Side Effects of Tumor Size Reducing Drugs | Experiment

Side Effects of Tumor Size Reducing Drugs | Experiment Manish Kumar Tiwari 1. Introduction: Objective: Pfizer have developed a new drug that appears to reduce the size of specific tumors but are concerned about what effect the drug might have on normal tissue. Outline how you would use DNA technology to address this issue. Cancer disease has large complexities in terms of genome variations at genetic level and epigenetic level. Immortalization and tumor genesis are the two fundamental characteristics of cancerous cells. This disease is caused by mutations in genes such as oncogenes, DNA repairing genes and tumor suppressor genes. Recent researches suggested that more than one mutations are needed for the cancers. One of the major drawbacks of the medicines or drugs that are used to treat cancer is its side effects on normal cells. The cells which are mostly affected by drugs are rapidly dividing cells such as blood cells, hair follicles cells, cells found in tract of reproductive organ and digestive system, and cells from immune system. Side effects on normal cells due to chemotherapy has become major challenges for researchers. Transcriptome or protein expression profiling for cancerous cells treated with specific drugs may provide useful information about possible side effects on normal cells. When a ny drugs or medicines are given for the treatment of any specific tumor disease, it binds with specific receptors (cell surface receptors, Cytoplasmic receptors or nuclear receptors) and leads to transcription and translation process and generate specific proteins that can be able to stop the cell cycle or initiation of apoptosis. But generally these drugs may also responsible to translation of unwanted proteins that can cause side effects on normal tissues. 2. Approach: Mode of action of many drugs that reduces the size of tumor are related to growth cycle (like mutagen, MAP kinase pathway) or DNA modifications (transcription, translation etc.). This method is more suitable for in vivo testing in rats or mammalian cancerous cell lines which has been described here. The added tumor size reducing drug must bind with specific receptors on the tumor cells. So first step is the identification of pathway via which it acts. In the downstream signaling of the pathway, some transcription factors will be activated and will bind to target promoter and reduces the size of tumor. So then transcription factors need to be quantified by qPCR as well as the sequence of their promoter through DNA Foot Printing. Now two plasmids need to be constructed (minimum two plasmids, if there are more transcription factors and promoters, more plasmids with different fluorescent proteins are needed) containing above identified promoter coupled with red fluorescent protein (RFP) and containing a tumor inducible promoter coupled with green fluorescent protein (GFP). Now for in vivo testing, mutant mouse are created and transfected with above two plasmids. During the growth, the known tumor inducing compounds/radiation is given to the mouse to induce the tumor. As the drug is added, it will cause induction of RFP through the body but level may be higher in tumor cells but GFP should be induced only in the tumor cells. If GFP is induced in other normal cells it means that this drug may cause side effect on that cells. A fluorescent mapping of mouse will reveal the efficacy and side effects of drug based on RFP and GFP intensity.  Ã‚   Figure 1: Schematic presentation of approach. The image of mice is taken from internet which has been used to explain the method. 3. Method: This method is suitable for in vivo testing in mice or mammalian cells culture. The main steps include quantification and identification of transcription factors and promoter sequences respectively, construction of suitable plasmids coupled with red and green fluorescent proteins, transfection of plasmids in mice body, tumor induction in mice body followed by drug injection and last fluorescent mapping using fluorescent detector. The instruments and techniques which will be used in this methods are qRT-PCR, DNase Foot Printing assay, Suitable plasmids vector, microinjections, Chemicals, fluorescent proteins (red and green), capillary electrophoresis, tumor inducing cells or chemicals or radiation and fluorescent detector. Validation of this method is important so validation could be possible by using this method for any known drug which side effects on normal cells has been identified completely. 3.1. Quantification of Transcription Factor: The exact quantification of transcription factor is the most important part of this method. Micro array or PCR is the good technique for quantification of transcription factors but in this method qPCR/QRT-PCR will be appropriate technique. First step is isolation of cancer cells from mammalian cancerous cell lines. Then inject target anti-cancer drug and incubate for some time because these drugs takes some time to start their function. After proper incubation, total or poly A RNA extraction is the next step. The solution which is used in extraction process should be RNase free otherwise it can degrade our RNA so that exact quantification could not be possible. Sample should be treated with DNase to remove genomic DNA contamination. Flow Chart 1: Steps involved in quantification of Transcription factors Electrophoresis and qPCR methods could be used for determination of purity and accurate concentration because these factors are very important for proper gene expression profiling. Then C DNA synthesis and validation of C DNA quality and quantity could be done by using qRT-PCR. For performing qRT-PCR assay there are two important steps such as selection of appropriate reference genes and designing of PCR primer labeled with fluorescent dye must be needed. For data analysis fluorescent detector can be used to detect transcription factors and their associated genes. Now once genes have been identified by using above method so the identification of their promoter sequence DNA Foot Printing assay will be performed. 3.2. Identification of Promoter Sequence: DNase Foot printing assay method can be used to identify target promoter sequence. Steps involved in this method is amplification of target DNA through PCR using fluorescent labeled primer at 5’ end. Then cleavage of the amplified DNA by using DNase enzyme followed by the capillary electrophoresis. The cleavage pattern will vary due to the presence of transcription factor, because the binding sites are protected by the protein from the cleavage. By using this method we can identify the promoter sequences. By using capillary electrophoresis we can identify the amount and size of DNA fragments and about the bases which are not cleaved by the DNase enzyme. Figure 2: Identification of promoter sequences through DNA Foot Printing assay. The graph between amount and size of DNA fragments in this figure is showing the bases which are protected by the transcription factor against DNase enzyme. 3.3. Construction of Suitable Plasmids: Construction of suitable expression vectors for mammalian cells, that can carry the desired promoter sequence coupled with fluorescent protein must be needed. The most important characteristics of vectors is presence of all elements that is suitable for expression in host cells. The important elements are promoter, stop and start codon, binding sites for ribosome, ORI region and appropriate selection markers. Some examples of vectors like adenoviral, PSV and pCMV are generally used for expression in mammalian cells. In this method, our expression vectors should contain promoter sequence labeled with red and green fluorescent protein and other important elements. Minimum two type of plasmid vectors need to be constructed. One plasmid should have promoter coupled with RFP which has not induced by the tumor inducible transcription factors. Other plasmid should have tumor inducible promoter coupled with green fluorescent protein. Our main idea is to inject these vectors into the mutated mice body so that we also need to remove the other elements of vectors that can cause any unwanted diseases in mutated mice. The vectors like pED and Pz can be used for the expression in mammalian cells. Figure 3: Construction of plasmids containing promoter coupled with Red and Green fluorescent protein. The very first step for the construction of the recombinant plasmid is the cleavage of both plasmid and target DNA with promoter sequence coupled with fluorescent protein using suitable restriction enzymes. The restriction enzymes creates sticky or blunt ends (depends on type of restriction enzyme used) in both plasmid and target DNA. Next step is the hybridization of both DNA and plasmid using DNA ligase enzyme. Selection of cells having plasmid with desired sequence is very important so further we need to selection of appropriate vector by using selection markers like antibiotic resistance genes. 3.4. Transfection: The transfer of desired plasmid inside the mice body could be possible through many ways such as microinjection, electroporation, shotgun method, through chemicals and viral infections. Transfection through viral infection has some limitations like limited carrying capacity of desired gene and unwanted inflammatory mutations. However, transfection through viral infection have some advantages like easy to handle, easy preparation and easily monitoring during the process. So, in this method transfection of plasmid in mice should be done directly through microinjection into the mice body. One another way for transfection of recombinant plasmids in mice is through recombinant Baculovirus. Baculovirus infects insect cells. Purified budded virus can be isolate from the infected insect cells with recombinant Baculovirus. This purified budded virus can be introduced inside the mice body. For the study of side effects on normal cells in whole body of mice it is very important that this recomb inant plasmids will reach every parts of body along with tumor affected parts. 3.5. Induction of Tumor in Mice: Mammalian cancerous cell lines or cell DNA extracted from virally infected cells can be able to induce cancer in mice. Once theses tumorgenic cells is injected inside the mice body it develop specific tumor. After developing cancer in mice body, anti-cancer drug is administered through injection to show the efficacy and side effects on cancerous and non-tumor cells. When drugs binds with specific target receptors, it will induce both promoters but with varying intensity. The promoter coupled with RFP will show intensities in both normal and tumor cells but may be higher in tumor cells. But GFP should be induced only in tumor cells if it is inducing in other normal cells with high intensity then it may cause side effects on those normal cells. 3.6. Fluorescent Mapping: Analysis of fluorescent mapping of these promoters in different locations of the mice body can provide useful information about possible side effects against designed anti-cancer drugs. For example if GFP will be induced in other cells like hair cells, heart cells, bone marrow cells than we can predict the side effects on these cells because the drug should not induce translational process in normal cells. If this drug induces promoters only in tumor cells then the chances of side effects may be less. We can study possible side effects against various drugs by using this method. Figure 4: This picture has been modified for illustrating the possible results that can be produced by this method. Region B and C in this figure are representing the cancerous cells where GFP has been expressed. Region A is representing the normal cells where GFP has been also expressed so this drug may cause side effects on this cells. 3.7. Validation of the Method: This approach has not been validated because this is the hypothesis only. For the testing of this method whether it is working efficiently or not need to be validated. An efficient approach has been described here. For the validation of this method we need to perform this method on known anti-cancer drugs for specific type of cancers. This method can be apply for known drugs which side effects on normal cells have been identified completely. If fluorescent mapping provide exact location in the body where GFP has been induced and if these locations are related with those areas where this specific drug causes side effects then this method will be validated. But proper validation need to be tested for various anti-tumor drugs which side effects has been completely known. 4. Discussion: There are so many side effects associated with anti-cancer drugs because these drugs mainly affects rapidly dividing cells and immune system. The drugs or medicines that are currently used have always some common side effects like typhlitis, diarrhea and hair loss but sometimes these drugs cause serious side effects like liver damage and cardiac arrest because these drugs are unable to differentiate rapidly growing normal and cancerous cells. So that development of proper efficient method for testing possible side effects for any anti-cancer drugs should be developed. In this section a good approach has been described for the identification of possible side effects on normal cells. The idea is based on the role of transcription factors induced by the drug- receptors interactions. As instance certain anti-tumor drugs causes anemia when used for the treatment of specific tumor. Generally the gene called HBB is responsible for anemia because this gene encode beta globins protein. It mea ns that these drugs also induces transcription factor that is responsible for activation of HBB gene. The fluorescent mapping of unknown anti-cancer drug against specific cancer can provides useful information about possible side effects. The figure 4 which has been modified to illustrate the possible results that can be achieved through this method. If the drug is not inducing GFP in normal cells except cancerous cells it means drug will not cause any side effects on normal cells but vice versa if GFP is expressing in other cells along with tumor cells so we can predict possible side effects on those cells because this method is also useful to find out what type of protein or transcription factors are expressed. By using bioinformatics data bases like PDB, Gene bank etc, functions of expressed proteins or transcription factors can be easily predict. The method which has been described above has not validate yet because this method is only a hypothesis that need further advancement and validation. 5. References: Lohmann, S., Herold, A., Bergauer, T., Belousov, A., Betzl, G., Demario, M., Dietrich, M., Luistro, L., Poignà ©e-Heger, M., Schostack, K., Simcox, M., Walch, H., Yin, X., Zhong, H. and Weisser, M. (2013). Gene expression analysis in biomarker research and early drug development using function tested reverse transcription quantitative real-time PCR assays. Methods, 59(1), pp.10-19. Swartzman, E., Shannon, M., Lieu, P., Chen, S., Mooney, C., Wei, E., Kuykendall, J., Tan, R., Settineri, T., Egry, L. and Ruff, D. (2010). Expanding applications of protein analysis using proximity ligation and qPCR. Methods, 50(4), pp.S23-S26. Genetics Home Reference, (2014). HBB gene. [online] Available at: http://ghr.nlm.nih.gov/gene/HBB [Accessed 23 Nov. 2014]. Dubensky, T., Campbell, B. and Villarreal, L. (1984). Direct transfection of viral and plasmid DNA into the liver or spleen of mice. Proceedings of the National Academy of Sciences, 81(23), pp.7529-7533. Caldana, C., Scheible, W., Mueller-Roeber, B. and Ruzicic, S. (2007). A quantitative RT-PCR platform for high-throughput expression profiling of 2500 rice transcription factors. Plant Methods, 3(1), p.7. Kim, T. and Eberwine, J. (2010). Mammalian cell transfection: the present and the future. Analytical and Bioanalytical Chemistry, 397(8), pp.3173-3178.

Food Debate reaction paper Essay

Our development of cheap, widespread food is essential to human sustainability. This may be true, but regardless of the facts i disagree with this . I believe a corn based food supply is environmentally destructive. To start off, growing too much corn is bad for the environment . It requires more nitrogen fertilizer than any other crop, and also requires pesticides . These substances are polluting the environment . Also, a corn based food supply is unhealthy. It is known to cause the disease â€Å"E. Coli†. Corn is also very high in salt and carbs, which is also unhealthy for us. A more diversified agrigulture would be a lot more healthier. It would also be a lot more secure in the means that rather than depending on one crop for such an important task, we could use a variety of healthier crops to supply efficiently. A variety of crops would also be more secure than a simply corn based food supply because if that single crop is effected or something goes wrong, it interferes with our entire food supply. On the other hand, if there are multiple sources that we use, if one crop is damaged we have other sources we can turn to. A development of a cheap, widespread food supply may seem like a good idea. It is cheap and easy to sustain, but it is still very unhealthy. The corn based food supply is unhealthy not only for the environment but also for humans. Corn and the chemicals used during the process to create other corn based foods, can cause diseases. It also has a lot of salt and carbohydrates, which are also bad for us. The growing of mass amounts of corn, requires mass amounts of pesticides. This chemical pollutes the environment. The price of a corn based food supply may be less pricey to produce, but the price you may eventually pay will be severe. A corn based food supply is a bad idea due to its unhealthy value towards humans and the environment.

Compare two of Palins meetings with people who are particularly memorable Essay

Pole to Pole is a piece of travel writing. It has also been made into a television show. The author of this piece of travel writing is Michael Palin, and it is about his journey from the North pole to the South pole. Michael Edward Palin, CBE (born 5 May 1943) is an English comedian, actor, writer and television presenter best known for being one of the members of the comedy group Monty Python and for his travel documentariesIn Pole to Pole, Michael Palin encounters several different people during his journey; these meetings were particularly memorable to him. Particularly the meetings, with Lyuba-Day 41 on board Junost- and Doctor Baela- witch doctor, day 108 in Zambia-. Palin meets Lyuba as he is travelling between countries on a boat, whereas he meets Dr Baela when he has just entered Mpulungu, but has been in the continent, Africa, for quite a long time. The use of language in these sections is effective because Palin portrays his attitude towards women. He does this by using a superlative to highlight his meeting with Lyuba. Palin also reinforces that he does not take Lyuba very seriously when he uses alliteration, â€Å"lovely Lyuba†, when he calls Lyuba â€Å"proprietress of the bar† he then implies a certain attitude towards women. This is because of the use of the word proprietress, meaning lady of the bar. This is also a contrast to Palin’s usual persona as Palin doesn’t normally criticize or seek to be sexist/ politically incorrect. When Palin visits Dr Baela he exaggerates the encounter by being dramatic. He also demonstrates that Dr Baela is weird and sort of unrealistic, when using emotive language such as â€Å"evil†, â€Å"shadow†, and â€Å"spirits†. This is because some people do not believe in such things as spirits and after life. When Dr Baela says what he thinks is wrong with Palin, Palin uses the word â€Å"concludes† in a sarcastic manner, this then suggests that Palin sees the meeting as a joke and does not take it seriously, as well as how strange he feels the meeting is. Therefore Palin does not take either of the encounters seriously. When Palin met Lyuba and Doctor Baela, both of these meetings were surprising and quite unexpected. These meetings were unexpected because Palin does not expect these people to act like they do, as well as being surprising. â€Å"Strangest encounter†, by using this Palin shows that his meeting with Lyuba was not just strange, but more than strange, this highlights the fact that he was not expecting this encounter with Lyuba, which is a contrast to life no the ship.This is comparable to Palin’s meeting with Doctor Baela, this is because Palin was surprised and didn’t expect Doctor Baela to act the way he did. â€Å"Only adds to the confusion.† By saying this Palin shows that he is confused, but he didn’t expect to be confused. â€Å"More seriously than I expected.† This quote also adds to the fact that Palin didn’t expect the day to go this way. I think Palin’s intention was to make both of these parts quite comical and laughable when he is with Dr Baela the incident begins comical but changes, I also feel that he was putting a strange and peculiar approach to the encounters. As reading this I would feel my response would be, that it was very different and an unforgettable experience. Therefore these encounters were both surprising and unexpected, this is because Palin did not expect the witch doctor to act the way he did which links in with the fact that he didn’t expect Lyuba to act the way she did. When meeting Lyuba, Palin uses a lot of sarcasm and humour in his language which is really effective, which differentiates this from the language Palin uses to make Doctor Baela sound completely silly, but also intimidating and that what he does is pointless. â€Å"If there’s nothing more normal in the world than sharing a water-logged packing case with a Russian Barmaid.† When Palin says this he says this in a very sarcastic way, saying â€Å"if there’s nothing more normal† he is being sarcastic that it actually is normal as well as being humorous and playful. This has an effect on the reader because it adds humour to the situation. â€Å"Which will ‘drive out’ any evil influence† By using the quotation marks either side of ‘drive out’ Palin shows the intention of making Baela sound pointless and useless. Using sarcasm and humour in the language makes Baela sound very unprofessional, and makes the reader feel that Baela is making a fool out of himself but everyone around him believes him although he talks a lot of nonsense, where as the language that he uses when he meets Lyuba then presents her to be quite intimidating, which adds to the affect of the flirting. I think when Palin is with Baela; his intention was to change a serious situation into a disappointing and peculiar one, as opposed to when he is with Lyuba and changing an awkward situation into a humorous one. I feel he does this because the reader may loose interest Palin uses a lot of description during these encounters with Lyuba and Doctor Baela. When Palin first sees Doctor Baela, he describes him extremely well, corresponding to his meeting with Lyuba when he uses very descriptive language. â€Å"He has pouting lips and big lazy eyes. He wears a head-dress of genet fur, a pink tunic with his name on the back and a pair of welding goggles.† By using this Palin describes what Doctor Baela looks like, in a very understandable and clear way. â€Å"Nonchalant British lounging† Using this in his diary entry for when he met Lyuba, Palin adds to the formality and description. Being this descriptive, as the reader I feel that this makes the situation clearer and more understandable similarly to when Palin is describing Doctor Baela. I feel that Palin’s intention was to show the reader really what was happening and to add to the fact that these situations were quite awkward. Overall I feel that these two meetings would be particularly memorable to Palin, I feel this because when someone goes through a long period of time not knowing where they are, not seeing friends or family, being some place different everyday, I would expect this to be quite emotional and that every different person he meets he would then remember because it is all a big opportunity and experience which he would like to gain the most out of. I chose Lyuba and Doctor Baela because they both are not the usual conversations or meetings you would have with a complete stranger, I also feel that these encounters will be quite unforgettable because Lyuba acted really flirty, quickly where as Doctor Baela acted quite rehearsed and quiet when it came to chatting. Therefore I feel that these meetings were particularly memorable to Palin’s journey.

Data Of Smes Rating Information Finance Essay - Free Essay Example

Sample details Pages: 8 Words: 2366 Downloads: 10 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? As discussed in the Chapter 3 on the methodology of the research, the data of SMEs rating information and the earnings information for the period has been computed. This chapter would analyze the obtained secondary data information and would provide the plan for hypothesis testing. 5.2. SECONDARY DATA ANALYSIS For the current study, secondary data on manufacturing and service SMEs was accessed from the CMIE-Prowess database. Initially by giving the search option in the Prowess data base adhering to the definitions of an SME as specified in chapter 4, the financial statement of manufacturing and service-based SMEs has been obtained. The lack of consistency of the data has been identified and it was mainly because of the following reasons: Don’t waste time! Our writers will create an original "Data Of Smes Rating Information Finance Essay" essay for you Create order 1. Information on earnings and accruals were not available for the required time; 2. Many companies were new; and 3. Many SMEs rating information were not available. Unfortunately, this drastically reduced the sample available for secondary data analysis to 50. 5.2.1. DESCRIPTIVE STATISTICS The following table (Table 5.1.) depicts the descriptive statistics for variables in the regressions. It is computed from the secondary data of Manufacturing and Services SMEs. Table 5.1. Descriptive Statistics ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t+1 Earn t ACC t CFO t Lev t Size t Cap Int t Liq t Mean 0.1200 0.2400 1.8971 1.4871 0.0184 0.3046 3.0378 0.0646 2.8593 Standard Error 0.1016 0.1264 0.1126 0.1423 0.0238 0.0335 0.0761 0.0101 0.3950 Median 0.0000 0.0000 1.9181 1.6457 0.0365 0.2670 2.9652 0.0320 1.8906 Standard Deviation 0.7183 0.8935 0.7963 1.0063 0.1682 0.2371 0.5379 0.0718 2.7934 Sample Variance 0.5159 0.7984 0.6341 1.0126 0.0283 0.0562 0.2893 0.0052 7.8032 Kurtosis 7.1147 2.1525 1.0811 -1.0021 14.5300 1.6161 0.3901 2.5296 7.3048 Skewness 1.5382 0.3917 -0.7036 -0.2597 -2.9950 1.0686 0.5198 1.6068 2.4717 Range 5.0000 5.0000 3.6621 3.4761 1.1197 1.0447 2.7613 0.3190 14.9807 Minimum -2.0000 -2.0000 0.0000 0.0000 -0.8588 0.0000 1.8904 0.0002 0.0328 Maximum 3.0000 3.0000 3.6621 3.4761 0.2610 1.0447 4.6517 0.3192 15.0134 Sum 6.0000 12.0000 94.8551 74.3542 0.9184 15.2292 151.8877 3.2296 142.9664 Note: Count= 50 Source: Self Computation Using Microsoft Excel INTERPRETATION The descriptive statistics figures for SMEs indicate that the median earnings for the firms stand at 1.91 and the mean for CFO and ACC are all found to be positive. The Kurtosis value of future change in ratings is found to be 2.15 which is close to the normality range of 3. The Skewness for the same is found to be 0.39 which is nearer to the normality range of 0.3. Hence to some extent the dependent value follows a normal distribution. The Skewness is found to be negative for all independent variables under discussion namely the earnings, cash flow and the accruals. The maximum value of earnings is found to be 3.6 and that of cash flow is 0.26. The standard deviation for all the variables are in the range less than 1 except for accruals which is found to be 1.0063. The future change in ratings has a maximum change of +3 and a minimum of -2 within the period under study. INFERENCE From the above descriptive statistics, it can be inferred that the change in ratings for the SMEs does not seem to have a greater upgrade or downgrade. However few companies have seen changes in their ratings. The earnings, accruals and cash flow measures are all varying with greater extent in the industry and thus could be a possible factor for the credit rating changes. CONCLUSION It can be concluded from the above table that the rating changes that the SMEs face today be it upgrade or downgrade depends on the accruals, cash flow and on the earnings as a change in these variables are found to be relatively high. The companies differ greatly in their size which has been taken as an independent variable for the study. 5.2.2. CORRELATION ANALYSIS Correlation is one of the most common and useful tools in statistics. Correlation describes the degree to which two variables are related. Correlation coefficient ranges between +1 and -1; +shows the variables are perfectly positively correlated and -1 shows the two variables are perfectly negatively correlated. The following table (Table 5.2.) depicts the correlation between the variables under study. Table 5.2. Correlation Matrix    ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t+1 Earn t ACC t CFO t Lev t Size t Cap Int t Liq t ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t 1                         ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t+1 0.240 1                      Earn t 0.491 0.362 1                   Earn t+1 0.447 0.281 0.592                   ACC t 0.226 0.354 0.524 1                CFO t 0.385 0.065 0.351 0.099 1             Lev t -0.182 0.059 -0.175 -0.005 -0.502 1          Size t 0.015 0.120 0.573 0.373 0.157 -0.024 1       Cap Int t -0.076 0.004 -0.305 -0.199 0.082 0.197 -0.482 1    Liq t 0.107 0.076 0.164 0.163 -0.217 0.279 -0.078 -0.198 1 Source: Self Computation Using Microsoft Excel INTERPRETATION A correlation analysis was performed to verify possible association between and among the variables, in order to verify whether there is any linear correlation between and among the variables of interest of the study (see Table 5.2). The correlation matrix for the SMEs indicates that ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt and ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+1 are positively correlated with Earnt, ACCt and CFOt. ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+1 is significantly related to Earnt and ACCt but it is not significantly related to CFO in the univariate analysis. ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt is found to be negatively correlated with Lev and CapInt. However ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+1 is found to be positively associated with all the variables. INFERENCE From the above table it can be inferred that the rating information is more dependent on the earnings than the cash flow measures based on the Pearson coefficients obtained. More significant correlation exists among the earnings information than that of the cash flow and accrual measures. The accrual measure has shown a very little significance when compared to that of the cash flow and earnings measures. CONCLUSION The SMEs change in their future rating depends more on the current earnings information than that of their accrued earnings and cash flows. It could be concluded that rating agencies take more of current earnings information rather future predicted cash flows. 5.2.3. REGRESSION ANALYSIS Regression analysis is the process of constructing a mathematical model or function that is being used to predict or determine the value of one variable by another variable. In the regression model the variable to be predicted is called a dependent variable and the variables upon which the dependent variable depends are called independent variables. For the research study, two models are developed one for earnings and the other for the cash flow. The dependent variable is the ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+1 and the independent variables are Earnt, ÃÆ'Ã… ½Ãƒ ¢Ã¢â€š ¬Ã‚ RATINGt, LEVt, SIZEt, SUBORDt, CAPINTENt, LIQt for the first model and ACCt, ÃÆ'Ã… ½Ãƒ ¢Ã¢â€š ¬Ã‚ CFOt, ÃÆ'Ã… ½Ãƒ ¢Ã¢â€š ¬Ã‚ RATINGt, LEVt, SIZEt, SUBORDt, CAPINTENt , LIQt for the second model. A summary of the model of regression analysis is given in table 5.3. Table 5.3. Model Summary Model R Square Adjusted R Square Std. Error of the Estimate 1 0.562 0.603 0.8508 2 0.488 0.533 0.7982 Source: Self Computation using XLSTAT INTERPRETATION R Square represents the proportion of standard deviation in the dependent variable (Future rating changes) which could be explained by the independent variables (Earnings, Change in ratings for the current year, Leverage, size, Liquidity, Capital Intensity, Subordinated debt). However the variable subordinated debt has not been considered for modelling as the value doesnt change for the entire sample. This is an overall measurement of the strength of the association and hence the extent to which any particular variable (independent) associated with the other (dependent variable) is not reflected. The value of R square for the first model is 0.562 which shows a relatively acceptable association between the dependent and the independent variables. Adjusted R Square is an adjustment to the R Square that expresses the effect on the addition of any other external predictors to the model. The value of adjusted R Square for the first model is found to be 0.603 and thus it indicates if an ad dition of external predictor to the model will add significant predictability to the dependent variable. The dependent variable (Future rating changes) which could be explained by the independent variables (Accruals, Cash Flow, Change in ratings for the current year, Leverage, size, Liquidity, Capital Intensity, Subordinated debt). However the variable subordinated debt has not been considered for this modelling too as the value doesnt change for the entire sample. The value of R square for the first model is 0.488 which shows a relatively acceptable association between the dependent and the independent variables. The value of R square is found to be lesser as the rating information does not necessarily consider the cash flow information and it is dependent on many other industry factors which are not included for the study. The value of adjusted R Square for the first model is 0.533. Std. error of the Estimate, also referred as the root mean square error represents the standard deviation of the error term and the square root of the mean square for the residual in the ANOVA Table 5.4 presented below. Table 5.4. ANOVA Table Model DF Sum of squares Mean squares F P value 1 Regression 6 8.563 1.427 2.008 0.0557 Residual 43 30.557 0.711       Corrected Total 49 39.120          2 Regression 7 6.974 0.996 1.302 0.0273 Residual 42 32.146 0.765 Corrected Total 49 39.120 Note: H2a0. Credit rating agencies do not fully incorporate information in earnings about future performance into their ratings. H2b0. Credit rating agencies do not fully incorporate information in accruals and cash flows about future performance into their ratings. Source: Self Computation using XLSTAT INTERPRETATION F test is used to test whether the model is statistically significant. The p-value of the F-test is looked at so as to see the overall model is significant. The P value is found to be 0.0557 and 0.0273, proving that the models are statistically significant. For the Model 1, the p value is greater than the significance level at 5 per cent. So the null hypothesis is accepted however the null hypothesis is rejected in the model 2 and it can be concluded that all mean values are not equal. It implies that there is a significant difference among all the independent variables. A summary of coefficients of Model 1 and Model 2 are given in the table 5.5 5.6. Model 1 Source Value Standard error t P Value Intercept 0.282 0.959 0.294 0.770 Earn t 0.546 0.205 2.658 0.011 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t 0.305 0.178 1.717 0.039 Lev t 0.339 0.599 0.901 0.373 Size t -0.223 0.335 -0.667 0.509 Cap Int t 0.268 2.157 0.291 0.772 Liq t -0.014 0.052 -0.272 0.787 Table 5.5. Coefficients for Model 1 Source: Secondary Data Table 5.6. Coefficients for Model 2 Model 2 Source Value Standard error t P Value Intercept 0.266 0.991 0.471 0.640 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Rating t 0.217 0.149 1.450 0.154 ACC t 0.312 0.139 2.243 0.030 CFO t 0.340 0.942 0.361 0.250 Lev t 0.273 0.700 0.391 0.698 Size t -0.025 0.311 -0.079 0.937 Cap Int t 0.273 2.337 0.354 0.725 Liq t 0.008 0.052 0.162 0.872 Source: Secondary Data INTERPRETATION The first variable (Intercept) represents the constant, is the predicted change in the future credit ratings when all other variables are not considered. From the Table 5.5, Coefficients for Model 1, the coefficient value tells us about the relationship of each variable with the independent variable. For example, let us look at these variables: 1. Earnings: The coefficient for earnings is 0.546. So for every unit increase in earnings, a 0.546 unit increase in future rating changes is predicted, holding all other variables constant. 2. Size: The coefficient for size is -0.223. So for every unit increase in growth, a 0.223 decrease in change in future credit ratings is predicted, holding all other variables constant. 3. Current Rating Changes: The coefficient for Current Rating Changes is 0.305. So for every unit increase in Current Rating Changes, a 0.305 unit increase in change in future credit ratings is predicted, holding all other variables constant. The relationship in the case of other variables also follows the same pattern. Similarly, From the Table 5.6, Coefficients for Model 2, the coefficient value tells us about the following relationship. 1. Accruals: The coefficient for Accruals is 0.312. So for every unit increase in accruals, a 0.312 unit increase in future rating changes is predicted, holding all other variables constant. 2. Cash Flow: The coefficient for cash flow is 0.340. So for every unit increase in cash flow, a 0.340 increase in change in future credit ratings is predicted, holding all other variables constant. 3. Current Rating Changes: The coefficient for Current Rating Changes is 0.217. So for every unit increase in Current Rating Changes, a 0.217 unit increase in change in future credit ratings is predicted, holding all other variables constant. The Beta values have an associated standard error indicating the extent to which these values would vary across different samples, and these standard errors are used to determine whether or not the B value differs significantly from zero. By standardizing the variables prior to regression, all the variables are put on the same scale and the magnitude of the coefficients are compared so as to see which has more effect. The t and p value are the statistical tools that are used to test for the significance in of a given coefficient. If the t test associated with the B value is significant then that predictor is making significant contribution to the model. Using an alpha of 0.05 for the first model, we may explain these variables as follows: 1. The coefficient for earnings (0.546) is significantly different because its p-value is 0.011, which is lesser than 0.05. 2. The coefficient for change in current ratings (0) is also significantly different and its p-value is 0.039, which is lesser than 0.05. 3. The coefficient for capital intensity is 0.268 is not significantly different as its p-value is 0.772, which is greater than 0.05 The relationship in the case of other variables also follows the same pattern. The regression equation for the model 1 is ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+1=0.282+0.305*ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+0.546*Earnt+0.339*Levt-0.223*Sizet+0.2683*Cap Intt-0.014255*Liqt The regression equation for the model 2 is ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt+1=0.2664+0.217*ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Ratingt-0.31239*ACCt+0.34039*CFOt+0.27331*Levt-0.0246*Sizet+0.27285*Cap Intt+0.00841237*Liqt 5.2.4. HYPOTHESIS TESTING To test the first major hypothesis the relation between accrual-based earnings and credit ratings and the relation between cash flows and credit ratings, the level of credit rating (Ratingt) is regressed on total earnings (Earnt) and several cash flow measures. The coefficient on Earnt is significantly positive with the value being 0.469 and the adjusted R square is 0.213 and thus indicating earnings being an important input in the determination of credit rating. In another regression, the coefficient of CFOt is 0.278 is also positive and significant and indicating the rating agencies reliability on the cash flow for credit ratings. The adjusted R square is found to be 0.145. Thus it could be found that the adjusted R square in cash flow model is just 68% of the adjusted R square of the earnings model as indicated by the ratio of RSquare cash flows / R Square earnings (0.145/0.213) This indicate that earnings are more related to credit ratings than the cash flow measures and earnings could mitigate the timing problems. Thus we are rejecting the null hypothesis of the relation between accrual-based earnings and credit ratings is not stronger than the relation between cash flows and credit ratings. 5.3. CONCLUSION From the secondary data analysis it is concluded that the level of credit rating is dependent on both the earnings accruals and the cash flow measures. However, it was proved statistically that the earnings information are more significant in credit rating than the cash flows. The two major model equations that has been designed to test the other hypotheses indicate that credit rating agencies does not fully incorporate earnings information in their future credit rating changes and that of accruals and cash flows information into credit rating process. The other independent variables and industry factors are to be included while arriving at the conclusion that there exists an inefficiency in credit rating for SMEs that are under consideration.