Effects of hypertension on subcortical nucleus morphological alternations in patients with type 2 diabetes.

Objectives: Type 2 diabetes mellitus(T2DM) and hypertension(HTN) are common comorbidities, and known to affect the brain. However, little is known about the effects of the coexisting HTN on brain in T2DM patients. So we aim to investigate the impact of HTN on the subcortical nucleus morphological alternations in T2DM patients. Materials & methods: This work was registered by the clinicaltrials.gov (grant number NCT03564431). We recruited a total of 92 participants, comprising 36 only T2DM patients, 28 T2DM patients with HTN(T2DMH) and 28 healthy controls(HCs) in our study. All clinical indicators were assessed and brain image data was collected for each participant. Voxel-based morphometry(VBM), automatic volume and vertex-based shape analyses were used to determine the subcortical nucleus alternations from each participant's 3D-T1 brain images and evaluate the relationship between the alternations and clinical indicators. Results: T2DMH patients exhibited volumetric reduction and morphological alterations in thalamus compared to T2DM patients, whereas T2DM patients did not demonstrate any significant subcortical alterations compared to HCs. Furthermore, negative correlations have been found between thalamic alternations and the duration of HTN in T2DMH patients. Conclusion: Our results revealed that HTN may exacerbate subcortical nucleus alternations in T2DM patients, which highlighted the importance of HTN management in T2DM patients to prevent further damage to the brain health.

Contrast enhanced magnetic resonance imaging-based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study.

Background: The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma. Methods: A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma. Results: Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753-0.881), 0.822 (95% CI: 0.727-0.916), and 0.845 (95% CI: 0.708-0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. Conclusions: The proposed contrast enhanced magnetic resonance imaging (MRI)-based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma.

[Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (2): Retrieval of Cloud Effective Radius and Water Path].

The cloud microphysical properties such as cloud effective radius and cloud water path are fundamental properties for understanding the cloud formation, radiative impacts and interactions with aerosol and precipitation. The downwelling infrared radiance spectra is studied here to retrieve microphysical properties of clouds. The sensitivity of the downwelling radiance spectra and cloud emissivity spectra to the liquid cloud and ice cloud effective radius and optical depth is analyzed. The look-up-tables are established for optically thin clouds (cloud optical depth less than 6) that rely on parameters of the slopes and differences of the emissivity spectra. These parameters include the difference in the emissivity between 862.1 and 934.9 cm(-1), the difference in the emissivity between 1 900.1 and 2 170.1 cm(-1), the slope of the cloud emissivity and the radiation between 900 and 1 000 cm(-1), the slope of the cloud emissivity and the radiation between 1 100 and 1 200 cm(-1). The look-up-tables are constrained by the incorporation of mean ozone band transmissivity between 1 050 and 1 060 cm(-1). Cloud effective radius and optical depth can be obtained with by least squares fitting between observed and modeled above-mentioned multiple spectral parameters. The cloud water path can then be derived from the experiential relationship. The inversion results are compared with the ARM baseline cloud microphysics product (MICROBASE). It is shown that, the cloud effective radius is roughly in the same order of magnitude while the water paths derived from both method are of large differences especially for the liquid cloud path. The algorithm proposed in this paper is efficient for retrieving microphysical properties of thin clouds with cloud optical depth less than 6.

[Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (1): Cloud Phase Determination].

As a key factor in the climate model, cloud phase is an important prerequisite to performing cloud property retrievals from remote sensor measurements. The ability to infer cloud phase using cloud emissivity spectra is investigated by numerical simulations. It is shown that for emissivity below 0.95, several spectral features such as the slopes, the ratios and the differences of the emissivity are consistent with the variation of cloud phase in some spectral regions. Specifically, these features include the slope of the cloud emissivity between 800 and 900 cm(-1), the slope of the cloud emissivity between 900 and 1 000 cm(-1), the difference in the mean emissivity between above-mentioned two regions, the ratio of the emissivity at 862.1 cm(-1) to the emissivity at 989.8 cm(-1), the difference in the emissivity between 862.1 and 989.8 cm(-1), the ratio of the emissivity at 1 900.1 cm-1 to the emissivity at 2 029.3 cm-1, the ratio of the mean emissivity for far-infrared region to the emissivity at 900 cm(-1). A cloud phase classifier is proposed based on support vector machines (SVM). A series of simulations including various cloud patterns are performed. The RBF kernel function parameters and the penalty factor of SVM are selected by using the genetic algorithm. The phase determination algorithm is applied for collecting data from the AERI at the SGP site. The results from the ground-based multisensor cloud phase classifier proposed by Shupe are used to validate the phase determination algorithm. It is found the two results are consistent in general. 30% clouds are indicated as opaque due to its high emissivity. The cloud with small lidar's depolarization is misclassified as clear sky by the Shupe method. It can be concluded that the proposed algorithm considering the spectral information (spectral slopes, ratios and differences) is efficient for cloud phase determination of thin cloud.

[A DTI study of the contralateral corticospinal tract modeled through simulated intracranial space-occupying lesions in macaque brain motor areas].

Recent studies found that a loss of motor function following corticospinal tract (CST) damage can, to some extent, be restored. Few studies, however, examine how space-occupying lesions in the brain motor area may affect the contralateral CTS structure and function. We performed a simulation of intracranial space-occupying lesions in the brain motor area by implanting of balloons into the brains of the two healthy macaques. Diffusion tensor imaging (DTI) was performed on the macaques' brains four times to measure the FA values of the contralateral CST operative area. The results showed that on the day of balloon implantation, the FA values had no obvious effect, but with time the effect increased, becoming increasingly apparent one week after removing the balloons. Experimental results demonstrated that this model was both feasible and reliable. After the simulated space-occupying lesions occurred in the brain motor area, DTI showed a compensatory response of the contralateral CTS, which remained for a short period of time even after the lesions were removed. This result suggests that the contralateral CST may then also contribute to recovery of limb function.