Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

The recent advancements under the umbrella of artificial intelligence (AI) open opportunities to tackle complex problems related to image analysis. Recently, the proliferation of COVID-19 brought multiple challenges to medical practitioners, such as precise analysis and classification of COVID-19 cases. Deep learning (DL) and transfer learning (TL) techniques appear to be attractive solutions. To provide the precise classification of COVID-19 cases, this article presents a customized Deep Convolutional Neural Network (DCNN) and pre-trained TL model approach. Our pipeline accommodated several popular pre-trained TL models, namely DenseNet121, ResNet50, InceptionV3, EfficientNetB0, and VGG16, to classify COVID-19 positive and negative cases. We evaluated and compared the performance of these models with a wide range of measures, including accuracy, precision, recall, and F1 score for classifying COVID-19 cases based on chest X-rays. The results demonstrate that our customized DCNN model performed well with randomly assigned weights, achieving 98.5% recall and 97.0% accuracy

HIV reactivity trends in a tertiary care teaching hospital in Himachal Pradesh: a ten-year ICTC based retrospective analysis

Background: Despite being a low HIV prevalence nation, India has the third largest number of PLHAs in the world. The study aimed to explore the prevalence, pattern of socio-demographic and epidemiological distribution among HIV sero-positive patients in this part of Himachal Pradesh. Objective was to estimate the prevalence of HIV infection among the clients who had attended the ICTC for a period of ten years, i.e. from 2008 to 2017.Methods: A retrospective descriptive analysis of secondary data from the National AIDS control program from the year 2008 through 2017 was done.Results: Overall prevalence of HIV positivity amongst the clients attending the centre was observed to be 2.1%. Out of the total 55610 clients tested for HIV infection, 40.4% were male, 25.4% were female (excluding ANCs) and 34.2% were Ante-natal cases. Overall, seropositivity was higher among males (58%) than females (40%). However, amongst the groups, higher prevalence has been observed to be present in the females (3.3%) over males (3%) and Ante-natal cases (0.12%). Belonging to the female sex [OR 1.99 (95% CI: 1.77-2.24)] and male sex [OR 2.07 (95% CI: 1.84- 2.33)] had higher odds of having HIV sero-positivity than Ante-natal cases [OR 0.04 (95% CI: 0.02-0.05)]. Heterosexual route of transmission was the major route seen in 70.1%. Maximum HIV seropositivity was in the age group of 25 - 34 years (35.4%).Conclusions: The trends over the last 10 years show no steady pattern. Hence, there is a need for scaled up and sustained efforts focused on the males of reproductive age group for the prevention and control of HIV infection

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid

Complex problems consisting of interdependent subtasks are represented by a direct acyclic graph (DAG). Subtasks of this DAG are scheduled by the scheduler on various grid resources. Scheduling algorithms for grid strive to optimize the schedule. Nowadays a lot of grid resources are attached by P2P approach. Grid systems and P2P model both are newfangled distributed computing approaches. Combining P2P model and grid systems we get P2P grid systems. P2P grid systems require fully decentralized scheduling algorithm, which can schedule interreliant subtasks among nonuniform computational resources. Absence of central scheduler caused the need for decentralized scheduling algorithm. In this paper we have proposed scheduling algorithm which not only is fruitful in optimizing schedule but also does so in fully decentralized fashion. Hence, this unconventional approach suits well for P2P grid systems. Moreover, this algorithm takes accurate scheduling decisions depending on both computation cost and communication cost associated with DAG’s subtasks

Changes in Anthropometric Indicators Using Therapeutic Food (F75/F-100) Versus Traditionally Used Home Based Food in the Treatment of Severe Acute Malnourished Children - A Comparative Study

Background& Objective: Shortage of suitable food, lack of purchasing power of the family as well as traditional views and taboos about what the baby should eat, often lead to a sufficient balanced diet, resulting in malnutrition. In children, malnutrition is synonymous with growth failure. Malnourished children are smaller and weigh less than they should be for their age and height. Aim of this study is to compare changes in anthropometric indicatorsbetween severe acute malnutrition (SAM) children of therapeutic food F75/F100 with traditionally used home based foods. Methods: This prospective and observational study was conducted in the Department of Pediatrics of G.S.V.M. Medical College, L.L.R. and Associated Hospitals, Kanpur. Logarithmic transformation was achieved by SPSS 20. Study was conducted between March 2011 to July 2011. Permission to perform the trial was obtained from Institutional Ethics Committee (IEC). Results: Weight gain was 7.525gm/kg/day±6.09 in hospitalized patient, whereas 1.013 gm/kg/day ±2.43 was weight gain in home treated patients. Height increase was more although statistically insignificant in hospital treated patients than in home treated patients. Increase in mid arm circumference in hospital treated was found to be highly significant. No significant increase in mid arm circumference was noticed between one at 14 days to one measured at 21 and 28 days in both the groups. Conclusion: Conclude that treatment of SAM is more effective and successful than home based therapy

An LSTM Based Approach for the Classification of Customer Reviews: An Exploratory Study

Significant research has been conducted to address the problem of identification and elimination of malicious content. The credibility of such information is always in question, especially in the E-commerce domain. This research proposes a classification model that automatically classifies customer reviews as credible or non-credible. This model encompasses a Long Short-Term Memory (LSTM) as a classification technique. The preliminary results have shown the potential of our model to classify customer reviews as credible / non-credible based on textual features