Publications by authors named "Tao Cui"

293 Publications

Nonselective beta-blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the United States.

Aliment Pharmacol Ther 2021 Aug 5;54(4):481-492. Epub 2021 Jul 5.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Background: Previous studies have demonstrated an association between nonselective beta-blockers (NSBBs) and lower risk of hepatocellular carcinoma (HCC) in cirrhosis. However, there has been no population-based study investigating the risk of HCC among cirrhotic patients treated using carvedilol.

Aims: To determine the risk of HCC among cirrhotic patients with NSBBs including carvedilol.

Methods: This retrospective cohort study utilised the Cerner Health Facts database in the United States from 2000 to 2017. Kaplan-Meier estimate, Cox proportional hazards regression, and propensity score matching (PSM) were used to test the HCC risk among the carvedilol, nadolol, and propranolol groups compared with no beta-blocker group.

Results: The final cohort comprised 107 428 eligible patients. The 100-month cumulative HCC incidence of NSBBs was significantly lower than the no beta-blocker group (carvedilol (11.24%) vs no beta-blocker (15.69%), nadolol (27.55%) vs no beta-blocker (32.11%), and propranolol (26.17%) vs no beta-blocker (28.84%) (P values < 0.0001). NSBBs were associated with a significantly lower risk of HCC (Hazard ratio: carvedilol 0.61 (95% CI 0.51-0.73), nadolol 0.74 (95% CI 0.63-0.87), propranolol 0.75 (95% CI 0.66-0.84) after PSM in the multivariate cox analysis. In subgroup analysis, NSBBs reduced the risk of HCC in cirrhosis with complications and non-alcoholic cirrhosis.

Conclusions: NSBBs, including carvedilol, were associated with a significantly decreased risk of HCC in patients with cirrhosis when compared with no beta-blocker regardless of complications status. Future randomised-controlled studies comparing the incidence of HCC among NSBBs should elucidate which NSBB would be the best option to prevent HCC in cirrhosis.
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http://dx.doi.org/10.1111/apt.16490DOI Listing
August 2021

CT appearances, patterns of progression, and follow-up of COVID-19: evaluation on thin-section CT.

Insights Imaging 2021 Jun 10;12(1):73. Epub 2021 Jun 10.

Department of Radiology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, Chaoyang District, Beijing, China.

Background: To retrospectively analyze CT appearances and progression pattern of COVID-19 during hospitalization, and analyze imaging findings of follow-up on thin-section CT.

Methods: CT findings of 69 patients with COVID-19 were evaluated on initial CT, peak CT, and pre-discharge CT. CT pattern were divided into four types on CT progression. Lesion percentage of pulmonary lobe (lobe score) was graded. Correlation analysis was made between scores and intervals. 53 patients were followed up by CT.

Results: Among 69 patients, 33.3% exhibited improvement pattern, 65.2% peak pattern, 1.5% deterioration pattern, and 0% fluctuation pattern. The lobe scores were positively correlated with most of intervals. It was more common to observe consolidation, pleural thickening and pleural effusion on the peak CT, and irregular line and reticulation on pre-discharge CT. The peak-initial interval were shortened when the initial CT with consolidation and pleural thickening. The intervals were extended when the irregular lines appeared on peak CT and reticulation on pre-discharge CT. Among 53 follow-up patients, 37.7% showed normal chest CT, and 62.3% showed viral pneumonia remained that mainly included GGO (100.0%) and irregular lines (33.3%).

Conclusions: COVID-19 displayed different appearances on CT as progressing. The peak pattern was the most common progression pattern. The CT appearances showed closely related to the intervals. The COVID-19 pneumonia can be remained or completely absorbed on CT with follow-up.
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http://dx.doi.org/10.1186/s13244-021-01019-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190726PMC
June 2021

Author Correction: mRNA structural dynamics shape Argonaute-target interactions.

Nat Struct Mol Biol 2021 Jun;28(6):533

Oncode Institute, Hubrecht Institute-KNAW and University Medical Center Utrecht, Utrecht, the Netherlands.

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http://dx.doi.org/10.1038/s41594-021-00610-9DOI Listing
June 2021

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.

NPJ Digit Med 2021 May 20;4(1):86. Epub 2021 May 20.

School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed for the text domain to the structured EHR domain. Med-BERT is a contextualized embedding model pretrained on a structured EHR dataset of 28,490,650 patients. Fine-tuning experiments showed that Med-BERT substantially improves the prediction accuracy, boosting the area under the receiver operating characteristics curve (AUC) by 1.21-6.14% in two disease prediction tasks from two clinical databases. In particular, pretrained Med-BERT obtains promising performances on tasks with small fine-tuning training sets and can boost the AUC by more than 20% or obtain an AUC as high as a model trained on a training set ten times larger, compared with deep learning models without Med-BERT. We believe that Med-BERT will benefit disease prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.
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http://dx.doi.org/10.1038/s41746-021-00455-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137882PMC
May 2021

Labor induction in pregnancy complicated by myelodysplastic syndrome: A case report.

Clin Case Rep 2021 Apr 14;9(4):2032-2035. Epub 2021 Feb 14.

Department of Gynecology and Obstetrics West China Second University Hospital Sichuan University Chengdu China.

Pregnancy may aggravate myelodysplastic syndrome. Cross-matched platelets can be used in cases of refractory thrombocytopenia. Vaginal delivery can be attempted if the platelet count is at least 20 × 10/L.
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http://dx.doi.org/10.1002/ccr3.3935DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077406PMC
April 2021

Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network.

AMIA Annu Symp Proc 2020 25;2020:1220-1229. Epub 2021 Jan 25.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.

Because they contain detailed individual-level data on various patient characteristics including their medical conditions and treatment histories, electronic health record (EHR) systems have been widely adopted as an efficient source for health research. Compared to data from a single health system, real-world data (RWD) from multiple clinical sites provide a larger and more generalizable population for accurate estimation, leading to better decision making for health care. However, due to concerns over protecting patient privacy, it is challenging to share individual patient-level data across sites in practice. To tackle this issue, many distributed algorithms have been developed to transfer summary-level statistics to derive accurate estimates. Nevertheless, many of these algorithms require multiple rounds of communication to exchange intermediate results across different sites. Among them, the One-shot Distributed Algorithm for Logistic regression (termed ODAL) was developed to reduce communication overhead while protecting patient privacy. In this paper, we applied the ODAL algorithm to RWD from a large clinical data research network-the OneFlorida Clinical Research Consortium and estimated the associations between risk factors and the diagnosis of opioid use disorder (OUD) among individuals who received at least one opioid prescription. The ODAL algorithm provided consistent findings of the associated risk factors and yielded better estimates than meta-analysis.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075517PMC
June 2021

Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD).

AMIA Annu Symp Proc 2020 25;2020:393-401. Epub 2021 Jan 25.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.

With vast amounts ofpatients' medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer's disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075520PMC
June 2021

HANDY: a device for assessing resistance to mechanical crushing of maize kernel.

Plant Methods 2021 Apr 26;17(1):44. Epub 2021 Apr 26.

College of Engineering, China Agricultural University, NO. 17 Qinghua East Road, Beijing, 100083, People's Republic of China.

Background: How to control the physical damage during maize kernel harvesting is a major problem for both mechanical designers and plant breeders. A limitation of addressing this problem is lacking a reliable method for assessing the relation between kernel damage susceptibility and threshing quality. The design, construction, and testing of a portable tool called "HANDY", which can assess the resistance to mechanical crushing in maize kernel. HANDY can impact the kernel with a special accelerator at a given rotating speed and then cause measurable damage to the kernel. These factors are varied to determine the ideal parameters for operating the HANDY.

Results: Breakage index (BI, target index of HANDY), decreased as the moisture content of kernel increased or the rotating speed decreased within the tested range. Furthermore, the HANDY exhibited a greater sensitivity in testing kernels at higher moisture level influence on the susceptibility of damage kernel than that in Breakage Susceptibility tests, particularly when the centrifugation speed is about 1800 r/min and the centrifugal disc type is curved. Considering that the mechanical properties of kernels vary greatly as the moisture content changes, a subsection linear (average goodness of fit is 0.9) to predict the threshing quality is built by piecewise function analysis, which is divided by kernel moisture. Specifically, threshing quality is regarded as a function of the measured result of the HANDY. Five maize cultivars are identified with higher damage resistance among 21 tested candidate varieties.

Conclusions: The HANDY provides a quantitative assessment of the mechanical crushing resistance of maize kernel. The BI is demonstrated to be a more robust index than breakage susceptibility (BS) when evaluating threshing quality in harvesting in terms of both reliability and accuracy. This study also offers a new perspective for evaluating the mechanical crushing resistance of grains and provides technical support for breeding and screening maize varieties that are suitable for mechanical harvesting.
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http://dx.doi.org/10.1186/s13007-021-00729-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074406PMC
April 2021

COVID-19 Trial Graph: A Linked Graph for COVID-19 Clinical Trials.

J Am Med Inform Assoc 2021 Apr 24. Epub 2021 Apr 24.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston.

Objective: Clinical trials are an essential part of the effort to find safe and effective prevention and treatment for COVID-19. Given the rapid growth of COVID-19 clinical trials, there is an urgent need for a better clinical trial information retrieval that supports searching by specifying criteria including both eligibility criteria and structured trial information.

Materials And Methods: We built a linked graph for registered COVID-19 clinical trials: the COVID-19 Trial Graph, to facilitate retrieval of clinical trials. Natural language processing (NLP) tools were leveraged to extract and normalize the clinical trial information from both their eligibility criteria free texts and structured information from ClinicalTrials.gov. We linked the extracted data using the COVID-19 Trial Graph and imported it to a graph database, which supports both query and visualization. We evaluated trial graph using case queries and graph embedding.

Results: The graph currently (as of 10-05-2020) contains 3,392 registered COVID-19 clinical trials, with 17,480 nodes and 65,236 relationships. Manual evaluation of case queries found high-precision and recall scores on retrieving relevant clinical trials searching from both eligibility criteria and trial-structured information. We observed clustering in clinical trials via graph embedding, which also showed superiority over the baseline (0.8704 vs. 0.8199) in evaluating whether a trial can complete its recruitment successfully.

Conclusions: The COVID-19 Trial Graph is a novel representation of clinical trials that allows diverse search queries and provides a graph-based visualization of COVID-19 clinical trials. High-dimensional vectors mapped by graph embedding for clinical trials would be potentially beneficial for many downstream applications, such as trial end recruitment status prediction, and trial similarity comparison. Our methodology also is generalizable to other clinical trials, such as cancer clinical trials.
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http://dx.doi.org/10.1093/jamia/ocab078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135317PMC
April 2021

[Effect of different orthodontic forces on the expression of T helper cell 17 cell-related cytokines in the pressure side of periodontal tissue in rats].

Hua Xi Kou Qiang Yi Xue Za Zhi 2021 Apr;39(2):143-152

Dept. of Orthodontics, Qingdao Stomatological Hospital Affiliated to Qingdao University, Qing-dao 266001, China.

Objectives: This study aimed to explore the changes in the expression of the characteristic transcription factor retinoid related orphan receptor γt (RORγt) and the cytokine interleukin-17 (IL-17) of T helper cell 17 (Th17) in the pressure side of the periodontal tissue of rats under different orthodontic forces. Their effects on the expression of osteoprotegerin (OPG) and the quantity of osteoclast (OC) were also explored. The role of Th17 cell in alveolar bone remodeling under different forces was preliminarily investigated.

Methods: A total of 108 rats were chosen and randomly divided into three groups. Mesial forces of 0, 50, and 100 g were loaded on the maxillary first molar in the three groups. The rats were executed at 0, 1, 3, 5, 7, and 14 days. The expression of RORγt mRNA was quantified by real-time quantitative polymerase chain reaction. The expression of IL-17 protein was quantified by enzyme linked immunosorbent assay. The expression levels of RORγt and OPG proteins were quantified, and the quantity of OC was counted via immunohistochemistry.

Results: The expression levels of RORγt and IL-17 and the quantity of OC increased first and then decreased in the 50 and 100 g groups, and the peak values of the two groups were on days 5 and 7, respectively. The expression levels in the 50 g group basically recovered to normal level on day 14, while that in the 100 g group remained at a high level. The expression levels in the 50 g group were higher than those in the 0 g group and lower than those in the 100 g group. The expression of OPG in the 50 g group decreased first, then increased, and finally decreased. It basically recovered to normal level on day 14. The expression of OPG in the 100 g group decreased first and then increased. It remained at a high level on day 14. The expression in the 50 g group was significantly higher than that in the 0 g group on day 7, while the expression in the 100 g group was significantly higher than that in the 0 g group on day 14.

Conclusions: RORγt, IL-17, and OPG were expressed regularly over time under different orthodontic forces, indicating that Th17 participated in the process of bone resorption on the pressure side of periodontal tissue by secreting IL-17.
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http://dx.doi.org/10.7518/hxkq.2021.02.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055766PMC
April 2021

Biochemical and structural characterization of the BioZ enzyme engaged in bacterial biotin synthesis pathway.

Nat Commun 2021 04 6;12(1):2056. Epub 2021 Apr 6.

Department of Pathogen Biology & Microbiology and General Intensive Care Unit of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Biotin is an essential micro-nutrient across the three domains of life. The paradigm earlier step of biotin synthesis denotes "BioC-BioH" pathway in Escherichia coli. Here we report that BioZ bypasses the canonical route to begin biotin synthesis. In addition to its origin of Rhizobiales, protein phylogeny infers that BioZ is domesticated to gain an atypical role of β-ketoacyl-ACP synthase III. Genetic and biochemical characterization demonstrates that BioZ catalyzes the condensation of glutaryl-CoA (or ACP) with malonyl-ACP to give 5'-keto-pimeloyl ACP. This intermediate proceeds via type II fatty acid synthesis (FAS II) pathway, to initiate the formation of pimeloyl-ACP, a precursor of biotin synthesis. To further explore molecular basis of BioZ activity, we determine the crystal structure of Agrobacterium tumefaciens BioZ at 1.99 Å, of which the catalytic triad and the substrate-loading tunnel are functionally defined. In particular, we localize that three residues (S84, R147, and S287) at the distant bottom of the tunnel might neutralize the charge of free C-carboxyl group of the primer glutaryl-CoA. Taken together, this study provides molecular insights into the BioZ biotin synthesis pathway.
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http://dx.doi.org/10.1038/s41467-021-22360-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024396PMC
April 2021

DIAGNOSTIC CHARACTERISTICS OF B-SCAN SWEPT-SOURCE OPTICAL COHERENCE TOMOGRAPHY FOR PATIENTS WITH POLYPOIDAL CHOROIDAL VASCULOPATHY.

Retina 2021 Mar 30. Epub 2021 Mar 30.

Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China; Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, People's Republic of China.

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http://dx.doi.org/10.1097/IAE.0000000000003172DOI Listing
March 2021

Correction to: JOINT for large-scale single-cell RNAsequencing analysis via soft-clustering and parallel computing.

BMC Genomics 2021 Mar 29;22(1):223. Epub 2021 Mar 29.

Department of Pharmacology and Physiology, Georgetown University Medical Center, Washington, DC, 20057, USA.

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http://dx.doi.org/10.1186/s12864-021-07408-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8008520PMC
March 2021

Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.

J Am Med Inform Assoc 2021 07;28(7):1393-1400

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Objective: Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract nervous system disorder-related events from vaccine safety reports.

Materials And Methods: We collected Guillain-Barré syndrome (GBS) related influenza vaccine safety reports from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016. VAERS reports were selected and manually annotated with major entities related to nervous system disorders, including, investigation, nervous_AE, other_AE, procedure, social_circumstance, and temporal_expression. A variety of conventional machine learning and deep learning algorithms were then evaluated for the extraction of the above entities. We further pretrained domain-specific BERT (Bidirectional Encoder Representations from Transformers) using VAERS reports (VAERS BERT) and compared its performance with existing models.

Results And Conclusions: Ninety-one VAERS reports were annotated, resulting in 2512 entities. The corpus was made publicly available to promote community efforts on vaccine AEs identification. Deep learning-based methods (eg, bi-long short-term memory and BERT models) outperformed conventional machine learning-based methods (ie, conditional random fields with extensive features). The BioBERT large model achieved the highest exact match F-1 scores on nervous_AE, procedure, social_circumstance, and temporal_expression; while VAERS BERT large models achieved the highest exact match F-1 scores on investigation and other_AE. An ensemble of these 2 models achieved the highest exact match microaveraged F-1 score at 0.6802 and the second highest lenient match microaveraged F-1 score at 0.8078 among peer models.
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http://dx.doi.org/10.1093/jamia/ocab014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279785PMC
July 2021

Prediction of post-vaccination Guillain-Barré syndrome using data from a passive surveillance system.

Pharmacoepidemiol Drug Saf 2021 05 23;30(5):602-609. Epub 2021 Feb 23.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Purpose: Severe adverse events (AEs), such as Guillain-Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3).

Methods: This study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance.

Results: Of the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty-four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS.

Conclusion: The prediction performance demonstrated the potential of developing risk-prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset.
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http://dx.doi.org/10.1002/pds.5196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8014460PMC
May 2021

JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.

BMC Genomics 2021 Jan 11;22(1):47. Epub 2021 Jan 11.

Department of Pharmacology and Physiology, Georgetown University Medical Center, Washington, DC, 20057, USA.

Background: Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.

Results: In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing.

Conclusions: Taken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.
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http://dx.doi.org/10.1186/s12864-020-07302-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798298PMC
January 2021

"Down the Rabbit Hole" of Vaccine Misinformation on YouTube: Network Exposure Study.

J Med Internet Res 2021 01 5;23(1):e23262. Epub 2021 Jan 5.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.

Background: Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines.

Objective: The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos.

Methods: Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model.

Results: Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos.

Conclusions: YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information.
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http://dx.doi.org/10.2196/23262DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815449PMC
January 2021

The complete chloroplast genome of , an endangered and economically important species.

Mitochondrial DNA B Resour 2020 27;5(3):2176-2177. Epub 2020 May 27.

Lincang Academy of Forestry Sciences, Lincang, PR China.

is an endangered and economically important species of the Arecaceae. The complete chloroplast genome sequence of this species is a circular molecule of 159,882 bp in size, including a pair of inverted repeats with length of 27,271 bp, separated by a large single-copy (87,645 bp) region and a small single-copy region (17,695 bp). In total, there are 131 genes, encoding 79 protein-coding genes, 40 tRNAs, and 10 rRNA genes, in which 123 genes, 69 CDSs, 37 tRNAs, and 10 rRNAs are unique, respectively. Phylogenetic inference confirmed the monophyly of the genus and its delimitation in subfamily Coryphoideae.
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http://dx.doi.org/10.1080/23802359.2020.1768944DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510677PMC
May 2020

Rapid on-site cytologic evaluation during endobronchial ultrasound-guided transbronchial biopsy.

J Formos Med Assoc 2021 06 18;120(6):1412-1413. Epub 2020 Dec 18.

Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China. Electronic address:

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http://dx.doi.org/10.1016/j.jfma.2020.12.006DOI Listing
June 2021

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health.

BMC Med Inform Decis Mak 2020 12 15;20(Suppl 10):269. Epub 2020 Dec 15.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX, 77030, USA.

Background: Dyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data.

Methods: The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope.

Results: Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data.

Conclusion: Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.
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http://dx.doi.org/10.1186/s12911-020-01287-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737278PMC
December 2020

Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019).

BMC Med Inform Decis Mak 2020 12 14;20(Suppl 4):315. Epub 2020 Dec 14.

Institute for Health Informatics and College of Pharmacy, University of Minnesota, Minneapolis, MN, USA.

In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.
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http://dx.doi.org/10.1186/s12911-020-01292-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734704PMC
December 2020

Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents.

BMC Med Inform Decis Mak 2020 12 14;20(Suppl 4):259. Epub 2020 Dec 14.

The University of Texas Health Science Center at Houston, School of Biomedical Informatics, 7000 Fannin Suite 600, Houston, 77030, TX, USA.

Background: Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction.

Methods: We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials.

Results: Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators.

Conclusions: Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.
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http://dx.doi.org/10.1186/s12911-020-01267-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734717PMC
December 2020

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.

BMC Med Inform Decis Mak 2020 12 14;20(Suppl 4):283. Epub 2020 Dec 14.

School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX, 77030, USA.

Background: Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.

Methods: First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.

Results And Conclusions: We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson's disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.
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http://dx.doi.org/10.1186/s12911-020-01274-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734713PMC
December 2020

Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection.

AIDS 2021 05;35(Suppl 1):S65-S73

School of Biomedical Informatics.

Objective: Young MSM (YMSM) bear a disproportionate burden of HIV infection in the United States and their risks of acquiring HIV may be shaped by complex multilayer social networks. These networks are formed through not only direct contact with social/sex partners but also indirect anonymous contacts encountered when attending social venues. We introduced a new application of a state-of-the-art graph-based deep learning method to predict HIV infection that can identify influential neighbors within these multiple network contexts.

Design And Methods: We used empirical network data among YMSM aged 16-29 years old collected from Houston and Chicago in the United States between 2014 and 2016. A computational framework GAT-HIV (Graph Attention Networks for HIV) was proposed to predict HIV infections by identifying influential neighbors within social networks. These networks were formed by multiple relations constituted of social/sex partners and shared venue attendances, and using individual-level variables. Further, GAT-HIV was extended to combine multiple social networks using multigraph GAT methods. A visualization tool was also developed to highlight influential network members for each individual within the multiple social networks.

Results: The multigraph GAT-HIV models obtained average AUC values of 0.776 and 0.824 for Chicago and Houston, respectively, performing better than empirical predictive models (e.g. AUCs of random forest: 0.758 and 0.798). GAT-HIV on single networks also delivered promising prediction performances.

Conclusion: The proposed methods provide a comprehensive and interpretable framework for graph-based modeling that may inform effective HIV prevention intervention strategies among populations most vulnerable to HIV.
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http://dx.doi.org/10.1097/QAD.0000000000002784DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058230PMC
May 2021

Meta-analysis on the effect of pituitary adenoma resection on pituitary function.

Neurol Neurochir Pol 2021 10;55(1):24-32. Epub 2020 Dec 10.

920th Hospital of the Combined Service Force of the Chinese People's Liberation Army, No. 212 Daguan Street, Xishan District, 650032 Kunming, China.

Objective: A meta-analysis was conducted on the effect of pituitary adenoma resection on pituitary function.

Methods: The Cochrane Library, Ovid, PubMed, the Excerpta Medica Database (EMBASE), and the Chinese Biomedical Literature Databases (CBM) were searched to find trials about the evaluation of pituitary target glands before and after pituitary adenoma resection. The databases were searched from the earliest available trials until the end of September 2019. Based on the inclusion and exclusion criteria, two researchers independently selected literature, extracted data, and evaluated the quality of the studies, and then used Revman 5.2 software to conduct a meta-analysis.

Results: Eleven clinical trials were included, with a total of 3,237 subjects. Meta-analysis showed that the number of patients with hypofunction of the thyroid and gonadal axes substantially decreased after pituitary tumour resection, and that the difference was statistically significant: odds ratio (OR) = 1.72 [95% confidence interval (CI), 1.18-2.52; P = 0.005] and OR = 2.06 (95% CI, 1.42-3.00; P = 0.0002). The number of patients with a poor total suprarenal gland axis after pituitary tumour resection did not change significantly compared to the number found before the operation; the difference was not statistically significant: OR = 1.04 (95% CI, 0.72-1.48; P = 0.85). However, the number of patients who had adrenal axis dysfunction both before and after the operation was significantly reduced, and the difference was statistically significant: OR = 1.46 (95% CI, 1.21-1.78; P = 0.0001).

Conclusion: The function of the thyroid and gonadal axes of pituitary gland tumour patients can be improved, to some extent, after pituitary tumour resection. Patients with pituitary tumours who have hypofunction of the adrenal axis can recover effectively after tumour resection.
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http://dx.doi.org/10.5603/PJNNS.a2020.0093DOI Listing
March 2021

Towards an ontology-based medication conversational agent for PrEP and PEP.

Proc Conf Assoc Comput Linguist Meet 2020 Jul;2020:31-40

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas.

HIV (human immunodeficiency virus) can damage a human's immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death. While HIV infections have decreased over the last decade, there is still a significant population where the infection permeates. PrEP and PEP are two proven preventive measures introduced that involve periodic dosage to stop the onset of HIV infection. However, the adherence rates for this medication is low in part due to the lack of information about the medication. There exist several communication barriers that prevent patient-provider communication from happening. In this work, we present our ontology-based method for automating the communication of this medication that can be deployed for live conversational agents for PrEP and PEP. This method facilitates a model of automated conversation between the machine and user can also answer relevant questions.
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http://dx.doi.org/10.18653/v1/2020.nlpmc-1.5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680642PMC
July 2020

Membrane Separation Coupled with Electrochemical Advanced Oxidation Processes for Organic Wastewater Treatment: A Short Review.

Membranes (Basel) 2020 Nov 12;10(11). Epub 2020 Nov 12.

Key Laboratory of Jiangsu Province for Chemical Pollution Control and Resources Reuse, School of Environment and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Research on the coupling of membrane separation (MS) and electrochemical advanced oxidation processes (EAOPs) has been a hot area in water pollution control for decades. This coupling aims to greatly improve water quality and focuses on the challenges in practical application to provide a promising solution to water shortage problems. This article provides a summary of the coupling configurations of MS and EAOPs, including two-stage and one-pot processes. The two-stage process is a combination of MS and EAOPs where one process acts as a pretreatment for the other. Membrane fouling is reduced when setting EAOPs before MS, while mass transfer is promoted when placing EAOPs after MS. A one-pot process is a kind of integration of two technologies. The anode or cathode of the EAOPs is fabricated from porous materials to function as a membrane electrode; thus, pollutants are concurrently separated and degraded. The advantages of enhanced mass transfer and the enlarged electroactive area suggest that this process has excellent performance at a low current input, leading to much lower energy consumption. The reported conclusions illustrate that the coupling of MS and EAOPs is highly applicable and may be widely employed in wastewater treatment in the future.
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http://dx.doi.org/10.3390/membranes10110337DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697808PMC
November 2020

Diagnostic accuracy of Augurix COVID-19 IgG test.

Eur J Clin Invest 2021 02 1;51(2):e13450. Epub 2020 Dec 1.

Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, China.

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http://dx.doi.org/10.1111/eci.13450DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744854PMC
February 2021

Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine.

JAMA Netw Open 2020 11 2;3(11):e2022025. Epub 2020 Nov 2.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston.

Importance: Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies.

Objective: To develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media.

Design, Setting, And Participants: This retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018.

Main Outcomes And Measures: The prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracted from their profiles, and geographic analyses were performed to identify the clustering of public perceptions of the HPV vaccine.

Results: A total of 1 431 463 English-language posts from 486 116 unique usernames were collected. Deep learning algorithms achieved F-1 scores ranging from 0.6805 (95% CI, 0.6516-0.7094) to 0.9421 (95% CI, 0.9380-0.9462) in mapping discussions to the constructs of behavior change theories. LOESS revealed trends in constructs; for example, prevalence of perceived barriers, a construct of HBM, deceased from its apex in July 2015 (56.2%) to its lowest prevalence in October 2018 (28.4%; difference, 27.8%; P < .001); Positive attitudes toward the HPV vaccine, a construct of TPB, increased from early 2017 (30.7%) to 41.9% at the end of the study (difference, 11.2%; P < .001), while negative attitudes decreased from 42.3% to 31.3% (difference, 11.0%; P < .001) during the same period. Interstate variations in public perceptions of the HPV vaccine were also identified; for example, the states of Ohio and Maine showed a relatively high prevalence of perceived barriers (11 531 of 17 106 [67.4%] and 1157 of 1684 [68.7%]) and negative attitudes (9655 of 17 197 [56.1%] and 1080 of 1793 [60.2%]).

Conclusions And Relevance: This cohort study provided a good understanding of public perceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.22025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666426PMC
November 2020

Pharmacokinetics, tissue distribution and excretion of a novel long-acting human insulin analogue - recombinant insulin LysArg in rats.

Xenobiotica 2021 Mar 16;51(3):307-315. Epub 2020 Nov 16.

Tianjin Institute of Pharmaceutical Research, State Key Laboratory of Drug Delivery Technologies and Pharmacokinetics , Tianjin, China.

As a novel long-acting recombinant human insulin analogue, it is necessary to carry out the preclinical research for insulin LysArg. The purpose of this study was to characterise the pharmacokinetic, tissue distribution and excretion of insulin LysArg and provide a reference for its development. Three methods were used to measure the content of insulin LysArg in biological samples after a single subcutaneous administration in rats, including radioassay, radioassay after precipitation with TCA and separation by HPLC. After Subcutaneous administration of recombinant insulin LysArg 1, 2, 4 U/kg in rats, it showed both C and AUC were positively correlated with the dose. In the meanwhile, after a single subcutaneous administration of recombinant insulin LysArg at 2 U/kg in rats, the amount of radioactivity in most organs was highest at 1.5 h and then decreased gradually, no accumulation was found. The highest level of insulin LysArg was observed in the kidney. Like other macromolecules, insulin LysArg was mainly excreted from urine. The study fully illustrated the pharmacokinetic pattern of insulin LysArg, provided valuable informations to support its further development about safety and toxicology.
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http://dx.doi.org/10.1080/00498254.2020.1847361DOI Listing
March 2021
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