Dilution and Spectrophotometer Analysis of Blue Dextran - Assignment Example

Experiment report Name: Institution name: Course Code: Instructor’s name: Date of submission: Part A Title: Dilution and spectrophotometer Analysis of Blue Dextran Introduction This experiment tires to understand impacts of dilution of Blue Dextran when it is subjected to a series of dilutions with the focus in change in absorbability of the due at various concentrations (Bates, 2008). In addition, the effects of concentration of wavelengths are investigated by use of a spectrophotometer. Methods During this experiment, a serial dilution was performed which involved the use of 1% Blue Dextran in triplicate where 24 test-tubes were filled with 2ml of water and the tubes were labeled in triplicate followed by conducting a series of doubled dilutions from ½ to 1/128 where it was expected that the last tube would contain almost a negligible amount of the reagent (Bothamley & Boyle 2009). This was followed by thorough mixing of the contents of the test tubes by vortexing. 2ml was then transferred from first tube to the second in each series and the mix was subjected to vortex, followed by transferring second dilution tube to the next and continuing the mixing and diluting until the series was completed. Using a flat bottomed microtitre, the column was divided into three sections (1-4; 5-8; 9-12). Using a pipette, 100µl was transferred to three consecutively numbered wells in the section plate (Cameron, 2002). In the consecutive wells in row B, directly below the ½ dilutions, 100µl of each ¼ dilutions were added and the process was continued until row H was reached and each of the triplicate dilutions was transferred to the section of microtitre plate. The wells in the microtitre plate were examined using a microtitre plate reader and a 630nm filter was selected during the dilution process. Absorbance of each dilution replicate against a reagent blank of purified water was measured by use of cuvettes and spectrometer (Carter, 2008). Absorbance spectra were performed to determine the best wavelength at which the samples could be measured. The results were recorded and analyzed. Results Microtitre plate results were tabulated as shown in the table below. 1 2 3 A 1.018 1.014 1.102 B 0.548 0.571 0.580 C 0.288 0.274 0.311 D 0.152 0.169 0.178 E 0.101 0.103 0.104 F 0.062 0.063 0.064 G 0.049 0.050 0.050 H 0.034 0.033 0.033 Table 1. Microtitre results for the experiment The spectrophotometer readings for various solutions were as follows: A 0.00, B 0.067, C 0.133, D 0.273, E 0.538, F 1.096, G 1.989 and H 2.482. Graphically, the results were as shown in the figure below. Figure 1. Graphical representaion of spectrophotometer readings A linear grapgh that passes throgh the origin was obtsiend when a gragh pf absorbanec versus wavelength was drawn for the Blue Dextran solution. It was also found that absorbance of the Blue Dextran was low at low wavelengths and high at high wavelengths with a mximum absorption at a wavelenght of 630nm (Clarkson, 2014). Discussion and Conclusion Based on the fact that maximum absorption of Blue Dextran takes place at a wavelength of 630nm, it is important to set the wavelength of the solution at this wavelength during the process of studying its characteristics. There are factors that may impact the accuracy of this experiment such as lack of accurate calibration of measuring instruments and inaccurate eye positioning during the process of taking readings (Gussak, Antzelevitch & Wilde, 2008). Part B Title: A comparison of Body characteristics and heart rate Abstract This is an experiment that uses statistical methods of analyzing raw data to determine the relationship between various body characteristics and heart rate. The main body characteristics used to accomplish this objective includes age, weight, height, percentage body fats and Heart rate Index. The statistical methods used include determination of correlation between these body characteristics and heart rate (Froelicher & Myers, 2007). A better understanding of the population involved in the research is facilitated through determination of mean and standard deviations for each variable. This results into a conclusion that there is a correlation between body characteristics and heart rate. Introduction There has been increased interest to understand the effects of body characteristics such as age, Body mass index (BMI), percentage fats in the body on heart rate. This is because despite the significance of understanding the role of these factors in affecting heart rate, there are few publish materials which can make an elaborate argument concerning these claims (Hottenrott, 2007). This has resulted into the need to conduct an experiment to determine the role of these characteristics of heart rate so that a recommendation can be reached when these factors need to be regulated so that heart rate is not affected to the detriment of a person but to their benefits (Bray & Ryan, 2006). It involves a primary experiment where the collected data was analyzed and descriptive statistics used to determine the characteristics of the variables as well as their relationship to the research question. Hypothesis: There is a positive correlation between heart rate and body compositions age, BMI and percentage body fats Aims and Objectives The study was aimed at finding the correlation between various body characteristics and heart rate through data collection and scientific analysis of the data to come up with a conclusion regarding the correlations. Methods During this experiment, 53 students were selected for investigation of the variables of the research. The variables being measured were age, height, weight and percentage fats in the bodies of participants. In addition, heart rate index was measured in each of the participants. This was followed by analysis of descriptive statistics of the variables such as mean and standard deviation. It was possible to understand the changes in characteristics of the variables measured through the calculation of descriptive statistics (Jevon & Ewens, 2012). The variables of body weight and height were used to compute another variable of Body mass Index (BMI). This was achieved by use of the formula BMI= body mass (kg)/ height (m2) (Kaplow, 2007). In order to find the percentage fats in the bodies of the participants, skins folds were performed in the triceps and sub-scapular areas of the bodies of the participants. The formula used to determine the percentage body fats in women was: 0.55 triceps + 0.31 sub scapular + 6.13 (Macfarlane et al., 2011) while in the case of male participants, the formula used was =0.43 triceps + 0.58 sub scapula + 1.47. This was followed by allowing the participants to engage in a 3 minute work load where they were involved in bicycle riding, which resulted into a workload of 100watts. Hear rates were determined through observation of wrist pulses or necks during rest. The next activity involved calculation of heart rate index which was achieved by use of the formula HFI = (resting HR + end exercise HR + recovery HR at 1 min -200)/10 (O'Hara, 2007). Results In order to understand the changes in variables, descriptive statistics were calculated and the following results were obtained: Age M= 24.7 SD =8.0, Body weight M= 70.8 SD =12.2, Height M =1.7 SD= 0.1, BMI M= 23.4 SD = 3.2 and percentage Body fat M=16.8, SD 5.9. The graph below shows these results. Figure 2. descriptive statistics of participants in the study The values of Pearson correlation wera obtained as shown in the table below. % Body Fat Heart Rate BMI Age Body Weight % Body Fat 1 0.372** 0.459** 0.004 0.213 Heart Rate 0.372** 1 -0.05 -0.251 -0.145 BMI 0.459** -0.05 1 0.155 0.837 Age 0.004 -0.251 0.155 1 0.156 Body Weight 0.213 -0.145 0.837 0.156 1 ** Correlation is significant at the 0.01 level (2 tailed) Table 2. Correlation between variables Discussion and Conclusion The correlation coefficient values enabled understanding whether there was a correlation between heart rate and various variables. It was found that all the variables had either a positive or negative correlation with heart rate. This implies that their impact either contributed to an increase in heart rate or a reduction in heart rate (Olubummo, 2010). The variable of percentage body fats showed a positive correlation of 0.372** to the variable of heart rate. This implies that when fats content in the body is high, there is a corresponding increase in heart rate and the reverse is true for a decrease in fats content in the body (Timby, 2009). The variable of BMI had a negative correlation of -0.05 to the variable of heart rate. This implies that people with high BMI are more likely to have a reduction in heart rate while those who have low BMI are likely to have high heart rate. This explains why people with obesity are more likely to experience high heart rate (Cameron, 2002). This is because they have a high BMI. The variable of age also had a negative coefficient to heart rate with a correlation value of -0.251. This implies that when a person is young, the heart rate is high while when the person is old, the heart rate is low. This is because younger individuals are active and can take part in activities which increase their heart rates while older people are weak and cannot take part in activities which increase their heart rates (Carter, 2008). It was also found that the correlation between heart rate and body weight was negative with a value of -0.145. Thus, it can be explained that those who are heavy in weight or overweight are more likely to have a low heart rates while those who are light in weight are more likely to have a high heart rate (Carter, 2008). This is because weight is accompanies with low level of activity which does not increase heart rates while light weight is associated with the ability to take part in exercises which increase heart rate (Clarkson, 2014). References Bates, M. (2008). Health fitness management: A comprehensive resource for managing and operating programs and facilities. Champaign, IL: Human Kinetics. Retrieved from Bothamley, J., & Boyle, M. (2009). Medical conditions affecting pregnancy and childbirth. Oxford: Radcliffe Publishing. Bray, G. A., & Ryan, D. H. (2006). Overweight and the metabolic syndrome: From bench to bedside. (Springer e-books.) New York: Springer. Cameron, O. G. (2002). Visceral sensory neuroscience: Interoception. GB: Oxford University Press. Carter, P. J. (2008). Lippincott's textbook for nursing assistants: A humanistic approach to caregiving. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins. Clarkson, S. (2014). Fit for birth and beyond : A guide for women over 35. Wollombi: Exisle Publishing. Froelicher, V. F., & Myers, J. N. (2007). Manual of exercise testing. Philadelphia, Pa: Mosby Elsevier. Gussak, I., Antzelevitch, C., & Wilde, A. A. M. (2008). Electrical diseases of the heart: Genetics, mechanisms, treatment, prevention. London: Springer. Hottenrott, K. (2007). Training with the heart rate monitor. Aachen: Meyer & Meyer Verlag. Jevon, P., & Ewens, B. (2012). Monitoring the critically ill patient. Chichester, West Sussex: Wiley-Blackwell. Kaplow, R., (2007). Critical care nursing: Synergy for optimal outcomes. Sudbury, Mass: Jones and Bartlett. Macfarlane, P. W., van Oosterom, A., Pahlm, O., Kligfield, P., Janse, M., & Camm, J. (Eds.). (2010). Comprehensive electrocardiology. London: Springer. O'Hara, R. B. (2007). Effects of Increased Leg Resistance Training and Reduced Aerobic Training on Selected Physiological Parameters in United States Air Force Men and Women. ProQuest. Olubummo, A. (2010). Human anatomy and physiology: Study notes. S.l.: Iuniverse Inc. Timby, B. K. (2009). Fundamental nursing skills and concepts. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins. Read More