Stroke is the leading cause of death in Chinese Residents. Intracerebral hemorrhage accounts for 10-20% of all stroke, and the degree of nerve function damage is often more severe than that of ischemic stroke. The corticospinal tract injury is an important mechanism for motor function defect after stroke. Diffusion tensor imaging (DTI) is the only functional magnetic resonance imaging to noninvasively detect white matter tracts, which can objectively evaluate the degree of fiber bundle damage. This article reviews the role of DTI in predicting the outcome of motor function in patients with intracerebral hemorrhage.Stroke is the leading cause of death in Chinese Residents. Intracerebral hemorrhage accounts for 10-20% of all stroke, and the degree of nerve function damage is often more severe than that of ischemic stroke. The corticospinal tract injury is an important mechanism for motor function defect after stroke. Diffusion tensor imaging (DTI) is the only functional magnetic resonance imaging to noninvasively detect white matter tracts, which can objectively evaluate the degree of fiber bundle damage. This article reviews the role of DTI in predicting the outcome of motor function in patients with intracerebral hemorrhage.

Objective:To explore independent predictors for poor outcome at 3 months in elderly patients with acute cerebral infarction(ACI)treated with intravenous thrombolysis(IVT), and to develop a nomogram-based predictive model.Methods:This was a retrospective cohort study.Clinical, laboratory and imaging data of 346 elderly patients with ACI treated with IVT from January 2016 to April 2021 in our hospital were collected.Poor outcome was defined as a modified Rankin Scale(mRS)score >2 at 3 months after the stroke.Logistic regression analysis was used to screen for independent factors predicting poor outcome in elderly ACI patients treated with IVT, and a corresponding nomogram model was developed using the R software.The ROC curve, calibration plots and decision curve analysis were used to evaluate discrimination, calibration and clinical application value of the nomogram model.Results:Among 346 candidates, 109 developed a poor outcome, representing a rate of 31.5%.Logistic regression analysis showed that symptomatic hemorrhagic transformation( OR=15.647, 95% CI: 8.913-27.454), stroke severity(moderate stroke, OR=3.322, 95% CI: 1.414-7.811; moderate-severe stroke, OR=8.169, 95% CI: 4.102-16.258; severe stroke, OR=9.653, 95% CI: 5.440-17.121), stroke-associated pneumonia( OR=2.239, 95% CI: 1.134-4.420), and heart failure( OR=2.758, 95% CI: 1.424-5.336)were independent predictors for poor outcome at 3 months in elderly ACI patients treated with intravenous thrombolysis(all P<0.05). With the area under curve(AUC-ROC)value at 0.85(95% CI: 0.80-0.89), the nomogram model, which was composed of the above four predictors, demonstrated good discrimination.On the calibration plot, the mean absolute error was 0.020, indicating that the model had good calibration.Decision curve analysis revealed that the model had good clinical application value. Conclusions:The nomogram model composed of symptomatic hemorrhagic transformation, stroke severity, stroke-associated pneumonia and heart failure may predict poor outcome at 3 months in elderly ACI patients treated with IVT, with high prediction accuracy and high clinical application value.

L-homophenylalanine (L-HPA) is an important non-natural amino acid that has been used as a key intermediate for the synthesis of Puli drugs for the treatment of hypertension. At present, L-HPA is synthesized using chemical methods, which has the disadvantages of expensive raw materials, tedious steps and serious pollution. Therefore, researchers have conducted in-depth research on the enzymatic production of L-HPA. This review summarizes the research progress on the enzymatic synthesis of L-HPA, including the dehydrogenase process, the transaminase process, the hydantoinase process, and the decarboxylase process, with the hope to facilitate the industrial production of L-HPA.
Amino Acids ; Environmental Pollution ; Industry ; Protein Biosynthesis