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Ischemic heart disease remains the leading cause of death globally, with acute coronary syndrome (ACS) being the main reason for emergency hospital admissions and thus representing a significant health care issue worldwide. Cardiac troponin I (cTnI) and cardiac troponin T (cTnT) are widely recognized biomarkers of cardiomyocyte injury and the gold-standard biomarkers for diagnosing myocardial infarction (MI). High-sensitivity cardiac troponin (hs-cTn) assays have the ability to accurately detect low cTn concentrations and document minor increases. However, in addition to MI, various other pathophysiological states can trigger elevated cardiac troponin levels, thus creating potential challenges in the diagnostic process. As cTn released into the bloodstream exists in heterogeneous forms, improving our understanding and accurately characterizing these forms across various etiologies might hold clinical significance. In this review, we add to the field by offering an overview of research on possible circulating forms of cTn, the mechanisms of cTn elevation, and the clinical significance of cTn following conditions such as MI, endurance exercise, and chronic kidney disease, thus highlighting the importance and challenge of understanding of the circulating forms of cTn and possible strategies for cTn immunodetection.

The negative impact of preanalytical errors on the quality of laboratory testing is now universally recognized. Nonetheless, recent technological advancements and organizational transformations in healthcare – catalyzed by the still ongoing coronavirus disease 2019 (COVID-19 pandemic) – have introduced new challenges and promising opportunities for improvement. The integration of value-based scoring systems for clinical laboratories and growing evidence linking preanalytical errors to patient outcomes and healthcare costs underscore the critical importance of this phase. Emerging topics in the preanalytical phase include the pursuit of a “greener” and more sustainable environment, innovations in self-sampling and automated blood collection, and strategies to minimize patient blood loss. Additionally, efforts to reduce costs and enhance sustainability through patient blood management have gained momentum. Digitalization and artificial intelligence (AI) offer transformative potential, with applications in sample labeling, recording collection events, and monitoring sample conditions during transportation. AI-driven tools can also streamline the preanalytical workflow and mitigate errors. Specific challenges include managing hemolysis and developing strategies to minimize its impact, addressing issues related to urine collection, and designing robust protocols for sample stability studies. The rise of decentralized laboratory testing presents unique preanalytical hurdles, while emerging areas such as liquid biopsy and anti-doping testing introduce novel complexities. Altogether, these advancements and challenges highlight the dynamic evolution of the preanalytical phase and the critical need for continuous innovation and standardization. This collective opinion paper, which summarizes the abstracts of lectures delivered at the two-day European Federation of Laboratory Medicine (EFLM) Preanalytical Conference entitled “New Insight in Preanalytical Quality” (Padova, Italy; December 12–13, 2025), provides a comprehensive overview of preanalytical errors, offers some important insights into less obvious sources of preanalytical vulnerability and proposes efficient opportunities of improvement.

The IFCC Task Force on Global Lab Quality (TF-GLQ) has recently released a new guidance for internal quality control (IQC) practice through an approach translating the general principles as stated in the ISO 15189:2022 standard to a series of practical recommendations. The paper contains however important inaccuracies and shortcomings that, in our opinion, make it a missed opportunity for providing a updated guidance for laboratory professionals. In particular, four important issues are discussed: a) how to design IQC strategies in the traceability era, b) how to define IQC acceptance limits, c) how to estimate measurement uncertainty using IQC data, and d) how to manage comparability between the results provided by different analyzers in the same laboratory. Our analysis underscores the necessity for a more systematic, updated, and evidence-based approach to produce an IQC recommendation in line with the IFCC tradition.

Objectives Accurate medical laboratory reports are essential for delivering high-quality healthcare. Recently, advanced artificial intelligence models, such as those in the ChatGPT series, have shown considerable promise in this domain. This study assessed the performance of specific GPT models-namely, 4o, o1, and o1 mini-in identifying errors within medical laboratory reports and in providing treatment recommendations. Methods In this retrospective study, 86 medical laboratory reports of Nucleic acid test report for the seven upper respiratory tract pathogens were compiled. There were 285 errors from four common error categories intentionally and randomly introduced into reports and generated 86 incorrected reports. GPT models were tasked with detecting these errors, using three senior medical laboratory scientists (SMLS) and three medical laboratory interns (MLI) as control groups. Additionally, GPT models were tasked with generating accurate and reliable treatment recommendations following positive test outcomes based on 86 corrected reports. χ2 tests, Kruskal-Wallis tests, and Wilcoxon tests were used for statistical analysis where appropriate. Results In comparison with SMLS or MLI, GPT models accurately detected three error types, and the average detection rates of the three GPT models were 88.9 %(omission), 91.6 % (time sequence), and 91.7 % (the same individual acted both as the inspector and the reviewer). However, the average detection rate for errors in the result input format by the three GPT models was only 51.9 %, indicating a relatively poor performance in this aspect. GPT models exhibited substantial to almost perfect agreement with SMLS in detecting total errors (kappa [min, max]: 0.778, 0.837). However, the agreement between GPT models and MLI was moderately lower (kappa [min, max]: 0.632, 0.696). When it comes to reading all 86 reports, GPT models showed obviously reduced reading time compared with SMLS or MLI (all p<0.001). Notably, our study also found the GPT-o1 mini model had better consistency of error identification than the GPT-o1 model, which was better than that of the GPT-4o model. The pairwise comparisons of the same GPT model’s outputs across three repeated runs showed almost perfect agreement (kappa [min, max]: 0.912, 0.996). GPT-o1 mini showed obviously reduced reading time compared with GPT-4o or GPT-o1(all p<0.001). Additionally, GPT-o1 significantly outperformed GPT-4o or o1 mini in providing accurate and reliable treatment recommendations (all p<0.0001). Conclusions The detection capability of some of medical laboratory report errors and the accuracy and reliability of treatment recommendations of GPT models was competent, especially, potentially reducing work hours and enhancing clinical decision-making.

Objectives Clinical laboratory errors pose a threat to patient safety and previous studies have demonstrated that pre-analytical error is the most common error type. Our study aimed to determine the types and frequency of errors occurring in clinical laboratory testing in contemporary practice. Methods Errors occurring in a core laboratory between 01/2022 and 05/2023 were recorded retrospectively. Errors were quantified using multiple data-streams including real-time manual technologist intervention, incidence reports filed by hospital staff/physicians, and retrospective assessment using automated reports from the lab information system (LIS). Errors were adjudicated and binned into pre-analytical, analytical, and post-analytical phases. Total test volumes were assessed in the LIS and electronic medical record. Results There were 37,680,242 billable results reported from approximately 11,000,000 specimens during the study period. In total, 87,317 errors occurred impacting 0.23 % (2,300 ppm) of billable results and approximately 0.79 % (7,900 ppm) of specimens. Among these errors, 85,894 (98.4 %, 984,000 ppm) were in the pre-analytical, 451 (0.5 %, 5,000 ppm) were in the analytical, and 972 (1.1 %, 11,000 ppm) occurred in the post-analytical phase. Hemolysis impacting specimen integrity (60,748/87,317, 69.6 %, 696,000 ppm) was the most common error. When excluding hemolysis, there were 26,569 errors documented (0.06 %, 600 ppm of billable results), among which 94.6 %, 1.7 % (17,000 ppm) and 3.7 % (37,000 ppm) were in the pre-analytical, analytical and post-analytical phase respectively. Conclusions Observed error rates were consistent with previous studies with pre-analytical errors comprising most errors. High prevalence of pre-analytical errors implies a need for enhanced tools for error detection and mitigation in the pre-analytical phase of testing.

Objectives Recent advances in information technology have renewed interest in patient-based real-time quality control (PBRTQC) as an alternative to internal quality control (IQC). However, since regulations mandate IQC, PBRTQC can only be implemented as a separate system. The additional labor required for PBRTQC may hinder widespread adoption. Therefore, a more efficient system that integrates IQC with PBRTQC is needed for laboratories to implement the methods effectively. Methods A QC system that integrates IQC with PBRTQC is proposed. The maximum average number of patients with unacceptable analytical errors (MaxANP TE ) was introduced as a critical metric to benchmark the efficiency of the integrated PBRTQC system against the IQC-only system using a modified Parvin patient risk model. With the historical data of serum sodium (Na), chloride (Cl), alanine aminotransferase (ALT), and creatinine (CREA) from Zhongshan Hospital, Fudan University, in 2019, the integrated system incorporating the simple PBRTQC model and the more advanced regression-adjusted real-time quality control (RARTQC) were compared with the IQC-only system. Results In most cases, the integrated system incorporating RARTQC models outperformed the IQC-only system, particularly for ALT, where QC events were reduced by up to 45 %. Based on these findings, we proposed strategies for laboratories to design the integrated system. Conclusions The study demonstrated the improvement of efficiency of the integrated PBRTQC system over the IQC-only system. These insights can help laboratories make informed decisions on adopting PBRTQC models and provide as evidence for revising regulation on IQC.

Objectives Macroprolactin (macro-PRL) mostly comprises a complex of PRL with IgG. The aim of this study was to clarify whether IgA-type macro-PRL exists and, if so, to elucidate the prevalence of and differences in laboratory data from IgG-type. Methods One hundred thirty patients with macroprolactinemia who were diagnosed through screening via the polyethylene glycol precipitation method followed by confirmation using gel filtration chromatography (GFC) were examined. IgA-type and IgG-type macro-PRLs were identified via Jacalin column/SDS‒PAGE and protein G columns, respectively. Results SDS‒PAGE under nonreducing conditions followed by western blotting with an IgA antibody revealed that the fraction bound to the Jacalin column was actually IgA. The PRL band was detected at the same position as the IgA band, which was purified with a Jacalin column, suggesting that PRL was bound to IgA. The finding that the PRL band was observed not only at the same position as IgA but also at the same position as the 23 kDa PRL reference suggested that some PRL dissociated from IgA during SDS‒PAGE. The prevalence rates of macro-PRL of only IgA, IgA plus IgG, only IgG, and non-IgA/non-IgG types were 7.7, 3.1, 83.8, and 5.4 %, respectively. Neither the PEG precipitation ratios of PRL nor the macro-PRL ratios on GFC differed between IgA- and IgG-type macro-PRLs, whereas both ratios were significantly lower in non-IgA/non-IgG-type macro-PRL. Conclusions IgA-type macro-PRL was demonstrated to exist. IgG-type macro-PRL was most prevalent, followed by IgA, non-IgA/non-IgG and IgA plus IgG-type macro-PRLs.

Objectives This study aimed to investigate the prevalence of macroprolactinemia and the changes in hyperprolactinemia status over time among hyperprolactinemic patients in the Turkish population. Methods A retrospective study was conducted on 3,013 patient records from a central university hospital between October 2023 and October 2024. Of these, 634 hyperprolactinemic samples (Prolactin >15.2 μg/L in males, >23.3 μg/L in females) were analyzed using polyethylene glycol (PEG) precipitation method. Post-PEG prolactin levels were measured using electrochemiluminescence immunoassay, with the recovery percentage (R%) calculated as the ratio of prolactin post-PEG precipitation to total prolactin, where R% values below 40 % were indicative of macroprolactinemia. The change in R% values was analyzed in 63 hyperprolactinemic patients who underwent restesting. Results Macroprolactinemia prevalence was found as 5.67 % of hyperprolactinemic patients with no macroprolactinemia cases observed at PRL levels above 85.40 μg/L. Male patients had significantly higher R% values than females (p=0.003). In the gray zone (R%: 40–60), 29.03 % of patients were classified as truly hyperprolactinemic. Repeated tests after an average of four months showed stable R% values in most cases, with only two patients changing classification between macroprolactinemia and true hyperprolactinemia. Conclusions The prevalence of macroprolactinemia in hyperprolactinemic Turkish patients was relatively low. As prolactin values increase, true hyperprolactinemia is more commenly observed instead of macroprolactinemia. Short-term retesting for macroprolactinemia may not be necessary unless clinical conditions change significantly. Accurate diagnosis requires method-specific cutoffs and further prospective studies using gold-standard methods like gel filtration chromatography.

Objectives Dried blood spot (DBS) specimen acceptance guidelines recommend rejecting specimens based on DBS size, often expressed as a diameter. Computer vision methods can estimate DBS size from images obtained from standalone equipment, smartphone cameras or existing laboratory instrumentation. However, no consensus definition of DBS diameter exists. We assessed how different DBS diameter definitions affect measurement and specimen rejection rates. Methods We compared computer vision estimates of DBS diameter on 1,991 DBS using two different calculation methods and on 22 DBS where paired images were taken from either side of the filter paper. We modelled the impact on specimen rejection rate in >163,000 DBS specimens. Results Two different calculation methods for DBS diameter showed a mean difference <0.1 mm for circular DBS. Greater variability was observed for incorrectly applied DBS with a mean (standard deviation) difference of 0.29 (0.41) mm. DBS diameter measured from the front of the filter paper was approximately 0.41 (0.25) mm larger than from the back of the filter paper. Changing the DBS diameter definition could more than double the number of insufficient DBS (<8 mm), potentially leading to 4,000 additional repeat collections annually in the UK newborn screening programme. Conclusions DBS diameter definition can have a small but important and easily avoidable impact on measurement, impacting specimen rejection rates. We recommend that DBS diameter is defined as the diameter of a circle with equal area to the DBS, when measured from the opposite side of the filter paper to blood application.

Objectives Primary aldosteronism (PA) is a common cause of secondary hypertension. The aldosterone-to-renin ratio (ARR) is the current recommended biomarker for PA screening, but it has limitations. This study evaluates another ratio, the aldosterone-to-angiotensin II ratio (AAIIR), as an alternative screening tool for PA. Methods Archived plasma samples for ARR from a group of 152 hypertensive patients undergoing PA screening were retrieved for AAIIR analyses. Both AAIIR and ARR were measured by liquid chromatography-mass spectrometry methods. Correlation analysis, sensitivity, specificity, and receiver operating characteristic (ROC) curve analysis were performed to assess the diagnostic performance of AAIIR relative to ARR. Results AAIIR showed a strong positive correlation with ARR (r=0.79, p<0.0001). The area under the ROC curve for AAIIR (0.94, 95 % CI: 0.90–0.98) was satisfactory and not significantly different from ARR (0.94, 95 % CI: 0.90–0.97, p=0.877). The optimal cutoff values were 577 (pmol/L)/(µg/L −h ) and 60 for ARR and AAIIR, respectively. The sensitivity of AAIIR was slightly higher than ARR (91 vs. 88 %), while the specificity was comparable (85 vs. 86 %). Conclusions AAIIR demonstrates a comparable diagnostic performance to ARR for PA screening, with potential advantages in efficiency and reliability. Further large-scale studies are needed to validate its efficacy and establish its role in routine clinical practice.

Objectives Diagnosing monoclonal gammopathy in chronic kidney disease (CKD) patients is challenging due to the complex interpretation of serum free light chain (FLC) levels. This study aimed to assess the FLC levels in a large cohort of Chinese CKD patients. Methods We enrolled 5,287 patients with all-cause nondialysis CKD from three medical centers between February 2018 and April 2023. Central 95 % reference ranges were established using data from one center and validated in an independent subpopulation from the other two centers. The crude prevalence of light chain monoclonal gammopathy of undetermined significance (LC-MGUS) was assessed against established norms for the entire cohort and subgroups based on estimated glomerular filtration rate (eGFR). Results A notable proportion of patients exceeded the standard reference limits for kappa (89.0 %), lambda (72.1 %), and FLC ratio (10.5 %), whereas non-eGFR-based renal reference interval identified only 0.3 % with abnormal FLC ratios. The iStopMM reference ranges showed higher out-of-range rates for absolute FLC levels in patients with preserved kidney function and lower rates in those with impaired kidney function, while the FLC ratio references remained robust across all groups. The crude prevalence of LC-MGUS was 10.4 % using standard ranges, predominantly kappa LC-MGUS (99.8 %). This prevalence decreased to 0.3 % with non-eGFR-based renal reference interval and 0.5 % with the iStopMM ranges. Using the newly established reference ranges, the crude prevalence was found to be 0.9 %. Conclusions Our findings suggest that current FLC reference ranges are inadequate for the Chinese population, underscoring the need for eGFR-based reference ranges tailored to this demographic.

Objectives In serum immunofixation electrophoresis, therapeutic monoclonal antibodies (tmAbs) can be misinterpreted as paraproteins. Strategies to circumvent these interferences include the use of anti-tmAb antibodies, which led to the development of the Hydrashift assay. However, this assay is exclusively available for the Sebia platform and limited to specific tmAbs, such as daratumumab (DmAb) and isatuximab (ImAb). Furthermore, we observed some cases in which complete shifting of tmAbs by the Hydrashift Assay was questionable. This study aimed to explore isoelectric focusing (IEF) as an alternative, widely applicable method for clinical laboratories. Methods First, the neutralization capacity of the DmAb and ImAb Hydrashift assay was assessed by testing undiluted tmAb samples. Second, DmAb and ImAb were spiked into paraprotein-containing samples in clinically relevant concentrations and analyzed by the Hydrashift assay and IEF. Presence of unshifted tmAb and identification of tmAb and paraproteins were examined in these samples and routinely collected patient samples. Results The Hydrashift assay reliably shifted up to 0.5 g/L DmAb and up to 1.5 g/L ImAb. IEF was found to be equally effective as the Hydrashift assay in identifying antibody interferences and detecting paraproteins. Additionally, IEF successfully identified two pseudo-monoclonal gammopathies as oligoclonal and detected other therapeutic antibodies. Conclusions Insufficient neutralization capacity of the Hydrashift Assay was not observed within the clinically relevant range of investigated tmAb concentrations. IEF offers a simple and flexible alternative to the Hydrashift assay for distinguishing tmAbs from paraproteins. Its advantages include broader applicability to various tmAbs and independency from specific commercial platforms.

Objectives Monoclonal gammopathies (MGs) are characterized by the presence of monoclonal immunoglobulins (M-proteins). Currently, recommendations for screening of MGs primarily rely on nephelometry, turbidimetry and electrophoresis, which have inherent limitations in sensitivity and throughput. This study aimed to evaluate a novel MALDI-TOF MS-based method, the intact M-protein Screening-Light Chain Assay (iMS-LC Assay), for direct M-protein detection and quantification without antibody enrichment. Methods Residual serum samples previously analyzed via serum protein electrophoresis (SPE) and immunofixation electrophoresis (IFE) were reduced to dissociate light chains from heavy chains. MALDI-TOF MS was then performed to determine the presence of M-protein characteristic pattern. The iMS-LC Assay’s analytical sensitivity, specificity, and screening efficacy in healthy populations were assessed. Results The iMS-LC Assay successfully detected all M-proteins identified by SPE and demonstrated higher sensitivity in analytical and diagnostic studies. It accurately quantified M-proteins at concentrations below 10 g/L, with a detection limit of 0.2 g/L and the ability to detect levels below 0.1 g/L. For samples with M-protein concentrations >1 g/L, intra-assay and inter-assay coefficients of variation were <10 %. In prospective screening of M-proteins in the healthy population, the iMS-LC Assay detected M-proteins at a prevalence of 3.15 %, higher than IFE (1.87 %) and SPE (0.94 %). Conclusions The iMS-LC Assay shows potential to replace SPE and drive advancements in the screening, diagnosis, and monitoring of MGs. Further validation of its clinical sensitivity and specificity is essential to determine its adequacy as a routine screening tool for M-proteins.

Objectives To investigate whether frozen storage duration influences κ-FLC index. Methods CSF and serum samples of patients with multiple sclerosis collected for routine diagnostic purposes had been stored at −20 °C. κ-FLC and albumin concentrations were measured at two different timepoints, i.e. before and after storage. The κ-FLC index was calculated as (CSF κ-FLC/serum κ-FLC)/(CSF albumin/serum albumin). Results A total of 70 patients were included showing median CSF κ-FLC concentration of 0.25 (25th-75th percentile: 0.12–0.43) mg/dL, serum κ-FLC concentration of 1.05 (0.86–1.34) mg/dL, CSF albumin concentration of 17.7 (14.6–26.1) mg/dL and serum albumin concentration of 4230 (3898–4488) mg/dL. The κ-FLC index was 44 (25–108). With increasing frozen storage duration, the absolute concentrations of CSF κ-FLC, serum κ-FLC, CSF albumin and serum albumin decreased, while the κ-FLC index remained stable. The observed changes in absolute concentrations evened out by using CSF/serum ratios of κ-FLC and albumin when calculating the κ-FLC index. Conclusions Frozen storage at −20 °C has no relevant impact on κ-FLC index.

Objectives To investigate whether renal function impacts CSF κ-FLC concentration and/or κ-FLC index. Methods Patients with non-inflammatory neurological diseases were eligible. κ-FLC index was calculated as (CSF κ-FLC/serum κ-FLC)/albumin quotient. Structural equation modeling (SEM) was used to evaluate the direct influence of GFR on serum κ-FLC concentration and albumin quotient (Q alb ), and via these two variables the indirect influence on CSF κ-FLC concentration. Results A total of 129 patients with a median age of 65 years and 42 % females were included. κ-FLC index ranged from 0.57 to 3.56 and glomerular filtration rate (GFR) ranged from 17 to 128 mL/min/1.73 m 2 . While a correlation of GFR with CSF κ-FLC concentration was observed (r= −0.52, p<0.001), there was no statistically significant correlation with κ-FLC index (r=0.14, p=0.113). SEM revealed that higher age was associated with lower GFR (β= −0.53), which led to higher serum κ-FLC concentration (β= −0.45) and higher Q alb (β= −0.17), while CSF κ-FLC concentration increased with serum κ-FLC concentration (β=0.75) and Q alb (β=0.39), indicating that GFR did not directly influence CSF κ-FLC concentration (RMSEA=0.043). Conclusions CSF κ-FLC concentration is not directly affected by renal function. The κ-FLC index compensates for renal function effects by factoring in serum κ-FLC concentration and Q alb . κ-FLC index can be interpreted without considering renal function.

Objectives Therapeutic drug monitoring (TDM) is critical for optimizing antipsychotic therapy, yet inter-laboratory comparability in China remains poor. Unstandardized practices risk suboptimal dosing and adverse effects, highlighting the need for standardization. This study evaluated TDM practices for six antipsychotics (olanzapine, clozapine, risperidone, quetiapine, aripiprazole, and paliperidone), focusing on inter-laboratory variability, quality control material suitability, and measurement system differences. Methods Four processed serum samples (two frozen, two lyophilized) containing the six antipsychotic drugs were distributed to over 100 laboratories. The analyses utilized LC-MS/MS, chemiluminescence immunoassays, and HPLC. Sample homogeneity, stability, matrix effects, and method precision, and accuracy were assessed systematically. Results Significant inter-laboratory variability occurred for six antipsychotics. Only quetiapine met the external quality assessment (EQA) requirements, with inter-laboratory coefficients of variation (CVs) ranging from 12.08 to 15.23 %. Others, especially olanzapine (CVs: 43.48–45.30 %), had poor precision (CVs>15 %). The study revealed that commercial in vitro diagnostic kits generally provided more consistent results compared to laboratory-developed tests. However, even with commercial kits, some drugs, such as aripiprazole, still showed substantial deviations. Lyophilized samples surpassed frozen ones in stability and EQA suitability, which exhibited comparable matrix effects across most assay systems. Conclusions Urgent standardization of Chinese TDM practices is needed, including harmonized systems and traceable calibrators. While commercial kits enhance result consistency, further efforts are required to harmonize performance across manufacturers. Lyophilized materials are optimal for EQA due to their stability and ease of transport. These findings offer actionable insights to improve the quality and comparability of antipsychotic TDM in clinical practice.

Objectives Potential coeliac disease (PCD) is defined by the presence of positive CD-specific autoantibodies with a normal/extremely mildly damaged intestinal mucosa. This study sought to examine the progression of PCD in children maintaining a gluten-containing diet and to identify risk factors associated with the onset of CD. A comparative literature review was conducted to assess the results in the context of existing evidence. Methods A retrospective cohort study was performed on 67 children diagnosed with PCD between January 2005 and January 2022, with a maximum follow up of 53 months. The associations between baseline clinical characteristics and the development of CD were assessed using hazard ratios (HR). Results Nineteen percent (19 %) (12/67, cumulative incidence) of PCD children, with a median age of 4.3 years, progressed to CD during a median follow up period of 30 months. A fluctuating trend in tissue transglutaminase IgA (tTG-IgA) levels was observed in 35.8 % (24/67) of the children, while 46.2 % (31/67) showed tTG-IgA negativization. In univariable analysis, the presence of autoimmune disease and one-year increase in age at diagnosis were significantly associated with CD progression [HR=17.7 (95%CI: 3.0–106.8; p=0.0017) and HR=1.3 (95%CI: 1.1–1.5; p=0.0125), respectively]. Conclusions Our study confirms that only a small proportion of PCD children progress to CD. It also highlights that advancing age and the presence of autoimmune disease are the main risk factors for the development of villous atrophy. A better understanding of tTG-IgA trend during follow up could help in the management of PCD children.

Objectives To describe the structure of vitamin D metabolome and investigate the possible cause of high serum levels of C3 epimers of 25-(OH)D in preterm infants, we compared the vitamin D metabolites in umbilical cord blood with serum samples taken at 28 days of age. Methods We analysed 40 preterm infants (29+0–32+6 weeks of gestation). Cholecalciferol, 25-(OH)D, and its C3-epimers were measured using liquid chromatography. A microsomal study with human liver and kidney microsomes was conducted to assess vitamin D metabolism. Identified metabolites were then examined in cord blood and serum samples. Results Cholecalciferol, 25-(OH)D, and its C3-epimers were significantly lower in cord blood compared to serum at 28 days of age (p<0.001 for all metabolites). Conversely, metabolites from the microsomal study (monohydroxylated-, dihydroxylated-, and mono-oxylated dihydroxylated-cholecalciferol and their C3-epimers) were significantly higher in cord blood (p<0.001 for all). Conclusions Our findings indicate that cholecalciferol, 25-(OH)D, and its C3-epimers increase during the first month of life, suggesting functional biosynthesis and postnatal accumulation of these metabolites. Conversely, based on microsomal study results, it seems that biotransformation responsible for a degradation of vitamin D during the first month of life in preterm infants is functionally impaired.

Objectives To explore the reasons behind the lack of commutability in protein solution reference materials (RMs) and seeks to eliminate these factors in order to develop commutable reference materials, using human insulin (hINS) as example, to meet the growing demand for standardization in in vitro diagnostics. Methods A concept for development of commutable protein solution RMs by matrix matching, structural-activity analysis, higher order structure adjustment, active concentration measurement, and commutability verification was investigated. This concept was applied in the development of hINS solution candidate certified RMs (cCRMs). Bovine serum solution (7 %) was used for matrix matching and hINS-Zn 2+ aggregates were found to be the key for commutability. hINS-Zn 2+ aggregates were constructed in vitro and the presence of the aggregates was confirmed through circular dichroism spectroscopy and mass photometry. The active concentration of the aggregate solution was analyzed using surface plasmon resonance. Then, six levels of hINS solution cCRMs were developed and the commutability was evaluated using both CLSI EP14 and IFCC approaches. Results The hINS solution cCRMs exhibited excellent homogeneity and remained stable for at least 6 months when stored at −70 °C. The relative uncertainties of these cCRMs ranged from 4.0 to 5.0 %. Both CLSI EP14 and IFCC commutability evaluations indicated good commutability between routine chemiluminescent immunoassay systems. Conclusions A new concept for developing commutable RMs with pure protein material, which avoids the challenges in developing commutable matrix RMs and should contribute to the standardization of clinical test results for proteins, was successfully applied to develop commutable hINS solution cCRMs.

Objectives Serum cholinesterase (ChE) 3.1.1.8 is measured to assess exposure to organophosphorus pesticides and determine deficiency related to prolonged apnea after the induction of anesthesia with certain drugs and less often as an indicator of liver function. Biological variation (BV) is an accepted endogenous source that contributes to the total variation in laboratory medicine. No data on the BV of serum ChE have been found in the European Federation of Clinical Chemistry and Laboratory Medicine BV database. Thus, this study aimed to contribute to the data on BV of serum ChE activity. Methods Detailed inclusion and exclusion criteria were used for the enrollment of 20 (10 women and 10 men, 8–10 weeks) ostensibly healthy volunteers from Turkey. The serum ChE activity was measured on Roche Cobas c501. Statistical analyses included the detection of outliers, control for the normality of distribution, checking steady-state condition, assessment for homogeneity, subgroup analysis, analysis of variance with 95 % confidence intervals, and estimation of analytical performance specifications (APS). Results After exclusion, 332 results were included in the study. The within-subject BV of men (3.5 % [2.9–4.2 %]) was lower than that of women (4.8 % [4.1–5.8 %]). Between-subject BV of men and women were 15.9 % [10.5–32.4 %] and 12.3 % [8.4–22.6 %], respectively. The index of individuality was 0.18 and reference change value (RCV) was +9.1 %/−8.3 %. The calculated desirable APS for imprecision and bias were 1.7 and 3.2 %, respectively. Conclusions We believe that this study will contribute to the BV data on serum ChE activity. The prominent individuality of serum ChE activity favors the use of RCV instead of population-based reference intervals for more reliable follow-up.

Objectives The aim of this study was to carry out a cross-analysis of the morphological abnormalities (MA) and the electrophoretic profile (EP) of blood samples suspect for heterozygous haemoglobinopathies (HTZ HGP). Screening for HTZ HGP was based on erythrocyte parameters provided by the Sysmex XN analysers. Methods A total of 596,000 blood samples was included in the study. According to the results of the mean corpuscular haemoglobin concentration (MCHC), the percentage of microcytes (Micro%) and the standard deviation of the red blood cell distribution width (RDW-SD), 842 different adults were screened as suspect for HTZ HGP and underwent simultaneous morphological analysis of red blood cells (RBCMA) and haemoglobin fraction analysis. Results The majority (72.8 %) of HTZ HGP suspects presented a pathological EP, mostly compatible with a confirmed β-thalassaemia trait (50.1 %) or a heterozygous β-haemoglobin variant (12.2 %). MA were identified in 360 (42.8 %) samples and 70 (8.3 %) of these had 3 or more MA. The most common MA was poikilocytosis (28.1 %). Patients with at least 1 MA detected were more likely to have a pathological EP (p=0.003). However, correlation between the number of MA detected and the type of EP was negligible. Conclusions Screening for HTZ HGP based on erythrocyte parameters measured on Sysmex XN analysers is a relevant tool with a positive predictive value of 72.8 % and definitely superior to microscopic RBCMA which now appears to be of low added value and obsolete in this indication.

Objectives In the early stage of gastric cancer (GC), identifying cancer-specific biomarkers is a key step in the disease screening process. This study aims to explore the clinical value of five novel protein biomarkers and their combination (5 MP) for GC early diagnosis. Methods The candidate biomarkers were mined from TCGA, GTEx, and CPTAC databases. The clinical value of the five biomarkers and 5 MP in the early diagnosis from healthy control, benign gastric disease (BGD), precancerous lesions (PLGC), early GC (EGC), and GC was evaluated by receiver operator characteristic curve (ROC), the area under the curve (AUC), sensitivity, specificity, and accuracy. Results Five candidate biomarkers, COL10A1, GKN1, GKN2, LIPF, and REG4, were mined from TCGA, GTEx, and CPTAC databases. In the training cohort, the five proteins were confirmed to be differentially expressed in the serum of control, BGD, EGC, and GC. COL10A1 has the highest AUC of a single protein in control vs. EGC (0.857). GKN2 has the highest AUC of a single protein in BGD vs. EGC (0.822). 5 MP has an AUC of 0.890 in Control vs. EGC, and 0.854 in BGD vs. EGC. In the validation cohort, 5 MP has an AUC of 0.834 in PLGC vs. GC, and 0.839 in PLGC vs. EGC. Conclusions Our findings suggest that COL10A1, GKN1, GKN2, LIPF, and REG4 are useful non-invasive serum biomarkers for GC diagnosis. Combination detection (5 MP) has enhanced early diagnosis of GC and distinguishing between benign and malignant gastric diseases.

Objectives The diagnostic accuracy of presepsin (P-SEP) in the newborn is still under evaluation. Methods In a multicenter study, we studied the accuracy of P-SEP as a diagnostic marker of late-onset sepsis (LOS) in critical newborns with underlying disorders, to define the most accurate cut-off to distinguish infected from uninfected patients. Results Sixty-nine/351 newborns without infections at admission developed LOS. The median P-SEP value at T0 (admission) was 518.0 ng/L (IQR 313.0–789.0), without significant differences related to underlying diseases (p=0.52). In neonates who developed LOS, P-SEP increased at the onset of infection (T1) (median: 816.0 ng/L) and after 24–48 h (median: 901.0 ng/L) compared with their value at admission (median: 560.0 ng/L) (p<0.01 and p=0.03, respectively). The area under the ROC curve at T1 was 0.71 (95 % CI 0.65–0.78) when all cases of sepsis were included in the analysis and increased to 0.74 (95 % CI 0.66–0.81) considering only confirmed sepsis. Approximately two-thirds of patients were correctly classified, setting the cut-off at 713 ng/L, with a negative predictive value of 89.0 %. Conclusions At a cut-off of 713 ng/L, P-SEP has good accuracy in diagnosing LOS in critically ill newborns. In uninfected newborns, the median value of P-SEP is not influenced by any underlying pathology.