Publications by authors named "H ATLAN"

COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning.

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Background: T-cell vaccination (TCV) for multiple sclerosis (MS) refers to treatment with autologous anti-myelin T-cells, attenuated by irradiation. Previously published clinical trials have been all open-labeled.

Aim: To evaluate the safety and efficacy of TCV in progressive MS, in a double-blind, controlled clinical trial.

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Until recently, the answer to that question was obvious: from fertilization. The discovery that artificial cellular constructs, by nuclear transfer from adult cells and by other techniques, can produce blastocyst-like structures, which under some conditions may develop into embryos upon uterine implantation, with no fertilization, leads us to consider successful uterine implantation as necessary for the qualification of what is an embryo. This implies replacement of essentialist definitions by evolutionary ones.

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We studied HIV-positive and -negative subjects for T-cell reactivity to rCD4, and found that 80% of 25 tested HIV-infected patients and 25% of controls manifested T-cell proliferation responses to rCD4. We mapped the major CD4 immunogenic epitopes among the CD4+ responders of both groups by testing T-cell proliferation responses to 31 synthetic overlapping peptides from the human CD4 molecule. Such responses to p1, p4, p14, p21, p28 and p29 were significantly higher in the eight infected patients and, with the exception of p14, these peptides differed from those found in three HIV-negative controls (p11, p14 and p27).

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A model of intentional actions is presented through the operation of two connected neural networks. A deterministic causal recurrent network relates a random initial state to an ordered final state. A perceptron-like, feed-forward network provides a memory mechanism that links the final states to the original initial states.

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