Objective: To investigate treatment strategies for chronic periapical periodontitis in prematurely erupted premolars and provide guidance for managing pulp and periapical diseases in young permanent teeth with immature roots. Methods: A regenerative endodontic procedure (REP) was performed on a prematurely erupted maxillary left first premolar (tooth 24) at Nolla stage Ⅶ with chronic apical periodontitis, following standardized protocols including root canal irrigation, disinfection, and coronal sealing. The case was followed up, and a literature review was conducted. Results: Clinical resolution of symptoms was observed on tooth 24, with sustained root development. After a 20-month follow-up, the tooth had restored biological function. Literature synthesis revealed that periapical infections in prematurely erupted permanent teeth predominently arise from pulp exposure and bacterial infection, with retrograde infection being rare. For young permanent teeth with necrotic pulp, regenerative endodontic procedures has been established as the treatment of choice to promote apical closure and root maturation. The critical steps of regenerative endodontic procedures include thorough disinfection, induced bleeding to form a fibrin scaffold, and coronal sealing to facilitate stem cell recruitment and differentiation. Conclusion Regenerative endodontic procedures represents an effective and viable treatment option for prematurely erupted young permanent teeth with chronic periapical periodontitis.

BACKGROUND: In the interim analysis of MONARCH plus, adding abemaciclib to endocrine therapy (ET) improved progression-free survival (PFS) and objective response rate (ORR) in predominantly Chinese postmenopausal women with HR+/HER2- advanced breast cancer (ABC). This study presents the final pre-planned PFS analysis. METHODS: In the phase III MONARCH plus study, postmenopausal women in China, India, Brazil, and South Africa with HR+/HER2- ABC without prior systemic therapy in an advanced setting (cohort A) or progression on prior ET (cohort B) were randomized (2:1) to abemaciclib (150 mg twice daily [BID]) or placebo plus: anastrozole (1.0 mg/day) or letrozole (2.5 mg/day) (cohort A) or fulvestrant (500 mg on days 1 and 15 of cycle 1 and then on day 1 of each subsequent cycle) (cohort B). The primary endpoint was PFS of cohort A. Secondary endpoints included cohort B PFS (key secondary endpoint), ORR, overall survival (OS), safety, and health-related quality of life (HRQoL). RESULTS: In cohort A (abemaciclib: n  = 207; placebo: n  = 99), abemaciclib plus a non-steroidal aromatase inhibitor improved median PFS vs . placebo (28.27 months vs . 14.73 months, hazard ratio [HR]: 0.476; 95% confidence interval [95% CI]: 0.348-0.649). In cohort B (abemaciclib: n  = 104; placebo: n  = 53), abemaciclib plus fulvestrant improved median PFS vs . placebo (11.41 months vs . 5.59 months, HR: 0.480; 95% CI: 0.322-0.715). Abemaciclib numerically improved ORR. Although immature, a trend toward OS benefit with abemaciclib was observed (cohort A: HR: 0.893, 95% CI: 0.553-1.443; cohort B: HR: 0.512, 95% CI: 0.281-0.931). The most frequent grade ≥3 adverse events in the abemaciclib arms were neutropenia, leukopenia, anemia (both cohorts), and lymphocytopenia (cohort B). Abemaciclib did not cause clinically meaningful changes in patient-reported global health, functioning, or most symptoms vs . placebo. CONCLUSIONS: Abemaciclib plus ET led to improvements in PFS and ORR, a manageable safety profile, and sustained HRQoL, providing clinical benefit without a high toxicity burden or reduced quality of life. TRIAL REGISTRATION ClinicalTrials.gov (NCT02763566).
Humans ; Female ; Fulvestrant/therapeutic use* ; Breast Neoplasms/metabolism* ; Aminopyridines/therapeutic use* ; Benzimidazoles/therapeutic use* ; Middle Aged ; Aromatase Inhibitors/therapeutic use* ; Aged ; Receptor, ErbB-2/metabolism* ; Adult ; Letrozole/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Anastrozole/therapeutic use*

The existing epilepsy seizure detection algorithms have problems such as overfitting and poor generalization ability due to high reliance on manual labeling of electroencephalogram's data and data imbalance between seizure and interictal periods. An unsupervised learning detection method for epileptic seizure that jointed graph attention network (GAT) and Transformer framework (GAT-T) was proposed. In this method, channel correlations were adaptively learned by GAT encoder. Temporal information was captured by one-dimensional convolution decoder. Combining outputs of the two mentioned above, predicted values for electroencephalogram were generated. The collective anomaly score was calculated and the detection threshold was determined. The results demonstrated that GAT-T achieved the average performance exceeding 90% (or 99%) with a 0.25 s (or 2 s) time segment length, which could effectively detect epileptic seizures. Moreover, the channel association probability matrix was expected to assist clinicians in the initial screening of the epileptogenic zone, and ablation experiments also reflected the significance of each module in GAT-T. This study may assist clinicians in making more accurate diagnostic and therapeutic decisions for epilepsy patients.
Humans ; Electroencephalography/methods* ; Epilepsy/physiopathology* ; Algorithms ; Seizures/physiopathology* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted

Efferocytosis refers to the process of phagocytes engulfing and clearing the cells after programmed cell death. In recent years, an increasing number of studies have shown that the mechanisms of efferocytosis are closely related to drug-induced liver injury, hepatic ischemia-reperfusion injury, viral hepatitis, cholestatic liver diseases, metabolic-associated fatty liver disease, alcoholic liver disease, and other liver disorders. This review summarized the research progress on the role of efferocytosis in liver diseases, with the hope of providing new targets for the prevention and treatment of liver diseases.
Humans ; Liver Diseases/metabolism* ; Animals ; Phagocytosis/physiology* ; Phagocytes ; Efferocytosis

It has been popular and challenging to undertake researches on the delay of acquired resistance of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). As key cells for tumor initiation, cancer stem cells (CSC) play an important role in the process of resistance to EGFR-TKI. Although preliminary studies found that traditional Chinese medicine (TCM) could inhibit CSC properties and delay EGFR-TKI resistance, the specific molecular mechanism remains unclear. By summarizing the empirical syndrome treatment of EGFR-TKI resistance via TCM and combining recent researches on TCM intervention in CSC to delay EGFR-TKI resistance, this review discussed the potential molecular pathways and mechanisms of deceleration in resistance to EGFR-TKI by TCM via inhibiting CSC characteristics, in order to expand the research ideas of TCM in combination with targeted therapy.
Humans ; Neoplastic Stem Cells/metabolism* ; Drug Resistance, Neoplasm/drug effects* ; ErbB Receptors/genetics* ; Protein Kinase Inhibitors/therapeutic use* ; Medicine, Chinese Traditional ; Neoplasms/drug therapy* ; Animals ; Drugs, Chinese Herbal/therapeutic use*

The connection between tendons and bones is called the tendon bone connection. With the continuous improvement of national sports awareness, excessive exercises and the related intensity are prone to damage the tendon bone connection. Tendon bone healing is a complex repair and healing process involving multiple factors, and good tendon bone healing is a prerequisite for its physiological function. The complexity of tendon bone structure also poses great challenges to the repair of tendon bone injuries. In recent years, researches have found that stem cells, growth factors, macrophages, and other factors are closely related to the healing process of tendon bone injuries, among which macrophages play an important role in the healing process. The authors reviewed relevant research literature in recent years and summarized the role of macrophages in tendon bone healing, in order to provide new ideas and directions for treatment strategies to promote tendon bone healing.
Humans ; Macrophages/metabolism* ; Wound Healing ; Animals ; Tendons/physiology* ; Bone and Bones/injuries* ; Tendon Injuries

Hepatocellular carcinoma (HCC) is one of the fastest growing cancers in the world, ranking fourth among the causes of cancer-induced death in the world. At present, the field of HCC treatment is developing rapidly, and immunotherapy has been recognized as a promising treatment method, in which T cells play a key role in HCC immunotherapy. However, in the case of virus infection or in tumor microenvironment (TME), T cells will be continuously stimulated by antigens and then fall into the state of T cell exhaustion (Tex). This state will not only reduce the immunity of patients but also lead to poor efficacy of immunotherapy. Therefore, to deeply analyze the mechanism of Tex and to explore effective strategies to reverse Tex is the key point in the immunotherapy for HCC. This review aims to summarize the mechanism of Tex in HCC patients, and the current situation and shortcomings of drug research and development to reverse Tex at this stage, in order to provide theoretical basis for the optimization of immunotherapy regimen for HCC patients.
Humans ; Carcinoma, Hepatocellular/therapy* ; Liver Neoplasms/therapy* ; Immunotherapy/methods* ; T-Lymphocytes/immunology* ; Tumor Microenvironment/immunology* ; Animals ; T-Cell Exhaustion

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

Objective Zinc finger protein 335 (Zfp335) plays a crucial role in the early development of thymic T cells and the differentiation of peripheral T cell subpopulations. The objective of this study is to investigate the role and underlying mechanisms of Zfp335 in the regulation of regulatory T cell (Treg) within tumor immunity. Methods The Zfp335 gene was specifically knocked out in Treg using tamoxifen (Zfp335^fl/fl FOXP3^creERT2), and the MC38 tumor model was established. On the 7th day after tumor inoculation, tumor size was observed and measured. Tumor size was monitored and recorded daily starting from day 7 post-inoculation. On day 12, tumors were harvested, and the proportions of CD4⁺ T cells, CD8⁺ T cells, and Treg were analyzed by flow cytometry. Additionally, the mitochondrial function of effector regulatory T cell (eTreg) was assessed. Results From day 10 post-tumor inoculation, tumor volume in the Zfp335^CKO group was significantly reduced compared to that of the wild-type (WT) group. Furthermore, the infiltration of CD4⁺ and CD8⁺ T cells, along with their respective effector cells, was significantly higher in the Zfp335^CKO group than in the WT group. The proportions of CD4⁺ and CD8⁺ T cells producing interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) were also significantly increased in the Zfp335^CKO group compared to that of the WT group. In addition, the percentage of CD8⁺ T cells secreting granzyme B (GzmB) was significantly higher in the Zfp335^CKO group than that in the WT group. In contrast, the proportion of Treg and inducible T cell co-stimulator (ICOS)⁺ Treg in the Zfp335^CKO group was significantly lower than that in the WT group. Finally, the expression level of Mitotracker Deep Red in eTreg from the Zfp335^CKO group was significantly reduced compared to that in the WT group. Conclusion During tumorigenesis, the specific deletion of Zfp335 impairs Treg activation, which is related to decreased mitochondrial function in eTreg. In Zfp335^CKO mice. Tumors exhibit increased infiltration of effector T cells, accompanied by elevated levels of cytotoxic cytokines, ultimately enhancing resistance to tumor progression.
Animals ; T-Lymphocytes, Regulatory/metabolism* ; Mice ; CD8-Positive T-Lymphocytes/immunology* ; Neoplasms/genetics* ; Cell Line, Tumor ; Mice, Inbred C57BL ; Mice, Knockout ; DNA-Binding Proteins/genetics* ; Female