Reduced Intestinal Tumorigenesis in APCmin Mice Lacking Melanin-Concentrating Hormone

Background: Melanin-concentrating hormone (MCH) is an evolutionary conserved hypothalamic neuropeptide that in mammals primarily regulates appetite and energy balance. We have recently identified a novel role for MCH in intestinal inflammation by demonstrating attenuated experimental colitis in MCH deficient mice or wild type mice treated with an anti-MCH antibody. Therefore, targeting MCH has been proposed for the treatment of inflammatory bowel disease. Given the link between chronic intestinal inflammation and colorectal cancer, in the present study we sought to investigate whether blocking MCH might have effects on intestinal tumorigenesis that are independent of inflammation. Methodology Tumor development was evaluated in MCH-deficient mice crossed to the APCmin mice which develop spontaneously intestinal adenomas. A different cohort of MCH−/− and MCH+/+ mice in the APCmin background was treated with dextran sodium sulphate (DSS) to induce inflammation-dependent colorectal tumors. In Caco2 human colorectal adenocarcinoma cells, the role of MCH on cell survival, proliferation and apoptosis was investigated. Results: APCmin mice lacking MCH developed fewer, smaller and less dysplastic tumors in the intestine and colon which at the molecular level are characterized by attenuated activation of the wnt/beta-catenin signaling pathway and increased apoptotic indices. Form a mechanistic point of view, MCH increased the survival of colonic adenocarcinoma Caco2 cells via inhibiting apoptosis, consistent with the mouse studies. Conclusion: In addition to modulating inflammation, MCH was found to promote intestinal tumorigenesis at least in part by inhibiting epithelial cell apoptosis. Thereby, blocking MCH as a therapeutic approach is expected to decrease the risk for colorectal cancer

The biopsychosocial model of stress in adolescence: self-awareness of performance versus stress reactivity

Extensive research among adults supports the biopsychosocial (BPS) model of challenge and threat, which describes relationships among stress appraisals, physiological stress reactivity, and performance; however, no previous studies have examined these relationships in adolescents. Perceptions of stressors as well as physiological reactivity to stress increase during adolescence, highlighting the importance of understanding the relationships among stress appraisals, physiological reactivity, and performance during this developmental period. In this study, 79 adolescent participants reported on stress appraisals before and after a Trier Social Stress Test in which they performed a speech task. Physiological stress reactivity was defined by changes in cardiac output and total peripheral resistance from a baseline rest period to the speech task, and performance on the speech was coded using an objective rating system. We observed in adolescents only two relationships found in past adult research on the BPS model variables: (1) pre-task stress appraisal predicted post-task stress appraisal and (2) performance predicted post-task stress appraisal. Physiological reactivity during the speech was unrelated to pre- and post-task stress appraisals and to performance. We conclude that the lack of association between post-task stress appraisal and physiological stress reactivity suggests that adolescents might have low self-awareness of physiological emotional arousal. Our findings further suggest that adolescent stress appraisals are based largely on their performance during stressful situations. Developmental implications of this potential lack of awareness of one’s physiological and emotional state during adolescence are discussed.Psycholog

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. Experimental Design: Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells. Results: A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion. Conclusions: This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints

Tobacco Use and Cardiovascular Disease among American Indians: The Strong Heart Study

Tobacco use among American Indians has a long and complicated history ranging from its utilization in spiritual ceremonies to its importance as an economic factor for survival. Despite this cultural tradition and long history, there are few studies of the health effects of tobacco in this population. The Strong Heart Study is a prospective observational study of cardiovascular disease (CVD) in 13 American Indian tribes in Arizona, Oklahoma, and North and South Dakota with 4,549 participants. Baseline examinations were followed by two examinations at regular intervals and 16 years of morbidity and mortality follow-up. Hazard ratios (HRs) for non-fatal CVD for current smokers vs. non-smokers after adjusting for other risk factors were significant in women (HR = 1.94, 95% CI 1.54 to 2.45) and men (HR = 1.59, 95% CI 1.16 to 2.18). Hazard ratios for fatal CVD for current smokers vs. non-smokers after adjusting for other risk factors were significant in women (HR = 1.64, 95% CI 1.04 to 2.58), but not in men. Individuals who smoked and who were diagnosed with diabetes mellitus, hypertension or renal insufficiency were more likely to quit smoking than those without these conditions. On average, American Indians smoke fewer cigarettes per day than other racial/ethnic groups; nevertheless, the ill effects of habitual tobacco use are evident in this population

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Semi-supervised discovery of differential genes

BACKGROUND: Various statistical scores have been proposed for evaluating the significance of genes that may exhibit differential expression between two or more controlled conditions. However, in many clinical studies to detect clinical marker genes for example, the conditions have not necessarily been controlled well, thus condition labels are sometimes hard to obtain due to physical, financial, and time costs. In such a situation, we can consider an unsupervised case where labels are not available or a semi-supervised case where labels are available for a part of the whole sample set, rather than a well-studied supervised case where all samples have their labels. RESULTS: We assume a latent variable model for the expression of active genes and apply the optimal discovery procedure (ODP) proposed by Storey (2005) to the model. Our latent variable model allows gene significance scores to be applied to unsupervised and semi-supervised cases. The ODP framework improves detectability by sharing the estimated parameters of null and alternative models of multiple tests over multiple genes. A theoretical consideration leads to two different interpretations of the latent variable, i.e., it only implicitly affects the alternative model through the model parameters, or it is explicitly included in the alternative model, so that the interpretations correspond to two different implementations of ODP. By comparing the two implementations through experiments with simulation data, we have found that sharing the latent variable estimation is effective for increasing the detectability of truly active genes. We also show that the unsupervised and semi-supervised rating of genes, which takes into account the samples without condition labels, can improve detection of active genes in real gene discovery problems. CONCLUSION: The experimental results indicate that the ODP framework is effective for hypotheses including latent variables and is further improved by sharing the estimations of hidden variables over multiple tests

Book Reviews: Spring 2020

Book Reviews, of:Dalsgaard (ed) – Thomas Pynchon in ContextChetwynd, Freer, Maragos (eds) – Thomas Pynchon, Sex, and GenderMogultay – The Ruins of Urban Modernity: Thomas Pynchon's Against the DayAlworth – Site Reading: Fiction, Art, Social FormMullins – Postmodernism in Pieces: Materializing the SocialHenry – New Media and the Transformation of Postmodern American Literature: From Cage to Connectionden Dulk – Existentialist Engagement in Wallace, Eggers, and Foer: A Philosophical Analysis of Contemporary American LiteratureAnderson – Postmodern Artistry in Medievalist Fiction: An International StudyHouser – Ecosickness in Contemporary U.S. Fiction: Environment and AffectPalleau-Papin (ed) – Under Fire; William T. Vollmann, The Rifles: A Critical Study [a note from the Book Reviews Editor: if you're interested in reviewing a book on any aspect of unconventional post-1945 US literature, please send an email proposing a review to [email protected]

Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

<p>Abstract</p> <p>Background</p> <p>Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.</p> <p>Methods</p> <p>First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.</p> <p>Results</p> <p>Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.</p> <p>Conclusion</p> <p>The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.</p