Time to Attain Full Enteral Feeds Among Preterm Fetal Growth Restricted Neonates With Absent/Reversed End-Diastolic Flow.

OBJECTIVES: To determine the difference in time to attainment of full enteral feeds between fetal growth restricted (FGR) preterm neonates with and without absent/reversed end-diastolic flow (AREDF). Secondary objectives were to compare the short-term outcomes including the incidence of necrotizing enterocolitis (NEC) and feed intolerance between the two groups and to determine the factors affecting the time to attainment of full enteral feeds (FEF) among preterm FGR neonates. METHODS: A prospective cohort study was conducted among consecutive preterm FGR neonates delivered at 28-36 weeks gestation admitted in level III NICU. An umbilical artery doppler ultrasound was performed antenatally for all participants to detect AREDF. FGR neonates with AREDF were taken as the study group and those without AREDF were taken as the comparison group. Time to attain FEF was defined as time taken to establish enteral feeds of 150 ml/kg/day and tolerating it for the next 3 consecutive days. Delayed attainment of FEF was taken as ≥10 days needed to attain FEF. RESULTS: The median (IQR) time to attainment of full feeds was longer among neonates with AREDF compared to those without AREDF [12 (8, 16.5) vs 8 (5, 10) days; P < 0.001]. Neonates with AREDF had more feed intolerance [RR, 95% CI = 1.51 (1.13 - 2.02); P = 0.004], higher mortality [RR, 95% CI = 2.5 (1.02 - 6.2); P = 0.036], prolonged time to regain birth weight [15 (11.5, 19) days, P = 0.035], longer NICU stay [10 (7, 15), P < 0.001] and longer hospital stay [33 (23, 49), P < 0.001]. Also, neonates with AREDF had more hypoglycemia [RR, 95% CI=2.15 (1.2-3.7); P = 0.004], hypoxic ischemic encephalopathy [RR, 95% CI 5.05 (1.13 - 22.4); P = 0.016], hypothyroidism [RR, 95% CI= 8.08 (1.02 - 63.4), P = 0.016], cholestasis (P = 0.007), prolonged parenteral nutrition requirement [10 (7, 15) days, P < 0.001] and oxygen requirement [4.5 (2, 8) days, P < 0.001]. Multivariable logistic regression showed, AREDF [aOR 95% CI 2.91 (1.49 - 5.68), P = 0.002], lower gestational age [aOR 95% CI 0.724 (0.604 - 0.867), P < 0.001] and thrombocytopenia at birth [aOR 95% CI 2.625 (1.342 - 5.136), P = 0.005] are significant predictors of delayed attainment of full feeds among preterm FGR neonates. CONCLUSION: Preterm FGR neonates with AREDF are slower to attain FEF, have more feed intolerance, higher mortality, need longer time to regain birth weight, prolonged NICU stay and hospital stay. AREDF, lower gestation, sepsis and thrombocytopenia at birth are significant predictors of delayed full feed attainment among preterm FGR neonates. It is essential to devise strategies to reduce morbidity and mortality among this group of preterm neonates.

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback-Leibler divergence.

IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback-Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings. Supplementary information: The online version contains supplementary material available at 10.1007/s12083-022-01301-y.

Neonatal seizures: Predictors of adverse outcome.

CONTEXT: Early detection of predictors of adverse outcome will be helpful for neonatologists to plan management, follow up and rehabilitation in advance so that neurological disability can be minimised. AIMS: The purpose of this study was to determine the factors affecting the adverse outcome of neonatal seizures. SETTINGS AND DESIGN: This is a prospective study conducted in the neonatal unit of a tertiary care hospital. One hundred and eight newborns consecutively admitted with seizures were included in this study. MATERIALS AND METHODS: Data was collected regarding perinatal history and seizure and evaluated for etiology. We conducted a retrospective analysis to identify the factors associated with adverse outcome after neonatal seizures. STATISTICAL ANALYSIS USED: Chi-square test with degree of freedom = 1 was used to find the variables significantly associated with adverse outcome (P < 0.05). RESULTS: Gestational age, birth weight, Apgar score at 5 min, seizure onset <24 hrs, status epilepticus, radiological findings and EEG findings were significantly associated with outcome. CONCLUSION: Mortality and severe neurological impairment after neonatal seizure is associated with prematurity, LBW, low Apgar score at 5 min, etiologies like meningitis, sepsis, severe HIE, brain malformations, grade 3 or 4 IVH or intracranial haemorrhage, seizure onset <24 hours, presence of status epilepticus, severely abnormal radiological and EEG findings.