eChapters

The liver is an organ that plays a central role in maintaining metabolic homeostasis, in its functions of metabolism, synthesis and storage of nutrients. The cells in the liver are parenchyma cells and non- parenchyma cells. The former include hepatocytes (80% of the cell population) and the later includes endothelial cells, Kupffer cells, lymphocytes and stellate cells. Liver dysfunction or failures are having different etiologies. Liver cancer has the fifth highest cancer incidence in the world, and is the third highest cause of cancer related deaths, with resection of the liver remaining the only curative option.

Following liver injury, the repair process involves two distinct phases: a regenerative phase, in which injured liver cells are replaced with regenerated hepatocytes; and a phase known as fibroplasia or fibrosis, in which connective tissue replace normal parenchyma tissue. The process even though initially beneficial becomes pathogenic when it is not controlled appropriately. Extensive accumulation of extra-cellular matrix (ECM) components ultimately leads to cirrhosis and liver failure. Thus, the ideal strategy to treat liver injury is to generate new hepatocytes replacing damaged cells without causing excessive ECM deposition.

Bone is the only tissue in the body able to heal without microscopical scarring. On the other hand fracture healing is a slow process causing long immobilization periods with consequently high costs for fracture healing giving rise to delayed healing, fibrous healing or non-healing with subsequent problems for both, the patient and the orthopedic surgeon. Fracture healing is complex phenomenon which involves numerous cells, regulators of cell function and biochemical interactions in the repair process. The healing potential of bone, in a fracture model is influenced by a variety of biochemical, biomechanical, cellular, hormonal and pathological mechanisms. A continuous occurring state of bone deposition, resorption and remodeling facilitates the healing process.

Biodegradable and bioinert ceramic materials such as hydroxyapatite (HA), tri­calcium phosphate (TCP), aluminum-calcium-phosphate (ALCAP) ceramics provides scaffold to support the attachment and migration of newly formed bone cells into the osseous defect and also help in formation of a vascular network through the newly formed bone. Bioceramic also act as a carrier to deliver stem cells for osteogenesis.

Specific bone-inductive proteins/growth factors can induce bone formation and healing in vivo. These morphogens are therefore, to be an ideal alternative to autogenous bone grafts. These findings initiating intensive research into bone regeneration orchestrated by putative soluble signals and lead to the discovery and identification of an entirely novel family of protein initiators collectively called the bone morphogenic protein (BMPs), which belong to transforming growth factors-ß (TGF- ß) super family. These growth factors might possess a biological activity that is site and tissue specific in the induction of bone formation. Different growth factors have been demonstrated to possess osteoinductive and fracture healing properties.

Collect the femur and tibia and clean them very well from blood, tendons, muscle, skin and other tissues (using scalpel)

Cut off epi-and metaphysis from the long bones with electric/manual bone saw
Transfer the open bones to a falcon tubes containing cell culture medium/DPBS without FBS but containing 1% PS (Penicillin-streptomycin)
Transfer the bones to a Petri dish containing 5 ml medium without serum (FBS) and without antibiotic (40-50 mm Petri dish)
Remove the pteri dish to the laminar cabinet
Collect the bone marrow by flushing out to a 50 ml falcon tube (use syringes and needles to flush out the bone marrow; flush out the bone marrow with complete (10% FBS, 1% PS, 90% DMEM-LG) culture medium
Centrifugation: 300rpm, 8 min (in the meanwhile prepare 5 ml of Ficoll into a 15ml falcon tube)
Aspire the medium

1.    Collect fat tissues from euthanized animals into a Falcon tube containing DPBS + 1% PS ( for transferring lab to lab)
2.    Wash 3 times with DPBS + 1% PS
3.    Inside a laminar cabinet, transfer the fat to a Petridis (use an sterile spatula to collect the fat)
4.    Cut the fat into small (mm) pieces using scalpel and forceps or scissors
5.    Transfer the fat into a Falcon tube (eg into a 50 ml Falcon tube you can add upto 10 ml fat tissues)
6.    Wash the fat by adding approx 40 ml DPBS; Centrifugation: 430g, 10 min (without break)
7.    Transfer the fat into a fresh 50 ml Falcon tube (with the help of sterile spatula); Repeat steps 5 to 7 for 2-3 times

1.    Collect the bone marrow from iliac crest of rabbit, dog, goat and sheep; sternum of pigs by 16-18 G bone marrow biopsy needle under sedation/anesthesia and maintain strict asepsis protocol
2.    To aspirate bone marrow, negative pressure should create in the syringe by pulling back the plunger. Syringe should be first flushed with heparin before bone marrow collection
3.    Collect approximately 2.5-5 ml of bone marrow from each animal
4.    Mix the bone marrow sample with equal volume of low DMEM having 10% FBS and 1 ml of PS and then layer on 5 ml of histopaque/Ficol-Paque Plus in a falcon tube
5.    Centrifuge at 3000 rpm for 30 min
6.    Collect stem cells from the interface with the help of a micropipette
7.    Cells wash thrice with DPBS 
8.    Centrifuged at 2000 rpm for 5 min

1.    Remove the growth media
2.    Wash 2 times with warm PBS (w/o Ca and Mg)
3.    Add 5ml of trypsin /EDTA 0.35 % (Invitrogen), place at 370C in a CO2 incubator for 4-5 min
4.    Wait until cells are detached/lifted and make sure by examining on microscope
5.    Add equal volume (5ml-1:1) of growth medium as appropriate and mix with 5 strokes
6.    Transfer into 15 ml centrifuge tube
7.    Centrifuge at room temperature for 4 min in 980 rpm
8.    Remove supernatant complete but carefully without touching the cell aliquot
9.    Re-suspend in 10ml DPBS (with Ca and Mg)-5 strokes
10.    Immediately after mixing transfer 100µl of cell suspension into Casy vial, containing 10ml Casyton

Experimental unit : An experimental unit is the material to which is applied the treatment and on which the variable under study is measured. In an agricultural field experiment, the plot of land, not the individual plant, will be the experimental unit; in a feeding experiment of cows,the whole cow is the experimental unit; in human experiments in which the treatment affects the individual, the individual will be the experimental unit.

Treatment : The different procedures under comparison in an experiment are the different treatments, i.e in an agricultural experiment, the different varieties of a crop or the different manures will be the treatments. In a dietary or medical experiment, the different diets or medicines, etc. are the treatments.

The difficulty in conducting a factorial experiment in an RBD is that, as the number of factors and/ or that of levels of the factors increase, the number of treatment combinations to be compared increases too. This in turn necessitates use of large sized blocks to accommodate all the treatment combinations i.e in a 2^4 – experiment there should be 16 plots in a block. But it has been found that the experimental error increases with an increase in the size of a block, for then it becomes less effective in controlling the heterogeneity of the units. A remedy has been found out : this is to divide a replicate (a complete block) into a number of equal blocks (incomplete blocks) and then to allocate the treatment combinations to these blocks so that only the unimportant treatment comparisons gets mixed up or entangled with the block comparisons. These treatment comparisons are then said to be confounded or mixed up with block effects. These effects cannot be separately tested or estimated. But the remaining treatment effects, which are not confounded with the block effects, are still capable of separate estimation and testing.

Split-plot Design

In field experiments, sometimes a factor has to be applied to a large experimental unit. This is true when the different methods of ploughing or irrigation are to be compared. And in such cases it is possible to introduce a second factor, which does not require large plots, with a small number of levels into the same experiment, at a little extra cost. This is done by splitting the plots (called whole plots of the first factor into as many sub-plots as there are levels of the second factor.)

A split-plot design with an RBD for the first set of treatments (called “the whole-plot treatments”) is obtained by allotting the whole-plot treatments at random to the whole plots of a block and then randomizing the second set of treatments (called “the sub-plot treatments”) to the sub-plots within each whole plot.

This enables us to test for the main effects of the sub-plot treatments and the interaction of the whole-plot treatments and the sub-plot treatments more efficiently than the main effects of the whole-plot treatments in a split-plot design.

In many experimental situations, it becomes necessary to repeat an experiment over time, (for a number of seasons or years). This repetition (or replication) of the experiment broadens the scope of the experiment in the sense that our recommendations will be applicable for a number of seasons. In the case of agricultural experiments, for example, there may be present treatment x season interaction. So the results of a series of experiment, performed for different seasons with the same set of treatments, will have wider applicability.

We shall consider the simplest case of a repetition of experiments of identical structure for a number of seasons. Let us consider randomized block experiments with t treatments in r blocks and conducted in p seasons. The analysis of the experiment in a season is based on the linear model. Before attempting a combined analysis for the p experiments, it is necessary to perform the analysis for p experiments separately and interpret the results separately. It may be of interest to find out whether differences among the treatments are the same in the different seasons so that ‘ best ‘ treatment may be recommended for all seasons or whether different treatments are to be recommended for different seasons.

We next consider the analysis of the combined experiments considering the seasons as a random sample from the population of seasons. Analysis of Variance of series of experiments

One of the objectives of experiments in cultivators’ field is to test technology for adoption in a targeted area i.e. NARP Zone, or a district. In that case interaction between district x treatment within a zone or between blocks x treatment within a district should not be significant. If the targeted area is a district, an experiment in cultivators’ fields must occur in at least two blocks. Also, if the targeted area is a zone, an experiment should occur in at least two districts in order to estimate interaction.

Analysis of cultivators’ field experiment

The on-farm trials were conducted in 25 villages on kharif rice during 1992-93. The treatments viz. T1-Farmers’ practice with local variety, T2-Farmers’ practice with improved variety, T3-Farmers’ practice with improved variety +recommended fertilizer and T4-Improved practice with improved variety +recommended fertilizer. Farmers level of NPK fertilizer was 40:0:0 and local variety was Suzata while recommended NPK fertilizer was 80:40:20.

A. Agronomic Considerations for Good Experimentation

For laying out of any experiment (Short / long term) at on-station and on farmers field, some of the agronomic considerations are very important for better results using experimental designs. Therefore, these are discussed here in this chapter.

1. Cropping History

The crop performance and its yield is affected by the crop grown and management followed during previous years. Therefore, for meaningful interpretation and factual reflection on yield trend due to the treatment proposed to be evaluated, the details of crop grown during previous 2 years/ seasons with details of management followed especially input applied needs to be recorded.

At country level, there is major paradiam shift from cropping systems towards farming system research in XI Plan period. The mandate of Project Directorate for Cropping Systems Research, Modipuram stands changed with focus on Farming Systems and re-named as Project Directorate for Farming Systems Research. w.e.f. 1.4.2009. The All India Co-ordinated Research Project on Cropping Systems with its 69 on-station and on-farm centres representing different agro-climatic zones of the country also stands re-named as AICRP on Integrated Farming Systems. As such, the much needed methodologies and procedures for planning, monitoring and interpretation of farming systems related data are not readily available. Therefore, some methodologies and procedures which can be adopted to execute Farming Systems Research for drawing meaningful conclusions are discussed here.

1.1 Classification

The raw or ungrouped data are in unorganized form and need be organized in meaningful and readily comprehensible form in order to facilitate further statistical analysis. Classification of data is the first step in this direction. It is the process of arranging thing in groups according to their resemblance and affinities. There are two basic types of classification namely-qualitative and quantitative. In qualitative classification, the basis of classification is some attribute or quality like: sex, literacy, religion, etc. A qualitative classification may be simple or manifold. The classification done with respect to one attribute is termed as ‘Simple Classification’. The classification in which two or more attributes are considered and several classes are formed is called ‘Manifold Classification’. In quantitative classification, the collected data are grouped in respect of characteristics, which can be measured and numerically expressed such as height, weight, etc.

7.1 Introduction

The theory of probability has its origin in the games of chance related to gambling such as throwing a die, tossing a coin, drawing cards from a pack of cards etc. Jerame Cardon (1501-1576), an Italian mathematician was the first man to write a book on the subject entitled “Book on Games and Chance”, which was published after his death in 1663. The systematic and scientific foundation of the mathematical theory of probability was laid in mid-seventeenth century by two French mathematicians B. Pascal (1623-1662) and Pierre de Fermat (1601-1665).

10.1 Introduction

The assumptions on which analysis of large samples is done generally do not hold well in case of small samples (n < 30). In case of large samples we had presumed that the random sampling distributions of statistics are approximately normal and further that the values obtained by sampling study are close to the population values and can be used in their place for the calculation of the standard error of the estimate. These assumptions do not hold well if the size of the samples may or may not be normally distributed and similarly it is not possible to substitute the mean or the standard deviation of a small sample in place of the parameter mean or standard deviation for the calculation of standard errors. Under such circumstances the analysis of small samples has to be done by techniques which are different from those applicable in case of large samples.

13.1 Introduction

In 1935 Prof. Ronald A. Fisher laid the foundation for the subject in his monumental work entitled “The Designs of Experiments”. Experimental designs concern the arranging of treatments or variables in such a manner that the inferences and conclusions regarding the effects of these treatments can be easily done and their reliability measured. Experiments are made with a view to find the validity of a particular hypothesis and to have an idea about the extent of the reliability that can be placed on a particular conclusion arrived. The selection of the design will have a very great bearing on the accuracy of the ultimate results. By a random selection of experimental units it is possible to remove the ambiguity about the casual interpretation of the observed associations. Random sampling is the most essential ingredient of all experimental designs. Besides, there are many devices for increasing the precision of the inference and the calculations.

14.1 Computer

Computer is an electronic device designed to accept and store data, process them and produce meaningful results under the direction of detailed step by instructions.

In 1822, Charles Babbage, a professor of Cambridge University designed a machine called an “Analytical Engine”, which could store data and perform arithmetic operations and printing out results. He is known as “Father of Computer”.

14.1.1 Basic anatomy of computer

The word Anatomy of a computer means inner structure of the computer. The study of anatomy of computer is necessary to acquire the knowledge and functioning of its inner components. Thus, essentially a computer consists of the following three main units

15.1 Computer Network

It is a group of computers and peripherals which are interconnected to share information and resources. A communication system that supports many users can be called a Network or we can say network is a communication system which interconnects many users who have something in common, either with respect to the type of data being sent or to the geographic areas that the users cover. There are three types of computer networks:

15.1.1 Local area network (LAN)

It is a small network (2 to 100 nodes) usually located within a single building or group of buildings belonging to an organization or an institute.

16.1 Entering Biological Data into Computer

After the forms have been designed, the database in ready for entry of biological data. Biological data is entered one record at a time. To enter the biological data, the user issues a command, which calls up and displays the appropriate form with blank fields. The user then keys in the data for each field in the appropriate spaces. In this manner, the user enters the data for the first record, then for the second record, and so on. In most database systems, the records are automatically assigned a number as they are entered.

16.2 Saving Biological Data into Computer

While entering data into the fields, the tab key or enter key is usually used to move to the next field. Pressing enter or tab key in the last field on the form saves the record in the database and moves to a new, blank form for the next record to be entered. In addition to using the tab or enter key to move forward through fields, one can directly go to any field on the form at any time by clicking on it with the mouse.