Index Of Agreement Research Articles

Development, Validation, and Diagnostic Accuracy of the Fetal Lack of Responsiveness Scale for Diagnosis of Severe Perinatal Hypoxia.

Background: There are limitations to predicting perinatal asphyxia, as current tools rely almost entirely on fetal cardiotocography (CTG). The fetal lack of responsiveness scale (FLORS) is a new diagnostic alternative based on the physiological phenomena associated with fetal hypoxia. Objectives: The objective of this study was to develop, validate, and assess the diagnostic accuracy of the FLORS for predicting severe perinatal hypoxia (SPH). Study Design: A two-phase retrospective observational cross-sectional analytical study was conducted. Phase 1 involved the formulation and retrospective validation of the FLORS. A total of 366 fetal CTG records were evaluated twice by seven readers. Phase 2 was a collaborative, retrospective, multicenter diagnostic test study that included 37 SPH and 366 non-SPH cases. Results: Phase 1: A numeric, physiology-based scale was developed and refined based on expert opinions. The median time to apply the scale per reading was 38 s. Cronbach's alpha, which is a reliability measure, was significant (p = 0.784). The kappa index for test-retest agreement was moderate to reasonable, with a median value of 0.642. For interobserver agreement, the kappa index per variable was as follows: baseline, 0.669; accelerations, 0.658; variability, 0.467; late/variable decelerations, 0.638; slow response decelerations, 0.617; and trend to change, 0.423. Phase 2, including 37 SPH and 366 non-SPH cases, showed a sensitivity of 62.2% and specificity of 75.4% for the 2-point score, whereas the 3-point score had a sensitivity of 35.1% and specificity of 89.9%. The area under the curve (AUC) was significant at 0.73 (CI 0.645-0.818). Conclusions: FLORS demonstrated significant internal consistency and observer agreement, with a promising sensitivity-specificity balance and significant AUC. Further research is needed to assess its impact on perinatal hypoxia and cesarean delivery.

Construção de uma escala de avaliação do ambiente de trabalho na atenção primária à saúde

Abstract Objective To describe the stages of constructing a scale to evaluate the work environment in Primary Health Care. Method methodological study with five stages: establishment of the conceptual structure; construction of questions and response scale; structuring; content validity with experts and semantic analysis with health professionals. Results construction of the conceptual structure with literature review, the analysis of researchers, experts and health professionals finalized the scale with 36 questions. The work environment is influenced by working conditions, administration and management issues, worker health, workloads, appreciation and motivation, violence and strategies for a healthy work environment. The Content Validity Index (CVI) and Percentage of agreement were performed, with values of 0.96 (CVI) and 96% agreement, respectively. Conclusions and implications for practice the scale was developed and showed agreement, according to the content validity test by experts and health professionals. Thus, the scale is capable of being used for other validation process and can contribute to the practice of health researchers in assessing the work environment.

Reliability and Validity of Gujarati Version of Nomophobia Questionnaire: A Cross-sectional Study

Introduction: Nomophobia is considered a modern-age phobia whereby individuals fear being without a smartphone. The present study focuses on translating the Nomophobia Questionnaire (NMP-Q) into Gujarati to measure nomophobia. Aim: To assess the reliability and validity of the Gujarati version of the NMP-Q, aiming to enhance research, public health initiatives and clinical assessments. Materials and Methods: This cross-sectional study was conducted from May 2023 to November 2023 at the Department of Physiotherapy, Vidhyadeep Institute of Physiotherapy, Surat, Gujarat, India and comprised two phases: 1) Face and content validation through expert clinical review; 2) Test-retest reliability. Face and content validity of the Gujarati NMP-Q were determined using the consensus method with a total of 200 participants aged between 15-18 years. To assess the reliability of the Gujarati NMP-Q, internal consistency and test-retest reliability were evaluated. Test-retest reliability was determined using the Intraclass Correlation Coefficient (ICC) and internal consistency was calculated using Cronbach's alpha. Results: A total of 200 participants (mean age 17.60±1.40 years) were included in the study. The Gujarati version of the NMP-Q demonstrated excellent test-retest reliability, indicated by a high ICC (0.986) and high internal consistency (0.972). The values for Content Validation Ratio (CVR), Item-level Content Validation Index (I-CVI), Modified Kappa (K) and Proportion of Agreement were all 1. Conclusion: The Gujarati version of the NMP-Q exhibits excellent reliability and good face and content validity. It is deemed adequate and valuable for assessing the level of fear of being without a smartphone in daily life among Gujaratispeaking school students.

Application of Decision Support Expert Systems for Improved gasoline yield in Refinery Catalytic Cracking

The Catalytic Cracking Unit (CCU) stands out as the primary factor responsible for enhancing gasoline production in the refinery. This paper provides a deep insight on the complexity of the factors within this unit, explore their broader implications, and offer a rationale for their technical significance. A survey approach involving the use of Principal Component Analysis (PCA) facilitated by StatistiXL software package and Kendall Coefficient of Concordance (KCC) to measure agreement among the respondents was employed. A total of 32 variables were identified, and questionnaires were developed using Rensis Likert's 5-point scale. These questionnaires were then administered to 100 respondents. Prior to this step, the Kendall Coefficient of Concordance was utilized to establish the sequential order of importance among these identified factors. The results obtained showed an index of agreement among the judges in ranking the variables W, as 0.54, the null hypothesis of disconcordance among the judges was rejected at a p-value of 0.01. The study identified 14 significant variables based on the KCC results, including Catalytic Cracking Charge, Mid Riser Temperature, Riser Inlet Temperature, Riser Outlet Temperature, and Stripper Level. Additionally, Principal Component Analysis (PCA) effectively condensed the set of 32 variables into four manageable clusters. The integration of data analytics techniques, such as KCC and PCA, alongside expert opinions and collected data enabled the assessment of the influence of independent variables on gasoline yield and the categorization of these variables. This study offers valuable insights for both academia and the industry, especially in the context of the Catalytic Cracking Unit.

Modelos alométricos para estimativa não destrutiva da área de folíolos da umbu-cajazeira (Spondias sp.)

ABSTRACT The umbu-cajazeira (Spondias sp.) is a fruit tree native to the semi-arid of Brazil, with significant environmental and agro-socioeconomic potential for the region. Determining leaflet area (LA) is essential for understanding the interactions between plant growth, physiology, nutrition, and environment. Thus, this research aims to construct allometric equations that non-destructively estimate the umbu-cajazeira (Spondias sp.) leaflet area, considering the leaflet dimensions (length and width). The leaflets were collected from mother plants of umbu-cajazeira (Spondias sp.) and individually measured for length (L), width (W), and LA using digitized images. These data were submitted for descriptive and regression analysis. The LA was estimated using, linear power, and exponential regression models based on the L and W of the leaflets. The best model and equation were chosen based on the following selection criteria: coefficient of determination, Willmott agreement index, root mean square error, and mean absolute error. The power model using LW presented the best equation ŷ = 0.76 * LW0.98 to estimate the leaflet area of umbu-cajazeira (Spondias sp.). This study provides a reliable, accurate, fast, and non-destructive approach for agronomic researchers and growers to determine the LA of the species.

Construction and validation of an interprofessional simulated scenario for the identification and management of sepsis.

Build and validate a realistic interprofessional simulation scenario for the identification and management of sepsis by doctors and nurses. Methodological study developed in 2021, in two stages: construction of the guide and content validation by expert judges; and development of the simulation and validation of the scenario by doctors and nurses. 15 specialists participated in the research in the first stage and eight care professionals in the second stage. 0.8 was adopted as a parameter for the Content Validation Index (CVI). The scenario guide addressed nosocomial sepsis with an abdominal focus in an adult patient and its validation obtained a total CVI of 0.97. All areas evaluated in the simulation scenario obtained agreement indices greater than 0.8. The construction and validation of the guide allowed the elaboration of guiding material for the development of an interprofessional simulated scenario, whose execution and validation process demonstrated its suitability in approaching the identification and management of sepsis by doctors and nurses. It is recommended to carry out future research evaluating its applicability to other situational contexts.

Assessing Kombucha: A Systematic Review of Health Effects in Human

Objective: To assess the new clinical evidence regarding the efficacy and safety of Kombucha for human health.Background: Kombucha is one of the most popular and rapidly expanding functional beverages globally. It has gathered significant attention, primarily due to its claimed health benefits, such as enhancing the immune system and potentially possessing antidiabetic properties.Methodology: Systematic review was performed following PRISMA 2020 guidelines without a meta-analysis. The aim was to analyze recent literature (within the past six years) on health publications and examine the trails of human benefits on kombucha consumption. Both authors independently conducted a comprehensive review and reached a conclusion. Tools were used to calculate Kappa agreement index between authors and create a PRISMA flowchart to assess study quality.Results: Five significant bibliometric reviews and four new human trials were identified. The initial trial examining the impact of Kombucha on intestinal microbiota showed neutral/negative results in healthy individuals. However, in a second microbiota trial, Kombucha significantly improved symptoms among patients with irritable bowel syndrome. Moreover, two recent clinical trials on diabetes strongly suggest that Kombucha enhances carbohydrate metabolism, indicating a potential antidiabetic effect for diabetic individuals. Nevertheless, it is important to note that these results should be considered a positive preliminary pilot assay due to the limited number of patients.Conclusions: Our research has shown that there is still controversy over the health benefits of Kombucha. Although there have been some limited clinical trials, the results are often contradictory, making it difficult to determine the positive effects of Kombucha on human health. The purpose of this review is to assess the conflicting information on the health benefits of consuming Kombucha.

Michigan appropriateness guide for intravenous catheters in pediatrics - miniMAGIC-Brasil: translation into Brazilian portuguese.

To perform the translation and adaptation of the Michigan Appropriateness Guide for Intravenous Catheters in Pediatrics (miniMAGIC) into Brazilian Portuguese. Methodological study performed in five recommended stages: initial translations; synthesis of the translations; back translations; assessment of the back translations; expert committee assessment. The expert committee was composed of three registered nurses and two doctors who had a Master's and/or PhD degree, and an expertise in intravenous therapy and pediatric and neonatal care. To assess the semantic, idiomatic, experiential and conceptual adequacy, a Likert scale was applied, in which 1, "not equivalent"; 2, "inequivalent"; 3, "cannot assess"; 4, "quite equivalent"; 5, "totally equivalent". The terms mostly analyzed as negative in equivalence and with a lower than 20 score were reviewed and submitted to a new assessment, with the Delphi Technique until consensus was obtained. The results were stored in electronic spreadsheets and treated with concordance index, with a minimum acceptable result of 0.80. The content of all recommendations, named as miniMAGIC-Brasil, was validated by the expert committee after two stages of evaluation. All recommendations had an overall agreement index of 0.91. The miniMAGIC-Brazil guide was validated in respect to the adequacy of the translation after two steps.

Estimation and Investigation of Photosynthetically Active Radiation Using Meteorological Parameters over Ikeja, Nigeria

Photosynthetically active radiation (PAR; 0.4 - 0.7μm) is a key driver in ecosystem biochemical processes, and thus a critical factor in agriculture productivity, ecosystem-atmosphere energy, and CO2 fluxes. In this study, the measured monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature dataset was utilized to estimate and investigate the Photosynthetically Active Radiation (PAR) for Ikeja (latitude 6.58 N, longitude 3.33 E and 39.4 m above sea level) located in the coastal region ofNigeria. The meteorological parameters used in this work were obtained from the archives of National Aeronautics and space Administration (NASA) for a period of thirty-eight years (1984-2021).The newly developed temperature PAR based models were statistically tested using the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test and index of Agreement (IA) to ascertain the accuracy and applicability of the models. The results show that, the highest and lowest values of PAR were found during the rainy season with 8.3426 MJ and 6.4936 MJ in the months of April and July respectively. Based on the developed models, the model equation (16g) is the most suitable for estimating PAR for Ikeja with R2, MBE, RMSE, MPE, t-test and IA values of 80.9 , 0.0062 MJ , 0.3415 MJ -0.1974 0.0601 and 92.8712 respectively.

Quantifying the spatial water salinity threshold of saline water irrigation by applying distributed WAVES model

CONTEXTSaline water irrigation presents a viable solution to alleviate freshwater scarcity. However, it's crucial to consider the threshold of salinity, as excessive levels can adversely affect crop yield, crop productivity and soil sustainability. Additionally, due to varying surface conditions and management practices at the regional level, a distributed approach to saline water irrigation management is necessary. OBJECTIVEThe primary goal of this research was to develop a regional agro-hydrological crop model. The validated regional model was then employed to identify the spatial water salinity threshold for saline water irrigation. METHODSIn this research, we utilized the WAVES (WAter Vegetation Energy and Solute model), which was adapted for mulching environment and validated in severely arid and saline fields. To account for spatial variations, we applied a distributed method to upscale the WAVES to the regional level. Multisource and multiscale data were used for regional model validation. Different scenarios of salinity levels for saline water irrigation were simulated, and a comprehensive criterion to determine water salinity threshold was proposed considering cotton yield, water productivity and soil salt accumulation. RESULTS AND CONCLUSIONSModel evaluation indicated a high level of accuracy for the developed regional model. The comparison between simulated and measured values for soil salt content, soil water content, cotton leaf area index, measured lint yield, and statistical yield resulted in indices of agreement of 0.90, 0.71, 0.93, 0.58, and 0.70, respectively. The validated regional model was employed to simulate various saline water irrigation scenarios. The findings revealed that under identical irrigation water salinity, cotton cultivation on sandy loam soil is more productive but has a larger yield reduction percentage compared to silty loam soil. Moreover, the research indicates a groundwater depth of 1.5 m is associated with higher cotton production. Additionally, a spatial pattern of the irrigation water salinity threshold for saline water irrigation was determined, suggesting that the water salinity in most areas of the Tarim Irrigation District should not exceed 6 g L−1. Notably, in the current practice of pumping groundwater in situ for irrigation, approximately 46% of the areas are at risk, indicating that it's not suitable for saline water irrigation. SIGNIFICANCEThis study developed a distributed model under a typical irrigation district characterized by mulching, severe drought and salinization and defined the distributed spatial threshold for saline water irrigation. These research results hold significant implications for guiding regional-scale saline water irrigation and groundwater extraction.

Modelling crop yield in a wheat–soybean relay intercropping system: A simple routine in capturing competition for light

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to several ecosystem services. However, modelling intercropping is challenging due to intensive parameterisation, complex calibration, and experiment scarcity. To facilitate future understanding, design and adaptation of intercropping, it is therefore necessary to develop simple modelling routines capable of simulating essential features. In this paper, we integrated a light competition module requiring four parameters into MONICA, a generic agroecosystem model, with the goal of simulating a wheat–soybean relay-row intercropping system. We tested three calibration approaches using data from two years of field experiments located in Müncheberg, Germany: sole cropping-based calibration, intercropping-based calibration and a default calibration method that incorporates both systems. Under both irrigated and rainfed conditions, MONICA successfully reproduced the aboveground biomass and yield of sole crops from field experiments, with RMSEA ranging from 0.64 t ha−1 to 2.74 t ha−1 and RMSEY ranging from 0.003 t ha−1 to 0.47 t ha−1. By taking light competition into account, the modified MONICA was able to simulate interactive performance in relay-row intercropping. Generally, MONICA overestimated the aboveground biomass and yield across the three calibration strategies, and simulations for wheat were more accurate than those for soybean. However, a comparison among the calibration strategies revealed that the intercropping-based strategy outperformed the others. It significantly improved the model efficiency for soybean yield in intercropping, increasing the Index of Agreement from 0.27 to 0.73, and it decreased the Mean Bias Error for yield by up to 76%. Our results demonstrate the feasibility of using a model that is simple in both calibration and inputs, yet detailed enough to simulate the complex aboveground light competition of intercropping. Additionally, they underscore the significance of cropping system specific calibration, highlighting the importance of calibrating crop performance specifically for intercropping in order to capture genotype-by-environment interactions.

Modelling the dispersion of particulate matter (PM10) via wind erosion from opencast mining-Moldova Nouă tailings ponds, Romania.

Historically, Romania is known as a mining site of mineral substances, including gold, silver, copper, lead, zin, uranium, manganese, salt, and coal, whereby their long periods of exploitation and extraction affected human health and the environment in various ways. In Moldova Nouă southwest region of Romania, we investigated the environmental impacts of mining activities on air quality over 2021. We quantified PM10 emission rates through in situ monitoring, dispersion modelling, and horizontal and vertical fluxes. Statistical metrics, including the fraction within factor 2 (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized mean gross error (NMGE), coefficient of efficiency (COE), index of agreements (IOAs), and Taylor diagram signifying standards deviation (SD), root mean squared error (RMSE), and correlation coefficient (R), were used to evaluate the reliability of modelling results against observation. Results conclude that PM10 dispersion agrees with MB, MGE, NMB, NMGE, COE, IOA, and Taylor diagram and moderately with FAC2 metrics. PM10 hotspot was investigated in the vicinity of the tailings ponds of 115.5µgm-3 annual mean, 563.7µgm-3 daily mean, 63.3µgm-2s-1 annual horizontal flux, and 3.0µgm-2s-1 annual vertical flux. PM10 dispersion was identified to expand to Moldova Nouă City and nearby country Serbia. Findings concluded that a windy air mass accumulation across the overburdened dumps and ponds causes the increase of PM10 in the air, resulting in the region's pollution. Therefore, results recommend adopting a strategic mitigation measure for residents, policymakers, stakeholders, and urban planners.

CROPGRO-soybean model – Validation and application for the southern Amazon, Brazil

Agricultural models are useful tools for predicting phenology, biomass, and crop yield under different environmental conditions during the growth period. Notwithstanding their use in the Southern Cone of Rondônia (southern Amazon, Brazil) is still limited. Thus, the objective in this work was to calibrate and validate the CROPGRO-Soybean model with soybean data collected in harvests of 2017 to 2019, being the data from the first growing period devoted to model calibration, and crop data from 2018/2019 to model validation. Six soybean cultivars were planted at the municipality of Vilhena, State of Rondônia, Brazil. The model was parameterized using climatic, hydric and physical soil properties, and growth data of the evaluated soybean cultivars. To evaluate the accuracy of model prediction we used the coefficient of determination (r2), percentage difference (Pd), root mean square error (RMSE), and Willmott’s index of agreement (d-value). The model accurately simulates the vegetative and reproductive stages, and the yields of the soybean cultivars under the climatic conditions of the Southern Cone of Rondônia. However, the CROPGRO-Soybean model tended to overestimate soil moisture content especially in upper soil layer (0.0–0.1 m depth). Taking the observed data, as the baseline, the model predicted the time to flowering, first pod, seeding, and physiological maturity, with a discrepancy of just one to seven days. In most of the soybean cultivars (five of the six cultivar evaluated), the differences between observed and simulated yields (growing cycle of 2018/2019 – validation harvest) ranged from –6 % to (underestimation) to 27 % (overestimation).

Modeling the Water Quality of the Middle Section of the Northern Great River (Dissolved Oxygen and Water Temperature) Using WASP8 Model

This study aims at to apply the WASP8 model on the middle section of The Great Northern River for the purpose of predicting the changes of both water temperature and the dissolved oxygen. At first, the model was calibrated depending on the field values that measured throughout the study period. The determination parameters and the index of agreement between the estimated values using the model and the actual measuring values showed the possibility of using the model to predict the tow studied indicators. The values of the determination parameter ranged from 0.76 to 0.86 in terms of the dissolved oxygen and regarding water temperature it was over 0.86 in the different studied sites. The values of index of agreement for all sites and the studied indicators ranged from 0.92 to 0.96. It was observed that the water of The Great Northern River is of a good quality in terms dissolved oxygen where the values ranged from 7 to 14 mg/L.

Artificial Neural Networks versus Multiple Linear Regressions to Predict the Christiansen Uniformity Coefficient in Sprinkler Irrigation

The Christiansen Uniformity Coefficient (CUC) describes the distribution of water in a sprinkler system. In this study, two types of models were developed to predict the Christiansen Uniformity Coefficient (CUC) of sprinkler irrigation systems: Artificial Neural Network (ANN), specifically the feed-forward neural networks, and multiple linear regression (MLR) models. The models were trained on a dataset of published research on the CUC of sprinkler irrigation systems, which included data on a variety of design, operating, and meteorological condition variables. In order to build the predictive model of CUC, 10 input parameters were used including sprinkler height (H), working pressure (P), nozzle diameter (D and da), sprinkler line spacing (SL), sprinkler spacing (SS), wind speed (WS), wind direction (WD), temperature (T), and relative humidity (RH). Fifty percent (50%) of the data was used to train ANN models and the remaining data for cross-validation (25%) and for testing (25%). Multiple linear regression models were built using the training data. Four statistical criteria were used to evaluate the model’s predictive quality: the correlation coefficient (R), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). Statistical analysis demonstrated that the best predictive ability was obtained when the models (ANN and MLR) utilized all the input variables. The results demonstrated that the accuracy of ANN models, predicting the CUC of sprinkler irrigation systems, is higher than that of the MLR ones. During the training stage, the ANN models were more accurate in predicting CUC than MLR, with higher R (0.999) and d (0.999) values and lower MAE (0.167) and RMSE (0.456) values. The R values of the MLR model fluctuated between 0.226 and 0.960, the d values oscillated from 0.174 to 0.979, the MAE values were in the range of 2.458% and 10.792%, and the RMSE values fluctuated from 2.923% to 13.393%. Furthermore, the study revealed that WS and WD are the most influential climatic parameters. The ANN model can be used to develop more accurate tools for predicting the CUC of sprinkler irrigation systems. This can help farmers to design and operate their irrigation systems more efficiently, which can save them time and money.

Nurseped: tecnologia educacional para a segurança no manejo de antibióticos endovenosos em pediatria

Objective: to develop and validate the content of a serious game on the safe management of intravenous medications in pediatrics. Method: methodological study for the development and content validation of an educational technology. The cases and challenges of the serious game were developed based on a literature review and validated by 11 nurses with training and experience in the area. Content validity and agreement indices were adopted to analyze agreement and internal consistency (minimum of 0.8). Results: the content is based on the main antibiotics used in the clinical management of infections in hospitalized children and patient safety. Absolute agreement was obtained in 60 of the 61 items evaluated, and the minimum obtained was 0.82 in the content validation index and 0.80 in agreement. Adjustments were suggested by experts in the response statement for a specific case and implemented to improve the quality of the technology content. Conclusion: the content of the serious game Nurseped was validated by nurse experts in child health regarding clinical cases, question statements and multiple-choice answers, in addition to feedback that presents the user with an evidence-based answer after getting the challenge right or wrong.

Avaliação do aplicativo móvel “Descomplicando a Amamentação” por familiares de recém-nascidos

Objective: to evaluate the semantics, appearance and usability of the mobile application “Descomplicando a Amamentação” for family members of newborns. Method: applied methodological study, developed with 20 family members of newborns users of educational technology, including pregnant women, postpartum women and family members of newborns. An evaluation instrument containing questions about semantics and appearance was applied, in addition to the System Usability Scale to evaluate usability. In data analysis, the Agreement Index was used, with a cutoff point of 0.8 (80%). Results: when evaluating semantics and appearance, the application achieved a Global Agreement Index of 0.99 (99%), varying from 0.95 (95%) to 1.0 (100%) between the items evaluated. In usability, a global average of 93 was achieved, presenting the best usability achievable in all system characteristics. Conclusion: the technology was considered understandable, relevant, and efficient, as well as easy to use and low inconsistency with high user satisfaction, demonstrating excellent potential for families.

Nurseped: educational technology for safety in the management of intravenous antibiotics in pediatrics.

to develop and validate the content of a serious game on the safe management of intravenous medications in pediatrics. methodological study for the development and content validation of an educational technology. The cases and challenges of the serious game were developed based on a literature review and validated by 11 nurses with training and experience in the area. Content validity and agreement indices were adopted to analyze agreement and internal consistency (minimum of 0.8). the content is based on the main antibiotics used in the clinical management of infections in hospitalized children and patient safety. Absolute agreement was obtained in 60 of the 61 items evaluated, and the minimum obtained was 0.82 in the content validation index and 0.80 in agreement. Adjustments were suggested by experts in the response statement for a specific case and implemented to improve the quality of the technology content. the content of the serious game Nurseped was validated by nurse experts in child health regarding clinical cases, question statements and multiple-choice answers, in addition to feedback that presents the user with an evidence-based answer after getting the challenge right or wrong.

Deep Learning patterns of Aβ accumulation in Alzheimer’s Disease

AbstractBackgroundAccumulation of amyloid (Aß) plaques is an early pathologic change of Alzheimer’s Disease (AD). However, the mechanisms and pathways by which amyloid spreads across the cerebrum are not fully understood. Deriving distinct dimensions of amyloid deposition and their associations with biomedical factors could be useful to understand how amyloid propagates. In this analysis, we identified two distinct subtypes of progression based on spatiotemporal variations using a recently developed data‐driven, deep learning clustering method called Surreal‐GAN1.MethodWe used data from 482 A+ and 801 A‐ subjects with 18F‐florbetapir PET from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; N = 832) and 11C‐PiB from the Preclinical AD consortium (PAC; N = 451). Standardized uptake value ratio (SUVR) maps (cerebellar GM reference) were transformed to reference space. We derived amyloid status using Gaussian mixture modelling of mean total cortical SUVR for each of 4 sites. We then applied non‐negative matrix factorization2 on the SUVR maps to identify data‐driven common patterns of deposition across the sample. Next, we applied Surreal‐GAN on a total of 1283 subjects to determine the amyloid spatiotemporal subtypes. Correlations with clinical and cognitive variables were evaluated.ResultsParticipants were 30%/22% female and had mean age 74.9 (±7.4)/ 67.9(±9.1) years for ADNI and PAC, respectively. We identified 24 components from NMF (Fig 1A) after excluding 6 as non‐target features. Surreal‐GAN showed optimal agreement indices for two patterns. While both patterns shared involvement of posterior cingulate/precuneus and inferior frontal cortex, pattern r1 shows relatively more occipital deposition and pattern r2 shows more frontal deposition (Fig 1B), as seen when comparing strongly r1 vs strongly r2 participants using voxel‐based morphometry (Fig 2). Both were associated with worse global cognitive function and worsening neurodegeneration (Table 1). r1 correlates with white matter hyperintensity volume, a measure of cerebrovascular disease, while pattern r2 is associated with the presence of APOE e4 alleles.ConclusionEmploying a data‐driven deep learning‐based approach, we derived more than one dissociable pattern of amyloid deposition, potentially differently influenced by vascular disease (r1/occipital) and genetics (r1/frontal). These findings could inform identification of subgroups of individuals who may or may not be responsive to particular clinical intervention.

Dosimetric impact of CT metal artifact reduction for spinal implants in stereotactic body radiotherapy planning.

Metal artifacts due to spinal implants may affect the accuracy of dose calculation for radiotherapy. However, the dosimetric impact of metal artifact reduction (MAR) for spinal implants in stereotactic body radiotherapy (SBRT) plans has not been well studied. The objective of this study was to evaluate the dosimetric impact of MAR in spinal SBRT planning with three clinically common dose calculation algorithms. Gammex phantom and 10 patients' computed tomography (CT) images were studied to investigate the effects of titanium implants. A commercial orthopedic MAR algorithm was employed to reduce artifacts. Dose calculations for SBRT were conducted on both artifact-corrected and uncorrected images using three commercial algorithms [analytical anisotropic algorithm (AAA), Acuros XB (AXB), and Monte Carlo (MC)]. Dose discrepancies between artifact-corrected and uncorrected cases were appraised using a dose-volume histogram (DVH) and 3-dimensional (3D) gamma analysis with different distance to agreement (DTA) and dose difference criteria. The gamma agreement index (GAI) was denoted as G(∆D, DTA). Statistical analysis of t-test was utilized to evaluate the dose differences of different algorithms. The phantom study demonstrated that titanium metal artifacts can be effectively reduced. The patient cases study showed that dose differences between the artifact-corrected and uncorrected datasets were small evaluated by gamma index and DVH. Gamma analysis found that even the strict criterion local G(1,1) had average values ≥93.9% for the three algorithms. For all DVH metrics, average differences did not exceed 0.7% in planning target volume (PTV) and 2.1% in planning risk volume of spinal cord (PRV-SC). Statistical analysis showed that the observed dose differences of MC method were significantly larger than those of AAA (P<0.01 for D98% of PTV and P<0.001 for D0.1cc of spinal cord) and AXB methods (P<0.001 for D98% and P<0.0001 for D0.1cc). Dosimetric impact of artifacts caused by titanium implants is not significant in spinal SBRT planning, which indicates that dose calculation algorithms might not be very sensitive to CT number variation caused by titanium inserts.