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INTRODUCTION: Recent genome‐wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear. METHODS: We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS‐implicated variants (rs34294852 and rs871269). RESULTS: CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10−5) and higher CSF phosphorylated tau (p‐tau) levels (P = 9.28 × 10−7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10−6) and CSF p‐tau levels (P = 4.38 × 10−9). DISCUSSION: These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels. Highlights: Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau.The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category.GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls.rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia. [ABSTRACT FROM AUTHOR]

Construction of fluorescent probes for zinc ion (Zn2+) and cadmium ion (Cd2+) is significant for the safety of humans. However, the discriminating recognition of Zn2+ and Cd2+ by a single probe remains challenging owing to their similar properties. Herein, a novel deoxycholic acid derivative containing 8‐hydroxyquinoline fluorophore has been facilely synthesized through click chemistry to form a clamp‐like probe. Using its perfect bonding cavity from 1,2,3‐triazole and quinoline, this molecule showed favorable solvent‐dependent fluorescent responses and distinguished Zn2+ and Cd2+ in different solvents. In ethanol aqueous solution, it displayed good selectivity and ratiometric fluorescence to Zn2+ with 30 nm spectroscopic red‐shifts. In acetonitrile aqueous solution, it exhibited good selectivity and ratiometric fluorescence to Cd2+ with 18 nm spectroscopic red‐shifts. Moreover, the unique microstructural features of the probe in assembly were used to reflect its recognition processes. Due to its merits of low detection limit and instant response time, the probe was utilized for sensing Zn2+ and Cd2+ in water, beer and urine with high accuracy. Meanwhile, this probe served as a handy tool and was employed to obtain inexpensive test strips for the prompt and semiqualitative analysis of Zn2+ and Cd2+ with the naked eye. [ABSTRACT FROM AUTHOR]

Fjords are representative marine ecosystems that play an important role in regulating coastal carbon cycling. As fingerprints of the dissolved organic matter (DOM) pool, fluorescent DOM (FDOM) is widely used to study the ocean carbon cycle. Here, we report on 1‐year of weekly observations of FDOM (from May 2018 to May 2019) in the Bedford Basin, a well‐studied northwestern Atlantic fjord. Results showed that differences in FDOM indices (biological index, fluorescence index, and humification indices) between the euphotic and aphotic layers changed during the investigation period, suggesting that classical definitions of these indices may not apply consistently. Spearman rank correlations showed that the surface FDOM was affected by physical and biological factors, while water mass renewal was the major control for the deeper layer. Humic‐like FDOM (FDOMH) changed following the Atlantic deep‐water intrusion, indicating that FDOMH may be used to trace water mass movements in this region. FDOMH shows a significantly positive linear correlation with apparent oxygen utilization (AOU). Their slopes were higher than those from the open ocean, suggesting that the Bedford Basin is a hotspot of RDOM production. Hence, we proposed a possible carbon sequestration mechanism that fjords play as "RDOM producing hot spots" and that the produced RDOM is then transported to the open ocean via water mass movement and finally stored in the ocean for extended periods of time. The proposed biogeochemical processes may also occur, more generally, in the global coastal ocean. Plain Language Summary: Fjords are representative marine systems and play an important role in regulating coastal carbon cycling; however, few high‐frequency long‐term time series observations of dissolved organic matter (DOM) have been reported to date. This study reports a 1‐year weekly observation of fluorescent DOM (FDOM) in the Bedford Basin, a 70‐m deep fjord located in Nova Scotia, Canada. The results suggested that FDOM indices are not reliable indicators as proposed originally, and the control factors of FDOM behaviors were different in the surface and bottom water, as well that humic‐like FDOM (FDOMH) have the potential to trace water mass movements. Significant positive linear relationships between apparent oxygen utilization and FDOMH were observed in the aphotic zone, and their slopes were higher than those from the open ocean, suggesting that the Bedford Basin is a hotspot of RDOM production. Similar results are widely observed in dark marginal seas. Therefore, we propose a possible coastal carbon sequestration mechanism in which fjord systems (even coastal oceans) act as RDOM‐producing factories, and the produced RDOM is then transported to the open ocean via water mass movement and finally stored in the ocean at longer timescales. Key Points: Weekly time series observations of fluorescent dissolved organic matter (FDOM) in the Bedford Basin are presentedFDOM signals trace deep seawater intrusionsFjords are potential hot spots of refractory dissolved organic matter (RDOM) production and serve as RDOM producing factories [ABSTRACT FROM AUTHOR]

In this study, a new compound PPD‐Arg (Tos) (PAT), an arginine derivative of 20(s)‐PPD, was synthesized via Fmoc‐Arg (Tos)‐OH and 20(s)‐PPD. The pharmacokinetic properties in rats, in vitro cytotoxicity, and cell apoptosis rates of protopanaxadiol (PPD) and PAT were determined. A sensitive bioanalytical method for pharmacokinetics using ultra‐performance liquid chromatography coupled with time‐of‐flight mass spectrometry was developed and validated. The result showed that the Tmax and t1/2 of PAT were significantly enhanced, indicating a long‐lasting effect in vivo. Compared to the PPD group, the PAT group showed higher bioavailability. PAT also exhibited higher antitumor efficacy than PPD against three cancer cells, especially the strongest inhibitory activity against Huh‐7, even more potent than the positive control of paclitaxel. Therefore, the apoptosis assay based on annexin V/propidium iodide–combined staining against Huh‐7 further demonstrated that PAT could induce apoptosis of Huh‐7 cells. Better pharmacokinetic properties and antitumor efficacy of the arginine derivative of 20(s)‐PPD were important. These findings could provide references for further clinical research on amino acid derivatives of PPD as antitumor agents. [ABSTRACT FROM AUTHOR]

A long‐standing enigma in oceanography is why terrestrial organic matter is "missing" in the global ocean, despite the considerable discharge into it every year. Although some explanations, such as mineralogical composition, hydrodynamic processes, and priming effect, have been proposed, we hypothesize that the essential mechanism behind the missing organic matter is microbial processing, for which the underlying coupled geochemical, molecular, and genetic evidence is unknown. An ultra‐large‐volume, long‐term river–seawater stratified simulation system was constructed to unravel the microbially driven fate of terrigenous particulate organic matter (POM) in oceans. Analysis of combining the molecular with POM chemical composition data suggests that Bacteroidetes could act as pioneers in the processing of terrigenous POM in oceans, degrading high‐molecular‐weight, high‐carbon compounds such as polysaccharides. Remaining low‐molecular‐weight nitrogenous organic matter is subsequently degraded by Planctomycetes and Proteobacteria. Isotopic signals show that this preferential degradation causes a distinct "aging" effect of POM, and along with nitrification enhanced by remineralization, causes a decrease in the POM C : N ratio. Degradation of terrigenous POM and bacterial biomass biosynthesis leads to positive deviations in δ15N and δ13C. Relatively refractory hydrocarbons, aromatic compounds, and phenols are accumulated by microbial processes in this system. This study provides mechanistic insights into the missing chemical and isotopic signals and microbially driven fate of terrigenous POM in the ocean, with important implications for how riverine material input affects marine carbon and nitrogen cycling. [ABSTRACT FROM AUTHOR]

Summary The 6G networks are envisioned to provide Artificial Intelligence (AI) for distributed devices through deploying machine learning on base stations. However, due to the concern of data privacy, devices are not willing to transmit their raw data to base stations (BSs) for machine learning training. Federated learning (FL) is a promising paradigm that can enable distributed machine learning while protecting data privacy by collaboratively training AI models without sharing raw data. But the traditional FL requires a central node to complete global model update which makes the traditional FL facing single point attack. Further, the traditional FL cannot allow the distributed model sharing among untrusted devices which results in low training efficiency on devices. Blockchain is a promising approach to address the above issues since it can establish a secure decentralized and transaction sharing environment for untrusted devices. In this paper, we integrate blockchain into FL to build a distributed model training and sharing platform, where distributed mobile devices execute local model training and base stations maintain blockchain‐based model sharing. Moreover, in order to accelerate the establishment of blockchain, we design a lightweight consensus strategy based on a delegate committee. To select high‐quality nodes to form delegate committee, we design a deep reinforcement learning (DRL)‐based selection algorithm. Numerical results demonstrate that the proposed framework can achieve a higher precision compared with the traditional FL, and the proposed DRL selection algorithm can reduce consensus latency compared with the benchmarks. [ABSTRACT FROM AUTHOR]

Methane is a critical greenhouse gas with significant impacts on environmental and global change. However, CH4 cycling processes and coupling mechanisms with the biogeochemical cycling of carbon, nitrogen, sulfur and metals in the environment remain elusive. To fill such knowledge gaps, we constructed a manually curated methane cycling database (MCycDB) for comprehensive and accurate analysis of methane cycling microbial communities. MCycDB contains 298 methane cycling gene families covering 10 methane metabolism pathways with 610,208 representative sequences, and associated reference sequences from the NCBI RefSeq database with 48 phyla and 2,197 genera, and five phyla and 100 genera for bacteria and archaea, respectively. Also, homologous groups from public orthology databases were identified and included in MCycDB to reduce false positive assignments. We applied MCycDB to profile methane cycling gene families and associated taxonomic groups from various environments. Gene families involved in methanogenesis were abundant in hot spring sediment and less abundant in freshwater, whereas the ones involved in aerobic oxidation of methane were abundant in permafrost and peatland. This study demonstrates that MCycDB is a useful tool for studying microbially‐driven methane cycling processes with high specificity, coverage and accuracy. [ABSTRACT FROM AUTHOR]

Although the chemical components of Panax notoginseng (PN) and Panax ginseng (PG) are similar, their bioactivities are different. In this study, the differential bioactivities of PN and PG were used as the research object. First, the different metabolites in the plasma after oral administration of PN and PG were analyzed using a UPLC‐Q/TOF‐MS‐based metabolomics approach. Afterward, the metabolite‐target‐ pathway network of PN and PG was constructed, and thus the pathways related to different bioactivities were analyzed. As a result, 7 different metabolites were identified in PN group, and 10 different metabolites were identified in the PG group. In the PN group, the metabolite N1 was related to hemostasis, N1 and N3 were related to inhibiting the nerve center, antihypertensive, and abirritation. The metabolites N1, N3, N4, N5, and N6 were related to liver protection. The results showed that the metabolites G1, G2, G3, G5, and G6 in PG group were related to heart protection, and G1, G2, G6, and G9 were related to increased blood pressure. There were 13 signaling pathways related to different biological activities of PN (8 pathways) and PG (5 pathways). These pathways further clarified the mechanism of action that caused the different bioactivities between PN and PG. In summary, metabolomics combined with network pharmacology could be helpful to clarify the material basis of different bioactivities between PN and PG, promoting the research on PN and PG. [ABSTRACT FROM AUTHOR]

Rationale: Panax ginseng (PG) and American ginseng (AMG) are both medicinal plants of the Panax genus in the Acanthopanax family. Although PG and AMG have similar components of ginsenosides, there are many differences of their bioactivities. In this study, the biochemical mechanisms of different bioactivities of PG and AMG were explored by researching the differential metabolites in plasma after administration of each of PG and AMG. Methods: In order to explore the material basis of differential bioactivities, two groups of mice were administrated orally with PG and AMG, and the method of metabolomics was used to identify the differential metabolites in plasma. Then network pharmacology was used based on the differential metabolites. Afterward, the metabolite–target–pathway network of PG and AMG was constructed; thus the pathways related to different bioactivities were analyzed. Results: Through principal component analysis and orthogonal projections to latent structures discriminant analysis, there were 10 differential metabolites identified in the PG group and 8 differential metabolites identified in the AMG group. Based on network pharmacology, the differential metabolites were classified and related to differential bioactivities of PG and AMG. In the PG group, there were 6 metabolites related to aphrodisiac effect and exciting the nervous system, and 5 metabolites associated with raised blood pressure. In the AMG group, 5 metabolites were classified as having the effect of inhibiting the nervous system, and 6 metabolites were related to antihypertensive effect. Conclusions: This study explored the material basis of the differential biological activities between PG and AMG, which is significant for the research of PG and AMG use and to promote human health. [ABSTRACT FROM AUTHOR]

Carbon cycling in the tropical margin is more extensive compared with other regions of the world. The goal of this study was to better understand the origins, transport, and burial of sedimentary organic matter (SOM) in the Gulf of Thailand (GOT) from the coastal margin of Southeast (SE) Asia, which serve as a major depository of fine‐grained sediments and the associated organic carbon (OC). The results revealed a variety of organic matter (OM) inputs and the selective transport of fine sediment, resulting in preferential dispersal of terrigenous SOM in the GOT. Bulk OC indices with low carbon/nitrogen ratios and enriched stable carbon isotope ratios (−24.2‰ to −20.4‰, and mean −21.4 ± 0.56‰) are likely related to the presence of marine‐derived OM and anthropogenic interference. A binary mixing model further clarified the significant contributions of terrestrial derived‐OM within the upper and central GOT. The n‐alkane compositions and principal component analysis indicated that a majority of the terrigenous SOM settles within the estuary in the upper GOT, while a selective dispersal of land‐based SOM through long‐distance transport toward the modern depocenter in the lower GOT. The characteristics of lower molecular weight n‐alkanes also suggest anthropogenic OM input from petroleum‐related contributions. Altogether, the depositional patterns and spatial heterogeneity of the SOM indicated by both the bulk and molecular signatures reveal the important roles of source variability and the selective dispersal of land‐based OM on the supply and accumulation of OC in the tropical coastal margin. Plain Language Summary: The accumulation and fate of sedimentary organic carbon (OC) in the tropical coastal system are significant components of the global carbon cycling. In order to elucidate the distribution and accumulation of organic matter (OM) in tropical regimes, this study first explored the concentration and composition of sedimentary OC based on the bulk and molecular‐level data, and then depicted the modern transport pattern of OM in the Gulf of Thailand (GOT). This study showed terrestrial input accounted for 5%–64% (20% ± 8% on average) of the OM and dominated in the upper GOT, the offshore depocenter in the northeastern GOT and north of Samui Island. Molecular composition of n‐alkanes and principal component analysis result showed that the hydrodynamic condition plays a significant role on the selective dispersal of terrigenous OM from the coastal region to the offshore depocenter. In addition, the contribution from petroleum‐derived OM is also revealed based on the molecular‐level evidences. The spatial heterogeneity of OM revealed the variability of the sources and hydrodynamic forces on accumulation and fate of OM in the tropical coastal margins Key Points: The bulk organic matter (OM) signals indicate spatial heterogeneity of various OM contributions to the Gulf of Thailand (GOT)The molecular‐level OM compositions suggest a selective dispersal of terrigenous sedimentary organic matter (SOM) toward fine sediment depocenter in the central GOTSource variability and hydrodynamic forces regulate the accumulation and fate of SOM in the tropical coastal margin [ABSTRACT FROM AUTHOR]

Background: Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed.Purpose: To develop and test an magnetic resonance imaging (MRI)-based radiomics nomogram for predicting the grade of STS (low-grade vs. high grade).Study Type: Retrospective POPULATION: One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71).Field Strength/sequence: Unenhanced T1-weighted (T1WI) and fat-suppressed T2-weighted images (FS-T2WI) were acquired at 1.5 T and 3.0 T.Assessment: Clinical-MRI characteristics included age, gender, tumor-node-metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression-free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS-T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS-T1， RS-FST2, and RS-Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors.Statistical Tests: Clinical-MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS-T1 model, RS-FST2 model, and RS-Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS-Combined model had AUCs of 0.916 (95%CI, 0.866-0.966, training set) and 0.879 (95%CI, 0.791-0.967, external validation set), and demonstrated good calibration and good clinical utility.Data Conclusion: The proposed noninvasive MRI-based radiomics models showed good performance in differentiating low-grade from high-grade STSs.Level Of Evidence: 3 TECHNICAL EFFICACY STAGE: 2. [ABSTRACT FROM AUTHOR]

Microorganisms play important roles in the biogeochemical cycling of sulphur (S), an essential element in the Earth's biosphere. Shotgun metagenome sequencing has opened a new avenue to advance our understanding of S cycling microbial communities. However, accurate metagenomic profiling of S cycling microbial communities remains technically challenging, mainly due to low coverage and inaccurate definition of S cycling gene families in public orthology databases. Here we developed a manually curated S cycling database (SCycDB) to profile S cycling functional genes and taxonomic groups for shotgun metagenomes. The developed SCycDB contains 207 gene families and 585,055 representative sequences affiliated with 52 phyla and 2684 genera of bacteria/archaea, and 20,761 homologous orthology groups were also included to reduce false positive sequence assignments. SCycDB was applied for functional and taxonomic analysis of S cycling microbial communities from four habitats (freshwater, hot spring, marine sediment and soil). Gene families and microorganisms involved in S reduction were abundant in the marine sediment, while those of S oxidation and dimethylsulphoniopropionate transformation were abundant in the soil. SCycDB is expected to be a useful tool for fast and accurate metagenomic analysis of S cycling microbial communities in the environment. [ABSTRACT FROM AUTHOR]

Background: Preoperative differentiation between malignant and benign soft-tissue masses is important for treatment decisions.Purpose/hypothesis: To construct/validate a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses.Study Type: Retrospective.Population: In all, 206 cases.Field Strength/sequence: The T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352-550/2.75-19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700-6370/40-120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.Assessment: Twelve machine-learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).Statistical Tests: 1) Demographic characteristics: a one-way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) values.Results: The LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.Data Conclusion: A machine-learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft-tissue masses.Evidence Level: 3 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:873-882. [ABSTRACT FROM AUTHOR]

Ocotillol, pseudo‐ginsenoside RT5 (RT5), and pseudo‐ginsenoside F11 (PF11) are ocotillol‐type saponins that have the same aglycone structure but with different numbers of glucose at the C‐6 position. In this study, the metabolites of ocotillol, RT5, and PF11 in rat plasma, stomach, intestine, urine, and feces after oral administration were investigated by ultra‐performance liquid chromatography coupled with time‐of‐flight mass spectrometry. The results showed that RT5 was easily biotransformed into metabolites in vivo, whereas PF11 and RT5 were difficult to be biotransformed. Hydrogenation, dehydrogenation, dehydration, deglycosylation, deoxygenation, hydration, phosphorylation, deoxidation, glucuronidation, and reactions combining amino acid were speculated to be involved in the biotransformation of ocotillol, RT5, and PF11. Based on the structural analysis of metabolites, it was deduced that hydrogenation, dehydration, deoxidation, and reactions combining amino acid occurred on the aglycone structure, whereas deglycosylation, hydration, and phosphorylation occurred on the glycosyl chain. Further, metabolites in plasma, urine, feces, and tissues were different: First, glucuronidation products were found in urine, stomach, intestine, and feces, but not in plasma. Second, the ocotillol prototype was not identified in urine samples. Third, the RT5 prototype was found in stomach, intestine, feces, and urine, but not in plasma. [ABSTRACT FROM AUTHOR]

Rationale: Panax ginseng C.A. Meyer (PG), which contains polysaccharides and ginsenosides as the major bioactive components, has been used to promote health and treat diseases for thousands of years in China. Total ginsenosides were extracted from a decoction of Panax ginseng (GD), which included both ginsenosides and polysaccharides, and dissolved in water to obtain a total ginsenosides aqueous solution (TGAS). To study their absorption and metabolism, the pharmacokinetics (PK) and metabolites of ginsenosides in vivo were investigated after the administration of GD and TGAS. Methods: Rat and mice plasma samples were collected after the administration of GD and TGAS. Ultra‐performance liquid chromatography coupled with time‐of‐flight mass spectrometry was used with the UNIFI platform to identify metabolites in the plasma sample. The pharmacokinetic parameters were calculated using a noncompartmental method in the Drug and Statistics software package. Results: Thirty ginsenoside metabolites were identified in mice plasma, of which only seven were found in the rat plasma after the administration of GD. The PK of ginsenosides Rb1, Rc, and Rd were also determined after the oral administration of GD and TGAS and showed significant differences in the pharmacokinetic parameters. Conclusions: There was no difference in the biotransformation pathways after the oral administration of GD and TGAS, indicating that there was no influence of polysaccharides on the biotransformation of ginsenosides in vivo. However, the pharmacokinetic parameters were different after the administration of GD and TGAS, possibly because of the polysaccharides in GD. This study should be of significance in exploring the basis of PG bioactivities and lays the foundation for the further development of new drugs using PG. [ABSTRACT FROM AUTHOR]

Background: Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning.Purpose: To assess the value of radiomics features in distinguishing histological grades of STSs.Study Type: Retrospective.Population: In all, 113 patients with pathology-confirmed low-grade (grade I), intermediate-grade (grade II), or high-grade (grade III) soft tissue sarcoma were collected.Field Strength/sequence: The 3.0T axial T1 -weighted imaging (T1 WI) with 550 msec repetition time (TR); 18 msec echo time (TE), 312 × 312 matrix, fat-suppressed fast spin-echo T2 WI with 4291 msec TR, 85 msec TE, 312 × 312 matrix.Assessment: Multiple machine-learning methods were trained to establish classification models for predicting STS grades. Eighty STS patients (18 low-grade [grade I]; 62 high-grade [grades II-III]) were enrolled in the primary set and we tested the model with a validation set with 33 patients (7 low-grade, 26 high-grade).Statistical Tests: 1) Student's t-tests were applied for continuous variables and the χ2 test were applied for categorical variables between low-grade STS and high-grade STS groups. 2) For feature subset selection, either no subset selection or recursive feature elimination was performed. This technology was combined with random forest and support vector machine-learning methods. Finally, to overcome the disparity in the frequencies of the STS grades, each machine-learning model was trained i) without subsampling, ii) with the synthetic minority oversampling technique, and iii) with random oversampling examples, for a total of 12 combinations of machine-learning algorithms that were assessed, trained, and tested in the validation cohort.Results: The best classification model for the prediction of STS grade was a combination of features selected by recursive feature elimination and random forest classification algorithms with a synthetic minority oversampling technique, which had an area under the curve of 0.9615 (95% confidence interval 0.8944-1.0) in the validation set.Data Conclusion: Radiomics feature-based machine-learning methods are useful for distinguishing STS grades.Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:791-797. [ABSTRACT FROM AUTHOR]

Background: Preoperative differentiation between malignant and benign tumors is important for treatment decisions.Purpose/hypothesis: To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses.Study Type: Retrospective.Population: Imaging data of 91 patients.Field Strength/sequence: T1 -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T2 -weighted images (T2 WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm.Assessment: Fat-suppressed FSE T2 WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T2 WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared.Statistical Tests: 1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ2 test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results.Results: The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone.Data Conclusion: This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model.Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:155-163. [ABSTRACT FROM AUTHOR]

Background: AD Genome‐wide association studies (AD‐GWAS) of late‐onset AD (LOAD) have identified single nucleotide polymorphisms (SNPs) within genes predominantly expressed in microglia (TREM2, TYROBP, CD33, CR1, PILRA, PLCG2, INPP5D, BIN1, MS4 gene family) or upregulated in microglia in association with disease (APOE, TREM2); these risk SNPs can either confer increased or decreased AD risk and implicate microglia function and neuroimmune mechanisms in disease etiology. One identified AD GWAS risk SNP is closely associated with the CD33 gene, confers protection, but is in linkage disequilibrium with rs12459419. The rs12459419 SNP is located at the 5′ intron‐exon junction of CD33 exon 2 and promotes exon skipping during CD33 pre‐mRNA splicing in microglia. Exon 2 skipping removes the immunoglobulin domain from full‐length CD33 (CD33M) leading to a truncated isoform (CD33‐D2). Method: Here, we generated exon‐2 deleted iPSCs (CD33Delta2/Delta2) from three patient iPSCs (CD33C/C) in order to investigate the impact of exon‐2 skipping on microglia function. These iPSC lines were differentiated to microglia in order to determine the mechanistic impact of exon‐2 skipping on microglia function. Result: Exon‐2 skipping results in a microglia signature identified in the ex vivo brain. Conclusion: The interferon microglia signature may be indicative of a protective microglia phenotype that mitigates AD risk [ABSTRACT FROM AUTHOR]

Norverapamil, the N‐demethylated derivative of verapamil, is a novel promising leading compound for attenuating multidrug resistance with less side effects compared with verapamil. However, the efficient synthetic method for norverapamil is absent. In this study, an innovative biotechnological method based on enzymatic catalysis was presented for the high‐efficient production of norverapamil. CYP105D1, a cytochrome P450 from Streptomyces griseus ATCC 13273, was identified to carry out a one‐step specific N‐demethylation of verapamil along with putidaredoxin reductase (Pdr) and putidaredoxin (Pdx) as the redox partner. Docking calculations rationalized the specific N‐demethylation observed in experiment and identified important amino acid residues for verapamil binding. Furthermore, a CYP105D1‐based whole‐cell system in E. coli BL21(DE3) was established and optimized for highly efficient N‐demethylation of verapamil. The bioconversion rate of verapamil by the whole cell system came up to 60.16% within 24 hours under the optimized conditions. These results demonstrated the high potential of CYP105D1‐based biocatalytic system for norverapamil production. [ABSTRACT FROM AUTHOR]

RATIONALE Neopanaxadiol (NPD) is one of the major ginsenosides in Panax ginseng C. A. Meyer (Araliaceae) that has been suggested to be a drug candidate against Alzheimer's disease. However, few data are available regarding its metabolism in rats. METHODS In this study, a method of ultraperformance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/QTOFMS) was developed to identify major metabolites of NPD in the stomach, intestine, urine and feces of rats, with the aim of determining the main metabolic pathways of NPD in rats after oral administration. RESULTS UPLC/QTOFMS revealed two metabolites in the stomach of rats, one metabolite in the intestine and two metabolites in feces. One metabolite, named M2, was isolated and purified from rats feces, which was identified as (20 S,22 S)-dammar-22,25-epoxy-3β,12β,20-triol based on extensive NMR spectroscopy and mass spectrometry data. The main metabolites of NPD in rats were the products of epoxidation, dehydrogenation and hydroxylation. NPD was predominantly metabolized by 20,22-double-bond epoxidation and rearrangement to yield an expoxidation product (M2). CONCLUSIONS Based on the profiles of the metabolites, possible metabolic pathways of NPD in rats were proposed for the first time. This study provides new and available information on the metabolism of NPD, which is indispensable for further research on metabolic pathways of dammarane ginsengenins in vivo. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Based on elemental geochemical data, we reconstructed the sediment provenance, surface productivity and bottom water redox conditions for the last 48 ka in the Ulleung Basin (Sea of Japan) and inferred the factors controlling them. Al2O3/TiO2 ratio and chemical index of alteration (CIA) suggest that sediment provenance changed during the glacial period (48-18 ka) compared to the deglacial (ca. 18-11 ka) and Holocene. Mass accumulation rates of total organic carbon (TOC), CaCO3, phosphorus, cadmium and excess barium reveal low paleoproductivity during low sea stand. During 18-11 ka, productivity increased due to increasing inflow of nutrient-rich water masses - the Oyashio and the East China Sea coastal water - in tandem with the rising sea level. Maximum productivity occurred during Younger Dryas and Pre-boreal periods when sea level was at ∼ −60 m and then gradually decreased as the Tsushima Warm Current inflow kicked off at ca. 9.3 ka, consistent with other paleoredox proxies, which reveal the presence of anoxic bottom water during ca. 12-9 ka. With the changes in paleoredox proxies and their ratios (TOC, Mo, U, Mn, C/S ratio and Uauthigenic and Mo contents), we hypothesized that the redox changes were mainly ventilation driven and were superimposed on the influence of circulation-induced productivity changes. The global climate and sea-level changes on a millennial timescale play a major role in enhancing paleoproductivity and restrict bottom water advection, subsequently driving the oxygenation of bottom water in the Ulleung Basin. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Acute inflammation has the potential for the recruitment of immune cells, inhibiting tumor angiogenesis, metastasis, and drug resistance thereby overcoming the tumor immunosuppressive microenvironment caused by chronic inflammation. Here, an acute inflammation inducer using bacteria outer membrane vesicles (OMVs) loaded in thermal‐sensitive hydrogel (named OMVs‐gel) for localized and controlled release of OMVs in tumor sites is proposed. OMVs trigger neutrophil recruitment and amplify acute inflammation inside tumor tissues. The hydrogel ensures drastic inflammation is confined within the tumor, addressing biosafety concerns that the direct administration of free OMVs may cause fatal effects. This strategy eradicated solid tumors safely and rapidly. The study further elucidates one of the possible immune mechanisms of OMVs‐gel therapy, which involves the assembly of antitumor neutrophils and elastase release for selective tumor killing. Additionally, tumor vascular destruction induced by OMVs‐gel results in tumor darkening, allowing for combinational photothermal therapy. The findings suggest that the use of OMVs‐gel can safely induce acute inflammation and enhance antitumor immunity, representing a promising strategy to promote acute inflammation application in tumor immunotherapy. [ABSTRACT FROM AUTHOR]