Exploration of MELK as a downstream of Del-1 and druggable targets in triple-negative breast cancer

PURPOSE

In our previous study, Developmental endothelial locus-1 (Del-1) was a promising predictive marker for breast cancer. However, the downstream targets of Del-1 remain unknown. Here, we sought to discover a druggable target downstream of Del-1 and investigate the mechanism by which it regulates the course of breast cancer.

METHODS

To investigate Del-1 downregulation effect on breast cancer, we performed transcriptome analysis using RNA sequencing of Del-1 knockdowned MDA-MB-231 cell line Plus, to investigate the expression of Del-1 and Maternal embryonic leucine zipper kinase (MELK), mRNA levels in eight different triple-Negative Breast Cancer (TNBC) cell lines were analyzed. High-throughput sequencing was performed on total RNA isolated. OTS167 was used for MELK inhibition. The effects of MELK on cell proliferation and invasion were determined using the MTT and Matrigel transwell assays. Furthermore, we examined MELK expression in breast cancer tissue.

RESULTS

Del-1 and MELK mRNA expression levels were significantly higher in the TNBC cell lines, MDA-MB-468, HCC-1806, and MBA-MB-231. Knocking down Del-1 with siRNA in HCC-1806 and MBA-MB-231 cells significantly decreased MELK expression and thus suggested a possible relationship between Del-1 and MELK. In MDA-MB-468 cells, a basal-like 1 TNBC cell line, OTS167 significantly inhibited breast cancer cell proliferation and promoted cell apoptosis. To further investigate the relationship between Del-1 and MELK, dual inhibition of both Del-1 and MELK was performed, which significantly reduced the viability of MDA-MB-468 and MBA-MB-231 cells.

CONCLUSION

We found that MELK acts downstream of Del-1 and is a promising druggable target, especially in basal-like and mesenchymal stem-like subtype.

Real-time heart rate variability according to ambulatory glucose profile in patients with diabetes mellitus

Background

Autonomic neuropathy commonly occurs as a long-term complication of diabetes mellitus (DM) and can be diagnosed based on heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. There are limited data on HRV using real-time ECG and ambulatory glucose monitoring in patients with DM. The aim of this study was to investigate real-time HRV according to ambulatory glucose levels in patients with DM.

Methods

A total of 43 patients (66.3 ± 7.5 years) with DM underwent continuous real-time ECG monitoring (225.7 ± 107.3 h) for HRV and ambulatory glucose monitoring using a remote monitoring system. We compared the HRV according to the ambulatory glucose profile. Data were analyzed according to the target in glucose range (TIR).

Results

There were no significant differences in the baseline characteristics of the patients according to the TIR. During monitoring, we checked ECG and ambulatory glucose levels (a total of 15,090 times) simultaneously for all patients. Both time- and frequency-domain HRVs were lower when the patients had poorly controlled glucose levels (TIR < 70%) compared with well controlled glucose levels (TIR > 70%). In addition, heart and respiratory rates increased with real-time glucose levels (P < 0.001).

Conclusions

Poorly controlled glucose levels were independently associated with lower HRV in patients with DM. This was further substantiated by the independent continuous association between real-time measurements of hyperglycemia and lower HRV. These data strongly suggest that cardiac autonomic dysfunction is caused by elevated blood sugar levels.

Trends in telemedicine utilization for mental illness during the COVID-19 pandemic: an analysis of a nationwide database in Korea

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has worsened mental health and reduced access to mental health services. During the pandemic, the demand for telemedicine has increased and related laws have been enacted. This study aimed to investigate telemedicine use for cases of major mental illnesses during the COVID-19 pandemic and to compare the characteristics of patients who received telemedicine service with those of patients who received in-person care.

METHODS

This population-based, cross-sectional, observational study was based on health insurance claims data, and included 2,749,872 patients who received outpatient treatment for mental illness from February 24, 2020 to June 30, 2022. Logistic regression was performed to assess the relationships between patient characteristics and telemedicine service use. Patients who received telemedicine services were analyzed in subgroups of each mental illness.

RESULTS

During the study period, 80,157 patients (2.9%), with an average age of 63 years, received at least one telemedicine treatment. There was a predominance of women and medical aid recipients. The lowest proportion of telemedicine treatments was for depression (2.1%), and the highest was for dementia (6.7%). The proportion of patients receiving telemedicine in long-term care hospitals was high (22.6%), with the highest odds ratio (OR) (5.84), compared with that in tertiary or general hospitals, followed by that in psychiatric hospitals and clinics. The proportions were high in the departments of internal medicine, neurology, and psychiatry. Patients aged > 80 years received most telemedicine treatment (OR: 1.23) across all diagnoses. Cases of dementia and other mental disorders had higher ORs (2.60 and 2.36, respectively) compared with cases of depression. Except for dementia and behavioral/emotional disorders, hospitalization increased the probability of telemedicine treatment. Comorbidities were positively associated with telemedicine treatment.

CONCLUSIONS

Older people and people with other physical illnesses were more likely to use telemedicine treatments temporarily provided during the pandemic. Telemedicine maintained continuity of treatment for patients with dementia and severe mental illnesses. Telemedicine can be useful for filling the medical gaps for vulnerable populations other than those with mild mental illnesses. This aspect should be considered for the future establishment of telemedicine systems.

Natural bio-based monomers for biomedical applications: a review

In recent years, synthetic and semi-synthetic polymer materials have been widely used in various applications. Especially concerning biomedical applications, their biocompatibility, biodegradability, and non-toxicity have increased the interest of researchers to discover and develop new products for the well-being of humanity. Among the synthetic and semi-synthetic materials, the use of natural bio-based monomeric materials presents a possible novel avenue for the development of new biocompatible, biodegradable, and non-toxic products. The purpose of this article is to review the information on the role of natural bio-based monomers in biomedical applications. Increased eco-friendliness, biocompatibility, biodegradability, non-toxicity, and intrinsic biological activity are some of the attributes which make itaconic, succinic, citric, hyaluronic, and glutamic acids suitable potential materials for biomedical applications. Herein, we summarize the most recent advances in the field over the past ten years and specifically highlight new and interesting discoveries in biomedical applications. Natural origin acid-based bio-monomers for biomedical applications.

Acupuncture to treat asymptomatic hyperuricemia: A protocol for systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Hyperuricemia (HUA) plays an important role in metabolic syndrome, cardiovascular disease, and kidney disease. HUA without resulting gout is referred to as asymptomatic HUA. The purpose of the present systematic review protocol is to provide methods to assess the effectiveness and safety of acupuncture-based treatment for asymptomatic HUA.

METHODS

To identify randomized controlled trials (RCTs) involving acupuncture-based treatment for asymptomatic HUA, a search will be carried out using the following eight electronic databases: MEDLINE, EMBASE, Cochrane Library, Korea Med, Oriental Medicine Advanced Searching Integrated System, Korean Studies Information Service System, China National Knowledge Infrastructure, and Japanese Institutional Repositories Online. Manual search and email contact with the author will also be conducted if necessary. Studies will be selected based on predefined criteria and summarized data regarding study participants, interventions, control groups, outcome measures, side effects, and risk of bias. No language restrictions will be imposed. Studies that evaluated any type of acupuncture will be eligible for inclusion, and the primary outcome will be the blood uric acid level. The methodological quality of the included RCTs will be assessed using the Cochrane risk of bias tool.

RESULTS

The present study will evaluate the efficacy and safety of acupuncture to treat HUA.

CONCLUSION

Our findings will establish the evidence for acupuncture-based treatment of HUA and will be informative for patients with HUA, clinicians, policy makers, and researchers.

REGISTRATION NUMBER

reviewregistry1054.

Image Pre-Processing Method of Machine Learning for Edge Detection with Image Signal Processor Enhancement

Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the information received from each pixel and performs certain processing operations for edge detection. Even with/without ISP, as an output of hardware (camera, ISP), the original image is too raw to proceed edge detection image, because it can include extreme brightness and contrast, which is the key factor of image for edge detection. To reduce the onerousness, we propose a pre-processing method to obtain optimized brightness and contrast for improved edge detection. In the pre-processing, we extract meaningful features from image information and perform machine learning such as k-nearest neighbor (KNN), multilayer perceptron (MLP) and support vector machine (SVM) to obtain enhanced model by adjusting brightness and contrast. The comparison results of F1 score on edgy detection image of non-treated, pre-processed and pre-processed with machine learned are shown. The pre-processed with machine learned F1 result shows an average of 0.822, which is 2.7 times better results than the non-treated one. Eventually, the proposed pre-processing and machine learning method is proved as the essential method of pre-processing image from ISP in order to gain better edge detection image. In addition, if we go through the pre-processing method that we proposed, it is possible to more clearly and easily determine the object required when performing auto white balance (AWB) or auto exposure (AE) in the ISP. It helps to perform faster and more efficiently through the proactive ISP.