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.

Tooth pulpitis is a prevalent oral disorder. Understanding the underlying mechanisms of pulpitis and developing effective treatment strategies hold great significance. Ferroptosis has recently emerged as a new form of cell death, but the role of ferroptosis in pulpitis remains largely unknown. In our study, single-cell RNA sequencing (scRNA-seq) was used to identify cellular heterogeneity between 3 pulpitis tissue and 3 healthy pulp tissue, and explored ferroptosis occurrence in pulpitis tissue and inflamed dental pulp cells (DPCs). In scRNA-seq, 40 231 cells (Pulpitis: 17 814; Healthy pulp: 22 417) were captured, and visualized into 12 distinct cell clusters. Differentially expressed ferroptosis-related genes (DE-FRGs) were almost presented in each cluster in pulpitis vs healthy pulp. ROS and Fe²⁺ levels significantly rose, and immunohistochemistry showed low expression of GPX4 and high expression of PTGS2 in pulpitis. In LPS-stimulated DPCs, thymosin α1 increased the expression of GPX4 and FTL, and decreased expression of TNF-α, IL-1β, IL-6, and Fe²⁺ levels. In rat pulpitis models, both prothymosin α (PTMA, precursor of thymosin α1) gelatin sponge placed at the hole of pulp (LPS-P(gs)) and PTMA injection in pulp (LPS-P(i)) significantly reduced infiltration of inflammatory cells and expression of PTGS2, and increased the expression of GPX4. In RNA sequencing, the expression of DE-FRGs were reversed when thymosin α1 were added in LPS-stimulated DPCs. Collectively, single-cell atlas reveals cellular heterogeneity between pulpitis and healthy pulp, and ferroptosis occurrence in pulpitis. Thymosin α1 may reduce ferroptosis in DPCs to alleviate pulpitis and thus potentially has the ability to treat pulpitis.
Ferroptosis/drug effects* ; Dental Pulp/drug effects* ; Animals ; Pulpitis/pathology* ; Rats ; Thymalfasin/pharmacology* ; Humans ; Male ; Thymosin/pharmacology* ; Disease Models, Animal ; Rats, Sprague-Dawley

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 clinical characteristics of a case of CNNM2 gene heterozygous mutation causing hypomagnesemia epilepsy mental retardation (HOMGSMR1) [MIM: 616418] in a child, and explore the association between genotype and phenotype.Methods:We followed up and retrospectively analyzed the clinical characteristics of a case of HOMGSMR1 caused by CNNM2 gene heterozygous mutation treated at Maternal and Child Health Care Hospital of Liuyang. Through whole exome sequencing of the family and bioinformatics analysis of the original data, we consulted databases and literature materials such as Online Mendelian Inheritance in Man (OMIM), ClinVar, gnomAD, GeneReviews, Pubmed, and China National Knowledge Infrastructure (CNKI), The American College of Medical Genetics and Genomics (ACMG) guidelines were used to rate heterozygous deletion mutations in the locked CNNM2 gene.Results:Patient, male, 3 months and 18 days old, mother gave birth to 1 child with 1 pregnancy, recurrent convulsions for more than 10 days, and multiple tests showed that blood magnesium levels were below normal, fluctuating between 0.51-0.55 mmol/L; After oral administration of " oxcarbazepine" and " magnesium sulfate", convulsions improved and blood magnesium concentration increased, but remained below normal, with the highest being 0.61 mmol/L. The sequencing results of the whole exome display of the family showed that the child carried a heterozygous deletion mutation in the exon region of the CNNM2 gene (c.838_843delATGGCCp. M280-A281del), which was not detected in their parents, indicating a new mutation. The large-scale population frequency database gnomAD did not include this mutation, and no literature reported this mutation. According to the ACMG guidelines, it was rated as a suspected pathogenic variant. The pathogenic variation of this gene can lead to autosomal dominant HOMGSMR1, which was consistent with genetic patterns.Conclusions:CNNM2 gene c. 838_ 843delATGGCC (p.M280_A281del) is a suspected pathogenic variant in this patient, with genotype and phenotype matching and heterozygous mutations following genetic patterns. Autosomal dominant inheritance is the molecular cause of clinical manifestations in this patient, and it is an unreported novel mutation.

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.