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PurposeIt is to examine clinical manifestations, early biochemical indicators, and risk factors for non-oliguric hyperkalemia (NOHK) in extremely low birth weight infants (ELBWI).Materials and MethodsWe collected clinical and biochemical data from 75 ELBWI admitted to Ajou University Hospital between Jan. 2008 and Jun. 2011 by reviewing medical records retrospectively. NOHK was defined as serum potassium ≥7 mmol/L during the first 72 hours of life with urine output ≥1 mL/kg/h.ResultsNOHK developed in 26.7% (20/75) of ELBWI. Among NOHK developed in ELBWI, 85% (17/20) developed within postnatal (PN) 48 hours, 5% (1/20) experienced cardiac arrhythmia and 20% (4/20) of NOHK infants expired within PN 72 hours. There were statistically significant differences in gestational age, use of antenatal steroid, and serum phosphorous level at PN 24 hours, and serum sodium, calcium, and urea levels at PN 72 hours between NOHK and non-NOHK groups (p-value <0.050). However, there were no statistical differences in the rate of intraventricular hemorrhage, arrhythmia, mortality occurred, methods of fluid therapy, supplementation of amino acid and calcium, frequencies of umbilical artery catheterization and urine output between the two groups.ConclusionNOHK is not a rare complication in ELBWI. It occurs more frequently in ELBWI with younger gestational age and who didn't use antenatal steroid. Furthermore, electrolyte imbalance such as hypernatremia, hypocalcemia and hyperphosphatemia occurred more often in NOHK group within PN 72 hours. Therefore, more use of antenatal steroid and careful control by monitoring electrolyte imbalance should be considered in order to prevent NOHK in ELBWI.

Most cyberattacks use malicious codes, and according to AV-TEST, more than 1 billion malicious codes are expected to emerge in 2020. Although such malicious codes have been widely seen around the PC environment, they have been on the rise recently, focusing on IoT devices such as smartphones, refrigerators, irons, and various sensors. As is known, Linux/embedded environments support various architectures, so it is difficult to identify the architecture in which malware operates when analyzing malware. This paper proposes an AI-based malware analysis technology that is not affected by the operating system or architecture platform. The proposed technology works intuitively. It uses platform-independent binary data rather than features based on the structured format of the executable files. We analyzed the strings from binary data to classify malware. The experimental results achieved 94% accuracy on Windows and Linux datasets. Based on this, we expect the proposed technology to work effectively on other platforms and improve through continuous operation/verification.

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