ObjectiveThis study aims to explore the neurotoxicity mechanism of Dictamni Cortex by integrating network toxicology and metabolomics techniques. MethodsThe neurotoxicity targets induced by Dictamni Cortex were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）， Traditional Chinese Medicine Information Database （TCM-ID）， and Comparative Toxicogenomics Database （CTD）. The target predictions of the components were performed by the Swiss Target Prediction tool. Neurotoxicity-related targets were collected from the Pharmacophore Mapping and Potential Target Identification Platform （PharmMapper）， GeneCards Human Gene Database （GeneCards）， DisGeNET Disease Gene Network （DisGeNET）， and Online Mendelian Inheritance in Man （OMIM）， and the intersection targets were identified. Protein-protein interaction （PPI） analysis， Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis， and Gene Ontology （GO） enrichment analysis were conducted. A "drug-compound-toxicity target-pathway" network was constructed via Cytoscape software to display the core regulatory network. Based on the prediction results， the neurotoxicity mechanism of Dictamni Cortex in mice was verified by using hematoxylin-eosin （HE） staining， Nissl staining， enzyme-linked immunosorbent assay （ELISA）， quantitative real-time fluorescence polymerase chain reaction （Real-time PCR）， and Western blot. The effects of Dictamni Cortex on the metabolic profile of mouse brain tissue were further explored by non-targeted metabolomics. ResultsNetwork toxicology screening identified 13 compounds and 175 targets in Dictamni Cortex that were related to neurotoxicity. PPI network analysis revealed that serine/threonine-protein kinase （Akt1） and tumor protein 53 （TP53） were the core targets. Additionally， GO/KEGG enrichment analysis indicated that Dictamni Cortex may regulate the phosphatidylinositol 3-kinase （PI3K）/Akt pathway and affect oxidative stress and cell apoptosis， thereby inducing neural damage. The "Dictamni Cortex-compound-toxicity target-pathway-neural damage" network showed that dictamnine， phellodendrine， and fraxinellone may be the toxic compounds. Animal experiments showed that compared with those in the blank group， the hippocampal neurons in the brain tissue of mice treated with Dictamni Cortex were damaged. The level of superoxide dismutase （SOD） and acetylcholine （ACh） in the brain tissue was significantly reduced， while the content of malondialdehyde （MDA） was significantly increased. The level of Akt1 and p-Akt1 mRNAs and proteins in the brain tissue was significantly decreased， while the level of TP53 was significantly increased. Non-targeted metabolomics results showed that Dictamni Cortex could disrupt the level of 40 metabolites in mouse brain tissue， thereby regulating the homeostasis of 13 metabolism pathways， including phenylalanine， glycerophospholipid， and retinol. Combined analysis revealed that Akt1， p-Akt1， and TP53 were significantly correlated with phenylalanine， glycerophospholipid， and retinol metabolites. This suggested that Dictamni Cortex induced neurotoxicity in mice by regulating Akt1， p-Akt1， and TP53 and further modulating the phenylalanine， glycerophospholipid， and retinol metabolism pathways. ConclusionDictamni Cortex can induce neurotoxicity in mice， and its potential mechanism may be closely related to the activation of oxidative stress， inhibition of the PI3K/Akt signaling pathway， and regulation of phenylalanine， glycerophospholipid， and retinol metabolism pathways.

ObjectiveThis study aims to investigate the effect of Dictamni Cortex on intestinal barrier damage in rats and its mechanism by untargeted metabolomics and targeted metabolomics for short-chain fatty acids （SCFAs）. MethodsRats were randomly divided into a control group， a high-dose group of Dictamni Cortex （8.1 g·kg^-1）， a medium-dose group （2.7 g·kg^-1）， and a low-dose group （0.9 g·kg^-1）. Except for the control group， the other groups were administered different doses of Dictamni Cortex by gavage for eight consecutive weeks. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in the ileal tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to detect the level of cytokines， including tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）， in the ileal tissue of rats. Quantitative real-time fluorescence polymerase chain reaction （Real-time PCR） technology was used to detect the expression level of tight junction proteins， including zonula occludens-1 （ZO-1）， Occludin， and Claudin-1 mRNAs， in the ileal tissue of rats to preliminarily explore the effects of Dictamni Cortex on intestinal damage. The dose with the most significant toxic phenotype was selected to further reveal the effects of Dictamni Cortex on the metabolic profile of ileal tissue in rats by non-targeted metabolomics combined with targeted metabolomics for SCFAs. ResultsCompared with the control group， all doses of Dictamni Cortex induced varying degrees of pathological damage in the ileum， increased TNF-α （P<0.01）， IL-6 （P<0.01）， and IL-1β （P<0.01） levels in the ileal tissue， and decreased the expression level of ZO-1 （P<0.05， P<0.01）， Occludin （P<0.01）， and Claudin-1 （P<0.05） in the ileal tissue， with the high-dose group showing the most significant toxic phenotypes. The damage mechanisms of the high-dose group of Dictamni Cortex on the ileal tissue were further explored by integrating non-targeted metabolomics and targeted metabolomics for SCFAs. The non-targeted metabolomics results showed that 21 differential metabolites were identified in the control group and the high-dose group. Compared with that in the control group， after Dictamni Cortex intervention， the level of 14 metabolites was significantly increased （P<0.05， P<0.01）， and the level of seven metabolites was significantly decreased （P<0.05， P<0.01） in the ileal contents. These metabolites collectively acted on 10 related metabolic pathways， including glycerophospholipids and primary bile acid biosynthesis. The quantitative data of targeted metabolomics for SCFAs showed that Dictamni Cortex intervention disrupted the level of propionic acid， butyric acid， acetic acid， caproic acid， isobutyric acid， isovaleric acid， valeric acid， and isocaproic acid in the ileal contents of rats. Compared with those in the control group， the level of isobutyric acid， isovaleric acid， and valeric acid were significantly increased， while the level of propionic acid， butyric acid， and acetic acid were significantly decreased in the ileal contents of rats after Dictamni Cortex intervention （P<0.05， P<0.01）. ConclusionDictamni Cortex can induce intestinal damage by regulating glycerophospholipid metabolism， primary bile acid biosynthesis， and metabolic pathways for SCFAs.

Objective To investigate the expansion margins of the planning target volume (PTV) and the planning organ at risk volume (PRV) in nasopharyngeal carcinoma patients immobilized with Styrofoam and head-neck-shoulder mask. Methods A convenient sample of 33 nasopharyngeal carcinoma patients who received radiotherapy at Huanggang Central Hospital from January to October 2024 were selected as the research subjects. All patients underwent cone beam CT scans during the first three treatments and weekly thereafter. After registration and calibration, the setup errors in the X (LAT), Y (LNG), and Z (VRT) directions were recorded. Statistical analysis was performed on the setup errors in each direction to determine differences, and the expansion margins for PTV and PRV were calculated using empirical formulas. Results A total of 229 cone beam CT images were collected. Statistical analysis found that the setup errors (systematic error ± random error) of the patients in the X, Y, and Z directions were 1.05 ± 0.72, 1.30 ± 0.80, and 1.29 ± 0.82 mm, respectively. The expansion margins for PTV in the left-right, superior-inferior, and anterior-posterior directions were 1.40, 1.76, and 1.8 mm, respectively. The expansion margins for PRV in these directions were 0.83, 1.02, and 1.05 mm, respectively. Conclusion For patients immobilized using Styrofoam and head-neck-shoulder mask, it is recommended that the expansion margins for PTV and PRV be set at 2 mm and 1 mm, respectively, in the left-right, superior-inferior, and anterior-posterior directions, and the PRV margin for the spinal cord be set at 3 mm in all directions.

Tooth pulpitis is a prevalent oral disorder. Understanding the underlying mechanisms of pulpitis and developing effective treatment strategies hold great significance. Ferroptosis has recently emerged as a new form of cell death, but the role of ferroptosis in pulpitis remains largely unknown. In our study, single-cell RNA sequencing (scRNA-seq) was used to identify cellular heterogeneity between 3 pulpitis tissue and 3 healthy pulp tissue, and explored ferroptosis occurrence in pulpitis tissue and inflamed dental pulp cells (DPCs). In scRNA-seq, 40 231 cells (Pulpitis: 17 814; Healthy pulp: 22 417) were captured, and visualized into 12 distinct cell clusters. Differentially expressed ferroptosis-related genes (DE-FRGs) were almost presented in each cluster in pulpitis vs healthy pulp. ROS and Fe²⁺ levels significantly rose, and immunohistochemistry showed low expression of GPX4 and high expression of PTGS2 in pulpitis. In LPS-stimulated DPCs, thymosin α1 increased the expression of GPX4 and FTL, and decreased expression of TNF-α, IL-1β, IL-6, and Fe²⁺ levels. In rat pulpitis models, both prothymosin α (PTMA, precursor of thymosin α1) gelatin sponge placed at the hole of pulp (LPS-P(gs)) and PTMA injection in pulp (LPS-P(i)) significantly reduced infiltration of inflammatory cells and expression of PTGS2, and increased the expression of GPX4. In RNA sequencing, the expression of DE-FRGs were reversed when thymosin α1 were added in LPS-stimulated DPCs. Collectively, single-cell atlas reveals cellular heterogeneity between pulpitis and healthy pulp, and ferroptosis occurrence in pulpitis. Thymosin α1 may reduce ferroptosis in DPCs to alleviate pulpitis and thus potentially has the ability to treat pulpitis.
Ferroptosis/drug effects* ; Dental Pulp/drug effects* ; Animals ; Pulpitis/pathology* ; Rats ; Thymalfasin/pharmacology* ; Humans ; Male ; Thymosin/pharmacology* ; Disease Models, Animal ; Rats, Sprague-Dawley

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.