Point-of-Care Testing and Personalized Medicine for Metabolic Disorders

Analytical Performances of the Novel i-STAT Alinity Point-of-Care Analyzer

Many Point-of-Care devices have been released over the past decade. However, data regarding their analytical performances in real-world situations remains scarce. Herein, we aimed to assess the analytical performances of the i-STAT Alinity system. We conducted an analytical performances study with the i-STAT Alinity device using cartridges CG4+ (pH, Pco2, Po2, lactate, bicarbonate and base excess); CHEM8+ (Na, K, Cl, ionized Ca, urea, creatinine, glucose, hematocrit and hemoglobin) and PT/INR (prothrombin time and international normalized ratio). We assessed the imprecision and compared the results to those obtained on existing instruments in the central laboratory. We found that the within-lab coefficients of variation (CV) were very low (<2%) or low (2−5%), except for creatinine and PT (CV = 5.2% and CV = 6.3%, respectively). For almost all the parameters, the results were strongly (R2 = 90−95%) or very strongly (R2 > 95%) correlated with those of the existing laboratory instruments, and the biases were very low (<2%) or low (2−5%). However, correlations of the PT and INR measurements with existing instruments were lower (R2 = 86.0% and 89.7%), and biases in the Po2 (7.9%), creatinine (5.4%) and PT (−6.6%) measurements were higher. The i-STAT Alinity appeared as a convenient device for measurements of numerous parameters. However, clinicians should interpret Po2, creatinine and PT results with caution.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

INTRODUCTION

Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.

METHODS

PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.

RESULTS

From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.

CONCLUSION

This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

Insulin Resistance in Schizophrenia

Schizophrenia and diabetes have been known to be linked disorders for decades. One reason is due to the fact that a major side effect of antipsychotic medication treatment is metabolic syndrome, which increases the risk of the patients developing type 2 diabetes and cardiovascular disorders. However, signs of metabolic syndrome in schizophrenia patients were identified more than 100 years ago, even before the development of antipsychotic drugs. This suggests that schizophrenia itself predisposes towards diabetes and, in turn, insulin resistance may be a risk factor for the development of schizophrenia. This review summarizes the findings surrounding this issue and places them into context with regards to increasing our understanding of the aetiology of schizophrenia and in support of biomarker and drug discovery efforts.