[circ_BACH2 affects the malignant biological behavior of papillary thyroid cancer by regulating miR-370-3p]

Objective: To investigate the influence of circ_BACH2 on the malignant biological behavior of papillary thyroid cancer and its molecular mechanism. Methods: Cancer tissues and paracancer tissues of 51 patients with papillary thyroid carcinoma from the Fourth Central Hospital of Tianjin between 2017 and 2019 were collected. Reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) was used to detect the expressions of circ_BACH2, miR-370-3p and G protein coupled receptor kinase interacting factor 1 (GIT1) mRNA in tissues and cells; flow cytometry to detect cell apoptosis and cell cycle; plate clone formation experiment to detect the number of cell clones; cell counting kit 8 (CCK-8) to detect cell proliferation; Transwell array to detect cell migration and invasion; western blot to detect protein expressions; dual luciferase report experiment to detect the targeting relationship between circ_BACH2, miR-370-3p and GIT1; the nude mouse tumor formation experiment to detect the effect of circ_BACH2 on tumors in mice. Results: Compared with adjacent tissues, the expressions of circ_BACH2 and GIT1 in papillary thyroid cancer tissues was increased, while the expression of miR-370-3p was decreased. Compared with Nthy-ori3-1 cells, the expressions of circ_BACH2 in papillary thyroid cancer cells TPC-1 and SW579 were increased, the mRNA and protein levels of GIT1 were increased, miR-370-3p expression was decreased. The expression level of GIT1 mRNA was negatively correlated with that of miR-370-3p (r=-0.634), and the expression level of circ_BACH2 was positively correlated with that of GIT1 (r=0.635). The expression level of circ_BACH2 was negatively correlated with that of miR-370-3p (r=-0.394, P＜0.05). Circ_BACH2 and miR-370-3p has a binding site at the 3' UTR of GIT1. After knocking down circ_BACH2, the proportion of G0/G1 cells in papillary thyroid cancer cells TPC-1 and SW579 was increased, the proportion of S-phase cells was decreased and the proportion of G2/M-phase cells did not change significantly. The cell absorbance value was lower than that in si-NC group. The number of cell clone formation was decreased (43±5 vs 100±6, 54±8 vs 100±9); the cell apoptosis rate was increased [(19.60±2.40)% vs (4.30±0.20)%, (18.10±2.10)% vs (5.10±0.23)%]; cell migration number was decreased (61±7 vs 134±15, 58±6 vs 112±11), the invasion number was also decreased (45±6 vs 113±11, 47±4 vs 92±9); the expressions of Snail and Twist1 were decreased, and the expression of E-cadherin was increased (P＜0.000). Inhibition of miR-370-3p expression reversed the effect of circ_BACH2 knockdown on proliferation, migration, invasion and apoptosis of thyroid papillary cancer cells. Overexpression of GIT1 reversed the effects of overexpression of miR-370-3p on proliferation, migration, invasion and apoptosis of thyroid papillary cancer cells. Mice injected with TPC-1 cells stably transfected with sh-circ_BACH2 showed a reduction in tumor volume [(535±91) mm3 vs (857±114) mm3] after 35 days of culture; tumor weight was decreased [(0.62±0.13) mg vs (1.06±0.15) mg, P＜0.05]; the expressions of circ_BACH2 and GIT1 were decreased, and the expression of miR-370-3p was increased in nude mouse tumor tissue. Conclusion: Silencing circ_BACH2 may inhibit the proliferation, migration and invasion of papillary thyroid cancer cells in vitro, promote cell apoptosis, and inhibit tumor growth in vivo through targeted regulation of miR-370-3p/GIT1.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

DA-SRN: Omics data analysis based on the sample network optimization for complex diseases

Effective biomarker identification and accurate sample label prediction are still challenging for complex diseases. Patient similarity network (PSN) analysis is a powerful tool in disease omics data analysis. The topology of PSN can reflect the discriminative ability of the corresponding feature space on which the sample network is built. In this study, a novel omics data analysis method based on the sample reference network (DA-SRN) is proposed to identify the potential biomarkers and predict the sample categories. DA-SRN defines the informative features and the sample reference network in optimizing the network structure by genetic algorithm. It labels the samples based on the graph neural network, the reference network and the selected informative features. DA-SRN was compared with nine efficient omics data analysis methods on the genomics, metabolomics and transcriptomics datasets to show its validation. The comparison results showed that it outperformed the other methods in area under receiver operating characteristic curve (AUROC), sensitivity, specificity and area under precision-recall curve (AUPRC) in most cases. Besides, the important metabolites identified by DA-SRN for the type 2 diabetes (T2D) metabolomics data were further examined. The pathway analysis revealed the close relationships between the identified metabolites and the critical metabolic pathways related to the occurrence and development of T2D. The experimental results illustrate that DA-SRN can extract the valuable information from the complex omics data by analyzing the sample relationship, and is promising in biomarker identification and sample discrimination for complex diseases.

Ex vivo instability of lipids in whole blood: preanalytical recommendations for clinical lipidomics studies

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h-24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.

Dynamic Network Construction for Identifying Early Warning Signals Based On a Data-Driven Approach: Early Diagnosis Biomarker Discovery for Gastric Cancer

During the development of complex diseases, there is a critical transition from one status to another at a tipping point, which can be an early indicator of disease deterioration. To effectively enhance the performance of early risk identification, a novel dynamic network construction algorithm for identifying early warning signals based on a data-driven approach (EWS-DDA) was proposed. In EWS-DDA, the shrunken centroid was introduced to measure dynamic expression changes in assumed pathway reactions during the progression of complex disease for network construction and to define early warning signals by means of a data-driven approach. We applied EWS-DDA to perform a comprehensive analysis of gene expression profiles of gastric cancer (GC) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Six crucial genes were selected as potential biomarkers for the early diagnosis of GC. The experimental results of statistical analysis and biological analysis suggested that the six genes play important roles in GC occurrence and development. Then, EWS-DDA was compared with other state-of-the-art network methods to validate its performance. The theoretical analysis and comparison results suggested that EWS-DDA has great potential for a more complete presentation of disease deterioration and effective extraction of early warning information.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Public concern for animal welfare and its correlation with ethical ideologies after coronavirus disease (COVID-19) in China

The outbreak of coronavirus disease (COVID-19) represents a major public health challenge and a serious threat to sustainable social development. A consideration of animal welfare is clearly justified, given the potential contribution of animals to the spread of the disease. The present study, therefore, sought to investigate the concern the Chinese people have for animal welfare (PCAW) and how their 'ethical ideology' (idealism and relativism) determines PCAW after COVID-19, through comparison with the same study, carried out in China in 2015. Our results demonstrated a significant improvement in Chinese PCAW after COVID-19. The adverse impact of COVID-19 may have resulted in a lowered idealism score and this decreased score served to neutralise significant correlations between idealism and PCAW, compared to the 2015 results. The global pandemic did not increase people's relativism score and a significant correlation was found between relativism and PCAW. Gender, age, educational level, public perception of animals after COVID-19, zoo and aquarium visiting were all shown to be predictor variables for PCAW. This study is one of the first to investigate Chinese PCAW after COVID-19 and can therefore provide knowledge that will potentially increase Chinese PCAW.

The bipolar charge plasma spectrometer (BCPS) based on the 2π-field-of-view double-channel electrostatic analyzer

In this paper, we propose a bipolar charge plasma spectrometer based on the double-channel electrostatic analyzer for simultaneously measuring thermal ions and electrons with a 2π hemispherical field-of-view. Both ions and electrons within the wide field-of-view enter the spectrometer, pass through the variable geometric factor channel, and are then separated by the double-channel electric fields. Two microchannel plates are accommodated at the exit of the analyzer for ion and electron detection. The main performance of the spectrometer has been obtained from on-ground calibration. With the electrostatic deflectors and the cylindrically symmetric structure, the spectrometer provides simultaneous measurements of thermal ion and electron velocity distributions with a shared field-of-view of 360° (azimuth angle) by 90° (elevation angle) and a broad energy range for both ions and electrons. The ion analyzer constant and the electron analyzer constant are 11.1 and 9.7, respectively. The detecting energy range of 33.3-44.4 keV for ions and 29.1-38.8 keV for electrons can be obtained by using the sweeping electrostatic analyzer voltage range of 3-4000 V. The ion and electron energy resolutions are 9.6% and 6.1%, respectively. The variable geometric factor function provides a large geometric factor adjusting range for both ion and electron measurements by two orders of magnitude, which fulfills the requirements of a large dynamic flux range for simultaneous measurements of space thermal plasma in the solar wind and magnetosphere.

A Novel Method for Constructing Classification Models by Combining Different Biomarker Patterns

Different biomarker patterns, such as those of molecular biomarkers and ratio biomarkers, have their own merits in clinical applications. In this study, a novel machine learning method used in biomedical data analysis for constructing classification models by combining different biomarker patterns (CDBP)is proposed. CDBP uses relative expression reversals to measure the discriminative ability of different biomarker patterns, and selects the pattern with the higher score for classifier construction. The decision boundary of CDBP can be characterized in simple and biologically meaningful manners. The CDBP method was compared with eight state-of-the-art methods on eight gene expression datasets to test its performance. CDBP, with fewer features or ratio features, had the highest classification performance. Subsequently, CDBP was employed to extract crucial diagnostic information from a rat hepatocarcinogenesis metabolomics dataset. The potential biomarkers selected by CDBP provided better classification of hepatocellular carcinoma (HCC)and non-HCC stages than previous works in the animal model. The statistical analyses of these potential biomarkers in an independent human dataset confirmed their discriminative abilities of different liver diseases. These experimental results highlight the potential of CDBP for biomarker identification from high-dimensional biomedical datasets and demonstrate that it can be a useful tool for disease classification.

Associations of Cardiovascular and Non-Cardiovascular Comorbidities with Dementia Risk in Patients with Diabetes: Results from a Large UK Cohort Study

BACKGROUND

Type 2 diabetes (T2D) is an established risk factor for dementia. However, it remains unclear whether the presence of comorbidities could further increase dementia risk in diabetes patients.

OBJECTIVES

To examine the associations between cardiovascular and non-cardiovascular comorbidities and dementia risk in T2D patients.

DESIGN

Population-based cohort study.

SETTING

The UK Clinical Practice Research Datalink (CPRD).

PARTICIPANTS

489,205 T2D patients aged over 50 years in the UK CPRD.

MEASUREMENTS

Major cardiovascular and non-cardiovascular comorbidities were extracted as time-varying exposure variables. The outcome event was dementia incidence based on dementia diagnosis or dementia-specific drug prescription.

RESULTS

During a median of six years follow-up, 33,773 (6.9%) incident dementia cases were observed. Time-varying Cox regressions showed T2D patients with stroke, peripheral vascular disease, atrial fibrillation, heart failure or hypertension were at higher risk of dementia compared to those without such comorbidities (HR [95% CI] = 1.64 [1.59-1.68], 1.37 [1.34-1.41], 1.26 [1.22-1.30], 1.15 [1.11-1.20] or 1.10 [1.03-1.18], respectively). Presence of chronic obstructive pulmonary disease or chronic kidney disease was also associated with increased dementia risk (HR [95% CI] = 1.05 [1.01-1.10] or 1.11 [1.07-1.14]).

CONCLUSIONS

A range of cardiovascular and non-cardiovascular comorbidities were associated with further increases of dementia risk in T2D patients. Prevention and effective management of these comorbidities may play a significant role in maintaining cognitive health in T2D patients.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Nitrogen doped titanium dioxide as an aesthetic antimicrobial filler in dental polymers

OBJECTIVE

To develop an aesthetic resin composite using a nitrogen-doped titanium dioxide (NTiO₂) filler that possesses antimicrobial properties against cariogenic bacteria.

METHODS

N-TiO₂ powder was manufactured by calcining commercial TiO₂ with urea. Free radical release from the N-TiO₂ powder under visible light irradiation was analysed using UV-Vis spectrophotometry. The N-TiO₂ powder was incorporated into a dental resin and the photocatalytic activity assessed using a dye under both visible light and dark conditions. Using XTT assay to measure the cellular metabolic activity, the antibacterial properties of the N-TiO₂ /resin composite discs were tested using Streptococcus mutans.

RESULTS

Doping nitrogen of TiO₂ resulted in a band gap shift towards the visible light spectrum, which enabled the powder to release reactive oxygen species when exposed to visible light. When incorporated into a dental resin, the N-TiO₂/resin composite still demonstrated sustained release of reactive oxygen species, maintaining its photocatalytic activity and showing an antibacterial effect towards Streptococcus mutans under visible light conditions.

SIGNIFICANCE

N-TiO₂ filled resin composite shows great promise as a potential aesthetic resin based adhesive for orthodontic bonding.

A computational strategy for metabolic network construction based on the overlapping ratio: Study of patients' metabolic responses to different dialysis patterns

BACKGROUND

Uremia is a worldwide epidemic disease and poses a serious threat to human health. Both maintenance hemodialysis (HD) and maintenance high flux hemodialysis (HFD) are common treatments for uremia and are generally used in clinical applications. In-depth exploration of patients' metabolic responses to different dialysis patterns can facilitate the understanding of pathological alterations associated with uremia and the effects of different dialysis methods on uremia, which may be used for future personalized therapy. However, due to variations of multiple factors (i.e., genetic, epigenetic and environment) in the process of disease treatments, identification of the similarities and differences in plasma metabolite changes in uremic patients in response to HD and HFD remains challenging.

METHODS

In this study, a computational strategy for metabolic network construction based on the overlapping ratio (MNC-OR) was proposed for disease treatment effect research. In MNC-OR, the overlapping ratio was introduced to measure metabolic reactions and to construct metabolic networks for analysis of different treatment options. Then, MNC-OR was employed to analyze HD-pattern-dependent changes in plasma metabolites to explore the pathological alterations associated with uremia and the effectiveness of different dialysis patterns (i.e., HD and HFD) on uremia. Based on the networks constructed by MNC-OR, two network analysis techniques, namely, similarity analysis and difference analysis of network topology, were used to find the similarity and differences in metabolic signals in patients under treatment with either HD or HFD, which can facilitate the understanding of pathological alterations associated with uremia and provide the guidance for personalized dialysis therapy.

RESULTS

Similarity analysis of network topology suggested that abnormal energy metabolism, gut metabolism and pyrimidine metabolism might occur in uremic patients, and maintenance of both HFD and HD therapies have beneficial effects on uremia. Then, difference analysis of network topology was employed to extract the crucial information related to HD-pattern-dependent changes in plasma metabolites. Experimental results indicated that the amino acid metabolism was closer to the normal status in HFD-treated patients; however, in HD-treated patients, the ability of antioxidation showed greater reduction, and the protein O-GlcNAcylation level was higher. Our findings demonstrate the potential of MNC-OR for explaining the metabolic similarities and differences of patients in response to different dialysis methods, thereby contributing to the guidance of personalized dialysis therapy.

Prognosis prediction of hepatocellular carcinoma after surgical resection based on serum metabolic profiling from gas chromatography-mass spectrometry

Comprehensive prognostic risk prediction of hepatocellular carcinoma (HCC) after surgical treatment is particularly important for guiding clinical decision-making and improving postoperative survival. Hence, we aimed to build prognostic models based on serum metabolomics data, and assess the prognostic risk of HCC within 5 years after surgical resection. A pseudotargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomics method was applied to analyze serum profiling of 78 HCC patients. Important metabolic features with discriminant ability were identified by a novel network-based metabolic feature selection method based on combinational significance index (N-CSI). Subsequently, phenylalanine and galactose were further identified to be relevant with mortality by the Cox regression analysis, while galactose and tyrosine were associated with recurrence and metastasis. Two models to predict risk of mortality (risk score of overall survival, RS_OS) and risk of recurrence and metastasis (risk score of disease-free survival, RS_DFS) were generated based on two panels of metabolites, respectively, which present favorable ability to predict prognosis of HCC, especially when combined with clinical staging system. The performance of models was further validated in an external independent cohort from 91 HCC patients. This study demonstrated that metabolomics is a powerful tool for risk screening of HCC prognosis.

Cystatin C alleviates H2O2-induced H9c2 cell injury

OBJECTIVE

At present, the incidence of acute myocardial infarction is increasing year by year, and it has become one of the diseases with the highest mortality rate in humans. Myocardial ischemia-reperfusion injury (MIRI) is a major problem in the treatment of myocardial infarction, but clinically there is no effective way to treat MIRI. This study used Cystatin C (Cys C) to treat cardiomyocytes and rats to investigate the effect of Cys C on MIRI.

MATERIALS AND METHODS

We used H2O2 to induce rat cardiomyocytes (H9c2 cells) injury and stimulated the cells with Cys C. Cell counting kit 8 (CCK8) assay was used to determine the optimal concentration of H2O2 and Cys C to stimulate H9c2 cells. We determined the effects of Cys C on oxidative stress and apoptosis levels in H9c2 cells by measuring the activity of dehydrogenase (LDH), superoxide dismutase (SOD) and malondialdehyde (MDA), and the expression of apoptosis-related molecules (caspase3/8/9, Bax and Bcl-2). Changes in the activity of the NF-κB signaling pathway in H9c2 cells were also detected. In addition, we made rat MIRI models by ligating the coronary arteries and used Cys C to treat rats to verify the effect of Cys C on MIRI.

RESULTS

According to the results of the CCK8 assay, 1000 μM of H2O2 and 15 μM of Cys C were used to stimulate H9c2 cells. Cys C alleviated H2O2-induced H9c2 cell injury, manifested as a decrease in LDH and MDA activity and an increase in SOD activity. Cys C also reduced the apoptosis level in H9c2 cells. The activity of NF-κB signaling pathway in injured H9c2 cells was increased, and stimulation of Cys C could inhibit the NF-κB signaling pathway in H9c2 cells. The application of Cys C in MIRI rats also verified its therapeutic effect on MIRI.

CONCLUSIONS

Cys C reduced the oxidative stress and apoptosis levels of cardiomyocytes by inhibiting the activity of NF-κB signaling pathway in cardiomyocytes, thereby reducing cardiomyocyte injury and treating MIRI.

Metabolome-Genome-Wide Association Study (mGWAS) Reveals Novel Metabolites Associated with Future Type 2 Diabetes Risk and Susceptibility Loci in a Case-Control Study in a Chinese Prospective Cohort

In a Chinese prospective cohort, 500 patients with new-onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected as case and control groups, and randomized into discovery and validation sets to discover the metabolite changes before T2D onset and the related diabetogenic loci. A serum metabolomics analysis reveals that 81 metabolites changed significantly before T2D onset. Based on binary logistic regression, eight metabolites are defined as a biomarker panel for T2D prediction. Pipecolinic acid, carnitine C14:0, epinephrine and phosphatidylethanolamine 34:2 are first found associated with future T2D. The addition of the biomarker panel to the clinical markers (BMI, triglycerides, and fasting glucose) significantly improves the predictive ability in the discovery and validation sets, respectively. By associating metabolomics with genomics, a significant correlation (p < 5.0 × 10-8) between eicosatetraenoic acid and the FADS1 (rs174559) gene is observed, and suggestive correlations (p < 5.0 × 10-6) between pipecolinic acid and CHRM3 (rs535514), and leucine/isoleucine and WWOX (rs72487966) are discovered. Elevated leucine/isoleucine levels increased the risk of T2D. In conclusion, multiple metabolic dysregulations are observed to occur before T2D onset, and the new biomarker panel can help to predict T2D risk.

Performance and simulated moment uncertainties of an ion spectrometer with asymmetric 2π field of view for ion measurements in space

Space plasma instruments provide 3D particle velocity distribution functions. Because of telemetry limitations, these cannot be transmitted in high time resolution and the plasma needs to be characterized by moments of the velocity distribution function. These moment uncertainties have vital effects on the reliability and accuracy of onboard plasma moments. We assess the measurement accuracy for magnetosheath and solar wind ions using an ion spectrometer with an asymmetric field of view designed for the all-sky measurement of low-energy ions in the magnetosheath and solar wind. We focus on moment uncertainties for the ideal spectrometer, not considering the background counts, which may have considerable effects on the uncertainties in real life. To obtain number density, bulk velocity, and temperature, different orders of moments are integrated assuming a Maxwellian velocity distribution. Based on the design specifications, we use simulations to estimate systematic and random errors for typical plasma conditions. We find that the spectrometer resolution is adequate for determining the density of solar wind (∼7% error) and magnetosheath ions (∼4% error). The resolution is also adequate for determining the temperature of solar wind (∼10% error) and magnetosheath ions (∼2% error). For high speed flows with a bulk velocity of 750 km/s and a temperature of 20 eV, the maximum density and temperature errors become 9% and 7%, respectively. The bulk velocity errors are less than 2% for all cases. The contributions of heavy ions to the systematic errors are less than 5% for magnetosheath ions and less than 8% for solar wind ions.

Demographic and clinical factors associated with immune reconstitution in HIV/HBV co-infected and HIV mono-infected patients: a retrospective cohort study

OBJECTIVES

To describe the clinical characteristics and factors associated with CD4 T-cell count and CD4/CD8 ratio restoration in HIV mono-infected and HIV/HBV co-infected individuals, and to explore liver and renal functional changes in both groups.

METHODS

A retrospective cohort study was performed including 356 HIV/HBV co-infected and 716 HIV mono-infected participants who initiated antiretroviral therapy (ART) during 2013-2017 in Beijing Youan Hospital, China. Demographic and clinical characteristics were compared between the two groups, using χ² and Mann-Whitney non-parametric tests. Bivariate and multivariate Cox regression models were used to test their association.

RESULTS

Baseline HIV viral load and ART regimen were found to be significantly associated with CD4 T-cell restoration among HIV-infected participants, whereas baseline HIV viral load was the only significant factor associated with CD4 T-cell restoration in HIV/HBV co-infected participants. The final model showed that baseline HIV viral load and ART regimen were significantly associated with CD4/CD8 ratio restoration among HIV-infected participants, while baseline HIV viral load was the significant factor. Liver and renal functions were similar at the endpoint (P > 0.05).

CONCLUSIONS

Baseline HIV viral load count was found to be the key factor affecting immune restoration in both HIV and HIV/HBV individuals. Future multi-wave prospective studies are needed to clarify the potential biological mechanism.

[The value of left ventricular longitudinal strain in the diagnosis and differential diagnosis of myocardial amyloidosis]

Objective: To investigate the characteristics of left ventricular longitudinal strain (LS) in myocardial amyloidosis (CA), hypertrophic cardiomyopathy (HCM) and Fabry disease (FD), as well as the correlation between left ventricular LS and these diseases. Methods: A total of 14 CA patients, 28 HCM patients and 5 FD patients who visited the Department of Cardiology of the First Affiliated Hospital of Suzhou University from June 2017 to November 2019 were retrospectively included. EchoPAC software was used to analyze left ventricular LS, and univariate logistic regression analysis was used to analyze the correlation between echocardiographic LS indexes and various myocardial hypertrophy diseases. The receiver operating characteristic (ROC) curve was used to assess the sensitivity and specificity of echocardiograph LS indexes in the diagnosis of various myocardial hypertrophy diseases. Results: There were significant differences in LS of left ventricular basal segment, inferior wall, posterior wall, lateral wall and posterior septum among the three groups (P<0.05). The absolute value of LS in the left ventricular basal segment decreased in the CA group; the absolute value of LS in left ventricular posterior wall and lateral wall decreased significantly in the FD group (P<0.05); the absolute values of LS in left ventricular basal segment, inferior wall, posterior septum, lateral wall and posterior wall increased significantly in the HCM group (P<0.05). The absolute value of LS < 7.9% in the left ventricular basal segment, or > 13.2% in the inferior wall and > 9.2% in the basal segment, or < 8.3% in the lateral wall and < 7.9% in the posterior wall were the indicators of high sensitivity and specificity in the diagnosis of CA, HCM and FD, respectively. Conclusions: Left ventricular LS was an important index to differentiate myocardial hypertrophy. Combined with their respective clinical characteristics, it could provide certain reference value for clinical practice.

Prospective Evaluation of Cognitive Health and Related Factors in Elderly at Risk for Developing Alzheimer's Dementia: A Longitudinal Cohort Study

BACKGROUND

The CHARIOT PRO Main study is a prospective, non-interventional study evaluating cognitive trajectories in participants at the preclinical stage of Alzheimer's disease (AD) classified by risk levels for developing mild cognitive impairment due to AD (MCI-AD).

OBJECTIVES

The study aimed to characterize factors and markers influencing cognitive and functional progression among individuals at-risk for developing MCI-AD, and examine data for more precise predictors of cognitive change, particularly in relation to APOE ε4 subgroup.

DESIGN

This single-site study was conducted at the Imperial College London (ICL) in the United Kingdom. Participants 60 to 85 years of age were classified as high, medium (amnestic or non-amnestic) or low risk for developing MCI-AD based on RBANS z-scores. A series of clinical outcome assessments (COAs) on factors influencing baseline cognitive changes were collected in each of the instrument categories of cognition, lifestyle exposure, mood, and sleep. Data collection was planned to occur every 6 months for 48 months, however the median follow-up time was 18.1 months due to early termination of study by the sponsor.

RESULTS

987 participants were screened, among them 690 participants were actively followed-up post baseline, of whom 165 (23.9%) were APOE ε4 carriers; with at least one copy of the allele. The mean age was 68.73 years, 94.6% were white, 57.4% were female, and 34.8% had a Family History of Dementia with a somewhat larger percentage in the APOE ε4 carrier group (42.4%) compared to the non-carrier group (32.4%). Over half of the participants were married and 53% had a Bachelor's or higher degree. Most frequently, safety events typical for this population consisted of upper respiratory tract infection (10.4%), falls (5.2%), hypertension (3.5%) and back pain (3.0%). Conclusion (clinical relevance): AD-related measures collected during the CHARIOT PRO Main study will allow identification and evaluation of AD risk factors and markers associated with cognitive performance from the pre-clinical stage. Evaluating the psycho-biological characteristics of these pre-symptomatic individuals in relation to their natural neurocognitive trajectories will enhance current understanding on determinants of the initial signs of cognitive changes linked to AD.

Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Predicts Prognosis of Hepatocellular Carcinoma after Curative Resection

Assessment and prediction of prognostic risk in patients with hepatocellular carcinoma (HCC) would greatly benefit the optimal treatment selection. Here, we aimed to identify the critical metabolites associated with the outcomes and develop a risk score to assess the prognosis of HCC patients after curative resection. A total of 78 serum samples of HCC patients were analyzed by liquid chromatography-mass spectrometry to characterize the metabolic profiling. A novel network-based feature selection method (NFSM) was developed to define the critical metabolites with the most discriminant capacity to outcomes. The metabolites defined by NFSM was further reduced by Cox regression analysis to generate a prognostic metabolite panel-phenylalanine and choline. Furthermore, univariate and multivariate Cox regression analyses were applied to combine the metabolite panel with the presence of satellite nodes to generate a global prognostic index (GPI) score for overall survival assessment. Compared with the current clinical classification systems, including the Barcelona-clinic liver cancer stage, tumor-node-metastasis stage, and albumin-bilirubin grade, the GPI score presented comparable performance, according to the time-dependent receiver operating characteristic curves and was validated in an independent cohort, which suggested that metabolomics could serve as a helpful tool to stratify the HCC prognostic risk after operation.