A quality-of-data aware mobile decision support system for patients with chronic illnesses

We present a mobile decision support system (mDSS) which runs on a patient Body Area Network consisting of a smartphone and a set of biosensors. Quality-of-Data (QoD) awareness in decision making is achieved by means of a component known as the Quality-of-Data Broker, which also runs on the smartphone. The QoD-aware mDSS collaborates with a more sophisticated decision support system running on a fixed back-end server in order to provide distributed decision support. This distributed decision support system has been implemented as part of a larger system developed during the European project MobiGuide. The MobiGuide system is a guideline-based Patient Guidance System designed to assist patients in the management of chronic illnesses. The system, including the QOD-aware mDSS, has been validated by clinicians and is being evaluated in patient pilots against two clinical guidelines

Application of a conceptual framework for the modelling and execution of clinical guidelines as networks of concurrent processes

We present a conceptual framework for modelling clinical guidelines as networks of concurrent processes. This enables the guideline to be partitioned and distributed at run-time across a knowledge-based telemedicine system, which is distributed by definition but whose exact physical configuration can only be determined after design-time by considering, amongst other factors, the individual patient's needs. The framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype that executes the guideline on a smartphone. The framework is shown to support the full development trajectory of a decision support system, including analysis, design and implementation

Structure and Molecular Mechanism of ER Stress Signaling by the Unfolded Protein Response Signal Activator IRE1

The endoplasmic reticulum (ER) is an important site for protein folding and maturation in eukaryotes. The cellular requirement to synthesize proteins within the ER is matched by its folding capacity. However, the physiological demands or aberrations in folding may result in an imbalance which can lead to the accumulation of misfolded protein, also known as “ER stress.” The unfolded protein response (UPR) is a cell-signaling system that readjusts ER folding capacity to restore protein homeostasis. The key UPR signal activator, IRE1, responds to stress by propagating the UPR signal from the ER to the cytosol. Here, we discuss the structural and molecular basis of IRE1 stress signaling, with particular focus on novel mechanistic advances. We draw a comparison between the recently proposed allosteric model for UPR induction and the role of Hsp70 during polypeptide import to the mitochondrial matrix

Mechanism of Hsp70 specialised interactions in protein translocation and the unfolded protein response

Hsp70 chaperones interact with substrate proteins in a coordinated fashion that is regulated by nucleotides and enhanced by assisting cochaperones. There are numerous homologues and isoforms of Hsp70 that participate in a wide variety of cellular functions. This diversity can facilitate adaption or specialisation based on particular biological activity and location within the cell. In this review, we highlight two specialised binding partner proteins, Tim44 and IRE1, that interact with Hsp70 at the membrane in order to serve their respective roles in protein translocation and UPR signaling. Recent mechanistic data suggest analogy in the way the two Hsp70 homologues (BiP and mtHsp70) can bind and release from IRE1 and Tim44 upon substrate engagement. These shared mechanistic features may underlie how Hsp70 interacts with specialised binding partners and may extend our understanding of the mechanistic repertoire that Hsp70 chaperones possess

Key Factors and AI-Based Risk Prediction of Malnutrition in Hospitalized Older Women

The numerous consequences caused by malnutrition in hospitalized patients can worsen their quality of life. The aim of this study was to evaluate the prevalence of malnutrition on the elderly population, especially focusing on women, identify key factors and develop a malnutrition risk predictive model. The study group consisted of 493 older women admitted to the Asunción Klinika Hospital in the Basque Region (Spain). For this purpose, demographic, clinical, laboratory, and admission information was gathered. Correlations and multivariate analyses and the MNA-SF screening test-based risk of malnutrition were performed. Additionally, different predictive models designed using this information were compared. The estimated frequency of malnutrition among this population in the Basque Region (Spain) is 13.8%, while 41.8% is considered at risk of malnutrition, which is increased in women, with up to 16.4% with malnutrition and 47.5% at risk of malnutrition. Sixteen variables were used to develop a predictive model obtaining Area Under the Curve (AUC) values of 0.76. Elderly women assisted at home and with high scores of dependency were identified as a risk group, as well as patients admitted in internal medicine units, and in admissions with high severity

The conceptual MADE framework for pervasive and knowledge-based decision support in telemedicine

Telemedicine systems are inherently distributed, but, especially in the context of the Internet-of-Things, their complete physical configuration may only be determined after design time by considering, for example, the individual patient's needs. Therefore, to enable pervasive and knowledge-based decision support to be provided in telemedicine, a conceptual framework was developed for modelling and executing clinical knowledge as networks of four types of concurrent processes: Monitoring (M), Analysis (A), Decision (D) and Effectuation (E). In this way, the required decision support functionality can, as presented in this article, be distributed at run-time by mapping different portions of the knowledge across the devices constituting the system. This MADE framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype knowledge-based system that executes the resulting MADE network. Thus it is shown to support the full development trajectory of a telemedicine system, including analysis, design and implementation