Novità nel mondo di Medicina di Laboratorio

BACKGROUND:

Exposure to drugs of abuse is frequently assessed using urine drug screening (UDS) immunoassays. Although fast and relatively inexpensive, UDS assays often cross-react with unrelated compounds, which can lead to false-positive results and impair patient care. The current process of identifying cross-reactivity relies largely on case reports, making it sporadic and inefficient, and rendering knowledge of cross-reactivity incomplete. Here, we present a systematic approach to discover cross-reactive substances using data from electronic health records (EHRs).

METHODS:

Using our institution's EHR data, we assembled a data set of 698651 UDS results across 10 assays and linked each UDS result to the corresponding individual's previous medication exposures. We hypothesized that exposure to a cross-reactive ingredient would increase the odds of a false-positive screen. For 2201 assay–ingredient pairs, we quantified potential cross-reactivity as an odds ratio from logistic regression. We then evaluated cross-reactivity experimentally by spiking the ingredient or its metabolite into drug-free urine and testing the spiked samples on each assay.

RESULTS:

Our approach recovered multiple known cross-reactivities. After accounting for concurrent exposures to multiple ingredients, we selected 18 compounds (13 parent drugs and 5 metabolites) to evaluate experimentally. We validated 12 of 13 tested assay–ingredient pairs expected to show cross-reactivity by our analysis, discovering previously unknown cross-reactivities affecting assays for amphetamines, buprenorphine, cannabinoids, and methadone.

CONCLUSIONS:

Our findings can help laboratorians and providers interpret presumptive positive UDS results. Our data-driven approach can serve as a model for high-throughput discovery of substances that interfere with laboratory tests.

BACKGROUND:

Early detection of hepatocellular carcinoma (HCC) among hepatitis B virus (HBV)-infected patients remains a challenge, especially in China. We sought to create an online calculator of serum biomarkers to detect HCC among patients with chronic hepatitis B (CHB).

METHODS:

Participants with HBV-HCC, CHB, HBV-related liver cirrhosis (HBV-LC), benign hepatic tumors, and healthy controls (HCs) were recruited at 11 Chinese hospitals. Potential serum HCC biomarkers, protein induced by vitamin K absence or antagonist-II (PIVKA-II), α-fetoprotein (AFP), lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3) and α-l-fucosidase (AFU) were evaluated in the pilot cohort. The calculator was built in the training cohort via logistic regression model and validated in the validation cohort.

RESULTS:

In the pilot study, PIVKA-II and AFP showed better diagnostic sensitivity and specificity compared with AFP-L3 and AFU and were chosen for further study. A combination of PIVKA-II and AFP demonstrated better diagnostic accuracy in differentiating patients with HBV-HCC from patients with CHB or HBV-LC than AFP or PIVKA-II alone [area under the curve (AUC), 0.922 (95% CI, 0.908–0.935), sensitivity 88.3% and specificity 85.1% for the training cohort; 0.902 (95% CI, 0.875–0.929), 87.8%, and 81.0%, respectively, for the validation cohort]. The nomogram including AFP, PIVKA-II, age, and sex performed well in predicting HBV-HCC with good calibration and discrimination [AUC, 0.941 (95% CI, 0.929–0.952)] and was validated in the validation cohort [AUC, 0.931 (95% CI, 0.909–0.953)].

CONCLUSIONS:

Our results demonstrated that a web-based calculator including age, sex, AFP, and PIVKA-II accurately predicted the presence of HCC in patients with CHB.

ClinicalTrials.gov Identifier:

NCT03047603

BACKGROUND:

Analytical characteristics of methods to measure biomarkers determine how well the methods measure what they claim to measure. Transparent reporting of analytical characteristics allows readers to assess the validity and generalizability of clinical studies in which biomarkers are used. Our aims were to assess the reporting of analytical characteristics of biomarkers used in clinical research and to evaluate the extent of reported characterization procedures for assay precision.

METHODS:

We searched 5 medical journals (Annals of Internal Medicine, JAMA: The Journal of the American Medical Association, The Lancet, The New England Journal of Medicine, and PLOS Medicine) over a 10-year period for the term "biomarker" in the full-text field. We included studies in which biomarkers were used for inclusion/exclusion of study participants, for patient classification, or as a study outcome. We tabulated the frequencies of reporting of 11 key analytical characteristics (such as analytical accuracy of test results) in the included studies.

RESULTS:

A total of 544 studies and 1299 biomarker uses met the inclusion criteria. No information on analytical characteristics was reported for 67% of the biomarkers. For 65 biomarkers (3%), ≥4 characteristics were reported (range, 4–8). The manufacturer of the measurement procedure could not be determined for 688 (53%) of the 1299 biomarkers. The extent of assessments of assay imprecision, when reported, did not meet expectations for clinical use of biomarkers.

CONCLUSIONS:

Reporting of the analytical performance of biomarker measurements is variable and often absent from published clinical studies. We suggest that readers need fuller reporting of analytical characteristics to interpret study results, assess generalizability of conclusions, and compare results among clinical studies.

BACKGROUND:

Despite implementation of the Athlete Biological Passport 10 years ago, blood doping remains difficult to detect. Thus, there is a need for new biomarkers to increase the sensitivity of the adaptive model. Transcriptomic biomarkers originating from immature reticulocytes may be reliable indicators of blood manipulations. Furthermore, the use of dried blood spots (DBSs) for antidoping purposes constitutes a complementary approach to venous blood collection. Here, we developed a method of quantifying the RNA-based 5'-aminolevulinate synthase 2 (ALAS2) biomarker in DBS.

MATERIALS:

The technical, interindividual, and intraindividual variabilities of the method, and the effects of storage conditions on the production levels of ALAS2 RNA were assessed. The method was used to monitor erythropoiesis stimulated endogenously (blood withdrawal) or exogenously (injection of recombinant human erythropoietin).

RESULTS:

When measured over a 7-week period, the intra- and interindividual variabilities of ALAS2 expression in DBS were 12.5%–42.4% and 49%, respectively. Following withdrawal of 1 unit of blood, the ALAS2 RNA in DBS increased significantly for up to 15 days. Variations in the expression level of this biomarker in DBS samples were more marked than those of the conventional hematological parameters, reticulocyte percentage and immature reticulocyte fraction. After exogenous stimulation of erythropoiesis via recombinant human erythropoietin injection, ALAS2 expression in DBS increased by a mean 8-fold.

CONCLUSIONS:

Monitoring of transcriptomic biomarkers in DBS could complement the measurement of hematological parameters in the Athlete Biological Passport and aid the detection of blood manipulations.

BACKGROUND:

The ratio of β-amyloid 1–42 (Aβ42) to Aβ40 in cerebrospinal fluid (CSF) may be useful for evaluating Alzheimer disease (AD), but quantification is limited by factors including preanalytical analyte loss. We developed an LC-MS/MS assay that limits analyte loss. Here we describe the analytical characteristics of the assay and its performance in differentiating patients with AD from non-AD dementia and healthy controls.

METHODS:

To measure Aβ42/Aβ40, we used unique proteolytically derived C-terminal peptides as surrogate markers of Aβ40 and Aβ42, which were analyzed and quantified by LC-MS/MS. The assay was analytically validated and applied to specimens from individuals with clinically diagnosed AD (n = 102), mild cognitive impairment (n = 37), and non-AD dementias (n = 22), as well as from healthy controls (n = 130). Aβ42/Aβ40 values were compared with APOE genotype inferred from phenotype, also measured by LC-MS/MS.

RESULTS:

The assay had a reportable range of 100 to 25000 pg/mL, a limit of quantification of 100 pg/mL, recoveries between 93% and 111%, and intraassay and interassay CV <15% for both peptides. An Aβ42/Aβ40 ratio cutoff of <0.16 had a clinical sensitivity of 78% for distinguishing patients with AD from non-AD dementia (clinical specificity, 91%) and from healthy controls (clinical specificity, 81%). The Aβ42/Aβ40 ratio decreased significantly (P < 0.001) with increasing dose of APOE4 alleles.

CONCLUSIONS:

This assay can be used to determine Aβ42/Aβ40 ratios, which correlate with the presence of AD.

BACKGROUND:

Small RNAs are key players in the regulation of gene expression and differentiation. However, many different classes of small RNAs (sRNAs) have been described with distinct biogenesis pathways and, as a result, with different biochemical properties. To analyze sRNAs by deep sequencing, complementary DNA synthesis requires manipulation of the RNA molecule itself. Thus, enzymatic activities during library preparation bias the library content owing to biochemical criteria.

METHODS:

We compared 4 different manipulations of RNA for library preparation: (a) a ligation-based procedure allowing only 5'-mono-phosphorylated RNA to enter the library, (b) a ligation-based procedure allowing additional 5'-triphosphates and Cap structures, (c) a ligation-independent, template-switch-based library preparation, and (d) a template-switch-based library preparation allowing 3'-phosphorylated RNAs to enter the library.

RESULTS:

Our data show large differences between ligation-dependent and ligation-independent libraries in terms of their preference for individual sRNA classes such as microRNAs (miRNAs), Piwi-interacting RNAs (piRNAs), and transfer RNA fragments. Moreover, the miRNA composition is different between both procedures, and more microRNA isoforms (isomiRs) can be identified after pyrophosphatase treatment. piRNAs are enriched in template-switch libraries, and this procedure apparently includes more different RNA species.

CONCLUSIONS:

Our data indicate that miRNAomics from both methods will hardly be comparable. Ligation-based libraries enrich for canonical miRNAs, which thus may be suitable methods for miRNAomics. Template-switch libraries contain increased numbers and different compositions of fragments and long RNAs. Following different interests for other small RNA species, ligation-independent libraries appear to show a more realistic sRNA landscape with lower bias against biochemical modifications.

BACKGROUND:

Increasing numbers of patients are presenting worldwide to emergency departments with suspected myocardial infarction. The use of point-of-care troponin assays might enable faster decision-making in this high-risk population and reduce the burden on emergency facilities. Here, we evaluate the diagnostic performance of a point-of-care high-sensitivity troponin I assay.

METHODS:

We conducted a prospective cohort study including patients presenting to the emergency department with suspected myocardial infarction from July 2013 to July 2016. A diagnostic algorithm for a high-sensitivity troponin I point-of-care assay was developed in a derivation data set with 669 patients and validated in an additional 610 patients.

RESULTS:

The derived 0/1 h algorithm for the point-of-care assay consisted of an admission troponin I <4 ng/L and a from 0 h to 1 h <3 ng/L for rule out and an admission troponin I ≥90 ng/L or a from 0 h to 1 h ≥20 ng/L for rule in of non-ST-elevation myocardial infarction. Application to the validation cohort showed a negative predictive value of 99.7% (95% CI, 98.1%–100.0%) and 48.0% of patients ruled out, whereas 14.6% were ruled in with a positive predictive value of 86.5% (95% CI, 77.6%–92.8%). The diagnostic performance of the point-of-care high-sensitivity assay was highly comparable to guideline-recommended use of a laboratory-based high-sensitivity troponin assay.

CONCLUSIONS:

The clinical application of a 0/1 h diagnostic algorithm based on a high-sensitivity troponin I point-of-care assay is safe, and diagnostic performance is comparable to a laboratory-based high-sensitivity troponin I assay.

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