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  • 1
    Online Resource
    Online Resource
    Effects of Donepezil Treatment on Brain Metabolites, Gut Microbiota, and Gut Metabolites in an Amyloid Beta-Induced Cognitive Impairment Mouse Pilot Model
    MDPI AG ; 2022
    In:  Molecules Vol. 27, No. 19 ( 2022-10-05), p. 6591-
    Details
    In: Molecules, MDPI AG, Vol. 27, No. 19 ( 2022-10-05), p. 6591-
    Abstract: Accumulated clinical and biomedical evidence indicates that the gut microbiota and their metabolites affect brain function and behavior in various central nervous system disorders. This study was performed to investigate the changes in brain metabolites and composition of the fecal microbial community following injection of amyloid β (Aβ) and donepezil treatment of Aβ-injected mice using metataxonomics and metabolomics. Aβ treatment caused cognitive dysfunction, while donepezil resulted in the successful recovery of memory impairment. The Aβ + donepezil group showed a significantly higher relative abundance of Verrucomicrobia than the Aβ group. The relative abundance of 12 taxa, including Blautia and Akkermansia, differed significantly between the groups. The Aβ + donepezil group had higher levels of oxalate, glycerol, xylose, and palmitoleate in feces and oxalate, pyroglutamic acid, hypoxanthine, and inosine in brain tissues than the Aβ group. The levels of pyroglutamic acid, glutamic acid, and phenylalanine showed similar changes in vivo and in vitro using HT-22 cells. The major metabolic pathways in the brain tissues and gut microbiota affected by Aβ or donepezil treatment of Aβ-injected mice were related to amino acid pathways and sugar metabolism, respectively. These findings suggest that alterations in the gut microbiota might influence the induction and amelioration of Aβ-induced cognitive dysfunction via the gut–brain axis. This study could provide basic data on the effects of Aβ and donepezil on gut microbiota and metabolites in an Aβ-induced cognitive impairment mouse model.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules27196591
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2008644-1
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  • 2
    Online Resource
    Online Resource
    Effects of Proton Irradiation on the Current Characteristics of SiN-Passivated AlGaN/GaN MIS-HEMTs Using a TMAH-Based Surface Pre-Treatment
    MDPI AG ; 2021
    In:  Micromachines Vol. 12, No. 8 ( 2021-07-23), p. 864-
    Details
    In: Micromachines, MDPI AG, Vol. 12, No. 8 ( 2021-07-23), p. 864-
    Abstract: This study investigated the combined effects of proton irradiation and surface pre-treatment on the current characteristics of Gallium Nitride (GaN)-based metal-insulator-semiconductor high-electron-mobility-transistors (MIS-HEMTs) to evaluate the radiation hardness involved with the Silicon Nitride (SiN) passivation/GaN cap interface. The impact of proton irradiation on the static and dynamic current characteristics of devices with and without pre-treatment were analyzed with 5 MeV proton irradiation. In terms of transfer characteristics before and after the proton irradiation, the drain current of the devices without and with pre-treatment were reduced by an increase in sheet and contact resistances after the proton irradiation. In contrast with the static current characteristics, the gate-lag characteristics of the device with pre-treatment were significantly degenerated. In the device with pre-treatment, the hydrogen passivation for surface states of the GaN cap was formed by the pre-treatment and SiN deposition processes. Since the hydrogen passivation was removed by the proton irradiation, the newly created vacancies resulted in the degeneration of gate-lag characteristics. After nine months in an ambient atmosphere, the gate-lag characteristics of the device with pre-treatment were recovered because of the hydrogen recombination. These results demonstrated that the radiation hardness of MIS-HEMTs was affected by the SiN/GaN interface quality.
    Type of Medium: Online Resource
    ISSN: 2072-666X
    URL: Article
    DOI: 10.3390/mi12080864
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2620864-7
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  • 3
    Online Resource
    Online Resource
    Machine Learning Predicts 30-Day Outcome among Acute Myeloid Leukemia Patients: A Single-Center, Retrospective, Cohort Study
    MDPI AG ; 2023
    In:  Journal of Clinical Medicine Vol. 12, No. 18 ( 2023-09-13), p. 5940-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 12, No. 18 ( 2023-09-13), p. 5940-
    Abstract: Acute myeloid leukemia (AML) is a clinical emergency requiring treatment and results in high 30-day (D30) mortality. In this study, the prediction of D30 survival was studied using a machine learning (ML) method. The total cohort consisted of 1700 survivors and 130 non-survivors at D30. Eight clinical and 42 laboratory variables were collected at the time of diagnosis by pathology. Among them, six variables were selected by a feature selection method: induction chemotherapy (CTx), hemorrhage, infection, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase. Clinical and laboratory data were entered into the training model for D30 survival prediction, followed by testing. Among the tested ML algorithms, the decision tree (DT) algorithm showed higher accuracy, the highest sensitivity, and specificity values (95% CI) of 90.6% (0.918–0.951), 70.4% (0.885–0.924), and 92.1% (0.885–0.924), respectively. DT classified patients into eight specific groups with distinct features. Group 1 with CTx showed a favorable outcome with a survival rate of 97.8% (1469/1502). Group 6, with hemorrhage and the lowest fibrinogen level at diagnosis, showed the worst survival rate of 45.5% (25/55) and 20.5 days. Prediction of D30 survival among AML patients by classification of patients with DT showed distinct features that might support clinical decision-making.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm12185940
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662592-1
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  • 4
    Online Resource
    Online Resource
    RF Transceiver for the Multi-Mode Radar Applications
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 5 ( 2021-02-24), p. 1563-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 5 ( 2021-02-24), p. 1563-
    Abstract: In this work, a multi-mode radar transceiver supporting pulse, FMCW and CW modes was designed as an integrated circuit. The radars mainly detect the targets move by using the Doppler frequency which is significantly affected by flicker noise of the receiver from several Hz to several kHz. Due to this flicker noise, the long-range detection performance of the radars is greatly reduced, and the accuracy of range to the target and velocity is also deteriorated. Therefore, we propose a transmitter that suppresses LO leakage in consideration of long-range detection, target distance, velocity, and noise figure. We also propose a receiver structure that suppresses DC offset due to image signal and LO leakage. The design was conducted with TSMC 65 nm CMOS process, and the designed and fabricated circuit consumes a current of 265 mA at 1.2 V supply voltage. The proposed transmitter confirms the LO leakage suppression of 37 dB at 24 GHz. The proposed receiver improves the noise figure by about 20 dB at 100 Hz by applying a double conversion architecture and an image rejection, and it illustrates a DC rejection of 30 dB. Afterwards, the operation of the pulse, FMCW, and CW modes of the designed radar in integrated circuit was confirmed through experiment using a test PCB.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21051563
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 5
    Online Resource
    Online Resource
    Prediction System for Prostate Cancer Recurrence Using Machine Learning
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 4 ( 2020-02-16), p. 1333-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 4 ( 2020-02-16), p. 1333-
    Abstract: Prostate cancer is the fourth most common cancer affecting South Korean males, and the biochemical recurrence (BCR) of prostate cancer occurs in approximately 25% of patients five years after radical prostatectomy. The ability to predict BCR would help clinicians and patients to make better treatment decisions. Therefore, in this study, we have proposed a web-based clinical decision support system that predicts the BCR of prostate cancer in Korean patients. The data were obtained from the Korean Prostate Cancer Registry (KPCR) database, which contained information about 7394 patients with prostate cancer who were treated at one of the six major medical institutions in South Korea between May 2001 and December 2014. We tested 13 prediction models and selected the gradient boosting classifier because it demonstrated excellent prediction performance. Using this model, we were able to create a web application and once clinical data from patients were entered, the three- and five-year post-surgery BCR predictions could be extracted. We developed a clinical decision support system to provide a prostate cancer BCR predictive function to facilitate postoperative follow-up and clinical management. This system will help clinicians develop a strategic approach for prostate cancer treatment by predicting the likelihood of prostate cancer recurrence.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10041333
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 6
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    Online Resource
    Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer
    MDPI AG ; 2022
    In:  Applied Sciences Vol. 12, No. 16 ( 2022-08-15), p. 8156-
    Details
    In: Applied Sciences, MDPI AG, Vol. 12, No. 16 ( 2022-08-15), p. 8156-
    Abstract: Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app12168156
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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  • 7
    Online Resource
    Online Resource
    Graphene/PVDF Composites for Ni-rich Oxide Cathodes toward High-Energy Density Li-ion Batteries
    MDPI AG ; 2021
    In:  Materials Vol. 14, No. 9 ( 2021-04-27), p. 2271-
    Details
    In: Materials, MDPI AG, Vol. 14, No. 9 ( 2021-04-27), p. 2271-
    Abstract: Li-ion batteries (LIBs) employ porous, composite-type electrodes, where few weight percentages of carbonaceous conducting agents and polymeric binders are required to bestow electrodes with electrical conductivity and mechanical robustness. However, the use of such inactive materials has limited enhancements of battery performance in terms of energy density and safety. In this study, we introduced graphene/polyvinylidene fluoride (Gr/PVdF) composites in Ni-rich oxide cathodes for LIBs, replacing conventional conducting agents, carbon black (CB) nanoparticles. By using Gr/PVdF suspensions, we fabricated highly dense LiNi0.85Co0.15Al0.05O2 (NCA) cathodes having a uniform distribution of conductive Gr sheets without CB nanoparticles, which was confirmed by scanning spreading resistance microscopy mode using atomic force microscopy. At a high content of 99 wt.% NCA, good cycling stability was shown with significantly improved areal capacity (Qareal) and volumetric capacity (Qvol), relative to the CB/PVdF-containing NCA electrode with a commercial-level of electrode parameters. The NCA electrodes using 1 wt.% Gr/PVdF (0.9:0.1) delivered a high Qareal of ~3.7 mAh cm−2 (~19% increment) and a high Qvol of ~774 mAh cm−3 (~18% increment) at a current rate of 0.2 C, as compared to the conventional NCA electrode. Our results suggest a viable strategy for superseding conventional conducting agents (CB) and improving the electrochemical performance of Ni-rich cathodes for advanced LIBs.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma14092271
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2487261-1
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  • 8
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    Online Resource
    The Ascosphaera apis Infection (Chalkbrood Disease) Alters the Gut Bacteriome Composition of the Honeybee
    MDPI AG ; 2023
    In:  Pathogens Vol. 12, No. 5 ( 2023-05-19), p. 734-
    Details
    In: Pathogens, MDPI AG, Vol. 12, No. 5 ( 2023-05-19), p. 734-
    Abstract: The declining honeybee populations are a significant risk to the productivity and security of agriculture worldwide. Although there are many causes of these declines, parasites are a significant one. Disease glitches in honeybees have been identified in recent years and increasing attention has been paid to addressing the issue. Between 30% and 40% of all managed honeybee colonies in the USA have perished annually over the past few years. American foulbrood (AFB) and European foulbrood (EFB) have been reported as bacterial diseases, Nosema as a protozoan disease, and Chalkbrood and Stonebrood as fungal diseases. The study aims to compare the bacterial community related to the Nosema ceranae and Ascosphaera apis infection on the gut of the honeybee and compare it with the weakly active honeybees. The Nosema-infected honeybees contain the phyla Proteobacteria as the significantly dominant bacterial phyla, similar to the weakly active honeybees. In contrast, the Ascosphaera (Chalkbrood) infected honeybee contains large amounts of Firmicutes rather than Proteobacteria.
    Type of Medium: Online Resource
    ISSN: 2076-0817
    URL: Article
    DOI: 10.3390/pathogens12050734
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2695572-6
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  • 9
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    Online Resource
    Study on the Propeller Rope Cutter Concerning Transient Torsional Vibration Due to Cutting Action
    MDPI AG ; 2022
    In:  Applied Sciences Vol. 12, No. 3 ( 2022-02-03), p. 1628-
    Details
    In: Applied Sciences, MDPI AG, Vol. 12, No. 3 ( 2022-02-03), p. 1628-
    Abstract: Nowadays, damage to ships due to marine debris at sea is increasingly reported. Specifically, a piece of rope or fishing net can wrap around the propeller shaft, stopping it from rotating. Although various efforts have been made, there are still numerous practical challenges. A rope cutter system, which was designed to cut suspended objects wrapped around the propeller shaft, offers a great advantage in protecting the propulsion system. Since the cutting action produces the transient torsional vibration, the connecting bolts should be stable and sufficiently rigid under normal conditions. However, in the event of an encounter with an object that is too hard to cut, the bolts must be broken so that the rope cutter is released. Those kinds of objects may not be long enough to wrap around the shaft, such as a piece of wood. Therefore, this research presents the novelty in maintaining the safety of the propulsion system installed with a rope cutter. In this study, a method for obtaining the maximum diameter of the connecting bolt as a safety device is described, and the torques required to cut ropes of various thicknesses were determined based on actual measurements. Finally, a series of experiments in the laboratory-scale and shipboard test on an actual ship show that the rope cutter does not significantly reduce the ship’s power performance or increase the structural vibration.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app12031628
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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  • 10
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    Online Resource
    Temporal and Spatial Variations of the Biochemical Composition of Phytoplankton and Potential Food Material (FM) in Jaran Bay, South Korea
    MDPI AG ; 2020
    In:  Water Vol. 12, No. 11 ( 2020-11-04), p. 3093-
    Details
    In: Water, MDPI AG, Vol. 12, No. 11 ( 2020-11-04), p. 3093-
    Abstract: Food material (FM) derived from biochemical components (e.g., proteins, lipids, and carbohydrates) of phytoplankton can provide important quantitative and qualitative information of the food available to filter-feeding animals. The main objective of this study was to observe the seasonal and spatial variations of the biochemical compositions of phytoplankton and to identify the major controlling factors of FM as a primary food source in Jaran Bay, a large shellfish aquaculture site in South Korea. Based on monthly sampling conducted during 2016, significant monthly variations in the depth-integrated concentrations of major inorganic nutrients and chlorophyll a within the euphotic water column and a predominance (49.9 ± 18.7%) of micro-sized phytoplankton ( 〉 20 μm) were observed in Jaran Bay. Carbohydrates were the dominant biochemical component (51.8 ± 8.7%), followed by lipids (27.3 ± 3.8%) and proteins (20.9 ± 7.4%), during the study period. The biochemical compositions and average monthly FM levels (411.7 ± 93.0 mg m−3) in Jaran Bay were not consistent among different bays in the southern coastal region of South Korea, possibly due to differences in controlling factors, such as environmental and biological factors. According to the results from multiple linear regression, the variations in FM could be explained by the relatively large phytoplankton and the P* (PO43− − 1/16 × NO3−) and NH4+ concentrations in Jaran Bay. The macromolecular compositions and FM, as alternatives food source materials, should be monitored in Jaran Bay due to recent changes in nutrient concentrations and phytoplankton communities.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w12113093
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2521238-2
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