Objective To investigate the effects of serum insulin-like growth factor 2(IGF2),leucine-rich repeat and fibronectin type Ⅲ domain containing 5(LRFN5)levels on neurological rehabilitation in patients with ischemic stroke(IS).Methods A total of 103 patients with IS who were treated in Shiyan Taihe Hospi-tal from October 2022 to March 2024 were selected as the study group.According to the modified Rankin scale(mRS)score,they were divided into 46 cases in the poor rehabilitation group and 57 cases in the good rehabili-tation group.Another 61 healthy individuals who underwent physical examinations in the hospital during the same period were selected as the control group.The levels of serum IGF2 and LRFN5 were detected by en-zyme-linked immunosorbent assay.Pearson and Spearman correlation analyses were used to analyze the corre-lations between the levels of serum IGF2 and LRFN5 in the poor rehabilitation group and the related indica-tors of neurological function.Logistic regression was used to analyze the influencing factors of poor neurologi-cal rehabilitation in patients with IS,and the receiver operating characteristic curve was plotted to analyze the diagnostic value of serum IGF2 and LRFN5 levels for poor neurological rehabilitation in patients with IS.Re-sults The serum IGF2 level in the study group was significantly lower than that in the control group,and the LRFN5 level was significantly higher than that in the control group.The difference was statistically significant(P<0.05).The scores of the National Institutes of Health Stroke Scale(NIHSS)at admission,the 90-day mRS score,the maximum diameter of the cerebral infarction lesion and the LRFN5 level in the poor rehabilita-tion group were significantly higher than those in the good rehabilitation group,and the IGF2 level was signifi-cantly lower than that in the good rehabilitation group.The differences were statistically significant(P<0.05).The level of serum IGF2 was negatively correlated with the NIHSS score at admission,the 90-day mRS score and the maximum diameter of cerebral infarction lesions.The level of LRFN5 was positively correlated with the NIHSS score at admission,the 90-day mRS score and the maximum diameter of cerebral infarction lesions.The level of IGF2 was negatively correlated with the level of LRFN5(P<0.05).The results of Logis-tic regression analysis showed that the 90-day mRS score and LRFN5 level were risk factors for poor neuro-logical function rehabilitation in patients with IS,and the IGF2 level was a protective factor for poor neurologi-cal function rehabilitation in patients with IS(P<0.05).The area under the curve(AUC)of the combined diagnosis of serum IGF2 and LRFN5 was 0.943(95%CI:0.902-0.984),which was significantly higher than AUC of the diagnosis by a single indicator(Zcombined with the two-IGF2=2.039,P=0.041;Zcombined with the two-LRFN5=2.139,P=0.032).Conclusion The IS patients have a significant reduction in serum IGF2 level and a signifi-cant increase in serum LRFN5 level,which are closely related to their neurological function recovery.The combination of IGF2 and LRFN5 has a certain diagnostic value for neurological function recovery in IS pa-tients.

This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male

Objective:To evaluate the diagnostic value of MRI findings in differentiating between tophus and giant cell tumors of the tendon sheath (GCTTS) in the knee.Methods:The study was a case-control study. The clinical and MRI data of patients diagnosed with knee tophus or GCTTS was retrospectively analyzed at the Affiliated Hospital of Qingdao University from September 2018 to September 2024. The study included 23 cases of tophus and 22 cases of GCTTS. MRI sequences, including T 1WI, fat-suppressed T 2WI, and proton density weighted imaging, were evaluated. Parameters including lesion signal intensity and homogeneity, margin, maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, synovial hyperplasia, joint effusion, and periarticular soft tissue swelling were recorded. Independent sample t-tests, χ2 tests, or Fisher exact tests were used to compare MRI findings between the two groups. Multivariate logistic regression was performed to identify independent predictive factors. Results:Significant differences in terms of maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, and periarticular soft tissue swelling between the two groups were found (all P<0.05). No significant differences for other parameters were observed (all P>0.05). Lesion location and ligament/tendon parenchymal involvement demonstrated the highest sensitivity and specificity for distinguishing the two diseases. The sensitivity and specificity values for lesion location were 0.78 and 0.95. The sensitivity and specificity values for ligament/tendon involvement were 0.78 and 1.00. Multivariate logistic regression identified lesion location (inside or outside the synovial cavity) as an independent predictor for differentiating tophus from GCTTS ( OR=31.48, 95% CI 1.58-625.69, P=0.024). Conclusion:The location of the lesion (inside or outside the synovial cavity) and involvement of ligament/tendon parenchyma are critical factors in differentiating knee tophus from GCTTS. Additionally, lesion location serves as an independent predictor for distinguishing between the two conditions.

Objective To investigate the effect of 3D technology combined with smartphones in problem-based learning(PBL)for neuroendoscopy.Methods 82 trainees who were enrolled from January 2021 to January 2023 were selected as the research subjects.A randomized controlled trial was conducted,and the subjects were divided into a control group and an experimental group.PBL and 3D technology combined with smartphone-assisted PBL were implemented respectively for two groups of students.The data were analyzed using t-test.Teaching satisfac-tion is evaluated by 2 test.Results The results of the in-operation examination and theoretical examination of the ex-perimental group students were found to be higher than those of the control group students(t=8.630,6.087,P＜0.001),the satisfaction scores of students and teachers showing that the satisfaction of the experimental group was higher than that of the control group(x2=4.213,6.301,7.026,P＜0.01).Conclusion In the PBL of neuroendosco-py,the use of 3D technology combined with smart phones as an auxiliary teaching system can effectively improve students'sense of participation,reduce the difficulty of skull base anatomy learning,and improve students'theo-retical and surgical assessment scores and teaching satisfaction.

Objective:To screen the risk factors for non-complete procedural success of transvenous lead extraction(TLE),and to establish a prediction model based on the results and evaluate its predictive efficacy.Methods:A total of 1 029 patients who underwent TLE in Peking University People's Hospital from January 2014 to December 2020 were enrolled and divided into training set(n=720)and validation set(n=309)using the random number method.There were no statistically significant differences among the variables in the training set and the validation set.The training set was divided into the complete procedural success(CPS)group(n=664)and the non-CPS group(n=56).Univariate analysis was employed to screen the relevant indicators of non-CPS,followed by binary logistic regression analysis to identify the independent risk factors of non-CPS.Subsequently,a predictive model and nomogram were constructed.The receiver operating characteristic(ROC)curve analysis was applied to evaluate the ability of the model to distinguish non-CPS from TLE patients in the training set and validation set.The Hosmer-Lemeshow goodness-of-fit test was used to assess the consistency between the predicted risk and the actual risk of the model.Results:Univariate analysis showed that the relevant variables with P<0.1 including the age at the first implantation of the lead,the number of leads extracted,the oldest dwell time of lead extracted,the presence of abandoned leads,non-manual traction for lead extracted,the number of extracted leads>3,bilateral lead implantation,and the indications for TLE.The binary logistic regression analysis revealed that the presence of abandoned leads(OR=2.252,95%CI:1.111-4.564,P=0.024),the oldest dwell time of the extracted leads(OR=1.009,95%CI:1.005-1.012,P<0.001),and the number of extracted leads>3(OR=3.177,95%CI:1.306-7.733,P=0.011)were independent risk factors for non-CPS of TLE.ROC curve analysis revealed that the area under the ROC curve(AUC)of the training set was 0.80(95%CI:0.75-0.85,P<0.001).The AUC of the validation set was 0.81(95%CI:0.72-0.90,P<0.001).The Hosmer-Lemeshow goodness-of-fit test indicated that the P values of both the training set(P=0.089)and the validation set(P=0.136)were greater than 0.05.Conclusions:The presence of abandoned leads,the oldest dwell time of lead extracted,and the number of extracted leads>3 are independent risk factors for non-CPS in patients undergoing TLE.The nomogram model based on the above factors has satisfactory predictive ability.

Objective In the clinical teaching of neurosurgery,the traditional Problem-Based Learning(PBL)teaching model faces systematic challenges such as the disconnection between the training cycle of specialized talents and technological iteration,the limitation of practical opportunities due to hospital infections,and the inefficient allocation of teaching resources.It provides a new path for teaching reform based on the deep integration of Artificial Intelligence and PBL,but it still needs to address issues such as differences in intern participation,insufficient technical adaptability of instructors,and fragmented resource allocation.Based on these problems,a collaborative mechanism of"technology development-talent cultivation"and a multi-dimensional optimization path of"intern participation-instructor training-hospital resource input"are proposed.On this basis,through collaborative strategies such as strengthening the incentive mechanism for autonomous learning,establishing a standardized instructor training system,and building a dynamic resource allocation platform,the management of neurosurgery clinical teaching is promoted towards intelligence,personalization,and systematization.

Objective:To screen the risk factors for non-complete procedural success of transvenous lead extraction(TLE),and to establish a prediction model based on the results and evaluate its predictive efficacy.Methods:A total of 1 029 patients who underwent TLE in Peking University People's Hospital from January 2014 to December 2020 were enrolled and divided into training set(n=720)and validation set(n=309)using the random number method.There were no statistically significant differences among the variables in the training set and the validation set.The training set was divided into the complete procedural success(CPS)group(n=664)and the non-CPS group(n=56).Univariate analysis was employed to screen the relevant indicators of non-CPS,followed by binary logistic regression analysis to identify the independent risk factors of non-CPS.Subsequently,a predictive model and nomogram were constructed.The receiver operating characteristic(ROC)curve analysis was applied to evaluate the ability of the model to distinguish non-CPS from TLE patients in the training set and validation set.The Hosmer-Lemeshow goodness-of-fit test was used to assess the consistency between the predicted risk and the actual risk of the model.Results:Univariate analysis showed that the relevant variables with P<0.1 including the age at the first implantation of the lead,the number of leads extracted,the oldest dwell time of lead extracted,the presence of abandoned leads,non-manual traction for lead extracted,the number of extracted leads>3,bilateral lead implantation,and the indications for TLE.The binary logistic regression analysis revealed that the presence of abandoned leads(OR=2.252,95%CI:1.111-4.564,P=0.024),the oldest dwell time of the extracted leads(OR=1.009,95%CI:1.005-1.012,P<0.001),and the number of extracted leads>3(OR=3.177,95%CI:1.306-7.733,P=0.011)were independent risk factors for non-CPS of TLE.ROC curve analysis revealed that the area under the ROC curve(AUC)of the training set was 0.80(95%CI:0.75-0.85,P<0.001).The AUC of the validation set was 0.81(95%CI:0.72-0.90,P<0.001).The Hosmer-Lemeshow goodness-of-fit test indicated that the P values of both the training set(P=0.089)and the validation set(P=0.136)were greater than 0.05.Conclusions:The presence of abandoned leads,the oldest dwell time of lead extracted,and the number of extracted leads>3 are independent risk factors for non-CPS in patients undergoing TLE.The nomogram model based on the above factors has satisfactory predictive ability.

Objective In the clinical teaching of neurosurgery,the traditional Problem-Based Learning(PBL)teaching model faces systematic challenges such as the disconnection between the training cycle of specialized talents and technological iteration,the limitation of practical opportunities due to hospital infections,and the inefficient allocation of teaching resources.It provides a new path for teaching reform based on the deep integration of Artificial Intelligence and PBL,but it still needs to address issues such as differences in intern participation,insufficient technical adaptability of instructors,and fragmented resource allocation.Based on these problems,a collaborative mechanism of"technology development-talent cultivation"and a multi-dimensional optimization path of"intern participation-instructor training-hospital resource input"are proposed.On this basis,through collaborative strategies such as strengthening the incentive mechanism for autonomous learning,establishing a standardized instructor training system,and building a dynamic resource allocation platform,the management of neurosurgery clinical teaching is promoted towards intelligence,personalization,and systematization.

Objective To investigate the effect of 3D technology combined with smartphones in problem-based learning(PBL)for neuroendoscopy.Methods 82 trainees who were enrolled from January 2021 to January 2023 were selected as the research subjects.A randomized controlled trial was conducted,and the subjects were divided into a control group and an experimental group.PBL and 3D technology combined with smartphone-assisted PBL were implemented respectively for two groups of students.The data were analyzed using t-test.Teaching satisfac-tion is evaluated by 2 test.Results The results of the in-operation examination and theoretical examination of the ex-perimental group students were found to be higher than those of the control group students(t=8.630,6.087,P＜0.001),the satisfaction scores of students and teachers showing that the satisfaction of the experimental group was higher than that of the control group(x2=4.213,6.301,7.026,P＜0.01).Conclusion In the PBL of neuroendosco-py,the use of 3D technology combined with smart phones as an auxiliary teaching system can effectively improve students'sense of participation,reduce the difficulty of skull base anatomy learning,and improve students'theo-retical and surgical assessment scores and teaching satisfaction.

Objective:To evaluate the diagnostic value of MRI findings in differentiating between tophus and giant cell tumors of the tendon sheath (GCTTS) in the knee.Methods:The study was a case-control study. The clinical and MRI data of patients diagnosed with knee tophus or GCTTS was retrospectively analyzed at the Affiliated Hospital of Qingdao University from September 2018 to September 2024. The study included 23 cases of tophus and 22 cases of GCTTS. MRI sequences, including T 1WI, fat-suppressed T 2WI, and proton density weighted imaging, were evaluated. Parameters including lesion signal intensity and homogeneity, margin, maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, synovial hyperplasia, joint effusion, and periarticular soft tissue swelling were recorded. Independent sample t-tests, χ2 tests, or Fisher exact tests were used to compare MRI findings between the two groups. Multivariate logistic regression was performed to identify independent predictive factors. Results:Significant differences in terms of maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, and periarticular soft tissue swelling between the two groups were found (all P<0.05). No significant differences for other parameters were observed (all P>0.05). Lesion location and ligament/tendon parenchymal involvement demonstrated the highest sensitivity and specificity for distinguishing the two diseases. The sensitivity and specificity values for lesion location were 0.78 and 0.95. The sensitivity and specificity values for ligament/tendon involvement were 0.78 and 1.00. Multivariate logistic regression identified lesion location (inside or outside the synovial cavity) as an independent predictor for differentiating tophus from GCTTS ( OR=31.48, 95% CI 1.58-625.69, P=0.024). Conclusion:The location of the lesion (inside or outside the synovial cavity) and involvement of ligament/tendon parenchyma are critical factors in differentiating knee tophus from GCTTS. Additionally, lesion location serves as an independent predictor for distinguishing between the two conditions.