Assessment of mild hypothermia combined with edaravone for the treatment of severe craniocerebral injury

Purpose: To study the clinical effect of combining mild hypothermia with edaravone in the treatment of severe craniocerebral injury. Methods: One hundred and twenty (120) patients with severe craniocerebral injury who were admitted to Tianjin Medical University General Hospital were assigned to control and study groups, respectively. Patients in the control group were given conventional treatment while those in the study group received combined treatment of mild hypothermia and edaravone, in addition to the conventional treatment received by control group. Clinical efficacy and prognosis were compared between the two groups. Results: The intracranial pressure (ICP) of both groups decreased after admission, but the decrease in ICP was more pronounced in the study group at various time points (p < 0.05). Blood lactic acid levels decreased in both groups after admission, while brain-derived neurotrophic factor (BDNF) levels increased. Improvement in blood lactic acid and BDNF was greater in the study group than in control group (p < 0.05). The treatment resulted in significant decrease in residual hematoma volume and edema range in the study group, relative to control (p < 0.05). There was a decrease in National Institutes of Health Stroke Scale (NIHSS) scores, and increase in Glasgow outcome scale (GOS) scores in both groups. However, improvement in NIHSS and GOS scores in the study group was superior to those in control group (p < 0.05). Conclusion: Mild hypothermia in combination with edaravone exerts a beneficial clinical effect in severe craniocerebral injury. The combined treatment rapidly reduces ICP and range of encephaledema, improves cerebral blood supply, promotes absorption of intracranial hematoma, and relieves nervous dysfunction

miR-597-3p inhibits invasion and migration of thyroid carcinoma SW579 cells by targeting RAB23

Introduction: Nowadays, it is indicated that miRNA was anomaly expressed in tumour. Previous studies have shown that miRNAs can regulate the proliferation, invasion, and migration of cancer cell-related processes. Meanwhile, current investigations show that RAB23 also plays an important role in cancer cell-related processes. But the potential mechanism remains unclear. Material and methods: SW579 cells were selected and transfected with miR-597-3p mimics. Then the expression of miR-597-3p and RAB23 were measured by quantitative real-time polymerase chain reaction (PCR) and western blotting, respectively. Subsequently, the abilities of proliferation, invasion, and migration of SW579 cells were researched. For further study, the Luciferase reporter assay proved that miR-597-3p could target the expression of RAB23, and the proteins of invasion and migration were also measured to clear the mechanism. Results: After being transfected with miR-597-3p mimics, the expression of miR-597-3p was remarkably increased and RAB23 was significantly decreased. The abilities of proliferation, invasion, and migration also decreased significantly. The miRTarase Database predicated and Luciferase reporter assay proved that RAB23 was the target gene of miR-597-3p. The expression of matrix metalloproteinase (MMP)-2, MMP-9, and N-cadherin was down-regulated, and the expression of E-cadherin was up-regulated. Conclusion: miR-597-3p could reduce the proliferation, invasion and migration abilities of SW579 cells, which may be related to the targeted inhibition of RAB23 expression and down-regulation of the expression levels of MMP-2, MMP-9 and N-cadherin proteins of SW579 cells

Observation of Majorana fermions with spin selective Andreev reflection in the vortex of topological superconductor

Majorana fermion (MF) whose antiparticle is itself has been predicted in condensed matter systems. Signatures of the MFs have been reported as zero energy modes in various systems. More definitive evidences are highly desired to verify the existence of the MF. Very recently, theory has predicted MFs to induce spin selective Andreev reflection (SSAR), a novel magnetic property which can be used to detect the MFs. Here we report the first observation of the SSAR from MFs inside vortices in Bi2Te3/NbSe2 hetero-structure, in which topological superconductivity was previously established. By using spin-polarized scanning tunneling microscopy/spectroscopy (STM/STS), we show that the zero-bias peak of the tunneling differential conductance at the vortex center is substantially higher when the tip polarization and the external magnetic field are parallel than anti-parallel to each other. Such strong spin dependence of the tunneling is absent away from the vortex center, or in a conventional superconductor. The observed spin dependent tunneling effect is a direct evidence for the SSAR from MFs, fully consistent with theoretical analyses. Our work provides definitive evidences of MFs and will stimulate the MFs research on their novel physical properties, hence a step towards their statistics and application in quantum computing.Comment: 4 figures 15 page

Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences from Longitudinal Electronic Health Records of US Military Veterans

Early prediction of Alzheimer's disease (AD) is crucial for timely intervention and treatment. This study aims to use machine learning approaches to analyze longitudinal electronic health records (EHRs) of patients with AD and identify signs and symptoms that can predict AD onset earlier. We used a case-control design with longitudinal EHRs from the U.S. Department of Veterans Affairs Veterans Health Administration (VHA) from 2004 to 2021. Cases were VHA patients with AD diagnosed after 1/1/2016 based on ICD-10-CM codes, matched 1:9 with controls by age, sex and clinical utilization with replacement. We used a panel of AD-related keywords and their occurrences over time in a patient's longitudinal EHRs as predictors for AD prediction with four machine learning models. We performed subgroup analyses by age, sex, and race/ethnicity, and validated the model in a hold-out and "unseen" VHA stations group. Model discrimination, calibration, and other relevant metrics were reported for predictions up to ten years before ICD-based diagnosis. The study population included 16,701 cases and 39,097 matched controls. The average number of AD-related keywords (e.g., "concentration", "speaking") per year increased rapidly for cases as diagnosis approached, from around 10 to over 40, while remaining flat at 10 for controls. The best model achieved high discriminative accuracy (ROCAUC 0.997) for predictions using data from at least ten years before ICD-based diagnoses. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.99) and consistent across subgroups of age, sex and race/ethnicity, except for patients younger than 65 (ROCAUC 0.746). Machine learning models using AD-related keywords identified from EHR notes can predict future AD diagnoses, suggesting its potential use for identifying AD risk using EHR notes, offering an affordable way for early screening on large population.Comment: 24 page