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Astragalus membranaceus is a medicinal plant mainly used in East Asia and contains abundant secondary metabolites. Despite the importance of this plant, the available genomic and genetic information is still limited. De novo transcriptome construction is recognized as an essential method for transcriptome research when reference genome information is incomplete. In this study, we constructed three individual transcriptome sets (unigene sets) for detailed analysis of the phenylpropanoid biosynthesis pathway, a major metabolite of A. membranaceus. Set-1 was a circular consensus sequence (CCS) generated using PacBio sequencing (PacBio-seq). Set-2 consisted of hybridized assembled unigenes with Illumina sequencing (Illumina-seq) reads and PacBio CCS using rnaSPAdes. Set-3 unigenes were assembled from Illumina-seq reads using the Trinity software. Construction of multiple unigene sets provides several advantages for transcriptome analysis. First, it provides an appropriate expression filtering threshold for assembly-based unigenes: a threshold transcripts per million (TPM) ≥ 5 removed more than 88% of assembly-based unigenes, which were mostly short and low-expressing unigenes. Second, assembly-based unigenes compensated for the incomplete length of PacBio CCSs: the ends of the 5`/3` untranslated regions of phenylpropanoid-related unigenes derived from set-1 were incomplete, which suggests that PacBio CCSs are unlikely to be full-length transcripts. Third, more isoform unigenes could be obtained from multiple unigene sets; isoform unigenes missing in Set-1 were detected in set-2 and set-3. Finally, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that phenylpropanoid biosynthesis and carbohydrate metabolism were highly activated in A. membranaceus roots. Various sequencing technologies and assemblers have been developed for de novo transcriptome analysis. However, no technique is perfect for de novo transcriptome analysis, suggesting the need to construct multiple unigene sets. This method enables efficient transcript filtering and detection of longer and more diverse transcripts.

Terpenoids are naturally occurring compounds involved in respiration, photosynthesis, membrane fluidity, and pathogen interactions and are classified according to the structure of their carbon skeleton. Although most terpenoids possess pharmacological activity, knowledge about terpenoid metabolism in medicinal plants is insufficient. Rehmannia glutinosa (R. glutinosa) is a traditional herb that is widely used in East Asia and has been reported to contain various terpenoids. In this study, we performed a comprehensive transcriptome analysis of terpenoid metabolism in R. glutinosa using two RNA sequencing platforms: Illumina and PacBio. The results show that the sterol, saponin, iridoid, and carotenoid pathways are active in R. glutinosa. Sterol and saponin biosynthesis were mevalonate pathway dependent, whereas iridoid and carotenoid biosynthesis were methylerythritol 4-phosphate pathway dependent. In addition, we found that the homologous genes of key enzymes involved in terpenoid metabolism were expressed differentially and that the differential expression of these genes was associated with specific terpenoid biosynthesis. The different expression of homologous genes encoding acetyl-CoA acetyltransferase, 3-hydroxy-3-methylglutaryl-CoA reductase, mevalonate kinase, mevalonate diphosphate decarboxylase, farnesyl pyrophosphate synthase, squalene synthase, and squalene epoxidase was associated with sterol and saponin biosynthesis. Homologous genes encoding 1-deoxy-D-xylulose 5-phosphate synthase were also differentially expressed and were associated with carotenoid and iridoid biosynthesis. These results suggest that the biosynthesis of specific terpenoids can be regulated by the homologous of key enzymes involved in plant terpenoid metabolism.

We experienced a case in which a central venous catheter (CVC) was misplaced into the wrong vein, which was mistaken for the internal jugular vein (IJV), identified by chest x-ray and ultrasound. The vertebral vein passes through the transverse foramina from the atlas to the 6th cervical vertebra. After exiting the transverse foramen of the 6th vertebra, the vein subsequently runs anterolateral to the vertebral artery and posterior to the IJV and drains the innominate vein. In this case, chest x-ray and ultrasound revealed that the inserted CVC had a course very similar to the vertebral vein. The misplacement of a CVC into the vertebral vein might occur from excessive rotation of the patient's head, which leads to alterations in the cervical vascular anatomy, and from deep insertion of the puncture needle. Therefore, it is advised, for safe CVC insertion, to minimize a patient's head rotation and to make use of ultrasound when the anatomical structures cannot be clearly identified.

As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a data feature reduction process, a fault prediction model training process using long short-term memory, and an interpretation process of the fault prediction model via an explainable AI method. In particular, the proposed framework can explain a fault prediction based on time-series data. Therefore, it indicates which part of the data was significant for the fault prediction not only in terms of sensor type but also in terms of time. Through extensive experiments, we evaluate the proposed framework using various fault data by comparing the prediction performance of fault prediction and by assessing how well the main pre-symptoms of the fault are extracted when predicting a fault.

Abstract Background Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations. Results We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications. Conclusions OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.

Background: Short-term hydroponic-cultured ginseng (sHCG), which is 1-year-old ginseng seedlings cultivated for 4 weeks in a hydroponic system, is a functional food item with several biological effects. However, the optimal extraction conditions for sHCG, and the bioactivity of its extracts, have not been evaluated. Methods: Chlorogenic acid (CGA) and ginsenoside contents were evaluated in sHCG, white ginseng (WG), and red ginseng (RG) using high-performance liquid chromatography. Response surface methodology (RSM) was used to optimize the extraction conditions (temperature and ethanol concentration) to maximize the yield of dry matter, CGA, and four ginsenosides (Re, Rg1, Rb1, and Rd) from sHCG. The optimal extraction conditions were applied to pilot-scale production of sHCG extracts. The expression levels of tumor necrosis factor (TNF)-α/interferon (IFN)-γ-induced thymic and activation-regulated chemokines (TARC/CCL17) were measured after treatment with sHCG, WG, and RG extracts, and the effects of their bioactive compounds (CGA and four ginsenosides) on human skin keratinocytes (HaCaTs) were evaluated. Results: CGA and four ginsenosides, which are bioactive compounds of sHCG, significantly inhibited TNF-α/IFN-γ-induced TARC/CCL17 expression. The optimal sHCG extraction conditions predicted by the RSM models were 80 °C and 60% ethanol (v/v). The sHCG extracts produced at the pilot scale under optimal conditions greatly alleviated TNF-α/IFN-γ-induced TARC/CCL17 production compared with WG and RG extracts. Conclusions: Pesticide-free sHCG extracts, which contain high levels of CGA and the ginsenosides Re, Rg1, Rb1, and Rd as bioactive compounds, may have therapeutic potential for atopic diseases.

Background: In Korea, fetuses are currently assessed using ultrasound data collected from foreign subjects. This study aimed to propose independent reference values for the transverse cerebellar diameter (TCD) in Korean fetuses to predict gestational age (GA) and compare the TCD values among different countries. Methods: A total of 1819 healthy singletons with GAs of 15–37 weeks were retrospectively analyzed. The TCD was measured in the transcerebellar plane of the fetal head. TCD percentiles were calculated for different GAs, and a regression model was created to predict GA. Bland–Altman analysis was performed to examine the differences in the mean TCD and variations in the TCD among different countries. Results: The TCD ranged from 1.48 to 4.76 cm with a mean of 2.7 ± 0.91 cm. In the regression model of TCD (cm) = 0.129 GA (weeks) – 0.527 (95% confidence interval 0.129–0.130, p < 0.001), the TCD explained 98.4% of the variance in GA. Significant differences in the mean TCD were found between Korea, the United States, the United Kingdom, Hong Kong, and India. TCD variations increased as the mean TCD increased. Conclusions: Fetal TCD is an important biometric index for predicting GA. Ethnic differences must be considered when using fetal TCD.

Background: This study investigated the optimal coefficient of variance (%CV) for preventing hypoglycemia based on real-time continuous glucose monitoring (rt-CGM) data in people with type 1 diabetes mellitus (T1DM) already achieving their mean glucose (MG) target. Methods: Data from 172 subjects who underwent rt-CGM for at least 90 days and for whom 439 90-day glycemic profiles were available were analyzed. Receiver operator characteristic analysis was conducted to determine the cut-off value of %CV to achieve time below range (%TBR)<54 mg/dL <1 and =0. Results: Overall mean glycosylated hemoglobin was 6.8% and median %TBR<54 mg/dL was 0.2%. MG was significantly higher and %CV significantly lower in profiles achieving %TBR<54 mg/dL <1 compared to %TBR<54 mg/dL ≤1 (all P<0.001). The cut-off value of %CV for achieving %TBR<54 mg/dL <1 was 37.5%, 37.3%, and 31.0%, in the whole population, MG >135 mg/dL, and ≥135 mg/dL, respectively. The cut-off value for %TBR<54 mg/dL=0% was 29.2% in MG ≥135 mg/dL. In profiles with MG ≥135 mg/dL, 94.2% of profiles with a %CV <31 achieved the target of %TBR<54 mg/dL <1, and 97.3% with a %CV <29.2 achieved the target of %TBR<54 mg/dL=0%. When MG was >135 mg/dL, 99.4% of profiles with a %CV <37.3 achieved %TBR<54 mg/dL <1. Conclusion: In well-controlled T1DM with MG ≥135 mg/dL, we suggest a %CV <31% to achieve the %TBR<54 mg/dL <1 target. Furthermore, we suggest a %CV <29.2% to achieve the target of %TBR<54 mg/dL =0 for people at high risk of hypoglycemia. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the role of CD8+T cells as host immune factors in pediatric patients with Helicobacter pylori gastritis. Methods: Gastric mucosal tissue and blood samples were collected from 39 children, including 11 children with H. pylori infection and 28 children as controls. Anti-CD8 and anti-T-bet antibodies were used for immunohistochemistry of the gastric mucosa. For the cell surface and intracellular staining, peripheral blood mononuclear cells were stained with anti-IL7Rα, anti-CX3CR1, anti-CD8, anti-T-bet, and anti-IFN-γ antibodies. Cytokines of sera such as tumor necrosis factor alpha (TNF-α) and CX3CL1 were analyzed using enzyme-linked immunosorbent assay (ELISA). Results: In the immunohistochemistry of gastric mucosa, the frequency of CD8+ and T-bet+ T cells cells was higher in the H. pylori-positive group than in the control group (26.9± 7.8% vs. 16.9±3.3%, p<0.001; 5.0±2.5% vs. 2.2±0.7%, p=0.001). Between the control and H. pylori-positive groups, the frequency of IL-7RαlowCX3CR1+ CD8+ and T-bet+ INF-γ+ CD8+ T cells were not significantly different between surface and intracellular staining, respectively (40.4±24.0% vs. 38.2±17.8%, p=0.914; 40.4±24.0% vs. 38.2±17.8%, p=0.914). In the ELISA, no significant differences in TNF-α and CX3CL1 concentrations were observed between the control and H. pylori-positive groups (34.3±12.1 pg/mL vs. 47.0±22.6 pg/mL, p=0.114/0.5± 0.1 pg/mL vs. 0.5±0.1 pg/mL, p=0.188). Conclusion: CD8+ T and Th1 cells, which secrete IFN-γ, might play important roles in the mucosal immunity of the stomach in children with H. pylori infection. [ABSTRACT FROM AUTHOR]

Abstract Background An increasing amount of evidence has demonstrated an association between an increase in the level of tumor necrosis factor superfamily 13 (TNFSF13) and immunoglobulin A nephropathy (IgAN) progression. We aimed to evaluate if the level of pre-transplant serum TNFSF13 is predictive of IgAN recurrence after kidney transplantation. Methods This analysis was based on the clinical and laboratory data of 69 patients with IgAN who underwent first kidney transplantation with no evidence of mesangial IgA deposits in zero-time transplantation biopsy. We measured pre-transplant serum TNFSF13, total IgA, and galactose-deficient IgA1 levels. Results The recurrence rate of IgAN over a median follow-up duration of 5.1 years was 15.9% (11/69 patients), with a mean time to the first recurrence of 1.7 years. The high pre-transplant TNFSF13 level was associated with IgAN recurrence after kidney transplantation among patients who received a graft from a living related donor. Conclusions This study highlights association of TNFSF13 levels in recurrent IgAN patients who undergo living related donor transplantation. Further research is needed to clarify mechanisms by which TNFSF13 affects the recurrence of IgA nephropathy.

Crude polysaccharides, extracted from two seaweed species (Hizikia fusiforme and Sargassum horneri) and Haliotis discus hannai (abalone) viscera, were evaluated for their inhibitory effect against SARS-CoV-2 propagation. Plaque titration revealed that these crude polysaccharides efficiently inhibited SARS-CoV-2 propagation with IC50 values ranging from 0.35 to 4.37 μg/mL. The crude polysaccharide of H. fusiforme showed the strongest antiviral effect, with IC50 of 0.35 μg/mL, followed by S. horneri and abalone viscera with IC50 of 0.56 and 4.37 μg/mL, respectively. In addition, immunofluorescence assay, western blot, and quantitative RT-PCR analysis verified that these polysaccharides could inhibit SARS-CoV-2 replication. In Vero E6 cells, treatment with these crude polysaccharides before or after viral infection strongly inhibited the expression level of SARS-CoV-2 spikes, nucleocapsid proteins, and RNA copies of RNA-dependent RNA-polymerase and nucleocapsid. These results show that these crude marine polysaccharides effectively inhibit SARS-CoV-2 propagation by interference with viral entry.