Practical Test Research Articles

Durable and deep response to CVD chemotherapy in SDHB-mutated metastatic paraganglioma: case report

IntroductionSuccinate dehydrogenase subunit B (SDHB)-mutated paragangliomas (PGLs) are rare neuroendocrine tumors characterized by increased malignancy, readily metastasizing, and poorer prognosis. Here we report a case of SDHB-mutated metastatic PGL, wherein the patient showed significant tumor shrinkage and complete symptom remission following chemotherapy. We aim to contribute additional evidence to the existing knowledge associated with SDHB-mutated PGLs.Case reportA 40-year-old male patient presented with recurrent hypoglycemia and hypertension crisis. Imaging revealed a huge left retroperitoneal tumor and multiple diffuse metastases in lungs. Catecholamine was also elevated, aligning with a diagnosis of metastatic PGL. Pathology also confirmed this diagnosis. Additionally, the immunohistochemistry indicated negative expression of SDHB and gene test showed somatic SDHB mutation. Given the SDHB mutation, cyclophosphamide-vincristine-dacarbazine (CVD) chemotherapy was initiated in critical conditions. Subsequently, a significant tumor shrinkage and complete biochemical response were observed after two treatment cycles. In September 2024, CT scan revealed new pulmonary lesions. The progression-free survival (PFS) with CVD chemotherapy was 24 months.ConclusionThis report reviews the distinct clinical and biochemical characteristics and treatment approaches of SDHB-mutated paragangliomas, emphasizing that the significance of incorporating both genetic testing and immunohistochemical analysis in clinical practice.

Prevalence Estimates of Cytochrome P450 Phenoconversion in Youth Receiving Pharmacotherapy for Mental Health Conditions.

Pharmacogenetics-predicted drug metabolism may not match clinically observed metabolism due to a phenomenon known as phenoconversion. Phenoconversion can occur when an inhibitor or inducer of a drug-metabolizing enzyme is present. Although estimates of phenoconversion in adult populations are available, prevalence estimates in youth populations are limited. To address this gap, we estimated the prevalence of phenoconversion in 1281 youth (6-24 years) receiving pharmacotherapy for mental health conditions and who had pharmacogenetics testing completed for four genes (CYP2B6, CYP2C19, CYP2D6, CYP3A4). Self-reported medication and cannabidiol/cannabis use were collected at the time of pharmacogenetics testing. Nearly, half (46%) of the cohort was estimated to be phenoconverted for one of the four genes examined. Comparison of metabolizer phenotype frequencies before and after adjustment for phenoconversion showed significantly more youth had actionable phenotypes for CYP2C19 (60.3% vs. 69.1%; P =< 0.001), CYP2D6 (49.3% vs. 63.0%; P =< 0.001), and CYP3A4 (8.5% vs.12.2%; P = 0.003) after phenoconversion adjustment. Of youth who were phenoconverted, 24% had a change in their metabolizer phenotype that would lead to current pharmacogenetics-based prescribing guidelines recommending a change to standard prescribing (dose adjustment, alternative medication). Our findings indicate a high prevalence of cytochrome P450 phenoconversion among youth receiving pharmacotherapy for mental health conditions. Adjustment for phenoconversion should be considered when implementing pharmacogenetics testing in youth populations to improve the clinical utility of this testing in practice.

Cardiorespiratory fitness (CRF) and its relationship with maximum oxygen consumption (VO2max) among 16-year-old junior players at Zakho football club

Background and Study Aim. Cardiorespiratory fitness (CRF) plays a crucial role in modern football performance, particularly in youth development where proper assessment and monitoring can significantly impact long-term athletic potential. While laboratory-based measurements of maximum oxygen consumption (VO2 max) are accurate, there is a need to validate practical field-based testing methods for youth players, especially in regions with unique environmental conditions like Iraqi Kurdistan. The present study investigated the relationship between Cardiorespiratory Fitness (CRF) and Maximum Oxygen Consumption (VO2 max) among 16-year-old football players at Zakho Football Club. Material and Methods. The descriptive correlational method was employed to examine the relationship between cardiorespiratory fitness (CRF) and maximum oxygen consumption (VO2 max) in 16-year-old junior football players. A random sample of twenty players was selected from a population of thirty-two players. The research utilized multiple assessment tools including the Cooper 12-minute test for CRF evaluation, heart rate monitoring through Garmin fēnix® 7 devices for measuring cardiovascular responses, and Cooper's formula for VO2 max estimation. Testing procedures were standardized and conducted at Dalal Sports Stadium during evening sessions to minimize environmental effects. Data analysis employed descriptive statistics and correlation coefficients. Results. The results revealed varying levels of cardiorespiratory fitness among players, with the majority demonstrating above-average performance levels. Statistical analysis showed a strong positive correlation between Cooper test performance and VO2 max values, validating the effectiveness of field-based testing for assessing cardiorespiratory fitness in Junior players. Heart rate response patterns indicated diverse cardiovascular adaptations across performance levels, suggesting the need for individualized training approaches. Conclusion. The findings contribute to understanding the relationship between field-based fitness assessments and physiological responses in Junior football, particularly in regions with unique environmental conditions. This research provides practical insights for optimizing Junior football development programs while highlighting the importance of considering individual adaptation patterns in training design.

Bounded Multivariate Contaminated Normal Mixture Model with an Application in Skin Cancer Detection

Introduction: In real-world datasets, outliers are a common occurrence that can have a significant impact on the accuracy and reliability of statistical analyses. Detecting these outliers and developing robust models to handle their presence is a crucial challenge in data analysis. For instance, natural images may have complex distributions of values due to environmental factors like noise and illumination, resulting in objects with overlapping regions and non-trivial contours that cannot be accurately described by Gaussian mixture models. In many real life applications, observed data always fall in bounded support regions. This leads to the idea of bounded support mixture models. Motivated by the aforementioned observations, we introduce a bounded multivariate cntaminated normal distribution for fitting data with non-Gaussian distributions, asymmetry, and bounded support which makes finite mixture models more robust to fitting, since rare observations are given less importance in calculations. Methods: A family of finite mixtures of bounded multivariate contaminated normal distributions is introduced. The model is well-suited for computer vision and pattern recognition problems due to its heavily-tailed and bounded nature, providing flexibility in modeling data in the presence of outliers. A feasible expectation- maximization algorithm is developed to compute the maximum likelihood estimates of the model parameters using a selection mechanism. Results: The proposed methodology is validated by conducting experiments on both simulated data and two real natural skin cancer images. We estimate the parameters by the proposed expectation-maximization algorithm. The obtained results shown that the proposed model has successfully enhanced accuracy in segmenting skin lesions. Conclusion: The reliable model-based clustering using finite mixtures of bounded multivariate contaminated normal distributions is introduced. An expectation-maximization algorithm was created to estimate parameters, with closed-form expressions utilized at the E-step. Practical tests on images for skin cancer detection showed enhanced accuracy in delineating skin lesions.

Analyzing and Designing Decision Support Systems for Stroke Patient Daily Treatment

Stroke is one of the neurological diseases that often requires complex management and treatment. Making the right decisions in diagnosing and treating stroke patients can have a significant impact on the recovery process and the quality of life for patients. To assist healthcare professionals and patients in making more informed decisions, this research has developed the "StrokeCare Navigator," a web-based Decision Support System (DSS) that utilizes the Decision Tree C4.5 algorithm. This system allows users, including healthcare professionals and patients, to input symptoms and patient medical information, providing an initial diagnosis of the type and severity of the stroke. Additionally, the system offers personalized treatment guidelines, treatment recommendations, and online patient monitoring services. The Decision Tree C4.5 model used in this system was developed through training with relevant patient data. Evaluation results indicate that the system can provide accurate and beneficial recommendations for healthcare professionals and patients. Testing and patient monitoring in clinical practice also demonstrate the potential to enhance stroke patient care. Therefore, StrokeCare Navigator is expected to be a valuable tool in stroke patient management and treatment, ultimately improving the prospects of patient recovery and providing better guidance for healthcare professionals. This research also provides deeper insights into the implementation of the Decision Tree C4.5 algorithm in the context of Decision Support Systems (DSS) in the medical field.

Naked eye three-dimensional teaching assistant system applied to undergraduate medical imaging education: A pilot study.

The traditional approach of using PowerPoint (PPT) presentations in medical imaging theory classes hinders the spatial thinking ability of most students. Consequently, the learning outcomes are often unsatisfactory. This article proposes a naked eye three-dimensional (3D) medical imaging teaching assistant app based on augmented reality (AR) technology to enhance learning interest, teaching interaction, and effectiveness. The control group consisted of 50 undergraduate students from the 2018 clinical medicine major who receive traditional teaching, while the experimental group includes 52 undergraduate students from the 2019 cohort who utilize an AR-based naked eye 3D teaching assistant app in addition to traditional teaching methods. Based on Bloom's cognitive learning taxonomy (Remember, Understand, Apply, Analyze, Evaluate, and Create), corresponding teaching curricula and assessment methods were designed in order to achieve more in-depth learning of the curriculum. The evaluation of the teaching effectiveness between the two groups relied on exam scores and student satisfaction questionnaires, with statistical analyses conducted using t-test and Mann-Whitney U-test in SPSS. The experimental group and control group showed statistically significant differences in the theoretical examination scores (62.06 ± 3.06 vs. 59.82 ± 3.38), practical testing scores (22.90 ± 2.35 vs. 21.06 ± 2.65), and total scores (84.96 ± 4.58 vs. 80.88 ± 6.01). Likert scores showed the experimental group scored significantly higher in enjoyment, satisfaction, participation, efficiency, and understanding. They also reported high convenience scores for the app and desired continued use. The naked eye 3D teaching assistant system is an innovative and effective teaching model for undergraduate medical imaging education, enhancing student interest, student interaction, and teaching effectiveness and promising future applications.

Knowledge, attitudes and demand toward cardiovascular polygenic risk testing in clinical practice: cross-sectional survey of patients.

The goal of this study was to assess patients' prior exposure and current level of knowledge of polygenic risk scores (PRSs). We also explored reactions to receiving a high-risk or low-risk score, and gauged the overall attitudes and demand patients have with regards to PRSs. We developed an online investigator-designed survey based on existing validated tools in genetic testing. There were two versions of the survey, one including a hypothetical high-risk PRS and one with a low-risk PRS. This survey was distributed to patients attending a cardiovascular clinic for primary or secondary prevention. A total of 226 participants responded to the survey. 177 patients (79%) had not read nor heard about polygenic testing. 209 patients (93%) had never discussed polygenic testing with their health care professional (HCP). 208 patients (93%) had never received polygenic testing. The average score on the knowledge quiz was 2.47/10 [95% C.I. (2.17, 2.78)]. Participants that received a high-risk survey scored 20.52/35 [95% C.I. (16.14, 24.9)] with regards to negative emotions while low-risk survey participants scored 17.96/35 [95% C.I. (13.98, 21.94)] (p < 0.001). Participants that received a high-risk survey scored 12.42/15 [95% C.I. (10.43, 14.41)] for demand and low-risk survey participants scored 12.22/15 [95% C.I. (9.66, 14.78)] (p = 0.549). Patients have limited prior exposure and knowledge of PRSs. Compared to receiving a low-risk score, participants receiving a high-risk score have more negative emotions and feelings of uncertainty. Despite the lack of knowledge, and the high rate of negative emotions and uncertainty, demand for PRSs in cardiology practice is high.

Enhancing Surface Finish in FDM 3D Printing: The Impact of a Spherical Ironing Tool

This study aims to evaluate the functionality and effectiveness of a new tool for ironing 3D printed components using FDM technology. The ironing process draws inspiration from the ball burnishing process, employing a spherical tool that, by exerting pressure, modifies the component's surface. The process involves heat transfer between the tool and the component to soften and deform the surface more easily. A theoretical analysis of the resulting surface roughness following this process was conducted to identify the parameters at play and their influence. It was found that the two main parameters are the fillet radius and the distance between passes. Additionally, practical tests were carried out to confirm the hypotheses, revealing the influence of three other parameters: tool Z-axis positioning, temperature, and movement speed. The surface after treatment exhibits an average roughness of Ra = 0.378 μm, significantly better than values obtained with ironing performed with the nozzle and with quality comparable to grinding. This lays the groundwork for possible future optimization of parameters based on the component's characteristics and the material used.

“Are we saying every time you drink, you’ll get cancer?” Lessons learned from a focus group study of communicating difficult-to-swallow health-risk information

This essay explores lessons learned when conducting focus group interviews with participants exposed to novel health-risk messages. Focus group participants exposed to new health-risk information might be defensive, affecting the quality of the data collected. Hence, accounting for potential participant defensiveness is of great importance for researchers who are developing and testing these health-risk messages. In this essay, I identify two forms of defensiveness that emerged in my focus group study evaluating cancer warning labels on alcoholic beverages: (1) actively counterarguing the health-risk message and (2) repeatedly modifying the health-risk message. I also offer four “lessons learned” to improve health-risk message testing in qualitative research practice: (a) communicate with empathy, (b) use personal stories, (c) forewarn participants, and (d) offer a self-affirmation opportunity. Overall, this essay contributes to the development of a typology of defensive strategies that focus group participants may use when exposed to novel health-risk messages. Additionally, it provides a methodological framework for guarding the integrity of the data and climate of the focus groups. These insights are valuable for health communication researchers and practitioners interested in conducting focus groups to assess health-risk messages.

International consensus statement on microbiome testing in clinical practice.

There is growing interest in the potential exploitation of the gut microbiome as a diagnostic tool in medicine, but evidence supporting its clinical usefulness is scarce. An increasing number of commercial providers offer direct-to-consumer microbiome diagnostic tests without any consensus on their regulation or any proven value in clinical practice, which could result in considerable waste of individual and health-care resources and potential drawbacks in the clinical management of patients. We convened an international multidisciplinary expert panel to standardise best practices of microbiome testing for clinical implementation, including recommendations on general principles and minimum requirements for their provision, indications, pre-testing protocols, method of analyses, reporting of results, and potential clinical value. We also evaluated current knowledge gaps and future directions in this field. We aimed to establish a framework to regulate the provision of microbiome testing and minimise the use of inappropriate tests and pave the way for the evidence-based development and use of human microbiome diagnostics in clinical medicine.

Experience with artificial intelligence algorithms for the diagnosis of vertebral compression fractures based on computed tomography: from testing to practical evaluation

BACKGROUND: Osteoporosis is often diagnosed at the stage with complications, i.e., low-energy fractures. Vertebral compression fractures, which are complications of osteoporosis and predictors of subsequent fractures, are often asymptomatic. Compression fractures can be found by computed tomography performed for other indications with vertebral morphometry. Approaches to using artificial intelligence algorithms designed for diagnosing vertebral compression fractures were analyzed. AIM: Testing artificial intelligence algorithms to conduct morphometric analysis of vertebrae on chest computed tomography scans and assess the possibility of their implementation in medical organizations of the Moscow Healthcare Department. MATERIALS AND METHODS: To set a clinical task for artificial intelligence algorithms, basic diagnostic requirements in the area of “vertebral compression fractures (osteoporosis)” were formulated. The testing of the artificial intelligence algorithms included the following stages: self-testing, functional and calibration testing, practical evaluation, and operation testing. The first three stages of testing were performed using previously generated datasets. At practical evaluation and operation testing, artificial intelligence algorithms analyzed the data from computed tomography performed in medical organizations. The expert group of radiologists assessed the diagnostic accuracy and functional capacity of the AI algorithms at all stages. The resulting quantitative metrics of the accuracy of artificial intelligence algorithms were compared with the required target values. RESULTS: From June 2021 to June 2022, two artificial intelligence algorithms (Nos. 1 and 2) with different methods of detecting compression fractures were tested. Both artificial intelligence algorithms successfully passed the self-testing (6 tests), functional (5 tests), and calibration (100 tests) stages. The area under the ROC curve for artificial intelligence algorithm No. 1 was 0.99 (95% CI, 0.98–1), and for artificial intelligence algorithm No. 2, it was 0.91 (95% CI, 0.85–0.96). Artificial intelligence algorithm No. 1 passed the practical evaluation stage without any significant remarks, whereas algorithm No. 2 was sent for fine-tuning. After the operation testing stage, the following accuracy metrics were obtained: the areas under the ROC curve for artificial intelligence algorithm Nos. 1 and 2 were 0.93 (95% CI, 0.89–0.96) and 0.92 (95% CI, 0.90–0.94), respectively. At all stages, both artificial intelligence algorithms demonstrated sufficient metrics for clinical validation. CONCLUSION: Artificial intelligence algorithms for the automatic diagnosis of vertebral compression fractures have been tested, demonstrating the high quality of their operation. artificial intelligence algorithms can be applied as a supplementary tool in the medical decision support system.

Nature loofah network‐inspired rapid thermal‐conductive and thermochromic‐based nanocomposites for efficient heat management in advanced electronic devices

AbstractThe development of 5G technology has raised concerns about the heat buildup in microelectronic components, which can hinder device performance and integration. Inspired by the structure of loofah, we designed thermally conductive substrates using polydimethylsiloxane (PDMS) and graphene oxide (GO) incorporated into copper foam (F‐Cu). We also created nano‐thermal management composites with organic thermochromic material‐GO/PDMS/F‐Cu. The composites had a thermal conductivity of 1.02 Wm−1 K−1 with 15 wt% content of (35‐, 45‐, and 65‐) organic thermochromic materials (OTM), showing efficient heat dissipation. Practical testing showed significantly improved cooling effects compared to pure copper foam, with a CPU temperature drop of 9.6°C. Additionally, the surface color changes at specific temperature thresholds, offering an innovative way to determine the temperature range of electronic devices. This material also provides a solution for monitoring the temperature of densely integrated electronic devices.Highlights Enables temperature visualization in microelectronics. The nanocomposite achieves a value of 1.08 Wm−1 K−1. Nanocomposites predict heat source temperature via thermochromic coating. The cyclic test exhibits commendable stability and reliability.

Comprehensive Review of Laboratory Science and its Impact on Medical Diagnostics

The development of molecular bioscience and diagnostic laboratories is the key to current healthcare schemes and patient management. Increased improvements in laboratory technologies such as molecular diagnostics, immunoassay, and automated laboratory systems have improved negativity, shortened the turnaround time, and facilitated early disease diagnosis. This extensive analysis assesses the historical development and contemporary stances of laboratory science, diagnostic technologies, and the role of laboratory tests in present-day medical practice. Also, problems like the test's accuracy, the cost of the test, and the incorporation of laboratory information into clinical practice are addressed. This review then provides recommendations that can enhance laboratory services' operations and their interaction with health systems for the benefit of patients.

Development of a STEM-based digital learning space platform to enhance students’ mathematical creativity in future learning classrooms

This study focuses on developing a web-based digital learning space (DLS) designed to support a STEM learning model that fosters students’ mathematical creativity. The DLS builds upon traditional learning management systems by introducing customizable elements tailored to individual teaching and learning environments (PTEs and PLEs). These features allow for the formation of diverse learning networks within the DLS. This enhanced version integrates frameworks for STEM learning, engaging activities, instructional videos, quizzes, evaluations, feedback tools, and a chat feature for direct communication with instructors. The research and development process employed a 4D model–define, design, develop, and disseminate–and involved validation from content and media experts, confirming the DLS’s reliability for broader testing. Results indicate that the STEM-based DLS is highly suitable for mathematics education, as shown by validation from experts, practicality tests from users, and effectiveness tests that measured students’ mathematical creativity. This study also found moderate improvement in students’ creative mathematical thinking (Ng = 0.554) within classrooms using the STEM-based DLS, highlighting its effectiveness for enhancing these skills. Thus, this STEM-focused DLS offers a mobile-friendly digital environment that effectively promotes students’ mathematical creativity.

A Fast flatness deviation evaluation algorithm for point cloud data

This paper proposes and develops a novel method, namely the Partially Iterative Algorithm (PIA), for high-speed assessment of flatness deviation for point cloud data, which is typically measured data obtained by advanced instruments for precision manufacturing, such as optical scanners and industrial computed tomography. Firstly, an enhanced flatness deviation model is established based on the minimum zone principle, which is strictly adhered to the latest ISO definition. Secondly, the proposed method is detailed, including the Dynamic Point Set (DPS), the update scheme, and the terminal condition. Thirdly, comparisons are conducted with typical methods for flatness deviation assessment, along with a practicability test via the simulated dataset and measuring dataset. The results show that the proposed methods can accurately and rapidly assess flatness deviation on point cloud data with massive measuring points.

The feasibility of using the go/no-go task as a dementia screening test assessed with a cross-sectional design

The Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery are screening tests for dementia. The go/no-go task offers an alternative approach for evaluating dementia patients. However, its role in screening for dementia remains unclear. We aimed to explore the feasibility of using the go/no-go task as a screening test for dementia via a cross-sectional design. Twenty-four Japanese individuals were evaluated using the go/no-go task, the MMSE, and the MoCA. The total MMSE and MoCA scores were correlated with the total number of errors in the go/no-go task (r=-0.699, p < 0.01; r=-0.756, p < 0.01). Moreover, When the MoCA cutoff value was 25 for MCI, the optimal cutoff score for the total number of error in the go/no-go task to detect MCI was 2, with an Area Under curve (AUC) of 0.98, a sensitivity of 0.94. When the MMSE cutoff value was 27 for MCI, the optimal cutoff score for the total number of error in the go/no-go task to detect MCI was 6, with an AUC of 0.89, a sensitivity of 0.76, showed respectively values close to 1. The go/no-go task is possible a practical screening test for dementia.

Unravelling impacts of the insecticide deltamethrin on neuronal sodium channels in honey bees: Molecular insights and behavioural outcomes

The current risk assessment framework for insecticides suffers from certain shortcomings in adequately addressing the effects of low doses on off-target species. To remedy this gap, a combination of behavioural assays and in vitro cellular approaches are required to refine the precision of toxicity assessment. The domestic honey bee has long been standing as an emblematic pollinator in ecotoxicology, and once more, it provides us with a practical testing model for this purpose. First, newly emerged bees (D1) were found more vulnerable than 6 days-old bees (D6) to deltamethrin, a widely used α-cyano-3-phenoxybenzyle pyrethroid. In D1 bees, the range of doses inducing mortality was shifted towards lower values (∼2-fold) with a correspondingly lower LD50 (11 ng/bee). Moreover, at low doses that do not induce mortality in laboratory conditions, the locomotor behaviour of D1 bees was more impacted than in D6 bees. This was evidenced by an increase in immobility time and a decrease in locomotor performance across all tested doses for D1 bees (0.75, 1.5 and 3 ng/bee) during automated 21 h-long observations. Behavioural disorders are linked to deltamethrin's disruption of voltage-gated sodium channels (NaVs) functions, as quantified in cultured neuronal cells. In the presence of deltamethrin, patch-clamp experiments revealed a concentration- and a use-dependent slowing of NaV kinetics. Channel's deactivation is slowed by three orders of magnitude at 10 μM deltamethrin. Two additional phenoxybenzyle pyrethroids, including the commonly used cypermethrin, elicited quantitatively similar effects on NaV kinetics. The integration of in vitro cellular assays and behavioural assays may facilitate a deeper understanding and prediction of insecticides toxicity.

Virtual Reality in Algorithm Programming Course: Practicality and Implications for College Students

Virtual Reality (VR) is one of the revolutionary technologies in education that can simulate learning interactively and realistically. The reliability of VR supports various variations in learning, including learning programming algorithms. This research aims to design and develop VR in learning programming algorithms. The ADDIE model is applied in this study to develop products through the stages of analysis, design, development, implementation, and evaluation. The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. Therefore, this VR learning design integrates various elements of students' learning styles, including visual, audiovisual, kinesthetic, and reading. The product developed is in the form of a VR application that presents learning by having a variety of learning styles. Audiovisual displays are presented in a VR environment, while kinesthetic interactions are obtained through user movements, and material information and readings are presented in a VR environment. The data collected included the results of a practicality and effectiveness test involving sixty-six students. The results of the practicality test show that this VR product is practical to use based on the indicators of convenience, attractiveness, efficiency, and benefits. Meanwhile, the effectiveness test shows that this VR product can improve learning outcomes better than traditional learning methods. Overall, the VR products developed have proven to be practical and effective in learning programming algorithms. The presence of VR, which presents a variety of realistic displays in a virtual environment, opens opportunities for further research related to distance learning through VR technology as well as an analysis of the impact of VR on health.

Development of A Sensor-Based Tool for Measurement of Sensor-Based Capturing Skills

This research aims to develop a tool for measuring catching-throwing skills using modern, effective, and efficient sensor technology. This tool is designed to increase accuracy in measuring catch-throwing skills. This type of research uses (R&amp;D). This research involved three experts in their field: an electrical expert, an evaluation and measurement expert, and a physicist. A total of 30 participants participated in testing in a small group of 12 people and a large group of 18 people with an age range of 15-20 years. Testing is carried out with questionnaires and direct tests. The statistical analysis used is r-correlation with the help of the SPSS application. The test results show a high alpha coefficient, confirming that this tool is consistent and reliable in collecting data. Three experts carried out practicality testing: Electrical Experts, Evaluation and Measurement Experts, and Physicists. The results show that this tool is efficient, with practicality levels of 98%, 92%, and 89%, respectively. In addition, an excellent level of effectiveness is demonstrated by expert assessment results, which reach 99%, 89%, and 85%. With this tool, researchers and teachers can evaluate object control skills more efficiently and accurately and assist in collecting more comprehensive data. Overall, this tool for measuring throwing and catching skills provides significant innovation and is expected to support research and education in measuring and developing children's motor skills.

Development of Learning Tools Based on the Culturally Responsive Transformative Teaching Model with a Socio-Scientific Issues Approach: Evaluation of Validity and Practicality

The importance of conducting this research is based on observations indicating that the implementation of the Kurikulum Merdeka on the theme of local wisdom has been limited to Sabtu Budaya (Cultural Saturday) activities, without further integration into the learning process, particularly in the development of learning tools. This research aims to develop a learning tool based on the CRTT model with a SSI approach that is both valid and practical. The type of research employed is Research and Development (R&amp;D), with a 4D development design limited to the define, design, and develop stages. The development stage is restricted to validation and practicality testing. Data collection techniques included expert validation sheets, student and teacher response questionnaires, and observation sheets for the implementation of learning activities. Data analysis techniques used Aiken's V validity index, Percentage of Agreement, and percentage method. The results of the content and construct validity tests indicate that the learning tool based on the CRTT model with the SSI approach is classified as highly valid, with the assessment agreement among validators being reliable. The results of the practicality test show that student responses had an average of 89.02%, categorized as practical, teacher responses were 92.81%, categorized as practical, and the implementation of learning activities reached 85.71%. These results indicate that the learning tool based on the CRTT model with the SSI approach is both valid and practical for use in the learning process.