The effects of escitalopram on myocardial apoptosis and the expression of Bax and Bcl-2 during myocardial ischemia/reperfusion in a model of rats with depression

BackgroundMajor depressive disorder (MDD) is an independent risk factor for coronary heart disease (CHD), and influences the occurrence and prognosis of cardiovascular events. Although there is evidence that antidepressants may be cardioprotective after acute myocardial infarction (AMI) comorbid with MDD, the operative pathophysiological mechanisms remain unclear. Our aim was therefore to explore the molecular mechanisms of escitalopram on myocardial apoptosis and the expression of Bax and Bcl-2 in a rat model of depression during myocardial ischemia/reperfusion (I/R).MethodsRats were divided randomly into 3 groups (n = 8): D group (depression), DI/R group (depression with myocardial I/R) and escitalopram + DI/R group. The rats in all three groups underwent the same chronic mild stress and separation for 21 days, at the same time, in the escitalopram + DI/R group, rats were administered escitalopram by gavage (10 mg/kg/day). Ligation of the rat¿s left anterior descending branch was done in the myocardial I/R model. Following which behavioral tests were done. The size of the myocardial infarction was detected using 1.5% TTC dye. The Tunel method was used to detect apoptotic myocardial cells, and both the Rt-PCR method and immunohistochemical techniques were used to detect the expression of Bcl¿2 and Bax.ResultsCompared with the D and DI/R groups, rats in Escitalopram + DI/R group showed significantly increased movements and sucrose consumption (P < .01). Compared with the DI/R group, the myocardial infarct size in the escitalopram + DI/R group was significantly decreased (P < .01). Compared with the D group, there were significantly increased apoptotic myocardial cells in the DI/R and escitalopram + DI/R groups (P < .01); however compared with the DI/R group, apoptotic myocardial cell numbers in the escitalopram + DI/R group were significantly decreased (P < .01). Compared with the DI/R group, there was a down-regulated Bax:Bcl-2 ratio in the escitalopram + DI/R group (P < .01).ConclusionsThese results suggest that in patients with AMI comorbid with MDD, there is an increase in pro-apoptotic pathways that is reversed by escitalopram. This suggests that clinically escitalopram may have a direct cardioprotective after acute myocardial infarction.

What's missing in missing data? Omissions in survey responses among parents of children with advanced cancer

Background: Missing data is a common phenomenon with survey-based research; patterns of missing data may elucidate why participants decline to answer certain questions. Objective: To describe patterns of missing data in the Pediatric Quality of Life and Evaluation of Symptoms Technology (PediQUEST) study, and highlight challenges in asking sensitive research questions. Design: Cross-sectional, survey-based study embedded within a randomized controlled trial. Setting: Three large children's hospitals: Dana-Farber/Boston Children's Cancer and Blood Disorders Center (DF/BCCDC); Children's Hospital of Philadelphia (CHOP); and Seattle Children's Hospital (SCH). Measurements: At the time of their child's enrollment, parents completed the Survey about Caring for Children with Cancer (SCCC), including demographics, perceptions of prognosis, treatment goals, quality of life, and psychological distress. Results: Eighty-six of 104 parents completed surveys (83% response). The proportion of missing data varied by question type. While 14 parents (16%) left demographic fields blank, over half (n=48; 56%) declined to answer at least one question about their child's prognosis, especially life expectancy. The presence of missing data was unrelated to the child's diagnosis, time from progression, time to death, or parent distress (p>0.3 for each). Written explanations in survey margins suggested that addressing a child's life expectancy is particularly challenging for parents. Conclusions and Relevance: Parents of children with cancer commonly refrain from answering questions about their child's prognosis, however, they may be more likely to address general cure likelihood than explicit life expectancy. Understanding acceptability of sensitive questions in survey-based research will foster higher quality palliative care research. © Copyright 2014, Mary Ann Liebert, Inc. 2014.

Estimation and extrapolation of optimal treatment and testing strategies

We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime for biologically similar subjects in a second health-care system when, for cultural, logistical, or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction.

Clinical aspects for survivin: a crucial molecule for targeting drug-resistant cancers

Drug resistance is frequently found in cancer patients who have prolonged chemotherapeutic treatments. Overcoming this phenomenon to make therapy available to these patients is one of the most important features in developing effective cancer therapeutic strategies. Identification of drug resistance causative molecules is one of the most focused areas of cancer research today. Many molecules have been identified in conferring cancer cells the property of drug resistance, and various small molecule inhibitors have been developed to target these molecules to restore the sensitivity of different traditional chemotherapeutic agents, which are frequently found to exhibit reduced potency during prolonged treatment, in cancer patients. Survivin, a member of the inhibitor of apoptosis proteins (IAP) family, has been identified as one of the most crucial biomarkers in the recognition of drug resistance. Survivin is overexpressed in tumor cells, helping in its proliferation and survival, and its overexpression is positively correlated with poor prognosis for cancer patients. Targeted therapeutic measures to inhibit survivin in cancers, particularly drug-resistant tumors, are the recent focus of research for cancer treatment.

Childhood wheeze phenotypes show less than expected growth in FEV1 across adolescence

Better characterization of childhood wheeze phenotypes using newer statistical methods provides a basis for addressing the heterogeneity of childhood asthma. Outcomes of these phenotypes beyond childhood are unknown.

The potential utility of a staging model as a course specifier: a bipolar disorder perspective.

Staging models are widely used in clinical medicine, and offer an insight into the progressive nature of many disorders. In general, the earlier stages of illness may be associated with a better prognosis and a higher treatment response. Once chronicity is reached, more complex and invasive treatments may be required, and the utility of treatments may decline. There is evidence that treatment response is greatest in the early phases of the disorder. There is also a progressive social and psychological burden of ongoing illness. This is paralleled by the twin notions of neuroprotection, which is supported by increasing evidence that structural changes in the disorder may be progressive and reversible with algorithm appropriate treatment, and that of early intervention, which posits that the optimal window for intervention is early in the illness course. A staging model compliments existing and proposed classifications of bipolar disorder, adding a temporal dimension to a cross sectional view. It may inform treatment choice and prognosis, and could have utility as a course specifier.

Microfluidic platforms for biomarker analysis

Biomarkers have been described as characteristics, most often molecular, that provide information about biological states, whether normal, pathological, or therapeutically modified. They hold great potential to assist diagnosis and prognosis, monitor disease, and assess therapeutic effectiveness. While a few biomarkers are routinely utilised clinically, these only reflect a very small percentage of all biomarkers discovered. Numerous factors contribute to the slow uptake of these new biomarkers, with challenges faced throughout the biomarker development pipeline. Microfluidics offers two important opportunities to the field of biomarkers: firstly, it can address some of these developmental obstacles, and secondly, it can provide the precise and complex platform required to bridge the gap between biomarker research and the biomarker-based analytical device market. Indeed, adoption of microfluidics has provided a new avenue for advancement, promoting clinical utilisation of both biomarkers and their analytical platforms. This review will discuss biomarkers and outline microfluidic platforms developed for biomarker analysis.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

Type D personality, suboptimal health behaviors and emotional distress in adults with diabetes: Results from Diabetes MILES-The Netherlands.

Type D personality - defined as high negative affectivity (NA) and high social inhibition (SI) - has been associated with adverse cardiovascular prognosis. We explored the differential associations of Type D personality and its constituent components with health behaviors, emotional distress and standard biomedical risk factors as potential risk mechanisms in adults with diabetes.

Locked nucleic acid aptamer based microfluidic devices for capturing tumor cells

Design and fabrication of novel microfluidic devices for sensitive and specific capture of circulating tumor cells using locked nucleic acid modified aptamers and antibodies targeting EpCAM/Nucleolin expression. These devices also allow re-usability, on-chip characterization of multiple markers and release of viable captured cells for further culture and in vitro characterization for cancer diagnosis, prognosis and therapeutic planning.

Prevalence and risk factors for stunting and severe stunting among under-fives in North Maluku province of Indonesia

BACKGROUND: Adequate nutrition is needed to ensure optimum growth and development of infants and young children. Understanding of the risk factors for stunting and severe stunting among children aged less than five years in North Maluku province is important to guide Indonesian government public health planners to develop nutrition programs and interventions in a post conflict area. The purpose of the current study was to assess the prevalence of and the risk factors associated with stunting and severe stunting among children aged less than five years in North Maluku province of Indonesia. METHODS: The health and nutritional status of children aged less than five years was assessed in North Maluku province of Indonesia in 2004 using a cross-sectional multi-stage survey conducted on 750 households from each of the four island groups in North Maluku province. A total of 2168 children aged 0-59 months were used in the analysis. RESULTS: Prevalence of stunting and severe stunting were 29% (95%CI: 26.0-32.2) and 14.1% (95%CI: 11.7-17.0) for children aged 0-23 months and 38.4% (95%CI: 35.9-41.0) and 18.4% (95%CI: 16.1-20.9) for children aged 0-59 months, respectively. After controlling for potential confounders, multivariate analysis revealed that the risk factors for stunted children were child's age in months, male sex and number of family meals per day (

Genomics based biomarker discovery in oral squamous cell carcinoma

 The present thesis showed signaling mechanisms and pathways essential for oral cancer progression through genomics approach. It has identified markers that are of diagnostic, prognostic and therapeutic importance. It has also shown that aspirin is a potential drug in oral cancer treatment.

Quick chip assay using locked nucleic acid modified epithelial cell adhesion molecule and nucleolin aptamers for the capture of circulating tumor cells

The role of circulating tumor cells (CTCs) in disease diagnosis, prognosis, monitoring of the therapeutic efficacy, and clinical decision making is immense and has attracted tremendous focus in the last decade. We designed and fabricated simple, flat channel microfluidic devices polydimethylsiloxane (PDMS based) functionalized with locked nucleic acid (LNA) modified aptamers (targeting epithelial cell adhesion molecule (EpCAM) and nucleolin expression) for quick and efficient capture of CTCs and cancer cells. With optimized flow rates (10 μl/min), it was revealed that the aptamer modified devices offered reusability for up to six times while retaining optimal capture efficiency (>90%) and specificity. High capture sensitivity (92%) and specificity (100%) was observed in whole blood samples spiked with Caco-2 cells (10-100 cells/ml). Analysis of blood samples obtained from 25 head and neck cancer patients on the EpCAM LNA aptamer functionalized chip revealed that an average count of 5 ± 3 CTCs/ml of blood were captured from 22/25 samples (88%). EpCAM intracellular domain (EpICD) immunohistochemistry on 9 oral squamous cell carcinomas showed the EpICD positivity in the tumor cells, confirming the EpCAM expression in CTCs from head and neck cancers. These microfluidic devices also maintained viability for in vitro culture and characterization. Use of LNA modified aptamers provided added benefits in terms of cost effectiveness due to increased reusability and sustainability of the devices. Our results present a robust, quick, and efficient CTC capture platform with the use of simple PDMS based devices that are easy to fabricate at low cost and have an immense potential in cancer diagnosis, prognosis, and therapeutic planning.

Stabilizing high-dimensional prediction models using feature graphs

We investigate feature stability in the context of clinical prognosis derived from high-dimensional electronic medical records. To reduce variance in the selected features that are predictive, we introduce Laplacian-based regularization into a regression model. The Laplacian is derived on a feature graph that captures both the temporal and hierarchic relations between hospital events, diseases, and interventions. Using a cohort of patients with heart failure, we demonstrate better feature stability and goodness-of-fit through feature graph stabilization.