Quantitative body shape analysis for obesity evaluation

Obesity is a public health concern as it is associated with a number of diseases, such as diabetes mellitus type 2, cardiovascular disease, some forms of renal failure and certain types of cancers. Growing evidence suggests that it is not only the amount of fat, but also its distribution in the body that is important to predict metabolic risk factors and adverse changes in organs. In this respect, it is necessary to develop convenient and inexpensive measures to characterize human body fat distribution and to investigate the unknown linkage between intrinsic adiposity and external body shape. This dissertation research aims to improve the obesity assessment by developing new quantitative measurements that comprehensively characterize body shape, and are highly relevant to intrinsic abdominal adiposity conditions. The proposed body shape descriptors were defined based on three-dimensional body images reconstructed from a custom-made stereovision body imaging system, which is particularly suitable for clinical use as an obesity monitoring equipment for its high portability and affordability. In this study, we developed a fully-automated algorithm to process T1-weighted magnetic resonance imaging (MRI) slices for abdominal adiposity measurements. This algorithm dramatically reduces the processing time and workload compared with traditional manual or semi-automatic methods for MRI processing, and greatly improves the repeatability and objectivity of fat assessments. A new obesity categorization method was then defined based on MRI adiposity data to depict characteristics of abdominal fat distribution, and the associations between the body shape descriptors and the MRI abdominal adiposity were explored. It was shown that the proposed body shape descriptors are able to capture the body shape differences between the subjects with dissimilar internal fat distribution (i.e., different categories), and to provide excellent prediction for the category of fat distribution through an optimized support-vector-machine classifier. The predictive models established in this dissertation demonstrate that the novel body shape descriptors were also effective for prediction of the volumes of abdominal visceral fat and subcutaneous fat accumulated in male and female adults. This dissertation introduces an innovative approach to assess obesity and fat distribution based on newly defined shape descriptors, and provides new findings that reveal the associations of intrinsic fat distribution with external body shapes, which enable both qualitative and quantitative assessment of obesity from body shape measurements.Biomedical Engineerin

Classifying Abdominal Fat Distribution Patterns by Using Body Measurement Data

This article aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI), to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs), and to develop a classifier to predict the fat distribution clusters using the BSDs

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Small molecule-protein interactions exemplified on short-chain dehydrogenases/reductases

The short-chain dehydrogenase/reductase (SDRs) family represents one of the largest enzyme superfamilies, with over 80 members in the human genome. Even though the human genome project has sequenced and mapped the entire human genome, the physiological functions of more than 70% of all SDRs are currently unexplored or insufficiently characterized. To start to fill this gap, the present thesis aimed to employ a combination of molecular modeling approaches and biological assessments for the identification and characterization of novel inhibitors and/or potential substrates of different SDRs. Due to their involvement in steroid biosynthesis and metabolism, SDRs are potential targets of endocrine disrupting chemicals (EDCs). To test the use of pharmacophore-based virtual screening (VS) applications and subsequent in vitro evaluation of virtual hits for the identification and characterization of potential inhibitors, 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) was selected as an example. 11β-HSD2 has an important role in the placenta by inactivating cortisol and protecting the fetus from high maternal glucocorticoid levels. An impaired placental 11β-HSD2 function has been associated with altered fetal growth and angiogenesis as well as a higher risk for cardio-metabolic diseases in later life. Despite this vital function, 11β-HSD2 is not covered in common off-target screening approaches. Several azole fungicides were identified as 11β-HSD inhibitors amongst approved drugs by testing selected virtually retrieved hits for inhibition of cortisol to cortisone conversion in cell lysates expressing recombinant human 11β-HSD2. Moreover, a significant structure-activity relationship between azole scaffold size, 11β-HSD enzyme selectivity and potency was observed. The most potent 11β-HSD2 inhibition was obtained for itraconazole (IC50 139 ± 14 nM), for its active metabolite hydroxyitraconazole (IC50 223 ± 31 nM), and for posaconazole (IC50 460 ± 98 nM). Interestingly, substantially lower inhibitory 11β-HSD2 activity of these compounds was detected using mouse and rat kidney homogenate preparations, indicating species-specific differences. Impaired placental 11β-HSD2 function exerted by these compounds might, in addition to the known inhibition of P-glycoprotein efflux transport and cytochrome P450 enzymes, lead to locally elevated cortisol levels and thereby could affect fetal programming. Successful employment of pharmacophore-based VS applications requires suitable and reliable in vitro validation strategies. Therefore, the following study addressed the re-evaluation of a potential EDC, the widely used flame retardant tetrabromobisphenol A (TBBPA), on glucocorticoid receptor (GR) and androgen receptor (AR) function. TBBPA was reported earlier in yeast-based reporter assays to potently interfere with GR and moderately with AR function. Human HEK-293 cell-based reporter assays and cell-free receptor binding assays did not show any activity of TBBPA on GR function, which was supported by molecular docking calculations. The antiandrogenic effect, however, could be confirmed, although less pronounced than in the HEK-293 cell system. Nevertheless, the evaluation of the relevant concentrations of an EDC found in the human body is crucial for an appropriate safety assessment. Considering the rapid metabolism of TBBPA and the low concentrations observed in the human body, it is questionable whether relevant concentrations can be reached to cause harmful effects. Thus, it is vital to take the limitations of each testing system including the distinct sensitivities and specificities into account to avoid false positive or false negative results. To extend the applications of in silico tools with demonstrated proof-of-concept, they were further employed to investigate novel substrate specificities for three different SDR members: the two multi-functional enzymes, 11β-HSD1 and carbonyl reductase (CBR) 1 as well as the orphan enzyme DHRS7. A role for 11β-HSD1 in oxysterol metabolism by metabolizing 7-ketocholesterol (7kC) has already been described. However, in contrast to the known receptors for 7α,25-dihydroxycholesterol (7α25OHC), i.e. Epstein-Barr virus-induced gene 2 (EBI2), or 7β,27-dihydroxycholesterol (7β27OHC), i.e. retinoic acid related orphan receptor (ROR)γ, no endogenous receptor has been identified so far for 7kC or its metabolite 7β-hydroxycholesterol. To explore the underlying biosynthetic pathways of such dihydroxylated oxysterols, the role of 11β-HSD1 in the generation of dihydroxylated oxysterols was investigated. For the first time, the stereospecific and seemingly irreversible oxoreduction of 7-keto,25-hydroxycholesterol (7k25OHC) and 7-keto,27-hydroxycholesterol (7k27OHC) to their corresponding 7β-hydroxylated metabolites 7β25OHC and 7β27OHC by recombinant human 11β-HSD1 could be demonstrated in vitro in intact HEK-293 cells. Furthermore, 7k25OHC and 7k27OHC were found to be potently inhibited the 11β-HSD1-dependent oxoreduction of cortisone to cortisol. Molecular modeling experiments confirmed these results and suggested competition of 7k25OHC and 7k27OHC with cortisone in the enzyme binding pocket. For a more detailed enzyme characterization, 11β-HSD1 pharmacophore models were generated and employed for VS of the human metabolome database and the lipidmaps structure database. The VS yielded several hundred virtual hits, including the successful filtering of known substrates such as endogenous 11-ketoglucocorticoids, synthetic glucocorticoids, 7kC, and several bile acids known to inhibit the enzyme. Further hits comprised several eicosanoids including prostaglandins, leukotrienes, cyclopentenone isoprostanes, levuglandins or hydroxyeicosatetraenoic acids (HETEs) and compounds of the kynurenine pathway. The important role of these compounds as well as 11β-HSD1 in inflammation emphasizes a potential association. However, further biological validation is of utmost necessity to explore a potential link. The closest relative of 11β-HSD1 is the orphan enzyme DHRS7, which has been suggested to act as tumor suppressor. Among others, cortisone and 5α-dihydrotestosterone have been identified as substrates of DHRS7, although effects in functional assays could only be observed at high concentrations that may not be of physiological relevance. Hence, the existence of other yet unexplored substrates of DHRS7 can be assumed, and the generation of homology models to study the structural features of the substrate binding site of DHRS7 was employed. The predictivity of the constructed models is currently limited, due to a highly variable region comprising a part of the ligand binding site but particularly the entry of the binding pocket, and requires further optimizations. Nevertheless, the models generally displayed a cone-shaped binding site with a rather hydrophobic core. This may suggest larger metabolites to be converted by DHRS7. Moreover, the flexible loops surrounding the binding pocket may lead to the induction of an induced fit upon ligand binding. However, further studies are crucial to confirm these findings. CBR1 is well-known for its role in phase I metabolism of a variety of carbonyl containing xenobiotic compounds. Several endogenous substrates of CBR1 have been reported such as prostaglandins, S-nitrosoglutathione or lipid aldehydes. The physiological relevance of these endogenous substrates, however, is not fully understood. Thus, the physiological roles of CBR1 was further explored by identifying a novel function for CBR1 in the metabolism glucocorticoids. CBR1 was found to catalyze the conversion of cortisol into 20β-dihydrocortisol (20β-DHF), which was in turn detected as the major route of cortisol metabolism in horses and elevated in adipose tissue derived from obese horses, humans and mice. Additionally, 20β-DHF was demonstrated as weak endogenous agonist of the GR, suggesting a novel pathway to modulate GR activation by CBR1-depenent protection against excessive GR activation in obesity. In conclusion, this thesis emphasized the employment of molecular modeling approaches as an initial filter to identify toxicological relevant compound classes for the identification of potential EDCs and, moreover, as valuable tools to identify novel substrates of multifunctional SDRs and to unravel novel functions for the large majority of yet unexplored orphan SDR members, while carefully considering the limitations of this strategy

Structure-based drug design of 11β-hydroxysteroid dehydrogenase type 1 inhibitors

The enzyme 11β-Hydroxysteroid Dehydrogenase 1 (11β-HSD1) catalyses the intracellular biosynthesis of the active glucocorticoid cortisol. Tissue specific dysregulation of the enzyme has been implicated in the development of metabolic syndrome and other associated diseases. Experiments with transgenic mice and prototype inhibitors show that inhibition of 11β-HSD1 in visceral adipose tissue and liver leads to a resistance of diet-induced hyperglycemia and a favourable lipid and lipoprotein profile as compared to controls. 11β-HSD1 inhibition has thus been proposed as an effective strategy to decrease intracellular glucocorticoid levels without affecting circulating glucocorticoid levels that are essential for stress responses. The clinical development of selective and potent drugs has therefore become a priority. In this research, a process of virtual screening employing the novel algorithm UFSRAT (Ultra Fast Shape Recognition with Atom Types) was used to discover compounds which had specific physicochemical and spatial atomic parameters deemed essential for inhibition of 11β-HSD1. The top scoring compounds were assayed for inhibitory activity against recombinant human and mouse enzyme, using a fluorescence spectroscopy approach. In addition, HEK-293 cell based assays with either human, mouse or rat enzymes were carried out using a scintillation proximity assay (SPA). The most potent compound competitively inhibited human 11β-HSD1 with a Kiapp value of 51 nM. Recombinant mouse and human enzyme were expressed, purified and characterised and used in a series of ligand binding assays. Further to this, an X-ray crystal structure of mouse 11β-HSD1 in complex with a tight binding inhibitor – carbenoxolone was solved

Impact of Pre-Mortem Factors on Meat Quality

Meat quality is associated with the chemical composition and metabolic state of skeletal muscle. This Special Issue aims to compile the recent literature with a focus on meat quality and pre-mortem factors that affect muscle metabolism. It includes nine research articles about various types of meat, as well as one review article about beef quality

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

Innovations in Medical Image Analysis and Explainable AI for Transparent Clinical Decision Support Systems

This thesis explores innovative methods designed to assist clinicians in their everyday practice, with a particular emphasis on Medical Image Analysis and Explainability issues. The main challenge lies in interpreting the knowledge gained from machine learning algorithms, also called black-boxes, to provide transparent clinical decision support systems for real integration into clinical practice. For this reason, all work aims to exploit Explainable AI techniques to study and interpret the trained models. Given the countless open problems for the development of clinical decision support systems, the project includes the analysis of various data and pathologies. The main works are focused on the most threatening disease afflicting the female population: Breast Cancer. The works aim to diagnose and classify breast cancer through medical images by taking advantage of a first-level examination such as Mammography screening, Ultrasound images, and a more advanced examination such as MRI. Papers on Breast Cancer and Microcalcification Classification demonstrated the potential of shallow learning algorithms in terms of explainability and accuracy when intelligible radiomic features are used. Conversely, the union of deep learning and Explainable AI methods showed impressive results for Breast Cancer Detection. The local explanations provided via saliency maps were critical for model introspection, as well as increasing performance. To increase trust in these systems and aspire to their real use, a multi-level explanation was proposed. Three main stakeholders who need transparent models have been identified: developers, physicians, and patients. For this reason, guided by the enormous impact of COVID-19 in the world population, a fully Explainable machine learning model was proposed for COVID-19 Prognosis prediction exploiting the proposed multi-level explanation. It is assumed that such a system primarily requires two components: 1) inherently explainable inputs such as clinical, laboratory, and radiomic features; 2) Explainable methods capable of explaining globally and locally the trained model. The union of these two requirements allows the developer to detect any model bias, the doctor to verify the model findings with clinical evidence, and justify decisions to patients. These results were also confirmed for the study of coronary artery disease. In particular machine learning algorithms are trained using intelligible clinical and radiomic features extracted from pericoronaric adipose tissue to assess the condition of coronary arteries. Eventually, some important national and international collaborations led to the analysis of data for the development of predictive models for some neurological disorders. In particular, the predictivity of handwriting features for the prediction of depressed patients was explored. Using the training of neural networks constrained by first-order logic, it was possible to provide high-performance and explainable models, going beyond the trade-off between explainability and accuracy