Kimiko Iijima

A high serum‐soluble interleukin‐2 receptor level is associated with a poor outcome of aggressive non‐Hodgkin's lymphoma

Abstract: Soluble interleukin‐2 receptor (sIL‐2R) is produced by activated T and B cells, and the level of this receptor is elevated in patients with non‐Hodgkins lymphoma (NHL). The present study demonstrated that the sIL‐2R level was high in the following groups of patients with aggressive NHL; those aged 60 yr, those with a poor PS, those in Ann Arbor stage III or IV, and those in the high–intermediate or high risk group according to the International Prognostic Index (IPI). Overall survival was significantly poorer when the sIL‐2R level was 2000 U/ml or more. In addition, the overall survival of patients in the low (L) and low–intermediate (L–I) risk groups with an sIL‐2R level of 3000 U/ml or more was significantly poorer, suggesting that the sIL‐2R level could be particularly useful for identifying patients with a poor prognosis among the L and L–I risk groups. Univariate analysis identified some significant prognostic factors, and multivariate analysis of these factors plus the five IPI prognostic factors showed that the sIL‐2R level was an independent prognostic indicator. In conclusion, the present findings established that the sIL‐2R level is a significant independent prognostic factor in patients with aggressive NHL.

TRALI after the infusion of marrow cells in a patient with acute lymphoblastic leukemia

BACKGROUND:  TRALI is one of the most serious, life‐threatening complications after blood transfusion. Antibodies against neutrophils or HLA molecules from the donor are thought to be the primary causative agents. Rarely, antibodies in the recipient may react with transfused neutrophils and initiate the same events, which raises the possibility that TRALI may also occur in an allogeneic PBPC transplantation setting.

High serum soluble CD44 is correlated with a poor outcome of aggressive non-Hodgkin's lymphoma

We measured soluble CD44 (sCD44) by enzyme-linked immunosorbent assay in 216 patients with non-Hodgkins lymphoma (NHL). The sCD44 level was significantly elevated in patients with NHL. A sCD44 level of > or =500 ng/ml showed a significantly decreased overall survival (OS) rate and progression free survival (PFS) rate. In the high-intermediate+high risk group (international prognostic index (IPI)), both the OS rate and the PFS rate were significantly decreased when the sCD44 level was > or =1000 ng/ml. Moreover, the results of a multivariate analysis showed that the five IPI prognostic factors and the sCD44 level were independent prognostic factors, thus suggesting that sCD44 levels could be a useful prognostic marker for aggressive lymphoma

Clinical significance of nm23-H1 proteins expressed on cell surface in non-Hodgkin's lymphoma

The nm23 gene was isolated as a metastasis suppressor gene that exhibits low expression in high-level metastatic cancer cells. Its gene is related to the prognosis of acute myelogenous leukemia (AML) and non-Hodgkins lymphoma (NHL). In this study, we examined the expression of nm23-H1 protein on the lymphoma cell surface of NHL. In 28 of 108 cases (25.9%), we observed ≥20% of cell surface nm23-H1 protein expression and expression was especially high in peripheral T cell lymphomas and extranodal NK/T cell lymphomas. We also observed a significant correlation between serum nm23-H1 level and cell surface nm23-H1 expression levels. In patients with high levels of cell surface nm23-H1 expression, overall and progression-free survival rates were significantly lower than those in patients with low surface nm23-H1 expression levels. When surface nm23-H1 and serum nm23-H1 were combined, patients with high levels of both exhibited a poorer prognosis than patients with a high level of one or the other. These results indicate that in addition to serum nm23-H1, cell surface nm23-H1 may be used as a prognostic factor in planning a treatment strategy. The nm23-H1 protein appears to be intimately related to biological aggressiveness of lymphoma and, therefore, might be a molecular target of NHL treatment.

Alcohol consumption is inversely correlated with insulin resistance, independent of metabolic syndrome factors and fatty liver diseases.

Background and Aim The role of alcohol consumption in insulin resistance remains unclear. The aim of this study was to examine the association between alcohol consumption and insulin resistance in a large asymptomatic population. Methods A total of 2463 asymptomatic Japanese men aged 28 years or above undergoing a comprehensive health checkup including an oral glucose tolerance test between May 2007 and April 2010 were recruited. Participants positive for hepatitis B or C virus, abstinent alcoholics, those taking hepatotoxic drugs, those with chronic renal or hepatic failure, and those under treatment for metabolic disorders were excluded. Fatty liver was defined ultrasonographically. Visceral and subcutaneous adipose tissues were measured with computed tomography. The homeostasis model assessment of insulin resistance (HOMA-IR) score was determined to estimate insulin resistance. The association between alcohol consumption and HOMA-IR score was investigated with multivariate regression analysis. Results A total of 1902 participants were eligible for this cross-sectional survey. A significant difference in distribution of each drinking category was noted between 249 participants with insulin resistance (HOMA-IR ≥2.5) and 1653 participants without insulin resistance (HOMA-IR <2.5; P=0.001). Light (40 to 140 g/wk), moderate (140 to 280 g/wk), and heavy alcohol consumption was inversely associated with HOMA-IR scores (coefficients=−0.125, −0.127, and −0.162; P=0.007, 0.011, and 0.006, respectively) with multivariate analysis after adjusting for potential confounding variables, including visceral and subcutaneous adipose tissues, metabolic profiles, fatty liver, and liver enzyme activities. Conclusions Alcohol consumption was inversely associated with insulin resistance, independent of central obesity, metabolic profiles, and fatty liver diseases.

Impact of stem cell source and conditioning regimen on erythrocyte recovery kinetics after allogeneic haematopoietic stem cell transplantation from an ABO-incompatible donor

Summary. We evaluated erythrocyte recovery in 121 allogeneic haematopoietic stem cell transplantation (HSCT) recipients. There were 35 major and minor ABO‐incompatible transplants, respectively, including 10 bi‐directionally ABO‐incompatible transplants. The use of peripheral blood stem cells facilitated erythrocyte recovery, regardless of the presence or absence of major ABO‐incompatibility, and was associated with a frequent detection of anti‐host isohaemagglutin early after minor ABO‐incompatible transplantation, which was not associated with clinically relevant haemolysis. The use of a reduced‐intensity regimen combining a purine analogue and busulphan did not delay erythrocyte recovery after major ABO‐incompatible transplantation, suggesting this regimen had a strong activity against host plasma cell.

Granulocytic Differentiation of Leukemic Cells With t(9;11)(p22;q23) Induced by All-Trans-Retinoic Acid

Acute leukemia patients with MLL (mixed linage leukemia) rearrangements tend to respond poorly to conventional therapies. We examined differentiation of human myeloid leukemia cells displaying the MLL-AF9 gene, using several differentiation agents. When MOLM-14 cells were treated with all-trans retinoic acid (ATRA) or 1beta,25-dihydroxyvitamin D3, significant induced differentiation was observed. Trichostatin A (TSA), an inhibitor of histone deacetylase, demonstrated enhance effects with ATRA in regard to growth inhibition and differentiation induction in MOLM-14 cells. Pretreatment with TSA before exposure to ATRA displayed increased effect. Based on these findings, combined treatment with ATRA and TSA may be clinically useful in therapy for acute leukemia displaying MLL-AF9 fusion gene.

Stable Response after Administration of Stem Cell Factor Combined with Granulocyte Colony-Stimulating Factor in Aplastic Anemia

We report successful treatment with 25 (xg/kg of recombinant methionyl human stem cell factor (SCF) combined with 400 μxg/m2 of recombinant human granulocyte colony-stimulating factor (G-CSF) in 2 patients with aplastic anemia refractory to immunosuppressive therapy. In one patient, hemoglobin levels increased from 6.4 g/dL to 11.3 g/dL after 36 weeks of SCF/ G-CSF treatment. Thereafter, the platelet count (24.0 X 109/L) began to improve without the therapy, and as of week 272, the platelet count was 125.0 X 109/L with a leukocyte count of 8.4 X 109/L and a hemoglobin level of 12.9 g/dL. In the other patient, more than 3 years of SCF/G-CSF treatment ameliorated hemoglobin levels and platelet counts from 5.8 g/dL to 15.9 g/dL and 8.0 X 109/L to 50.0 X 109/L, respectively. After cessation of SCF/G-CSF treatment, the positive response was sustained, and the platelet count improved further to 71.0 X 109/L as of week 242. These observations suggest the clinical benefit of SCF/G-CSF administration to patients with refractory aplastic anemia.

Prediction of glucose metabolism disorder risk using a machine learning algorithm (Preprint)

Background A 75-g oral glucose tolerance test (OGTT) provides important information about glucose metabolism, although the test is expensive and invasive. Complete OGTT information, such as 1-hour and 2-hour postloading plasma glucose and immunoreactive insulin levels, may be useful for predicting the future risk of diabetes or glucose metabolism disorders (GMD), which includes both diabetes and prediabetes. Objective We trained several classification models for predicting the risk of developing diabetes or GMD using data from thousands of OGTTs and a machine learning technique (XGBoost). The receiver operating characteristic (ROC) curves and their area under the curve (AUC) values for the trained classification models are reported, along with the sensitivity and specificity determined by the cutoff values of the Youden index. We compared the performance of the machine learning techniques with logistic regressions (LR), which are traditionally used in medical research studies. Methods Data were collected from subjects who underwent multiple OGTTs during comprehensive check-up medical examinations conducted at a single facility in Tokyo, Japan, from May 2006 to April 2017. For each examination, a subject was diagnosed with diabetes or prediabetes according to the American Diabetes Association guidelines. Given the data, 2 studies were conducted: predicting the risk of developing diabetes (study 1) or GMD (study 2). For each study, to apply supervised machine learning methods, the required label data was prepared. If a subject was diagnosed with diabetes or GMD at least once during the period, then that subject’s data obtained in previous trials were classified into the risk group (y=1). After data processing, 13,581 and 6760 OGTTs were analyzed for study 1 and study 2, respectively. For each study, a randomly chosen subset representing 80% of the data was used for training 9 classification models and the remaining 20% was used for evaluating the models. Three classification models, A to C, used XGBoost with various input variables, some including OGTT data. The other 6 classification models, D to I, used LR for comparison. Results For study 1, the AUC values ranged from 0.78 to 0.93. For study 2, the AUC values ranged from 0.63 to 0.78. The machine learning approach using XGBoost showed better performance compared with traditional LR methods. The AUC values increased when the full OGTT variables were included. In our analysis using a particular setting of input variables, XGBoost showed that the OGTT variables were more important than fasting plasma glucose or glycated hemoglobin. Conclusions A machine learning approach, XGBoost, showed better prediction accuracy compared with LR, suggesting that advanced machine learning methods are useful for detecting the early signs of diabetes or GMD. The prediction accuracy increased when all OGTT variables were added. This indicates that complete OGTT information is important for predicting the future risk of diabetes and GMD accurately.

Therapeutic effectiveness of ranimustine chemotherapy for elderly essential thrombocythemia

Background:  We studied the clinical effectiveness of ranimustine (MCNU) chemotherapy for essential thrombocythemia (ET).