Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

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Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

ObjectiveTo establish a risk recognition model for coronary artery stenosis by using a machine learning method and to identify the key causative factors. MethodsPatients aged ≥18 years，diagnosed with coronary heart disease through coronary angiography from January 2013 to May 2020 in two prominent hospitals in Shanxi Province， were continuously enrolled. Logistic regression，back propagation neural network （BPNN）， and random forest（RF）algorithms were used to construct models for detecting the causative factors of coronary artery stenosis. Sensitivity （TPR）， specificity （TNR）， accuracy （ACC）， positive predictive value （PV+）， negative predictive value （PV-）， area under subject operating characteristic curve （AUC）， and calibration curve were used to compare the discrimination and calibration performance of the models. The best model was then employed to predict the main risk variables associated with coronary stenosis. ResultsThe RF model exhibited superior comprehensive performance compared to logistic regression and BPNN models. The TPR values for logistic regression，BPNN，and RF models were 75.76%， 74.30%， and 93.70%， while ACC values were 74.05%， 72.30%， and 79.49%， respectively. The AUC values were：logistic regression 0.739 9； BPNN 0.723 1； RF 0.752 2. Manifestations such as chest pains，abnormal ST segments on ECG，ventricular premature beats with hypertension， atrial fibrillation， regional wall motion abnormalities（RWMA） by color echocardiography， aortic regurgitation（AR）， pulmonary insufficiency （PI）， family history of cardiovascular diseases，and body mass index（BMI）were identified as top ten important variables affecting coronary stenosis according to the RF model. ConclusionsRandom forest model shows the best comprehensive performance in identification and accurate assessment of coronary artery stenosis. The prediction of risk factors affecting coronary artery stenosis can provide a scientific basis for clinical intervention and help to formulate further diagnosis and treatment strategies so as to delay the disease progression.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

OBJECTIVE: To observe the effects of electroacupuncture (EA) at "Shenting" (GV24) and "Baihui" (GV20) on mitochondrial autophagy in hippocampal neurons and silent information regulator sirtuin 1 (Sirt1)/forkhead box O3 (FOXO3)/PTEN-inducible kinase 1 (PINK1)/Parkin pathway in rats with learning-memory impairment after cerebral ischemia reperfusion injury. METHODS: A total of 35 male SD rats were randomly divided into a sham operation group (9 rats) and a modeling group (26 rats). In the modeling group, middle cerebral artery occlusion method was used to establish the middle cerebral artery ischemia-reperfusion (MCAO/R) model, and 18 rats of successful modeling were randomly divided into a model group and an EA group, 9 rats in each one. EA was applied at "Shenting" (GV24) and "Baihui" (GV20) in the EA group, 30 min a time, once a day for 14 days. After modeling and on 7th and 14th days of intervention, neurologic deficit score was observed; the learning-memory ability was detected by Morris water maze test; the morphology of neurons in CA1 area of hippocampus was detected by Nissl staining; the mitochondrial morphology was observed by transmission electron microscopy; the protein expression of Beclin-1, microtubule-associated protein 1 light chain 3B (LC3B), P62, Sitrt1, FOXO3, PINK1 and Parkin was detected by Western blot. RESULTS: After modeling, the neurologic deficit scores in the model group and the EA group were higher than that in the sham operation group (P<0.001); on 7th and 14th days of intervention, the neurologic deficit scores in the model group were higher than those in the sham operation group (P<0.001), the neurologic deficit scores in the EA group were lower than those in the model group (P<0.05, P<0.01). After modeling, the escape latency in the model group and the EA group was prolonged compared with that in the sham operation group (P<0.001); on 9th-13th days of intervention, the escape latency in the model group was prolonged compared with that in the sham operation group (P<0.001), the escape latency in the EA group was shortened compared with that in the model group (P<0.05, P<0.01, P<0.001). The number of crossing plateau in the model group was less than that in the sham operation group (P<0.001); the number of crossing plateau in the EA group was more than that in the model group (P<0.05). In the model group, in CA1 area of hippocampus, the number of neurons was less, with sparse arrangement, nuclear fixation, deep cytoplasmic staining, and reduction of Nissl substance; the morphology of mitochondrion was swollen, membrane structure was fragmented, and autophagic lysosomes were formed. Compared with the model group, in the EA group, in CA1 area of hippocampus, the number of neurons was increased, the number of cells of abnormal morphology was decreased, and the number of Nissl substance was increased; the morphology of mitochondrion was more intact and the number of autophagic lysosomes was increased. Compared with the sham operation group, in the model group, the protein expression of Beclin-1, FOXO3, PINK1, Parkin and the LC3BⅡ/Ⅰ ratio in hippocampus were increased (P<0.01, P<0.001), while the protein expression of P62 was decreased (P<0.05). Compared with the model group, in the EA group, the protein expression of Beclin-1, Sirt1, FOXO3, PINK1, Parkin and the LC3BⅡ/Ⅰratio in hippocampus were increased (P<0.001, P<0.01), while the protein expression of P62 was decreased (P<0.001). CONCLUSION EA at "Shenting" (GV24) and "Baihui" (GV20) can relieve the symptoms of neurological deficits and improve the learning-memory ability in MCAO/R rats, its mechanism may relate to the modulation of Sirt1/FOXO3/PINK1/Parkin pathway and the enhancement of mitochondrial autophagy.
Animals ; Electroacupuncture ; Male ; Rats, Sprague-Dawley ; Rats ; Forkhead Box Protein O3/genetics* ; Reperfusion Injury/metabolism* ; Ubiquitin-Protein Ligases/genetics* ; Brain Ischemia/complications* ; Mitochondria/genetics* ; Autophagy ; Protein Kinases/genetics* ; Sirtuin 1/genetics* ; Humans ; Memory Disorders/psychology* ; Signal Transduction

BACKGROUND: G protein-coupled receptor kinase 2 (GRK2) could participate in the regulation of diverse cells via interacting with non-G-protein-coupled receptors. In the present work, we explored how paroxetine, a GRK2 inhibitor, modulates the differentiation and activation of immune cells in rheumatoid arthritis (RA). METHODS: The blood samples of healthy individuals and RA patients were collected between July 2021 and March 2022 from the First Affiliated Hospital of Anhui Medical University. C57BL/6 mice were used to induce the collagen-induced arthritis (CIA) model. Flow cytometry analysis was used to characterize the differentiation and function of dendritic cells (DCs)/T cells. Co-immunoprecipitation was used to explore the specific molecular mechanism. RESULTS: In patients with RA, high expression of GRK2 in peripheral blood lymphocytes, accompanied by the increases of phosphatidylinositol 3 kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR). In animal model, a decrease in regulatory T cells (T regs ), an increase in the cluster of differentiation 8 positive (CD8 + ) T cells, and maturation of DCs were observed. Paroxetine, when used in vitro and in CIA mice, restrained the maturation of DCs and the differentiation of CD8 + T cells, and induced the proportion of T regs . Paroxetine inhibited the secretion of pro-inflammatory cytokines, the expression of C-C motif chemokine receptor 7 in DCs and T cells. Simultaneously, paroxetine upregulated the expression of programmed death ligand 1, and anti-inflammatory cytokines. Additionally, paroxetine inhibited the PI3K-AKT-mTOR metabolic pathway in both DCs and T cells. This was associated with a reduction in mitochondrial membrane potential and changes in the utilization of glucose and lipids, particularly in DCs. Paroxetine reversed PI3K-AKT pathway activation induced by 740 Y-P (a PI3K agonist) through inhibiting the interaction between GRK2 and PI3K in DCs and T cells. CONCLUSION Paroxetine exerts an immunosuppressive effect by targeting GRK2, which subsequently inhibits the metabolism-related PI3K-AKT-mTOR pathway of DCs and T cells in RA.
G-Protein-Coupled Receptor Kinase 2/metabolism* ; Arthritis, Rheumatoid/immunology* ; Animals ; Dendritic Cells/metabolism* ; Paroxetine/therapeutic use* ; Proto-Oncogene Proteins c-akt/metabolism* ; Mice ; Humans ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Male ; Phosphatidylinositol 3-Kinases/metabolism* ; Lymphocyte Activation/drug effects* ; Female ; T-Lymphocytes/metabolism* ; Middle Aged

Cellular senescence, stable cell cycle arrest that can be triggered in normal cells in response to various intrinsic and extrinsic stressors, has been highlighted as one of the most important mechanisms involved in kidney diseases. It not only serves as a fundamental biological process promoting normal organogenesis and successful wound repair but also contributes to organ dysfunction, tissue fibrosis, and the generalized aging phenotype. Moreover, senescent cells exhibit reduced regenerative capacity, which impairs renal function recovery from injuries. Importantly, senescent cells are involved in immune regulation via secreting a diverse array of proinflammatory and profibrotic factors known as senescence-associated secretory phenotype (SASP) with autocrine, paracrine, and endocrine activities. Thus, eliminating detrimental senescent cells or inhibiting SASP production holds great promise for developing innovative therapeutic strategies for kidney diseases. In this review, we summarize the current knowledge of the intricate mechanisms and hallmarks of cellular senescence in kidney diseases and emphasize novel therapeutic targets, including epigenetic regulators, G protein-coupled receptors, and lysosome-related proteins. Particularly, we highlight the recently identified senotherapeutics, which provide new therapeutic strategies for treating kidney diseases.
Humans ; Cellular Senescence/genetics* ; Kidney Diseases/pathology* ; Senescence-Associated Secretory Phenotype/physiology* ; Animals ; Epigenesis, Genetic/physiology*