Sequential and dynamic RNA:RNA base-pairing interactions between U6atac and U12 snRNAs predicted to form Helix 1a and Helix 1b

In eukaryotes, pre-mRNA splicing is important step for gene expression. Splicing is a two-step process which is carried out by a multi-megadalton molecular weight ribonucleoprotein (RNP) machinery called spliceosome. Spliceosome converts pre- mRNA to mRNA by removing non-coding sequence (introns) and splice together coding sequence (exons). Mammalian pre-mRNA are spliced by two different class of spliceosomes which are known as U2- and U12- dependent spliceosomes. U12 dependent spliceosome is composed of five small nuclear RNAs (snRNA). As compared to U2-dependent spliceosome, there is very less known about the catalytic process of U12-dependent splicing. U6atac and U12 snRNA are central to U12- dependent splicing. Therefore, to understand importance of U6atac and U12 snRNA interaction during splicing we have created a series of 2nd site nucleotide mutations in both U6atac and U12 snRNA to test for their functionality in in vivo splicing assays. Our work will help to better understand the catalytic process of minor class spliceosome and involvement of these snRNA in mammalian gene expression and genetic disorders.https://engagedscholarship.csuohio.edu/u_poster_2016/1012/thumbnail.jp

A rare case of cerebellar toxicity after prolonged use of metronidazole: a case report

Metronidazole, a commonly used antiprotozoal and antibacterial medication is usually a safe and rarely reported to cause serious side effects. Major nervous side effects are peripheral ones, while central toxicity is rare. Following the discontinuation of the medication, clinical improvement is seen in the most cases. A 62 years old female patient was presented to hospital after experiencing the symptoms of an unsteady gait, difficulty in walking, impaired coordination of arms and legs, slurring of the speech, headache, tingling and numbness of both the feet and ascending limb weakness following intake of 400 mg metronidazole TDS daily for 2 months. The motor system examination revealed reduced muscle power, and DTR (Deep tendon reflex) was found to be 2+, except ankle reflex absent, while examination of sensory system showed, decrease pain and joint vibration sense up to the neck with absent planter reflex. The axial magnetic resonance imaging study of the brain showed bilateral symmetric hyperintensity involving both dentate nuclei in FLAIR image. The patient’s clinical conditions, on the other hand was deteriorated even after the discontinuation of the medication, hence injection methylprednisolone was given as an empirical therapy and was proved to be successful, and patient was recovered completely

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach

Assaying the Splicing Activity of Novel Human Disease Variants of U4atac snRNA

In eukaryotes, pre-messenger RNA (pre-mRNA) splicing is an essential process in gene expression. Splicing is carried out by a dynamic multi-megadalton RNA-protein complex known as the spliceosome. Sequential transesterification reactions catalyzed by the spliceosome convert pre-mRNA to mRNA by removing the intervening sequences (introns) and joining the coding sequences (exons) together. Small nuclear RNAs (snRNAs) are essential splicing factors. Biallelic mutations of the human RNU4ATAC gene, which codes for U4atac snRNA, have been identified in patients diagnosed with Microcephalic Osteodysplastic Primordial Dwarfism type I (MOPD I). MOPD I is an autosomal recessive disorder characterized by extreme intrauterine growth retardation, multiple organ abnormalities, and typically early death. The mutations that have been studied biochemically reduce U4atac snRNA function and impair minor class (U12-dependent) intron splicing. Four novel patient mutations, 37 G\u3eA, 46 G\u3eA, 48 G\u3eA and 118 T\u3eC, have recently been discovered. To evaluate the functional effects of these newly discovered mutations on U12-dependent splicing, we incorporated each of these mutations into a modified human RNU4ATAC gene construct by site directed mutagenesis. Following verification of the mutations by DNA sequencing, we prepared DNA for use in an in vivo splicing assay that is based on genetic suppression. These mutations are expected to affect the binding of proteins to U4atac snRNA that are important in formation of the catalytically active form of the spliceosome. We do not yet know how the consequent defective U12-dependent splicing affects gene expression and yields the MOPD I disease pathologies, but this study allows us to better understand the mechanistic basis of MOPD I and will serve as an important foundation for further studies and possible therapeutic intervention in the future.https://engagedscholarship.csuohio.edu/u_poster_2015/1008/thumbnail.jp

Effect of gestational weight gain on pregnancy outcome of Indian mothers

Background: Gestational weight gain (GWG) and pre-pregnancy body mass index (BMI) play important roles in determining the pregnancy outcome. The weight gain recommendations by the IOM are based on Western WHO BMI cut-offs, making it difficult to generalize their findings to Asian Indians. We aimed to compare GWG among healthy pregnant women across different BMI with the IOM guidelines-2009. We also aimed to evaluate associated feto-maternal outcomes with GWG among the pregnant women enrolled in the study.Methods: A retrospective cohort study conducted at department of obstetrics and gynecology, from April 2019 to November 2019. Postnatal mothers whose weight was registered at first trimester of pregnancy and at term and delivered in SSG hospital were included. According to IOM Women were divided into: Group 1 less than recommended weight gain and Group 2 recommended weight gain.Results: Significant difference was seen in the baby weight between the two groups (p value 0.05), and no association was seen between GWG and preterm deliveries (p >0.05).Conclusions: Majority of patients in the both groups had term delivery. Women gaining less than recommended weight gain during pregnancy had new born with significantly lower birth weight. There was no association of mode of deliveries and GWG

Sentiment analysis on film review in Gujarati language using machine learning

Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer

“A Comparative study to assess the knowledge and attitude towards mental illness among adults of urban and rural area of Kheda District, Gujarat.”

Mental illness has always been significant challenge and is becoming more and more relevant in today’s fast paced world. According to the world Health organization Mental Health is “a state of well being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully and is able to make a contribution to his or her community.” Whereas mental illness refers collectively to health conditions involving significant changes in emotion, thinking or behavior or a combination of there and associated with distress and/or problems function in social work or family activities. There is a misconception that people with mental illness are violent, which contributes to the significant of mental illness. Material and Method: A Quantitative non-experimental, comparative approach used in this research study. Sample size for the present study was consisting of 100 adults. The instrument used for the data collection is self structured knowledge questionnaire and Likert scale for attitude questionnaire. The data analysis was done by using descriptive and inferential statistical in terms of mean, mean percentage, standard deviation and chi square. Result: The result revealed that, In knowledge regarding mental illness urban adults (mean=10.38) have more knowledge as compare to rural adults (mean=6.04) and In attitude regarding mental illness urban adults (mean=43.66) have more attitude as compare to rural adults (mean=30.42). Conclusion: The study concluded that Urban adults have more knowledge as compare to Rural adults. Urban adults have more positive attitude as compare to rural adults

Efficacy and superiority of an innovative method (IM) of intravenous (IV) fluid drip drop rate calculation using IV set and its comparison with conventional methods (CM)

Background: Almost every indoor patient requires some form of intravenous (IV) fluids and its infusion rate should be proper as recommended for best treatment outcomes. To overcome the same, a simple, quick and easily applicable new method for drip drop rate calculation is proposed, which is user-friendly at bedside and doesn’t require mathematical skills or help.Methods: Author compared this novel innovative method (IM) of IV fluid drip drop rate method for both regular macro and micro drop infusion set against conventional mathematical calculation method (MC) of infusion in various IV fluid indoor orders and assessed for time-to-initiation of treatment (TI) required and its accuracy. Ten resident doctors and ten nursing staff participated to grade both conventional and novel methods by using pre-printed forms of various parameters like time consumption, comfort level, accuracy and applicability in ward and these both methods were scored on a scale of 1 to 10.Results: Conventional method (CM) required 14.23±1.10seconds, while novel method (IM) required average 3.63±0.73seconds for calculation of drop rate. Average grading for conventional method was 3.63±0.49 and for novel method was 7.84±0.6 out of 10.Conclusions: Novel method of IV fluid drip drop rate formula is easy, quick and superior in comparison to conventional method and it doesn’t require any additional instrumental help. It is good alternative to conventional formula for IV drip drop rate calculation in absence of infusion pump

Effect of postpartum counseling on adoption of family planning within six months in women delivered in SSG Hospital: an interventional study

Background: Increased family planning helps to reduce maternal mortality and infant mortality. Unmet need is spacing of birth for younger women and is the limitation of births for older women; both of which can be fulfilled by rigorous counseling. so the present study was conducted to measure the proportion of targeted women adopting family planning methods after postpartum counseling and to find out the type and timing of adoption.Methods: A Non-randomized control trial was conducted. The study was carried out at the postpartum delivery ward of the obstetrics and gynecology department of SSG Hospital. 103 participants in the intervention group and 103 participants in the control group were interviewed.Counseling and leaflet were given to the intervention group. The washout period was kept for one week. In the next week in the control group usual counseling was given by the counselor (standard of care). Second interview of the same participants was done telephonically or home visits after the 6 months to see the adoption of family planning method.Results: Within six months of delivery, acceptance of contraceptive methods was more in intervention group (72.85%) than in control group (48.52%). Condom was the most common type of contraceptive intervention used in both interventional group (51.45%) and control group (36.76%) followed by Copper T use which was 14.21% in interventional group and 8.82% in control group. Majority of the women adopted contraceptives within two months of intervention.Conclusions: Counseling may help in adoption of family planning methods among postpartum women

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach