Objective:To investigate the differences in brain age and brain age gap (BAG) between patients with prolonged disorders of consciousness (pDoC) and healthy controls (HC).Methods:A retrospective cross-sectional study was performed; 43 patients with pDoC admitted to Rehabilitation Medicine Center, Zhujiang Hospital, Southern Medical University from January 2020 to October 2022 were enrolled; 26 healthy volunteers recruited at the same time and 187 healthy subjects from the publicly available healthy control dataset Nathan Kline Institute-Rockland Sample (NKI-RS) were chosen as HC group. The clinical and imaging data of these subjects were collected. A brain age estimation model was constructed based on amplitude of low-frequency fluctuation (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) for healthy individuals, and the pDoC group was used as the test set. A two-sample t-test was used to compare the brain age and BAG differences between the pDoC group and HC group. Pearson correlation analysis was used to explore the correlation between BAG and coma recovery scale-revised (CRS-R) in the pDoC group. Results:The chronological age and estimated brain age in the HC group were (41.54±9.61) and (42.32±10.65) years, respectively, without significant difference ( t=-0.254, P=0.801). The chronological age and estimated brain age in the pDoC group were (49.91±12.03) and (62.79±15.00) years, respectively, with significant difference ( t=-4.341, P<0.001). The BAG in the HC and pDoC groups were (0.78±4.59) and (12.88±7.17) years, respectively, with significant difference ( t=-7.822, P<0.001). Correlation analysis results showed that in the pDoC patients, no correlation was noted between BAG and CRS-R score on the day of imaging scan or 6 months after the day of imaging scan ( r=0.090, P=0.738; r=0.205, P=0.674); no correlation was noted between BAG and difference in CRS-R score (difference value of CRS-R score 6 months after the day of imaging scan-CRS-R score on the day of imaging scan, r=0.246, P=0.687). Conclusion:Compared with the HC subjects, patients with pDoC exhibit an abnormal increase in brain age, suggesting that pDoC caused by severe brain injury may lead to accelerated brain aging.

Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state(VS)patients with different prognoses,and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis.Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University.Patients were stratified into good prognosis(n=22)and poor prognosis(n=30)at 1-year follow-up based on Coma Recovery Scale-Revised(CRS-R)scores.The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging,and for the first time,the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model.To optimize model construction,the least absolute shrinkage and selection operator regression was employed for feature screening,and synthetic minority over-sampling technique for data balancing.The prognostic prediction model was subsequently developed using a support vector machine algorithm and validated via leave-one-out cross-validation.Model performance was evaluated using area under receiver operating characteristic curve,along with accuracy,sensitivity,specificity,and F1 score metrics.Results Following LASSO regression feature screening,the pontine crossing tract,medial lemniscus,tapetum,splenium of corpus callosum,and visual subscale scores were identified as key predictors.A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients,achieving a high predictive performance(AUC=0.894).Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.

Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state(VS)patients with different prognoses,and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis.Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University.Patients were stratified into good prognosis(n=22)and poor prognosis(n=30)at 1-year follow-up based on Coma Recovery Scale-Revised(CRS-R)scores.The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging,and for the first time,the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model.To optimize model construction,the least absolute shrinkage and selection operator regression was employed for feature screening,and synthetic minority over-sampling technique for data balancing.The prognostic prediction model was subsequently developed using a support vector machine algorithm and validated via leave-one-out cross-validation.Model performance was evaluated using area under receiver operating characteristic curve,along with accuracy,sensitivity,specificity,and F1 score metrics.Results Following LASSO regression feature screening,the pontine crossing tract,medial lemniscus,tapetum,splenium of corpus callosum,and visual subscale scores were identified as key predictors.A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients,achieving a high predictive performance(AUC=0.894).Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.

Objective:To investigate the differences in brain age and brain age gap (BAG) between patients with prolonged disorders of consciousness (pDoC) and healthy controls (HC).Methods:A retrospective cross-sectional study was performed; 43 patients with pDoC admitted to Rehabilitation Medicine Center, Zhujiang Hospital, Southern Medical University from January 2020 to October 2022 were enrolled; 26 healthy volunteers recruited at the same time and 187 healthy subjects from the publicly available healthy control dataset Nathan Kline Institute-Rockland Sample (NKI-RS) were chosen as HC group. The clinical and imaging data of these subjects were collected. A brain age estimation model was constructed based on amplitude of low-frequency fluctuation (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) for healthy individuals, and the pDoC group was used as the test set. A two-sample t-test was used to compare the brain age and BAG differences between the pDoC group and HC group. Pearson correlation analysis was used to explore the correlation between BAG and coma recovery scale-revised (CRS-R) in the pDoC group. Results:The chronological age and estimated brain age in the HC group were (41.54±9.61) and (42.32±10.65) years, respectively, without significant difference ( t=-0.254, P=0.801). The chronological age and estimated brain age in the pDoC group were (49.91±12.03) and (62.79±15.00) years, respectively, with significant difference ( t=-4.341, P<0.001). The BAG in the HC and pDoC groups were (0.78±4.59) and (12.88±7.17) years, respectively, with significant difference ( t=-7.822, P<0.001). Correlation analysis results showed that in the pDoC patients, no correlation was noted between BAG and CRS-R score on the day of imaging scan or 6 months after the day of imaging scan ( r=0.090, P=0.738; r=0.205, P=0.674); no correlation was noted between BAG and difference in CRS-R score (difference value of CRS-R score 6 months after the day of imaging scan-CRS-R score on the day of imaging scan, r=0.246, P=0.687). Conclusion:Compared with the HC subjects, patients with pDoC exhibit an abnormal increase in brain age, suggesting that pDoC caused by severe brain injury may lead to accelerated brain aging.

Objective: To study the diagnostic accuracy and the distance between the root of maxillary posterior tooth and the maxillary sinus using panoramic radiography and cone beam computer tomography; to provide basic information for clinicians to treat diseases in the maxillary posterior region. Methods: Eighty patients were included in this study. A total of 671 specimens were measured for the distance between the root tip and the maxillary sinus floor in both imaging modalities. Results : The roots that did not contact the sinus floor or contacted but did not project into the sinus cavity showed an agreement of 82% and 70% when using panoramic radiography. Forty-eight percent of the roots that projected into the sinus cavity in panoramic radiography showed protrusion into the sinus with cone beam computer tomography (CBCT). For panoramic radiography and CBCT showing root projections into the sinus cavity, the average distances were 2.19 ± 1.82 mm and 1.47 ± 1.01 mm, respectively. There was a significant difference between the two values (P ＜ 0.05). Conclusion Panoramic radiography is more accurate when roots of maxillary posterior teeth do not contact the sinus floor or contact it. However, it has a lower accuracy rate when the tooth roots protrude into the sinus.

Objective To analyze the relationship between pain sensation, emotion and recognition in three dimensions. Methods By using questionnaires which contained general information questionnaire, Cancer Pain Questionnaire, Self-reporting Inventory (SCL-90), Pain Beliefs and Perceptions Inventory (PBPI) to investigate pain sensation, emotion and recognition of 46 patients with cancer pain. Results There were 13(28.3%) cases sufferd from mild pain,17 (37.0%) cases were moderate pain, 16 (34.8% )cases were severe pain.As to the result of SCL- 90,patients showed obvious symptom in somatization, depression, anxiety and hostility.They holded deep belief of that pain was very mysterious. There was a significant correlation between pain severity and depression(rs=0.377) , anxiety(rs=0.388) on the condition that confidence level was 0.01;there was also a significant correlation between pain degree and interpersonal sensitivity(rs=0.308), hostility(rs=0.320) on the condition that confidence level was 0.05. As to pain beliefs, pain degree had a significant correlation with it in the dimension of pain as mystery (rs=0.529) and pain was persistent(rs=0.680) on the condition that confidence level was 0.01. Conclusions The survey shows a positive correlation between pain severity,emotion of pain(such as anxiety,depression, hostility and interpersonal sensitivity)and beliefs about pain as mystery or permanent.

To establish HPLC chromatographic fingerprints to control the quality of Chinese herbal medicine. In this study, fingerprints were established based on HPLC-DAD chromatographs. And with these fingerprints, content variations of three important active components catalpol, 5-hydroxymethylfurfural and acteoside in Rehmannia rhizome were analyzed during processing, as well as changes of the fingerprints. Fingerprints comparing with the standard prepared Rehmannia fingerprints which came from the mean of prepared ones randomly chosen for standard was done to seek optimal processing time. The results indicated that catalpol decreased quickly as braising prolonged and almost vanished in the end. While the active component of 5-HMF increased linearly throughout the process of braising. And the content of acteoside did not show obvious change. Similarity to standard prepared Rehmannia reached summit after braising for 26 hours. So 26 hours could be considered to be the optimum time for braising prepared Rehmannia. Chromatographic fingerprint is convenient for revealing changes of constituents and for accurately controlling quality during processing prepared Rahmannia.
Chromatography ; Chromatography, High Pressure Liquid ; methods ; Dermatoglyphics ; Drugs, Chinese Herbal ; Furaldehyde ; analogs & derivatives ; chemistry ; Glucosides ; chemistry ; Iridoids ; chemistry ; Phenols ; chemistry ; Phytotherapy ; Plant Preparations ; Plant Structures ; Rehmannia ; chemistry ; Rhizome ; chemistry