List Of Classes Research Articles

Prevalence of depression and its correlates among undergraduate health science students in Mogadishu, Somalia: a cross-sectional study

BackgroundDepression among undergraduate health science students is increasingly recognized as a significant public health issue globally. These students face immense academic pressure, often leading to stress and mental exhaustion, particularly for students studying in a humanitarian crisis situation. However, there is a scarcity of information in this regard in higher education institutions in Somalia. Hence, this investigation was conducted to assess the prevalence of depression and its correlates health science students in Somalia.MethodsA cross-sectional study was conducted on 321 health science students from some universities in Mogadishu, Somalia. The data was collected from students studying medicine, nursing and midwifery, laboratory science and public health at SIMAD university, Banadir university, Mogadishu university and Jamhuriya university, all located in Mogadishu. Simple random sampling based on class list was used to select the participants and a web-based self-administered Patient Health Questionnaire (PHQ-9) was used to screen for depression. The collected data were cleaned and checked for completeness before exporting into SPSS version 27 Statistical software for analysis, where descriptive statistics as well as logistic regression analysis was done to determine the independent predictors of the outcome variable. Significant association was determined at a 95% confidence interval and p-value < 0.05.ResultThe prevalence of depression among the students was 58.6%. A total of 41.5% (133/321) of the participants were free from depression (Normal), while 30.8% had mild depression, 27.1% had moderate depression and 0.6% had severe depression. Analysis of the association between the sociodemographic variables and depressive state of the participant showed statistical significance (p-value = 0.045) for course of study. Students studying nursing and midwifery had the highest case of depression with 68.6% (59/86), followed by public health 66.7% (30/45), laboratory science 52.3% (34/65), and medicine having the least number of depressed students with 52.0% students (65/125). Similarly, gender of students was also found to be statistically significant (p-value = 0.001) with female students having 65% depression which is 41.1% of the total students studied.ConclusionThe prevalence of depression among health science students in Mogadishu was high and positively associated with gender, having trouble with authorities, emotional problems, as well as experience of sexual abuse or violence. Hence, routine screening and monitoring of students’ mental health state on the campus and providing mental health services is necessary to address this growing problem.

An exploratory study evaluating the 20 medications most commonly associated with suicidal ideation and self-injurious behavior in the FAERS database.

A number of pharmaceuticals, including antidepressants and antiepileptics, have a strong correlation with suicide risk. However, it is not entirely clear which of these medications are more strongly associated with suicide-related behaviors. This study aims to elucidate the drugs responsible for drug-associated suicidal ideation or self-injurious, recognizing the severe consequences associated with such outcomes. However, it is not entirely clear which specific medications are associated with higher levels of suicide-related behavior. Real-world data from the FDA adverse event reporting system database were analyzed to identify medications correlated with suicidal ideation or self-injurious. The reporting intensity of the High-Level Term "suicidal ideation or self-injurious behavior" and its Preferred Terms across distinct categories was assessed using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR). We identified the top 20 drugs with the highest reporting frequencies, spanning sedative-hypnotics, antidepressants, antipsychotics, antiepileptics, antihypertensives, antipyretic/analgesic drugs, and antihyperglycemic drugs. Ranking these medications according to ROR, the top five medications with ROR values related to suicidal ideation or self-injurious were alprazolam, zolpidem, amphetamine, quetiapine, and fluoxetine. Further analysis showed that suicide-related adverse events were more frequently reported in females. Antiepileptics had the highest frequency of reported adverse events in the 51-55 year age group, compared to 16-20 years for antidepressants and 46-50 years for sedative-hypnotics. Our study provides valuable information for clinical drug selection by presenting a potential list of medication classes commonly associated with drug-associated suicidal ideation or self-injurious behavior. We observed a large number of adverse event reports of suicidal ideation with duloxetine and relatively few reports of suicide attempts. Acetaminophen and amlodipine had substantial adverse event reports of completed suicides, but may not be associated with drug-induced suicidal behavior. On the other hand, some drugs mentioned in this study, such as quetiapine, aripiprazole, and lamotrigine, are recommended to be used after assessing the risk level of suicide in patients.

Software Metric Based Impact Analysis of Code Smells ‐ A Large Scale Empirical Study

ABSTRACTContextCode smells are indicators of poor design and implementation choices that negatively affect software quality and maintainability. Moreover, it is difficult and time‐consuming to work with a long list of the code smells, as not all of those smells have equal impact on the system. So, understanding the individual impact of the code smells is significant while performing refactorings on a priority basis.ObjectiveDespite significant research efforts aimed at detecting and refactoring these code smells, understanding their individual impact on software quality metrics such as size, complexity, coupling, etc. remains still unclear.MethodologyTo mitigate this research gap, we present an empirical investigation on the impact analysis of code smells based on the 25 software quality metrics such as size, cyclomatic complexity, coupling, etc. To the best of our knowledge, this is the largest empirical study about the impact analysis of code smells with respect to the number of software metrics. Particularly for this study, we identify 13 code smells in 35 open‐source software systems, and analyze (1) the relationship between code smells and software metrics, (2) which code smells are highly impactful that affect the metrics, and (3) which impactful smells occur frequently in the systems.ResultsThe results show varying degrees of correlation‐based impact between specific code smells and software metrics, with some smells showing strong correlations with multiple metrics. Three categories of impact for the code smells have been identified, namely High, Moderate and Low, where Long Method, Anti Singleton, Complex Class, Large Class and Long Parameter List smells have high impact, but their frequencies are not high except Anti Singleton; Refused Parent Bequest, Spaghetti Code and Blob have moderate impact; and rest of the smells have low impact. We also observe that perceptions about the impact of code smells vary from developer to developer and they most cases refactor the smells based on their intuition.ConclusionOur findings will help them to refactor the smells on an objective‐based instead of an intuition‐based, which will be more significant to improve the software quality. For example, refactoring the smells having a high impact on the coupling between objects metric can be an objective. Furthermore, our results will not only assist developers in prioritizing refactoring activities but also provide researchers with valuable insights to innovate tools that prioritize refactoring based on the impact of code smells. These tools will help developers target the most impactful smells and thus enhance the overall quality and maintainability of software systems.

Generating SPARQL Queries over CIDOC-CRM using a Two-Stage Ontology Path Patterns Method in LLM Prompts

In this paper, we focus on the task of exploiting the capabilities of Large Language Models (LLMs) to generate SPARQL Queries for answering natural questions over cultural Knowledge Graphs (KGs) expressed according to the ISO standard ontology CIDOC-CRM. Since CIDOC-CRM is an event-based model, usually we have to follow long paths for answering a question, thereby, the challenge is how to construct the prompt for aiding the LLM to produce the right SPARQL query. We propose and comparatively evaluate methods based on the creation of ontology path patterns of a configurable path radius (or length). Then, we construct a new dedicated benchmark that includes 100 natural questions and the corresponding SPARQL queries over two real KGs from the cultural domain describing artworks. Finally, we present comparative results about the effectiveness and efficiency over the benchmark by using ChatGPT-3.5. The most effective method follows a two-stage process that predicts and uses the most appropriate path patterns of \(r\leq 4\) . This method achieves 3.5 \(\times\) higher accuracy than the baseline method (0.66 versus 0.19), that includes in the prompt only the list of properties and classes of the KG. Benchmark: https://github.com/mountanton/CIDOC-QA-using-LLMs

Ensemble methods with feature selection and data balancing for improved code smells classification performance

Code smells are software flaws that make it challenging to comprehend, develop, and maintain the software. Identifying and removing code smells is crucial for software quality. This study examines the effectiveness of several machine-learning models before and after applying feature selection and data balancing on code smell datasets. Extreme Gradient Boosting, Gradient Boosting, Adaptive Boosting, Random Forest, Artificial Neural Network (ANN), and Ensemble model of Bagging, and the two best-performing Boosting techniques are used to predict code smell. This study proposes an enhanced approach, which is an ensemble model of the Bagging and Boosting classifier (EMBBC) that incorporates feature selection and data balancing techniques to predict code smells. Four publicly available code smell datasets, Blob Class, Data Class, Long Parameter List, and Switch Statement, were considered for the experimental work. Classes of datasets are balanced using the Synthetic Minority Over-Sampling Technique (SMOTE). A feature selection method called Recursive Feature Elimination with Cross-Validation (RFECV) is used. This study shows that the ensemble model of Bagging and the two best-performing Boosting techniques performs better in Blob Class, Data Class, and Long Parameter List datasets with the highest accuracy of 99.21%, 99.21%, and 97.62%, respectively. In the Switch Statement dataset, the ANN model provides a higher accuracy of 92.86%. Since the proposed model uses only seven features and still provides better results than others, it could be helpful to detect code smells for software engineers and practitioners in less computational time, improving the system's overall performance.

Room Interior Generation Using Stable Diffusion Models

The present paper puts forward an innovative method for the fine tuning of Stable Diffusion with the use of Dreambooth, which is a very fast technique in the image generation task. In particular, our approach covers the topic of room interior generation. It offers a quick option to instruct the algorithm with the most recent concepts, without the need to retrain it. Using a shortened list of instance and class prompts as examples we introduce a revolutionary training pipeline where class and instance information interacts to order a model's learning process. By conducting elaborate trials where we demonstrate that our methodology out-perform the competitors in the realistic visualization, which is based on the given theme. We use an extensive evaluation process to prove the effectiveness of the method on many datasets, which can ensure its generalization capacity to unseen various layouts of the rooms and their interior design. On one hand, precise ablation studies are carried out in order to evaluate the influence of the distinguished components in the given model. This paper shows the encompassing work of Dreambooth as a tool of choice for the personalized room interior synthesis. The new possibilities to fine-tune the generative models are also becoming a subject for futurological research in the interior design area.

Peran Metode Cerita Islami dalam Menanamkan Moral Keagamaan Mata Pelajaran Akidah Akhlak di Kelas III MI Model Panyabungan, Mandailing Natal, Sumatera Utara

This article investigates how Islamic tales might be used to teach moral lessons to young children. The research hinges on the idea that Islamic storytellers can effectively teach religious morals to primary school students by using relatable, real-world examples. class absences, class lists, class decisions, and classes that will be awarded are just some of the activities carried out by MI educators in the Panyabungan Model. Researchers play integral roles throughout the entire process, from ideation to implementation to data collecting and analysis and finally report writing, in this method of conducting qualitative research. Methods of information gathering that include keeping detailed notes and conducting interviews. Secondary statistics come from the documentation of Grade III A in the MI Model of Cooperative Education, while primary data come from teachers and students in that grade. According to the results of this research, there is a multi-step process involved in integrating the Islamic storytelling technique into the third-year curriculum of Akidah Akhlak at the MI Model Panyabungan. First and foremost, a person's upbringing, environment, and access to educational materials can all strengthen or weaken Islamic worldviews. First, Michigan's school system Each Panyabunga model has spent countless hours working with infants and toddlers, gaining invaluable knowledge along the way. Both sets of kids have religious backgrounds and were encouraged by their parents to get an education. Books, the Internet, and audiobooks are three of the most accessible educational resources for teachers. However, time, classroom management, and storytelling tools are three areas where Islamic storytelling falls short.

The community of inquiry as a tool for measuring student engagement in blended massive open online courses (MOOCs): a case study of university students in a developing country

While massive open online courses (MOOCs) promise to democratise access to education, the literature reveals a nuanced understanding of engagement in these settings, especially in resource-constrained environments. Blended MOOCs combine MOOCs and physical classroom settings of contents and instructions. This study extends this discourse by focusing on blended MOOCs, which remain under-explored in the context of developing countries. The blended MOOC at the University of Cape Coast (UCC), Ghana, deals with third-party MOOCs as open educational resources (OERs) integrated with campus-based courses. UCC students have been using such blended MOOCs since 2016, when all level 100 students were mandated to enrol in a course entitled Information Technology Skills (ITS101). ITS101 is aligned to courses in a MOOC platform called Alison as an OER. Students' engagement is key to their continued use and satisfaction with online learning, such as MOOCs. However, among all the e-learning modes, students' engagement is the lowest in MOOCs, leading to high dropout rates. Blended MOOCs are one of the techniques recommended to reverse the undesirability of MOOCs, including engagement. However, few studies have been conducted on students' engagement in blended MOOCs, especially among university students in sub-Saharan Africa using MOOCs as OERs. Thus, this paper aims to measure student engagement in blended MOOCs using the revised Community of Inquiry for university students in a developing country. The rationale is to determine whether factors affect engagement positively or negatively. A two-stage cluster sampling technique was used to determine the participants for this study. A list of blended MOOC classes offered at UCC was obtained from the staff's mailing list. In the first stage, academic levels (100, 200, 300 and 800) were randomly selected from the strata using a lottery sampling technique. In the second stage, another simple random selection of blended MOOC courses or classes was used within each selected academic level. All students in the selected classes were then included in the study. Partial Least Squares Structural Equation Modelling was used to validate the model on the predictive relationships existing among the four presences (cognitive, learning, social and teaching) and engagement. Results from the structural model analysis proved a statistically significant predictive relationship among the constructs within the model. Learning presence had the most significant effect on student engagement. Thus, it should be included as one of the presences in the community of inquiry.

Использование машинного обучения для тематической разметки текстовых материалов корпуса устной речи

The research aims to determine the effectiveness of the thesaurus method for forming a list of topic classes when using machine learning for the topic classification of text materials of sociolinguistic interviews. The paper considers the potential of using machine learning in the topic annotation of linguistic corpus materials. The polytopical nature of the analyzed material is due to its genre belonging to dialogical speech. The hierarchical structure of the topics, identified as a result of a preliminary introspective analysis of the texts, can be described using a thesaurus. The results of using the unsupervised machine learning method are discussed involving two sets of topic class names: a list of topics used in manual text annotation and an extended list of micro-topics whose names were selected from a Russian language thesaurus. The paper is novel in that it is the first to propose the thesaurus method for selecting topic labels for the zero-shot classification of weakly structured Russian texts. The research findings show that using a more detailed lexical description for topic classes improves the classification result.

The discovery of novel noncoding RNAs in 50 bacterial genomes.

Structured noncoding RNAs (ncRNAs) contribute to many important cellular processes involving chemical catalysis, molecular recognitionand gene regulation. Few ncRNA classes are broadly distributed among organisms from all three domains of life, but the list of rarer classes that exhibit surprisingly diverse functions is growing. We previously developed a computational pipeline that enables the near-comprehensive identification of structured ncRNAs expressed from individual bacterial genomes. The regions between protein coding genes are first sorted based on length and the fraction of guanosine and cytidine nucleotides. Long, GC-rich intergenic regions are then examined for sequence and structural similarity to other bacterial genomes. Herein, we describe the implementation of this pipeline on 50 bacterial genomes from varied phyla. More than 4700 candidate intergenic regions with the desired characteristics were identified, which yielded 44 novel riboswitch candidates and numerous other putative ncRNA motifs. Although experimental validation studies have yet to be conducted, this rate of riboswitch candidate discovery is consistent with predictions that many hundreds of novel riboswitch classes remain to be discovered among the bacterial species whose genomes have already been sequenced. Thus, many thousands of additional novel ncRNA classes likely remain to be discovered in the bacterial domain of life.

A hybrid knowledge graph for efficient exploration of lithostratigraphic information in open text data

Rocks formed during different geologic time record the diverse evolution of the geosphere and biosphere. In the past decades, substantial geoscience data have been made open access, providing invaluable resources for studying the stratigraphy in different regions and at different scales. However, many open datasets have information recorded in natural language with heterogeneous terminologies, short of efficient approaches to analyze them. In this research, we constructed a hybrid Stratigraphic Knowledge Graph (StraKG) to help address this challenge. StraKG has two layers, a simple schema layer and a rich instance layer. For the schemas, we used a short but functional list of classes and relationships, and then incorporated community-recognized terminologies from geological dictionaries. For the instances, we used natural language processing techniques to analyze open text data and obtained massive records, such as rocks and spatial locations. The nodes in the two layers were associated to establish a consistent structure of stratigraphic knowledge. To verify the functionality of StraKG, we applied it to the Baidu encyclopedia, the largest online Chinese encyclopedia. Three experiments were implemented on the topics of stratigraphic correlation, spatial distribution of ophiolite in China, and spatio-temporal distribution of open lithostratigraphic data. The results show that StraKG can provide strong knowledge reference for stratigraphic studies. Used together with data exploration and data mining methods, StraKG illustrates a new approach to analyze the open and big text data in geoscience.

Trabajo colaborativo como método de enseñanza innovador: beneficios y desafíos

IntroductionThe use of innovative teaching methodologies, such as collaborative work, constitutes a significant change for both teachers and students. This method requires greater autonomy from students, and significantly more effort from both students and teachers due to the importance of careful planning and the monitoring of learning. Thus, this teaching model promotes the development of various skills which include critical thinking and self-management. MethodWe carried out a cross-sectional observational study of a collaborative activity used in several classes in the subject Diagnostic Imaging and Physical Medicine in the third year of an undergraduate medicine degree. The students were divided into 13 groups of 8 people, separated using the order of the class list to avoid grouping friends. Each group was given a different clinical case with a radiological image of a bone tumour, and the students were asked to describe it individually at home applying the theory they had been taught in previous Semiology of bone tumours classes. Subsequently and autonomously, they then had to describe it to a classmate from the same group. Later, they described it in groups of 2 and 4 during class time before the group of 8 students finally had to describe it in front of the whole class and try to provide a diagnosis. During the preparatory classes, the teacher went around the groups answering questions. After the final description, the teacher provided feedback. The activity was evaluated through a questionnaire filled out by the students. ResultsThe results of the survey reveal the opinions of the students. Overall, they found the methodology to be appropriate, and 60% of those surveyed said that it facilitated the acquisition of knowledge, is an innovative form of self-learning, increased their level of involvement and improved their relationships with their colleagues. Likewise, more than 50% of the participants believe that collaborative work is an appropriate method for their training, providing them with a greater sense of participation. ConclusionCollaborative work is found to be an appropriate methodology to improve the acquisition of knowledge. However, it requires greater effort and involvement from both teachers and students, which means that students are not keen on applying it to other subjects. Students consider as a drawback the potential for imbalances of participation.

A note on polarized varieties with high nef value

Abstract We study the classification problem for polarized varieties with high nef value. We give a complete list of isomorphism classes for normal polarized varieties with high nef value. This generalizes classical work on the smooth case by Fujita, Beltrametti and Sommese. As a consequence we obtain that polarized varieties with slc singularities and high nef value are birationally equivalent to projective bundles over nodal curves.

School eye screening survey in the Gaza Strip/Palestine

Aim: To estimate prevalence of vision impairment and associated refractive error in basic school children in the Gaza Strip in the State of Palestine. The study will also provide an estimate of the use of corrective spectacles among children with refractive error. Associated factors like age, sex, type of school, residence, parents’ education, parents’ occupation, other indicators of socio‐economic status and vision problems in the child and the family will be studied.Methods: This is the first comprehensive study of school children vision impairment in the Gaza Strip.The method is an adaptation of WHO's Assessment of the prevalence of visual impairment attributable to refractive error or other causes in school children (RESC) methodology.Study population are students in the first and the nineth grades from public and United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) schools in the Gaza Strip.The sampling unit (cluster) was classes in the target grade levels and the strata were grade (grade 1 and grade 9), type of school (public and UNRWA) and school gender (all male, all female and co‐ed schools).Based on an expected prevalence of blindness of 20% among target population, 95% confidence level, 20% non‐response rate (refusals and absent students/total number of students), 3.4% margin of error and a design effect of 1.35 to account for clustering, a sample size 1100 students was determined. 28 classes were selected with probability proportionate to size from a list of classes of target grades along with number of children enrolled within each class in public and UNRWA schools in the Gaza Strip.Results: The actual number of students was 1094 in 15 UNRWA and 13 public schools (15 first grade classes and 13 ninth grade classes). The total non‐response rate was 12%.Data collection was completed on 18.5.2023. I did not have time to clean and analyse the data. Results will be presented at the conference.Conclusions: Conclusions will be presented at the conference.

A spermidine riboswitch class in bacteria exploits a close variant of an aptamer for the enzyme cofactor S-adenosylmethionine

Natural polyamines such as spermidine and spermine cations have characteristics that make them highly likely to be sensed by riboswitches, such as their general affinity to polyanionic RNA and their broad contributions to cell physiology. Despite previous claims that polyamine riboswitches exist, evidence of their biological functions has remained unconvincing. Here, we report that rare variants of bacterial S-adenosylmethionine-I (SAM-I) riboswitches reject SAM and have adapted to selectively sense spermidine. These spermidine-sensing riboswitch variants are associated with genes whose protein products are directly involved in the production of spermidine and other polyamines. Biochemical and genetic assays demonstrate that representatives of this riboswitch class robustly function as genetic "off" switches, wherein spermidine binding causes premature transcription termination to suppress the expression of polyamine biosynthetic genes. These findings confirm the existence of natural spermidine-sensing riboswitches in bacteria and expand the list of variant riboswitch classes that have adapted to bind different ligands.

Hip Fracture Post-operative Mortality and Polypharmacy: A New Risk Predictor?

Introduction Hip fractures include intra- and extracapsular fractures with hip hemiarthroplasty, intramedullary (IM nailing), dynamic hip screw (DHS) fixation and cannulated cancellous screws being the main treatment options. The Nottingham Hip Fracture Score (NHFS) is used to predict the risk of 30-day mortality with some studies investigating its use for one-year mortality. This study aims to investigate the impact of polypharmacy on post-operative hip fracture mortality and the correlation with NHFS predicted mortality. Methods A retrospective single-centre analysis was carried out on hip fracture patients aged 65 years and over who underwent operative management. Primary outcome measures were 30-day and one-year mortality along with the presence of polypharmacy. Secondary outcome measures were mortality based on procedure type, NHFSs for polypharmacy and non-polypharmacy groups, number of medications, American Society of Anesthesiologists (ASA) grade, age and gender. Polypharmacy was defined as five or more long-term medications from a selected list of drug classes. Results Thirty-day mortality was 19.3% for the polypharmacy group and 2.4% for the non-polypharmacy group (P≤0.00001), while one year mortality was 50.9% for the polypharmacy group and 2.4% for the non-polypharmacy group (P≤0.00001), the NHFS was 5.16 (±1.38) on average for the polypharmacy group and 5.07 (±1.47) for the non-polypharmacy group. Thirty-day mortality was 10/116 (8.6%) for the hemiarthroplasty patients, 3/66 (4.5%) for the DHS fixation patients and 1/32 (4.5%) for the IM nailing patients. One-year mortality was 33/116 (28.4%) for the hemiarthroplasty patients, 11/66 (16.7%) for the DHS fixation patients and 4/32 (12.6%) for the IM nailing patients. Conclusion Polypharmacy correlated with a significantly higher one-year and 30-day postoperative mortality after hip fractures with the NHFS predicting no difference in mortality. This finding could assist in decision making and help facilitate discussions with patients and family members regarding post-operative mortality risks. The NHFS may also benefit from integrating polypharmacy possibly leading to more accurate risk predictions. The IM nailing and DHS fixation patients were found to have a lower 30-day and one-year mortality than the hemiarthroplasty patients.

The Philadelphia Bray Schools: A Story of Black Education in Early America, 1758–1845

Abstract: The Associates of Dr. Bray, an Anglican charity, supported schools for Black children in Philadelphia operated by Christ Church and the African Episcopal Church of St. Thomas from 1758 to 1845. This narrative history of the schools complements open-source data accessible online through the Magazine of Early American Datasets . These spreadsheets present information pertaining to the schools' operations gleaned from the Associates' archive in the United Kingdom. Besides the very few class lists from the period before the American Revolution, there are more than 90 lists for the Philadelphia schools covering the period from 1821 to 1845 that identify almost four hundred individual pupils and their curricula, providing valuable insight into one of the few opportunities for Black education in early America.

The KI-ASIC Dataset

We present a novel dataset captured from a BMW X5 test carrier within the German research project KI-ASIC for use in radar sensor development and autonomous driving research. Our work aims at providing a blueprint for the process of creating labeled datasets for the development of neural networks for pattern recognition in radar data in the automotive environment. With a variety of different sensor types such as wide angle color cameras, a high-resolution color stereo camera, an Ouster OS1-64 laser scanner and three novel Infineon radar sensors, we recorded over 100,000 scenes of real traffic scenarios as well as defined test scenarios with a frequency of 10 Hz. The scenarios in real traffic contain inner-city situations, but also scenes from rural areas with static and dynamic objects. Besides, the defined test scenarios are based on the NCAP scenarios and focus mostly on turning, overtaking and follow-up maneuvers. The data from the different sensors is calibrated, synchronized and timestamped including raw and rectified information. Our dataset also contains labels for all detected objects from a defined class list with distance and angle properties. The content of the paper aims at the description of the recording test carrier, the format of the provided sensor data and the structure of the overall dataset

Learning Experiences of Nursing Students with Case-Based Learning Technique: A Qualitative Stu

Objective: The aim of this study was to determine the experiences of nursing students regarding the application of the Surgical Diseases Nursing course, which was carried out with case-based learning techniques and group work. Materials and Methods: The study was carried out using the phenomenology pattern with the focus group interview technique. The sample of the study consisted of second-year students (n=84) who received Surgical Diseases Nursing. Students were divided into 14 groups according to the class list by random sampling method. Practice trainers guided the groups in line with the case-based learning technique for 8 weeks. The groups took the real case (patient) and gave nursing care in the clinical setting. The groups used the “Surgical Diseases Nursing Patient Evaluation” form to collect data. The students evaluated the result by planning the nursing care of the case (patient) and then applying the care plan with group work. In the last two weeks of the academic year, student groups made case presentations in the classroom. Results: The experiences of the students are explained in three main themes and 9 sub-themes. The main themes were; case presentation experience, case presentation in nursing practice, and a good case presentation. Conclusion: As a result, case-based learning techniques and group work facilitate learning, combine practice with theory, and can contribute to the professional development of students.

Development of a next generation pertussis vaccine: An unbiased biochemical approach to identify novel CD4+ T cell recognized antigens from Bordetella pertussis

Abstract Waning acellular pertussis vaccine (aPV) efficacy has brought about the reemergence of pertussis despite global vaccine coverage. Decreasing vaccine efficacy is correlated with the emergence of vaccine escape mutants lacking bacterial antigens expressed in current laboratory strains. Recent work shows that systemic and mucosal CD4+ T cells are necessary for improved vaccine efficacy and increased duration of immunity. However, the antigens recognized by these T cells are undefined. We hypothesize that the efficacy and duration of aPV-induced immunity will be improved by the addition of new bacterial antigens that induce a strong CD4+ T cell response. Here we used the natural antigen processing machinery of mouse bone marrow derived dendritic cells to identify peptides derived from the laboratory Bp strain Bp536 and a clinical isolate, H762, that are presented on MHC Class II. Bioinformatics analysis selected the highest affinity peptides for both mice and human MHC Class II. The immunogenicity of each peptide was tested ex vivo using ELISA and an Activation Induced Marker (AIM) assay. We stimulated splenic T cells from convalescent or whole cell vaccine (wPV) immunized mice and human PBMCs. This unbiased biochemical approach generated a list of Bp-specific MHC Class II epitopes that are recognized by CD4+ T cells. These data suggest that Bp expresses multiple novel CD4+ T cell recognized antigens that may improve vaccine efficacy and the duration of protection when incorporated in a next generation aPV.