Hemolysis by Saponin Is Accelerated at Hypertonic Conditions

Saponins are a large group of organic amphiphilic substances (surfactants) mainly extracted from herbs with biological activity, considered as one of the main ingredients in numerous remedies used in traditional medicine since ancient times. Anti-inflammatory, antifungal, antibacterial, antiviral, antiparasitic, antitumor, antioxidant and many other properties have been confirmed for some. There is increasing interest in the elucidation of the mechanisms behind the effects of saponins on different cell types at the molecular level. In this regard, erythrocytes are a very welcome model, having very simple structures with no organelles. They react to changing external conditions and substances by changing shape or volume, with damage to their membrane ultimately leading to hemolysis. Hemolysis can be followed spectrophotometrically and provides valuable information about the type and extent of membrane damage. We investigated hemolysis of erythrocytes induced by various saponin concentrations in hypotonic, isotonic and hypertonic media using measurements of real time and end-point hemolysis. The osmotic pressure was adjusted by different concentrations of NaCl, manitol or a NaCl/manitol mixture. Unexpectedly, at a fixed saponin concentration, hemolysis was accelerated at hypertonic conditions, but was much faster in NaCl compared to mannitol solutions at the same osmotic pressure. These findings confirm the colloid-osmotic mechanism behind saponin hemolysis with pore formation with increasing size in the membrane.

OP0111 RHEUMATOID ARTHRITIS SEROLOGIC PHENOTYPE AT DIAGNOSIS AND SUBSEQUENT RISK FOR PNEUMONIA IDENTIFIED USING MACHINE LEARNING APPROACHES

Background:

Patients with rheumatoid arthritis (RA) are at increased risk of serious infections, with considerable excess morbidity and mortality after pneumonia. RA-related autoantibodies such as anti-cyclic citrullinated peptide (CCP) and rheumatoid factor (RF) may be generated at inflamed pulmonary mucosa prior to clinical RA onset. Therefore, patients with seropositive RA may be at increased risk for pneumonia after RA diagnosis due to subclinical pulmonary injury.

Objectives:

We investigated whether seropositive RA was associated with increased pneumonia risk compared to seronegative RA.

Methods:

We performed a retrospective cohort study among RA patients seen at a health care system in Boston, MA. RA patients were identified using a previously validated electronic health record (EHR) algorithm incorporating billing codes, natural language processing (NLP) of notes, medications, and laboratory results at 97% specificity1. We constructed an incident RA cohort using NLP for the index date of initial mention of RA. All patients were required to have both CCP and RF data from clinical care to determine serologic RA phenotype. We used semi-supervised machine learning approaches to identify pneumonia using billing codes and terms extracted using NLP, with the Centers for Disease Control definition of pneumonia from medical record review as a gold standard. The area under the receiver operating curve (AUROC) for this billing code+NLP pneumonia algorithm was 0.94 compared to the standard rule-based pneumonia algorithm (billing code on inpatient discharge) AUROC of 0.86 (p<0.001). Smoking status was extracted using NLP methods. Other covariates, including a previous validated weighted RA multimorbidity score2, were determined using structured EHR data. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pneumonia adjusting for potential confounders.

Results:

We analyzed a total of 4,110 patients with incident RA and both CCP/RF data available. Mean age at index date was 53.0 years (SD 14.8), 77.2% were female, and 79.8% were CCP+ or RF+. During 32,248 patient-years of follow-up (mean 7.8 years/patient), we identified 240 pneumonia cases. Patients with seropositive RA had a HR of 1.99 (95%CI 1.30-3.01, Table) for pneumonia compared to patients with seronegative RA, adjusted for age, sex, smoking, index year, ESR level, glucocorticoid use, DMARD use, and weighted RA multimorbidity score. While CCP+ RA (HR 1.91, 95%CI 1.23-2.97) and RF+ RA (HR 2.07, 95%CI 1.35-3.16) had increased pneumonia risk compared to seronegative RA, the CCP+RF- RA subgroup had no association with pneumonia (HR 0.67, 95%CI 0.23-1.93).

Conclusion:

Patients with incident seropositive RA, particularly RF+ RA, had increased risk for pneumonia throughout the RA disease course that was not explained by measured confounders including smoking status, multimorbidity, medications, and ESR level. Further studies should investigate how RF+ may predispose RA patients to later develop pneumonia after clinical RA diagnosis.

References:

[1]Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res. 2010;62(8):1120–1127.

[2]Radner H, Yoshida K, Mjaavatten MD, et al. Development of a multimorbidity index: Impact on quality of life using a rheumatoid arthritis cohort. Semin Arthritis Rheum. 2015;45(2):167–173.

Disclosure of Interests:

Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Optum, Janssen, Gilead, Weixing Huang: None declared, Bing Lu: None declared, Sicong Huang: None declared, Andrew Cagan: None declared, Vivian Gainer: None declared, Sean Finan: None declared, Guergana Savova: None declared, Daniel Solomon Grant/research support from: Funding from Abbvie and Amgen unrelated to this work, Elizabeth Karlson: None declared, Katherine Liao: None declared

Inflammatory profile dysregulation in nuclear workers occupationally exposed to low-dose gamma radiation

Chronic inflammation is a common denominator linking a wide range of health conditions, including tissue response to radiation exposure. This pilot study investigates whether inflammatory cytokines-interleukins IL-6, -8, -10, monocyte chemoattractant protein-1 (MCP-1) and tumor necrosis factor α (TNFα)-can be used as early biomarkers of radiation-induced adverse health effects in occupationally exposed individuals. The study included 33 workers externally exposed to gamma radiation from the nuclear industry with cumulated doses from 0.11 to 190 mSv and 42 non-exposed controls of comparable age and socio-economic status. IL-6, IL-8, MCP-1, TNFα and IL-10 were analyzed by enzyme-linked assay (ELISA) in blood plasma samples. Total antioxidant status (TAS) of blood plasma was determined by a colorimetric assay. The radiation-exposed and control groups measured significantly different levels of MCP-1, TNFα and IL-10. Seventy-five percent of radiation workers had either high MCP-1 levels or low IL-10 levels and 30% had all three cytokines dysregulated. Approximately 50% of workers showed upregulated antioxidant status, which appeared to compensate the pro-inflammatory cytokine shift in these individuals. In contrast, only 2% of the control subjects were found to have three dysregulated cytokines, and all of them measured within the normal TAS range. The present study may represent an important step towards the establishment of a reliable set of biomarkers for health-risk estimation in population cohorts exposed to low radiation doses.

Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

HSP90 Inhibitor Geldanamycin as a Radiation Response Modificator in Human Blood Cells

Heat shock protein 90 (Hsp90) is a highly conserved molecular chaperone, involved in the folding, assembly, stabilization and activation of numerous proteins with unrelated amino acid sequences and functions. Geldanamycin (GA), a natural benzoquinone, can inhibit the chaperone activity of Hsp90. It has been shown that GA can produce superoxide anions and increase the intracellular oxidative stress, which, in addition to the direct inhibition of Hsp90, might also contribute to the modifying effects of the inhibitor on the early response in human mononuclear cells exposed to ionizing radiation. The present study shows that GA antagonizes the radiation-induced suppression on MnSOD and catalase, key enzymes of the radical scavenging systems. By significantly up-regulating catalase levels over the entire range of doses from 0.5 to 4 Gy, the inhibitor of Hsp90 exerted adaptive protection and modified the early radiation response of the human blood cells.

The soluble receptor ST2 is positively associated with occupational exposure to radiation

Purpose Radiation exposure, besides the risk of cancer, may also increase the risk of non-cancer diseases, including cardiovascular disease (CVD). This study investigates whether the soluble form of the ST2 receptor (sST2), an emerging prognostic marker in patients with CVD, can be used to monitor the CVD risk in individuals occupationally exposed to radiation. Materials and methods sST2 in blood plasma from 69 individuals, 45 workers from the nuclear industry and 24 controls, was analyzed using enzyme-linked assay (ELISA). Total antioxidant status (TAS) of blood plasma and levels of reactive oxygen species (ROS) in lymphocytes were determined by colorimetric and fluorescence assays. Results The data suggest a 5-fold increase in the number of subjects with sST2 levels above the clinical threshold and a 10-fold increase in the number of subjects with TAS levels outside the reference range in the exposed group when compared to the group of non-exposed individuals. The strongest up-regulation of TAS was measured in the group of younger workers with cumulative doses not exceeding 50 mSv. Conclusion The present study may represent an initial step towards the establishment of sST2 as a biomarker for CVD risk estimation in the context of radiation exposure.

The emerging role of electronic medical records in pharmacogenomics

Health-care information technology and genotyping technology are both advancing rapidly, creating new opportunities for medical and scientific discovery. The convergence of these two technologies is now facilitating genetic association studies of unprecedented size within the context of routine clinical care. As a result, the medical community will soon be presented with a number of novel opportunities to bring functional genomics to the bedside in the area of pharmacotherapy. By linking biological material to comprehensive medical records, large multi-institutional biobanks are now poised to advance the field of pharmacogenomics through three distinct mechanisms: (i) retrospective assessment of previously known findings in a clinical practice-based setting, (ii) discovery of new associations in huge observational cohorts, and (iii) prospective application in a setting capable of providing real-time decision support. This review explores each of these translational mechanisms within a historical framework.

Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents

OBJECTIVE

To evaluate the effectiveness of a lexico-syntactic pattern (LSP) matching method for ontology enrichment using clinical documents.

METHODS

Two domains were separately studied using the same methodology. We used radiology documents to enrich RadLex and pathology documents to enrich National Cancer Institute Thesaurus (NCIT). Several known LSPs were used for semantic knowledge extraction. We first retrieved all sentences that contained LSPs across two large clinical repositories, and examined the frequency of the LSPs. From this set, we randomly sampled LSP instances which were examined by human judges. We used a two-step method to determine the utility of these patterns for enrichment. In the first step, domain experts annotated medically meaningful terms (MMTs) from each sentence within the LSP. In the second step, RadLex and NCIT curators evaluated how many of these MMTs could be added to the resource. To quantify the utility of this LSP method, we defined two evaluation metrics: suggestion rate (SR) and acceptance rate (AR). We used these measures to estimate the yield of concepts and relationships, for each of the two domains.

RESULTS

For NCIT, the concept SR was 24%, and the relationship SR was 65%. The concept AR was 21%, and the relationship AR was 14%. For RadLex, the concept SR was 37%, and the relationship SR was 55%. The concept AR was 11%, and the relationship AR was 44%.

CONCLUSION

The LSP matching method is an effective method for concept and concept relationship discovery in biomedical domains.

Frame semantics and the domain of functioning, disability and health

We explore the use of FrameNet (FN) frames and their frame elements to represent content from the International Classification of Functioning, Disability and Health (ICF) Self-Care chapter. Terms were extracted from the ICF and mapped to FN frames. The mappings were validated by an expert. FN provided relevant and nearly complete coverage of ICF terms, suggesting FN may be an important resource to leverage for semantic language processing and knowledge representation in this domain.