Relevant Course Research Articles

Differentiation of MS lesions through analysis of microvascular distribution

Abstract Conventional MRI is crucial for diagnosing multiple sclerosis (MS) but lacks precision, leading to the clinico-radiological paradox and misdiagnosis risk, especially when confronted with unspecific lesions not related to MS. Advancements in perfusion-weighted imaging (PWI) with an algorithm designed for diseases with anticipated contrast agent extravasation offer insight into microvascular impairment and flow heterogeneity. Our study aimed to assess these factors in MS patients and their association with clinically relevant white matter injury and disease course. We evaluated 60 adults with white matter lesions (WML), including 50 diagnosed with MS or MS syndromes and 10 non-diseased symptomatic controls (SC) with unspecific WML. MRI included conventional three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), 3D magnetization-prepared 2 rapid acquisition gradient-echo (MP2RAGE), post-contrast 3D T1-weighted (T1) images and Dynamic Susceptibility Contrast (DSC) PWI at 3T. WML masks of “unspecific T2-FLAIR lesions”, “MS T2-FLAIR lesions”, and “MS T1-lesions” were manually outlined and validated by a neuroradiologist. DSC-derived parameters were analyzed in WML masks and healthy-appearing tissue. MS T2-FLAIR lesions showed increased flow heterogeneity and vasodilation compared to unspecific T2-FLAIR lesions in SC, as well as compared to unspecific T2-FLAIR lesions within the MS group. MS T1-lesions exhibited more homogenized flow. Our findings suggest that DSC-PWI combined with lesion delineation, can provide clinically relevant differentiation of MS lesions from unspecific WML, highlighting potential microvascular pathology previously overlooked in MS.

Body fatness across the adult life course and ovarian cancer risk.

Excess body fatness in late adulthood has been observed to increase ovarian cancer risk, but the association is relatively weak. Body fatness can change over time, and timing may differently influence risk. We used a life course epidemiology approach to identify whether the relation between body fatness and ovarian cancer risk is best described by a critical period, accumulation or sensitive period hypothesis. In a population-based case-control study of ovarian cancer in Montreal, Canada (2011-16), data on body mass index (BMI) at each decade starting at age 20 was available. Among 363 cases and 707 controls aged ≥ 50 years, we used a Bayesian relevant life course exposure model to estimate the relative importance of BMI for three pre-specified periods across the adult life course, i.e., early childbearing years, late childbearing years, and peri/postmenopause, on ovarian cancer risk. The accumulation hypothesis best described BMI in relation to ovarian cancer overall, with an odds ratio (OR) for the lifetime effect of BMI (per 5kg/m2 increase) of 1.10 (95% credible interval [CrI]: 0.90-1.35). For invasive ovarian cancer, the OR (95% CrI) for the lifetime effect was 1.16 (0.92-1.48), with BMI during early childbearing years showing the highest relative importance, suggesting this may be a sensitive period. For borderline cancer, the lifetime effect OR was not strongly supportive of an association (OR: 0.90, 95% CrI: 0.53-1.32). The results suggest that a sensitive period of early childbearing years is a candidate hypothesis for further investigation.

A Curricular Review of Radiology Education in a Master of Physician Assistant Studies Program.

Physician assistants/associates (PAs) are expected to be competent in ordering and interpreting diagnostic imaging. However, there are no further details outlining the educational expectations of PAs as it relates to radiology upon graduation. This can result in significant variability in the radiology curricula that PA students are taught and, consequently, hinder PAs' ability to work within their full scope of practice. Therefore, the purpose of this study was to map the radiology curriculum in a Master of Physician Assistant Studies (MPAS) program to elucidate radiological educational training before graduation. Quantitative curricular mapping was used to assess the 2021 to 2022 MPAS program for radiological involvement. Relevant course and session objectives related to radiology education were identified. In addition, educational learning material was reviewed for diagnostic imaging content. Formal radiological training was observed in 8 of 27 courses in the preclinical curriculum, with 4.35% of the total session objectives directed to radiological education. This formal exposure comprises 18.9 hours (1.71%) of curricular time. Informal diagnostic imaging exposure increased radiology education to approximately 29.5 hours (2.67%) of curricular time. One course (Diagnostic Imaging) focuses exclusively on radiology teaching and accounts for approximately 50% of the total radiologic teaching. X-ray ordering and interpretation received the greatest emphasis throughout the curriculum, while ultrasound received the least attention. Further integration of formal radiological education into PA programs should be considered with specific attention directed toward point-of-care ultrasound exposure and ordering/interpretation skills.

Strategies for LinkedIn-Based Guest Speaker Recruitment and Establishment of Engaging and Comfortable Classroom Discussions: A Case Study from an Operations Management Course

This article describes workable strategies for instructors to introduce highly interactive question and answer (Q&A) sessions with accomplished guest speakers from industry. Two innovative strategies serve as the key factors behind the series’ success: (1) using LinkedIn to partner with guest speakers, especially university alumni, who possess expertise in specific operations management disciplines, and (2) implementing a novel structured Q&A approach that enables alignment with learning objectives while maximizing quality student engagement. Our approach accesses the resumes of tens of thousands of college alumni listed on LinkedIn and utilizes free cloud-based services such as Google Forms and Google Sheets for question submission (by students), review (by instructors), and selection (by the guests). These successfully implemented strategies (1) match qualified speakers to relevant course content, (2) boost student engagement by ensuring the participation of all students, (3) promote content alignment by first filtering out irrelevant student questions, and (4) increase speaker preparation and comfort level.

Bridging the gap: Implementing a pilot radiology-focused curriculum in an acute care Nurse Practitioner program.

Knowledge in diagnostic radiology and performance of point-of-care ultrasound are integral to acute care nurse practitioners (ACNPs) in the inpatient settings, especially true in specialty areas such as interventional radiology and critical care. However, existing ACNP training programs, including at our institution, often lack robust relevant course content. To address this educational gap, we designed and implemented a pilot acute care radiology course, incorporating a flipped classroom approach and hands-on simulation activities led by expert faculty. Our course evaluation revealed high levels of student satisfaction, self-reported confidence, and perceived course effectiveness. The course aligns with key competencies outlined by the American Association of Colleges of Nursing, demonstrating its potential to advance ACNP education in radiology content.

Status Epilepticus in Children at a Tertiary Care Center in India: Etiology, Preventable Risk Factors, and Short-Term Outcome

Abstract Introduction Status epilepticus (SE) is a common neurological emergency in children. Recent guidelines suggest initiating treatment after 5 minutes of seizure activity. Objective This study analyzes the clinical, laboratory parameters, and treatment outcomes of children with SE to identify preventable risk factors. Materials and Methods Prospective observational study of 150 children. They were evaluated for relevant history, clinical features, laboratory investigations, treatment, course, and outcome. Results The risk factors that had an impact on outcome in children with SE were identified as red flag signs on initial pediatric advanced life support (PALS) assessment, malnutrition, abnormal head circumference and number of seizures before child presented to hospital. The strongest predictor of outcome was stable status of children on initial PALS assessment (odds ratio = 20.174 [1.117, 364.393], p = 0.042). Presence of abnormal head size was 86% less likely to have favorable outcome and if the child had lesser number of seizures before child presented to hospital (single), it was 3.7 times likely that it would yield a favorable outcome. Conclusion Aggressive treatment of seizures, identification of red flag signs on initial PALS assessment, identification and treatment of malnutrition, anemia, hypocalcemia, and sodium derangements, strengthening vaccination to prevent central nervous system infections, and early intervention for developmental delay, can all help to combat morbidity and mortality in children with SE.

An Examination of Students’ Perspectives of Medical English Course Quality in Guangdong Medical Universities

Phenomenon: In China, medical English courses are critical to medical education, equipping Chinese students with the linguistic tools necessary for international medical practice and collaboration. However, a disconnect persists between the pedagogical approaches of medical practitioners and language educators, leading to a curriculum that emphasizes grammatical accuracy over practical communication skills. This misalignment results in student disengagement and falls short of addressing the real-world demands of the medical profession. With the growing importance of English proficiency in the global health sector, the need for significant improvements in medical English education is evident. This study delves into the underlying causes of student demotivation and aims to reconcile educational delivery with the evolving expectations of the medical field. Insights gained from this research will inform targeted interventions, promising to enhance medical English courses and support improved educational experiences for Chinese medical undergraduates. Approach: This cross-sectional quantitative study surveyed 3,046 second-year medical students from four medical universities in Guangdong Province, China, leveraging means-analysis and Expectancy-Disconfirmation Theory (EDT) as its foundation. The research was conducted at the end of the 2022–2023 academic year, utilizing a questionnaire to assess students’ perceptions of their medical English courses. Importance-Performance Analysis (IPA) was the primary analytical tool to discern discrepancies between students’ expectations and experiences. Findings: The IPA revealed that course content, classroom environment, and instructor effectiveness were pivotal factors influencing the perceived quality of the medical English courses. Students expressed a need for practical and relevant course material, with current content and textbooks falling short of preparing them for future medical communication demands. Additionally, while learning technologies were acknowledged, there was a discernible preference against their excessive application, suggesting a misalignment between student satisfaction and learning outcomes. Insights: This study highlights the need for innovative staffing models, refined qualifications for part-time instructors, development of collaborative and practical teaching materials, and focused training for medical English instructors. It also emphasizes the judicious integration of e-learning to enhance the learning experience. These insights aim to improve instruction quality by informing potential pedagogical adjustments and resource allocations in medical English education.

Needs Analysis of Students in Dialectology Learning at Toraja Christian University of Indonesia

This research aims to analyze the needs of students at the Toraja Christian University of Indonesia in dialectology learning. The study is beneficial in providing profound insights into the needs and preferences of students in dialectology learning, assisting the university in developing a more relevant and effective dialectology course. Furthermore, by understanding the student's needs, the university can allocate learning resources more efficiently, including providing more suitable teaching materials, effective teaching methods, and improving learning facilities. This research is a mixed-methods study, combining both quantitative and qualitative analyses. Data for this study were obtained from 46 students in the Indonesian Language and Literature Education Program at Toraja Christian University of Indonesia who enrolled in the Dialectology course during the academic year 2022/2023. Data were collected through survey and interview techniques. The results of the research indicate that students' needs in dialectology learning include understanding the learning materials, effective teaching methods, required learning resources, and desired practical experiences. Students' expectations and motivations in dialectology learning vary, ranging from deepening their understanding of language and culture and learning about dialectal differences in their society to contributing to language preservation.

Enhancing online learning: sentiment analysis and collaborative filtering from Twitter social network for personalized recommendations

Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines sentiment analysis of learners’ reviews on social networks with collaborative filtering methods to provide more personalized and relevant course recommendations. To achieve this, we explored different models to analyze the sentiments of tweets related to online courses. Additionally, we used collaborative filtering based on k-nearest neighbors (KNN). Our results demonstrate that integrating sentiment analysis provides more relevant recommendations. This has also been shown based on the calculation of root mean square error (RMSE) compared to a traditional approach. In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of their course recommendations, tailoring them to each individual learner’s needs.

Addressing the Gap: Perspectives on Developing English for Specific Purposes Course Books for Business Management in Mongolia

This research paper explores the necessity for the development of English for Specific Purposes (ESP) course books tailored to the needs of professionals in Business Management in Mongolia. Despite the availability of English for Academic Purposes (EAP) books, there remains a significant gap in resources catering to the specific language requirements of professionals in this field. English teachers in Mongolia recognize the importance of ESP materials in enhancing language proficiency and employability among Business Management students. Drawing upon interviews and surveys with English teachers, this paper highlights the challenges faced due to the absence of relevant ESP course books and proposes of recommendations for their creation and implementation.

Students satisfaction, self-efficacy and achievement in an emergency online learning course

This study aimed to evaluate the impact of an emergency online learning course on students’ satisfaction, self-efficacy and achievement. This study used a convergent mixed methods approach with an action research design to explore students’ experiences and outcomes in an emergency online science course. This study involved 25 voluntary participants from a private college in Manila, Philippines, who were enrolled in the Science, Technology and Society online course during the 2019–2020 academic year. Data were collected using a variety of instruments, including questionnaires, reflective journals and semi-structured interviews. The results showed that the developed emergency online learning course positively impacted students’ satisfaction, efficacy and achievement. Students were satisfied with their interactions with classmates and teachers and the course content. They also expressed confidence in their ability to perform online tasks independently and master the subject through pre-recorded videos. This study suggests that effective student-teacher interaction, peer relationships, relevant and relatable course content, well-designed lesson materials, clear assessment tasks, differentiated tasks to meet individual learning preferences and teacher creativity are essential factors for student satisfaction, efficacy and achievement in emergency online learning courses.

Preclinical comparison of [177Lu]Lu-rhPSMA-10.1 and [177Lu]Lu-rhPSMA-10.2 for endoradiotherapy of prostate cancer: biodistribution and dosimetry studies

BackgroundRadiohybrid PSMA-targeted ligands (rhPSMA) have been introduced as a novel platform for theranostic applications. Among a variety of rhPSMA-ligands developed for radioligand therapy, two stereoisomers [177Lu]Lu-rhPSMA-10.1 and -10.2 have been synthesized and initially characterized in preclinical experiments with the aim to provide an optimized binding profile to human serum albumin, a reduction of charge, and thus accelerated kidney excretion, and unaffected or even improved tumor uptake. As both isomers showed similar in vitro characteristics and tumor uptake at 24 h post injection in tumor bearing mice and in order to identify the isomer with the most favorable pharmacokinetics for radioligand therapy, we carried out in-depth biodistribution and dosimetry studies in tumor-bearing and healthy mice.ResultsrhPSMA-10.1 and -10.2 were radiolabeled with lutetium-177 according to the established procedures of other DOTA-based PSMA ligands and displayed a high and comparable stability in all buffers and human serum (> 97%, 24 h). Biodistribution studies revealed fast clearance from the blood pool (0.3–0.6%ID/g at 1 h) and other background tissues within 48 h. Distinctive differences were found in the kidneys, where [177Lu]Lu-rhPSMA-10.1 displayed lower initial uptake and faster excretion kinetics compared to [177Lu]Lu-rhPSMA-10.2 expressed by a 1.5-fold and ninefold lower uptake value at 1 h and 24 h in healthy animals, respectively. Tumor uptake was comparable and in the range of 8.6–11.6%ID/g for both isomers over 24 h and was maintained up to 168 h at a level of 2.2 ± 0.8 and 4.1 ± 1.4%ID/g for [177Lu]Lu-rhPSMA-10.1 and [177Lu]Lu-rhPSMA-10.2, respectively.ConclusionOur preclinical data on biodistribution and dosimetry indicate a more favorable profile of [177Lu]Lu-rhPSMA-10.1 compared to [177Lu]Lu-rhPSMA-10.2 for PSMA-targeted radioligand therapy. [177Lu]Lu-rhPSMA-10.1 shows fast kidney clearance kinetics resulting in excellent tumor-to-organ ratios over a therapy relevant time course. Meanwhile, [177Lu]Lu-rhPSMA-10.1 is currently being investigated in clinical phase I/II studies in patients with mCRPC (NCT05413850), in patients with high-risk localized PC (NCT06066437, Nautilus Trial) and after external beam radiotherapy (NCT06105918).

Exploring Students' Perspectives on the Implementation of a Self-Organised Learning Environment

This critical participatory action research explored undergraduate female Saudi students' perspectives on the implications of a self-organised learning environment (SOLE) via their mobile devices. Data was gathered from interviews and focus groups using a convenience sample of 27 undergraduate students and the researcher's reflective journals. The proposed intervention gave students opportunities for engagement and enhanced a spirit of cooperation, which led to the students enjoying their classes. Furthermore, SOLE encouraged the students to evaluate their ideas before posting them. Participating in SOLE helped the students to understand and recall the relevant course content. However, some challenging aspects, e.g., misleading information, emerged during the study's first phase, along with some technical issues.

A Bayesian functional approach to test models of life course epidemiology over continuous time.

Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

An Empirical Study on the Blended Teaching Mode of College Oral English Guided by the POA Theory: A Preliminary Exploration

This study is based on the theory of “Production-Oriented Approach” (POA) proposed by Professor Wen Qiufang. It relies on the online course “Practical College Oral English” on the “Cool Learning Liaoning” platform. Using the POA theory as a foundation, relevant course designs were developed, and a blended teaching approach was implemented, combining online and offline components. During the fall semester of the academic year 2023, two groups of students, an experimental group and a control group, were selected for a cross-institutional elective course in “Practical College Oral English”. Two different teaching methods, POA and non-POA, were employed, and data collected were subjected to statistical analysis using SPSS. This empirical research aimed to analyze the effectiveness of the POA theory in the context of a blended teaching model.

An Online Course Recommender System in e-Learning Using Learners’ Profile and Learning Behavior-Based Mechanism

The advent of online learning platforms, such as Coursera and Udemy, has facilitated global access to diverse educational opportunities. However, integrating recommender algorithms into these online learning systems presents a notable challenge. To address this challenge, this research paper proposes an architecture for online course recommender systems that leverages learners’ profiles and learning behavior. The proposed architecture comprises six core components: 1) User Management Engine, 2) Online Course Content Management Engine, 3) Learning Behavior Observer and Recorder, 4) Profile and Learning Behavior Builder Engine, 5) Course Recommendation and Filtering Engine, and 6) Feedback Engine. These components collectively form the foundation of the online course recommender system. In order to facilitate practical implementation, a prototype of the online course recommender system has been developed. To evaluate the system’s performance, a comparative analysis was conducted, to compare the personalized recommended list with the non-personalized recommended list. The analysis revealed an average precision of 77.60% for the personalized recommended list, while the non-personalized recommended list achieved an average precision of 65.60%. These findings highlight the superiority of the personalized approach in generating more accurate and relevant online course recommendations.

An Explainable Content-Based Course Recommender Using Job Skills

The large number of courses offered in universities and online studies made it difficult for students to choose the courses that suit their interests and career goals, which led students to lose many opportunities to be employed in the job they wanted. To keep pace with the rapid development of technology, and instead of relying on the job title as was previously done, the employers began to identify the skills required for a job. The competencies of the candidates are then examined and evaluated according to those requirements. Thus, it has become necessary for students to take courses that suit their future professional interests, ensuring that they are employed in the job they desire and supporting their long-term career success. Fortunately, the emergence of skills-based employment has provided an opportunity for universities and colleges to create a clearer path to the courses offered to allow students to take courses that match their future career interests. In this study, we used K-Mean clustering algorithm, TF-idf approach, and content-based filtering algorithm to provide relevant courses for students based on the required job with an explanation of why these courses are recommended. Our result illustrates that our method offers many advantages compared with other recommender systems. our system converts a simple course recommendation into a tool for discovering skills. Since many recommendation systems work as black boxes, we designed our system to recommend the relevant course with explaining why these courses are recommended, which will add a factor of transparency to our system and confirms the reliability of the system to the students.

Maternal social support during and after pregnancy and child cognitive ability: examining timing effects in two cohorts.

Maternal anxiety, depression, and stress during and after pregnancy are negatively associated with child cognitive development. However, the contribution of positive maternal experiences, such as social support, to child cognitive development has received less attention. Furthermore, how maternal experience of social support during specific developmental periods impacts child cognitive development is largely unknown. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 5784) and the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction study (PREDO; n = 420), we investigated the associations between maternal perceived social support during and after pregnancy and child's general cognitive ability at 8 years of age, assessed with the Wechsler Intelligence Scale for Children (WISC). Bayesian relevant life course modeling was used to investigate timing effects of maternal social support on child cognitive ability. In both cohorts, higher maternal perceived social support during pregnancy was associated with higher performance on the WISC, independent of sociodemographic factors and concurrent maternal symptoms of depression and anxiety. In ALSPAC, pregnancy emerged as a sensitive period for the effects of perceived social support on child cognitive ability, with a stronger effect of social support during pregnancy than after pregnancy on child cognitive ability. Our findings, supported from two prospective longitudinal cohorts, suggest a distinct role of maternal perceived social support during pregnancy for cognitive development in children. Our study suggests that interventions aimed at increasing maternal social support during pregnancy may be an important strategy for promoting maternal and child well-being.

Using a Learning Management System to Actively Engage Students in a Biomedically Relevant Introductory Physics Course

Using a Learning Management System to Actively Engage Students in a Biomedically Relevant Introductory Physics Course

Investigating the Role of the Accumulation of Depressive Symptoms for the Risk of Dementia

AbstractBackgroundDepressive symptoms vary over the life course and may result from a multitude of causes. With consolidating evidence about depressive symptoms as risk factor of dementia, and previous findings suggesting shared biological pathways of depression and dementia, involving e.g., inflammation, it remains unclear if depressive symptoms reflect a causal risk factor, or an early sign of dementia. We sought to explore the role of depressive symptoms accumulation over time regarding dementia incidence in old age.MethodWe applied the functional relevant life course exposure model (fRLM) to the Survey of Health Aging and Retirement in Europe (SHARE). More precisely, we modelled the accumulation of depressive symptoms, measured with the EURO‐D scale, over time utilizing functional regressions. Data from waves 2 (2006) to 8 (2020) of SHARE was used to examine the association of depressive symptoms with incident self‐report physician‐diagnosis of dementia.ResultIn total, 3,717 participants aged 65 to 70 without dementia at baseline (M [SD] age at baseline = 67.42 [1.70] years) with complete data, participation in at least 2 waves prior to dementia diagnosis or 3 waves prior to loss to follow‐up (M [SD] participation = 3.67 [0.88] waves, MD [SD] follow‐up = 8.0 [2.9] years) were included (58.7% female). Higher exposure to depressive symptoms over time was associated to higher dementia incidence (OR [95% CI] = 3.66 [1.84‐5.87]). Moreover, relevance of depressive symptoms for dementia incidence varied across time windows, suggesting differential associations of depressive symptoms to dementia risk as a function of age. Highest relevance of exposure to depressive symptoms was observed for age 72‐75, i.e., close to diagnosis (Median [SD] age at diagnosis = 76 [3.26] years).ConclusionWhile we observed an association of depressive symptoms with dementia risk, our novel approach extends on previous findings suggesting highest relevance, i.e., predictive ability of depressive symptoms close to diagnosis. Given a build‐up of pathological changes relating to dementia starting as early as two decades prior to cognitive impairment and diagnosis, this suggests depressive symptoms not to be a causal risk factor but rather an early sign or accompanying symptom of dementia.