High-dose (5-7 mg/kg/day) liposomal amphotericin B was evaluated prospectively during the period 1995-2001 in 41 episodes of systemic candidiasis occurring in 37 neonates (36 of the 37 were premature infants with very low birth weights). Median age at the onset of systemic candidiasis was 17 days. Candida spp. were isolated from blood in all patients and from urine, skin abscesses and peritoneal fluid in 6, 5 and 1 neonates, respectively. Candidiasis was due to Candida parapsilosis in 17 cases, Candida albicans in 15 cases, Candida tropicalis in 5 cases, Candida guilliermondii in 2 cases, Candida glabrata in 2 cases and an unidentified Candida sp. in 1 case. Twenty-eight, five and eight infants received 7, 6-6.5 and 5 mg/kg/day, respectively. Median duration of therapy was 18 days; median cumulative dose was 94 mg/kg. Fungal eradication was achieved in 39 of 41 (95%) episodes; median duration of therapy until fungal eradication was 8.7+/-4.5 days. Fungal eradication was achieved after 10.9+/-4.8 days in patients who had received previous antifungal therapy compared to 8.2+/-4.3 days in those treated with liposomal amphotericin B as first-line therapy. One patient died due to systemic candidiasis on day 12 of therapy. High-dose liposomal amphotericin B was effective and safe in the treatment of neonatal candidiasis. Fungal eradication was more rapid in patients treated early with high doses and in patients who received high-dose liposomal amphotericin B as first-line therapy.

AmBisome represents an effective, safe and convenient antifungal agent in the therapy of systemic fungal infections in very low birth weight infants.

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Purpose To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes. Methods The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data. Results Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models. Conclusions Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists' counselling and their patients in adjusting the appropriate treatment strategy.