Teaching package improves mothers knowledge on vaccine preventable diseases and vaccination: a Quasi experimental study

Background: Today vaccination is a very essential part of child’s health. Vaccination programme is the key step for the vaccine preventable diseases in children. Objectives of current study were 1. To assess the knowledge of mothers regarding selected vaccine preventable diseases and vaccination. 2. To find the effectiveness of teaching package on knowledge regarding selected vaccine preventable diseases and vaccination among mothers. 3. To find the association between pre-test knowledge score and demographic variables.Methods: An evaluative approach with quasi experimental - Two group pre-test and post-test design was adopted. The sample comprised of 100 mothers in selected hospitals of Mangalore who were selected by purposive sampling technique and assigned to control and experimental group. On first day pre-test was conducted with a structured knowledge questionnaire to both control and experimental group and teaching package was given only to the experimental group followed by post-test and information booklet to both the groups on 7th day.Results: The mean and standard deviation of post-test knowledge score of mothers in experimental group (27.80 ± 3.010) was much greater than pre-test value (10.44 ± 2.323). There is no change in pre and post-test knowledge score in control group (9.74 ± 1.805). The calculated’ value t98=34.54 was greater than the table value 1.68 at 0.05 level of significance. This indicates that the teaching package was effective in improving the level of mothers knowledge.  Conclusion: The study findings concluded that the mothers were benefited by teaching package on vaccination and vaccine preventable diseases. Furthermore mass health education programs can be conducted to create awareness among general public.

Preon Prophecies by the Standard Model

The Standard Model of quarks and leptons is, at first sight, nothing but a set of {\it ad hoc} rules, with no connections, and no clues to their true background. At a closer look, however, there are many inherent prophecies that point in the same direction: {\it Compositeness} in terms of three stable preons.Comment: 13 pages, 8 eps-figures, invited talk at Beyond the Desert '03, Schloss Ringberg, Bavaria, June 2003; to be published in the Proceeding

Environmental and genetic influences on neurocognitive development: the importance of multiple methodologies and time-dependent intervention

Genetic mutations and environmental factors dynamically influence gene expression and developmental trajectories at the neural, cognitive, and behavioral levels. The examples in this article cover different periods of neurocognitive development—early childhood, adolescence, and adulthood—and focus on studies in which researchers have used a variety of methodologies to illustrate the early effects of socioeconomic status and stress on brain function, as well as how allelic differences explain why some individuals respond to intervention and others do not. These studies highlight how similar behaviors can be driven by different underlying neural processes and show how a neurocomputational model of early development can account for neurodevelopmental syndromes, such as autism spectrum disorders, with novel implications for intervention. Finally, these studies illustrate the importance of the timing of environmental and genetic factors on development, consistent with our view that phenotypes are emergent, not predetermined

Data Talks: Obesity-Related Influences on US Mortality Rates

Background: In the US, obesity is an epidemiologic challenge and the population fails to comprehend this complex public health issue. To evaluate underlying obesity-impact patterns on mortality rates, we data-mined the 1999-2016 Center for Disease Control WONDER database’s vital records.Methods: Adopting SAS programming, we scrutinized the mortality and population counts. Using ICD-10 diagnosis codes connected to overweight and obesity, we obtained the obesity-related crude and age-adjusted causes of death. To understand divergent and prevalence trends we compared and contrasted the tabulated obesity-influenced mortality rates with demographic information, gender, and age-related data.Key Results: From 1999 to 2016, the obesity-related age-adjusted mortality rates increased by 142%. The ICD-10 overweight and obesity-related death-certificate coding showed clear evidence that obesity factored in the male age-adjusted mortality rate increment to 173% and the corresponding female rate to 117%. It also disproportionately affected the nation-wide minority population death rates. Furthermore, excess weight distributions are coded as contributing features in the crude death rates for all decennial age-groups.Conclusions: The 1999-2016 data from ICD-10 death certificate coding for obesity-related conditions indicate that it is affecting all segments of the US population

Delaware’s 1999-2017 Leading Causes of Death Information Illustrates Its Obesity and Obesity-Related Life-Limiting Disease Burdens

Using commercially available but powerful big data analytics, this non-clinical obesity and underlying causes of death observational study, analyzed the very large US Centers for Disease Control and Prevention’s (CDC) State of Obesity records, the CDC WONDER data, and the US census records. Compared to the 1999-to-2017 US obesity rate increase of 29.8%, an uncontrolled increase in Delaware’s obesity rate (81.7%) was observed. During the same time period, CDC WONDER death certificate archives disclosed that there was a 60.53% surge in crude Delawarean mortality rate when obesity was listed as a single underlying cause of death. When any mention of obesity was documented on the death certificate, Delaware’s 1999-2017 crude mortality rate advanced by 75.69% and its age-adjusted rate rose by 53.18%. Likewise, except for one year, Delaware’s African American/Black population experienced higher crude mortality rate averages but however, between the years of 1997 and 2017, its Caucasian/White inhabitants had an enormous 87.34% death rate increase. With additional available CDC mortality data, Delaware males saw substantially larger age-adjusted death rate increases (79.87%) than their female counterparts (28.92%).Diabetes, circulatory system diseases, and neoplasms (cancer), are three common obesity comorbidities. For these three conditions, Delaware’s 1999-2017 mortality rate figures mimic the falling national patterns of mortality rate averages, when each disease is listed as the single underlying cause of death, including observations where there are disproportionate numbers of cases that affect the African American/Black race

COVID-19 Impacts at a Small Mid-Atlantic Liberal-Arts College with Implications for STEM Education

During the COVID-19 pandemic, with very little preparation and within a brief span of 48 hours, the Wesley College STEM faculty and students triaged into a remote-only form of instruction. Wesley College STEM student COVID-19 impact surveys showed underlying gaps in economic equity, increased family responsibilities, struggles to stay motivated, social isolation, and higher levels of psychological stress. Yet, the crisis demonstrated new ways in which technology can be harnessed and allowed STEM students to reconsider how jobs and skills should be aligned. A STEM faculty COVID-19 check-in survey and interview responses revealed a quick realization that faculty could not rely solely on Wesley’s Jenzebar learning management system (MyWesley). To engage their students and to create a supportive learning environment, STEM faculty sought new strategies and approaches for a diverse set of STEM learners. For synchronous e-teaching, the faculty used the Microsoft-Teams and the Zoom video conferencing platforms. Faculty only adopted MyWesley to execute dedicated asynchronous tasks (laboratory assignments, reports, exams). The STEM students were overwhelmingly positive about STEM faculty availability during the crisis. Still, both faculty and students indicated a much stronger preference for the face-to-face delivery of their course content via a traditional classroom setting

Evaluating BERT-based scientific relation classifiers for scholarly knowledge graph construction on digital library collections

The rapid growth of research publications has placed great demands on digital libraries (DL) for advanced information management technologies. To cater to these demands, techniques relying on knowledge-graph structures are being advocated. In such graph-based pipelines, inferring semantic relations between related scientific concepts is a crucial step. Recently, BERT-based pre-trained models have been popularly explored for automatic relation classification. Despite significant progress, most of them were evaluated in different scenarios, which limits their comparability. Furthermore, existing methods are primarily evaluated on clean texts, which ignores the digitization context of early scholarly publications in terms of machine scanning and optical character recognition (OCR). In such cases, the texts may contain OCR noise, in turn creating uncertainty about existing classifiers’ performances. To address these limitations, we started by creating OCR-noisy texts based on three clean corpora. Given these parallel corpora, we conducted a thorough empirical evaluation of eight Bert-based classification models by focusing on three factors: (1) Bert variants; (2) classification strategies; and, (3) OCR noise impacts. Experiments on clean data show that the domain-specific pre-trained Bert is the best variant to identify scientific relations. The strategy of predicting a single relation each time outperforms the one simultaneously identifying multiple relations in general. The optimal classifier’s performance can decline by around 10% to 20% in F-score on the noisy corpora. Insights discussed in this study can help DL stakeholders select techniques for building optimal knowledge-graph-based systems

Manipulating In-House Designed Drug Databases For The Prediction Of pH-Dependent Aqueous Drug Solubility

Chemical, pharmacokinetic, and pharmacodynamics properties are available in the package inserts of every Food and Drug Administration (FDA) approved prescription drug, including all available chemotherapy drugs. These inserts follow a specific format imposed by the FDA. Whether chemotherapy drugs are administered via the parenteral route or alimentary tract, a significant factor affecting their bioavailability, elimination, and consequently, the drug’s effectiveness and potency, is its state of aqueous solubility. Water solubility has always lent itself poorly to the different predictive and experimental measures employed in the determination of a useful quantitative assessment. In this project, we first built a chemical structure-based searchable database for 85 FDA approved chemotherapy drugs and then used Bio-Rad’s KnowItAll® Informatics suite to focus on the drugs pH-dependent water solubility prediction. We compared the predicted values for water solubility to the available values reported in the drug inserts, testing the practical utility and the predictive ability of our model in reporting such a clinically relevant, underreported pharmacokinetic parameter. A relational cancer drug database (MySQL) was created to further facilitate analysis and/or prediction of a chemotherapy compound’s missing pharmacokinetic properties.