Microbial infections are a prevalent challenge in the prevention and treatment of oral diseases. Antibiotic therapy faces clinical limitations due to its single-target mechanism and tendency to induce resistance with repeated use, necessitating novel antibacterial strategies. Stimuli-responsive antibacterial materials, whose antimicrobial activity can be modulated by external stimuli, offer advantages such as remote controllability, potential for localized precision treatment, and a reduced risk of inducing resistance. Among these materials, mechanical force-triggered piezoelectric materials exhibit significant antibacterial activity in the biomedical field owing to their unique piezoelectric effect, excellent stability, and good biocompatibility. Research has shown that piezoelectric materials can convert mechanical energy into electrical energy in response to external forces, which enables antibacterial effects without requiring an external power source. The underlying mechanisms primarily include direct electric field effects, generation of reactive oxygen species, and immune modulation. Preliminary applications in treating oral infections (e.g., dental caries, periodontitis, and peri-implantitis) have confirmed their stability and biocompatibility, establishing a foundation for clinical translation. However, long-term efficacy and biosafety in the complex oral microenvironment require further validation. Future research should focus on optimizing material preparation protocols to enhance antibacterial efficacy and stability, further investigating the underlying antimicrobial mechanisms, and systematically evaluating their therapeutic outcomes and safety profiles across various types of oral infections. This review summarizes the antibacterial effects, mechanisms, stability, safety, and research progress of piezoelectric materials in the stomatologic field, aiming to provide new insights for further research and application in this area.

AIM: To investigate the changes of retinal nerve fiber layer(RNFL)thickness, retinal thickness and blood flow density in different stages of diabetic retinopathy(DR)patients based on optical coherence tomography angiography(OCTA).METHODS: A retrospective analysis was conducted on 382 patients(382 eyes)diagnosed with DR in our hospital from February 2023 to February 2024. According to the staging criteria, the patients were divided into mild group(n=121), moderate group(n=133), severe group(n=72), and proliferative group(n=56). The general clinical data of the four groups of patients was compared; OCTA was used to scan and collect data from all patients, and the RNFL thickness, retinal thickness, and blood flow density were compared among the four groups of patients.RESULTS: There was no statistically significant difference in age, gender, hypertension, chronic kidney disease, and random blood glucose among patients in the mild, moderate, severe, and proliferative groups(all P>0.05). As the stage of DR worsened, the duration of the disease gradually prolonged(P<0.05). The thickness of the RNFL(superior, inferior, temporal, nasal, and average thickness)and retinal thickness significantly increased with the severity of DR(all P<0.001); however, there was no statistically significant difference in inferior RNFL thickness between the moderate and mild groups(P>0.05). The blood flow density in the superficial and deep retinal layers, as well as in the choroidal capillary layer, significantly decreased with the progression of DR(all P<0.05). Nevertheless, there was no statistically significant difference in superficial retinal blood flow density between the moderate and severe groups(P>0.05).CONCLUSION: OCTA can accurately observe the changes in RNFL thickness, retinal thickness, and blood flow density in patients with DR at different stages, which can serve as sensitive indicators for monitoring DR progression.

Objective To investigate the role and mechanism of RNA-Specific adenosine deaminase 3 (Adar3) in regulating macrophage polarization during Coxsackievirus B3(CVB3)-induced viral myocarditis (VM). Methods Bone marrow-derived macrophages (BMDM) from mice were cultured in vitro and induced into M1/M2 macrophages using interferon-gamma (IFN-γ)/lipopolysaccharide (LPS) or interleukin 4 (IL-4), respectively. The mRNA expression levels of Adar1, Adar2, and Adar3 in each group of cells were assessed by real-time quantitative PCR (qRT-PCR). Specific siRNAs targeting the Adar3 gene were designed, synthesized, and transiently transfected into M2 macrophages. The mRNA levels of M2 polarization-related marker genes-including arginase 1 (Arg1), chitinase 3-like molecule 3 (YM1/Chi3l3), and resistin-like molecule alpha (RELMα/FIZZ1)-were detected by qRT-PCR. RNA sequencing was performed to analyze the signaling pathways affected by Adar3. The expression levels of Wnt/β-catenin signaling pathway were further validated using qRT-PCR and Western blot. The adeno-associated virus overexpressing Adar3 was designed, synthesized, and injected into mice via tail vein. Three weeks later, a myocarditis mouse model was established. After an additional week, the phenotype and function of cardiac macrophages, as well as multiple indicators of VM (including echocardiography, body weight, histopathology and serology) were examined. Additionally, the protein levels of the Wnt/β-catenin signaling pathway were assessed. Results Compared to M0-type macrophages, the expression level of Adar3 was significantly increased in M2-type macrophages. After transfection of Adar3 siRNA, the mRNA levels of Arg1, YM1 and FIZZ1 in M2 macrophages were downregulated. RNA sequencing revealed 149 upregulated genes and 349 downregulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and subsequent validation experiments indicated that Adar3 modulated the Wnt/β-catenin signaling pathway. In vivo experiments demonstrated that Adar3 overexpression alleviated the cardiac dysfunction of VM mice. The proportion of M1 macrophages in the heart decreased, while the proportion of M2 macrophages increased. At the same time, the Adar3 overexpression activated the Wnt/β-catenin signaling pathway. Conclusion Adar3 promotes macrophage polarization toward the M2 phenotype by activating the Wnt/β-catenin signaling pathway, thereby alleviating VM.
Animals ; Adenosine Deaminase/metabolism* ; Macrophages/immunology* ; Wnt Signaling Pathway/genetics* ; Myocarditis/immunology* ; Mice ; Coxsackievirus Infections/metabolism* ; Male ; Mice, Inbred BALB C ; Enterovirus B, Human/physiology* ; beta Catenin/genetics*

The underlying cause of low response rates to existing immunotherapies is that tumor cells dominate tumor immune escape through surface antigen deficiency and inducing tumor immunosuppressive microenvironment (TIME). Here, we proposed an in situ tumor cell engineering strategy to disrupt tumor immune escape at the root by restoring tumor cell MHC-I/tumor-specific antigen complex (MHC-I/TSA) expression to promote T-cell recognition and by silencing tumor cell CD55 to increase the ICOSL⁺ B-cell proportion and reverse the TIME. A doxorubicin (DOX) and dual-gene plasmid (MAC pDNA, encoding both MHC-I/ASMTNMELM and CD55-shRNA) coloaded drug delivery system (LCPN@ACD) with tumor targeting and charge/size dual-conversion properties was prepared. LCPN@ACD-induced ICD promoted DC maturation and enhanced T-cell activation and infiltration. LCPN@ACD enabled effective expression of MHC-I/TSA on tumor cells, increasing the ability of tumor cell recognition and killing. LCPN@ACD downregulated tumor cell CD55 expression, increased the proportion of ICOSL⁺ B cells and CTLs, and reversed the TIME, thus greatly improving the efficacy of αPD-1 and CAR-T therapies. The application of this in situ tumor cell engineering strategy eliminated the source of tumor immune escape, providing new ideas for solving the challenges of clinical immunotherapy.

OBJECTIVES: We propose a novel deep learning segmentation algorithm (DSRF) based on multi-scale supervision and residual feedback strategy for precise segmentation of the optic chiasm and optic nerves in CT images of nasopharyngeal carcinoma (NPC) patients. METHODS: We collected 212 NPC CT images and their ground truth labels from SegRap2023, StructSeg2019 and HaN-Seg2023 datasets. Based on a hybrid pooling strategy, we designed a decoder (HPS) to reduce small organ feature loss during pooling in convolutional neural networks. This decoder uses adaptive and average pooling to refine high-level semantic features, which are integrated with primary semantic features to enable network learning of finer feature details. We employed multi-scale deep supervision layers to learn rich multi-scale and multi-level semantic features under deep supervision, thereby enhancing boundary identification of the optic chiasm and optic nerves. A residual feedback module that enables multiple iterations of the network was designed for contrast enhancement of the optic chiasm and optic nerves in CT images by utilizing information from fuzzy boundaries and easily confused regions to iteratively refine segmentation results under supervision. The entire segmentation framework was optimized with the loss from each iteration to enhance segmentation accuracy and boundary clarity. Ablation experiments and comparative experiments were conducted to evaluate the effectiveness of each component and the performance of the proposed model. RESULTS: The DSRF algorithm could effectively enhance feature representation of small organs to achieve accurate segmentation of the optic chiasm and optic nerves with an average DSC of 0.837 and an ASSD of 0.351. Ablation experiments further verified the contributions of each component in the DSRF method. CONCLUSIONS The proposed deep learning segmentation algorithm can effectively enhance feature representation to achieve accurate segmentation of the optic chiasm and optic nerves in CT images of NPC.
Humans ; Tomography, X-Ray Computed/methods* ; Optic Chiasm/diagnostic imaging* ; Optic Nerve/diagnostic imaging* ; Algorithms ; Nasopharyngeal Carcinoma ; Deep Learning ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods*

In combined orthodontic-orthognathic treatment, the maxillary palatine suture is closed in most patients with insufficient maxillary width, and bony expansion of the maxilla cannot be achieved by dental expansion or rapid palatal expansion (RPE) which causes buccal inclination of the maxillary posterior teeth leading to unstable results. Therefore, segmental LeFort Ⅰ osteotomy and surgically assisted RPE are often used in clinical practice. In recent years, with the application of implant anchorage technology, implant anchorage assisted RPE has been gradually applied in orthognathic treatment. This article reviewed the indications, contraindications, complications, efficacy and long-term stability in different treatment approaches including segmental LeFort Ⅰ osteotomy, surgically assisted RPE and implant-supported maxillary skeletal expansion.

Objective To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.Methods We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone,tumor core,and whole tumor on T1,T2,and T1-weighted contrast-enhanced(T1CE)and fluid attenuated inversion recovery(FLAIR)sequences.The cases were divided into a training set(271 cases)and a test set(117 cases).Random survival forest algorithms were used to select the radiomics features associated with overall survival(OS)in the training set to construct a radiomic score(Rad-score),based on which the patients were classified into high-and low-risk groups for Kaplan-Meier survival analysis.Cox proportional hazard regression models for the 3 different tumor zones were constructed,and their performance for predicting 1-and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients.The best-performing model was used for constructing a nomogram for survival predictions.Results Five radiomics features from the tumor core,7 from the peritumoral edema zone,and 5 from the whole tumor were selected.In both the training and test sets,the high-and low-risk groups had significantly different OS(P<0.05),and age,IDH status and Rad-score were independent factors affecting OS.The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set,0.764 and 0.800 in the test set,and 0.938 and 0.917 in external validation,respectively.Conclusion The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Objective:To analyze the surveillance data of population in rabies exposure and post-exposure prophylaxis (PEP) clinics in Shandong province from 2019 to 2023, explore risk factors for exposure, and provide a basis for further standardizing PEP.Methods:Based on surveillance data from the rabies exposure treatment clinic vaccination information system, population, and spatial administrative division data in Shandong province from 2019 to 2023, descriptive epidemiological methods were applied to analyze using SPSS 18.0.Results:The number of individuals receiving post-exposure treatment at PEP clinics in Shandong province from 2019 to 2023 was over 1 million annually and showed an increasing trend year by year, with the highest number of visits occurring from May to August each year. The number of female patients is increasing year by year. The 15-year-old and younger age group accounted for 31.69%-36.86% of clinic visits. Grade Ⅲ exposures accounted for 53.40%, and limbs accounted for approximately 89.81% of exposed areas. Approximately 1.94% is multi site exposure. The main injuring animals were dogs, followed by cats, and the proportion of cats is increasing year by year. The proportion of doctor′s office visitors who self-treated wounds increased from 32.32% to 45.46%, while the proportion of outpatient wound treatment decreased from 76.07% to 66.24%. The whole course vaccination rate after initial exposure is 63.79%. The whole course vaccination rate for the 15-29-year-old age group was lower than that for other age groups. The usage rate of passive immune preparations among grade Ⅲ exposed individuals is 35.66%. From 2019 to 2023, 8 cases of rabies were reported, none of whom received standard post-exposure treatment.Conclusions:The epidemic characteristics and exposure risk of people exposed to suspected rabies animals in Shandong province have changed. It is necessary to pay attention to the female population, the population with low vaccination rate between 15 to 29 years of age, cat bitten people, and carry out continuous monitoring on the exposed population, and timely adjust the prevention and control strategies.

Objective To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.Methods We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone,tumor core,and whole tumor on T1,T2,and T1-weighted contrast-enhanced(T1CE)and fluid attenuated inversion recovery(FLAIR)sequences.The cases were divided into a training set(271 cases)and a test set(117 cases).Random survival forest algorithms were used to select the radiomics features associated with overall survival(OS)in the training set to construct a radiomic score(Rad-score),based on which the patients were classified into high-and low-risk groups for Kaplan-Meier survival analysis.Cox proportional hazard regression models for the 3 different tumor zones were constructed,and their performance for predicting 1-and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients.The best-performing model was used for constructing a nomogram for survival predictions.Results Five radiomics features from the tumor core,7 from the peritumoral edema zone,and 5 from the whole tumor were selected.In both the training and test sets,the high-and low-risk groups had significantly different OS(P<0.05),and age,IDH status and Rad-score were independent factors affecting OS.The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set,0.764 and 0.800 in the test set,and 0.938 and 0.917 in external validation,respectively.Conclusion The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Objective:To screen the characteristic gut microbiota and fecal metabolites related to the efficacy of oxaliplatin-based neoadjuvant chemotherapy in patients with colorectal cancer liver metastasis, to analyze the relationship between gut microbiota and fecal metabolites, and to evaluate the predictive value of relevant markers for the efficacy of neoadjuvant chemotherapy in patients with colorectal cancer liver metastasis.Methods:This is a case-control study, 34 patients with colorectal cancer liver metastasis who were treated in Qilu Hospital of Shandong University from October 2021 to July 2022 were selected as the research objects, and were divided into chemotherapy effective group (20 cases) and chemotherapy ineffective group (14 cases) according to the efficacy evaluation criteria. Logistic regression was used to construct a prediction model to screen the microbiota and metabolic markers capable of predicting the effect of chemotherapy, and the receiver operating characteristic (ROC) curve and survival analysis curve were plotted to evaluate the predictive effect of related microbiota and metabolites on the efficacy of neoadjuvant chemotherapy.Results:There was no significant difference in the α and β diversity of gut microbiota between the patients in the chemotherapy effective group and in the ineffective group (all P>0.05). In terms of species, the relative abundance of 5 species was up-regulated and 10 species were down-regulated in the chemotherapy-effective group compared with the chemotherapy-ineffective group, and the difference was statistically significant (all P<0.05), among which Prevotella salivae could effectively predict the chemotherapy effect (AUC=0.750, P=0.007), with a sensitivity of 80.0% and a specificity of 71.4%. The overall survival of patients with high abundance (17 cases) was lower than that of patients with low abundance (17 cases) ( χ 2=5.239, P=0.022). In terms of metabolites, 20 metabolites were up-regulated and 4 metabolites were down-regulated in the chemotherapy-effective group compared with the chemotherapy-ineffective group, and the difference was statistically significant (all P<0.05), among which threonine and prostaglandin F2α-1-ethanolamide could distinguish between patients who responded to chemotherapy and those who did not respond to chemotherapy (AUC=0.743, 0.707, all P<0.05), and the overall survival of patients with high levels of relative abundance (17 cases) was higher than that of patients with low levels (17 cases) ( χ 2=4.748, 5.407, all P<0.05). The Logistic regression model of Prevotella salivae and prostaglandin F2α-1-ethanolamide was obtained through screening analysis, and the ROC curve results showed that the model had a good predictive value (AUC=0.836, sensitivity: 90.0%, specificity: 78.6%), and the overall survival of patients with high predict probability (17 cases) predicted by the model was higher than that of patients with low predict probability (17 cases) ( χ 2=9.260, P=0.002). Conclusion:Prevotella salivae and prostaglandin F2α-1-ethanolamide can be used as predictive biomarkers of neoadjuvant chemotherapy for colorectal cancer liver metastasis, and the model has good clinical reference value for prognosis assessment of patients in this cohort.