Spinal cord injury (SCI) is a neurological disorder that occurs after a direct or indirect violent injury to the spinal cord, often resulting in sensory and motor dysfunction below the injury level. Patients with SCI are often paralyzed in bed due to impaired nerve function and there has been no effective treatment for limb paralysis after SCI. As a cutting-edge technology with a multidisciplinary integration of neuroscience, computer science, biological engineering, electronic engineering and psychology, brain-computer interface (BCI) provides a new program for the rehabilitation of SCI patients by changing the traditional brain signal output pathways and realizing the direct connection between the brain and external devices. In order to further understand the application of BCI in SCI rehabilitation, the authors reviewed the classification, basic principles of BCI and the research progress of the application of BCI in SCI rehabilitation, which may provide references for the clinical transformation of BCI.

Objective To develop and validate a deep learning model based on chest CT images to accurately distinguish between drug-resistant(DR-TB)and-sensitive tuberculosis(DS-TB).Methods A retrospective study was conducted on 722 cases of confirmed secondary tuberculosis admitted in our center from January 2019 to December 2022.According to the results of antimicrobial susceptibility test,they were divided into 357 DS-TB cases and 365 DR-TB cases.Pre-existing U-Net segmentation model was employed to segment the lung parenchyma regions in CT images.The dataset was randomly partitioned into a training set and a testing set in an 8:2 ratio.Six 3D deep learning architectures(3D Swin Transformer,3D ShuffleNet v2,3D ViT,3D MobileNet v2,3D DenseNet,and 3D ResNet18)were employed to evaluate the discriminative efficiency between DS-TB and DR-TB.Hyperparameters were optimized by five-fold cross-validation on the training set to construct the optimal model.The performance of the constructed model was assessed using area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),and F1-score.Six radiologists independently evaluated DR-TB identification on the test set,and their performance was compared with the best-performing deep learning model.Results The AUC value in DR-TB prediction was 0.583,0.704,0.698,0.758,0.736,and 0.841,respectively,for 3D Swin Transformer,3D ShuffleNet v2,3D ViT,3D MobileNet v2,3D DenseNet,and 3D ResNet18.The 3D ResNet18 model demonstrated optimal performance,achieving a sensitivity of 0.935(95%CI:0.880～0.987),a specificity of 0.642(95%CI:0.492～0.757),a PPV of 0.750(95%CI:0.663～0.835),an NPV of 0.896(95%CI:0.809～0.976),an AUC value of 0.841,and a F1-score of 0.832.The radiologists got a F1-score of 0.571,0.450,0.675,0.623,0.617 and 0.635,respectively,and the F1-score of the 3D ResNet18 model is all higher than that of the radiologists.The highest-performing radiologist achieved sensitivity,specificity,PPV and NPV of 0.701(95%CI:0.605～0.802),0.567(95%CI:0.447～0.684),0.651(95%CI:0.549～0.757),and 0.623(95%CI:0.500～0.754),with all these values lower than those of the 3D ResNet18 model(P<0.05).Class activation mapping showed that the 3D ResNet18 model could focus on key lesion areas.The class activation mapping demonstrated that the 3D ResNet18 model could effectively focus on critical lesion regions.Conclusion Our 3D ResNet18 model shows the best predictive performance in identifying DR-TB,and is expected to assist clinical diagnosis for DR-TB.

Objective To compare the effectiveness of the heart rate variability threshold(HRVT),muscle oxygenation threshold(MOT),and their combined threshold(COMT)in predicting the ventila-tory threshold(VT)and lactate threshold(LT).Methods Twenty male athletes at or above the nation-al level were recruited to perform an incremental exercise test to determine VT and LT,during which gas exchange,muscle oxygenation,and heart rate variability were collected.The HRVT,MOT,and COMT methods were then applied,and their predictive validity was evaluated by comparing the correla-tions and agreements between heart rate(HR)and power output(PO)at VT and LT.Results For HR,VT1 showed strong correlation and agreement with MOT1(r=0.958,ICC=0.944)and COMT1(r=0.957,ICC=0.951),with COMT1 having the smallest mean bias(1.4 bpm)and the narrowest limits of agreement(LOA)(-5.4 to 8.2 bpm).Moreover,VT2 demonstrated the highest correlation and agree-ment with COMT2(r=0.985,ICC=0.982),with a mean bias of-0.7 bpm and LOA of-5.0 to 3.7 bpm.Meanwhile,LT showed high correlation and good agreement with COMT2(r=0.884,ICC=0.754)and MOT2(r=0.886,ICC=0.738),with mean biases of-6.8 bpm and-7.3 bpm,and LOA of-17.5 to 3.8 bpm and-17.9 to 3.2 bpm,respectively.For PO,VT1 had the best correlation and agreement with MOT1(r=0.836,ICC=0.808),with a mean bias of-0.2 W and LOA of-11.7 to 11.3 W.VT2 performed best correlation and agreement with COMT2(r=0.910,ICC=0.900),with a mean bias of 2.3 W and LOA of-8.9 to 13.5 W.In LT prediction,MOT2(r=0.736,ICC=0.692)outperformed COMT2(r=0.635,ICC=0.618),with mean biases of-2.6 W and 0.2 W,and LOA of-26.0 to 20.7 W and-26.6 to 27.1 W,respectively.Conclusion All three indicators—HRVT,MOT,and COMT—demonstrates high validity in predicting VT and LT.MOT1 performs best for predicting PO at VT1,while COMT2 shows the highest consistency for predicting both HR and PO at VT2.Howev-er,for LT prediction,MOT2 is more advantageous for predicting PO,whereas COMT2 is more accu-rate for predicting HR.Therefore,method selection should be tailored to the specific threshold type and measurement target.

DiGeorge(DGS)syndrome is an autosomal dominant disorder caused by 22q11.2 microdeletions,most patients developed the disease in childhood.22q11.2 deletion syndrome,and the mutation types are dominated by haploid deletion of this gene.We report a young patient with hypoparathyroidism(parathyroidism)induced by DGS syndrome combined with hypocalcemic heart failure.Genetic testing revealed pathogenic copy number variants associated with the clinical phenotype of the subject.About 2 674 kb of deletion variation was detected at q11.21 position on chromosome 22,which contained the TBX1 gene and was a pathogenic mutation.This paper discusses the clinical features,pathogenesis and current treatment of DGS,and emphasizes the importance of early screening,early diagnosis and treatment,and regular follow-up of heart failure,aiming to enhance the awareness of clinicians and geneticists on DGS syndrome.

DiGeorge(DGS)syndrome is an autosomal dominant disorder caused by 22q11.2 microdeletions,most patients developed the disease in childhood.22q11.2 deletion syndrome,and the mutation types are dominated by haploid deletion of this gene.We report a young patient with hypoparathyroidism(parathyroidism)induced by DGS syndrome combined with hypocalcemic heart failure.Genetic testing revealed pathogenic copy number variants associated with the clinical phenotype of the subject.About 2 674 kb of deletion variation was detected at q11.21 position on chromosome 22,which contained the TBX1 gene and was a pathogenic mutation.This paper discusses the clinical features,pathogenesis and current treatment of DGS,and emphasizes the importance of early screening,early diagnosis and treatment,and regular follow-up of heart failure,aiming to enhance the awareness of clinicians and geneticists on DGS syndrome.

Spinal cord injury (SCI) is a neurological disorder that occurs after a direct or indirect violent injury to the spinal cord, often resulting in sensory and motor dysfunction below the injury level. Patients with SCI are often paralyzed in bed due to impaired nerve function and there has been no effective treatment for limb paralysis after SCI. As a cutting-edge technology with a multidisciplinary integration of neuroscience, computer science, biological engineering, electronic engineering and psychology, brain-computer interface (BCI) provides a new program for the rehabilitation of SCI patients by changing the traditional brain signal output pathways and realizing the direct connection between the brain and external devices. In order to further understand the application of BCI in SCI rehabilitation, the authors reviewed the classification, basic principles of BCI and the research progress of the application of BCI in SCI rehabilitation, which may provide references for the clinical transformation of BCI.

Objective To evaluate the effectiveness of low-volume high-intensity interval training(LV-HIIT)protocols defined by the W'Balance(W'BAL)model in achieving maximal activation of both anaerobic and aerobic energy systems.Methods Twenty-eight national-level athletes(age:20±1 years old;height:174±9 cm;weight:65.1±9.4 kg)completed an incremental exercise test,followed by six supra-critical power(supra-CP)and five sub-CP constant-load tests to determine VO2max,maximal accumulated oxygen deficit(MAOD),critical power(CP),and W prime(W').They then performed three randomly ordered LV-HIIT protocols(each of 4-minute total duration),with a 10-second inter-val for passive recovery between bouts.The protocols consisted of 10s(HIIT10/10),20s(HIIT20/10),and 30 s(HIIT30/10).Exercise intensities were individually prescribed using the W'BAL model.Accu-mulated oxygen deficit(AOD)and net AOD(NAOD)were calculated for each protocol.Results Final 10-s oxygen uptake(VO2)reached 77％,88％,and 89％of VO2max in HIIT10/10,HIIT20/10,and HIIT30/10,respectively(P<0.05).VO2 in HIIT10/10 was significantly lower than in HIIT20/10 and HIIT30/10(P<0.05),with no significant difference between the latter two(P>0.05).AOD did not differ significantly from MAOD among the three protocols(P>0.05),whereas NAOD was significantly lower than MAOD in all cases(P<0.05).Additionally,NAOD in HIIT10/10 was significantly lower than in HIIT20/10 and HI-IT30/10(P<0.05),with no significant difference between the latter two(P>0.05).Conclusion The HIIT20/10 and HIIT30/10 intensity established by the W'BAL model can substantially elicit maximal activation of both anaerobic and aerobic energy systems.While the W'BAL model demonstrates potential for set-ting exercise intensities in LV-HIIT,future studies are necessary to develop specific W'recovery rate models for targeted populations and to refine CP models that are better suited for intermittent exercises.

Objective To compare the effectiveness of the heart rate variability threshold(HRVT),muscle oxygenation threshold(MOT),and their combined threshold(COMT)in predicting the ventila-tory threshold(VT)and lactate threshold(LT).Methods Twenty male athletes at or above the nation-al level were recruited to perform an incremental exercise test to determine VT and LT,during which gas exchange,muscle oxygenation,and heart rate variability were collected.The HRVT,MOT,and COMT methods were then applied,and their predictive validity was evaluated by comparing the correla-tions and agreements between heart rate(HR)and power output(PO)at VT and LT.Results For HR,VT1 showed strong correlation and agreement with MOT1(r=0.958,ICC=0.944)and COMT1(r=0.957,ICC=0.951),with COMT1 having the smallest mean bias(1.4 bpm)and the narrowest limits of agreement(LOA)(-5.4 to 8.2 bpm).Moreover,VT2 demonstrated the highest correlation and agree-ment with COMT2(r=0.985,ICC=0.982),with a mean bias of-0.7 bpm and LOA of-5.0 to 3.7 bpm.Meanwhile,LT showed high correlation and good agreement with COMT2(r=0.884,ICC=0.754)and MOT2(r=0.886,ICC=0.738),with mean biases of-6.8 bpm and-7.3 bpm,and LOA of-17.5 to 3.8 bpm and-17.9 to 3.2 bpm,respectively.For PO,VT1 had the best correlation and agreement with MOT1(r=0.836,ICC=0.808),with a mean bias of-0.2 W and LOA of-11.7 to 11.3 W.VT2 performed best correlation and agreement with COMT2(r=0.910,ICC=0.900),with a mean bias of 2.3 W and LOA of-8.9 to 13.5 W.In LT prediction,MOT2(r=0.736,ICC=0.692)outperformed COMT2(r=0.635,ICC=0.618),with mean biases of-2.6 W and 0.2 W,and LOA of-26.0 to 20.7 W and-26.6 to 27.1 W,respectively.Conclusion All three indicators—HRVT,MOT,and COMT—demonstrates high validity in predicting VT and LT.MOT1 performs best for predicting PO at VT1,while COMT2 shows the highest consistency for predicting both HR and PO at VT2.Howev-er,for LT prediction,MOT2 is more advantageous for predicting PO,whereas COMT2 is more accu-rate for predicting HR.Therefore,method selection should be tailored to the specific threshold type and measurement target.

Objective To investigate the correlation between the Balkwill angle and occlusal plane angles and temporomandibular joint(TMJ)morphology in adults with skeletal Class Ⅱ division 2 malocclusion.Methods Thirty-five adult patients with skeletal Class Ⅱ division 2 low angle(study group)and 35 adult patients with skeletal Class Ⅰ average angle(control group)were included.The Invivo Dental 5 software was employed to acquire the data of Balkwill angle,occlusal plane angle(FH-OP),posterior occlusal plane angle(FH-POP),mandibular occlusal plane angle(FH-MOP),DPO(vertical distance from condylar center to the MOP)and the TMJ measurement items.Results The mean values of FH-OP,FH-POP and FH-MOP were lower in the study group than control group(P＜0.05).The average value of Balkwill angle and DPO were higher in the study group than the control group(P＜0.05).Sig-nificant differences were found in the measurement results of the mediolateral diameters of the condyle,width of condylar head,the ar-ticular eminence inclination and height,superior joint space between two groups.DPO had a medium correlation with mediolateral di-ameters of the condyle,glenoid fossa depth and articular eminence height.And FH-MOP angle had a medium correlation with mediolat-eral diameters of the condyle and articular eminence height.There was a medium correlation between the Balkwill angle and articular eminence height,a weak correlation with mediolateral diameters of the condyle,articular eminence inclination,condylar length and glenoid fossa depth.Conclusion The results indicated that the DPO had a significant impact on TMJ morphology,followed by FH-MOP,and finally Balkwill angle in skeletal Class Ⅱ division 2 low angle malocclusion.

Objective To evaluate the effectiveness of low-volume high-intensity interval training(LV-HIIT)protocols defined by the W'Balance(W'BAL)model in achieving maximal activation of both anaerobic and aerobic energy systems.Methods Twenty-eight national-level athletes(age:20±1 years old;height:174±9 cm;weight:65.1±9.4 kg)completed an incremental exercise test,followed by six supra-critical power(supra-CP)and five sub-CP constant-load tests to determine VO2max,maximal accumulated oxygen deficit(MAOD),critical power(CP),and W prime(W').They then performed three randomly ordered LV-HIIT protocols(each of 4-minute total duration),with a 10-second inter-val for passive recovery between bouts.The protocols consisted of 10s(HIIT10/10),20s(HIIT20/10),and 30 s(HIIT30/10).Exercise intensities were individually prescribed using the W'BAL model.Accu-mulated oxygen deficit(AOD)and net AOD(NAOD)were calculated for each protocol.Results Final 10-s oxygen uptake(VO2)reached 77％,88％,and 89％of VO2max in HIIT10/10,HIIT20/10,and HIIT30/10,respectively(P<0.05).VO2 in HIIT10/10 was significantly lower than in HIIT20/10 and HIIT30/10(P<0.05),with no significant difference between the latter two(P>0.05).AOD did not differ significantly from MAOD among the three protocols(P>0.05),whereas NAOD was significantly lower than MAOD in all cases(P<0.05).Additionally,NAOD in HIIT10/10 was significantly lower than in HIIT20/10 and HI-IT30/10(P<0.05),with no significant difference between the latter two(P>0.05).Conclusion The HIIT20/10 and HIIT30/10 intensity established by the W'BAL model can substantially elicit maximal activation of both anaerobic and aerobic energy systems.While the W'BAL model demonstrates potential for set-ting exercise intensities in LV-HIIT,future studies are necessary to develop specific W'recovery rate models for targeted populations and to refine CP models that are better suited for intermittent exercises.