BACKGROUND Dysfunction of the glymphatic system in the brain in different stages of altered glucose metabolism and its influencing factors are not well characterized. AIM To investigate the function of the glymphatic system and its clinical correlates in patients with different glucose metabolism states, the present study employed diffusion tensor imaging along the perivascular space (DTI-ALPS) index. METHODS Sample size was calculated using the pwr package in R software. This cross-sectional study enrolled 22 patients with normal glucose metabolism (NGM), 20 patients with prediabetes, and 22 patients with type 2 diabetes mellitus (T2DM). A 3.0T magnetic resonance imaging was used to evaluate the function of the glymphatic system. The mini-mental state examination (MMSE) was used to assess general cognitive function. The DTI-ALPS index of bilateral basal ganglia and the mean DTI-ALPS index was calculated. Further, the correlation between DTI-ALPS and clinical features was assessed. RESULTS The left-side, right-side, and mean DTI-ALPS index in the T2DM group were significantly lower than that in the NGM group. The right-side DTI-ALPS and mean DTI-ALPS index in the T2DM group were significantly lower than those in the prediabetes group. DTI-ALPS index lateralization was not observed. The MMSE score in the T2DM group was significantly lower than that in the NGM and prediabetes group. After controlling for sex, the left-side DTI-ALPS and mean DTI-ALPS index in the prediabetes group were positively correlated with 2-hour postprandial blood glucose level; the left-side DTI-ALPS index was negatively correlated with total cholesterol and low-density lipoprotein level. The right-side DTI-ALPS and mean DTI-ALPS index were negatively correlated with the glycosylated hemoglobin level and waist-to-hip ratio in the prediabetes group. The left-side, right-side, and mean DTI-ALPS index in the T2DM group were positively correlated with height. The left-side and mean DTI-ALPS index in the T2DM group were negatively correlated with high-density lipoprotein levels. CONCLUSION Cerebral glymphatic system dysfunction may mainly occur in the T2DM stage. Various clinical variables were found to affect the DTI-ALPS index in different glucose metabolism states. This study enhances our understanding of the pathophysiology of diabetic brain damage and provides some potential biological evidence for its early diagnosis.

Parkinson's disease is a progressive degenerative nervous system disease. Recent studies have shown that secondary changes in the GABA system play directly affect the pathogenesis of PD. There is still much debate about GABA concentrations because currently, GABA concentrations in the brain tissue are obtained indirectly by measuring its concentration in the blood and cerebrospinal fluid. These results are unreliable. Magnetic resonance spectroscopy (MRS) is the only noninvasive method for evaluating the concentration of metabolites in living brain tissue and has been widely applied in research and clinical practice. In addition, combining MEGA-PRESS technology with LCModel software for quantitative GABA measurements is largely recognized. At present, the PD monkeys model in primates has been increasingly proficient. Primates are more similar to humans in terms of brain structure and function than other animals. However, 3.0 T MRS studies involving the PD monkey model to measure metabolites in living subjects with PD are still rare. The study was performed at 3.0 T MRI with control monkeys and PD monkeys that were injected methyl-phenyl-tetrahydropyridine (MPTP) in one side of common carotid artery before and 3 months after successful model establishment to measure GABA concentrations in the bilateral striatum. Behavioral observations were performed for all animals, and the behavioral score was recorded. After 3 months, the GABA concentration in the bilateral striatum was measured in both groups by high-performance liquid chromatography (HPLC). The data obtained from magnetic resonance spectroscopy (MRS) were compared with the actual measured GABA concentrations in tissues isolated from the corresponding regions, and their correlations with the behavior score were analyzed. The research objectives are to investigate the changes of γ-aminobutyric acid (GABA) concentration in the bilateral striatum of monkeys with Parkinson's disease (PD) and the value of quantitatively measuring its concentration by noninvasive 3.0 T spectroscopy. (1) The MRS results showed that the GABA concentration in the injured side of the striatum of the PD monkeys was higher than in the contralateral side, but the difference was not statistically significant (P = 0.154). Compared with that the blank control group, the GABA concentration in the striatum of the PD monkeys increased, but there was no difference between the groups (P = 0.381; P = 0.425). (2) The GABA concentration that determined from the isolated specimens by HPLC in the injured side of the striatum of the PD monkeys was significantly higher than that in the contralateral side (P < 0.01). Compared with the blank control group, the PD monkeys had higher GABA concentrations in both sides of the striatum, and there was a significant difference in the lesion side (P = 0.004), while there was a non-significant difference in the contralateral side (P = 0.475). (3) The mean GABA concentration in the injured striatum of PD monkeys determined by MRS was not significantly correlated with the behavioral score (r = 0.146, P = 0.688). The mean GABA concentration in the injured striatum determined from the isolated specimens was positively correlated with the behavioral score in the same period (r = 0.444, P = 0.038). The GABA concentration in the injured striatum of PD monkeys is increased and positively correlated with behavioral changes. Validity of noninvasive 3.0 T MRS to detect PD neurotransmitter changes is limited.

The clinical treatment of patients with severe diabetic foot (DF) is difficult. Recently, the First Affiliated Hospital of Guangxi Medical University began to apply tibial transverse transport (TTT) in patients with DF. This treatment has achieved significant effects, but its mechanism of action is unclear. Recently, microcirculation and the pathogenesis of diabetes have become the foci of research in this field. The evaluation of the possible mechanism of microcirculation reconstruction requires relevant indicators. The aim of this study was to investigate the value of computed tomography perfusion in evaluations of the curative effects of TTT and establish corresponding quantitative evaluation indicators. Twelve patients with DF treated with TTT were recruited as the research participants. All diabetic feet were divided into the transport foot (TF) group and nontransport foot (NTF) group according to whether the patients underwent TTT. All patients underwent CT shuttle scanning preoperatively and 8 weeks after surgery. The shuttle scan data were transferred to Siemens VPCT body software and postprocessed with Customized Tumor2. We chose the TF posterior tibial artery from a distance of approximately 15 cm to the bifurcation of the plantar medial artery and the lateral plantar artery as the input artery. We selected the centre of the bilateral medial plantar muscle group on the coronal and axial regions of interest. We applied a deconvolution approach to obtain data from both sides of the plantar tissue perfusion. Skin temperature (ST) detection was performed with an ST gun to measure the average ST values in the dorsal and plantar areas, the first and fifth heads of the phalanges, and the medial and lateral malleolus points of both feet of patients with DF preoperatively and 8 weeks after surgery. The preoperative and postoperative ST values of the patients in the TF group were 30.73 ± 1.86 °C and 32.22 ± 1.51 °C, respectively. The preoperative and postoperative ST values for the patients in the NTF group were 30.93 ± 2.65 °C and 32.07 ± 2.09 °C, respectively. There were significant differences in the preoperative and postoperative data between the TF (P = 0.001) and NTF (P = 0.013) groups. In the patients with DF who underwent TTT, there were both preoperative and postoperative differences inside the medial plantar muscle group in the relative blood volume (rBV) value and relative mean transit time (rMTT) (P = 0.027, P = 0.026, respectively). The postoperative BV in the NTF group was increased compared with the preoperative BV (P = 0.006). There were significant differences in relative BV, relative mean transit time, and ST between the two groups before and after surgery, and the postoperative BV in the NTF group and ST values in the two groups were increased compared with the preoperative values. The BV in the NTF group and the ST values in the two groups were effective indicators in evaluating the changes between preoperative and postoperative perfusion. These results indicate that TTT could increase plantar tissue perfusion as assessed by BV and ST; this increase was among the reasons for the surface healing of severe DF ulcers. Preoperative CT perfusion evaluation can provide relevant information of blood vessels and microcirculation for clinical operation, and postoperative CT perfusion evaluation can provide postoperative efficacy evaluation for clinical operation. All patients received information about the study and signed a specific informed consent. Approval for this study was granted by the regional ethics committee [Regional Ethics Committee of the First Affiliated Hospital of Guangxi Medical University, China [(2018-(KY-E-069].

Background and purpose: The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients.Methods: A total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10‐fold cross validation was applied to the training dataset.Results: A model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations.Conclusions: Radiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% confidence interval [CI] 0.774 – 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using fractional flow reserve (FFR) and FAI, could significantly improve the prediction of MACE in patients with CAD, thereby potentially aiding clinical decision making.

Objective: Telomerase reverse transcriptase (TERT) promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas.Methods: This was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3,948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 gray level run length matrix, 14 gray level dependence matrix, 16 gray level run length matrix, 16 gray level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis.Results: Among the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status.Conclusion: The LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models.