**4.1 Hierarchy of immune reactions to prosthetic wear particles** *(Gallo)*

Aseptic loosening and osteolysis can be understood to be the result of chronic high-grade inflammatory disease induced by exposure of local tissue to massive quantities of prosthetic particles. For this reason predominantly investigated is the role of the immune system in its pathophysiology. Currently, there is a large body of evidence supporting the concept of the immune system acting major influence on particle disease. In this section, we will focus on the role of the different parts of the immune system in the process of aseptic loosening and osteolysis.

Basically the immune system consists of two tightly associated segments: *the innate immune system and the adaptive immune system*. The former consists of a set of pattern-recognition receptors (PRRs) that evolved to recognize pathogen-associated molecular patterns (PAMPs), triggering rapid and robust response to invading pathogens following their recognition. The adaptive immune system evolved to produce a highly specific response to unique antigen presentation after innate instructions are conveyed to the second line of host-immunity associated in particular with T and B cells. A distinction should be made between at least the *inflammatory TH1 response* inducing and perpetuating inflammation via expression of IFN-γ, IL-2, TNF-alpha and activation of macrophages, NK cells, cytotoxic lymphocytes, and *anti-inflammatory TH2 and TH3 responses* associated characteristically with expression of IL-4, IL-5 and TGF-β and IL-10 cytokines, respectively (Matzinger 2007). From a functional point of view we can distinguish *cells/pathways/molecules that trigger the immune response to wear debris and those that regulate host response,* assuming the latter may play an important role in resolving inflammation and prevention of tissue damage.

The immune response is triggered after recognition of special surface motifs on invading pathogen (PAMPs) by PRRs. Intact pathogens can be composed of a number of PAMPs which induce activation of multiple PRRs simultaneously and, vice versa, different PRRs can recognize the same PAMP (Kumar et al. 2011). Currently, there is growing evidence that activation of PRRs is an important part of host adverse reaction against prosthetic particles (Lahdeoja et al. 2010). This could be explained by adherence of immunoreactive substances (lipopolysaccharides, lipoteichoic acid, fragments of bacteria, components of complement etc.) on wear debris particles. Some researchers have demonstrated that i) *PAMPs adhere to the particles*; ii) *adherent PAMPs increase significantly the biological response to the particles*; iii) *PAMPs do this by activating their PRRs* (Greenfield et al. 2010). In addition, particles deliberated from THA are coated immediately with serum proteins creating *a protein-particle complex* that may result in a damage/danger-associated molecular pattern picture *irrespective of endotoxin* (Sun et al. 2003). Engagement of TLRs leads to activation of the NF-κβ signalling pathway with subsequent expression of pro-inflammatory cytokines, strongly boosting the immune response (Pearl et al. 2011). In the same way also other receptors (e.g. NOD-like receptor proteins) may participate in activation of the immune response to wear debris (St Pierre et al. 2010). Some of these are structurally involved in the cytosolic multi-protein complex known as the "inflammasome" which is considered a critical part of the inflammatory response. After appropriate stimulus, IL-1β and IL-18 convert from their inactive precursor molecules (pro-1β and pro-IL-18) into biologically active molecules that are then released from inflammasomes (Caicedo et al. 2009). From the clinical viewpoint, lipopolysaccharide has been detected in periprosthetic tissues without any other signs of infection in patients (Nalepka, Lee et al. 2006). In addition, some patients who underwent revision arthroplasty due to aseptic loosening had positive culture and/or PCR detected bacteria (Dempsey et al. 2007). In one recent study, it was even found that the extension of osteolysis correlated with proportion of positive sonication cultures (Sierra et al. 2011).

Participation of the adaptive immune system in host response to prosthetic wear debris is still a subject of controversy despite the fact that several reports have described lymphocytes in periprosthetic tissues, retrieved from patients with aseptically failed metal-on-metal (MoM) or non-MoM implants (Fujishiro et al. 2011; Ng et al. 2011). One recent study revealed increased serum concentration of cobalt and chromium in patients with MoM THA which positively correlated with increased proportion of HLA DR+ CD8+ T-cells in these patients (Hailer et al. 2011). In the same study, it was proposed that macrophages around THA could create haptenic metal ions in the context of self peptides (metal ion-altered self) as antigens in combination with MHC class II molecules, leading to T-cell priming. Weyand et al proposed antigen-recognition events when they characterised T cells in periprosthetic membranes (Weyand et al. 1998). They found identical T-cell receptor sequences suggesting identical antigen specificity and in addition, transcription of IL-2 and IFN-γ, indicating functional activity of at least some lymphocytes. Moreover, in this study, IFN-γ transcription correlated with extension of bone loss. Sensitization to various heavy metals after THA is described elsewhere in this chapter (*Part 4.5*).

### **4.2 Chemokines and cellular chemotaxis during the inflammatory response to wear particles from orthopaedic implants** *(Gibon, Goodman, Gallo)*

The biological reaction to polymers, ceramics and metallic wear particles is *a non-specific foreign body and chronic inflammatory response*. A type IV lymphocyte-driven hypersensitivity reaction involving a specific antigen has been found for a small percentage of cases with predominantly metal-on-metal bearing surfaces (*Part 4.5*). This section will focus on the inflammatory processes associated with wear particles from orthopaedic implants, emphasizing local and systemic cell signaling mediated by chemotactic cytokines called chemokines.

### **4.2.1 The inflammatory reaction to wear particles**

328 Recent Advances in Arthroplasty

Aseptic loosening and osteolysis can be understood to be the result of chronic high-grade inflammatory disease induced by exposure of local tissue to massive quantities of prosthetic particles. For this reason predominantly investigated is the role of the immune system in its pathophysiology. Currently, there is a large body of evidence supporting the concept of the immune system acting major influence on particle disease. In this section, we will focus on the role of the different parts of the immune system in the process of

Basically the immune system consists of two tightly associated segments: *the innate immune system and the adaptive immune system*. The former consists of a set of pattern-recognition receptors (PRRs) that evolved to recognize pathogen-associated molecular patterns (PAMPs), triggering rapid and robust response to invading pathogens following their recognition. The adaptive immune system evolved to produce a highly specific response to unique antigen presentation after innate instructions are conveyed to the second line of host-immunity associated in particular with T and B cells. A distinction should be made between at least the *inflammatory TH1 response* inducing and perpetuating inflammation via expression of IFN-γ, IL-2, TNF-alpha and activation of macrophages, NK cells, cytotoxic lymphocytes, and *anti-inflammatory TH2 and TH3 responses* associated characteristically with expression of IL-4, IL-5 and TGF-β and IL-10 cytokines, respectively (Matzinger 2007). From a functional point of view we can distinguish *cells/pathways/molecules that trigger the immune response to wear debris and those that regulate host response,* assuming the latter may play an

The immune response is triggered after recognition of special surface motifs on invading pathogen (PAMPs) by PRRs. Intact pathogens can be composed of a number of PAMPs which induce activation of multiple PRRs simultaneously and, vice versa, different PRRs can recognize the same PAMP (Kumar et al. 2011). Currently, there is growing evidence that activation of PRRs is an important part of host adverse reaction against prosthetic particles (Lahdeoja et al. 2010). This could be explained by adherence of immunoreactive substances (lipopolysaccharides, lipoteichoic acid, fragments of bacteria, components of complement etc.) on wear debris particles. Some researchers have demonstrated that i) *PAMPs adhere to the particles*; ii) *adherent PAMPs increase significantly the biological response to the particles*; iii) *PAMPs do this by activating their PRRs* (Greenfield et al. 2010). In addition, particles deliberated from THA are coated immediately with serum proteins creating *a protein-particle complex* that may result in a damage/danger-associated molecular pattern picture *irrespective of endotoxin* (Sun et al. 2003). Engagement of TLRs leads to activation of the NF-κβ signalling pathway with subsequent expression of pro-inflammatory cytokines, strongly boosting the immune response (Pearl et al. 2011). In the same way also other receptors (e.g. NOD-like receptor proteins) may participate in activation of the immune response to wear debris (St Pierre et al. 2010). Some of these are structurally involved in the cytosolic multi-protein complex known as the "inflammasome" which is considered a critical part of the inflammatory response. After appropriate stimulus, IL-1β and IL-18 convert from their inactive precursor molecules (pro-1β and pro-IL-18) into biologically active molecules that are then released from inflammasomes (Caicedo et al. 2009). From the clinical viewpoint, lipopolysaccharide has been detected in periprosthetic tissues without any other signs of infection in patients (Nalepka, Lee et al. 2006). In addition, some patients who underwent revision arthroplasty due to aseptic loosening had positive culture and/or PCR detected

**4. Local host response to prosthetic wear debris**

aseptic loosening and osteolysis.

**4.1 Hierarchy of immune reactions to prosthetic wear particles** *(Gallo)*

important role in resolving inflammation and prevention of tissue damage.

Resident macrophages are amongst the first cells involved in particle-associated inflammation and initiate cell recruitment via the release of chemokines. Macrophage activation may occur with or without phagocytosis, by cell membrane contact with particles (Goldstein et al. 1975). Several receptors in the outer membrane of macrophages (e.g. CD11b, CD14, Toll-like Receptors and others) are involved in the activation of macrophages after contact with particulate debris. These receptors act through transmembrane proteins and different intracellular pathways and result in the release of cytokines, chemokines and other substances that induce cell recruitment. One of the most important intracellular pathways involved in signal transduction is the *mitogen-activated protein kinase (MAP-Kinase) pathway*. The MAP-kinase pathway then activates transcription factors such as the Nuclear Factor Kappa B (NFB), which activates a cluster of genes for the production of pro-inflammatory cytokines, chemokines and related substances (Tuan et al. 2008). Besides macrophages, other resident cells are involved in the inflammatory process including fibroblasts, mesenchymal stem cells, osteoblasts, lymphocytes and others. Retrieval studies demonstrated that the periprosthetic cells produced high levels of pro-inflammatory cytokines and other factors including TNF, IL-1, IL-6, M-CSF, RANKL, and others (*Part 4.3*).

Aseptic Loosening of Total Hip Arthroplasty as a Result of Local Failure of Tissue Homeostasis 331

through the blood stream to the areas of particle generation. Recent murine studies involving continuous intra-osseous infusion of ultra high molecular weight polyethylene (UHMWPE) particles have demonstrated an increase in the systemic trafficking of reporter macrophages to the particle infusion site using non-invasive imaging (bioluminescence) and immunofluorescence microscopy (Ren et al. 2010). Furthermore, a decrease in bone mineral density has been observed within the UHMWPE particle-infused femora (Ren et al. 2011). This suggests that wear particles stimulate a systemic response, mediated by chemokines released locally. Recent unpublished studies in our laboratory have demonstrated that inhibition of the MCP-1-CCR2 chemokine-receptor axis can mitigate this response, including

Fig. 3. Biologic reaction induced by orthopaedic wear particles on host cells; note the complex interaction of different cell types and factors to the presence of wear particles. Chemokines play a central role in cell recruitment. This diagram does not include the type 4

*B ligand, TLR = toll-like receptor, FGF = fibroblast growth factor, OPG = osteoprotegerin, TGF*

*, PGE2 = prostaglandin E2*

*stimulating factor, MCP-1 = monocyte chemoattractant protein-1, MIP-1 = macrophage inhibitory protein-1, ROS = reactive oxygen species, NO = nitric oxide, VEGF = vascular endothelial growth*

*, GM-CSF = granulocyte macrophage colony-*

 *=*

 *B, IFN = interferon, RANKL = receptor activator of nuclear factor*

reversing in part, particle-induced bone loss.

immune reaction to metallic wear particles.

 *= tumor necrosis factor*

*B = nuclear factor*

*IL = interleukin, TNF*

*transforming growth factor*

*factor, NF-*


*Edited by Olga Babusikova, Sinisa Dovat and Kimberly J. Payne*

The purpose of this book is to provide a comprehensive review of the scientific advances in T-cell malignancies and to highlight the most relevant findings that will help the reader understand both basic mechanisms of the disease and future directions that are likely to lead to novel therapies. In order to assure a thorough approach to these problems, contributors include basic scientists, translational researchers and clinicians who are experts in this field. Thus, the target audience for this book includes both basic scientists who will use this book as a review of the advances in our fundamental knowledge of the molecular mechanisms of T-cell malignancies, as well as clinicians who will use this book as a tool to understand rationales for the development of novel treatments for these diseases.

ISBN 978-953-307-400-9

T-Cell Leukemia

Photo by cgtoolbox / iStock


**1. Introduction**

Current demand for energy consumption is predicated on burning fossil fuels to provide dependable and resilient energy networks. Unquestionably, one of the most significant issues for scientists and engineers is the requirement for energy. Energy production techniques from the preceding century are now acknowledged as being inappropriate because of rising atmospheric carbon dioxide (CO2) emissions [1]. The significant contribution of non-renewable energy sources raises environmental issues since they release greenhouse gases into the atmosphere, which have detrimental effects on human health and the ecosystem. As they are generally accessible, clean, and free of pollution, renewable energy sources (RESs) have gained popularity and attention across the globe. By 2030, it is anticipated that RESs like wind, solar, hydro, geothermal, and biomass would dominate world's electricity production and surpass all other energy sources [2]. Among the renewables, photovoltaics have experienced the fastest increase over the past few decades. Currently, wind energy production exceeds that of photovoltaic (PV), although PV is more widely accessible and wind turbines require extremely specialized site conditions. As an alternative, photovoltaics have become a viable option in the fight against climate change. The process of turning light into electricity using semiconducting materials that display the

**Citation:** Qaiyum, S.; Margala, M.; Kshirsagar, P.R.; Chakrabarti, P.; Irshad, K. Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques. *Sustainability* **2023**, *15*, 11081. https://doi.org/10.3390/su151411081

Academic Editors: Prince Winston David and Praveen Kumar B

Received: 30 May 2023 Revised: 6 July 2023 Accepted: 10 July 2023 Published: 15 July 2023

**Copyright:** © 2023 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/).

photovoltaic effect is known as photovoltaic (PV). The three main PV cell technologies monocrystalline silicon, polycrystalline silicon, and thin film—control the global market. Among these, thin film solar cells offer several advantages over traditional silicon-based PV cells. They can be manufactured using low-cost techniques, such as roll-to-roll deposition or printing methods, which can potentially reduce production costs and enable large-scale manufacturing. Thin film solar cells are also lightweight and flexible, making them suitable for various applications, including building-integrated photovoltaics, portable electronics, and off-grid installations. However, the efficiency of thin film solar cells is generally lower than that of silicon-based cells [3].

The amazing rise in human living standards and the resulting rise in electricity demand have led to a number of super-large-scale power system flaws that are now more obvious than ever [4]. The traditional fossil fuel-based power plants are unable to provide enough energy to keep up with the rising demand for electricity. Early in the twenty-first century, the idea of a microgrid (MG) for integrating clean renewable energy sources (RESs) was put forth.  Energy storage systems (ESSs) and RERs are both used in the microgrid's power generation or DERs. A new sort of contemporary active power distribution system for the use and advancement of renewable energy is the microgrid [5]. However, microgrids make it more challenging to maintain a balance between energy production and consumption, and the incorporation of RESs complicates power grid operations.

PV plant power generation frequently experiences significant fluctuations, including voltage irregularities, reserve power flow issues, and power distribution problems. Additionally, energy users show unpredictable usage of power due to a variety of factors, including alterations in the environment and user activity. Therefore, it is important to analyze the performance of the system to provide better consumer services and to maintain a reliable and sustainable system.

Accurate forecasting of PV panels is a challenging task since it depends only on the weather conditions such as temperature, humidity, etc. [6]. Prediction can be carried out using many techniques such as physical mode, machine learning (ML), and deep learning (DL) [2]. Each prediction method has its own advantages and disadvantages. Physical methods, for instance, can anticipate the shifting patterns of the environment with high precision, but they need a lot of processing capacity since they require a massive amount of data. Physical techniques encounter unforeseen estimating errors and are inappropriate for short-term forecasting horizons, which raises further problems. Similar to this, the majority of the statistical models used to anticipate renewable energy are linear in design, which restricts their application to forecasting issues with wider time horizons. ML-based prediction provides findings that are more accurate than those produced by statistical and physical methods owing to its data mining and feature extraction capabilities. However, ML-based prediction models require shallow models as the foundation of their learning strategies. Trees, regressors, and neural networks with zero or one hidden layer are examples of common shallow patterns. Often, extensive knowledge and expertise are needed while training such shallow models. Therefore, it is frequently difficult to investigate shallow structures theoretically. Hence, shallow models have certain disadvantages in real-world applications. Lately, it has been determined that DL-based approaches to energy generation and power load forecasting performed better than ML-based approaches. Unlike ML, DL-based methods do not have problems with manually chosen feature selection, complex samples, or ineffective generalization competence [7]. Consequently, it is impossible to ignore the dynamics of renewable resource energy generation behavior. The limited use of MGs in literature work prevents real-world data from being taken into account while controlling energy distribution. For better energy management of real-world data, a complete framework is needed. Furthermore, power trading between different market players is completely ignored, and prior statistical information of uncertain renewable resource energy production was assumed to be perfectly understood. This is the driving force behind

the suggested method, which combines deep learning approaches with distributed energy management to enhance the effectiveness and dependability of the proposed system. Objectives of this work are as follows:


**3. Overall survival**

Overall survival is around 66.7% at 3 years [40]. In the study by Méndez Romero et al, overall survival at 1 year was 75%18 and in the study by Bujold et al, 55% (24–54 Gy in six fractions) [12]. And in the study by Su et al, overall survival at 1 year was 94%20. Kang et al reported an OS at 2 years after SBRT (42–60 Gy in three fractions) of 69% [6]. In the study by Sanuki et al, overall survival at 3 years was 70%, with no difference between doses of 35 and 40 Gy [9]. Overall survival at 1, 2, and 3 years in the study by Rim et al. was 72.6, 57.8, and 48.3%, respectively [29].

When the intention of the treatment is neoadjuvant, the aim is to prevent progression of patients on the waiting list for liver transplantation and to prevent them from leaving the waiting list. SBRT is an effective treatment as a bridge to transplantation. One study retrospectively included 10 patients with hepatocellular carcinoma on the transplant list treated with SBRT. Two patients had Child Pugh B and one had Child Pugh C, the median tumor size was 3.4 cm (2.5–5.5 cm), and the median dose was 51 Gy in 3 fractions. Four patients had received previous treatment with TACE. All patients were successfully transplanted. On anatomic-pathologic review, three patients had complete response and three patients had minimal remainder. The 5-year overall survival and progression-free survival were 100%, and there were no toxicities greater than or equal to grade 3 [41]. Mannina et al analyzed their experience using SBRT in 38 patients with hepatocellular carcinoma, and all patients were transplanted [40]. Complete response was observed in 45% of lesions and partial response in 23%, with poor concordance between radiological and pathological evaluation. Overall survival at 1, 2, 3, and 5 years was 92, 86, 77, and 73%, respectively [42].


*3.4. Generation of Inflammatory Responses*

Inflammatory responses may also contribute to IR-induced salivary gland dysfunction. Extracellular ATP (eATP), a damage-associated molecular pattern (DAMP) that commonly activates neighboring cells due to ATP release from adjacent damaged cells, is released from primary parotid gland cells immediately following 2–10 Gy IR exposure [48]. Additionally, levels of the inflammation-associated lipid, prostaglandin E2 (PGE2), are increased in parotid acinar cell culture supernatant 24–72 h following 5 Gy IR, with reduced levels of eATP and PGE2 release shown in mice deficient in the ATP-activated, P2X7 purinergic receptor (P2X7R), which correlates with improved saliva flow by days 3–30 post-IR [48]. Surprisingly, these pathways do not impact cell death induction in parotid glands post-IR [48], but may play a role in the inflammatory response to IR-induced salivary gland damage. It also has been observed that mRNA levels of the inflammatory cytokine interleukin (IL)-6 in irradiated salivary glands increased at 3 h post-13 Gy IR exposure and were reduced by 6 h post-IR, but increased again by day 14 post-IR. Interestingly, this increase correlated with elevated serum IL-6 levels at 6–12 h post-IR and again by day 14 [67]. IL-6 is a pro-inflammatory cytokine with diverse functions. However, the exact role that IL-6 plays in salivary glands post-IR has not been well defined. These data suggest that IR-induced damage to salivary glands leads to diverse inflammatory responses that should be further investigated.