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Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed. This study aims to propose a clinically interpretable ensemble approach for the timely and accurate prediction of CA within 24 hours, regardless of patient heterogeneity, including variations across different populations and ICU subtypes. Additionally, we conducted patient-independent evaluations to emphasize the model's generalization performance and analyzed interpretable results that can be readily adopted by clinicians in real-time. Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU-Collaborative Research Database (eICU-CRD). To address the problem of underperformance, we constructed our framework using feature sets based on vital signs, multiresolution statistical analysis, and the Gini index, with a 12-hour window to capture the unique characteristics of CA. We extracted 3 types of features from each database to compare the performance of CA prediction between high-risk patient groups from MIMIC-IV and patients without CA from eICU-CRD. After feature extraction, we developed a tabular network (TabNet) model using feature screening with cost-sensitive learning. To assess real-time CA prediction performance, we used 10-fold leave-one-patient-out cross-validation and a cross-data set method. We evaluated MIMIC-IV and eICU-CRD across different cohort populations and subtypes of ICU within each database. Finally, external validation using the eICU-CRD and MIMIC-IV databases was conducted to assess the model's generalization ability. The decision mask of the proposed method was used to capture the interpretability of the model. The proposed method outperformed conventional approaches across different cohort populations in both MIMIC-IV and eICU-CRD. Additionally, it achieved higher accuracy than baseline models for various ICU subtypes within both databases. The interpretable prediction results can enhance clinicians' understanding of CA prediction by serving as a statistical comparison between non-CA and CA groups. Next, we tested the eICU-CRD and MIMIC-IV data sets using models trained on MIMIC-IV and eICU-CRD, respectively, to evaluate generalization ability. The results demonstrated superior performance compared with baseline models. Our novel framework for learning unique features provides stable predictive power across different ICU environments. Most of the interpretable global information reveals statistical differences between CA and non-CA groups, demonstrating its utility as an indicator for clinical decisions. Consequently, the proposed CA prediction system is a clinically validated algorithm that enables clinicians to intervene early based on CA prediction information and can be applied to clinical trials in digital health.

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Anisakidosis is a foodborne parasitic infection caused by the consumption of raw or uncooked seafood that contains third stage larvae from the Anisakidae family. This infection has been observed across the globe, with a particularly high prevalence in South Korea and Japan. Consequently, there is a necessity to compare and analyze the optimal detection methods with a view to preventing Anisakis outbreaks. In this study, a species-specific Taqman-based qPCR method was developed for the detection of the internal transcribed spacer region and mtDNA genes of Anisakis simplex and Pseudoterranova decipiens . Parasite-specific primer/probe sets were selected based on the data from domestic and foreign detection methods. In addition, we have designed our own primer/probe sets based on the target region of each parasite. A comprehensive literature review and a self-creation process were undertaken to select thirteen detection method sets for A. simplex and P. decipiens . The sensitivity of these sets was then evaluated by comparing the C q values from extracted DNA. The concentrations of six primer/probe sets detected through the screening process were then compared to optimize the test method. The resultant optimized method demonstrated a limit of detection of 0.0019 ng/µL for A. simplex and 0.0001 ng/µL for P. decipiens . The specificity test also confirmed that there was no cross-activity with the five parasite samples and the three types of anisakids plasmid DNA. This study would contribute development of a rapid detection method for anisakidosis, providing a foundation for proactive responses to food poisoning outbreaks.

Background Flavonoids are a diverse family of natural phenolic compounds commonly found in fruits and vegetables. Epidemiologic studies showed that flavonoids also reduce the risk of colon cancer. Tectochrysin is one of the major flavonoids of Alpinia oxyphylla Miquel. However, the anti-cancer effects and the molecular mechanisms of tectochrysin in colon cancer cells have not yet been reported. We investigated whether tectochrysin could inhibit colon cancer cell growth at 1, 5, 10 μg/ml. In in vivo study, we injected a tectochrysin treatment dose of 5 mg/kg to each mouse. Results Tectochrysin suppressed the growth of SW480 and HCT116 human colon cancer cells. The expression of DR3, DR4 and Fas were significantly increased, and pro-apoptotic proteins were also increased. Tectochrysin treatment also inhibited activity of NF-κB. A docking model indicated that tectochrysin binds directly to the p50 unit. In in vivo , tumor weights and volumes in mice were reduced when treated with tectochrysin. Tectochrysin leads to apoptotic cell death in colon cancer cells through activation of death receptors expression via the inhibition of NF-κB. Conclusions Tectochrysin can be a useful agent for the treatment of colon cancer cell growth as well as an adjuvant agent for chemo-resistant cancer cells growth.

The efficient storing and utilizing of industrial waste heat can contribute to the reduction of CO2 and primary energy. Thermochemical heat storage uses a chemical and/or an adsorption-desorption reaction to store heat without heat loss. This study aims to assess the long-term operational feasibility of thermochemical material based composite honeycombs, so that a new thermochemical heat storage and peripheral system were prepared. The evaluation was done by three aspects: The compressive strength of the honeycomb, heat charging, and the discharging capabilities of the thermochemical heat storage. The compressive strength exceeded 1 MPa and is sufficient for safe use. The thermal performance was also assessed in a variety of ways during 100 cycles, 550 h in total. By introducing a new process, the amount of thermochemical-only charging was successfully measured for the first time. Furthermore, the heat charging capabilities were measured at 55.8% after the end of the experiment. Finally, the heat discharging capability was decreased until 60 cycles and there was no further degradation thereafter. This degradation was caused by charging at a too high temperature (550 °C). In comparative tests using a low temperature (450 °C), the performance degradation became slow, which means that it is important to find the optimal charging temperature.

Rationale Two biomarkers: concentration ratio of O -desmethylvenlafaxine/venlafaxine and concentration sum of venlafaxine +  O -desmethylvenlafaxine were adopted to indicate venlafaxine responses, but neither is validated. Objectives To evaluate the ability of two biomarkers in reflecting venlafaxine pharmacokinetic variations, and to further examine their relationship with venlafaxine treatment outcomes. Methods Two well-defined influencing factors: CYP2D6 genotypes and drug interactions were enriched into a three-period crossover study to produce venlafaxine pharmacokinetic variations: In each period, healthy CYP2D6 extensive metabolizers (EM group; n  = 12) and CYP2D6*10/*10 intermediate metabolizers (IM group; n  = 12) were pretreated with clarithromycin (CYP3A4 inhibitor), or nothing (control), or clarithromycin + paroxetine (CYP3A4 + CYP2D6 inhibitors), before administration of a single-dose of 75 mg venlafaxine. Both biomarkers were evaluated (1) for their relationship with the influencing factors in healthy volunteers and (2) for their relationships with the venlafaxine responses/adverse events reported in two patient studies. Results Significant venlafaxine pharmacokinetic variations were observed between the EM and IM groups (geometric mean ratio [95 % CI] of area under the curve, 3.0 [1.8–5.1] in the control period), and between the control and clarithromycin + paroxetine periods (4.1 [3.5–4.7] and 2.0 [1.7–2.4] in the EM and IM group, respectively). O -Desmethylvenlafaxine/venlafaxine was superior to venlafaxine +  O -desmethylvenlafaxine to reflect the influencing factors. In the patient studies, O -desmethylvenlafaxine/venlafaxine > 4 showed high precision in predicting venlafaxine responders/partial-responders (92 %) and patients without venlafaxine-related adverse events (88 %); the O -desmethylvenlafaxine/venlafaxine < 4 and venlafaxine +  O -desmethylvenlafaxine > 400 ng/ml combination showed higher precision (100 %) than O -desmethylvenlafaxine/venlafaxine < 4 alone (65 %) in predicting venlafaxine non-responders. Conclusion We propose using O -desmethylvenlafaxine/venlafaxine for CYP2D6 phenotyping, and O -desmethylvenlafaxine/venlafaxine with venlafaxine +  O -desmethylvenlafaxine for predicting venlafaxine treatment outcomes in future prospective studies.

Pharmacogenomics is the study of the association between inter-individual genetic differences and drug responses. Researches in pharmacogenomics have been performed in compliance with the use of several genotyping technologies. In this study, a total of 392 single-nucleotide polymorphisms (SNPs) located in 141 pharmacogenes, including 21 phase I, 13 phase II, 18 transporter and 5 modifier genes, were selected and genotyped in 150 subjects using the GoldenGate assay or the SNaPshot technique. These variants were in Hardy-Weinberg equilibrium (HWE) (P>0.05), except for 22 SNPs. Genotyping of the 392 SNPs revealed that the minor allele frequencies of 47 SNPs were <0.05, 105 SNPs were monomorphic and 22 variants were not in HWE. Also, based on previous studies, we predicted the association between the polymorphisms of certain pharmacogenes, such as cytochrome P450 2D6, cytochrome P450 2C9, vitamin K epoxide reductase complex, subunit 1, cytochrome P450 2C19, human leukocyte antigen, class I, B and thiopurine S-methyltransferase, and drug efficacy. In conclusion, our study demonstrated the allele distribution of SNPs in 141 pharmacogenes as determined by high-throughput screening. Our results may be helpful in developing personalized medicines by using pharmacogene polymorphisms.

Issue Title: Special Issue: ICE-2005 International Conference on Electroceramics Perovskite-type oxygen permeable ceramic membranes, La^sub 0.6^Sr^sub 0.4^CoO^sub 3^ and La^sub 0.7^Sr^sub 0.3^Ga^sub 0.6^Fe^sub 0.4^O^sub 3^, were investigated to apply for an oxy-fuel combustion system. Effects of surface modification on oxygen permeability were scrutinized for monolithic membranes. Asymmetric membranes, composed of a dense thick-film type membrane and a porous support, were prepared. Several methods were explored to form a crack-free membrane film and processing conditions were established. Porous support, which gives rigidity to the specimens and gaseous path to the membrane surface, was inquired with viewpoints of porosity, mechanical properties and gas permeability. Finally, we designed and developed a laboratory scale oxygen production module system using tubular asymmetric membranes.[PUBLICATION ABSTRACT]

Aichivirus-A (AiV-A), a member of the Kobuvirus genus of the family Picornaviridae, was first reported in stool samples of patients with non-bacterial gastroenteritis in Aichi Prefecture, Japan, in 1989. AiV has been reported from in various aquatic environments, such as surface water and sewage, can be transmitted via the fecal–oral route through contaminated water. As AiV is known to acute gastroenteritis worldwide, developing methods for AiV detection from contaminated environments and food is required. In the present study, we established an effective polymerase chain reaction (PCR) method to detect AiV. Various real-time reverse transcription (RT)-PCR and conventional PCR methods for AiV detection were compared, and the limit of detection was confirmed by comparing the sensitivity at varied primer concentrations and PCR conditions. The final detection limits were 10 2 copy/μL in conventional PCR, and 10 1 copy/μL in the real-time RT-PCR. The optimized method used in this study might aid in detecting AiV contamination.

This study describes a method for discriminating Rangifer antlers from true Cervus antlers using agarose gel electrophoresis, capillary electrophoresis, quantitative real-time PCR, and allelic discrimination. Specific primers labeled with fluorescent tags were designed to amplify fragments from the mitochondrial D-loop genes for various Cervus subspecies and Rangifer tarandus differentially. A 466-bp fragment that was observed for both Cervus and Rangifer antlers served as a positive control, while a 270-bp fragment was specifically amplified only from Rangifer antlers. Allelic discrimination was used to differentiate between Cervus and Rangifer antlers, based on the amplification of specific alleles for both types of antlers. These PCR-based assays can be used for forensic and quantitative analyses of Cervus and Rangifer antlers in a single step, without having to obtain any sequence information. In addition, multiple PCR-based assays are more accurate and reproducible than a single assay for species-specific analysis and are especially useful in this study for the identification of original Cervus deer products from fraudulent Rangifer antlers.