Streszczenia

V Congress of Polish Society of Neuroendocrinology 21–22 September, 2018 KrakówV Congress of Polish Society of Neuroendocrinology 21–22 September, 2018 Krakó

The Effects of Altered Prenatal Melatonin Signaling on Adult Behavior and Hippocampal Gene Expression of the Male Rat: A Circadioneuroendocrine-Axis Hypothesis of Psychopathology

Disturbances in melatonin - the neurohormone that signals environmental darkness as part of the circadian circuit of mammals - have been implicated in various psychopathologies in humans. At present, experimental evidence linking prenatal melatonin signaling to adult physiology, behavior, and gene expression is lacking. We hypothesized that administration of melatonin (5 mg/kg) or the melatonin receptor antagonist luzindole (5 mg/kg) to rats in utero would permanently alter the circadian circuit to produce differential growth, adult behavior, and hippocampal gene expressionin the male rat. Prenatal treatment was found to increase growth in melatonin-treated animals. In addition, subjects exposed to melatonin prenatally displayed increased rearing in the open field test and an increased right turn preference in the elevated plusmaze. Rats administered luzindole prenatally, however, displayed greater freezing and grooming behavior in the open field test and improved learning in the Morris water maze. Analysis of relative adult hippocampal gene expression with RT-PCR revealed increasedexpression of brain-derived neurotrophic factor (BDNF) with a trend toward increased expression of melatonin 1A (MEL1A) receptors in melatonin-exposed animals whereas overall prenatal treatment had a significant effect on microtubule-associated protein 2(MAP2) expression. Our data support the conclusion that the manipulation of maternal melatonin levels alters brain development and leads to physiological and behavioral abnormalities in adult offspring. We designate the term circadioneuroendocrine (CNE)axis and propose the CNE-axis hypothesis of psychopathology

Classification of Distal Growth Plate Ossification States of the Radius Bone Using a Dedicated Ultrasound Device and Machine Learning Techniques for Bone Age Assessments

X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We present a low-cost, mobile system that uses one-dimensional ultrasound radio frequency signals to obtain a robust binary classifier enabling the determination of bone age from data of girls and women aged 9 to 24 years. These data were acquired as part of a clinical study conducted with 148 subjects. Our system detects the presence or absence of the epiphyseal plate by moving ultrasound array transducers along the forearm, measuring reflection and transmission signals. Even though classical digital signal processing methods did not achieve a robust classifier, we achieved an F1 score of approximately 87% for binary classification of completed bone growth with machine learning approaches, such as the gradient boosting machine method CatBoost. We demonstrate that our ultrasound system can classify the fusion of the distal growth plate of the radius bone and the completion of bone growth with high accuracy. We propose a non-ionizing alternative to established X-ray imaging methods for this purpose

Statistical Models and Analysis of Growth Processes in Biological Tissue

The mechanisms that control growth processes in biology tissues have attracted continuous research interest despite their complexity. With the emergence of big data experimental approaches there is an urgent need to develop statistical and computational models to fit the experimental data and that can be used to make predictions to guide future research. In this work we apply statistical methods on growth process of different biological tissues, focusing on development of neuron dendrites and tumor cells. We first examine the neuron cell growth process, which has implications in neural tissue regenerations, by using a computational model with uniform branching probability and a maximum overall length constraint. One crucial outcome is that we can relate the parameter fits from our model to real data from our experimental collaborators, in order to examine the usefulness of our model under different biological conditions. Our methods can now directly compare branching probabilities of different experimental conditions and provide confidence intervals for these population-level measures. In addition, we have obtained analytical results that show that the underlying probability distribution for this process follows a geometrical progression increase at nearby distances and an approximately geometrical series decrease for far away regions, which can be used to estimate the spatial location of the maximum of the probability distribution. This result is important, since we would expect maximum number of dendrites in this region; this estimate is related to the probability of success for finding a neural target at that distance during a blind search. We then examined tumor growth processes which have similar evolutional evolution in the sense that they have an initial rapid growth that eventually becomes limited by the resource constraint. For the tumor cells evolution, we found an exponential growth model best describes the experimental data, based on the accuracy and robustness of models. Furthermore, we incorporated this growth rate model into logistic regression models that predict the growth rate of each patient with biomarkers; this formulation can be very useful for clinical trials. Overall, this study aimed to assess the molecular and clinic pathological determinants of breast cancer (BC) growth rate in vivo

Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually significantly underrepresented compared to the non-target class. This makes it difficult to train standard classification models like Support Vector Machine (SVM), Decision Trees, and Nearest Neighbor techniques on biomedical datasets because they assume an equal class distribution or an equal misclassification cost. Resampling techniques by either oversampling the minority class or under-sampling the majority class have been proposed to mitigate the class imbalance problem but with minimal success. We propose a method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain. We hypothesize that by maximizing the variance and preserving as much variability in well-engineered features prior to applying a classifier model will boost the differentiation of the thyroid nodules (benign or malignant) through effective model building. Our proposed a hybrid approach of applying Regression and Rule-Based techniques to build our Feature Engineering and a Bayesian Classifier respectively. In the Feature Engineering model, we transformed images pixel intensity values into a high dimensional structured dataset and fitting a regression analysis model to estimate relevant kernel parameters to be applied to the proposed filter method. We adopted an Elastic Net Regularization path to control the maximum log-likelihood estimation of the Regression model. Finally, we applied a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of the thyroid lesion. This is performed to establish the conditional influence on the textural feature to the random factors generated through our feature engineering model and to evaluate the success criterion of our approach. The proposed approach was tested and evaluated on a public dataset obtained from thyroid cancer ultrasound diagnostic data. The analyses of the results showed that the classification performance had a significant improvement overall for accuracy and area under the curve when then proposed feature engineering model was applied to the data. We show that a high performance of 96.00% accuracy with a sensitivity and specificity of 99.64%) and 90.23% respectively was achieved for a filter size of 13 × 13

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Translational studies on bipolar disorder and anorexia nervosa

Translational medicine aims at closing the gap between basic and clinical sciences in an integrative way. Psychiatry is one of the few medical specialties in which diagnosis is primarily based on clinical observation and all mental disorders are defined by abnormal behaviors and cognitions. The lack of biomarkers supporting diagnostic and therapeutic procedures has been a challenge in psychiatry. A better biological understanding is needed to move the field forward, it will enhance diagnostics and treatment, while reducing the stigma that surrounds mental disorders that are so poorly understood. Over the last years, advances in fundamental sciences like genetics and neuroscience have made it clear that there is shared biology between many psychiatric disorders and that integration of methods might lead to new understandings. The studies presented in this thesis focus on bipolar disorder (BD) and anorexia nervosa (AN), both severe mental disorders with high suicide rates, high heritability, and both lacking in biological understanding. BD, formerly known as manic-depressive disorder, is a mood disorder, characterized by manic or hypomanic episodes, often in combination with depressive episodes. AN is an eating disorder characterized by severe weight loss together with pathological behaviors. This thesis includes five main studies on the biology underlying these disorders, based on large, well characterized cohorts, covering several methods, including genetic, imaging and protein markers, as well as preliminary data on the establishment of in vitro models. Specifically, in study I, we attempted to replicate previously published findings on the association between subphenotypes of bipolar disorder and genetic variations in the AKT1 gene. Using frequentist and Bayesian approaches, as well as publicly available results from genome-wide association studies (GWAS), we were able to reject previously proposed associations. In study II, we explored the effects of genetic variations in genes involved in glutamate regulation on glutamate levels in two brain regions and their associations with other phenotypes. We found that the minor allele of rs3812778/rs3829280 in the 5’-untranslated region of SLC1A2, coding for a glutamate transporter, is associated (1) with increased glutamate levels in the anterior cingulate cortex, (2) with increased expression levels, in several brain regions, of the transmembrane receptor gene CD44, which is implicated in inflammation and brain development, as well as (3) with an increased risk for rapid-cycling in bipolar disorder, potentially linking CD44/SLC1A2 to a more severe phenotype of BD. In study III, we investigated the effects of clinical and genetic parameters on lithium pharmacokinetics in order to better understand lithium biology and improve lithium dose prediction models for bipolar patients, using the ratio between serum lithium and daily lithium intake, as outcome. We were able to confirm the association of several clinical predictors. Although no genome-wide significant locus was found, we report that genetic variation is important and might influence the outcome. Finally, based on the results obtained in the study, we developed a prediction algorithm that can be tested in the clinic. In study IV, we investigated the involvement of neuronal degeneration in AN by studying neurofilament light chain (NfL), a known marker of neurodegeneration, in a case-control setting and found increased levels of NfL in patients with active AN in two different cohorts. In study V, we studied the involvement of inflammation in AN, using a panel of 92 inflammatory markers in a case-control setting and report an aberrant inflammatory profile in patients with active AN, but not in patients that have recovered from AN. These studies exemplify possible approaches that can be taken in translational psychiatry. The integration of clinical, technical and analytical approaches illustrates important learning outcomes for an aspiring clinical scientist in psychiatr

Sarcopenia: age-related skeletal muscle changes from determinants to physical disability.

Human aging is characterized by skeletal muscle wasting, a debilitating condition which sets the susceptibility for diseases that directly affect the quality of life and often limit life span. Sarcopenia, i.e. the reduction of muscle mass and/or function, is the consequence of a reduction of protein synthesis and an increase in muscle protein degradation. In addition, the capacity for muscle regeneration is severely impaired in aging and this can lead to disability, particularly in patients with other concomitant diseases or organ impairment. Immobility and lack of exercise, increased levels of proinflammatory cytokines, increased production of oxygen free radicals or impaired detoxification, low anabolic hormone output, malnutrition and reduced neurological drive have been advocated as being responsible for sarcopenia. It is intriguing to notice that multiple pathways converge on skeletal muscle dysfunction, but the factors involved sometimes diverge to different pathways, thus intersecting at critical points. It is reasonable to argue that the activity of these nodes results from the net balance of regulating mechanisms, as in the case of the GH/IGF-1 axis, the testosterone and Cortisol functions, the pro- and anti-inflammatory cytokines and receptors. Both genetic and epigenetic mechanisms operate in regulating the final phenotype, the extent of muscle atrophy and reduction in strength and force generation. It is widely accepted that intervention on lifestyle habits represents an affordable and practical way to modify on a large scale some detrimental outcomes of aging, and particularly sarcopenia. The identification of the molecular chain able to reverse sarcopenia is a major goal of studies on human aging

Her2 challenge contest: a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues

Aims Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. Methods and Results The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the “ground truth” (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. Conclusions This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring