Background: The in-hospital mortality rate in patients undergoing percutaneous transhepatic biliary drainage (PTBD) for malignant obstructive jaundice (MOJ) is high. There are few reports on the risk factors associated with hospital death after MOJ, with most of them being retrospective analyses of single factors. Therefore, this study aimed to assess pre-, intra-, and post-procedure risk factors that were independently associated with increased in-hospital mortality in MOJ patients who underwent PTBD. Methods: One-hundred fifty-five patients with MOJ who underwent initial PTBD were included in this study. A total of 25 pre-, 4 intra-, and 6 post-procedure factors potentially related to in-hospital mortality were assessed by univariate and multivariate analyses. Results: The in-hospital mortality rate was 16.8% (26/155). Of 25 pre-procedure variables analyzed, Child-Pugh classification C, creatinine level ≥6.93 μmol/L, and quality-of-life score (≤30) were found to be significant in univariate and multivariate analyses. Increased mortality was observed in patients with 2 or more risk factors, which was significantly different from patients with no risk factors or one risk factor ( P < .01). None of the intra-procedure factors were important in identifying patients at risk of death. Multivariate analysis indicated post-PTBD cholangitis and unsuccessful drainage as post-procedure risk factors that correlated with in-hospital death. Conclusion: It was identified that in-hospital mortality was associated with 3 pre-procedure and 2 post-procedure risk factors, such as the liver function classification, quality-of-life score of cancer patients, creatinine level, PTBD-associated biliary duct infection, and unsuccessful drainage.

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Background Treatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under‐ or overtreatment. Purpose To develop and validate an MRI texture‐based model to predict the mitotic index and its risk classification. Study Type Retrospective. Population Ninety‐one patients with histologically‐confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort. Field Strength/Sequence T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and dynamic contrast‐enhanced three‐dimensional volumetric interpolated breath‐hold examination (3D‐VIBE) at 1.5T. Assessment GIST images were manually segmented by two independent radiologists using ITK‐SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus. Statistical Tests The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk. Results The texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922). Data Conclusion The texture‐based model can be used to predict GIST mitotic index and risk classification preoperatively. Level of Evidence 2. Technical Efficacy Stage 3

Transcranial direct-current stimulation (DCS) offers a method for noninvasive neuromodulation usable in basic and clinical human neuroscience. Laser-speckle contrast imaging (LSCI), a powerful, low-cost method for obtaining images of dynamic systems, can detect regional blood-flow distributions with high spatial and temporal resolutions. Here, we used LSCI for measuring DCS-induced cerebral blood flow in real-time. Results showed that the change-rate of cerebral blood flow could reach approximately 10.1 ± 5.1% by DCS, indicating that DCS can increase cerebral blood flow and alter cortical hemodynamic responses. Thus, DCS shows potential for the clinical treatment and rehabilitation of ischemic strokes.

Diffusion of water molecules closely related to physiological and pathological information of brain tissue. Low-intensity transcranial ultrasound stimulation (TUS) has advantages of noninvasive, high spatial resolution and penetration depth. Previous studies have demonstrate that TUS can modulate neuronal activity and alter cortical hemodynamic. However, how TUS affect diffusion of water molecules remain unclear. In this paper, in order to evaluate the effect of low-intensity TUS on the diffusion of water molecules in brain tissue, diffusion-weighted magnetic resonance (MR) imaging was performed in 19 healthy Sprague-Dawley rats in sham surgery group (six rats) and TUS group (thirteen rats) Subsequently, rats were stimulated by low-intensity transcranial ultrasound for 5 min in TUS group. Finally, rats of sham surgery group and TUS group were imaged again by diffusion-weighted MR imaging. The apparent diffusion coefficient (ADC) was measured in caudate putamen region and middle brain motor-related region of each rat in sham surgery group and TUS group. Surgery-related and TUS-related changes were calculated using a statistical analysis. The mean ADC values of marked regions of six rats in sham surgery group were 0.743 ± 0.031 (pre-surgery) and 0.745 ± 0.029 (post-surgery). The mean ADC values of marked regions of 13 rats in TUS group were 0.749 ± 0.032 (pre-TUS) and 0.712 ± 0.033 (post-TUS) Compared to the pre-TUS values, the mean ADC values of the rats decreased 4.9% (*P < 0.05) post-TUS. These results of this study demonstrate that low-intensity TUS can restrict the diffusion of water molecules in brain tissue.

The relationship between extracellular space (ECS) diffusion parameters and brain drug clearance is not well-studied, especially in the context of Parkinson's disease (PD). Therefore, we used a rodent model of PD to explore the distribution and clearance of a magnetic resonance tracer. Forty male Sprague Dawley rats were randomized into four different groups: a PD group, a Madopar group (PD + Madopar treatment), a sham group, and a control group. All rats received an injection of the extracellular tracer gadolinium-diethylene triaminepentacetic acid (Gd-DTPA) directly into the substantia nigra (SN). ECS diffusion parameters including the effective diffusion coefficient (D*), clearance coefficient (k'), ratio of the maximum distribution volume of the tracer (Vd-max%), and half-life (t1/2) were measured. We found that all parameters were significantly increased in the PD group compared to the other three groups (D*: F = 5.774, p = 0.0025; k': F = 20.00, P < 0.0001; Vd-max%: F = 12.81, P < 0.0001; and t1/2: F = 23.35, P < 0.0001). In conclusion, the PD group exhibited a wider distribution and lower clearance of the tracer compared to the other groups. Moreover, k' was more sensitive than D* for monitoring morphological and functional changes in the ECS in a rodent model of PD.

Background: Low-intensity transcranial ultrasound (LITUS) may have a therapeutic effect on Parkinson’s disease (PD) patients to some extent. Fractional anisotropy (FA) and relaxation time T2∗ that indicate the integrity of fiber tracts and iron concentrations in brain tissue have been used to evaluate the therapeutic effects of LITUS.Purpose: This study aims to use FA and T2∗ values to evaluate the therapeutic effects of LITUS in a PD rat model.Materials and Methods: Twenty Sprague-Dawley rats were randomly divided into a hemi-PD group (n = 10) and a LITUS group (n = 10). Single-shot spin echo echo-planar imaging and fast low-angle shot T2WI sequences at 3.0 T were used. The FA and T2∗ values on the right side of the substantia nigra (SN) pars compacta were measured to evaluate the therapeutic effect of LITUS in the rats.Results: One week after PD-like signs were induced in the rats, the FA value in the LITUS group was significantly larger compared with the PD group (0.214 ± 0.027 vs. 0.340 ± 0.032, t = 2.864, P = 0.011). At the 5th and 6th weeks, the FA values in the LITUS group were significantly smaller compared with the PD group (5th week: 0.290 ± 0.037 vs. 0.405 ± 0.027, t = 2.385, P = 0.030; 6th week: 0.299 ± 0.021 vs. 0.525 ± 0.028, t = 6.620, P &lt; 0.0001). In the 5th and 6th weeks, the T2∗ values in the injected right SN of the LITUS group were significantly higher compared with the PD group (5th week, 12.169 ± 0.826 in the LITUS group vs. 7.550 ± 0.824 in the PD group; 6th week, 11.749 ± 0.615 in the LITUS group vs. 7.550 ± 0.849 in the PD group).Conclusion: LITUS had neuroprotective effects and can reduce the damage of 6-OHDA-induced neurotoxicity in hemi-PD rats. The combination of FA and T2∗ assessments can potentially serve as a new and effective method to evaluate the therapeutic effects of LITUS.

BackgroundHistological grade is one of the most important prognostic factors of endometrial carcinoma (EC) and when selecting preoperative treatment methods, conducting accurate preoperative grading is of great significance.PurposeTo develop a magnetic resonance imaging (MRI) radiomics-based nomogram for discriminating histological grades 1 and 2 (G1 and G2) from grade 3 (G3) EC.MethodsThis was a retrospective study included 358 patients with histologically graded EC, stratified as 250 patients in a training cohort and 108 patients in a test cohort. T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) were performed via 1.5-Tesla MRI. To establish ModelADC, the region of interest was manually outlined on the EC in an apparent diffusion coefficient (ADC) map. To establish the radiomic model (ModelR), EC was manually segmented by two independent radiologists and radiomic features were extracted. The Radscore was calculated based on the least absolute shrinkage and selection operator regression. We combined the Radscore with carbohydrate antigen 125 (CA125) and body mass index (BMI) to construct a mixed model (ModelM) and develop the predictive nomogram. Receiver operator characteristic (ROC) and calibration curves were assessed to verify the prediction ability and the degree of consistency, respectively.ResultsAll three models showed some amount of predictive ability. Using ADC alone to predict the histological risk of EC was limited in both the cohort [area under the curve (AUC), 0.715; 95% confidence interval (CI), 0.6509–0.7792] and test cohorts (AUC, 0.621; 95% CI, 0.515–0.726). In comparison with ModelADC, the discrimination ability of ModelR showed improvement (Delong test, P &lt; 0.0001 for both the training and test cohorts). ModelM, established based on the combination of radiomic and clinical indicators, showed the best level of predictive ability in both the training (AUC, 0.925; 95% CI, 0.898–0.951) and test cohorts (AUC, 0.915; 95% CI, 0.863–0.968). Calibration curves suggested a good fit for probability (Hosmer–Lemeshow test, P = 0.673 and P = 0.804 for the training and test cohorts, respectively).ConclusionThe described radiomics-based nomogram can be used to predict EC histological classification preoperatively.