Aloe QDM complex enhances specific cytotoxic T lymphocyte killing in vivo in metabolic disease mice.

We developed spontaneous diet-induced metabolic disease in mice by feeding them a high-fat diet for 23 weeks and administered Aloe QDM complex for 16 weeks to examine its restorative effect on immune disorders and metabolic syndrome. A series of immune functional assays indicated Aloe QDM complex enhanced lymphocyte proliferation and antigen-specific immunity as determined by the restored functions of cytotoxic T lymphocytes (CTL) and IgG production. The elevated serum TNF-α level was also regulated by Aloe QDM complex treatment, which suggested its complex therapeutic potential. As for metabolic phenotypes, oral administration of Aloe QDM complex significantly improved diabetic symptoms, including high fasting glucose levels and glucose tolerance, and distinctly alleviated lipid accumulation in adipose and hepatic tissue. The simultaneous restoration of Aloe QDM complex on metabolic syndrome and host immune dysfunction, especially on the specific CTL killing was first elucidated in our study.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

Rapid and ultra-sensitive detection of African swine fever virus antibody on site using QDM based-ASFV immunosensor (QAIS).

African swine fever (ASF) is a swine viral disease that could cause highly contagious and extremely high mortality, causing huge economic losses to the pig industry. As there is currently no vaccine and effective treatment methods. Therefore, early monitoring is one of the most important solutions to prevent and control ASF. In this study, the dual QDM recombinant virus protein 30 and 54 (P30 and P54) probes and pre-incubation in vitro were proposed for the first time as QDM based-ASFV immunosensor (QAIS) for the ultra-sensitive quantitative detection of ASFV antibodies in serum. In the range from serum dilution of 1:1000 to 1:64000, it showed a good linear relationship (R2 = 0.9947), and its detection sensitivity was 1:64000 dilution. Compared with commercial enzyme-linked immunosorbent assay (ELISA) and colloidal gold immunochromatographic strip (CGICS), its detection sensitivity was improved by at least one order of magnitude and four orders of magnitude respectively. In addition, the whole ASFV antibody screening test can be completed in 25 min with simple operation. The performance and practicability of the established QAIS sensor have been verified by ASF-ELISA kit, and its coincidence rate was as high as 98.7% in 151 clinical samples. We firmly believe that the proposed QAIS sensor could potentially be applied to point-of-care testing (POCT) for quantitative ASFV antibody in pig farms.

Bias correction of climate model outputs influences watershed model nutrient load predictions.

Waterbodies around the world experience problems associated with elevated phosphorus (P) and nitrogen (N) loads. While vital for ecosystem functioning, when present in excess amounts these nutrients can impair water quality and create symptoms of eutrophication, including harmful algal blooms. Under a changing climate, nutrient loads are likely to change. While climate models can serve as inputs to watershed models, the climate models often do not adequately represent the distribution of observed data, generating uncertainties that can be addressed to some degree with bias correction. However, the impacts of bias correction on nutrient models are not well understood. This study compares 4 univariate and 3 multivariate bias correction methods, which correct precipitation and temperature variables from 4 climate models in the historical (1980-1999) and mid-century future (2046-2065) time periods. These variables served as inputs to a calibrated Soil and Water Assessment Tool (SWAT) model of Lake Erie's Maumee River watershed. We compared the performance of SWAT outputs driven with climate model outputs that were bias-corrected (BC) and not bias-corrected (no-BC) for dissolved reactive P, total P, and total N. Results based on graphical comparisons and goodness of fit metrics showed that the choice of BC method impacts both the direction of change and magnitude of nutrient loads and hydrological processes. While the Delta method performed best, it should be used with caution since it considers historical variable relationships as the basis for predictions, which may not hold true under future climate. Quantile Delta Mapping (QDM) and Multivariate Bias Correction N-dimensional probability density function transform (MBCn) BC methods also performed well and work well for non-stationary climate scenarios. Furthermore, results suggest that February-July cumulative load in the Maumee basin is likely to decrease in the mid-century as runoff and snowfall decrease, and evapotranspiration increases with warming temperatures.

Quantitative data mining in signal detection: the Singapore experience.

Background: In Singapore, the Health Sciences Authority (HSA) reviews an average of 20,000 spontaneous adverse event (AE) reports yearly. Potential safety signals are identified manually and discussed on a weekly basis. In this study, we compared the use of four quantitative data mining (QDM) methods with weekly manual review to determine if signals of disproportionate reporting (SDRs) can improve the efficiency of manual reviews and thereby enhance drug safety signal detection.Methods: We formulated a QDM triage strategy to reduce the number of SDRs for weekly review and compared the results against those derived from manual reviews alone for the same 6-month period. We then incorporated QDM triage into the manual review workflow for the subsequent two 6-month periods and made further comparisons against QDM triage alone.Results: The incorporation of QDM triage into routine manual reviews resulted in a reduction of 20% to 30% in the number of drug-AE pairs identified for further evaluation. Sequential Probability Ratio Test (SPRT) detected more signals that mirror human manual signal detection than the other three methods.Conclusions: The adoption of QDM triage into our manual reviews is a more efficient way forward in signal detection, avoiding missing important drug safety signals.

Assessing the Quality of Decision Making in the Development and Regulatory Review of Medicines: Identifying Biases and Best Practices.

BACKGROUND: Although the quality of decision making (QDM) in the development and regulatory review of medicines influences the delivery of new products, there appears to be no suitable instrument to assess QDM in this area. The aim of this study was to assess differences in QDM using a validated instrument, the Quality of Decision-Making Orientation Scheme (QoDoS), to identify best practices and biases affecting individuals and their organization, as well as to assess differences in decision-making behaviors between pharmaceutical companies and regulatory agencies. QoDoS also enables the measurement against 10 quality decision-making practices (QDMPs) that underpin a quality process. METHODS: QoDoS, consisting of 47 items that assess individual and organizational decision-making approaches and influences, was completed by 76 participants from regulatory agencies and pharmaceutical companies. RESULTS: Having a systematic, structured approach to aid in decision making is achieved to a greater extent at an individual level (72%) compared with that of the organization (38%). Key differences between company and agency decision making were uncovered. While it was recognized that both stakeholders felt that their decision making could be improved (100% agencies; 92% companies), training in the science of decision making was rarely provided. CONCLUSIONS: QoDoS has the ability to measure differences in QDM between individuals and organizations within companies and agencies. The benefits of assessing QDMPs with QoDoS include enabling an increased awareness of biases and best practices that should be incorporated into a decision-making framework; increasing productivity and reducing uncertainty around decision making, thereby resulting in more predictable outcomes for organizations. In addition, it provides a basis for discussion of the issues in decision making within an organization as well as between stakeholders to encourage a level of partnership. Finally, measurements of QDM will enable trust, consistency, transparency, and timeliness to be built into critical decisions that affect medicines' availability.

Developing a modular architecture for creation of rule-based clinical diagnostic criteria.

BACKGROUND: With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. METHODS AND RESULTS: The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation. CONCLUSION: Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.

Dietary Aloe QDM Complex Reduces Obesity-Induced Insulin Resistance and Adipogenesis in Obese Mice Fed a High-Fat Diet.

Obesity-induced disorders contribute to the development of metabolic diseases such as insulin resistance, fatty liver diseases, and type 2 diabetes (T2D). In this study, we evaluated whether the Aloe QDM complex could improve metabolic disorders related to blood glucose levels and insulin resistance. Male C57BL/6 obese mice fed a high-fat diet for 54 days received a supplement of Aloe QDM complex or pioglitazone (PGZ) or metformin (Met) and were compared with unsupplemented controls (high-fat diet; HFD) or mice fed a regular diet (RD). RT-PCR and western blot analysis were used to quantify the expression of obesity-induced inflammation. Dietary Aloe QDM complex lowered body weight, fasting blood glucose, plasma insulin, and leptin levels, and markedly reduced the impairment of glucose tolerance in obese mice. Also, Aloe QDM complex significantly enhanced plasma adiponectin levels and insulin sensitivity via AMPK activity in muscles. At the same time, Aloe QDM decreased the mRNA and protein of PPARγ/LXRα and scavenger receptors in white adipose tissue (WAT). Dietary Aloe QDM complex reduces obesity-induced glucose tolerance not only by suppressing PPARγ/LXRα but also by enhancing AMPK activity in the WAT and muscles, both of which are important peripheral tissues affecting insulin resistance. The Aloe QDM complex could be used as a nutritional intervention against T2D.

Dietary Aloe Reduces Adipogenesis via the Activation of AMPK and Suppresses Obesity-related Inflammation in Obese Mice.

BACKGROUND: Metabolic disorders, including type II diabetes and obesity, present major health risks in industrialized countries. AMP-activated protein kinase (AMPK) has become the focus of a great deal of attention as a novel therapeutic target for the treatment of metabolic syndromes. In this study, we evaluated whether dietary aloe could reduce obesity-induced inflammation and adipogenesis. METHODS: Male C57BL/6 obese mice fed a high-fat diet for 54 days received a supplement of aloe formula (PAG, ALS, Aloe QDM, and Aloe QDM complex) or pioglitazone (PGZ) and were compared with unsupplemented controls (high-fat diet; HFD) or mice fed a regular diet (RD). RT-PCR and western blot analysis were used to quantify the expression of obesity-induced inflammation. RESULTS: Aloe QDM complex down-regulated fat size through suppressed expression of scavenger receptors on adipose tissue macrophages (ATMs) compared with HFD. Both white adipose tissue (WATs) and muscle exhibited increased AMPK activation through aloe supplementation, and in particular, the Aloe QDM complex. Obesity-induced inflammatory cytokines (IL-1ß and -6) and HIF1α mRNA and protein were decreased markedly, as was macrophage infiltration by the Aloe QDM complex. Further, the Aloe QDM complex decreased the translocation of NF-κB p65 from the cytosol in the WAT. CONCLUSION: Dietary aloe formula reduced obesity-induced inflammatory responses by activation of AMPK in muscle and suppression of proinflammatory cytokines in the WAT. Additionally, the expression of scavenger receptors in the ATM and activation of AMPK in WAT led to reduction in the percent of body fat. Thus, we suggest that the effect of the Aloe QDM complex in the WAT and muscle are related to activation of AMPK and its use as a nutritional intervention against T2D and obesity-related inflammation.

Dietary Aloe Improves Insulin Sensitivity via the Suppression of Obesity-induced Inflammation in Obese Mice.

BACKGROUND: Insulin resistance is an integral feature of metabolic syndromes, including obesity, hyperglycemia, and hyperlipidemia. In this study, we evaluated whether the aloe component could reduce obesity-induced inflammation and the occurrence of metabolic disorders such as blood glucose and insulin resistance. METHODS: Male C57BL/6 obese mice fed a high-fat diet for 54 days received a supplement of aloe formula (PAG, ALS, Aloe QDM, and Aloe QDM complex) or pioglitazone (PGZ) and were compared with unsupplemented controls (high-fat diet; HFD) or mice fed a regular diet (RD). RT-PCR and western blot analysis were used to quantify the expression of obesity-induced inflammation. RESULTS: Aloe QDM lowered fasting blood glucose and plasma insulin compared with HFD. Obesity-induced inflammatory cytokine (IL-1ß, -6, -12, TNF-α) and chemokine (CX3CL1, CCL5) mRNA and protein were decreased markedly, as was macrophage infiltration and hepatic triglycerides by Aloe QDM. At the same time, Aloe QDM decreased the mRNA and protein of PPARγ/LXRα and 11ß-HSD1 both in the liver and WAT. CONCLUSION: Dietary aloe formula reduces obesity-induced glucose tolerance not only by suppressing inflammatory responses but also by inducing anti-inflammatory cytokines in the WAT and liver, both of which are important peripheral tissues affecting insulin resistance. The effect of Aloe QDM complex in the WAT and liver are related to its dual action on PPARγ and 11ß-HSD1 expression and its use as a nutritional intervention against T2D and obesity-related inflammation is suggested.