**Studies on Effects of Aircraft Noise on Behavior of Rats, Their Plasma Norepinephrine Levels and Cell Morphology of the Temporal Lobe**

Guo-qing Di

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62586

#### **Abstract**

To study the physiological effects of airport noise exposure on organisms, Sprague-Dawley (SD) rats were exposed in soundproof chambers to previously recorded aircraftrelated noise for 65 d. As a comparison, unexposed control rats were also used. According to aircraft flight schedules, aircraft noise was replayed and its weighted equivalent continuous perceived noise levels (*L*WECPN) were adjusted to 75 and 80 dB for the two experimental groups. Rat behaviors were observed through an open field test and the concentrations of plasma norepinephrine (NE) were tested by high-perform‐ ance liquid chromatography-fluorimetric detection (HPLC-FLD). The morphologies of neurons and synapses in the temporal lobe were also examined by transmission electron microscopy (TEM). Our results indicated that SD rats of experiment group exposed to airport noise of 80 dB had significantly lower line crossing number (*P* < 0.05) and significantly longer center area duration (*P* < 0.05) compared with that of control group. After 29 d of airport noise exposure, the concentrations of plasma NE of experiment group were significantly higher than that of control group (*P* < 0.05). It was deter‐ mined that the neuron and synapsis of the temporal lobe of experiment group exposed to 80 dB for 65 d showed signs of damage. In conclusion, exposing rats to long-term aircraft noise affects their behaviors, plasma NE levels, and cell morphology of the temporal lobe. Of course, the differences in the hearing sensitivity to different sound frequencies and circadian rhythms between rats and humans can bring variances in physiological effects under the same noise exposure. Therefore, if this study results are applied into humans, it should be further confirmed.

**Keywords:** Aircraft noise, open field test, norepinephrine, neuron, synapse

© 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.


**1. Introduction**

After the Paris Agreement 2015, decarbonization of industry and energy-intensive industrial processes has been proposed as one of the key measures to limit the increase of the global average temperature well below 2 ◦C. Within decarbonization of industry, waste heat recovery is dedicated to play a major role.

Systematic approaches to design and synthesize heat recovery systems, especially heat exchanger networks (HENs), have been subject to research since the early 1980s with the introduction of the graphical Pinch analysis method [1]. In addition to the graphical Pinch analysis method, mathematical programming has been well established as a complementary approach to HEN synthesis. Both sequential approaches [2] and simultaneous approaches [3], where targeting and network synthesis are performed separately and simultaneously, respectively, have been developed and reported in the literature. In addition, hybrid methods, which combine graphical methods and mathematical programming [4], have been suggested. Among others, Axelsson et al. [5], Bengtsson et al. [6], as well as Escobar and Trierweiler [7] have applied graphical and mathematical methods to case studies, including industrial applications, and reported results and comparisons.

The above-mentioned methods do not account for the variation and uncertainty in operating data that often occur in industrial applications. One example is the variation of input conditions for HENs, i.e., temperatures and flowrates, but also uncertainty in design characteristics, such as heat transfer coefficients of heat exchangers (HEXs). The consequences of neglecting these variations and uncertainties as well as using average or nominal values for design purposes can be increased utility demand (see, e.g., [8]). Additionally, more severe operability issues may occur, e.g., the controllability of target values may not be guaranteed. In order to face these challenges, different strategies have been proposed and a good overview is provided by Kang and Liu [9] in their recent review paper on the synthesis of flexible HENs. Some literature is mentioned here. Kotjabasakis and Linnhoff [10] developed an approach to mitigate the unwanted response of a HEN to variations based on sensitivity tables and systematic utilization of downstream paths. Hafizan et al. [11] utilized the plus-minus principle, introduced by Linnhoff and Vredeveld [12], to visualize the impact of process modifications on the minimum utility target, to derive heuristics for HEN design synthesis based on graphical methods if the respective HEN is subject to disturbances in inlet temperatures.

In addition to graphical approaches (e.g., Pinch-based), mathematical programming has great potential when dealing with variations and uncertainty in operating data. Floudas and Grossmann [13] introduced a multi-period approach to the HEN synthesis problem, which was automated [14] and developed further in the late 1980s [15]. More recently, the multi-period approach has been developed further by a number of authors. In this context, Aaltola [16] extended the SYNHEAT model of Yee and Grossmann [3] to multiple periods. Additionally, Verheyen and Zhang [17] as well as Short et al. [18] improved the model of Aaltola [16] by considering more specific HEX design data. The multi-period HEN synthesis strategy was further applied by Tveit et al. [19] to industrial applications.

Compared to the synthesis of HENs for greenfield problems, the rearrangement of an existing HEN (retrofitting problem) is a different problem. In contrast to greenfield design situations, a retrofitting problem is often characterized by additional constraints and limitations. Extensive work exists on retrofitting methodologies based on graphical insights (e.g., Lai et al. [20], Kamel et al. [21], Bonhivers et al. [22]), mathematical programming (e.g., Ciric and Floudas [23], Asante and Zhu [24], Athier et al. [25]), and those methodologies that can be characterized as hybrid methodologies combining graphical analysis and mathematical programming (e.g., Smith et al. [26], Jiang et al. [27], Akpomiemie and Smith [28]). A comprehensive overview on different retrofitting methodologies can be found in [29].

The above-mentioned retrofit methodologies for HENs do not account for variations and uncertainties in operating data, e.g., in inlet temperatures or heat capacity flowrates. Usually, steady-state or average values are considered, i.e., the reported methodologies are suited for retrofitting single-period HENs, only. However, the capability to account for variations and uncertainties is essential in certain industry applications. Persson and Berntsson [8] showed that considering annual average values to calculate the steam savings potential of a retrofit heat integration project in a pulp mill can lead to an overestimation of 15% compared to the steam savings potential calculated with monthly average values. Persson and Berntsson further concluded in [30] that the steam savings potential decreases even further if it is calculated with average values of shorter time periods (e.g., daily or 10-min periods). In contrast to the results reported by Persson and Berntsson as well as the excessive amount of methodologies available for retrofitting single-period HENs, little attention has been paid to HEN retrofit methodologies that account for variations and uncertainties in operating data. It is worth mentioning that this imbalance between only a few methodologies for multi-period HEN retrofit in comparison with the reported methodologies for single-period HEN retrofit was observed by authors previously publishing in the field (see, e.g., [31]). In fact, to our knowledge, only two systematic retrofitting methodologies for multi-period HENs are reported in the literature. These two retrofitting methodologies are described in the following paragraph.

Papalexandri et al. [32] developed a multi-period Mixed Integer (Non-)Linear Program (MI(N)LP) model, which is based on multi-period hyperstructures, including the considered retrofit alternatives. Furthermore, Papalexandri et al. [32] proposed an iterative scheme between the developed multi-period MI(N)LP and a flexibility analysis subproblem to identify the retrofit alternative, which is operable

over a priori defined variations and yields the minimum total annualized cost. Another retrofitting methodology was reported by Kang and Liu [31], who introduced a two-step method to achieve retrofit design proposals that can operate cost efficiently at multiple periods. In a first step, the multi-period HEN synthesis model as it is used in greenfield design problems is employed. In the second step, existing exchangers are relocated in order to meet the required area demands identified in step one.

Although the above-mentioned retrofit approaches account for variations and uncertainty in operating data, there are several reasons why these approaches are not easily applied to large and complex industrial applications. Commonly, complexities, such as splitting or recirculation of streams, are present in industrial heat recovery systems, which cause nonlinearities in the mathematical formulation of these systems. Therefore, the resulting multi-period MINLP, including retrofit design proposals incorporated as hyper structures as proposed in [32], becomes complex, which inevitably leads to difficultness in finding feasible solutions. Additionally, the flexibility analysis subproblem for large and complex industrial applications is difficult to solve even with state-of-the-art global MINLP solvers. Moreover, depending on the problem complexity itself, the solution produced by common multi-period HEN synthesis formulations and the current network layout may differ essentially. Consequently, relocating existing exchangers to meet identified area demands as proposed in [31] can easily result in inefficient trial and error procedures.

The above reported methodologies are based on mathematical programming, which coincides with the benefits of mathematical programming when dealing with variations in operating data. However, in comparison to these methodologies, graphical insights-based methods have great potential to be applied to large and complex applications due to their beneficial user interaction. Recently, different strategies have been reported in the literature to utilize approaches based on graphical insights in the design process of multi-period HEN retrofitting case studies. A common strategy to deal with variations in operating conditions from a Pinch analysis perspective is to develop different retrofit proposals for a number of selected sets of operating conditions (e.g., annual, seasonal, and monthly average values) [8]. The different design proposals are then evaluated and may be combined to achieve an, over all considered operating points, operable and energy-efficient retrofit proposal. Another strategy is to develop different retrofit proposals by employing graphical insights-based retrofitting methods (e.g., Bridge analyses [22] or advanced composite curves [33]) for a specific nominal point and analyze the network's response to variations to obtain insights and identify the best performing retrofit design proposal [34]. Additionally, Langner et al. [35] proposed to decouple the design and analysis steps in retrofitting processes. By means of this decoupling, well-proven (single period) retrofit design methodologies can be utilized to generate different design proposals, which thereafter are evaluated and adjusted in a trial and error manner with respect to flexibility and energy efficiency [35]. In conclusion, approaching a retrofit problem subject to variations in operating data from a Pinch analysis perspective relies to a large extent on trial and error procedures as different design proposals must be evaluated and manually combined. Good results can be obtained, but the design process itself is often very inefficient due to the trial and error character of the respective strategies.

As pointed out, systematic methodologies for retrofitting industrial HENs subject to variation in operating data are needed, but the available methodologies fulfill this demand only partly. Therefore, we propose a new framework in this paper to achieve flexible and cost-efficient retrofit measures by combining the beneficial designer interaction of graphical approaches (e.g., Pinch based) at an early stage in the design process with the efficiency of mathematical programming. By means of this combination, inefficient trial and error procedures can be avoided. The proposed framework is based on single-period (e.g., Pinch based) retrofit methodologies, (structural) flexibility analysis, critical point analysis, and multi-period optimization. The proposed framework is outlined in Section 3 of this paper. In the following section, the theoretical background of (structural) flexibility analysis and critical point analysis is provided.


**4. Relaxation processes for energy and spin in quantum dots important for photovoltaics applications**

Possible applications of quantum dots in photovoltaics [6] call for the studies and understanding of their optical and transport properties. These properties are either controlled or strongly related to the energy and spin relaxation processes in these systems. The energy relaxation is important since it determines transport properties of the photovoltaic devices such as the probability of electron escape from the quantum dot to the glass layer and the transparent conductive oxide and, as a result, the integral photovoltaic efficiency. The spin relaxation is important since it is directly related to the coupling mechanisms responsible for the energy relaxation and allows one to find the efficient mechanisms of both the energy and the spin relaxation. In addition, the spin states are critically important for the formation of the spectra of quantum dots with several carriers (electrons and/or holes) and, thus, spin relaxation can determine the photovoltaic efficiency of the charged quantum dots in the regime of strong light irradiation, where the light intensity is sufficiently strong to produce more than one carrier per quantum dot [8].

There are different types of quantum dots applicable in photovoltaics. Let us consider ellipsoidal quantum dots serving as optical absorbers and, subsequently, source of the photo excited electrons upon their escape from the dot. The main source of the energy relaxation in single-electron quantum dots is due to various types of electronphonon coupling, which will be analysed below. In addition, we will analyse the mechanisms of the energy relaxation in multi-electron quantum dots taking into account the electron-electron interaction. We will demonstrate that a novel mechanism of spin relaxation, different from the conventional admixing mechanism, can be relevant for

the quantum dots based on III-V group materials. Let us begin with a model of a semiconductor quantum dot applicable in photovoltaics. These dots have a variety of different properties related to their sizes and shapes. Although this variety extends the ability to use the quantum dots for nonmonochromatic light sources such as sunlight, on the other hand, it decreases the controllability of their applications. Direct systemdependent analysis of properties of quantum dots is difficult and can basically be done with the effective mass approximation as included in the Hamiltonian

$$H\_0 = \frac{p^2}{2m^\*} + U\_\epsilon(r). \tag{9}$$

Where *p*2 2*m*∗ , is the electron kinetic energy, p is the electron momentum, m∗ is the electron effective mass, and U (r) is the effective confining potential of the quantum dot. Frequently, for model calculations the confinement is taken in the form of an anisotropic oscillator:

$$U\_{\varepsilon}(r) = 2m^\* \left[ \Omega\_{\parallel}^2 (\varkappa^2 + \jmath^2) + \Omega\_x^2 x^2 \right] / 2,\tag{10}$$

where Ωk and Ω*z*, are the corresponding frequencies of the anisotropic oscillator. However, this potential is well-suitable only for the description of the low-energy states of the photoexcited electrons, where wavefunction is well-localized near the potential minimum. Another form of the potential is given by *Ue*ð Þ*r* = 0 for r inside the quantum dot, *Ue*ð Þ*r* = U for r outside the quantum dot, where the inside/outside boundary determines the quantum dot shape. Usual model shapes of quantum dot are ellipsoidal with an example presented in **Figure 6**. The typical scale of the quantum kinetic energy is determined by the z � axis extension of the quantum dot *ω* as *ℏ*2 *m*∗*ω*2 with the corresponding quantity of the order of 10 meV for *ω* � 10 nm and *m*∗ of the order of 0.1 of the free electron mass. For highly excited states, with the energies above the excitation threshold of the order of 100 meV, but less than the confinement potential U, a classical description in terms of the electron trajectories becomes possible. Here emission of phonons leads to the relaxation to the lowerenergy quantum states. To study the relaxation, we present electron-phonon coupling Hamiltonian (assuming the crystal volume � 1) in the form:

#### **Figure 6.**

*(a) Two model ellipsoidal quantum dot on the surface of a glass. The vertical size of the quantum dot is denoted as w, typically of the order of 10 nm. (b) Classical trajectory of highly photoexcited electron in the quantum dot.*

*Quantum Dots - Recent Advances, New Perspectives and Contemporary Applications*

$$\mathcal{V}\_{e-ph} = \frac{D\sqrt{\hbar}}{\sqrt{2\zeta\_r}} \sum\_{q} \sqrt{q} (de) \left( e^{-iqr} a\_q^\dagger + H.c \right) \tag{11}$$

Where *a*† *q* is the creation operator for the phonon with momentum q, D is the deformation potential (D = �5.5 eV in GaAs), ζ is the crystal density, d = q/q is the phonon propagation direction, e is the phonon polarization, c is the speed of the longitudinal sound mode, and summation is taken over the momenta. We have chosen a single longitudinal phonon branch with (de) = 1, since for the transverse branches (de) = 0, taking into account only the strongest interaction with the deformational potential of the acoustic phonons.

There are different regimes of energy relaxation. The first regime is relevant for the highly-excited semiclassical states, leading to their energy loss and subsequent quantization with localization in the quantum states. The second regime is relevant for the localized states, where phonon-induced transitions occur between the low-energy quantum states in the dots. We begin with the most important for the photovoltaics semiclassical regime, where the electron states can be presented as plane waves. For the relaxation of these states shown in **Figure 6(b)** one can use the classical Boltzmann equation based on Fermi's golden rule. The energy-dependent phonon emission rate 1/τ (E) per single phonon mode with wavevector q leading to the energy relaxation is given by this rule as [9]:

$$\frac{1}{\pi(E)} = \frac{2\pi D^2 \hbar}{\hbar 2\zeta c} \left[ q \delta \left[ E\_f - E\_i + \hbar \Omega(q) \right] \frac{d^3 k\_f}{(2\pi)^3} \right] \tag{12}$$

where the initial and final energy is *Ei* ¼ *ℏ*2*k*2 *i* 2*m*∗ and *Ef* ¼ *ℏ*2*k*2 *f* 2*m*∗ respectively, final electron wavevector kf = ki � q, and δ [..] � function corresponds to the energy conservation with (q) = cq. Taking into account that the characteristic wave vector ki of a photoexcited electron is of the order of 106 cm�1 or higher, at the corresponding phonon momentum its energy *ℏck* i is of the order of less than one meV, that is 10 K, much less than the electron energy. As a result, the electron scattering is quasi-elastic and the occupation number of the phonons *nB* ¼ exp *ℏ*Ωð Þ*q T* � 1 h i � �1 , equal to T/*ℏ*Ω at the room temperature T, is large. Therefore, the emission probability is greatly enhanced by the factor nB + 1. However, this occupation factor increases the phonon absorption probability as well and these processes partially compensate each other. This process leads to the energy dependence of 1 *τ*ð Þ *E* determined by the electron density of states, proportional to ffiffiffi *E* p and by the absolute value of the phonon momentum, also behaving as ffiffiffi *E* p and resulting in:

$$\frac{1}{\pi(E)} \approx \frac{1}{\pi\_D} \frac{E}{\hbar \Omega\_D} \left(\frac{m^\* c^2}{\hbar \Omega\_D}\right)^{1/2} \tag{13}$$

where *τD* is the nominal momentum relaxation time Ref. [9] (*τD* = 2.5 ps in GaAs), and D is the Debye phonon frequency. The time-dependence of the energy due to nearly balanced quasi-elastic emission and absorption of phonon is given by: *dE t*ð Þ *dt* ¼ *ℏ*Ωð Þ *E τ*ð Þ *E* , where the energy goes to phonon subsystem. The numerical value of eτ is rather long, being of the order of 10�7 s as a result. The resulting time dependence of energy has the form:

*Quantum Dots as Material for Efficient Energy Harvesting DOI: http://dx.doi.org/10.5772/intechopen.106579*

$$E(t) = \frac{E(\mathbf{0})}{\left(\sqrt{\frac{E(\mathbf{0})}{2\sqrt{\hbar\tau}}}t + \mathbf{1}\right)^2} \tag{14}$$

For the initial energy E(0) = 100 meV this estimate yields the value τ � 10 ps, demonstrating that energy relaxation of photoexcited electrons is fast and can set up the initial condition for the diffusion through the glass layer. Taking into account that the electron velocity is of the order of 108 cm/s, electron during this time hits the quantum dot boundary at least 100 times. As a result, the electron can escape from the quantum dot if the escape probability per single boundary collision is higher than 10�2 , thus, requiring for a relatively transparent boundary between the quantum dot and the glass (see **Figure 6(a)**).

Quantum dots are often single-electron charged since they can absorb electrons from the bulk of the semiconductors. Therefore, complex states built by a photoexcited electron-hole pair inside the dot and the resident electron, usually called 'trions' Ref. [9], can be formed, as shown in **Figure 7(a)**. The formation of trions extends the absorption range of quantum dots. The spectrum and the structure of a trion strongly depends on electron-electron and electron-hole coulomb interaction between electrons. The Hamiltonian of the system becomes:

$$H\_0 = \frac{p\_1^2}{2m^\*} + \frac{p\_2^2}{2m^\*} + \frac{P^2}{2m\_h^\*} + U\_\epsilon(r\_1) + U\_\epsilon(r\_2) + U\_h(R) + \frac{e^2}{\epsilon|r\_1 - r\_2|} + \left[\frac{e^2}{\epsilon|r\_1 - R|} + \frac{e^2}{\epsilon|r\_2 - R|}\right] \tag{15}$$

where r1 and r2 are positions of electrons (with momenta p1 and p2, respectively), R is the position of the hole (with mass *m*∗ *h* and the momentum P with the confinement potential Uh (R)), and o is the quantum dot material dielectric constant. The process ˛ leading to the overall decrease in the escape probability is shown in **Figure 7(b)**. Note that this process reduces the energy of the upper electron and increases the energy of the lower one.

#### **Figure 7.**

*(a) Trion in a photoexcited quantum dot. Electron positions are r1 and r2, and the hole position is R. (b) Interaction-induced transition between initial (left) and final (right) states in a quantum dot. Note that, due to the Pauli principle, this transition is possible only for the singlet electron spin state.*

Despite this partial increase, the escape probability, strongly dependent on the electron energy, decreases. The corresponding energy and time scales are given by the following matrix element, where we neglected the exchange effects take only the direct interactions as: *VC* ¼ Ð *ψ*1 *f*ð Þ *r*1 *ψ*2 *f*ð Þ *r*2 *ψh f* ð Þ *R HCψ*1 *i*ð Þ *r*1 *r*2Þ*ψh i* ð Þ *R d*3 *r*1*d*3 *r*2*d*3 *R* �

where the coulomb term

$$H\_C = \frac{e^2}{\varepsilon |r\_1 - r\_2|} + \left[ \frac{e^2}{\varepsilon |r\_1 - R|} + \frac{e^2}{\varepsilon |r\_2 - R|} \right] \tag{16}$$

The probability of this process is given by the time scale corresponding to the energy scale of the Hamiltonian (16), typically of the order of *e*2 *ϵω*, that is higher than 1 meV. Therefore, these processes are very fast, being, in general, energy-conserving and dependent on the total spin of participating electrons. This energy value corresponds to short time scales of these processes, of the order of a picosecond. Then, on the top of these fast transitions, a relatively slow energy relaxation due to phonons occurs, complicated by the transitions between the localized states in the quantum dots. Yet, the energy relaxation weakly depends on these fast electron transitions since these states are localized and, therefore, the estimate of long τ[loc] given in the previous subsection is valid here as well.

Thus, understanding of energy and spin relaxation processes in quantum dots suitable for applications in photovoltaics is crucial. The energy relaxation is due to the phonon emission. It was found the relaxation times of semiclassical states of the order of 1 ps, sufficient for electron escape from the quantum dot and its contribution to the photovoltaic effects. In addition, the redistribution of electrons among the energy levels and subsequent energy evolution can be caused by the electron-electron interactions. Spin relaxation in quantum dots is caused by direct spin-phonon coupling and leads to the spin relaxation times of the order of 1�102 microsecond, being the longest relaxation process in quantum dots.

### **Acknowledgements**

This work was supported by the National Centre for Research and Development under the project No. POIR.01.02.00-00-0265/17-00.

*Quantum Dots as Material for Efficient Energy Harvesting DOI: http://dx.doi.org/10.5772/intechopen.106579*


**Abstract**

Traditional single-mode fiber capacity issues will be mitigated by using space-division multiplexing in future 5G, IoT, and M2M networks. Multi-core fibers are expected as a good candidate for overcoming the capacity limit of a current optical communication system. This chapter describes the recent progress on the Multi-core fibers technology for the application of high capacity space-division multiplexing to be utilized for long-distance transmission systems. Further various optical approaches that enable key functions are discussed, including SDM MUX/ DeMUX, switches, transceivers to enable next generation optical network. Moreover, issues like crosstalk, non-linearity is a potential limitation on the achievable data-rates in optical fiber transmission systems using multi-core fibers will be discussed.

**Keywords:** All-optical signal processing, Crosstalk, Fiber capacity, Optical Network, Space division multiplexing


**5. Use of statin and antiplatelet therapy**

The use of statin is the other core element to prevent the premature death and disability in diabetes. The mechanisms of beneficial effects of statins in CVD are not completely understood. Some beneficial effects appear to occur independently of lowering of LDL cholesterol. Thus, most trials of statins and CVD outcomes tested specific doses of statins against placebo or other statins rather than aiming for specific LDL cholesterol goals, suggesting that the initiation and intensification of statin therapy should be based on risk profile [25, 32]. Before starting statin, lipid profile should be required at least once; a fasting sample is not routinely needed [32]. Antiplatelet therapy in diabetes is generally recommended for those with history of CVD and dual antiplatelet therapy is reasonable for up to 1 year after an acute coronary syndrome [25, 32].

**7. Antihyperglycemic medications**

**7.2. Risk of hypoglycemia**

is given below [21]:

**Figure 3**. A few pertinent points are further discussed below.

**Research perspectives:** Compared with the BMI cut point of 30.0 kg/m2

bution is observed at corresponding BMI cut points of 21.0 kg/m2

**7.1. Reduction of insulin resistance and beta-cell workload**

Type 2 diabetes is a chronic metabolic condition characterized by insulin resistance and insufficient pancreatic insulin production from beta-cell, resulting in high blood glucose levels [39]. Difficulty in utilizing the available insulin increases the workload of beta-cell. While using antihyperglycemic medications, the issues that need to be considered are shown in

Aboriginals in Canada [38]. The BMI cut points are, however, higher, for the lipid and blood pressure factors than for the glucose factor in South Asians, Chinese, and Aboriginals in the study [38] pointing out the need to study BMI cut points

in different populations similarly considering the glucose, lipid and blood pressure factors separately.

among Europeans, a similar glucose factor distri-

http://dx.doi.org/10.5772/intechopen.71193

in Chinese and 21.8 kg/m2

in

183

in South Asians, 20.6 kg/m2

Principle of Management of Type 2 Diabetes: From Clinical, Public Health…

Beta-cell failure is central to the ultimate development and progression of type 2 diabetes and it antedates and predicts diabetes onset and progression [40]. Subjects with normal glucose tolerance with 2-hour plasma glucose 120–139 mg/dL may have already lost 50% of beta-cell function, whereas subjects with impaired glucose tolerance with 2-hour plasma glucose 180– 199 mg/dL have lost up to 80% of beta-cell function. Thus, when the diagnosis of diabetes is made, the patient may have already lost 80% of their beta-cell function [41, 42]. The available clinical studies with appropriate protocols, however, indicate that existing therapy may not reverse or arrest progression of beta-cell dysfunction in type 2 diabetes [40]. Weight loss reduces insulin resistance and beta-cell workload and physical activity increases insulin utilization and also helps to reduce beta-cell workload and bodyweight. Both help to normalize the blood glucose and are also useful for cardiovascular health. Promotion of physical activity and exercise and loss of excess bodyweight for as long as possible are the cornerstones of management in type 2 diabetes. Medications favoring these two aspects are preferred for as long as possible.

Hypoglycemia can manifest in different ways (**Figure 4**). Insulin is a known cause of hypoglycemia. The risk of hypoglycemia associated with the use of other antihyperglycemic medications

**Figure 3.** Factors to consider while prescribing antihyperglycemic medications in type 2 diabetes.

**Research perspectives:** Studies indicate that lower statin doses achieve lipid improvements in Chinese, Japanese and Koreans patients comparable with those observed with higher doses in Caucasians [33]. However as the mechanism of action of statins may not be just related to the lowering of LDL cholesterol, the relation of such observation with cardiovascular events need to be studied.


**5. Prognosis**

The prognosis of HNMM is relatively dismal, often due to late diagnosis, with 5-year overall survival rate of 25% [116–121] and higher rates of local recurrence and distant metastases than cutaneous melanomas [10, 122, 123].

Distant metastasis is the most common cause of treatment failure. The most common sites for distant metastases are the lungs, followed by the liver, bones and brain [124].

Local recurrence is frequent and commonly associated to positive surgical margins. Advanced age is associated with decreased survival [59, 83, 98, 124–126]. Present of distant metastases, advanced T-category, ulceration, vascular invasion, deep infiltration and male gender are associated with a poorer prognosis too [8, 97].

The multidisciplinary team approach can help reduce morbidity and mortality once optimize treatment, reduce costs and minimize adverse events, while maximizing the chances of recovery. A collaborative interprofessional team includes surgery, medical oncology, radiation oncology, radiology, nuclear medicine and pathology [127]. A multidisciplinary team workup will provide proper appraisal evidence based decision-making, and the most helpful treatment planning and care.