Detection and treatment of lung cancer still remain a clinical challenge. This study aims to validate exosomal microRNA‐96 (miR‐96) as a serum biomarker for lung cancer and understand the underlying mechanism in lung cancer progression. MiR‐96 expressions in normal and lung cancer patients were characterized by qPCR analysis. Changes in cell viability, migration and cisplatin resistance were monitored after incubation with isolated miR‐96‐containing exosomes, anti‐miR‐96 and anti‐miR negative control (anti‐miR‐NC) transfections. Dual‐luciferase reporter assay was used to study interaction between miR‐96 and LIM‐domain only protein 7 (LMO7). Changes induced by miR‐96 transfection and LMO7 overexpression were also evaluated. MiR‐96 expression was positively correlated with high‐grade and metastatic lung cancers. While anti‐miR‐96 transfection exhibited a tumour‐suppressing function, exosomes isolated from H1299 enhanced cell viability, migration and cisplatin resistance. Potential miR‐96 binding sites were found within the 3′‐UTR of wild‐type LMO7 gene, but not of mutant LMO7 gene. LMO7 expression was inversely correlated with lung cancer grades, and LMO7 overexpression reversed promoting effect of miR‐96. We have identified exosomal miR‐96 as a serum biomarker of malignant lung cancer. MiR‐96 promotes lung cancer progression by targeting LMO7. The miR‐96‐LMO7 axis may be a therapeutic target for lung cancer patients, and new diagnostic or therapeutic strategies could be developed by targeting the miR‐96‐LMO7 axis.
High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80-200[Formula: see text]Hz), fast ripples (FRs, 200-500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman's rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.
Freezing of gait (FOG) is a common and debilitating symptom in Parkinson’s disease (PD). The current study investigated alterations of resting-state spontaneous brain activity in PD patients with FOG. A total of 29 patients with FOG, 28 patients without FOG and 31 controls were included. All subjects underwent resting-state functional MRI, and the amplitude of low-frequency fluctuation (ALFF) was calculated to measure the spontaneous brain activity. Between-group differences and correlations with FOG severity (both subjective and objective measures) were analyzed. Compared to those without FOG, patients with FOG showed increased ALFF in right anterior cingulate cortex (ACC) and left inferior parietal lobule (IPL), as well as decreased ALFF in right superior frontal gyrus (SFG), bilateral cerebellum and left thalamus. Correlation analyses demonstrated that ALFF within the right SFG, right ACC and bilateral pallidum were positively correlated with FOG; while ALFF within the thalamus, putamen, cerebellum and sensorimotor regions were negatively correlated. Our results indicate that FOG is associated with dysfunction within frontal-parietal regions, along with increased inhibitory outputs from basal ganglia. Additionally, altered activity of cerebellum implicates its role in the pathophysiology of FOG. These findings provide further insight into the underlying neural mechanisms of FOG in PD patients.
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