Many studies have examined the negative impact on smartphone addiction in adolescents. Recent concerns have focused on predictors of smartphone addiction. This study aimed to investigate the ...association of adolescents' smartphone addiction with family environment (specifically, domestic violence and parental addiction). We further investigated whether self-control and friendship quality, as predictors of smartphone addiction, may reduce the observed risk.
We used the 2013 national survey on internet usage and utilization data from the National Information Agency of Korea. Information on exposure and covariates included self-reported experience of domestic violence and parental addiction, sociodemographic variables, and other variables potentially related to smartphone addiction. Smartphone addiction was estimated using a smartphone addiction proneness scale, a standardized measure developed by national institutions in Korea.
Adolescents who had experienced domestic violence (OR = 1.74; 95% CI: 1.23-2.45) and parental addiction (OR = 2.01; 95% CI: 1.24-3.27) were found to be at an increased risk for smartphone addiction after controlling for all potential variables. Furthermore, on classifying adolescents according to their level of self-control and friendship quality the association between domestic violence and parental addiction, and smartphone addiction was found to be significant in the group with adolescents with lower levels of self-control (OR = 2.87; 95% CI: 1.68-4.90 and OR = 1.95; 95% CI: 1.34-2.83) and friendship quality (OR = 2.33; 95% CI: 1.41-3.85 and OR = 1.83; 95% CI: 1.26-2.64).
Our findings suggest that family dysfunction was significantly associated with smartphone addiction. We also observed that self-control and friendship quality act as protective factors against adolescents' smartphone addiction.
The rate of detection of thyroid nodules and carcinomas has increased with the widespread use of ultrasonography (US), which is the mainstay for the detection and risk stratification of thyroid ...nodules as well as for providing guidance for their biopsy and nonsurgical treatment. The Korean Society of Thyroid Radiology (KSThR) published their first recommendations for the US-based diagnosis and management of thyroid nodules in 2011. These recommendations have been used as the standard guidelines for the past several years in Korea. Lately, the application of US has been further emphasized for the personalized management of patients with thyroid nodules. The Task Force on Thyroid Nodules of the KSThR has revised the recommendations for the ultrasound diagnosis and imaging-based management of thyroid nodules. The review and recommendations in this report have been based on a comprehensive analysis of the current literature and the consensus of experts.
Although three-dimensional (3D) bioprinting technology has gained much attention in the field of tissue engineering, there are still several significant engineering challenges to overcome, including ...lack of bioink with biocompatibility and printability. Here, we show a bioink created from silk fibroin (SF) for digital light processing (DLP) 3D bioprinting in tissue engineering applications. The SF-based bioink (Sil-MA) was produced by a methacrylation process using glycidyl methacrylate (GMA) during the fabrication of SF solution. The mechanical and rheological properties of Sil-MA hydrogel proved to be outstanding in experimental testing and can be modulated by varying the Sil-MA contents. This Sil-MA bioink allowed us to build highly complex organ structures, including the heart, vessel, brain, trachea and ear with excellent structural stability and reliable biocompatibility. Sil-MA bioink is well-suited for use in DLP printing process and could be applied to tissue and organ engineering depending on the specific biological requirements.
The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions ...of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.
Microrobots facilitate targeted therapy due to their small size, minimal invasiveness, and precise wireless control. A degradable hyperthermia microrobot (DHM) with a 3D helical structure is ...developed, enabling actively controlled drug delivery, release, and hyperthermia therapy. The microrobot is made of poly(ethylene glycol) diacrylate (PEGDA) and pentaerythritol triacrylate (PETA) and contains magnetic Fe3O4 nanoparticles (MNPs) and 5‐fluorouracil (5‐FU). Its locomotion is remotely and precisely controlled by a rotating magnetic field (RMF) generated by an electromagnetic actuation system. Drug‐free DHMs reduce the viability of cancer cells by elevating the temperature under an alternating magnetic field (AMF), a hyperthermic effect. 5‐FU is released from the proposed DHMs in normal‐, high‐burst‐, and constant‐release modes, controlled by the AMF. Finally, actively controlled drug release from the DHMs in normal‐ and high‐burst‐release mode results in a reduction in cell viability. The reduction in cell viability is of greater magnitude in high‐burst‐ than in normal‐release mode. In summary, biodegradable DHMs have potential for actively controlled drug release and hyperthermia therapy.
A degradable hyperthermia microrobot (DHM), which encapsulates 5‐fluorouracil and Fe3O4 nanoparticles is developed and demonstrates the potential for actively controlled drug release and hyperthermia therapy. The locomotion of the DHM is remotely controlled by a rotating magnetic field. The alternating magnetic field not only increases the temperature of the DHMs, but also actively controls the drug release from the DHMs.
Computational calculations and experimental studies reveal that the CoOOH phase and the intermediate-spin (IS) state are the key factors for realizing efficient Co-based electrocatalysts for the ...oxygen evolution reaction (OER). However, according to thermodynamics, general cobalt oxide converts to the CoO
phase under OER condition, retarding the OER kinetics. Herein, we demonstrate a simple and scalable strategy to fabricate electrodes with maintaining Fe-CoOOH phase and an IS state under the OER. The changes of phase and spin states were uncovered by combining in-situ/operando X-ray based absorption spectroscopy and Raman spectroscopy. Electrochemical reconstruction of chalcogenide treated Co foam affords a highly enlarged active surface that conferred excellent catalytic activity and stability in a large-scale water electrolyzer. Our findings are meaningful in that the calculated results were experimentally verified through the operando analyses. It also proposes a new strategy for electrode fabrication and confirms the importance of real active phases and spin states under a particular reaction condition.
The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by ...blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.
Background/Purpose
The current study aimed to develop a prediction model using a multi‐marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma.
Methods
Multi‐center cohort of ...1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi‐biomarker Enzyme‐Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19‐9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers.
Results
Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non‐pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively.
Conclusions
This study demonstrates a significant diagnostic performance of the multi‐marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.
Osthole is a natural coumarin found in a variety of plants and has been reported to have diverse biological functions, including antimicrobial, antiviral, immunomodulatory, and anticancer effects. ...Here, we investigated the natural derivative osthole as a promising anticancer compound against ovarian cancer and evaluated its ability to suppress and abrogate tumor progression. In addition, we found the endoplasmic reticulum‐mitochondrial axis‐mediated anticancer mechanisms of osthole against ES2 and OV90 ovarian cancer cells and demonstrated its calcium‐dependent pharmacological potential. Mechanistically, osthole was found to target the phosphatidylinositol 3‐kinase/mitogen‐activated protein kinase signaling pathway to facilitate tumor suppression in ovarian cancer. Furthermore, we identified the effects of osthole in a three‐dimensional tumor‐formation model using the zebrafish xenograft assay, providing convincing evidence of the pharmacological effects of osthole within the anchorage‐independent tumor microenvironment. These findings suggest that osthole has strong potential as a pharmacological agent for targeting ovarian cancer.
Our current results indicate that osthole suppresses ovarian cancer through multiple intracellular mechanisms, including the inhibition of cell proliferation, the induction of apoptotic cell death, disruption of calcium homeostasis, reduced reactive oxygen species production, and endoplasmic reticulum (ER)‐mitochondria axis interference. These data suggest that osthole has pharmacological potential as a chemotherapeutic agent capable of targeting human ovarian cancer.
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