USEFULNESS OF CONVENTIONAL KNOWLEDGE BASED CLINICAL EXAMINATION (SHORT CASE) VERSUS OBJECTIVE STRUCTURED CLINICAL EXAMINATION (OSCE) IN ACQUIRING CLINICAL COMPETENCE IN FINAL YEAR MBBS STUDENTS

Introduction: The way medical students learn is largely determined by the way they are assessed. There is a need to rationalize the examination system by giving due emphasis on internal assessment, and supplementing the traditional short case examination with more valid and reliable instruments for the assessment of clinical skills. To compare the Aims And Objectives: marks/score pattern between short case and OSCE and to study the students and faculty feedback about short case method of assessment versus OSCE. This study was conducted on 60 nal year MBBS students at the end of thei Methodology: r clinical posting in skin department of IRT Perundurai Medical College Hospital, Erode. Clinical assessment was rst done on short case and then by administering OSCE. The results were analyzed using ANOVA. Two scenarios were chosen namely Hansen's disease and psoriasis. Marks obtained by the students were only Results: marginally higher in OSCE than short case assessment. Students performed better in OSCE leprosy. 26 out of 60 students scored 70% or above by short case method whereas 32 out of 60 scored 70% or above by OSCE method. The student's feedback regarding both in general was positive. Students preferred short case assessment in terms of method and time. Logistical difculties were noted in OSCE. The practical Conclusion: clinical examinations are of key importance in the assessment of clinical competence of medical students. Students perform better in OSCE because it is objective, fair, unbiased, without examiners marking variability, without fear of examiner and anxiety. Students and faculty sensitization regarding nuances of OSCE is the need of the hour.

Thymic T-Cell Production Is Associated With Changes in the Gut Microbiota in Young Chicks

Increasing studies show that gut microbiota play a central role in immunity, although the impact of the microbiota on mediation of thymic T cells throughout life is not well understood. Chickens have been shown to be a valuable model for studying basic immunology. Here, we show that changes in the gut microbiota are associated with the development of thymic T cells in young chickens. Our results showed that T-cell numbers in newborn chicks sharply increased from day 0 and peaked at day 49. Interestingly, the α-diversity score pattern of change in gut microbiota also increased after day 0 and continued to increase until day 49. We found that early antibiotic treatment resulted in a dramatic reduction in gut alpha diversity: principal component analysis (PCA) showed that antibiotic treatment resulted in a different cluster from the controls on days 9 and 49. In the antibiotic-treated chickens, we identified eight significantly different (p < 0.05) microbes at the phylum level and 14 significantly different (p < 0.05) microbes at the genus level, compared with the controls. Importantly, we found that antibiotic treatment led to a decreased percentage and number of T cells in the thymus when measured at days 9 and 49, as evaluated by flow cytometry. Collectively, our data suggest that intestinal microbiota may be involved in the regulation of T cells in birds, presenting the possibility that interventions that actively modify the gut microbiota in early life may accelerate the maturation of humoral immunity, with resulting anti-inflammatory effects against different pathogens.

Negative effect of dietary elimination on breastfeeding mothers having infants with and without food allergy

Background: There has been no trial evaluating the psychopathology in breastfeeding mothers of infants with food allergy (FA). Objective: To investigate the effect of dietary elimination on maternal psychopathology, specifically stress/anxiety and mother-to-infant bonding and explore the importance of sociodemographic features on these variables. Methods: Breastfeeding mothers following an elimination diet due to FA in their children aged 1-to-12 months were compared with the healthy controls. Physician-diagnosed FA group were divided into IgE-, non-IgE-mediated and infants with some minor symptoms which were not enough to make the diagnosis of FA were classified as Indecisive symptoms for FA group. Mothers completed standardized questionnaires including Symptom Checklist 90R, Beck Depression/Anxiety Inventories (BDI/BAI), Postpartum Bonding Questionnaire (Bonding). Results: Of 179 mother-infants, 64 were healthy, 89 were FA, 16 were indecisive symptoms for FA. The mean age of the mothers and infants were 31.1±4.7 years and 6.3±3.6 months. The physician diagnosed FA groups had higher scores for anxiety (p=0.008), anger (p=0.042), depression (p<0.001), obsession (p=0.002), phobia (p=0.008), somatization (p=0.002) and general symptom index (GSI) (p=0.001), BDI (p<0.001), BAI (p=0.008) and Bonding [attachment (p=0.001), anger (p=0.019) and total (p=0.036)] than the healthy. The indecisive symptoms for FA group had a similar score pattern to physician-diagnosed FA except interpersonal sensitivity, BDI and attachment. Conclusion: Breastfeeding mothers of infants with FA were anxious, depressive and had many psychopathologies which affected bonding. Interventions targeting negativity in caregivers’ social relationships are urgently needed.

A statistical framework for QTL hotspot detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These methods may suffer from the problems of ignoring the correlation structure among traits, neglecting the magnitude of LOD scores for the QTL, or paying a very high computational cost, which often lead to the detection of excessive spurious hotspots, failure to discover biologically interesting hotspots composed of a small-to-moderate number of QTL with strong LOD scores, and computational intractability, respectively, during the detection process. In this article, we describe a statistical framework that can handle both types of data as well as address all the problems at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computational cost and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top γn,α profile, are introduced into the framework. The trait grouping attempts to group the traits controlled by closely linked or pleiotropic QTL together into the same trait groups and randomly allocates these QTL together across the genomic positions separately by trait group to account for the correlation structure among traits, so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top γn,α profile is designed to outline the LOD-score pattern of QTL in a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD-score distributions. Real examples, numerical analysis, and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots, and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

Cluster-based information retrieval using pattern mining

This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.

Teknik Instruksional Pertanyaan Guru di Tahap Searching for the Theoritical Background of the Driving Question pada Project Based Learning untuk Meningkatkan Skor Pattern Concept Map

<p>The aim of the study was to calculate the score pattern on the concept map (CM) of the students by applying instructional instruction technique of teacher in searching for the theoretical background of the driving question on project based learning. The subjects were 32 high school students. The research is a classroom action research with research procedure including: planning to prepare RPP and its completeness, implementation on activity of action, observation to calculate CM pattern score and reflection for next action. Triangulation validation test includes: verification of conformity of CM pattern score and documentation based on expert pattern CM and interview to represent learners' skill structure. Reduction is done to select the completeness of data, presenting the data and drawing conclusions based on the complete data pattern. Data analysis with qualitative descriptive. The results showed that the pattern score based on the expert pattern of CM on precycle was obtained from 40% -60% range with 46,875% of total learner got score below average and 0% above average. Cycle I obtained a score range of 40% -100% with 9.375% showing scores below average and 43.75% above average. Cycle II obtained a score range of 40% -80% with 15.625% indicating scores below average and 12.5% above average, thus the instructional technique question in the searching phase for the theoretical background of the driving question project based learning increases the score pattern CM both individual and classical.</p><p> </p>