The safety and efficacy of harvesting peripheral blood hematopoietic stem cells (PBSC) were evaluated in 38 children weighing 20 kg or less, with the smallest patient weighing 7 kg. The patients had a median age of 42 months and included 26 children with acute leukemias or lymphoma and 12 with various solid tumors. A total of 81 aphereses were performed, mostly in the recovery phase of chemotherapy, with or without granulocyte colony-stimulating factor, using a CS-3000 cell separator and regular procedure no. 3. Blood was withdrawn at a mean rate of 30 mL/min (range, 17 to 46 mL/min) through a temporary radial arterial catheter (20 to 24 guage) and returned through a larger catheter in a peripheral vein. Morbidity related to PBSC harvest was low and all aphereses were completed within 3 hours. The volume of blood per kilogram processed for each apheresis ranged from 85 to 615 mL (median, 270 mL). The median number of colony-forming units-- granulocyte-macrophage (CFU-GM) and CD34+ cells collected were, respectively, 34 x 10(4)/kg and 15 x 10(6)/kg per apheresis and 126 x 10(4)/kg and 31 x 10(6)/kg per patient. Thirty-three patients (87%) required only a single apheresis to collect the minimum requirement of 10 x 10(4) CFU-GM/kg, including 28 patients (74%) from whom 30 x 10(4) CFU-GM/kg was obtained in a single apheresis. Twenty-three of the patients subsequently underwent autografts with PBSC. The median number of days required to achieve an absolute granulocyte count of 0.5 x 10(9)/L and a platelet count of 50 x 10(9)/L were, respectively, 10 (range, 6 to 15) and 14 (range, 9 to 46). The patients remained dependent on platelet transfusion support for a median of 10 days (range, 5 to 35). Thus, harvesting PBSC in very small children with active cancers is effective and safe and does not involve the risk of anesthesia or multiple invasive marrow aspirations.

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Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

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The purpose of this study was to develop a measure for self-monitoring and self-regulation of momentary mood states. The Two-Dimensional Mood Scale (TDMS), consisting of eight words selected on the basis of pleasure and arousal, was created as an efficient measure of self-monitoring. In Study 1, the validity and reliability of the TDMS were examined by administering the measure to 904 university students. Structural equation modeling revealed that mood states were constructed of two components, arousal and pleasure, and factor analysis found two factors, vitality and stability. In Study 2, differences between two mood manipulations, activation (exercise) and relaxation techniques were examined in 224 university students. The results showed that exercise induced higher vitality (η P ). The TDMS can be combined with various mood manipulations to enable individuals to self-regulate and alter negative psychological states.