Early chloride intake does not parallel that of sodium in extremely-low-birth-weight infants and may impair neonatal outcomes.

BACKGROUND AND OBJECTIVE: Accurate data on the optimal chloride (Cl) intake in premature infants are scarce. The aim of the present study was to describe Cl intakes in the first 10 days of life and to assess the relations between high Cl intakes and corrected serum Cl level or markers of severe acidosis in infants <28 weeks' gestation. METHODS: Retrospective cohort study including all of the infants <28 weeks admitted to the neonatal intensive care unit during a 3-year period and cared for from birth until day 10 or more. RESULTS: Fifty-six infants were included. Cumulative total Cl intakes reached 9.6 ± 3.7 mmol/kg at day 3 and 49.2 ± 13.5 mmol/kg at day 10. Inadvertent intakes (from intravenous fluids other than parenteral nutrition) represented on average 70% of total Cl intakes in the first 3 days. Difference between Cl and sodium intakes reached 7.8 ± 4.8 mmol/kg at day 10 and mainly originated from parenteral nutrition. By multivariate analysis, cumulative Cl intake >10 mmol/kg during the first 3 days was an independent risk factor of base excess <-10 mmol/L. Cumulative Cl intake >45 mmol/kg during the first 10 days was an independent risk factor of corrected chloremia >115 mmol/L and of base excess <-10 mmol/L. CONCLUSIONS: Cumulative Cl intake >10 mmol/kg during the first 3 days (ie, 3.3 mmol · kg (-1) · day(-1) on average) and >45 mmol/kg during the first 10 days (ie, 4.5 mmol · kg (-1) · day(-1) on average) may have unwanted metabolic consequences and should be avoided. Imbalance between electrolytes provided by the parenteral nutrition solution need to be detected and corrected.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.