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PubMed2002

Attitudes among students and teachers on vertical integration between clinical medicine and basic science within a problem-based undergraduate medical curriculum.

Important elements in the curriculum at the Faculty of Health Sciences in Linköping are vertical integration, i.e. integration between the clinical and basic science sections of the curriculum, and horizontal integration between different subject areas. Integration throughout the whole curriculum is time-consuming for both teachers and students and hard work is required for planning, organization and execution. The aim was to assess the importance of vertical and horizontal integration in an undergraduate medical curriculum, according to opinions among students and teachers. In a questionnaire 102 faculty teachers and 106 students were asked about the importance of 14 different components of the undergraduate medical curriculum including vertical and horizontal integration. They were asked to assign between one and six points to each component (6 points = extremely important for the quality of the curriculum; 1 point = unimportant). Students as well as teachers appreciated highly both forms of integration. Students scored horizontal integration slightly but significantly higher than the teachers (median 6 vs 5 points; p=0.009, Mann-Whitney U-test), whereas teachers scored vertical integration higher than students (6 vs 5; p=0.019, Mann-Whitney U-test). Both students and teachers considered horizontal and vertical integration to be highly important components of the undergraduate medical programme. We believe both kinds of integration support problem-based learning and stimulate deep and lifelong learning and suggest that integration should always be considered deeply when a new curriculum is planned for undergraduate medical education.

Attitude of Health Personnel↗
PubMed1993

Automatic knowledge base refinement: learning from examples and deep knowledge in rheumatology.

MESICAR is a second generation expert system which contains very general descriptions of rheumatological disorders in the primary medical care field. With the help of a detailed hierarchical description of the human anatomy the system is able to support diagnostic decisions. The paper describes how machine learning techniques are used to automatically construct more specific disease descriptions for common, frequently occurring cases. The system MESICAR-LEARN implements a learning method which integrates analytical and empirical learning techniques. Cases diagnosed by MESICAR form the training examples, and MESICAR's knowledge base is used as domain theory. The learned concepts are integrated into a hierarchy of disease descriptions. They support efficient and fast reasoning on common cases in addition to the general diagnostic support afforded by MESICAR's deep knowledge.

Algorithms↗
PubMed2006

The deep inferior epigastric perforator flap for breast reconstruction, the learning curve explored.

The deep inferior epigastric perforator (DIEP) flap has been used as the principle tool for secondary breast reconstruction in our department. This article details our experience in learning and improving the technique with the help of an external team of experienced surgeons. In our initial 65 DIEP flap breast reconstructions our total flap loss rate was reduced from 9.5 to 0%, partial flap loss rate from 31 to 0%, and fat necrosis rate from 17 to 4.3%. We illustrate how a surgical team, which might initially have considered the complication rate from DIEP flap breast reconstruction too high, can benefit from a staged approach to this complex, but useful, reconstruction technique.

Anastomosis, Surgical↗
PubMed1997

Learning in dental education.

Learning in dental education has long been a subject of academic research. There are different types of student learning which directly or indirectly contribute to the learning outcomes. In this paper, the various classifications of different types of learning; superficial, deep, individual, collaborative and resource-based learning are explored. In order to achieve the learning objectives, different teaching methods such as group-work, rôle-play and problem-based learning are employed. The types of resources used in present day teaching are diverse and multiple: such as print-based, computer-based technologies and audio-visual technologies. In order to assess the effectiveness of such teaching, different methods of assessment have been adopted for use in higher education. In the context of good teaching in higher education, there are a number of constraints placed upon the individual and the institution. These constraints are discussed and recommendations made whereby they may be overcome.

Education, Dental↗
PubMed2003

Approaches to learning: psychometric testing of a study process questionnaire.

BACKGROUND: One method of evaluating students' learning is to measure surface, deep and achieving approaches to learning using a questionnaire. In comparison with research on student nurses' learning styles, there has been little examination of their 'approaches to learning'. Much of the 'approaches to learning' research has been conducted with higher education students in Australia and Hong Kong and this kind of measurement is viewed as a valid and reliable way to assess learning. AIM: The aim of study reported here was to establish the validity of an 'approaches to learning' questionnaire, the study process questionnaire, for use with student nurses by undertaking psychometrical testing, including exploratory factor analysis. METHOD: The study process questionnaire is a 42-item questionnaire measuring surface, deep and achieving approaches to learning. It was distributed to 300 student nurses attending a common foundation programme in a higher education establishment in the United Kingdom (UK) in July 2000. Principal components analysis was conducted to determine the validity of the deep, surface and achievement scales in the questionnaire. RESULTS: A new factor structure was identified comprising three main scales which were similar in content but not identical to the original questionnaire. The deep factor correlated positively and significantly with grade performance average and sociology examination results. CONCLUSION: The study process questionnaire is a valid and useful tool for nurse teachers to gain knowledge about student nurses' approaches to learning. Deep learning appears to influence academic performance. More work is required to elucidate the complex nature of deep learning.

Achievement↗
PubMed2026

ClearDepthIAS enables automated high-throughput quantification of roots in soil-grown taproot crops.

Understanding root system architecture is critical for improving crop productivity and resilience, yet phenotyping root traits such as root growth angle and rooting depth remains technically challenging, especially at high throughput. Here, we present ClearDepthIAS, a high-throughput imaging and analysis platform that enables nondestructive, automated quantification of root architecture traits in taproot system crops. By capturing and stitching 360° images of roots growing along the transparent walls of pots and applying deep learning-based segmentation (ClearDepth-WRT), we measured wall root shallowness (WRS)-a proxy for root growth angle-with high precision. We demonstrated for the tap root systems of soybean and canola that the system accurately detects root tips, quantifies their vertical distribution, and extracts biologically meaningful traits such as root area, distribution indices, and growth angles. Validation experiments in canola and soybean demonstrated that WRS can correlate with root crown architecture in mature plants, both in greenhouse and field settings. Furthermore, WRS and root distribution indices derived from ClearDepthIAS are predictors of early root architecture and can be correlated with root biomass distribution across soil depths under field conditions; however, environmental interactions may influence these relationships and weaken or even negate such correlations, as observed when comparing field to field variation in root system architecture. Our system enables efficient phenotyping of genetically diverse populations, with medium to high trait heritability, supporting its utility for genome-wide association studies and breeding. ClearDepthIAS accelerates the development of root ideotypes for improved resource acquisition and carbon sequestration, offering a scalable tool for supporting climate-resilient agriculture.

Plant Roots↗
PubMed2026

Biallelic loss of RB1 in hepatocellular carcinoma as synthetic lethal target for artificial intelligence-guided therapy.

The retinoblastoma (RB1) gene is a critical tumor suppressor that regulates cell cycle progression and genomic stability. Although RB1 alterations have been reported in hepatocellular carcinoma (HCC), the biological and clinical consequences of biallelic RB1 inactivation (RB1-Bi) remain poorly defined. We performed a comprehensive allele-specific genomic analysis of HCC patients from the TCGA-LIHC (n&#x2009;=&#x2009;355) and in-house AMC (n&#x2009;=&#x2009;206) cohorts, collectively comprising the AMC-TCGA discovery cohort. In this combined cohort, RB1-Bi was identified in 14.6% of tumors, was enriched in poorly differentiated HCCs and was independently associated with significantly reduced overall survival (adjusted hazard ratio 3.32, 95% CI 1.93-5.72, p&#x2009;<&#x2009;0.001). Additionally, a deep learning-based histopathology model using hematoxylin and eosin-stained slides (i.e., FR-MIL model) accurately predicted RB1-Bi status (F1 score 84.39% [95% CI, &#xb1;0.02]), making it readily identifiable in routine clinical practice. The prevalence and prognostic impact of RB1-Bi, as well as FR-MIL model performance, were consistent across independent validation cohorts, including advanced-stage tumors and external institutions. High-throughput drug screening in isogenic HCC models revealed that RB1-Bi HCC cells were particularly sensitive to inhibitors targeting mitotic regulators (e.g., AURKA, PLK1, KSP) and DNA damage response pathways (e.g., PARP inhibitors). Synthetic lethal interactions between RB1-Bi and these compounds were demonstrated in vitro and in vivo, and combination treatment with mitotic and PARP inhibitors had synergistic effects with acceptable tolerability. We conclude that RB1-Bi represents a clinically actionable biomarker that identifies a high-risk HCC subtype with specific therapeutic vulnerabilities, offering new opportunities for precision medicine.

Humans↗
PubMed1996

Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children.

Learning from patient records may aid knowledge acquisition and decision making. Existing inductive machine learning (ML) systems such us NewId, CN2, C4.5 and AQ15 learn from past case histories using symbolic and/or numeric values. These systems learn symbolic rules (IF... THEN like) which link an antecedent set of clinical factors to a consequent class or decision. This paper compares the learning performance of alternative ML systems with each other and with respect to a novel approach using logic minimization, called LML, to learn from data. Patient cases were taken from the archives of the Paediatric Surgery Clinic of the University Hospital of Crete, Heraklion, Greece. Comparison of ML system performance is based both on classification accuracy and on informal expert assessment of learned knowledge.

Abdomen, Acute↗
PubMed2003

Approaches to learning, need for cognition, and strategic flexibility among university students.

BACKGROUND: Considerable research has described students' deep and surface approaches to learning. Other research has described individuals' self-regulated learning and need for cognition. There is a need for research examining the relationships among these constructs. AIMS: This study explored relationships among approaches to learning (deep, surface), need for cognition, and three types of control of learning (adaptive, inflexible, irresolute). Theory suggested similarities among the deep approach, need for cognition, and adaptive control (aspects of self-regulated learning); and among surface, inflexible, and irresolute control (aspects of an ineffective approach to learning). One-factor and two-factor models were proposed. SAMPLE: Participants were 226 Canadian military college students. METHOD: Participants completed the following questionnaires: the Study Process Questionnaire (Biggs, 1978), the Need for Cognition Scale (Cacioppo & Petty, 1982), and the Strategic Flexibility Questionnaire (Cantwell & Moore, 1996). RESULTS: Confirmatory factor analysis supported the identification of the six scale factors. Second order confirmatory factor analysis indicated three factors representing constructs underlying these factors. CONCLUSIONS: Neither the one- nor two-factor models accounted adequately for the data. Self-regulated learning was defined by measures of the deep approach to learning, need for cognition, and adaptive control of learning. The second factor divided into one factor consisting of irresolute control, the surface approach, and negative need for cognition; and another consisting of inflexible and negative adaptive control. Substantial relationships among scales support the need for further theory development.

Adolescent↗
PubMed2003

University students' approaches to learning first-year mathematics.

This study assessed reliability and validity of the Approaches to earning Mathematics Questionnaire, for 218 university students. The study also identified the relationship between subscales. Internal consistency as Cronbach alpha was .77 for the Surface Approach to Learning scale and .88 for the Deep Approach to Learning scale. Principal components analysis yielded a two-factor solution accounting for only 34.6% of variance. The factors were interpreted as Surface Approach and Deep Approach to learning mathematics, as in Australia. The former subscale scores were negatively correlated -.2 with the latter subscale scores.

Adolescent↗
PubMed2004

Epistemological exploration: generalization of learning styles and analytical skills between academic and religious materials.

Among 139 students (mean age 21.8, SD=3.5), use of Schmeck's Deep Processing learning style (looking for conceptual understanding) on academic materials correlated modestly with its use on religious materials. The same was true for Elaborative Processing (looking for associations and applications). Both Deep and Elaborative Processing of academic materials correlated with better Analytical Skills. Only Elaborative Processing of religious materials correlated with Religiousness. Religiousness correlated with poorer Analytical Skills on academic materials and with a more Concrete Divine Concept; however, specific religious affiliation made a difference. Our understanding of the role of contents of materials and characteristics of learners on the types of learning strategies used and competence with cognitive skills is still very limited.

Adult↗
PubMed1999

Intercalated degrees, learning styles, and career preferences: prospective longitudinal study of UK medical students.

OBJECTIVES: To assess the effects of taking an intercalated degree (BSc) on the study habits and learning styles of medical students and on their interest in a career in medical research. DESIGN: Longitudinal questionnaire study of medical students at application to medical school and in their final year. SETTING: All UK medical schools. PARTICIPANTS: 6901 medical school applicants for admission in 1991 were studied in the autumn of 1990. 3333 entered medical school in 1991 or 1992, and 2695 who were due to qualify in 1996 or 1997 were studied 3 months before the end of their clinical course. Response rates were 92% for applicants and 56% for final year students. MAIN OUTCOME MEASURES: Study habits (surface, deep, and strategic learning style) and interest in different medical careers, including medical research. Identical questions were used at time of application and in final year. RESULTS: Students who had taken an intercalated degree had higher deep and strategic learning scores than at application to medical school. Those with highest degree classes had higher strategic and deep learning scores and lower surface learning scores. Students taking intercalated degrees showed greater interest in careers in medical research and laboratory medicine and less interest in general practice than their peers. The effects of the course on interest in medical research and learning styles were independent. The effect of the intercalated degree was greatest in schools where relatively few students took intercalated degrees. CONCLUSIONS: Intercalated degrees result in a greater interest in research careers and higher deep and strategic learning scores. However, the effects are much reduced in schools where most students intercalate a degree. Introduction of intercalated degrees for all medical students without sufficient resources may not therefore achieve its expected effects.

Attitude↗
PubMed2006

Learning in oral biology: a comparison between deep and surface approaches.

It has been suggested that students approach their learning in at least two qualitatively different ways. In the surface approach, students see tasks as being imposed, for which they develop coping strategies focused on reproduction of essentials and memorising information for assessment rather than for understanding. In the deep approach, students seek to understand ideas to allow them to relate and integrate knowledge from other parts of their study and thereby develop conceptual frameworks from which they can derive solutions to novel problems. To these two approaches, a third, achieving approach, is often added, in which students aim to obtain the highest grades, whether or not they grasp the learning material. In this study we used a subject-specified version of Biggs' Study Process Questionnaire to obtain data about the way dental and dental technology students perceived and approached their learning in oral biology. Questionnaires were distributed to 62 second-year dental students and 23 second-year dental technology students. Within each group the dependent variables examined were deep, surface or achieving approaches to learning. Analysis of these data showed that significant differences between dental and dental technology students centred on their approaches to learning. However, there were no significant differences attributable to gender, country of origin or ethnicity. While dental students, who had a relatively well developed understanding of the nature of their studies in oral biology at the start of their course, adopted deep learning strategies, dental technology students, who had had no prior experience of university education per se, were significantly more surface-orientated.

Achievement↗
PubMed1995

Differential roles of cerebellar cortex and deep cerebellar nuclei in the learning of the equilibrium behavior: studies in intact and cerebellectomized lurcher mutant mice.

Three- to 6-month-old lurcher mutant mice (+/lc), which exhibit a massive loss of neurons in the cerebellar cortex and in the inferior olivary nucleus but whose deep cerebellar nuclei are essentially intact, were trained daily, for 9 days, to maintain their equilibrium upon a rota rod rotating at 20 or 30 revolutions per minute (rpm). Their scores were measured and their behavior upon the rotating rod quantified in comparison to those of matched control (+/+) mice. Lurcher mice were able to learn to maintain their equilibrium efficiently when rotated at 20 rpm but were not when rotated at 30 rpm. After cerebellectomy, the equilibrium capabilities of the animals were much altered, especially in +/lc. These results show that the deep cerebellar nuclei are sufficient for motor learning, provided the task is not too difficult (20 rpm), but that the cerebellar cortex is required when the task is more difficult (30 rpm). Therefore, it can be concluded that the adaptive motor capabilities of lurcher mice are less developed than those of control animals.

Animals↗
PubMed2025

A comparative study highlights superiority of LSTM in crop genomic prediction.

We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic prediction (GP) has been developed as an important method supporting crop breeding. By utilizing the phenotype values result from GP, breeders could make decisions in the seedling stage that consequently benefit for cost saving. In recent years, machine learning emerged as an efficient technology to solve modeling problems in many fields, including crop breeding. However, numerous modeling approaches have hindered the application of GP since breeders struggle to choose. Therefore, a comprehensively methodological research with guiding significance is extremely necessary. In the present study, we systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods. As for genomic feature processing, we found feature selection (SNP filtering approach) performed better than feature extraction (PCA method). Specifically, the feature relationship dependent methods (GBLUP, RNN, and LSTM) as well as DNN architecture showed superior performance with feature selection. Marker density analysis showed positive correlation with prediction accuracy in a limited threshold. Comparison on effect of population size demonstrated a positive correlation between trait genetic complexity and the optimal population size required. By testing fifteen modeling methods, we found LSTM network displayed superior performance, achieving the highest average STScore (0.967) across six datasets. Further research using all cell states or the latest cell states of LSTM inputs demonstrated its architecture particularly adept with capturing additive and epistatic QTL effects among SNPs. In conclusion, our findings provide basic principles for implementing GP in breeding project to maximize prediction accuracy while maintaining cost-effectiveness.

Plant Breeding↗
PubMed2006

Learning approaches to physiology of undergraduates in an Indian medical school.

Inventories monitoring students' learning approaches are widely used in medical education research. It is important that teaching interventions adopted in medical schools aim to develop a deep approach to learning in medical students. To study the changes in medical students' approaches to learning before and after the incorporation of clinically orientated physiology teaching (COPT) in the undergraduate physiology curriculum, using the Short Inventory of Approaches to Learning (SIAL). Medical students (n = 223) at Melaka Manipal Medical College (Manipal Campus) undertake a 9-week learning block of endocrine, reproductive and renal physiology in Year 1. During this period, COPT was incorporated along with regular didactic lectures with the intention of enhancing the use of the deep approach and decreasing the use of the surface and strategic approaches to learning taken by the students. The SIAL, which focuses on the learning approaches of students to physiology, was distributed both before and after COPT. The implementation of COPT seemed to affect the learning approaches of students as measured by the SIAL. After the introduction of COPT, there was a significant increase in the use of the deep learning approach, while the majority of subscales for the surface approach showed decreased use. Nevertheless, use of the strategic approach was found to have increased after COPT. The SIAL was found to be a fairly reliable tool with which to determine the learning approaches of medical students. Clinically orientated physiology teaching was successful in enhancing use of the deep approach to learning and reducing use of the surface approach among undergraduate medical students.

Clinical Medicine↗
PubMed2026

Investigating cross-organism prediction of prokaryotic essential proteins using unsupervised language model and ensemble strategy.

Cross-organism prediction of essential proteins is a critical task for drug discovery and microbial engineering, yet the generalizability of existing machine learning models across diverse species remains a significant challenge. In this study, we propose DeepPEP, a large language model-based framework designed to reliably transfer essential protein annotations between distantly related organisms. Utilizing 66 curated prokaryotic datasets, we systematically evaluated DeepPEP's cross-organism performance under various conditions. Initial pairwise predictions revealed a correlation between performance and evolutionary distance; however, further investigation demonstrated that integrating training data from multiple organisms yields superior predictive power. In a benchmark scenario designed to simulate real-world applications, DeepPEP outperformed the state-of-the-art tool Geptop 2.0, showcasing a robust ability to identify species-specific essential proteins. Finally, a case study on novel genomes confirmed the model's practical effectiveness. Our results suggest that DeepPEP is a powerful strategy for prokaryotic essential protein prediction, and the rigorous evaluation framework established in this study provides a new benchmark for the field.

Large Language Models↗