Objective: To investigate the role of butyric acid-producing bacteria in long bone homeostasis in mice with periodontitis under a high-fat/high-sugar diet and to provide new insights for the prevention and treatment of periodontitis and related bone metabolic diseases. Methods: This study has been approved by the Animal Welfare and Ethics Committee of the Experimental Animal Center. Initially, 14 mice were randomly divided into the CON group (the control group) and the LIG group (the periodontitis group). Mice in the LIG group had experimental periodontitis induced by ligating the second maxillary molars bilaterally and were fed a high-fat and high-sugar diet. After 8 weeks, samples were collected. Micro-computed tomography (Micro-CT) was used to analyze alveolar bone resorption and various parameters of the proximal tibia trabecular bone, including bone mineral density (BMD), bone volume per tissue volume (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), and trabecular separation (Tb.Sp). After decalcification, hematoxylin and eosin (HE) staining was performed on maxillary bone sections to assess periodontal tissue inflammation and connective tissue destruction. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect related genes in the distal femur and proximal tibia bone tissues, including osteocalcin (OCN), osteogenic transcription factor (Osterix), osteoprotegerin (OPG), tartrate resistant acid phosphatase (TRAP), osteoclast-associated receptor (OSCAR), receptor activator of nuclear factor kappa-B (RANK), and receptor activator of nuclear factor kappa-B ligand (RANK-L). Subsequently, the other 28 mice were randomly divided into the CON group (the control group), LIG group (the periodontitis group), CON + butyric acid-producing bacteria (BP) group, and LIG + BP group. The breeding, sampling, and sample detection methods remained the same. Finally, the other 28 mice were randomly divided into the CON group (the control group), LIG group (the periodontitis group), CON + sodium butyrate (SB) group, and LIG + SB group. The breeding, sampling, and sample detection methods remained the same. Results: ①Periodontitis modeling was successful. Compared with the CON group, the LIG group exhibited significant alveolar bone resorption of the maxillary second molar, aggravated periodontal tissue inflammation, and connective tissue destruction. ②Periodontitis exacerbated long bone resorption in mice fed a high-fat high-sugar diet. Compared with the CON group, the LIG group had significantly lower BMD, BV/TV, Tb.N, and Tb.Th (P<0.05), and significantly higher Tb.Sp (P<0.05). HE staining of the proximal tibia showed that the trabeculae in the LIG group were sparse and disordered, with some areas showing fractures or dissolution. The expression of osteoblast markers (OCN, Osterix, OPG) was significantly lower in the LIG group (P<0.05), while the expression of the osteoclast marker TRAP showed an increasing trend (P>0.05). The ratio of RANK-L/OPG was significantly higher in the LIG group compared with the CON group (P<0.05). ③ Supplementation with butyric acid-producing bacteria alleviates periodontitis-induced disruption of long bone homeostasis in mice fed a high-fat/high-sugar diet. Compared with the LIG group, BMD and Tb.Th were significantly higher in the LIG + BP group. HE staining of the proximal tibia showed that bone resorption was mitigated in the LIG + BP group compared with the LIG group. The expression of OCN and Osterix was significantly higher in the LIG + BP group, while the expression of osteoclast-specific genes (OSCAR, RANK, RANK-L) was significantly lower (P<0.05). ④ Supplementation with butyrate alleviates periodontitis-induced disruption of long bone homeostasis in mice fed a high-fat/high-sugar diet. Compared with the LIG group, BV/TV and Tb.N were significantly higher in the LIG + SB group, and Tb.Sp was significantly lower (P<0.05). HE staining of the proximal tibia showed that bone resorption was mitigated in the LIG + SB group compared with the LIG group. The expression of Osterix, OPG, OSCAR, TRAP, and RANK was significantly lower in the LIG + SB group compared with the LIG group (P<0.05). Conclusion Periodontitis disrupts the long bone homeostasis of mice fed a high-fat high-sugar diet, aggravating long bone resorption. Supplementation with butyric acid-producing bacteria or butyrate can effectively alleviate the disruption of long bone homeostasis caused by periodontitis.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective:To compare the differences in clinical characteristics among the patients at clinical high risk for bipolar disorder(CHR-BD),the patients with bipolar disorder(BD),and the healthy controls(HC)at low risk,and to provide the basis for the diognasis and treatment of CHR-BD.Methods:For the first time,the BD risk criteria and prospective structured assessment tools were jointly used in outpatients aged 16-30 years,and 43 CHR-BD patients were included to ensure the accuracy of the assessment.Meanwhile,33 BD patients and 32 HC subjects were also enrolled.The clinical symptoms,neurocognitive function,and global functional levels of the subjects in the three groups were evaluated using observer-rated and self-rated tools.The CHR-BD and BD groups were combined,and Logistic regression analysis was used to identify the independent influencing factors related to diagnostic status;Pearson or Spearman correlation analysis was used to analyze the correlations between the global functional levels and the symptoms or neurocognitive characteristics of the patients in CHR-BD and BD groups.Results:There were statistically significant differences in the scores of symptom and global functional level scales among HC,CHR-BD,and BD groups(P<0.05).Compared with HC group,the scores of mood symptoms(anxiety,depression,and mania/hypomania),psychotic symptoms,total affective temperament questionnaire scores,and some dimensions(cyclothymic,depressive,irritable,and anxious temperaments)in CHR-BD and BD groups were significantly increased(P<0.001),while the global functional levels were significantly decreased(P<0.001).Compared with BD group,the lowest global functional level score in the past year in CHR-BD group was significantly increased(P=0.022),while the current global functional level score was significantly decreased(P=0.005).No significant differences were observed in neurocognitive function scores among the three groups(P>0.05).The lowest global functional level score in the past year was an independent influencing factor for BD diagnosis[odds ratio(OR)=0.952,95%confidence interval(CI):0.917-0.988,P=0.010].In both CHR-BD and BD patients,the current global functional levels were negatively correlated with depressive(r=-0.417,P=0.005;r=-0.617,P<0.001)and anxiety symptoms(r=-0.360,P=0.018;r=-0.506,P=0.003).In BD patients,the current global functional level was negatively correlated with lifetime manic/hypomanic symptoms(r=-0.360,P=0.039),psychotic symptoms(r=-0.502,P=0.003),and affective temperament scores(r=-0.479,P=0.005),while the lowest global functional level in the past year was negatively correlated with lifetime manic/hypomanic symptoms(r=-0.391,P=0.024).Conclusion:CHR-BD patients share similar mood symptom characteristics with BD patients,and their global functional levels are negatively correlated with depressive and anxiety symptoms.BD patients exhibit worse lowest global functional levels in the past year,and their global functional levels are negatively correlated with manic/hypomanic symptoms.

AIM: To investigate the effects of Conbercept on various optical coherence tomography(OCT)biomarkers in patients with retinal vein occlusion-related macular edema(RVO-ME), and to analyze the correlation of these biomarker changes with visual prognosis.METHODS: Retrospective study. A total of 57 patients(57 eyes)with RVO-ME, including 25 patients(25 eyes)with central retinal vein occlusion(CRVO)and 32 patients(32 eyes)with branch retinal vein occlusion(BRVO), were enrolled in this study. All the patients received intravitreal injection of conbercept once a month, three times in total. The preoperative and postoperative best-corrected visual acuity(BCVA), and changes in OCT biomarkers, including central macular thickness(CMT), the length of disorganization of the retinal inner layers(DRIL), the number of hyperreflective dots(HRD), the area of intraretinal fluid(IRF), the area of subretinal fluid(SRF), and the length of ellipsoid zone(EZ)disruption were compared. Furthermore, the relationship of these changes with BCVA was analyzed.RESULTS:Compared with the baseline, at 3 mo post-treatment, BCVA(LogMAR)was improved, CMT was decreased, the length of DRIL was shortened, the number of HRD was reduced, the area of IRF was decreased, the area of SRF was reduced, and the length of EZ disruption was shortened(all P<0.05). Spearman correlation analysis showed that there was no correlation between the changes in CMT, the length of DRIL, the number of HRD, the area of IRF, the area of SRF and the change in BCVA before and after treatment(P>0.05). However, the change in the length of EZ disruption was positively correlated with the change in BCVA(rs=0.34, P=0.011), and the R2 value of the fitting curve between the change in the length of EZ disruption and the change in BCVA was 0.113(P=0.011). When comparing the pre- and post-treatment changes in BCVA, the length of DRIL, the number of HRD, the area of IRF, the area of SRF, and the length of EZ disruption between patients in the CRVO group and BRVO group, no significant differences were observed(all P>0.05). In contrast, a significant difference was found in the change in CMT between the two groups(P=0.002).CONCLUSION:Conbercept effectively improves multiple OCT biomarkers in patients with RVO-ME. Repair of EZ disruption is a key driver of visual recovery, and its stability may serve as a novel indicator for personalized decision-making in anti-vascular endothelial growth factor therapy.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Ferroptosis is a form of cell death elicited by an imbalance in intracellular iron concentrations, leading to enhanced lipid peroxidation. In neurological disorders, both oxidative stress and mitochondrial damage can contribute to ferroptosis, resulting in nerve cell dysfunction and death. The ubiquitin-proteasome system (UPS) refers to a cellular pathway in which specific proteins are tagged with ubiquitin for recognition and degradation by the proteasome. In neurological conditions, the UPS plays a significant role in regulating ferroptosis. In this review, we outline how the UPS regulates iron metabolism, ferroptosis, and their interplay in neurological diseases. In addition, we discuss the future application of small-molecule inhibitors and identify potential drug targets. Further investigation into the mechanisms of UPS-mediated ferroptosis will provide novel insights and strategies for therapeutic interventions and clinical applications in neurological diseases.
Ferroptosis/physiology* ; Humans ; Proteasome Endopeptidase Complex/metabolism* ; Nervous System Diseases/metabolism* ; Animals ; Ubiquitin/metabolism* ; Iron/metabolism*

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child