Periodontitis is a chronic inflammatory disease, and its occurrence and development are closely related to the imbalance of local innate immune responses. The caspase family plays a crucial role in regulating inflammatory responses and cell death pathways in periodontal innate immune cells (such as gingival epithelial cells, neutrophils, macrophages, dendritic cells, and natural killer cells). These proteases exhibit a dual regulatory effect on cellular functions. On one hand, apoptotic pathways mediated by caspase-3/7/9 enable the programmed clearance of senescent or damaged cells, while pyroptosis pathways mediated by caspase-1/4 contribute to immune defense and pathogen elimination, collectively helping to maintain tissue homeostasis. On the other hand, excessive activation of the caspase-1/gasdermin D pathway, as well as inflammatory amplification pathways involving caspase-4/6/8, promotes the release of inflammatory cytokines such as IL-1β and IL-18, leading to the disruption of the epithelial barrier and exacerbation of periodontal tissue damage. Caspase regulation exhibits both commonality and cell specificity. In gingival epithelial cells, caspase-1 mediates pyroptosis and inflammation activation, caspase-3 regulates apoptosis and proliferation signaling, and caspase-4 participates in differentiation regulation and pathogen-selective immune responses, collectively adapting to physiological and pathological changes. Neutrophils can utilize the caspase-1/gasdermin D signaling pathway to drive the release of neutrophil extracellular traps without triggering typical pyroptosis. In macrophages, caspase-1 and caspase-8 synergistically promote polarization toward the M1 phenotype, while caspase-3 acts as an apoptosis executor to facilitate macrophage transition to the M2 phenotype in specific microenvironments. This article reviews caspase’s specific mechanism of action in periodontal innate immune-related cells, aiming to provide a new theoretical basis for targeted regulation of caspase in the treatment of periodontitis.

OBJECTIVES: To explore the promoting effect of ginsenoside Rb3 (Rb3) on osteogenesis in periodontitis environment, and to explain its mechanism. METHODS: Human periodontal ligament stem cells (hPDLSCs) were cultured by tissue block method and identified by flow cytometry. Cell counting kit-8 (CCK8) method and calcein acetoxymethyl ester/propidium iodide staining were used to detect the effect of Rb3 on the viability of hPDLSCs cells. In vitro cell experiments were divided into control group, 10 μg/mL lipopolysaccharides (LPS) group, 10 μg/mL LPS+100 μmol/L Rb3 group and 10 μg/mL LPS+200 μmol/L Rb3 group. Alkaline phosphatase (ALP) staining was used to detect the ALP activity of hPDLSCs in each group after osteogenesis induction. The expression of hPDLSCs interleukin-6 (IL-6), interleukin-8 (IL-8), runt-related transcription factor 2 (RUNX2) and transforming growth factor-β (TGF-β)genes in each group after osteogenesis was detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. Western blot was used to detect the protein expression of hPDLSCs phosphorrylated extracellular signal-regulated kinase (p-ERK) in each group. Sprague-Dawley rats were randomly divided into the control group, ligation group and ligation+Rb3 group. The left molar-maxillary tissue was subjected to micro-computed tomography (micro-CT) scanning. After the scanning, the left molar-maxilla was made into periodontal tissue sections. Hematoxylin-eosin (HE) staining was used to detect the infiltration and loss of adhesion of inflammatory cells. Masson staining was used to detect the destruction of gingival collagen fibers. Immunofluorescence staining was used to detect the protein expression of RUNX2 and p-ERK. The expression of TGF-β in rat gingival tissue was detected by qRT-PCR. The protein expression of IL-6 in peripheral serum of rats was detected by enzyme-linked immunosorbent assay (ELISA). Flow cytometry was used to detect the proportion of Treg cells in rat heart blood. The experimental data were statistically analyzed by Graph Pad Prism10.1.2 software. RESULTS: Rb3 had no effect on the cell activity of hPDLSCs. The results of qRT-PCR and ALP staining showed that Rb3 could inhibit the gene expression of IL-6 and IL-8 in inflammatory hPDLSCs, promote TGF-β gene and promote the osteogenic differentiation of inflammatory hPDLSCs. Western blot showed that Rb3 inhibited the protein expression of inflammatory hPDLSCs p-ERK. The results from micro-CT, Masson staining, and HE staining demonstrated that Rb3 promotes alveolar bone formation in rats with periodontitis, while simultaneously inhibiting the destruction of periodontal fibrous tissue, reducing attachment loss, and suppressing inflammatory cell infiltration. The results of flow cytometry showed that Rb3 could promote the differentiation of Treg cells in peripheral blood of periodontitis rats. The results of ELISA and qRT-PCR showed that Rb3 could inhibit the protein expression of IL-6 and promote the gene expression of TGF-β in periodontitis rats. Immunofluorescence results showed that Rb3 could promote the protein expression of RUNX2 and inhibit the protein expression of p-ERK in periodontitis rats. CONCLUSIONS Rb3 can reduce the inflammatory reaction of periodontal tissues in periodontitis rats, and promote the osteogenic differentiation of hPDLSCs by regulating p-ERK pathways.
Animals ; Ginsenosides/pharmacology* ; Osteogenesis/drug effects* ; Periodontitis/metabolism* ; Rats ; Periodontal Ligament/cytology* ; Humans ; Core Binding Factor Alpha 1 Subunit/metabolism* ; Stem Cells/drug effects* ; Interleukin-6/metabolism* ; Rats, Sprague-Dawley ; Interleukin-8/metabolism* ; Cells, Cultured ; MAP Kinase Signaling System/drug effects* ; Transforming Growth Factor beta/metabolism* ; Signal Transduction ; Male ; Phosphorylation ; Lipopolysaccharides ; Extracellular Signal-Regulated MAP Kinases/metabolism* ; Alkaline Phosphatase/metabolism*

OBJECTIVES: This study aimed to explore the therapeutic effect and mechanism of ginsenoside Rb3 on experimental periodontitis and bone resorption in rats. METHODS: Male SD rats were randomly divided into a control group, a ligation group, an Rb3 group, and a doxycycline (Dox) group for in vivo experiments. A periodontitis model was established by ligating the maxillary second molar, and samples were collected after 3 weeks of drug treatment. Micro-CT assessment of alveolar bone resorption was performed, and hematoxylin-eosin (HE) staining was used to observe pathological changes in periodontal and visceral tissues. Tartrate resistant acid phosphatase (TRAP) staining was applied to detect the formation of osteoclasts in periodontal tissues, and enzyme-linked immunosorbent assay (ELISA) was adopted to detect the serum levels of interleukin (IL)-6, IL-8, immunoglobulin (Ig)M, and IgG. Quantitative polymerase chain reaction (qPCR) was employed to detect the expression of factors related to gingival inflammation and osteoclast formation. Immunofluorescence staining was used to detect phospho-extracellular signal-regulated kinase (p-ERK) expression. In vitro experiments were conducted by pretreating RAW264.7 cells with drugs and adding lipopolysaccharides (LPS) stimulation from Porphyromonas gingivalis (P. gingivalis). IL-1β and IL-6 mRNA expression was detected by qPCR, and Western blot was used to detect the effect of Rb3 on the mitogen-activated protein kinases (MAPKs) signaling pathway. RESULTS: Compared with the control group, the ligation group showed significant periodontitis and bone resorption. Compared with the ligation group, the Rb3 group showed a decrease in alveolar bone resorption and osteoclast formation; p-ERK/ERK ratio, IL-1β, IL-6, and nuclear factor of activated T cells (NFATc1) mRNA levels and downstream gene expression in periodontal tissues; serum IL-6, IL-8, IgG, and IgM levels. Rb3 reduced IL-8 and IL-1β mRNA expression levels and p-ERK/ERK and p-p38 MAPK/p38 MAPK ratios in RAW264.7 cells induced by P. gingivalis LPS stimulation. CONCLUSIONS Rb3 inhibits inflammation and bone resorption in experimental periodontitis in rats. Compared with Dox, Rb3 has better effects in inhibiting pro-inflammatory factors and osteoclast gene expression and may exert anti-inflammatory effects by activating the MAPK signaling pathway.
Animals ; Ginsenosides/therapeutic use* ; Rats, Sprague-Dawley ; Male ; Periodontitis/pathology* ; Rats ; Osteoclasts/drug effects* ; Interleukin-1beta/metabolism* ; Interleukin-6/blood* ; Mice ; Alveolar Bone Loss ; Interleukin-8/blood* ; Immunoglobulin G/blood* ; RAW 264.7 Cells ; Transcription Factors

Biosensors have become powerful tools for real-time monitoring of specific small molecules and precise control of gene expression in biological systems. High-throughput sensors for 1, 4-butanediamine biosynthesis can greatly improve the screening efficiency of high-yielding 1, 4-butanediamine strains. However, the strategies for adapting the characteristics of biosensors are still rarely studied, which limits the applicability of 1, 4-butanediamine biosensors. In this paper, we propose the development of a 1, 4-butanediamine biosensor based on the transcriptional regulator PuuR, whose homologous operator puuO is installed in the constitutive promoter P_gapA of Escherichia coli to control the expression of the downstream superfolder green fluorescent protein (sfGFP) as the reporter protein. Finally, the biosensor showed a stable linear relationship between the GFP/OD₆₀₀ value and the concentration of 1, 4-butanediamine when the concentration of 1, 4-butanediamine was 0-50 mmol/L. The promoters with different strengths in the E. coli genome were used to modify the 1, 4-butanediamine biosensor, and the functional properties of the PuuR-based 1, 4-butanediamine biosensor were explored and improved, which laid the groundwork for high-throughput screening of engineered strains highly producing 1, 4-butanediamine.
Biosensing Techniques/methods* ; Escherichia coli/metabolism* ; Promoter Regions, Genetic/genetics* ; Green Fluorescent Proteins/metabolism* ; Transcription Factors/genetics* ; Escherichia coli Proteins/genetics* ; Diamines/metabolism* ; Gene Expression Regulation, Bacterial

ObjectiveTo explore effect of Xianlian Jiedu prescription on the recurrence of colorectal cancer （CRC） and investigate the related mechanisms. MethodsA postoperative recurrence model was established in 25 Balb/c mice by injecting CT26 cells subcutaneously into the armpit， followed by surgical removal of 99% of the subcutaneous tumor. The mice were randomly divided into model group， low-dose Xianlian Jiedu prescription （XLJDP-L） group （6.45 g·kg^-1·d^-1）， medium-dose Xianlian Jiedu prescription （XLJDP-M） group （12.9 g·kg^-1·d^-1）， high-dose Xianlian Jiedu prescription （XLJDP-H） group （25.8 g·kg^-1·d^-1）， and 5-fluorouracil （5-FU） group （1×10^-3 g·kg^-1·d^-1）. The mice were euthanized after 14 days of continuous intervention， and recurrent tumor tissue was harvested. Hematoxylin and eosin （HE） staining was used to observe pathological and morphological changes in the recurrent tumor tissue. Immunohistochemistry （IHC） was employed to assess the expression of proliferating cell nuclear antigen （Ki67）， vascular endothelial growth factor （VEGF）， and platelet-endothelial cell adhesion molecule （CD31） in recurrent tumor tissue. The Western blot was used to detect the protein expression levels of angiopoietin-2 （ANG-2）， VEGF， phosphorylated-protein kinase B （p-Akt）， protein kinase B （Akt）， phosphorylated-phosphatidylinositol 3-kinase （p-PI3K）， and phosphatidylinositol 3-kinase （PI3K） in recurrent tumor tissue. ResultsBefore treatment， there were no statistical differences in tumor volume， tumor weight， and body mass among the XLJDP-L， XLJDP-M， and XLJDP-H groups and the 5-FU group compared to the model group， indicating model stability. After treatment， compared with those in the model group， the tumor volume and tumor weight in the XLJDP-L， XLJDP-M， and XLJDP-H groups and the 5-FU group were significantly reduced （P<0.01）， showing dose dependency. Meanwhile， there were no significant differences in body weight among the XLJDP-L， XLJDP-M， and XLJDP-H groups and the 5-FU group compared to the model group. HE staining showed that compared with that in the model group， tumor tissue in the XLJDP-L， XLJDP-M， and XLJDP-H groups and the 5-FU group had loosely arranged cells， increased intercellular spaces， small and shriveled nuclei， light staining， fewer mitotic figures and atypical nuclei， and increased necrotic areas. IHC showed that compared with those of the model group， the positive rates of Ki67， VEGF， and CD31 in the recurrent tumor tissue of the XLJDP-L， XLJDP-M， and XLJDP-H groups and the 5-FU group were significantly reduced （P<0.01） in a dose-dependent manner. Western blot results showed that compared with those of the model group， the protein expression levels of ANG-2 and VEGF in the recurrent tumor tissue of the XLJDP-L， XLJDP-M， and XLJDP-H groups and the 5-FU group were significantly downregulated （P<0.05， P<0.01）， and the p-Akt/Akt and p-PI3K/PI3K ratios were significantly decreased in a dose-dependent manner （P<0.05， P<0.01）. ConclusionXianlian Jiedu prescription significantly inhibits the recurrence of CRC in mice after subcutaneous tumor surgery. The mechanism may involve regulating the PI3K/Akt pathway and downregulating key angiogenic proteins such as ANG-2， VEGF， and CD31.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective To investigate the feasibility of simultaneous multi-slice(SMS)acquisition combined with single-shot echo-planar imaging(SSEPI)multi-model diffusion weighted imaging(DWI)for breast lesions.Methods Totally 108 cases of breast lesions were retrospectively enrolled and divided into malignant group(n=66)and benign group(n=42)based on pathology.3.0T MR scanner was used to acquire SSEPI and SMS-SSEPI multi-b values DWI,7 derived parameters were obtained through post-processing with mono-exponential,fractional-order calculus(FROC)and continuous-time random walk(CTRW)models.Then the imaging quality and derived parameters of SMS-SSEPI and SSEPI DWI were compared between groups.Spearman correlation analysis was performed to explore the relationships of corresponding parameters between SMS-SSEPI DWI and SSEPI DWI.Diagnostic performance of each parameter for distinguishing malignant and benign lesions was evaluated according to the area under the receiver operating characteristic curve(AUC).Results Background noise score of SMS-SSEPI DWI was lower than that of SSEPI DWI(P＜0.05),whereas no significant difference of overall imaging quality,normal anatomical structure depiction,lesion conspicuity,geometric distortion,signal-to-noise ratio(SNR)nor contrast-to-noise ratio(CNR)was found between SMS-SSEPI DWI and SSEPI DWI(all P＞0.05).Parameters derived from SMS-SSEPI DWI were all moderately to highly positively correlated with those from SSEPI DWI(rs=0.66-0.98).Malignant lesions exhibited significantly lower apparent diffusion coefficient(ADC),diffusion coefficient based on FROC(DFROC),fractional order derivative in space(βFROC),diffusion coefficient based on CTRW(DCTRW),temporal diffusion heterogeneity index(αCTRW)and spatial diffusion heterogeneity index(βCTRW)values,but higher spatial parameter(μFROC)value than benign lesions(all P＜0.05).AUC of SMS-SSEPI DWI derived parameters for differentiating malignant from benign lesions were 0.699-0.900,of those from SSEPI DWI were 0.654-0.887,while in both SMS-SSEPI DWI and SSEPI DWI,DFROC had the highest diagnostic efficacy(AUC=0.900,0.887).Conclusion SMS-SSEPI DWI could be used to effectively differentiate malignant and benign breast lesions.

Oral squamous cell carcinoma(OSCC)is the most prevalent malignant tumor in the head and neck region,with a relatively high incidence and mortality rate.Long non-coding RNA(lncRNA)is an important class of non-coding RNA molecules that play a cru-cial role in the occurrence and progression of OSCC.Urothelial carcinoma associated 1(UCA1)is highly expressed in OSCC,where it regulates the biological behavior of tumor cells through miRNA sponge activity and activation of downstream signaling pathways.This ar-ticle reviews the mechanisms of action of UCA1 in OSCC and its research progress in areas such as tumor proliferation,invasion,me-tastasis,drug resistance,diagnosis,treatment and prognosis.This is expected to provide a theoretical foundation for the potential clini-cal applications of UCA1 in OSCC.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective To investigate the feasibility of simultaneous multi-slice(SMS)acquisition combined with single-shot echo-planar imaging(SSEPI)multi-model diffusion weighted imaging(DWI)for breast lesions.Methods Totally 108 cases of breast lesions were retrospectively enrolled and divided into malignant group(n=66)and benign group(n=42)based on pathology.3.0T MR scanner was used to acquire SSEPI and SMS-SSEPI multi-b values DWI,7 derived parameters were obtained through post-processing with mono-exponential,fractional-order calculus(FROC)and continuous-time random walk(CTRW)models.Then the imaging quality and derived parameters of SMS-SSEPI and SSEPI DWI were compared between groups.Spearman correlation analysis was performed to explore the relationships of corresponding parameters between SMS-SSEPI DWI and SSEPI DWI.Diagnostic performance of each parameter for distinguishing malignant and benign lesions was evaluated according to the area under the receiver operating characteristic curve(AUC).Results Background noise score of SMS-SSEPI DWI was lower than that of SSEPI DWI(P＜0.05),whereas no significant difference of overall imaging quality,normal anatomical structure depiction,lesion conspicuity,geometric distortion,signal-to-noise ratio(SNR)nor contrast-to-noise ratio(CNR)was found between SMS-SSEPI DWI and SSEPI DWI(all P＞0.05).Parameters derived from SMS-SSEPI DWI were all moderately to highly positively correlated with those from SSEPI DWI(rs=0.66-0.98).Malignant lesions exhibited significantly lower apparent diffusion coefficient(ADC),diffusion coefficient based on FROC(DFROC),fractional order derivative in space(βFROC),diffusion coefficient based on CTRW(DCTRW),temporal diffusion heterogeneity index(αCTRW)and spatial diffusion heterogeneity index(βCTRW)values,but higher spatial parameter(μFROC)value than benign lesions(all P＜0.05).AUC of SMS-SSEPI DWI derived parameters for differentiating malignant from benign lesions were 0.699-0.900,of those from SSEPI DWI were 0.654-0.887,while in both SMS-SSEPI DWI and SSEPI DWI,DFROC had the highest diagnostic efficacy(AUC=0.900,0.887).Conclusion SMS-SSEPI DWI could be used to effectively differentiate malignant and benign breast lesions.