Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

This study sought to assess the therapeutic effect of celecoxib (CEL)-loaded polylactic acid-glycolic acid copolymer (PLGA) microspheres on rheumatoid arthritis in rats after intra-articular injection.The celecoxib-loaded microspheres (CEL-MS) were prepared by the O/W solvent volatilization method with PLGA as carrier.In order to investigate the therapeutic effect of CEL-MS on rheumatoid arthritis in rats after intra-articular injection, a rat model of adjuvant arthritis (AA) was constructed by complete Freund''s adjuvant, and the evaluation indicators of the therapeutic effect were rat paw swelling, arthritis index,spleen index and joint synovial histopathological examination. The results showed that the microspheres had a smooth spherical morphology with a particle size of (2.1 ± 0.3) μm and a drug loading efficiency of (20.8 ± 0.6)%.The results of the in vivo efficacy test showed that intra-articular injection of CEL-MS compared to the CEL suspension oral and the celecoxib suspension intra-articular injection in adjuvant arthritis rat model can significantly reduce joint swelling and arthritis index, thus effectively inhibiting synovial inflammation.The above results indicate that intra-articular injection of CEL-MS has a good therapeutic effect on rheumatoid arthritis in rats.

Objective:To assess the short-term efficacy of ustekinumab (UST) in the treatment of intractable Crohn′s disease (CD) .Methods:Clinical data of intractable CD patients with the administration of UST in the Sixth Affiliated Hospital of Sun Yat-sen University, the Second Affiliated Hospital of Zhejiang University School of Medicine and Xinqiao Hospital of Army Medical University from March 1st to September 30th, 2020 were analyzed retrospectively. All of the patients started UST at the recommended dose at 0, 8th, 16th/20th weeks. The clinical response and remission were evaluated by Crohn′s disease activity index (CDAI) . The endoscopic response and remission were evaluated by simple endoscopic score for Crohn′s disease (SES-CD) . The life quality was evaluated by inflammatory bowel disease questionnaire (IBDQ) . The clinical and endoscopic responses and IBDQ scores were analyzed statistically at 8th week and (or) at 16th/20th week. The differences in body mass index (BMI) , albumin, hemoglobin, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) among 0, 8th, 16th/20th weeks after UST treatment were compared and analyzed.Results:A total of 18 CD patients were enrolled, including 12 males and 6 females. The age was 30.5 (26.0, 38.0) years old while the disease duration was 6.5 (1.9, 10.0) years. According to Montreal classification, 15 (83.3%) patients were diagnosed at the age from 17 to 40 years old, the disease involved the ileocolon in 13 (72.2%) patients and the non-stricturing non-penetrating type was in 14 (77.8%) patients. Ten (55.6%) patients were complicated with perianal disease and 6 (33.3%) patients had undergone surgeries. Eleven (61.1%) patients had received treatment of glucocorticoids, 13 (72.2%) patients had received immunosuppressants, and 16 (88.9%) patients had received biologics. The clinical remission was observed in 72.2% (13/18) patients while 77.8% (14/18) had clinical response at 8th week after first use of UST. The clinical remission was observed in 88.9% (16/18) patients while 94.4% (17/18) had clinical response at 16th/20th week. Endoscopic remission rate was 28.6% (4/14) while endoscopic response rate was 78.6% (11/14) at 16th/20th week. The level of BMI [ (21.0 ± 0.5) kg/m 2 and (21.7 ± 0.4) kg/m 2 vs. (19.9 ± 0.6) kg/m 2, all P<0.05], albumin[ (41.7 ± 3.7) g/L and (41.7 ± 3.7) g/L vs. (39.6 ± 4.6) g/L, all P<0.05], and hemoglobin[ (134.9 ± 12.0) g/L and (135.9 ± 12.3) g/L vs. (126.7 ± 15.8) g/L, all P<0.05] at 8th week and at 16th/20th week were increased significantly than those at 0 week. And the CRP at 8th week and at 16th/20th week were significantly lower than that at 0 week [10.2 (5.1, 17.5) mg/L and 7.6 (3.2, 14.7) mg/L vs. 15.5 (12.3, 31.4) mg/L, all P< 0.05]. However, compared with the baseline value at 0 week, there were no significant difference in ESR and IBDQ score at 8th, 16th/20th weeks (all P>0.05) . Conclusion:UST can effectively improve clinical symptoms and ameliorate endoscopic manifestations of intractable CD patients in the short term.

Objective:To assess the short-term efficacy of ustekinumab (UST) in the treatment of intractable Crohn′s disease (CD) .Methods:Clinical data of intractable CD patients with the administration of UST in the Sixth Affiliated Hospital of Sun Yat-sen University, the Second Affiliated Hospital of Zhejiang University School of Medicine and Xinqiao Hospital of Army Medical University from March 1st to September 30th, 2020 were analyzed retrospectively. All of the patients started UST at the recommended dose at 0, 8th, 16th/20th weeks. The clinical response and remission were evaluated by Crohn′s disease activity index (CDAI) . The endoscopic response and remission were evaluated by simple endoscopic score for Crohn′s disease (SES-CD) . The life quality was evaluated by inflammatory bowel disease questionnaire (IBDQ) . The clinical and endoscopic responses and IBDQ scores were analyzed statistically at 8th week and (or) at 16th/20th week. The differences in body mass index (BMI) , albumin, hemoglobin, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) among 0, 8th, 16th/20th weeks after UST treatment were compared and analyzed.Results:A total of 18 CD patients were enrolled, including 12 males and 6 females. The age was 30.5 (26.0, 38.0) years old while the disease duration was 6.5 (1.9, 10.0) years. According to Montreal classification, 15 (83.3%) patients were diagnosed at the age from 17 to 40 years old, the disease involved the ileocolon in 13 (72.2%) patients and the non-stricturing non-penetrating type was in 14 (77.8%) patients. Ten (55.6%) patients were complicated with perianal disease and 6 (33.3%) patients had undergone surgeries. Eleven (61.1%) patients had received treatment of glucocorticoids, 13 (72.2%) patients had received immunosuppressants, and 16 (88.9%) patients had received biologics. The clinical remission was observed in 72.2% (13/18) patients while 77.8% (14/18) had clinical response at 8th week after first use of UST. The clinical remission was observed in 88.9% (16/18) patients while 94.4% (17/18) had clinical response at 16th/20th week. Endoscopic remission rate was 28.6% (4/14) while endoscopic response rate was 78.6% (11/14) at 16th/20th week. The level of BMI [ (21.0 ± 0.5) kg/m 2 and (21.7 ± 0.4) kg/m 2 vs. (19.9 ± 0.6) kg/m 2, all P<0.05], albumin[ (41.7 ± 3.7) g/L and (41.7 ± 3.7) g/L vs. (39.6 ± 4.6) g/L, all P<0.05], and hemoglobin[ (134.9 ± 12.0) g/L and (135.9 ± 12.3) g/L vs. (126.7 ± 15.8) g/L, all P<0.05] at 8th week and at 16th/20th week were increased significantly than those at 0 week. And the CRP at 8th week and at 16th/20th week were significantly lower than that at 0 week [10.2 (5.1, 17.5) mg/L and 7.6 (3.2, 14.7) mg/L vs. 15.5 (12.3, 31.4) mg/L, all P< 0.05]. However, compared with the baseline value at 0 week, there were no significant difference in ESR and IBDQ score at 8th, 16th/20th weeks (all P>0.05) . Conclusion:UST can effectively improve clinical symptoms and ameliorate endoscopic manifestations of intractable CD patients in the short term.

Objective:To monitor the epidemiological characteristics of viral etiology in children with influenza-like illness and to guide the prevention and management of acute respiratory tract infections in childhood.Methods:Nasopharyngeal swabs were collected from the outpatient children seeking medical care in Children′s Hospital of Fudan University, Shanghai for influenza-like illness between January 2015 and December 2018. Multiplex real-time polymerase chain reaction was performed to detect respiratory syncytial virus (RSV), influenza virus (Flu), adenovirus (ADV), parainfluenza virus (PIV, type Ⅰ to type Ⅳ) and enterovirus (EV), and the epidemiological data were analyzed. Chi-square test was used for statistical analysis.Results:A total of 2 271 patients with influenza-like illness were enrolled, age range from two months to 182 months old, 1 280 cases(56.4%) were positive for the target respiratory viruses tested on respiratory samples. The detection rates of FluA, FluB, PIV, EV, ADV, RSV were 15.1%(343/2 271), 12.5%(284/2 271), 8.4%(191/2 271), 7.8%(177/2 271), 5.1%(116/2 271) and 6.7%(152/2 271), respectively.The detection rates of influenza virus were statistically different among the age groups ( χ2=39.33, P<0.05), which showed an increasing trend with the increasing ages. The detection rate of RSV was 9.7%(35/361) in infant group from zero to 12 months old, which was higher than other age groups. Usually, FluA had two epidemic peaks during the winter and summer seasons, the epidemics of FluB and RSV peaked during the winter season, and EV and PIV were more prevalent in the summer season. Conclusions:Influenza virus remains the most common viral pathogen responsible for childhood influenza-like illness in Shanghai.Influenza virus has high incidence in winter.Widely influenza vaccination is highly recommended for the effective prevention the influenza outbreaks.Continuous monitoring the epidemic trend of viral respiratory infections is imperative for the prevention and control of diseases.

Objective:To investigate the serotype features of group B Streptococcus (GBS) vaginal colonization in late pregnancies and their relationship with early-onset neonatal GBS disease (GBS-EOD). Methods:Thirty-two strains were isolated from neonates delivered by GBS-positive mothers and hospitalized for GBS-EOD in Xiamen Maternal and Child Care Hospital from June 2016 to June 2018. Another 266 strains were isolated from vaginal samples from randomly selected late pregnant women who received antenatal screening and delivered in the same hospital during the same period with an allocation ratio of 12∶1. A total of 298 strains from mothers and 32 strains from neonates were involved. Every isolate was serotyped with latex agglutination assay. GBS infection caused by eleven serotypes and the correlation between GBS serotypes in late pregnant women and neonatal GBS-EOD were analyzed. Qualitative variables were compared using Chi-square or Fisher's exact test. A correlation analysis was presented by the column contact number C. Multiple analysis of multiple sample rates was performed with Post hoc testing. Differences between groups were analyzed according to the adjusted standardized residual. Results:A total of nine serotypes were identified among the 298 strains isolated from the mothers. The most prevalent serotype wasⅢ [55.0% (164/298)], followed byⅠb [16.4% (49/298)], Ⅰa [11.1% (33/298)], Ⅴ [9.4% (28/298)], Ⅱ [5.0% (15/298)], non-typable [NT, 1.0% (3/298)], and Ⅵ, Ⅷ and Ⅸ [0.7% (2/298) in each]. Neither Ⅳ nor Ⅶ serotype was identified. The 32 strains isolated from neonates with GBS-EOD belonged to five serotypes, which were Ⅲ [18/32 (56.3%)], Ⅰa [8/32 (25.0%)], Ⅰb [3/32 (9.4%)], Ⅱ [2/32 (6.2%)] and Ⅴ [1/32 (3.1%)]. The positive rates of GBS Ⅲ serotype in neonates with pneumonia, sepsis, and meningitis were 6/13, 7/14, and 5/5. However, no statistically significant difference was observed in the distribution of the five serotypes in GBS-EOD neonates ( P=0.654). Thirty neonates (93.7%) were cured, while two (6.3%) died. There were statistically significant differences among neonatal GBS-EOD caused by vertical transmission with Ⅰa, Ⅰb, Ⅱ, Ⅲ and Ⅴ, Ⅵ, Ⅷ, Ⅸ and NT serotypes ( P=0.046, contingency coefficient: 0.183). Further analysis showed that the adjusted absolute value of the standardized residual of serotype Ⅰa was 2.7 (>2), and the difference was statistically significant. However, the adjusted absolute value of the standardized residual of serotype Ⅲ was only 0.1, which was not statistically significant. Conclusions:Serotype Ⅲ is the most prevalent GBS serotype in late pregnant women and GBS-EOD neonates, and also the predominant serotype in infants with early-onset meningitis. Serotype Ⅰa could be highly vertically transmitted, while the virulence of serotypes Ⅲ and Ⅰa strains of GBS are the strongest.

Objective:To explore the characteristics of cerebellar metabolites of autistic children using the magnetic resonance spectrum (MRS) and analyze their correlation with clinical symptoms.Methods:An autism group ( n=14) and a control group ( n=8) both underwent bilateral MRS scans of their cerebella. The NAA, Cho and Cr absolute values were recorded along with the NAA/Cr and Cho/Cr ratios. Those values were correlated with clinical symptoms of autism (the CARS and ABC scales), as well as with age. Results:There were no significant differences between the two groups in cerebellar metabolite levels, but the autism group exhibited significantly higher Cho/Cr ratios in the right cerebellum than in the left. There was a significant positive correlation between the Cho/Cr ratio in the right cerebellum and CARS scores, and between the NAA/Cr ratio in the right cerebellum and age.Conclusions:There is metabolic abnormality in the left and right cerebellar hemispheres of autistic children. The metabolic abnormality is related to the severity of clinical symptoms. However, metabolism in the cerebellum improves gradually with age.

This article reports a rare pediatric case with diagnosis of dyskeratosis congenita complicated with cytomegalovirus colitis. The clinical and genetic characteristics of dyskeratosis congenita and cytomegalovirus colitis were discussed in detail.