Informatics Platform Research Articles

Development, Implementation, And Evaluation of An Informatics Platform for Home Based Self- Management of Chronic Obstructive Pulmonary Disease: A Study Protocol

Background: The burden of chronic obstructive pulmonary disease to patients, their caretakers, and health system is gradually increasing and is associated with the morbidity, mortality, disability adjusted life years and higher costs. Self-management of COPD is a vital strategy for its management. This study aims to develop, implement, and evaluate an informatics platform for the home-based self-management of Chronic Obstructive Pulmonary Disease. Method: A systematic review will be conducted to synthesize and appraise the evidence on self-management informatics tool or platform for COPD. An evidence-based COPD self-management application will be developed and implemented to 35 COPD patients at identified hospitals of New Delhi using prospective non-randomized study. A mixed methods study will be conducted to evaluate the effectiveness of this informatics platform at selected hospitals of Delhi. Outcomes will be measured in terms of self-management for COPD related exacerbations, number of health care facility admissions for COPD and hospitalization days, adherence to the informatics platform, number of exacerbations requiring hospital visits, health related quality of life. Conclusion: The development and evaluation of home-based self-management of informatics platform for COPD would be an innovative strategy for the COPD patients in India.

Social Determinants of Health and Racial Disparities in Cardiac Events in Breast Cancer.

Racial disparities have been reported for breast cancer and cardiovascular disease (CVD) outcomes. The determinants of racial disparities in CVD outcomes are not yet fully understood. We aimed to examine the impact of individual and neighborhood-level social determinants of health (SDOH) on the racial disparities in major adverse cardiovascular events (MACE; consisting of heart failure, acute coronary syndrome, atrial fibrillation, and ischemic stroke) among female patients with breast cancer. This 10-year longitudinal retrospective study was based on a cancer informatics platform with electronic medical record supplementation. We included women aged ≥18 years diagnosed with breast cancer. SDOH were obtained from LexisNexis, and consisted of the domains of social and community context, neighborhood and built environment, education access and quality, and economic stability. Race-agnostic (overall data with race as a feature) and race-specific machine learning models were developed to account for and rank the SDOH impact in 2-year MACE. We included 4,309 patients (765 non-Hispanic Black [NHB]; 3,321 non-Hispanic white). In the race-agnostic model (C-index, 0.79; 95% CI, 0.78-0.80), the 5 most important adverse SDOH variables were neighborhood median household income (SHapley Additive exPlanations [SHAP] score [SS], 0.07), neighborhood crime index (SS = 0.06), number of transportation properties in the household (SS = 0.05), neighborhood burglary index (SS = 0.04), and neighborhood median home values (SS = 0.03). Race was not significantly associated with MACE when adverse SDOH were included as covariates (adjusted subdistribution hazard ratio, 1.22; 95% CI, 0.91-1.64). NHB patients were more likely to have unfavorable SDOH conditions for 8 of the 10 most important SDOH variables for the MACE prediction. Neighborhood and built environment variables are the most important SDOH predictors for 2-year MACE, and NHB patients were more likely to have unfavorable SDOH conditions. This finding reinforces that race is a social construct.

INTUITION: a data platform to integrate human epilepsy clinical care and support for discovery.

To make appropriate clinical decisions, clinicians consider many types of data from multiple sources to arrive at a diagnosis and plan. However, the current health systems have siloed data, making it challenging to develop information platforms that integrate this process into a single place for comprehensive clinical evaluation and research. INTUITION is a human brain integrative data system that facilitates multimodal data integration, unified storage, cohort selection, and analysis of multidisciplinary datasets. In this article, we describe the use of INTUITION to include electronic health records together with co-registered neuroimaging and EEG from patients who undergo invasive brain surgery for epilepsy. In addition to providing clinically useful visualizations and analytics to help guide surgical planning, INTUITION also links a bank of human brain epileptic tissues from specific brain locations to quantitative EEG, imaging, histology, and omics studies in a unique, completely integrated informatics platform. Having a clinically useful platform for integrating multimodal datasets can not only aid in clinical management decisions but also in creating a unique resource for research and discovery when linked to spatially mapped tissue samples.

Comprehensive Metabolite Profiling of Two Edible Garcinia Species Based on UPLC-ESI-QTOF-MSE Coupled with Bioactivity Assays.

In China, the endemic species Garcinia yunnanensis and native Garcinia xanthochymus are known as edible and medicinal plants. However, a systematic metabolomic and bioactivity evaluation of different plant parts from both species is lacking. In this study, comprehensive investigations of 11 plant parts of G. yunnanensis and 10 of G. xanthochymus employing UPLC-ESI-QTOF-MSE-based metabolomic analysis in conjunction with three bioactivity assays were undertaken. A customized chemotaxonomic-based in-house library containing 6456 compounds was constructed and coupled to the Progenesis QI informatic platform for metabolite annotations. From these two species, a total of 235 constituents were characterized using multiple criteria. Differences in metabolite profiles between the plant parts within each species were uncovered using multivariate analysis. Based on orthogonal partial least-squares discriminant analysis (OPLS-DA), 23 markers were identified as highly differential metabolites from G. xanthochymus and 20 from G. yunnanensis. Comparative assessment of the biological assays revealed the activity variations among different plant parts. The seeds of both species and G. yunnanensis latex exhibited excellent cytotoxic and antibacterial activities, while G. xanthochymus roots and G. yunnanensis arils showed strong anti-inflammatory effects. S-plot analysis identified 26 potential biomarkers for the observed activities, including the known cytotoxic agent cycloxanthochymol and the anti-inflammatory compound garcimultiflorone B, which likely explains some of the potent observed bioactivity.

The role of a molecular informatics platform to support next generation risk assessment

Chemoinformatics has been successfully employed in safety assessment through various regulatory programs for which information from databases, as well as predictive methodologies including computational methods, are accepted. One example is the European Union Cosmetics Products Regulations, for which Cosmetics Europe (CE) research activities in non-animal methods have been managed by the Long Range Science Strategy (LRSS) program. The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of in silico and in vitro data. To this end, ChemTunes•ToxGPS® has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes•ToxGPS® platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for Perilla frutescens through the workflows of threshold of toxicological concern (TTC), in silico predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced in silico tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the workgroup and international regulatory entities.

Abstract 4275: Averon: informatics platform to discover Actionable Vulnerabilities Enabled by Rewired Oncogenic Networks

Abstract Missense mutations can change protein structure, cellular localization, and functions. Such changes lead to the rewiring of protein-protein interaction (PPI) networks, activation of oncogenic pathways, and acquisition of cancer hallmarks. However, the translation of the landscape of oncogenic mutations into clinically actionable biological models for cancer target discovery remains a major challenge. We address this challenge by leveraging the power of computational systems biology supported by high-throughput screening technologies. While some mutations can disrupt the PPIs, others may induce new PPIs that are not natural for the wild-type counterparts. Recently, we have established a quantitative High Throughput differential Screening (qHT-dS) platform [1] to discover such mutant-enabled or neomorph PPIs (neoPPIs). The screening of more than 13,000 mutant interactions revealed a landscape of gain-of-interactions encompassing both oncogenic and tumor suppressor mutations. This emerging neoPPI landscape may offer new mutant-directed therapeutic approaches for precision medicine. However, to infer clinically actionable mechanistic insights into how neoPPIs promote tumorigenesis special computational tools are needed. To inform the neoPPI-based target discovery, we develop a set of innovative informatics tools for discovering Actionable Vulnerabilities Enabled by Rewired Oncogenic Networks (Averon). Implemented in a widely-used Jupyter Notebook format, the Averon streamlines the identification of the oncogenic programs and clinically significant genes that are regulated by neoPPIs in cancer patients. The Averon can recapitulate well-established connectivity between known mutant-dependent PPIs and specific oncogenic pathways and reveal new, previously unknown mechanisms of neoPPI-mediated oncogenic signaling. To inform new therapeutic strategies in neoPPI-dependent cancers, Averon connects neoPPI-regulated genes with available approved drugs and clinical compounds. Together, the Averon provides a powerful informatics environment to determine therapeutically actionable vulnerabilities created by mutant-regulated protein-protein interactions to inform new personalized therapeutic strategies in cancer. Acknowledgments: This work was supported in part by NCI’s Informatics Technology for Cancer Research (ITCR) Program (R21CA274620, A.A.I.), Winship Cancer Institute #IRG-17-181-06 from the American Cancer Society (A.A.I.). Cancer Target Discovery and Development (CTD2) Network (U01CA217875, H.F.), NCI Emory Lung Cancer SPORE (P50CA217691, H.F.), Career Enhancement Program (A.A.I., P50CA217691), Winship Cancer Institute (NIH 5P30CA138292).

Abstract 4617: A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform

Abstract Detecting and analyzing large numbers of proteins using whole-slide imaging is critical for a comprehensive picture of immune response to cancer. Many existing approaches for high-plex proteomics face issues around simplicity, speed, scalability, and big data analysis. Here, we present an integrated workflow from sample preparation through downstream analysis that addresses many key concerns around high plex proteomics. The CosMx Spatial Molecular Imager (SMI) and AtoMx Spatial Informatics Platform (SIP) comprise of a turnkey, end-to-end workflow that efficiently handles highly multiplex protein analysis at plex sizes exceeding 110 targets. We demonstrate an extension of our commercially available 64-plex human immuno-oncology panel to higher numbers of targets and show how the cloud computing-enabled AtoMx SIP allows flexible construction of analytic pipelines for cell typing and spatial analyses. The CosMx protein assay uses antibodies conjugated with oligonucleotides, which are detected using universal, multi-analyte CosMx readout reagents. The CosMx Human Immuno-oncology panel was optimized to comprehensively profile lymphoid and stromal lineages within the tumor microenvironment as well as markers of cancer signaling and progression. Each CosMx SMI antibody was validated on multi-organ FFPE tissue microarrays covering prevalent solid tumor types with matched controls, and 52 human FFPE cell lines, including overexpression lines for key targets such as GITR, CD278, PD-L1, and PD-1. CosMx SMI uses a deep learning algorithm to segment whole cells and a semi-supervised algorithm to classify cell types. The AtoMx SIP provides full analysis support, including a whole-slide image viewer, and methods for performing built-in or fully customizable analyses for cell typing, ligand-receptor analysis, neighborhood analysis and spatial differential expression. Within the cancer sample profiled, we performed in-depth single-cell proteomic profiling across different cell populations. We detected TLS, characterized TLS maturation, and identified immune interactions with the tumor microenvironment. The CosMx SMI assay profiled the composition and spatial organization of infiltrating immune cells within and around the tumor microenvironment. We found that markers of T cell activation and exhaustion varied across the tumor landscape. CosMx SMI is a high-plex spatial multi-omics platform that enables detection of more than 110 proteins at subcellular resolution in real-world FFPE tissues. The extensibility of the CosMx protein assay to large numbers of protein targets and our flexible, scalable bioinformatic platform provides a straightforward and robust solution for comprehensive immune phenotyping with full spatial context. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Tien Phan-Everson, Zachary Lewis, Giang Ong, Yan Liang, Emily Brown, Liuliu Pan, Aster Wardhani, Mithra Korukonda, Carl Brown, Dwayne Dunaway, Edward Zhao, Dan McGuire, Sangsoon Woo, Alyssa Rosenbloom, Brian Filanoski, Rhonda Meredith, Kan Chantranuvatana, Brian Birditt, Hye Son Yi, Erin Piazza, Jason Reeves, John Lyssand, Vik Devgan, Michael Rhodes, Gary Geiss, Joseph Beechem. A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4617.

Abstract 5323: Accelerating the drug discovery process with an automated high-throughput protein production and characterization platform for AI-driven antibody development of immunotherapy

Abstract The demonstrated effectiveness and vast potential of large molecule therapeutics have driven biopharmaceutical companies to change the ways by which they discover and develop novel biologic therapies. While the conventional antibody discovery process via animal immunization and in vivo hypermutation can generate sufficient hits for functional screening, this approach can only cover a limited subset of sequence diversity. In silico, artificial intelligence (AI) driven methods, by contrast, have the potential to cover 2-3 orders of magnitude more sequence diversity compared to conventional methods and more rapidly produce a drug candidate with a superior safety and efficacy profile. However, this approach requires wet lab validation of predicted sequences as well as targeted data generation to improve AI-predicted rankings. Traditional wet lab methods for protein generation and characterization are expensive, time consuming, and prone to human error. These limitations restrict the overall potential benefit of the AI-driven antibody discovery process. To accelerate this process, which consists of a complex series of cycles spanning different functional teams, a substantial increase in the throughput of the end-to-end production and characterization workflow is established in-house such that 104 antibodies with property data can be generated and analyzed within a short period of time. BioMap has implemented a highly integrated and automated protein expression, purification, and characterization platform linked with a unified informatics system to eliminate as much manual operation as possible. This high-throughput robotic platform interweaves multiple procedures including plasmid preparation, cell dispensing, mammalian transfection, protein purification, and characterization, and it is coupled to our in-house informatics system and database. Empowered by state-of-the-art liquid handling system and well-established experimental protocols, the integrated facility is capable of the delivery of 103 protein samples from plasmids in ten days within a single batch. Benefiting from our robust transient expression platform, the production scale ranges from 0.5 to 30 mL with an average antibody yield of 300 mg/L. The high-throughput workflow brings a challenge to track the provenance of each protein and manage data flow across different stations. To address this bottleneck, a well-defined informatics platform and database have been developed to provide sample registration, interface with lab instruments, experimental process tracking, and automated data recording and analysis. The implementation of this system provides a massive amount of high-quality data available for AI training and validation with a very short turnover time and enables AI-driven development of next-generation biologics. Citation Format: Zhehao Xiong, Wei Jiang, Lijun Xia, Jianhua Huang, Cheng-chi Chao. Accelerating the drug discovery process with an automated high-throughput protein production and characterization platform for AI-driven antibody development of immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5323.

Abstract 4224: University of Chicago Comprehensive Cancer Center catchment area informatics platform

Abstract Every cancer center has a surrounding geolocation known as a catchment area (CA). Data about this CA can help cancer centers better understand the population and type of patients the center is targeting. In addition, collecting, analyzing, and visualizing this data can help the cancer center better understand population needs and gain more insights to provide predictive care instead of reactive care. However, the multidimensionality and complexity of the CA data requires the build of a specialized platform that can handle this heterogenous data. Motivated by this need and to answer specific questions about the CA, the UCCCC developed a new informatics platform that provides access to analytical tools, data handling, and an exchange environment. The goal of this platform is also to support a collaborative community to study CA dynamics related to health disparity and cancer. Here we present a new Geoinformatics platform that offers capabilities for using and modifying spatial data. It facilitates interrogating, modifying, executing, and mapping data. Data includes imagery, features, and base maps linked to spreadsheets and tables. The data is kept in the RDBMS (Relational Database Management System). Other query software tools are needed to deal with the database system including a server to launch the GIS application with specific RAM, GPU, and good SSD to back up the database. The database could be saved on google firebase to handle real-time queries. Thus, the maps can be easily shared and embedded in apps and accessible by virtually everyone, everywhere. The datasets used were downloaded from conventional websites (listed in the appendix) and internal datasets. The goal is to answer basic questions including the count of patients in that area, socioeconomic status, distribution in terms of demography (race, age, gender), physical environment, and other factors. Furthermore, it will be able to find correlations across many variables given the response variable, such as type of disease (lung, ovarian, breast cancer). Solving Critical Problems in Cancer Control using Spatial Science as well as being able to select a geographic location/region/area and plot the data across different dimensions, possibly on a map or another type of view. Geographic information needs a way to integrate data from several sources with varying degrees of accuracy. The platform provides real-time changes in the CA, so it needs to be updated frequently to stay current. The tool will connect the dataset to maps, integrating location data (where things are) with all kinds of descriptive information (how things are). Thus, a unique method of retrieval and manipulation is required for a significant portion of the data stored in GIS for practical usage. Citation Format: Ahmed Fadiel Metwaly, Adam Thomas Koster, Mohammad Abbasi, Adekunle Odunsi. University of Chicago Comprehensive Cancer Center catchment area informatics platform. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4224.

39.2: Invited Paper: First, Faster, Further: Competitive Advantage with Next‐Generation Materials Development

We have entered a paradigm-changing era in the way chemists innovate. Many fields, such as automotive engineering and particle physics, rely today on accurate simulation before experimentation. In recent years, chemistry has entered a new phase of chemical solution design powered by a rich set of physics-based and augmented intelligence capabilities. This talk will present select case studies illustrating some of our latest physics-based simulation technology for developing and optimizing OLED materials. We will also introduce an enterprise informatics platform (LiveDesignTM) focused on chemical discovery, enabling multidisciplinary teams to amplify their development cycle with collaboration on a global scale.

Informatics approaches to improve the quality of dental care.

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.

Designing, Development, and Evaluation of an Informatics Platform for Enhancing Treatment Adherence in Latent Tuberculosis Infection Patients: A Study Protocol

Introduction: Digital health interventions are gradually being incorporated into the management of tuberculosis to ensure treatment adherence, but only a small number of trials focusing on latent tuberculosis infection (LTBI) care have tested and evaluated them. It is anticipated that 170 million persons with LTBI may eventually develop active TB; thus, treatment of LTBI patients is an important aspect, along with ensuring treatment adherence. Digital platforms can be beneficial to ensure treatment adherence in LTBI patients, as various studies have shown the positive impact of digital interventions in improving patients’ treatment adherence and treatment outcome. This study aims to explore the various available digital interventions worldwide for treatment adherence in LTBI patients and develop an informatics platform for enhancing treatment adherence in LTBI patients. Methods: This will be a quasi-experimental study divided into three phases. In the first phase, a scoping review method will be used to conduct a systematic literature review using the PRISMA tool to report on various digital interventions focused on treatment adherence in LTBI patients. In the second phase, a text message-based digital platform will be developed, and in the third phase of the study, an evaluation of the digital platform will be done using qualitative and quantitative questionnaires. The study will be conducted using a mixed-methods approach between January 2023 and December 2023. The sample size will be 162 participants, of whom 81 will be assigned to an intervention group and 81 will receive the usual care from the respective chest clinic as a control group. Results: A descriptive analysis of demographic variables and other variables will be done. Continuous variables will be described as mean ± standard deviation (M ± SD), medians (inter-quartile ranges) (M (IQR)), and medians (5th percentile to 95th percentile) (P5-P95). A two-sample independent T-test, the chi-square test, and the Mann-Whitney test will be used for comparisons between groups. Treatment success between control and intervention will be compared through a chi-square test. Conclusions: The key finding of the study will be an understanding of the efficiency of digital platforms for improving treatment adherence in latent TB patients in India.

The Design, Development, and Implementation of a Web-Enabled Informatics Platform to Enhance the Well-being of Individuals Aged 18-24 Years: Protocol for an Experimental Study.

Well-being is multidimensional, complex, and dynamic in nature. It is an amalgam of physical and mental health, essential for disease prevention and the promotion of a healthy life. This study aims to explore the features that impact the well-being of individuals between 18 and 24 years of age in an Indian setting. It further aims to design, develop, and evaluate the usefulness and effectiveness of a web-based informatics platform or stand-alone intervention to enhance the well-being of individuals aged 18-24 years in an Indian setting. This study follows a mixed method approach to identify factors influencing the well-being of individuals in the age group of 18-24 years in an Indian setting. The college-going students in this age group from the states of Uttarakhand (urban settings of Dehradun) and Uttar Pradesh (urban settings of Meerut) will be enrolled. They will be randomly allocated to the control and intervention groups. The participants in the intervention group will have access to the web-based well-being platform. This study will examine the factors that influence the well-being of individuals aged 18-24 years. It will also facilitate the design and development of the web-based platform or stand-alone intervention, which will enhance the well-being of individuals in the age group of 18-24 years in an Indian setting. Furthermore, the results of this study will help generate a well-being index for individuals to plan tailored interventions. The 60 in-depth interviews have been conducted as of September 30, 2022. The study will help understand the factors that influence the well-being of individuals. The findings of this study will help in the design and development of the web-based platform or stand-alone intervention to enhance the well-being of individuals in the age group of 18-24 years in an Indian setting. PRR1-10.2196/38632.

Adherence to psychiatric medications and diagnosis

Introduction Patients with mental disorders frequently become non-adherent during their long term prescribed treatment. This situation frequently triggers clinical worsening and hospital admission. Therefore, non-adherence may result in poorer long term clinical outcomes and has economic implications for health-care providers (Carlos De las Cuevas et al. Neuropsychopharmacol Hung 2021; 23(4):347-362). Objectives - To describe the adherence to oral and long acting injectable treatment in the sample of patients that were admitted to the short stay hospital unit during the period of study. - To describe the adherence to treatment amongst psychiatric diagnosis in the sample of study. Methods It was a retrospective observational study with a duration of three months. Data was collected from all patients admitted to the short stay hospital unit during the period of study and there were no specific exclusion criteria. Descriptive statistics were performed. To assess the adherence to pharmachological treatment the patient report, the family report and the pharmacy dispensation according to the existent informatic prescription platform was considered. Regarding the long acting injectable treatment the formulary of administration in the clinical history was checked. Results During the period of study 172 patients were admitted to the short stay hospital unit. Of those, 146 patients had a previous pharmacologic prescription. Data of treatment was not possible to obtain in 7 patients. In the sample of study, 83.5% were on oral and 16.5% on long acting injectable treatment. The general adherence to treatment in the sample was 61.87%. In the oral treatment group the adherence was 58.4% and in the long acting injectable treatment group was 65.2%. Amongst the different psychiatric diagnoses the outcomes of adherence to treatment were: 60.4% in schizophrenia and related psychosis, 62.5% in bipolar disorder, 78.6% in depression, 58.3% in personality disorders and 62% in addictive disorders. Conclusions In our descriptive study adherence to treatment was higher in the long acting injectable treatment group, agreeing with the existent scientific literature. The results of adherence for schizophrenia and bipolar disorder are similar to the ones found in scientific literature but differ from the ones for depression, being higher in our sample (Judit Lazary et al. Neuropsychopharmacol Hung 2021;23(4): 347-362). Moreover, in scientific literature it is found a similar prevalence of adherence across diagnosis (for schizophrenia, bipolar disorder and depression) whereas in our sample patients with depression showed a different and higher adherence to treatment (Judit Lazary et al. Neuropsychopharmacol Hung 2021;23(4): 347-362). In our sample, patients with personality disorders had the lowest adherence to treatment. Disclosure of Interest None Declared

Methods to Enhance Causal Inference for Assessing Impact of Clinical Informatics Platform Implementation.

Hospitals are increasingly likely to implement clinical informatics tools to improve quality of care, necessitating rigorous approaches to evaluate effectiveness. We leveraged a multi-institutional data repository and applied causal inference methods to assess implementation of a commercial data visualization software in our pediatric cardiac intensive care unit. Natural experiment in the University of Michigan (UM) Cardiac Intensive Care Unit pre and postimplementation of data visualization software analyzed within the Pediatric Cardiac Critical Care Consortium clinical registry; we identified N=21 control hospitals that contributed contemporaneous registry data during the study period. We used the platform during multiple daily rounds to visualize clinical data trends. We evaluated outcomes-case-mix adjusted postoperative mortality, cardiac arrest and unplanned readmission rates, and postoperative length of stay-most likely impacted by this change. There were no quality improvement initiatives focused specifically on these outcomes nor any organizational changes at UM in either era. We performed a difference-in-differences analysis to compare changes in UM outcomes to those at control hospitals across the pre versus postimplementation eras. We compared 1436 pre versus 779 postimplementation admissions at UM to 19 854 (pre) versus 14 160 (post) at controls. Admission characteristics were similar between eras. Postimplementation at UM we observed relative reductions in cardiac arrests among medical admissions, unplanned readmissions, and postoperative length of stay by -14%, -41%, and -18%, respectively. The difference-in-differences estimate for each outcome was statistically significant (P<0.05), suggesting the difference in outcomes at UM pre versus postimplementation is statistically significantly different from control hospitals during the same time. Clinical registries provide opportunities to thoroughly evaluate implementation of new informatics tools at single institutions. Borrowing strength from multi-institutional data and drawing ideas from causal inference, our analysis solidified greater belief in the effectiveness of this software across our institution.

Третья платформа информации и Big Data

Despite the fact that the introduction of Big Data in many areas of life is an inevitable reality of the near future, there are difficulties with the classification of information, the allocation of knowledge, storage, security and scalability issues. The paper describes the concept of the third platform of informatization and Big Data, which play an important role in this process, is described. The problems associated with the need for efficient processing and analysis of Big Data are posed. The spheres of life in which new technologies are being actively introduced and developed are shown and considered. The purpose of the study is to discuss the trends in the implementation of Big Data within the framework of the third informatization platform. The problems of Big Data and some ways to solve them are investigated. The interrelations and patterns in the field of Big Data and their impact on the development of information technologies are considered. Research methodology: analytical research methods based on the analysis of data and concepts of the third informatization platform are used. The results of the study: a deep analysis was carried out for full immersion in the issue of the third platform of informatization and the concept of Big Data. The advantages of introducing technology components into the spheres of life are shown, as well as the problems of Big Data and some ways of their solution are studied. The main theoretical results of the study are presented, confirming the interrelationships and patterns between the elements of developing information technologies. The practical significance of the third informatization platform and its components is discussed. Recommendations related to the application of these technologies in real conditions are proposed, as well as their mutual influence of technologies to achieve more effective results.

Research on the Development Path of Sports Informatization Based on Big Data and Cloud Computing

Big data and cloud computing have improved information technology applications in the physical education industry. After learning big data and cloud computing, this paper discusses the status of China's physical education industry. We conclude the important role of big data and cloud computing in the construction of physical education informatization based on China's national conditions and current economic development level. In addition, this paper proposes the construction method and implementation plan of physical education informatization platform based on big data and cloud computing technology to give some reference opinions to other research scholars.

Design and QoS Optimization Model of Party Building Cloud Platform Based on FoRTAN

Using FoumNPowERsTAⅡoNv4. o to develop a simulation program is the core focus of this paper. It has a good wlND0ws interface, which can intuitively and conveniently carry out a man-to-machine dialogue, which is convenient for simulation racing. Its application example shows that the program has universality and practicability. The informatization work of party building in colleges and universities can be carried out according to different situations: confidential work can be combined with the campus informatization platform; collaborative work between units can introduce third-party questionnaires and cloud collaboration documents to connect the data communication network with information. Intranet integration and unified deployment. With the growth of communication data volume, higher requirements are put forward for the quality of service of converged network QoS and the resource consumption of base stations and users.

Metabolic profiling on the analysis of different parts of Schisandra chinensis based on UPLC-QTOF-MS with comparative bioactivity assays.

The Schisandra chinensis is an important edible plant, and previous phytochemical research focused on the S. chinensis fruit (SF) due to its long history as traditional Chinese medicine. Schisandra chinensis fruit was used as an astringent tonic to astringe the lungs and the kidneys, replenish energy, promote the production of body fluids, tonify the kidney, and induce sedation. The components of S. chinensis, such as its stems (SS), leaves (SL), and roots (SR), have drawn little attention regarding their metabolites and bioactivities. In this study, a strategy of combining a chemical database with the Progenesis QI informatics platform was applied to characterize the metabolites. A total of 332 compounds were tentatively identified, including lignans, triterpenoids, flavonoids, tannins, and other compound classes. Heatmap and principal component analysis (PCA) showed remarkable differences in different parts of the plants. By multiple orthogonal partial least-squares discriminant analyses (OPLS-DA), 76 compounds were identified as potential marker compounds that differentiate these different plant parts. Based on the variable influence on the projection score from OPLS-DA, the active substances including gomisin D, schisandrol B, schisantherin C, kadsuranin, and kadlongilactone F supported the fact that the biological activity of the roots was higher than that of the fruit. These substances can be used as marker compounds in the plant roots, which likely contribute to their antioxidant and anti-inflammatory activities. The plant roots could be a new medicinal source that exhibits better activity than that of traditional medicinal parts, which makes them worth exploring.

Social Service Workers’ Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform

To gain insight into current use of social-media platforms in human services delivery, we systematically surveyed 172 social-service workers from six agencies in a Midwest US city to gather data about social-media usage among social-service providers, potential challenges and benefits of using social media, and whether a social-media-based informatics platform could be valuable. Quantitative analyses showed that approximately half of participants have used social media to collect client-related information; nearly one-quarter indicated “often” or “nearly daily” use. Adjusting for the effects of worker characteristics, social-media use was associated with the type of agency involved and with increased tenure in social services. Adjusted results also showed that participants’ comfort with using the potential application was greater in those agencies substantially involved with investigative/legal work. However, trust in the information collected by the potential application was a stronger, independent predictor of comfort using the tool. Qualitative analyses identified numerous challenges and ethical concerns, and positive and negative aspects of a social-media-based informatics platform. If the platform is to be created, work must be done carefully, fully considering ethical issues rightly raised by social service workers, existing agency policies, and professional standards. Future research should investigate ways to negotiate these complex challenges.