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.

ObjectiveTo systematically investigate the identification characteristics of medicinal amber， elucidate its microscopic features， crystal structural properties， and elemental composition， and thereby provide a scientific foundation for quality control and authenticity verification. MethodsThirty-nine batches of amber samples were collected and analyzed through integrated techniques including morphological analysis， microscopic identification， powder X-ray diffraction （XRD）， Raman spectroscopy， Fourier-transform infrared （FTIR） spectroscopy， and inductively coupled plasma mass spectrometry （ICP-MS） to evaluate their morphological attributes， phase composition， molecular vibrational modes， and trace element profiles. Among them， the XRD experiment used Cu Kα radiation （λ=1.540 6 Å）， with a scanning angle range of 10° to 70° （2θ） and a step size of 0.02°， the Raman spectroscopy experiment employed a 785 nm laser， with a spectral measurement range of 3 400 to 50 cm^-1， a laser power of 300 mW， a laser intensity of 30%， and a scanning time of 100 to 1 000 ms， the infrared spectroscopy experiment used a carbon-sulfur lamp， with a scanning range of 4 000 to 500 cm^-1， a resolution of 4 cm^-1， and 3 scans， the ICP-MS experiment utilized frequency power of 1.2 kW， a double-pass cyclonic spray chamber， a sample introduction system flow rate of 0.7-1.0 L·min^-1， and an auxiliary gas flow of 0.2 L·min^-1. ResultsUnder orthogonal polarized light microscopy， medicinal amber exhibited an isotropic homogeneous structure， with partial samples containing inorganic impurities such as AsS and SiO₂. FTIR spectra revealed characteristic absorption peaks at 2 932-2 939 cm^-1 （C-H stretching vibrations）， 1 705-1 728 cm^-1 （C=O stretching vibrations）， and 880-887 cm^-1 （C=C deformation vibrations）， confirming the oxidative polymerization of terpenoid resin. Raman spectroscopy further identified distinctive peaks at 2 925 cm^-1， 2 870 cm^-1 （saturated C-H stretching）， and 1 648 cm^-1 （C=C stretching）， consistent with the structural features of oxidized-polymerized resin. ICP-MS analysis demonstrated that S， Al， Si， Fe， Na， and Ca were the predominant trace elements in medicinal amber. ConclusionThis study comprehensively evaluated medicinal amber's morphological attributes， phase composition， molecular vibrational modes， and trace elements through multimodal analytical techniques. The findings establish data support for establishing quality standards for medicinal amber and distinguishing it from synthetic resin imitations.

ObjectiveTo systematically investigate the identification characteristics of medicinal amber， elucidate its microscopic features， crystal structural properties， and elemental composition， and thereby provide a scientific foundation for quality control and authenticity verification. MethodsThirty-nine batches of amber samples were collected and analyzed through integrated techniques including morphological analysis， microscopic identification， powder X-ray diffraction （XRD）， Raman spectroscopy， Fourier-transform infrared （FTIR） spectroscopy， and inductively coupled plasma mass spectrometry （ICP-MS） to evaluate their morphological attributes， phase composition， molecular vibrational modes， and trace element profiles. Among them， the XRD experiment used Cu Kα radiation （λ=1.540 6 Å）， with a scanning angle range of 10° to 70° （2θ） and a step size of 0.02°， the Raman spectroscopy experiment employed a 785 nm laser， with a spectral measurement range of 3 400 to 50 cm^-1， a laser power of 300 mW， a laser intensity of 30%， and a scanning time of 100 to 1 000 ms， the infrared spectroscopy experiment used a carbon-sulfur lamp， with a scanning range of 4 000 to 500 cm^-1， a resolution of 4 cm^-1， and 3 scans， the ICP-MS experiment utilized frequency power of 1.2 kW， a double-pass cyclonic spray chamber， a sample introduction system flow rate of 0.7-1.0 L·min^-1， and an auxiliary gas flow of 0.2 L·min^-1. ResultsUnder orthogonal polarized light microscopy， medicinal amber exhibited an isotropic homogeneous structure， with partial samples containing inorganic impurities such as AsS and SiO₂. FTIR spectra revealed characteristic absorption peaks at 2 932-2 939 cm^-1 （C-H stretching vibrations）， 1 705-1 728 cm^-1 （C=O stretching vibrations）， and 880-887 cm^-1 （C=C deformation vibrations）， confirming the oxidative polymerization of terpenoid resin. Raman spectroscopy further identified distinctive peaks at 2 925 cm^-1， 2 870 cm^-1 （saturated C-H stretching）， and 1 648 cm^-1 （C=C stretching）， consistent with the structural features of oxidized-polymerized resin. ICP-MS analysis demonstrated that S， Al， Si， Fe， Na， and Ca were the predominant trace elements in medicinal amber. ConclusionThis study comprehensively evaluated medicinal amber's morphological attributes， phase composition， molecular vibrational modes， and trace elements through multimodal analytical techniques. The findings establish data support for establishing quality standards for medicinal amber and distinguishing it from synthetic resin imitations.

Persistent inflammation plays a pivotal role in the initiation and progression of renal fibrosis. Activation of the pattern recognition receptor retinoic acid-inducible gene-I (RIG-I) is implicated in the initiation of inflammation. This study aimed to investigate the upstream mechanisms that regulates the activation of RIG-I and its downstream signaling pathway. Eight-week-old male C57BL/6 mice were used to establish unilateral ureteral obstruction (UUO)-induced renal fibrosis model, and the renal tissue samples were collected 14 days later for analysis. Transforming growth factor-β (TGF-β)-treated mouse renal tubular epithelial cells were used in in vitro studies. The results demonstrated that, compared to the control group, UUO kidney exhibited significant fibrosis, which was accompanied by the increases of RIG-I, p-NF-κB p65 and inflammatory cytokines, such as TNF-α and IL-1β. Additionally, the protein level of the E3 ubiquitin ligase RNF125 was significantly downregulated and predominantly localized in the renal tubular epithelial cells. Similarly, the treatment of tubular cells with TGF-β induced the increases in RIG-I, p-NF-κB p65 and inflammatory cytokines while decreasing RNF125. Co-immunoprecipitation (Co-IP) assays confirmed that RNF125 was able to interact with RIG-I. Overexpression of RNF125 promoted the ubiquitination of RIG-I, and accelerated its degradation via the ubiquitin-proteasome pathway. Overexpression of RNF125 in UUO kidneys and in vitro tubular cells effectively mitigated the inflammatory response and renal fibrosis. In summary, our results demonstrated that the decrease in RNF125 under pathological conditions led to reduction in RIG-I ubiquitination and degradation, activation of the downstream NF-κB signaling pathway and increase in inflammatory cytokine production, which promoted the progression of renal fibrosis.
Animals ; Fibrosis ; Male ; Ubiquitination ; Mice ; Mice, Inbred C57BL ; DEAD Box Protein 58 ; Ubiquitin-Protein Ligases/physiology* ; Inflammation/metabolism* ; Ureteral Obstruction/complications* ; Kidney/pathology* ; Signal Transduction ; Transforming Growth Factor beta/pharmacology*

The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*