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The present study aims to elucidate the metabolomic profile of Arthrospira platensis grown in a bioreactor in Bulgaria. The results show that Arthrospira platensis has a high content of mannose, 137.02 mg g -1 , and vitamin A (retinol)-10.3 μg/100 g. High concentrations of calcium, sulfur, and zinc distinguish its elemental composition. The freeze-dried powder contained 15.81 ± 0.45% dietary fiber, 50.16 ± 0.25% total protein content, and 1.22 ± 0.11% total fat content. Among the unsaturated fatty acids with the highest content is α-linolenic acid (25.28%), while among the saturated fatty acids, palmitic acid prevails (22.55%). Of the sterols in the sample, β-sitosterol predominated. There is no presence of microcystins LR, RR, YR, and nodularin. Therefore, Arthrospira platensis grown in a Bulgarian bioreactor is safe for use in the pharmaceutical and food industries. Many of the organic compounds found have applications in medicine and pharmacology and play an important role in biochemical processes in the body. Therefore, Arthrospira platensis grown in Bulgaria has a high potential for use as an independent food supplement or in combination with other natural products.

Practically all aboveground plants parts of Passiflora vines can be included in the compositions of dietary supplements, medicines, and cosmetics. It has a diverse chemical composition and a wide range of biologically active components that determine its diverse pharmacological properties. Studies related to the chemical composition of the plant are summarized here, and attention has been paid to various medical applications-(1) anti-inflammatory, nephroprotective; (2) anti-depressant; (3) antidiabetic; (4) hepatoprotective; (5) antibacterial and antifungal; and (6) antipyretic and other. This review includes studies on the safety, synergistic effects, and toxicity that may occur with the use of various dietary supplements based on it. Attention has been drawn to its application in cosmetics and to patented products containing passionflower.

The brain endocannabinoid system has been shown to play a role in many physiological processes, including mood, learning and memory. It is also involved in the pathogenesis of anxiety, depression, mood disorders, as well as neurodegenerative disorders, although the exact mechanisms by which cannabinoid receptors interfere in these disorders are not well established. The aim of the present study was to evaluate the effects of cannabinoid ligands HU‑210 (CB1 receptor agonist) and SR 141716A (CB1 receptor antagonist) on learning and memory processes of rats with depressive - like state, induced by bilateral olfactory bulbectomy. The bilateral olfactory bulbectomy (OBX) is a validated model of depression, which can be used also as an animal model of Alzheimer's disease. We found that the subchronic treatment of OBX rats with HU 210 and SR 141716A exerted modulatory effect on rat's performance in both active avoidance (shuttle box) and passive avoidance (step through) tests. HU 210 ameliorated the memory deficits of OBX rats; however, the scores of the sham‑operated controls had not been reached. SR 141716A modified the avoidance performance in OBX rats and showed a memory enhancing effect in the sham‑operated rats. Our findings suggest that CB1 receptors might be involved in avoidance learning and memory acquisition in OBX rats.

In the context of home-based healthcare monitoring systems, it is desirable that the results obtained from biochemical tests - tests of various body fluids such as blood and urine - are objective and automatically generated to reduce the number of man-made errors. The authors present the StripTest reader - an innovative smartphone-based interpreter of biochemical tests based on paper-based strip colour using image processing techniques. The working principles of the reader include image acquisition of the colour strip pads using the camera phone, analysing the images within the phone and comparing them with reference colours provided by the manufacturer to obtain the test result. The detection of kidney damage was used as a scenario to illustrate the application of, and test, the StripTest reader. An extensive evaluation using laboratory and human urine samples demonstrates the reader's accuracy and precision of detection, indicating the successful development of a cheap, mobile and smart reader for home-monitoring of kidney functioning, which can facilitate the early detection of health problems and a timely treatment intervention.

Introduction: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status., Methods: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set., Results: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objectives: To obtain a balanced view on the role and place of expert knowledge and learning methods in building Bayesian networks for medical image interpretation., Methods and Materials: The interpretation of mammograms was selected as the example medical image interpretation problem. Medical image interpretation has its own common standards and procedures. The impact of these on two complementary methods for Bayesian network construction was explored. Firstly, methods for the discretisation of continuous features were investigated, yielding multinomial distributions that were compared to the original Gaussian probabilistic parameters of the network. Secondly, the structure of a manually constructed Bayesian network was tested by structure learning from image data. The image data used for the research came from screening mammographic examinations of 795 patients, of whom 344 were cancerous., Results: The experimental results show that there is an interesting interplay of machine learning results and background knowledge in medical image interpretation. Networks with discretised data lead to better classification performance (increase in the detected cancers of up to 11.7%), easier interpretation, and a better fit to the data in comparison to the expert-based Bayesian network with Gaussian probabilistic parameters. Gaussian probability distributions are often used in medical image interpretation because of the continuous nature of many of the image features. The structures learnt supported many of the expert-originated relationships but also revealed some novel relationships between the mammographic features. Using discretised features and performing structure learning on the mammographic data has further improved the cancer detection performance of up to 17% compared to the manually constructed Bayesian network model., Conclusion: Finding the right balance between expert knowledge and data-derived knowledge, both at the level of network structure and parameters, is key to using Bayesian networks for medical image interpretation. A balanced approach to building Bayesian networks for image interpretation yields more accurate and understandable Bayesian network models., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The MetaPath knowledge base was developed for the purpose of archiving, sharing and analyzing experimental data on metabolism, metabolic pathways and crucial supporting metadata. The MetaPath system grew out of the need to compile and organize the results of metabolism studies into a systematic database to facilitate data comparisons and evaluations. Specialized MetaPath data evaluation tools facilitate the review of pesticide metabolism data submitted for regulatory risk assessments as well as exchange of results of complex analyses used in regulation and research. Customized screen editors called Composers were developed to automate data entry into MetaPath while also streamlining the production of agency specific study summaries such as the Data Evaluation Records (DER) used by the US EPA Office of Pesticide Programs. Efforts are underway through an Organization for Economic Co-operation and Development (OECD) work group to extend the use of DER Composers as harmonized templates for rat metabolism, livestock residue, plant residue and environmental degradation studies., (Published by Elsevier Inc.)

The recent increased interest in information fusion methods for solving complex problem, such as in image analysis, is motivated by the wish to better exploit the multitude of information, available from different sources, to enhance decision-making. In this paper, we propose a novel method, that advances the state of the art of fusing image information from different views, based on a special class of probabilistic graphical models, called causal independence models. The strength of this method is its ability to systematically and naturally capture uncertain domain knowledge, while performing information fusion in a computationally efficient way. We examine the value of the method for mammographic analysis and demonstrate its advantages in terms of explicit knowledge representation and accuracy (increase of at least 6.3% and 5.2% of true positive detection rates at 5% and 10% false positive rates) in comparison with previous single-view and multi-view systems, and benchmark fusion methods such as naïve Bayes and logistic regression., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Objectives: Receptors for luteinizing hormone-releasing hormone (LHRH) can be utilized for targeted chemotherapy of cytotoxic LHRH analogs. The compound AEZS-108 (previously AN-152) consists of [D-Lys⁶]LHRH linked to doxorubicin. The objectives of this first study in humans with AESZ-108 were to determine the maximum tolerated dose and to characterize the dose-limiting toxicity, pharmacokinetics, preliminary efficacy, and hormonal effects., Methods: The study included 17 women with histologically confirmed epithelial cancer of the ovary, endometrium, or breast that was metastatic or unresectable and for which standard curative or palliative measures could not be used or were no longer effective or tolerated. In each patient, immunohistochemistry of primary tumor or metastatic lesion confirmed that the tumors expressed LHRH receptors., Results: One patient each received intravenous doses of 10, 20, 40, or 80 mg/m² of AEZS-108, six received 160 mg/m² and seven 267 mg/m² at 3 week intervals. Dose-limiting leukopenia and neutropenia were observed at the highest dose. A total of 6 patients, 3 patients each in both upper dose groups, showed responses to AEZS-108. The half-life of AESZ-108 was estimated to be about 2h., Conclusions: The maximum tolerated dose of AESZ-108 in the absence of supportive medication is 267 mg/m² and this dose is recommended as starting dose for therapeutic Phase II studies., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Neural networks applied in control loops and safety-critical domains have to meet more requirements than just the overall best function approximation. On the one hand, a small approximation error is required; on the other hand, the smoothness and the monotonicity of selected input-output relations have to be guaranteed. Otherwise, the stability of most of the control laws is lost. In this article we compare two neural network-based approaches incorporating partial monotonicity by structure, namely the Monotonic Multi-Layer Perceptron (MONMLP) network and the Monotonic MIN-MAX (MONMM) network. We show the universal approximation capabilities of both types of network for partially monotone functions. On a number of datasets, we investigate the advantages and disadvantages of these approaches related to approximation performance, training of the model and convergence., (2009 Elsevier Ltd. All rights reserved.)

Mammographic reading by radiologists requires the comparison of at least two breast projections (views) for the detection and the diagnosis of breast abnormalities. Despite their reported potential to support radiologists, most mammographic computer-aided detection (CAD) systems have a major limitation: as opposed to the radiologist's practice, computerized systems analyze each view independently. To tackle this problem, in this paper, we propose a Bayesian network framework for multi-view mammographic analysis, with main focus on breast cancer detection at a patient level. We use causal-independence models and context modeling over the whole breast represented as links between the regions detected by a single-view CAD system in the two breast projections. The proposed approach is implemented and tested with screening mammograms for 1063 cases of whom 385 had breast cancer. The single-view CAD system is used as a benchmark method for comparison. The results show that our multi-view modeling leads to significantly better performance in discriminating between normal and cancerous patients. We also demonstrate the potential of our multi-view system for selecting the most suspicious cases.

The hormonal regulation of haemostasis is a problem which has not received much attention. The data concerning the influence of hormones from the hypothalamic-pituitary-thyroid axis are scarce, contradictory and based mainly on clinical observations. The objective of the current research is to study the influence of the Thyrotropin releasing hormone (TRH), Thyroid stimulating hormone (TSH), Triiodothyronine (T3) and Thyroxin (T4) on the activity level of the vitamin K-dependent plasma factors of blood coagulation--factor II (F II), factor VII (F VII), factor IX (F IX) and factor X (F X). This study was carried out on 40 male Wistar rats. The necessary quantity of blood was obtained by cardiac puncture under ether narcosis. The indicators studied were activated partial thromboplastin time (aPTT), protothrombin time (PT), F II, F VII, F IX and F X, and were determined by means of Diagnostica Stago tests and with the help of an automatic coagulometer. The hormones studied were: TRH (0.06 mg/kg b.w.), TSH (1 MU/kg b.w.), T3 (0.08 mg/kg b.w.) T4 (0.08 mg/kg b.w.) prolonged aPTT (p<0.001) and PT (p<0.001). TRH and T3 significantly reduced the activity level of factors II, VII, IX and X; T4 only reduced the level of F II (p<0.05), and TSH did not induce significant changes in the haemocoagulation factors studied. The TRH, TSH, T3 and T4 hormones, although elements of one and the same axis, have an ambiguous effect on the vitamin K-dependent factors of blood coagulation. The results obtained show that the determined changes in the activity levels of the vitamin K-dependent plasma factors of blood coagulation are undoubtedly related to the hypocoagulation observed in the intrinsic and extrinsic pathways under the influence of the hormones of the thyroid axis.

Anthracycline cardiotoxicity is cumulative and can cause congestive heart failure. The cardiotoxicity caused by 5-fluorouracil (5-FU) is acute and is usually observed during the first cycle of chemotherapy. We present the case of a female patient operated on for colorectal cancer and receiving her first postoperative chemotherapy cycle. Three hours after the initiation of continuous 5-FU infusion she developed signs of acute heart failure (AHF) and pulmonary edema. The patient did not have any previous history of heart disease. Symptoms resolved 24 hours from the onset of the episode after the initiation of the relevant emergency therapy. One of the most common symptoms related to 5-FU cardiotoxicity is chest pain. In case of such a toxicity treatment should be switched to another antineoplastic agent.

The hormonal regulation of hemostasis has had little attention in research, and the existing literature data are relatively contradictory. The possible effects of the hormones of the hypothalamic-pituitary-thyroid axis on hemocoagulation and the fibrinolytic system are studied here. The study was conducted on 80 white male rats of the Wistar breed. The necessary blood quantity was obtained by cardiac puncture realized under ether narcosis. The basic parameters of the hemocoagulation and fibrinolytic activity of the plasma were determined by Diagnostica Stago tests (France), using an automatic coagulometer (Italy). The hormones employed in the study: Thyreotropin releasing hormone (0.06 mg/kg bw), Thyroid stimulating hormone (1 MU/kg bw), Triiodothyronine (0.08 mg/kg bw), and Thyroxin (0.08 mg/kg bw) applied s.c. for three consecutive days, extended the activated partial thromboplastine time (p less than 0.001), proto-thromboplastine time (p less than 0.001), thrombin time (p less than 0.001), reptilase time (p less than 0.001), and shortened the euglobin clot lysis time of (p less than 0.001). These data indicate that each of the hormones used causes significant changes in hemostasis by suppressing the coagulability by the intrinsic and extrinsic system pathways, and transformation of fibrinogen into fibrin. The shortened euglobim clot lysis time may be recognized as a manifestation of increased levels of plasma plasminogen activators. The results obtained show that hypothalamic-pituitary-thyroid hormones are significant regulators of hemostasis, since they cause an expressed hypocoagulation and increase plasma fibrinolytic activity.

Twenty-one propolis samples produced by 12 different Meliponinae species were analyzed by GC-MS. Several chemical types of stingless bees' propolis could be grouped, according to the prevailing type of compounds like: 'gallic acid", "diterpenic" and "triterpenic" types. The results confirm that neither the bee species nor the geographical location determine the chemical composition of Meliponinae propolis and the choice of its plant source, respectively. This could be explained by the fact that Meliponinae forage over short distances (maximum 500 m) and thus use as propolis source the first plant exudate they encounter during their flights. The antibacterial, antifungal and cytotoxic activities of the samples were also investigated. Most samples had weak or no activity against E. coli, weak action against Candida albicans. Some of them showed significant activity against St. aureus., presumably connected to the high concentration of diterpenic acids. Samples rich in diterpenic acids possessed also high cytotoxic activity (Artemia salina test).

The chemical composition of propolis from Bulgaria, Turkey, Greece and Algeria was investigated by GC-MS. All of them contained mainly flavonoids and esters of caffeic and ferulic acids, which indicated that their main source are buds of poplars of the taxonomic section Aegieros. Some Turkish samples contained a low percent of diterpenic acids, while in Algerian samples significant amounts of a hydroxyditerpenic acid (M=322, its structure not determined by its MS) were found. All samples showed significant antibacterial and weak to moderate antifungal activity.

Phytochemical study of leaves of Uvaria chamae resulted in the isolation for the first time for the genus Uvaria of benzylisoquinoline alkaloids (+)-armepavine (1) and racem. O,O-dimethylcoclaurine (2). The aporphines nornantenine (3), nantenine (4) and corydine (7) are new for the species. The alkaloids were found to express cytotoxic activity against L 929 transformed cells. The highest activity was shown by 1, 3, and 5. At a concentration corresponding to their IC50 against L929 cells, they were nontoxic against mouse thymocytes.