Tool For Comparison Research Articles

Hospital variation in quality indicators for patient blood management in total knee and hip arthroplasty: a retrospective cohort study

BackgroundAnaemia, blood loss, and blood transfusion are critical aspects of patient care in major orthopaedic surgery. We assessed hospital adherence to guideline-recommended Patient Blood Management (PBM) care, analysed variations between hospitals, and validated two composite indicators of hospital PBM performance in patients undergoing total knee arthroplasty (TKA) or total hip arthroplasty (THA). MethodsThis retrospective cohort study included all primary TKA and THA procedures performed during 2021 across 39 hospitals in Spain. We assessed hospital adherence to key guideline-recommended PBM interventions using nine individual quality indicators and two types of composite quality indicators (cQIs): opportunity-based (cQI1) and all-or-none (cQI2). We validated these cQIs by analysing their associations with the adjusted total transfusion index using linear regression. ResultsWe included 8561 patient episodes from 33 hospitals in the analysis. Delivery of PBM care was similar for TKA and THA. Patients received 62% of the analysed PBM interventions and only 12% of patients underwent the full PBM pathway. Higher hospital cQIs scores were associated with a lower adjusted total transfusion index, both in TKA and THA. The greatest association was found for cQI1 in THA patients (β=−1.18 [95% confidence interval −2.00 to −0.36]; P=0.007). ConclusionsHospital adherence to guideline-recommended patient blood management care in total hip and knee arthroplasty was suboptimal and varied across centres. Using data that are widely available in hospitals, quality indicators and composite scores could become valuable tools for patient blood management monitoring and comparisons between healthcare organisations.

How to map biomes: Quantitative comparison and review of biome‐mapping methods

AbstractBiomes are large‐scale ecosystems occupying large spaces. The biome concept should theoretically facilitate scientific synthesis of global‐scale studies of the past, present, and future biosphere. However, there is neither a consensus biome map nor universally accepted definition of terrestrial biomes, making joint interpretation and comparison of biome‐related studies difficult. “Desert,” “rainforest,” “tundra,” “grassland,” or “savanna,” while widely used terms in common language, have multiple definitions and no universally accepted spatial distribution. Fit‐for‐purpose classification schemes are necessary, so multiple biome‐mapping methods should for now co‐exist. In this review, we compare biome‐mapping methods, first conceptually, then quantitatively. To facilitate the description of the diversity of approaches, we group the extant diversity of past, present, and future global‐scale biome‐mapping methods into three main families that differ by the feature captured, the mapping technique, and the nature of observation used: (1) compilation biome maps from expert elicitation, (2) functional biome maps from vegetation physiognomy, and (3) simulated biome maps from vegetation modeling. We design a protocol to measure and quantify spatially the pairwise agreement between biome maps. We then illustrate the use of such a protocol with a real‐world application by investigating the potential ecological drivers of disagreement between four broadly used, modern global biome maps. In this example, we quantify that the strongest disagreement among biome maps generally occurs in landscapes altered by human activities and moderately covered by vegetation. Such disagreements are sources of bias when combining several biome classifications. When aiming to produce realistic biome maps, biases could be minimized by promoting schemes using observations rather than predictions, while simultaneously considering the effect of humans and other ecosystem engineers in the definition. Throughout this review, we provide comparison and decision tools to navigate the diversity of approaches to encourage a more effective use of the biome concept.

Sex difference in body image, exercise motivation and social comparison among Instagram users: a cross sectional study.

In the 21 st century, impact of social media, particularly Social Networking Sites (SNSs) has been linked to a wide range of human beliefs and expectations. Growing body of research has indicated that body image concerns along with exercise motivation and social comparison are on the rise among young adults. The present study aimed to examine the sex difference in body image, exercise motivation and social comparison among people who use Instagram in the age group 20-30. A total of 212 participants (men=106, women=106) aged 20-30 years, who are users of Instagram completed Body Self Image Questionnaire Short Form as a measure of Body image, Exercise Motivation Inventory - 2 as a measure of Exercise Motivation and Instagram as a Tool for Social Comparison as a measure of Social Comparison. Results showed that a significant difference in body image exist across gender with body image issues higher among females and significant difference in exercise motivation across gender with exercise motivation higher among males. No sex differences were seen in social comparison. It was concluded that body image concerns are higher among females and the drive for exercise is higher among males who used Instagram. It was found that body image concerns were higher among people who exercised regularly as well as among those who followed fitness related pages on Instagram as compared to those who did not. These results provide an insight into the sex differences between the variables and future directions can be aimed at conducting an in-depth analysis using body image, exercise motivation and social comparison.

P21 A Framework and Set of Tools for Graphical Comparison of the Sensitivity of Different Instruments for Health Utilities and Other Preference Based Values

P21 A Framework and Set of Tools for Graphical Comparison of the Sensitivity of Different Instruments for Health Utilities and Other Preference Based Values

Research Paper For Driving Mitra

Abstract: This project introduces a comprehensive online portal to alleviate the challenges associated with the manual search for suitable driving schools, instructors, and schedules. The portal aims to streamline the process for aspiring drivers by maintaining an extensive database of licensed driving schools and certified instructors, facilitating easy access based on user preferences and geographical proximity. Each driving instructor will have a detailed profile, enabling users to make informed decisions by considering qualifications, experience, and teaching style. Real-time reviews and ratings from past learners contribute valuable insights to guide prospective students. The portal incorporates a cost- comparison tool to empower users in evaluating fees across different driving schools and instructors, ensuring transparency and budget-conscious decision-making. Scheduling convenience is enhanced through integrated features that allow users to input preferred time slots, eliminating the need for manual inquiries.

Design an ESG Rating System: A Case Study of the Chinese Dairy Industry

The Environmental, Social, and Governance (ESG) rating system has become a crucial framework for evaluating companies, but its lack of standardisation poses challenges. This study presents a case study of the Chinese dairy industry to design a standardised ESG rating system. The proposed system comprises four levelsaspect, theme, information, and indicatorswith quantitative and qualitative performance indicators assigned weights based on their significance. Publicly available data from Chinese dairy companies are analysed to calculate weighted ESG scores, allowing for clear and comparable ratings. The case study focuses on environmental impact, social responsibility, governance standards, and negative aspects, offering insights into the industry's commitment to sustainability. The designed ESG rating system aims to address the current lack of standardisation, providing a tool for fair comparisons and guiding companies toward improvement. The case study underscores the importance of ESG considerations in a sector with significant environmental and social implications, contributing to more informed and responsible decision-making.

Homogeneous multi-payload antibody-drug conjugates.

Many systemic cancer chemotherapies comprise a combination of drugs, yet all clinically used antibody-drug conjugates (ADCs) contain a single-drug payload. These combination regimens improve treatment outcomes by producing synergistic anticancer effects and slowing the development of drug-resistant cell populations. In an attempt to replicate these regimens and improve the efficacy of targeted therapy, the field of ADCs has moved towards developing techniques that allow for multiple unique payloads to be attached to a single antibody molecule with high homogeneity. However, the methods for generating such constructs-homogeneous multi-payload ADCs-are both numerous and complex owing to the plethora of reactive functional groups that make up the surface of an antibody. Here, by summarizing and comparing the methods of both single- and multi-payload ADC generation and their key preclinical and clinical results, we provide a timely overview of this relatively new area of research. The methods discussed range from branched linker installation to the incorporation of unnatural amino acids, with a generalized comparison tool of the most promising modification strategies also provided. Finally, the successes and challenges of this rapidly growing field are critically evaluated, and from this, future areas of research and development are proposed.

Advancing Health Equity through 15-min Cities and Chrono-urbanism.

Implementing the 15-min city and chrono-urbanism aims to improve sustainability and quality of life by ensuring residents' proximity to essential services. The 15-min city model is gaining global traction, with localized adaptations to suit communities' needs. Beyond environmental motivations, 15-min cities can benefit public health through enhanced walkability, social cohesion, and universal accessibility. However, research examining the intersection of health and equity among chrono-urbanism and the 15-min city remains limited. This study aims to develop a framework to integrate health and equity into chrono-urbanism and 15-min city plans. We describe the potential benefits and risks of the 15-min approach for urban planning, daily behaviors, and health outcomes. Potential benefits of 15-min cities for health equity include proximity to destinations, increased physical activity, strengthened social capital, reduced emissions, and traffic calming. Risks that must be mitigated include gentrification, variable proximity definitions, infrastructure upgrades, and inadequate cultural sensitivity. Recommendations to integrate 15-min cities into planning activities include conducting comprehensive baseline assessments, aligning goals with sustainability, economic development, flexible zoning, inclusive public spaces, and diverse community engagement tactics. We recommend interventions targeting marginalized communities and developing standardized measurement tools for comparison, monitoring, and evaluation. A nuanced, equitable approach to implementing 15-min cities can help urban plans support health equity across diverse populations.

Eco-decisional well-being networks as a tool for community decision support.

Community decision making based on the sustainability of ecosystem services is an integrated process that involves multiple complex decisions and is greatly aided by an understanding of how those decisions are interrelated. The interrelatedness of decisions can be understood and even measured based on connections between actions and services and influence of services on domains of human well-being. These connections can be formed into a network structure so that quantifiable properties of networks can be applied to understanding decision impacts. We developed an eco-decisional network based on weighted social-ecological networks as a tool for integrated decision making based on ecosystem services and human well-being. Nodes are actions, services, or domains of human well-being and they are linked by weighted influence derived from community stakeholder input. Examination of the eco-decisional network, as well as comparison to pattern in the random networks, suggest there are important patterns of influence among different influence pathways from actions to community well-being, which describe community priorities and define unique roles through which chosen sets of actions can influence human well-being. The eco-decisional network is generalized across communities but can also be made community specific, which provides a tool for comparison between communities in decisional priorities (network properties), as well as comparisons between proposed actions within a community (network paths). The well-studied properties of networks, well-established network theory, as well as established network metrics make this approach promising for application to integrated decision making and for communicating possible outcomes to stakeholders. The result is a guidance tool for connecting propose actions to ecosystem services and human well-being.

Measuring General Health Literacy in Chinese adults: validation of the HLS19-Q12 instrument.

Health literacy measurement lays a solid foundation to identify associations with health outcomes and monitor population health literacy levels over time. In mainland China, most existing health literacy instruments are either knowledge-based or practice-based, making health literacy results incomparable between China and other countries. This study aimed to examine the reliability and validity of the 12-item Health Literacy Population Survey (HLS19-Q12) in a general population of Chinese adults. A cross-sectional study was conducted to recruit primary carers of students from 11 schools in Zhengzhou, Henan Province, using convenience cluster sampling. Participants completed an online self-administered survey that collected information on key sociodemographics, health literacy (HLS19-Q12 and a comparison tool: Health Literacy Questionnaire (HLQ)), and health-related outcomes. Using the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) checklist as a guideline, we tested internal consistency, test-retest reliability, content validity, structural validity, concurrent predictive validity, and convergent validity of the HLS19-Q12. Overall, 14,184 participants completed the full survey. The HLS19-Q12 showed excellent internal consistency (Cronbach's α = 0.93), moderate test-retest reliability (intra-class correlation coefficient = 0.54), satisfactory content validity (based on the 12-matrix health literacy model), and strong structural validity (comparative fit index = 0.94, Tucker and Lewis's index of fit = 0.93, root mean square error of approximation = 0.095). Concurrent predictive validity results showed health literacy was associated with both health determinants and health-related outcomes. The HLS19-Q12 had weak to strong correlations (coefficients = 0.24 to 0.42) with the nine scales of the HLQ. Respondents had an average score of 81.6 (± 23.0) when using the HLS19-Q12, with 35.0% and 7.5% having problematic and inadequate levels of health literacy, respectively. The HLS19-Q12 is a reliable and valid instrument to measure health literacy in our sample. Further validation is needed with a more nationally representative sample of Chinese adults. The HLS19-Q12 could be used as a comprehensive, skills-based, and easy-to-administer health literacy assessment tool integrated into population surveys and intervention evaluations.

Using theory knitting to conceptualize social phenomena in the design and evaluation of dance programs for people living with Parkinson’s disease

Dance programs for people living with Parkinson’s disease (PwPD) offer participants an opportunity to exercise, engage in artistic self-expression, and form new relationships. While it is understood that the social dimension of dance programs for PwPD contributes to dancer satisfaction and program sustainability, the social mechanisms instrumental to program success are under-examined. Engaging with theory from wider disciplines, or “theory knitting” can help program designers and evaluators examine the mechanisms and contextual factors that make classes socially meaningful with greater detail and specificity. This study identified and examined three theoretical frameworks that program planners and evaluators could use to conceptualize social engagement in dance for PwPD contexts and inform practice. Each theory was assessed for fit using the T-CaST theory comparison and selection tool developed by Birken et al. (2018). As an example, we used anthropologist Victor Turner’s (1970; 1977) theory of liminality and communitas to identify five key areas for fostering a sense of social connection in dance for PwPD contexts: (1) selecting a meaningful dance space (2) creating a joyous atmosphere (3) marking entrance into the liminal time and space with rituals (4) embodying liminality and anti-structure and (5) inverting power relations and embracing fluid roles.

Dynamic response of magneto-electro-elastic composite plates lying on visco-Pasternak medium subjected to blast load

This is the first endeavor to present a finite element approach to predict free oscillation and transient characteristics of magneto-electro-elastic (MEE) composite rectangular and elliptical plates resting on the visco-Pasternak medium in a hygro-thermal environment subjected to blast load. The variation of electric and magnetic potentials along the thickness direction of the MEE plate is determined via the Maxwell equation and magneto-electric boundary condition. The governing equations of motion for the MEE plate are obtained using refined higher-order shear plate theory and Hamilton's principle. Stiffness matrices, mass matrices, damping matrices, and force vectors of the plate are derived using a four-node quadrilateral element with eight degrees of freedom per node approximated using C1-order non-conforming Hermite and Lagrange functions. In addition, the Navier close-form solutions for rectangular plates with simply supported boundary conditions are used as a useful comparison tool for the numerical solutions. Dynamic response results of plates were obtained using Newmark's direct integration written by Matlab's programming. The accuracy of the method is verified through numerical comparison with confidence statements. The results show that the applied magnetic and electric potential highly inspires the transient responses. These results can be used in vibration control studies of structures and structures subjected to explosive or low-velocity impact loads.

Control System UV-C Lamp and Room Temperature Based Internet of Things

At the beginning of 2020, the world was shocked by the emergence of a new virus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-nCoV- 2) and the name of the disease is Coronavirus Disease 2019 (COVID-19). Common symptoms of COVID-19 are acute respiratory distress, fever, cough and shortness of breath. One of the efforts to overcome the transmission of COVID-19 is by sterilizing the room. This research designs an automatic room sterilization device using UV-C lamps that can be controlled using the Blynk application. There are 2 working principles of this tool, namely automatic, the lamp will turn on according to a predetermined setpoint and manual, the lamp will turn on manually like a normal lamp. Hardware used to make this tool are NodeMCU ESP8266, DHT11 sensor, PIR sensor, UV-C lamp, and Relay. As for the software used are Arduino IDE and Blynk Application. This research has 3 tests, the first is testing the lamp automation system where the UV-C lamp response is in accordance with the notification in the Blynk application. The second test is a human detection system with a PIR sensor where the sensor can detect with a maximum radius of 5m. The third test of room temperature monitoring system with DHT11 sensor with digital thermometer comparison tool. The average error value is 0.88% with the accuracy of the DHT11 sensor of 99.12%

Abstract 2338: Benchmarking long- and short-read somatic structural variation callers using a multi-technology panel of six tumor/normal cell lines

Abstract Better structural variation (SV) detection could improve cancer diagnosis, treatment and prevention. Yet many cancer genomics studies have only reliably detected single nucleotide variants and small indels due to technological limitations of short-read sequencing. While structural variants can be inferred from discordant short read pairs, as much as 70% of SVs remain undetected because of the mappability difficulties. This is especially true for SVs in regions where GC content is greater than 45% or regions which harbor tandem repeats and segmental duplications. Advances in long-read sequencing enabled direct observation of SVs with recall and precision over 95% across a larger portion of the genome. To harness this technology, we developed Severus - a long-read SV caller that provides phased detection of both simple germline SVs as well as complex rearrangements commonly found in cancer genomes. Such rearrangements are built into a breakpoint graph to reconstruct derived tumor chromosome structure. To benchmark Severus and other long-read SV calling methods, we sequenced six tumor/normal cell line pairs using Nanopore, HiFi, short-read and Hi-C sequencing technologies. We combined the calls from multiple tools and technologies to generate benchmarking sets of somatic SV for each tumor cell line, and evaluated short- and long-read tools against this benchmark set. Current tools for SV merging and comparison (such as truvari) were primarily designed for long indels, rather than breakends that are more common in somatic SVs. Another challenge is that different tools may represent the same variants differently in a VCF output. To enable robust somatic SV outputs comparison, we developed a tool called Minda that translates an input VCF entries into a unified representation, and then compares the calls in either pairwise or multi-tool mode. As expected, in our benchmarking of cell six tumor cell line pairs, short-read methods had substantially lower recall and precision, compared to long-read methods. Among the long-read methods, Severus consistently had substantially better recall and precision, compared to Sniffles2, nanomonsv and SAVANA. An analysis of publicly available long-read sequencing of a melanoma cell line (for which curated benchmark SV set is available) produced consistent results. Citation Format: Ayse Keskus, Asher Bryant, Tanveer Ahmad, Ataberk Donmez, Isabel Rodriguez, Nicole Rossi, Yi Xie, Byunggil Yoo, Rose Milano, Hong Lou, Jimin Park, Joshua Gardner, Brandy McNulty, Karen Miga, Mike Dean, Midhat Farooqi, Benedict Paten, Mikhail Kolmogorov. Benchmarking long- and short-read somatic structural variation callers using a multi-technology panel of six tumor/normal cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2338.

Non-contact Respiration Monitoring Using Bio- Radar Sensor Based on Linear Regression Classifier

Tuberculosis (TB) is an infectious disease that mainly attacks the lungs, caused by the bacterium Mycobacterium tuberculosis. To reduce its spread, hospitals use special rooms for TB patients and health workers follow strict Standard Operating Procedures (SOP). Recent advances in medical technology have led to the development of contactless respiratory monitoring techniques, such as bio-radar sensors that utilize the Doppler principle to detect lung movement. This research aims to explore the application of bio-radar sensors for contactless respiratory rate monitoring and then combine it with machine learning methods, specifically using linear regression algorithms, to translate bio-radar output into measurable respiratory rate values. By training a regression model using a processed raw data set to identify inspiration and expiration, where 1 is inspiration and 0 is expiration. To test the performance of the contactless breathing module, it was compared to a patient monitor. The module and comparison tool were run simultaneously with 10 measurement distance points for 10 patients or respondents with each distance point taken three times. The data that has been obtained from the results of comparisons between modules and comparison tools is entered into machine learning data analysis techniques, namely accuracy, precision and recall. The accuracy results were 74.9%, precision 71.4% and recall 83.3%. This research has proven that bio radar can be used to detect lung movement.

Fuzzy Logic Method to Control Evenly Distributed and Stable Waterbath Temperature with Four Heaters

Water baths are commonly used in scientific fields to incubate samples at specific temperatures. The temperature of the water bath must be controlled precisely, because even the slightest temperature variation can affect the results of the experiment. It can handle imprecise, uncertain and incomplete information, making it suitable for temperature control in water baths. This research aims to determine the distribution and stability of fuzzy logic control to control the temperature of a water bath with four heaters. Even heat distribution from the four heaters will ensure consistent water bath temperature throughout the bath. This research uses an Arduino microcontroller to process the temperature sensor output from the DS18B20, then the processed temperature value will be displayed on the TFT LCD. The independent variable in this research is the temperature value, while the dependent variable is the DS18B20 temperature sensor. The largest error value from the module measurements is at a temperature setting of 30 ºC on the 2nd temperature sensor with an error value of 1.43%. Meanwhile, the smallest error value is found at a temperature setting of 35 ºC on the 1st and 4th temperature sensors with an error value of 0.01%. Data collection used a digital thermometer comparison tool with 10 repetitions as a temperature sensor reference tool. The results obtained using this sensor are more stable and have a high accuracy value. The results of the research show that the temperature difference between points 1 to 4 when viewed from the error percentage is very small, or it can be said that the temperature distribution is even. The conclusion from these results is that the module has a stable temperature value and the error value is low and is still within the tolerance limit. permitted is ±5%.

Evaluating the strength of evidence of elemental profiling of polymers with LA-ICP-MS

LA-ICP-MS is a powerful technique for obtaining a forensic elemental profile of polymer evidence materials. However, the lack of homogeneous polymer reference standards hampers database creation and reliably matching a sample to a specific source. Therefore, the current study aims to evaluate the strength of evidence of forensic polymer comparisons by applying a matrix-matched reference standard with known concentrations for elements of interest. Four datasets of tapes, electrical wires, tubing, and jerrycans were compiled using LA-ICP-MS. It was found that quantification with the new PVC standard or by simply using the response of one element as internal standard significantly reduced the between-run variation. For each class of polymeric materials, characteristic elements could be identified with PCA and LDA. To facilitate classification, elemental concentrations were found to be typical for specific colors and types of polymeric materials. For forensic comparison, a score-based Bayesian likelihood ratio model and the t-test overlap method performed better than the feature-based model and 4-sigma criterion, in terms of rates of misleading evidence. Normalization to 13C and quantification with the PVC standard with and without prior normalization to 13C slightly reduced rates of misleading evidence. The t-test method showed an overall average false inclusion rate of only 0.45% and a false exclusion rate of 2.4%. Maximum calibrated likelihood ratios of 0.014 to 1778 were found for the tape dataset. In conclusion, this study demonstrates that with the use of proper standards, quantitative elemental profiling with LA-ICP-MS is a promising tool for forensic classification and comparison of polymers.

Analysis of maritime piracy trends and patterns using spatial autocorrelation in Africa

ABSTRACT Many studies have analyzed causation factors for piracy, but failed to quantitatively examine how piracy trends in regions have changed. The research aimed to establish the changing patterns of maritime piracy in Africa. The study utilized world piracy data from the National Geospatial Intelligence Agency, which was analyzed using spatial auto-correlation tools. Getis-Ord Gi* statistic identified piracy clusters and hot spot comparison tool compared the hot spot layers in East and West Africa. Autocorrelation analysis results suggest changing frequency of piracy incidents in the study areas before and after 2012. The mean Getis-Ord Gi* values for East Africa were 0.35946 and 0.07839 before and after 2012, illustrating agreater change. However, West Africa mean values were 0.60917 and 0.43408 pre and post 2012, suggesting minimal change. The comparison analysis results generated a smaller similarity value for East Africa (0.43350) and a larger index for West Africa (0.9867). West Africa recorded many incidents in the study period; hence, piracy intensity was almost similar. The study revealed high similarities in piracy events in West Africa, and low similarities in East Africa pre and post 2012. These have implications for understanding the changing dynamics of maritime piracy in Africa, and asserting spatial analysis.

Inspecting Pond Fabric Using Unmanned Aerial Vehicle-Assisted Modeling, Smartphone Augmented Reality, and a Gaming Engine

Historical farm ponds have been designed, maintained, and established as heritage sites or cultural landscapes. Has their gradually evolving function resulted in changes to the landscape influenced by their degenerated nature and the new urban fabric? This study aimed to assess the interaction between urban fabrics and eight farm ponds in Taoyuan by determining the demolition ratio of ponds subject to the transit-oriented development (TOD) of infrastructure and to evaluate land cover using historical maps, unmanned aerial vehicle (UAV)-assisted 3D modeling, smartphone augmented reality (AR), and a gaming engine to inspect and compare well-developed or reactivated ponds and peripheries. A 46% reduction in pond area around Daxi Interchange was an important indicator of degeneration in the opposite direction to TOD-based instrumentation. Three-dimensional skyline analysis enabled us to create an urban context matrix to be used in the simulations. Nearly 55 paired AR comparisons were made with 100 AR cloud-accessed models from the Augment® platform, and we produced a customized interface to align ponds with landmark construction or other ponds using Unreal Engine®. Smartphone AR is a valuable tool for situated comparisons and was used to conduct analyses across nine categories, from buildings and infrastructure to the intensity and stage of development. The gaming engine handled large point models with high detail and was supported by a customized blueprint. We found that 3D virtual dynamics highlighted the evolving interstitial space and role substitution of the agricultural fabric. This combination of heterogeneous platforms provides a practical method of preserving heritage and enables conflict resolution through policy and TOD instrumentation.

On the gamma radiation response of commercially available 3D printing materials for medical dosimetry

3D printing technology has rapidly spread for decades, allowing the fabrication of medical implants and human phantoms and revolutionizing healthcare. The objective of this study is to evaluate some radiological properties of commercially available 3D printing materials as potential tissue mimicking materials. Among fifteen materials, we compared their properties with nine human tissues. In all materials and tissues, exposure and energy absorption buildup factors were calculated for photon energies between 0.015 and 15 MeV and penetration depths up to 40 mean free path. Furthermore, the Geant4 Monte Carlo toolkit (version 10.5) was used to simulate their percentage depth dose distributions. In addition, equivalent atomic numbers, effective atomic numbers, attenuation coefficients, and CT numbers have been examined. All parameters were considered in calculating the average relative error (σ), which was used as a statistical comparison tool. With σ between 6 and 7, we found that Polylactic Acid (PLA) was capable of simulating eye lenses, blood, soft tissue, lung, muscle, and brain tissues. Moreover, Polymethacrylic Acid (PMAA) material has a σ value of 4 when modeling adipose and breast tissues, respectively. Aside from that, variations in 3D printing materials’ infilling percentage can affect their CT numbers. We therefore suggest the PLA for mimicking soft tissue, muscle, brain, eye lens, lung and blood tissues, with an infill of between 92.7 and 94.3 percent. We also suggest an 89 percent infill when simulating breast tissue. Furthermore, with a 96.7 percent infill, the PMAA faithfully replicates adipose tissue. Additionally, we found that a 59 percent infill of Fe-PLA material is comparable to cortical bone. Due to the benefits of creating individualized medical phantoms and equipment, the results might be seen as an added value for both patients and clinicians.