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lems in Engineering. 2016. 13 p. DOI: 10.1155/2016/8673809; ID 8673809

and Applications. New York: Nova Sci. Publ; 2017. pp. 201-275

tion. 2017;309:1-16. DOI: 10.1016/j.amc.2017.03.048

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4(3):109-117. DOI: 10.1177/027836498500400308

10.1016/j.amc.2015.04.125

20 Matrix Theory-Applications and Theorems

1081-3810.1635

laa.2003.12.039

We introduce and study a matrix which has the exponential function as one of its eigenvectors. We realize that this matrix represents a set of finite differences derivation of vectors on a partition. This matrix leads to new expressions for finite differences derivatives which are exact for the exponential function. We find some properties of this matrix, the induced derivatives and of its inverse. We provide an expression for the derivative of a product, of a ratio, of the inverse of vectors, and we also find the equivalent of the summation by parts theorem of continuous functions. This matrix could be of interest to discrete quantum mechanics theory.

Keywords: exact finite differences derivative, exact derivatives on partitions, exponential function on a partition, discrete quantum mechanics


*Article* **Nutrition Status and Renal Function as Predictors in Acute Myocardial Infarction with and without Cancer: A Single Center Retrospective Study**

**Naoki Itaya 1, Ako Fukami 1, Tatsuyuki Kakuma 2 and Yoshihiro Fukumoto 1,\***

1 Division of Cardiovascular Medicine, Department of Internal Medicine, Kurume University School of Medicine, Kurume 830-0011, Japan<EMAIL_ADDRESS>(N.I<EMAIL_ADDRESS>(A.F.)

2 Biostatistics Center, Kurume University, Kurume 830-0011, Japan<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>Tel.: +81-94-231-7562

**Abstract:** *Background:* Clinical characteristics of nutrition status in acute myocardial infarction (AMI) patients with cancer remains unknown. Therefore, this study aimed to clarify the differences of clinical parameters, including nutrition status, between AMI patients with and without history of cancer. *Methods and Results:* This retrospective cohort study, using the database of AMI between 2014 and 2019 in Kurume University Hospital, enrolled 411 patients; AMI patients without cancer (*n* = 358, 87.1%) and with cancer (*n* = 53, 12.9%). AMI patients with cancer were significantly older with lower body weight, worse renal function, and worse nutrition status. Next, we divided the patients into 4 groups by cancer, age, and plaque area, detected by coronary image devices. The prediction model indicated that nutrition, lipid, and renal functions were significant predictors of AMI with cancer. The ordinal logistic regression model revealed that worse nutrition status, renal dysfunction, lower uric acid, and elevated blood pressure were significant predictors. Finally, we were able to calculate the probability of the presence of cancer, by combining each factor and scoring. *Conclusions:* Worse nutrition status and renal dysfunction were associated with AMI with cancer, in which nutrition status was a major different characteristic from those without cancer.

**Keywords:** onco-cardiology; nutrition status; cancer; acute myocardial infarction

#### **1. Introduction**

Acute myocardial infarction (AMI) is mainly caused by coronary arteries thrombus due to atherosclerotic plaque rupture or endothelial erosion, and sometimes coronary artery spasm, microvascular thrombus, or others [1]. The formation of fibrofatty lesions in the atherosclerotic vulnerable plaques occurs during the atherosclerosis progression [2], which have been treated by lipid-lowering therapy to stabilize during these 2 decades [3,4]. Further, it has been reported that rapid plaque progression of moderately severe vulnerable plaques is the critical step prior to AMI in most patients [5–7]. Actually, accumulating evidence demonstrates that atherosclerotic risk factors, including hypertension, dyslipidemia, diabetes mellitus, and cigarette smoking, develop atherosclerotic lesions through immune system [2]. The atherosclerotic plaques often contain lipid core and fibrous cap, which consist of smooth muscle cells, macrophages, angiogenesis, and adventitial inflammation [8]. Taken together, when focused on vulnerable coronary atherosclerosis, not stable atheroma, it is considered that AMI most likely occurs due to rapidly progressed coronary atherosclerosis, caused by traditional atherosclerotic risk factors, including age; however, even small atherosclerotic plaques can cause AMI in some patients, which might be associated with some other risk factors.

In cancer patients, the mechanisms of atherosclerosis progression might be different. Especially, clonal hematopoiesis with indeterminate potential (CHIP) has been reported to increase the risk of AMI [9]. CHIP carriers had a 4.0-fold (95% confidence interval 2.4–6.7)

**Citation:** Itaya, N.; Fukami, A.; Kakuma, T.; Fukumoto, Y. Nutrition Status and Renal Function as Predictors in Acute Myocardial Infarction with and without Cancer: A Single Center Retrospective Study. *Nutrients* **2021**, *13*, 2663. https:// doi.org/10.3390/nu13082663

Academic Editor: Pramod Khosla

Received: 22 June 2021 Accepted: 28 July 2021 Published: 30 July 2021

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

**Copyright:** © 2021 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/).

higher risk of myocardial infarction than non-carriers [9]. It has been also reported that each of DNA methyltransferase 3A (DNMT3A), ten-eleven translocation-2 (TET2), additional sex combs-like 1 (ASXL1), and Janus kinase 2 (JAK2) mutation was associated with coronary artery disease, and that CHIP carriers with these mutations also had high levels of coronary artery calcification [9]. Further, TET2-deficient bone marrow cells enlarge atherosclerotic plaques by infiltrating macrophages, and TET2-deficiency increases cytokines/chemokines such as C-X-C motif ligand (CXCL)-1, CXCL 2, CXCL 3, interleukin (IL)-6 and IL-1β [10]. Also, Heyde et al., have reported that CHIP causes hematopoietic stem cell proliferation, promotes arteriosclerosis, and leads to a vicious cycle of proliferation of hematopoietic stem cells [11]. Above all, CHIP is an independent coronary risk factor in patients with premature menopause, especially in patients with spontaneous premature menopause [12]. Thus, coronary risk factors in the clinical settings might be different between cancer and non-cancer patients.

During these 4 decades, cancer is the first cause of death in Japan, and cardiovascular diseases (CVDs) are the second [13]. Because of recent improvement of cancer prognosis due to the advancement of cancer early detection, surgery, and anticancer drug treatment, the number of cancer survivors has increased [6,7]. Cancer and CVDs have common risks of lifestyle factors, such as smoking, obesity, and unhealthy food intake, in which healthy lifestyle is oppositely associated with a longer life expectancy free from major chronic diseases, including cancer and CVDs [14]. However, the clinical characteristics, especially in the aspects of nutrition status, remains scant in AMI patients with cancer.

Therefore, the purpose of the present study was to clarify the differences of clinical parameters in AMI with and without history of cancer, focusing on traditional coronary risk factors and nutrition status, and to develop a statistical model to evaluate the presence of cancer in patients with AMI, using database of AMI in Kurume University Hospital.

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

#### *2.1. Study Design*

This study was retrospective cohort study using the database of AMI in Cardiovascular Medicine, Kurume University Hospital. We enrolled 437 patients, who were treated by primary percutaneous coronary intervention (PCI) due to AMI in Cardiovascular Medicine, Kurume University Hospital from January 2014 to December 2019. We excluded patients if the patients had following reasons; (1) AMI occurred due to PCI complication, (2) PCI due to acute stent thrombosis, (3) unsuccessful PCI, and (4) coronary bypass surgery due to multi-vessel coronary artery disease.

#### *2.2. Data Collection*

Baseline demographic data were collected based on the medical records, including age, sex, height, body weight, waist, medications, traditional risk factors (hypertension, glucose intolerance/diabetes mellitus and dyslipidemia), blood pressure (BP), pulse rate, heart rate, and comorbidities (coronary artery disease, hypertensive heart disease, cardiomyopathy, valvular heart diseases, and congenital heart diseases). All cardiovascular diseases were diagnosed by expert cardiologists. AMI was diagnosed according to fourth universal definition of myocardial infarction [15].

All patients were treated by PCI combined with the use of image devices, such as intravascular ultrasound (IVUS) or optical coherence tomography (OCT). We retrospectively measured the coronary atherosclerotic plaque area by image devices at the culprit lesions.

#### *2.3. Blood Sampling*

Peripheral blood was drawn from the antecubital vein for measurements of blood cell counts, lipid profiles including total cholesterol (T.chol), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and triglyceride, liver and renal function markers including creatinine (Cr) and estimated glomerular filtration rate (eGFR), glycemic parameters of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c), uric acid, troponin, and N-terminal pro-brain natriuretic peptide (NT-pro-BNP). These chemistries were measured at a commercially available laboratory in Kurume University Hospital.

#### *2.4. Definition of Comorbidities*

Hypertension was defined as the use of antihypertensive drugs and/or systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg. Dyslipidemia was defined as the use of lipid-lowering drugs and/or plasma LDL-c ≥ 140 mg/dL and/or triglycerides ≥ 150 mg/dL, and/or HDL-c < 40 mg/dL. Diabetes mellitus was diagnosed using antidiabetic drugs and/or fasting plasma glucose ≥ 110 mg/dL or HbA1c ≥ 6.5%.

#### *2.5. Evaluation of Nutrition Status*

The nutrition status was evaluated using the Glasgow Prognostic Score (GPS) [16], modified GPS (mGPS) [17,18] and Controlling Nutritional Status (CONUT) [19].

#### *2.6. Statistical Analysis*

All continuous variables were given as the mean ± standard deviations (SDs) or median with interquartile range. Categorical data were presented as number (*n*) or percentage (%). For intergroup univariate comparisons, an unpaired *t* test was applied in continuous variables and chi-square test in categorical variables.

The mean ± SDs and frequencies were presented by the two groups with and without cancer (Tables 1 and 2).

**Table 1.** *Cont.*

Data are mean ± SD or median (interquartile rage).Abbreviations: BP: blood pressure, AST: aspartate aminotransferase, ALT: alanine aminotransferase, LDH: Lactate dehydrogenase, γGTP:γ-Glutamyl transpeptidase, ALP: alkaline phosphatase, NT-pro BNP: N-terminal pro-brain natriuretic peptide, eGFR: estimated glomerular filtration rate, HDL: high-density lipoprotein, LDL: low-density lipoprotein, PT-INR: Prothrombin time-international normalized ratio, APTT: activated partial thromboplastin time, FDP: fibrin/fibrinogen degradation products, AOD: aortic dimension, LAD: left atrial dimension, IVST: interventricular septum thickness, PWT: posterior wall thickness, LVDd: left ventricular diameter at end diastole LVDs: left ventricular diameter at end systole, EF: ejection fraction, CONUT: Controlling Nutrition Status, mGPS: modified Glasgow Prognostic Score, GPS: Glasgow Prognostic Score, CSA: average reference lumen cross-sectional area, MLA: minimal lumen area, PA: plaque area.

**Table 2.** Comorbidities and medicine of acute myocardial infarction patients with and without cancer.

**Table 2.** *Cont.*

To evaluate the impact of various risk factors of myocardial infarction on cancer and non-cancer patients, following three analytical steps were taken. First, the classification and regression tree (CART) model was employed to define risk groups (Table 3). Cancer status (yes or no) was used as the response variable and plaque area, age and sex were used as predictors. The fitted probability for the levels of the response was calculated, and the split is chosen to minimize the residual log-likelihood chi-square. Second, principal component analyses were performed to derive synthetic variables based on five sets of risk factors. Specifically, three measurements of nutrition, two measurements for lipid, glucose, blood pressure and renal function were subjected in the principal component analyses which render a way to avoid collinearity problems among highly correlated measurements within each set of risk factor (Tables 4 and 5). Finally, ordinal logistic regression was employed to evaluate effect of each synthetic variables on the risk groups derived from the CART. Effect of each synthetic variable was interpreted based on the odds ratio (Tables 6 and 7), and the

predicted probability of the risk groups were calculated (Figure 5). To compare the results with logistic regressing model with cancer/non-cancer as response variable, an additional table was added (Table 8).

**Table 3.** Divided groups by age and atherosclerotic plaque area.

**Table 4.** Comportments of Synthetic variable with weights.

Abbreviations: CONUT; Controlling Nutrition Status, GPS; Glasgow Prognostic Score, mGPS; modified Glasgow Prognostic Score, T.chol; total cholesterol, LDL-c; low-density lipoprotein cholesterol, FPG; Fasting plasma glucose, HbA1c; hemoglobin A1c, sBP; systolic blood pressure, Cr; Creatinine, eGFR; estimated glomerular filtration rate, cons \*; constant term used to calculate principal component score along with weight of each variable.

**Table 5.** Mean and SD of six risk variables by Four Groups.

*p* value \* based on One-Way Analysis of Variance (ANOVA). Data are means (SD). The equation of nutrition was expressed as "Nutrition = (−1.307) + 0.243 \* CONUT + 0.898 \* GPS + 0.846 \* mGPS". Similarly, other equations were expressed as "Lipid = (−4.947) + 0.018 \* LDL-c + 0.015 \* T.chol", "Glucose = (−5.448) + 0.594 \* HbA1c + 0.0092 \* FPG", "Blood pressure = (−5.559) + 0.038 \* Pulse pressure + 0.027 \* systolic BP", "Renal function = (1.151) + 0.469 \* Cr + (−0.025) \* eGFR". Nutrition, lipid, and renal functions were significant predictors of risk grouping.

**Table 6.** Estimate of the ordinal logistic regression model.

**Table 7.** Estimates of odds ratio from ordinal logistic regression model.

Odds ratio \*: G1 is used as reference group.

**Table 8.** Estimates of odds ratio from ordinal logistic regression model.

Statistical significance was defined as *p* value < 0.05. All statistical analyses were performed using JMP Pro 13.0 and SAS software (Release 9.3; SAS Institute, Cary, NC, USA).

#### *2.7. Description of Ordinal Logistic Regression Model*

Each subject denoted by *i* (*i* = 1, ... , *N)* is classified into risk group denoted as "G1", "G2", "G3" or "G4", and group membership is represented by random variable *Y*. *Y* takes value = 1 if G1, *Y* = 2 if G2, *Y* = 3 if G3 and *Y* = 3 if G4. Ordinal logistic regression is used to model *Yi*. Let *X* be a vector of covariate defined as *X* = (*X*1, ··· , *X*6, *W*) where *X*1, ··· , *X*6 are 6 risk variables. *W* is a dummy variable, which takes value 0 or 1 for non-smoker and smoker respectively. Given covariate vector *X*, cumulative probability of *Y* is written as *P*(*Y* ≤ *k*|*X*), and ordinal logistic regression model is defined as

$$\text{logit}(P(Y \le k | X)) = \log \left\{ \frac{P(Y \le k | X)}{1 - P(Y \le k | X)} \right\} = a\_k - X^\prime \beta \tag{1}$$

where *αk* is an intercept with *α*4 = 0, and β is 7 × 1 parameter vector. From definition of the model, the expected predicted probabilities for G1, G2, G3 and G4 are given by

$$\begin{aligned} P\_1 &= P(Y=1|X) = \frac{\exp(a\_1 - X^\prime \beta)}{1 - \exp(a\_1 - X^\prime \beta)},\\ P\_2 &= P(Y=2|X) = \frac{\exp(a\_2 - X^\prime \beta)}{1 - \exp(a\_2 - X^\prime \beta)} - \frac{\exp(a\_1 - X^\prime \beta)}{1 - \exp(a\_1 - X^\prime \beta)},\\ P\_3 &= P(Y=3|X) = \frac{\exp(a\_3 - X^\prime \beta)}{1 - \exp(a\_3 - X^\prime \beta)} - \frac{\exp(a\_2 - X^\prime \beta)}{1 - \exp(a\_2 - X^\prime \beta)},\\ P\_4 &= P(Y=4|X) = 1 - \frac{\exp(a\_3 - X^\prime \beta)}{1 - \exp(a\_3 - X^\prime \beta)} \end{aligned} \tag{2}$$

Finally, the predicted probabilities *P*ˆ 1, *P*ˆ 2, *P*ˆ 3, *P*ˆ 4 were obtained by plugging in parameter estimates *α*ˆ *k*, *β*ˆ into the expected predicted probabilities, and *α*ˆ *k* − *X β*ˆ is referred to as the linear predicted score in the Figure 5.

Since our model is defined as logit(*P*(*Y* ≤ *k*|*X*)) = *αk* − *X β*, log odds ratio of belonging lower risk group when Nutrition score increase one unit is given by

$$\text{logit}(P(Y \le k | X\_1 = a + 1)) - \text{logit}(P(Y \le k | X\_1 = a)) = -\beta\_1 \tag{3}$$

where *β*1 is the parameter estimate of "Nutrition", thus, odds ratio is given by exp (−*β*1). However, interpretation of odds ratio for a risk variable is easier for a subject being classified into higher risk group. To this end, we reversed the order of group membership (i.e., Y \* = 5 − Y) and model Y \* instead of Y where G1 was set as a reference group. Table 6 shown parameter estimates for modeling Y \* where OR for nutrition is calculated as exp (0.232) = 1.26.

#### **3. Results**

*3.1. Clinical Differences of Traditional Coronary Risk Factors and Nutrition Status with and without Cancer*

Among 437 enrolled patients in the present study, 26 patients were excluded (Figure 1).

**Figure 1.** Enrollment of patients.

In the remaining 411 patients, there were 358 AMI patients without cancer (87.1%) and 53 with cancer (12.9%). To compare with AMI patients without cancer, those with cancer were significantly older with significantly lower body weight, lower diastolic blood pressure, anemia, worse renal function, lower albumin and cholesterol levels, and worse nutrition status, evaluated by CONUT score, GPS, and mGPS (Table 1).

Next in comorbidities, those with cancer had significantly higher prevalence of diabetes mellitus, chronic kidney disease, history of coronary artery bypass surgery treatment, and peripheral artery disease, who also had more frequent drug interventions by clopidogrel, ticlopidine, other antiplatelet agents, anti-cancer agents, and steroid (Table 2).

#### *3.2. Risk Model of Cancer*

To divide the patients into some groups to evaluate the presence of cancer, we first examined the association between age and coronary atherosclerotic plaque area (Figure 2).

Obviously, there was no cancer patient in Age < 60 years-old group. Then, the CART divided the patients by plaque area < 9.39 mm2 and those ≥9.39 mm2, which was the cut-off value of the presence and absence of cancer. According to this grouping process, we divided the whole patients into 4 groups to develop a new response variable to evaluate the presence of cancer (Figure 3 and Table 3). As we considered that if AMI occurred in patients with smaller coronary atherosclerotic plaques, the patients had higher risks, we defined the 4 groups as described in Figure 3 and Table 3.

**Figure 2.** Association between age and coronary atherosclerotic plaque area. There was no significant association between age and coronary plaque area.

**Figure 3.** Disposition of patients. As there was no cancer patient in age < 60 years old, we first divided the patients into 2 groups according to age of 60 years old. Next, the CART defined that the cut-off value of coronary atherosclerotic plaque area (PA) to divide the patients into with and without cancer was 9.39 mm2. Then, we divided the patients ≥ 60 years old into 2 groups according to plaque area of 9.39 mm2, with and without cancer. Due to the small number of cancer patients, we made 4 groups as shown above. Group 1; non-cancer, age < 60 years old, Group 2; non-cancer, age ≥ 60 years old, plaque area ≥ 9.39 mm2, Group 3; non-cancer, age ≥ 60 years old, plaque area < 9.39 mm2, Group 4; cancer, age ≥ 60 years old.

Next, we have developed the prediction model, which consisted of nutrition, lipid, glucose, blood pressure, and renal function (Table 4).

The equation of nutrition was expressed as "Nutrition = (−1.307) + 0.243 \* CONUT + 0.898 \* GPS + 0.846 \* mGPS". Similarly, other equations were expressed as "Lipid = (−4.947) + 0.018 \* LDL-c + 0.015 \* T.chol", "Glucose = (−5.448) + 0.594 \* HbA1c + 0.0092 \* FPG", "Blood pressure = (−5.559) + 0.038 \* Pulse pressure + 0.027 \* systolic BP", "Renal function = (1.151) + 0.469 \* Cr + (−0.025) \* eGFR". Using these equations, we performed analysis of variance, which indicated that nutrition, lipid, and renal functions were significant predictors of risk grouping (Table 5, Figure 4).

The odds of a subject being classified into higher risk group (i.e., group membership j (=1,2,3,4) increase) were 1.26 times when value of the synthetic variable "Nutrition" increased 1 point (Table 7).

Variables with odds ratio greater than 1 had similar interpretation. On the other hand, variables with odds ratio less than 1 had reverse effects. For example, when value of the synthetic variable "Lipid" increased 1 point, the odds of a subject being classified into higher risk group were 0.88 times compared with odds of being classified into lower risk group (Table 7). The additional logistic regression model, where cancer/non-cancer was used a response factor with age and plaque area as adjusting variables, showed the insignificant increase in nutrition (Table 8). Direction of effect of each synthetic variable could be also seen in the Table 5. Finally, we were able to calculate the probability of the presence of cancer, by combining each factor and scoring (Figure 5).

**Figure 5.** Predicted probabilities of classification to 4 risk groups. Linear predicted Score is referred to as linear combination of parameter estimates with its corresponding variable scores in the ordinal logistic regression model.

#### **4. Discussion**

The major findings of the present study were that (a) the prevalence of cancer in AMI patients was 13%, (b) AMI patients with cancer were older with worse nutrition status and renal dysfunction, (c) nutrition status and renal function were consistent predictors for obtaining cancer in AMI, and (d) we were able to calculate the probability of the presence of cancer, by combining each factor and scoring. To the best of our knowledge, this is the first study that provides the evidence of differential risk factors including nutrition status in AMI patients between with and without cancer in details.

#### *4.1. Prevalence of Cancer in AMI*

In the US National Inpatient Sample database between 2004 and 2014 report, among 6,563,255 AMI patients, there were 5,966,955 with no cancer, 186,604 with current cancer (2.8%), and 409,697 with a historical diagnosis of cancer (6.2%) [20]. Among 175,146 patients in Swedish registries of first AMI between 2001 and 2014, there were 16,237 patients (9.3%), who had received care for cancer in the 5 years before AMI [21]. The prevalence of cancer in AMI patients was 13% in the present study, which was relatively higher that these nationwide data, probably because the enrolled patients in the study might have complicated conditions such as being hospitalized in our University Hospital.

#### *4.2. Differences of Traditional Risk Factors and Nutrition Status in AMI between with and without Cancer*

Hypertension, diabetes, current smoking, family history, and dyslipidemia are well known as traditional risk factors in AMI [22], and it has been recently reported that aging, higher prevalence of dyslipidemia and hypertension, and lower prevalence of obesity, diabetes mellitus, and smoking were risk factors in breast cancer survivors compared with general female population [23]. However, the differential risk factors for AMI with cancer compared with non-cancer remains scant. The present study indicated for the first time that renal dysfunction and worse nutrition status were the strongest risk factors in AMI patients with cancer. Besides these factors, univariate analyses (Table 1) showed that AMI

with cancer patients had significantly lower levels of albumin, T.chol, and LDL-c than those without cancer, which seemed to be caused by deterioration of nutrition status, which were different from traditional coronary risk factors.

#### *4.3. Prediction and Scoring of Cancer in AMI*

In the present study, we have developed formulas regarding nutrition status, lipid, glucose blood pressure, and renal function (Table 4). Using these formulas, we are able to avoid to evaluate several data separately, such as T.chol and LDL-c, or Cr and eGFR, and to consider these factors as whole. Further, we have established the predicted scoring system for AMI with cancer (Figure 5), which means that we are able to predict if the patients with AMI have cancer, by calculating this score. This might be useful in clinical settings to recognize the presence of cancer.

#### *4.4. Limitations*

Several limitations should be acknowledged in the present study. First, the present study was an observational retrospective cohort study from a single center. Thus, there might be some bias. Second, we were not able to divide cancer patients into active cancer and history of cancer, due to the limited number of patients with cancer. Also, we were not able to evaluate cancer stage in this study. Third, we were not able to distinguish coronary plaque rupture from coronary artery erosion as causes of AMI. Fourth, the enrolled patients in the present study were hospitalized in University Hospital, which may also cause some bias. Fifth, due to the limited number of enrolled patients, we were not able to examine sexdifference in the present study. Taken together, further investigations should be necessary in larger multi-center cohort studies.

#### **5. Conclusions**

The present study demonstrates that the prevalence of cancer in AMI was 13%, and that worse nutrition status and renal dysfunction were associated with AMI with cancer, in which nutrition status was a major different characteristic from non-cancer. Further, we have developed formulas to predict the presence of cancer in AMI.

**Author Contributions:** Conceptualization, N.I. and Y.F.; methodology, A.F. and T.K.; software, A.F. and T.K.; validation, T.K.; formal analysis, A.F.; investigation, N.I.; resources, Y.F.; data curation, N.I.; writing—original draft preparation, N.I.; writing—review and editing, Y.F.; visualization, A.F.; supervision, Y.F.; project administration, Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the institutional review board at Kurume University (No. 20181, 18 November 2020 approved).

**Informed Consent Statement:** Informed consent was waved due to the retrospective nature and opt-out was used in the study.

**Data Availability Statement:** The data presented in this paper are available on request from the corresponding author.

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

#### **References**


**5. Conclusion**

The characteristic waves of an electrocardiogram (ECG) signal and the heart sounds of the phonocardiogram (PCG) signal provide important indicators for the diagnosis of heart disease; they reflect physiological processes of the heart and autonomic nervous system. The analysis and interpretation of these physiological signals *Mathematical Morphology and the Heart Signals DOI: http://dx.doi.org/10.5772/intechopen.104113*

make it possible to highlight new phenomena, which is sometimes possible to explain at the physiological level, and which leads to a better understanding of the overall functioning of the heart.

The automatic analysis of ECG and PCG signals provides the cardiologist with the information needed to diagnose cardiac pathologies. The implementation of reliable algorithms for the processing of ECG and PCG signals, making it possible to detect the useful information carried by the ECG and PCG signals remains a major concern for technicians.

A set of algorithms using morphology transforms has been developed. These algorithms concerning:

Morphological filtering which uses two morphological operators opening and closing for the correction of the baseline.

In noise suppression, the Top Hat transformation was used. It combines the subtraction of the closing and opening morphology operators.

For the detection of ECG signal parameters, typically the QRS complex, the T wave, and the P wave, two techniques have been presented which are called multi-scale morphology and morphological operators.

At the PCG signal level, we used a morphological filter for noise suppression and heart murmur detection.

These techniques, which are based on mathematical morphology, are very effective in estimating rapid changes in the morphology of ECG and PCG signals.


**Abstract**

This chapter focuses on the design and fabrication of different types of flexible and inflexible wearable fractal for modern wireless applications with body-areanetworks (BANs). A wearable antenna is intended to be a part of clothing used for modern wireless communication purposes. Fractal technology allowed us to design compact antennas and integrate multiple communication services into one device. The proposed antennas were simulated and measured by CST simulator version 2017 and Agilent N9918A VNA respectively. Furthermore, these antennas were fabricated using folded copper. The measured results agree well with the simulated results.

**Keywords:** fractal, wearable antenna, metamaterial, SAR, Sierpinski carpet, crown rectangular, textile antenna

#### **1. Introduction**

Conventional antenna designs which include planar dipoles, monopoles, planar inverted-Fs (PIFAs), and microstrip patches were used in recent research for wearable antennas design [1]. Wearable microstrip antennas are planar. This made them a practical antenna type due to their low cost, low profile, light weight, small size and eases for fabrication to be worn or carried on human body [2]. A wearable antenna is a body-worn antenna which designed from textile materials as antenna substrates to form the "smart clothes" or in the other mean, is an antenna which designed and meant to be a part of clothing or integrated into a personal accessory (such as shoes, glasses, buttons, and helmets) [3]. The wearable antennas are divided into two main categories: flexible and inflexible wearable antennas [4].

Nowadays, the compact antenna with a better performance and multi-bands working frequencies is one of the main trends in modern wireless communications systems [5]. One of the most important techniques used to reduce the antenna's dimensions is the fractal geometries. A fractal is a fragmented or split geometric shape that can be subdivided into parts; each of this is a reduced-size copy of the whole. Fractal antennas have more benefits such as; high radiation efficiency, high gain, wide bandwidth and reduced size etc. Generally, fractals are self-similar and independent of scale. There are many shapes of fractals such as Sierpinski's gasket; Cantor's comb, Von Koch's snowflake, the Mandelbrot set, and the Lorenz attractor see **Figure 1** [6].

**Figure 1.** *The different geometries of fractals in natural [6].*


**2. Methodology**

PubMed, Google Scholar, EBSCOhost, Mendeley, and MedLine Plus were used to conduct the electronic literature search. The search was confined to relevant publications and articles published between January 2020 and April 2022. If a manuscript was relevant to the issue of genetic mutations or variations of SARS-CoV-2, it was chosen. To narrow and guide the search process, the listed keywords were sought after. COVID-19, SARS-CoV-2, variants of interest (VOI), variants of concern (VOC), genetic variances, and genetic mutations are among them.

## **3. Variant being monitored**

Viruses are known to change over time. SARS-CoV-2 has undergone multiple modifications since the start of the COVID-19 pandemic in Wuhan in late 2019 [9]. Comparing the SARS-CoV-2 pathogenic S protein sequence to the Wuhan-Hu-1 reference protein sequence showed about 96.5% of the original S protein sequence has undergone mutations [9]. These mutations lead to genetic differences, which results in the emergence of new variants of the virus [9]. Centers for Disease Control and Prevention (CDC), in collaboration with the SIG, added a fourth class of variant classification named VBM on September 21, 2021, as depicted in **Table 1** [22]. The other classes include VOI, VOC, and VOHC [22].

**Note.** *Researchers and public health authorities globally are using the Pango nomenclature to track the transmission and spread of SARS-CoV-2, including variations of concern and interest. This table contains information on various lineages and their distribution [10–21].*

#### **Table 1.**

*Lineage list.*

The VBM class includes variants under surveillance for mutations leading to increased receptor binding, reduced neutralization by host immune systems, decreased efficacy of treatments, and an increase in disease severity and transmissibility but have not yet been deemed a public health threat by SIG at this time [22]. The VBM class also includes previously designated VOIs and VOCs that are no longer detected or have a decreased prevalence in the population [22]. The variants belonging to the VBM class do not pose a significant risk to public health [22]. Regardless of the minimal risk presented by the VBM class, they are closely monitored for new data [22]. VBM class that warrants more concern can have their classification changed to VOI or VOC if required by SIG [2].


*Article* **Polycyclic Aromatic Hydrocarbons (PAHs) Exposure Triggers Inflammation and Endothelial Dysfunction in BALB/c Mice: A Pilot Study**

**Gabriel A. Rojas 1,2, Nicolás Saavedra 1 , Kathleen Saavedra 1 , Montserrat Hevia 1 , Cristian Morales 1 , Fernando Lanas 3 and Luis A. Salazar 1,\***

**Abstract:** The particulate matter present in air pollution is a complex mixture of solid and liquid particles that vary in size, origin, and composition, among which are polycyclic aromatic hydrocarbons (PAHs). Although exposure to PAHs has become an important risk factor for cardiovascular disease, the mechanisms by which these compounds contribute to increased cardiovascular risk have not been fully explored. The aim of the present study was to evaluate the effects of PAH exposure on systemic pro-inflammatory cytokines and markers of endothelial dysfunction. An intervention was designed using a murine model composed of twenty BALB/c male mice separated into controls and three groups exposed to a mixture of phenanthrene, fluoranthene, and pyrene using three different concentrations. The serum levels of the inflammatory cytokines and gene expression of adhesion molecules located on endothelial cells along with inflammatory markers related to PAH exposure in aortic tissue were determined. Furthermore, the expression of the ICAM-1 and VCAM-1 proteins was evaluated. The data showed significant differences in IL-6 and IFN-γ in the serum. In the gene expression, significant differences for ICAM-1, VCAM-1, and E-Selectin were observed. The results suggest that phenanthrene, fluoranthene, and pyrene, present in air pollution, stimulate the increase in serum inflammatory cytokines and the expression of markers of endothelial dysfunction in the murine model studied, both relevant characteristics associated with the onset of disease atherosclerosis and cardiovascular disease.

**Keywords:** air pollution; endothelium; inflammation; cardiovascular disease

#### **1. Introduction**

Air pollution is a serious global public health problem. According to the Ambient Air Quality Guide of the World Health Organization (WHO), 95% of the world population lives in areas exceeding the recommended values [1]. The most recent estimates revealed that 4.2 million deaths (7.6% of total global mortality and 700,000 more deaths in 2015 compared with 1990) are attributable to ambient particulate matter2.5 (PM2.5) [2]. PM is a widespread complex mixture of solid and liquid particles suspended in air that vary in size, shape, origin, and composition. PM is an air pollutant known as a human carcinogen (group I, IARC, 2013). The composition of PM can substantially vary between geographical regions, sources of emissions, and even weather or seasons [3]. Its chemical composition comprises inorganic ions (e.g., sulfates, nitrates, ammonium, and soluble metals), insoluble metals, elemental carbon, and organic compounds including PAHs, polychlorinated biphenyls, biological components (allergens), microbial agents, and water. The carbonaceous part of air pollution is regarded as more involved in adverse health effects, and some PAHs

**Citation:** Rojas, G.A.; Saavedra, N.; Saavedra, K.; Hevia, M.; Morales, C.; Lanas, F.; Salazar, L.A. Polycyclic Aromatic Hydrocarbons (PAHs) Exposure Triggers Inflammation and Endothelial Dysfunction in BALB/c Mice: A Pilot Study. *Toxics* **2022**, *10*, 497. https://doi.org/10.3390/ toxics10090497

Academic Editors: Virgínia Cruz Fernandes and Diogo Pestana

Received: 21 July 2022 Accepted: 23 August 2022 Published: 27 August 2022

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

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

are considered as particularly important [4]. Thus, PM and PAH are among the most health-relevant air pollutants [5,6].

The three main ways in which air pollution causes damage to the cardiovascular system have been proposed: (a) secretion of pro-inflammatory mediators or oxidative stress in the circulatory system; (b) imbalance of the autonomic nervous system; and (c) direct penetration of particles or components in the circulatory system, which affect numerous tissues within the cardiovascular system [7]. Reports indicate that the smallest particles increase the risk of cardiovascular events, with PM2.5 being specifically associated with an increased risk of myocardial infarction, stroke, arrhythmia, and exacerbation of heart failure symptoms in susceptible patients [8].

PM accumulation, especially redox active components (e.g., metals and PAHs), can cause oxidative stress and inflammation in lung tissue [9]. The inflammatory response to exposure to PM is characterized by the increased expression of pro-inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6), which are secreted by cells of the innate immune system [10]. In addition, the adaptive immune system releases interleukin-1β (IL-1β), interleukin-4 (IL-4), and IL-6 [11]. These cytokines are released into the circulatory system, increasing the liver production of C-reactive protein (CPR) and fibrinogen, IL-6, IL-1β, interferon-gamma (IFN-γ), interleukin-8 (IL-8), and TNF-α [8]. This increase in cytokines associated with exposure to PM has been described in various studies [12–15].

Pathological stimuli in the endothelium trigger a phenotype-modifying adaptive response, a process known as endothelial activation, characterized by increased expression of adhesion molecules Selectin-P (P-Selectin), Selectin-E (E-Selectin), intercellular adhesion molecule 1 (ICAM-1), and vascular cell adhesion molecule 1 (VCAM-1) [16,17]. This process compromises the barrier function of the endothelium, which promotes leukocyte diapedesis, increases vascular tone by decreasing nitric oxide production, and reduces resistance to thrombosis [18,19].

Among the toxic components found in PM2.5 are PAHs, whose main emission sources are the domestic burning of coal and wood, power plants that burn fossil fuels and biomass, industrial processes, and vehicular traffic [20]. Concentrations of these compounds vary depending on multiple factors such as the season of the year, geographic location, and demographics, among others. Thus, for Temuco, Chile, it was determined that the dominant individual PAHs were phenanthrene (35–45%), fluoranthene (11–15%), and pyrene (9–12%), with the phenanthrene domain reflecting a typical characteristic of emissions from biomass combustion, especially burning wood for heating or cooking [21].

Most of these studies focused their interest on the PM relationship with cardiovascular disease development; however, the mechanisms by which PAH presents in PM contribute to the increased cardiovascular risk have not been explored in depth. Therefore, the objective was to evaluate the effects of PAH exposure on markers of inflammation and endothelial dysfunction in a murine model of BALB/c mice.

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

#### *2.1. Animals*

Twenty male BALB/c mice were randomly assigned to four equal groups of five animals each including a control group (C = no exposure) and three groups exposed to 10 µg, 30 µg, and 50 µg of a PAH mixture, composed of 55% phenanthrene (Sigma-Aldrich, St. Louis, MO, USA), 25% fluoranthene (Sigma-Aldrich, St. Louis, MO, USA), and 20% pyrene (Sigma-Aldrich, St. Louis, MO, USA), proportionally to the most representative distribution of the PAHs previously described [21]. Dimethylsulfoxide (DMSO, Sigma-Aldrich, St. Louis, MO, USA) was used as a solvent. The animals were kept in the Bioterio of the University of La Frontera, receiving a standard diet and ad libitum water.

#### *2.2. Experimental Protocol*

The animals received a 2-week acclimatization treatment according to the instillation protocol described above [22]. The intervention groups underwent nasal instillation of a volume of 10 µL using a micropipette. The intranasal instillation induced an apnea reflex followed by a deep inspiration. In addition, the control group was instilled with the vehicle solution (DMSO) using the same volume. The intervention protocol consisted of instillation for 5 days a week for 5 weeks. We recorded the weight of the animals once a week. To assess the animals' level of stress and spontaneous activity, the cylinder test was applied every two weeks, where the upright exploration attempts in a transparent cylinder were quantified [23]. Thus, low attempts or immobility indicated the level of activity of the animal. To assess the general condition of the animals, the Morton and Griffiths 'Animal Supervision Protocol' was applied once a week [24]. The Scientific Ethics Committee of the Universidad de La Frontera (No 105\_18) approved the experimental protocol.

#### *2.3. Sampling Extraction*

Euthanasia was performed with a mixture of ketamine/xylazine using a lethal intraperitoneal dose of 200 mg/kg of ketamine-16 mg/kg of xylazine. Whole blood sampling was performed by cardiac puncture and centrifuged at 2000 rpm for 15 min. Once the serum was separated from the blood clot, the samples were stored at −80 ◦C for later analysis. The thoracic aorta was removed and stored at −80 ◦C in a sterile tube with 1 mL of RNAlaterTM stabilizer solution (Ambion Inc., Austin, TX, USA).

## *2.4. Cytokine Analysis*

We analyzed the serum levels of IL-6, IL-10, IL-17A, INF-γ, and TNF-α with the 6-Plex Kit of the Bio-Plex Pro TM Mouse Cytokine Th17 Panel A (BioRad, Hercules, CA, USA) following the manufacturer's instructions, with the MAGPIX® system (Luminex, Austin, TX, USA). Twenty samples were tested in duplicate in a 96-well plate including an 8-point standard curve in duplicate and two wells as the negative control. Data collection was performed with xPONENT 4.2® software (Luminex, Austin, TX, USA). We adjusted the cytokine values according to the weight of each animal.

## *2.5. Gene Expression by RT-qPCR*

Gene expression was analyzed by quantitative real-time polymerase chain reaction (RT-qPCR). Specific primers were used for ICAM-1, VCAM-1, E-Selectin, P-Selectin, platelet endothelial cell adhesion molecule (Pecam-1), endothelial nitric oxide synthase (eNOS), aryl hydrocarbon receptor (Ahr), Kelch-type ECH-associated protein 1 (Keap 1), transcription factor p65 (RelA), inhibitor of nuclear factor kappa-B kinase subunit beta (IKK-β), IL-6, and TNF-α, together with the reference genes for ribosomal protein L32 (RPL32) and beta2-microglobulin (B2M) (Table 1). Frozen aortic tissue samples were lysed using 2 mL prefilled tubes with ceramic beads (MP biomedical, Solon, OH, USA) in a benchtop BeadBugTM homogenizer (Benchmark Scientific, Sayreville, NJ, USA) for 60 s at 3500 rpm, adding 1 mL of TRIzol® reagent (Invitrogen, Waltham, MA, USA). Once the tissue was completely homogenized, the TRIzol® reagent protocol recommended by the manufacturer was followed to extract the total RNA, and subsequent evaluation by spectrophotometry (NanoQuant Infinite® 200 PRO, Tecan®, Männedorf, Switzerland) and fluorometry (Quantus™ Fluorometer, Promega, Madison, WI, USA) to determine the purity (260/280 nm ratio) and the amount of RNA extracted, respectively. A ratio between 1.8 and 2.0 was considered as acceptable. The total RNA samples were diluted to ensure a final concentration of 30 ng/µL. The synthesis of cDNA was carried out through reverse transcription using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). A qPCR was performed to quantify the expression of each of the selected genes and housekeeping gene using the Fast SYBR® Green Master Mix Kit (Applied Biosystems, Foster City, CA, USA) following the manufacturer's protocols. For qPCR analysis, LinRegPCR® software was used, which established a linearity window and calculated

the PCR efficiencies per sample. With the average PCR efficiency per sample, Ct value, and fluorescence threshold established, the initial concentration per sample expressed in arbitrary fluorescence units was calculated [25]. To analyze the specificity of the primers, the melting curve was evaluated.

**Table 1.** The primer sequences used for the PCR analysis.

ICAM-1—Intercellular adhesion molecule 1; VCAM-1—Vascular cell adhesion molecule 1; E-Selectin—Selectin, endothelial cell; P-Selectin—Selectin platelet; PECAM-1—Platelet/endothelial cell adhesion molecule 1; eNOS— Nitric oxide synthase endothelial cell; Ahr—Aryl-hydrocarbon receptor; Keap1—Kelch-like ECH-associated protein 1; RelA—Transcription factor p65; IKK-β—Inhibitor of nuclear factor kappa-B kinase subunit beta; IL-6— Interleukin 6; TNF-α—Tumor necrosis factor α; RPL32—Ribosomal protein L32; B2M—Beta-2 microglobulin.

#### *2.6. Western Blotting*

Protein levels of ICAM-1 and VCAM-1 were quantified using α/β-tubulin as a loading control. We performed total protein extraction from aortic tissue using the TRIzol® reagent protocol. Total proteins were quantified using the Pierce BCA Colorimetric Assay Kit (Thermo Scientific ™, Rockford, IL, USA) in a 96-well multiplate, in triplicate. The protein extract was diluted 3:1 in 4× Laemmli sample buffer (Bio-Rad, Hercules, CA, USA) before adding β-mercaptoethanol in a 1:9 ratio (Bio-Rad). The final protein concentration used for the immunodetection of each sample was 40 µg. The samples were denatured at 95 ◦C for 5 min, and then loaded onto a 4–20% Mini-PROTEAN® TGX ™ electrophoresis gel (Bio-Rad). Afterward, electrophoresis was applied at 100 V for 15 min and then 200 V for 30 min. To differentiate the molecular mass of the bands, the Precision Plus Protein ™ Kaleidoscope standard (Bio-Rad) with a volume of 10 µL was used. The proteins were then transferred to the PVDF immunoblot membrane (Bio-Rad) for 1.5 h at 350 mAmp. Once the transfer was complete, the membrane was stained with Ponceau Red Solution S (Biotium, Fremont, CA, USA) to verify the transfer. Subsequently, the membrane was blocked with 5% NFDM/TBS-Tween for 1 h and then incubated with the primary antibodies at 4 ◦C overnight according to the manufacturer's instructions. VCAM-1 (1:1000, 5% BSA, 1X TBS, 0.1% Tween®20; Cell Signaling 32653, Danvers, MA, USA), ICAM-1 (1:1000, 5% NFDM, 1X TBS, 0.1% Tween®20; Abcam, ab179707), and as the loading control α/β-tubulin (1:1000, 5% BSA, 1X TBS, 0.1% Tween®20; Cell Signaling 2148, Danvers, MA, USA). Subsequently, the membrane was washed with TBS-Tween and incubated with the HRP-conjugated secondary antibody (1:3000, 5% NFDM 1X TBS, 0.1% Tween®20, goat anti-rabbit IgG; Cell Signaling 7074, Danvers, MA, USA) for 1 h at room temperature. Antigen-antibody binding bands were detected using G: BOX Chemi XRQ (SYNGENE, Frederick, MD, USA) chemiluminescence equipment using the SuperSignal ™ West Femto Maximum Sensitivity Substrate Kit (Thermo Scientific ™, Rockford, IL, USA), following the manufacturer's recommendations. The densitometric analysis of the bands was performed using the ImageJ 1.51j8 open-source software (https://imagej.nih.gov/ij/index.html (accessed on 22 August 2022), National Institutes of Health, Bethesda, MD, USA)

#### *2.7. Statistical Analysis* distribution of the values obtained, the Shapiro–Wilk normality test was performed. For

*2.7. Statistical Analysis*

Data were analyzed using Prism 8.0.2 software (GraphPad, San Diego, CA, USA). The results are ex-pressed as the means ± standard error of the mean. To evaluate the distribution of the values obtained, the Shapiro–Wilk normality test was performed. For the comparison of the groups, Welch's ANOVA with the Dunnett's multiple comparisons test or its non-parametric simile Kruskal–Wallis and a multiple comparison analysis were used through Dunn's test. A two-way ANOVA was used for group comparison analyses with two variables. A *p*-value < 0.05 was established for statistical significance. the comparison of the groups, Welch's ANOVA with the Dunnett's multiple comparisons test or its non-parametric simile Kruskal–Wallis and a multiple comparison analysis were used through Dunn's test. A two-way ANOVA was used for group comparison analyses with two variables. A *p*-value < 0.05 was established for statistical significance. **3. Results**  *3.1. Animals*

Data were analyzed using Prism 8.0.2 software (GraphPad, San Diego, CA, USA). The results are ex-pressed as the means ± standard error of the mean. To evaluate the

(accessed on 22 August 2022), National Institutes of Health, Bethesda, MD, USA)

Subsequently, the membrane was washed with TBS-Tween and incubated with the HRPconjugated secondary antibody (1:3000, 5% NFDM 1X TBS, 0.1% Tween®20, goat anti-rabbit IgG; Cell Signaling 7074, Danvers, MA, USA) for 1 h at room temperature. Antigenantibody binding bands were detected using G: BOX Chemi XRQ (SYNGENE, Frederick, MD, USA) chemiluminescence equipment using the SuperSignal™ West Femto Maximum Sensitivity Substrate Kit (Thermo Scientific™, Rockford, IL, USA), following the manufacturer's recommendations. The densitometric analysis of the bands was performed using the ImageJ 1.51j8 open-source software (https://imagej.nih.gov/ij/index.html

#### **3. Results** The initial weight per group of animals did not show significant differences (*p* =

#### *3.1. Animals*

The initial weight per group of animals did not show significant differences (*p* = 0.091). The mean weight was as follows: control = 24.20 ± 1.07 g; group 10 µg = 21.40 ± 0.40 g; group 30 µg = 21.80 ± 0.74 g; group 50 µg = 20.80 ± 0.37 g. The weekly weight of each animal was recorded, which did not show significant differences between the groups (*p* = 0.058). No differences were found in the spontaneous activity of the animals evaluated with the cylinder test (*p* = 0.919; Figure 1). 0.091). The mean weight was as follows: control = 24.20 ± 1.07 g; group 10 µg = 21.40 ± 0.40 g; group 30 µg = 21.80 ± 0.74 g; group 50 µg = 20.80 ± 0.37 g. The weekly weight of each animal was recorded, which did not show significant differences between the groups (*p* = 0.058). No differences were found in the spontaneous activity of the animals evaluated with the cylinder test (*p* = 0.919; Figure 1).

*Toxics* **2022**, *10*, x FOR PEER REVIEW 5 of 14

**Figure 1.** Monitoring of the general condition of the animals. (**A**) Evolution of weight per week by group (*p* = 0.058). (**B**) Comparison of the exploration attempts per group in the cylinder test (*p* = **Figure 1.** Monitoring of the general condition of the animals. (**A**) Evolution of weight per week by group (*p* = 0.058). (**B**) Comparison of the exploration attempts per group in the cylinder test (*p* = 0.919). Two-way ANOVA. (*n* = 5 per group).

#### 0.919). Two-way ANOVA. (*n* = 5 per group). *3.2. Serum Cytokines*

*3.2. Serum Cytokines*  We observed significant differences between the intervention and the control groups for the IL-6 levels (*p* = 0.026) [Control v/s 10 µg, *p* = 0.025; Control v/s 30 µg, *p* = 0.024; Control v/s 50 µg, *p* = 0.256] and IFN-γ (*p* = 0.039) [Control v/s 10 µg, *p* = 0.039; Control v/s We observed significant differences between the intervention and the control groups for the IL-6 levels (*p* = 0.026) [Control v/s 10 µg, *p* = 0.025; Control v/s 30 µg, *p* = 0.024; Control v/s 50 µg, *p* = 0.256] and IFN-γ (*p* = 0.039) [Control v/s 10 µg, *p* = 0.039; Control v/s 30 µg, *p* = 0.050; Control v/s 50 µg, *p* = 0.195]. In contrast, TNF-α (*p* = 0.145), IL-10 (*p* = 0.576), and IL-17A (*p* = 0.296) did not show differences between the groups (Figure 2).

#### 30 µg, *p* = 0.050; Control v/s 50 µg, *p* = 0.195]. In contrast, TNF-α (*p* = 0.145), IL-10 (*p* = *3.3. Gene Expression*

0.576), and IL-17A (*p* = 0.296) did not show differences between the groups (Figure 2). We observed significant differences for the gene expression of ICAM-1 (*p* = 0.047) [Control v/s 10 µg, *p* = 0,944]; [Control v/s 30 µg, *p* = 0.655]; [Control v/s 50 µg, *p* = 0.041], VCAM-1 (*p* = 0.023) [Control v/s 10 µg, *p* = 0.981]; [Control v/s 30 µg, *p* = 0.910]; [Control v/s 50 µg, *p* = 0.023]; and E-Selectin (*p* = 0.048) [Control v/s 10 µg, *p* > 0.9999]; [Control v/s 30 µg, *p* > 0.999]; [Control v/s 50 µg, *p* = 0.033]. No differences were found for P-Selectin (*p* = 0.986), Pecam-1 (*p* = 0.705), and eNOS (*p* = 0.396) (Figure 3). Regarding markers related to PAH exposure and inflammatory indicators, no differences were identified in Ahr (*p* = 0.789); Keap1 (*p* = 0.507); RelA (*p* = 0.679); IKK-β (*p* = 0.450); IL-6 (*p* = 0.878); TNF-α (*p* = 0.760) (Figure 4).

**Figure 2.** The quantification of weight-adjusted serum inflammatory cytokines in animals exposed to the PAHs and controls. (**A**) IL-6 (*p* = 0.026) [Control v/s 10 µg *p* = 0.025; Control v/s 30 µg *p* = 0.024; Control v/s 50 µg *p* = 0.256]. (**B**) IFN-γ (*p* = 0.039 [Control v/s 10 µg *p* = 0.039; Control v/s 30 µg *p* = 0.050; Control v/s 50 µg *p* = 0.195]). (**C**) TNF-α (*p* = 0.145). (**D**) IL-10 (*p* = 0.576). (**E**) IL-17A (*p* = 0.296). Data are presented as the mean ± SEM. The dashed line represents the mean value of the control group. Welch's ANOVA with Dunnett's multiple comparisons test (Control, *n* = 4; 10 µg, *n* = 5; 30 µg, *n* = 4; 50 µg, *n* = 5). **Figure 2.** The quantification of weight-adjusted serum inflammatory cytokines in animals exposed to the PAHs and controls. (**A**) IL-6 (*p* = 0.026) [Control v/s 10 µg *p* = 0.025; Control v/s 30 µg *p* = 0.024; Control v/s 50 µg *p* = 0.256]. (**B**) IFN-γ (*p* = 0.039 [Control v/s 10 µg *p* = 0.039; Control v/s 30 µg *p* = 0.050; Control v/s 50 µg *p* = 0.195]). (**C**) TNF-α (*p* = 0.145). (**D**) IL-10 (*p* = 0.576). (**E**) IL-17A (*p* = 0.296). Data are presented as the mean ± SEM. The dashed line represents the mean value of the control group. Welch's ANOVA with Dunnett's multiple comparisons test (Control, *n* = 4; 10 µg, *n* = 5; 30 µg, *n* = 4; 50 µg, *n* = 5).

*3.3. Gene Expression*

0.760) (Figure 4).

We observed significant differences for the gene expression of ICAM-1 (*p* = 0.047) [Control v/s 10 µg, *p* = 0,944]; [Control v/s 30 µg, *p* = 0.655]; [Control v/s 50 µg, *p* = 0.041], VCAM-1 (*p* = 0.023) [Control v/s 10 µg, *p* = 0.981]; [Control v/s 30 µg, *p* = 0.910]; [Control v/s 50 µg, *p* = 0.023]; and E-Selectin (*p* = 0.048) [Control v/s 10 µg, *p* > 0.9999]; [Control v/s 30 µg, *p* > 0.999]; [Control v/s 50 µg, *p* = 0.033]. No differences were found for P-Selectin (*p* = 0.986), Pecam-1 (*p* = 0.705), and eNOS (*p* = 0.396) (Figure 3). Regarding markers related to PAH exposure and inflammatory indicators, no differences were identified in Ahr (*p* = 0.789); Keap1 (*p* = 0.507); RelA (*p* = 0.679); IKK-β (*p* = 0.450); IL-6 (*p* = 0.878); TNF-α (*p* =

**Figure 3.** The relative gene expression of endothelial dysfunction markers in the aortic tissue of animals exposed to PAHs and controls. (**A**) ICAM-1 (*p* = 0.047) † [Control v/s 10 µg, *p* = 0.944]; [Control v/s 30 µg, *p* = 0.655]; [Control v/s 50 µg, *p* = 0.041]. (**B**) VCAM-1 (*p* = 0.023) † [Control v/s 10 µg, **Figure 3.** The relative gene expression of endothelial dysfunction markers in the aortic tissue of animals exposed to PAHs and controls. (**A**) ICAM-1 (*p* = 0.047) † [Control v/s 10 µg, *p* = 0.944]; [Control v/s 30 µg, *p* = 0.655]; [Control v/s 50 µg, *p* = 0.041]. (**B**) VCAM-1 (*p* = 0.023) † [Control v/s 10 µg, *p* = 0.981]; [Control v/s 30 µg, *p* = 0.910]; [Control v/s 50 µg, *p* = 0.023]. (**C**) E-Selectin (*p* = 0.048) ‡ [Control v/s 10 µg, *p* > 0.9999]; [Control v/s 30 µg, *p* > 0.999]; [Control v/s 50 µg, *p* = 0.033]. (**D**) P-Selectin (*p* = 0.986) ‡ . (**E**) Pecam-1 (*p* = 0.705) ‡ . (**F**) eNOS (*p* = 0.396) † . Relative quantification was calculated using the reference genes RPL32 and B2M. Data are presented as the means ± SEM. † Welch's ANOVA with Dunnett's multiple comparisons test. ‡ Kruskal–Wallis with Dunn's post hoc analysis (Control, *n* = 4; 10 µg, *n* = 4; 30 µg, *n* = 4; 50 µg, *n* = 4).\* *p* < 0.05.

*p* = 0.981]; [Control v/s 30 µg, *p* = 0.910]; [Control v/s 50 µg, *p* = 0.023]. (**C**) E-Selectin (*p* = 0.048) ‡ [Control v/s 10 µg, *p* > 0.9999]; [Control v/s 30 µg, *p* > 0.999]; [Control v/s 50 µg, *p* = 0.033]. (**D**) P-Selectin (*p* = 0.986) ‡. (**E**) Pecam-1 (*p* = 0.705) ‡. (**F**) eNOS (*p* = 0.396) †. Relative quantification was calculated using the reference genes RPL32 and B2M. Data are presented as the means ± SEM. † Welch's ANOVA with Dunnett's multiple comparisons test. ‡ Kruskal–Wallis with Dunn's post hoc

analysis (Control, *n* = 4; 10 µg, *n* = 4; 30 µg, *n* = 4; 50 µg, n = 4).\* *p* < 0.05.

**Figure 4.** The relative gene expression of exposure-related PAHs and inflammatory markers in aortic tissue from PAH exposed animals and the controls. (**A**) Ahr (*p* = 0.789) ‡. (**B**) Keap1 (*p* = 0.507)‡. (**C**) RelA (*p* = 0.679) ‡. (**D**) IKK-β (*p* = 0.450) †. (**E**) IL-6 (*p* = 0.878) ‡. (**F**) TNF-α (*p* = 0.760) †. Gene expression was normalized using the RPL32 and ACTB as reference genes. Data are presented as mean ± SEM. † Welch's ANOVA test. ‡ Kruskal–Wallis test (Control, *n* = 4; 10 µg, *n* = 4; 30 µg, *n* = 4; 50 µg, *n* = 4). **Figure 4.** The relative gene expression of exposure-related PAHs and inflammatory markers in aortic tissue from PAH exposed animals and the controls. (**A**) Ahr (*p* = 0.789) ‡ . (**B**) Keap1 (*p* = 0.507) ‡ . (**C**) RelA (*p* = 0.679) ‡ . (**D**) IKK-β (*p* = 0.450) † . (**E**) IL-6 (*p* = 0.878) ‡ . (**F**) TNF-α (*p* = 0.760) † . Gene expression was normalized using the RPL32 and ACTB as reference genes. Data are presented as mean ± SEM. † Welch's ANOVA test. ‡ Kruskal–Wallis test (Control, *n* = 4; 10 µg, *n* = 4; 30 µg, *n* = 4; 50 µg, *n* = 4).

#### *3.4. Protein Expression*

The protein expression of ICAM-1 (*p* = 0.117) and VCAM-1 (*p* = 0.210) did not show significant differences in the aortic tissue, although we observed an elevated expression in the intervention groups compared to the control group (Figure 5).

*3.4. Protein Expression*

**Figure 5.** The effect of exposure to PAHs on the protein expression of adhesion molecules in aortic tissue. (**A**) Relative protein expression of ICAM-1 (*p* = 0.117). (**B**) Relative protein expression of VCAM-1 (*p* = 0.210). (**C**) Representative Western blots are shown for ICAM-1, VCAM-1, and α/βtubulin. Data are presented as the mean ± SEM. Welch's ANOVA test. (Control, *n* = 4; 10 µg, *n* = 4; 30 µg, *n* = 4; 50 µg, *n* = 4). **Figure 5.** The effect of exposure to PAHs on the protein expression of adhesion molecules in aortic tissue. (**A**) Relative protein expression of ICAM-1 (*p* = 0.117). (**B**) Relative protein expression of VCAM-1 (*p* = 0.210). (**C**) Representative Western blots are shown for ICAM-1, VCAM-1, and α/βtubulin. Data are presented as the mean ± SEM. Welch's ANOVA test. (Control, *n* = 4; 10 µg, *n* = 4; 30 µg, *n* = 4; 50 µg, *n* = 4).

The protein expression of ICAM-1 (*p* = 0.117) and VCAM-1 (*p* = 0.210) did not show significant differences in the aortic tissue, although we observed an elevated expression

in the intervention groups compared to the control group (Figure 5).

#### **4. Discussion 4. Discussion**

The concentrations and doses of PAHs used for this protocol were established according to previous studies using PM [26] and pollution generated by diesel combustion [27] as we did not find studies using PAH nasal instillation. Some studies have reported that exposure to PAH increases circulating pro-inflammatory cytokines, IL-6, IL-8, and TNF-α being the most studied markers [28,29]. In this study, a significant increase in serum IL-6 and IFN-γ was identified. Another report carried out on workers at a coal plant showed an increased concentration of IL-6 in plasma, demonstrating a dose-dependent relationship with PAH metabolites in urine [28]. IL-6 represents a good indicator of cytokine cascade activation, accurately reflecting the inflammatory state, in addition to its high stability since the half-life of IL-6 is longer than that of other pro-inflammatory cytokines [30]. On the other hand, IFN-γ induces the overexpression of additional proinflammatory cytokines such as IL-12, IL-15, TNF-α, IFN-γ-inducible protein-10 (IP-10), inducible nitric oxide synthase (iNOS), among others, inducing the activation of proinflammatory transcription factors such as the nuclear factor kappa light chain enhancer of activated B cells (NF-κB) [31]. However, in a study carried out on asthmatic and non-The concentrations and doses of PAHs used for this protocol were established according to previous studies using PM [26] and pollution generated by diesel combustion [27] as we did not find studies using PAH nasal instillation. Some studies have reported that exposure to PAH increases circulating pro-inflammatory cytokines, IL-6, IL-8, and TNF-α being the most studied markers [28,29]. In this study, a significant increase in serum IL-6 and IFN-γ was identified. Another report carried out on workers at a coal plant showed an increased concentration of IL-6 in plasma, demonstrating a dose-dependent relationship with PAH metabolites in urine [28]. IL-6 represents a good indicator of cytokine cascade activation, accurately reflecting the inflammatory state, in addition to its high stability since the half-life of IL-6 is longer than that of other pro-inflammatory cytokines [30]. On the other hand, IFN-γ induces the overexpression of additional pro-inflammatory cytokines such as IL-12, IL-15, TNF-α, IFN-γ-inducible protein-10 (IP-10), inducible nitric oxide synthase (iNOS), among others, inducing the activation of pro-inflammatory transcription factors such as the nuclear factor kappa light chain enhancer of activated B cells (NF-κB) [31]. However, in a study carried out on asthmatic and non-asthmatic children exposed to air pollution from traffic and followed for 6 years, there were no differences in circulating IFNγ concentrations [32]. However, a study evaluating three different areas of environmental pollution according to their volatile components identified increased IFN-γ and TNF-α concentrations in industrialized and high-traffic areas compared to low-traffic areas [33]. Furthermore, a study evaluating different sources of pollutants derived from wood combustion identified a significant increase in blood TNF-α [34], showing that exposure to

particles derived from wood combustion containing PAH can upregulate pro-inflammatory cytokines including IL-6 and TNF-α.

IL-10 has pleiotropic effects on immunoregulation and inflammation, downregulating the expression of Th1 cytokines, class II MHC, and co-stimulatory molecules in macrophages. IL-10 also improves B cell survival, proliferation, and antibody production [35]. This cytokine can block the activity of NF-kB, participating in the regulation of the JAK-STAT signaling pathway. By comparing different pro-inflammatory and antiinflammatory cytokines associated with different levels of environmental pollution, Dobreva et al. showed that air pollutants, mostly PM2.5, modulated cytokine production by altering the TNF-α (pro-inflammatory) and IL-10 (anti-inflammatory) [36].

An increase in the pro-inflammatory cytokines affects the regulation of vascular tone, cell adhesion, inflammation, proliferation, and the phenotype of smooth muscle cells as well as the formation of atheroma plaques [16]. Endothelial activation is characterized by an increase in the expression of adhesion molecules, leukocyte diapedesis, increased vascular tone due to decreased nitric oxide production, and reduced resistance to thrombosis [18]. The process of endothelial activation has been described as the factor initiating atheroma plaques in the vascular tissue. Thus, TNF-α enhances the expression of adhesion molecules in vascular endothelial cells. An in vitro study determined that stimulation of human coronary artery endothelial cells exposed to TNF-α increased ICAM-1 expression [37]. Our data indicate increased expression of the ICAM-1, VCAM-1 and E-Selectin genes, showing significant differences in the group exposed to 50 µg PAHs. Additionally, ICAM-1 and VCAM-1 protein expression was elevated in the PAH-exposed groups, although it did not reach statistical significance, being able to explain this by post-transcriptional regulations that should be studied in more depth.

We also observed a nonsignificant increase in IL-6 and TNF-α. Endothelial activation by TNF-α is carried out by two main mechanisms: activation of NF-κB and MAPK. The activation of NF-kB plays a central role in the regulation of multiple cellular processes such as inflammation, immune response, differentiation, proliferation, apoptosis, and cancer, thus it is considered as a master regulator of inflammatory responses [38]. Furthermore, TNF-α, IL-1β, IL-6, and IFN-γ expression are elevated within the great elastic arteries of old mice and humans [39]. This pro-inflammatory arterial phenotype is associated with increased NF-κB activity. When translocated to the nucleus, NF-κB activates the transcription of genes involved in the production of pro-inflammatory cytokines [40]. As we did not observe an increase in the gene expression of RelA and IKK-β, it becomes necessary to identify the mechanisms by which exposure to PAHs stimulates the increased expression of adhesion molecules in vascular tissue.

Based on the findings of this study, it is necessary to expand the studies that can investigate the mechanisms by which PAHs can generate deleterious effects on the cardiovascular system, being able to speculate that these compounds, due to their nature, manage to enter the cardiovascular system directly via bloodstream, affecting the vascular endothelium manage to enter the cardiovascular system directly via bloodstream, affecting the vascular endothelium. Thus, evidence shows that the passage of small molecules (PM0.1) into the blood directly affects the vascular system. Within this group of particles, we can find particles derived from fossil fuels and wood combustion [7,41–43]. In this sense, reports have determined that PAHs bind to AhR, leading to the release of the latter from the multiprotein complex and its consequent translocation to the nucleus, where it dimerizes with the nuclear translocator AhR (ARNT), leading to binding to the xenobiotic response element (XRE) in the promoter region of target genes to stimulate transcription [44]. Furthermore, it has been proposed that AhR-mediated pathways are linked to responses to oxidative stress through the dissociation of the nuclear factor erythroid 2-related factor 2 (Nrf2) and the inhibitory protein keap1 [45].

Regarding the behavior of the variables studied, it was originally expected that there would be a dose–response relationship, a situation that was not observed in this investigation. In this regard, it is important to point out that the responses to the different

pollutants did not always maintain this behavior, finding little evidence indicating that exposure to increasing doses of PM leads to vasomotor dysfunction and the progression of atherosclerotic plaque [46]. Furthermore, it has been suggested that systemic or pulmonary inflammation is not a prerequisite for dysfunction in the vasomotor response and accelerated the progression of atherosclerosis in animals exposed to PM [46]. Therefore, in the investigation, we wanted to review both the markers of systemic inflammation and the specific effect on tissue, particularly aortic tissue, evaluating markers of endothelial dysfunction that are recognized as potent factors for vasomotor dysfunction and atherosclerotic plaque formation. However, it has been documented that different types of PAH generate effects through different pathways in the tissues. An example of this is benzo[a]pyrene (B[a]P), which is considered to have a low affinity for AhR, but with potent effects on Ca2+ induction, a mechanism related to endothelial dysfunction. In contrast, pyrene, which seems to have an even greater effect on Ca2+ induction, but a non-nuclear Ahr stimulation pathway [47], showed differences in the effects of various PAHs in their pathophysiological mechanism of action. This is relevant to exemplify that the effect observed in tissue may be influenced by several metabolic pathways, which requires further investigation.

The PAH doses used in this research are within the ranges published in various studies and are adequately summarized in the review by Møller et al. [46]. However, it is important to point out that there is a great variation in the representation of PAHs in the particulate matter in the air, which depends on the concentrations of its different types, emission sources, time of year, place of measurement, and PM level, among others. These factors make direct extrapolation somewhat complex to perform. Thus, for example, residents living in rural areas generally inhale higher concentrations of PM-bound PAHs (4.2–655 ng/m3 ) [48] than residents living in urban areas (0.4–11, 9 ng/m3 ) [49]. In this study, a mixture of phenanthrene, fluoranthene, and pyrene was obtained, which are characteristic of the winter season associated with the combustion of wood for heating and cooking.

Although we have reported interesting findings regarding the role of exposure to PAHs in pro-inflammatory states and cardiovascular health, this study had limitations of a small number of animals per group, considering that no previous data were found regarding the model used for PAH management. In addition, the intervention contemplated 5 weeks of exposure, which could be a bit limited considering the long-term deleterious effects that people exposed to high levels of air pollution have been shown to manifest.

#### **5. Conclusions**

Our results suggest that PAHs of phenanthrene, fluoranthene, and pyrene, present in PM, partially stimulate the production of serum inflammatory cytokines, which have been associated with the development of various diseases related to high exposure to air pollution, in addition to being a relevant factor for the development of endothelial dysfunction and atherosclerotic disease. Furthermore, an increase in the expression of adhesion molecules related to endothelial dysfunction was found, an initial mechanism in the atherogenesis process that contributes to the formation, progression, and complications of the atherosclerotic plaque, alterations that are usually subclinical and poorly diagnosed. In this studied murine model, we showed that both mechanisms associated with the development of cardiovascular disease were manifested and may represent a model that allows for investigating the cellular and molecular mechanisms associated with exposure to PAHs present in PM.

**Author Contributions:** Conceptualization, G.A.R., N.S., K.S., F.L. and L.A.S.; Methodology, G.A.R., N.S. and L.A.S.; Software, G.A.R., M.H., C.M., K.S. and N.S.; Validation, G.A.R., M.H. and N.S.; Formal analysis, G.A.R. and N.S.; Investigation, G.A.R., M.H. and C.M.; Resources, N.S., F.L. and L.A.S.; Data curation, G.A.R. and N.S.; Writing—original draft preparation, G.A.R., N.S., M.H., C.M. and K.S.; Writing—review and editing, G.A.R., N.S. and L.A.S.; Visualization, G.A.R. and N.S.; Supervision, N.S., F.L. and L.A.S.; Project administration, N.S., F.L. and L.A.S.; Funding acquisition, N.S., K.S., F.L. and L.A.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by FONDECYT-Chile [Grant numbers 1171618 and 1171765].

**Institutional Review Board Statement:** The Scientific Ethics Committee of the Universidad de La Frontera (No 105\_18) approved the experimental protocol.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data are described in the manuscript.

**Acknowledgments:** The authors are grateful to the Bioterio of the University of La Frontera, especially its manager, Patricio Mena Toboada.

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

#### **References**