Chronic brain hypoperfusion (CBH) induces the accumulation of abnormal cellular proteins, accompanied by cognitive decline, and the autophagic-lysosomal system is abnormal in dementia. Whether CBH accounts for autophagic-lysosomal neuropathology remains unknown. Here, we show that CBH significantly increased the number of autophagic vacuoles (AVs) with high LC3-II levels, but decreased SQSTM1 and cathepsin D levels in the hippocampi of rats following bilateral common carotid artery occlusion (2VO) for 2 weeks. Further studies showed that microRNA-27a (Mir27a) was upregulated at 2 weeks compared with the sham group. Additionally, LAMP-2 proteins were downregulated by Mir27a overexpression, upregulated by Mir27a inhibition, and unchanged by binding-site mutations or miR-masks, indicating that lamp-2 is the target of Mir27a. Knockdown of endogenous Mir27a prevented the reduction of LAMP-2 protein expression as well as the accumulation of AVs in the hippocampi of 2VO rats. Overexpression of Mir27a induced, while the knockdown of Mir27a reduced, the accumulation of AVs and the LC3-II level in cultured neonatal rat neurons. The results revealed that CBH in rats at 2 weeks could induce inefficient lysosomal clearance, which is regulated by the Mir27a-mediated downregulation of LAMP-2 protein expression. These findings provide an insight into a novel molecular mechanism of autophagy at the miRNA level.
Background/Aims: The purpose of the present study was to clarify whether chronically elevated plasma neuropeptide Y (NPY) might affect heart function and cardiac remodeling in rats. Methods: Male Wistar rats were administered NPY (85 μg for 30 days) by mini-osmotic pump subcutaneously implanted between the scapulae. Associated indices for heart function, cardiac remodeling and hypertrophy were evaluated. Results: Compared to the sham group, the baseline systolic blood pressure (SBP) in rats administered NPY was significantly increased; cardiac function was significantly decreased, as indicated by reduced ejection fraction (EF), left ventricular end-systolic pressure (LVESP), maximum change velocity of left ventricular pressure in the isovolumic contraction or relaxation period (±dp/dtmax) and increased left ventricular end-diastolic pressure (LVEDP); hematoxylin-eosin (H&E) staining detection displayed enlarged cell areas and a consistent increase in heart-to-body weight ratios (HW/BW) was observed; quantitative real time PCR (qRT-PCR) and Western blot analysis showed markedly increased expressions of β-myosin heavy chain (β-MHC), calcineurin (CaN) and phosphorylated p38 proteins, while no changes were found in the expressions of p38 total protein and the phosphorylations of JNK and ERK. Conclusion: This study reported for the first time that long-term elevated plasma concentration of NPY could induce cardiac dysfunction and cardiac hypertrophy and this phenomenon could, in part, be mediated by the Ca2+/CaM-dependent CaN pathway and p38 mitogen-activated protein kinase (MAPK) signal pathway in rats.
Background
Early prediction of patients’ deterioration is helpful in early intervention for patients at greater risk of deterioration in Intensive Care Unit (ICU). This study aims to apply machine learning approaches to heterogeneous clinical data for predicting life-threatening events of patients in ICU.
Methods
We collected clinical data from a total of 3151 patients admitted to the Medical Intensive Care Unit of Peking Union Medical College Hospital in China from January 1st, 2014, to October 1st, 2019. After excluding the patients who were under 18 years old or stayed less than 24 h at the ICU, a total of 2170 patients were enrolled in this study. Multiple machine learning approaches were utilized to predict life-threatening events (i.e., death) in seven 24-h windows (day 1 to day 7) and their performance was compared.
Results
Light Gradient Boosting Machine showed the best performance. We found that life-threatening events during the short-term windows can be better predicted than those in the medium-term windows. For example, death in 24 h can be predicted with an Area Under Curve of 0.905. Features like infusion pump related fluid input were highly related to life-threatening events. Furthermore, the prediction power of static features such as age and cardio-pulmonary function increased with the extended prediction window.
Conclusion
This study demonstrates that the integration of machine learning approaches and large-scale high-quality clinical data in ICU could accurately predict life-threatening events for ICU patients for early intervention.
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