**1. Introduction**

Matrix metalloproteinase-8 (MMP-8), also known as neutrophil collagenase or collagenase-2, is a Zn+2 -dependent endopeptidase of the MMP family. It is primarily expressed and secreted by neutrophils as a zymogen (pro-MMP-8), and is activated by the reactive oxygen species released from activated neutrophils, thus rendering MMP-8 as having a key role in both acute and chronic inflammation [1]. Tissue inhibitors of metalloproteinases (TIMPs) regulate the activity of MMP-8 by forming a 1:1 stoichiometric inhibitory complex [2]. However, imbalances in MMP-8 and TIMP levels can lead to increased collagen degradation and pathological remodeling of the extracellular matrix [1]. Although it was initially believed that MMP-8′ s only function was to degrade collagen, later studies have identified both non-collagenous and non-structural substrates [1], thereby implicating MMP-8 in various pathological processes (see Table 1 for the full listing). In recent years, a wealth of preclinical and clinical data have emerged that correlate high MMP-8 levels with the potential for diagnosing diseases [3]. Likewise, MMP-8 inhibitors have also been suggested as potential therapeutics for treating some of these pathologies [3].

**Table 1.** Matrix metalloproteinase-8 (MMP-8) in various diseases.

Most early attempts to develop MMP inhibitors focused on developing peptide-based agents mimicking endogenous substrate sequences aided by a hydroxamate group, which ligates the critical Zn+2 co-factor [3,16]. Unfortunately, the high affinity of hydroxamates to Zn+2 containing proteins, which include several MMPs, as well as a disintegrin and metalloproteinase (ADAM) and ADAM with thrombospondin motifs (ADAMTS) family members, introduced a significant non-selectivity of inhibition, causing severe side-effects in the clinical trials [3,16]. Later, researchers focused on the development of peptides with a lower affinity to Zn+2 by including phosphonates, thiolates, and carboxylates in the structure (see Table S1) [3,16–18]. Although such peptides demonstrated significantly reduced side-effects, they failed to show sufficient efficacy in the clinical trials [3]. This resulted in a rather negative perception of MMPs as drug targets in the community [3].

After nearly a decade of no progress in clinical trials with MMP inhibitors, a better understanding of the MMP biology has led to the insight that the initial clinical trials were perhaps held prematurely [3,16]. Learning from both the fundamental advances made so far and the limitations of the broad-spectrum non-selective MMP inhibitors, researchers have focused on the development of selective small molecule MMP inhibitors. This has led to the development of selective MMP-2, -9, and -13 inhibitors. Several of these molecules are now being tested in clinical trials [3,16].

In contrast to these successes, MMP-8 appears to be a rather challenging target. Although small molecule inhibitors are likely to prevent and treat multiple diseases (Table 1), few reports are available on the development of MMP-8 inhibitors. In fact, only two publications have reported MMP-8 inhibitors in the last decade [19,20]. Thus, the vast chemical space offered by small molecules appears to be almost untouched for MMP-8 inhibitor development.

Natural glycosaminoglycans (GAGs) modulate various functions of MMPs, including cell localization, conformation, and stability [21,22]. As one would expect, based on the extensive literature behind GAG–protein interactions [23,24], GAGs are expected to affect MMP activity, depending on the length of their polymeric chains and the structure of the local sulfated microdomains [21,25]. In fact, distinct GAGs have been found to inhibit MMP-9, which is closely related to MMP-8 [26]. Furthermore, the potency of natural GAGs against MMPs is quite promising. However, the heterogeneity of these GAGs is problematic for drug development. Additionally, polymeric natural GAGs generally display unwanted side-effects; they are impossible to chemically synthesize, and their bio-analytical characterization is a major challenge. These challenges reduce the enthusiasm to pursue natural GAGs as drugs [27].

In contrast, sulfated small molecules that mimic GAGs offer much better prospects. These molecules, referred to as non-saccharide GAG mimetics (NSGMs), have now been shown to bind and functionally modulate multiple GAG-binding proteins [28–34]. For example, distinct NSGMs have been identified as ligands of fibroblast growth factor receptor-1 [28], antithrombin [33,34], neutrophil elastase [29], coagulation factor Xia [30,35], plasmin [31], and viral glycoprotein D [32], each of which are known to bind to heparin. In fact, several NSGMs have been found to display one-to-one correspondence with specific GAG sequences [28,36]. In terms of drug-like properties, NSGMs are fully synthetic, which bodes well for analytical ease and scalability of preparation [27,37]. Their much smaller molecular size (MW 500–2000) in comparison with natural GAGs (MW 15,000–100,000) enables a rational and/or computational design for identifying advanced analogs [28]. Additionally, NSGMs are highly water-soluble owing to the presence of one or more sulfate groups, which enables easier formulation, e.g., direct instillation into the lung. This property could have important value for lung disorders, such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), where MMP-8 inhibition has shown to be beneficial.

Over the past decade, we have developed a library of NSGMs (Figure 1) with 12 different chemical scaffolds (apigenin, benzofuran, catechin, glucoside, gossypetin, inositol, luteolin, morin, phloretin, quercetin, quinazolinone, and resveratrol). The library contains mimetics with different chain lengths (monomer, dimer, and trimer), as well as varying sulfation levels (mono-sulfate through dodeca-sulfate), which affords an excellent potential to mimic the diversity of the chain length and sulfation patterns of GAGs.

The rationale behind the development of the NSGM library was that while the sulfate groups will offer GAG-like protein recognition features, the aromatic scaffold will impart hydrophobic characteristics. These dual ionic and non-ionic forces of recognition are likely to enhance selectivity for the target protein of interest. Using this concept, distinct NSGMs have been developed as promising anticoagulants [35], antifibrinolytics [31], anticancer [39], anti-inflammatory [29], and antiviral agents [32]. More importantly, our work has shown that initial "hits" can be systematically developed through an appropriate hit-to-lead optimization strategy into potent and selective candidates, with favorable pharmacokinetic properties and minimum adverse side-effects [30,36,40,42].

In this work, we screen a library of NSGMs using computational and in vitro techniques to identify promising inhibitors of MMP-8. Our work shows, for the first time, that MMP-8 can be inhibited by targeting electropositive GAG binding sites, and offers at least one promising small molecule inhibitor of MMP-8.

**Figure 1.** Structures of the 58 non-saccharide glycosaminoglycans (GAGs) mimetics (NSGMs) studied against MMP-8. The synthesis and characterization of these molecules has been previously reported [30,31,38–41].


**3. Microturbines**

Research and implementation activities on microturbines are currently proceeding in different scientific units and companies. Microturbines are investigated to be applied as expansion machines in many technical applications, such as power generating systems and CHPs [109], unmanned airplanes [110] and hybrid cars [111]. The microturbine implementation was possible thanks to recent progress in different fields of science (especially materials science, thermodynamics, fluid mechanics and computer aided design). Advanced computer aided design (CAD) techniques and numerical flow modeling are widely used to design microturbines, and computer numerical control (CNC) machines are used for manufacturing microturbine parts. By the direction of the working fluid flow through the microturbine, they can be classified into radial and axial machines and by the type of the applied working fluid into gas microturbines and steam microturbines [112]. A characteristic feature of microturbines is their high rotational speed, ranging from several dozen to several hundred thousand revolutions per minute [113]. Therefore, very precise tools must be applied to manufacture microturbines. The microturbine shaft is coupled with the shaft of the generator in a different way than in the case of standard large-power turbines (i.e., small-scale, specially designed high-speed generators are applied and electronic systems are used to convert the generated current into a current of frequency and voltage that can be transferred directly to the grid). What is more, due to high rotational speeds of the microturbine shaft and large heat load, magnetic or foil bearings are often applied instead of classically used slide bearings [113]. Compared to volumetric expanders, microturbines are featuring smaller dimensions, a smaller number of moving parts, lower friction losses and higher efficiency [114]. They are also lighter. However, working fluid flow through microturbine and machine cost are much higher.

## *3.1. Gas Microturbines*

In recent years, gas microturbines have gained a reputation as a refined technology and are boldly entering a variety of municipal and industrial facilities where reliable, independent electricity and/or heat generation at competitive prices is required. The leaders of this technology are mainly British [115,116], Italian [117] and American [118–120] companies. Currently, their offer includes devices with a power of 50-several hundred kW, and in the future, it is planned to gradually expand this range. Recently, microturbines have had a number of original applications. One of the largest sports and recreation centers in London used a micro-turbine to generate 80 kW of electricity and 150 kW of heat for its own facilities and equipment, including a swimming pool, sports hall and other rooms using the Bowman Power TG80CG gas microturbine [115]. This original 80% efficient power plant has been supplemented with a conventional boiler to cover peak heat loads. The microturbine itself is only a slightly more complex design than a typical low power turbo generator. What distinguishes this device from classic machines of this type is a high-speed, four-pole self-excited generator and dedicated software controlling the operation of the unit. The alternator was made of rare earth metals of extremely high density, which allowed for such a significant reduction of elements that the turbine and generator rotors were placed on one shaft, thus eliminating the troublesome mechanical transmission. This single shaft assembly rotates at over 100,000 rpm producing an output voltage with a frequency in the range of 1000 to 3000 Hz. A special power electronic converter converts them into voltage with a mains frequency of 50 or 60 Hz and an ideal sinusoidal shape and value. Thus, the unit becomes a reliable power source with a quality that meets the most stringent requirements [121]. Manufacturers produce microturbines in two main types: with and without exhaust gas heat recovery. In microturbines without heat recovery, a compressed mixture of natural gas and air is burnt at constant pressure, and the resulting hot exhaust gas stream expands in the gas turbine, driving the generator. These systems follow a simple thermodynamic cycle and are cheaper and more reliable than microturbines with heat recovery. The latter devices have an exchanger in which part of the heat contained in the turbine exhaust gas stream is transferred to the inlet air. As a result, microturbines with heat recovery are characterized by higher efficiency (fuel savings up to 30–40%), comparable to diesel-based combined heat and power plants. In some implementations the exhaust/air heat exchanger has been replaced with a hot water boiler. In other applications, the microturbine exhaust stream without heat recovery is routed to the furnace, eliminating traditional gas burners. Currently, microturbines with a capacity of 25–250 kW and an electricity generation efficiency of 30% are offered in the world. With combined production of electricity and heat, this ratio can reach 80%. Gas microturbines have many advantages and offer a number of advantages, especially when used in small- and micro-power distributed energy. A small number of rotating and moving parts, compact design, small dimensions and weight-facilitate assembly and maintenance. At the same time, very low emission of pollutants and noise level allow their use in virtually every facility. Microturbines can be supplied by different types of fuel, e.g., dairy cattle biogas [122], syngas [123] and biofuels [124].

In [125] authors presented very small model of gas microturbine, featuring only 500 W of power. The turbine is called the Ultra Micro Gas Turbine (UMGT). The test bench of this turbine is presented in Figure 18.

**Figure 18.** The test bench of the Ultra Micro Gas Turbine (UMGT) power generator [125].

The elements of the tested gas turbine are shown in the Figure 19.

**Figure 19.** Elements of the first integrated test rig: (**a**) compressor, (**b**) turbine, (**c**) rotor shaft with compressor and turbine, (**d**) radial-thrust integral static air bearing, (**e**) graphite hot-bulb igniter and (**f**) annular-type combustor with 12 nozzles [125].

Furthermore, the technology of ultra-small gas turbines is presented in [126–130]. Ansaldo Energia [131–133] and Capstone [134] are one of the world's leading manufacturers of micro gas turbines. A view of the Capstone 30 kW gas turbine is shown in Figure 20.

**Figure 20.** View of the Capstone 30 kW gas turbine [134].

Ansaldo Energia is a global manufacturer in a distributed generation market with its AE-T100 Gas Microturbine, available in three different versions: natural gas-fired AE-T100NG, biogas-fired AE-T100B, and fed by heat from external combustion: An example ofEFGT technology is shown in Figure 21. The AE-T100 is producing 100 kWe of electrical power and about 200 kWth of thermal power. The efficiency of this system is up to 90%. A broad power range is achieved by good modularity of this systems and can spread by adding additional units.

**Figure 21.** View of the external combustion gas turbine schematic layout [135].

## *3.2. Steam Microturbines*

A characteristic feature of microturbines is their high durability and reliability, which results from their relatively simple design, as they have only one rotating element in the form of a shaft assembly with rotors and a generator [136–139]. High rotational speeds are also typical of this type of turbine machine, thanks to which, with small overall dimensions, the microturbines enable a high power output. However, high rotational speeds lead to complications in the design of the clutch connecting the turbine to the generator and bearings [140–144].

Many research centers around the world conduct research and development work on the continuous improvement of steam microturbine technology. Works on small steam turbines are carried out by, among others, The Institute of Fluid Flow Machinery of the Polish Academy of Sciences in Gda ´nsk and the Institute of Turbomachinery of the Łód´z University of Technology.

As part of the research tasks proceeded at Institute of Fluid Flow Machinery of the Polish Academy of Sciences, several alternative solutions for devices enabling the conversion of thermal energy into electricity were developed and tested. With the assumed power level and limitations resulting from the target place of operation of domestic conditions, steam microturbines turned out to be the optimal solution. Among the examined expansion devices, the most promising results were obtained for the variant of the four-stage radial microturbine and the single-stage radial microturbine. It was decided to couple the steam microturbine with the ORC system [145–147]. The working fluid in the ORC system is a low-boiling fluid. To drive the microturbine, it is firstly heated in a heat exchanger to the temperature at which the state changes from liquid to gas (evaporation takes place).

The gaseous medium at the appropriate pressure is fed to the microturbine blade system, causing its acceleration and then maintaining a constant rotational speed. The mechanical energy of the shaft rotation is then converted into electricity (by means of a generator) which, after appropriate preparation, can be used e.g., in a household. The lowboiling fluid used to drive the microturbine circulates in a closed system; after condensation, it flows through the pump and then it is reheated [148].

The four-stage microturbine developed at Institute of Fluid Flow Machinery of the Polish Academy of Sciences has two centripetal and two centrifugal stages. The shaft is supported by two radial-thrust gas bearings in which a low-boiling fluid is used as the lubricant. At the nominal rotational speed of approx. 24,000 rpm, the microturbine allows to obtain ca. 2 kW of electric power [148]. A cross-section through the turbine and the rotor disk is shown in Figure 22.

**Figure 22.** Cross-section and manufactured rotor disc of the four-stage radial microturbine made by Institute of Fluid Flow Machinery [148].

A single-stage microturbine has also been developed at the Institute of Fluid Flow Machinery (see, Figure 23). It is characterized by a centrifugal stage where the flow velocity is more than twice larger than the velocity of sound. It was built on the basis of the experience gained in the implementation of a four-stage microturbine. It uses the previously proven radial-thrust gas bearings, lubricated with a low-boiling fluid. The nominal rotational speed of this micro-turbine is ca. 30,000 rpm, which allows it to generate ca. 2.5 kW of electricity.

**Figure 23.** Radial, single-stage steam microturbine installed on the Institute of Fluid Flow Machinery test stand [148].

The developed steam microturbines were tested in the laboratory of Fluid Flow Machinery in conditions corresponding to their operation in the ORC system, using their own control system. Research has shown that prototype microturbine solutions have many advantages. Thanks to the use of high rotational speeds, machines with a compact structure and small dimensions were developed. Unlike other devices that enable the conversion of heat into electricity, steam microturbines are characterized by high durability and reliability because they do not have wear parts and parts that require periodic replacement or repair. Since the same low-boiling fluid is used in the flow system and the bearings, the risk of mixing the working medium, e.g., with oil, has been eliminated. The test results also confirmed the very low vibration level and quiet operation of the developed turbines. The mentioned advantages of microturbines mean that they can be successfully used in domestic ORC micro-cogeneration installations, as well as in other installations requiring a small and reliable device that enables electricity generation [148].

As mentioned earlier, work on small steam turbines is also carried out at the Institute of Turbomachinery of the Łód ´z University of Technology [136].

The experimental steam turbine with a nominal power of 50 kW was built on the basis of the Institute of Turbomachinery project. The turbine is powered by a steam generator that uses waste heat from a biogas combustion engine. Firstly, the following live steam parameters were considered: temperature equal to 613 K, pressure equal to 12 bar, while the mass flow rate was 0.075 kg/s. The turbine works in condensing mode. For these parameters, the isentropic drop in enthalpy is as high as 732 kJ/kg, which excludes a single-stage design, except for the Curtis two-ring (or three-ring) stage, operating in the range of very high Mach numbers. Therefore, after discussion, the live steam temperature was limited. Ultimately, the following turbine design parameters were established [136]:

The rotational speed was imposed due to the unavailability of a suitable gear.

A small backpressure turbine was also designed at Institute of Turbomachinery of the Łód ´z University of Technology. It generates 165 kW of power. The turbine was designed to cooperate with the existing technological installation; therefore, its operating parameters were strictly defined and were not subject to any discussion. These parameters were established as follows:

In this case, the isentropic enthalpy drop is only 119.7 kJ/kg, which greatly facilitates the adoption of the advantageous design solution. After preliminary calculations, it was assumed that the turbine would be implemented as a single action stage, powered on the entire circuit.

Experimental research on the application of a microturbine (featuring a maximum power of 1.9 kW) in domestic ORC CHP systems using ethanol as a working fluid was also proceeded at the Gda ´nsk University of Technology. The results of these experiments were reported in [149–152]. The experimental tests were proceeded for varied thermodynamic parameters of the working fluid at the inlet and at the outlet of the microturbine. The pressure at the inlet to the machine was varied between 0.36 and 0.6 MPa, while the working fluid flow was varied between 15 and 20 g/s. The maximum temperature of the working medium at the inlet to the machine was equal to 143 ◦C. For these experimental conditions, the obtained electric power of the ORC system was ranging between 0.66 and 0.76 kW, electrical efficiency was ranging between 6.40 and 6.65% and the total efficiency of the ORC CHP system was ranging between 22.53 and 23.54% [150].

There are also suppliers on the world market that offer microturbine technology. One of them is Spirax Sarco. This technology is described in more detail in [153]. An example of a Spirax Sarco steam microturbine in a container version is presented in Figure 24.

**Figure 24.** View of the Spirax Sarco steam microturbine [153].

Other companies that have in their portfolio steam microturbines are e.g., Siemens Dressel-Rand and General Electric.

#### **4. Volumetric Expanders (Vane, Lobe, Screw, Piston, Wankel, Gerotor)**

Volumetric expanders can be applied in small CHP steam and ORC systems as an alternative to the earlier described microturbines. The principle of operation of volumetric expander differs from that of turbine. In the case of volumetric machine, the working fluid expansion proceeds in a working chamber which volume is limited by the cylinder and the displacer. The operation of volumetric expander is cyclical and working chamber volume changes during machine operation. For this reason, gas expansion processes are proceeding periodically. Compared to microturbines, volumetric expanders are featuring simpler design and lower investment costs. What is more, they are also featuring lower rotational speeds, higher pressure drops that can be obtained in one stage, lower mass flows of the working fluid and the possibility of wet-gas expansion. In selected cases, it is possible to design and implement oil-free volumetric expanders.


**4. Controversy**

No consensus exists on the personality of dermatological patients. Buske-Kirschbaum suggests that these patients have a common psychological profile (Buske-Kirschbaum et al., 2004). Despite the existence of features found in such patients, other authors do not accept the existence of a profile that differentiates them from the healthy population (Verhoeven et al., 2008). Similarly, research on personality variables in a Spanish dermatological population (Antuña-Bernardo, 2000), who were assessed using the Eysenck Personality Questionnaire, found that there were no differences between the healthy population and patients with various skin diseases, including psoriasis. However, they were found to have lower quality of life and above-average neuroticism scores.

Personality in Patients with Psoriasis 219

differences between specific subgroups of dermatological patients, particularly in relation to

There are few personality studies on dermatological patients, and on psoriasis patients in particular, indicating a need for further research in personality psychology and psychodermatology. Such studies would lead to a better understanding of this group of patients and to help them better manage stress and the impact this has on their disease, quality of

Alexander, E. J. (1951). Psychosomatic Medicine-Its Principles and Applications. *American*

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Brown, R. (2004). Psychological mechanisms of medically unexplained symptoms: an integrative conceptual model. *Sychol Bull, 130*, 793-812. ISSN: 0146-1672. Buske-Kirschbaum, A., Ebrecht, M., Kern, S., Hollig, H., Gierens, A., & Hellhammer, D.

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what qualitatively characterizes psoriasis patients as one such subgroup.

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life, and their relationships with others.

**6. References**

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There may be several reasons why no personality differences have been found between the healthy population and psoriasis patients. Firstly, by including all skin diseases, those less associated with psychological variables may have obscured the influence of distinctive personality patterns, thus hindering the detection of differences between the 2 populations. Second, few studies have analyzed the strategies used or differences in the way psoriasis patients manage their emotions compared to the healthy population (Fortune, Richards, Main, & Griffiths, 2002), whereas, in comparison, psychological research on dermatologic patients has mainly focussed on variables such as stress, anxiety and depression. By placing too much emphasis on variables such as depression and anxiety, or on psychopathological abnormalities based on diagnostic categories, there is an increased risk of losing specificity in the search for potential differences between the healthy population and psoriasis patients. An increase in psychiatric disorders can be observed in the latter group, but it would be more useful to know how these abnormalities arise, how are they qualitatively different, and what characterizes this group of patients. The relevance of personality may have become lost as a research aim, leading to a reduction in the number of empirical studies investigating personality in this group of patients. Finally, another possibility is that instruments used in the past to assess personality were not designed to measure specific personality variables in this group or were not sufficiently sensitive to detect subtle differences. This could explain why some studies have failed to find any differences; questionnaires were used that were not designed to assess profound personality structures. According to some authors, sensitive instruments are now available for assessing the association between personality and disease (Friedman, 1990).

### **5. Conclusions**

The most important conclusion is that personality differences were found between the healthy population and psoriasis patients. These differences suggest that there is a greater tendency among psoriasis patients to be acquiescent and to restrain the expression of negative emotions in order to bond with others. The literature and the results from the study presented here shows a decreased capacity to adjust to the environment, which could, due to poor stress management, increase the risk of suffering psoriasis in susceptible individuals.

The number of subjects in the study presented here was sufficient to establish differences between psoriasis patients and healthy participants and the results are consistent with those obtained by other authors; however, further studies are needed that include a greater number of psoriasis patients, thereby providing stronger support to the results obtained here, as well as a greater degree of generalizability.

If we wish to understand the psychological characteristics of psoriasis patients and their characteristic personality styles, future studies should compare the personality of psoriasis patients to that of patients with other skin diseases associated with psychological traits in order to identify the different adaptation styles, if any.

Finally, the relevance of these findings lies in their deepening our understanding of the psychological problems of psoriasis patients.

If psychopathological abnormalities, such as depression and anxiety, are observed in dermatological patients as a group, then this should lead to investigating potential differences between specific subgroups of dermatological patients, particularly in relation to what qualitatively characterizes psoriasis patients as one such subgroup.

There are few personality studies on dermatological patients, and on psoriasis patients in particular, indicating a need for further research in personality psychology and psychodermatology. Such studies would lead to a better understanding of this group of patients and to help them better manage stress and the impact this has on their disease, quality of life, and their relationships with others.

#### **6. References**

218 Psoriasis

There may be several reasons why no personality differences have been found between the healthy population and psoriasis patients. Firstly, by including all skin diseases, those less associated with psychological variables may have obscured the influence of distinctive personality patterns, thus hindering the detection of differences between the 2 populations. Second, few studies have analyzed the strategies used or differences in the way psoriasis patients manage their emotions compared to the healthy population (Fortune, Richards, Main, & Griffiths, 2002), whereas, in comparison, psychological research on dermatologic patients has mainly focussed on variables such as stress, anxiety and depression. By placing too much emphasis on variables such as depression and anxiety, or on psychopathological abnormalities based on diagnostic categories, there is an increased risk of losing specificity in the search for potential differences between the healthy population and psoriasis patients. An increase in psychiatric disorders can be observed in the latter group, but it would be more useful to know how these abnormalities arise, how are they qualitatively different, and what characterizes this group of patients. The relevance of personality may have become lost as a research aim, leading to a reduction in the number of empirical studies investigating personality in this group of patients. Finally, another possibility is that instruments used in the past to assess personality were not designed to measure specific personality variables in this group or were not sufficiently sensitive to detect subtle differences. This could explain why some studies have failed to find any differences; questionnaires were used that were not designed to assess profound personality structures. According to some authors, sensitive instruments are now available for assessing the

The most important conclusion is that personality differences were found between the healthy population and psoriasis patients. These differences suggest that there is a greater tendency among psoriasis patients to be acquiescent and to restrain the expression of negative emotions in order to bond with others. The literature and the results from the study presented here shows a decreased capacity to adjust to the environment, which could, due to poor stress management, increase the risk of suffering psoriasis in susceptible

The number of subjects in the study presented here was sufficient to establish differences between psoriasis patients and healthy participants and the results are consistent with those obtained by other authors; however, further studies are needed that include a greater number of psoriasis patients, thereby providing stronger support to the results obtained

If we wish to understand the psychological characteristics of psoriasis patients and their characteristic personality styles, future studies should compare the personality of psoriasis patients to that of patients with other skin diseases associated with psychological traits in

Finally, the relevance of these findings lies in their deepening our understanding of the

If psychopathological abnormalities, such as depression and anxiety, are observed in dermatological patients as a group, then this should lead to investigating potential

association between personality and disease (Friedman, 1990).

here, as well as a greater degree of generalizability.

order to identify the different adaptation styles, if any.

psychological problems of psoriasis patients.

**5. Conclusions**

individuals.

Personality in Patients with Psoriasis 221

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**Part 3**

**Treatment**


**4. Methodology**

In March 2020, Twitter was taken over with #EndHomophobiaInNigeria, a reaction to the murder of a gay man in Imo state, Nigeria. The incident portrayed the dark side of queer hook-up culture. This led to the coming together of LGBT Nigerians and activists to condemn this act of violence and advocate against homophobia using the hashtag. I collected a total of 87 tweets posted between March 2020 and August 31, 2022. March 2020 captures the emergence of the EndHomophobiaInNigeria campaign. However, till 2022, the hashtag continued to be in use since sexual minorities continue to suffer backlash as a result of their gender identities. Out of the 87 tweets, 82 were pro-gay while only five were against the movement.  Only 34 pro-gay tweets provided the sample for this study. The tweets are numbered TWT1-TWT35. ("TWT" stands for "tweet.") In the analysis, however, only a few relevant tweets from the various discursive categories are reproduced.

I adopted a qualitative approach to analyze the contents of the tweets, which I viewed as identity discourse. The tweets were subjected to CDA and linguistic analysis. CDA allows an examination of the contributions of language to identity and ideological formations as identifiable within the context of same-sex relations, while SFL provides linguistic tools for analyzing discourse engagements. Ideological and identity discourses very often demonstrate evidence of the positive "we" representation and the negatives "other" representation. The categories applied in the analysis include actors' descriptions and disclaimers (dissociating from negative identities). Under SFL, transitivity, mood, and modality are considered in the analysis of how the identities of the in-group are defined. For instance, the kind of processes associated with the "we" and their ideological functions are explored. The analysis is done in relation to the Nigerian sociopolitical context.


**Abstract**

The Coronavirus Disease 2019, regularly referred to as "COVID-19", has had an unprecedented impact on not only the state of graduate medical education (GME) for post-doctoral trainees, but also their well-being and welfare. Trainees comprise approximately 14% of physicians in the United States. This crucial portion of personnel in healthcare has irrefutably represented the resilience that personifies the medical community. The prevalence of physical and emotional exertion by these trainees, necessitated by the pandemic, has precipitated behavioral health ailments like mood disorders including depression and anxiety, diminished satisfaction in their corresponding specialties and impaired their ability to achieve balance between professional and personal responsibilities. This excerpt examines the pervasiveness of the adverse psychosocial implications the COVID-19 pandemic has had on this susceptible practitioner population in addition to the examination of physical and emotional exhaustion that exacerbate physician burnout including the implementation of policies and procedures to address the emergent problem of physician burnout throughout the COVID-19 pandemic by the GME. Also, this excerpt examines the adaptation of GME, including the reformation and implementation of innovative policies and procedures that has incontestably created an imprint on medical education for descendants of ACGME residency and fellowship programs in the United States.

**Keywords:** ACGME, behavioral health, burnout, Coronavirus Disease 2019, COVID-19, fellow, fellowship, GME, pandemic, psychiatry, public health, resident, residency, safety, vulnerability, welfare, well-being


*Article* **In-Season Quantification and Relationship of External and Internal Intensity, Sleep Quality, and Psychological or Physical Stressors of Semi-Professional Soccer Players**

**Hadi Nobari 1,2,3,\* , Roghayyeh Gholizadeh 3 , Alexandre Duarte Martins 4,5,6 , Georgian Badicu 7 and Rafael Oliveira 4,5,8**

**Simple Summary:** In this study, we analyse the relationship of the in-season variations of external, internal and well-being measures across different periods of a semi-professional soccer season (early-, mid- and end-season) and describe TM and TS for the entire period of the analysis. The main findings of our study revealed that increasing the training intensity affects the well-being of the players and consequently the training intensity management. Coaches and their staff should consider the results of this study, because despite the relationship between external and internal intensity, each has a unique effect on the perception of the player's training intensity management.

**Abstract:** The purpose of this study was two-fold: (a) to describe and analyse the relationship of the in-season variations of external and internal intensity metrics as well as well-being measures across different periods of a semi-professional soccer season (early-, mid- and end-season); and (b) to describe training monotony (TM) and training strain (TS) for 20 weeks in a semi-professional soccer season. Eighteen semi-professional players (age: 29 ± 4.1) from the Asian First League team participated in this study. The players were monitored for 20 consecutive weeks during in-season for external training intensity, internal training intensity and well-being parameters. The in-season was organized into three periods: early-season (weeks 1–7); mid-season (weeks 8–13); and end-season (weeks 14–20). Total distance (TD), high-speed running distance (HSRD), sprint distance, rate of perceived exertion (RPE), session-RPE (s-RPE), TM, TS, heart rate average and maximum, as well as sleep quality, stress and muscle soreness were collected. Results revealed that TD, HSRD and sprint distance (total values) were meaningfully greater during end-season than in the early-season. RPE showed a significantly highest value during the end-season (4.27 AU) than in early- (3.68 AU) and mid-season (3.65 AU), *p* < 0.01. TS showed significant differences between early-season with mid-season (*p* = 0.011) and end-season (*p* < 0.01), and the highest value occurred in week 17 during end-season (6656.51 AU), while the lowest value occurred in week 4 during early-season (797.17 AU). The average TD periods showed a moderate to large correlation with RPE, sleep and s-RPE at early-, mid- and end-season. Increasing the training intensity without considering the well-being of the players affects the performance of the team. Examining processes of the relationship between training intensity and other psychological indicators among players will probably be effective in training planning. Sports coaches and fitness professionals should be wary of changes in TM and TS that

**Citation:** Nobari, H.; Gholizadeh, R.; Martins, A.D.; Badicu, G.; Oliveira, R. In-Season Quantification and Relationship of External and Internal Intensity, Sleep Quality, and Psychological or Physical Stressors of Semi-Professional Soccer Players. *Biology* **2022**, *11*, 467. https://doi.org/ 10.3390/biology11030467

Academic Editor: Anne Delextrat

Received: 4 February 2022 Accepted: 16 March 2022 Published: 18 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

affect players performance. Therefore, to better control the training, more consideration should be given by the coaches.

**Keywords:** load; heart rate; high-speed running; monotony; muscle soreness; sprint; sleep; strain; stress

#### **1. Introduction**

Nowadays, it is almost mandatory to monitor intensity and well-being to know the effects of exercise training programs on soccer players, and to individualize the training process in semi-professional soccer teams [1]. Through the season, there are several variations on training and well-being measures, and their monitoring is essential to apply the most successful strategy for recovery and competition [2].

In a recent study by Impellizzeri et al. [3], intensity monitoring definition was updated and defined in two dimensions: external (the physical demands imposed by the design and mode of exercise), and internal (the psychophysiological impact of external intensity). Usually, distances of different speed thresholds and accelerometry-based variables are the main measures reported as external intensity, while heart rate and rated perceived exertion (RPE) are the main measures reported as internal intensity [4].

Furthermore, well-being monitoring also represents a non-invasive valid and quick tool for collecting information associated with the status and readiness of the players to the training process and competition [5,6]. One example of an instrument that allows this monitoring process is the Hooper questionnaire [5], which include four categories: delayed onset muscle soreness (DOMS), fatigue, sleep, and stress.

Monitoring external, internal and well-being measures is part of daily strategies used to quantify the training session effects [6], but it can also allow the identification of intensity variations across the season [7]. In this sense, additional analyses can be performed to specific data obtained. For instance, two traditional indexes known as training monotony (TM) and training strain (TS) have been used to analyse such week variations. TM is used to analyse the intensity variability within the week, while TS is used to analyse the intensity variability multiplied by the accumulated intensity of the week [8]. The relationship between both indexes is supported by their formulas where TM is calculated by dividing the daily mean load by the standard deviation while TS is calculated through the product of weekly load by TM [8]. Usually, both indexes are based on the training duration multiplied by RPE (s-RPE) [8].

However, despite such a diversity of intensity measures, few studies have analysed the relationship between external, internal and well-being measures [9–11]. For instance, Haddad et al. [10] did not observe any relationship between the Hooper Index (HI) categories and RPE through submaximal exercises in junior soccer players, but Clemente et al. [9] showed negative correlations between s-RPE with DOMS, sleep, fatigue, and stress in weeks with two matches across training data from a full professional soccer season [10]. Moreover, Oliveira et al. [11] analysed associations in 10 in-season mesocycles (full-season) between all external, internal and wellbeing measures, and found negative correlations between stress and total distance [11]. In addition, the same authors found positive correlations between fatigue and s-RPE, between DOMS and s-RPE, and between HI total score and total distance [11].

The above-mentioned results were obtained in small sample sizes (n ranged between 17–35), which is common for soccer studies, and thus more research is needed to analyse the relationship between external, internal and well-being measures simultaneously, since there were multiple occasions where such analysis was not presented. Therefore, this study aims: (a) to describe and analyse the relationship of the in-season variations of external, internal and well-being measures across different periods of a semi-professional soccer season (early-, mid- and end-season); and (b) to describe TM and TS for the entire period of the analysis in a semi-professional soccer season.

#### **2. Materials and Methods** were examined and monitored (age, 29 ± 4.1 years; height, 179.6 ± 4.7 cm; body mass, 74.9 ± 3.9 kg). All players participated in the in-season (>80%) [9].

**2. Materials and Methods**

*Biology* **2022**, *11*, x FOR PEER REVIEW 3 of 15

period of the analysis in a semi-professional soccer season.

#### *2.1. Participants* The exclusion criteria were adopted from a previous study, namely, players with in-

*2.1. Participants*

In this study, eighteen male semi-professional soccer players from Iran's First League were examined and monitored (age, 29 ± 4.1 years; height, 179.6 ± 4.7 cm; body mass, 74.9 ± 3.9 kg). All players participated in the in-season (>80%) [9]. jury or that did not participate in training for at least two consecutive weeks and non-field positions such as the goalkeepers due to differences in intensity in training and matches [12]. All participants were familiarised with the training protocols prior to investigation.

In this study, eighteen male semi-professional soccer players from Iran's First League

soccer season (early-, mid- and end-season); and (b) to describe TM and TS for the entire

The exclusion criteria were adopted from a previous study, namely, players with injury or that did not participate in training for at least two consecutive weeks and non-field positions such as the goalkeepers due to differences in intensity in training and matches [12]. All participants were familiarised with the training protocols prior to investigation. The Ardabil University of Medical Sciences' ethical committee code IR.ARUMS.REC.1399.545 was authorized for this study, which followed the Declaration of Helsinki's guidelines. The Ardabil University of Medical Sciences' ethical committee code IR.AR-UMS.REC.1399.545 was authorized for this study, which followed the Declaration of Helsinki's guidelines. *2.2. Experimental Design*  A descriptive longitudinal approach of 20 in-season consecutive weeks was used.

#### *2.2. Experimental Design* Specifically, for the present study, all players participated in 47 training sessions and 20

A descriptive longitudinal approach of 20 in-season consecutive weeks was used. Specifically, for the present study, all players participated in 47 training sessions and 20 matches. Only data from regular training sessions was considered for analysis which means that data from resistance training, competitions, rehabilitation and/or recuperation sessions was excluded. All session were planned by the coach and staff, and the researchers only controlled the initial and final 30 min of the sessions. The analysed period ranged from early season (30 October 2017) until the end of the season (18 March 2018). The present in-season was organized into three periods: early-season (weeks 1–7); mid-season (weeks 8–13); and end-season (weeks 14–20) (Figure 1). Players' weekly averages and accumulated values were used for analysis. matches. Only data from regular training sessions was considered for analysis which means that data from resistance training, competitions, rehabilitation and/or recuperation sessions was excluded. All session were planned by the coach and staff, and the researchers only controlled the initial and final 30 min of the sessions. The analysed period ranged from early season (30 October 2017) until the end of the season (18 March 2018). The present in-season was organized into three periods: early-season (weeks 1–7); mid-season (weeks 8–13); and end-season (weeks 14–20) (Figure 1). Players' weekly averages and accumulated values were used for analysis. Figure 1 shows the distributions of weeks per periods of the season, as well as the number of training sessions and matches.

**Figure 1.** Weekly (W) distribution of training sessions and number of matches across the season. **Figure 1.** Weekly (W) distribution of training sessions and number of matches across the season.

*2.3. External Intensity Monitoring*  Figure 1 shows the distributions of weeks per periods of the season, as well as the number of training sessions and matches.

All training sessions were monitored using GPS (GPSPORTS systems Pty Ltd.,

#### Model: SPI HPU; Canberra, Australia). The GPS included 15 Hz position GPS and a tri-*2.3. External Intensity Monitoring*

axial accelerometer, and this device has previously shown high validity and reliability [13]. For data collection, belts were placed on the players' shoulders and chests. At the end All training sessions were monitored using GPS (GPSPORTS systems Pty Ltd., Model: SPI HPU; Canberra, Australia). The GPS included 15 Hz position GPS and a tri-axial accelerometer, and this device has previously shown high validity and reliability [13]. For data collection, belts were placed on the players' shoulders and chests. At the end of the sessions, belts were collected and checked by the team's GPS manager. Then, devices entered the dock system to download the information to the Team AMS software. After this procedure and before next session, all belts were recharged. The SPI IQ Absolutes were adjusted for GPS default zone throughout the season.

The measures used for analysis were: TD; HSRD covered between 18 to 23 km/h−1 ; and sprint distance covered above >23 km/h−1 . Data were considered in daily average values and the total of each period analysed, respectively.

#### *2.4. Internal Intensity Monitoring*

Players were monitored daily using a Borg's CR10 scale [14], adapted by Foster et al. [15]. This scale showed validity and reliability for quantifying session intensity [16].

Thirty minutes after each session, players individually provided their RPE value using a tablet to avoid non-valid scores. The RPE values provided were also multiplied by the training duration, to obtain the s-RPE [15,17]. Previously, all players were familiarized with the RPE scale.

Through s-RPE, TM (mean of training load during the seven days of the week divided by the SD of the training load of the seven days) [12,18,19] and TS (sum of the training load for all training sessions during a week multiplied by TM) [12,18,19] were calculated.

A flashing RED light was used to track HR. We placed each unit perpendicular to the bag, the logo on the unit was facing backwards and the RED light was on. HPUs are designed to automatically collect athlete's HR data in one session. In addition to the GPS receiver, the SPI Pro X unit consists of a tri-axial accelerometer for estimating the forces on the player, and an integrated HR monitor. The following variable was selected: HR average (HRavg). Then, weekly HRavg was calculated by the average value for the entire week for each period, respectively. The way this information was recorded was similar to previous studies [12,20,21]. Daily average data were used for RPE, s-RPE and HRavg.

#### *2.5. Well-Being Monitoring*

Approximately thirty minutes before sessions, players provided the HI scores [5] with the same procedures of the RPE. HI is a questionnaire that includes fatigue, stress, DOMS (scale of 1–7, in which 1 is very, very low and 7 is very, very high), and quality of sleep of the night that preceded the evaluation (scale of 1–7, in which 1 is very, very bad and 7 is very, very good). However, due to the purposes of the coach, fatigue was not considered in the present study. Daily average data was used for each category.

#### *2.6. Statistical Analysis*

Descriptive statistics were used to characterize the sample. Shapiro–Wilk was used to test normality of results. Results were presented as mean ± SD. The relationship between all variables at the different periods was verified using bivariate correlations [22] (Pearson's product–moment correlation coefficient (*r*)). The correlations' effect size (ES) were calculated using the following criteria: <0.1, (trivial); 0.1–0.3, (small); >0.3–0.5, (moderate); >0.5–0.7, (large); >0.7–0.9, (very large); and >0.9, (virtually perfect)

All variables obtained a normal distribution (Shapiro–Wilk, *p* > 0.05), a repeated measures ANOVA test was used with the Bonferroni post hoc test was used to compare variables for periods throughout the in-season. Statistical significance was set at *p* ≤ 0.05. Hedge's g ES was also calculated to determine the magnitude of pairwise comparisons through the following formula: (mean 1–mean 2)/SD \* pooled [23]. Then, the Hopkins threshold was applied: g ≤ 0.2, (trivial); 0.2 to ≤0.6, (small); 0.6 to ≤1.2, (moderate); 1.2 to ≤2.0, (large); 2.0 to ≤4.0, (very large); and >4.0, (nearly perfect) [24,25]. All data were analysed using IBM SPSS Statistics (version 22, IBM Corporation (SPSS Inc., Chicago, IL, USA).

#### **3. Results**

Table 1 shows the differences between the early-, mid- and end-season for all measures. To organize the results section, five sub-sections will address external, internal and well-being monitoring, correlations, monotony, and strain descriptions, respectively. To simplify the results description, only moderate to nearly perfect ES's will be described here.

**Table 1.** Descriptive statistics (mean ± standard deviation (SD)) of the external, and internal and well-being measures in the early-, mid- and end-season.

Abbreviations: EarS, early-season; MidS, mid-season; EndS, end-season; AU, arbitrary units; RPE, rate of perceived exertion; Avg, average; TDur, Training duration; Min, minutes; s-RPE, session rate of perceived exertion; DOMS, delayed onset muscle soreness; TM, training monotony; TS, training strain; TD, total distance; Km, kilometres; HSRD, high-speed running distance; SD, speed distance; HRavg, heart rate average; Bpm, beats per minute.

#### *3.1. External Intensity Monitoring*

In relation to TD (average values), a significant difference was found between early- vs. end-season, while TD in total values showed two significant differences, early- vs. endseason (*p* = 0.022), and mid- vs. end-season (*p* = 0.003), both with moderate ES. The HSRD (in average and total values) showed significant differences between all periods of the in-season. The sprint distance shows a significant difference between mid- vs. end-season.

#### *3.2. Internal Intensity Monitoring*

The RPE showed the highest values in the end-season (4.27 AU), with significant differences between the early- and mid-season. Average and total training duration showed the lower values in the early-season (58.91 and 989.61 min, respectively). They both showed significant differences with mid- and end-season. Regarding to s-RPE, it showed higher values in end-season (333.27 AU) compared to early- and mid-season. TS showed significant differences between early-season with mid- and end-season.

#### *3.3. Well-Being Monitoring*

**4. Discussion**

ternal intensity.

There were no meaningful differences for quality of sleep, stress, or DOMS. *Biology* **2022**, *11*, x FOR PEER REVIEW 11 of 15

#### *3.4. Correlations of All Measures for Each Period*

Tables 2–5 show the correlation coefficient of all measures in the study on the early-, mid-, end-season, and full-season, respectively. *3.5. Training Monotony and Training Strain Descriptions*

#### *3.5. Training Monotony and Training Strain Descriptions* Figure 2 shows an overall view of the weekly average for TM and TS calculated

Figure 2 shows an overall view of the weekly average for TM and TS calculated through the s-RPE across 20 weeks. Overall, Figure 2 shows that the highest TM occurred in week 12 during mid-season (9.60 AU), while the lowest value occurred in week 4 during early-season (1.92 AU). The highest TS occurred in week 17 during end-season (6656.51 AU), while the lowest value occurred in week 4 during early-season (797.17 AU). through the s-RPE across 20 weeks. Overall, Figure 2 shows that the highest TM occurred in week 12 during mid-season (9.60 AU), while the lowest value occurred in week 4 during early-season (1.92 AU). The highest TS occurred in week 17 during end-season (6656.51 AU), while the lowest value occurred in week 4 during early-season (797.17 AU).

**Figure 2.** Training monotony (TM) and training strain (TS) variations calculated using the session rate of perceived exertion (s-RPE) across 20 weeks in different moments of a semi-professional soccer season. **Figure 2.** Training monotony (TM) and training strain (TS) variations calculated using the session rate of perceived exertion (s-RPE) across 20 weeks in different moments of a semi-professional soccer season.

The aim of the present study was to quantify and analyse the relationship of the external and internal training intensity metrics as well as the well-being measures in different periods of a semi-professional soccer season (early-, mid- and end-season); and to describe TM and TS for the entire period of the analysis in a semi-professional soccer season. In the case of external training intensity metrics, it was observed that the total HSRD and sprint distance in comparison to the early and mid-season increased at the end-season. Previous studies had used various methods to examine these factors. According to the coaches, there could be variances in training and performance depending on the degree of play that led to differences in the values reported in different periods [26]. The

previous periods, which in turn can affect the high total HSRD and TD. The average HSRD showed a positive correlation with RPE changes. In many studies, changes in internal intensity exhibit high correlation with low speeds and low-intensity distance, but not with high speeds. Possible reasons include GPS error at high speeds, individual ability/requirement to reach high speeds, and the nonlinear relationship between speeds and internal intensity. One aspect that internal intensity does not take into account is moving at higher speeds (>14 km·h−1) and high accelerations (>2 m·s−2) [27,28]. According to previous results, RPE also showed the highest values in the end-season (4.27 AU), which shows a significant difference between the early and mid-season, and follows the same line of ex-

Analysis of internal intensity measured by psychological variables such as RPE is highly preferred because of its potential for integrating different types of stimuli and ease of use [8]. Various factors may affect RPE. As an example, situations such as scoring, scoring opportunities, ball control, tackle, good play on set, winning turnover, increasing ball possession or ability to block the attack, or even non-technical/tactical training can have an impact the perceived exertion of a player [29]. Therefore, given the team's matches and training seasons at the end-season compared to previous periods, a higher RPE value can

average; \*, moderate effect; #, large effect; §, very large effect; £, virtually perfect effect.

**Table 2.** Correlation analysis between the measures in study on the early-season.

average; \*, moderate effect; #, large effect; §, very large effect; £, virtually perfect effect.

**Table 3.** Correlation analysis between the measures in study on the mid-season.

average; \*, moderate effect; #, large effect; §, very large effect.

**Table 4.** Correlation analysis between the measures in study on the end-season.

average; \*, moderate effect; #, large effect; §, very large effect; £, virtually perfect effect.

**Table 5.** Correlation analysis between the measures in study on the full-season.

#### **4. Discussion**

The aim of the present study was to quantify and analyse the relationship of the external and internal training intensity metrics as well as the well-being measures in different periods of a semi-professional soccer season (early-, mid- and end-season); and to describe TM and TS for the entire period of the analysis in a semi-professional soccer season.

In the case of external training intensity metrics, it was observed that the total HSRD and sprint distance in comparison to the early and mid-season increased at the end-season. Previous studies had used various methods to examine these factors. According to the coaches, there could be variances in training and performance depending on the degree of play that led to differences in the values reported in different periods [26]. The number of training sessions and team competitions at the end-season was higher than the previous periods, which in turn can affect the high total HSRD and TD. The average HSRD showed a positive correlation with RPE changes. In many studies, changes in internal intensity exhibit high correlation with low speeds and low-intensity distance, but not with high speeds. Possible reasons include GPS error at high speeds, individual ability/requirement to reach high speeds, and the nonlinear relationship between speeds and internal intensity. One aspect that internal intensity does not take into account is moving at higher speeds (>14 km·h −1 ) and high accelerations (>2 m·s −2 ) [27,28]. According to previous results, RPE also showed the highest values in the end-season (4.27 AU), which shows a significant difference between the early and mid-season, and follows the same line of external intensity.

Analysis of internal intensity measured by psychological variables such as RPE is highly preferred because of its potential for integrating different types of stimuli and ease of use [8]. Various factors may affect RPE. As an example, situations such as scoring, scoring opportunities, ball control, tackle, good play on set, winning turnover, increasing ball possession or ability to block the attack, or even non-technical/tactical training can have an impact the perceived exertion of a player [29]. Therefore, given the team's matches and training seasons at the end-season compared to previous periods, a higher RPE value can be justified. Furthermore, s-RPE was considered an important global indicator of training intensity and intensity in team sports [29,30]. However, Haddad et al. suggested that s-RPE is not sufficient to identify health indicators such as subjective fatigue, DOMS, stress, and sleep level of young soccer players [10]. In this case, Hooper and Mackinnon [5] suggested a self-assessment-based psychometric questionnaire, which includes well-being related to sleep, stress, fatigue, and DOMS. It is considered one of the best questionnaires for estimating well-being and monitoring the perceived health of soccer players [6,19].

In the present study, no significant difference was observed in well-being measures between the periods of the season. Evaluating RPE was found very important instrument in correlating overtraining of athletes with physical demands on the body. However, changes in TM and TS were also not significant during the season. It is not clear why it fluctuates, exhibiting a W-shaped diagram, during the season. Several factors such as match position, match result, opponent quality, tactics system, and training program can affect these results. Contextual factors such as tactical formation and suspension of a match can affect the overall workload during a match, and further into the previous or next training session [21]. This result may have been a strategy of the coach to prepare the team for the next period to achieve better results. On the other hand, the rise in TS had led to a decline in TM in the coming weeks, which can be seen in the indicators of well-being (considering not being meaningful). The degree of sleep quality gradually decreased compared to the early-season, and the quality of sleep also improved with the increase in TS and decrease in TM in the last weeks of the end-season. Additionally, DOMS and stress were higher than the endseason. Past studies had also shown that increasing the training intensity and competitions impairs sleep quality [31,32]. On the other hand, the changes were consistent with previous literature, where the high diversity of TM and TS in the mid-season reflected that players prefer greater training intensity for motivation [33].

The average of TD showed a moderate to high correlation with RPE, sleep and s-RPE at early-, mid- and end-seasons. In a similar study, the daily intensity over the course of a week revealed a high and moderate correlation with peak power and change of direction at different periods of the seasons in elite youth soccer players [34]. Another study examined the daily training intensity and perceived wellness characteristics and showed that the amount of training intensity was linked to sleep perceived by elite football players. This condition was also reported in our study [6].

In the other part of results, there was a large association between TD and sprint distance and between TD and HSRD in the early-, mid- and end-season. It can be inferred that this high level of correlation indicates the effect of external training intensity indices on each other. Numerous studies have reported a negative correlation between training intensity and strength indices. However, the methods of measuring internal training intensity were different from the present study [35], which reinforces the need for more studies to confirm the present results. Additionally, a review article assessed the symptoms of perceived stress and it showed that both categories were sensitive to acute changes [36]. On the other hand, increasing HRavg is associated with increasing TM and TS, which indicates that increasing external intensity increases HRavg. On the other hand, the increase in TM leads to overtraining based on a previous study [37], which is one of the consequences of overtraining, and consequently increases the heart rate during training and competition.

In the early-, mid-, and end-season, there was a large and negative relationship between Avg TD and DOMS, as well as between sleep and Avg HSRD. Similar to the results of this study, previous research has linked perceived sleep, stress, fatigue, and DOMS to daily perceived intensity at the professional level [38].

Meanwhile, there are limitations to this study that need to be considered. First, we can point to the lack of pre-season information, which affects the overall results. Second, the number of athletes participating in this study was relatively small, which makes it difficult to generalize the results. Third, in future studies, changes in acute and chronic training can also be considered along with external and internal training intensity to obtain complete information. Fourth, stress from HI questionnaire was not considered in the present study which could present more details on data analysis. The final limitation of this study was the lack of internal and external load monitoring in resistance training and competitions sessions which should be considered in future studies.

#### **5. Conclusions**

Coaches and their staff should consider the results of this study. Despite the relationship between external and internal intensity, each metric had a unique effect on the perception of the player's training intensity management, and special attention should be paid to each player when monitoring a training session.

Sport coaches and fitness professionals should be wary of changes in TM and TS that affect players' good responses, because based on the results, increasing these metrics can have a negative effect on indicators such as DOMS and sleep.

This study shows the relationship between training intensity and other psychological indicators among players. Examining these processes will probably be effective in training planning. Therefore, in order to better control the training, more consideration should be given by the coaches, so that the team performance can be maximized, and better results can be obtained. The results serve as a useful tool for providing coaches and their staff information on determining perception factors.

**Author Contributions:** Conceptualization, H.N., R.G., A.D.M. and R.O.; methodology, H.N. and R.G.; data collection, H.N. and A.D.M.; analysis, H.N. and R.O.; writing—original draft preparation, A.D.M., G.B. and R.O.; writing—review and editing, R.G., R.O., G.B., H.N. and R.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Portuguese Foundation for Science and Technology, I.P., Grant/Award Number UIDP/04748/2020.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ardabil University of Medical Sciences authorized the ethical committee code IR.ARUMS.REC.1399.545 for this study.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** The authors thank Sepahan Football Club for your full cooperation in this study.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


**1. Introduction**

Implantation requires a complex crosstalk between the endometrium and the blastocyst and is highly regulated by a variety of factors, such as soluble growth factors, hormones, prostaglandins, adhesion molecules, and the extracellular matrix (ECM) [1–5]. These factors, produced by the receptive endometrium in response to the presence of the blastocyst and vice versa, are able to synchronize the development of the embryo to the blastocyst stage and the differentiation of the uterus to the receptive state [6,7]. This complex network of signaling accounts for implantation being one of the major limiting steps in mammalian reproduction. Indeed, the implantation rate in humans is about 30% per cycle [8,9]. Alterations of these signaling pathways may result in pathological conditions leading to infertility.

The WHO has designated infertility as "a disease of the reproductive system defined by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse" [10,11]. Infertility is one of the main health issues in all societies worldwide, with a prevalence of 3.5–16.7% in developed countries and 6.9–9.3% in developing countries [12,13] and may be a consequence of low embryo quality, male problems, or female dysfunctions. Female fertility problems account for 20–35% of infertility cases and may derive from a wide variety of causes such as age, anatomical, endocrine and immunological problems, and several pathological conditions affecting the endometrium [14–19]. These conditions may lead to defects in blastocyst implantation in the maternal uterus, resulting in implantation failure, a common cause of impaired fertility [20]. The term "implantation failure" actually implies a series of conditions in which the embryo does not implant in the maternal endometrium after both spontaneous and in vitro fertilization (IVF) [21]. A condition in which implantation failure occurs after the transfer of three or more good quality embryos is defined recurrent implantation failure (RIF) and it is only applicable to assisted reproductive technology (ART) [21,22]. According to ASRM and ESHRE definitions, RIF is considered a distinct pathological condition from recurrent pregnancy loss [21,23,24].

The present review describes and discusses the molecular mechanisms underlying the implantation process, focusing on factors implicated in the complex blastocyst–endometrium crosstalk, which are crucial for successful implantation. Further research for new factors involved in the dialogue between the blastocyst and the endometrium would allow to reduce the current rates of implantation failure, allowing many couples with infertility problems to reach a successful pregnancy.