BackgroundWeight loss of greater than 7% from birth weight indicates possible feeding problems. Inadequate oral intake causes weight loss and increases the bilirubin enterohepatic circulation. The objective of this study was to describe the association between total serum bilirubin (TSB) levels and weight loss in healthy term infants readmitted for hyperbilirubinemia after birth hospitalization.MethodsWe reviewed medical records of breastfed term infants who received phototherapy according to TSB levels readmitted to Caja Petrolera de Salud Clinic in La Paz, Bolivia during January 2005 through October 2008.ResultsSeventy-nine infants were studied (64.6% were males). The hyperbilirubinemia readmission rate was 5% among breastfed infants. Term infants were readmitted at a median age of 4 days. Mean TSB level was 18.6 ± 3 mg/dL. Thirty (38%) had significant weight loss. A weak correlation between TSB levels and percent of weight loss was identified (r = 0.20; p < 0.05). The frequency of severe hyperbilirubinemia (> 20 mg/dL) was notably higher among infants with significant weight loss (46.7% vs. 18.4%; p < 0.05). The risk of having severe hyperbilirubinemia was approximately 4 times greater for infants with significant weight loss (OR: 3.9; 95% CI: 1.4-10.8; p < 0.05).ConclusionsSignificant weight loss could be a useful parameter to identify breastfed term infants at risk of severe hyperbilirubinemia either during birth hospitalization or outpatient follow-up visits in settings where routine pre-discharge TSB levels have not been implemented yet.

The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid (RNA) transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks.

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