Biochemia medica最新文献

[This corrects the article DOI: 10.11613/BM.2025.010702.].

Adrenocorticotropic hormone (ACTH) has historically been considered an unstable hormone after venous sampling, necessitating stringent conditions for the transport of blood samples to the laboratory to ensure accurate measurement. However, recent investigations suggest that ACTH may be more stable than previously assumed, raising the possibility of more flexible handling conditions. This prompted us to conduct a systematic review using the MEDLINE database to ascertain the stability of ACTH in blood samples. We included 9 studies in our final analysis from 405 reports. Our findings reveal that all studies reported a mean percentage difference (PD%) in ACTH concentrations relative to baseline below the 10% threshold when uncentrifuged tubes were stored under refrigerated conditions for 2, 4, 6, and 8 hours. In contrast, the mean PD% exceed the 10% threshold in 5 out of 7 studies investigating a storage duration of 24 hours under refrigerated conditions. Nearly all studies reported a mean PD% in ACTH concentrations relative to baseline below 10% when uncentrifuged tubes were stored at room temperature for 2, 4, and 6 hours. However, for storage durations of 8, 12, and 24 hours at room temperature, most studies observed a mean PD% exceeding 10%. In summary, our findings suggest that ACTH remains stable in uncentrifuged tubes containing EDTA for 6 h at room temperature and at least 8 h under refrigerated conditions. Our findings can assist clinical laboratories in reviewing their acceptance criteria for sample transport regarding time and temperature.

The application of advanced artificial intelligence (AI) models and algorithms in clinical laboratories is a new inevitable stage of development of laboratory medicine, since in the future, diagnostic and prognostic panels specific to certain diseases will be created from a large amount of laboratory data. Thanks to machine learning (ML), it is possible to analyze a large amount of structured numerical data as well as unstructured digitized images in the field of hematology, cytology and histopathology. Numerous researches refer to the testing of ML models for the purpose of screening various diseases, detecting damage to organ systems, diagnosing malignant diseases, longitudinal monitoring of various biomarkers that would enable predicting the outcome of each patient's treatment. The main advantages of advanced AI in the clinical laboratory are: faster diagnosis using diagnostic and prognostic algorithms, individualization of treatment plans, personalized medicine, better patient treatment outcomes, easier and more precise longitudinal monitoring of biomarkers, etc. Disadvantages relate to the lack of standardization, questionable quality of the entered data and their interpretability, potential over-reliance on technology, new financial investments, privacy concerns, ethical and legal aspects. Further integration of advanced AI will gradually take place on the basis of the knowledge of specialists in laboratory and clinical medicine, experts in information technology and biostatistics, as well as on the basis of evidence-based laboratory medicine. Clinical laboratories will be ready for the full and successful integration of advanced AI once a balance has been established between its potential and the resolution of existing obstacles.

Diagnostic tests are important means in clinical practice. To assess the performance of a diagnostic test, we commonly need to compare its results to those obtained from a gold standard test. The test sensitivity is the probability of having a positive test in a diseased-patient; the specificity, a negative test result in a disease-free person. However, none of these indices are useful for clinicians who are looking for the inverse probabilities, i.e., the probabilities of the presence and absence of the disease in a person with a positive and negative test result, respectively, the so-called positive and negative predictive values. Likelihood ratios are other performance indices, which are not readily comprehensible to clinicians. There is another index proposed that looks more comprehensible to practicing physicians - the number needed to misdiagnose. It is the number of people who need to be tested in order to find one misdiagnosed (a false positive or a false negative result). For tests with continuous results, it is necessary to set a cut-off point, the choice of which affects the test performance. To arrive at a correct estimation of test performance indices, it is important to use a properly designed study and to consider various aspects that could potentially compromise the validity of the study, including the choice of the gold standard and the population study, among other things. Finally, it may be possible to derive the performance indices of a test solely based on the shape of the distribution of its results in a given group of people.

Introduction: Sphingolipids, essential to trophoblast and endothelial function, may impact inflammation in preeclampsia. However, their specific role in late-onset preeclampsia remains unclear. To address this research gap, we analyzed sphingolipid profiles in pregnancies at high risk for preeclampsia development to identify potential biomarkers and clarify their role in disease pathogenesis.

Materials and methods: We monitored 90 pregnant women at high risk for preeclampsia development across four gestational points. These women were later categorized into the group of women with high risk who did not develop preeclampsia (HRG) (70 women) or the preeclampsia group (PG) (20 women). Sphingolipids (sphingosine, sphinganine, sphingosine-1-phosphate (S1P), ceramides C16:0/C24:0, and sphingomyelin C16:0) were quantified via liquid chromatography-tandem mass spectrometry.

Results: Sphingolipid profiles revealed distinct patterns between groups. Concentrations of S1P in the HRG increased from the 1st trimester to delivery (P < 0.001). We did not notice significant changes in S1P during pregnancy in the PG but compared with the HRG we found significantly lower concentrations at each test point from the 2nd trimester until delivery (P = 0.020, P = 0.013, P = 0.011, respectively). Ceramides C16:0 and C24:0 demonstrated significant increases over time in HRG (P < 0.001, both). Sphingomyelin C16:0 increased significantly across pregnancy in both groups (P < 0.001 in HRG and P = 0.006 in PG), with no significant differences between groups.

Conclusions: We identified S1P as a potential biomarker for late-onset preeclampsia, with lower concentrations observed in PG compared to HRG. Rising sphingomyelin concentrations in both cohorts might serve as a relevant cardiovascular risk indicator in pregnancies at high risk for preeclampsia.

Introduction: Reliable and accurate measurement of blood glucose concentration is of crucial importance for making clinical decisions in diagnosis diabetes, gestational diabetes and impaired fasting glucose tolerance.

Materials and methods: Survey was performed in form of questionnaire. Questionnaire was sent to all Croatian laboratories (N = 204) in electronic form using SurveyMonkey cloud-based software (SurveyMonkey, Inc., San Mateo, USA) as an extra-analytical module of the Croatian EQA (External Quality Assessment) provider Croatian center for external quality assessment (CROQALM) in June 2023.

Results: In total 148 (73%) of laboratories responded to the survey. Large proportion of laboratories never use glucose inhibitor tubes for random glucose measurement (more than half) or for glucose function tests (one quarter). Only three laboratories use recommended glycolysis inhibitor citrate. Many other inhibitors are also used, even if some of them are not recommended for plasma glucose measurement. Glucose is almost never (93%) sampled on ice when glucose inhibitor tube is not available.

Conclusions: Laboratories in Croatia do not follow the recommended procedures regarding glycolysis inhibitors for glucose determination.

Introduction: Ferroportin (Fpn) is the only known iron exporter and plays an essential role in iron homeostasis. Serum concentrations of Fpn in health and/or diseased states are still mostly unknown. Therefore, the aim of this study was to determine the concentration of Fpn in the serum of women of reproductive age (WRA) for the first time, and to establish whether there is a difference in the concentration of Fpn according to ferritin status.

Materials and methods: This research included 100 WRA (18-45 years, C-reactive protein (CRP) < 5 mg/L, hemoglobin > 120 g/L). Serum Fpn was measured using Enzyme Linked Immunosorbent Assay (ELISA) method on the analyzer EZ Read 800 Plus (Biochrom, Cambridge, UK). Reference interval was calculated using the robust method.

Results: The median concentration of Fpn in the whole study group was 9.74 (5.84-15.69) µg/L. The subgroup with ferritin concentration > 15 µg/L had a median Fpn concentration 15.21 (10.34-21.93) µg/L, which significantly differed from Fpn concentration in the subgroup with ferritin concentration ≤ 15 µg/L (5.93 (4.84-8.36) µg/L, P < 0.001). The reference limits for the Fpn were 2.26-29.81 µg/L with 90% confidence intervals (CI) of 1.78 to 2.83 and 25.37 to 34.33, respectively.

Conclusions: The proposed reference interval could help in the future research on iron homeostasis both in physiological conditions and in various disorders, because this is the first study that measured Fpn concentration in a certain gender and age group of a healthy population.

This case report describes interference from heterophilic antibodies in D-dimer assay. The interference was suspected due to discrepancies between D-dimer concentrations in the original sample and diluted samples, as well as inconsistent clinical findings. The patient's medical history, laboratory results, and imaging studies were considered in the investigation. Heterophilic antibodies, likely developed during the SARS-CoV-2 infection, were identified as the probable cause of interference. The interference was confirmed through various methods, including dilution studies, blocking heterophilic antibodies, and comparing results with an alternative D-dimer method. This case highlights the importance of recognizing and addressing interference in D-dimer testing, emphasizing the need for collaboration between clinicians and laboratory specialists.

Ovarian cancer is the 8th most common malignancy in women and the deadliest gynecological cancer. Due to the non-specific symptoms and the lack of effective diagnostic methods, late diagnosis remains the main barrier for improving the poor prognosis. Human epididymis protein 4 (HE4) is a protein overexpressed in ovarian cancer, but not in healthy individuals or benign conditions. The aim of this review article is to evaluate the laboratory aspect and potential clinical application of urine HE4. The methodology is presented, together with discussion on preanalytical, analytical and postanalytical phase of HE4 detection using urine. Moreover, we present the diagnostic role of urine HE4 in differential diagnosis, chemotherapy response, detection of recurrence and detection of low-malignant potential tumors. It has been found that urine HE4 presents as a promising, non-invasive tumor marker for detection and monitoring of ovarian cancer. However, standardization of the HE4 detection process is needed prior to implementation in clinical diagnostics.

Introduction: In highly stressed circumstances, such as COVID-19 pandemic, biomarkers of the vaccine-induced immunity could be especially convenient. The main aim of our study was to determine C-C motif ligand 20 (CCL20) concentration after Ad26.COV2.S vaccination in regard to more common proinflammatory molecules and its correlation with anti-SARS-CoV-2 antibody concentration. Secondly, we investigated inflammatory and immunologic profile differences between patients with and without arterial hypertension.

Materials and methods: The study included 84 subjects vaccinated with Ad26.COV2.S vaccine. Concentration of CCL20, interleukin (IL) 6, C-reactive protein (CRP) was investigated before, 7 and 14 days after vaccination and concentration of anti-SARS-CoV-2 IgG antibody 7 and 14 days after vaccination. All the markers were measured by well-established laboratory methods.

Results: There were no statistically significant changes of CCL20 and IL-6 concentration after the vaccination. The obtained results have shown statistically significant differences for CRP (P = 0.006) concentrations between 3 time points and SARS-CoV-2 IgG antibody (P < 0.001) concentrations between 2 time points. CCL20 did not correlate with IL-6, CRP or anti-SARS-CoV-2 IgG antibody concentration. Statistically significant difference for CRP (P = 0.025) concentration between 3 time points was observed in the subgroup of subjects with arterial hypertension.

Conclusions: Although our results did not show changes in CCL20 concentration after the vaccination, possibly due to the study timeframe, further investigations on chemokine profile post SARS-CoV-2 immunization could facilitate the recognition of specific patterns of response (supra- or sub-optimal) to the vaccine.