Plant dehydroascorbate reductase moonlights as membrane integrated ion channel.

Plant dehydroascorbate reductases (DHARs) are only known as soluble antioxidant enzymes of the ascorbate-glutathione pathway. They recycle ascorbate from dehydroascorbate, thereby protecting plants from oxidative stress and the resulting cellular damage. DHARs share structural GST fold with human chloride intracellular channels (HsCLICs) which are dimorphic proteins that exists in soluble enzymatic and membrane integrated ion channel forms. While the soluble form of DHAR has been extensively studied, the existence of a membrane integrated form remains unknown. We demonstrate for the first time using biochemistry, immunofluorescence confocal microscopy, and bilayer electrophysiology that Pennisetum glaucum DHAR (PgDHAR) is dimorphic and is localized to the plant plasma membrane. In addition, membrane translocation increases under induced oxidative stress. Similarly, HsCLIC1 translocates more into peripheral blood mononuclear cells (PBMCs) plasma membrane under induced oxidative stress conditions. Moreover, purified soluble PgDHAR spontaneously inserts and conducts ions in reconstituted lipid bilayers, and the addition of detergent facilitates insertion. In addition to the well-known soluble enzymatic form, our data provides conclusive evidence that plant DHAR also exists in a novel membrane-integrated form. Thus, the structure of DHAR ion channel form will help gain deeper insights into its function across various life forms.

A unified graph model based on molecular data binning for disease subtyping.

Molecular disease subtype discovery from omics data is an important research problem in precision medicine. The biggest challenges are the skewed distribution and data variability in the measurements of omics data. These challenges complicate the efficient identification of molecular disease subtypes defined by clinical differences, such as survival. Existing approaches adopt kernels to construct patient similarity graphs from each view through pairwise matching. However, the distance functions used in kernels are unable to utilize the potentially critical information of extreme values and data variability which leads to the lack of robustness. In this paper, a novel robust distance metric (ROMDEX) is proposed to construct similarity graphs for molecular disease subtypes from omics data, which is able to address the data variability and extreme values challenges. The proposed approach is validated on multiple TCGA cancer datasets, and the results are compared with multiple baseline disease subtyping methods. The evaluation of results is based on Kaplan-Meier survival time analysis, which is validated using statistical tests e.g, Cox-proportional hazard (Cox p-value). We reject the null hypothesis that the cohorts have the same hazard, for the P-values less than 0.05. The proposed approach achieved best P-values of 0.00181, 0.00171, and 0.00758 for Gene Expression, DNA Methylation, and MicroRNA data respectively, which shows significant difference in survival between the cohorts. In the results, the proposed approach outperformed the existing state-of-the-art (MRGC, PINS, SNF, Consensus Clustering and Icluster+) disease subtyping approaches on various individual disease views of multiple TCGA datasets.

The risk stratification of coronary vascular disease as linked to homocysteine, its modulating genes, genetic polymorphisms, conventional predictors, and with antihypertensive medicaments.

Cardiovascular disease (CVD) have multifactorial nature, and owing to their disparate etiological roots, it is difficult to ascertain exact determinants of CVD. In the current study, primary objective was to determine association of single nucleotide polymorphisms (SNP) in folate pathway genes, homocysteine, antihypertensive medication, and of known risk factors in relation to CVD outcomes. The participants numbered 477 (controls, n = 201, ischemic heart disease patients, n = 95, and myocardial infarction cases, n = 181, respectively). SNPs that were queried for homocysteine pathway genes included, "methylene tetrahydrofolate reductase (MTHFR)" gene SNPs rs1801133 and rs1801131, "methyltransferase (MTR)" SNP rs1805087, "paraoxonase 1 (PON1)" SNP rs662, and angiotensin-converting enzyme (ACE) gene polymorphisms rs4646994. Medication data were collected through questionnaire, and serum-based parameters were analyzed through commercial kits. The analysis of variance and multiple comparison scrutiny revealed that age, gender, family history, cholesterol, creatinine, triglyceride, high density lipoproteins (HDL), homocysteine, beta-blocker, ACE inhibitors, MTHFR and PON1 SNPs related to coronary artery disease (CAD). On regression, rs662 SNPs and C-reactive protein had nonsignificant odds ratio, whereas age, gender, creatinine, and HDL were nonsignificant. Family history, cholesterol, homocysteine, beta blocker, and ACE inhibitors, homocysteine, rs1801133 and rs1801131 SNP maintained significance/significant odds for CAD. The current study indicates an intricate relationship between genetic variants, traditional factors, and drug usage in etiogenesis of arterial disease. Differences in SNPs, their modulated effects in consensus with medicinal usage may be related to ailment outcomes affecting coronary vasculature.

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation.

The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user's perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

Smart Extraction and Analysis System for Clinical Research.

BACKGROUND: With the increasing use of electronic health records (EHRs), there is a growing need to expand the utilization of EHR data to support clinical research. The key challenge in achieving this goal is the unavailability of smart systems and methods to overcome the issue of data preparation, structuring, and sharing for smooth clinical research. MATERIALS AND METHODS: We developed a robust analysis system called the smart extraction and analysis system (SEAS) that consists of two subsystems: (1) the information extraction system (IES), for extracting information from clinical documents, and (2) the survival analysis system (SAS), for a descriptive and predictive analysis to compile the survival statistics and predict the future chance of survivability. The IES subsystem is based on a novel permutation-based pattern recognition method that extracts information from unstructured clinical documents. Similarly, the SAS subsystem is based on a classification and regression tree (CART)-based prediction model for survival analysis. RESULTS: SEAS is evaluated and validated on a real-world case study of head and neck cancer. The overall information extraction accuracy of the system for semistructured text is recorded at 99%, while that for unstructured text is 97%. Furthermore, the automated, unstructured information extraction has reduced the average time spent on manual data entry by 75%, without compromising the accuracy of the system. Moreover, around 88% of patients are found in a terminal or dead state for the highest clinical stage of disease (level IV). Similarly, there is an ∼36% probability of a patient being alive if at least one of the lifestyle risk factors was positive. CONCLUSION: We presented our work on the development of SEAS to replace costly and time-consuming manual methods with smart automatic extraction of information and survival prediction methods. SEAS has reduced the time and energy of human resources spent unnecessarily on manual tasks.

The Mining Minds digital health and wellness framework.

BACKGROUND: The provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people's conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems. METHODS: This work presents Mining Minds, a novel framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized support. Mining Minds embraces some of the most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, as well as modern concepts and methods, such as context-awareness, knowledge bases or analytics, to holistically and continuously investigate on people's lifestyles and provide a variety of smart coaching and support services. RESULTS: This paper comprehensively describes the efficient and rational combination and interoperation of these technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support. Moreover, this work presents a realization of the key architectural components of Mining Minds, as well as various exemplary user applications and expert tools to illustrate some of the potential services supported by the proposed framework. CONCLUSIONS: Mining Minds constitutes an innovative holistic means to inspect human behavior and provide personalized health and wellness support. The principles behind this framework uncover new research ideas and may serve as a reference for similar initiatives.

Towards A Self Adaptive System for Social Wellness.

Advancements in science and technology have highlighted the importance of robust healthcare services, lifestyle services and personalized recommendations. For this purpose patient daily life activity recognition, profile information, and patient personal experience are required. In this research work we focus on the improvement in general health and life status of the elderly through the use of an innovative services to align dietary intake with daily life and health activity information. Dynamic provisioning of personalized healthcare and life-care services are based on the patient daily life activities recognized using smart phone. To achieve this, an ontology-based approach is proposed, where all the daily life activities and patient profile information are modeled in ontology. Then the semantic context is exploited with an inference mechanism that enables fine-grained situation analysis for personalized service recommendations. A generic system architecture is proposed that facilitates context information storage and exchange, profile information, and the newly recognized activities. The system exploits the patient's situation using semantic inference and provides recommendations for appropriate nutrition and activity related services. The proposed system is extensively evaluated for the claims and for its dynamic nature. The experimental results are very encouraging and have shown better accuracy than the existing system. The proposed system has also performed better in terms of the system support for a dynamic knowledge-base and the personalized recommendations.

Ontology-Based High-Level Context Inference for Human Behavior Identification.

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users.

On Curating Multimodal Sensory Data for Health and Wellness Platforms.

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user's lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user's sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user's lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval.

Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.

Recommendations service for chronic disease patient in multimodel sensors home environment.

With advanced technologies in hand, there exist potential applications and services built around monitoring activities of daily living (ADL) of elderly people at nursing homes. Most of the elderly people in these facilities are suffering from different chronic diseases such as dementia. Existing technologies are mainly focusing on non-medication interventions and monitoring of ADL for addressing loss of autonomy or well-being. Monitoring and managing ADL related to cognitive behaviors for non-medication intervention are very effective in improving dementia patients' conditions. However, cognitive functions of patients can be improved if appropriate recommendations of medications are delivered at a particular time. Previously we developed the Secured Wireless Sensor Network Integrated Cloud Computing for Ubiquitous-Life Care (SC(3)). SC(3) services were limited to monitoring ADL of elderly people with Alzheimer's disease and providing non-medication recommendations to the patient. In this article, we propose a system called the Smart Clinical Decision Support System (CDSS) as an integral part of the SC(3) platform. Using the Smart CDSS, patients are provided with access to medication recommendations of expert physicians. Physicians are provided with an interface to create clinical knowledge for medication recommendations and to observe the patient's condition. The clinical knowledge created by physicians as the knowledge base of the Smart CDSS produces recommendations to the caregiver for medications based on each patient's symptoms.

An adaptive semantic based mediation system for data interoperability among Health Information Systems.

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

Development of Magnesium Aluminate (MgAl2O4) Nanoparticles for refractory crucible application.

Ceramics are the oxides of metals and nonmetals with excellent compressive strength. Ceramics usually exhibit inert behavior at high temperatures. Magnesium aluminate (MgAl2O4), a member of the ceramic family, possesses a high working temperature up to 2000°C, low thermal conductivity, high strength even at elevated temperatures, and good corrosion resistance. Moreover, Magnesium Aluminate Nanoparticles (MANPs) can be used in the making of refractory crucible applications. This study focuses on the thermal behavior of Magnesium Aluminate Nanoparticles (MANPs) and their application in the making of refractory crucibles. The molten salt method is used to obtain MANPs. The presence of MANPs is seen by XRD peaks ranging from 66° to 67°. The determination of the smallest crystallite size of the sample is achieved by utilizing the Scherrer formula and is found to be 15.3 nm. The SEM micrographs provided further information, indicating an average particle size of 91.2 nm. At 600°C, DSC curves show that only 0.05 W/g heat flows into the material, and the TGA curve shows only 3% weight loss, which is prominent for thermal insulation applications. To investigate the thermal properties, crucibles of pure MANPs and the different compositions of MANPs and pure alumina are prepared. During the sintering, cracks appear on the crucible of pure magnesium aluminate. To explore the reason for crack development, tablets of MgAl2O4 are made and sintered at 1150°C. Ceramography shows the crack-free surfaces of all the tablets. Results confirm the thermal stability of MANPs at high temperatures and their suitability for melting crucible applications.

Endotracheal tube cuff position in relationship to the walls of the trachea: A retrospective computed tomography-based analysis.

Background: The use of cuffed endotracheal tubes (ETTs) has become the standard of care in pediatric practice. The rationale for the use of a cuffed ETT is to minimize pressure around the cricoid while providing an effective airway seal. However, safe care requires that the cuff lie distal to the cricoid ring following endotracheal intubation. The current study demonstrates the capability of computed tomography (CT) imaging in identifying the position of the cuff of the ETT in intubated patients. Methods: The study included patients ranging in age from 1 month to 10 years who underwent neck and chest CT imaging that required general anesthesia and endotracheal intubation. The location of the ETT and of the cuff within the airway was determined from axial CT images at three levels (proximal, middle, and distal). Anatomical orientations were tabulated, and percent chances of each orientation were determined for the ETT and the cuff. Results: The study cohort included 42 patients ranging in age from 1 to 114 months. An ETT with a polyvinylchloride cuff was used in 24 patients, and an ETT with a polyurethane cuff was used in 18 patients. The ETT was located near the posterior wall of the trachea in approximately 24-38% of patients, being most likely to be centrally located at the proximal end and at its mid-portion. The middle part of the cuff was most likely to be positioned in the mid-portion of the trachea but tended to skew anteriorly at both the proximal and distal ends. Conclusion: This is the first study using CT imaging to identify the uniformity of cuff inflation within the trachea in children. With commonly used cuffed ETTs, cuff inflation and the final position of ETT cuff within the tracheal lumen were not uniform. Future investigations are needed to determine the reasons for this asymmetry and its clinical implications.

Personalized-detailed clinical model for data interoperability among clinical standards.

OBJECTIVE: Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalized-detailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems. MATERIALS AND METHODS: We consider electronic health record (EHR) standards, openEHR, and HL7 CDA instances transformation using P-DCM. P-DCM concepts associated with openEHR and HL7 CDA help in transformation of instances among these standards. We investigated two datasets: (1) data of 100 diabetic patients, including 50 each of type 1 and type 2, from a local hospital in Korea and (2) data of a single Alzheimer's disease patient. P-DCMs were created for both scenarios, which provided the basis for deriving instances for HL7 CDA and openEHR standards. RESULTS: For proof of concept, we present case studies of encounter information for type 2 diabetes mellitus patients and monitoring of daily routine activities of an Alzheimer's disease patient. These reflect P-DCM-based customized mappings generation with openEHR and HL7 CDA standards. Customized mappings are generated based on the relationship of P-DCM concepts with CDA and openEHR concepts. CONCLUSIONS: The objective of this work is to achieve semantic data interoperability among heterogeneous standards. This would lead to effective utilization of resources and allow timely information exchange among healthcare systems.

AtSWEET11 and AtSWEET12 transporters function in tandem to modulate sugar flux in plants.

The sugar will eventually be exported transporter (SWEET) members in Arabidopsis, AtSWEET11 and AtSWEET12 are the important sucrose efflux transporters that act synergistically to perform distinct physiological roles. These two transporters are involved in apoplasmic phloem loading, seed filling, and sugar level alteration at the site of pathogen infection. Here, we performed the structural analysis of the sucrose binding pocket of AtSWEET11 and AtSWEET12 using molecular docking followed by rigorous molecular dynamics (MD) simulations. We observed that the sucrose molecule binds inside the central cavity and in the middle of the transmembrane (TM) region of AtSWEET11 and AtSWEET12, that allows the alternate access to the sucrose molecule from either side of the membrane during transport. Both AtSWEET11 and AtSWEET12, shares the similar amino acid residues that interact with sucrose molecule. Further, to achieve more insights on the role of these two transporters in other plant species, we did the phylogenetic and the in-silico analyses of AtSWEET11 and AtSWEET12 orthologs from 39 economically important plants. We reported the extensive information on the gene structure, protein domain and cis-acting regulatory elements of AtSWEET11 and AtSWEET12 orthologs from different plants. The cis-elements analysis indicates the involvement of AtSWEET11 and AtSWEET12 orthologs in plant development and also during abiotic and biotic stresses. Both in silico and in planta expression analysis indicated AtSWEET11 and AtSWEET12 are well-expressed in the Arabidopsis leaf tissues. However, the orthologs of AtSWEET11 and AtSWEET12 showed the differential expression pattern with high or no transcript expression in the leaf tissues of different plants. Overall, these results offer the new insights into the functions and regulation of AtSWEET11 and AtSWEET12 orthologs from different plant species. This might be helpful in conducting the future studies to understand the role of these two crucial transporters in Arabidopsis and other crop plants.

Zinc2+ ion inhibits SARS-CoV-2 main protease and viral replication in vitro.

Zinc deficiency is linked to poor prognosis in COVID-19 patients while clinical trials with zinc demonstrate better clinical outcomes. The molecular targets and mechanistic details of the anti-coronaviral activity of zinc remain obscure. We show that zinc not only inhibits the SARS-CoV-2 main protease (Mpro) with nanomolar affinity, but also viral replication. We present the first crystal structure of the Mpro-Zn2+ complex at 1.9 Å and provide the structural basis of viral replication inhibition. We show that Zn2+ coordinates with the catalytic dyad at the enzyme active site along with two previously unknown water molecules in a tetrahedral geometry to form a stable inhibited Mpro-Zn2+ complex. Further, the natural ionophore quercetin increases the anti-viral potency of Zn2+. As the catalytic dyad is highly conserved across SARS-CoV, MERS-CoV and all variants of SARS-CoV-2, Zn2+ mediated inhibition of Mpro may have wider implications.

Retrospective analysis of anti-inflammatory therapies during the first wave of COVID-19 at a community hospital.

BACKGROUND: Our understanding of the severe acute respiratory syndrome coronavirus 2 has evolved since the first reported cases in December 2019, and a greater emphasis has been placed on the hyper-inflammatory response in severely ill patients. The purpose of this study was to determine risk factors for mortality and the impact of anti-inflammatory therapies on survival. AIM: To determine the impact of various therapies on outcomes in severe coronavirus disease 2019 patients with a focus on anti-inflammatory and immune-modulating agents. METHODS: A retrospective analysis was conducted on 261 patients admitted or transferred to the intensive care unit in two community hospitals between March 12, 2020 and June 17, 2020. Totally 167 patients received glucocorticoid (GC) therapy. Seventy-three patients received GC alone, 94 received GC and tocilizumab, 28 received tocilizumab monotherapy, and 66 received no anti-inflammatory therapy. RESULTS: Patient survival was associated with GC use, either alone or with tocilizumab, and decreased vasopressor requirements. Delayed administration of GC was found to decrease the survival benefit of GC therapy. No difference in survival was found with varying anticoagulant doses, convalescent plasma, tocilizumab monotherapy; prone ventilation, hydroxychloroquine, azithromycin, or intravenous ascorbic acid use. CONCLUSION: This analysis demonstrated the survival benefit associated with anti-inflammatory therapy of GC, with or without tocilizumab, with the combination providing the most benefit. More studies are needed to assess the optimal timing of anti-inflammatory therapy initiation.

Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method.

BACKGROUND AND OBJECTIVE: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. METHODS: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification. RESULTS: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. CONCLUSION: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.