Biochemia medica最新文献

Introduction: Glycomics, focusing on the role of glycans in biological processes, particularly their influence on the folding, stability and receptor interactions of glycoconjugates like antibodies, is vital for our understanding of biology. Changes in immunoglobulin G (IgG) N-glycosylation have been associated with various physiological and pathophysiological conditions. Nevertheless, time-consuming manual sample preparation is one of the limitations in the glycomics diagnostic implementation. The study aimed to develop an automated method for sample preparation on the Tecan Freedom Evo 200 platform and compare its efficiency and precision with the manual counterpart.

Materials and methods: The initial method development included 32 pooled blood plasma technical replicates. An additional 24 pooled samples were used in the method comparison along with 78 random duplicates of plasma samples collected from 10,001 Dalmatians biobank to compare the manual and automated methods.

Results: The development resulted in a new automated method. For the automated method, glycan peaks comprising 91% of the total sample glycan showed a variation of less than 5% while 92% of the total sample showed a variation of less than 5% for the manual method. The results of the Passing-Bablok regression indicated no differences between the automated and manual methods for 12 glycan peaks (GPs). However, for 8 GPs systematic difference was present, while both systematic and proportional differences were present for four GPs.

Conclusions: The developed automated sample preparation method for IgG glycan analysis reduced exposure to hazardous chemicals and offered a simplified workflow. Despite slight differences between the methods, the new automated method showed high precision and proved to be highly comparable to its manual counterpart.

This case report describes a case of pseudonormokalemia, true hypokalemia. Often, only laboratory values outside the normal range gain attention and false normal results are at risk of not being noticed. However, a disease state may be masked by another pathological process. Here, a 50-year old male was admitted to the Department of Internal Medicine due to sepsis from a dental infection. Initially, serum potassium measurement revealed a normal value of 4 mmol/L (reference interval 3.8-5.1 mmol/L). Thrombocyte number was above 500x10⁹/L. Due to our policy to recommend a repeated measurement of potassium in whole blood or heparin plasma if a patient has thrombocytosis, pseudonormokalemia was identified because the heparin plasma potassium value was only 2.9 mmol/L (reference interval 3.5-4.8 mmol/L). The physiological difference between serum and plasma concentration is no more than 0.3 mmol/L. In this case, potassium concentration were falsely elevated in the serum sample, probably caused by the high number of platelets releasing potassium during clotting. Interpretative comments in patients with thrombocytosis over 500x10⁹/L recommending plasma potassium measurement are helpful. The best way to eliminate pseudohyperkalemia and pseudonormokalemia phenomena caused by thrombocytosis is to completely change towards heparin plasma as the standard material.

Introduction: We compared the quality control efficiency of artificial intelligence-patient-based real-time quality control (AI-PBRTQC) and traditional PBRTQC in laboratories to create favorable conditions for the broader application of PBRTQC in clinical laboratories.

Materials and methods: In the present study, the data of patients with total thyroxine (TT4), anti-Müllerian hormone (AMH), alanine aminotransferase (ALT), total cholesterol (TC), urea, and albumin (ALB) over five months were categorized into two groups: AI-PBRTQC group and traditional PBRTQC group. The Box-Cox transformation method estimated truncation ranges in the conventional PBRTQC group. In contrast, in the AI-PBRTQC group, the PBRTQC software platform intelligently selected the truncation ranges. We developed various validation models by incorporating different weighting factors, denoted as λ. Error detection, false positive rate, false negative rate, average number of the patient sample until error detection, and area under the curve were employed to evaluate the optimal PBRTQC model in this study. This study provides evidence of the effectiveness of AI-PBRTQC in identifying quality risks by analyzing quality risk cases.

Results: The optimal parameter setting scheme for PBRTQC is TT4 (78-186), λ = 0.03; AMH (0.02-2.96), λ = 0.02; ALT (10-25), λ = 0.02; TC (2.84-5.87), λ = 0.02; urea (3.5-6.6), λ = 0.02; ALB (43-52), λ = 0.05.

Conclusions: The AI-PBRTQC group was more efficient in identifying quality risks than the conventional PBRTQC. AI-PBRTQC can also effectively identify quality risks in a small number of samples. AI-PBRTQC can be used to determine quality risks in both biochemistry and immunology analytes. AI-PBRTQC identifies quality risks such as reagent calibration, onboard time, and brand changes.

Introduction: Many studies report vitamin D (25-OH-D) deficiency, although there is no consensus among scientific societies on cut-offs and reference intervals (RI). The aim of this study is to establish and compare RI for serum 25-OH-D by direct and indirect methods.

Materials and methods: Two studies were performed in Zaragoza (Spain). A retrospective study (N = 7222) between January 2017 and April 2019 was used for RI calculation by indirect method and a prospective study (N = 312) with healthy volunteers recruited in August 2019 and February 2020 for direct method. Seasonal differences were investigated. Measurements were performed on Cobas C8000 (Roche-Diagnostics, Basel, Switzerland) using electrochemiluminescence immunoassay technology.

Results: Reference intervals (2.5-97.5 percentile and corresponding 95% confidence intervals, CIs) were as follows: by indirect method 5.6 ng/mL (5.4 to 5.8) - 57.2 ng/mL (55.2 to 59.8), in winter 5.4 ng/mL (5.2 to 5.7) - 55.7 ng/mL (53.6 to 58.4), while in summer 5.9 ng/mL (5.4 to 6.2) - 59.9 ng/mL (56.3 to 62.9). By direct method 9.0 ng/mL (5.7 to 9.5) - 41.4 ng/mL (37.6 to 48.0), in winter 7.4 ng/mL (3.9 to 8.6) - 34.6 ng/mL (30.6 to 51.5), while in summer 13.3 ng/mL (10.1 to 14.1) - 44.1 ng/mL (38.9 to 66.0). In both methods, RIs were higher in summer. A significant difference was observed in 25-OH-D median values between the two methods (P < 0.001).

Conclusions: Reference interval calculation according to the studied area may be a useful tool to adapt the deficiency cut-offs for 25-OH-D. Our data support 25-OH-D values over 12.0 ng/mL for healthy population as sufficient, therefore current recommendations should be updated. In addition, differences in seasonality should be taken into account.

Introduction: Diabetic kidney disease (DKD) is one of the major microvascular complications of type 1 diabetes mellitus (T1DM). Some studies suggest that changes of renal tubular components emerge before the glomerular lesions thus introducing the concept of diabetic tubulopathy with urinary neutrophil gelatinase-associated lipocalin (uNGAL) as a potential marker of DKD. This concept was not confirmed in all studies.

Materials and methods: In 198 T1DM patients with median age 15 years and diabetes duration over one year, an albumin/creatinine ratio (ACR) was determined and uNGAL measured in spot urine sample. Urine samples for ACR and uNGAL were also collected in the control group of 100 healthy children of similar age.

Results: There was no significant difference in uNGAL concentration or uNGAL/creatinine between T1DM children and healthy subjects (6.9 (2.8-20.1) ng/mL vs 7.9 (2.9-21.0) ng/mL, P = 0.969 and 6.8 (2.2-18.4) ng/mg vs 6.5 (1.9-13.4) ng/mg, P = 0.448, respectively) or between T1DM subjects with albuminuria A2 and albuminuria A1 (P = 0.573 and 0.595, respectively). Among T1DM patients 168 (85%) had normal uNGAL concentrations, while in 30 (15%) patients uNGAL was above the defined cut-off value of 30.9 ng/mL. There was no difference in BMI, HbA1c and diabetes duration between patients with elevated uNGAL compared to those with normal uNGAL.

Conclusions: We found no significant difference in uNGAL concentration or uNGAL/creatinine between T1DM children and healthy subjects or between albuminuria A2 and albuminuria A1 T1DM subjects. Therefore, uNGAL should not be recommended as a single marker for detecting diabetic kidney disease in children and adolescents.

Hairy cell leukemia (HCL) represents 2% of all leukemia cases, with men aged above 55 years being the most affected. The most common symptoms of this type of leukemia include splenomegaly, monocytopenia, and neutropenia. In the basic blood count examination, leukopenia with monocytopenia and granulocytopenia, as well as aplastic anemia and/or thrombocytopenia occur. The mutation of β-rapidly accelerated fibrosarcoma (BRAF) proto-oncogene, which can be found in nearly 100% of patients, is an important feature of HCL. Immunophenotypic analysis of the HCL cells reveals high expression of B-lineage antigens, including CD19, CD20, and CD22. Additionally, CD11c, CD25, CD103, and CD123 belong to specific markers of HCL. Lactate dehydrogenase activity and β-2-microglobulin concentration are also important in the patient's assessment. The differential diagnosis between HCL, hairy cell leukemia variant (HCL-V) and splenic marginal zone lymphoma (SMZL) is of first importance. Currently, the main treatment for HCL involves the use of purine analogues, excluding pregnant women, individuals with severe infections, and those with relapsing HCL.

Introduction: Reverse osmosis (RO) membrane, key component of water-purifying equipment, is often stored in protection fluid containing substances such as glycerol, which may contaminate the water at replacement. This study aims to explore the effects of RO membrane replacement on clinical chemistry and immunoassay, particularly triglyceride (TG), providing reference for managing test interference caused by RO membrane replacement.

Materials and methods: The RO membrane of water-purifying equipment A, which provided water to C16000 biochemistry analyzer (Abbott Laboratories, Abbott Park, USA) and E801 electrochemiluminescence analyzer (Roche, Basel, Switzerland), was replaced. Water resistivity was recorded, and quality control (QC) tests were performed on C16000 and E801. Moreover, TG was measured in 29 of selected serum samples on C16000 at 0.5h and 10.5h after RO membrane replacement and on reference biochemistry analyzer BS2000M (Mindray Biomedical Electronics Co., Shenzhen, China), which was connected to water-purifying equipment B without RO membrane replacement. Finally, blank, calibrator 1 and calibrator 2 of TG reagent were measured on C16000 before and at 0.5h, 2.5h and 10.5h after RO membrane replacement. All statistical analyses of data were done using GraphPad Prism (GraphPad Software Inc., San Diego, USA), and a value of P < 0.05 was considered statistically significant.

Results: After RO membrane replacement, all QC results of clinical chemistry and immune tests passed except TG that showed positive bias of 536% and 371% at two levels, respectively. Moreover, TG results of the same serum samples were significantly higher at 0.5h than 10.5h after RO membrane replacement. Meanwhile, there was worse agreement and correlation of TG results between C16000 and BS2000M at 0.5h than 10.5h after replacement. Furthermore, the absorbance of TG blank, calibrator 1 and calibrator 2 was significantly higher at 0.5h and 2.5h after replacement than before replacement, and the absorbance gradually returned to normal value at 10.5h after replacement.

Conclusions: Replacement of RO membrane could cause significant interference to TG test while have no effects on other laboratory tests performed in the study, which may be due to glycerol contamination. Our data provides important reference for management of test interference caused by RO membrane replacement. Clinical laboratory should observe the effects of RO membrane replacement on laboratory tests through both water quality monitoring and QC detection.

This paper examines the application of super-superiority margins in study power calculations. Unlike traditional power calculations, which primarily aim to reject the null hypothesis by any margin, a super-superiority margin establishes a clinically significant threshold. Despite potential benefits, this approach, akin to a non-inferiority calculation but in an opposing direction, is rarely used. Implementing a super-superiority margin separates the notion of the likely difference between two groups (the effect size) from the minimum clinically significant difference, without which inconsistent positions could be held. However, these are often used interchangeably. In an audit of 30 recent randomized controlled trial power calculations, four studies utilized the minimal acceptable difference, and nine utilized the expected difference. In the other studies, this was unclarified. In the post hoc scenario, this approach can shed light on the value of undertaking further studies, which is not apparent from the standard power calculation. The acceptance and rejection of the alternate hypothesis for super-superiority, non-inferiority, equivalence, and standard superiority studies have been compared. When a fixed minimal acceptable difference is applied, a study result will be in one of seven logical positions with regards to the simultaneous application of these hypotheses. The trend for increased trial size and the mirror approach of non-inferiority studies implies that newer interventions may be becoming less effective. Powering for superiority could counter this and ensure that a pre-trial evaluation of clinical significance has taken place, which is necessary to confirm that interventions are beneficial.

Introduction: Pyruvate kinase M2 (PKM2) was involved in the pathophysiology of atherosclerosis and coronary artery disease (CAD). We tested whether plasma PKM2 concentrations were correlated with clinical severity and major adverse cardiovascular events (MACEs) in CAD patients.

Materials and methods: A total of 2443 CAD patients and 238 controls were enrolled. The follow-up time was two years. Plasma PKM2 concentrations were detected by enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone, Wuhan, China) using SpectraMax i3x Multi-Mode Microplate Reader (Molecular Devices, San Jose, USA). The predictors of acute coronary syndrome (ACS) were assessed by logistic regression analysis. The association between PKM2 concentration in different quartiles and MACEs was evaluated by Kaplan-Meier (KM) curves with log-rank test and Cox proportional hazard models. The predictive value of PKM2 and a cluster of conventional risk factors was determined by Receiver operating characteristic (ROC) curves. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were utilized to evaluate the enhancement in risk prediction when PKM2 was added to a predictive model containing a cluster of conventional risk factors.

Results: In CAD patients, PKM2 concentration was the independent predictor of ACS (P < 0.001). Kaplan-Meier cumulative survival curves and Cox proportional hazards analyses revealed that patients with a higher PKM2 concentration had higher incidence of MACEs compared to those with a lower PKM2 concentration (P < 0.001). The addition of PKM2 to a cluster of conventional risk factors significantly increased its prognostic value of MACEs.

Conclusion: Baseline plasma PKM2 concentrations predict the clinical severity and prognosis of CAD.