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  1. Article ; Online: Three New Benzophenone Derivatives from Selaginella tamariscina

    Jiayin Long / Qingqing Mao / Yujie Peng / Lei Liu / Yin Hong / Honglin Xiang / Ming Ma / Hui Zou / Junwei Kuang

    Molecules, Vol 28, Iss 4582, p

    2023  Volume 4582

    Abstract: Six compounds including three new benzophenones, selagibenzophenones D-F ( 1 – 3 ), two known selaginellins ( 4 – 5 ) and one known flavonoid ( 6 ), were isolated from Selaginella tamariscina . The structures of new compounds were established by 1D-, 2D- ... ...

    Abstract Six compounds including three new benzophenones, selagibenzophenones D-F ( 1 – 3 ), two known selaginellins ( 4 – 5 ) and one known flavonoid ( 6 ), were isolated from Selaginella tamariscina . The structures of new compounds were established by 1D-, 2D-NMR and HR-ESI-MS spectral analyses. Compound 1 represents the second example of diarylbenzophenone from natural sources. Compound 2 possesses an unusual biphenyl-bisbenzophenone structure. Their cytotoxicity against human hepatocellular carcinoma HepG2 and SMCC-7721 cells and inhibitory activities on lipopolysaccharide-induced nitric oxide (NO) production in RAW264.7 cells were evaluated. Compound 2 showed moderate inhibitory activity against HepG2 and SMCC-7721 cells, and compounds 4 and 5 showed moderate inhibitory activity to HepG2 cells. Compounds 2 and 5 also exhibited inhibitory activities on lipopolysaccharide-induced nitric oxide (NO) production.
    Keywords Selaginella ; Selaginella tamariscina ; benzophenone ; selagibenzophenones D-F ; cytotoxicity ; NO inhibitory effects ; Organic chemistry ; QD241-441
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease

    Robert P. Adelson / Anurag Garikipati / Jenish Maharjan / Madalina Ciobanu / Gina Barnes / Navan Preet Singh / Frank A. Dinenno / Qingqing Mao / Ritankar Das

    Diagnostics, Vol 14, Iss 1, p

    2023  Volume 13

    Abstract: Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data ... ...

    Abstract Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55–88 years old ( n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24–48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24–48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.
    Keywords mild cognitive impairment ; Alzheimer’s disease ; machine learning ; disease progression ; Medicine (General) ; R5-920
    Subject code 006
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

    Angier Allen / Anna Siefkas / Emily Pellegrini / Hoyt Burdick / Gina Barnes / Jacob Calvert / Qingqing Mao / Ritankar Das

    Applied Sciences, Vol 11, Iss 5576, p

    2021  Volume 5576

    Abstract: Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have ... ...

    Abstract Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUC adversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.
    Keywords digital twins ; variational autoencoder ; machine learning ; algorithm ; stroke ; disease forecasting ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 310
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

    Ashwath Radhachandran / Anurag Garikipati / Nicole S. Zelin / Emily Pellegrini / Sina Ghandian / Jacob Calvert / Jana Hoffman / Qingqing Mao / Ritankar Das

    BioData Mining, Vol 14, Iss 1, Pp 1-

    2021  Volume 15

    Abstract: Abstract Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support ... ...

    Abstract Abstract Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. Conclusions A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights ...
    Keywords Acute heart failure ; Mortality ; Machine learning ; Prediction ; Clinical decision support ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Analysis ; QA299.6-433
    Subject code 310
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Expanding known viral diversity in plants

    Shixing Yang / Qingqing Mao / Yan Wang / Jingxian He / Jie Yang / Xu Chen / Yuqing Xiao / Yumin He / Min Zhao / Juan Lu / Zijun Yang / Ziyuan Dai / Qi Liu / Yuxin Yao / Xiang Lu / Hong Li / Rui Zhou / Jian Zeng / Wang Li /
    Chenglin Zhou / Xiaochun Wang / Quan Shen / Hui Xu / Xutao Deng / Eric Delwart / Tongling Shan / Wen Zhang

    Environmental Microbiome, Vol 17, Iss 1, Pp 1-

    virome of 161 species alongside an ancient canal

    2022  Volume 15

    Abstract: Abstract Background Since viral metagenomic approach was applied to discover plant viruses for the first time in 2006, many plant viruses had been identified from cultivated and non-cultivated plants. These previous researches exposed that the viral ... ...

    Abstract Abstract Background Since viral metagenomic approach was applied to discover plant viruses for the first time in 2006, many plant viruses had been identified from cultivated and non-cultivated plants. These previous researches exposed that the viral communities (virome) of plants have still largely uncharacterized. Here, we investigated the virome in 161 species belonging to 38 plant orders found in a riverside ecosystem. Results We identified 245 distinct plant-associated virus genomes (88 DNA and 157 RNA viruses) belonging to 27 known viral families, orders, or unclassified virus groups. Some viral genomes were sufficiently divergent to comprise new species, genera, families, or even orders. Some groups of viruses were detected that currently are only known to infect organisms other than plants. It indicates a wider host range for members of these clades than previously recognized theoretically. We cannot rule out that some viruses could be from plant contaminating organisms, although some methods were taken to get rid of them as much as possible. The same viral species could be found in different plants and co-infections were common. Conclusions Our data describe a complex viral community within a single plant ecosystem and expand our understanding of plant-associated viral diversity and their possible host ranges.
    Keywords Plant virome ; Phytocommunity ; Virus host switching ; Co-infection ; Phylogenetic analysis ; Environmental sciences ; GE1-350 ; Microbiology ; QR1-502
    Subject code 580
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Using electronic health record collected clinical variables to predict medical intensive care unit mortality

    Jacob Calvert / Qingqing Mao / Jana L. Hoffman / Melissa Jay / Thomas Desautels / Hamid Mohamadlou / Uli Chettipally / Ritankar Das

    Annals of Medicine and Surgery, Vol 11, Iss C, Pp 52-

    2016  Volume 57

    Abstract: Background: Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. ... ...

    Abstract Background: Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU. Objective: Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR. Methods: We have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset. Results: AutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26. Conclusions: Through the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.
    Keywords Clinical decision support systems ; Mortality prediction ; Electronic health records ; Medical informatics ; Medicine (General) ; R5-920
    Subject code 310
    Language English
    Publishing date 2016-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: COVID-19 Evidence Accelerator

    Mark Stewart / Carla Rodriguez-Watson / Adem Albayrak / Julius Asubonteng / Andrew Belli / Thomas Brown / Kelly Cho / Ritankar Das / Elizabeth Eldridge / Nicolle Gatto / Alice Gelman / Hanna Gerlovin / Stuart L Goldberg / Eric Hansen / Jonathan Hirsch / Yuk-Lam Ho / Andrew Ip / Monika Izano / Jason Jones /
    Amy C Justice / Reyna Klesh / Seth Kuranz / Carson Lam / Qingqing Mao / Samson Mataraso / Robertino Mera / Daniel C Posner / Jeremy A Rassen / Anna Siefkas / Andrew Schrag / Georgia Tourassi / Andrew Weckstein / Frank Wolf / Amar Bhat / Susan Winckler / Ellen V Sigal / Jeff Allen

    PLoS ONE, Vol 16, Iss 3, p e

    A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients.

    2021  Volume 0248128

    Abstract: Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged ... ...

    Abstract Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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