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Killer‐cell immunoglobulin‐like receptors (KIRs) are important because of their key roles in NK cell development and function. Some KIR genes have been associated with the incidence of haematological malignancies. This study was designed to determine whether the inheritance of specific KIR genes is associated with susceptibility to acute myelogenous leukaemia (AML) in Persians living in south‐western Iran. KIR genes and KIR2DS4 variants were typed by polymerase chain reaction–sequence‐specific primer (PCR‐SSP) in 167 patients with AML and 169 healthy controls. Our results showed 10% of patients—mostly females—were classified as M3. Flt3 mutations were detected in 26% of patients, most of whom had internal tandem duplication (ITD). The frequency of activating KIRs (aKIRs)—mainly KIR3DS1—was higher in patients, whereas inhibitory KIRs (iKIRs)—particularly KIR3DL1 and KIR2DL1—were more common among controls. The incidence of the KIR2DS4fl allele was higher among patients with non‐M3 AML than controls. We also found a higher frequency of 4 or more iKIR genes in the controls and a higher frequency of 4 or more aKIR genes in the patients. Individuals with more iKIR than aKIR belonged predominantly to the control group. Individuals with the telomeric AA genotype who had inherited the KIR2DS4fl allele were more frequent in the patient group. According to our results, increased frequency of aKIRs in patients with AML may lead to the hyperactivation of NK cells against malignant cells with reduced or lack of HLA class I molecules followed by NK cell exhaustion which allow malignant cells to progress.

The logistic regression (LR) model for assessing differential item functioning (DIF) is highly dependent on the asymptotic sampling distributions. However, for rare events data, the maximum likelihood estimation method may be biased and the asymptotic distributions may not be reliable. In this study, the performance of the regular maximum likelihood (ML) estimation is compared with two bias correction methods including weighted logistic regression (WLR) and Firth's penalized maximum likelihood (PML) to assess DIF for imbalanced or rare events data. The power and type I error rate of the LR model for detecting DIF were investigated under different combinations of sample size, moderate and severe magnitudes of uniform DIF (DIF = 0.4 and 0.8), sample size ratio, number of items, and the imbalanced degree (τ). Indeed, as compared with WLR and for severe imbalanced degree (τ = 0.069), there were reductions of approximately 30% and 24% under DIF = 0.4 and 27% and 23% under DIF = 0.8 in the power of the PML and ML, respectively. The present study revealed that the WLR outperforms both the ML and PML estimation methods when logistic regression is used to evaluate DIF for imbalanced or rare events data.

Background: The complexity of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) makes the clinical course of the disease develop rapidly, causing severe and deadly complications. Identifying effective laboratory biomarkers able to predicting patients based on their risk. This study aimed to look for those serobiomarkers in hospitalized patients with Coronavirus Disease 2019 (COVID‐19). Materials and Methods: In this retrospective observational study, 114 patients with COVID-19, admitted to Valian hospital in Aligudarz, City, Iran from October to December 2020 were examined. The disease outcome was followed along with the hospital course of every patient at the time of analysis. Laboratory investigations of all patients were monitored at the time of admission. A comparative analysis was done between the survivors (n=73) and non-survivors (n=41). Statistical analysis was conducted using SPSS. Results: Of the 114 patients, 40.4% (n=41) were non-survivor, and there were significant differences in Hemoglobin (Hb), Hematocrit (Hct), Platelet (PLT), Alkaline Phosphatase (ALP), Total Bilirubin, Fasting Blood Suger (FBS), Total Iron-Binding Capacity (TIBC), Lactate Dehydrogenase (LDH), Blood Urea Nitrogen (BUN), Creatinine (Cr), Albumin (ALB), and C-Reactive Protein (CRP) between survivors and non-survivors. Conclusion: The laboratory parameters have fundamental roles in poor prognosis and mortality prediction rated among patients with COVID-19 in the first admission. Thus, it is highly recommended to collaborate among hematologists, health managers, and clinical especialists.