Does Preterm Status Hinder the Timely Diagnosis of Intestinal Atresia?

Intestinal atresia is one of the most common causes of bowel obstruction in newborns. Unfortunately, this diagnosis is often missed or delayed in extremely preterm infants because of complications of prematurity including feeding intolerance and necrotizing enterocolitis. Here we report 2 cases of jejunoileal atresia in extremely preterm infants who were diagnosed beyond 30 days of age. Case 1 had jejunoileal atresia type IIIa, whereas case 2 had type IV jejunoileal atresia complicated by short bowel syndrome. Ideally, intestinal atresia should be diagnosed as early as possible in a patient's hospital course to attain full enteral feedings earlier and avoid prolonged hospitalization. Planned surgical intervention prior to the development of sepsis or bowel perforation is associated with better outcomes. Ultimately, this report serves as a reminder to physicians that intestinal atresia remains an important differential for infants, including preterm infants, with feeding intolerance in the first days to weeks of age.

Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool.

PURPOSE: Necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP) are devastating diseases in preterm neonates, often requiring surgical treatment. Previous studies evaluated outcomes in peritoneal drain placement versus laparotomy, but the accuracy of the presumptive diagnosis remains unknown without bowel visualization. Predictive analytics provide the opportunity to determine the etiology of perforation and guide surgical decision making. The purpose of this investigation was to build and evaluate machine learning models to differentiate NEC and SIP. METHODS: Neonates who underwent drain placement or laparotomy NEC or SIP were identified and grouped definitively via bowel visualization. Patient characteristics were analyzed using machine learning methodologies, which were optimized through areas under the receiver operating characteristic curve (AUROC). The model was further evaluated using a validation cohort. RESULTS: 40 patients were identified. A random forest model achieved 98% AUROC while a ridge logistic regression model reached 92% AUROC in differentiating diseases. When applying the trained random forest model to the validation cohort, outcomes were correctly predicted. CONCLUSIONS: This study supports the feasibility of using a novel machine learning model to differentiate between NEC and SIP prior to any intended surgical interventions. LEVEL OF EVIDENCE: level II TYPE OF STUDY: Clinical Research Paper.

Spin crossover in tetranuclear Fe(II) complexes, {[(tpma)Fe(µ-CN)]4}X4 (X = ClO4(-), BF4(-)).

Two Fe(II) complexes, {[(tpma)Fe(µ-CN)]4}X4 (X = ClO4(-) (1a), BF4(-) (1b); tpma = tris(2-pyridylmethyl)amine), were prepared by reacting the {Fe(tpma)}(2+) building block with (Bu4N)CN. The crystal structures of 1a and 1b feature a tetranuclear cation composed of cyanide-bridged Fe(II) ions, each capped with a tetradentate tpma ligand. The Fe4(µ-CN)4 core of the complex is strongly distorted, assuming a butterfly-like geometry. Both complexes exhibit gradual temperature-driven spin crossover (SCO) associated with the high-spin (HS) ↔ low-spin (LS) transition at two out of four metal centers. The evolution of HS and LS Fe(II) ions with temperature was followed by a combination of X-ray crystallography, magnetic measurements, and Mössbauer spectroscopy. Only the Fe(II) ions surrounded by six N atoms undergo the SCO. A comparison of the temperature-dependent SCO curves for the samples stored under solvent and the dried samples shows that the former exhibit a much more abrupt SCO. This finding was interpreted in terms of the increased structural disorder and decreased crystallinity caused by the loss of the interstitial solvent molecules in the dried samples.