Immediate effects of a vibrating foam roller on dorsiflexion rom, balance, and gait in stroke patients: a randomized controlled trial.

The purpose of this study was to investigate the immediate effects of vibration foam rolling on dorsiflexion range of motion (ROM), balance, and gait in stroke patients. Thirty stroke patients volunteered to participate and were randomly assigned to the vibrating foam roller group (n=15) and the foam roller group (n=15). The vibrating foam roller group performed a 30-min foam roller exercise program, with participants subjected to vibration at 28 Hz. The foam roller group performed the same exercise program as the vibrating foam roller group, but without vibration. Dorsiflexion lunge test, limits of stability, and Timed Up and Go were used to evaluate dorsiflexion ROM, balance, and gait before and after each intervention. The results revealed that the vibration foam roller group showed significant differences in dorsiflexion ROM and gait after the intervention, while the foam roller group exhibited a significant difference only in dorsiflexion ROM (P<0.05). In comparisons between the vibration foam roller group and the foam roller group, significant differences were observed in dorsiflexion ROM and gait (P<0.05). However, there were no significant differences in balance, both before and after the intervention, as well as in the comparisons between the two groups (P>0.05). This study confirmed that a single-session vibrating foam roller exercise program improves dorsiflexion ROM and gait in stroke patients. Further studies with extended exercise program durations are needed to address limitations and explore long-term effects.

A Survey on the Use of Outcome Measures during Physical Therapy Interventions by Physical Therapists in Korea.

The purpose of this study was to determine the current status of patient care provided by Korean physical therapists (KPTs) in clinical practice by studying the outcome measures (OMs) used in physical therapy interventions among KPTs with experience in treating patients. A total of 225 KPTs with experience in treating patients in clinical settings participated in the study and completed the online questionnaire. The questionnaire included questions about the use of OMs and the reasons for using them, as well as the types, benefits, and barriers of OMs. The participants' responses were analyzed and reported in terms of frequencies and percentages. A total of 220 questionnaires were analyzed. The results show that the majority of KPTs in clinical practice used OMs during interventions. The main reasons for using OMs were to check the patient's condition and to determine the direction and effectiveness of treatment. In terms of the types of OMs used, the highest percentage of subjects used both patient-reported OMs (PROMs) and performance-based OMs (PBOMs). They chose OMs that were quick and easy to use and used them voluntarily. Barriers to and reasons for not using OMs were similar, including lack of benefits, lack of time, and problems with patient performance and uncooperative behavior. When analyzing the effect of demographic characteristics on the use of OMs, we found that physical therapists specializing in musculoskeletal and neurological systems, physical therapists with longer treatment times, and physical therapists who valued OMs were more likely to use them. Based on the results of this study, it is recommended that improvements in the work environment and healthcare system are needed to enhance the professionalism of KPTs working in the field of physical agent therapy by improving their awareness of Oms and improving the quality of physical therapy interventions.

Electro-Responsive Conductive Blended Hydrogel Patch.

The proposed electro-responsive hydrogel has great benefit for transdermal drug delivery system (TDDS) applications. To improve the physical or chemical properties of hydrogels, a number of researchers have previously studied the mixing efficiencies of the blended hydrogels. However, few studies have focused on improving the electrical conductivity and drug delivery of the hydrogels. We developed a conductive blended hydrogel by mixing alginate with gelatin methacrylate (GelMA) and silver nanowire (AgNW). We demonstrated that and the tensile strength of blended hydrogels were increased by a factor of 1.8 by blending GelMA and the electrical conductivity was enhanced by a factor of 18 by the addition of AgNW. Furthermore, the GelMA-alginate-AgNW (Gel-Alg-AgNW) blended hydrogel patch enabled on-off controllable drug release, indicating 57% doxorubicin release in response to electrical stimulation (ES) application. Therefore, this electro-responsive blended hydrogel patch could be useful for smart drug delivery applications.

Development and Validation of a Joint Attention-Based Deep Learning System for Detection and Symptom Severity Assessment of Autism Spectrum Disorder.

Importance: Joint attention, composed of complex behaviors, is an early-emerging social function that is deficient in children with autism spectrum disorder (ASD). Currently, no methods are available for objectively quantifying joint attention. Objective: To train deep learning (DL) models to distinguish ASD from typical development (TD) and to differentiate ASD symptom severities using video data of joint attention behaviors. Design, Setting, and Participants: In this diagnostic study, joint attention tasks were administered to children with and without ASD, and video data were collected from multiple institutions from August 5, 2021, to July 18, 2022. Of 110 children, 95 (86.4%) completed study measures. Enrollment criteria were 24 to 72 months of age and ability to sit with no history of visual or auditory deficits. Exposures: Children were screened using the Childhood Autism Rating Scale. Forty-five children were diagnosed with ASD. Three types of joint attention were assessed using a specific protocol. Main Outcomes and Measures: Correctly distinguishing ASD from TD and different levels of ASD symptom severity using the DL model area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall. Results: The analytical population consisted of 45 children with ASD (mean [SD] age, 48.0 [13.4] months; 24 [53.3%] boys) vs 50 with TD (mean [SD] age, 47.9 [12.5] months; 27 [54.0%] boys). The DL ASD vs TD models showed good predictive performance for initiation of joint attention (IJA) (AUROC, 99.6% [95% CI, 99.4%-99.7%]; accuracy, 97.6% [95% CI, 97.1%-98.1%]; precision, 95.5% [95% CI, 94.4%-96.5%]; and recall, 99.2% [95% CI, 98.7%-99.6%]), low-level response to joint attention (RJA) (AUROC, 99.8% [95% CI, 99.6%-99.9%]; accuracy, 98.8% [95% CI, 98.4%-99.2%]; precision, 98.9% [95% CI, 98.3%-99.4%]; and recall, 99.1% [95% CI, 98.6%-99.5%]), and high-level RJA (AUROC, 99.5% [95% CI, 99.2%-99.8%]; accuracy, 98.4% [95% CI, 97.9%-98.9%]; precision, 98.8% [95% CI, 98.2%-99.4%]; and recall, 98.6% [95% CI, 97.9%-99.2%]). The DL-based ASD symptom severity models showed reasonable predictive performance for IJA (AUROC, 90.3% [95% CI, 88.8%-91.8%]; accuracy, 84.8% [95% CI, 82.3%-87.2%]; precision, 76.2% [95% CI, 72.9%-79.6%]; and recall, 84.8% [95% CI, 82.3%-87.2%]), low-level RJA (AUROC, 84.4% [95% CI, 82.0%-86.7%]; accuracy, 78.4% [95% CI, 75.0%-81.7%]; precision, 74.7% [95% CI, 70.4%-78.8%]; and recall, 78.4% [95% CI, 75.0%-81.7%]), and high-level RJA (AUROC, 84.2% [95% CI, 81.8%-86.6%]; accuracy, 81.0% [95% CI, 77.3%-84.4%]; precision, 68.6% [95% CI, 63.8%-73.6%]; and recall, 81.0% [95% CI, 77.3%-84.4%]). Conclusions and Relevance: In this diagnostic study, DL models for identifying ASD and differentiating levels of ASD symptom severity were developed and the premises for DL-based predictions were visualized. The findings suggest that this method may allow digital measurement of joint attention; however, follow-up studies are necessary for further validation.

Machine Learning-Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children.

Importance: Early detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children's mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life. Objective: To evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study. Design, Setting, and Participants: In this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm-based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models. Main Outcomes and Measures: The average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features. Results: The final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992). Conclusions and Relevance: In this diagnostic study, an ML method for early detection or screening using digital phenotypes in children's daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

A Method of Generating Real-Time Natural Light Color Temperature Cycle for Circadian Lighting Service.

The light intensity and color temperature of natural light periodically change and promote the circadian entrainment of the human body. In addition, the color temperature cycle of natural light that is unique to each region is formed by its location and geographic and environmental factors, affecting the health of its residents. Research on lighting and construction to provide the color temperature of real-time natural light has continued to provide the beneficial effect of natural indoor lighting. However, lighting technology that provides the real-time color temperature of natural light could not be realized since it is challenging to select a color temperature cycle zone due to abrupt color temperature changes at sunrise and sunset. Such drastic shifts cause an irregular measurement of color temperature over time due to general weather or atmospheric conditions. In a previous study, a method of generating a color temperature cycle using deep learning was introduced, but the performance at the beginning and end of the color temperature cycle was unreliable. Therefore, this study proposes generating a real-time natural light color temperature cycle for the circadian lighting service. The characteristics of the daily color temperature cycle were analyzed based on the measured natural light characteristics database, and a data set for learning was established. To improve the color temperature cycle generation performance, a deep learning (TadGAN) model was implemented by searching for the lowest point of the color temperature at the start and end points of the color temperature cycle and applying the boot and ending datasets to these points. The color temperature cycle zone was accurately detected in real-time in the experiment, and the generation performance of the color temperature cycle was maintained at the beginning and end of the color temperature cycle. The mean absolute error decreased by about 67%, confirming the generation of a more accurate real-time color temperature cycle.

Antioxidant, Enzyme, and H2O2-Triggered Melanoma Targeted Mesoporous Organo-Silica Nanocomposites for Synergistic Cancer Therapy.

The multi-stimuli responsive drug delivery system has recently attracted attention in cancer treatments, since it can reduce several side effects and enhance cancer therapeutic efficacy. Herein, we present the intracellular antioxidant (glutathione, GSH), enzyme (hyaluronidase, HAase), and hydrogen peroxide (H2O2) triggered mesoporous organo-silica (MOS) nanocomposites for multi-modal treatments via chemo-, photothermal, and photodynamic cancer therapies. A MOS nanoparticle was synthesized by two-types of precursors, tetraethyl orthosilicate (TEOS) and bis[3-(triethoxysilyl)propyl] tetrasulfide (BTES), providing large-sized mesopores and disulfide bonds cleavable by GSH. Additionally, we introduced a new ß-cyclodextrin-hyaluronic acid (CDHA) gatekeeper system, enabling nanocomposites to form the specific interaction with the ferrocene (Fc) molecule, control the drug release by the HAase and H2O2 environment, as well as provide the targeting ability against the CD44-overexpressing melanoma (B16F10) cells. Indocyanine green (ICG) and doxorubicin (Dox) were loaded in the MOS-Fc-CDHA (ID@MOS-Fc-CDHA) nanocomposites, allowing for hyperthermia and cytotoxic reactive oxygen species (ROS) under an 808 nm NIR laser irradiation. Therefore, we demonstrated that the ID@MOS-Fc-CDHA nanocomposites were internalized to the B16F10 cells via the CD44 receptor-mediated endocytosis, showing the controlled drug release by GSH, HAase, and H2O2 to enhance the cancer therapeutic efficacy via the synergistic chemo-, photothermal, and photodynamic therapy effect.

TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light.

This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.

Clinical features and management of snake bites in 70 dogs in Korea.

BACKGROUND: Snakebites remain a devastating and life-threatening environmental hazard. While the management of snakebites has been well described in humans, few clinical data and guidelines exist for dogs, especially in Korea. OBJECTIVES: This retrospective study evaluated the clinical features of 70 dogs with snakebite wounds in Korea. METHODS: The medical records of 72 dogs that presented to three animal hospitals from June 2008 to July 2021 were reviewed; among these, 70 dogs that met the inclusion criteria were enrolled. Their signalment, history, clinical signs, physical examination, blood analysis, treatment, and prognosis were also evaluated. RESULTS: Of 70 dog owners, 35 (50%) witnessed the bite, with a mean time between bite and hospital presentation of 9.7 ± 4.1 h in 58 dogs. Blood smears were evaluated in 45 dogs, of which 28 (62%) showed echinocytosis. Anemia and acute kidney injury were found in 21 (29%) and 2 dogs (3%), respectively. A total of 37 dogs (53%) were hospitalized, 5 (7%) of which died. CONCLUSIONS: The most significant finding was the high prevalence of echinocytosis. The data from this retrospective study could inform the management of dogs bitten by snakes in Korea.

Near-Infrared Light-Triggered Generation of Reactive Oxygen Species and Induction of Local Hyperthermia from Indocyanine Green Encapsulated Mesoporous Silica-Coated Graphene Oxide for Colorectal Cancer Therapy.

Near-infrared (NIR) light-mediated photothermal therapy (PTT) and photodynamic therapy (PDT) have widely been used for cancer treatment applications. However, a number of limitations (e.g., low NIR absorption capacity of photothermal agents, insufficient loading efficiency of photosensitive molecules) have hindered the widespread use of NIR-mediated cancer therapy. Therefore, we developed a mesoporous silica-coated reduced graphene oxide (rGO) nanocomposite that could provide a high encapsulation rate of indocyanine green (ICG) and enhance PTT/PDT efficiency in vitro and in vivo. The ICG-encapsulated nanocomposite not only enhances the photothermal effect but also generates a large number of tumor toxic reactive oxygen species (ROS). By conjugation of polyethylene glycol (PEG) with folic acid (FA) as a tumor targeting moiety, we confirmed that ICG-encapsulated mesoporous silica (MS)-coated rGO nanocomposite (ICG@MS-rGO-FA) exhibited high colloidal stability and intracellular uptake in folate receptor-expressing CT-26 colorectal cancer cells. Upon NIR laser irradiation, this ICG@MS-rGO-FA nanocomposite induced the apoptosis of only CT-26 cells via enhanced PTT and PDT effects without any damage to normal cells. Furthermore, the ICG@MS-rGO-FA nanocomposite revealed satisfactory tumor targeting and biocompatibility in CT-26 tumor-bearing mice, thereby enhancing the therapeutic effects of PTT and PDT in vivo. Therefore, this tumor-targeted ICG@MS-rGO-FA nanocomposite shows a great potential for phototherapy applications.

Development of High-Intensity Focused Ultrasound Therapy for Inferior Turbinate Hypertrophy.

OBJECTIVES: Inferior turbinate (IT) hypertrophy is the main cause of chronic nasal obstruction. We developed a high-intensity focused ultrasound (HIFU) ablation device to treat patients with IT hypertrophy. METHODS: First, computed tomography images of patients with no evidence of sinonasal disease were evaluated to measure and compare the IT, medial mucosal thickness (MT), and space between the nasal septum and IT according to clinical characteristics such as septal deviation. A HIFU prototype was developed based on the above human anatomical studies. The experimental study was performed in five pigs; the nasal volume and histological changes at 1 and 4 weeks postoperatively were evaluated to compare the efficacy of HIFU turbinoplasty with that of radiofrequency turbinoplasty and a control group. RESULTS: The mean medial MT of the anterior, middle, and posterior portions of the IT were 4.66±1.14, 4.23±0.97, and 6.17±1.29 mm, respectively. The mean medial space was 2.65±0.79 mm. The diameter and focal depth of the prototype were 4 mm and 3 mm, respectively. HIFU showed no postoperative complications, including bleeding or scar formation. After HIFU treatment, the nasal volume increased by 196.62 mm3 (7.8%) and 193.74 mm3 (8.3%) at 1 week and 4 weeks, compared with the increase of 87.20 mm3 (3.1%) and 213.81 mm3 (9.0%), respectively, after radiofrequency therapy. A qualitative histological analysis after radiofrequency turbinoplasty showed epithelial layer disruption at 1 week and increased fibrosis, along with decreased glandular structure, at 4 weeks. The HIFU group had an intact epithelial layer at 1 week postoperatively. However, significant differences were observed at 4 weeks, including increased fibrosis and decreased glandular structure. CONCLUSION: The efficacy and safety of HIFU turbinoplasty were demonstrated in an animal study. Our. RESULTS: warrant further human clinical trials.

A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images.

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.

Risk of Atrial Fibrillation According to Cancer Type: A Nationwide Population-Based Study.

BACKGROUND: Patients with cancer have an increased risk of atrial fibrillation (AF). However, there is a paucity of information regarding the association between cancer type and risk of AF. OBJECTIVES: This study sought to evaluate the risk of AF according to the type of cancer. METHODS: We enrolled 816,811 patients who were diagnosed with cancer from the Korean National Health Insurance Service database between 2009 and 2016. Age- and sex-matched noncancer control subjects (1:2; n = 1,633,663) were also selected. Newly diagnosed AF was identified based on the type of cancer. RESULTS: During a median follow-up of 4.5 years, AF was newly diagnosed in 25,356 patients with cancer (6.6 per 1,000 person-years). In multivariable Fine and Gray's regression analysis, cancer was an independent risk factor for incident AF (adjusted subdistribution hazard ratio [aHR]: 1.63; 95% confidence interval [CI]: 1.61 to 1.66). Multiple myeloma showed a higher association with incident AF (aHR: 3.34; 95% CI: 2.98 to 3.75). Esophageal cancer showed the highest risk among solid cancers (aHR: 2.69; 95% CI: 2.45 to 2.95), and stomach cancer showed the lowest association with AF risk (aHR: 1.27; 95% CI 1.23 to 1.32). CONCLUSIONS: Although patients with cancer were found to have a higher risk of AF, the impact on AF development varied by cancer type.

Association between exercise habits and stroke, heart failure, and mortality in Korean patients with incident atrial fibrillation: A nationwide population-based cohort study.

BACKGROUND: There is a paucity of information about cardiovascular outcomes related to exercise habit change after a new diagnosis of atrial fibrillation (AF). We investigated the association between exercise habits after a new AF diagnosis and ischemic stroke, heart failure (HF), and all-cause death. METHODS AND FINDINGS: This is a nationwide population-based cohort study using data from the Korea National Health Insurance Service. A retrospective analysis was performed for 66,692 patients with newly diagnosed AF between 2010 and 2016 who underwent 2 serial health examinations within 2 years before and after their AF diagnosis. Individuals were divided into 4 categories according to performance of regular exercise, which was investigated by a self-reported questionnaire in each health examination, before and after their AF diagnosis: persistent non-exercisers (30.5%), new exercisers (17.8%), exercise dropouts (17.4%), and exercise maintainers (34.2%). The primary outcomes were incidence of ischemic stroke, HF, and all-cause death. Differences in baseline characteristics among groups were balanced considering demographics, comorbidities, medications, lifestyle behaviors, and income status. The risks of the outcomes were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting (IPTW) during a mean follow-up of 3.4 ± 2.0 years. The new exerciser and exercise maintainer groups were associated with a lower risk of HF compared to the persistent non-exerciser group: the hazard ratios (HRs) (95% CIs) were 0.95 (0.90-0.99) and 0.92 (0.88-0.96), respectively (p < 0.001). Also, performing exercise any time before or after AF diagnosis was associated with a lower risk of mortality compared to persistent non-exercising: the HR (95% CI) was 0.82 (0.73-0.91) for new exercisers, 0.83 (0.74-0.93) for exercise dropouts, and 0.61 (0.55-0.67) for exercise maintainers (p < 0.001). For ischemic stroke, the estimates of HRs were 10%-14% lower in patients of the exercise groups, yet differences were statistically insignificant (p = 0.057). Energy expenditure of 1,000-1,499 MET-min/wk (regular moderate exercise 170-240 min/wk) was consistently associated with a lower risk of each outcome based on a subgroup analysis of the new exerciser group. Study limitations include recall bias introduced due to the nature of the self-reported questionnaire and restricted external generalizability to other ethnic groups. CONCLUSIONS: Initiating or continuing regular exercise after AF diagnosis was associated with lower risks of HF and mortality. The promotion of exercise might reduce the future risk of adverse outcomes in patients with AF.

N2O dynamics in the western Arctic Ocean during the summer of 2017.

The western Arctic Ocean (WAO) has experienced increased heat transport into the region, sea-ice reduction, and changes to the WAO nitrous oxide (N2O) cycles from greenhouse gases. We investigated WAO N2O dynamics through an intensive and precise N2O survey during the open-water season of summer 2017. The effects of physical processes (i.e., solubility and advection) were dominant in both the surface (0-50 m) and deep layers (200-2200 m) of the northern Chukchi Sea with an under-saturation of N2O. By contrast, both the surface layer (0-50 m) of the southern Chukchi Sea and the intermediate (50-200 m) layer of the northern Chukchi Sea were significantly influenced by biogeochemically derived N2O production (i.e., through nitrification), with N2O over-saturation. During summer 2017, the southern region acted as a source of atmospheric N2O (mean: + 2.3 ± 2.7 µmol N2O m-2 day-1), whereas the northern region acted as a sink (mean - 1.3 ± 1.5 µmol N2O m-2 day-1). If Arctic environmental changes continue to accelerate and consequently drive the productivity of the Arctic Ocean, the WAO may become a N2O "hot spot", and therefore, a key region requiring continued observations to both understand N2O dynamics and possibly predict their future changes.

Conductive GelMA-Collagen-AgNW Blended Hydrogel for Smart Actuator.

Blended hydrogels play an important role in enhancing the properties (e.g., mechanical properties and conductivity) of hydrogels. In this study, we generated a conductive blended hydrogel, which was achieved by mixing gelatin methacrylate (GelMA) with collagen, and silver nanowire (AgNW). The ratio of GelMA, collagen and AgNW was optimized and was subsequently gelated by ultraviolet light (UV) and heat. The scanning electron microscope (SEM) image of the conductive blended hydrogels showed that collagen and AgNW were present in the GelMA hydrogel. Additionally, rheological analysis indicated that the mechanical properties of the conductive GelMA-collagen-AgNW blended hydrogels improved. Biocompatibility analysis confirmed that the human umbilical vein endothelial cells (HUVECs) encapsulated within the three-dimensional (3D), conductive blended hydrogels were highly viable. Furthermore, we confirmed that the molecule in the conductive blended hydrogel was released by electrical stimuli-mediated structural deformation. Therefore, this conductive GelMA-collagen-AgNW blended hydrogel could be potentially used as a smart actuator for drug delivery applications.

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User's Location.

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays' information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user's location obtained with mobile devices' help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model's data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user's location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90-95% in the summers, as well as in winters.

CuS/rGO-PEG Nanocomposites for Photothermal Bonding of PMMA-Based Plastic Lab-on-a-Chip.

We developed copper sulfide (CuS)/reduced graphene oxide (rGO)-poly (ethylene glycol) (PEG) nanocomposites for photothermal bonding of a polymethyl methacrylate (PMMA)-based plastic lab-on-a-chip. The noncontact photothermal bonding of PMMA-based plastic labs-on-chip plays an important role in improving the stability and adhesion at a high-temperature as well as minimizing the solution leakage from microchannels when connecting two microfluidic devices. The CuS/rGO-PEG nanocomposites were used to bond a PMMA-based plastic lab-on-a-chip in a short time with a high photothermal effect by a near-infrared (NIR) laser irradiation. After the thermal bonding process, a gap was not generated in the PMMA-based plastic lab-on-a-chip due to the low viscosity and density of the CuS/rGO-PEG nanocomposites. We also evaluated the physical and mechanical properties after the thermal bonding process, showing that there was no solution leakage in PMMA-based plastic lab-on-a-chip during polymerase chain reaction (PCR) thermal cycles. Therefore, the CuS/rGO-PEG nanocomposite could be a potentially useful nanomaterial for non-contact photothermal bonding between the interfaces of plastic module lab-on-a-chip.

Photo-Oxidative Protection of Chlorophyll a in C-Phycocyanin Aqueous Medium.

In this study, potential protection of chlorophyll a from illumination and oxidation-induced decomposition has been examined using C-phycocyanin (C-PC) aqueous medium. Photo-oxidation resistance of chlorophyll a was monitored in various aqueous media using ultraviolet-visible spectroscopy and direct-infusion atmospheric pressure chemical ionization mass spectrometry analysis. The spectroscopy results showed that chlorophyll a in C-PC medium experienced the lowest rate of conversion to its derivatives; thus, it was demonstrated that chlorophyll a was mostly intact in the C-PC medium. Furthermore, the C-PC treated with chlorophyll a showed the lowest concentrations of malondialdehyde, and chlorophyll a in C-PC medium did not cause serious damage to human liver cells in vitro after intensive illumination. Therefore, we propose a new method of protecting chlorophyll a from photodegradation and oxidation using C-PC aqueous medium.

Near-Infrared Light-Triggered Thermo-responsive Poly(N-Isopropylacrylamide)-Pyrrole Nanocomposites for Chemo-photothermal Cancer Therapy.

The combination therapy based on multifunctional nanocomposites has been considered as a promising approach to improve cancer therapeutic efficacy. Herein, we report targeted multi-functional poly(N-isopropylacrylamide) (PNIPAM)-based nanocomposites for synergistic chemo-photothermal therapy toward breast cancer cells. To increase the transition temperature, acrylic acid (AAc) was added in synthetic process of PNIPAM, showing that the intrinsic lower critical solution temperature was changed to 42 °C . To generate the photothermal effect under near-infrared (NIR) laser irradiation (808 nm), polypyrrole (ppy) nanoparticles were uniformly decorated in PNIPAM-AAc. Folic acid (FA), as a cancer targeting ligand, was successfully conjugated on the surplus carboxyl groups in PNIPAM network. The drug release of PNIPAM-ppy-FA nanocomposites was efficiently triggered in response to the temperature change by NIR laser irradiation. We also confirmed that PNIPAM-ppy-FA was internalized to MDA-MB-231 breast cancer cells by folate-receptor-mediated endocytosis and significantly enhanced cancer therapeutic efficacy with combination treatment of chemo-photothermal effects. Therefore, our work encourages further exploration of multi-functional nanocarrier agents for synergistic therapeutic approaches to different types of cancer cells.