Background: Chemotherapy is a standard therapeutic regimen to treat triple-negative breast cancer (TNBC); however, chemotherapy alone does not result in significant improvement and often leads to drug resistance in patients. In contrast, combination therapy has proven to be an effective strategy for TNBC treatment. Whether metformin enhances the anticancer effects of cisplatin and prevents cisplatin resistance in TNBC cells has not been reported. Methods: Cell viability, wounding healing, and invasion assays were performed on Hs 578T and MDA-MB-231 human TNBC cell lines to demonstrate the anticancer effects of combined cisplatin and metformin treatment compared to treatment with cisplatin alone. Western blotting and immunofluorescence were used to determine the expression of RAD51 and gamma-H2AX. In an in vivo 4T1 murine breast cancer model, a synergistic anticancer effect of metformin and cisplatin was observed. Results: Cisplatin combined with metformin decreased cell viability and metastatic effect more than cisplatin alone. Metformin suppressed cisplatin-mediated RAD51 upregulation by decreasing RAD51 protein stability and increasing its ubiquitination. In contrast, cisplatin increased RAD51 expression in an ERK-dependent manner. In addition, metformin also increased cisplatin-induced phosphorylation of γ-H2AX. Overexpression of RAD51 blocked the metformin-induced inhibition of cell migration and invasion, while RAD51 knockdown enhanced cisplatin activity. Moreover, the combination of metformin and cisplatin exhibited a synergistic anticancer effect in an orthotopic murine model of 4T1 breast cancer in vivo. Conclusions: Metformin enhances anticancer effect of cisplatin by downregulating RAD51 expression, which represents a novel therapeutic target in TNBC management.

Introduction Oligomeric amyloid-ß is a major toxic species associated with Alzheimer’s disease pathogenesis. Methods used to measure oligomeric amyloid-β in the blood have increased in number in recent years. The Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ) is a specific method to measure oligomerization tendencies in the blood. The objective of this study was to determine the association between amyloid-ß oligomerization in the plasma and structural changes of the brain. Methods We studied 162 subjects composed of 92 community-based normal healthy subjects, 17 with subjective cognitive decline, 14 with mild cognitive impairment and 39 with Alzheimer’s disease dementia. All subjects underwent MDS-OAβ and three-dimensional T1 magnetic resonance imaging. To determine the structural changes of the brain that are statistically correlated with MDS-OAβ level, we used voxel-based morphometry with corrections for age and total intracranial volume covariates. Results We found brain volume reduction in the bilateral temporal, amygdala, parahippocampal and lower parietal lobe and left cingulate and precuneus regions (family-wise error, p < 0.05). Reduction was also found in white matter in proximity to the left temporal and bilateral lower parietal lobes and posterior corpus callosum (family-wise error, p < 0.05). Brain volume increment was not observed in any regions within grey or white matter. Discussion Findings suggest that substantial correlation exists between amyloid ß oligomerization in the blood and brain volume reduction in the form of Alzheimer’s disease despite of uncertainty in the casual relationship. Electronic supplementary material The online version of this article (10.1186/s13195-019-0499-7) contains supplementary material, which is available to authorized users.

Multiple neurological complications have been associated with the coronavirus disease-19 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2. This is a narrative review to gather information on all aspects of COVID-19 in elderly patients with cognitive impairment. First, the following three mechanisms have been proposed to underlie the neurological complications associated with COVID-19: 1) direct invasion, 2) immune and inflammatory reaction, and 3) hypoxic brain damage by COVID-19. Next, because the elderly dementia patient population is particularly vulnerable to COVID-19, we discussed risk factors and difficulties associated with cognitive disorders in this vulnerable population. We also reviewed the effects of the patient living environment in COVID-19 cases that required intensive care unit (ICU) care. Furthermore, we analyzed the impact of stringent social restrictions and COVID-19 pandemic-mediated policies on dementia patients and care providers. Finally, we provided the following strategies for working with elderly dementia patients: general preventive methods; dementia care at home and nursing facilities according to the activities of daily living and dementia characteristics; ICU care after COVID-19 infection; and public health care system and government response. We propose that longitudinal follow-up studies are needed to fully examine COVID-19 associated neurological complications, such as dementia, and the efficacy of telemedicine/telehealth care programs.

INTRODUCTION: Oligomeric amyloid ß (Aß) is one of the major contributors to the pathomechanism of AD; Aß oligomerization in plasma can be measured using a Multimer Detection System-Oligomeric Aß (MDS-OAß) after incubation with spiked synthetic Aß. METHODS: We evaluated the clinical sensitivity and specificity of the MDS-OAß values by inBlood TM OAß test using heparin-treated plasma samples from 52 AD patients in comparison with 52 community-based subjects with normal cognition (NC). The inclusion criterion was proposed by the NINCDS-ADRDA and additionally required for the least 6 months of follow-up from the initial clinical diagnosis in the course of AD. RESULTS: The MDS-OAβ values were 1.43 ± 0.30 ng/ml in AD and 0.45 ± 0.19 (p <0.001) in NC, respectively. Using a cutoff value of 0.78 ng/ml, the results revealed that 100% sensitivity 92.31% specificity. DISCUSSION: MDS-OAß to measure plasma Aβ oligomerization is a valuable blood-based biomarker for clinical diagnosis of AD, with high sensitivity and specificity.

The point prevalence of atopic dermatitis (AD) among Korean adults visiting the Health Service Center for health check-ups was assessed. AD was diagnosed based on the questionnaires filled by patients and through physical examination by dermatologists. A total of 3,563 persons completed the questionnaires, and 2,032 persons were examined by the dermatologists. According to the questionnaires, the prevalence of AD was 7.1%, and from the dermatologists' examination, it was 2.6% overall, which decreased with age significantly. The prevalence in men was higher than that of women. Grading the severity of AD according to their eczema area and severity index scores, 70.6% were classified as being mild, 25.5% moderate, and 3.9% severe. Interestingly, concomitance of psoriasis and AD was found in 0.5% of those examined by the dermatologists. Our results suggest that AD is one of the most common skin diseases not only in children but also in adults in Korea.

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Meteorin-like (metrnl) is a recently identified adipomyokine that beneficially affects glucose metabolism; however, its underlying mechanism of action is not completely understood. We here show that the level of metrnl increases in vitro under electrical pulse stimulation and in vivo in exercised mice, suggesting that metrnl is secreted during muscle contractions. In addition, metrnl increases glucose uptake via the calcium-dependent AMPKa2 pathway in skeletal muscle cells and increases the phosphorylation of HDAC5, a transcriptional repressor of GLUT4, in an AMPKa2dependent manner. Phosphorylated HDAC5 interacts with 14-3-3 proteins and sequesters them in the cytoplasm, resulting in the activation of GLUT4 transcription. An intraperitoneal injection of recombinant metrnl improved glucose tolerance in mice with high-fat-diet-induced obesity or type 2 diabetes, but not in AMPK b1b2 muscle-specific null mice. Metrnl improves glucose metabolism via AMPKa2 and is a promising therapeutic candidate for glucose-related diseases such as type 2 diabetes.Abbreviations ACC, acetyl-CoA carboxylase; AMPK, AMP-activated protein kinase; BAPTA-AM, 1,2-Bis (2-aminophenoxy) ethane-N,N,N 0 ,N 0 -tetraacetic acid tetrakis (acetoxymethyl ester; EPS, electrical pulse stimulation; GLUT4, glucose transporter type 4; GST, glutathione S-transferase; GTT, glucose tolerance test; HDAC5, histone deacetylase 5; HFD, high-fat diet; ICC, immunocytochemistry; MAPK, mitogen-activated protein kinase; TBC1D1, TBC1 domain family member 1.

BackgroundNeuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data.MethodsMulti-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer’s disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination.ResultsThe ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The ‘time orientation’ and ‘3-word recall’ score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment.ConclusionsThe machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.