An active learning card game to teach microbial pathogenesis to undergraduate biology majors.

Interactive classroom activities are an effective way to reinforce knowledge and promote student engagement. In this paper, we introduce the Pathogenesis Card Game (PCG), an innovative card game that engages students in a battle between microbial pathogens and the host immune system. Each student is given a set of cards that consist of either common host defenses or common pathogen evasion strategies. In pairs, students play a host defense card versus a pathogenesis card. Host defense cards include neutralize (antibody production), eat (phagocytosis), and destroy (degranulation). Pathogenesis cards present evasive strategies including mimic (molecular mimicry), escape (hemolysin production), hide (polysaccharide capsule), block (antioxidant defense), cut (protease secretion), and disguise (antigenic variation). Students develop a mastery of microbial pathogenesis through active gameplay by deliberating the outcome of each unique host-pathogen interaction. Furthermore, they learn the role of cells in the immune system and how pathogens can evade these immune defenses. PCG was piloted in a 300-level introductory microbiology course for 22 undergraduate students, comprising primarily biology and nursing majors. Both quantitative and qualitative student evaluations of the activity strongly suggest that PCG is an engaging, effective, and useful way to teach microbial pathogenesis. This activity provides a 60-minute lesson plan and corresponding materials that can be used to facilitate the introduction of pathogenesis to a typical undergraduate microbiology course. PCG offers instructors a framework to teach microbial pathogenesis and gives students the opportunity to construct their own knowledge about pathogen immune evasion in an engaging and interactive way.

CREATE'ing improvements in first-year students' science efficacy via an online introductory course experience.

With a primary objective to engage students in the process of science online, we transformed a long-standing laboratory course for first-year science students into a more accessible, immersive experience of current biological research using a narrow and focused set of primary literature and the Consider, Read, Elucidate a hypothesis, Analyze and interpret data, Think of the next Experiment (CREATE) pedagogy. The efficacy of the CREATE approach has been demonstrated in a diversity of higher education settings and courses. It is, however, not yet known if CREATE can be successfully implemented online with a large, diverse team of faculty untrained in the CREATE pedagogy. Here, we present the transformation of a large-enrollment, multi-section, multi-instructor course for first-year students in which the instructors follow different biological research questions but work together to reach shared goals and outcomes. We assessed students' (i) science self-efficacy and (ii) epistemological beliefs about science throughout an academic year of instruction fully administered online as a result of ongoing threats posed by COVID-19. Our findings demonstrate that novice CREATE instructors with varying levels of teaching experience and ranks can achieve comparable outcomes and improvements in students' science efficacy in the virtual classroom as a teaching team. This study extends the use of the CREATE pedagogy to large, team-taught, multi-section courses and shows its utility in the online teaching and learning environment.

A case study to engage students in the research design and ethics of high-throughput metagenomics.

Case studies present students with an opportunity to learn and apply course content through problem solving and critical thinking. Supported by the High-throughput Discovery Science & Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network, our interdisciplinary team designed, implemented, and assessed two case study modules entitled "You Are What You Eat." Collectively, the case study modules present students with an opportunity to engage in experimental research design and the ethical considerations regarding microbiome research and society. In this manuscript, we provide instructors with tools for adopting or adapting the research design and/or the ethics modules. To date, the case has been implemented using two modalities (remote and in-person) in three courses (Microbiology, Physiology, and Neuroscience), engaging over 200 undergraduate students. Our assessment data demonstrate gains in content knowledge and students' perception of learning following case study implementation. Furthermore, when reflecting on our experiences and student feedback, we identified ways in which the case study could be modified for different settings. In this way, we hope that the "You Are What You Eat" case study modules can be implemented widely by instructors to promote problem solving and critical thinking in the traditional classroom or laboratory setting when discussing next-generation sequencing and/or metagenomics research.

Extremophile enzyme activity lab: using catalase from Pyrobaculum calidifontis to highlight temperature sensitivity and thermostable enzyme activity.

There are places on earth that are considered to possess extreme physico-chemical characteristics as they relate to life. Surprisingly, there are microbes that have adapted various strategies that enable them to form robust communities in these environments. The microbes that live in these environments, called extremophiles, are described as being thermophilic, psychrophilic, halophilic, acidophilic, alkaliphilic, barophilic, and so on. Given that extremophiles were not discovered until relatively recently due to a view point that the environments in which they inhabited were not conducive to life, it is reasonable to conclude that the concept of extremophiles may be hard to grasp for students. Herein is described a laboratory exercise adapted from laboratory exercises that use mesophilic catalase enzymes to illustrate the influence of physico-chemical parameters on enzyme activity. Catalase is an enzyme that accelerates the degradation of hydrogen peroxide to water and oxygen gas. In addition to mesophilic catalases, the catalase from Pyrobaculum calidifontis, a hyperthermophile with an optimal growth temperature of 90°C, is used to highlight the adaptation of an enzyme to an extreme environment. A visual comparison of bubble production by the hyperthermophilic and mesophilic enzymes after heating at high temperatures dramatically illustrates differences in thermostability that will likely reinforce concepts that are given in a pre-laboratory lecture that discusses not only the extremophiles themselves but also their applications in biotechnology and possible role in the field of astrobiology.

A customizable series of microbiology lab activities exploring a foodborne outbreak to enhance student recruitment to a biology program.

As educators at a small university, we are constantly trying to find new and innovative ways of getting high school students interested in a degree in Biology at our school. Thus, we designed an outreach program to draw interested high school students to our campus and participate in a day-long outbreak investigation. The investigation is composed of six distinct activities, each taking between 15 min and 1 h of active time. These activities can be used in conjunction or individually to engage students with basic epidemiology and microbiology. The modules included in this recruitment event are outbreak interviews, DNA fingerprinting analysis, Gram staining, examination of microbial diagnostic tests, use of high-performance liquid chromatography to analyze toxins, and examination of potential food preparation contamination. Our first event was a success, with all participants reporting that they enjoyed their time at the University and found the faculty and staff helpful. One of the students even said, "I wish all school was like this." The goal of this event was to increase potential student interest and enrollment in our program. We hope that in sharing our experience here we can provide other instructors with a menu from which to pick and choose inexpensive, easy, and engaging activities for high school and introductory college students.

Computed tomography-based automated measurement of abdominal aortic aneurysm using semantic segmentation with active learning.

Accurate measurement of abdominal aortic aneurysm is essential for selecting suitable stent-grafts to avoid complications of endovascular aneurysm repair. However, the conventional image-based measurements are inaccurate and time-consuming. We introduce the automated workflow including semantic segmentation with active learning (AL) and measurement using an application programming interface of computer-aided design. 300 patients underwent CT scans, and semantic segmentation for aorta, thrombus, calcification, and vessels was performed in 60-300 cases with AL across five stages using UNETR, SwinUNETR, and nnU-Net consisted of 2D, 3D U-Net, 2D-3D U-Net ensemble, and cascaded 3D U-Net. 7 clinical landmarks were automatically measured for 96 patients. In AL stage 5, 3D U-Net achieved the highest dice similarity coefficient (DSC) with statistically significant differences (p < 0.01) except from the 2D-3D U-Net ensemble and cascade 3D U-Net. SwinUNETR excelled in 95% Hausdorff distance (HD95) with significant differences (p < 0.01) except from UNETR and 3D U-Net. DSC of aorta and calcification were saturated at stage 1 and 4, whereas thrombus and vessels were continuously improved at stage 5. The segmentation time between the manual and AL-corrected segmentation using the best model (3D U-Net) was reduced to 9.51 ± 1.02, 2.09 ± 1.06, 1.07 ± 1.10, and 1.07 ± 0.97 min for the aorta, thrombus, calcification, and vessels, respectively (p < 0.001). All measurement and tortuosity ratio measured - 1.71 ± 6.53 mm and - 0.15 ± 0.25. We developed an automated workflow with semantic segmentation and measurement, demonstrating its efficiency compared to conventional methods.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. CONCLUSION: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

Children's subjective uncertainty-driven sampling behaviour.

Are children and adults sensitive to gaps in their knowledge, and do they actively elicit information to resolve such knowledge gaps? In a cross-situational word learning task, we asked 5-year-olds, 6- to 9-year-olds and adults to estimate their knowledge of newly learned word-object associations. We then examined whether participants preferentially sampled objects they reported not knowing the label in order to hear their labels again. We also examined whether such uncertainty-driven sampling behaviour led to improved learning. We found that all age groups were sensitive to gaps in their knowledge of the word-object associations, i.e. were more likely to say they had correctly indicated the label of an object when they were correct, relative to when they were incorrect. Furthermore, 6- to 9-year-olds and adults-but not 5-year-olds-were more likely to sample objects whose labels they reported not knowing. In other words, older children and adults displayed sampling behaviour directed at reducing knowledge gaps and uncertainty, while younger children did not. However, participants who displayed more uncertainty-driven sampling behaviour were not more accurate at test. Our findings underscore the role of uncertainty in driving 6- to 9-year-olds' and adults' sampling behaviour and speak to the mechanisms underlying previously reported performance boosts in active learning.

Sensitivity of Vanessa cardui to Temperature Variations: A Cost-Effective Experiment for Environmental Education.

Temperature increases mediated through climate change threaten the survival of species. It is of foremost importance to engage citizens and future generations in understanding the mechanisms through which temperatures impose their effects. For educators, this is not straightforward, as tools for examining the impact of temperature over the lifetime of an animal are prohibitively expensive. At the same time, environmental educators need guidance on the appropriate study systems to use with a balance between the species having an obvious response and ensuring the outcomes are ethical and sustainable. In our study, we created and tested a cost-effective experiment meant to be used for environmental education purposes. More specifically, we tested the sensitivity of the painted lady butterfly Vanessa cardui to temperature variations using a homemade incubator. We describe the design of this experiment and report findings on survival rate, morphological variations, development time of various stages and wingspan of adults across a range of biologically relevant temperatures. The information provided gives educators options for testing a variety of hypotheses with regards to the impacts of temperature using an affordable and flexible set-up. Furthermore, the findings can be used by students to develop an understanding of the ramifications of the butterflies' responses in an ecological context.

Active learning using adaptable task-based prioritisation.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for a new kidney segmentation task, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

The impact of three learning methods on dental students' satisfaction and knowledge acquisition.

OBJECTIVES: Health professionals' education presents unique challenges including clinical application of knowledge and interprofessional collaboration. Additionally, institutions suffer from faculty shortages while class sizes increase. Therefore, educators seek innovative andragogical techniques utilizing minimal resources. Several active learning methods have been introduced as a solution. The aim of this study is to assess the impact of lecture (LBL), case (CBL), and team (TBL) based learning on the students' satisfaction and knowledge attainment. METHODS: A total of 134 sophomore dental students are taught pediatric dentistry course using LBL, CBL, and TBL techniques. At the end of the course, students are invited to participate in a satisfaction survey. Statistical analysis is conducted using a two-sided chi-square goodness of fit test. Students' comments are used for qualitative analysis. Final exam analysis is administered via Examsoft software. RESULTS: A total of 98% of the students participated in the survey out of which 83% met the inclusion criteria (N = 110). Students chose LBL as the method that helped them learn in a comprehensive way, provided the most comfortable environment, and presented the overall highest satisfaction with statistically significant difference (p value = 0.001). LBL questions scored the highest point biserial and discrimination index. The percentage of correct answers and difficulty level was highest for TBL. CONCLUSION: Students preferred LBL over the other learning methods. Final exam psychometrics showed favorable results for LBL and TBL. Students could potentially benefit from combining both techniques. However, more research is needed to assess the effectiveness of various teaching methods on the short- and long-term learning outcomes.

Virtual self-care simulations for third-year pharmacy skills laboratory courses in three institutions.

BACKGROUND AND PURPOSE: This study was designed to determine whether a virtual, self-care activity improved knowledge and confidence in third-year student pharmacists. EDUCATIONAL ACTIVITY AND SETTING: Third-year student pharmacists (n = 386) from three institutions participated in the virtual self-care simulation during their respective practice laboratory course. A pre- and post-assessment collected 10 knowledge and five confidence questions, self-reported on 0-100 scale, mapped to learning outcomes and pharmacy standards. Responses for participants who provided consent and had linked assessments were analyzed. Additionally, students participated in a perception assessment following the simulation with the post-assessment. Each knowledge question was scored as binary (correct/incorrect), presented as percentage, and significance identified with a McNemar's test. Total knowledge score and confidence changes were presented as means with standard deviations and significance with a paired t-test. Student perceptions were presented as frequencies and percentages. FINDINGS: Total knowledge assessment demonstrated a significant improvement (p < 0.001) for the entire cohort of 198 study participants. Upon additional analysis, a single institution led the cohort to significant increase, with variable improvement and significance for each individual question. Confidence improved for the entire cohort of students and at each institution individually. The students perceived the virtual self-care activity favorably. SUMMARY: The third-year student virtual self-care activity improved knowledge and confidence with varying significance between institutions. Future studies will focus on the impact of continued reinforcement of self-care activities on student growth in knowledge and confidence.

Belongingness, Well-Being and an Interview With Dr Pauline Dow.

The Supreme Court decision that race conscious admissions policies in universities are unconstitutional has created questions and concerns about the sustainability of diversity, equity and inclusion programs in many organizations and across sectors. This editorial provides hopeful examples of how belongingness is being embraced as a core value in many organizations. In an interview with education expert, Dr Pauline Dow, we discuss ways that the education sector creates trust and belongingness and explore best practices and leadership development methods that allow teachers to thrive. Lessons learned in the educator sector offer insights into how belongingness can be cultivated in other sectors, organizations and communities.

From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product.

Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most informative based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.

Is everything everywhere? A hands-on activity to engage undergraduates with key concepts in quantitative microbial biogeography.

The ubiquity and ease with which microbial cells disperse over space is a key concept in microbiology, especially in microbial ecology. The phenomenon prompted Baas Becking's famous "everything is everywhere" statement that now acts as the null hypothesis in studies that test the dispersal limitation of microbial taxa. Despite covering the content in lectures, exam performance indicated that the concepts of dispersal and biogeography challenged undergraduate students in an upper-level Microbial Ecology course. Therefore, we iteratively designed a hands-on classroom activity to supplement the lecture content and reinforce fundamental microbial dispersal and biogeography concepts while also building quantitative reasoning and teamwork skills. In a class period soon after the lecture, the students formed three-to-five-person teams to engage in the activity, which included a hands-on dispersal simulation and worksheet to guide discussion. The simulation involved stepwise neutral immigration or emigration and then environmental selection on a random community of microbial taxa represented by craft poms. The students recorded the results at each step as microbial community data. A field guide was provided to identify the taxonomy based on the pom phenotype and a reference to each taxon's preferred environmental niches. The worksheet guided a reflection of student observations during the simulation. It also sharpened quantitative thinking by prompting the students to summarize and visualize their and other teams' microbial community data and then to compare the observed community distributions to the idealized expectation given only selection without dispersal. We found that the activity improved student performance on exam questions and general student satisfaction and comfort with the biogeography concepts. Activity instructions and a list of needed materials are included for instructors to reproduce for their classrooms.

Continuing professional development in general dentistry-experiences of an online flipped classroom.

INTRODUCTION: Continuing professional development is a lifelong learning process. One pedagogical approach that can be used is active learning. Flipped classroom is a method that has been shown to improve deeper conceptual understanding. In an online setting, the method saves travel, time, and costs. To our knowledge, flipped classroom is rarely used in continuing professional education. This study in general dentistry explored experiences of an online flipped classroom course in continuing professional development. MATERIALS AND METHODS: Fifteen dental hygienists, clinically active in general dental care, were interviewed. They were recruited from an online course on the latest classification system for periodontal diseases. The course had been conducted using an active learning and flipped classroom model. The interviews were semi-structured. Data were extracted using qualitative content analysis. RESULTS: The experiences of the dental hygienists could be summarized in three themes: Stimulation of knowledge gain through self-paced studies, The ease of virtual networking among colleagues, and Fostering of direct practical application through collaboration. CONCLUSION: New and emerging communication technology seems to open new possibilities for continuing professional development in general dentistry. Study participants felt that, in an online environment, mixing asynchronous and synchronous communication in a flipped classroom model facilitated learning in continuing professional development. Online active learning seems to work well in continuous professional development in general dentistry. After the course, the participating dental hygienists stated that they were able to use their new knowledge clinically and felt confident doing so.

The Inverted Case Study Technique: Changing the Traditional Case-Study Approach to Prioritize Physiological Mechanisms Over Correct Diagnosis and Treatment.

The traditional case study has been used as a learning tool for the past 100 years and in our program, graduate physiology students are presented with a real-world scenario and must determine the diagnosis and treatment of the patient. We found that students defaulted to memorization of disease with treatment and bypassed gaining an understanding of the mechanistic physiology behind disease and treatment. To adjust our student's approach, we developed a novel way to enhance student learning. In order to accomplish this shift from memorization to physiological mastery, we created the Inverted Case Study. This approach diverges from the traditional model in that students are given the diagnosis and treatment beforehand and are tasked with explaining the actual physiology of the case. In this way, students can no longer rely on the memorization of symptoms-disease-treatment, but rather gain a solid understanding of the physiological mechanisms of the disease since that is the focus of ICST. The inverted case study approach is an effective approach to apply and hone critical thinking skills.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

The effect of flipped learning on nursing students' Asepsis knowledge and learning skills: A randomized controlled study.

AIM: This study was conducted to evaluate the effect of the flipped learning model on nursing students' asepsis knowledge and learning skills. BACKGROUND: The flipped learning model enables students to pursue their learning with online support whenever and wherever they want. Students have the responsibility for their learning activities. The flipped learning model is an effective method to improve nursing students' knowledge and skills related to the principles of asepsis with online innovative approaches. DESIGN: This study has a pre-test post-test open-label, randomized controlled design. METHOD: The study sample consisted of 107 first-year nursing students randomized into experimental (n = 53) and control (n = 54) groups. The experimental group students were trained utilizing the flipped learning model. The data were collected through the "Descriptive Characteristics Form of Nursing Students ", the "Principles of Asepsis Knowledge Test" and the " Self-directed Learning Skills Scale". RESULTS: It was determined that the post-test knowledge score of the experimental group was statistically significantly higher (p=0.000) than the control group and the median of the retention test knowledge score was statistically significantly higher (p=0.000) than the control group. There was a statistically significant increase (p<0.05) in the median score of the self-directed learning skills scale "self-control" sub-dimension of the experimental group. CONCLUSION: Flipped learning increased nursing students' knowledge related to the principles of asepsis and enabled them to take responsibility for learning. This model had a positive effect on students' higher order thinking skills such as critical organization and decision making. It is recommended to use the flipped learning within the scope of nursing education and especially in gaining basic skills. TWEETABLE ABSTRACT: A success in today's education; flipped learning.

Source-free active domain adaptation for diabetic retinopathy grading based on ultra-wide-field fundus images.

Domain adaptation (DA) is commonly employed in diabetic retinopathy (DR) grading using unannotated fundus images, allowing knowledge transfer from labeled color fundus images. Existing DAs often struggle with domain disparities, hindering DR grading performance compared to clinical diagnosis. A source-free active domain adaptation method (SFADA), which generates features of color fundus images by noise, selects valuable ultra-wide-field (UWF) fundus images through local representation matching, and adapts models using DR lesion prototypes, is proposed to upgrade DR diagnostic accuracy. Importantly, SFADA enhances data security and patient privacy by excluding source domain data. It reduces image resolution and boosts model training speed by modeling DR grade relationships directly. Experiments show SFADA significantly improves DR grading performance, increasing accuracy by 20.90% and quadratic weighted kappa by 18.63% over baseline, reaching 85.36% and 92.38%, respectively. This suggests SFADA's promise for real clinical applications.