Enhancing the antioxidant capacity and quality attributes of fermented goat milk through the synergistic action of Limosilactobacillus fermentum WXZ 2-1 with a starter culture.

This study evaluated 75 strains of lactic acid bacteria (LAB) isolated from traditional dairy products in western China for their probiotic properties. Among them, Limosilactobacillus fermentum WXZ 2-1, Lactiplantibacillus plantarum TXZ 2-35, Companilactobacillus crustorum QHS 9, and Companilactobacillus crustorum QHS 10 demonstrated potential probiotic characteristics. The antioxidant capacity of these 4 strains was assessed, revealing that L. fermentum WXZ 2-1 exhibited the highest antioxidant capacity. Furthermore, when cocultured with Streptococcus salivarius ssp. thermophilus and Lactobacillus delbrueckii ssp. bulgaricus, L. fermentum WXZ 2-1 demonstrated a synergistic effect in growth medium and goat milk. To explore its effect on goat milk fermentation, different amounts of L. fermentum WXZ 2-1 were added to goat milk, and its physicochemical properties, antioxidant activity, flavor substances, and metabolomics were analyzed. The study found that the incorporation of L. fermentum WXZ 2-1 in goat milk fermentation significantly improved the texture characteristics, antioxidant capacity, and flavor of fermented goat milk. These findings highlight the potential of L. fermentum WXZ 2-1 as a valuable probiotic strain for enhancing the functionality and desirability of fermented goat milk, contributing to the development of novel functional foods with improved health benefits and enhanced quality attributes.

Semantic standards of external exposome data.

An individual's health and conditions are associated with a complex interplay between the individual's genetics and his or her exposures to both internal and external environments. Much attention has been placed on characterizing of the genome in the past; nevertheless, genetics only account for about 10% of an individual's health conditions, while the remaining appears to be determined by environmental factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent them, environmental exposures, especially the external exposome, need to be systematically explored. However, the heterogeneity of the external exposome data sources (e.g., same exposure variables using different nomenclature in different data sources, or vice versa, two variables have the same or similar name but measure different exposures in reality) increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. To solve the issue, the development of semantic standards using an ontology-driven approach is inevitable because ontologies can (1) provide a unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly express and model the context of the variables and relationships between those variables. We conducted a review of existing ontology for the external exposome and found only four relevant ontologies. Further, the four existing ontologies are limited: they (1) often ignored the spatiotemporal characteristics of external exposome data, and (2) were developed in isolation from other conceptual frameworks (e.g., the socioecological model and the social determinants of health). Moving forward, the combination of multi-domain and multi-scale data (i.e., genome, phenome and exposome at different granularity) and different conceptual frameworks is the basis of health outcomes research in the future.

Molecular Basis of Artemisinin Derivatives Inhibition of Myeloid Differentiation Protein 2 by Combined in Silico and Experimental Study.

Artemisinin (also known as Qinghaosu), an active component of the Qinghao extract, is widely used as antimalarial drug. Previous studies reveal that artemisinin and its derivatives also have effective anti-inflammatory and immunomodulatory properties, but the direct molecular target remains unknown. Recently, several reports mentioned that myeloid differentiation factor 2 (MD-2, also known as lymphocyte antigen 96) may be the endogenous target of artemisinin in the inhibition of lipopolysaccharide signaling. However, the exact interaction between artemisinin and MD-2 is still not fully understood. Here, experimental and computational methods were employed to elucidate the relationship between the artemisinin and its inhibition mechanism. Experimental results showed that artemether exhibit higher anti-inflammatory activity performance than artemisinin and artesunate. Molecular docking results showed that artemisinin, artesunate, and artemether had similar binding poses, and all complexes remained stable throughout the whole molecular dynamics simulations, whereas the binding of artemisinin and its derivatives to MD-2 decreased the TLR4(Toll-Like Receptor 4)/MD-2 stability. Moreover, artemether exhibited lower binding energy as compared to artemisinin and artesunate, which is in good agreement with the experimental results. Leu61, Leu78, and Ile117 are indeed key residues that contribute to the binding free energy. Binding free energy analysis further confirmed that hydrophobic interactions were critical to maintain the binding mode of artemisinin and its derivatives with MD-2.

Analysis of Serum Metabolomics in Rats with Osteoarthritis by Mass Spectrometry.

Osteoarthritis is a common multifactorial chronic disease that occurs in articular cartilage, subchondral bone, and periarticular tissue. The pathogenesis of OA is still unclear. To investigate the differences in serum metabolites between OA and the control group, liquid chromatography/mass spectrometry (LC/MS)-based metabolomics was used. To reveal the pathogenesis of OA, 12 SD male rats were randomly divided into control and OA groups using collagenase to induce OA for modeling, and serum was collected 7 days after modeling for testing. The OA group was distinguished from the control group by principal component analysis and orthogonal partial least squares-discriminant analysis, and six biomarkers were finally identified. These biomarkers were metabolized through tryptophan metabolism, glutamate metabolism, nitrogen metabolism, spermidine metabolism, and fatty acid metabolism pathways. The study identified metabolites that may be altered in OA, suggesting a role in OA through relevant metabolic pathways. Metabolomics, as an important tool for studying disease mechanisms, provides useful information for studying the metabolic mechanisms of OA.

User-centered design of a web-based crowdsourcing-integrated semantic text annotation tool for building a mental health knowledge base.

BACKGROUND: One in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap. OBJECTIVE: We aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach. METHODS: Following UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality. RESULTS: The SUS score increased from 70.3 ± 12.5 to 81.1 ± 9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7 ± 20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8 ± 13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5 ± 69.2 s on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences. CONCLUSIONS: We successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.

An ontology-based documentation of data discovery and integration process in cancer outcomes research.

BACKGROUND: To reduce cancer mortality and improve cancer outcomes, it is critical to understand the various cancer risk factors (RFs) across different domains (e.g., genetic, environmental, and behavioral risk factors) and levels (e.g., individual, interpersonal, and community levels). However, prior research on RFs of cancer outcomes, has primarily focused on individual level RFs due to the lack of integrated datasets that contain multi-level, multi-domain RFs. Further, the lack of a consensus and proper guidance on systematically identify RFs also increase the difficulty of RF selection from heterogenous data sources in a multi-level integrative data analysis (mIDA) study. More importantly, as mIDA studies require integrating heterogenous data sources, the data integration processes in the limited number of existing mIDA studies are inconsistently performed and poorly documented, and thus threatening transparency and reproducibility. METHODS: Informed by the National Institute on Minority Health and Health Disparities (NIMHD) research framework, we (1) reviewed existing reporting guidelines from the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network and (2) developed a theory-driven reporting guideline to guide the RF variable selection, data source selection, and data integration process. Then, we developed an ontology to standardize the documentation of the RF selection and data integration process in mIDA studies. RESULTS: We summarized the review results and created a reporting guideline-ATTEST-for reporting the variable selection and data source selection and integration process. We provided an ATTEST check list to help researchers to annotate and clearly document each step of their mIDA studies to ensure the transparency and reproducibility. We used the ATTEST to report two mIDA case studies and further transformed annotation results into sematic triples, so that the relationships among variables, data sources and integration processes are explicitly standardized and modeled using the classes and properties from OD-ATTEST. CONCLUSION: Our ontology-based reporting guideline solves some key challenges in current mIDA studies for cancer outcomes research, through providing (1) a theory-driven guidance for multi-level and multi-domain RF variable and data source selection; and (2) a standardized documentation of the data selection and integration processes powered by an ontology, thus a way to enable sharing of mIDA study reports among researchers.

Molecular Mechanism of S1P Binding and Activation of the S1P1 Receptor.

Sphingosine-1-phosphate (S1P) is a lipidic mediator in mammals that functions either as a second messenger or as a ligand. In the latter case, it is transported by its HDL-associated apoM carrier and circulated in blood where it binds to specific S1P receptors on cell membranes and induces downstream reactions. Although S1P signaling pathways are essential for many biological processes, they are poorly understood at the molecular level. Here, the solved crystal structures of the S1P1 receptor were used to evaluate molecular dynamics (MD) simulations to generate greater detailed molecular insights into the mechanism of S1P signaling. The MD simulations provided observations at the coarse-grained and atomic levels indicating that S1P may access the receptor binding pocket directly from solvents. Lifting of the bulky N-terminal cap region of the receptor precedes initial S1P binding. Glu1213.29 guides S1P penetration, and together with Arg2927.34 is responsible for the stabilization of S1P in the binding pocket, which is consistent with experimental predictions. The complete binding of S1P is followed by receptor activation, wherein Trp2696.48 moves toward the transmembrane helix (TM) 7, resulting in the formation of an enhanced hydrogen bond network in the lower region of TM7. The distance between TM3 and TM6 is subsequently increased, resulting in the opening of the intracellular binding pocket that enables G protein binding. Further analysis of the force distribution network in the receptor yielded a detailed molecular understanding of the signal transmission network that is activated upon agonist binding.

OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.

BACKGROUND: There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB. METHODS: We developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB. RESULTS: We conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB. CONCLUSIONS: We presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.

An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival.

BACKGROUND: Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. METHODS: Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. RESULTS: Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. CONCLUSIONS: Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.

Using Social Media Data to Understand the Impact of Promotional Information on Laypeople's Discussions: A Case Study of Lynch Syndrome.

BACKGROUND: Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople. OBJECTIVE: This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople's discussions on a social media platform (Twitter) in terms of topic awareness and attitudes. METHODS: We used topic modeling and sentiment analysis techniques on Lynch syndrome-related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome-related tweets?; (2) how promotional Lynch syndrome-related information on Twitter affects laypeople's discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople's discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome-related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople's discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome-related tweets and conducted sentiment analysis on each layperson's tweet to gauge the writer's attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs. RESULTS: Of all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome-related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople's discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople's discussions were similar to the distributions in promotional Lynch syndrome-related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople's discussions had a strong correlation with promotional Lynch syndrome-related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople's discussions and their attitudes. CONCLUSIONS: There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health.

Enzymo-chemical preparation, physico-chemical characterization and hypolipidemic activity of granular corn bran dietary fibre.

Corn bran dietary fibre (CF) was paid more attention for its anticancer and hypolipidemic activities. In this paper, corn bran was firstly decomposed to the threadlike fibre (CF1) by multiple enzymes and then further modified to the granular fibre (CF2) by alkali under high pressure and high temperature (APT). The two types of fibres were characterized by scanning electron microscope (SEM) and near-infrared spectrophotometer (IR), and investigated by hydration measurements and nitrite adsorption assays. The results showed that CF2 had more much specific surface area, and displayed 4.7, 6.3 and 30-fold increases in water retention (WR), swelling capacity (SC) and nitrite absorption (NA), compared with CF1, respectively. The rat feeding trials showed that the granular fibre could decrease total cholesterol (TC), triglyceride (TG) and low density lipoprotein-cholesterol (LDLC) by 41.4 %, 20.7 % and 56.5 %, respectively. These excellent physiological activities indicate that CF2 will be a potentially available dietary ingredient in functional food industries, and meanwile imply that the enzymochemical method is a desired strategy for CF processing.

Concept and investigation of Selective Forward Osmosis (SFO) for Salt-Salt Separation as a Pretreatment of Seawater for Resource Utilization.

Seawater utilization is crucial for the sustainable human development. Despite growing interest in forward osmosis (FO) due to its unique properties, conventional FO membranes with salt-water selectivity have limitations in applying to specific salt-salt separation processes, which hinders their application in resource utilization. In this work, a new concept, "selective forward osmosis (SFO)", was proposed, which ingeniously employed an SFO membrane consisting of an ion-selective layer on a denser substrate. The denser substrate is designed to control water flux so as to alleviate the solution dilution and improve the salt-salt separation. Moreover, the sucrose and pure water were used separately as feed solution to provide different water flux to influence the various salt fluxes, showing that pure water feed could enhance the salt-salt separation efficiency, although it could dilute the draw solution to some extent. Therefore, pure water was selected as feed in the subsequent experiments. The optimized SFO membrane achieved high Na2SO4/NaCl selectivity (∼54.8) and MgCl2/NaCl selectivity (∼9.2) in single-salt draw solutions. With mixed-salt and heavy-metal-mixed-salt draw solutions, the Mg2+/Na+ selectivity was enhanced to ∼14.5, and further to 29.3. In real seawater tests, the SFO system effectively permeated monovalent elements (such as Na flux of ∼68.6 g m-2 h-1) while maintaining a higher rejection for bivalent elements (such as Mg flux of ∼0.08 g m-2 h-1), showing high selectivities for Mg/Na, U/Na, Sr/Na, Ni/Na, and Ca/Na. These results demonstrate the potential of SFO for resource utilization, especially in complex saline environments. This work contributes a new route for salt-salt separation in the pretreatment of seawater resources.

Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.

Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes-homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria.

Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for Alzheimer's disease and related dementias.

BACKGROUND: Cognitive tests and biomarkers are the key information to assess the severity and track the progression of Alzheimer's' disease (AD) and AD-related dementias (AD/ADRD), yet, both are often only documented in clinical narratives of patients' electronic health records (EHRs). In this work, we aim to (1) assess the documentation of cognitive tests and biomarkers in EHRs that can be used as real-world endpoints, and (2) identify, extract, and harmonize the different commonly used cognitive tests from clinical narratives using natural language processing (NLP) methods into categorical AD/ADRD severity. METHODS: We developed a rule-based NLP pipeline to extract the cognitive tests and biomarkers from clinical narratives in AD/ADRD patients' EHRs. We aggregated the extracted results to the patient level and harmonized the cognitive test scores into severity categories using cutoffs determined based on both relevant literature and domain knowledge of AD/ADRD clinicians. RESULTS: We identified an AD/ADRD cohort of 48,912 patients from the University of Florida (UF) Health system and identified 7 measurements (6 cognitive tests and 1 biomarker) that are frequently documented in our data. Our NLP pipeline achieved an overall F1-score of 0.9059 across the 7 measurements. Among the 6 cognitive tests, we were able to harmonize 4 cognitive test scores into severity categories, and the population characteristics of patients with different severity were described. We also identified several factors related to the availability of their documentation in EHRs. CONCLUSION: This study demonstrates that our NLP pipelines can extract cognitive tests and biomarkers of AD/ADRD accurately for downstream studies. Although, the documentation of cognitive tests and biomarkers in EHRs appears to be low, RWD is still an important resource for AD/ADRD research. Nevertheless, providing standardized approach to document cognitive tests and biomarkers in EHRS are also warranted.

High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data.

Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.

Differential expression of Ago2-mediated microRNA signaling in adipose tissue is associated with food-induced obesity.

Adipose tissue is a major component for the regulation of energy homeostasis by storage and release of lipids. As a core element of RNA-induced silencing complex, argonaute2 (Ago2) plays critical role in maintenance of systemic metabolic demand. Here, we show that high-fat-diet-fed mice exhibit an increase in body mass alongside systematic insulin resistance and altered rate of energy expenditure. Interestingly, Ago2 expression is associated with obesity and an increased amount of adipose tissue. Moreover, increased levels of Ago2 inhibited the expression of AMPKα by promoting its targeting by miR-148a, the most abundant microRNA in adipose tissues. Those results suggested that Ago2-miR-148a-AMPKα signaling pathway play an important function in the developing obesity and adiposity, and will further provide basic research data for the potential clinical treatment of obesity.

A scoping review of semantic integration of health data and information.

OBJECTIVE: We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS: We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS: The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION: SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

Simulating Colorectal Cancer Trials Using Real-World Data.

PURPOSE: Using real-world data (RWD)-based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS: We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration-approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record-derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS: We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION: In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.

Berberine Potentiates Insulin Secretion and Prevents ß-cell Dysfunction Through the miR-204/SIRT1 Signaling Pathway.

Pancreatic ß-cell dysfunction is a key link during the progression of type 2 diabetes (T2DM), and SIRT1 participates in the regulation of various physiological activities of islet ß-cells. However, as a key link in signal transduction, it is not clear how SIRT1 is regulated. By TargetScan prediction, we found that miR-204, which is enriched in islets, has highly complementary binding sites with SIRT1. Therefore, we speculate that miR-204 may be the upstream regulatory target of SIRT1 in islets and thus participate in the occurrence of ß-cell dysfunction. In this study, we explored the association between miR-204 and ß-cell dysfunction, the therapeutic effects of berberine (BBR) on ß-cell function and the possible mechanisms. We found that miR-204 increased and SIRT1 mRNA and protein levels decreased significantly in islets both in vivo and in vitro. MIN6 cells induced by palmitic acid exhibited increased apoptosis, and the accumulation of insulin and ATP in the supernatant decreased. Importantly, palmitic acid treatment combined with miR-204 silencing showed opposite changes. MiR-204 overexpression in MIN6 cells increased apoptosis and decreased insulin and ATP production and SIRT1 expression. SIRT1 overexpression reversed the damage to ß-cells caused by miR-204. The BBR treatment effectively improved insulin synthesis, reduced miR-204 levels, and increased SIRT1 expression in islet tissue in diabetic mice. Overexpression of miR-204 reversed the protective effect of BBR on apoptosis and insulin secretion in MIN6 cells. Our study identifies a novel correlation between miR-204 and ß-cell dysfunction in T2DM and shows that administration of BBR leads to remission of ß-cell dysfunction by regulating the miR-204/SIRT1 pathway.

When text simplification is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information?

OBJECTIVE: Dietary supplements are widely used. However, dietary supplements are not always safe. For example, an estimated 23 000 emergency room visits every year in the United States were attributed to adverse events related to dietary supplement use. With the rapid development of the Internet, consumers usually seek health information including dietary supplement information online. To help consumers access quality online dietary supplement information, we have identified trustworthy dietary supplement information sources and built an evidence-based knowledge base of dietary supplement information-the integrated DIetary Supplement Knowledge base (iDISK) that integrates and standardizes dietary supplement related information across these different sources. However, as information in iDISK was collected from scientific sources, the complex medical jargon is a barrier for consumers' comprehension. The objective of this study is to assess how different approaches to simplify and represent dietary supplement information from iDISK will affect lay consumers' comprehension. MATERIALS AND METHODS: Using a crowdsourcing platform, we recruited participants to read dietary supplement information in 4 different representations from iDISK: (1) original text, (2) syntactic and lexical text simplification (TS), (3) manual TS, and (4) a graph-based visualization. We then assessed how the different simplification and representation strategies affected consumers' comprehension of dietary supplement information in terms of accuracy and response time to a set of comprehension questions. RESULTS: With responses from 690 qualified participants, our experiments confirmed that the manual approach, as expected, had the best performance for both accuracy and response time to the comprehension questions, while the graph-based approach ranked the second outperforming other representations. In some cases, the graph-based representation outperformed the manual approach in terms of response time. CONCLUSIONS: A hybrid approach that combines text and graph-based representations might be needed to accommodate consumers' different information needs and information seeking behavior.