9- and 13-HODE regulate fatty acid binding protein-4 in human macrophages, but does not involve HODE/GPR132 axis in PPAR-γ regulation of FABP4.

BACKGROUND: Both activation of monocytes and increased serum fatty acid binding protein-4 (FABP4) occur in diabetes and are associated with increased atherosclerosis. The oxidized lipid, 9-hydroxyoctadecadienoic acid (9-HODE) increases FABP4 in macrophages, and is a ligand for G protein-coupled receptor 132 (GPR132). We investigated the involvement of GPR132 in mediating the 9-, 13-HODE stimulation of FABP4 secretion, and whether GPR132 expression is increased in monocytes from patients with type 2 diabetes. METHODS: The effects of siRNA silencing of GPR132 gene and of the PPAR-γ antagonist T0070907 were studied in THP-1 cells. Serum levels of FABP4 and other adipokines were measured in patients with diabetes, and monocyte subpopulations were analyzed using flow cytometry. GPR132 mRNA was quantified in isolated CD14+ cells. RESULTS: 9-HODE and 13-HODE increased FABP4 expression in THP-1 monocytes and macrophages, and also increased GPR132 expression. Silencing of GPR132 did not influence the increase in FABP4 with 9-HODE, 13-HODE, or rosiglitazone (ROSI). By contrast, T0070907 inhibited the effect of all three ligands on FABP4 expression. Diabetic subjects had increased serum FABP4, and activated monocytes. They also expressed higher levels of GPR132 mRNA in CD14+ cells. CONCLUSIONS: We conclude that GPR132 is an independent monocyte activation marker in diabetes, but does not contribute to PPAR-γ-mediated induction of FABP4 by HODEs.

Hydroxyoctadecadienoic acids: Oxidised derivatives of linoleic acid and their role in inflammation associated with metabolic syndrome and cancer.

Linoleic acid (LA) is a major constituent of low-density lipoproteins. An essential fatty acid, LA is a polyunsaturated fatty acid, which is oxidised by endogenous enzymes and reactive oxygen species in the circulation. Increased levels of low-density lipoproteins coupled with oxidative stress and lack of antioxidants drive the oxidative processes. This results in synthesis of a range of oxidised derivatives, which play a vital role in regulation of inflammatory processes. The derivatives of LA include, hydroxyoctadecadienoic acids, oxo-​octadecadienoic acids, epoxy octadecadecenoic acid and epoxy-keto-octadecenoic acids. In this review, we examine the role of LA derivatives and their actions on regulation of inflammation relevant to metabolic processes associated with atherogenesis and cancer. The processes affected by LA derivatives include, alteration of airway smooth muscles and vascular wall, affecting sensitivity to pain, and regulating endogenous steroid hormones associated with metabolic syndrome. LA derivatives alter cell adhesion molecules, this initial step, is pivotal in regulating inflammatory processes involving transcription factor peroxisome proliferator-activated receptor pathways, thus, leading to alteration of metabolic processes. The derivatives are known to elicit pleiotropic effects that are either beneficial or detrimental in nature hence making it difficult to determine the exact role of these derivatives in the progress of an assumed target disorder. The key may lie in understanding the role of these derivatives at various stages of development of a disorder. Novel pharmacological approaches in altering the synthesis or introduction of synthesised LA derivatives could possibly help drive processes that could regulate inflammation in a beneficial manner. Chemical Compounds: Linoleic acid (PubChem CID: 5280450), 9- hydroxyoctadecadienoic acid (PubChem CID: 5312830), 13- hydroxyoctadecadienoic acid (PubChem CID: 6443013), 9-oxo-​octadecadienoic acid (PubChem CID: 3083831), 13-oxo-​octadecadienoic acid (PubChem CID: 4163990), 9,10-epoxy-12-octadecenoate (PubChem CID: 5283018), 12,13-epoxy-9-keto-10- trans -octadecenoic acid (PubChem CID: 53394018), Pioglitazone (PubChem CID: 4829).

A framework for feature extraction from hospital medical data with applications in risk prediction.

BACKGROUND: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. RESULTS: Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). CONCLUSIONS: The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Disease burden evaluation of fall-related events in the elderly due to hypoglycemia and other diabetic complications: a clinical review.

A hypoglycemia-induced fall is common in older persons with diabetes. The etiology of falls in this population is usually multifactorial, and includes microvascular and macrovascular complications and age-related comorbidities, with hypoglycemia being one of the major precipitating causes. In this review, we systematically searched the literature that was available up to March 31, 2014 from MEDLINE/PubMed, Embase, and Google Scholar using the following terms: hypoglycemia; insulin; diabetic complications; and falls in elderly. Hypoglycemia, defined as blood glucose <4.0 mmol/L (70 mg/dL) requiring external assistance, occurs in one-third of elderly diabetics on glucose-lowering therapies. It represents a major barrier to the treatment of diabetes, particularly in the elderly population. Patients who experience hypoglycemia are at a high risk for adverse outcomes, including falls leading to bone fracture, seizures, cognitive dysfunction, and prolonged hospital stays. An increase in mortality has been observed in patients who experience any one of these events. Paradoxically, rational insulin therapy, dosed according to a patient's clinical status and the results of home blood glucose monitoring, so as to achieve and maintain recommended glycemic goals, can be an effective method for the prevention of hypoglycemia and falls in the elderly. Contingencies, such as clinician-directed hypoglycemia treatment protocols that guide the immediate treatment of hypoglycemia, help to limit both the duration and severity of the event. Older diabetic patients with or without underlying renal insufficiency or other severe illnesses represent groups that are at high risk for hypoglycemia-induced falls and, therefore, require lower insulin dosages. In this review, the risk factors of falls associated with hypoglycemia in elderly diabetics were highlighted and management plans were suggested. A target hemoglobin A1c level between 7% and 8% seems to be more appropriate for this population. In addition, the first-choice drugs should have good safety profiles and have the lowest probability of causing hypoglycemia - such as metformin (in the absence of significant renal impairment) and incretin enhancers - while other therapies that may cause more frequent hypoglycemia should be avoided.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.

OBJECTIVES: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. SETTING: A regional cancer centre in Australia. PARTICIPANTS: Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. PRIMARY AND SECONDARY OUTCOME MEASURES: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). RESULTS: The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. CONCLUSIONS: Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments.

BACKGROUND: To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1-6 month risk. METHODS: 7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator). RESULTS: In the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 - 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 - 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83). CONCLUSION: Predictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.

Multifunctional and multitargeted nanoparticles for drug delivery to overcome barriers of drug resistance in human cancers.

The recurrence and metastatic spread of cancer are major drawbacks in cancer treatment. Although chemotherapy is one of the most effective methods for the treatment of metastatic cancers, it is nonspecific and causes significant toxic damage. The development of drug resistance to chemotherapeutic agents through various mechanisms also limits their therapeutic potential. However, as we discuss here, the use of nanodelivery systems that are a combination of diagnostics and therapeutics (theranostics) is as relatively novel concept in the treatment of cancer. Such systems are likely to improve the therapeutic benefits of encapsulated drugs and can transit to the desired site, maintaining their pharmaceutical properties. The specific targeting of malignant cells using multifunctional nanoparticles exploits theranostics as an improved agent for delivering anticancer drugs and as a new solution for overriding drug resistance.