Automock Automated Mock Backend Generation for JavaScript based Applications

Modern web development is an intensely collaborative process. Frontend Developers, Backend Developers and Quality Assurance Engineers are integral cogs of a development machine. Frontend developers constantly juggle developing new features, fixing bugs and writing good unit test cases. Achieving this is sometimes difficult as frontend developers are not able to utilize their time completely. They have to wait for the backend to be ready and wait for pages to load during iterations. This paper proposes an approach that enables frontend developers to quickly generate a mock backend that behaves exactly like their actual backend. This generated mock backend minimizes the dependency between frontend developers and backend developers, since both the teams can now utilize the entire sprint duration efficiently. The approach also aids the frontend developer to perform quicker iterations and modifications to his or her code

An observational study to determine accuracy of various methods used to assign gestational age and correlate with outcome

Background: Appropriate estimation of gestational age is paramount in obstetric care. Uncertain gestational age may lead to adverse pregnancy outcome like low birth weight, spontaneous or iatrogenic preterm delivery and perinatal mortality independent of maternal characteristics. In India, seeking of early medical attention in pregnancy is still not the norm. Three methods to estimate the estimated date of delivery are available, namely, menstrual history, clinical examination and by ultrasound.  This study attempts to analyse the accuracy of the three methods used and their correlation with maternal and fetal outcome.Methods: 260 patients presenting to the outpatient department were enrolled irrespective of the gestational age but as soon as they got registered. Ultrasonography was advised if the patient did not have one. EDD was calculated by various methods and was recorded. If significant discrepancy existed, EDD was reassigned. Patients were followed up till the time of their delivery. After the delivery of the baby gestational age was assessed by the neonatologist and was compared with the gestational age at the time of delivery by the three methods. Maternal and fetal outcomes were compared in the form of avoided inductions and maturity of the baby at the time of delivery by all the three methods.Results: The kappa coefficient for the agreement between dating by ultrasound scan and neonatologist was 0.415 whereas for menstrual dates and clinical examination it was 0.197 and 0.369 respectively thus it can be interpreted that the accuracy of ultrasonography may be slightly better than menstrual dates and clinical examination. 75 patients required reassigning of EDD, Induction of labor for supposed post-term pregnancy was avoided in 13% of the patients.Conclusions: Ultrasonography was found to be accurate for determination of term /preterm/ post-term births followed by clinical examination and then the menstrual EDD. Induction of labor for supposed post-term pregnancy was avoided in 13% of the patients in whom EDD was ”assigned” thus stressing that EDD should be reassigned when there is discrepancy between menstrual EDD, Ultrasonography EDD and EDD by clinical examination

Clinical presentation of autoimmune disorders in pregnancy

Background: Autoimmune disorders have a significant impact over the health of an individual. This heterogenous group of disorders affects pregnancy in a multitude of ways. Pregnancies with autoimmune disorders are usually cared for by a multidisciplinary team of doctors.Methods: Pregnancies with autoimmune disorders were studied over a one-year period in one unit of a medical college teaching hospital set up. Obstetric and neonatal outcomes were studied.Results: Ten patients were studied. Average age was 29.9 years. Majority presented in early second trimester. Eight patients were ANA positive. Two patients had antiphospholipid antibody syndrome, for whom low molecular weight heparin was helpful. Hypothyroidism was seen in two patients. Bad obstetric history was seen in most patients. Successful neonatal outcome was seen in six patients. One patient had Evans syndrome. There were no maternal mortalities. There was one perinatal mortality.Conclusions: Autoimmune disorders in pregnancy when managed in a tertiary care centre with multidisciplinary approach can result in good obstetric and neonatal outcomes

An approach to diagnosis and management of acute fatty liver of pregnancy

Acute fatty liver of pregnancy is a rare life threatening cause of jaundice in the third trimester of pregnancy and early postpartum period and is associated with a poor outcome. The maternal and fetal outcome can be improved by a high index of suspicion, early diagnosis and prompt delivery. We report a case of a 30 year old parous lady with 36 weeks twin pregnancy with Acute Fatty Liver of Pregnancy [AFLP] and coagulation failure, she responded to prompt induction of labour and appropriate management of the coagulopathy and related complications. We provide a review of literature on jaundice in pregnancy and the clinical approach to management

INLINE PATCH PROXY FOR XEN HYPERVISOR

Application softwares running on end user or application servers are always prone to various attacks. These attacks not only harm applications but also waste network resources. Solutions to these problems are available as patches since a long time. Generally, people have been reluctant to patch their systems immediately, because patches are perceived to be unreliable and disruptive to apply. To address this problem we propose an inline patch proxy solution for Xen hypervisor. Inline solutions provided are vulnerability-specific, exploit-generic network solutions installed on end systems. The Inline patch module examines the incoming or outgoing traffic, vulnerable to applications, and removes these vulnerabilities to maintain secure traffic. The motive of the idea is to reduce the time difference between the release of a software patch and its actual deployment. Currently patching is promised by software developers, generally within hours (Varies as per the service level agreements) of occurrence of a vulnerable attack. The proposed idea is based on the reducing this time gap to a few seconds by placing the proposed module within the system. For unexposed attacks, time is needed to create new signatures which are generated in update server and pulled by the software running on host

Postoperative sore throat with 0.05% betamethasone gel and 2% lignocaine jelly used as a lubricant for ProSeal LMA (PLMA) insertion

AbstractPostoperative sore throat (POST) is a minor complication after general anaesthesia. Many agents have been used as lubricant to reduce the incidence of POST with variable efficacy. We conducted a study to compare the incidence of POST with 0.05% betamethasone gel and 2% lignocaine jelly as a lubricant for PLMA insertion in patients undergoing general anaesthesia.Sixty subjects were divided randomly into two groups. Patients in Group I (n=30) had 2.5ml of 0.05% of betamethasone gel while the Group II had 2.5ml of 2% lignocaine jelly applied on the cuff of PLMA. After standard induction and insertion of PLMA cuff inflated to 60cm of H2O and was maintained at the same throughout the surgery. In PACU, patients were inquired about sore throat at immediate and 24h post operative period.POST was not observed in any of the patients of Group I. In group II 33% of the patients had 1st degree and 10% had 2nd degree of sore-throat in immediate post-operative period. After 24h 16% patients had 1st degree sore-throat and 3% patients had 2nd degree of sore-throat in Group II patients. We conclude that lubricating cuff of PLMA with 0.05% of betamethasone gel is effective in reducing the incidence of POST

Studying the Effects of Sex-related Differences on Brain Age Prediction using brain MR Imaging

While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant in the context of machine learning for medical imaging applications as these models are increasingly being used for diagnosis and treatment planning. In this paper, we study biases related to sex when developing a machine learning model based on brain magnetic resonance images (MRI). We investigate the effects of sex by performing brain age prediction considering different experimental designs: model trained using only female subjects, only male subjects and a balanced dataset. We also perform evaluation on multiple MRI datasets (Calgary-Campinas(CC359) and CamCAN) to assess the generalization capability of the proposed models. We found disparities in the performance of brain age prediction models when trained on distinct sex subgroups and datasets, in both final predictions and decision making (assessed using interpretability models). Our results demonstrated variations in model generalizability across sex-specific subgroups, suggesting potential biases in models trained on unbalanced datasets. This underlines the critical role of careful experimental design in generating fair and reliable outcomes