Until now, the relationship of obstructive sleep apnoea (OSA) with diabetic retinopathy (DR) was controversial. This meta-analysis was performed to obtain definitive conclusion on this topic. Relevant articles were searched on databases of Pubmed, Google Scholar, and Chinese National Knowledge Infrastructure (CNKI). The articles were selected according to inclusion and exclusion criteria. Odds ratio (OR) with 95% confidence interval (CI) was used to evaluate the relationship of OSA with risk of DR. I2 and P value were used to assess the presence of heterogeneity. I2 ≥ 50% or P < 0.05 indicated significant heterogeneity. Sensitivity analysis was performed to evaluate the robustness of pooled results. Begg's funnel plot and Egger's regression analysis were adopted to assess publication bias. 6 eligible studies were selected in the present meta-analysis. The pooled results indicated that OSA was significantly associated with increased risk of DR (OR = 2.01, 95% CI = 1.49–2.72). Subgroup analysis based on type of diabetes mellitus suggested that OSA was related to DR in both Type 1 and Type 2 diabetes mellitus. Sensitivity analysis demonstrated that pooled results were robust. No significant publication bias was observed (P = 0.128). The results indicate that OSA is related to increased risk of DR.

The complex etiopathogenesis of Alzheimer's disease (AD) has limited progression in the identification of effective therapeutic agents. Amyloid precursor protein (APP) and presenilin‑1 (PS1) are always overexpressed in AD, and are considered to be the initiators of the formation of β‑amyloid plaques and the symptoms of AD. In the present study, a transgenic AD model, constructed via the overexpression of APP and PS1, was used to verify the protective effects of ginsenoside Rg1 on memory performance and synaptic plasticity. AD mice (6‑month‑old) were treated via intraperitoneal injection of 0.1‑10 mg/kg ginsenoside Rg1. Long‑term memory, synaptic plasticity, and the levels of AD‑associated and synaptic plasticity‑associated proteins were measured following treatment. Memory was measured using a fear conditioning task and protein expression levels were investigated using western blotting. All the data was analyzed by one-way analysis of variance or t‑test. Following 30 days of consecutive treatment, memory in the AD mouse model was ameliorated in the 10 mg/kg ginsenoside Rg1 treatment group. As demonstrated by biochemical experiments, ginsenoside Rg1 treatment reduced the accumulations of β‑amyloid 1‑42 and phosphorylated (p)‑Tau in the AD model. Additionally, brain-derived neurotrophic factor (BDNF) and p‑TrkB synaptic plasticity‑associated proteins were upregulated following ginsenoside Rg1 application. Correspondingly, long‑term potentiation (LTP) was restored following ginsenoside Rg1 application in the AD mice model. Taken together, ginsenoside Rg1 repaired hippocampal LTP and memory, likely through facilitating the clearance of AD‑associated proteins and through activation of the BDNF‑TrkB pathway. Therefore, ginsenoside Rg1 may be a candidate drug for the treatment of AD.

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Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has become a powerful tool for the diagnosis of breast cancer in the clinical setting due to its high sensitivity and specificity. Pharmacokinetic parameters, including Ktrans and area under the curve (AUC), and texture features derived from DCE-MRI have been used to specify the characteristics inside tumors. In the present study, 56 patients (average age 45.3±11.1; range 25-69 years) with histopathologically proved breast tumors were analyzed using the pharmacokinetic parameters and texture features. Malignant tumors displayed higher Ktrans and AUC values than the benign, Ktrans exhibited a significantly difference between the malignant and benign tumors (P=0.001) compared with the AUC values (P=0.029); texture features from DCE-MRI images and pharmacokinetic parameter maps also showed a good diagnostic ability. Alongside the routine method, principal components analysis (PCA) and Fisher discriminant analysis (FDA) were employed on these texture features to differentiate the breast lesions automatically. The Factor-1 scores of PCA were used to divide the patients into two groups, and the diagnosing accuracies of the FDA method on the texture features from DCE-MRI images, Ktrans maps, AUC maps were 93, 98 and 98%, with a cross validation accuracies of 82, 77 and 77%, respectively. To conclude, pharmacokinetic parameters, texture features and the combined computer-assisted classification method were discussed. All method involved in this study may be a potential assisted tool for radiological analysis on breast.

Aims: Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). Methods:We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. Results:In the feature evaluation of SVO-AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis.In SVO-AIS, SVD performed better than regular clinical data, which is the opposite | 1025 WANG et al.