Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

Modelo numérico mecanobiológico para a obtenção da matriz de rigidez estrutural e da densidade mineral na remodelagem óssea

Neste trabalho é proposto um modelo mecanobiológico de remodelagem óssea para a estimativa de variações, provocadas por perturbações mecânicas ou biológicas, na matriz de rigidez estrutural da escala macroscópica e na densidade mineral em uma região do osso. Na cooperação entre as áreas da saúde e da engenharia, como nos estudos estruturais de biomecânica no sistema esquelético, as propriedades mecânicas dos materiais devem ser conhecidas, entretanto os ossos possuem uma constituição material altamente complexa, dinâmica e variante entre indivíduos. Sua dinâmica decorre dos ciclos de absorção e deposição de matriz óssea na remodelagem óssea, a qual ocorre para manter a integridade estrutural do esqueleto e adaptá-lo aos estímulos do ambiente, sejam eles biológicos, químicos ou mecânicos. Como a remodelagem óssea pode provocar alterações no material do osso, espera-se que suas propriedades mecânicas também sejam alteradas. Na literatura científica há modelos matemáticos que preveem a variação da matriz de rigidez estrutural a partir do estímulo mecânico, porém somente os modelos mais recentes incluíram explicitamente processos biológicos e químicos da remodelagem óssea. A densidade mineral óssea é um importante parâmetro utilizado no diagnóstico de doenças ósseas na área médica. Desse modo, para a obtenção da variação da rigidez estrutural e da densidade mineral óssea, propõe-se um modelo numérico mecanobiológico composto por cinco submodelos: da dinâmica da população de células ósseas, da resposta das células ao estímulo mecânico, da porosidade óssea, da densidade mineral óssea e, baseado na Lei de Voigt para materiais compósitos, da rigidez estrutural. Os valores das constantes das equações dos submodelos foram obtidos de literatura. Para a solução das equações do modelo, propõe-se uma implementação numérica e computacional escrita em linguagem C. O método de Runge-Kutta-Dorman-Prince, cuja vantagem consiste no uso de um passo de solução variável, é utilizado no modelo para controlar o erro numérico do resultado do sistema de equações diferenciais. Foi realizada uma avaliação comparativa entre os resultados obtidos com o modelo proposto e os da literatura dos modelos de remodelagem óssea recentes. Conclui-se que o modelo e a implementação propostos são capazes de obter variações da matriz de rigidez estrutural macroscópica e da densidade mineral óssea decorrentes da perturbação nos parâmetros mecânicos ou biológicos do processo de remodelagem óssea.

ADAMIS: a database for medical information systems

This paper describes the design and implementation of ADAMIS (‘A database for medical information systems’). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the ‘simplified medical query language’ (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.

Lipid assisted membrane entry in specific phospholipid targeted protein systems

The purpose of this thesis project is to study changes in the physical state of cell membranes during cell entry, including how these changes are connected to the presence of ceramide. The role of enzymatical manipulation of lipids in bacterial internalization is also studied. A novel technique, where a single giant vesicle is chosen under the microscope and an enzyme coupled-particle attached to the micromanipulator pipette towards the vesicle, is used. Thus, the enzymatic reaction on the membrane of the giant vesicle can be followed in real-time. The first aim of this study is to develop a system where the localized sphingomyelinase membrane interaction could be observed on the surface of the giant vesicle and the effects could be monitored with microscopy. Domain formation, which resembles acid sphingomyelinase (ASMase), causes CD95 clustering in the cell membrane due to ceramide production (Grassmé et al., 2001a; Grassmé et al., 2001b) and the formation of small vesicles inside the manipulated giant vesicle is observed. Sphingomyelinase activation has also been found to be an important factor in the bacterial and viral invasion process in nonphagocytic cells (Grassmé et al., 1997; Jan et al., 2000). Accordingly, sphingomyelinase reactions in the cell membrane might also give insight into bacterial or viral cellular entry events. We found sphingomyelinase activity in Chlamydia pneumonia elementarybodies (EBs). Interestingly, the bacterium enters host cells by endocytosis but the internalization mechanism of Chlamydia is unknown. The hypothesis is that sphingomyelin is needed for host cell entry in the infection of C. pneumonia. The second project focuses on this subject. The goal of the third project is to study a role of phosphatidylserine as a target for a membrane binding protein. Phosphatidylserine is chosen because of its importance in fusion processes. This will be another example for the importance of lipids in cell targeting, internalization, and externalization.

Control of uncertain compartmental systems

Os sistemas compartimentais são frequentemente usados na modelação de diversos processos em várias áreas, tais como a biomedicina, ecologia, farmacocinética, entre outras. Na maioria das aplicações práticas, nomeadamente, aquelas que dizem respeito à administração de drogas a pacientes sujeitos a cirurgia, por exemplo, a presença de incertezas nos parâmetros do sistema ou no estado do sistema é muito comum. Ao longo dos últimos anos, a análise de sistemas compartimentais tem sido bastante desenvolvida na literatura. No entanto, a análise da sensibilidade da estabilidade destes sistemas na presença de incertezas tem recebido muito menos atenção. Nesta tese, consideramos uma lei de controlo por realimentação do estado com restrições de positividade e analisamos a sua robustez quando aplicada a sistemas compartimentais lineares e invariantes no tempo com incertezas nos parâmetros. Além disso, para sistemas lineares e invariantes no tempo com estado inicial desconhecido, combinamos esta lei de controlo com um observador do estado e a robustez da lei de controlo resultante também é analisada. O controlo do bloqueio neuromuscular por meio da infusão contínua de um relaxante muscular pode ser modelado como um sistema compartimental de três compartimentos e tem sido objecto de estudo por diversos grupos de investigação. Nesta tese, os nossos resultados são aplicados a este problema de controlo e são fornecidas estratégias para melhorar os resultados obtidos.

Staging systems in bipolar disorder: an International Society for Bipolar Disorders Task Force report

We discuss the rationale behind staging systems described specifically for bipolar disorders. Current applications, future directions and research gaps in clinical staging models for bipolar disorders are outlined.

Advances in molecular genetic systems in malaria

Robust tools for analysing gene function in Plasmodium parasites, which are the causative agents of malaria, are being developed at an accelerating rate. Two decades after genetic technologies for use in Plasmodium spp. were first described, a range of genetic tools are now available. These include conditional systems that can regulate gene expression at the genome, transcriptional or protein level, as well as more sophisticated tools for gene editing that use piggyBac transposases, integrases, zinc-finger nucleases or the CRISPR-Cas9 system. In this Review, we discuss the molecular genetic systems that are currently available for use in Plasmodium falciparum and Plasmodium berghei, and evaluate the advantages and limitations of these tools. We examine the insights that have been gained into the function of genes that are important during the blood stages of the parasites, which may help to guide the development and improvement of drug therapies and vaccines.

Dendrimers as potential platform in nanotechnology-based drug delivery systems

The dendrimers of poly (amidoamine) (PAMAM) are nanoparticles which have proven succeed in transporting drugs due to high solubility, low toxicity and ability to control drugs release. Studies have explored the biological potential of dendrimers such as to transport genes, development of vaccines, antiviral, antibacterial and anticancer therapies. This review of literature on the PAMAM dendrimers discusses the architecture and general construction of dendrimers and intrinsic properties of the PAMAM. This study also describes how the PAMAM interact with many drugs and the potential of these macromolecules as well as drug nanocarriers in transdermal routes of administration, ocular, respiratory, oral and intravenous administration. Dendrimers promises good future prospects for the biomedicine.

Dynamic decision networks for decision-making in self-adaptive systems:a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

Subject identification via ECG fiducial-based systems:influence of the type of QT interval correction

Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.

Kinetic resolution of the interactions between agrochemical products and adjuvant systems upon mixing

The addition of an adjuvant to a pesticide usually occurs in a mix-tank, before spray application to the crop. Their interaction is potentially crucial to overall efficacy but has received little attention from a physical-chemical perspective. Study was undertaken by laser diffraction, Raman spectroscopy, and small-angle X-ray scattering to resolve these physical processes. It was shown that migration of the pesticide into the adjuvant droplet occurred in all cases studied. The level of transfer was dependent upon adjuvant level, adjuvant solubility, and surfactant level. For suspension pesticides, dissolution of crystallites within the droplet occurred to a degree limited by solubility. The results directly demonstrate the transfer of the pesticide into the adjuvant carrier. This indicates that for emulsion-based pesticides, application to the target is likely as a homogeneously mixed droplet, whereas for suspension pesticides, solubility may limit transfer and dissolution, leading to heterogeneity in the applied particles.

Mitigation of N₂O emissions from surface-irrigated cropping systems using water management and the nitrification inhibitor DMPP

Soils under irrigated agriculture are a significant source of nitrous oxide (N2O) owing to high inputs of nitrogen (N) fertiliser and water. This study investigated the potential for N2O mitigation by manipulating the soil moisture deficit through irrigation scheduling in combination with, and in comparison to, using the nitrification inhibitor, 3,4-dimethylpyrazole phosphate (DMPP). Lysimeter cores planted with wheat were fitted with automated chambers for continuous measurements of N2O fluxes. Treatments included conventional irrigation (CONV), reduced deficit irrigation (RED), CONV-DMPP and RED-DMPP. The total seasonal volume of irrigation water applied was constant for all treatments but the timing and quantity in individual irrigation applications varied among treatments. 15N-labelled urea was used to track the source of N2O emissions and plant N uptake. The majority of N2O emissions occurred immediately after irrigations began on 1 September 2014. Applying RED and DMPP individually slightly decreased N2O emissions but when applied in combination (RED-DMPP) the greatest reductions in N2O emissions were observed. There was no effect of treatments on plant N uptake, 15N recovery or yield possibly because the system was not N limited. Half of the plant N and 53% to 87% of N2O was derived from non-fertiliser sources in soil, highlighting the opportunity to further exploit this valuable N pool.