**10th MATBUD'2023 Scientific-Technical Conference**

Editors

**Katarzyna Mróz Tomasz Tracz Tomasz Zdeb Izabela Hager**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Katarzyna Mróz Cracow University of Technology Poland Izabela Hager Cracow University of

Technology Poland

Tomasz Tracz Cracow University of Technology Poland

Tomasz Zdeb Cracow University of Technology Poland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

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**3. Clinical presentation**

*Pneumothorax*

space

to loud music [24, 25].

2.Communication between the atmosphere (direct or indirect) and the pleural

PSP has an incidence of 7.4 to 18 cases per 100,000 population each year in males and 1.2 to 6 cases per 100,000 populations each year in females [17, 18]. Risk factors for PSP include tall thin people, male sex, and smoking. The recurrence range is 25–50%, and most recurrences occur in the first year [19]. Female gender, tall male, low body weight, and persistent smoking are associated with a high rate of recurrence [20]. In the largest epidemiologic study of PSP from Bobbio et al. with 42,595 patients, they found that the mean age was significantly greater in women than in men (41 ± 19 vs. 37 ± 19 p < 0.0001), rehospitalization was more frequent in women than in men in patients aged <50 years (p < 0.0001). In the 50–64 years age group, surgical procedures and rehospitalizations were more frequent in men than in women (p = 0.002 and p < 0.0001, respectively). The most commonly performed procedures were thoracoscopic resection of blebs (52% of cases) and talc pleurodesis (24% of cases). Surgery was associated with younger age, secondary pneumothorax, and ICU surveillance (p < 0.001) [21]. Moderate smoking (22 cigarettes/day) increases the risk of first episode of PSP up to 22 times. PSP usually occurs at rest, so the lack of physical activity should be avoided in the counseling of these patients [17]. Thoracic endometriosis may lead to catamenial pneumothorax and should be considered in women with PSP temporally related to menstruation [22]. Malnutrition in patients with anorexia nervosa may lead to the development of PSP. Birt-Hogg-Dubé syndrome (which predisposes patients to benign skin tumors and renal cancers) is an autosomal dominant condition defined as a rare cause of PSP [23]. Precipitating factors include atmospheric pressure changes and exposure

An increased frequency of PSP is seen in patients with Marfan syndrome and homocystinuria. Marfan syndrome is a common inherited connective tissue disorder with typical skeletal, ocular, and cardiovascular manifestations. Pulmonary involvement occurs less frequently, with PSP being the most frequently reported. Karpman et al.'s study in 2011 found a prevalence of pneumothorax in patients with Marfan syndrome between and 11%. The increased risk of pneumothorax has been attributed to the presence of apical blebs, bullae, and abnormal connective tissue constituents in the lung parenchyma or increased mechanical stresses in the lung apices due to the tall body habitus. Patients who have Marfanoid features such as long stature, hyperextendable joints, and dislocated lens should be studied by CT scanning to identify blebs and bullae. This may allow risk stratification for pneumothorax in patients with this syndrome and also favors identification of aortic root disease, which leads to aneurysmal dilation, aortic regurgitation, and dissection [26, 27]. A multidisciplinary approach is fundamental in these patients and their family, who must be thoroughly investigated, to confirm the disease and to initiate the treatment, thus decreasing mortality, especially due to cardiovascular causes; also a medical genetics consultation should be provided for genetic

PSP recurrence rates are typically cited as between 16 and 52%, which makes

counseling about future risk difficult and creates uncertainty regarding the optimal management. Thoracic Society guidelines advise that pneumothorax recurrence is an indication for surgery (whether second ipsilateral or first contralateral) [29]. Unfortunately, there is no consensus on which treatment offers the best reduction in risk of recurrence [6]. A systematic review demonstrates a 32% PSP recurrence rate, with almost all the risk in the first year. Recurrence rates

3.Presence of gas-producing organisms in the pleural space

**16**

counseling [28].

Symptoms in primary spontaneous pneumothorax may be minimal or absent. These clinical symptoms depend on proportion and the size of the pneumothorax. Patients may present an abrupt onset of pleuritic chest pain associated with dyspnea and shortness of breath, and some patients may experience shoulder tip pain [29]. Severe symptoms are not common, and when this happens it suggests a tension pneumothorax. Typical examination findings in primary spontaneous pneumothorax include ipsilateral decreased breath sounds on auscultation, percussion hyperresonance, and thoracic hypoexpansion. The presence of observable breathlessness has influenced subsequent management in previous guidelines [29, 31]. Hemodynamic compromise is unusual in PSP. Arterial blood gas measurements are frequently abnormal in patients with pneumothorax. Arterial oxygen tension is lower according to the extent of the pneumothorax but oxygen saturations are adequate, and pulmonary function tests are poor predictors of the presence or size of a pneumothorax [32].

The clinical features in tension pneumothorax are shortness of breath, dyspnea, tachypnea, respiratory distress, hypoxemia, hypotension, tachycardia and ipsilateral decreased air entry, and percussion hyperresonance. This condition requires an urgent thoracic decompression when the diagnosis is suspected, and the clinicians should be prepared to perform urgent thoracic decompression without chest radiographic confirmation in these patients [31].


**1. Introduction**

Small non-coding RNAs (sncRNAs) regulate genes at the post-transcriptional level in animals, plants, and arthropods, including ticks [1–5]. MicroRNAs (miRNAs) are a class of sncRNA, between 18 and 25 nucleotides in length, and they are now known to be important in arthropod immunity and host–pathogen interactions through their involvement in several cellular processes, including development, immunity, and pathogen responses in arthropods [2,6–10]. In animals, miRNAs regulate post-transcriptional gene expression, most often by binding to the 30 -untranslated region (30 -UTR) of target genes. While perfect complementarity of 2–8 nucleotides at the 50 end of the miRNA (seed region) is necessary for miRNA regulation, the remaining sequence can harbor mismatches or bulges [2,11,12]. miRNAs are transcribed as primary miRNA transcripts before processing by Drosha and Pasha proteins into pre-miRNAs. These are then exported to the cytoplasm and processed

**Citation:** Kumar, D.; Downs, L.P.; Embers, M.; Flynt, A.S.; Karim, S. Identification of microRNAs in the Lyme Disease Vector *Ixodes scapularis*. *Int. J. Mol. Sci.* **2022**, *23*, 5565. https://doi.org/10.3390/ijms 23105565

Academic Editors: Denis Sereno and Michail Kotsyfakis

Received: 14 April 2022 Accepted: 10 May 2022 Published: 16 May 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/).

by Dicer into mature miRNAs, which are loaded onto the microRNA-induced silencing complex (miRISC) before targeting complementary mRNA for degradation [13,14].

Several proteomic and transcriptomic studies have identified differentially expressed transcripts and proteins in uninfected and infected ticks [1,15–18], but few have investigated the role of pathogens in the differential modulation of the post-transcriptional tick and host machinery. Although there are over 800 tick species and despite their importance as vectors of human and animal diseases, ticks are underrepresented in available miRNA resources. For example, the miRbase database contains 49 *Ixodes scapularis* miRNAs and 24 *Rhipicephalus microplus* miRNAs, while MirGeneDB 2.1 contains 64 *Ixodes scapularis* miRNAs. However, small RNA sequencing (RNA-seq) and computational approaches are accelerating the discovery of miRNAs from species with incomplete genome sequencing, assembly, and annotation.

*Ixodes scapularis* is a primary vector of human pathogens, including the Lyme disease agent *Borrelia burgdorferi*, which infects vertebrates and ticks through evolved complex mechanisms. There is now a good understanding of tick immune pathways and their interactions with *B. burgdorferi* [19–21], but it is still uncertain how *B. burgdorferi* avoids clearance. *B. burgdorferi* must traverse tick salivary glands during transmission [22]. Since saliva/salivary gland proteins can enhance *B. burgdorferi* transmission into the vertebrate host, characterization of molecular interactions at the tick-bite site and the tick salivary glands is expected to facilitate vaccine development [23,24], since promoting immunity against tick salivary proteins could neutralize tick bites and pathogen transmission. Once *B. burgdorferi* is acquired by ticks from infected hosts, it resides in the tick gut and only migrates to the salivary gland during subsequent blood feeding, which generally lasts for 3–7 days [25–28]. While it is known that other tick-borne pathogens, such as *Anaplasma marginale*, must replicate inside salivary glands for efficient transmission [29], the details of *B. burgdorferi* replication are less well understood. Indeed, borrelial spirochetes invade the tick salivary gland via an unknown mechanism [22] and might be carried to the host dermis via tick saliva. Several salivary gland genes are upregulated in *B. burgdorferi*-infected *Ixodes scapularis* nymphs compared with uninfected ones [30], suggesting a significant role for salivary gland gene regulation in *B. burgdorferi* infection and transmission.

To fill the knowledge gap on miRNA expression in *Ixodes scapularis* salivary glands, here, we performed miRNA profiling of partially fed *B. burgdorferi*-infected and uninfected tick salivary glands to identify miRNAs that might play a role in *B. burgdorferi* survival, colonization, transmission, and host immunomodulation. In doing so, we detected 254 miR-NAs, of which 25 were high confidence miRNAs and 51 low confidence miRNAs. Forty-one of the identified miRNAs were present as *I. scapularis* miRNAs in miRBase (v22.1). Gene ontology and network analysis of target genes of differentially expressed miRNAs have predicted roles in metabolic and cellular development, cellular component biogenesis, and biological regulation processes. Several KEGG pathways, including sphingolipid metabolism; valine, leucine and isoleucine degradation; lipid transport and metabolism; exosome biogenesis and secretion; and phosphate-containing compound metabolic processes, were predicted as targets of differentially expressed miRNAs.

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

#### *2.1. Ethics Statement*

All animal experiments were performed in strict accordance with the recommendations in the NIH Guide for the Care and Use of Laboratory Animals. The Institutional Animal Care and Use Committee of the University of Southern Mississippi approved the protocol for blood feeding of field-collected ticks (protocol # 15101501.1).

#### *2.2. Ticks and Tissue Dissections*

Ticks were purchased from the Oklahoma State University Tick-Rearing Facility. Adult male and female *I. scapularis* were kept according to standard practices [31] and maintained in the laboratory as described in our previously published work [32,33]. Unfed female adult *I. scapularis* were infected with laboratory-grown *B. burgdorferi* strain B31.5A19 using the capillary feeding method at Tulane National Primate Research Center [34]. *Borrelia*-infected and uninfected ticks (*n* = 45 in each group) were placed on each ear of a rabbit host for tick blood feeding. Blood-fed adult female *I. scapularis* were dissected within 60 min of removal from the rabbit. Tick tissues were dissected and washed in M-199 buffer as described previously [35]. Salivary glands and midguts from individual *I. scapularis* were stored in RNAlater (Life Technologies, Carlsbad, CA, USA) at −80 ◦C until use.

#### *2.3. RNA Isolation, cDNA Synthesis, and PCR-Based B. burgdorferi Detection in Tick Tissues*

The TRIzol method was used for RNA extraction from individually dissected midgut tissues, and cDNA was synthesized as described previously [36,37]. *B. burgdorferi* was detected in tick midguts using the *flaB* gene in a PCR assay [36,38]. After testing for *B. burgdorferi* infection in tick midguts, the corresponding salivary gland tissues from the same uninfected/infected ticks (*n* = 10 salivary glands from each group) were pooled in separate tubes and RNA isolated using the TRIzol method [37].

#### *2.4. Small RNA Sequencing (RNA-Seq)*

Small RNA libraries were made using the Illumina TruSeq Kit following the manufacturer's protocol (Illumina, San Diego, CA, USA). Briefly, short adapter oligonucleotides were ligated to each end of the small RNAs in the sample, cDNA made with reverse transcriptase, and PCR used to add sample-specific barcodes and Illumina sequencing adapters. The final concentration of all sequencing libraries was determined using a Qubit fluorometric assay (Thermo Fisher Scientific, Waltham, MA, USA), and the DNA fragment size of each library was assessed using a DNA 1000 high-sensitivity chip on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). After purification by polyacrylamide gel electrophoresis, sample libraries were pooled and sequenced on an Illumina NextSeq 500 (single end 36 base) using the TruSeq SBS kit v3 (Illumina, San Diego, CA, USA) and protocols defined by the manufacturer. Four small RNA libraries of clean and *B. burgdorferi*-infected, partially fed, and pooled salivary glands were sequenced. RNA library preparation and indexing were performed at the University of Mississippi Medical Center (UMMC) sequencing facility. The small RNA sequencing data used in this study was deposited in the National Center for Biotechnology Information (NCBI) under the Sequence Read Archive (SRA) accession number PRJNA837369.

#### *2.5. Data Analysis*

A schematic of the experimental plan and data analysis is shown in Supplementary Figure S1. miRDeep2 v.<IP_ADDRESS> [39,40] was used to process RNA-seq data. To predict novel miRNAs, the reads from all samples were combined. The mapper function in miRDeep2 first trims the adapter sequences from the reads and converts the read files from FASTQ to FASTA format. Reads shorter than 18 bases were discarded and the remaining reads mapped to the *I. scapularis* reference genome using default miRDeep2 mapper function parameters. Reads mapping to the genome were used to predict novel miRNAs. The *Drosophila melanogaster* genome was also used as another reference genome, and mapped reads were aligned to available miRNAs of *D. melanogaster* in miRBase (v22) and quantified. Reads were mapped to the reference genomes of *D. melanogaster* and *I. scapularis* and locations of potential miRNA read accumulations identified. The regions immediately surrounding the mapped reads were examined for miRNA biogenesis features including mature miRNAs, star and precursor reads, and stem-loop folding properties. miRDeep2 models the miRNA biogenesis pathway and uses a probabilistic algorithm to score compatibility of the position and frequency of sequencing reads with the secondary structure of the miRNA precursor.

For miRNA expression, a count table was generated using bedtools multicov, which counts alignments from indexed BAM files that overlap intervals in BED files provided from the miRDeep2 analysis.


**7.1 Experiment: impact of probiotic yeast on blood fecal biomarkers in dairy heifers and growing animals**

Based upon the above discussion, we have conducted two research experiments on dairy animals by using the IPY concept to improve the gut health. In experiment 1, eight dairy heifers (87 ± 5 kg and 6–7 months) were divided into two equal groups (control n = 4 and probiotic n = 4)[80]. Control group animals fed on NRC recommended diet and probiotic group animals fed control diet FPY (Yea-Sac1026 ; 5 g/animal). After 120 days results showed that the FPY significantly affected the serum glucose, and urea levels in dairy heifers [24].

*1 n = 4 per treatment.*

*2 Control feed without yeast.*

*3 Probiotic feed compose of control feed supplemented with 2.5×10 07 cfu/g commercially available probiotic yeast (Yac-Sac1026) at the rate of 5 g per animal/day \* ± Standard error of the mean.*

*4 Before treatment (day 0).*

*5 After treatment (day 120).*

#### **Table 1.**

*Blood serum metabolites (Means ± SEM) in dairy heifers fed on control and foreign probiotic yeast.*

*a , b Values on the same row with different superscripts differ significantly (P < 0.05).1 n = 3 per treatment. 2 Control feed without yeast.*

*3 LAB-Probiotic feed compose of control feed supplemented with 3.13 × 1007 cfu/g laboratory produces probiotic yeast (QAUSC03) at the rate of 8 g/day/animal.*

*4 COM-Probiotic feed compose of control feed supplemented with 2.5×10 07 cfu/g commercially probiotic yeast (Yac-Sac1026) at the rate of 10g/day/animal.*

*5 Before treatment (day 0).*

*6 After treatment (day 120) \* ± SEM = standard error of the mean.*

#### **Table 2.**

*Effect of indigenous Vs foreign probiotic yeast on blood parameters (Means ± SEM) in lactating dairy cattle.*

**47**

**Figure 7.**

**Figure 8.**

*Understanding the Mechanism of Action of Indigenous Target Probiotic Yeast: Linking…*

That means, we had a proof of positive impact of PFY on animal health. We had isolated the yeast from dairy animals fed on yeast. After careful assessment of the probiotic potential, we conducted another experiment to determine the impact of FPY Vs IPY on the health of lactating dairy cattle. Mix breed (*Sahiwal* and *Sahiwal×Jersey,* n = 9*,* with 4-5-liter milk per day) animal were selected for blood and fecal flora study. Animals were divided into three groups. Group 1 fed on 8 g IPY with 3.13 × 1007 CFU/g; group 2 fed on 10 g FPY with 2.5 ×1 007 CFU/g FPY, group 3 fed only control diet with no probiotic (Figure). After 90 days, results showed that the gut associated microbial flora and blood biochemical parameters were improved in the presence IPY as compare to the FPY (**Tables 1** and **2**). We highlighted that improved animal health condition might be due to improved digestive enzymes produced from well propagated IPY. The VFAs have a capability to reduce the triglycerol and cholesterol in liver cells and might be change the animal lipid profile. Results of the ruminal gut microflora showed that the average*,* beneficial *Pediococcus* and *Weisella* species (CFU/g) counts increased while pathogenic E.coli species (CFU/g) counts decreased in IPY fed

*Total Enterococcus count (CFU/g) in the ruminal gut of dairy heifers fed on control feed (control, ♦; no yeast)*

*or commercial probiotic feed (COM-P, ■; control feed plus commercial yeast) (n = 4).*

*Total Lactococcus count (CFU/g) in the ruminal gut of dairy heifers fed on control feed (control, ♦; no yeast) or*

*commercial probiotic feed (COM-P, ■; control feed plus commercial yeast) (n = 4).*

*DOI: http://dx.doi.org/10.5772/intechopen.95822*

*Understanding the Mechanism of Action of Indigenous Target Probiotic Yeast: Linking… DOI: http://dx.doi.org/10.5772/intechopen.95822*

#### **Figure 7.**

*Saccharomyces*

**animal probiotic?**

**heifers and growing animals**

serum glucose, and urea levels in dairy heifers [24].

**Parameters Feeding regime**

*Blood serum metabolites (Means ± SEM) in dairy heifers fed on control and foreign probiotic yeast.*

*(Yac-Sac1026) at the rate of 5 g per animal/day \* ± Standard error of the mean.*

 *Values on the same row with different superscripts differ significantly (P < 0.05).1*

Before treatment5 14.55 ± \*0.57 14.18 ± 0.21 15.54 ± 0.32

Before treatment 75.70 ± 1.24 73.99 ± 2.51 75.08 ± 2.30

*LAB-Probiotic feed compose of control feed supplemented with 3.13 × 1007 cfu/g laboratory produces probiotic yeast*

*COM-Probiotic feed compose of control feed supplemented with 2.5×10 07 cfu/g commercially probiotic yeast*

*Effect of indigenous Vs foreign probiotic yeast on blood parameters (Means ± SEM) in lactating dairy cattle.*

± 0.58 12.31b

± 0.71 77.42a

**Control2 IPY3 FPY4**

± 0.22 13.68ab ± 0.90

± 0.54

± 1.28 78.97a

*n = 3 per treatment.*

**7. Experimental proofs: who is better; indigenous or foreign microbe as**

**7.1 Experiment: impact of probiotic yeast on blood fecal biomarkers in dairy**

Based upon the above discussion, we have conducted two research experiments on dairy animals by using the IPY concept to improve the gut health. In experiment 1, eight dairy heifers (87 ± 5 kg and 6–7 months) were divided into two equal groups (control n = 4 and probiotic n = 4)[80]. Control group animals fed on NRC recommended diet and probiotic group animals fed control diet FPY (Yea-Sac1026

5 g/animal). After 120 days results showed that the FPY significantly affected the

**Items Feeding regime p-Value**

Before treatment4 30.10 ± \*0.711 31.14 ± 0.974 0.012 After treatment5 33.34 ± 0.432 29.23 ± 0.494 0.01

Before treatment 62.67 ± 4.04 60.86 ± 2.80 0.605 After treatment 63.31 ± 2.60 65.47 ± 2.84 0.600

*Probiotic feed compose of control feed supplemented with 2.5×10 07 cfu/g commercially available probiotic yeast*

**Control2 FPY3**

;

**46**

*a , b*

*1*

*2*

*3*

*4*

*5*

**Table 1.**

*2*

*3*

*4*

*5*

*6*

**Table 2.**

**Urea (mg/100 ml)1**

**Urea (mg/100 ml)**1

**Glucose (mg/100 ml)**

*n = 4 per treatment.*

*Control feed without yeast.*

*Before treatment (day 0).*

*After treatment (day 120).*

**Glucose (mg/100 ml)**

*Control feed without yeast.*

*Before treatment (day 0).*

*(QAUSC03) at the rate of 8 g/day/animal.*

*(Yac-Sac1026) at the rate of 10g/day/animal.*

After treatment6 14.18a

After treatment 73.84b

*After treatment (day 120) \* ± SEM = standard error of the mean.*

*Total Lactococcus count (CFU/g) in the ruminal gut of dairy heifers fed on control feed (control, ♦; no yeast) or commercial probiotic feed (COM-P, ■; control feed plus commercial yeast) (n = 4).*

#### **Figure 8.**

*Total Enterococcus count (CFU/g) in the ruminal gut of dairy heifers fed on control feed (control, ♦; no yeast) or commercial probiotic feed (COM-P, ■; control feed plus commercial yeast) (n = 4).*

That means, we had a proof of positive impact of PFY on animal health. We had isolated the yeast from dairy animals fed on yeast. After careful assessment of the probiotic potential, we conducted another experiment to determine the impact of FPY Vs IPY on the health of lactating dairy cattle. Mix breed (*Sahiwal* and *Sahiwal×Jersey,* n = 9*,* with 4-5-liter milk per day) animal were selected for blood and fecal flora study. Animals were divided into three groups. Group 1 fed on 8 g IPY with 3.13 × 1007 CFU/g; group 2 fed on 10 g FPY with 2.5 ×1 007 CFU/g FPY, group 3 fed only control diet with no probiotic (Figure). After 90 days, results showed that the gut associated microbial flora and blood biochemical parameters were improved in the presence IPY as compare to the FPY (**Tables 1** and **2**).

We highlighted that improved animal health condition might be due to improved digestive enzymes produced from well propagated IPY. The VFAs have a capability to reduce the triglycerol and cholesterol in liver cells and might be change the animal lipid profile. Results of the ruminal gut microflora showed that the average*,* beneficial *Pediococcus* and *Weisella* species (CFU/g) counts increased while pathogenic E.coli species (CFU/g) counts decreased in IPY fed

lactating cows than other groups which leads to improve GIT microbial balance (**Figures 7** and **8**).

It can be concluded IPY improves the, gut health, and wellbeing of lactating dairy cattle in cost effective manner. IPY strain may adopt well in the cattle gut than FPY [80].


*2.8. Gelation Properties*

Visual observation of gel formation was performed according to the method described by Yi et al. [41]. Specifically, 3% and 10% (*w*/*v*) dispersion samples (hydrolysates and insect meals) in McIlvaine buffer (pH 4.0, 5.5 and 7.0) were stirred and stored at 4 ◦C for 12 h. The dispersion was heated at 86 ± 1 ◦C in a water bath for 10 min, cooled for 1 min in an ice-water bath and stored at 4 ◦C for 12 h. Gelation was confirmed if the gel was not deformed when the tube was overturned [41] and the gel formation was determined using dynamic oscillatory measurements. Since no gelation properties were visually observed for hydrolysates, only edible insect meals were tested at 30%, 20% and 10% (*w*/*v*) dispersion in McIlvaine buffer (pH 4.0, 5.5. and 7.0) at two ionic strengths (0 and 1 M

NaCl). Measurements were made with an ARES-G2 rheometer (TA Instrument, New Castle, DE, USA) used with DIN geometry (diameter 27.70 mm, gap 5.849 mm) with a cup (diameter 29.9 mm). Two temperature ramps were used, 25 to 85 ◦C and 85 to 5 ◦C, at a rate of 5 ◦C/min. The final temperature of each ramp was kept constant for 10 min. The oscillatory parameters for both temperature ramps were 1.0 Hz for angular frequency and 0.05% for strain. An amplitude step was used with a strain of 0.05% to 500% and angular frequency of 1.0 Hz at 5 ◦C. Data were analyzed using TRIOS software (TA Instrument). It was considered gel if the elastic behavior (G') was greater than viscoelastic behavior (G") [50].


**Part 4**

**Aortic Sclerosis/Aortic Stenosis**

118 Aortic Valve

Watkin RW, Lang S, Lambert PA, Littler WA & Elliott TS (2006). The serological diagnosis of

Wilson W, Taubert KA, Gewitz M, Lockhart PB, Baddour LM, Levison M, et al. (2007).

Research Interdisciplinary Working Group. *Circulation*; 116(15):1736-54.

Prevention of infective endocarditis: guidelines from the American Heart Association: a guideline from the American Heart Association Rheumatic Fever, Endocarditis, and Kawasaki Disease Committee, Council on Cardiovascular Disease in the Young, and the Council on Clinical Cardiology, Council on Cardiovascular Surgery and Anesthesia, and the Quality of Care and Outcomes

staphylococcal infective endocarditis. *J Infect*; 53:301–307.

**6**

*USA*

**The Progression of**

**Aortic Sclerosis to Aortic Stenosis**

Uzma Jalal Serageldin Raslan and Farouk Mookadam *Department of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ,*

Aortic stenosis is the most frequent valvular heart disease in the western world and its incidence continues to rise. Until recently, aortic valve sclerosis (AVS) was considered to be a normal degenerative process associated with aging. For this reason, the common and well recognized soft, basal ejection murmur of aortic sclerosis was generally regarded by physicians to be of little or no clinical significance. In the last decade, AVS has been the focus of both clinical and animal research. AVS has emerged as a biomarker for cardiovascular risk, and ultimately leads to aortic stenosis in 16% of adults (Stewart et al., 1997; Cosmi et al., 2002). Calcific aortic valve disease ranges from aortic sclerosis, defined as focal, irregular thickening of aortic valve leaflets with no hemodynamically significant derangement (i.e., peak velocity of ≤ 2 m/s and no significant aortic regurgitation), to severe calcification (with impaired leaflet motion and an aortic jet velocity of ≥ 2.5 m/s) referred to as aortic stenosis. The paradigm of aortic stenosis has shifted from being considered a degenerative aging process; it is now recognized as a dynamic inflammatory process with features similar to atherosclerotic plaque. These features include endothelial disruption, focal deposition of low density lipoprotein (LDL) cholesterol and lipoprotein A, accumulation of macrophages and

Aortic valve sclerosis is present in approximately 25% of people 65 to 74 years old and in 50 % of people over 84 years (Lindroos et al., 1993; Stewart et al., 1997; Otto et al., 1999), while aortic stenosis affects 2% of the population over 65 years old, 3 % over 75 years old, and 4% over 85 years old (Stewart, 1997). The severity of aortic sclerosis on a scale of 0-3 has been quantified by echocardiography for echogenicity, thickening, or calcification of the

• 2- Moderate (Minor involvement of two leaflets or extensive involvement of one leaflet) • 3- Severe (Extensive involvement of two leaflets or involvement of all three leaflets) In adults, valvular aortic stenosis is due to degenerative calcific changes of a trileaflet valve, rheumatic disease, or secondary calcification of a congenitally bicuspid valve (Roberts, 1970; Selzer, 1987). In developed countries, the most common cause of adult acquired aortic

T lymphocytes, and calcification (Freeman et al., 2004).

**2. Epidemiology and etiology**

• 0- Normal (No involvement)

valve leaflet as follows (Chandra et al., 2004):

• 1- Mild (Minor involvement of one leaflet)

**1. Introduction**


3. Integrated batch production and maintenance scheduling for single item processed on a deteriorating machine with a due date

The modeling starts with an inventory holding cost for in-process and completed batches, modeling of system constraints of the problem, model of the problem, algorithm, and an example to show how the algorithm works to solve the model.

#### 3.1 Inventory holding costs for in-process batch and completed batch

Holding cost concept is developed from [3]. Let q parts of an item be scheduled by a minimization of total actual flow time criterion. The q parts are divided into N batches L[i] (i = 1, 2, ..., N) where the sizes of each batch are Q[i](i = 1, 2, ..., N). If all parts in a batch have been processed completely, then the batch is called as completed batch. If a batch is still containing any part not yet or being processed, it is called as in-process batch.

In an assumption raw materials arrive just in the time when they are required, i.e., in the beginning of a batch processing, the holding cost is only for in-processed batch and completed batch. The formulation of holding cost is conducted for inprocess batch firstly and then for completed batch. The position of batch L[i] in a single machine manufacturing system by a backward approach during a planning horizon is shown in Figure 3.

An assumption for in-process batch is that parts in a batch shall wait in the batch until all parts in the batch have already been processed. Therefore, in the interval

Figure 3. Batch position in a single machine manufacturing system.

Integrated Batch Production and Maintenance Scheduling to Minimize Total Production… DOI: http://dx.doi.org/10.5772/intechopen.85004

(0,t) in the in-process batch L[i], there are Q[i] work-in-process parts (parts not yet or being processed). In interval (t, 2 t) there are (Q[i]-1) work-in-process parts and l finished part, until in interval ((Q[i]–1)t,Q[i]t), there are I work-in-process parts and (Q[i]–1) finished parts.

The amount of holding cost for the finished part, designated f1, is as follows:

$$f\_1 = c\_1t + c\_12t + c\_13t + \dots + c\_1t\left(Q\_{\left[i\right]} - \mathfrak{Z}\right) + c\_1t\left(Q\_{\left[i\right]} - \mathfrak{Z}\right) + c\_1t\left(Q\_{\left[i\right]} - \mathfrak{Z}\right).$$

The amount of holding cost for the in-process part, designated f1, is as follows:

$$f\_{\mathcal{I}} = c\_2 t Q\_{[\bar{t}]} + c\_2 t \left( Q\_{[\bar{t}]} \mathbf{-1} \right) + c\_2 t \left( Q\_{[\bar{t}]} \mathbf{-2} \right) + \dots + c\_2 \mathbf{3} t + c\_2 \mathbf{2} t + c\_2 t.$$

By summation f1 and f1, f2 and f2 in a reverse order, a simpler result is found, i.e.:

$$\mathbf{f}\_1 = \frac{c\_1}{2} \left[ t Q\_{[i]} \left( Q\_{[i]} - \mathbf{1} \right) \mathbf{d} \mathbf{n} \mathbf{f}\_2 = \frac{c\_2}{2} \left[ t \left( Q\_{[i]} + \mathbf{1} \right) \left( Q\_{[i]} \right) \right] \right]$$

Then, the holding cost of i th in-process batch is the addition of f1 and f2, that is:

$$f\_1 + f\_2 = \frac{c\_1}{2} \left[ t Q\_{[i]} \left( Q\_{[i]} - \mathbf{1} \right) + \frac{c\_2}{2} \left[ t \left( Q\_{[i]} + \mathbf{1} \right) \left( Q\_{[i]} \right) \right] \text{ or }$$

$$f\_1 + f\_2 = \frac{c\_1 + c\_2}{2} t Q\_{[i]}^2 + \frac{c\_2 - c\_1}{2} t Q\_{[i]} \tag{1}$$

Based on Eq. (1), the total holding cost of the in-process batch of all batches may be written as follows:

$$\begin{aligned} \frac{c\_1 + c\_2}{2} t Q\_{[N]}^2 + \frac{c\_2 - c\_1}{2} t Q\_{[N]} + \frac{c\_1 + c\_2}{2} t Q\_{[N-1]}^2 + \frac{c\_2 - c\_1}{2} t Q\_{[N-1]} + \dots \\ \frac{c\_1 + c\_2}{2} t Q\_{[2]}^2 + \frac{c\_2 - c\_1}{2} t Q\_{[2]} + \frac{c\_1 + c\_2}{2} t Q\_{[1]}^2 + \frac{c\_2 - c\_1}{2} t Q\_{[1]} = \frac{c\_1 + c\_2}{2} t \sum\_{i=1}^N Q\_{[i]}^2 + \frac{c\_2 - c\_1}{2} t \sum\_{i=1}^N Q\_{[i]} \end{aligned} \tag{2}$$

The holding cost of completed batch may be formulated as follows:

$$\begin{aligned} &\mathbf{c}\_{1}\left\{\left(t\mathbf{Q}\_{[N-1]}+s\right)+\ldots+\left(t\mathbf{Q}\_{[2]}+s\right)+\left(t\mathbf{Q}\_{[1]}+s\right)\right\}\mathbf{Q}\_{[N]}\\ &+\mathbf{c}\_{1}\left\{\left(t\mathbf{Q}\_{[N-2]}+s\right)+\ldots+\left(t\mathbf{Q}\_{[2]}+s\right)+\left(t\mathbf{Q}\_{[1]}+s\right)\right\}\mathbf{Q}\_{[N-1]}+\ldots\\ &\mathbf{c}\_{1}+\mathbf{c}\_{1}\left\{\left(t\mathbf{Q}\_{[2]}+s\right)+\left(t\mathbf{Q}\_{[1]}+s\right)\right\}\mathbf{Q}\_{[3]}+\mathbf{c}\_{1}\left\{\left(t\mathbf{Q}\_{[1]}+s\right)\right\}\mathbf{Q}\_{[2]}=\mathbf{c}\_{1}\sum\_{i=1}^{N-1}\left\{\sum\_{j=1}^{i}\left(t\mathbf{Q}\_{[j]}+s\right)\right\}\mathbf{Q}\_{[i+1]}.\end{aligned} \tag{3}$$

Next, total holding cost (TolC) is computed by adding up the holding cost of inprocess batch in Eq. (2) and completed batch in Eq. (3) to yield Eq. (4).

$$ToI C = c\_1 \sum\_{i=1}^{N-1} \left\{ \sum\_{j=1}^{i} \left( t Q\_{[j]} + s \right) \right\} Q\_{[i+1]} + \frac{c\_1 + c\_2}{2} t \sum\_{i=1}^{N} Q\_{[i]}^2 + \frac{c\_2 - c\_1}{2} t \sum\_{i=1}^{N} Q\_{[i]}.\tag{4}$$

The first term of Eq. (4) is total holding cost in completed batch, and the second and third terms are the total holding cost, while the part is being processed in batch (in-process batch) in one production run.

The input-output diagram of the integrated batch production and maintenance

Figure 2 shows the input parameters model: the number of parts scheduled, due date, Weibull distribution function f(t), unit processing time, PM interval length, inventory holding cost, rework cost, setup time between batches, and the probability of nonconforming part on the machine in the status of in-control and out of control. The output of the model are the size of batches and the schedule, PM

The model will address trade-off issues on production costs and maintenance costs, where production costs will consist of inventory holding costs (in-process and

nonconforming parts, while the maintenance cost consists of PM cost and CM cost. The model will answer how the batch production and maintenance scheduled

3. Integrated batch production and maintenance scheduling for single

batches, modeling of system constraints of the problem, model of the problem, algorithm, and an example to show how the algorithm works to solve the model.

The modeling starts with an inventory holding cost for in-process and completed

Holding cost concept is developed from [3]. Let q parts of an item be scheduled by a minimization of total actual flow time criterion. The q parts are divided into N batches L[i] (i = 1, 2, ..., N) where the sizes of each batch are Q[i](i = 1, 2, ..., N). If all parts in a batch have been processed completely, then the batch is called as completed batch. If a batch is still containing any part not yet or being processed, it is

In an assumption raw materials arrive just in the time when they are required, i.e., in the beginning of a batch processing, the holding cost is only for in-processed batch and completed batch. The formulation of holding cost is conducted for inprocess batch firstly and then for completed batch. The position of batch L[i] in a single machine manufacturing system by a backward approach during a planning

An assumption for in-process batch is that parts in a batch shall wait in the batch until all parts in the batch have already been processed. Therefore, in the interval

scheduling to minimize total cost is shown in Figure 2.

Industrial Engineering

schedule, number of CM, and number of nonconforming parts.

minimize the total cost. Drawing influence diagram follows [7].

finished part of inventory holding costs), setup cost, and rework cost for

item processed on a deteriorating machine with a due date

3.1 Inventory holding costs for in-process batch and completed batch

called as in-process batch.

horizon is shown in Figure 3.

Batch position in a single machine manufacturing system.

Figure 3.

32

ΛðÞ¼ t

Integrated Batch Production and Maintenance Scheduling to Minimize Total Production…

Let a system (machine) with a Weibull failure time distribution have a shape parameter of β = 1.69 and a scale parameter α = 2,857.14, then, based on ROCOF cumulative function, the first, the second, and so on estimated failure times that

with scale parameter α and shape parameter β:

β ¼ 1 then t = 2857.14. If ΛðÞ¼ t t

β ¼ 3 then t = 5473.33. If ΛðÞ¼ t t

could be written as follows can be found:

DOI: http://dx.doi.org/10.5772/intechopen.85004

3.3 Estimation of nonconforming parts

for k=2 may be written as follows:

nonconforming parts may be written as

A condition for two production runs and two PMs.

If ΛðÞ¼ t t

α

If ΛðÞ¼ t t

Figure 5.

35

α

t α β

α

α

From the calculation above, the time interval between machine failure times can be estimated, where the time between failures of a machine is diminishing over time. It indicates that the machine has increasing failure rate distribution.

This research developed a policy in that PM is carried out before an expected first failure time based on cumulative ROCOF function. An example of a condition for a case of two production runs and two PMs is shown in Figure 5. In the second production run, there is no nonconforming part, because the out-of-control state takes place in the first production run, so that the number of nonconforming parts

M2 ¼ p2 xnumber of parts processed in interval B½ � N11 � s þ α;C½ � 11 (10)

In the same way for g production runs and g PMs, the number of nonconforming

Mg ¼ p2 xnumber of parts processed in interval B½ � N11 � s þ α;C½ � 11 (11)

E Mð Þ¼ Mk, k ¼ 1, 2, …, g (12)

E Wð Þ¼ cwE Mð Þ (13)

parts will always be of the same form, except if applied to k = 1, 2, …, g, so that

Under an assumption that the probability that the nonconforming part processed under in-control state is p1 = 0, then the expected number of

so that the expected network cost may be computed by

β ¼ 2 then t = 4305.82.

β ¼ 4 then t = 6489.03.

(9)

Figure 4.

Batch position in a single machine manufacturing system.

Eq. (4) and Figure 3 are to be developed into a formulation of holding cost for g production run and PM interval inserted sequentially as shown in Figure 4.

By considering any changes taking place in each production run and total PM for g production runs and g PM intervals, total holding cost will become Eq. (5).

$$c\_1 \sum\_{i=1}^{N-1} \left\{ \sum\_{j=1}^i \left( t Q\_{\left[i\_1\right]} + s \right) \right\} Q\_{\left[ (i+1)\_1 1 \right]} + \frac{c\_1 + c\_2}{2} t \sum\_{i\_1=1}^{N\_1} Q\_{\left[ i\_1 1 \right]}^2 + \frac{c\_2 - c\_1}{2} t \sum\_{i\_1=1}^{N\_1} Q\_{\left[ i\_1 1 \right]} +$$

$$\sum\_{k=2}^g \left[ c\_1 \sum\_{i\_k=1}^{N\_k-1} \left\{ \sum\_{j=1}^i \left( t Q\_{\left[ i\_k k \right]} + s \right) \right\} Q\_{\left[ (i+1)\_k k \right]} + \frac{c\_1 + c\_2}{2} t \sum\_{i\_k=1}^{N\_k} Q\_{\left[ i\_k k \right]}^2 +$$

$$\frac{c\_2 - c\_1}{2} t \sum\_{i\_k=1}^{N\_k} Q\_{\left[ i\_k k \right]} + c\_1 \sum\_{i\_k=1}^{N\_k} Q\_{\left[ i\_k k \right]} \left( (k-1) t\_{PM} + \sum\_{j\_k=1}^{N\_{\left[ k-1 \right]}} \left( t Q\_{\left[ j\_k k \right]} + s \right) \right) \right]. \tag{5}$$

#### 3.2 ROCOF function

Rate of occurrence of failures (ROCOF) is a concept that is useful in the modeling of failures over time and the effect of PM (and CM) actions [8]. The ROCOF characterizes the probability that a failure occurs in the interval [t,t + δt]. The ROCOF is given by an intensity function

$$\lambda(t) = \lim\_{\delta t \to 0} \frac{P\{N(t + \delta t) - N(t) \ge 1\}}{\delta t} \tag{6}$$

where N(t) is the number of failures in the interval [0,t). Since the probability of two or more failures in the interval [t,t + δt] is zero as δt–›0, we have the intensity function equal to the derivative of the conditional expected number of failures, so that

$$
\lambda(t) = \frac{d}{dt} E\{N(t)\}. \tag{7}
$$

When the failures are minimally repaired and the time to repair is negligible, then ROCOF function λðÞ¼ t r tð Þ, the failure rate function. The cumulative ROCOF function is given by

$$\Lambda(t) = \int\_0^t \lambda(t)dt\tag{8}$$

A ROCOF function that has been used extensively is the Weibull ROCOF. The cumulative ROCOF (or the expected total number of failures) is given by the function

Integrated Batch Production and Maintenance Scheduling to Minimize Total Production… DOI: http://dx.doi.org/10.5772/intechopen.85004

$$A(t) = \left(\frac{t}{a}\right)^{\beta} \tag{9}$$

with scale parameter α and shape parameter β:

Let a system (machine) with a Weibull failure time distribution have a shape parameter of β = 1.69 and a scale parameter α = 2,857.14, then, based on ROCOF cumulative function, the first, the second, and so on estimated failure times that could be written as follows can be found:

If ΛðÞ¼ t t α β ¼ 1 then t = 2857.14. If ΛðÞ¼ t t α β ¼ 2 then t = 4305.82. If ΛðÞ¼ t t α β ¼ 3 then t = 5473.33. If ΛðÞ¼ t t α β ¼ 4 then t = 6489.03.

From the calculation above, the time interval between machine failure times can be estimated, where the time between failures of a machine is diminishing over time. It indicates that the machine has increasing failure rate distribution.

#### 3.3 Estimation of nonconforming parts

Eq. (4) and Figure 3 are to be developed into a formulation of holding cost for g

By considering any changes taking place in each production run and total PM for

c1 þ c2 2 t ∑ N1 i1¼1 Q2 ½ � i11 þ

> c1 þ c2 2 t ∑ Nk ik¼1 Q2 ½ � ikk þ

> > Nð Þ k�1 j k¼1

� � !#

tQ j k ½ �k þ s

δt (6)

dt ENt f g ð Þ : (7)

λð Þt dt (8)

Q ð Þ iþ1 k ½ �k þ

Q½ � ikk ð Þ k � 1 tPM þ ∑

Rate of occurrence of failures (ROCOF) is a concept that is useful in the modeling

where N(t) is the number of failures in the interval [0,t). Since the probability of two or more failures in the interval [t,t + δt] is zero as δt–›0, we have the intensity function equal to the derivative of the conditional expected number of failures, so that

d

When the failures are minimally repaired and the time to repair is negligible, then ROCOF function λðÞ¼ t r tð Þ, the failure rate function. The cumulative ROCOF

ðt

0

A ROCOF function that has been used extensively is the Weibull ROCOF. The

PNt f g ð Þ� þ δt N tð Þ≥1

of failures over time and the effect of PM (and CM) actions [8]. The ROCOF characterizes the probability that a failure occurs in the interval [t,t + δt]. The

λðÞ¼ t

ΛðÞ¼ t

cumulative ROCOF (or the expected total number of failures) is given by the

c2 � c1 2 t ∑ N1 i1¼1

Q½ � i11 þ

: (5)

production run and PM interval inserted sequentially as shown in Figure 4.

g production runs and g PM intervals, total holding cost will become Eq. (5).

Q ð Þ iþ1 1 ½ �1 þ

c1 ∑ N�1 i¼1

Figure 4.

Industrial Engineering

∑ g

"

k¼2

c2 � c1 2

3.2 ROCOF function

function is given by

function

34

∑ i j¼1

c1 ∑ Nk�1 ik¼1

> t ∑ Nk ik¼1

tQ j 1 ½ �1 þ s � � ( )

Batch position in a single machine manufacturing system.

∑ i j¼1

ROCOF is given by an intensity function

tQ j k ½ �k þ s � � ( )

> Nk ik¼1

λðÞ¼ t lim δt!0

Q½ � ikk þ c1 ∑

This research developed a policy in that PM is carried out before an expected first failure time based on cumulative ROCOF function. An example of a condition for a case of two production runs and two PMs is shown in Figure 5. In the second production run, there is no nonconforming part, because the out-of-control state takes place in the first production run, so that the number of nonconforming parts for k=2 may be written as follows:

$$M\_2 = p\_2 \text{number of parts processed in interval} \left[ B\_{[N\_11]} - \mathfrak{s} + a, C\_{[11]} \right] \tag{10}$$

In the same way for g production runs and g PMs, the number of nonconforming parts will always be of the same form, except if applied to k = 1, 2, …, g, so that

$$M\_{\mathfrak{g}} = p\_2 \text{ number of parts processed in interval} \left[ B\_{[N\_1 1]} - \mathfrak{s} + a, C\_{[11]} \right] \tag{11}$$

Under an assumption that the probability that the nonconforming part processed under in-control state is p1 = 0, then the expected number of nonconforming parts may be written as

$$E(M) = M\_k, k = 1, 2, \dots, \text{g} \tag{12}$$

so that the expected network cost may be computed by

$$E(\mathcal{W}) = \mathbf{c}\_{\mathbf{w}} E(\mathcal{M}) \tag{13}$$

Figure 5. A condition for two production runs and two PMs.


**2. Multifield Modeling of Early-Age Concrete**

The plurality and unrecognizability of phenomena occurring during concrete curing, leads to extensive experimental research and the development of a model, in which concrete is defined as a material with non-stationary properties. Mathematical models, available in the literature, takes into account four aspects of concrete hardening. It can be said that in the early age concrete there is a strong coupling of thermal, chemical, moisture and mechanical phenomena. In this study complex models presented i.e., in Azenha's [6,12] and Di Luzio-Cusatis's [7] papers were considered. However, having in mind the application of model to on-line monitoring of the structure under the construction process and limited number of measurement parameters, the thermo-chemical models described below, in Sections 2.1 and 2.2, have been chosen.