Objective To investigate the effect of peptidoglycan (PGN) on the secretion of pro-inflammatory cytokines by dendritic cells (DCs) and the regulation of T helper 17 (Th17) responses in experimental autoimmune uveitis. Methods Bone marrow cells from naive mice were cultured with granulocyte macrophage-colony-stimulating factor and interleukin (IL)-4 to induce DCs. DCs cultured for six days were randomly divided into two groups: PGNtreated group and control group. The DCs in PGNtreated group were stimulated with PGN and the same volume of phosphate buffered saline was added to the DCs as control group. The relative mRNA expression levels of IL-23, tumor necrotic factor alpha; (TNF-alpha;), IL-6,IL-1beta;were measured by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Peptide fragment of interphotoreceptor retinoidbinding protein (IRBP1-20)specific T cells, which were isolated from the spleen and draining lymph nodes of C57BL/6 mice immunized with IRBP1-20 peptide fragments 13 days earlier, were co-cultured with PGN-treated or untreated DCs, respectively. Total RNA from T cells cocultured for two days were isolated and the relative expression of retinoic acid receptor-related orphan receptor gamma;t (ROR-gamma;t), IL-17, T-box expression in T cells (T-bet), interferon gamma; (IFN-gamma;) mRNA were detected by realtime RT-PCR. On the second, the fifth and the seventh day, the cocultured T cells were analyzed by flow cytometry to detect the percentages of IFN-gamma;, IL-17 positive cells. Results The real-time RT-PCR results revealed that the level of IL-23, IL-1beta;, IL-6, TNF-alpha; mRNA from PGNstimulated DCs were significantly increased compared to the control group (t=-14.363, -5.627, -3.85, -28.151; P＜0.05). The level of RORgamma;t, IL-17 mRNA from the T cells cocultured with PGN-stimulated DCs were greatly increased compared with the control group (t=-5.601, -19.76；P＜0.05). However, the level of T-bet, IFN-gamma; mRNA from the T cells cocultured with PGNstimulated DCs were significantly decreased compared with the control group (t=4.717, 11.207; P＜0.05). Data of flow cytometry showed that at two days, five days, seven days after cocultured with PGN-treated DCs, the percentages of IL-17 positive T cells were increased compared to the control group (t=-2.944, -3.03, -4.81; P＜0.05), and the percentages of IFN-gamma; positive T cells had no remarkable change (t=-1.25, -0.18, -2.16; P＞0.05). Conclusion PGN can promote the secretion of Th17-related cytokines by DCs, which favors proliferation and differentiation of Th17 in experimental autoimmune uveitis.
Patient-specific volumetric modulated arc therapy (VMAT) quality assurance (QA) process is an important component of the implementation process of clinical radiotherapy. The tolerance limit and action limit of discrepancies between the calculated dose and the delivered radiation dose are the key parts of the VMAT QA processes as recognized by the AAPM TG-218 report, however, there is no unified standard for these two values among radiotherapy centers. In this study, based on the operational recommendations given in the AAPM TG-218 report, treatment site-specific tolerance limits and action limits of gamma pass rate in VMAT QA processes when using ArcCHECK for dose verification were established by statistical process control (SPC) methodology. The tolerance limit and action limit were calculated based on the first 25 in-control VMAT QA for each site. The individual control charts were drawn to continuously monitor the VMAT QA process with 287 VMAT plans and analyze the causes of VMAT QA out of control. The tolerance limits for brain, head and neck, abdomen and pelvic VMAT QA processes were 94.56%, 94.68%, 94.34%, and 92.97%, respectively, and the action limits were 93.82%, 92.54%, 93.23%, and 90.29%, respectively. Except for pelvic, the tolerance limits for the brain, head and neck, and abdomen were close to the universal tolerance limit of TG-218 (95%), and the action limits for all sites were higher than the universal action limit of TG-218 (90%). The out-of-control VMAT QAs were detected by the individual control chart, including one case of head and neck, two of the abdomen and two of the pelvic site. Four of them were affected by the setup error, and one was affected by the calibration of ArcCHECK. The results show that the SPC methodology can effectively monitor the IMRT/VMAT QA processes. Setting treatment site-specific tolerance limits is helpful to investigate the cause of out-of-control VMAT QA.
ObjectiveTo observe and analyze the subfoveal choroidal thickness (SFCT), large choroidal vessel layer thickness (LCVT), and their early changes, and to evaluate their prognostic value for treatment outcomes in eyes with polypoidal choroidal vasculopathy (PCV) receiving anti-vascular endothelial growth factor (VEGF) therapy. MethodsA retrospective clinical study. A total of 120 patients (120 eyes) with unilateral PCV diagnosed and confirmed at Tangshan Eye Hospital from January 2020 to August 2024 were included. All affected eyes received intravitreal anti-VEGF injections. SFCT and LCVT were measured using swept-source optical coherence tomography (SS-OCT) before treatment and at 1, 3, 6, and 12 months after treatment. Based on the 12-month follow-up outcomes, the eyes were divided into a good prognosis group (69 eyes) and a poor prognosis group (51 eyes). Subretinal fluid (SRF) absorption was assessed by SS-OCT at 1 month after the first treatment (early stage). Repeated measures ANOVA was used to compare the dynamic changes of SFCT and LCVT between the two groups. Binary logistic regression, joint modeling, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and mediation analysis were used to evaluate the predictive value of SFCT and LCVT for prognosis. ResultsCompared with the good prognosis group, the poor prognosis group had significantly greater disease duration, best-corrected visual acuity, branching vascular network area, maximum linear lesion distance, proportion of choroidal vascular hyperpermeability, pigment epithelial detachment height, and pre-treatment SFCT and LCVT (P＜0.05). The good prognosis group had significantly lower SRF height (SRFH) before treatment and at 1 month after treatment, and a greater reduction in SRFH (ΔSRFH) within the first month, compared with the poor prognosis group (P＜0.05). At 1 month after treatment, complete SRF absorption was observed in 65 eyes (94.20%, 65/69) in the good prognosis group and 32 eyes (62.75%, 32/51) in the poor prognosis group; the complete SRF absorption rate was significantly higher in the good prognosis group (χ2=18.731, P＜0.001). Repeated measures ANOVA showed that SFCT and LCVT significantly decreased after treatment at different time points in both groups (P＜0.05); at each post-treatment time point, SFCT and LCVT were significantly higher in the poor prognosis group than in the good prognosis group (P＜0.05). Cohen's d effect size analysis showed that the intergroup difference in LCVT was greater than that in SFCT. ROC curve analysis showed that pre-treatment SFCT and LCVT effectively predicted early complete SRF absorption, and LCVT had significantly better diagnostic performance than SFCT [area under the ROC curve (AUC)=0.82, 0.76; P=0.025]. Joint modeling analysis showed that longitudinal changes in SFCT and LCVT were significantly associated with the risk of poor prognosis (P＜0.01); for every 50 μm increase in LCVT and SFCT, the relative risk of poor prognosis was 1.615 and 1.512, respectively. The Akaike information criterion value of the LCVT joint model (3 719.42) was lower than that of the SFCT model (3 852.67). At 1 month after treatment, ΔLCVT and ΔSFCT were both significant predictors of poor prognosis; in all adjusted models, the odds ratio of ΔLCVT was lower than that of ΔSFCT, indicating better predictive value. ROC curve analysis showed that both ΔSFCT and ΔLCVT had significant predictive value for poor prognosis, and ΔLCVT had significantly better predictive performance than ΔSFCT (AUC=0.81, 0.74). DCA showed that both ΔSFCT and ΔLCVT provided clinical net benefit, with ΔLCVT showing higher benefit. Mediation analysis showed that ΔLCVT and ΔSFCT partially improved prognosis by promoting early SRF absorption, with the indirect effect of ΔLCVT (39.35%) being higher than that of ΔSFCT (32.70%). ConclusionsBoth SFCT and LCVT can serve as predictors of prognosis in PCV eyes treated with anti-VEGF therapy. After controlling for the effect of SFCT, LCVT maintains independent predictive value, and its predictive efficacy is superior to that of SFCT.
ObjectiveTo systematically summarize recent advancements in the application of artificial intelligence (AI) in key components of radiotherapy (RT), explore the integration of technical innovations with clinical practice, and identify current limitations in real-world implementation. MethodsA comprehensive analysis of representative studies from recent years was conducted, focusing on the technical implementation and clinical effectiveness of AI in image reconstruction, automatic delineation of target volumes and organs at risk, intelligent treatment planning, and prediction of RT-related toxicities. Particular attention was given to deep learning models, multimodal data integration, and their roles in enhancing decision-making processes. ResultsAI-based low-dose image enhancement techniques had significantly improved image quality. Automated segmentation methods had increased the efficiency and consistency of contouring. Both knowledge-driven and data-driven planning systems had addressed the limitations of traditional experience-dependent approaches, contributing to higher quality and reproducibility in treatment plans. Additionally, toxicity prediction models that incorporated multimodal data enabled more accurate, personalized risk assessment, supporting safer and more effective individualized RT. ConclusionsRT is a fundamental modality in cancer treatment. However, achieving precise tumor ablation while minimizing damage to surrounding healthy tissues remains a significant challenge. AI has demonstrated considerable value across multiple technical stages of RT, enhancing precision, efficiency, and personalization. Nevertheless, challenges such as limited model generalizability, lack of data standardization, and insufficient clinical validation persist. Future work should emphasize the alignment of algorithmic development with clinical demands to facilitate the standardized, reliable, and practical application of AI in RT.