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Objectives: The purpose of this study was to explore and evaluate the characteristics and quality of infant and maternal sleep (total sleep time, sleep efficiency, and wakefulness after sleep onset) using an actigraph.Methods: The characteristics and quality of sleep were assessed in 22 dyads of mothers and infants aged 3–12 months. The quality of infant and mother sleep was collected using an actigraph for 12 consecutive days; this was repeated three times at 4-week intervals, and characteristics of infant sleep were measured using the Brief Infant Sleep Questionnaire-Revised.Results: Bedsharing with infants at 7–12 months of age was higher (12.5%) than with those at 3–6 months of age (50.0%). Regarding the sleeping position, half of the infants aged 3–6 months adopted the lateral (25.0%) and prone (25.0%) positions, and the remaining half (50.0%) aged 7–12 months adopted the lateral position. It was found that 45.5% of mothers of infants used breastfeeding and pacifiers to induce sleep in infants and to help their babies go back to sleep when they woke up. The total sleep time and sleep efficiency of infants and mothers were 434.68 minutes per day and 73.1% and 379.53 minutes per day and 82.8%, respectively, indicating that both infants and mothers had low sleep quality.Conclusions: To safeguard infants’ sleep, it is necessary to provide appropriate information and education to parents and necessary interventions for resolving sleep problems and improving the quality of sleep for both infants and mothers.

Wnt signaling plays a critical role in embryonic development, and its deregulation is closely linked to the occurrence of a number of malignant tumors, including breast and colon cancer. The pathway also induces Snail-dependent epithelial-to-mesenchymal transition (EMT), which is responsible for tumor invasion and metastasis. In this study, we show that Wnt suppresses mitochondrial respiration and cytochrome C oxidase (COX) activity by inhibiting the expression of 3 COX subunits, namely, COXVIc, COXVIIa, and COXVIIc. We found that Wnt induced a glycolytic switch via increased glucose consumption and lactate production, with induction of pyruvate carboxylase (PC), a key enzyme of anaplerosis. In addition, Wnt-induced mitochondrial repression and glycolytic switching occurred through the canonical β-catenin/T-cell factor 4/Snail pathway. Short hairpin RNA–mediated knockdown of E-cadherin, a regulator of EMT, repressed mitochondrial respiration and induced a glycolytic switch via Snail activation, indicating that EMT may contribute to Wnt/Snail regulation of mitochondrial respiration and glucose metabolism. Together, our findings provide a new function for Wnt/Snail signaling in the regulation of mitochondrial respiration (via COX gene expression) and glucose metabolism (via PC gene expression) in tumor growth and progression. Cancer Res; 72(14); 3607–17. ©2012 AACR.

PDF file - 3.8MB, Supplemental Figures S1-S7 include the Induction of EMT in MCF-7 cells (Figure S1), the effects of Wnt on mitochondrial mass (Figure S2), Ponceau S staining corresponding to Figure 4A (Figure S3), ChIP assays using Wnt3a-treated or Snail-transfected MCF-7 cells (Figure S4), mitochondrial repression and glycolytic switch by PC shRNA (Figure S5), the effects of E-cadherin shRNA and Snail shRNA on the expression of COXVIc, COXVIIa, COXVIIc, and PC (Figure S6), and a schematic representation showing a new function of Wnt/Snail (Figure S7)

PDF file - 373K, Supplemental Tables S1-S5 include shRNA target sequences (Table S1), primers for RT-PCR, real-time PCR, and ChIP assays (Table S2), regulation of COX subunits and assembly factors (Table S3), Snail binding site in promoter region (Table S4), and effects of PC shRNA and Wnt3a on the doubling time of MCF-7 cells (Table S5)

PDF file - 152K, File includes Western blotting, RT-PCR, real-time PCR, and nuclear fractionation

Objectives: A primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normal cells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical features of brain tumors and to perform a classification using artificial intelligence (AI) techniques.Methods: AI techniques can help radiologists to diagnose primary brain tumors without using any invasive measurement techniques. In this paper, we focused on deep learning (DL) and machine learning (ML) techniques for texture, morphological, and statistical feature classification of three tumor types (namely, glioma, meningioma, and pituitary). T1-weighted magnetic resonance imaging (MRI) 2D scans were used for analysis and classification (multiclass and binary). A total of 102 features were calculated for each tumor, and the 20 most significant features were selected using the three-step feature selection method, which included removing duplicate features, Pearson correlations, and recursive feature elimination.Results: From the predicted results of multiclass and binary classification, a long short-term memory binary classification (glioma vs. meningioma) showed the best performance, with an average accuracy, recall, precision, F1-score, and kappa coefficient of 97.7%, 97.2%, 97.5%, 97.0%, and 94.7%, respectively.Conclusions: The early diagnosis of primary brain tumors is very important because it can be the key to effective treatment. Therefore, this research presents a method for early diagnoses by effectively classifying three types of primary brain tumors.

Kinship testing using genetic markers such as short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) is crucial for forensic analysis. Although STR markers have superior discriminatory power due to their highly polymorphic properties, they have several weak points in determining extended distant or complex relationships because of high mutation rates and low success rates in degraded samples. Therefore, SNPs are regarded as promising tools in forensic science because they have low mutation rates and small amplicon sizes. Herein, we propose an SNP panel consisting of 1400 autosomal SNPs obtained from the Korean National Standard Reference Variome (KoVariome) database. To evaluate its performance, in-silico analysis was performed using whole-genome sequencing (WGS) data from 21 Korean families. Subsequently, to estimate pairwise relatedness, kinship coefficients were calculated using PLINK, and Welch's one-way ANOVA test with Games-Howell's pairwise comparison test was performed. As a result, the average kinship coefficients of first- (parent-offspring and full siblings), second- (grandparent-grandchildren and aunt/uncle-niece/nephew), and third- (first cousin and grandniece/grandnephew) degree relatives, and unrelated were 0.24, 0.11, - 0.054, and - 0.0082, respectively. Consequently, relatives (first and second degree) were distinguished from non-relatives; however, further studies are required to investigate more effective SNP markers for discriminating extended kinship. Nevertheless, the results of this study go beyond the scope of screening using the discovered 1400 SNPs in Korean families and suggest the applicability of kinship analysis in the Korean population.

Histologically poor differentiation is associated with lymph node metastasis. Thus, pathological evaluation of biopsy specimens is crucial when treating stomach cancers. Deep learning of WSIs is challenging because the images are enormous. Given the computing limitations, patch-level supervised learning methods have been proposed. However, valuable information is lost when dividing WSIs into smaller patches. Another drawback is the need for pixel-level annotation by a pathologist. It is acceptable to differentiate, i.e., grade, gastric cancer at the holistic tissue level (i.e., under low magnification). We developed a weakly supervised learning technique for tissue-level gastric adenocarcinoma histological differentiation (well-to-moderately or poorly differentiated) and applied global reasoning to tissue-level features. The tissue-level AUROCs of the histological differentiation classifiers were 0.953, 0.969, and 0.943, respectively when data from five hospitals were subjected to threefold cross-validation. Comparison of the Grad-CAM heatmaps of the trained classifier and the pathologists’ annotations confirmed that our weakly supervised model exhibited performed well.

Assessing the pattern of nuclear chromatin is essential for pathological investigations. However, the interpretation of nuclear pattern is subjective. In this study, we performed the texture analysis of nuclear chromatin in breast cancer samples to determine the nuclear pleomorphism score thereof. We used three different algorithms for extracting high‐level texture features: the gray‐level co‐occurrence matrix (GLCM), gray‐level run length matrix (GLRLM), and gray‐level size zone matrix (GLSZM). Using these algorithms, 12 GLCM, 11 GLRLM, and 16 GLSZM features were extracted from three scores of breast carcinoma (Scores 1–3). Classification accuracy was assessed using the support vector machine (SVM) and k‐nearest neighbor (KNN) classification models. Three features of GLCM, 11 of GLRLM, and 12 of GLSZM were consistent across the three nuclear pleomorphism scores of breast cancer. Comparing Scores 1 and 3, the GLSZM feature large zone high gray‐level emphasis showed the largest difference among breast cancer nuclear scores among all features of the three algorithms. The SVM and KNN classifiers showed favorable results for all three algorithms. A multiclass classification was performed to compare and distinguish between the scores of breast cancer. Texture features of nuclear chromatin can provide useful information for nuclear scoring. However, further validation of the correlations of histopathologic features, and standardization of the texture analysis process, are required to achieve better classification results. © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

The therapeutic potential of tonsil-derived mesenchymal stem cells (TMSCs) has been proved in several in vitro and in vivo models based on their antioxidative capacity. Oxidative stress is involved in the formation of vocal fold scars and the aging of vocal folds. However, few studies have examined the direct correlation between oxidative damage and reconstitution of extracellular matrix (ECM) in the vocal fold fibrosis. We, therefore, sought to investigate the impact of oxidative stress on cell survival and ECM production of human vocal fibroblasts (hVFFs) and the protective effects elicited by TMSCs against oxidative damages in hVFFs. hVFFs were exposed to different concentrations of tert-butyl hydroperoxide in the presence or absence of TMSCs. Cell viability and reactive oxygen species (ROS) production were assessed to examine the progression of oxidative stress in vitro. In addition, expression patterns of ECM-associated factors including various collagens were examined by real-time PCR and immunocytochemical analysis. We found that both cell viability and proliferation capacity of hVFFs were decreased following the exposure to tBHP in a dose-dependent manner. Furthermore, tBHP treatment induced the generation of ROS and reactive aldehydes, while it decreased endogenous activity of antioxidant enzymes in hVFF. Importantly, TMSCs could rescue these oxidative stress-associated damages of hVFFs. TMSCs also downregulated tBHP-mediated production of proinflammatory cytokines in hVFFs. In addition, coculture with TMSC could restore the endogenous matrix metalloproteinase (MMP) activity of hVFFs upon tBHP treatment and, in turn, reduce the oxidative stress-induced ECM accumulation in hVFFs. We have, therefore, shown that the changes in hVFF proliferative capacity and ECM gene expression induced by oxidative stress are consistent with in vivo phenotypes observed in aging vocal folds and vocal fold scarring and that TMSCs may function to reduce oxidative stress in aging vocal folds.

Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Context Since pediatric palliative care (PPC) aims to improve the health-related quality of life (HRQoL) of children with life-limiting conditions (LLC), assessment of their HRQoL and identification of its determinants is crucial. Objectives To examine the clinical and family factors associated with HRQoL of children with LLC Methods This was a cross-sectional study of 136 pediatric patients with LLC who were enrolled in the PPC services at Seoul National University Hospital in South Korea. Patients’ HRQoL was measured using the Pediatric Quality of Life 4.0. Clinical and family characteristics were gathered from the medical records and PPC registry database. Results Most children with LLC have a poor HRQoL at their enrollment for PPC services with significant variation in their total HRQoL scores according to the diagnostic categories. Patients with nonmalignant conditions showed significantly lower HRQoL scores than patients with malignancy. Lower HRQoL scores were associated with more caregiver depressive symptoms. In a multivariable regression model, total HRQoL scores of patients were significantly associated with diagnostic categories and caregiver's depressive symptoms after controlling for other clinical and family variables. Physical health summary scores were significantly associated with diagnostic categories and caregiver depressive symptoms. Psychosocial health summary scores were significantly associated with patient location and caregiver's depressive symptoms. Conclusion The HRQoL of children with LLC receiving PPC differed among underlying disease categories. Lower HRQoL was associated with more caregiver depressive symptoms. These findings suggest the needs for optimized intervention in palliative care for children with nonmalignant conditions and family-centered intervention to address caregivers’ psychosocial problems.

The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&amp, E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.

Prostate carcinoma is caused when cells and glands in the prostate change their shape and size from normal to abnormal. Typically, the pathologist&rsquo, s goal is to classify the staining slides and differentiate normal from abnormal tissue. In the present study, we used a computational approach to classify images and features of benign and malignant tissues using artificial intelligence (AI) techniques. Here, we introduce two lightweight convolutional neural network (CNN) architectures and an ensemble machine learning (EML) method for image and feature classification, respectively. Moreover, the classification using pre-trained models and handcrafted features was carried out for comparative analysis. The binary classification was performed to classify between the two grade groups (benign vs. malignant) and quantile-quantile plots were used to show their predicted outcomes. Our proposed models for deep learning (DL) and machine learning (ML) classification achieved promising accuracies of 94.0% and 92.0%, respectively, based on non-handcrafted features extracted from CNN layers. Therefore, these models were able to predict nearly perfectly accurately using few trainable parameters or CNN layers, highlighting the importance of DL and ML techniques and suggesting that the computational analysis of microscopic anatomy will be essential to the future practice of pathology.

Purpose mTORC1 and mTORC2 inhibition by Ku-0063794 could confer profound anticancer effects against cancer cells because it eliminates feedback activation of Akt. Herein, we aimed to determine anticancer effects of docetaxel and Ku-0063794, individually or in combination, against breast cancer cells, especially triple-negative breast cancer (TNBC) cells.Materials and Methods MCF-7 breast cancer and MDA-MB-231 TNBC cell lines for in vitro studies and mouse xenograft model for in vivo studies were used to investigate the effect of docetaxel, Ku-0063794, or their combination.Results In the in vitro experiments, combination therapy synergistically reduced cell viability and induced higher apoptotic cell death in breast cancer cells than the individual monotherapies (p < 0.05). Western blot analysis and flow cytometric analysis showed that the combination therapy induced higher apoptotic cell death than the individual monotherapies (p < 0.05). In the in vivo experiment, docetaxel and Ku-0063794 combination therapy reduced the growth of MDA-MB-231 cells xenografted in the nude mice better than in the individual monotherapies (p < 0.05). Immunohistochemistry showed that the combination therapy induced the highest expression of cleaved caspase-3 and the lowest expression of Bcl-xL in the MDA-MB-231 cells xenografted in the nude mice (p < 0.05). Western blot analysis and immunofluorescence, incorporating both in vitro and in vivo experiments, consistently validated that unlike individual monotherapies, docetaxel and Ku-0063794 combination therapy significantly inhibited epithelial-mesenchymal transition (EMT) and autophagy (p < 0.05).Conclusion These data suggest that docetaxel and Ku-0063794 combination therapy has higher anticancer activities over individual monotherapies against MDA-MB-231 TNBC cells through a greater inhibition of autophagy and EMT.

Aims: To prevent Alzheimer’s disease (AD) from progressing to dementia, early prediction and classification of AD are important and they play a crucial role in medical image analysis. Background: In this study, we employed a transfer learning technique to classify magnetic resonance (MR) images using a pre-trained convolutional neural network (CNN). Objective: To address the early diagnosis of AD, we employed a computer-assisted technique, specifically the deep learning (DL) model, to detect AD. Methods: In particular, we classified Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects using whole slide two-dimensional (2D) images. To illustrate this approach, we made use of state-of-the-art CNN base models, i.e., the residual networks Res- Net-101, ResNet-50, and ResNet-18, and compared their effectiveness in identifying AD. To evaluate this approach, an AD Neuroimaging Initiative (ADNI) dataset was utilized. We also showed uniqueness by using MR images selected only from the central slice containing left and right hippocampus regions to evaluate the models. Results: All three models used randomly split data in the ratio of 70:30 for training and testing. Among the three, ResNet-101 showed 98.37% accuracy, better than the other two ResNet models, and performed well in multiclass classification. The promising results emphasize the benefit of using transfer learning, specifically when the dataset is low. Conclusion: From this study, we know that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD.

Background The number of technology-dependent children (TDC) is increasing in South Korea, but available healthcare services after their discharge are poor. This study aimed to examine how TDC and caregivers live at home and identify their difficulties and needs regarding home care with few services to support them. Methods This cross-sectional study was conducted in a tertiary hospital for children in South Korea. A self-reported questionnaire was completed by primary caregivers of TDC who were younger than 19 years and had been dependent on medical devices for more than 3 months. Technologies included home mechanical ventilation, oxygen supplementation, suction equipment, enteral feeding tube, and home total parenteral nutrition. Patterns of healthcare use and home care of TDC and caregivers’ perception toward child were assessed. Results A total of 74 primary caregivers of TDC completed a self-reported questionnaire. About 60% children were aged under 5 years. There were 31.1% children who required both respiratory and nutritional support. On average, caregivers took care of a child for 14.4 (±6.1) hours, slept for 5.6 (±1.6) hours, and spent 2.4 h per day on personal activities. Children used hospital services for 41.3 (±45.6) days in 6 months, and most (78.1%) were transported through private car/ambulance. Participants (75.6%) reported taking more than an hour to get to the hospital. More than 80% of caregivers responded that child care is physically very burdensome. The only statistically significant relationships was between economic status and financial burden (p = 0.026). Conclusions Caregivers of TDC reported having significant time pressure regarding childcare-related tasks, insufficient time for personal activities, and inefficient hospital use because of inadequate medical services to support them in South Korea. Thus, it is necessary to support caregivers and develop a home care model based on current medical environment.

Background The increase in the number of pediatric patients with complex health conditions necessitates the application of advance care planning for children. Earlier, withdrawal of life-sustaining treatment was taboo in the medical society in South Korea due to the history of such practice being punishable by law, and physicians tended to pursue aggressive treatment. With changes in public opinion on end-of-life care, the Korean government enacted a new law that protect human dignity by respecting patients’ self-determination and facilitating advance care planning. However, little is known about current state of advance care planning for pediatric patients. The study aimed to assess perceptions regarding advance care planning among South Korean pediatricians and clarify any differences in perception among pediatric subspecialties. Methods This study was an observational cross-sectional survey that used a web-based self-report questionnaire. Participants comprised of pediatricians currently caring for children with life-limiting conditions in 2018. Results Of the 96 respondents, 89 were included in the analysis. In a hypothetical patient scenario, more hemato-oncologists and intensivists than neonatologists and neurologists preferred to provide comfort care than aggressive treatment. While 72.2% of hemato-oncologists reported that they usually or always discuss advance care plans with parents during treatment, more than half of other pediatricians reported that they seldom do so. Furthermore, 65% of respondents said that they never discuss advance care planning with adolescent patients. Moreover, there were no notable differences among subspecialties. The most prevalent answers to factors impeding advance care planning were lack of systemic support after performing advance care planning (82.0%) and uncertain legal responsibilities (70.8%). Conclusions The pediatricians differed in their experiences and attitudes toward advance care planning based on their subspecialty. Consequently, institutional support and education should be provided to physicians so that they can include children and families in discussions on prognosis.

Paediatric palliative care (PPC) is regarded as standard care for children and young people (CYP) with life-limiting conditions (LLCs). There is a lack of knowledge about the rate of CYP with LLCs, hampering the development of PPC. This retrospective study aimed to examine population-based statistics of South Korean CYP with LLCs and the pattern of healthcare use and costs in their last year of life, analysing the National Health Insurance Service claims database for the period 2013–2015. In 2015, the number of CYP (≤24 years old) living with LLCs was 133,177, with those who died accounting for 1,032. Prevalence of LLC and mortality rate per 100,000 were highest among under-1-age group (2,151.7 and 82.7, respectively). In the last year of life, 91.8% of deceased CYP with LLCs were hospitalized at least once and the average length of stay was 101.2 days (standard deviation = 104.1). Deceased CYP with cancer spent more on healthcare than non-cancer CYP (64,266 vs. 40,694 US dollar, p

Recently, a method of identifying body fluids using DNA methylation has been developed (Frumkin et al., 2011). An existing multiplex assay using 9 CpG markers could differentiate 5 body fluids: semen, blood, saliva, menstrual blood, and vaginal fluid. To validate this technique, we evaluated the previously described body fluid identification method by means of single base extension (SBE). DNA methylation was applied to 22 samples in 18 forensic cases; seven of these were semen, three were blood, eight were saliva, three were vaginal fluid, and one was menstrual blood. Total of 18 samples were tested, the DNA methylation profiles were coincident from preliminary tests (acid phosphatase (AP), leucomalachite green (LMG, Sigma Aldrich, St Louis, MO, USA) and SALIgAE®) except one sample which displayed an all-negative result. After applying the DNA methylation method to forensic samples, we determined that it could be very useful for differentiating vaginal secretions from menstrual blood, for which there is no conventional preliminary testing method.

Purpose: The purpose of this study was to review interventions available to nurses caring for siblings of children with cancer. Methods: Searches of CINAHL, MEDLINE, PubMed, EMBASE, and RISS identified ten intervention studies published from January, 2000 to November, 2016. Results: Ten studies on interventions for siblings of pediatric cancer patients were identified as follows: 4 included camps, 4 included support groups, and 2 provided individual interventions. Theoretical frameworks were mainly cognitive behavioral theory and most studies were led by psychologists and multidisciplinary teams. The purpose of the interventions was primarily enhancing psychosocial adjustment. Siblings’ fear of disease, self-esteem, and social support were improved significantly after the interventions. Findings were inconsistent with regard to depression, anxiety, behavioral problems, post-traumatic stress, health-related quality of life, and siblings’ perceptions of the illness. Conclusion: Study findings showed the potential for enhancing emotional and behavioral outcomes in siblings of children with cancer. However, the number of studies was very small, and several methodological limitations were identified. In the future, more randomized controlled trials with larger samples are needed to extend the evidence base. Moreover, future research should identify sibling’s characteristics and circumstances most likely to bring benefits to the siblings.

Background: Despite advances in medical technology, resources for pediatric palliative care (PPC) for children with serious illnesses are limited in South Korea. Physicians' awareness of and willin...

Reactive oxygen species (ROS) are important cellular second messengers involved in various aspects of cell signaling. ROS are elevated in multiple types of cancer cells, and this elevation is known to be involved in pathological processes of cancer. Although high levels of ROS exert cytotoxic effects on cancer cells, low levels of ROS stimulate cell proliferation and survival by inducing several pro‑survival signaling pathways. In addition, ROS have been shown to induce epithelial‑mesenchymal transition (EMT), which is essential for the initiation of metastasis. However, the precise mechanism of ROS‑induced EMT remains to be elucidated. In the present study, it was indicated that ROS induce EMT by activating Snail expression, which then represses E‑cadherin expression in MCF‑7 cells. It was further indicated that distal‑less homeobox‑2 (Dlx‑2), one of the human Dlx gene family proteins involved in embryonic development, acts as an upstream regulator of ROS‑induced Snail expression. It was also revealed that ROS treatment induces the glycolytic switch, a phenomenon whereby cancer cells primarily rely on glycolysis instead of mitochondrial oxidative phosphorylation for ATP production, even in the presence of oxygen. In addition, ROS inhibited oxidative phosphorylation and caused cytochrome c oxidase inhibition via the Dlx‑2/Snail cascade. These results suggest that ROS induce EMT, the glycolytic switch and mitochondrial repression by activating the Dlx‑2/Snail axis, thereby playing crucial roles in MCF‑7 cancer cell progression.

Background: In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain. Materials and Methods: The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images. Results: The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC. Conclusion: The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

Metastasis is a major obstacle to the efficient and successful treatment of cancer. Initiation of metastasis requires epithelial-mesenchymal transition (EMT) that is regulated by several transcription factors, including Snail and ZEB1/2. EMT is closely linked to the acquisition of cancer stem cell (CSC) properties and chemoresistance, which contribute to tumor malignancy. Tumor suppressor p53 inhibits EMT and metastasis by negatively regulating several EMT-inducing transcription factors and regulatory molecules; thus, its inhibition is crucial in EMT, invasion, metastasis, and stemness. Metabolic alterations are another hallmark of cancer. Most cancer cells are more dependent on glycolysis than on mitochondrial oxidative phosphorylation for their energy production, even in the presence of oxygen. Cancer cells enhance other oncogenic metabolic pathways, such as glutamine metabolism, pentose phosphate pathway, and the synthesis of fatty acids and cholesterol. Metabolic reprogramming in cancer is regulated by the activation of oncogenes or loss of tumor suppressors that contribute to tumor progression. Oncogenic metabolism has been recently linked closely with the induction of EMT or CSC phenotypes by the induction of several metabolic enzyme genes. In addition, several transcription factors and molecules involved in EMT or CSCs, including Snail, Dlx-2, HIF-1α, STAT3, TGF-β, Wnt, and Akt, regulate oncogenic metabolism. Moreover, p53 induces metabolic change by directly regulating several metabolic enzymes. The collective data indicate the importance of oncogenic metabolism in the regulation of EMT, cell invasion and metastasis, and adoption of the CSC phenotype, which all contribute to malignant transformation and tumor development. In this review, we highlight the oncogenic metabolism as a key regulator of EMT and CSC, which is related with tumor progression involving metastasis and chemoresistance. Targeting oncometabolism might be a promising strategy for the development of effective anticancer therapy.

Purpose Survivin is a typical antiapoptotic protein. It is copiously expressed during human fetal development but is infrequently present in adult tissues. In this experiment, we researched the treatment effect of the secretome that adipose-derived stem cells (ASCs) transfected with survivin. Methods First of all, we generated survivin-overexpressing ASCs transfected with a plasmid comprising a gene encoding survivin. The secreted substances released from survivin-overexpressing ASCs (survivin-secretome) were collected, and were determined their in vitro and in vivo therapeutic potential, especially in the model of liver impairment. Results In vitro, the survivin-secretome significantly increased cell viability and promoted the expression of proliferation-related markers (proliferating cell nuclear antigen [PCNA], phospho-signal transducer and activator of transcription 3 (p-STAT3), hepatocyte growth factor [HGF], vascular endothelial growth factor [VEGF]) and anti-apoptosis-related markers (myeloid cell leukemia-1 [Mcl-1] and survivin) (P < 0.05). In vivo using 70% hepatectomy mice, the survivin-secretome group exhibited the lowest serum levels of interleukin-6, tumor necrosis factor-α (P < 0.05). The serum levels of liver transaminases (alanine aminotransferase and aspartate aminotransferase) were also the lowest in the survivin-secretome group (P < 0.05). The survivin-secretome group also exhibited the highest liver regeneration on the 7th day after 70% partial hepatectomy (P < 0.05). In the subsequent liver specimen analysis, the specimens of survivin-secretome exhibited the highest expression of p-STAT3, HGF, VEGF, PCNA, and Mcl-1 and the lowest expression of bcl-2-like protein 4 (P < 0.05). Conclusion Taken together, secretome secreted by survivin-overexpressing ASCs could be an effective way to improve liver regeneration and repair for liver injury treatment.

Rapidly growing malignant tumors frequently encounter hypoxia and nutrient (e.g., glucose) deprivation, which occurs because of insufficient blood supply. This results in necrotic cell death in the core region of solid tumors. Necrotic cells release their cellular cytoplasmic contents into the extracellular space, such as high mobility group box 1 (HMGB1), which is a nonhistone nuclear protein, but acts as a proinflammatory and tumor-promoting cytokine when released by necrotic cells. These released molecules recruit immune and inflammatory cells, which exert tumor-promoting activity by inducing angiogenesis, proliferation, and invasion. Development of a necrotic core in cancer patients is also associated with poor prognosis. Conventionally, necrosis has been thought of as an unregulated process, unlike programmed cell death processes like apoptosis and autophagy. Recently, necrosis has been recognized as a programmed cell death, encompassing processes such as oncosis, necroptosis, and others. Metabolic stress-induced necrosis and its regulatory mechanisms have been poorly investigated until recently. Snail and Dlx-2, EMT-inducing transcription factors, are responsible for metabolic stress-induced necrosis in tumors. Snail and Dlx-2 contribute to tumor progression by promoting necrosis and inducing EMT and oncogenic metabolism. Oncogenic metabolism has been shown to play a role(s) in initiating necrosis. Here, we discuss the molecular mechanisms underlying metabolic stress-induced programmed necrosis that promote tumor progression and aggressiveness.

The study of immune cell migration is important for understanding the immune system network, which is associated with the response to foreign cells. Neutrophils act against foreign cells before any other immune cell, and they must be able to change shape and squeeze through narrow spaces in the extracellular matrix (ECM) during migration to sites of infection. Conventional in vitro migration assays are typically performed on two-dimensional substrates that fail to reproduce the three-dimensional (3-D) nature of the ECM. Here we present an in vitro method to simulate the 3-D migration of neutrophils using an electrospun nanofibrous membrane, which is similar to the ECM in terms of morphology. We examined the properties of neutrophil movement and the effects of gravity and the presence of IL-8, which has been widely used as a chemotactic attractant for neutrophils. The number of neutrophils passing through the nanofibrous membrane were higher, and their movement was more active in the presence of IL-8. Also, we confirmed that neutrophils could migrate against gravity toward IL-8 through a nanofibrous membrane.