The protein truncation test (PTT) is a simple and fast method to screen for biologically relevant gene mutations. The method is based on the size analysis of products resulting from in vitro transcription and translation. Proteins of lower mass than the expected full-length protein represent translation products derived from truncating frame shift or stop mutations in the analyzed gene. Because of the low sensitivity of the conventional PTT mutations can be detected only in those samples, which harbor a high relative number of mutated gene copies. This disadvantage can be overcome by technical modifications and advanced forms of the PTT. Modifications like gene capturing and the digital PTT lower the detection limit and thus allow the use of the PTT in the detection of mutations in body fluids. Another disadvantage of the conventional PTT is the use of radioactive labels for protein detection. Recently, modifications like fluorescent labels or the use of tagged epitopes were established, which allow the detection of the nonradioactive translation product. When several epitopes in different reading frames are used, the mutation detection spectrum can be expanded to all possible frame shift mutations. These modifications transform the PTT into a powerful nonradioactive technique to detect mutations with high sensitivity.

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Self-monitoring of blood glucose is a key aspect of diabetes management. Depending on the technology used, however, various substances can jeopardize the reliability of the measurements and precipitate complications with potentially life-threatening consequences when blood glucose was deemed well-controlled. As such, it is important for all involved to be aware of those factors. Officially suggested procedures for testing and alternatives have each their own advantages and limitations, and interferences may be found beyond the substances to be tested provided by the various pertinent institutions. This article reviews these pros and cons and illustrates how interference testing beyond established standards contributes to patient safety. Once identified, interfering substances are included in product labeling and health care professionals and users need to be trained to be aware of these risks.

Many self-monitoring of blood glucose (SMBG)-systems demonstrate positive glucose bias at lower and negative bias at higher hematocrit (HCT) levels. Variations can thus lead to measurement errors and possibly to over- or underdosage of insulin. ISO 15197:2013 demands that manufacturers publish HCT-ranges within which BG-meters perform within certain tolerance limits. But how wide should these ranges be? In this cross-sectional study, we collect real-world data from hospital outpatients to assess the frequency of out-of-range HCT-values compared to healthy individuals. Data from more than 1 million patients were collected from hospitals in Leeuwarden (NL) and Prague (CZ) and stratified by outpatient status, sex and age. Data were compared to common HCT labelled range limits and data from 1780 healthy Czech subjects to assess differences in distribution. Compared to healthy subjects, outpatients from Prague were distributed with significantly higher dispersion of values. Independent of the location, low HCT-values are common in age groups known to also have high prevalence of diabetes. Real-world data indicate that BG-meters labelled to perform only within the frequently used 30-55% range would leave thousands of patients at risk of falsely high or low results. Adequate SMBG-systems should be chosen carefully, particularly for patients with increased and decreased HCT-values.Age groupLocationTotal number Female / MaleNumber <30% HCT  F / MPercentage <30% HCT  F / MPrevalence of Diabetes (Eurostat)  F / M55-64NL27329 / 335461043 / 13243.8% / 3.9%6.2% / 10.3%55-64CZ22212 / 19035748 / 9703.4% / 5.1%10.9% / 11.6%65-74NL32004 / 441811958 / 28496.1% / 6.4%12.0% / 15.0%65-74CZ33598 / 313211987 / 19245.9% / 6.1%18.1% / 19.5%75+NL42435 / 450803431 / 45358.1% / 10.1%14.4% / 19.7%75+CZ26645 / 227061923 / 20417.2% / 9.0%27.3% / 25.3% Disclosure T. Zima: Research Support; Self; Roche Diabetes Care Health and Digital Solutions. H. Storm: None. M. Spacek: None. S. Wardat: Employee; Self; Roche Diagnostics Corporation. O. Hauss: Consultant; Self; Roche Diabetes Care GmbH. R. Hinzmann: Employee; Self; Roche Diagnostics Corporation.