=Paper=
{{Paper
|id=Vol-2255/paper13
|storemode=property
|title=The Portraits Creating Method by Correlation Analysis of Hormone-Producing Cells Data
|pdfUrl=https://ceur-ws.org/Vol-2255/paper13.pdf
|volume=Vol-2255
|authors=Olha Ryabukha,Ivanna Dronyuk
|dblpUrl=https://dblp.org/rec/conf/iddm/RyabukhaD18
}}
==The Portraits Creating Method by Correlation Analysis of Hormone-Producing Cells Data==
https://ceur-ws.org/Vol-2255/paper13.pdf
The Portraits Creating Method by Correlation Analysis
of Hormone-Producing Cells Data
Olha Ryabukha1[0000-0001-6220-4381] and Ivanna Dronyuk2[0000-0003-1667-2584]
1 Lviv Medical Institute, Polishchuka str. 76, 79018 Lviv, Ukraine
oriabuha@ukr.net
2 Lviv Polytechnic National University, S. Bandera str. 12, 79013 Lviv, Ukraine
ivanna.droniuk@gmail.com
Abstract. Method of using the correlation analysis for studies of the hormone-
producing cells activity is presented as exemplified by the follicular thyrocyte.
Electronograms of the thyroid ultrathin sections are studied, prepared using rou-
tine technologies. Studying any type of follicular thyrocyte’s activities (synthe-
sis, secretion, transport of hormones, energy supply of these processes) involves
the successive application of such stages: research objective formulation and
isolation from the entire set of organelles’ cells ultrastructures – implementers
of the respective activity (the studied cluster formation); transformation of lin-
guistic (qualitative) information on the cluster elements into quantitative (nu-
meric) indices; determining of intra-cluster (intra-system) interconnections and
interdependencies; graphic representation of correlation links in correlation por-
traits and their analysis. In need to deepen the study, a module can be formed of
several clusters in which the elements most relevant for solving the task are de-
termined. Subsequently, the correlation analysis establishes the connections be-
tween the elements of the studied clusters, which are reflected in the correlation
portraits, analyze the information obtained and develop an integrative model of
the module’s activities. The suggested method is not connected with the strict
determinism of the input information, but it permits to correctly transform qual-
itative/binary indices into quantitative ones and to generalize the results ob-
tained. Establishment of interdependencies between elements of investigated
clusters is an integral research approach that enables the use of correlation por-
traits as the basis for the further development of an expert system for solving
problems in cytomorphology and cytophysiology.
Keywords: correlation analysis, correlation portrait, follicular thyrocyte.
1 Introduction
The human body is the object of multi-directional influences that are acting simulta-
neously. Thus, in particular, the state of a sick person’s organism is caused by a num-
ber of internal and external factors: the features of premorbid development and life,
working conditions, living conditions, the state of all body systems during a disease,
etc. By virtue of this, the application of mathematical methods in the study of the
organism/system/organ/cell activity is a very complicated process, because it involves
the need to consider a large number of various factors whose significance is unequal
[1].
The task of medical diagnostics is the search for the expression
X * ( x1* , x2* , , xn* ) d j D (d1 , d 2 , , d m ) ,
where X* is a set of a particular patient’s state parameters, and D is a set of diagnoses
inherent in the given field of medicine. Development of mathematical approaches to
the study of biological systems requires the use of a mathematical apparatus, which is
an integral part of formalized cognition. The present stage of the medicine develop-
ment as a science is characterized by quite frequent implementation of mathematical
methods into the practice of medical diagnostics, being intended to objectivize the
results obtained and to serve as the basis for making decisions on the belonging of
pathological manifestations observed in the patient to this or that nosological group
[2, 3]. Difficulties associated with solving diagnostic tasks are due to a number of
reasons, the most important of which is the necessity to know a large number of the
patient's condition parameters, which is steadily increasing with the development of
medical science. This makes it quite problematic to remember them and reduces the
opportunity of quickly taking them into account when diagnosing, even the by the
most skilled expert. At the same time, the analytical dependencies between the pa-
rameters of the patient's condition and the diagnosis in their classical sense are usually
absent, and these parameters in particular may be of a various nature: quantitative
(age, height, body weight, content of certain substances, etc.), qualitative (the nature
of pain sensations, mood sensations, etc.) or binary (presence or absence of a state or
a process – yes/no).
The method of correlation analysis has a long history of application. Since the task
of the method is to establish connections between the parameters under study, it is
successfully used in various branches of biology and medicine. The peculiarity of the
correlation analysis lies in the search of interdependence between two or more indi-
ces, which nature and pronouncement are established by the pair correlation coeffi-
cient, calculated by the formula. Durable application of the method contributed to
accumulating the information on biomedical processes at their multiple levels (genet-
ic, phenotypic, physiological, etc.), which predicated the development of tools to
facilitate its visualization, analysis and interpretation [4]. An example of such scien-
tific tools is the correlation grids, permitting to trace the interdependencies in large
amounts of quantitative data. Despite its widespread use, the method is not designed
for work with qualitative or binary data on the studied biosystem status.
The concept of fuzzy sets arose in response to the requirements of the classical
theory of systems to provide artificial precision, which can not be achieved in the
biological objects life process. The theory of fuzzy sets is a means of formalizing
uncertainties [5, 6], arising in the biological system, and methods for solving prob-
lems inherent in a living organism.
It applies qualitative data, relatively simple mathematical methods, namely the no-
tions of membership function and the highest and the least expressiveness of any sign
(max-y and min-y). For example, in the study of the thyroid gland pathology, the
signs may have the following form: hypothyroidism may be non threatening, moder-
ate, severe, etc.; cytoplasmic reticulum may be narrowed, moderately expressed, ex-
panded, etc.
The principle of linguistic diagnostic data implies that the causal relationships be-
tween the biological system status parameters, which can be the organism/organ/cell
(cause), and the diagnosis (consequence) are initially described by words of the lan-
guage used, thereafter they are formalized as a collection of fuzzy logical statements
in the “if… – then” sort. This principle may be exemplified by the following descrip-
tion of the thyrocyte morpho-functional status: “If the cell’s shape is cubic, the elec-
tron density of the cytoplasm is moderate, the electron density of the colloid is mod-
erate, apical microvilli are thin, short, and their number is moderate; mitochondria in
sufficient quantities, the elements of the granular cytoplasmic reticulum are moderate-
ly expressed, the Golgi complex elements are expressed moderately, the number of
free and bound ribosomes and the polysomes is moderate, the number of lysosomal
bodies is moderate, the number of apical secretory granules is moderate, then the
thyrocyte’s functional status is balanced“. This permits to take into account the status
quality, which considerably broadens the the researcher’s opportunities. Diagnosing
on the principles of Fuzzy Logic requires special training of the researcher for carry-
ing out mathematical transformations, which significantly impedes the final result
obtaining. In addition, in order to avoid excessive complications in the biological
processes simulation, one has to neglect a sufficiently large number of actual facts.
However, in biological systems, there are no minor or unnecessary processes, there-
fore their neglecting leads to reduction in the reliability of conclusions based on such
incomplete data.
Therefore, both of the described methods do not permit studying peculiarities of
changes in cell structures involved in the hormonopoiesis to the full extent. The ne-
cessity in formalizing qualitative and binary information on the studied biological
object/system status is acute in cytology traditionally applying such a heuristic meth-
od as a linguistic description. In this case, the completeness of the information ob-
tained and its interpretation depend on the researcher’s qualification, thus subjectiviz-
ing the final conclusion. The use of mathematical statistics for the quantitative param-
eters processing, despite the possibility of comparison, does not allow to carry out an
expanded analysis of the data obtained to the full extent and to generalize them, which
is very important for establishing the regularities of the cells activity and highlighting
peculiarities of their changes in response to the various factors’ effects.
2 Purpose of the study
The purpose of the study was to develop, based on follicular thyrocyte, a method for
analyzing the morpho-functional status of hormone-producing cells, permitting to
transform qualitative (linguistic)/binary information into objective (quantitative) indi-
ces, which can be subjected to further mathematical transformations, analysis and
generalizing.
3 Materials and methods
The object of the study were electron diffraction patterns of the thyroid glands tissue
ultrathin (4-6 μm) sections of white outbred male rats, prepared for electron micro-
scopic studies using routine technologies, which were studied using methods of elec-
tron diffraction patterns semi-quantitative analysis and determination of hormone-
producing cells special possibilities, by means of separate elements of such compo-
nents as mathematical statistics, phase interval, Fuzzy logic, correlation analysis [7];
forming separate clusters of ultrastructural elements is carried out based on the сyto-
physiology data on the functional role of each cell organelle [8].
At all stages of the study, international requirements for the humane treatment of
vertebrate animals were observed in accordance with the “Guidelines for Accomoda-
tion and Care of Animals” (Strasbourg, 2006, Annex 4) and Helsinki Declaration on
humane endpoints to experiment animals.
4 The portraits creating method by correlation analysis of
hormone-producing cells data: description
In our opinion, the promising trend of studies in cytology, should be based on a com-
bination of adequate methods, which are elements of mathematical statistics, cluster
and correlation analysis, the phase interval method, the concept of fuzzy sets, all
available quantitative, qualitative and binary data being used for the research needs.
In this case, the research process will be comprehensive and objective. We have de-
veloped a method for studying morphological and functional characteristics of hor-
mone-producing cells, including seven stages (see Fig. 1).
At the first stage, according to the task of studying a certain field of the cell activi-
ty (synthetic, secretory, transport, energy) create separate clusters of the studied struc-
tural elements/signs/manifestations that are implementers of this field. For example,
the status of the intrafollicular colloid electronic density, apical microvilli, lysosomal
bodies, secretory granule indicates the secretory cluster of the thyrocyte activity (see
Table 1).
At the second stage, each ultrastructural/substructural element (form, number, sub-
cellular location, etc.) of the cluster and its state (reduced, moderate, increased) is
assigned an alphanumeric index. Using the principle of the phase interval method for
comparing the studied system status with two diametrically opposite reference stand-
ards, which can be conventionally defined as “health” and “studied pathological pro-
cess/disease”, a digital assessment of each cluster element is carried out, i.e., qualita-
tive or binary features are transformed into quantitative ones. Special tables being
applied, the results obtained are expressed in percentage or points (see Table 2). The
cluster components are studied and valued in several fields of sight (see Table 3, Ta-
ble 4).
Stage 1
Problem definition and the studied cluster formation
Stage 2
Formalization of the cluster constituent elements status
and transformation of the quantitative data obtained
Stage 3
Establishment of intra-cluster (intra-system)
interdependencies
Stage 4
Creating a correlation portrait and analyzing the established
interdependencies
Stage 5
Formation of a separate module from different clusters
studies with the definition of meaningful elements
Stage 6
Establishment and analysis of modular
(intra-cluster) interconnections
Stage 7
Development of the integrative model of module activity
Fig. 2. Structure of the correlation portraits creating method for study the hormone-
producing cells activity.
Averaged digital data is used for mathematical transformations at the third stage of
the study, when, using a correlation analysis, the existence of links is established be-
tween the studied cluster constituent elements. For this purpose, the correlation coef-
ficients, being calculated by the Pearson formula, are used.
Table 5. The cluster of ultrastructures-realizators of follicular thyrocytes secretory potential.
Ultrastruc- The researched fea- The quality of the Quality designation
tural ele- ture of the ultra- rеsearched ultra- of the rеsearched
ment structural element structural element ultrastructural ele-
feature ment feature
insignificant E1
Intrafollicu- electron
density moderate E2
lar colloid significant E3
insignificant H1
Microvilli quantity moderate H2
of the apical significant H3
cytosolic insignificant H4
membrane density, moderate H5
length
significant H6
insignificant G1
quantity moderate G2
significant G3
small G4
Lysosome size medium G5
bodies big G6
insignificant G7
electron moderate G8
density
significant G9
insignificant M1
quantity moderate M2
significant M3
insignificant M4
Secretory electron
moderate M5
granules density
significant M6
apical cellular pole M7
allocation along the whole M8
cytosolic membrane
topographic connection present M9
with lysosome bodies absent M10
Table 6. Scale of the evaluation of the features severity in the semi-quantitative analysis of
electronograms.
Numerical assessment
Feature severity Graphic
degree symbol (points) (percentage)
Feature absent - 0 0
weak + 1 25
moderate ++ 2 50
significant +++ 3 75
maximal ++++ 4 100
Note. 0 points - state of unattended pathology under study ("disease"); 4 points - state of the studied pathol-
ogy complete absence ("health").
Table 7. Results of transforming qualitative data of follicular thyrocyte secretory activity clus-
ter into quantitative indices.
Designation / numerical rating (points)
Е1 Е2 Е3 М1 М2 М3 М4 М5 М6 М7 М8 М9 М10
A #1 0 1 3 3 1 0 0 3 1 2 2 1 3
#2 0 1 2 3 1 0 0 3 1 3 3 2 2
#3 0 0 3 3 2 0 0 4 2 3 3 2 3
#4 0 1 2 3 1 0 0 3 1 2 2 1 3
#5 0 1 3 2 1 0 0 3 1 2 2 1 3
mean
values 0 0,8 2,6 2,8 1,2 0 0 3,2 1,2 2,4 2,4 1,4 2,8
B #6 0 1 3 3 1 0 0 0 4 1 3 4 0
#7 0 1 2 3 1 0 0 0 3 1 4 3 0
#8 0 0 3 3 0 0 0 0 4 1 4 4 0
#9 0 1 2 4 1 0 0 0 4 0 3 4 0
#10 0 1 3 4 1 0 0 0 4 1 3 4 0
mean
values 0 0,8 2,6 3,4 0,8 0 0 0 3,8 0,8 3,4 3,8 0
Note. A - series with adding 100 μg of organic iodine into the white male rats ratio under the conditions of
thyroid hyperthyroidism; B - series with adding 100 μg of inorganic iodine into the white male rats ratio
under the conditions of thyroid hyperthyroidism; # - studied electronograms.
Table 8. Results of transforming qualitative data of follicular thyrocyte secretory activity clus-
ter into quantitative indices.
Designation / numerical rating (points)
Н1 Н2 Н3 Н4 Н5 H6 G1 G2 G3 G4 G5 G6 G7 G8 G9
A #1 3 1 0 3 1 0 3 1 0 1 0 3 0 0 4
#2 2 2 0 2 1 0 3 2 0 2 0 4 0 0 3
#3 2 1 0 3 1 0 3 1 0 1 0 2 0 0 3
#4 2 2 0 2 2 0 2 1 0 1 0 3 0 0 4
#5 3 1 0 3 1 0 2 1 0 1 0 3 0 0 4
mean
values 2,4 1,4 0 2,6 1,2 0 2,6 1,2 0 1,2 0 3 0 0 3,6
B #6 0 1 3 0 1 3 3 1 0 0 0 4 0 0 4
#7 0 1 4 0 0 4 4 1 0 0 0 3 0 0 3
#8 0 1 3 0 1 3 4 0 0 0 0 4 0 0 4
#9 0 1 4 0 1 3 3 1 0 0 0 4 0 0 4
#10 0 2 3 0 1 3 3 1 0 0 0 4 0 0 4
mean
values 0 1,2 3,4 0 0,8 3,2 3,4 0,8 0 0 0 3,8 0 0 3,8
Note. A - series with adding 100 μg of organic iodine into the white male rats ratio under the conditions of
thyroid hyperthyroidism; B - series with adding 100 μg of inorganic iodine into the white male rats ratio
under the conditions of thyroid hyperthyroidism; # - studied electronograms.
The fourth stage is devoted to designing the intra-cluster (intra-system) correlation
portraits, which permits to visualize the traced correlations. Analysis of the estab-
lished connections is performed taking into account their strength, quantity and direc-
tion (sign). The positive value of the pair correlation coefficient indicates the same
direction of change in the studied indices, the negative means that with an increase in
one of the indices another indice associated with it reduces; the value rxy = 1.0 indi-
cates the existence of a direct proportional feedback between the x and y, r xy = -1.0
means inversely proportional feedback. In the structural organization of the interrela-
tions between the indices, the most significant are considered very strong and strong
connections, which on the Chaddok correlation scale are respectively within 1.0 ≥ | r |
> 0.9 and 0.9 ≥ | r | ≥ 0.7; in the absence of such connections, the noticeable (0.7 ≥ | r |
> 0.5) and moderate (0.5 ≥ | r | > 0.3) connections are studied. We find it irrelevant to
analyze the weak (0.3 ≥ | r | > 0.1) connections, since it can distort results because of
the fact that the biological system’s functioning as a whole is due to the presence of
various connections between its constituent parts. Functional analysis of the traced
correlations is carried out based on cytophysiology [8], taking into account the role of
each cluster ultrastructure and its status significance for the organelle activity.
As an example of the cytophysiological information visualizing possibilities, we
report the study on the follicular thyrocytes secretory capacity in white male rats un-
der the conditions of administering iodine of different chemical nature against the
background of thyreoidin hyperthyroidism (see Fig. 3).
Fig. 4. Graphic representation of the correlation portraits structure for the secretory features of
the thyroid glands follicular thyrocytes in white male rats receiving 100 μg of organic (A) and
inorganic (B) iodine in the model conditions of thyreoidin hyperthyroidism.
The results processing was performed using the software: for the digital parameters -
StatSoft Statistica v6.0 package, for correlation tables and portraits – Microsoft Office
2010 package (Microsoft Excel spreadsheet and Microsoft Word editor, respectively).
If it is necessary to carry out an in-depth intra-cluster/intra-system study, several
more steps are to be added. For this purpose the module of these clusters have been
formed. At the fifth stage, the elements significant for implementing the research in
the studied area of the cell activity are determined, in particular, those elements of the
studied object, which in the studied conditions, are the most sensitive to the studied
influence ("point of reference") and elements of the surrounding systems, changes in
which under studied conditions are the most pronounced ("point of attraction"). For
example, in the case of taking iodine in some pathological conditions (hypothyroid-
ism, etc.), the dose of iodine consumed (“the point of action”), affecting the state of
ultrastructure of a certain protein-synthesizing organelle (“the point of response”),
causes changes in the structure of the “attraction points”, which are other cells’ orga-
nelles of the thyroid gland, functionally associated with follicular thyrocytes.
At the sixth stage, the presence/absence of correlation dependencies between
“points of response” and “points of attraction” is determined and analyzed, the results
obtained are interpreted.
The seventh, final, stage is devoted to the development of integrative mathematical
complexes/models as a result of the interaction between “points of response” and
“points of attraction” that are present in the studied biological object [9]. The peculi-
arity of intra-thyroid control of the thyroid gland activity as an organ is the simultane-
ous presence of cells in its parenchyma that produce specific thyroid hormones, and
the cells producing the hormones-antagonists. This can cause some difficulties in the
development of integrative mathematical research models. At the same time, applica-
tion of the suggested approach permits to simultaneously take into account the fea-
tures of cell activity in different areas. In this case, the “response system” are cells of
the follicular epithelium, and the “system of action” and the “target of action” are
certain substances and their doses affecting the follicular thyrocyte, the “object of
response” is the thyrocyte’s structures producing a specific hormone, the “points of
response” are the form of thyrocyte and the form and degree of its nucleus electron
density, the “attraction system” is the C-cell as a functional antagonist of the thyro-
cyte, “attraction points” in the C-cell are its cytoplasm and nucleus (the number of
organelles in the cytoplasm, the nomenclature of prevailing organelles and their sta-
tus, the electron density of nuclear chromatin, etc.) [9]. Subsequent analysis, pro-
cessing and integration of the data obtained can give reason to suppose that an in-
crease in the number of C-cells and their ultrastructural elements, such as granular
cytoplasmic reticulum and the Golgi complex, under the condition of a certain exten-
sion of the said organelles’ substructures at flattening of thyrocytes and their nuclei,
will indicate a reduced background of the thyrocyte functional activity. The experi-
mental proof of these theoretical assumptions creates the prospect of adjusting the
activity of the thyroid gland by affecting the C-cells activity.
The presented method of studying hormone-producing cells was implemented by
us into studying the features of follicular thyrocytes synthetic capacity, in particular
the search for markers of changes in their morpho-functional status under the influ-
ence of iodine-containing compounds [10]. In general, the use of mathematical tech-
nologies as a tool for studying the biological system permits to take into account a
significant number of its qualitative characteristics, which significantly expands the
capabilities of the researcher-theorist to determine its status and possible changes, and
the researcher-clinician – to establish the correct diagnosis and choose a strategy of
treatment.
Thus, the methods of analysis, on which the mathematical support of biomedical
diagnostics is based, particularly in cytology, are not sufficiently adapted to work
with high-quality information. At the same time, it is linguistic (non-numeric) infor-
mation that is a prerequisite for increasing the amount of information obtained about
the studied object, since it contains the greatest information about all the subtle as-
pects of its condition. Our approach, suggested for work with linguistic information
while performing cell status analysis, is in line with the idea of expediency of using
step-by-step algorithms for the medical data analysis [11].
The similar idea is suggested by [12] who believe that the process of medical diag-
nosis should take place stagewise, with the first stage being processing of linguistic
information obtained from the patient. To some extent, the approach presented is
consistent with the views of [13] concerning the necessity of all the morpho-
functional module components joint activities, aimed at achieving a general beneficial
result.
At testing the informativity of the studies results, obtained with their help, and clar-
ifying the availability of the results that was carried out in our work [7], carried out
from the standpoint of cybernetic insight into the cell as a complex self-regulating
system, it was established that the presented method of studying hormone-producing
cells is not associated with the strict determinism of the input information. Instead, it
permits the correct transformation of qualitative/binary indices into quantitative ones
and integration of the results obtained.
5 Conclusion
The suggested objectivizing of the database with a collection of morpho-functional
information on the thyrocyte as a hormone producing cell is carried out using StatSoft
Statistica and Microsoft Office (Word and Excel) software packages in accordance
with our method’s stages developed with a detailed description of each constituent
element in the studied clusters. Establishment of interdependencies between them is
an integral research approach that enables the creation of correlation portraits as the
basis for the further development of an expert system for solving problems in cyto-
morphology and cytophysiology.
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