Ilgalaikė senyvo amžiaus asmenų globa Lietuvoje: visuomenės nuostatos ir paslaugų teikėjų požiūriai

[full article and abstract in Lithuanian; abstract in English] In this paper, building on the welfare culture and public value management theories, we analyze the situation of long-term care (LTC) of older persons in Lithuania. As the normative vantage point of the study, we hold that the objective of developing and maintaining a harmonious and sustainable LTC system requires developing a system that is not only economically efficient but ensures that LTC services are of the right quality, are accessible and conform to public expectations as well as to the stakeholders’ and service providers’ attitudes. The review of prior research on social policy and LTC conducted in Lithuania shows that the attitudes of LTC providers as a community, as well as those of different stakeholders, toward the role of the state, family and other institutions in the provision of LTC vary to a considerable extent, between the social work and health care systems in particular. However, this strand of research is lacking a broader conceptualization, i.e., a situation analysis in relation to the macro level of LTC developments, public values and stakeholder attitudes to LTC as a distinct field of social policy. Next, we outline the LTC situation in Lithuania. Then, we interpret public attitudes and the attitudes of social service providers toward the responsibility for the LTC as a complex reaction to the current unsatisfactory LTC situation in the country. Surveys show that an expectation still prevails in the society that LTC should be provided by family members and other close persons, which emphasizes a need for an active discussion of LTC responsibilities and the actual situation among the politicians and society at large. The findings of our LTC stakeholder survey (n=260) show that LTC providers also believe families to be the most important pillar supporting LTC. On the other hand, a more or less equal distribution of LTC providers into two groups regarding the increase or reduction of taxes and investment into social protection indicates a need for a wider discussion of this issue among LTC providers, as the current situation points to an ab­sence of an interest group in the field of LTC that would hold a clear position about service demand and the possible means for meeting it. We conclude with recommendations on the enhancement of democratic deliberations and other instruments proposed by public value management, which would in turn allow to better conform to the attitudes and expectations of all the stakeholders and match their interests in the LTC domain.[straipsnis ir santrauka lietuvių kalba; santrauka anglų kalba] Straipsnyje, remiantis gerovės kultūros prieiga ir viešųjų vertybių vadybos idėjomis, analizuojama ir įvertinama ilgalaikės senyvo amžiaus asmenų globos ir slaugos (IGS) problematika Lietuvoje. Pradžioje apžvelgiami Lietuvos mokslininkų darbai, susiję su socialine politika ir IGS, bei apibūdinama šios srities būklė šalyje. Visuomenės nuostatos ir paslaugų teikėjų požiūriai į atsakomybę už rūpinimąsi senyvo amžiaus asmenimis bei į socialinių paslaugų plėtros poreikį IGS srityje interpretuojami kaip kompleksinė reakcija į nepatenkinamą IGS situaciją. Straipsnis baigiamas rekomendacijomis stiprinti viešųjų vertybių vadybos propaguojamus demokratinius svarstymus ir kitus instrumentus, kuriais būtų geriau atliepiama į visų suinteresuotų šalių vertybines nuostatas ir lūkesčius ir būtų geriau derinami jų interesai IGS srityje

Can a systems approach produce a better understanding of mood disorders?

Background: One in twenty-five people suffer from a mood disorder. Current treatments are sub-optimal with poor patient response and uncertain modes-of-action. There is thus a need to better understand underlying mechanisms that determine mood, and how these go wrong in affective disorders. Systems biology approaches have yielded important biological discoveries for other complex diseases such as cancer, and their potential in affective disorders will be reviewed. Scope of review: This review will provide a general background to affective disorders, plus an outline of experimental and computational systems biology. The current application of these approaches in understanding affective disorders will be considered, and future recommendations made. Major conclusions: Experimental systems biology has been applied to the study of affective disorders, especially at the genome and transcriptomic levels. However, data generation has been slowed by a lack of human tissue or suitable animal models. At present, computational systems biology has only be applied to understanding affective disorders on a few occasions. These studies provide sufficient novel biological insight to motivate further use of computational biology in this field. General significance: In common with many complex diseases much time and money has been spent on the generation of large-scale experimental datasets. The next step is to use the emerging computational approaches, predominantly developed in the field of oncology, to leverage the most biological insight from these datasets. This will lead to the critical breakthroughs required for more effective diagnosis, stratification and treatment of affective disorders

Systems biology approach in understanding metabolic reprogramming in breast cancer.

Cancer is increasingly being viewed as a metabolic disease. Research shows the importance of metabolism to cancerous traits such as metastasis, invasion, drug resistance, growth, evasion of apoptosis or immune system. Therefore, understanding how metabolism adapts to support the growth of tumours could lead towards the optimization of current therapeutic approaches and the development of new treatment options, which would help to overcome multi-drug resistance traits. In this study we represent a systems biology approach to identify biomarkers and therapeutic options of breast cancer through the analysis of breast cancer metabolism. The metabolism of breast cancer was explored in the context of personalized genome scale metabolic models (GSMNs) by combing gene expression data with currently the most comprehensive GSMN Recon2 by using the GEBRA algorithm. GEBRA algorithm ensures maximum congruency between gene expression data and metabolic genes present in the GSMN while satisfying various constraints (stoichiometry, mass balance, thermodynamic) to generate metabolic landscapes. We performed analysis using microarray data derived from the Metabric study, consisting of 2,000 individual breast tumours and 131 matched normal breast tissue samples. In addition, this study was complemented with publicly available gene expression datasets of breast cancer cell lines (MCF-7, MDA-MB-231, and MCF-10a), and breast cancer stem cells. The performed metabolic analysis of the Metabric Discovery set (997 patients) identified a novel poor prognosis group, which was reproduced in the validation set (995 samples). We further explored the hypothetical metabolic adaptations elicited by the poor prognosis tumours and established serotonin production to be an important trait of the poor prognosis group. In addition, the analysis of breast cancer stem cells suggested the prostaglandin synthesis pathway to play a major role in breast cancer stem cell maintenance and development. Our data supports the reconsideration of the synergistic use of selective serotonin uptake inhibitors (SSRIs) and prostaglandin synthase inhibitors along with the chemotherapeutic regimens used to treat breast cancers

Systems biology approach in understanding metabolic reprogramming in breast cancer.

Cancer is increasingly being viewed as a metabolic disease. Research shows the importance of metabolism to cancerous traits such as metastasis, invasion, drug resistance, growth, evasion of apoptosis or immune system. Therefore, understanding how metabolism adapts to support the growth of tumours could lead towards the optimization of current therapeutic approaches and the development of new treatment options, which would help to overcome multi-drug resistance traits. In this study we represent a systems biology approach to identify biomarkers and therapeutic options of breast cancer through the analysis of breast cancer metabolism. The metabolism of breast cancer was explored in the context of personalized genome scale metabolic models (GSMNs) by combing gene expression data with currently the most comprehensive GSMN Recon2 by using the GEBRA algorithm. GEBRA algorithm ensures maximum congruency between gene expression data and metabolic genes present in the GSMN while satisfying various constraints (stoichiometry, mass balance, thermodynamic) to generate metabolic landscapes. We performed analysis using microarray data derived from the Metabric study, consisting of 2,000 individual breast tumours and 131 matched normal breast tissue samples. In addition, this study was complemented with publicly available gene expression datasets of breast cancer cell lines (MCF-7, MDA-MB-231, and MCF-10a), and breast cancer stem cells. The performed metabolic analysis of the Metabric Discovery set (997 patients) identified a novel poor prognosis group, which was reproduced in the validation set (995 samples). We further explored the hypothetical metabolic adaptations elicited by the poor prognosis tumours and established serotonin production to be an important trait of the poor prognosis group. In addition, the analysis of breast cancer stem cells suggested the prostaglandin synthesis pathway to play a major role in breast cancer stem cell maintenance and development. Our data supports the reconsideration of the synergistic use of selective serotonin uptake inhibitors (SSRIs) and prostaglandin synthase inhibitors along with the chemotherapeutic regimens used to treat breast cancers

Synergistic interaction between lipid-loading and doxorubicin exposure in Huh7 hepatoma cells results in enhanced cytotoxicity and cellular oxidative stress: Implications for acute and chronic care of obese cancer patients

There has been a dramatic increase in the number of clinically obese individuals in the last twenty years. This has resulted in an increasingly common scenario where obese individuals are treated for other diseases, including cancer. Here, we examine interactions between lipid-induced steatosis and doxorubicin treatment in the human hepatoma cell line Huh7. The response of cells to either doxorubicin, lipid-loading or a combination were examined at the global level by DNA microarray, and for specific endpoints of cytotoxicity, lipid-loading, reactive oxygen species, anti-oxidant response systems, and apoptosis. Both doxorubicin and lipid-loading caused a significant accumulation of lipid within Huh7 cells, with the combination resulting in an additive accumulation. In contrast, cytotoxicity was synergistic for the combination compared to the individual components, suggesting an enhanced sensitivity of lipid-loaded cells to the acute hepatotoxic effects of doxorubicin. We demonstrate that a synergistic increase in reactive oxygen species and deregulation of protective anti-oxidant systems, most notably metallothionein expression, underlies this effect. Transcriptome analysis confirms synergistic changes at the global level, and is consistent with enhanced pro-inflammatory signalling in steatotic cells challenged with doxorubicin. Such effects are consistent with a potentiation of progression along the fatty liver disease spectrum. This suggests that treatment of obese individuals with doxorubicin may increase the risk of both acute (i.e. hepatotoxicity) and chronic (i.e. progress of fatty liver disease) adverse effects. This work highlights the need for more study in the growing therapeutic area to develop risk mitigation strategie

MUFINS: Multi-Formalism Interaction Network Simulator

Systems Biology has established numerous approaches for mechanistic modelling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organisation challenge. We present MUFINS software, implementing a unique set of approaches for multiformalism simulation of interaction networks. We extend the constraint-based modelling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modelling of networks simultaneously describing gene regulation, signalling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signalling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through analysis of 262 individual tumour transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualisation, which facilitates use by researchers who are not experienced in coding and mathematical modelling environments

MUFINS: multi-formalism interaction network simulator.

Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments