Background Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method. Methods We retrospectively reviewed 1157 patients with spontaneous ICH who underwent initial computed tomography (CT) scan within 6 h and follow-up CT scan within 72 h from symptom onset in our hospital between September 2013 and August 2018. Hematoma region was manually segmented at each slice to guarantee the measurement accuracy of hematoma volume. Hematoma expansion was defined as a proportional increase of hematoma volume > 33% or an absolute growth of hematoma volume > 6 mL from initial CT scan to follow-up CT scan. Univariate and multivariate analyses were performed to assess the association between clinical variables and hematoma expansion. SVM machine learning model was developed to predict hematoma expansion. Findings 246 of 1157 (21.3%) patients experienced hematoma expansion. Multivariate analyses revealed the following 6 independent factors associated with hematoma expansion: male patient (odds ratio [OR] = 1.82), time to initial CT scan (OR = 0.73), Glasgow Coma Scale (OR = 0.86), fibrinogen level (OR = 0.72), black hole sign (OR = 2.52), and blend sign (OR = 4.03). The SVM model achieved a mean sensitivity of 81.3%, specificity of 84.8%, overall accuracy of 83.3%, and area under receiver operating characteristic curve (AUC) of 0.89 in prediction of hematoma expansion. Interpretation The designed SVM model presented good performance in predicting hematoma expansion from routinely available variables. Fund This work was supported by , China, , China (LQ15H180002), the , China (Y20180112), Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of , and , China. The funders had no role in study design, data collection, data analysis, interpretation, writing of the report.

Highlights Brain network dysfunction is the core mechanism of Parkinson’s disease. Fixel-based analysis is a novel method to detect fiber-specific white matter alterations. Degeneration of corpus callosum fiber density was related to the motor symptoms. Increased cortical spinal tract fiber density may compensate the motor impairments in Parkinson’s disease. Fiber alterations were different across disease stages of Parkinson’s disease.

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Basic fibroblast growth factor (bFGF) has shown great therapeutic effects for diabetic nephropathy (DN). However, its clinical applications are limited due to its short half-life, low stability and poor penetration. Herein, a bFGF-loaded liposome (bFGF-lip) was constructed and combined with ultrasound-targeted microbubble destruction (UTMD) to overcome these drawbacks. bFGF-lip exhibited spherical morphology with a diameter of 171.1 ± 14.2 nm and a negative zeta potential of -5.15 ± 2.08 mV, exhibiting a sustained-release profile of bFGF. DN rat models were successfully induced by streptozotocin. After treatment with bFGF-lip + UTMD, the concentration of bFGF in kidney of DN rats was significantly enhanced in comparison with free bFGF treatment. Additionally, the morphology and the function of the kidneys were obviously recovered after bFGF-lip + UTMD treatment as shown by ultrasonography and histological analyse. The molecular mechanism was associated with the inhibition of renal inflammation. After treatment with bFGF-lip + UTMD, the activation of NF-κB was obviously reduced in the renal tissues, and downstream inflammatory mediators including TGF-β1, MCP-1, IL-6 and IL-1β were also down regulated. In addition, inflammation-induced cellular apoptosis of renal tubular cells was also significantly inhibited by detecting Bax, caspase-3 and Bcl-2. Therefore, bFGF-lip in combination with UTMD might be a potential strategy to reverse the progression of early DN.