**1. Introduction**

Women have risk developing breast cancer in non-breastfeeding women or infertile conditions. The risk of breast cancer was higher among women who currently or recently used contemporary hormonal contraceptives than among who had never used hormonal contraceptives [1].

At the time when women began to feel the complaint with her breasts, few women hasten to check themselves completely. The condition of the emergence of symptoms is the right time for handling so that the rate of recovery is high. Some women are open to complain and hasten for therapy. Stigma is present in women when breast cancer is present in them. This is a management bottleneck in the days before being diagnosed with breast cancer. Attendance of others such as family and healthcare workers who pay attention, fast management, and plenary caring are needed during this period.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Most women diagnosed with breast cancer are already in a late condition where the breasts have given an unpleasant condition, foul smell, and excessive fluid. The existence of women in the family as wives, mothers, and caregivers of families becomes disrupted, and some women undergo self-care.

cancer than women with little or no dense breast tissue. Breast density can only be seen on a mammogram, but dense breasts also make a mammogram harder to read. On a mammogram, fatty tissue looks dark, while dense tissue looks white, like tumors, so it can hide

Caring of Breast Cancer Patient

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**e.** Reproductive history. Estrogen is the main hormone associated with breast cancer. Estrogen affects the growth of breast cells. Experts believe that it plays an important role in the growth of breast cancer cells as well. The type of exposure and how long cells are exposed to estrogen affect the chances that breast cancer will develop. Menopause at a younger age decreases the length of time the breast tissue is exposed to estrogen and other hormones. Women who experience early menarche, late menopause, and late pregnancy at risk of breast cancer. Early menopause is linked with a lower risk of breast cancer. Pregnancy interrupts the exposure of breast cells to circulating estrogen. It also lowers the total number of menstrual cycles a woman has in her lifetime. Women who have their first full-term pregnancy after the age of 30 have a slightly higher risk of breast cancer than

**f.** Exposure to ionizing radiation. Woman who have received radiation therapy to the chest,

**g.** Hormone replacement therapy. Woman who is taking hormone replacement therapy for

**h.** Oral contraception. Oral contraceptives that contain both estrogen and progesterone can slightly increase the risk for breast cancer, especially among women who have used oral contraceptives for 10 or more years. The higher risk disappears after the woman stops taking oral contraceptives. However, current and recent (less than 10 years since last use) users have a slightly greater risk than women who have never used oral

**i.** Atypical hyperplasia. Atypical hyperplasia is a noncancerous (benign) condition where there are a greater number of abnormal (atypical) cells in the breast tissue. Atypical hyper-

**j.** Alcohol. Drinking alcohol increases a woman's risk for breast cancer. Even low levels of alcohol consumption (just over 1 drink per day) can increase a woman's risk. The risk increases with the amount of alcohol consumed. One possible reason for the link between alcohol and breast cancer is that alcohol is thought to cause higher levels of estrogen. Alcohol may also lower levels of some essential nutrients that protect against cell damage,

**k.** Being obese. Obesity increases the risk for breast cancer in postmenopausal women. Studies show that women who have never taken hormone replacement therapy and who have a body mass index (BMI) of 31.1 or higher have a 2.5 times greater risk of developing breast cancer than those with a BMI of 22.6 or lower. Ovarian hormones, estrogens in particular, play an important role in breast cancer. Many of the risk factors for breast cancer are believed to result from the overall dose of estrogen the breast tissue receives over time. The ovaries make most of the body's estrogen, but after menopause fat tissue produces

women who have at least one full-term pregnancy at an earlier age.

neck, and armpit area have a higher risk of developing breast cancer.

plasia increases a woman's risk of developing breast cancer.

such as folate (a type of vitamin B), vitamin A, and vitamin C.

a long time increases the risk of breast cancer.

a tumor.

contraceptives.

Caring in women with breast cancer requires a variety of understanding of the development, roles and functions of women with breast cancer, and psycho-spiritual and psychosocial needs. The context of understanding conditions under conditions of diagnosis, therapy, and follow-up after therapy is a caring breast cancer approach.


**1. Introduction**

Japan has a large aging population; the 2019 White Paper on Aging Society reported that 28.1% of the population is over the age of 65 years [1]. In addition, soaring medical costs have become a social problem. One of the causes is that there is a discrepancy between the average life expectancy and the period of healthy life expectancy during which a person can live an independent life [1]. Frailty, the decline in motor and cognitive function in individuals with advanced age, is one of the reasons healthy life expectancy remains lower than average life expectancy. The main cause for frailty is sarcopenia, a progressive age-related weakness of the muscles. The definition of sarcopenia was based on the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP) in 2010 [2]. In Japan, the criteria of the Asian Working Group for Sarcopenia (AWGS) are generally recommended for diagnosis, and in 2017 the Japanese Association on Sarcopenia and Frailty created the sarcopenia clinical practice guidelines [3–5].

The main cause of sarcopenia is aging [2–4,6]. Secondary factors include physical inactivity, illness, and nutrition, and it is likely that multiple factors overlap to cause sarcopenia. Specifically, with regard to nutrition, reports indicate that deficiency of protein and vitamin D intake increases the risk of sarcopenia [7–21]. The leading problem regarding nutrients in Japanese people is high-salt intake. The World Health Organization (WHO) has recommended an intake of no more than 5 g of salt/day [22]. In Japan, there is an average salt intake of 11.0 g/day for men and 9.3 g/day for women. Recently in Japan, salt intake has been declining slightly each year [23].

Many studies report that excessive salt intake is one of the causes of various diseases, such as hypertension [24,25], where sodium in the skeletal muscle accumulates more in older people than in younger people, and patients with refractory hypertension have increased tissue sodium(Na (+)) content when compared with normotensive controls [26]. In addition, the sodium-potassium-chloride symporter 1 (NKCC1) is highly expressed in mammalian skeletal muscle. The physiologic function of NKCC1 in myogenesis is unclear. However, NKCC1 protein levels increased skeletal myoblast differentiation, and NKCC1 inhibitors markedly suppressed skeletal myoblast differentiation [27]. It has also been reported that excess sodium leads to a downregulation of expression of NKCC [28]. Therefore, we hypothesized that the risk of sarcopenia, which is associated with decreased muscle mass and strength, is related to the amount of salt intake, because Na (+) is stored in tissues and NKCC1 is involved in muscle hypertrophy and suppression.

Incidentally, renalase, a recently discovered enzyme released by the kidneys, may break down blood-derived catecholamine and regulate blood pressure. A report of loss of renalase function can result in increased blood pressure (hypertension), increased heart rate (tachycardia), increased vascular resistance (vasoconstriction), and increased catecholamine response [29]. Human and animal studies have suggested that high levels of dietary salt may lower blood and renal renalase levels. Due to blood pressure increasing with salt intake, the renalase levels that breaks down catecholamines, may also be reduced [30–32]. For the relationship between renalase and skeletal muscle, exercise load increased blood levels of renalase, independent of renal secretion, and increased gene expression of renalase in skeletal muscles [33–35]. In a study based on disuse atrophy, there was a study that showed muscle proteolysis decreased and muscle mass increased in renalase knockout mice [36]. On the other hand, skeletal muscle has β2 adrenergic receptors involved in catecholamines, and β2-adrenergic receptor stimulation increases muscle mass by promoting muscle protein synthesis and/or attenuating protein degradation. However, excessive stimulation of -adrenergic receptors negates their beneficial effects [37]. By these reports, we considered that renalase, which is related to catecholamines that increase with salt intake, may also be involved in skeletal muscle atrophy. Therefore, we speculated that the decreased level of renalase might be a marker for sarcopenia.

We aimed to clarify the relationship between salt intake, blood renalase concentrations, and sarcopenia risk for Japanese adults.

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

#### *2.1. Study Design*

Healthy adult volunteers (*n* = 122, age (standard deviation [SD]): 56.3 (12.3) years) were recruited from the community using local advertisements, and participants were selected from Tsukuba and nearby urban areas (Ibaraki, Japan). Participants with an estimated glomerular filtration rate of 90 mL/min/1.73 m2 or less were excluded because renal function is involved in sodium uptake and renalase levels [24,30–32,38–40]. In addition, eight participants who showed more than four times the standard deviation of the mean blood test of all 122 participants were excluded, because there was a possibility of other diseases. The items are renalase, Interleukin-6 (IL-6), triglyceride (TG), insulin, glucose (Glu), glycosylated hemoglobin (HbA1c), aspartate transaminase (AST), and alanine aminotransferase (ALT). Therefore, 114 participants were analyzed.

We calculated estimated salt intake using spot urine testing and divided the participants into two groups with an average estimated salt intake of 9.37 g/day [1]. The participants with lower salt intake than average was designated as "low-salt," and the participants with a higher intake were designated as "high-salt." Furthermore, since the age range of the selection criteria for this study is 20 years and over, volunteers from a very wide range of age groups participated, we also analyzed after stratifying by age. Participants were divided according to the overall average age (56.3 (12.3)) designated "younger" and "older." That is, "younger" is younger than the average age of participants and "older" is older than the average age. Overall, the participants were divided into four groups ("younger low-salt," "younger high-salt," "older low-salt," "older high-salt") and blood test results, anthropometric measurements, and assessment of physical performance were compared.

This study was approved by the Ethical Review Board of University of Tsukuba, and all participants provided written, informed consent as per the requirements of the Declaration of Helsinki (IRB approval No. H30-161).

## *2.2. Data Collection*

#### 2.2.1. Blood Test and Urinalysis

Participants were asked to avoid strenuous exercise, live a normal life the day before and were banned from eating and drinking except water after 20:00 p.m., before testing to control for exercise and nutrition. They were asked to arrive at the laboratory between 8:00 a.m. and 11:00 a.m.

First, participants provided urine samples. We conducted urinalysis to measure the concentration of sodium and creatinine at Tsukuba i-Laboratory Limited Liability Partnership (Ibaraki, Japan), using the ion-selective electrode method to measure urinary sodium and the enzyme method to measure urinary creatinine. We calculated the 24-h salt intake using the following formula [41,42]:

Estimated 24 h Creatinine (mg/day) = −2.04 × age + 14.89 × weight (kg) + 16.14 × height (cm) − 2244.45, (1)

$$\text{Estimated 24 h varying sodium (mEq/day)} = 21.98 \times \text{uninary sodium/10/(1)}^{0.392},\tag{2}$$

$$\text{Estimated 24-h salt intake (g/day)} = \text{(2)} / 17. \tag{3}$$

Next, blood samples were collected from the antecubital vein. In this study, we measured some parameters in the blood that could pose a risk for sarcopenia. IL-6 (pg/mL) for inflammation, urea nitrogen (UN) (mg/dL) and cystatin C (CysC) (mg/L) for renal function, TG (mg/dL) for fat accumulation that causes obesity, albumin (Alb) (g/dL) for nutritional status, Glu (mg/dL), insulin (μU/mL) and HbA1c (%), for glucose metabolism, and AST (U/L) and ALT (U/L) for liver function. In addition, since renalase (mg/L) has been reported to be involved in blood pressure control function and skeletal muscle atrophy by metabolizing catecholamines, it was measured as a marker candidate for sarcopenia. Whole blood was used to measure HbA1c, which was measured using the enzyme method. The remaining blood was brought to room temperature of 20 to 25 ◦C and then centrifuged at

3000 rpm for 10 min to obtain a serum. It was then stored at −80 ◦C until measurement. Serum was used for measurement of concentrations of the following parameters: IL-6, UN, CysC, TG, Alb, insulin, Glu, AST, ALT, and renalase. The measurement was carried out at Tsukuba i-Laboratory Limited Liability Partnership (Ibaraki, Japan) by chemiluminescent enzyme immunoassay (insulin), ultraviolet (UN, Glu), latex coagulating nephelometry (CysC), the enzyme method (TG), the bromocresol purple method (Alb), and the Japan Society of Clinical Chemistry (JSCC) Standardization Corresponding Method (AST, ALT). IL-6 was measured by Chemiluminescent Enzyme Immunoassay at Jichi Medical University Hospital. Renalase was measured by enzyme-linked immunosorbent assay (ELISA) using the FAD-Dependent Amine Oxidase ELISA Kit (Cloud-Clone Corp, Houston, TX, USA). In addition, the estimated glomerular filtration value (eGFR) was calculated using the renal function presumption formula and serum CysC concentrations to evaluate kidney function. Moreover, eGFR using the serum CysC concentration was calculated using the equation for Japanese people as shown in the chronic kidney disease (CKD) clinical practice guidelines [40] as follows: eGFR (mL/min/1.73 m2) = (104 × [Concentration of serum CysC (mg/dL)]−1.019 × 0.996(Age) − 8".

#### 2.2.2. Anthropometric Measurements

Heights were measured using a stadiometer. Weight and body compositions were then measured using bioelectrical impedance analysis (Inbody 770; Inbody, Tokyo, Japan). The body composition measurement included skeletal muscle mass index (SMI; kg/m2), body fat percentage (BFP; %), and mass of muscle of upper limbs (ULMM; kg) and lower limbs (LLMM; kg) [43,44]. Then we calculated the body mass index (BMI; kg/m2)

#### 2.2.3. Assessment of Physical Performance

Handgrip strength (HGS) (kg), knee extensor muscle strength (KES) (kg), single-leg stance time (SLT) (s), maximum gait speed (MGS) (m/s), long seat type body anteflexion (FleX) (cm), and the chair rise test (30CS) (number of repetitions) were used as indices of physical performance [3–5,45–51]. HGS was determined using a handheld dynamometer (T.K.K.5401; Takei Kiki Kogyo, Niigata, Japan). The participants being tested alternated between their left and right hand and were measured twice. The mean of the maximal values for each hand was used for analysis [49,50]. Then, the HGS was divided by body weight (BW). KES was determined using a handheld dynamometer (μTas F-1; ANIMA, Tokyo, Japan). Both lower limbs were measured twice, and the average of their highest score was registered as the result. Then, the KES was divided by body weight [48,50]. SLT was measured as an indicator of balance performance. Participants balanced as long as possible on one leg with their eyes open. The participant could choose which foot on which to balance. The maximum value was 60 s. If the maximum value was achieved, it was completed once; if not, the test was carried out twice and the longest time was used [47,50]. MGS was calculated using the time to cover a distance of 10 m on a straight walking course. To ensure that the participants reached their maximal walking speed, the 10 m was measured within a 16-m track [46,50]. FleX was determined using the long seat type body anteflexion measurement device (T.K.K.5412; Takei Kiki Kogyo, Niigata, Japan) [45]. The measurement was performed twice, and the largest value was used as the data. Muscular function of lower extremities was measured by 30CS as described by Jones et al. [51]. Participants were instructed to stand up as often as possible within 30 s. The score was the total number of stands executed correctly (stretched knee and hip) within 30 s. The measurement was performed only once.

#### *2.3. Statistical Analysis*

Data were analyzed using SPSS Statistics 26.0 (IBM, Armonk, IL, USA). Significance was set at *p* < 0.05. All values are expressed as mean (standard deviation) or median [interquartile range] unless stated otherwise. A chi-square test was performed to confirm that there was no difference in the ratio of males and females in each group. Normal distribution was confirmed by a Kolmogorov–Smirnov test. In the comparison between two groups, an independent t-test was performed in the case of normal

distribution, and the Mann–Whitney U test was performed in other cases. For unequal variance, Welch's correction was applied. The correlation was analyzed by Pearson's correlation coefficient and Spearman's correlation coefficient. In the comparison between the four groups, one-way analysis of variance (ANOVA) for normality was performed, and Kruskal–Wallis for non - normality testing was performed otherwise. If there was a significant difference, either the Tukey test or the Bonferroni test was applied as a post hoc test. For multiple analysis, multiple linear regression analysis was performed with salt intake as the dependent variable.

#### **3. Results**

#### *3.1. Comparison with Estimated Daily Salt Intake*

The normality of each parameter is shown in Table 1. All parameters except BFP, HGS/BW, KES/BW, Flex, UN, Alb, HbA1c, and AST showed a non-normal distribution. The comparison between the "low-salt" and "high-salt" groups is shown in Table 1.  However, there were significant differences in SBP, DBP, weight, BMI, BFP, HGS/BW, Flex, 30CS, IL-6, TG, insulin, and ALT.

These data are shown as mean (standard deviation) or median [interquartile range]. For the normality test, the Kolmogorov—Smirnov test was performed. Two-group comparison is the result of the independent T test or Mann—Whitney-U test [§].  Correlation with Estimated Salt Intake*

The correlations between estimated salt intake and sarcopenia-related parameters are shown in Table 2. As for the correlation coefficient, the correlation coefficient of Pearson was analyzed in the case of normality, and the correlation coefficient of spearman was analyzed in the case of non-normality. There were significant differences in SBP, Weight, BMI, SMI, ULMM, BFP, HGS/BW, Flex, 30Cs, IL-6, TG, Insulin, and ALT. SBP, Weight, SMI, ULMM, HGS/BW, Flex, 30CS, IL-6, TG, Insulin, and ALT was weakly correlated and BMI and BFP was moderately correlated.

**Table 2.** Correlation of sarcopenia-related parameters based on estimated salt intake.

This table shows the correlation between estimated salt intake and sarcopenia-related parameters. As for the correlation coefficient, the Pearson correlation coefficient was shown in the case of normality, and the Spearman correlation coefficient was shown in the case of non-normality [§]. \*: *p* < 0.05. sec: seconds, BW: body weight, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index, SMI: skeletal muscle mass index, ULMM: mass of muscle of upper limbs, LLMM: mass of muscle of lower limbs, BFP: body fat percentage, HGS/BW: handgrip strength/weight, KES: knee extensor muscle strength, SLT: single-leg stance time, MGS: maximum gait speed, Flex: long seat type body anteflexion, 30CS: chair rise test, IL-6: Interleukin-6, UN: urea nitrogen, CysC: cystatin C, TG: triglyceride, Alb: albumin, Glu: glucose, HbA1c: Glycosylated hemoglobin, AST: Aspartate transaminase, ALT: Alanine aminotransferase.

## *3.3. Comparison of Estimated Salt Intake and Age*

The comparison between the "younger low-salt," "younger high-salt," "older low-salt," and "older high-salt" groups is shown in Table 3. There were significant differences in SBP, DBP, weight, BMI, SMI, ULMM, LLMM, BFP, HGS/BW, 30CS, IL-6, UN, TG, Glu, HbA1c, and AST.

**Table**

long seat type body anteflexion, 30CS: chair rise test, IL-6: Interleukin-6,

hemoglobin, AST: Aspartate transaminase,

 ALT: Alanine

 UN: urea nitrogen, CysC: cystatin C, TG: triglyceride, Alb: albumin, Glu: glucose, HbA1c: Glycosylated

aminotransferase.

In the comparison between the four groups, one-way analysis of variance (ANOVA) for normality was performed while Kruskal-Wallis was performed for non-normality testing. Significant differences were found in SBP, DBP, Weight, BMI, SMI, ULMM, LLMM, BFP, HGS/BW, 30CS, IL-6, UN, TG, Glu, HbA1c, and AST. A post hoc test was calculated only on the parameters that were significantly different. Figure 1 shows the p-values of the parameters that were significantly different in the post hoc test. When Younger and Older are stratified, significant differences were found in SPB, DBP, BMI, HGS/BW, 30CS, IL-6, and TG only in the older group. BFP was significantly different in both younger and older groups. weight, SMI, ULMM, LLMM, UN, Glu, HbA1c, and AST were significantly different between the younger group and the older group.

**Figure 1.** *Cont.*

**Figure 1.** For comparison of the four groups, one-way ANOVA or Kruskal-Wallis test was performed, followed by a post hoc test. \*: *p* < 0.05. sec: seconds, BW: body weight, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index, SMI: skeletal muscle mass index, ULMM: mass of muscle of upper limbs, LLMM: mass of muscle of lower limbs, BFP: body fat percentage, HGS/BW: handgrip strength/weight, KES: knee extensor muscle strength, SLT: single-leg stance time, MGS: maximum gait speed, Flex: long seat type body anteflexion, 30CS: chair rise test, IL-6: interleukin-6, UN: urea nitrogen, CysC: cystatin C, TG: triglyceride, Alb: albumin, Glu: glucose, HbA1c: glycosylated hemoglobin, AST: aspartate transaminase, ALT: alanine aminotransferase.

#### *3.4. Multivariate Analysis of Estimated Salt Intake and Sarcopenia Parameters*

Multiple linear regression analysis used salt intake as the dependent variable. The independent variable populated all parameters related to sarcopenia risk. We then implemented the stepwise method. The adopted factors were BFP, ULMM, IL-6, SLT, LLMM, 30CS, ALT, and insulin, and the multiple regression equation for predicting salt intake (Y) was Y = 0.12 × (BFP) + 1.44 × (ULMM) + 0.46 × (IL-6) + 0.03 × (SLT) − 0.42 × (LLMM) − 0.08 × (30CS) + 0.05 × (ALT) − 0.14 × (insulin) + 5.09. The multiple correlation coefficient was 0.46, the adjusted coefficient of determination was 0.42, and the adjusted multiple correlation coefficient was 0.68. The value of the Burbin Watson test was 2.40. Residual analysis showed that the model was reliable.

#### **4. Discussion**

This study was conducted with a focus on salt intake, which pertains to nutritional intake. It is known that the salt intake of Japanese people is the highest in the world [52,53]. In this study, the estimated daily salt intake using the urine test values was high, with an average value of 9.32 g/day (2.14). This amount is close to the reported average Japanese salt intake and is almost twice the recommended amount given by the WHO [22,23].

All parameters that showed a significant difference are values indicating the degree of obesity and the amount of fat in the body. In addition, salt intake, BFP, and BMI showed positive correlations. These results suggest that excessive salt intake may contribute to fat accumulation and are consistent with previous studies reporting an association between excessive salt intake and obesity [18,54–56].  Excessive salt intake can lead to fat accumulation and sarcopenia risk, however, participants who consume excess salt may not have dietary controls other than salt, which may lead to fat accumulation and obesity. Since no dietary survey was conducted in this study, this cannot be clarified. In terms of assessment of physical performance, some parameters, such as muscle weakness, were significantly lower in the high-salt group than the low-salt group. Muscle weakness is one of the causes of sarcopenia [50,61]. However, according to the criteria of the AWGS [3], there were only five in the older group that had reduced grip strength. The high-salt: low-salt group ratio was 3:2. Furthermore, no decrease in the parameter (SIM) indicating muscle mass loss could be seen. Therefore, in this study, we can only say that there is a possibility of sarcopenia risk, and not sarcopenia. Regarding blood tests, the fact that IL-6 and insulin were significantly higher in the high-salt group may suggest insulin resistance. Insulin resistance contributes to obesity and aging, and skeletal muscle insulin secretion resistance is involved in the pathogenesis of sarcopenia. IL-6 has also been shown to induce insulin resistance. Therefore, when homeostasis model assessment as an index of insulin resistance (HOMA-IR) was calculated [62], the average value of low salt content and high salt content was 1.18 (0.69):1.49 (0.91), and HOMA-IR also increased significantly. Increased ALT in blood tests is associated with liver dysfunction. There are many reports of the risk of sarcopenia due to liver dysfunction, and there are also diagnostic criteria for liver disease in Japan [4]. Increases in all these parameters are associated with sarcopenia risk [4,12–19].

Next, we investigated the relationship between estimated salt intake, age, and a possibility of sarcopenia risk. There were 16 parameters with a significant difference. Parameters were stratified into the younger and older groups, the items that showed a significant difference only in older groups were the parameters related to fat and the parameters indicating the decrease in muscle strength or physical function. In addition, only BFP had significantly higher mean values in the high-salt group compared to the low-salt group in both the younger and older groups. In other words, the results of this study showed that excessive intake of salt was related with accumulated fat parameters in the bodies in both younger and older groups. The comparison of muscle strength in this study did not follow the sarcopenia definition of EWGSOP and AWGS, but HGS/BW, which indicates muscle strength, and 30CS, which indicates physical function, were significantly decreased in the high-salt group [2–4]. Therefore, in the older group, the high-salt group had fat accumulation and muscle weakness.

The expression of the renalase may also be related to salt intake and sarcopenia [30–32]. However, we observed no significant relation between blood renalase levels and sarcopenia in this study. A previous study reported that the daily intake of salt was 18 g, much higher than the 4.0–13.8 g/day of this experiment [30]. The results of this study did not reveal a link between renalase, salt intake, and sarcopenia.

Our study compared people with high and low salt intakes and found that those with high-salt intake had higher fat-related parameters. However, we could not investigate causal relationships and mechanisms. For example, fat accumulation is associated with a variety of factors. Salt intake can contribute to fat accumulation. It can also be inferred that fat accumulation changes depend on diet and physical activity. It cannot be ruled out that factors that have not been measured or considered may have confounded the observational results in this study. First, a detailed dietary and physical activity survey would be required to elucidate the association with salt intake. A second limitation of this study is water intake. "Inbody 770", which measures body composition, is affected by the amount of water in the body because it uses the bioelectrical impedance analysis method. Therefore, if the body composition is to be measured strictly, water restriction should be controlled from the previous day. The third, this study recruited adults over the age of 20, participants ranging from 22 to 81 years old, with an average age of 56 years. Further recruitment of participants over the age of 60 or 65 should have been recruited to investigate the association between salt intake and sarcopenia.

Our study does not directly show that excessive salt intake causes sarcopenia, but a diet with excessive salt is associated with fat accumulation and muscle weakness. Furthermore, aging without improving diet can lead to the development of sarcopenia. In addition, past papers have reported that skeletal muscle mass and skeletal muscle strength are maximized in the 20s and 30s [63], and there is a standard that the target age for sarcopenia is 60 or 65 years [3]. On the other hand, there are reports targeting people over 40 years old [64]. Controlling the diet from a young age can prevent various diseases, including the prevention of sarcopenia.


*2.6. Statistical Analysis*

≤ ≤ All analyses were performed in triplicate, and the results are presented as mean ± standard deviation (*n* = 3). The mean differences between different samples were analyzed by one-way analysis of variance (ANOVA) and Tukey's honestly significant differences (HSD) multiple rank test at *p* ≤ 0.05. ANOVA was carried out via Minitab 19.0 (Minitab, LLC, State College, PA, USA) and GraphPad Prism 7.05 Software for Windows (GraphPad 7.05 Software, San Diego, CA, USA, www.graphpad.com). For correlations between polyphenol content and antioxidant activities, Pearson's correlation coefficient at *p* ≤ 0.05 and multivariate statistical analysis including a principal component analysis (PCA), XLSTAT-2019.1.3 were used by Addinsoft Inc. New York, NY, USA.


**Chapter 9**

**143**

**129**

Preface

Both developing and underdeveloped countries have adopted strategies to produce economically optimal agricultural products to feed the world's growing population, which is expected to reach 9 billion in the first half of this century according to the United Nations Food and Agriculture Organization (FAO). Since the main goal of these strategies was to increase the amount of production, soil productivity was prioritized, whereas the fertility factor of soil was ignored. To increase productivity, chemical fertilizers were used excessively and uncontrollably, which caused many short- and long-term problems in the fertility of the soil, continuity of biodiversity, and the sustainability of the balance in nature. Improper fertilizer applications led to an increase in soil salinity, insufficiency of nutrient elements in soil, increased diseases and pests, pollution of the environment, agricultural lands, and drinking water and rivers, and contributed to climate change, which increased the biotic and abiotic stresses on plants and harmed sustainability. As a result, the use of chemical fertilizers decreased agricultural production in the long term. This outcome triggered the efforts to develop agricultural activities involving organic inputs to regain biodiversity, maintain the ecological balance, and decrease the adverse effects of

By definition, a fertilizer is any material of natural or synthetic origin that is applied

to soil or plant tissues to provide at least one, but often more, of the nutrients required for plant growth. Most fertilizers that are currently used in commercial agriculture provide the three main nutrients: nitrogen, phosphorus, and potassium. These fertilizers are produced industrially from petroleum and petroleum-based products. On the contrary, organic fertilizers are derived from natural sources such as vegetable matter (e.g., compost and crop residues), animal manure, natural rocks, algae extracts, and so on. They are mostly richer in composition compared to chemical fertilizers and provide more nutritional elements as well as beneficial microorganisms. One of the main advantages of using organic fertilizers is that they increase the resistance of plants against biotic and abiotic stress conditions by boosting their immune systems. Moreover, they provide a slow, long, and sustained

Several natural resources, biological products (beneficial bacteria, lichens, algae, and fungi), mineralization products of plant and animal residues (leonardite, humic acid, rock phosphate, bat manure), fermentation products of waste by worms (worm manure), and products obtained from waste of slaughterhouses (amino acid products) are used in agricultural production as organic fertilizers. Although the use of these environmentally friendly products enables the regeneration of natural resources, improves the productivity and fertility parameters of soil, and contributes to carbon footprint management and sustainability, the substitution of chemical fertilizers with these organic products by producers is still slow due to lack of awareness. These products will get greater recognition only when their benefits

Besides the aforementioned natural sources, solid wastes of organic origin in the agricultural and industrial wastes can be converted into new organic products to

chemicals on human health.

release of nutritional elements for plant uptake.

are explained thoroughly and scientifically.

The Insects as a Workforce for Organic Fertilizers Production – Insect Frass *by Regina Menino and Daniel Murta*


*Article* **Miniature DC-DC Boost Converter for Driving Display Panel of Notebook Computer**

**Seok-Hyeong Ham 1 and Hyung-Jin Choe 2,\***

Received: 26 June 2019; Accepted: 29 July 2019; Published: 30 July 2019

**Abstract:** This paper proposes a miniature DC-DC boost converter to drive the display panel of a notebook computer. To reduce the size of the circuit, the converter was designed to operate at a switching frequency of 1 MHz. The power conversion efficiency improved using a passive snubber circuit that consisted of one inductor, two capacitors, and two diodes; it reduced the switching losses by lowering the voltage stress of the switch and increased the voltage gain using charge pumping operations. An experimental converter was fabricated at 2.5 cm × 1 cm size using small components, and tested at input voltage 5 V ≤ *VIN* ≤ 17.5 V and output current 30 mA ≤ *IO* ≤ 150 mA. Compared to existing boost converters, the proposed converter had ~7.8% higher power conversion efficiency over the entire range of *VIN* and *IO*, only ~50% as much voltage stress of the switch and diodes, and a much lower switch temperature *TSW* = 49.5 ◦C. These results indicate that the proposed converter is a strong candidate for driving the display panel of a notebook computer.

**Keywords:** backlight; DC-DC converter; passive snubber; voltage stress

#### **1. Introduction**

Notebook or tablet computers are powered by a Li ion battery pack [1–4]. Typically, the battery pack is composed of 2–4 Li ion battery cells. Each Li ion cell has a terminal voltage of 2.6–4.2 V depending on the charging state, so the voltage of the battery pack changes in the range of 5–17 V [5–7]. To supply power to each part of the computer, the pack voltage is converted to several voltages by DC-DC converters.

Many computers use a light-emitting diode (LED) backlight unit (BLU) for the liquid crystal display (LCD) panel. For a 17 inch panel, the voltage required to drive the LED backlight can be as high as 40 V [8]. The DC-DC boost converter for the LED BLU in a notebook or tablet computer should have a circuit topology that can reduce the overall size and weight [9–16]. The conventional boost converter (Figure 1) has the simplest structure, and can be implemented in small sizes by operating at a high switching frequency *fS*. However, the switching loss increases as *fS* increases, and thereby decreases the power conversion efficiency η*e* and can cause overheating of the switch. This problem has been solved using soft-switching converters [17–21].

**Figure 1.** Schematic of conventional DC-DC boost converter.

Soft-switching converters [17–21] use active snubbers to reduce switching loss. These snubbers are implemented using an auxiliary switch, diodes, and energy storage elements. The auxiliary switch requires a separate gate drive circuit that is controlled by a sophisticated algorithm. The snubber of Reference [17] discharges the drain-source capacitor *CDS* of the main switch before the switch turns on, so zero-voltage (ZV) turn-on of the switch is possible. The snubbers of References [18,19] force the switch current to zero before the switch turns off, so zero-current (ZC) turn-off of the switch is possible. The snubber of Reference [20] achieves ZV turn-off of the switch by reducing the rate of voltage increase during the turn-off transient, and achieves ZC turn-on of the switch by reducing the rate of current increase during the turn-on transient. However, the converters of References [17–20] have some limitations when they operate at high *fS*; they use a resonance between an inductor (*L*) and a capacitor (*C*) in the active snubber, the inductance *L* of L should be small enough to achieve ZV or ZC switching in a short time; the current peak of the switch or diodes increases as *L* decreases, therefore, this change increases the conduction loss and reduces the maximum output power. In the converter of Reference [21], the capacitance *CDS* discharges through L of the snubber when the auxiliary switch turns on. The ZV turning on of the main switch is achieved by discharging *CDS* completely. The *L* required to achieve the ZV turning on of the switch decreases as *fS* increases; as a consequence, the switching loss in the auxiliary switch increases. Therefore, simultaneous reduction of switching losses in the main and auxiliary switches is very difficult at high *fS*. The converter of Reference [9] uses a passive snubber to reduce the switching loss. The switching loss is reduced by lowering the turn-on slope of the switch current; the inductor *LS* that is connected in series with the switch controls the turn-on slope. However, the energy stored in LS is released as a voltage spike because the current path of *LS* is blocked when the switch is turned off. To ensure that this voltage spike does not exceed the maximum voltage limit of the switch, *LS* should be less than 50 nH at high switching frequencies; This value of *LS* was calculated using Equation (9) of the design guide in Reference [9]. The low value of the chip type inductor has a low maximum current limit. This low inductance also causes undesired resonance due to the parasitic inductance or capacitance of the circuit. Because of this, the converter of Reference [9] is difficult to use at high switching frequencies. Additionally, the switch current significantly increases the conduction loss in *LS* because the components of small size have non-negligible series resistance.

To improve upon the aforementioned drawbacks in the existing converters, this paper proposes a miniature DC-DC boost converter for the display panel of a notebook computer. The proposed converter (Figure 2) uses a passive snubber to reduce the switching loss; the capacitor *C*2 is connected in parallel with the switch, and the inductor *L*1 is connected in series with *C*2, and the value of *L*1 and *C*2 are large enough to prevent resonance at high *fS*. This lowers *VSW* to *VO*-*VC*1 and eliminates the switching loss. In addition, *L*1 is placed apart from the main current path to reduce the conduction loss in the passive snubber. The switch operates under a hard-switching condition to achieve fast switching, but the switching loss is reduced by lowering *VSW* (Figure 3). The switching loss *PSW* is calculated using the waveforms of *iSW* and *VSW* as:

$$P\_{sw} = \frac{1}{T\_s} \int\_{t\_0}^{t\_2} V\_{sw}(t)i\_{sw}(t)dt + \frac{1}{T\_s} \int\_{t\_3}^{t\_5} V\_{sw}(t)i\_{sw}(t)dt$$

Compared with the conventional converter, the proposed converter has a similar current peak, but has *VSW*(*t*1) and *VSW*(*t*4) lowered by ~1/2, so *PSW* is reduced.

**Figure 2.** Schematic of proposed DC-DC boost converter.

**Figure 3.** Voltage and current waveforms of the switch: (**a**) conventional boost converter, (**b**) proposed converter.

The proposed converter has a higher voltage conversion ratio than the conventional boost converter, and therefore has a wide range of output voltage for a given range of input voltage. Section 2 describes the circuit structure and operating principle of the proposed converter, Section 3 gives the design guidelines, Section 4 shows the experimental results, and Section 5 concludes the paper.

#### **2. Circuit Structure and Operating Principles of the Proposed Converter**

The proposed converter consists of two capacitors *C*1 and *C*2, two diodes *D*1 and *D*2, and an inductor *L*1, in addition to the conventional boost components *Lb*, *DO*, *CO*, and a switch (SW). *C*1 is located between boost inductor *Lb* and output diode *DO*, and acts as a charge pump capacitor to increase the voltage gain. *L*1 and *C*2 reduce the voltage stress of SW by dividing the output voltage *VO*. *D*1 and *D*2 provide a charging or discharging path for *C*2.

The operating modes (Figure 4) and key waveforms (Figure 5) of the proposed converter include three sequential modes of operation.

*Mode* 1 (*t*0~*t*1): The first operation (Figure 4a) starts at *t* = *t*0 by turning ON SW. During this operation, *D*2 turns ON, and *D*1 and *D*O turn OFF. *vLb* = *VIN* and *vL*1 = *VC*2 − *VC*1, so

$$i\_{Lb}(t) = \frac{V\_{IN}}{L\_{\emptyset}}(t - t\_0) + i\_{Lb}(t\_0) \tag{1}$$

$$i\_{L1}(t) = i\_{\rm C1}(t) = -i\_{\rm C2}(t) = \frac{V\_{\rm C2} - V\_{\rm C1}}{L\_1}(t - t\_0) \tag{2}$$

*C*1 is charged and *C*2 is discharged when SW is turned ON. *Mode* 1 ends when SW is turned OFF.

*Mode* 2 (*t*1~*t*2): The second operation (Figure 4b) starts at *t* = *t*1 when SW turns OFF. During *mode* 2, *D*2 stays ON, and *D*1 and *DO* are turned ON. When SW is turned OFF, the anode voltage of *D*1 is *VC*2 because *VLb* = *VC*2 − *VIN* according to the voltage second valance law. The cathode voltage of *D*1 is *VC*2 − Δ*VC*2 because C2 was discharged in *Mode* 1, so *D*1 is turned ON. After *D*1 is turned ON, the anode voltage of *DO* is *VC*1 + *VC*2 and cathode voltage is *VO* − Δ*VO* because *CO* was discharged in *Mode* 1. Therefore, *DO* is turned ON because *VC*1 + *VC*2 = *VO* in steady state. In the same manner, after *D*1 and *DO* are turned ON, *D*2 is turned ON because the anode voltage of *D*2 is *VC*1 + *VC*2 and the cathode voltage is *VO* − Δ*VO*. *vLb* = *VIN* − *VC*2 and *vL*1 = −*VC*1, so

$$i\_{Lb}(t) = \frac{V\_{IN} - V\_{C2}}{L\_b}(t - t\_1) + i\_{Lb}(t\_1), \ i\_{L1}(t) = -\frac{V\_{C1}}{L\_1}(t - t\_1) + i\_{L1}(t\_1).$$

**Figure 4.**

**Figure 5.** Operating waveforms of the proposed DC-DC boost converter.

The voltage stress of SW is *VC*2 because *D*1 is turned ON when SW is turned OFF. The output voltage *VO* is divided into *VC*1 and *VC*2, so the voltage stress of SW is smaller than *VO*, which is the voltage stress of the conventional boost converter.

*Mode* 3 (*t*2~*t*3): The last operation (Figure 4c) starts at *t* = *t*2 when *D*2 turns OFF. *D*1 and *DO* stay ON during this mode. *vLb* = *VIN* − *VC*2, so

$$i\_{Lb}(t) = \frac{V\_{IN} - V\_{C2}}{L\_b}(t - t\_1) + i\_{Lb}(t\_1)$$

In this mode, *vL*1 = 0 because *iL*1 = 0. *Mode* 3 ends when SW is turned ON for the next switching cycle.

The voltage–second balance law of inductances *Lb* and *L*1 yields

$$(V\_{IN}D + (V\_{IN} - V\_{C2})(1 - D) = 0\tag{3}$$

$$(V\_{C2} - V\_{C1})D - V\_{C1}(1 - D - \alpha) = 0\tag{4}$$

where *D* is the duty of switching and α*TS* is the duration of *Mode* 3. Solving Equations (3) and (4) for *VC*1 and *VC*2 yields

$$V\_{C2} = \frac{V\_{IN}}{1 - D},\ V\_{C1} = \frac{D}{1 - a} \frac{V\_{IN}}{1 - D}.\tag{5}$$

Equation (5) and *VO* = *VC*1 + *VC*2 then give the voltage conversion ratio as

$$\frac{V\_O}{V\_{IN}} = \frac{1}{1-D} \frac{1-a+D}{1-a} \tag{6}$$

The average current of *L*1, *IL*1 for one switching period *TS* is equal to the average output current *IO*, so

$$I\_O = \frac{V\_{C2} - V\_{C1}}{2L\_1} D^2 T\_S + \frac{V\_{C1}}{2L\_1} (1 - D - a)^2 T\_S \tag{7}$$

Inserting Equation (5) into Equation (7) and solving for α yields

$$\alpha = \frac{(V\_{IN}DT\_S - 2I\_OL\_1)(1 - D)}{V\_{IN}DT\_S} \tag{8}$$

Combining Equations (6) and (8) yields the output voltage

$$V\_O = \frac{2V\_{IN}(V\_{IN}D^2T\_S + I\_OL\_1(1-D))}{(1-D)(V\_{IN}D^2T\_S + 2I\_OL\_1(1-D))}\tag{9}$$

*VO* versus *D* (Figure 6) for *L*1 = 10 μH, *fS* = 1 MHz, *IO* = 150 mA, *VIN* = 5 V, and 0.1 ≤ *D* ≤ 0.9 was calculated using Equation (9) for the proposed converter, but the conventional boost converter was calculated using their voltage gain equations. To have *VO* = 40 V for given *VIN*, *D* = 0.775 was applied to the proposed converter, whereas *D* = 0.875 was applied to the conventional boost converter. At given *VIN* and *D*, the proposed converter had higher *VO* than the conventional boost converters.

**Figure 6.** *VO* versus duty *D* for the experimental boost converters; *VIN* = 5 V, 0.1 ≤ *D* ≤ 0.9.

#### **3. Design Guideline**

#### *3.1. Boost Inductor Lb*

The inductance *Lb* of *Lb* should be determined so that the proposed converter operates in a continuous conduction mode (CCM). The current through *Lb* is minimum at *t*0 and maximum at *t*1. The following equation is obtained using the average of *iLb* and Equation (1),

$$i\_{Lb}(t\_1) - i\_{Lb}(t\_0) = \frac{V\_{IN}DT\_S}{L\_b},\ i\_{Lb}(t\_1) + i\_{Lb}(t\_0) = 2I\_{IN}.\tag{10}$$

Solving Equation (10) for *iLb*(*t*0) yields

$$i\_{Lb}(t\_0) = I\_{IN} - \frac{V\_{IN}DT\_S}{2L\_b}$$

The condition *iLb*(*t*0) > 0 should be satisfied to operate the proposed converter in CCM. This condition yields

$$L\_b > \frac{V\_{IN} D T\_S}{2I\_{IN}}\tag{11}$$

For 5 ≤ *VIN* ≤ 17.5 V, *VO* = 40 V, *fS* = 1 MHz, and 30 ≤ *IO* ≤ 150 mA, *Lb* = 33 μH was determined using Equations (9) and (11).

#### *3.2. Snubber Inductor L1*

A small value of *L*1 causes a drastic current decrease of *D*2 in mode 2. Therefore, the limit of *L*1 is calculated using the following condition for a soft turn-off of *D*2.

$$\frac{V\_{C1}}{L\_1} << \frac{I\_{D2,\text{max}}}{t\_{D2,full}}$$

where *ID2,max* is the highest diode current and *tfall* is the diode turn-off time; *L*1 0.2 μH for*ID2,max* = 1 A and *tfall* = 10 ns. The diode turn-off loss increases as *L*1 decreases, so *L*1 = 10 μH was chosen to provide an operating margin and to reduce turn-off loss of *D*2.

#### *3.3. Snubber Capacitors C1 and C2*

The capacitance of *C*1 is determined by the condition that Δ*VC*1 << *VC*1 because *VC*1 should be almost constant for one switching period *TS*, where Δ*VC*1 is the voltage ripple of *C*1. During *Mode 1*, *C*1 charges through *L*1; this results in a voltage ripple Δ*VC*1 of C1; Δ*VC*1 = (*VC*2 − *VC*1)(*DTS*) 2 /(2*C*1*L*1) because the current through *C*1 decreases linearly from 0 A at *t*0 to (*VC*2 − *VC*1)*DTS*/*L*1 at *t*1.

Therefore,

$$C\_1 >> \frac{V\_{C2} - V\_{C1}}{2V\_{C1}L\_1} \left(DT\_S\right)^2. \tag{12}$$

For *VIN* = 5 V, *VO* = 40 V, *fS* = 1 MHz, and *L*1 = 10 μH, *C*1 = 1 μF was determined using Equations (5) and (12). *C*2 has been chosen to be the same as *C*1 because Δ*VC*1 = Δ*VC*2 and *iC*1 = −*iC*2 in *Mode 1*.

#### **4. Experimental Results**

The proposed boost converter (Figure 7) was designed to operate at *VIN* = 5 V, *VO* = 40 V, 30 ≤ *IO* ≤ 150 mA, and *fS* = 1 MHz. The inductances were set at *Lb* = 33 μH and *L*1 = 10 μH. Capacitances *C*1 and *C*2 were both set at 1 μF. The experimental circuit was implemented using the following miniature components (Table 1): TPS55340 DC-DC controller (Texas Instruments Inc., Dallas, TX, USA), which has an internal *n*-MOS switch (40 V, 5A), RB160M-60TR diodes (Rohm Co.), IFSC-1515AH-01 (Vishay Inc.) and SSMC252008R47SC (SST Inc.) chip inductors, and multilayer ceramic chip capacitors of a size 3.2 mm × 1.6 mm. The chip inductors and capacitors have series resistances given in the parentheses. To ensure a fair comparison, the conventional boost converter used the same components.

**Figure 7.** Photograph of the proposed DC-DC boost converter.

**Table 1.** Component values for the experimental boost converters.

The TPS55340 regulates the output voltage with current mode PWM control, and has an internal oscillator. The pulse width modulation of the gate pulse *Vg* is achieved in the control circuit for the proposed converter (Figure 8) as follows; each clock pulse resets the flip-flop and the ramp generator, which sets *Vg* to the 'high' state; the output *Vc* of the error amplifier increases as *VO* increases; the comparator output changes from 'low' to 'high' when the output voltage of the ramp generator exceeds *Vc*, which changes the inverter output *Vg* to the 'low' state. When the output current *IO* increases/decreases abruptly, *VO* decreases/increases instantly, which increases/decreases *Vc*. Thus, the pulse width of *Vg* increases/decreases to keep *VO* constant.

**Figure 8.** Block diagram of the control circuit for the proposed converter.

The voltage and current waveforms of SW (Figure 9) were measured at *VIN* = 5 V, *VO* = 40 V, *IO* = 150 mA, and *fS* = 1 MHz. The voltage stress of the proposed converter (Figure 9a) was lower than that of the conventional boost converter (Figure 9b) because the voltage stress was *VO* − *VC*1 in the proposed converter but *VO* in the conventional boost converter. The current stresses of the proposed converter and the conventional boost converter were similar. The current waveforms of the diodes are shown in Figure 10. The diodes current stresses of the proposed converter was lower than the conventional boost converter. The time-averaged values <*iD*1>, <*iD*2>, and <*iD*o> were all equal to *IO*.

The curves of η*e* on *IO* (Figure 11a) were measured at 30 ≤ *IO* ≤ 150 mA, *VIN* = 5 V, *VO* = 40 V, *fS* = 1 MHz. At *IO* = 30 mA, η*e* was 84.0% for the proposed converter and 76.9% for the conventional boost converter. At *IO* = 150 mA, η*e* was 80.4% for the proposed converter and 72.6% for the conventional boost converter. The curves of η*e* on *VIN* (Figure 11b) were measured at 5 ≤ *VIN* ≤ 17.5 V, *IO* = 150 mA, *VO* = 40 V, *fS* = 1 MHz; η*e* at *VIN* = 17.5 V was 89.1% for the proposed converter and 88.6% for the conventional boost converter. The η*e* of the proposed converter was up to 7.8% higher than the conventional boost converter at *VIN* = 5 V. The proposed converter had a high η*e* over the entire input voltage and output current ranges.

**Figure 9.** Voltage and current waveforms of switch measured at *VIN* = 5 V, *VO* = 40 V, *IO* = 150 mA, and *fS* = 1 MHz. (**a**) Proposed converter, (**b**) conventional boost converter.

**Figure 10.** Diode waveforms of the proposed converter. (**a**) *Do*, (**b**) *D*1, (**c**) *D*2, and diode waveform of the conventional boost converter. (**d**) *Do* measured at *VIN* = 5 V, *VO* = 40 V, *IO* = 150 mA and *fS* = 1 MHz.

**Figure 11.** (**a**) η*e* versus *IO* at *VIN* = 5 V, *VO* = 40 V, *fS* = 1 MHz, and 30 ≤ *IO* ≤ 150 mA. (**b**) η*e* versus *VIN* at *IO* = 150 mA, *VO* = 40 V, *fS* = 1 MHz, and 5 ≤ *VIN* ≤ 17.5 V.

The temperatures of SW and inductors (Figure 12) were measured for 30 min using a digital temperature recorder (GL-220, GRAPHTEC), while the converters were operated at *VIN* = 5 V, *VO* = 40 V, *fS* = 1 MHz, and *IO* = 150 mA. In the conventional boost converter the switch stabilized at 86.6 ◦C and the inductor stabilized at 91.7 ◦C, but in the proposed converter, the switch stabilized at 49.5 ◦C and the inductor stabilized at 62.9 ◦C. These data indicate that the proposed converter generated less heat loss than the conventional boost converter.

**Figure 12.** Temperature of switch and inductors versus operation time for the experimental converters, measured at *VIN* = 5 V, *VO* = 40 V, *IO* = 150 mA, and *fS* = 1 MHz.

The circuit losses were calculated using a circuit simulator at *VO* = 40 V, *IO* = 150 mA, *fs* = 1 MHz, and *VIN* = 5 V (Figure 13a) and 15 V (Figure 13b). The total power losses were 1.59 W (proposed) and 2.31 W (conventional) at *VIN* = 5 V, and they were 0.68 W (proposed) and 0.78 W (conventional) at *VIN* = 15 V. These results show that the proposed converter had lower power loss than the conventional boost converter at both *VIN* = 5 V and 15 V. The losses in the switch were 0.65 W (proposed) and 1.41 W (conventional) at *VIN* = 5 V, and they were 0.38 W (proposed) and 0.63 W (conventional) at *VIN* = 15 V. The proposed converter reduced the switching loss by decreasing *VSW.* Additionally, the snubber loss of the proposed converter was 0.16 W at *VIN* = 5 V, and it was 0.15 W at *VIN* = 15 V. The proposed converter reduced the snubber losses by placing *L*1 at the output stage and eliminating the resonant current in the snubber circuit.

**Figure 13.** Circuit losses for *VO* = 40 V, *IO* = 150 mA and *fS* = 1 MHz, calculated at (**a**) *VIN* = 5 V and (**b**) *VIN* = 15 V.

The voltage and current stress (Table 2) of the proposed and conventional boost converter were measured at *VIN* = 5 V, *VO* = 40 V, *IO* = 150 mA, and *fS* = 1 MHz. Without parasitic components, the voltage stress of SW and diodes were as follows: (1) in the conventional boost converter, *VSW* = *VO* because *DO* was turned ON when SW was turned OFF, and *VDo* = *VO* because SW was turned ON when *DO* was turned OFF; (2) in the proposed converter, *VSW* = *VO* − *VC*1 because *DO*, *D*1, and *D*2 were turned ON when SW was turned OFF, *VDo* = *VD*1 = *VO* − *VC*1 because SW was turned ON when *D*O and *D*1 are turned OFF, and *VD*2 = *VO* − *VC*2 because *D*1 and *D*O were turned ON, and SW was turned OFF when *D*2 was turned OFF. The proposed converter had lower voltage stress of SW and diodes than the conventional boost converter because *VO* = *VC*1 + *VC*2. The current stress of SW was similar in the conventional boost converter and the proposed converter. The current stresses of diodes were lower by half in the proposed converter than in the conventional boost converter.

**Table 2.** Voltage and current stresses of components in the experimental boost converters.

The voltage stress, current stress, voltage gain, and efficiency of the existing boost converter were calculated using a circuit simulator at *VIN* = 5 V, *VO* = 40 V, *IO* = 150 mA, and *fs* = 1 MHz (Table 3). The efficiency η*e* was 72.6% for the conventional boost converter, 80.4% for the proposed converter, 75.1% for the converter of Reference [12], and 76.6% for the converter of Reference [21]. The proposed converter had the highest η*e* for given input and output conditions. The converters of References [12,21] had a lower efficiency than that of the proposed converter due to a high current stress and the loss of auxiliary switch, respectively. The voltage stress of the proposed converter was lower than that of the other converters because the voltage stress is *VO*-*VC*1 in the proposed converter but *VO* in the other converters. The current stresses of the proposed converter and the conventional boost converter were similar, but the converter of Reference [12] had a high current stress because it used resonance to

reduce the switching loss at high frequency. At given *VIN* and *VO*, the proposed converter had the smallest duty, so the proposed converter had the highest voltage gain.

**Table 3.** Comparison with existing boost converters.

#### **5. Conclusions**

A miniature DC-DC boost converter for driving a display panel of a notebook computer was proposed. This converter operates at a switching frequency of 1 MHz to miniaturize the circuit. The switching loss is reduced by using a passive snubber that lowers the voltage stresses of the switch. The conduction losses of the snubber and switch is also minimized by preventing high peak current due to the resonance at high *fS*. The experimental converter was fabricated in 2.5 cm × 1 cm size and tested at 5 ≤ *VIN* ≤ 17.5 V, 30 ≤ *IO* ≤ 150 mA. Compared to previous boost converters, the proposed converter had a higher voltage conversion ratio, ~7.8% higher power conversion efficiency over the entire range of *VIN* and *IO*, ~1/2 as much voltage stress of the switch and diodes, and a much lower switch temperature. The results indicate that the proposed converter is a strong candidate for driving the display panel of a notebook computer.

**Author Contributions:** S.-H.H. conceived the main idea for the proposed converter and performed overall analysis and experiment. H.-J.C. led the project and gave technical advice.

**Acknowledgments:** This research was supported by the Tongmyong University Research Grants 2019(2019A003-1).

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

#### **References**

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

*Article*


2.2.1. Mito Stress Test

To evaluate mitochondrial function, the Mito Stress test was performed using a Seahorse XF96 Cell Analyzer according to the manufacturer's recommendations. Briefly, myoblasts from patients and controls were seeded to Seahorse XF96 cell culture microplates (2.5 × 104 cells per well) in skeletal muscle cell growth medium supplemented with 10% fetal bovine serum (FBS). After a 24-h incubation at 37 ◦C, the cells were washed twice with a pre-warmed assay medium (XF base medium supplemented with 10 mM glucose, 2 mM glutamine, and 1 mM sodium pyruvate; pH 7.4).

Oxygen-consumption rate (OCR) values were simultaneously measured following sequential injections of inhibitors of mitochondrial oxidative phosphorylation: (I) oligomycin (2 µM), an ATP synthase inhibitor; (II) carbonyl cyanide p-(trifluoromethoxy) phenylhydrazone (FCCP, 2 µM), an uncoupler; (III) antimycin A (0.5 µM) (inhibitor of complex III) and (IV) rotenone (0.5 µM) (inhibitor of complex I), inhibiting uncoupled respiration. Key parameters of mitochondrial function such as basal respiration (BR), ATP production rate (ATP-R) and spare respiratory capacity (SRC) were analyzed using the above-described measurements. The data was normalized to cell numbers by measurement of Hoechst dye staining of nuclei with excitation and emission wavelengths 355 nm and 465 nm using a Tecan Infinite TM M1000 and plotted as OCR (pmol/min/U fluorescence/well ± SD), accordingly.