Laboratory medicine最新文献

Objective: Pathological tumor (pT) staging plays a crucial role in prostate cancer (PCa) diagnosis. This study aimed to identify pT stage-associated biomarkers and explored their utility in PCa prognosis.

Methods: GSE69223 was used to identify potential targets differentially expressed between level 2 of pT staging (pT2) and level 3 of pT staging (pT3). Quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry were performed on tissues from patients with PCa to screen the pT stage-associated targets and to explore the prognostic value of these targets in PCa.

Results: CENPI and SLC38A11 were most significantly upregulated, whereas ANO6 and KANK2 were mostly decreased in pT3 tumors compared with pT2 staging. ANO6 levels were negatively associated with preoperative prostate-specific antigen (PSA) levels, lymph node staging (N staging), Gleason score, and overall survival (OS); CENPI was positively associated with preoperative PSA levels, N staging, and OS, but was not associated with the Gleason score; SLC38A11 and KANK2 were not associated with OS. ANO6 and KANK2 were correlated with neutrophil markers, whereas CENPI was correlated with macrophage M2 types.

Conclusion: We identified 4 reliable PCa biomarkers associated with pT staging that would be valuable for diagnosing and determining PCa prognosis.

Background: Interleukin-6 (IL-6) is a proinflammatory cytokine that is associated with many inflammatory diseases. This validation study evaluates the automated Roche Elecsys IL-6 electrochemiluminescent immunoassay that has been granted emergency use authorization by the US Food and Drug Administration.

Methods: The Elecsys IL-6 assay was evaluated for precision, linearity, interference (by hemoglobin, bilirubin, triglycerides, and biotin) and clinical performance was compared to the V-PLEX Human IL-6 immunoassay (Meso Scale Discovery), performed by a reference laboratory.

Results: The Elecsys IL-6 assay is precise (intra-assay <3% coefficient of variation [CV], interassay <5% CV), exhibits an analytical measurable range of 1.5-4790 pg/mL, and is tolerant of significant interferences (H < 2522, I <62, L<2101, biotin <50 ng/mL). Comparison with the V-PLEX assay revealed a 2.95 slope bias in patient samples evaluated for IL-6 concentration (n = 43, range = 1.5-1891 pg/mL, y = 2.95x - 32.7, r2 = 0.84). Bland-Altman analysis revealed an absolute mean bias of 152 pg/mL (SD = 254 pg/mL), or a mean percentage difference of 73%.

Conclusion: The Roche IL-6 assay showed good analytical performance. The large systematic bias compared with another reference method precludes using multiple methods to monitor IL-6 response. The random-access nature of an automated IL-6 assay on the Roche platform makes the test available on demand.

Background: Hashimoto's thyroiditis (HT) is an autoimmune disease that is frequently associated with other autoimmune conditions.

Objective: To perform serological screening for rheumatoid arthritis (RA) in patients with HT.

Methods: Our study included 88 consecutive serum specimens of patients with confirmed HT and 88 sex- and age-matched healthy subjects. All study participants were tested for anti-cyclic citrullinated peptides antibodies (CCP-Ab) and rheumatoid factor (RF). CCP-Ab and RF were performed using ELISA commercial kits. Statistical analysis was conducted using Epi Info, version 3.

Results: Out of 88 patients with HT, 15 (17.0%) had CCP-Ab or RF. The frequency of serological markers of RA was significantly higher in patients than in control individuals (17.0% vs 4.5%; P = .007). RF was more frequent in patients than in the control group, and the difference was statistically significant (13.6% vs 3.4%; P = .01). Isolated RF-IgM was absent in all controls and present in 6 patients with HT (6.8% vs 0%; P = .02). Out of 14 male patients, 3 (21.4%) had antibodies of RA. There was no significant difference in age between patients with CCP-Ab or RF and those without.

Conclusion: A high frequency of serological markers of RA was highlighted in patients with HT.

Background: Nonmedullary thyroid cancer (NMTC) comprises approximately 90% of all thyroid cancers, and about 3% to 9% of NMTC cases have a familial origin. Familial NMTC (FNMTC) in the absence of a documented familial cancer syndrome such as Cowden syndrome is characterized by the occurrence of thyroid cancer of follicular cell origin in 2 or more first-degree relatives.

Methods: Whole-exome sequencing (WES) was used to identify pathogenic genetic variants in 2 Persian families with FNMTC. The purpose of this work is to assess the pathogenic status of these variants as well as the cosegregation status of the variants observed in the examined families.

Results: By analyzing WES data in the first family, SRGAP1: NM_020762: exon16: c.C1849T was identified as a pathogenic variant. This variant was confirmed by Sanger sequencing. In the second family, the variant FOXE1: NM_004473: exon1: c.531_532insCGCGA was identified but was not confirmed by Sanger sequencing.

Conclusion: Based on the data, SRGAP1 can be a potential candidate gene for susceptibility to FNMTC in the first family. However, additional analyses like whole genome sequencing and copy number variations are required to ascertain the disease status in second family.

Granulocyte transfusions are indicated for patients with severe neutropenia and evidence of bacterial or fungal infection who are unresponsive to standard antimicrobial therapy. With a limited expiration time of 24 hours after collection, granulocytes are often transfused before results of infectious-disease screening tests are available, and before a transfusion service can perform a risk assessment if postdonation information is provided after the collection. The case we describe herein demonstrates a clinical scenario meeting indications for granulocyte transfusion, coupled with the clinical management undertaken after the granulocyte donor disclosed a positive result for a COVID-19 self-test taken 1 day after donation. In this case, the patient did not develop new COVID-19 symptoms and tested negative for COVID-19 after transfusion of the implicated unit. These findings add to the body of evidence in the literature that COVID-19 is not transmitted via blood transfusion.

Objective: To compare platelet count results of specimens that yield platelet clump flags to platelet count results on these specimens after vortexing.

Method: Specimens that generated platelet count flags on Sysmex XN 3000 instruments were vortexed and rerun. Only data from specimens demonstrating elimination of platelet clump flags were used in this study. Pearson r analysis was performed on data.

Results: Comparison of complete blood count results (white blood cell count, red blood cell count, hemoglobin, hematocrit, and platelet count) all yielded Pearson r scores >0.9.

Conclusion: Additional patient comfort and safety concerns, as well as concerns over additional specimen collection and processing costs, may be avoided by vortexing and rerunning specimens flagged for platelet clumps when the platelet count is normal.

We present 3 cases of discordant results from screening hemoglobin A1c (HbA1c) measured by ion-exchange high-performance liquid chromatography (HPLC) all due to various forms of interference and flagged by the instrument as "suspected hemoglobin E (HbE)." The first case was due to a rare hemoglobin variant, later confirmed to be hemoglobin Hoshida, the second due to "true" heterozygous HbE, and the third a result of analytical artifact causing splitting of the HbA1c peak without an underlying variant hemoglobin. We examine the similarities in these cases along with the laboratory work-up to classify each cause of interference to demonstrate the wide array of potential causes for the suspected HbE flag and why it warrants proper work-up. Because there is no standardized method of reporting out hemoglobin variant interference in HbA1c measurement, we discuss our laboratory's process of investigating discordant HbA1c measurements and reporting results in cases with variant interference as 1 possible model to follow, along with discussing the associated laboratory, ethical, and clinical considerations. We also examine the structure of hemoglobin Hoshida, HbE, and conduct a brief literature review of previous reports.

Objective: To assess the diagnostic value of urinary transferrin (Tf) in early diabetic nephropathy (DN) to propose a more sensitive and noninvasive biomarker for screening and monitoring DN in clinical practice.

Methods: We searched 3 databases from their inception to May 2023, to identify studies investigating the diagnostic value of Tf in patients with DN. Meta-DiSc software, version 1.4, and Stata software, version 15.1 (StataCorp) were used to conduct a meta-analysis and evaluate the diagnostic accuracy of urine Tf levels for DN.

Results: The meta-analysis included 6 relevant studies investigating the diagnostic value of Tf level for DN. Urinary Tf as a diagnostic marker demonstrated a combined sensitivity of 0.82 (95% CI, 0.71-0.89) and specificity of 0.88 (0.84-0.92). the positive diagnostic likelihood ratio was 7.07 (4.57-10.93), the negative diagnostic likelihood ratio was 0.20 (0.12-0.35), and the diagnostic odds ratio was 34.49 (13.61-87.44). Also, the area under the receiver operating characteristic curve was 0.92 (0.89-0.94), indicating that urinary Tf has a decent discriminative ability in diagnosing DN.

Conclusion: Tf level is a valuable biological marker for early diagnosis and monitoring of DN in clinical practice. It has statistically significant predictive value for patients in the early phases of DN.

Background: A substantial number of patients with multiple myeloma (MM) who have bone destruction are initially admitted into the orthopedic service at the hospital. However, routine laboratory testing usually fails to identify these patients, thus delaying optimal therapy. Therefore, there is a clear medical need for early diagnosis of MM in these patients.

Methods: Between 2019 and 2021, 42 patients receiving treatment for orthopedic conditions had normal hemoglobin (Hb), total protein (TP), albumin (ALB), creatinine (CREA), and blood calcium (Ca) levels before their surgical procedure(s) but were subsequently pathologically confirmed to have MM, based on their presenting orthopedic symptoms. During the same period, 52 patients with orthopedic conditions were pathologically excluded from the diagnosis of MM and were recruited into our control group. Serum free light chain (sFLC) testing was performed in 94 consecutive patients in the orthopedic service using Siemens N Latex FLC kits. The levels of Hb, TP, ALB, CREA, and Ca were also measured. All 42 patients with MM were divided into group A (n = 25: κ proliferation) and group B (n = 17: λ proliferation) by the pathology department.

Results: There were no significant differences in levels of Hb, TP, ALB, CREA, and Ca between group A and group B and the control group. However, the sFLC κ/λ ratio of group A and B was also significantly different from that of the control group (P < .001). The results of serum immunofixation electrophoresis (IFE) testing demonstrated negative results in 14 cases (58.3%) in group A and 4 cases (25.0%) in group B.

Conclusions: Some patients with orthopedic conditions who do not have typical MM laboratory results, such as those with abnormal Hb, TP, ALB, CREA, and Ca levels before their operation(s), actually have MM. MM should be highly suspected in patients with unexplained bone lesions and with an abnormal sFLC κ/λ ratio. Further tissue or bone marrow biopsy is needed in these patients even if serum and urine IFE results are negative and light chain ratio is normal.

Objective: Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, but their performance has not been entirely satisfactory. This study aimed to develop a more accurate LDL-C prediction model using machine learning methods.

Methods: The study involved predicting directly measured LDL-C, using individual characteristics, lipid profiles, and other laboratory results as predictors. The models applied to predict LDL-C values were multiple regression, penalized regression, random forest, and XGBoost. Additionally, a novel 2-step prediction model was developed and introduced. The machine learning methods were evaluated against the Friedewald, Martin, and Sampson equations.

Results: The Friedewald, Martin, and Sampson equations had root mean squared error (RMSE) values of 12.112, 8.084, and 8.492, respectively, whereas the 2-step prediction model showed the highest accuracy, with an RMSE of 7.015. The LDL-C levels were also classified as a categorical variable according to the diagnostic criteria of the dyslipidemia treatment guideline, and concordance rates were calculated between the predictive values obtained from each method and the directly measured ones. The 2-step prediction model had the highest concordance rate (85.1%).

Conclusion: The machine learning method can calculate LDL-C more accurately than existing equations. The proposed 2-step prediction model, in particular, outperformed the other machine learning methods.