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**4. Protozoan infections**

Different protozoan (Plasmodium, Leishmania, Trypanosoma, Toxoplasma, and Entamoeba) PAMPs induced pathogenic reactions through TLR signaling pathway.

#### **4.1 Malaria**

Malaria, one of the most life-threatening diseases of human history, has infected about 219 million people over 90 countries with around 1 million deaths per year. Plasmodium, an intracellular protozoan parasite, is the causative agent of malaria. It is transmitted by infected female Anopheles mosquito biting, and four species of *Plasmodium* are responsible for human malarial infection. Among *Plasmodium falciparum*, *Plasmodium vivax*, *Plasmodium ovale,* and *Plasmodium malariae*, *P. falciparum* is the deadliest. Recently, another species named *Plasmodium knowlesi* has been found to infect humans [30]. In the early presymptomatic stage, a very low level of plasmodium can induce inflammatory response [31]. The innate immune genes such as TLRs, PRRs, and inflammatory cytokines are already upregulated, and these lead to elevate the level of TNF, IFN, and IL-12 from plasmodium-infected peripheral blood mononuclear cells (PBMCs) up to 48 h of infection [32, 33]. These inflammatory responses are associated with the pathophysiological condition and clinical symptoms of malaria including anemia, cerebral malaria, and ultimate death [34]. Cerebral malaria is caused due to overexpression and binding of adhesion molecules such as intracellular adhesion molecule 1 (ICAM-1), vascular cellular adhesion molecule 1 (VCAM-1), endothelial/leukocyte adhesion molecule (ELAM-1), and CD36 [35] on brain endothelial cell receptors. Thus, inflammatory response leads to sequestration of infected red blood cells in host brain [36]. Furthermore, TNF and IFN suppress hematopoiesis and lead to anemia during malarial infection [37]. The potential immunomodulators of the malarial parasites are: (1) plasmodial glycosylphosphatidylinositol (GPI) anchors, (2) hemozoin, and (3) plasmodial DNA. All of these three molecules are referred as "malaria toxin" released during schizogony and cause inflammation and symptoms of malaria [38, 39].

Homodimer of TLR4 and heterodimer of TLR1/TLR2 and TLR4/TLR6 can bind to GPIs released during erythrocytic phase of *P. falciparum* infection [40]. GPI induces TLR-mediated proinflammatory cytokines (TNFα and IL-1) [41] and

**67**

**Figure 2.**

*TLR signaling during Plasmodium infection.*

*TLR Signaling on Protozoan and Helminthic Parasite Infection*

derived molecules also induce Th2 response via IL-4-inducing factor (released by PI3K-Akt-NF-κB signaling) in DC [53].

Leishmaniasis is one of the deadliest parasitic infections with an estimation of 200,000–400,000 worldwide infections each year. A protozoan parasite is the causative agent of this disease, which is transmitted to humans by the biting of female Phlebotomus sandfly. The pathology of this infection and causative parasitic species includes cutaneous (i.e., *L. major*, *L. mexicana*, and *L. guyanensis*), mucocutaneous (i.e., *L. amazonensis* and *L. braziliensis*), or visceral leishmaniasis (*L. donovani* and *L. chagasi*) [54]. Several reports indicate that few Leishmania-derived molecules could interact with innate immune receptors (TLRs) of host and result in inflammatory

**4.2 Leishmaniasis**

nitric oxide [42] release from macrophages. It also induces cerebral malaria at later course of infection [43]. Plasmodium 2-Cys peroxiredoxin also acts as a TLR4 ligand in monocyte and mast cells and causes cytokine production [44]. Hemozoin is released during each life cycle of *P. falciparum* infection and makes a complex with plasmodial DNA. This complex acts as a TLR9 ligand and leads to the production of proinflammatory cytokines (TNFα and IL-1β) [45]. Hemozoin DNA complex induces cerebral malaria by caspase 1-mediated inflammasome (NLRP3) formation by TLR9 in case of *P. chabaudi* infection [46] but is absent in *P. berghei* spoprozoite infection [47]. Although in case of both mice and humans, Plasmodium infection renders no TLR stimulation in dendritic cells. The infant exposed to TLR-mediated cytokine profiles (IL-10) is associated with higher risk of *P. falciparum* maternal infection during delivery [48]. RNA of *P. chabaudi* acts as a ligand for TLR7 and induces IFNγ, IL-10, IL-12, and TNF release at 24 h of infection [49]. In case of *P. vivax* infection, TLR5 and TLR7 hinder parasitic growth, but TLR9 is associated with high inflammation and cytokine production [50]. The 19 kDa C-terminal fragment of merozoite surface protein 1 (MSp1) in *P. vivax* acts as a ligand for TLR5 [51]. *P. yoelii* infection in peritoneal macrophages enhances TLR and parasite-specific immune response [52] (**Figure 2**). Other than Th1 response, malaria parasite-

*DOI: http://dx.doi.org/10.5772/intechopen.84711*

#### *TLR Signaling on Protozoan and Helminthic Parasite Infection DOI: http://dx.doi.org/10.5772/intechopen.84711*

nitric oxide [42] release from macrophages. It also induces cerebral malaria at later course of infection [43]. Plasmodium 2-Cys peroxiredoxin also acts as a TLR4 ligand in monocyte and mast cells and causes cytokine production [44]. Hemozoin is released during each life cycle of *P. falciparum* infection and makes a complex with plasmodial DNA. This complex acts as a TLR9 ligand and leads to the production of proinflammatory cytokines (TNFα and IL-1β) [45]. Hemozoin DNA complex induces cerebral malaria by caspase 1-mediated inflammasome (NLRP3) formation by TLR9 in case of *P. chabaudi* infection [46] but is absent in *P. berghei* spoprozoite infection [47]. Although in case of both mice and humans, Plasmodium infection renders no TLR stimulation in dendritic cells. The infant exposed to TLR-mediated cytokine profiles (IL-10) is associated with higher risk of *P. falciparum* maternal infection during delivery [48]. RNA of *P. chabaudi* acts as a ligand for TLR7 and induces IFNγ, IL-10, IL-12, and TNF release at 24 h of infection [49]. In case of *P. vivax* infection, TLR5 and TLR7 hinder parasitic growth, but TLR9 is associated with high inflammation and cytokine production [50]. The 19 kDa C-terminal fragment of merozoite surface protein 1 (MSp1) in *P. vivax* acts as a ligand for TLR5 [51]. *P. yoelii* infection in peritoneal macrophages enhances TLR and parasite-specific immune response [52] (**Figure 2**). Other than Th1 response, malaria parasitederived molecules also induce Th2 response via IL-4-inducing factor (released by PI3K-Akt-NF-κB signaling) in DC [53].


**5. Conclusion**

In conclusion, endotoxin contamination of fluid for dialysis therapy is an important aspect of patient safety and well-being. Regardless of the microbiological quality of the water coming into the hemodialysis machine, the dialysis membrane is the final barrier between potentially contaminated dialysis fluid, and the blood of the patient. We have examined how manipulation of specific fiber membrane parameters (geometric properties, materials, coatings, chemical modifications) can be utilized to improve the endotoxin retentive properties to limit trans-membrane flux, whether they contribute to adsorptive improvements, sieving improvements, or both. Membrane structure, surface chemistry, material, and surface coatings all have an impact on how endotoxin is filtered and adsorbed from solution. In addition to better understanding endotoxin-membrane interactions, studies of endotoxin removal by membrane modifications will result in better approaches to manufacture dialysis membranes that remove endotoxin from solution quickly and with improved efficiency.

As future hemodialysis membranes are designed to further improve upon convective removal of larger middle molecular solutes such as B2M, the opportunity for pyrogenic materials to enter the blood stream via back filtration becomes greater. An improved comprehension of how endotoxin is removed using a fiber membrane can lead to new improvements and future product designs, by streamlining concepts from development to production.


**1. Introduction**

IgA nephropathy (IgAN), defined by prevalent mesangial IgA deposits [1], displays a variable clinical course, ranging from persistent mild microscopic hematuria with or without mild proteinuria and normal renal function to nephrotic syndrome or, rarely, a rapidly progressive course of kidney function loss. Moreover, in approximately 30% of cases, it leads to end-stage kidney disease over a 20-year follow-up period [2]. This variability represents a challenge for the clinician and has elicited the search for clinical and pathologic features predictive of progression [3] but definitively demonstrates that IgAN cannot be considered as a benign condition.

Shortly after the identification of IgAN as a histological pattern of glomerular injury with a variable clinical course, the presence of crescents attracted particular interest as a distinct pathological feature associated with severity. In small but well-described case series, crescents were found to be present in patients with macroscopic hematuria and worse renal function [4] and with a rapid progressive course with loss of renal function in cases with more than 30% of glomeruli involved with crescents [5]. A relationship between crescents and transplanted kidney dysfunction with loss of grafts in patients with recurrent IgAN was also reported [6] and further supported by the observation of an increased recurrence rate of in patients with crescentic forms of IgAN in their native kidneys, despite baseline immunosuppressive therapy [7]. Moreover, the value of crescents to classify children with IgA vasculitis nephritis (IgAVN) established by the International Study for Kidney Disease in Children (ISKDC) [8] suggested a possible similar role for crescents as a histologic marker

**Citation:** Trimarchi, H.; Haas, M.; Coppo, R. Crescents and IgA Nephropathy: A Delicate Marriage. *J. Clin. Med.* **2022**, *11*, 3569. https:// doi.org/10.3390/jcm11133569

Academic Editors: Hitoshi Suzuki, Jan Novak and Giacomo Garibotto

Received: 13 April 2022 Accepted: 18 June 2022 Published: 21 June 2022

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**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/).

and as a potential tool to assess treatment decisions concerning immunosuppression in primary IgAN, due to the strong similarities between both entities.

These observations have led to the general thought that finding crescents in kidney biopsies of IgAN cases was a prognostic indicator associated with poor outcomes [9]. Furthermore, results from many clinical studies supported the value of crescents as markers of progression that benefited from immunosuppressive approaches [10,11].

Interestingly, the predictive value of crescents was difficult to demonstrate with recent sophisticated statistical approaches on large cohorts enrolling patients with or without corticosteroid-immunosuppressive treatments, since patients with crescents were those most frequently treated [12]. Other reasons that complicate this analysis are the different definitions employed to identify crescents; the inclusion of extra-capillary proliferation with either cellular, fibrocellular, or fibrous components; and the relative number of crescents in relation to the total number of glomeruli encountered in a kidney biopsy. Different prognostication models employing artificial intelligence provided differing conclusions regarding the inclusion of crescents as predictive markers of progression in IgAN [13]. In this regard, the variability of these results led the Kidney Disease Improving Global Outcomes (KDIGO) 2021 guidelines for the management of IgAN to recommend that either the presence or the relative number of crescents should not be used to determine the likely progression of IgAN nor should they suggest the choice of immunosuppression [14]. KDIGO also made the controversial proposal that even the presence of crescents in >50% of glomeruli in a kidney biopsy in the absence of a concomitant change in glomerular filtration rate (GFR) does not constitute a rapidly progressive situation and ought not to bias therapy.

Despite these negative authoritative considerations, for most nephrologists, the value of crescents remains an unsolved conundrum, since it is commonly recognized that cellular crescents are often associated with a rapid progression of kidney disease and clinically severe proteinuria and hematuria in patients with IgAN, particularly when the relative number is high in a kidney biopsy. Unfortunately, the issue of the value of crescents in IgAN cannot be explored in experimental IgAN, since in these models mostly mesangial proliferation and glomerular sclerosis, but not crescents, are reproduced [15].

The aim of this review is to report recent data on the debated issue of the value of active (cellular and fibrocellular) crescents in the pathogenesis and clinical progression of IgAN, their predictive value as a histological marker, and the impact of immunosuppressive treatment on renal function.

#### **2. Molecular Factors Involved in Crescent Formation in IgAN**

In IgAN, fibrinogen and fibrin-related molecules are present in crescents (Figure 1). Moreover, components of the glomerular basement membrane, such as collagen types IV and V, laminin, fibronectin, and cytokeratin, are persistently positive within all stages of crescents. Vimentin, usually located in podocytes and parietal epithelial cells as well as in interstitial cells, is also present in all stages of crescents in IgAN, suggesting that at early stages of crescent development in IgAN podocytes may play a key role. In addition, the presence of intrinsic basement membrane constituents is consistent with GBM rupture, likely related at least in part to necrotizing lesions within the glomerular tuft, representing a precursor to crescent formation. Fibrinoid necrosis may be seen in IgAN, most often in cases with active crescentic lesions, although in most studies from Europe and North America, this is relatively uncommon, especially when compared to, for example, anti-neutrophil cytoplasmic antibody (ANCA) glomerulonephritis and vasculitis. Nevertheless, in a French study of 128 adults with IgAN, glomerular necrotizing lesions, which were seen in 9 cases, were significantly associated with development of end-stage kidney disease (ESKD) or the doubling of serum creatinine by univariate (but not multivariable) analysis [16]. In an Italian study, glomerular necrotizing lesions were seen in 35 of 340 IgAN patients and were associated with a greater probability of progression to ESKD only in patients who were not treated with immunosuppression [17]. Notably, glomerular necrotizing lesions tend to be seen more frequently in patients from Asia, perhaps related to more frequent

activation of the lectin pathway of complement in these patients (see below). Shen et al. [18] examined 60 Chinese patients with active IgAN who underwent a kidney biopsy, followed by corticosteroid therapy and a follow-up biopsy within 6 months of the initial biopsy. In this cohort, 31 patients had one or more glomerular necrotizing lesions, 51 had one or more cellular or fibrocellular crescents, and 22 had E1 lesions on the initial biopsy. After steroid therapy, necrotizing lesions, active crescents, and E1 lesions were seen on 2, 15, and 5 follow-up biopsies, respectively, and none of these lesions on the initial biopsy were associated with a composite endpoint of ESKD or 30% decline in eGFR [18]. The apparent higher steroid sensitivity of necrotizing lesions compared with crescents is consistent with the former representing an earlier form of a related inflammatory process, although clearly this requires validation.

**Figure 1.** Main cells and molecules involved in crescent development in IgAN. In IgAN, crescent formation could be divided into two steps. First of all, there is an initial damage to the endothelium

of the glomerular filtration barrier (rents )from where podocytes and parietal cells interact within and outside the surrounding glomerulus. The second step is the consequence of this interaction: The development of the crescent itself. Main molecules involved: Fibrinogen and fibrin-related antigens , type IV and V collagens, laminin, fibronectin, cytokeratin persistently positive at all stages of crescents, as well as vimentin, distributed in podocytes and parietal epithelial cells (PECs). At early stages of crescent formation in IgAN, podocytes play a key role, while the accumulation of basement membrane components adds to the progression of the crescents (Figure 1). In IgAN, endothelial proliferation associates with crescent appearance. Crescent formation in IgAN is associated with activation of the lectin and alternative pathways .

It appears that monocytes and macrophages may not play a critical role in the development of crescents in IgAN, in contrast with what occurs in other glomerular diseases, in which these cells are active players in crescent pathogenesis [19,20]. This characteristic may explain why the appearance of some or occasional crescents in kidney biopsies may not be indicative of severe lesions when compared to other forms of glomerulonephritis with crescents, unless the GBM is not preserved. In IgAN, endocapillary hypercellularity was found more frequently in cases with crescents [18], although numbers of glomerular CD68+ macrophages were found to be associated with endocapillary hypercellularity but not crescents [21]. A possible explanation for this may be the difficulty of accurate histologic diagnosis of endocapillary hypercellularity even by skillful nephropathologists, especially in the presence of segmental glomerulosclerosis that is often associated with fibrocellular crescents [21,22].

Several studies have reported that, in IgAN, complement activation plays a role in crescent formation. Complement components and factors related to complement activation are partly produced by intrinsic glomerular cells including mesangial and endothelial cells [23]. Podocytes also display complement component receptors at their cell surface. It has been reported that there is an increased intensity of properdin and factor B staining in murine IgAN with more severe glomerular injury including crescent formation, indicating the involvement of an activated alternative pathway of complements [24]. IgAN patients with increased glomerular mannose-binding lectin (MBL)-associated serine protease type 1 (MASP-1) deposition presented with higher levels of proteinuria and increased proportions of extracapillary proliferation, glomerular sclerosis, and renal dysfunction [25]. IgAN with crescents has been reported to present with higher levels of tissue C5b-9, MASP 1/3, MASP2, properdin, and factor B than IgAN without crescents, indicating an activation of both the lectin and alternative pathways of complement [26].

Glomerular C4d staining in the absence of C1q deposits is considered a typical sign of lectin pathway activation. C4d-positive IgAN biopsies are associated with a worse prognosis and a trend to develop ESKD [24]. In a cohort of 100 IgAN Chinese patients with various proportions of crescents, signs of complement activation in urine samples were significantly increased in comparison to healthy controls in cases with crescents involving >50% of glomeruli, presenting with high levels of the common complement pathway—C3a, C5a, and C5b-9—as well as markers of an alternative pathway—Bb—and the lectin pathway—C4d and MBL [27]. The levels of urinary C4d showed a highly significant linear association with the number of crescents. In a subgroup of these patients, immunohistochemistry was performed on renal biopsy tissue. Glomerular staining for C4d (>25% of glomeruli) was observed in 20% of cases in the group with crescents <25% of glomeruli, in 70% of cases with 25–49% crescents, and all cases with crescents in ≥50%. Positive glomerular staining was seen predominantly in the mesangial area, very often within the crescents and sclerotic lesions. Glomerular C5b-9 deposition and C3d staining were observed in almost all crescentic cases. These data stress the role of lectin pathway activation in Chinese patients with crescentic IgAN.

#### **3. Turning from MEST to MEST-C Score**

In studies of the cohort of 265 patients on which the original Oxford classification was based [28], the presence or absence of cellular/fibrocellular crescents was not a significant predictor of the rate of eGFR decline or a composite outcome of ESKD or a ≥50% decline in eGFR. This was also true in several validation studies [29–32], which like the original Oxford study excluded patients with an eGFR of <30 mL/min at the time of biopsy and/or progression to ESKD within 12 months of the biopsy. However, other studies with less restrictive entry criteria found crescents to be a prognostic indicator of a poor outcome [33–36]. Noteworthily, Katafuchi et al. found similar results to those in the original Oxford study in patients meeting entry criteria for the latter, although in their entire cohort of 702 patients and in those 286 patients not meeting the entry criteria of the original Oxford study, crescents independently predicted the development of ESKD [33].

Twenty studies published between 2009 and 2016 evaluating the association between crescents and kidney outcome involving more than 5000 patients with IgAN were included in a meta-analysis [37]. Nine of these studies [28,30–32,35,38–41] compared measures of kidney function between patients with no crescents or any crescents. Those patients with crescents had lower eGFR levels (*p* = 0.023); higher proteinuria (*p* = 0.024); more frequent M1 (*p* = 0.003), E1 (*p* < 0.001), S1 (*p* = 0.016), and T1/2 (*p* < 0.001) lesions; and received immunosuppressive therapy more frequently (*p* < 0.001) than those without crescents. Pooled results also showed that crescents were associated with progression to ESKD (*p* < 0.001), suggesting that potential inclusion of crescents in the Oxford Classification needed further evaluation.

In addition, two studies of pediatric patients with IgAN from Japan [35] and Sweden [36] without restrictive entry criteria also found cellular or fibrocellular crescents to be predictive of a poor outcome (eGFR <60 mL/min/1.73 m2 and ESKD or eGFR reduction >50%, respectively) by univariate analysis and by multivariable analysis (including eGFR, mean arterial pressure, and proteinuria at the time of biopsy) in the Japanese cohort.

In response to these findings, a working group of the International IgA Nephropathy Network (IIgANN) performed a multicenter study of the impact of crescents on renal outcomes in over 3000 patients with IgA nephropathy pooled from four previously studied, well-defined cohorts: the European VALIGA study cohort [42], two large Asian cohorts [31,33], and the original Oxford cohort [28] that included patients from four continents. Notably, while each of the three former studies validated the findings of the original Oxford study with regard to the impact of M, S, and T scores on renal outcomes, just one [33] showed crescents to be an independent predictor of a poor outcome (ESKD), although in the VALIGA study [42] an association with eGFR loss was noted only for patients not treated with immunosuppression. The working group study [10] was limited to cellular and fibrocellular (at least 10% cells) crescents, as in the original Oxford study identification of fibrous crescents showed poor inter-observer reproducibility [43]. In this study, one or more crescents were seen in 36% of biopsies (<10% crescents in 61%). The presence of crescents was associated with a faster rate of renal function decline compared to no crescents and a reduced survival from a combined event of ESKD or a ≥50% decline in eGFR in a multivariable analysis including eGFR at the time of biopsy and time-averaged proteinuria and mean arterial pressure during follow up [28]. This study also validated the association of the Oxford M, S, and T scores with the combined event in all patients although, in patients treated with corticosteroids and/or other immunosuppressive agents, only the T score remained a significant, independent predictor of increased risk of the combined event. The lack of an impact of crescents in patients treated with immunosuppressive agents is consistent with a repeat biopsy study from China that showed complete resolution of cellular/fibrocellular crescents following immunosuppressive therapy in 36/51 patients having one or more crescents on their initial biopsy [18]. Notably, however, the presence of cellular or fibrocellular crescents in ≥1/4 of the glomeruli was independently associated with a combined event even in patients who received corticosteroids and/or other immunosuppressive agents [18]. Thus, a revised version of the Oxford classification for IgA nephropathy published in 2017 includes a C (crescent) score in addition to the original MEST scores: C0 (no cellular or fibrocellular crescents), C1 (crescents in <25% of glomeruli, suggesting a poor prognosis in patients not receiving immunosuppressive therapy), and C2 (crescents in ≥25% of glomeruli) [21].

While the predictive value of crescents on renal outcomes was not evident in the initial VALIGA study [44], Coppo et al. [42] performed a follow-up study of this patient cohort based on a prolonged follow-up period (median 7 years, as compared with 4.7 years in the original study) of up to 35 years. This longer-term analysis of the VALIGA cohort, which included adults and children, showed that, in patients who never received corticosteroids/and or other immunosuppressive treatment, the presence of crescents (C1 + C2) was related to the rate of renal functional decline, independent of the MEST score and other risk factors. There was not a significant effect of crescents on the composite endpoint of

50% loss of eGFR or ESKD, perhaps because the patients enrolled had a modest median annual eGFR loss (1.8 mL/min/year) and the fraction of patients with crescents was only 10.5% (C1 = 8.6%; C2 = 1.9%).

#### **4. Risk Prediction Models for IgAN and Crescents in Adults**

In recent years, several models using novel mathematical statistics or artificial intelligence approaches have been developed to predict, at the time of kidney biopsy, the risk of progression of IgAN toward ESKD or 50% decline in eGFR [45]. Variables most commonly included were age, gender, blood pressure, creatinine, eGFR, proteinuria, and renal biopsy lesions according to the Oxford classification. The most recognized, due to the great number of included cases from global multiethnic cohorts, is that developed by the IIgANN, which considered two models with and without ethnicity and used clinical predictors and MEST scores at the time of renal biopsy to predict the risk of 50% decline in eGFR or ESKD at 8 years [45].

This IIgANN prediction tool was validated in external cohorts and was updated for its use also in children [46] and for its employment up to two years from the time of biopsy [47]. This tool, now available for clinical use online in a mobile-app calculator, has been recommended by 2021 KDIGO glomerulonephritis guidelines [14]. Each MEST score component was required to be entered in the prediction formula, but crescents were excluded, as their presence or absence was apparently not associated with clinical outcomes. Crescents were significantly associated with race/ethnicity (more frequent in Japanese ethnicity) and with use of corticosteroid/immunosuppressive therapy after biopsy (56% vs. 36%, *p* < 0.001). In the recently published prediction tool, at time points after renal biopsy, the value of crescents (present or absent) was re-checked, adding this variable in the post-biopsy models, but there was no improvement in in model fit or in calibration indices [47].

Other prediction models generated in different cohorts, reported the effects of crescents in untreated patients. Among 3380 Korean patients, crescents improved the discrimination performance of the prediction model only in patients not receiving corticosteroids/immunosuppressive agents [48]. However, this study did not employ the Oxford classification. A more recent report, using the MEST score, investigated 545 Korean patients to generate a prognostication model that considered C1 (found in 24% of patients) and C2 (found in 1.3%). When adding crescents to their full model with clinical data and MEST score, the predictor was not superior to the full model. However, the prediction performance of crescents was significantly improved after 5 years of follow up in the subgroup of 426 patients not treated with corticosteroid-immunosuppressive drugs (*p* < 0.02) [49].

In recent years, artificial intelligence has arisen in the medical field to assess new prediction models, claiming that machine learning techniques may outperform conventional statistical models, with an improved capability to identify variants relevant to clinical outcome. A prediction model using an artificial intelligence statistical approach developed in 2047 Chinese patients with a median of 10 years of follow up did not include crescents due to non-significance [50]. In a Caucasian cohort mostly involving the VALIGA European patients, the artificial intelligence tool showed a performance value of 0.82 in patients with a follow up of 5 years. Crescents were included, although these were not associated with significantly increased risk to develop ESKD by Cox proportional hazard models [13].

#### **5. Risk Prediction Models for IgAN and Crescents in Children**

The IIgANN gathered 1060 pediatric cases of IgAN from various continents. As in adults, crescents were more frequent in patients of East Asian ethnicity (65.9% of Japanese and 45.7% of Chinese versus 25.5% of Caucasians) [46]. Children received steroid therapy in 58% of the cases, more frequently in the presence of crescents (70% of children with crescents versus 48% without crescents). No association of crescents (absent versus present) with the secondary outcome of 30% reduction in eGFR or end-stage kidney disease was found, even after adjusting for immunosuppression. The prediction performance was the

same in subgroups of treated versus untreated by immunosuppression. Any prediction improvement associated with crescents is confounded by the effects of other predictor variables already included in the model, such as race/ethnicity. However, the prediction model performed equally well in treated and untreated cases, indicating that the combination of risk factors contained in the models predicted outcome similarly in treated and untreated children.

In a pediatric cohort from Japan, a threshold of 30% of glomeruli with crescents was found to be predictive of development of an eGFR below 60 mL/min/1.73 m2 by both univariate analysis and a multivariate analysis including MEST scores and proteinuria at the time of biopsy [35].

#### **6. IgAN with Crescents Involving >50% of Glomeruli**

Crescentic IgAN, defined as >50% cellular crescentic glomeruli on kidney biopsy, was investigated in 113 Chinese adult patients [11]. At biopsy, the mean serum creatinine level was 4.3 mg/dL, and the mean percentage of crescents was 66%. Kidney survival rate at 5 years after biopsy was 45.8%. Multivariable Cox regression revealed initial serum creatinine as the only independent risk factor for end-stage kidney disease (*p* = 0.002). Notably, the percentage of crescents was not independently associated with ESKD.

A recent report from Japan focused on children with >50% glomeruli with crescents, accounting for 25 cases (4.9% of the whole cohort of 515 Japanese children with IgAN) [51]. No prior history of urinary abnormalities was reported in 16/25 children, who were referred from school screening programs. These children with crescentic IgAN had more frequent gross hematuria (76%), proteinuria ≥ 1 g/day, and shorter duration from clinical onset to renal biopsy, median 4 months versus 8 months. There was a significantly increased frequency of M1 (80%) and E1 (83%) in comparison with the other children, although T>0 was rare in both groups. At the time of renal biopsy, renal function was actually well preserved (eGFR 120 mL/min) in comparison to the other children (eGFR 104 mL/min). Although eighteen children with crescentic IgA were treated with prednisolone or prednisolone plus other immunosuppressive agents, only 8/25 patients with these lesions had remission of proteinuria (6 of whom received immunosuppression) and 4 (16%) progressed to an endpoint of eGFR < 60 mL/min/1.73 m2 or ESKD compared to 13 (2.7%) of the remaining 490 patients, the majority of whom had C1 (n = 228) or C2 (n = 40). Children with crescentic IgAN had significantly lower survival from this composite endpoint at 13 years post-biopsy (77.1% vs. 92.6%, *p* < 0.0001). Failure of treatment to induce a remission of proteinuria and a higher percentage of tubular atrophy/interstitial fibrosis were also predictors of an unfavorable outcome [51].

#### **7. Timing of Renal Biopsy and Crescents**

A challenge in establishing the value of percentage of crescents as a risk factor for progression in IgAN is represented by the timing of renal biopsy, due to changes which may occur over time [52]. A study in Japanese children with IgAN [53] considered the time elapsed from the diagnosis of urinary abnormalities—mostly detected after school screening programs—and renal biopsy and reported that a shorter time from onset to renal biopsy was associated with higher glomerular percentage of crescents, in addition to higher M and E lesions. This suggests that crescents are associated with disease onset and then likely undergo a healing process into sclerotic lesions, which are commonly detected in biopsies performed years after onset.

#### **8. Treatment and Crescents in IgAN**

According to all the data presented in this review, the literature reports showing benefits of corticosteroid/immunosuppressive treatment in patients (adults and children) having IgAN with crescents are uncontrolled and retrospective. The coincidence of other MEST lesions with C, the variable morphology of crescents (cellular or fibrocellular, segmental or global glomerular involvement) and association with other lesions not included

in the MEST scores (e.g., necrosis, interstitial inflammation) render the understanding of the role of crescents as an independent feature promoting progression of IgAN very difficult. Moreover, the reports describe patients treated with corticosteroids and other immunosuppressive drugs at varying doses and over varying intervals, with different levels of supportive care (e.g., renin–angiotensin system (RAS) inhibitors).

A recently published Argentinian–Spanish retrospective study assessed the impact of steroids plus mycophenolate in a cohort of 25 patients with progressive IgAN [54]. Progressive IgAN was defined by a decrease in eGFR of at least 10 mL/min in the 12 months prior to the start of treatment and proteinuria ≥0.75 g/day despite maximum tolerated doses of RAS blockade, and hematuria ( ≥5 red blood cells per high power field) at the beginning of treatment. The mean interval between the performance of kidney biopsy and the onset of therapy was 4.5 ± 11.9 months. Ten patients (47.6%) had C1 scores and three (14.3%) displayed C2 scores (patients with crescents in >50% of glomeruli were excluded). The mean duration of immunosuppression treatment was 24.7 ± 15.2 months. In the 12 months prior to treatment, the median rate of kidney function decline had been 23 mL/min/year. After the onset of treatment, the median eGFR slope was 5 mL/min/year (*p* = 0.001 with respect to the 12 months prior to treatment). Proteinuria decreased from 1.8 g/day (range 1.0–2.5) at baseline to 0.6 g/day (range 0.3–1.2) at the end of treatment (*p* = 0.01), and hematuria disappeared in 40% of patients. There were no serious adverse effects requiring treatment discontinuation. In this study, in which >50% of the population presented with active crescents in their biopsies, the addition of immunosuppression decreased the rate of decline of kidney function plus the degree of proteinuria and hematuria.

There are few data on the predictive impact of MEST-C scores in randomized clinical trial settings. An exploratory analysis was performed in 70 available renal biopsies from 162 randomized STOP-IgAN trial participants and correlated the results with clinical outcomes [55]. Kidney biopsies had been performed from 6.5 to 95 months (median 9.4) prior to randomization. This secondary analysis of STOP-IgAN biopsies indicated that M1, T1/2, and C1/2 scores were associated with worse renal outcomes. In particular, patients with glomerular crescents (C1/2 scores) in their biopsies were more likely to develop ESKD during the 3-year trial phase, but this trend was only significant in patients under supportive care.

As commented previously, consideration of crescents in managing the treatment of IgAN has been strongly discouraged by KDIGO 2021, which does not support the value of MEST score in general as a guide of treatment and, in particular, disregards the value of percentage of crescents as an independent risk factor supporting a more aggressive therapy. According to KDIGO guidelines, the presence of crescents in kidney biopsy, even when involving ≥50% of glomeruli, in the absence of a concomitant rise in serum creatinine does not constitute a rapidly progressive form of IgAN, which is instead defined as a >50% decline in eGFR over >3 months, where reversible causes are excluded. KDIGO guidelines suggest repeating renal biopsy in case of insufficient benefits from supportive care, but this is in contrast with the suggestion not to use pathology scores to guide treatment. A few uncontrolled studies have repeated renal biopsies in selected cases [18,56] after corticosteroid or other immunosuppressive drugs had been administered and showed a reduction in crescents in most treated cases. Notably, in one repeat-biopsy study, it was found that, in patients with crescents on their original biopsy, those who continued to have crescents on their repeated biopsy were significantly more likely to develop ESKD than those whose second biopsy showed resolution of crescents [56].

Recently, a single-center Chinese study investigated 140 patients enrolled between 2008 and 2016 with C1 and proteinuria <1 g/day who received supportive care (n = 52) or steroid-based immunosuppressive therapy (n = 88) [57]. The primary outcome was the rate of renal function decline. Baseline data showed a population with mild renal disease, median proteinuria of 0.6 g/day, and a median fraction of crescents of 7% (5–12%), with a follow-up time of 69 months. The rate of renal function decline was slower in the steroid-based immunosuppressive therapy group than in the supportive care group.

Multivariable linear regression analyses showed that steroid-based immunosuppressive therapy significantly slowed the rate of renal function decline (*p* = 0.013) after adjusting for age, sex, mean arterial blood pressure, proteinuria, eGFR, M1, E1, S1, T1-2, the fraction of crescents, and use of RAS inhibitors. Similar findings were seen in 66 patients (33 from each group) who were matched for baseline demographic, clinical, and pathologic findings. In the matched cohort, the rate of renal function decline was also slower in the steroid-based immunosuppressive therapy group. The conclusion was that steroid-based immunosuppressive therapy may slow down the rate of renal function decline of IgAN patients with C1 and proteinuria ≤1 g/day.

Novel therapies are under evaluation for the treatment of IgAN (Table 1). These drugs are presently targeting persistent proteinuria despite several months of optimized supportive care, but the perspective is that at least some of them could be of benefit in the control of the most active and progressive cases such as those with high presence of crescents.

**Table 1.** New drugs under evaluation for treatment of IgA nephropathy: selected from studies published in the U.S. National Library of Medicine Clinical Trials.gov.

The agents being tested include (a) drugs blocking B cell function and survival by inhibiting B-lymphocyte stimulator (BLyS, also known as B cell activating factor, BAFF) and APRIL (a proliferation-inducing ligand); (b) complement activation inhibitors, targeting the lectin complement pathway (mannose-associated serine protease, MASP), the alternative complement pathway (factor B) or the common complement pathway (C3, C5, and its proinflammatory activation product C3a); and (c) other hemodynamic and inflammatory pathways such as endothelin, angiotensin II, nuclear factor erythroid-derived 2-related factor agonist (Table 1). Future perspectives are expected from the next results of these studies for treating patients with active histological forms of IgAN, including crescentic IgAN, independently from the persistence of proteinuria despite optimal supportive care.


**3. Basic principle of NFC**

NFC works based on the principle of near field magnetic communication. This principle of inductive coupling is applied to all communications based on near field magnetism between transmitting and receiving devices. **Figure 3** shows the

**Figure 3.**

*Inductive coupling between transmitter and receiver coils [9].*

simplified concept of inductive coupling. When a primary coil generates alternating magnetic field, secondary coil which is placed in the vicinity of the primary coil inductively coupled with the primary coil and generates induced alternating magnetic field according to the Faraday's law. This is the basic principle in transferring power wirelessly between the devices in near field region. The above stated principle also applied on RFID systems that are based on inductive coupling. Even though, there are some differences in other components such as network system and protocols between NFC and general RFID systems.

#### **3.1 Inductive coiled system**

The inductive coupled NFC system can be modeled by using expressions of selfinductance, mutual inductance and resistances [19]. A generalized analytical expression for calculation of self-inductance of circular or rectangular shaped coil is explained below. **Figure 4** shows a representation of single turn circular coil while illustrating the magnetic field pattern surrounding two circular coils [20].

The inductance, *L*0, for a single turn circular coil can be given by Eq. (1) as seen below [21]:

$$L\_0 = \mu\_0 r l n \left(\frac{2r}{d}\right) \tag{1}$$

Where *μ*0 is permeability of free space, *r* is radius of the coil and *d* is the diameter of the wire. The single turn coil inductance can be utilized to calculate

*Near-Field Communications (NFC) for Wireless Power Transfer (WPT): An Overview*

*L* ¼ *N*2

where *N* is number of turns in a coil. This equation provides appropriate approximation for a cylindrical inductor, but for the case of spiral inductor this equation provides general parameter studies. A detailed study for calculation of inductance of spiral inductors is provided by S. Alturi et al.

Another important figure of merit is mutual inductance of two coupled coils.

*M* ¼ *μπN*1*N*2*r*2

2

Also, the coupling factor between 2 coils can be expressed in Eq. (5).

*k* ¼ *M*ffiffiffiffiffiffiffiffiffiffi *L*1*L*2

Where *k* is coupling factor and lies between 0 and 1, *L*1 and *L*2 are inductance of

Power transfer efficiency between loop antennas for NFC system is expressed as the figure of merit that depends on inductive coupling. As NFC operates at small distance range between transmitter and receiver antennas, its efficiency depends on coupling between the antennas for wireless power transfer [25]. **Figure 5** shows schematic of two mutual magnetic coupled coil antennas for the WPT system [27]. To improve the power efficiency, an impedance matching on both coil antennas (receiver and transmitter) is required. In case of magnetic coupling between receiver and transmitter coil antennas, eddy currents are generated due to alternating magnetic field. This cause a shift of resonant peaks of the input impedance, which shifts resonance frequency of maximum power transfer. Insertion of a high permeability soft magnetic ferrite sheet between coil antenna and metal conductor shifts the frequency back to original resonance frequency [26]. When both circuits resonate at peak frequency, maximum transfer of power is achieved. **Figure 6** depicts the simplified equivalent circuit model of wireless power transfer

In case of complex geometries, numerical methods can be applied to calculate

1*r*2 2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *r*2

where, *N*1 and *N*2 are the number of turns in first and second coil, respectively, *r*1 and *r*2 are the radius of first and second coil, respectively, *x* is the axial separation and *μ* is the permeability. Eq. (4) is valid only for *r*2 < *r*1 ≪ *x*, i.e., the magnetic field generated by current *I1* in first coil should be homogeneous in the mutually bounded area of the 2nd coil. Detailed calculation for mutual inductance for

*μ*0*rln* 2*r d*

*L* ¼ *N*2

The mutual inductance between the two coils can be expressed in Eq. (4).

*L*0 (2)

� � (3)

1 þ *x*2 � �3 q (4)

p (5)

inductance of multiturn coil, *L* and is given by Eq. (2).

*DOI: http://dx.doi.org/10.5772/intechopen.96345*

(2004) [22].

cylindrical coils is presented in [23].

first and second coil, respectively.

systems [26].

**99**

inductances of complex coil systems [24].

**3.2 Wireless power transfer (WPT) efficiency**

**Figure 4.** *Depiction of two magnetic inductive coil system (primary and secondary) [20].*

*Near-Field Communications (NFC) for Wireless Power Transfer (WPT): An Overview DOI: http://dx.doi.org/10.5772/intechopen.96345*

Where *μ*0 is permeability of free space, *r* is radius of the coil and *d* is the diameter of the wire. The single turn coil inductance can be utilized to calculate inductance of multiturn coil, *L* and is given by Eq. (2).

$$L = N^2 L\_0 \tag{2}$$

$$L = N^2 \mu\_0 r l n \left(\frac{2r}{d}\right) \tag{3}$$

where *N* is number of turns in a coil. This equation provides appropriate approximation for a cylindrical inductor, but for the case of spiral inductor this equation provides general parameter studies. A detailed study for calculation of inductance of spiral inductors is provided by S. Alturi et al. (2004) [22].

Another important figure of merit is mutual inductance of two coupled coils. The mutual inductance between the two coils can be expressed in Eq. (4).

$$M = \frac{\mu \pi N\_1 N\_2 r\_1^2 r\_2^2}{2\sqrt{\left(r\_1^2 + \varkappa^2\right)^3}}\tag{4}$$

where, *N*1 and *N*2 are the number of turns in first and second coil, respectively, *r*1 and *r*2 are the radius of first and second coil, respectively, *x* is the axial separation and *μ* is the permeability. Eq. (4) is valid only for *r*2 < *r*1 ≪ *x*, i.e., the magnetic field generated by current *I1* in first coil should be homogeneous in the mutually bounded area of the 2nd coil. Detailed calculation for mutual inductance for cylindrical coils is presented in [23].

Also, the coupling factor between 2 coils can be expressed in Eq. (5).

$$k = \frac{M}{\sqrt{L\_1 L\_2}}\tag{5}$$

Where *k* is coupling factor and lies between 0 and 1, *L*1 and *L*2 are inductance of first and second coil, respectively.

In case of complex geometries, numerical methods can be applied to calculate inductances of complex coil systems [24].

#### **3.2 Wireless power transfer (WPT) efficiency**

Power transfer efficiency between loop antennas for NFC system is expressed as the figure of merit that depends on inductive coupling. As NFC operates at small distance range between transmitter and receiver antennas, its efficiency depends on coupling between the antennas for wireless power transfer [25]. **Figure 5** shows schematic of two mutual magnetic coupled coil antennas for the WPT system [27]. To improve the power efficiency, an impedance matching on both coil antennas (receiver and transmitter) is required. In case of magnetic coupling between receiver and transmitter coil antennas, eddy currents are generated due to alternating magnetic field. This cause a shift of resonant peaks of the input impedance, which shifts resonance frequency of maximum power transfer. Insertion of a high permeability soft magnetic ferrite sheet between coil antenna and metal conductor shifts the frequency back to original resonance frequency [26]. When both circuits resonate at peak frequency, maximum transfer of power is achieved. **Figure 6** depicts the simplified equivalent circuit model of wireless power transfer systems [26].

Using Eq. (8) and (9):

Substituting Eq. (10) into Eq. (8):

*DOI: http://dx.doi.org/10.5772/intechopen.96345*

*Vs* ¼ *Is Rs* þ *jωLs* þ

*Zs* ¼ *Vs Is*

calculated as:

0 @

**101**

*η* ¼ *jωM jωLL* þ 1

power:

*IL* ¼ *Is*

1 *jωCs*

� � � �

model. The input impedance is given by [26]:

*jωCL* þ *RL* � � 0 @

*Near-Field Communications (NFC) for Wireless Power Transfer (WPT): An Overview*

*jωM*

*jωCL* þ *RL* � �

*jωLL* þ 1

*jωM*

*RL*

� � þ *ω*2*M*2

0 @

*jωLL*þ 1 *jωCL* þ*RL* � �

*jωCs*

*jωCL* þ *RL* � �

1

A (10)

Að Þ *jωM* (11)

*CsCL*

1 A

1 A (12)

(13)

(14)

(15)

1

*jωLL* þ 1

0 @

The input impedance is calculated based on the simplified equivalent circuit

¼ ð Þ *jωCs Rs* þ 1 � *ω*2 ð Þ *LsCs* ð Þ *jωCL RL* þ 1 � *ω*2 ð *LLCL*Þ � *ω*4*M*2

The power transfer efficiency is given by ratio of output power to the input

*η* ¼ *POut Pin*

By substituting Eq. (10)–(12) in Eq. (13), the effective power efficiency is

*Rs* þ *jωLs* þ 1

where, *M* is Mutual inductance between the coil antennas, *Ls* and *LL* are the inductance of transmitter and receiver coils, *Cs* and *CL* are matching capacitor for transmitter and receiver coils, *Rs* and *RL* are internal resistance and load resistance

The transferred power is maximum at resonance frequency when load current in the circuit becomes maximum, at a given resonance frequency, Eq. (15) describes

> *M*2 ¼ *R*2 *L ω*2 0

The wireless power transfer efficiency using inductive coupling can be greater than 90% within a limited transmission range. During the last decade, methods such as magnetic resonance coupling for WPT have been widely studied by researchers to increase the efficiency of power transmission with greater distance range. Twoloop and four-loop coil systems were studied for magnetic resonance coupling based WPT system [30]. In order to achieve maximum power transfer efficiency, several studies have been done such as loop to coil coupling manipulation [29], automated impedance matching [31], adaptive frequency tuning [32], circuit structure

For resonance coupling system, assuming *C* ¼ *Cs* ¼ *CL:*

1 A

of the coils and *Vs* and *VL* are source and load voltages.

the condition for maximum power transfer efficiency [29]:

2

0 @

0 @

*jωCs*ð Þ ð Þ *jωCL RL* þ 1 � *ω*2*LLCL*

¼ *I* 2 *LRL I* 2 *s Zs*

� *Is*

#### **Figure 5.**

*Schematic drawing of mutual magnetic coupled coils for wireless power transfer systems [26].*

**Figure 6.** *A simplified equivalent circuit model of wireless power transfer systems [27].*

The resonance frequency *ω* for coil antenna is given by,

$$\rho = \frac{1}{\sqrt{L\_s C\_s}} = \frac{1}{\sqrt{L\_L C\_L}}\tag{6}$$

Base on Kirchhoff voltage law and above circuit diagram, *Vs* can be calculated as [28]:

$$
\begin{bmatrix}
joL\_s + \frac{\mathbf{1}}{jwC\_s} + R\_s & jo\mathbf{M} \\\\jo\mathbf{M} & joL\_L + \frac{\mathbf{1}}{joC\_L} + R\_L
\end{bmatrix}
\begin{bmatrix}
I\_s \\ I\_L
\end{bmatrix} = \begin{bmatrix}
V\_s \\ \mathbf{0}
\end{bmatrix} \tag{7}
$$

From Eq. (7), *Vs* can be calculated [29]:

$$W\_s = I\_s \left( R\_s + j\alpha L\_s + \left(\frac{1}{j\alpha C\_s}\right) - I\_L(j\alpha \mathbf{M}) \right) \tag{8}$$

$$\mathbf{O} = I\_L \left( j a L\_L + \left( \frac{\mathbf{1}}{j a \mathbf{C}\_L} \right) + R\_L \right) - I\_s (j a \mathbf{M}) \tag{9}$$

*Near-Field Communications (NFC) for Wireless Power Transfer (WPT): An Overview DOI: http://dx.doi.org/10.5772/intechopen.96345*

Using Eq. (8) and (9):

$$I\_L = I\_s \left(\frac{j\alpha\mathcal{M}}{j\alpha L\_L + \left(\frac{1}{j\alpha C\_L} + R\_L\right)}\right) \tag{10}$$

Substituting Eq. (10) into Eq. (8):

$$W\_s = I\_s \left( R\_s + joL\_s + \left(\frac{1}{joC\_s}\right)\right) - I\_s \left(\frac{joM}{joL\_L + \left(\frac{1}{joC\_L} + R\_L\right)}\right) (joM) \tag{11}$$

The input impedance is calculated based on the simplified equivalent circuit model. The input impedance is given by [26]:

$$Z\_t = \frac{V\_s}{I\_s} = \frac{((j\rho C\_t)R\_t + \mathbf{1} - \rho^2 L\_t C\_t)((j\rho C\_L)R\_L + \mathbf{1} - \rho^2 L\_L C\_L) - \rho^4 \mathbf{M}^2 C\_t C\_L}{j\rho C\_t((j\rho C\_L)R\_L + \mathbf{1} - \rho^2 L\_L C\_L)} \tag{12}$$

The power transfer efficiency is given by ratio of output power to the input power:

$$\eta = \frac{P\_{\text{Out}}}{P\_{\text{in}}} = \frac{I\_L^2 R\_L}{I\_s^2 Z\_s} \tag{13}$$

For resonance coupling system, assuming *C* ¼ *Cs* ¼ *CL:*

By substituting Eq. (10)–(12) in Eq. (13), the effective power efficiency is calculated as:

$$\eta = \left(\frac{j\alpha\mathcal{M}}{j\alpha L\_L + \left(\frac{1}{j\alpha C\_L} + R\_L\right)}\right)^2 \frac{R\_L}{\left(\left(R\_s + j\alpha L\_s + \left(\frac{1}{j\alpha C\_s}\right) + \left(\frac{1}{j\alpha L\_L + \left(\frac{1}{j\alpha C\_L} + R\_L\right)}\right)\right)\right)} \tag{14}$$

where, *M* is Mutual inductance between the coil antennas, *Ls* and *LL* are the inductance of transmitter and receiver coils, *Cs* and *CL* are matching capacitor for transmitter and receiver coils, *Rs* and *RL* are internal resistance and load resistance of the coils and *Vs* and *VL* are source and load voltages.

The transferred power is maximum at resonance frequency when load current in the circuit becomes maximum, at a given resonance frequency, Eq. (15) describes the condition for maximum power transfer efficiency [29]:

$$M^2 = \frac{R\_L^2}{a\_0^2} \tag{15}$$

The wireless power transfer efficiency using inductive coupling can be greater than 90% within a limited transmission range. During the last decade, methods such as magnetic resonance coupling for WPT have been widely studied by researchers to increase the efficiency of power transmission with greater distance range. Twoloop and four-loop coil systems were studied for magnetic resonance coupling based WPT system [30]. In order to achieve maximum power transfer efficiency, several studies have been done such as loop to coil coupling manipulation [29], automated impedance matching [31], adaptive frequency tuning [32], circuit structure

manipulation [33], improving WPT for future portable consumer electronics using large transmitter coil system [34] and improving efficiency by four-coil system for deep brain simulation [35]. There are studies, which focused on misalignment between receiver and transmitter coil for applications such as wireless EV charging system [36] and wireless mobile phone charging system [37, 38]. With the rapid development in consumer electronics, there is a substantial increase in applications of NFC based wireless power transfer technology.


**2.3. Pathology of the CF lung**

As might be expected, the buildup of thick mucus in the CF lungs often has severe clinical implications The significant reduction or loss of mucus clearance often results in infection and leads to inflammation due to the dramatic ~1,500-fold increase in airway neutrophils [14]. Coupling the inflammation and buildup of mucus, it is not surprising that the overall lung capacity of CF patients decreases dramatically throughout life. This can be tested using a series of pulmonary function tests (PFTs) [15]. These often involve a spirometry test, which is a measure of the forced efflux volume in one second (FEV1), or how much air the patient can forcefully exhale in one second. This is a hallmark test for overall lung volume and strength. Clinically it has been shown that the FEV1 of a CF patient will be approximately 10% below the expected for a healthy individual of the same age [16].

In older patients, the prolonged effects of the disease often lead to chronic infections. These invading organisms can then be cultured and analyzed to determine the best treatment strategy. While we will be covering the type of infections further in this chapter, it is important to note that clinically, this also affects the patient in other ways, primarily leading to inflam‐ mation of the respiratory system and decreased airway capacity [17]. This inflammation is often brought on by increased neutrophil accumulation in the lungs, which not only serve to act as a preliminary means of immune defense, but also to recruit macrophages. These neutrophils and macrophages will phagocytose bacteria and dead immune cells, but also produce pro-inflammatory cytokines that exacerbate the inflammatory process. These cytokines not only attract other immune cells, but also serve as a trigger for the release of proteases and elastases by the immune cells. Normally, these help eradicate the bacteria that triggered this response, but theior over-production in the CF lung can actually damage epithelial cells, leading to the fibrotic nature of cells associated with this disease [18].


**Author details**

*Geopolymers and Other Geosynthetics*

especially when high loads are applied.

tion is no longer allowed [17–19].

by the Neoloy® composite material.

**5. Conclusions**

also maintaining a degree of plastic deformation. Therefore, a linear viscoelastic behavior can be determined, in which there is an initial elastic deformation caused by the stresses generated upon it, followed by a time-dependent delayed deformation, known as material creep. There may be some permanent flow in the material,

When the unload is permitted to the material, within linear viscoelastic behavior, an inverse process begins, with a certain recovery at the moment, continued by a recovery along the time recovering fluence dependent on the time. The material may or may not reach the original dimensions. If permanent flow occurs in the charging process, there will be a residual deformation even when the load applica-

Polyolefin geocells can be considered an excellent reinforcement in the structures of the roads as long as they meet certain specifications, such as resistance to long-term deformation. For the geocells tested, the best performance was obtained

Taking into account the results of the laboratories, the increase factor of MIF modules is directly connected to creep resistance. When confinement is eventually lost due to large deformations in the geocells, the increase will be zero, and the behavior of the granular material belonging to the pavement structure will be the same as if it were not considered reinforced, decreasing its modulus, requiring a

All polymeric material that is exposed to weather, for greater resistance that it possesses, suffers the loss of properties. This could be evidenced when comparing tests with the same material and category, but with different time of exposure to the effects caused by nature (UV rays, air, water, etc.). A lesser deformation was

The deformation behavior of HDPE exposed to high temperatures is low compared to Neoloy® material. For this reason, it could be determined that the HDPE geocells, although they have a regular behavior, cannot be located in the base layer of the pavement structures, since these could have temperatures as tested in the "SIM test" or higher, generating a double deformation. One of them would be due to time and the other due to temperature, leading to failure in a very short period of time. To determine the MIF, it is necessary to know the resistance of the material of the geocell to the long-term deformation, since it is a vital part of achieving high performance, during the entire life of the structure of pavement reinforced with

The geocells, composed by Neoloy® with a resistance to the wide strip of 15 kN/m, are the most suitable to achieve a subgrade improvement. These geocells have the ability to be used in the granular layers adjacent to the asphalt folder, decreasing thicknesses. The use of these minimum aspects is recommended to guarantee the functionality, serviceability, and long-term operability of structures

greater thickness to comply with the conditions of the structure.

displayed in the sample that had been under condition for a year.

**146**

geocells.

where geocells are involved.

Juan Carlos Ruge1 \*, Julian Gonzalo Gomez1,2 and Carlos Andres Moreno2

1 Catholic University of Colombia, Bogotá, Colombia

2 Fluent Business Group, Mexichem Colombia S.A.S, Bogota, Colombia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**3. Mucosa-associated lymphoid tissue (MALT)**

The immune system can recognize a wide range of unknown antigens and elicit an appropriate respond due to the lymphocytes that have a wide variety of antigen receptors [16]. This system has evolved into a system of secondary lymphoid organs such as the spleen, lymph nodes, Peyer's patches, and other MALT, in line with the defense targets [17]. Highly organized secondary lymphoid organs contain architectural domains that facilitate sequential cellular interactions between antigen-presenting cells and lymphocytes and efficiently promote the activation, selection, and differentiation of B and T cells [16]. Therefore, the immunological response becomes more effective.

#### *Bronchus-Associated Lymphoid Tissue (BALT) Histology and Its Role in Various Pathologies DOI: http://dx.doi.org/10.5772/intechopen.99366*

MALT can function independently of the systemic immune system and therefore encompasses the mucosal immune system, which is a crucial part of immunopathology [18]. It plays an important role in immunological defense by eliciting immune responses against specific antigens encountered along the surfaces of all mucosal tissues [19]. Although MALT is anatomically divided into regions, these regions are functionally interconnected under the name of the common mucosal immune system. In this way, events such as antigen presentation and B-cell activation in a mucosal region can trigger the secretion of immunoglobulin A (IgA) in the mucosal regions of different organs [18, 20]. Due to MALT, which mainly functions to produce and secrete IgA along the mucosal surfaces in antigen-specific, T helper 2-dependent reactions, T helper 1 and cytotoxic T-cell-mediated reactions can occur. This may then result in immunotolerance [20, 21].

The best-known representatives of MALT, which contains approximately half of the lymphocytes of the immune system, [22] are gut-associated lymphoid tissue (GALT), nasal-associated lymphoid tissue (NALT), and BALT. However, structures such as conjunctival-associated lymphoid tissue (CALT), larynx-associated lymphoid tissue, and duct-associated lymphoid tissue (DALT) are other MALT representatives [20, 21].

MALT is divided into the two following functional parts: inducer sites and effector sites. Inducer sites include secondary lymphoid tissues, where the clonal expansion of B cells and IgA class transition occur in response to antigen-specific T-cell activation [19]. GALT, BALT, NALT, and CALT in mice, dogs [23], and baboons [24] and DALT in cynomolgus macaques [25] constitute these inducing sites. These sites are known as secondary immune tissues where antigen sampling occurs, and immune responses are initiated. Although there are many differences between inducing sites in various organs, they all contain the same functional segments as follows: lymphoid follicles, interfollicular zones, subepithelial dome zones, and follicle-associated epithelium or lymphoepithelium containing microfold (M) cells [19].

Effector sites distributed as diffuse lymphoid tissue throughout the lamina propria layer on all mucosal surfaces [26] are known as the transport sites of IgA along the mucosal epithelium. After activation and IgA class transition, T- and B cells migrate from inducing sites to these sites [19]. CD4+ and CD8+ T cells, IgA-, IgG- and IgM-plasma cells, B cells, antigen-presenting dendritic cells, and macrophages [19] constitute the cellular content of these effector regions where secreted IgA (S-IgA) is secreted along the mucosal epithelium [27]. Mast cells and eosinophils can occasionally be seen in the interfollicular area. Thus, all the cell types required to initiate an immune response are present here.


**Author details**

Leander Kwabena Brown Washington DC Metro Alumni Chapter, Mississippi Valley State University National Alumni Association, Riverdale, MD, United States

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Samuel L. Zelinka \*, Grant T. Kirker, Amy B. Bishell and Samuel V. Glass**

US Forest Service Forest Products Laboratory, Madison, WI 53726, USA<EMAIL_ADDRESS>(G.T.K<EMAIL_ADDRESS>(A.B.B<EMAIL_ADDRESS>(S.V.G.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +1-608-231-9277

Received: 16 January 2020; Accepted: 5 March 2020; Published: 7 March 2020

**Abstract:** Acetylation is one of the most common types of wood modification and is commercially available throughout the world. Many studies have shown that acetylated wood is decay resistant at high levels of acetylation. Despite its widespread use, the mechanism by which acetylation prevents decay is still not fully understood. It is well known that at a given water activity, acetylation reduces the equilibrium moisture content of the wood cell wall. Furthermore, linear relationships have been found between the acetylation weight percent gain (WPG), wood moisture content, and the amount of mass loss in decay tests. This paper examines the relationships between wood moisture content and fungal growth in wood, with various levels of acetylation, by modifying the soil moisture content of standard soil block tests. The goal of the research is to determine if the reduction in fungal decay of acetylated wood is solely due to the reduction in moisture content or if there are additional antifungal effects of this chemical treatment. While a linear trend was observed between moisture content and mass loss caused by decay, it was not possible to separate out the effect of acetylation from fungal moisture generation. The data show significant deviations from previously proposed models for fungal moisture generation and suggest that these models cannot account for active moisture transport by the fungus. The study helps to advance our understanding of the role of moisture in the brown rot decay of modified wood.

**Keywords:** acetylated wood; wood modification; brown rot decay; fungal decay; soil block test; wood moisture content; soil bottle assays

#### **1. Introduction**

When wood is used in exterior applications it is subject to biodeterioration from wood decay fungi. Wood modifications, such as acetylation, are often used to protect wood from decay [1]. Unlike preservative treatments where the chemical treatments are targeted to repel or kill wood decay fungi, acetylation modifies many wood properties and a complete understanding of how acetylation prevents decay is a topic of active research. Ringman et al. reviewed the literature and concluded that diffusion inhibition through the reduction of void volume was the only mechanism that was consistent with all observed behaviors in modified wood [2]. Researchers have further proposed that diffusion likely occurs through regions of softened hemicelluloses and that acetylation may prevent decay by arresting diffusion by increasing the moisture-induced glass transition temperature of the hemicelluloses [3–5]. Recent work by Hunt et al. has confirmed that acetylated wood indeed has a higher relative humidity threshold for the diffusion of potassium ions [6]. Furthermore, Ringman et al. have suggested several possible alternative mechanisms for the inhibition of diffusion beyond cell wall plasticization [7].

Much of what is known about how treatments and wood modifications affect fungal decay comes from laboratory soil bottle tests, such as the American Wood Protection Association (AWPA) E-10 soil block assay, and the corresponding European tests, such as EN-113 [8,9]. In these tests, a fungal monoculture is cultivated in a sterile bottle with soil and a wood feeder strip and then a wood test block is added once the fungus is established. The efficacy of the treatment is typically examined by measuring the mass loss (ML) of the wood test block, which is calculated using the following equation:

$$ML = \frac{m\_{\text{od, prec}} - m\_{\text{od, post}}}{m\_{\text{od, prec}}} \tag{1}$$

where *m* is the mass of the specimen and the subscripts "pre" and "post" refer to the mass before and after the soil block test, respectively, and the subscript "od" stands for oven dried. Note that in this paper, the mass, *m*, includes the added mass of the acetylation chemical, where applicable. In the standard soil block tests, the wood moisture content is not controlled, but instead the soil is preconditioned to a certain moisture content (specified as a percent of the water holding capacity of the soil), and the moisture content of the wood test block is affected by capillary uptake from the soil and feeder strip, water vapor sorption from the humidity within the soil bottle, the moisture produced from the breakdown of wood polymers, and the moisture translocated from the fungus.

Soil block tests have shown that increasing the level of acetylation results in lower mass loss [10–14]. Typically, the mass loss of the test block after the test is plotted as a function of the weight percent gain (WPG, a measure of the intensity of the wood modification). An example of such a plot is shown in Figure 1a from the thesis of Forster [10]. Note that the mass used in all calculations includes the mass of the treatment chemical. These plots are helpful for determining the level of modification at which decay is negligible; however, such plots cannot give us insight as to the mechanism by which the wood modifications provide protection. In addition to measuring the weight loss caused by decay, Forster [10] also measured the moisture content at the end of the decay test and normalized to the dry mass after degradation, which we show in Figure 1b. We can see that there is a strong correlation between the moisture content and the mass loss, and, in fact, when the wood moisture content at the end of the test is plotted against the mass loss (with the WPG plotted implicitly, Figure 1c), we see that the mass loss increases with the moisture content at the end of the test.

**Figure 1.** The interrelationship between the amount of acetylation, mass loss caused by decay, and wood moisture content at the end of the test. (**a**), mass loss as a function of weight percent gain. (**b**) final moisture content as a function of weight percent gain. (**c**) mass loss as a function of the final moisture content. The data replotted from Forster [10], -= *Gloeophyllum trabeum* and •= *Coniophora puteana*.

The fact that the wood moisture content in soil bottle tests varies with acetylation level has important implications for understanding the mechanism of protection as well as understanding whether wood that appears to be decay resistant in soil bottle tests may be subject to decay under different environmental conditions [7]. Decay fungi are most active when the wood moisture content is between 40% and 80% moisture content (MC) [15]. In the absence of fungi, wood moisture content would depend on the water activity of the environment and the kinetics of water absorption [16]. In the soil block tests, the moisture content of the wood specimen would also depend on moisture generation from the breakdown of wood polymers by the fungus and active water translocation by the

fungus. It has been shown that for untreated wood in the standard soil block tests, the wood moisture content increases during the first 3 weeks of the decay test and then levels off in an optimal range for fungal growth [17]. Measurements of this type are lacking for acetylated wood. Given the differences in moisture properties between acetylated and unmodified wood and the various phenomena that influence wood moisture content during decay tests, from the available literature data, it is unclear whether acetylation would provide protection against decay under higher wood moisture contents.

Thybring reviewed literature data and developed a model based upon assumed cell wall chemistry and structure that suggested that it is not surprising that wood moisture content and mass loss are correlated [18]. The model includes the theoretical amount of water produced by degradation of cellulose and hemicellulose, which is 0.56 g of water per 1 g of polymer, and is similar to an earlier model developed by Peterson and Cowling in the 1970s [19]. Thybring suggested that instead of plotting conventional moisture content as a function of mass loss, a "reduced moisture content" should instead be used where the MC is calculated with the original dry mass [18]. Since there are several definitions associated with "reduced moisture content", we use the nomenclature "*MCO*", where the subscript stands for the moisture content calculated from the original dry mass, i.e.,

$$\text{MCC}\_{\mathcal{O}} = \frac{m\_{\text{post}} - m\_{\text{od, post}}}{m\_{\text{od, pre}}} \tag{2}$$

in this paper to describe the "reduced moisture content" described in the work of Thybring [18]. Thybring stated that *MCo* should be directly proportional to the mass loss as the consumption of lignocellulosic biomass results in the production of water [18]. However, Thybring did not provide additional experimental tests to confirm whether this model can fully explain the effects of moisture dynamics on decay in acetylated wood.

In this paper, we attempt to separate the moisture exclusion effects of acetylation from other mechanisms of wood protection provided by acetylation. To do this, we examine the relationship between the soil moisture content and the wood moisture content at different levels of acetylation in the presence and absence of fungus. By adjusting the soil moisture content, we are able to test different levels of acetylation at the same wood moisture content in the absence of fungus. Furthermore, performing parallel experiments with and without fungus allow us to investigate the moisture generated by the fungus.

#### **2. Materials and Methods**

#### *2.1. Materials*

Southern pine (*Pinus spp.*) sapwood was examined. The southern pine was cut into 10 mm cubes ("mini-blocks"). Four different levels of acetylation were examined: 0 WPG (control, untreated), 8 WPG, 11 WPG, and 18 WPG. The acetylation was performed in a similar fashion to our previous work [6,20]. Wood was vacuum-impregnated with acetic anhydride and cured in an oven at 140 ◦C for various times to provide a range of WPGs. The specimens were then rinsed to remove unbound acetic acid. For each different test and treatment combination.

#### *2.2. Soil Block Tests*

With the exception of the soil moisture content, the soil block tests followed the AWPA E-10 standard [9]. The soil bottles were made by adding soil, water, and a feeder strip of southern pine sapwood to a flint glass jar with a metal lid and autoclaving it. In this study, *Rhodonia placenta* (Fr.) Niemelä, K.H. Larss. and Schigel 2005 (Mad-698-R) was examined. Following the autoclave, two 5-mm plugs from the leading edge of a fungus grown on 2% malt extract plus a 0.1% yeast extract agar plate incubated in a 30 ◦C, 70% RH room were placed in each bottle, one at each end of the feeder, and this was allowed to colonize the bottle for 3 weeks before introducing an autoclaved test block to the jar. The test was allowed to continue for 8 weeks, after which the blocks were removed. Prior

to autoclaving the test block, an initial oven-dry weight was determined. After removal from the jar, the mycelia were removed, the test block was weighed, oven dried at 105 ◦C overnight, and reweighed. The mass loss caused by decay was determined by Equation (1) and the moisture content after the test (MC) was determined by the following equation:

$$\text{MCC} = \frac{m\_{\text{post}} - m\_{\text{od, post}}}{m\_{\text{od, post}}} \tag{3}$$

In some experiments, the wood moisture content without the fungus was examined. In these tests, the soil, water, feeder strip, and test block were all autoclaved at the same time and the bottle was disassembled after eight weeks and the block was examined.

#### *2.3. Soil Water Holding Capacity*

The E-10 standard specifies that the testing should utilize a soil moisture content that is 130% of the soil water holding capacity (SWHC). For our soil, collected from an agricultural site, the SWHC was found to be 31% soil moisture content (by mass, dry basis). SWHC was varied in the tests as one of the main variables.

In the control experiments, which were intended to follow the AWPA standard, the soil moisture content prior to soil bottle preparation was not independently verified and was assumed to be the same as the previous experiment. Therefore, it was inadvertently lower than expected and, as a result, the "standard" measurements were conducted with a soil moisture content of 38.3% MC or 124% SWHC instead of the desired 130%.

In addition to the tests at 124% SWHC, additional experiments were run at 120% SWHC and 140% SHWC on all levels of the acetylated wood.

#### *2.4. Wood Moisture Content Preconditioning*

In preliminary experiments, the starting moisture content of the wood block was examined as another variable. It is expected that the moisture content of the blocks will change throughout the test as the specimen comes into equilibrium with the soil. If the moisture uptake of the acetylated wood has different kinetics than untreated wood, preconditioning the wood may affect the final moisture content or the amount of decay exhibited by the block. Figure 2 shows data collected at 124% SWHC for blocks that had been autoclaved in the presence of liquid water to bring their starting moisture contents above 40% MC; the actual MCs varied between 40% and 55% MC. The starting moisture content was shown to have little effect on the amount of mass loss; a t-test with unequal variance (Welch test) showed no statistical differences between the mass loss for dry and pre-wetted blocks 0%, 8%, and 11% WPG (*p* = 0.05). The final moisture content of the blocks was shown to be statistically different (*p* = 0.05) for the 8% WPG and 18% WPG but not the 0% and 11% WPG conditions. Because preconditioning had such a small effect on the mass loss, except at the highest level of acetylation, it was not investigated further.

**Figure 2.** (**a**) mass loss and (**b**) moisture content as a function of percent weight loss from preliminary experiments where specimen pre-wetting was examined. The dry blocks had starting moisture contents (MCs) of less than 10%, and the pre-wetted blocks had starting moisture contents between 40% and 55%. Error bars represent the standard deviation from 5 replicates.

#### **3. Results**

#### *3.1. E*ff*ect of Acetylation and Soil Moisture Content*

In order to assess the effects of soil moisture on the final block MC, a preliminary trial was conducted using the standard E10 soil bottle setup only without fungi. We refer to the moisture content from this test as *MCster* and calculate it as:

$$\text{MC}^{\text{ster}} = \frac{m\_{\text{post}}^{\text{ster}} - m\_{\text{od}\_r \text{post}}^{\text{ster}}}{m\_{\text{od}\_r \text{ pre}}^{\text{ster}}} \tag{4}$$

where the superscript "*ster*" indicates sterile, uninoculated controls. Soil water holding capacities were varied at 110%, 120%, 140%, and 150% (Figure 3). A Tukey Honest Significance Difference (HSD) test was performed on the data. For each SWHC, the *MCster* of acetylated wood was significantly different (at p = 0.05) from the untreated controls, however, the amount of acetylation did not cause significant difference in *MCster*.

These results indicate that the standard soil bottles at 130% SWHC should achieve an MC that is well suited for fungal decay activity.

The effect of SWHC and acetylation on mass loss are presented in Figure 4. The amount of decay decreases with increasing levels of acetylation. This is not surprising as many others have demonstrated similar results [10]. Furthermore, the wood acetylated to 18% WPG showed almost no mass loss; this is in line with previous works suggesting that wood acetylated to 20% WPG is very decay resistant [11,21]. The most interesting results for this investigation, however, are the results of the 8% and 11% WPG. These two levels of acetylation show measurable amounts of decay but less decay than the untreated wood. These groups will later be used to examine the interactions between acetylation, wood moisture content, and susceptibility to decay.

**Figure 3.** Effect of the soil water holding capacity on the wood moisture content after 8 weeks with no fungus present (*MCster*). The error bars represent the standard deviation from five replicates.

**Figure 4.** Percent weight loss caused by decay as a function of the soil water holding capacity and the level of acetylation. The error bars represent the standard deviation from five replicates.

The data in Figure 4 are also useful for understanding the effect of the soil moisture content on the decay fungi; for reference, the AWPA standard soil bottle test specifies 130% SWHC. For all treatments, the soil water holding capacity affected the mass loss caused by the fungus. It appears that for the three soil moisture contents examined, *Rhodonia placenta* is most active at 120% SWHC. For the 0%, 8%, and 11% WPG samples, the mass loss increases with the decreasing SWHC. For the samples acetylated to 18% WPG, the 124% SWHC exhibited a higher mass loss than the 120% SWHC group (*p* = *0.01*), however, all the SWHCs had a mass loss of less than 10%.

#### *3.2. Relationships Between Moisture Content and Mass Loss*

A major motivation of this work was to understand the relationship between acetylation, wood moisture content, and decay. This is illustrated in Figure 5, which plots the mass loss against the post-test moisture content calculated three different ways. In all subfigures, the level of acetylation is plotted implicitly; the four data points correspond with the average moisture content and mass loss of the four levels of acetylation with the 18% WPG specimen being the closest to the origin and the control specimen having the highest moisture content and mass loss.

**Figure 5.** Relationship between mass loss and the moisture content at the end of the experiment calculated three different ways from Equations (3), (5), and (6) in rows 1, 2, and 3. The columns represent different soil water holding capacities. The error bars represent the standard deviation from five replicates.

In the first row, the data are plotted as a function of the moisture content at the end of the test calculated in the traditional manner (Equation (3)). A clear linear trend can be seen with higher mass losses having higher moisture content, similar to literature data (Figure 1c). Because the fungus that causes the mass loss itself produces water, it is not possible to determine from the data the first row whether the lower mass loss is a result of the lower wood moisture contents or whether the lower moisture contents are a result of the lower mass loss.

To examine the extra effect of fungal growth on the final moisture content, we plot "Δ*MC*" as a function of mass loss in the middle row of Figure 5. We define Δ*MC* using the following equation:

$$
\Delta \text{MC} = \text{MC} - \text{MC}^{\text{star}} = \frac{m\_{\text{post}}^{\text{inac}} - m\_{\text{od.post}}^{\text{inac}}}{m\_{\text{od.post}}^{\text{inac}}} - \frac{m\_{\text{post}}^{\text{star}} - m\_{\text{od.post}}^{\text{star}}}{m\_{\text{od.post}}^{\text{star}}} \tag{5}
$$

where "*inoc*" indicates inoculated specimens and "*ster*" indicates sterile uninoculated controls. We are able to calculate Δ*MC* because we ran the identical experiment two times; once where wood was exposed to the soil bottle for 8 weeks in the absence of fungus and the second time where it was placed in the soil bottle for 8 weeks with the culture of *R. placenta*. In theory, Δ*MC* should represent the extra amount of moisture generated by the fungus beyond any changes in the wood moisture content that occur from being in contact with moist soil.

Two observations can be made from the Δ*MC*. The first is that all samples show a linearly increasing trend with mass loss. That is, higher amounts of fungal metabolism are associated with higher final moisture contents. This is not surprising as water is a byproduct of fungal metabolism. The slope of the line in the middle column of Figure 5 cannot, however, be directly related to the stoichiometry of the metabolic reaction since the slope is in units of water per gram of post-test dry mass and the metabolic reaction can only be understood in terms of grams of water per grams of polymer consumed (mass loss and moisture content both need to be expressed with respect to pre-test dry mass).

The second, and more surprising observation from the middle row of Figure 5 is that some of the data from the 120% and 140% SWHC experiments are negative. That is, in the experiments conducted with fungus, the moisture content was lower than in the experiments without the fungus. The reason for this behavior is not clear. One potential explanation of this result is that the block was potentially inhibited from reaching the higher MC due to physiological envelopment by the fungus, thereby reducing the overall block MC. The blocks in the soil bottle begin at less than 10% MC and get to 100% MC without fungus after 8 weeks of exposure in the test. This suggests that the fungus is either inhibiting the block from taking up moisture or physically excluding moisture from entering the block. Similar trends were also reported by Peterson and Cowling, where a durable species (Sitka spruce) exposed to *T. versicolor* exhibited a lower final moisture content (68%) after 8 weeks in comparison to an uninoculated block (98%) [19]. They concluded that *T. versicolor* can adjust the MC of the wood by the transfer of water through its own mycelium.

In short, both the plots of Δ*MC* and show linear trends with increasing fungal mass loss. While not surprising, it unfortunately not possible to separate out the moisture effects and acetylation from these plots. Thybring suggested that instead of examining the conventional moisture content, *MCO* should instead be analyzed. Δ*MCO* represents the moisture generated by the fungus and, in theory, should be linearly related to the mass loss with a slope of 0.56 g g-1 [18].

We plot Δ*MCO* as a function of mass loss in the third row of Figure 5. We define Δ*MCO* using the following equation:

$$
\Delta MC\_O = MC\_O - MC^{str} = \frac{m\_{post}^{inc} - m\_{od,post}^{inc}}{m\_{od, prc}^{inc}} - \frac{m\_{post}^{str} - m\_{od,post}^{str}}{m\_{od, post}^{str}} \tag{6}
$$

where the superscripts and subscripts have the same meanings as Equations (1)–(5). Δ*MC* is calculated here the same way as Thybring [18]. The theoretical slope of 0.56 g g-1 is shown in each of the subfigures in the third row for reference. The data roughly follow the theoretical line for the 124% SWHC condition, however, the data at 120% and 140% SWHC deviate strongly from the expected values.

It is worth further discussion of why Thybring suggested that Δ*MCO* should be proportional to the mass loss and why this was not observed in some of our experiments. The calculations of Thybring assume that biomass is converted to water and carbon dioxide and that this extra water is maintained within the wood. Clearly, this is true instantaneously as the stoichiometry of the reaction dictates the amount of water generated by the biodegradation. However, the composition of the polymers changes during degradation becoming less hydrophilic and as degradation progresses there are more fungal hyphae in the wood which are hydrated by the fungi. Furthermore, the post-test moisture content is measured at the end of an eight-week experiment and both the fungus and the wood are passively and actively transporting moisture throughout the experiment. Peterson and Cowling have already demonstrated that fungi actively lower the wood moisture content in soil bottle experiments on unmodified wood [19]. While the relationship between Δ*MCO* and mass loss must be true instantaneously, this analysis does not account for moisture redistribution and therefore cannot reliably predict the moisture content at the end of the test. Therefore, one possible explanation of the data is that some of the moisture generated by the fungus was translocated by the fungus before the end of the test. If that were the case, however, it is unclear why the data for the 124% SWHC experiment closely follow the expected behavior.

Although every effort was made to examine the effect of wood moisture content on mass loss independently of fungal moisture generation, it was not possible to separate out the effect of acetylation from fungal moisture generation and to a lesser extent fungal activity in different soil moisture contents. While higher amounts of acetylation were correlated with both lower moisture contents and lower mass losses, it is impossible to fully separate the effect of reduced hygroscopicity on decay resistance from these experiments.

#### **4. Conclusions**

The pre-wetting of the wood was examined as a potential way to better understand the effect of the moisture excluding properties of acetylation on the decay process. However, pre-wetting had only minor effects on the mass loss except for the 18% WPG samples.

As observed in previous studies, the moisture content at the end of the test is linearly related to the amount of mass loss. However, it was not possible to separate out the moisture generation from the fungus from the reduced hygroscopicity of the wood for acetylated samples.

The parameter Δ*MCO*, proposed by Thybring represents the moisture generated by the fungus and should in theory be linearly proportional to mass loss [18]. While the data roughly follow the theoretical line for the 124% SWHC condition, the data at 120% and 140% SWHC deviate from the expected values. Similar deviations were also observed by Peterson and Cowling who observed that Δ*MCO* overestimated moisture generation due to the fungus by roughly three times [19].

While the reduction in wood moisture content caused by acetylation may play a role in the decay resistance of acetylated wood, the fungus actively controls the moisture in the wood and it is not possible to independently control the wood moisture content during the decay process. Because of active water transport by the fungi, it is not possible to understand the role of moisture reduction in the decay process of acetylated wood by altering the soil bottle moisture content.

**Author Contributions:** Conceptualization, S.L.Z. and G.T.K..; methodology, A.B.B., S.L.Z., and G.T.K. formal analysis, S.L.Z, A.B.B., and S.V.G.; investigation, A.B.B.; writing—original draft preparation, S.L.Z.; writing—review and editing, S.L.Z., G.T.K., A.B.B., and S.V.G.; visualization, S.L.Z., A.B.B., and S.V.G. All authors have read and agreed to the published version of the manuscript.

**Acknowledgments:** We thank Forest Products Laboratory chemist Linda Lorenz for acetylating the samples. This work was inspired through discussions with Rebecka Ringman and Annica Pilgård of RISE (Sweden), and further refined through heated debates with Emil Thybring of the University of Copenhagen.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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