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Human T-cell lymphotropic virus type 1 and 2 (HTLV-1/2) are oncogenic retroviruses linked etiologically to human diseases. In Chile, these viruses have been studied in ethnic populations, or patients diagnosed clinically with HTLV-1 associated myelopathy/tropical spastic paraparesis, but have not been studied in patients with malignant hematological diseases. The aim of this study was to determine the seroprevalence and viral prevalence of HTLV-1/2 among patients with malignant hematological diseases. Eighty-eight patients with malignant hematological diseases were tested by enzyme-linked immunosorbent assay (ELISA) for IgG anti-HTLV-1/2 and nested-PCR for the tax gene. The seroprevalence by ELISA was 3.4% and the viral prevalence by nested-PCR tax was 18.2%. HTLV-1 was found in 17% and HTLV-2 in 1% of the patients tested. HTLV-1/2 was found in 17.4% of patients with non-Hodgkin's lymphomas, 28.6% of patients with Hodgkin's lymphomas, 80% of patients with chronic lymphocytic leukemia, 11.4% of patients with acute lymphoblastic leukemia, and 22.2% of patients with acute myeloid leukemia. A high prevalence of HTLV-1/2 was found in patients with malignant hematological diseases. A high proportion of patients were seronegative to HTLV-1/2 infection, similar to other HTLV-1/2 associated disorders. Because 50% of patients positive for HTLV-1/2 were below 30 years old, it is suggested that vertical transmission could have played an important role in these patients.

Molecular astronomy is a field that is blooming in the era of large observatories such as the Atacama Large Millimeter/Submillimeter Array (ALMA). With modern, sensitive, and high spectral resolution radio telescopes like ALMA and the Square Kilometer Array, the size of the data cubes is rapidly escalating, generating a need for powerful automatic analysis tools. This work introduces MolPred, a pilot study to perform predictions of molecular parameters such as excitation temperature (T ex ) and column density (log(N)) from input spectra by the use of neural networks. We used as test cases the spectra of CO, HCO + , SiO and CH 3 CN between 80 and 400 GHz. Training spectra were generated with MADCUBA, a state-of-the-art spectral analysis tool. Our algorithm was designed to allow the generation of predictions for multiple molecules in parallel. Using neural networks, we can predict the column density and excitation temperature of these molecules with a mean absolute error of 8.5% for CO, 4.1% for HCO + , 1.5% for SiO and 1.6% for CH 3 CN. The prediction accuracy depends on the noise level, line saturation, and number of transitions. We performed predictions upon real ALMA data. The values predicted by our neural network for this real data differ by 13% from the MADCUBA values on average. Current limitations of our tool include not considering linewidth, source size, multiple velocity components, and line blending.

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