An investigation into equations for estimating water requirements and the development of new equations for predicting total water intake

The primary purpose of this study was to investigate agreement among five equations by which clinicians estimate water requirements (EWR) and to determine how well these equations predict total water intake (TWI). The Institute of Medicine has used TWI as a measure of water requirements. A secondary goal of this study was to develop practical equations to predict TWI. These equations could then be considered accurate predictors of an individual’s water requirement. ^ Regressions were performed to determine agreement between the five equations and between the five equations and TWI using NHANES 1999–2004. The criteria for agreement was (1) strong correlation coefficients between all comparisons and (2) regression line that was not significantly different when compared to the line of equality (x=y) i.e., the 95% CI of the slope and intercept must include one and zero, respectively. Correlations were performed to determine association between fat-free mass (FFM) and TWI. Clinically significant variables were selected to build equations for predicting TWI. All analyses were performed with SAS software and were weighted to account for the complex survey design and for oversampling. ^ Results showed that the five EWR equations were strongly correlated but did not agree with each other. Further, the EWR equations were all weakly associated to TWI and lacked agreement with TWI. The strongest agreement between the NRC equation and TWI explained only 8.1% of the variability of TWI. Fat-free mass was positively correlated to TWI. Two models were created to predict TWI. Both models included the variables, race/ethnicity, kcals, age, and height, but one model also included FFM and gender. The other model included BMI and osmolality. Neither model accounted for more than 28% of the variability of TWI. These results provide evidence that estimates of water requirements would vary depending upon which EWR equation was selected by the clinician. None of the existing EWR equations predicted TWI, nor could a prediction equation be created which explained a satisfactory amount of variance in TWI. A good estimate of water requirements may not be predicted by TWI. Future research should focus on using more valid measures to predict water requirements.^

Feminine Concepts of Leadership and Power: A New Framework for Development Ethics and Education Development

The last twenty years have been a period of growth in education development, development ethics, and female leadership studies. Literature indicates meaningful connections between these disciplines and points towards reassessment of obstacles to systemic change. A new term enpowerment is coined to define a proposed framework for ethical development practice.

Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

Positron Emission Tomography (PET) Tumor Segmentation and Quantification: Development of New Algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.