The anti-inflammatory properties of the cannabinoid 2 receptor (CB2R) in injury and inflammatory diseases have been widely substantiated. Specifically, the anti-inflammatory effect of CB2R may be achieved by regulating macrophage polarisation. Several research findings suggested that the activation of CB2R could attenuate inflammation by reducing pro-inflammatory M1 macrophage polarisation and promoting anti-inflammatory M2 polarisation. However, considering CB2R inhibits fibrosis and M2 promotes fibrosis, that the activation of CB2R may lead to an increase in M2 macrophages seems contradictory. Therefore, we hypothesised that the activation of CB2R to attenuate inflammation is not achieved by up-regulating M2 macrophages. We established an incised wound model using mouse skin and used this to evaluate the effect of CB2R agonists (JWH133 or GP1a) and an antagonist (AM630) on wound healing. At various post-injury intervals, we used western blot analysis, immunofluorescence staining, enzyme-linked immunosorbent assay and quantitative reverse transcription polymerase chain reaction assays to determine CB2R protein expression, M1/M2 macrophage infiltration, and the protein and gene expression of M1/M2-associated markers and cytokines in skin lesions. Activation of CB2R significantly reduced M1 macrophage infiltration and slightly increased M2 macrophage infiltration. Similarly, gene expression and protein levels of M1-associated markers and cytokines (interleukin [IL]-6, IL-12, CD86 and inducible nitric oxide synthase) were significantly down-regulated after CB2R agonist administration; in contrast, markers and cytokines were increased in the CB2R antagonist–treated group. Conversely, the administration of agonists slightly increased gene expression and protein levels of M2-associated markers and cytokines (IL-4, IL-10, CD206 and arginase-1 [Arg-1]); however, a statistical significance at most time points post-injury was not noted. In summary, our findings suggested that during incised skin wound healing in mice, increased levels of CB2R may affect inflammation by regulating M1 rather than M2 macrophage subtype polarisation. These results offer a novel understanding of the molecular mechanisms involved in the inhibition of inflammation by CBR2 that may lead to new treatments for cutaneous inflammation.

Ferroptosis and iron-related redox imbalance aggravate traumatic brain injury (TBI) outcomes. NRF2 is the predominant transcription factor regulating oxidative stress and neuroinflammation in TBI, but its role in iron-induced post-TBI damage is unclear. We investigated ferroptotic neuronal damage in the injured cortex and observed neurological deficits post-TBI. These were ameliorated by the iron chelator deferoxamine (DFO) in wild-type mice. In Nrf2-knockout (Nrf2^−/−) mice, more sever ferroptosis and neurological deficits were detected. Dimethyl fumarate (DMF)-mediated NRF2 activation alleviated neural dysfunction in TBI mice, partly due to TBI-induced ferroptosis mitigation. Additionally, FTH-FTL and FSP1 protein levels, associated with iron metabolism and the ferroptotic redox balance, were highly NRF2-dependent post-TBI. Thus, NRF2 is neuroprotective against TBI-induced ferroptosis through both the xCT-GPX4- and FTH-FTL-determined free iron level and the FSP1-regulated redox status. This yields insights into the neuroprotective role of NRF2 in TBI-induced neuronal damage and its potential use in TBI treatment.

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.