OBJECTIVE: To explore the genetic etiology of 46 Chinese pedigrees affected with Hereditary multiple exostoses (HME) and provide genetic counseling and reproductive intervention. METHODS: Whole-exome sequencing and Sanger sequencing were carried out on 87 patients from the 46 pedigrees to analyze the variants of EXT1 and EXT2 genes. Pathogenicity of the variants was assessed based on the guidelines from the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP). Prenatal diagnosis and preimplantation genetic testing (PGT) were provided for couples with identified pathogenic mutations. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: LL-SC-SG-2014-010). RESULTS: In total 17 and 22 pathogenic variants were respectively identified in the EXT1 and EXT2 genes, among which 5 EXT1 and 12 EXT2 variants were unreported previously. Three patients with no family history were found to harbor de novo variants of the EXT1 gene. Twenty nine couples had opted for PGT or underwent prenatal diagnosis following natural conception, and 17 healthy babies were born. CONCLUSION This study has clarified the genetic etiology of 45 HME pedigrees and identified 17 novel variants, which has enriched the mutational spectrum of the EXT1 and EXT2 genes. Reproductive intervention through PGT and prenatal diagnosis have prevented the recurrence of HME in these families.
Humans ; Female ; Male ; Pedigree ; Exostoses, Multiple Hereditary/diagnosis* ; N-Acetylglucosaminyltransferases/genetics* ; Adult ; Exostosin 1 ; Asian People/genetics* ; Genetic Testing ; Exostosin 2 ; Mutation ; China ; Prenatal Diagnosis ; Pregnancy ; Genetic Counseling ; Preimplantation Diagnosis ; Exome Sequencing ; East Asian People

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To investigate the relationship between deep medullary vein(DMV)score and enlarged perivascular space(EPVS)score with cranial MRI to explore the mechanisms of EPVS occurrence and development.Methods Totally 118 patients with cerebral small vessel disease in some hospital had their clinical and imaging data analyzed retrospectively.On T2-weighted images,EPVS scores ranging from 0 to 4 were assigned to the basal ganglia and centrum semiovale regions based on the number of EPVS.For DMV scoring on magnetic susceptibility weighted images,the brain lobes were divided into six regions,including bilateral frontal,parietal and occipital lobes,and scored according to the significance and continuity of the DMV signals(0 to 3 points),and the scores of the regions were summed up as the total DMV score(0 to 18 points).Spearman correlation analysis was employed to investigate the correlation between DMV and EPVS scores.Kruskal-Wallis test was used to analyze the differences among groups with different EPVS scores.Additionally,multinomial ordinal regression analysis was conducted to explore the factors influencing EPVS.Results No significant correlation was found between EPVS scores in centrum semiovale and DMV scores(r2=0.015,P=0.191),while DMV scores and EPVS scores in basal ganglia showed a significant positive correlation(r2=0.558,P<0.000 1).Univariate analysis revealed statisti-cally significant differences in age,hypertension and DMV scores when different scoring groups of EPVS in the basal ganglia region were compared(P<0.05);multinomial ordinal regression analysis showed that age,hypertension and DMV scores were independently correlated with EPVS scores in the basal ganglia region.Conclusion There is a positive correlation between DMV scores and EPVS scores in the basal ganglia.Age,hypertension,and DMV score are independent influe-ncing factors for EPVS in the basal ganglia region.[Chinese Medical Equipment Journal,2025,46(4):52-56]

Objective To optimize the appointment scheduling for MRI examinations to provide new ideas for solving the problems of MRI examinations in resource optimization.Methods A simulation model of time-sharing MRI examination appointment rules was established by conducting a field survey on the MRI examination process in the hospital imaging department.The appointment scheduling rule for MRI examination slots was optimized with the empirical distributions of MRI examination time and patient tardiness time as the input parameters and the weighted averages of patient waiting time,doctor overtime time and equipment idle time as the objective functions.The optimal time slot length and the number of examination sites for each time slot were determined based on the multiple-population differential evolution algorithm,and the response of the appointment scheduling rule to parameter variations was investigated by sensitivity analysis.Results Simulation results showed that the optimal time slot length was 15 min,and that the optimized rule significantly gained advantages over the existing rule in terms of interference resistance when the patient tardiness rate and the number of devices varied.Conclusion The optimized appointment scheduling for MRI examinations based on the multiple-population differential evolution algorithm contributes to enhancing the patient experience and the efficiency of MRI examinations.[Chinese Medical Equipment Journal,2025,46(6):88-92]

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To optimize the appointment scheduling for MRI examinations to provide new ideas for solving the problems of MRI examinations in resource optimization.Methods A simulation model of time-sharing MRI examination appointment rules was established by conducting a field survey on the MRI examination process in the hospital imaging department.The appointment scheduling rule for MRI examination slots was optimized with the empirical distributions of MRI examination time and patient tardiness time as the input parameters and the weighted averages of patient waiting time,doctor overtime time and equipment idle time as the objective functions.The optimal time slot length and the number of examination sites for each time slot were determined based on the multiple-population differential evolution algorithm,and the response of the appointment scheduling rule to parameter variations was investigated by sensitivity analysis.Results Simulation results showed that the optimal time slot length was 15 min,and that the optimized rule significantly gained advantages over the existing rule in terms of interference resistance when the patient tardiness rate and the number of devices varied.Conclusion The optimized appointment scheduling for MRI examinations based on the multiple-population differential evolution algorithm contributes to enhancing the patient experience and the efficiency of MRI examinations.[Chinese Medical Equipment Journal,2025,46(6):88-92]

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective To explore the effects of robot assisted training (RAT) combined with neural mobi-lization (NM) training on the recovery of upper limb functions in stroke patients. Methods A total of 110 stroke patients who met the inclusion criteria were selected as the subjects and randomly divided into a control group (n=28),RAT group (n=27),NM group (n=28),and combination group (n=27). All patients underwent routine upper limb occupational therapy. Additionally,the patients in the RAT group were treated with upper limb rehabilitation robots,those in the NM group underwent neural mobilization for treatment,those in the combination group were managed with robot-assisted training for upper limb rehabilitation and neural mobilization. Before treat-ment and 4 weeks after treatment,the modified Ashworth scale (MAS),Fugl-Meyer assessment upper extremity (FMA-UE),functional test for the hemiplegic upper extremity Hong Kong version (FTHUE-HK),and modified Barthel index (MBI) were used to assess the effects. The surface electromyographic signals of the biceps and triceps at the maximum isometric voluntary contraction (MIVC) position during elbow flexion and extension were measured,the integrated electromyographic values (iEMG) were recorded and the synergistic contraction rate (CR) was calculated. Results There was no statistically significant difference (P>0.05) between the four groups in the general information and pre-treatment assessments of MAS,FMA-UE,FTHUE-HK,MBI,iEMG,and CR. After 4 weeks,significant improvements were observed in all indicators compared to the pre-treatment assessments (P<0.05),with the exception of the triceps brachii CR,biceps brachii CR,and elbow extension MIVC biceps brachii iEMG in the control group.Among the group comparisons,all indicators showed statistically significant differences in mean or distribution (P<0.05),except for MAS and triceps brachii CR. The RAT group,NM group,and combination group all demonstrated significant improvements compared to the control group (P<0.05). Nota-bly,the combination group exhibited a greater degree of improvement than the RAT and NM groups. Conclusion RATcombined with NM can reduce upper limb muscle tone in stroke patients. This approacheffectively promotes the establishment of normal movement patterns,improve upper limb motor function,and enhance activities of daily living. This combination is effective and worthy of further clinical promotion and application.

Objective To investigate the relationship between deep medullary vein(DMV)score and enlarged perivascular space(EPVS)score with cranial MRI to explore the mechanisms of EPVS occurrence and development.Methods Totally 118 patients with cerebral small vessel disease in some hospital had their clinical and imaging data analyzed retrospectively.On T2-weighted images,EPVS scores ranging from 0 to 4 were assigned to the basal ganglia and centrum semiovale regions based on the number of EPVS.For DMV scoring on magnetic susceptibility weighted images,the brain lobes were divided into six regions,including bilateral frontal,parietal and occipital lobes,and scored according to the significance and continuity of the DMV signals(0 to 3 points),and the scores of the regions were summed up as the total DMV score(0 to 18 points).Spearman correlation analysis was employed to investigate the correlation between DMV and EPVS scores.Kruskal-Wallis test was used to analyze the differences among groups with different EPVS scores.Additionally,multinomial ordinal regression analysis was conducted to explore the factors influencing EPVS.Results No significant correlation was found between EPVS scores in centrum semiovale and DMV scores(r2=0.015,P=0.191),while DMV scores and EPVS scores in basal ganglia showed a significant positive correlation(r2=0.558,P<0.000 1).Univariate analysis revealed statisti-cally significant differences in age,hypertension and DMV scores when different scoring groups of EPVS in the basal ganglia region were compared(P<0.05);multinomial ordinal regression analysis showed that age,hypertension and DMV scores were independently correlated with EPVS scores in the basal ganglia region.Conclusion There is a positive correlation between DMV scores and EPVS scores in the basal ganglia.Age,hypertension,and DMV score are independent influe-ncing factors for EPVS in the basal ganglia region.[Chinese Medical Equipment Journal,2025,46(4):52-56]

Objective To explore the effect of visual deprivation training combined with proprioceptive training on balance function of hemiplegic patients after stroke.Methods A total of 80 stroke patients with hemiplegia in Jiangsu Shengze Hospital were selected from May,2022 to March,2024,and randomly divided into control group(n=20),proprioceptive training group(n=20),visual de-privation group(n=20)and combined group(n=20).All the groups received routine rehabilitation training,while the proprioceptive training group added proprioceptive training,the visual deprivation group added balance training under visual deprivation,and the combined group added visual deprivation training and proprioceptive training,for four weeks.They were assessed with ProKin Balance Test and Training System,Berg Balance Scale(BBS),10-metre walking test(10MWT),Fugl-Meyer Assessment-Lower Extremities(FMA-LE)and Functional Gait Assessment(FGA)before and after treatment.Results The intra-group effect(F>96.618,P<0.001)and interaction effect(F>5.444,P<0.01)were significant in mean longitudinal velocity and mean horizontal velocity.The intra-group effect(F>177.671,P<0.001),inter-group effect(F>3.761,P<0.05)and interaction effect(F>7.555,P<0.001)were significant in movement el-lipse area and movement length both with eyes open and closed.The intra-group effect(F>221.902,P<0.001)and interaction effect(F>7.586,P<0.001)were significant in the time of 10MWT,and the scores of BBS,FMA-LE and FGA;and the inter-group effect were significant in FGA score(F=5.258,P<0.01).Post Hoc test showed that all the indicators were better in the combined group and the visual deprivation group than in the con-trol group(P<0.05);as well as in the proprioceptive training group than in the control group(P<0.05)except mean longitudinal velocity with eyes open,mean horizontal velocity with eyes closed,and movement length with eyes open;while all the indicators were better in the combined group than both in the visual deprivation group and the proprioceptive training group(P<0.05);there was no significant difference between the visual depriva-tion group and the proprioceptive training group for all the indicators(P>0.05).Conclusion Both visual deprivation training and proprioceptive training can improve balance,lower limb motor function and walking of hemiplegic stroke patients,and the combination is more effective.