Treatment of recurrent respiratory tract infections with a polyvalent bacterial lysate: results of an open, prospective, multinational study.

This multicenter, open study, carried out in 14 countries in Europe, Latin America, and Asia, recruited 4965 patients suffering from recurrent respiratory tract infections to investigate the safety and acceptability of the oral bacterial lysate immunomodulator LW 50020. Patients remained in the study for 4 months (two 4-week courses of LW 50020 separated by a 28-day treatment-free interval and follow-up). The incidence of all adverse events was 7.2%; that of adverse drug reactions was 0.6%. Adverse drug reactions were mild to moderate and not more frequent in the large subgroup of patients (77%) with a known history of allergies or underlying respiratory diseases; however, the incidence of adverse events in this subgroup was twofold higher than in the study population as a whole, probably indicating a generally increased vulnerability to disease. No clinically relevant changes in laboratory variables followed treatment. Comparison of the first study period (first course of LW 50020 and drug-free interval) with the second study period (second course of LW 50020 and follow-up) showed an overall reduction of at least 50% in the number, severity, and duration of respiratory tract infections, the number of antibiotic and symptomatic treatments, and the number of days absent from school or work. Tolerability and acceptability were assessed as good or very good in 99% of patients who completed the study.

Emergence of algorithmic language in genetic systems.

In genetic systems there is a non-trivial interface between the sequence of symbols which constitutes the chromosome, or 'genotype', and the products which this sequence encodes--the 'phenotype'. This interface can be thought of as a 'computer'. In this case the chromosome is viewed as an algorithm and the phenotype as the result of the computation. In general, only a small fraction of all possible sequences of symbols makes any sense for a given computer. The difficulty of finding meaningful algorithms by random mutation is known as the brittleness problem. In this paper we show that mutation and crossover favor the emergence of an algorithmic language which facilitates the production of meaningful sequences following random mutations of the genotype. We base our conclusions on an analysis of the population dynamics of a variant of Kitano's neurogenetic model wherein the chromosome encodes the rules for cellular division and the phenotype is a 16-cell organism interpreted as a connectivity matrix for a feed-forward neural network. We show that an algorithmic language emerges, describe this language in extenso, and show how it helps to solve the brittleness problem.