, King's College London, 5th Floor Bermondsey Wing, Guy's Campus, London Bridge, London, SE1 9RT, UK.
Background: Depression and anxiety are common in chronic kidney disease (CKD) and worsen clinical outcomes. Psycho-behavioural interventions offer a promising, non-pharmacological approach. However, most evidence comes from people with kidney failure with distinct treatment needs, limiting relevance to earlier stages of CKD, where timely support may enhance self-management and slow progression.
View Article and Find Full Text PDFUtility and performance of cerebrospinal fluid cytology in discriminating central nervous system infections and brain tumors.
J Neurooncol
September 2025
Department of Neurology, Xiangya Hospital, Central South University, No.87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan Province, China.
Background And Objective: Differentiating central nervous system infections (CNSIs) from brain tumors (BTs) is difficult due to overlapping features and the limited individual indicators, and cerebrospinal fluid (CSF) cytology remains underutilized. To improve differential diagnosis, we developed a model based on 9 early, cost-effective cerebrospinal fluid parameters, including CSF cytology.
Methods: Patients diagnosed with CNSIs or BTs at Xiangya Hospital of Central South University between October 1st, 2017 and March 31st, 2024 were enrolled and divided into the training set and the test set.
Using Medication Dispensation Data to Identify Clusters with Similar Prescribing Patterns in Older Adults Living with Dementia.
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFIdentification and prioritization of gene sets associated with schizophrenia risk by network analysis.
Psychopharmacology (Berl)
September 2025
Institute of Cardiovascular Research, Sleep Medical Center, Department of Psychiatry, Fundamental and Clinical Research on Mental Disorders Key Laboratory of Luzhou, Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan Province, 646000, China.
Rationale: Genome-wide association studies (GWASs) are used to identify genetic variants for association with schizophrenia (SCZ) risk; however, each GWAS can only reveal a small fraction of this association.
Objectives: This study systematically analyzed multiple GWAS data sets to identify gene subnetwork and pathways associated with SCZ.
Methods: We identified gene subnetwork using dmGWAS program by combining SCZ GWASs and a human interaction network, performed gene-set analysis to test the association of gene subnetwork with clinical symptom scores and disease state, meanwhile, conducted spatiotemporal and tissue-specific expression patterns and cell-type-specific analysis of genes in the subnetwork.