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: 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.

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: To elucidate the rules of temporal and spatial variations in distal skin potential at Hegu (LI4) under different stimulation modes by extracting nonlinear characteristic parameters from array multichannel data and adopting multivariate statistical analysis. METHODS: Seven healthy subjects were selected and the surface potential at the left Quchi (LI11) was collected using 14×9 array multichannel electrodes. Using Hegu (LI4) on the left as the stimulation point, four stimulation modes were applied, i.e. being quiescent, point pressing, moxibustion, and manual needling manipulation. Electrical signals were collected for 30 s in each mode, with a 5-min interval between operations, and a sampling frequency of 16 384 Hz. The data was denoised using ensemble empirical mode decomposition (EEMD), and sample entropy (SaEn) features were extracted. Statistical analysis was conducted on these data using factor analysis and multivariate analysis of variance. RESULTS: The SaEn values of most electrode channels were higher under point pressing, moxibustion and manual needling manipulation compared with those under quiescent condition. Under manual needling manipulation, the SaEn value of the electrode channel reached the peak in the first time interval (1-5 s) and it was declining thereafter. Factor analysis showed that the specificity of activation channels was concentrated at the left Quchi (LI11) (loading capacity ≥0.90). Analysis of variance indicated that the significant differences were presented in average sample entropy (SaEn()) values of activation channels among different stimulation modes at Hegu (LI4) (P<0.001), but there was no statistically significant interaction effect between groups and time intervals (P>0.05). CONCLUSION Through nonlinear characteristic parameter extraction and multivariate statistical analysis, we have uncovered the complex temporal and spatial dynamical rules of distal skin potential at Hegu (LI4) under various stimulation modes and successfully identified the specific activation characteristics at Quchi (LI11).
Humans ; Moxibustion ; Acupuncture Points ; Male ; Adult ; Female ; Young Adult ; Acupuncture Therapy/instrumentation*

OBJECTIVE: To explore the pain-location interaction between pressure-sensitive points on the body surface and traditional acupoints in patients with chronic non-specific low back pain (CNLBP) under different disease courses, using Euclidean distance and multivariate statistical analysis. METHODS: A pressure-sensitive point detection was performed on 30 CNLBP patients with varying disease courses. A constant pressure was applied using an FDK20 algometer within a designated lumbar area, a total of 50 points were tested, and the tested points were numbered; the visual analogue scale (VAS) pain score was recorded simultaneously. MatlabR2022a9.12. software was used to extract the positions of pressure-sensitive points, and preprocessing and normalization of point location and VAS scores data were conducted. Under constraint conditions (VAS≥8.0 ∩ Euclidean distance to acupoint≤0.5), the proportion of pressure-sensitive points within the Euclidean distance threshold to each acupoint (PVD_acupoint) was calculated, followed by multivariate statistical analysis. RESULTS: ①Constrained analysis of PVD_acupoint showed that PVD_{Qihaishu (BL24)} and PVD_{Dachangshu (BL25)} were positively correlated with disease course (r=0.55, P<0.01). ②Factor analysis and silhouette analysis revealed that PVD_{Shenshu (BL23)} and PVD_{Dachangshu (BL25)} exhibited trends consistent with disease course progression (P>0.05), with different degree (P<0.01). CONCLUSION The PVD_acupoint value based on Euclidean distance can characterize the pressure sensitivity features of traditional acupoints associated with disease. Multivariate statistical analysis of PVD_acupoint confirms that selecting the acupoint combination of Shenshu (BL23) and Dachangshu (BL25) for CNLBP is associated with the distribution of surrounding pressure-sensitive points and the pathological characteristics of the condition.
Humans ; Acupuncture Points ; Low Back Pain/physiopathology* ; Male ; Female ; Middle Aged ; Adult ; Aged ; Acupuncture Therapy ; Young Adult ; Pressure

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*