Lauesenparrott2513

Colorectal cancer (CRC) is one of the most frequently diagnosed cancers worldwide. Based on clinical data, CRC could be cured by surgery with favorable outcomes if diagnosed at an early stage. The present study aimed to determine whether circ-FMN2, circ-LMNB1, and circ-ZNF609 may serve as potential biomarkers for CRC.

Expression levels of circ-FMN2, circ-LMNB1, and circ-ZNF609 were detected in serum samples from 88 CRC patients and 68 healthy volunteers by real-time quantitative PCR (RT-qPCR). The correlation between circRNA expressions and clinicopathological parameters was analyzed subsequently. The ROC curve analysis and survival curves were calculated and compared in order to explore the diagnostic and prognostic values of circ-RNAs in CRC.

The results verified that circ-FMN2, circ-LMNB1, and circ-ZNF609 were significantly elevated in serum samples of CRC patients compared with healthy controls (p < 0.01). Increased circ-FMN2, circ-LMNB1, and circ-ZNF609 expressions were markedly positively correlated with histological grade (p < 0.0001, p = 0.0014, p = 0.0303), lymph nodes metastasis (p < 0.0001, p < 0.0001, p = 0.0093), and TNM stage (p = 0.0055, p = 0.0110, p < 0.000). Meanwhile, the ROC curve analysis verified the diagnostic accuracy of circ-FMN2, circ-LMNB1, and circ-ZNF609 with AUC of 0.9153 (95% CI = 0.8707 ~ 0.9599), 0.9627 (95% CI = 0.9351 ~ 0.9903), and 0.8711 (95% CI = 0.8151 ~ 0.9270), respectively. LC-2 mw Furthermore, the CRC patients with high circ-FMN2, circ-LMNB1, and circ-ZNF609 had significantly worse outcomes than those with low expression (p = 0.0267, p = 0.0145, p = 0.0194).

The present study elucidated that circ-FMN2, circ-LMNB1, and circ-ZNF609 may function as potential diagnostic and prognostic indicators for CRC detection.

The present study elucidated that circ-FMN2, circ-LMNB1, and circ-ZNF609 may function as potential diagnostic and prognostic indicators for CRC detection.Multiple myeloma is a tumour of antibody-secreting plasma cells characterized by clonal expansion and accumulation of monotypic plasma cells in the bone marrow. It is an incurable malignant neoplasm accounting for 10% all hematological malignancies. Globally, the annual percentage of new cancer cases and deaths attributed to multiple myeloma is estimated at about 0.8% and 1%, respectively. Furthermore, its global incidence ranges from 0.5 - 12/100,000 population. It causes hypercalcemia, renal insufficiency, anemia, thrombocytopenia, leucopenia, bone lesions, bone fractures, spinal stenosis, and endorgan damages. This neoplasm occurs due to a complex cytogenetic and chromosomal aberrations. These aberrations affect the expression and functions of microRNAs. Abnormal expression of these microRNAs plays an important role in the pathogenesis and angiogenesis of multiple myeloma and could have a potential role in the diagnosis, prognostic stratification, and treatment of multiple myeloma. This review aimed at summarising the expression of microRNAs and the implication of their dysregulation in the pathogenesis, diagnosis, and treatment of multiple myeloma.

Acute lymphoblastic leukemia (ALL) is a common pediatric leukemia caused by lymphoid precursor proliferation. We analyzed immunophenotyping and hematological findings, as the risk of relapse, of pediatric ALL patients at diagnosis and relapse.

Peripheral blood and bone marrow samples of 30 pediatric ALL patients were collected at diagnosis and at relapse. The latter was evaluated for immunophenotyping and cytochemical staining (Periodic Acid Schiff stain (PAS)), while hematological findings were assessed in the former one.

The percentage of PAS-positive patients, TdT, and CD4 expression were significantly higher at diagnosis than relapse (p = 0.027, 0.004, and 0.043, respectively), whereas the platelet/lymphocyte ratio (PLR) and neutrophil/lymphocyte ratio were significantly lower at diagnosis (p = 0.004 and 0.032, respectively). There were correla-tions between immunophenotyping and hematology data, including a) a negative correlation between CD4 expression with blast percentage (r = -0.927, p = 0.003) and hemoglobin level (r = -0.991, p < 0.001) at diagnosis and TdT expression with platelet count (r = -0.441, p = 0.017) at relapse, and b) a positive correlation between CD3 expression with PLR (r = 0.367, p = 0.046) at relapse.

Results suggest that changes in immunophenotyping and hematology findings could be applied as relapse prognostic factors in ALL.

Results suggest that changes in immunophenotyping and hematology findings could be applied as relapse prognostic factors in ALL.

The performance of 17 routine chemical detection methods was evaluated by the Sigma (σ) index, and separate quality control standards were established according to the sigma values of different detection methods.

The internal quality control (IQC) and external quality assessment (EQA) data of 17 assays in the biochemical laboratory of our hospital were collected from January to June 2019. Referring to the total allowed error (TEa) standards established in the Health Industry Standards of the People's Republic of China (WS/T 403-2012), the sigma metric of each assay was calculated, the performance level for inspection was evaluated, the quality goal index (QGI) was calculated for items with analysis performance < 5 sigma, and the main causes of poor performance were determined to guide quality improvement.

For level 1 internal quality control (IQC), five assays (AMY, Crea, UA, TP, and Na) showed a performance of ≥ 6 sigma levels. Five assays (GGT, LDH, ALP, K, and Ca) had a performance lower than 3 sigma. For level 2 IQC, nine assays (ALT, AST, CK, AMY, Crea, UA, TP, Na, and Mg) achieved 6 sigma, and four assays (GGT, LDH, ALP, and K) achieved less than 3 sigma. Among the 12 assays with a sigma value < 5, the precision of 1 assay should be improved first, the accuracy of 6 assays should be improved next, and both the precision and the accuracy of 5 assays should be improved.

The sigma metric is the best tool for evaluating the performance of different test methods. Assays with high sigma values can be evaluated with single-rule quality control, while assays with low values should be evaluated with strict quality control rules.

The sigma metric is the best tool for evaluating the performance of different test methods. Assays with high sigma values can be evaluated with single-rule quality control, while assays with low values should be evaluated with strict quality control rules.