The role of pentraxin 3 and oxidative status in the prognosis of multiple myeloma.

Multiple myeloma (MM) is a bone marrow malignancy characterized by plasma cell proliferation. It was aimed to investigate pentraxin 3 (PTX3) levels, oxidative/antioxidative status, and their correlation in MM. In the study, four groups were established, including newly diagnosed MM (NDMM), MM in remission (Rem-MM), relapsed/refractory MM (RRMM) patients, and a healthy control group. PTX3 levels were measured using enzyme-linked immunosorbent assay, and the total antioxidant status (TAS) and total oxidant status (TOS) were assessed with an autoanalyzer. The oxidative stress index (OSI) was calculated using the formula: OSI (arbitrary unit) = TOS (µmol H2O2 Eq/L)/TAS (mmol Trolox Eq/L) × 100. The study involved comparing PTX3, TAS, TOS, and OSI levels among these four groups. PTX3 levels were significantly elevated in NDMM and RRMM groups compared to controls and the Rem-MM group (NDMM vs control; p < 0.001, NDMM vs Rem-MM; p < 0.001, RRMM vs control; p < 0.001, and RRMM vs Rem-MM; p = 0.006). TAS was higher in NDMM and RRMM groups versus controls (p = 0.009 and p < 0.001, respectively), and TOS was higher in rem-MM group versus NDMM and control groups (p < 0.001 and p = 0.016, respectively). OSI was higher in the Rem-MM group than in NDMM and RRMM groups (p < 0.001 and p = 0.009, respectively). Multivariate analysis confirmed associations between MM groups and PTX3 levels. Receiver operating characteristic analysis revealed high specificity (90%) and sensitivity (79%) for PTX3 in NDMM at a >0.56 ng/mL cut-off value. This study suggests that PTX3 levels may have diagnostic and prognostic potential in MM and its relationship with oxidative stress requires further exploration.

The Leucine-Rich α2-Glycoprotein-1 Levels in Patients with Multiple Myeloma.

INTRODUCTION: Angiogenesis is considered important in the pathogenesis of multiple myeloma (MM), as well as in the targeted treatment of the disease. Leucine-rich α2-glycoprotein 1 (LRG1) is a protein that participates in angiogenesis and its effect on solid organ tumors has been investigated recently. This study aimed to investigate the relationship between MM and LRG1. METHODS: The MM patients who admitted to Hatay Mustafa Kemal University Hematology Clinic between September 2021 and October 2022 were included in the study. The study consists of a total of 4 groups: newly diagnosed MM (NDMM), relapsed refractory MM (RRMM), MM in remission (Rem-MM), and control group. Demographic data were retrieved from hospital records. Blood samples of our study groups were centrifuged at 1,500 × g for 10 min and serum was collected. LRG1, IL-6, IL-8, TGF-ß1, HIF-1α, FGF-2, and VEGF levels were analyzed in all groups by ELISA method, and statistical analysis was performed. RESULTS: A total of 112 individuals, including NDMM (n: 27), RRMM (n: 18), Rem-MM (n: 42), and control group (n: 25), were enrolled in the study. Based on the analyses, the NDMM group exhibited significantly elevated levels of LRG1 (p < 0.001), TGF-1 (p < 0.001), and HIF-1α (p = 0.046, p < 0.001, and p = 0.003 compared to the RRMM, Rem-MM, and control groups, respectively) compared to the other groups. LRG1 levels were positively correlated with creatinine (r: 0.363, p = 0.001), calcium (r: 0.344, p = 0.001), total protein (r: 0.473, p < 0.001), erythrocyte sedimentation rate (r: 0.547, p < 0.001), lactate dehydrogenase (r: 0.321, p = 0.003), beta-2-microglobulin (r: 0.312, p = 0.017), IL-6 (r: 0.478, p < 0.001), IL-8 (r: 0.240, p = 0.03), TGF-ß1 (r: 0.521, p < 0.001), and HIF-1α (r: 0.321, p = 0.003) levels and were negatively correlated with hemoglobin (r: -0.512, p < 0.001) and albumin (r: -0.549, p < 0.001) levels. Receiver operating characteristics (ROC) analysis revealed the association of LRG1 with the highest AUC value of 0.959 (95% CI: 0.904-1, p < 0.001) and the optimal cut-off value of 534.95 ng/mL (sensitivity: 93% and specificity: 99%) in the NDMM group compared to the control group. CONCLUSION: In this study, providing data for the first time on LRG1 levels in the setting of MM. LRG1 levels were found to be significantly higher in NDMM patients and in our study discriminate this patient population from RRMM, Rem-MM, and normal controls. Therefore, LRG1 seems to a potential biomarker that should be evaluated in future studies addressing the diagnosis, staging, follow-up, prognosis, and treatment target of MM.

Evaluation of Serum Creatinine Levels with Reference Change Value in Patients Receiving Colistin Treatment.

OBJECTIVE: In this study, we aimed to evaluate the serum creatinine (SCr) levels with the reference change value (RCV) in patients receiving colistin treatment. METHODS: We retrospectively recorded the SCr levels of 47 patients receiving colistin treatment before treatment and on days 3 and 7 after treatment. RCV was calculated with the asymmetrical RCV formula (Z = 1.64, P < .05). Percent (%) increase in the SCr results of the patients was compared with RCV and values exceeding RCV were regarded as statistically significant. RESULTS: The RCV was calculated as 15.6% for SCr. Compared with pretreatment values, SCr value on day 3 was 32/47 and on day 7 it was 36/47; as these results exceeded RCV, they were considered statistically significant. CONCLUSION: Use of RCV in the interpretation of results between serial measurements will provide a more rapid and sensitive method when making decisions.

Evaluation of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in Turkish patients with chronic plaque psoriasis.

INTRODUCTION: This study evaluates the relationship between disease activity and neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in patients with chronic plaque psoriasis. METHODS: Clinical and biochemical data were retrieved through retrospective examination of patients' and healthy subjects' medical records. NLR and PLR values were calculated from the hemogram results. This study included 46 patients (25 males, 21 females; 36.58 ± 9.82 years) diagnosed with chronic plaque psoriasis and a control group of 46 healthy volunteers (21 males, 25 females; 34.02 ± 8.41 years). RESULTS: NLR and PLR were significantly elevated in patients with chronic plaque psoriasis (p = 0.0001 and p = 0.003, respectively). PASI was positively correlated with NLR, PLR, and serum CRP levels (r = 0.313, p = 0.034; r = 0.394, p = 0.017; r = 0.359, p = 0.014, respectively). CONCLUSION: NLR and PLR are low-cost tests that can be used to determine the severity of current systemic inflammation in patients with chronic plaque psoriasis.

Interpretation of Biochemical Tests Using the Reference Change Value in Monitoring Adverse Effects of Oral Isotretinoin in 102 Ethnic Turkish Patients.

OBJECTIVES: The aim of this study was to model the use of reference change values (RCVs) for the follow-up of 4 parameters of patients using oral isotretinoin which is gaining widespread popularity for monitoring the side effects of the treatment. METHOD: 102 patients received 30 mg/day oral isotretinoin for 24 weeks for the diagnosis of acne vulgaris. RESULTS: Repetitive measurements of the patients were interpreted with RCVs, after comparing the first and second doses based on RCVs: TC, TG, AST and ALT results increased in 12%, 20%, 14% and 12% of the patients respectively. When the first dose was compared with the last dose, the increases were 20%, 29%, 22% and 18% respectively interpreted as significant changes based on laboratory medicine. CONCLUSIONS: A more sensitive follow-up is possible in the monitorization of adverse effects by using RCVs method.

The use of reference change values in clinical laboratories.

BACKGROUND: The use of Reference Change Values (RCV) has been advocated as very useful for monitoring individuals. Most of these are performed for monitoring individuals in acute situations and for following up the improvement or deterioration of chronic diseases. In our study, we aimed at evaluating the RCV calculation for 24 clinical chemistry analytes widely used in clinical laboratories and the utilization of this data. METHODS: Twenty-four serum samples were analyzed with Abbott kits (Abbott Laboratories, Abbott Park, IL, USA), manufactured for use with the Architect c8000 (Abbott Laboratories, Abbott Park, IL, USA) auto-analyzer. We calculated RCV using the following formula: RCV = Z x 2 1/2x (CVA2 + CVw2)1/2. Four reference change values (RCV) were calculated for each analyte using four statistical probabilities (0.95, and 0.99, unidirectional and bidirectional). Moreover, by providing an interval after identifying upper and lower limits with the Reference Change Factor (RCF), serially measured tests were calculated by using two formulas: exp (Z x 2 1/2 x (CV(A)2 + CVw2)½/100) for RCF(UP) and (1/RCF(UP)) for RCF(DOWN). RESULTS: RCVs of these analytes were calculated as 14.63% for glucose, 29.88% for urea, 17.75% for ALP, 53.39% for CK, 46.98% for CK-MB, 21.00% amylase, 8.00% for total protein, 8.70% for albumin, 51.08% for total bilirubin, 86.34% for direct bilirubin, 6.40% for calcium, 15.03% for creatinine, 21.47% for urate, 14.19% for total cholesterol, 46.62% for triglyceride, 20.51% for HDL-cholesterol, 29.59% for AST, 46.31% for ALT, 31.54% for GGT, 20.92% for LDH, 19.75% for inorganic phosphate, 3.05% for sodium, 11.75% for potassium, 4.44% for chloride (RCV, p < 0.05, unidirectionally). CONCLUSIONS: We suggest using RCV as well as using population-based reference intervals in clinical laboratories. RCV could be available as a tool for making clinical decision, especially when monitoring individuals.