=Paper= {{Paper |id=Vol-2654/paper23 |storemode=property |title=Advanced selection of ideal building blocks for ‘‘smart’’ biocompatible surfaces: prediction and study of polymer properties and streaming video recognition method for observation of the cellular behavior and economic effect |pdfUrl=https://ceur-ws.org/Vol-2654/paper23.pdf |volume=Vol-2654 |authors=Yurij Stetsyshyn,Uriy Kryvenchuk,Yuliia Chyrkova,Tetiana Helzhynska,Pylyp Skoropad,Liubomyr Pohreliuk,Yurij Panchenk |dblpUrl=https://dblp.org/rec/conf/cybhyg/StetsyshynKCHSP19 }} ==Advanced selection of ideal building blocks for ‘‘smart’’ biocompatible surfaces: prediction and study of polymer properties and streaming video recognition method for observation of the cellular behavior and economic effect== https://ceur-ws.org/Vol-2654/paper23.pdf

Advanced selection of ideal building blocks for ‘‘smart’’
biocompatible surfaces: prediction and study of polymer
properties and streaming video recognition method for
observation of the cellular behavior and economic effect

Yurij Stetsyshyn [0000-0002-6498-2619], Uriy Kryvenchuk[0000-0002-2504-5833],
Yuliia Chyrkova [0000-0001-7315-9520], Tetiana Helzhynska[0000-0003-3280-5199],
Pylyp Skoropad [0000-0003-3559-6580], Liubomyr Pohreliuk[0000-0003-1482-5532],
Yurij Panchenko [0000-0003-1760-177X]

Lviv Polytechnic National University, Lviv 79013, Ukraine
yrstecushun@ukr.net, yurii.p.kryvenchuk@lpnu.ua,
yuliia.l.chyrkova@lpnu.uatanyazvarich@ukr.net,
skorfil@yahoo.com,liubomyr@inoxoft.com, yupanchenko1@gmail.com

Abstract. In this work we developed some aspects for advanced selec-
tion of ideal building blocks for ‘‘smart’’ biocompatible surfaces using
prediction of polymer properties from additive group contributions. Es-
pecial interest was included in prediction of wettability and surface en-
ergies of “smart” polymer brush coatings which may strong impact on
many biological processes rely on feedback-controlled interactions with
biomolecules and cells. Idea for identification of another crucial aspect
for application of the polymers as biocompatible materials, behavior of
the cells was developed using streaming video recognition method.

Keywords: Streaming video recognition method, cellular behavior, prediction
of polymer properties.

1 Introduction

Nowadays, information technologies and artificial intelligence offer lots of opportuni-
ties for chemical, physical, biological and material sciences. Polymers form an im-
portant, amazing and challenging class of the materials. They are widespread with
applications ranging from daily products, e.g., plastic packaging and containers, to
know-how, e.g., high-energy density capacitors, polymer light-emitting diodes, con-
trolled release dosage forms and “smart” biomaterials. Their chemistry and morphol-
ogy are extremely vast and complex, leading to fundamental barriers in polymer stud-
ies. Recent successes in rationally designing polymer as biomaterials via experiment-
computation synergies indicate that there may be opportunities for theoretical predic-
tion, machine learning, informatics approaches and advanced technologies in this new
research and development area.

Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons Li-
cense Attribution 4.0 International (CC BY 4.0). CybHyg-2019: International Workshop on
Cyber Hygiene, Kyiv, Ukraine, November 30, 2019.
One of most important trends in application of the polymer as biomaterials are sur-
face modification with polymer brushes which allow to add to surfaces a valuable and
desired properties. In the most general sense, polymer brushes are defined as thin
polymer films in which the individual polymer chains are tethered by one chain end to
a solid interface. Depending on the density at which polymer chains are anchored to
the surface and their molecular weight, surface-anchored polymer chains can adopt
various conformations, from the so-called “mushroom” or “pancake” regime at low
grafting densities to the high density “brush” regime.
One of the most important characteristic of the grafted polymer brushes are surface
wettability. Surface wettability is an important parameter and is the first that was
controlled in almost all surface modification strategies. Wettability mainly depends on
surface chemical functionality, although surface roughness also plays an important
role. This phenomenon is closely related to another surface properties like reactivity,
absorption, and even mechanical properties. In general, are numerous ways to modify
surface wettability using polymer brushes, including grafting different types of poly-
mer brushes, copolymer brushes, binary polymer brushes and using stimuli-
responsive polymer brushes that able to change wettability upon an external trigger.
In our opinion, modifications with different polymer brushes that are sensitive to sur-
rounding area are the simplest way to change surface wettability. Prediction of the
polymer properties allows to solve important questions about availability their appli-
cation as biocompatible materials. As was mentioned before, one of most important
properties are wettability that can be calculated theoretically but all parameters such
as low critical solution temperature and other may be considered here.
To solve the problems described above we should use advanced theoretical predic-
tions and machine learning technologies. We propose to use prediction of polymer
properties from additive group contributions but the same time all possible polymer
parameters should be considered to eliminate the possible errors. Another side our
study is detailed analysis of biocompatibility (cellular behavior) that can be realized
using streaming video recognition method.

2 State of arts

In the macromolecular area the amount of literature sources are extremely large but
really often are problem that neither directly measured properties before or reliable
methods to calculate them can not find. To predict properties of the polymers the
simple and very successful method based on the concept of additive group contribu-
tions was firstly proposed by van Krevelen [1]. In general, thousands of chemical
substances may be virtually decomposed to the number of structural and functional
groups. The assumption that a physical property of a compound, e.g. a polymer, is in
some way determined by a sum of contributions made by the structural and functional
groups in the molecule or in the repeating unit of the polymer, forms the basis of a
method for estimating and correlating the properties of a very large number of com-
pounds or polymers, in terms of a much smaller number of parameters which charac-
terize the contributions of individual groups. These group contributions are called as
increments. The basic assumption of the group-contribution method is additivity. This
method was valid only when the influence of any one group in a structural unit of a
polymer was not affected by the nature of the other groups that are essential limitation
for this technique.
In this work we developed some aspects for advanced selection of ideal building
blocks for ‘‘smart’’ biocompatible surfaces using prediction of polymer properties
from additive group contributions. Especial interest was included in prediction of
wettability and surface energies of “smart” polymer brush coatings which may strong
impact on many biological processes rely on feedback-controlled interactions with
biomolecules and cells.
Approach based on works developed by Girifalco and Good was used here [1].
Wetting contact angles were calculated using equations:
1
  2
cos  2  s   1 (1)
 l 
1
4(Vs Vl ) 3
 1 1
(2)
(Vs 3  Vl 3 )2
where, Φ - ratio of liquid and solid molar volumes in Eqs.1 and 2
s - surface tension of solid, mN/m;
l - surface tension of liquid, mN/m (values from Tables);
Vs, Vl - molar volume of polymer and liquid, respectively (values from Tables).
Polymer surface energy was calculated using:
4
s   Ps V  (3)
 s 

where, Ps – molar parachor (molar parachor is a useful means of estimating surface
tensions); Vs - molar volume of polymer.
The parachor was introduced by Sugden, who first gave a list of atomic constants.
Later the atomic and group contributions were slightly modified and improved by
Mumford and Phillips and by Quayle (see Table 1) [1].
Calculation of the wetting contact angle and surface energy (surface tention)
are presented by van Krevelen for solid poly(methyl methacrylate) (see fig. 1).
CH3
CH2 C
C n
O OCH3
Fig. 1. Unit of poly (methyl methacrylate)
Table 1. Atomic and structural contributions to the parachor

Units Values assigned by
Sugden Mumford and Quayle
Phillips
–CH2– 39.0 40.0 40.0
>C< 4.8 9.2 9.0
–H 17.1 15.4 15.5
–O– 20.0 20.0 19.8
–O2 (in esters) 60.0 60.0 54.8
>N– 12.5 17.5 17.5
–S– 48.2 50.0 49.1
–F 25.7 25.5 26.1
–Cl 54.3 55.0 55.2
–Br 68.0 69.0 68.0
–I 91.0 90.0 90.3
Double bond 23.2 19.0 16.3–19.1
Triple bond 46.4 38.0 40.6
Three-membered ring 16.7 12.5 12.5
Four-membered ring 11.6 6.0 6.0
Five-membered ring 8.5 3.0 3.0
Six-membered ring 6.1 0.8 0.8

Table 2. Contributions of the functional groups of poly(methyl methacrylate) to the parachor

Units Parachor [(mN/m)1/4×(cm3/mol)] Calculation
1 –CH2– 39.0

1 >C< 4.8
2–CH3 112.2 2×(CH2+H)=2×(39.0+17.1)
1 –COO– 64.8 1×(C+ O2(in ester))=( 4.8+60)
220.8

Molar volume of polymer was taken from Tables in [1] and equal to 86.5 cm3/mol.
Contributions of the functional groups of poly(methyl methacrylate) to the parachor
were calculated in Table 2.
Polymer surface energy was calculated with Eqs. 3 as (220.8/86.5)4 and equal to 42.5
mN/m.
According to Eqs. (2), where Vl (molar volume of water) is equal to 18 (value from
Table in [1]) calculated Φ=1.
In turn, using Eqs. (3), where λl for water is 72.8 (surface tension of liquid) the
contact angle with water (cos θ) was determined as: 2×1×(42.5/72.8)1/2-1=0.42 so that
θ≈65°.
An experimental value of wetting contact angle for poly(methyl methacrylate) (θ≈
67°) has been published by Ma [2], which is in good correspondence with
theoretically calculated.
To continue mentioned above analysis, poly(N-isopropylamide) – (pNIPAM),
poly(di(ethylene glycol)methyl ether methacrylate) – (pOEGMA188), poly(N-
methacryloylleucine) - (pNML) were selected (see. Fig.2). All presented here
polymers are thermoresponsive and able to temperature-induced transitions which are
expressed as reorientation of the polymer macromolecules (conformational changes)
resulting in essential changes of wetting etc. Temperature-responsive changes for
these polymers are based on low critical solution temperature (LCST).
First presented here polymer - PNIPAM are perhaps the most heavily studied
thermoresponsive polymers. Although this group consists of few tens of the
thermoresponsive polymers, only poly(N-isopropylacrylamide) and its copolymers
have practical applications. In modern literature, there are numerous references
devoted to this polymer, and special attention is paid to poly(N-isopropylacrylamide)
(pNIPAM) grafted brushes. LCSTs of the pNIPAM and its copolymers are nearly 32
°C, which is close to the physiological temperatures of the human body. pNIPAM
has been utilized in various biomedical applications such as biosensors, thermally
modulated drug and gene delivery systems, pNIPAM conjugated proteins for
thermally modulated enzyme function etc [3-9].
Second polymer, poly(di(ethylene glycol)methyl ether methacrylate) –
(pOEGMA188) are biocompatible, uncharged, water-soluble, nontoxic,
nonimmunogenic and therefore the most commonly applied synthetic polymers in the
biomedical field [10-11].
Last presented here polymer is pNML which is based on natural amino acid.
Application of pNML as the “smart” polymer offers at least two advantages: firstly,
the leucine fragments of PNML, unlike other synthetic polymers, can provide peptide-
mimetic properties for a polymer [12]. Secondly, the phase transition temperature of
PNML can be tuned by modification of the free carboxyl groups. Moreover, free
carboxyl groups can be further used for the attachment of biomolecules.
pNIPAM pOEGMA188 pNML
CH3 CH3
CH2 CH CH2 C CH2 C
n n n
C O C O C O
O NH
NH
HC COOH
CH
H3C CH3 CH2
O 2 CH
CH3
CH3 CH3
Fig. 2. Chemical structures of some polymers for biomedical applications.

In Table 3 are presented values of molar volumes and parachors described above
polymers and calculated wetting contact angles in according to to Eqs. (1) and (2).
Table 3. Molecular volumes, parachors and calculated wetting contact angles for pre-
sented above polymers
Polymer Molar volumes Parachor Calculated θ
[cm3/mol] [(mN/m)1/4×(cm3/mol)] [deg]
pNIPAM 107.6 290.2 ≈56
pOEGMA 160 411 ≈69
pNLM 173.3 424.9 ≈76
Calculated wetting contact angles were compared with experimental wetting con-
tact angles obtained in our previous works [12-14]. Figs. 3-5 show the temperature
dependences of the wetting contact angles for the pNIPAM, pOEGMA and pNLM
grafted brush coatings, respectively. As we can see, values of the wetting contact
angles for pNIPAM and pNLM at top of the curve are in good accordance with theo-
retically calculated (see Table 3).
60
Contact angle [deg]

20
20 25 30 35o
40
Temperature [ C]
Fig. 3.Temperature dependences of the wetting contact angles for the pNIPAM grafted brush
coatings. Solid lines are guide for eyes.

80
Contact angle [deg]

20
0 20 40
0
Temperature [ C]
Fig. 4.Temperature dependences of the wetting contact angles for the pOEGMA
grafted brush coatings. Solid lines are guide for eyes.
85

Contact angle [deg]
80

60
0 10 20 30
0
Temperature [ C]
Fig. 5.Temperature dependences of the wetting contact angles for the pNML grafted brush
coatings. Solid lines are guide for eyes.

In contrary, for pOEGMA calculated value is essentially another than experimental.
For all cases, temperature-induced changes with sharp transition at are visible. Pre-
sented above model are probably simple enough to pay attention to all parameters
such as interaction with water and temperature-depended interaction with water, in
these cases named as the low critical solution temperature (LCST). Presented above
curves demonstrate the changes in wetability during increasing of the temperature.
Additional parameters such as LCST may be included in model. LCST is temperature
when the polymer molecules attract one another but not solvent [15-29].
For polymers that exhibit an LCST behavior in aqueous solution, the responsive pol-
ymer is soluble due to extensive hydrogen bonding interactions with the surrounding
water molecules at temperature and restricted intra- and intermolecular hydrogen
bonding between polymer molecules before LCST. Upon heating, hydrogen bonding
with water is disrupted, and intra- and intermolecular hydrogen bonding/hydrophobic
interactions dominate, which results in a transition in solubility. The transition tem-
perature of a polymer in solution is one of the most important parameters to take into
account when considering applications under a given set of conditions. For instance,
many of the thermoresponsive polymers reported to have potential use in biomedical
applications have transition temperatures between room temperature and body tem-
perature. In grafted polymer brushes LCST induces transition from hydrated loose
coils to hydrophobic collapsed chains. The LCST can be readily tuned by incorporat-
ing hydrophilic or hydrophobic character by copolymerization with hydrophobic or
hydrophilic comonomers or end group transformations. By increasing the hydrophilic
nature of the polymers, the overall hydrogen bonding ability of the macromolecules is
increased, which leads to higher transition temperatures. Incorporating hydrophobic
groups lowers the LCST. Furthermore, the addition of hydrophobic groups causes a
disruption of the structure of water around the macromolecules. This enhances the
interaction of hydrophobic species, further facilitating aggregation.
We would like omit here discussion about LCST as part theoretical model presented
here but this moment may be developed in details to predict properties of grafted
polymer brushes. Another important aspect to predict properties of biocompatible
surfaces to have information on cellular behavior on different coatings [18, 30].

Fig. 6. Phase contrast microscopy images of L929 mouse fibroblasts on oligo(ethylene glycol)-
based thermoswitchable substrates after 44 h of incubation at 37 °C (left) and 30 min after
cooling the sample down to 25 °C (right). Scale bars correspond to 100 mm. Cartoons (top
panel) show a schematic view of the polymer coatings at 37 and 25 °C [18].

Fig. 6 show phase contrast microscopy images of L929 mouse fibroblasts on oligo
(ethylene glycol)-based thermoswitchable substrates after 44 h of incubation at 37 °C
(left) and 30 min after cooling the sample down to 25 °C (right). Cartoons (top panel)
show a schematic view of the polymer coatings at 37 and 25 °C. As we see, morphol-
ogy of the cell is principally different at temperature transition from 37 °C to 25°C. It
depends on sharp changes in physic-chemical properties of grafted brush coatings at
relatively small changes in temperature. Analysis of the cellular behavior at dynamic
observations is important aspect for understanding of the biocompatibility which may
be realized using streaming video recognition method.

3 Streaming video recognition method

The analysis showed that for the recognition of groups of different objects or individ-
ual objects, the python is best suited as a programming language and a superstructure
of pandas for pattern recognition. In the Python ecosystem, pandas are the most ad-
vanced and fastest growing library for processing and analyzing data. To work effec-
tively with pandas, you need to master the most important data structures of the li-
brary: DataFrame and Series. Without understanding what they are, it is impossible to
conduct a qualitative analysis in the future. You can often find ready-made solutions
for face recognition in Python, but there is nothing on the network for cell recogni-
tion. Therefore, it was decided to adapt python libraries for such tasks. To do this, you
need to understand the basic concepts.

3.1 Series
The Series structure / object is an object that looks like a one-dimensional array (a
Python list, for example), but its distinguishing feature is the presence of associated
labels, indexes, along each element of the list. This feature turns it into an associative
array or dictionary in Python.
>>> import pandas as pd
>>> my_series = pd.Series ([5, 6, 7, 8, 9, 10])
>>> my_series
0 5
16
27
38
49
5 10
dtype: int64 >>>
In the string representation of the Series object, the index is on the left and the el-
ement is on the right. If the index is not explicitly specified, then pandas automatical-
ly creates a RangeIndex from 0 to N-1, where N is the total number of elements. It is
also worth noting that Series has a type of stored elements, in our case it is int64,
because integer values are transmitted. The Series object has attributes through which
you can get a list of elements and indexes, these are values and index, respectively. A
list with indexes in length must match the number of items in Series.

3.2 DataFrame
It is best to imagine a DataFrame as a regular table, and that’s right, because a Da-
taFrame is a tabular data structure. Any table always has rows and columns. The col-
umns in the DataFrame are Series objects whose rows are their immediate elements.
The easiest way to construct a DataFrame is to use the Python dictionary:
>>> df = pd.DataFrame({
... 'country': ['Poland', 'England', 'USA', 'Ukraine'],
... 'population': [17.04, 143.5, 9.5, 45.5],
... 'square': [2724902, 17125191, 207600, 603628]
... })
>>> df
country population square
0 Poland23.04 2724902
1 England 143.50 17125191
2 USA 199.50 207600
3 Ukraine 23.50 603628
A DataFrame has 2 indexes: row and column. If the row index is not explicitly
specified (for example, the column by which you want to build them), then pandas
sets the RangeIndex integer index from 0 to N-1, where N is the number of rows in
the table.
Access to rows by index is possible in several ways:
.loc - used to access by string label;
.iloc - used to access by numerical value (starting from 0).
Pandas supports all the most popular data storage formats: csv, excel, sql, clip-
board, html. Most often you have to work with csv files. For example, to save our
DataFrame with countries, just write:
>>> df.to_csv('filename.csv')
The to_csv functions still receive various arguments (for example, a separator be-
tween columns), which can be found in the official documentation for more details.

You can read data from a csv file and turn it into a DataFrame with the read_csv
function.
>>> df = pd.read_csv('filename.csv', sep=',')
The sep argument indicates split columns. There are many more ways to form a
DataFrame from various sources, but most often use CSV, Excel and SQL. For exam-
ple, using the read_sql function, pandas can execute an SQL query and form the nec-
essary DataFrame based on the response from the database.

3.3 OpenCV

OpenCV uses machine learning algorithms to search for cells in an image. Because
the cell is so complex, there is no simple test that will tell you whether a cell has been
found or not. Instead, there are thousands of little patterns and features to match. Al-
gorithms break down cell detection tasks into thousands of smaller tasks, each of
which is easy to solve. These tasks are also called classifiers. For something like a
cell, you may have 6,000 or more classifiers, all of which must match the cell's detec-
tion (of course, within error). But therein lies the problem: the cell recognition algo-
rithm starts at the top left of the image and moves down the small blocks of data,
looking at each block, constantly asking, "Is this a cell?… Is this a cell?… Is this a
cell?" Because the block has 6,000 or more tests, you may have to perform millions of
calculations that stop your computer from stopping. To bypass this, OpenCV uses
cascades. What is a cascade? The best answer can be found in the dictionary: “water-
fall or series of waterfalls.” Like a series of waterfalls, the OpenCV cascade breaks
down the problem of detecting cells into several stages. For each block it does a very
rough and fast test. If it passes, a slightly more detailed test is done. The algorithm
can have 30 to 50 of these stages or cascades and it will detect cells only then The
advantage is that most images will return negative during the first few steps, which
means that the algorithm does not spend time testing all 6000 functions. and on it.
Instead of taking hours, cell recognition can now be performed in real time. Although
the theory may seem complicated, in practice it is quite simple. The cascades them-
selves are just a bunch of XML files that contain OpenCV data that is used to detect
objects. You initialize your code with the desired cascade, and then it does the work
for you.
What if you want to use a camera? OpenCV captures each frame from the camera,
and then you can detect the cells by processing each frame. This task will require a
powerful computer, because there are many frames and everyone needs to perform
logical operations described above.

3.4 System architecture

However, not only the software part plays a key role in recognizing the behavior of
cells. There is a need to develop a system for controlling cell growth, as well as to
automate it. After conducting an analysis, it was decided to develop its own system,
as there are almost no similar systems in the public domain in the world. Figure 7
shows the architecture of the cell growth control system.
On a substrate preliminary spend on all cases. They need to be believed at a tem-
perature of 37 degrees Celsius, while the vibration temperature is not observed in the
forge, and you need to increase the growth rate.
The microscope supports a high-speed picture that adjusts the program and infor-
mation. The resulting usage is recorded as the current video for use with a high-
resolution camera. The video stream is transmitted to the server, where it is further
processed.
For the first time, the video is divided into frames for using OpenCV. Each frame
is recognized in fast real time, in fact instantly receives information.

Fig. 7. Architecture of cell growth control system

The system monitors the growth of cells, and when the cell boundaries begin to
approach each other, the system automatically corrects the temperature of the lining to
25 degrees Celsius. The cells begin to acquire an oval-shaped shape and detach from
the substrate. All manipulations with the thermostat, the camera, the recognized
frames are logged on the server, and the system informs the laboratory assistant about
the key moments.

4 Conclusions

Recent successes in rationally designing polymer as biomaterials via experiment-
computation synergies indicate that there may be opportunities for theoretical predic-
tion, machine learning, informatics approaches and advanced technologies in this new
research and development area. In this work we developed some aspects for advanced
selection of ideal building blocks for ‘‘smart’’ biocompatible surfaces using predic-
tion of polymer properties from additive group contributions. Especial interest was
included in prediction of wetability and surface energies of “smart” polymer brush
coatings which may strong impact on many biological processes rely on feedback-
controlled interactions with biomolecules and cells. Obtained results suggest that only
complex approach including all possible parameters (not only parachor and molar
volume but also LCST and interaction between polymer and water) of the polymer
system may be successful applied to predict wetability and surface energy.
Another crucial aspect for application of the polymers as biocompatible materials
are their biocompatibility based on cellular behavior. The analysis showed that today
in the fields of biotechnology, chemistry there is no publicly available system that
able successfully to observe the cellular growth and development. Manual control for
such production processes is obsolete and increases the number of defects. The analy-
sis showed that when automating the process of growing cells, the shortage will de-
crease by 29.3% compared to manual control (Figure 8). Also, in the future it is
planned to modernize the system to ensure a minimum of human influence.

Fig. 8. The ratio of the number of defects in manual and automatic control.

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