New structures and algorithms for adaptive system identification and channel equalization

The main drawback of the ADF is that it takes lot of iteration and fails to identify nonlinear systems. BAF converges fast while maintaining the same performance as ADF but its performance degrades at nonlinear conditions.In this thesis we propose an ANN, which provides better and faster converges when employed for identifying nonlinear systems. This network employs chebyschev based nonlinear inputs updated with the RLS algorithm. Through extensive computer simulation it is demonstrated that CFLANN updated with RLS is a better candidate compared to FLANN and MLP in terms of less complex structure, less number of input simple needed and does accurate identification

Effect of black tea consumption on onset of action of benzodiazepines in children: A case–control study

Introduction: Benzodiazepines (lorazepam and diazepam) are the drugs that have calming effects, but caffeine of black tea is a stimulant. Hence, taking black tea along with benzodiazepines might block the calming effects of the latter. In our locality, giving black tea to the children is a regular sociocultural practice by their parents. Objective: To know the effect of black tea consumption on onset of action of benzodiazepines in children. Methods: An observational analytic matched case–control study was done in our department from January 2015 to June 2015 subjected to interview schedule by simple consecutive sampling, and data were analyzed using SPSS version 24 software after proper consent and ethical committee approval. Inclusion criteria of cases were any child taking black tea routinely between 1 and 15 years of age attending our outpatient department or inpatient department requiring intravenous (IV) benzodiazepines medications, and exclusion criteria were critically ill children, having chronic liver or kidney diseases, and children taking anticonvulsants regularly. Children of identical age groups, not taking black tea at all, requiring IV benzodiazepines were taken as controls. Results: An independent t-test showed a significant difference in the onset of action of lorazepam in black tea drinkers (M=5.44 h, standard deviation [SD]=2.43h) and in non-drinkers (M=1.65 h, SD=0.74h); t (99.06)=13.94h, p=0.016 and for diazepam in drinkers (M=1.65 h, SD=0.74h) and in non-drinkers (M=0.93 h, SD=0.37h); t (98.23)=16.58h, p=0.005. Conclusion: Black tea delays the onset of actions of benzodiazepines. Hence, it is advisable not to give black tea to the children, and further studies on this aspect are warranted

Role of Predictive Modeling in Healthcare Research: A Scoping Review

The huge preponderance of inferences drawn in empirical medical research follows from model-based relations (e.g. regression). Here, we described the role of predictive modeling as a complement to this approach. Predictive models are usually probabilistic model which gives a good quality fit to our data. In medical research, it’s very common to use regression models for predictive purposes. Here in this article, we described the types of predictive modeling (Linear and Non-linear) used in medical research and how effectively the researchers take decisions based on predictive modeling, and what precautions, we have to take while building a predictive model. Finally, we consider a working example to illustrate the effectiveness of the predictive model in healthcare

Hyperglycemia in transported neonates: A tertiary care experience

Introduction: Hyperglycemia is common in low birth weight and sick neonates which affect them adversely due to hyperosmolarity. As these neonates need often referral to higher setup, maintenance of euglycemia during transport should be emphasized. Objective: To know the prevalence of hyperglycemia on admission among outborn neonates and analyze the association of different transport variables and other clinical parameters with it. Methods: An observational, analytical, and cross-sectional study was designed and conducted on outborn neonates, enrolled by simple consecutive sampling from November 2014 to October 2016. All data were analyzed using SPSS version 24 and Microsoft Excel version 16 software. Results: Out of 394 outborn neonates, 33.75% were hyperglycemic. 76.4% newborns were transported by ambulance, 94.9% were stabilized before referral, 61.2% had accompanying paramedics, 86.5% neonates given intravenous fluid (IVF) during transport, 61.4% transported by moderately equipped, and 38.6% by poorly equipped vehicle. Admission hyperglycemia was significantly associated with variables such as gestational age, birth asphyxia, type of transport vehicle, duration of transport, IVF during transport, hypoxic ischemic encephalopathy, and neonatal jaundice with p<0.05. Logistic regression model taking variable which shows a strong association, we can predict 70.3% time correctly the hyperglycemia on admission. Conclusion: Prevalence of hyperglycemia found to be quite common in referred neonates. Although there is quite improvement in neonatal transport due to the implementation of various government transport schemes for patients, specialized neonatal transport service with accompanied skill personnel and care during transport is a long way to go

Impact of Machine Learning and Prediction Models in the Diagnosis of Oral Health Conditions

Introduction: Recent developments in data science and the employment of machine learning algorithms (ML) have revolutionized health sciences in the prediction of diseases using laboratory data. Oral diseases are observed in all age groups and are estimated to affect about a 3.5billion people as per WHO 2022 statistics. Using the existing diagnostic data and taking advantage of ML and prediction models would benefit developing a prediction model for diagnosing oral diseases. Hence, it is quite essential to understand the basic terminologies used in the prediction model. Methods: We retrieve various research papers using Scopus, PubMed, and google scholar databases, where prediction models were used in dentistry. The idea of this review is to explore current models, model validation, discrimination, calibration, and bootstrapping methods used in prediction models for oral diseases. Results: The current advancement of ML techniques plays a significant task in the diagnosis and prognosis of oral diseases. Conclusion: The use of prediction models using ML techniques can improve the accuracy of the treatment methods in oral health. This article aims to provide the required framework, data sets, and methodology to build ML and prediction models for oral diseases

Pulse oximetry as a screening tool for congenital heart disease in neonates: A diagnostic study

Introduction: Many studies have been done for screening of congenital heart disease (CHD) in the neonatal period utilizing pulse oximetry as a screening tool along with routine clinical assessment, but none of them from our province. Objective: The objective of the study was to find out the diagnostic accuracy of pulse oximeter at three different sites as a screening tool to diagnose CHD among neonates. Methods: A diagnostic study was conducted in neonatal intensive care unit of a tertiary care hospital of Odisha from October 2016 to September 2018 after approval from the Institutional Ethics Committee. Three hundred and seventy-four neonates (both inborn and outborn) with gestational age >34 weeks were included in the study. Oxygen saturation (SpO2) in the right hand (RH), right foot (RF), and left foot (LF) was estimated by pulse oximeter among all participants after 10 min of postnatal life. All the study subjects were evaluated by two-dimensional (2D) echocardiography for the detection of CHDs. All the diagnostic accuracy tests (sensitivity [Sn], specificity [Sp], positive predictive value, negative predictive value, and diagnostic odds ratio) were calculated taking 2D echocardiography as the gold standard with software, and for all statistical purpose, p<0.05 was considered statistically significant. Results: Cutoff value of the RH SpO2 was 90.0% with Sn of 68.80% and Sp of 98.20%; area under curve (AUC) 0.851 (0.766 and 0.914), p<0.001, for the RF, SpO2 was 90.0% with Sn 78.0% and Sp 92.1%; AUC 0.865 (0.782 and 0.925), p<0.001, and for LF, it was 87% with Sn 77.1% and Sp 94.0%; AUC 0.864 (0.781 and 0.924), p<0.001. Conclusion: Along with the clinical skills, pulse oximetry can be used as an early screening tool for the detection of CHD in the neonatal period and of three different sites, RF found to be better

Serum ferritin as a diagnostic marker for cardiac iron overload among beta-thalassemia major children

Introduction: Beta-thalassemia major is the most common chronic hemolytic anemia. It is a well-comprehended fact that the toxic effects of iron overload particularly the cardiomyopathy are the major complication that roots from beta-thalassemia major children. Therefore, timely diagnosis is crucial to optimize the long-term gain. Objective: The objective of the study is to find the cutoff level of serum ferritin for early diagnosis of cardiac iron overload. Materials and Methods: This study was an observational analytical cross-sectional diagnostic study which was conducted from November 2016 to October 2018. With due approval of Institutional Ethics Committee and after taking proper informed consent from the parents and/or legal heir, 105 thalassemic children were enrolled in the study by simple consecutive sampling after satisfying the pre-defined inclusion and exclusion criteria. In this study, two-dimensional Doppler echocardiography was used to detect cardiac iron overload. Serum ferritin levels were estimated, and cutoff values were calculated for each of the echocardiographic parameters of cardiac iron overload, i.e. ejection fraction (EF), left ventricular end-diastolic diameter (LVEDD), and left ventricular end-systolic diameter (LVESD) by receiver operating characteristic curve analysis. Sensitivity (Sn), specificity (Sp), positive predictive value, and negative predictive value were calculated with considering p<0.05 as statistically significant. Results: The mean age of the study participants was 9±3 years. Cutoff value of serum ferritin for detecting abnormality in EF was 3286 ng/ml with Sn of 76.1% and Sp of 88.1%. Similarly, for detecting abnormal LVEDD, cutoff value of serum ferritin was 4640 ng/ml with Sn of 70.1% and Sp of 98.6%, and for LVESD, it was 3286 ng/ml with Sn of 90% and Sp of 70.5%. Conclusion: The serum ferritin level can be used as a reliable marker of myocardial iron overload among childhood beta-thalassemia and hence can be used as an important screening tool

Comparison of Some Prediction Models and their Relevance in the Clinical Research

In healthcare research, predictive modeling is commonly utilized to forecast risk variables and enhance treatment procedures for improved patient outcomes. Enormous quantities of data are being created as a result of recent advances in research, clinical trials, next-generation genomic sequencing, biomarkers, and transcriptional and translational studies. Understanding how to handle and comprehend scientific data to offer better treatment for patients is critical. Currently, multiple prediction models are being utilized to investigate patient outcomes. However, it is critical to recognize the limitations of these models in the research design and their unique benefits and drawbacks. In this overview, we will look at linear regression, logistic regression, decision trees, and artificial neural network prediction models, as well as their advantages and disadvantages. The two most perilous requirements for building any predictive healthcare model are feature selection and model validation. Typically, feature selection is done by a review of the literature and expert opinion on that subject. Model validation is also an essential component of every prediction model. It characteristically relates to the predictive model's performance and accuracy. It is strongly recommended that all clinical parameters should be thoroughly examined before using any prediction model

Survival of malarial acute kidney injury in children: A prospective analytical study

Introduction: The renal involvement has been reported in Plasmodium falciparum, Plasmodium malariae, and recently in Plasmodium vivax infection. Although malaria is highly endemic in the rural locality of Odisha and a significant proportion of severe malaria causes acute renal complication, there is no definite study on the survival of malarial acute kidney injury (AKI) in children of the setup of the current study. Objective: The objective of the study was to find out the survival of malarial AKI in children. Methods: A prospective analytical study was conducted from October 2016 to September 2018 in the postgraduate department of pediatrics, of a tertiary care hospital in Odisha, after approval from the Institutional Ethics Committee. Children with smear-positive and/or quantitative buffy coat (QBC) positive malaria were included in the study. All the relevant data (age, gender, duration of hospital stays, stages of AKI, signs, and symptoms of AKI, serum urea and creatinine, electrolytes, and routine hemogram) were collected, validated and results were analyzed in terms of one-way ANOVA and Kaplan–Meier survival analysis. Results: Out of 202 malarial cases, 50.4% (102) cases were found to be suffering from malarial AKI. Out of 102 malarial AKI children, 68% were affected due to falciparum infection, 12% due to vivax, and rest 20% due to mixed infection. The median duration of survival in days between three stages of AKI was significant as evidenced by Tarone-Ware Chi-square=48.365 (df=2), p=0.000. Conclusion: Mortality was 6% and all of these deaths belong to Stage 3 AKI; furthermore, the morbidities are more in Stage 3 as compared to other stages

Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.Comment: Compilation of the contributions (148 pages) as presented in the `Hot QCD Matter 2022 conference', held from May 12 to 14, 2022, jointly organized by IIT Goa & Goa University, Goa, Indi