Integrated biomarker profiling of the metabolome associated with type 2 diabetes mellitus among Tibetan in China.

INTRODUCTION: Metabolomic signatures of type 2 diabetes mellitus (T2DM) in Tibetan Chinese population, a group with high diabetes burden, remain largely unclear. Identifying the serum metabolite profile of Tibetan T2DM (T-T2DM) individuals may provide novel insights into early T2DM diagnosis and intervention. METHODS: Hence, we conducted untargeted metabolomics analysis of plasma samples from a retrospective cohort study with 100 healthy controls and 100 T-T2DM patients by using liquid chromatography-mass spectrometry. RESULTS: The T-T2DM group had significant metabolic alterations that are distinct from known diabetes risk indicators, such as body mass index, fasting plasma glucose, and glycosylated hemoglobin levels. The optimal metabolite panels for predicting T-T2DM were selected using a tenfold cross-validation random forest classification model. Compared with the clinical features, the metabolite prediction model provided a better predictive value. We also analyzed the correlation of metabolites with clinical indices and found 10 metabolites that were independently predictive of T-T2DM. CONCLUSION: By using the metabolites identified in this study, we may provide stable and accurate biomarkers for early T-T2DM warning and diagnosis. Our study also provides a rich and open-access data resource for optimizing T-T2DM management.

Impairments in intrinsic functional networks in type 2 diabetes: A meta-analysis of resting-state functional connectivity.

Type 2 diabetes mellitus (T2DM) is associated with abnormal communication among large-scale brain networks, revealed by resting-state functional connectivity (rsFC), with inconsistent results between studies. We performed a meta-analysis of seed-based rsFC studies to identify consistent network connectivity alterations. Thirty-three datasets from 30 studies (1014 T2DM patients and 902 healthy controls [HC]) were included. Seed coordinates and between-group effects were extracted, and the seeds were divided into networks based on their location. Compared to HC, T2DM patients showed hyperconnectivity and hypoconnectivity within the DMN, DMN hypoconnectivity with the affective network (AN), ventral attention network (VAN) and frontal parietal network, and DMN hyperconnectivity with the VAN and visual network. T2DM patients also showed AN hypoconnectivity with the somatomotor network and hyperconnectivity with the VAN. T2DM illness durations negatively correlated with within-DMN rsFC. These DMN-centered impairments in large-scale brain networks in T2DM patients may help to explain the cognitive deficits associated with T2DM.

Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms.

OBJECTIVE: To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images. METHODS: A total of 151 ASD children and 151 age-matched typically developing (TD) controls were included in this study. The data from these subjects were assigned to training and validation datasets. An additional 20 ASD children and 25 TD controls were acquired, whose data were utilized in an independent test set. All subjects underwent cMRI and diffusion-weighted imaging examination of the brain. We developed a series of DL models to separate ASD from TD based on the cMRI and ADC data. The seven models used include five single-sequence models (SSMs), one dominant-sequence model (DSM), and one all-sequence model (ASM). To enhance the feature detection of the models, we embed an attention mechanism module. RESULTS: The highest AUC (0.824 ~ 0.850) was achieved when applying the SSM based on either FLAIR or ADC to the validation and independent test sets. A DSM using the combination of FLAIR and ADC showed an improved AUC in the validation (0.873) and independent test sets (0.876). The ASM also showed better diagnostic value in the validation (AUC = 0.838) and independent test sets (AUC = 0.836) compared to the SSMs. Among the models with attention mechanism, the DSM achieved the highest diagnostic performance with an AUC, accuracy, sensitivity, and specificity of 0.898, 84.4%, 85.0%, and 84.0% respectively. CONCLUSIONS: This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. KEY POINTS: • Deep learning models based on conventional MRI and ADC can be used to diagnose ASD. • The model (DSM) based on the FLAIR and ADC sequence achieved the best diagnostic performance with an AUC of 0.836 in the independent test sets. • The attention mechanism further improved the diagnostic performance of the models.

[A study of hemodynamic changes in the arteries of rabbit's eye caused by acute high intraocular pressure].

OBJECTIVE: Using the color Doppler flow imaging (CDFI) to measure and to study the hemodynamic changes of rabbit's retrobulbar artery caused by changes of intraocular pressure (IOP). To provide information for screening of drugs that improves the blood circulation and decrease the IOP. METHODS: This is a self-contrast research performed at the left eye: high IOP was induced, then the hemodynamic changes of rabbit's retrobulbar artery at different IOP stages were observed. RESULTS: Rabbit's ophthalmic artery (OA), ciliary artery (CA), short posterior ciliary artery (SPCA), central retinal artery (CRA) are low resistant artery. When the IOP raised rapidly, the velocity of blood flow became slower and resistance force index became higher. The hemodynamic changes could not return to normal even after the IOP decreased to the normal level. CONCLUSION: A short-term high IOP can influence the hemodynamic pattern of retro-bulbar arteries. Color Doppler flow imaging is a valuable method for monitoring these changes.