Exstrophy of cloaca sequence (OEIS complex) with multiple cardiac malformations

Omphalocele, Exstrophy, Imperforate anus, Spinal defects (OEIS) complex is the most severe birth defect within the exstrophyepispadias complex. There is exstrophy of the cloaca, failure of fusion of the genital tubercles and pubic rami, omphalocele and incomplete development of the lumbosacral vertebrae with hydromyelia. The diagnosis of OEIS complex mainly relies on sonographic findings. Our case presented with microcephaly, omphalocele, syndactyly, hydromyelia, imperforate anus, single cloacal opening, bifid clitoris, prominent unfused pubic rami and left renal agenesis. In addition, multiple severe cardiac malformations were found on echocardiography. Prognosis is poor when the OEIS complex is compounded by life-threatening malformations. We report a rare case of a preterm neonate presenting with features of OEIS complex with multiple cardiac malformations

Bohring Opitz Syndrome: A case of a rare genetic disorder

Bohring-Opitz syndrome (BOS) is a rare genetic disorder, characterized by feeding difficulties, developmental delay, microcephaly, micrognathia, limb anomalies, and typical phenotypic facial features. The cause of the syndrome is identified as de novo heterogeneous mutations in the ASXL1 gene, but other mutations have been described in some patients. Most patients die in early childhood due to infections and comorbidities. As molecular confirmation by genetic studies is not always possible, this syndrome is diagnosed on the basis of distinctive clinical features. We report a case of the 6-month-old male child having gastroesophageal reflux and physical features of microcephaly, sloping forehead, sparse hair, craniosynostosis, telecanthus, hypertelorism, prominent eyes, posteriorly rotated ears, high-arched palate, micrognathia, pes planus, and typical BOS posture. A multidisciplinary approach is required for managing these patients

Mobile phone use by young children and parent's views on children's mobile phone usage

Aims: This study aims to explore the prevalence of mobile phone use among young children aged 6 months to 4 years. We studied the usage patterns, optimal age for use, and the attitudes of parents toward their child's mobile phone use. Methods: We conducted a cross-sectional study in a pediatric OPD of a tertiary teaching hospital for a period of 2-months. Ethics committee approval and informed consent was taken before conducting the research. A predesigned and validated questionnaire was used to collect data. We calculated a sample size of 90 children at a 95% confidence level. Chi-square test and Fischer's exact test were used as a test of significance at 5% level of significance. Results: We observed that 73.34% of children were using mobile phones and mobile phone usage increased with age. Children used mobile phones for educational purposes (43.9%), and for less than an hour a day (57.6%). In the 3-4 year age group, 19% used mobile phones for 3 hours or more.While 93.3% of parents felt they shouldn't give their child a phone, 71.4% children of these parents still used one. Conclusions: Our study highlights a high prevalence of mobile phone use among young children aged 6 months to 4 years. Although parents aimed to limit their child's phone usage, the reality was different. We recommend that guidelines on mobile phone use be followed in India

Concurrent malaria and dengue fever: A need for rapid diagnostic methods

Malaria and dengue fever are endemic in the South-East Asian region including India. Both the illnesses share similar symptomatology, but differ in certain respects such as different- causative organisms and mosquito vector with diverse habitat. Hence, concurrent malaria and dengue fever in the same patient is said to be unusual. There have been cases of concurrent malaria and dengue, but they are scarce from highly endemic region like ours. Here, we describe three unusual cases of Plasmodium vivax and dengue co-infection diagnosed by use of rapid diagnostic tests. Early diagnosis and timely intervention is crucial in managing such patients

COVID‐19 and preeclampsia: the unique and the mutually nonexclusive clinical manifestations

Preeclampsia (PE) is a serious, unpredictable hypertensive disorder of pregnancy present in around 8–10% of all pregnancies resulting in high rate of maternal and fetal morbidity and mortality. With the pathophysiology partially known, delivery is the only cure for PE. The disease sets due to multiple pathologic processes involving endothelial cell activation, inflammation, multiorgan damage and syncytiotrophoblast stress. Though the primary target organ is lungs in COVID-19, other systemic manifestations which include endothelial dysfunction, dysregulated angiogenesis, thrombosis, liver injury, thrombocytopenia, hypertension and kidney damage overlap with PE. COVID-19 patients show a higher incidence of PE as compared to their noninfected counterparts and vice versa. Similar pathophysiology and clinical features make differential diagnosis challenging. For effective and specific management, it is important to differentiate actual PE from COVID-19 with PE like features. There are contradictory reports about the accuracy of diagnostic tools in distinguishing PE from severe COVID-19 with PE like features. With the available data, it can only be stated that PE is a common adverse pregnancy event, which may be exacerbated by, or may exacerbate, COVID-19. Future research should focus on cohesive understanding of the pathophysiology of the clinical manifestations, and preventive strategies during pregnancy

Weighted Multiclass Intrusion Detection System

Attackers are continuously coming up with new attack strategies since cyber security is a field that is continually changing. As a result, it’s important to update and enhance the system frequently to ensure its efficiency against fresh threats. Unauthorised entry, usage, or manipulation of a computer system or network by a person or programme is referred to as an intrusion. There are numerous ways for an incursion to happen, including using software flaws, phishing scams, or social engineering techniques. A realistic solution to handle the risks brought on by the interconnectedness and interoperability of computer systems is to use deep learning architectures to build an adaptive and resilient network intrusion detection system (IDS) to identify and categorise network attacks. Artificial neural networks (ANNs) or deep learning can help adaptive intrusion detection systems (IDS) with learning capabilities identify well-known and unique or zero-day network behavioural patterns, which can significantly reduce the risk of compromise. The NSL-KDD dataset, which represents both synthetically manufactured attack actions and real-world network communication activity, is used to show the effectiveness of the model. Model trained with this dataset to detect a wide range of attack patterns, which help in building an effective IDS