Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Seasonal influenza virus infection has been associated with a variety of neurologic complications. We report a case of novel influenza A (H1N1) encephalitis in an infant aged 3 months with an upper respiratory infection, who presented seizures. The infection was confirmed in nasopharyngeal aspirate and cerebrospinal fluid. Treatment with oseltamivir was started. He was discharged without any neurologic sequelae.

Reference intervals are commonly used as a decision-making tool. In this review, we provide an overview on “big data” and reference intervals, describing the rationale, current practices including statistical methods, essential prerequisites concerning data quality, including harmonization and standardization, and future perspectives of the indirect determination of reference intervals using routine laboratory data.

The aim of this study was to determine reference intervals in an outpatient population from Vall d’Hebron laboratory using an indirect approach previously described in a Dutch population (NUMBER project). We used anonymized test results from individuals visiting general practitioners and analysed during 2018. Analytical quality was assured by EQA performance, daily average monitoring and by assessing longitudinal accuracy between 2018 and 2020 (using trueness verifiers from Dutch EQA). Per test, outliers by biochemically related tests were excluded, data were transformed to a normal distribution (if necessary) and means and standard deviations were calculated, stratified by age and sex. In addition, the reference limit estimator method was also used to calculate reference intervals using the same dataset. Finally, for standardized tests reference intervals obtained were compared with the published NUMBER results. Reference intervals were calculated using data from 509,408 clinical requests. For biochemical tests following a normal distribution, similar reference intervals were found between Vall d’Hebron and the Dutch study. For creatinine and urea, reference intervals increased with age in both populations. The upper limits of Gamma-glutamyl transferase were markedly higher in the Dutch study compared to Vall d’Hebron results. Creatine kinase and uric acid reference intervals were higher in both populations compared to conventional reference intervals. Medical test results following a normal distribution showed comparable and consistent reference intervals between studies. Therefore a simple indirect method is a feasible and cost-efficient approach for calculating reference intervals. Yet, for generating standardized calculated reference intervals that are traceable to higher order materials and methods, efforts should also focus on test standardization and bias assessment using commutable trueness verifiers.

Objectives Administration of busulfan is extending rapidly as a part of a conditioning regimen in patients undergoing hematopoietic stem cell transplantation (HSCT). Monitoring blood plasma levels of busulfan is recommended for identifying the optimal dose in patients and for minimizing toxicity. The aim of this research was to validate a simple, rapid, and cost-effective analytical tool for measuring busulfan in human plasma that would be suitable for routine clinical use. This novel tool was based on liquid chromatography coupled to mass spectrometry. Methods Human plasma samples were prepared using a one-step protein precipitation protocol. These samples were then resolved by isocratic elution in a C18 column. The mobile phase consisted 2 mM ammonium acetate and 0.1% formic acid dissolved in a 30:70 ratio of methanol/water. Busulfan-d8 was used as the internal standard. Results The run time was optimized at 1.6 min. Standard curves were linear from 0.03 to 5 mg/L. The coefficient of variation (%CV) was less than 8%. The accuracy of this method had an acceptable bias that fell within 85–115% range. No interference between busulfan and the interfering compound hemoglobin, lipemia, or bilirubin not even at the highest concentrations of compound was tested. Neither carryover nor matrix effects were observed using this method. The area under the plasma drug concentration-time curves obtained for 15 pediatric patients who received busulfan therapy prior to HSCT were analyzed and correlated properly with the administered doses. Conclusions This method was successfully validated and was found to be robust enough for therapeutic drug monitoring in a clinical setting.

ResumenLos intervalos de referencia son habitualmente empleados como herramienta de apoyo a las decisiones clínicas. En esta revisión se resumen los aspectos relacionados con el big data y los intervalos de referencia, las prácticas actuales, incluyendo los métodos estadísticos, los requisitos de calidad de los datos, incluyendo la armonización y la normalización, y las perspectivas de futuro para la determinación indirecta de intervalos de referencia mediante datos de laboratorio de rutina.

Objectives The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CVI estimates and calculate confidence intervals (CI), using the EFLM-BVD CVI estimates as gold standard for comparison. Methods Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18–75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database. Results RS2 and RS3 had the best correlation for the CVI estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CVI value was obtained. Conclusions Our study presents a new strategy for obtaining robust CVI estimates using an indirect method together with the value of symmetric RCV to select the target population. The CVI estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.

Resumen Objetivos Durante la pandemia causada por el virus SARS-CoV-2 ha surgido la necesidad de identificar variables predictivas que permitan una rápida identificación de aquellos pacientes que desarrollarán la COVID-19 severa para una rápida intervención. Este estudio ha desarrollado y validado un modelo capaz de realizar un pronóstico de severidad de la COVID-19. Métodos A partir de datos analíticos, demográficos y comorbilidades de pacientes visitados en el Servicio de Urgencias con sintomatología compatible de COVID-19, se ha realizado un estudio descriptivo y comparativo de pacientes con PCR-RT positiva y negativa para SARS-CoV-2 y de pacientes con enfermedad COVID-19 moderada y severa. La cohorte COVID-19 positiva ha servido para el desarrollo de un modelo de regresión logística. Resultados Se han incluido 410 pacientes COVID positivo (303 con enfermedad moderada y 107 con enfermedad severa) y 81 COVID negativo. Las variables predictivas del modelo son: lactato deshidrogenasa, proteína C reactiva, proteínas totales, urea y plaquetas. La calibración interna mostró un área bajo la curva ROC (AUC) de 0,88 (IC95%: 0,85–0,92), con un porcentaje de clasificaciones correctas del 85,2% a un valor de corte de 0,5. La validación externa (100 pacientes) obtuvo un AUC de 0,79 (IC95%: 0,71–0,89), con un 73% de clasificaciones correctas. Conclusiones El modelo predictivo desarrollado permite seleccionar desde el Servicio de Urgencias, con una única extracción de sangre y con magnitudes habituales en un Laboratorio Clínico, aquellos pacientes que con mayor probabilidad desarrollarán COVID-19 severa, proporcionando una importante herramienta para la planificación y la toma de decisiones clínicas.