Concepts for the development of person-centred, digitally-enabled, Artificial Intelligence-assisted ARIA care pathways (ARIA 2024)

The traditional healthcare model is focused on diseases (medicine and natural science) and does not acknowledge patients' resources and abilities to be experts in their own life based on their lived experiences. Improving healthcare safety, quality and coordination, as well as quality of life, are important aims in the care of patients with chronic conditions. Person-centred care needs to ensure that people's values and preferences guide clinical decisions. This paper reviews current knowledge to develop (i) digital care pathways for rhinitis and asthma multimorbidity and (ii) digitally-enabled person-centred care (1). It combines all relevant research evidence, including the so-called real-world evidence, with the ultimate goal to develop digitally-enabled, patient-centred care. The paper includes (i) Allergic Rhinitis and its Impact on Asthma (ARIA), a two-decade journey, (ii) Grading of Recommendations, Assessment, Development and Evaluation (GRADE), the evidence-based model of guidelines in airway diseases, (iii) mHealth impact on airway diseases, (iv) from guidelines to digital care pathways, (v) embedding Planetary Health, (vi) novel classification of rhinitis and asthma, (vi) embedding real-life data with population-based studies, (vii) the ARIA-EAACI strategy for the management of airway diseases using digital biomarkers, (viii) Artificial Intelligence, (ix) the development of digitally-enabled ARIA Person-Centred Care and (x) the political agenda. The ultimate goal is to propose ARIA 2024 guidelines centred around the patient in order to make them more applicable and sustainable

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Axiomatic based decomposition for conceptual product design

This paper describes a structured methodology for decomposing the conceptual design problem in order to facilitate the design process and result in improved conceptual designs that better satisfy the original customer requirements. The axiomatic decomposition for conceptual design method combines Alexander's network partitioning formulation of the design problem with Suh's Independence Axiom. The axiomatic decomposition method uses a cross-domain approach in a House of Quality context to estimate the interactions among the functional requirements that are derived from a qualitative assessment of customer requirements. These interactions are used in several objective functions that serve as criteria for decomposing the design network. A new network partitioning algorithm is effective in creating partitions that maximize the within-partition interactions and minimize the between-partition interactions with appropriate weightings. The viability, usability, and value of the axiomatic decomposition method were examined through analytic comparisons and qualitative assessments of its application. The new method was examined using students in engineering design capstone courses and it was found to be useable and did produce better product designs that met the customer requirements. The student-based assessment revealed that the process would be more effective with individuals having design experience. In a subsequent assessment with practicing industrial designers, it was found that the new method did facilitate the development of better designs. An important observation was the need for limits on partition size (maximum of four functional requirements.) Another issue identified for future research was the need for a means to identify the appropriate starting partition for initiating the design

Schizophrenia: from the brain to peripheral markers. A consensus paper of the WFSBP task force on biological markers

Objective. The phenotypic complexity, together with the multifarious nature of the so-called "schizophrenic psychoses", limits our ability to form a simple and logical biologically based hypothesis for the disease group. Biological markers are defined as biochemical, physiological or anatomical traits that are specific to particular conditions. An important aim of biomarker discovery is the detection of disease correlates that can be used as diagnostic tools. Method. A selective review of the WFSBP Task Force on Biological Markers in schizophrenia is provided from the central nervous system to phenotypes, functional brain systems, chromosomal loci with potential genetic markers to the peripheral systems. Results. A number of biological measures have been proposed to be correlated with schizophrenia. At present, not a single biological trait in schizophrenia is available which achieves sufficient specificity, selectivity and is based on causal pathology and predictive validity to be recommended as diagnostic marker. Conclusions. With the emergence of new technologies and rigorous phenotypic subclassification the identification of genetic bases and assessment of dynamic disease related alterations will hopefully come to a new stage in the complex field of psychiatric research