Current medications used to treat the inflammatory pain either have limited effectiveness or may be associated with serious side effects. Non-psychoactive phytocannabinoids may have a better safety profile and could be used as an alternative approach to treat this condition. The aim of this study was to test the potential of three phytocannabinoids, cannabinol (CBN), tetrahydrocannabivarin (THCV) and cannabidiol (CBD), in treating inflammatory pain. The inflammatory pain was triggered in male rats by a single intra-articular knee injection of the complete Freund’s adjuvant (CFA). One week later, rats were given four daily administrations of ibuprofen, CBN, THCV or CBD. Our data demonstrated that CFA injection triggered an inflammatory response expressed as damage of the synovial tissue, reduced locomotor activity, increased mechanical and, to a lesser extent, thermal pain sensitivity, and loss of body weight. All phytocannabinoids reduced mechanical hyperalgesia and had no or a minor effect on locomotor activity. Treatment with CBN also lowered thermal hypersensitivity. Treatment with CBN and THCV recovered the body weight of CFA-injected rats. However, administration of CBD reduced body weight and elevated blood monocyte and granulocyte levels above those of the CFA-injected control animal group. We conclude that CBN and THCV may have potential in managing inflammatory pain.

The serotonin system is the main therapeutic target for selective serotonin reuptake inhibitors (SSRIs) in treating depression, yet the mechanism of action of SSRIs remains incompletely understood. To investigate the molecular and transcriptional effects of SSRI administration on serotonin neurons, we performed spatial transcriptomics, a spatially resolved RNA-sequencing method in intact brain tissue. Mouse brain sections containing the dorsal raphe nucleus and adjacent midbrain structures were analyzed, revealing six distinct serotonergic subpopulations with unique molecular signatures and spatial distributions. Both acute and chronic fluoxetine treatment induced a large number of changes in gene expression in the dorsal raphe nucleus. Notably, Htr1a expression increased following acute treatment but decreased after chronic administration, supporting previous findings on serotonin transporter blockade effects on 5-HT1A autoreceptors. Gene enrichment and network analysis identified key pathways modulated by SSRI administration, including Ras, MAPK and cAMP signaling pathways as well as pathways involved in axonal guidance. Additionally, we observed treatment-dependent opposing transcriptional changes in neuropeptides, particularly Thyrotropin-releasing hormone (Trh) and Prodynorphin (Pdyn), with distinct spatial localization within the dorsal raphe nucleus. Collectively, our transcriptomic and in situ hybridization analyses reveal spatial and cell-type-specific heterogeneity in SSRI action within the dorsal raphe nucleus, providing new insights into the molecular basis of SSRI treatment effects.

Analyzing animal behavior is crucial for decoding brain function, modeling neurological disorders, and assessing therapeutics. Recent advances in pose-estimation tools like DeepLabCut and SLEAP have revolutionized behavioral analysis by enabling precise tracking of animal body movements. However, these tools do not automate behavioral classification. Unsupervised learning algorithms address this gap by identifying clusters of recurring behavioral motifs from pose-tracking data without requiring pre-labeled datasets, reducing observer bias and uncovering novel patterns. This study compares four recent unsupervised learning algorithms—B-SOiD, BFA, VAME, and Keypoint-MoSeq—analyzing their methodological approaches, clustering efficiency, and ability to produce meaningful behavioral classifications. By offering a qualitative and quantitative evaluation, this paper aims to aid researchers in selecting the most suitable tool for their specific research needs.