An integrated deep learning-based model of spatial cells that combines self-motion with sensory information.

A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial world. We present a general, deep learning-based modeling framework that describes the emergence of the spatial-cell responses and can also explain responses that involve a combination of path integration and vision. The first layer of the model consists of head direction (HD) cells that code for the preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal component analysis (PCA) of the PI-cell responses showed the emergence of cells with grid-like spatial periodicity. We show that the Bessel functions could describe the response of these cells. The output of the PI layer is used to train a stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting the wider applicability of the proposed modeling framework beyond the two simulated studies.

Detecting human CD34+ and CD34- hematopoietic stem and progenitor cells using a Sysmex automated hematology analyzer.

In clinical medicine, particularly in the newly developing stem cell therapies required to support the practice of regenerative medicine, the measurement of both CD34+ and CD34- hematopoietic stem cells (HSC)/hematopoietic progenitor cells (HPC) is important in obtaining more accurate information about the total HSC/HPC content in various stem/progenitor cell sources. We report the results of an investigation into methods of detecting CD34+ and CD34- HSC/HPC using the immature information (IMI) channel incorporated into the Sysmex XE-2100 and SE-9000 automated hematology analyzers. In this study, CD34+ and CD34- HSC/HPC were separated by immunologic methods and quantified by flow cytometry (FACScan) and IMI channel analysis. In addition, CD34-/CD133+ HSC were prepared by a sequential antibody-based positive selection strategy. These cells appeared in the same area as CD34+ cells in the IMI channel of the automated hematology analyzer. These findings confirmed that an automated hematology analyzer can be used to measure both CD34+ and CD34- HSC. These results may explain the difference in HSC/HPC counts sometimes observed between the automated hematology analyzer and flow cytometric methods for CD34+ measurement. The results of this study demonstrated the potential of automated cell counting methods for measuring HSC content in cellular products for both research and clinical applications.

Hemoglobin A1c levels in non-diabetic patients with end-stage renal disease (ESRD) receiving hemodialysis.

Hemoglobin A1c (HbA1c) level represents an established tool to monitor glycemic control in diabetic patients, but the previous commonly used tests of HbA1c in patients with end-stage renal disease (ESRD) may not be reliable because of the presence of anemia, assay interference from uremia, and decreased red blood cell (RBC) life span. HbA1c level measured by turbidimetric immunoassay method is not affected by the above factors. We enrolled 40 non-diabetic ESRD patients receiving hemodialysis and 55 non-diabetic patients without ESRD for this study. HbA1c was analyzed by turbidimetric immunoassays with Synchron CX system. We found that the average HbA1c level in non-diabetic ESRD patients receiving hemodialysis was 5.99% and in the control group was 5.45% (p<0.05). There was no significant difference in fasting glucose levels and Hct % between the two groups (p>0.05). Our limited data indicate that HbA1c levels are elevated in nondiabetic ESRD patients receiving hemodialysis. We propose that the elevated HbA1c level may be due to the repetitive exposure of patients' RBCs to the high glucose level in dialysate (200 mg/dl) or may reflect true glucose intolerance in non-diabetic patients with ESRD.

Serum leptin concentration in patients infected with human immunodeficiency virus.

OBJECTIVE: To assess the potential effect of serum leptin levels in human immunodeficiency virus (HIV)-related wasting. METHODS: Morning serum leptin levels of 94 randomly chosen HIV-infected patients were measured and correlated with age, sex, weight, height, body mass index (BMI), routine blood chemistries (SMA 18), complete blood cell count, HIV viral load, and CD4/CD8 ratio. RESULTS: The mean serum leptin level was 7.0 +/- 6.9 ng/mL. Leptin levels were significantly higher in the 38 female patients than in the 56 male patients (10.0 +/- 8.4 ng/mL versus 5.0 +/- 4.9 ng/mL; P<0.001). Leptin levels were positively correlated with BMI (r = 0.71; P<0.05). The correlation of leptin levels with BMI was improved when the results were analyzed stratified by the sex of the patients (r = 0.74 for female patients; r = 0.81 for male patients). CONCLUSION: This study showed that the serum leptin levels in HIV-infected patients with BMI between 18 and 25 kg/m 2 were comparable to leptin levels in lean, healthy subjects. Leptin distribution was positively correlated with BMI, as expected. These data do not support the hypothesis for a major role of serum leptin in HIV-related wasting.