**Comparing the Impact of Multi-Session Left Dorsolateral Prefrontal and Primary Motor Cortex Neuronavigated Repetitive Transcranial Magnetic Stimulation (nrTMS) on Chronic Pain Patients**

**Sascha Freigang 1, \* , Christian Lehner 1 , Shane M. Fresnoza 2,3 , Kariem Mahdy Ali 1 , Elisabeth Hlavka 1 , Annika Eitler 1 , Istvan Szilagyi 4 , Helmar Bornemann-Cimenti 5 , Hannes Deutschmann 6 , Gernot Reishofer 6 , Anže Berlec 1 , Senta Kurschel-Lackner 1 , Antonio Valentin 7 , Bernhard Sutter 1 , Karla Zaar 1 and Michael Mokry 1**

	- Institute of Psychology, University of Graz, 8010 Graz, Austria<EMAIL_ADDRESS>
2

**Abstract:** Repetitive transcranial stimulation (rTMS) has been shown to produce an analgesic effect and therefore has a potential for treating chronic refractory pain. However, previous studies used various stimulation parameters (including cortical targets), and the best stimulation protocol is not yet identified. The present study investigated the effects of multi-session 20 Hz (2000 pulses) and 5 Hz (1800 pulses) rTMS stimulation of left motor cortex (M1-group) and left dorsolateral prefrontal cortex (DLPFC-group), respectively. The M1-group (*n* = 9) and DLPFC-group (*n* = 7) completed 13 sessions of neuronavigated stimulation, while a Sham-group (*n* = 8) completed seven sessions of placebo stimulation. The outcome was measured using the German Pain Questionnaire (GPQ), Depression, Anxiety and Stress Scale (DASS), and SF-12 questionnaire. Pain perception significantly decreased in the DLPFC-group (38.17%) compared to the M1-group (56.11%) (*p* ≤ 0.001) on the later sessions. Health-related quality of life also improved in the DLPFC-group (40.47) compared to the Sham-group (35.06) (*p* = 0.016), and mental composite summary (*p* = 0.001) in the DLPFC-group (49.12) compared to M1-group (39.46). Stimulation of the left DLPFC resulted in pain relief, while M1 stimulation was not effective. Nonetheless, further studies are needed to identify optimal cortical target sites and stimulation parameters.

**Keywords:** chronic pain; low back pain; repetitive transcranial magnetic stimulation; neuromodulation; dorsolateral prefrontal cortex; primary motor cortex


**1. Introduction**

The cheese-making process of rennet coagulated cheeses consists in the formation of a three-dimensional network of paracasein, in which fat globules and part of the milk whey are entrapped. The quantity of milk constituents recovered into cheese is strictly dependent on the quality of the milk (casein content, casein micelle structure, and integrity) and conditions of the cheese-making process (for example, pre-acidification of processed milk, type and quantity of rennet, cooking temperature, acidification of the cheese mass), and thus varies depending on the cheese type.

The assessment of the e fficiency of the cheese-making process (ECMP) is crucial for the profitability of cheese-factories. The best way to quantify the ECMP is to measure the recovery of milk constituents into cheese through a mass balance determination. This can be obtained by measuring the quantity of a constituent in the processed milk and the resulting cheese. However, to perform this kind of analysis, it is necessary to also measure the weight of both the processed milk and cheeses. This is not always possible, especially in artisanal cheese factories, as those involved in Parmigiano Reggiano production, where the weight of the processed milk is estimated using a wooden measuring stick with a sensitivity, are not acceptable for research purposes.

Although less reliable than a mass balance, a rough and simple alternative way to estimate the ECMP is to measure the estimated cheese-making losses (ECL). In this method, the estimated loss of

a milk constituent is expressed by the ratio between its concentration in the cheese whey (C-whey) (that remains in the vat after the extraction of the cheese mass) and that in the processed vat milk (V-milk). Consequently, the determination of ECL is easier and faster to perform, since for a single milk constituent it is necessary to only measure its concentration in V-milk and C-whey. No weight of milk, cheese, or whey is needed, and it is not necessary to sample and analyze the cheese. Moreover, to date, the concentration of most milk constituents can be rapidly assessed by applying the mid infrared technology (MIR) [1–3]. The MIR technology is widely used by a lot of laboratories which provide analysis and technological consulting services to dairy farms producing Parmigiano Reggiano cheese. In these laboratories, this kind of analysis is routine, cheap, and certified ISO/IDF. The ECLs have been used in several studies to estimate the e ffect of breed [4], storage conditions [5], and somatic cells [6,7] on e fficiency in Parmigiano Reggiano cheese-making.

Parmigiano Reggiano is a hard, cooked, and long-ripened protected designation of origin (PDO) cheese made from raw milk, following a strict manufacture procedure [8]. In case of raw milk cheese, the quality of milk-in terms of chemical composition and microbial characteristics is one of the main factors influencing the e fficiency of the cheese-making process. Seasonal variations of milk characteristics at the herd level were reported in several studies [9,10]. Significant variations of the chemical and microbial quality of milk employed for Parmigiano Reggiano cheese throughout the year have also been reported [11,12]. These variations could have repercussions on ECLs and curd fines, as reported by Formaggioni et al. [13] and by Franceschi et al. [5]. However, these papers considered a limited number of cheese-making trials and milk traits, since minerals were not taken into account and only two periods of the year were considered. Moreover, in the majority of PDO cheese manufacture, such as Parmigiano Reggiano, where milk standardization and automation of the processes are not implemented, a strong variability in the ECMP is expected among dairy farms [14]. However, the quantification of this variability has never been carried out.

The aim of this research was to evaluate the influence of the season and cheese-factory on ECLs on the quantity of curd particles lost in the whey (curd fines) in Parmigiano Reggiano cheese manufacture carried out in field conditions.

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

#### *2.1. Cheese-Making Process*

Cheeses were produced by the approved method of the Consortium [8,15]. A natural whey starter culture (about 2.5–3 L for every 100 kg V-milk), obtained by the spontaneous acidification of previous day milk whey (C-whey), was added to the V-milk before coagulation. It was then heated to 33 ◦C and clotted in 10–12 min with 2.5 g for every 100 kg V-milk of calf rennet (1:125,000 units). The curd was broken up into small granules (approximately the size of a rice grain) and cooked. During this operation, the temperature was increased in two steps to 55 ◦C within 10–15 min; during this phase the curd was stirred continuously. After cooking the broken-up curd particles, thy were deposited by simple decantation at the bottom of the vat, where they aggregated and blended together spontaneously. In this step, the temperature was 55–53 ◦C and the process lasted 45–60 min. The cheese mass was then removed from the vat, divided into two parts, and placed in special molds called "fascere" for two days. During this period, the cheese wheels were naturally cooled and periodically turned over to allow an homogeneous drying. Furthermore, pH decreased from about 6.0 (at the extraction from the vat) to about 5.1 at the end of the two days. This is related to the activity of thermophilic lactic bacteria added with the natural whey starter, which converts lactose to lactic acid. The cheese wheels were then placed into a saturated brine for a period of 20–25 days. Finally, the cheese entered the ripening phase, a process that lasts about 24 months, and, at the end of the ripening, the cheese wheels resulted in a cylindrical in shape, with a slightly convex side, 22–24 cm high, 40–45 cm diameter, weight 35–36 kg.

#### *2.2. Experimental Design and Sampling Procedure*

The research involved 288 Parmigiano Reggiano cheese-making trials, carried out in 3 cheese factories (CF1, CF2, and CF3), throughout two years.

Briefly, every cheese factory vat (that was filled with the milk of the same herd throughout the two years of the experimental period) was selected (6 vats in CF1, 3 in CF2, and 3 CF3). Each selected vat was sampled once a month, and all the vats of the same cheese factory were sampled on the same day. Each vat always contained the milk from the same farm. From each cheesemaking, samples of V-milk and C-whey were collected, following the International Dairy Federation standard [16]. V-milk samples were collected at the beginning of the cheese making process, before the addition of the natural whey starter culture. C-whey samples were collected after the extraction of the cheese mass and stirring of the whey.

#### *2.3. Analytical Methods*

The following traits were determined or calculated on both V-milk and C-whey. Total N (TN) and non casein N (NCN) were measured on milk and acid whey at pH4.6, respectively, by Kjeldahl, from which the values of crude protein (TN × 6.38/1000) and casein ((TN-NCN) × 6.38/1000) were calculated [7]. Fat content was assessed by the mid infrared method using a FT 6000 (Foss Electric, DK-3400 Hillerød Denmark); dry matter was measured after oven drying at 102 ◦C and ash was measured after muffle calcination at 530 ◦C [17]; total Ca and Mg were determined on a chloridric ash solution by atomic absorption spectrometry (AAS) with a wavelength reading at 422.7 and 285.2 nm, respectively; and total P was assessed on a chloridric ash solution following the colorimetric method [18] with a wavelength reading at 750.0 nm. Titratable acidity was measured only on V-milk by titration of 50 mL of milk with 0.25 N sodium hydroxide with the Soxhlet–Henkel method [7]. Rennet coagulation properties were also measured on V-milk, using Formagraph (Foss Electric, DK-3400 Hillerød Denmark) [4]. The analysis was performed adding 0.2 mL (1:100) of rennet solution (1:19,000; Chr. Hansen, I-20094 Corsico MI, Italy) to milk samples (10 mL). The rennet coagulation properties, milk clotting time, curd firming time, and curd firmness, were measured at 35 ◦C. Milk clotting time is the time from the addition of rennet to the onset of gelation. Curd firming time is the time from the onset of gelation till the signal attains a width of 20 mm. Curd firmness is the width of the signal 30 min after the addition of rennet. To record curd firming time values in milk samples that do not reach a width of 20 mm within 30 min, the analysis was prolonged to 60 min. The curd fines were determined in C-whey by the gravimetric method proposed by van den Berg et al. [19]. In this method, 250 of C-whey were centrifuged at 2000 g for 30 min. The pellet was resuspended in distilled water and filtered on a Whatman 40 filter paper. The filter was dried at 102 ◦C for 2 h and weighed.

ECLs of dry matter, protein, casein, fat, calcium, phosphorus, and magnesium were calculated as follows:

$$\text{ECL} = \text{[C-whey]} \times 100 \text{[V-milk]}$$

where ECL is expressed as percentage; C-whey = concentration in whey, expressed as g/100 g (mg/100 g for Ca, P, Mg); V-milk = concentration in milk, expressed as g/100 g (mg/100 g for Ca, P, Mg).

#### *2.4. Statistical Analysis*

The significance of the differences between seasons and cheese-factories were tested by analysis of variance, using the software for statistical analysis SPSS (IBM SPSS Statics 23, Armonk, New York 10504-1722, NY, USA), according to the following univariate model:

$$\mathbf{Y}\_{\rm ijk} = \mu + \mathbf{S}\_{\rm i} + \mathbf{C}\_{\rm j} + \varepsilon\_{\rm ijk}$$

where Yijk = dependent variable; μ = overall mean; Si = effect of season (i = 1, ... , 4; winter, from January to March; spring, from April to June; summer, from July to September, Autumn, from October to

December); Cj = effect of cheese-factory (j = 1, ... , 3; CF1, CF2, CF3); εijk = residual error. The Bonferroni post-hoc test was employed to evaluate the significance of the differences between means.

Data were also processed by the Pearson product moment correlation coefficient to measure the degree of linear relationship between milk constituents and ECLs.


**Table 4.**

*Weights of the main criteria.*

$$S\_k(\mathbf{x}\_i) = \sum\_j^n f\_j^k(\mathbf{x}\_i) w\_{kj} \tag{2}$$

presented in **Table 5**. All the criteria were scored between 0.000 and 1.000. For all the criteria, the scores closer to 1.000 indicate the high level of success for the criterion, whereas the scores closer to 0.000 indicate failure. In this section, the

In this criterion aiming to measure the urban design quality of Turkish cities in

In this criterion, taking the environmental quality as a basis, there are three subcriteria. None of these sub-criteria could exceed the score of 0.30 (appearance and

terms of the physical design, there are five sub-criteria. Constituting the main principles of the physical design in general, the highest score among the five criteria was 0.35, whereas the lowest one was 0.15. Since it has a score of 0.35, "diversity" is the criterion with the highest score, but it is much lower than the intermediate level (0.50). Moreover, the mean score of these five criteria was found to be 0.21. From this aspect, the score of the main criteria is 0.19. Given these values, it can be understood that urban design quality is not at a good level in Turkey. All the scores were found to be below 0.35. There are many problems to solve before Turkish cities can come to the forefront with their planned development. It is an inevitable necessity to reconsider the integrity of planning and design in both planning and designing processes. It is understood from these results that, without considering the identities and characters of the cities, they were either damaged or neglected and not included in the planning and design processes. Rather than emphasizing or enhancing the diversity, an inclination towards stereotyping and monotony has been exhibited. Rather than making the cities more readable and defined, the complexity and irregularity have become the dominant approach. The physical and functional continuity has been destructed by the point regulations. There are many undefined areas. The effects of guidance, coverage, and closeness have decreased. Rather than flexible regulations meeting the current necessities, strict and old-

evaluations are made based on the scores of criteria.

school implementations have become prominent.

**8.2 Second main criterion**

**343**

**8.1 First main criterion**

*Scores by the main criteria and sub-criteria.*

*Assessing the Urban Design Quality of Turkish Cities DOI: http://dx.doi.org/10.5772/intechopen.89779*

**Table 5.**

where *j* = 1,2, … ,9; *Sk*ð Þ *Xi* is the element evaluation *X I* and j criteria for all sub-criteria k.

$$T(\mathbf{x}\_i) = \sum\_{j}^{n} \mathbf{S}\_k(\mathbf{x}\_i) w\_k \tag{3}$$

*T*(*xi*) is the evaluation of all criteria for the element k.


*4.6. Perl's Stain*

Tissue slides were dewaxed in xylene and rehydrated in a series of alcohol solutions using decreasing concentrations. Perl's stain was performed using the Iron Stain Kit (Sigma Aldrich, Taufkirchen, Germany, HT20) according to the manufacturer's protocol. Slides were then washed in distilled water, counterstained with Nuclear Fast Red solution (Sigma Aldrich, N3020), rapidly dehydrated, and mounted in Entellan (Merck, Darmstadt, Germany, 107961). Pictures were acquired using an Axioskop 40 (Zeiss, Oberkochen, Germany).


**4.3 Asymmetric nucleation by one-handed CPL**

Solid CO is formed via CO deposition from the vapor phase in molecular clouds. The crystallinity of solid CO, either amorphous or crystalline, can be determined by the CO flux in a molecular cloud [5]. Because the CO flux in a molecular cloud is much smaller than the critical flux in which amorphous CO (a-CO) is formed, α-CO should be formed. When one-handed CPL is irradiated during the nucleation of α-CO, the formed crystals might have an enantiomeric excess (**Figure 7**). When there are no metal or high-index nanoparticles on icy grains, α-CO can absorb UV-CPL, which may result in excess enantiomeric crystals. In this case, the formation of one-handed α-CO would only occur in the shallow part of the molecular cloud because the UV-ray penetration depth is not so large (see **Figure 6**). However, when there are metal or high-index nanoparticles on the icy grains, the peak absorption wavelength could be transferred to the visible wavelength region, and the peak could be enhanced compared to that of the UV region [65–67], resulting in excess enantiomeric crystals, possibly up to several tens of percent. This could be supported by laboratory experiments on chiral crystallization [68, 69] and theoretical work [70–72]. In this case, the

formation of one-handed α-CO would occur not only in shallow parts of a molecular cloud but also in deeper parts because the penetration depth of visible rays is considerably deep (see **Figure 6**).

A similar process might occur during the crystallization of a-H2O to form chiral ice crystals and hydrogen-ordered cubic and hexagonal ices (see **Table 2**) in protosolar nebula, as shown in **Figure 1**. The crystallization temperature of a-H2O under a 105 -years' timescale is 90 K [12]. The penetration depth of UV to visible CPL in protoplanetary disks is smaller than that in molecular clouds. However, icy grains could be moved to the surface of the disk by turbulent motion [73] and irradiated with CPL, resulting in the formation of one-handed, hydrogen-ordered H2O crystals. When ice crystals were recondensed during the cooling of the solar nebula (**Figure 1**), one-handed, hydrogen-ordered H2O crystals might be formed by the same mechanism.

The crystallization of a-CH3OH and a-NH3 also occurred in the protoplanetary disk. Crystallization temperatures of a-CH3OH and a-NH3 under a 105 -years' timescale can be estimated from those in the laboratory (a-CH3OH: 100 K [14, 42] and a-NH3: 80 K [15, 45]) and from the assumption that the slopes of a-CH3OH and a-NH3 in the plot of the timescale of crystallization vs. the inverse of the temperature lie between those of H2O [12] and CO2 [6]. We found that the crystallization temperatures of a-CH3OH and a-NH3 under the 105 -years' timescale were 40–60 K and 20–40 K, respectively. The formation of one-handed α-CH3OH, NH3 I, and NH3 hydrates might also occur, as in the crystallization of a-H2O. In this way, various kinds of homochiral ice crystals could be formed in protoplanetary disks.

## **5. Conclusion and outlook**

The results of this study indicate the possibility that there were/are many chiral ice crystals in space and that homochiral ice crystals might form by the irradiation of CPL in the star-forming region. These findings have important implications for the origin of the homochirality of organic molecules in space, and the pursuit of the following three suggested areas of study would further our understanding of this.

The crystallinity of CH3OH and NH3 in space and the formation mechanism of α-CH3OH, NH3 I, and their hydrates in protoplanetary disks are still unclear. Therefore, astronomical observations of the crystallinity of these ices are highly desirable.

For chemical reactions on icy grains, only a-H2O ice has been considered as a substrate. The adsorption and subsequent surface diffusion of atoms (H, C, N, and O), small molecules (e.g., CO, CO2, and H2CO), and radicals (e.g., OH, HCO, and NH), followed by surface two-body reactions to form larger molecules on a-H2O at low temperatures have been calculated using astrochemical reaction network models [74]. However, this study indicated the possibility of the growth of single ice crystals on grains. On the surface of α-CO, the adsorption behavior of atoms differs greatly from that on a-H2O [6]. Therefore, it is expected that atoms, except for C and small molecules/radicals, are not adsorbed on the surface of singe-crystalline H2O ice I. Instead, larger molecules/radicals diffuse easily on the surface of singe-crystalline H2O ice I, which leads to the formation of more complex organic molecules. Furthermore, the asymmetric adsorption/synthesis of organic molecules on homochiral ice crystals might also proceed.

The search for enantiomeric surfaces on achiral ice crystals, as investigated in minerals [2, 4], is another important subject that should be explored.


2.2.6. Statistical Analysis

The statistical data on the WPI-TA hydrogels' swelling under conditions with different pH, both with and without TA, the TA release and the cytotoxic activity of the hydrogels were calculated using Microsoft Excel. Means and standard deviations were obtained from 3–6 independent experiments.

The data on the kinetics of swelling of hydrogels loaded with TA incubated in PBS at different pH values were plotted as "mean ± standard error" (*n* = 6). The viability of Hep2 cells incubated for 24 and 48 h with hydrogels containing different TA/WPI ratio was presented as "mean ± standard error" (*n* = 4). Differences between treatments were analyzed using two-way analysis of variance (ANOVA, Microsoft Excel 2016). [26] Calculations were carried out using Microsoft Excel software. Values of *p* ≤ 0.05 were considered significant (Tables S1–S4).


**5.3. UPLC and fluorescence methods**

**4. Conclusions and applications**

to this analysis.

108 Honey Analysis

**5. Analytical techniques**

**5.1. Fluorescence spectroscopy**

fluorescence units (RFU).

**5.2. HPLC**

Fluorescence analysis is a novel technique to determine manuka honey authenticity. Two unique compounds have been found in manuka honey, Leptosperin and Lepteridine, and these compounds are responsible for the MM1 and MM2 fluorescence described in the honey. In New Zealand, Leptosperin and Lepteridine are present only in *L. scoparium* nectar and therefore, these compounds are reliable chemical markers for manuka honey. However, the concentrations of these compounds do not predict DHA and MGO concentrations in a honey. Fluorescence spectroscopy is a rapid technique with high throughput, and relatively simple fluorescence screening assessments are gaining increasing attention in food processing sys‐ tems. Technology for assessing fluorescence is developing rapidly and handheld fluorometers are available. A handheld fluorometer could be used in the field by beekeepers, alternatively

Fluorescence assessment of manuka honey is an independent method separate from liquid chromatography coupled to detectors such as DAD or mass spectrometry. The use of two sets of wavelengths in combination, which can be screened simultaneously, adds robustness

Therefore, analysis of the MM1 and MM2 wavelengths is an efficient way of screening New Zealand honeys to ensure that attribution of floral source is appropriate and manuka honeys are wholly or predominantly sourced from *L. scoparium*, and these honeys that do not display

Honey fluorescence was analysed by scanning fluorescence spectroscopy according to methods described previously [36]. Honey samples were diluted with distilled water to 2% w/v, and loaded as 100 μL aliquots into a flat‐bottom microplate (OptiplateTM‐384, black). Fluorescence measurements were carried out on a Gemini EM Dual‐Scanning Microplate Spectrofluorometer (Molecular Devices Inc., Sunnyvale, CA, USA) operated with the SoftMax® Pro software. A fluorescence top read setting with automatic calibration and sen‐ sitivity at an ambient temperature was adopted for analysis at both MM1 and MM2 marker wavelengths. Fluorescence intensity was expressed as arbitrary units, in this case relative

Leptosperin, Lepteridine, methyl syringate and 2‐methoxyacetophenone concentrations were quantified on a Dionex UltimateTM 3000 reverse‐phase HPLC system (Thermo Fisher

in market by retailers and may be of use to regulatory authorities.

characteristics of manuka honey are not inappropriately labelled.

Scientific, New Zealand) with diode‐array detection (DAD).

Honey samples (0.5 g) were weighed into a polypropylene extraction tube and solubilised in 9.5 mL of 10% acetonitrile containing 0.1% formic acid in Type 1 water by shaking and ultra‐ sonic agitation. After centrifugation to remove particulates, an aliquot was diluted a further fivefold for analysis by ThermoFisher Ultimate‐3000 UPLC with an RS fluorescence detector ( ex264nm–em365nm), using a Waters XSelect HSS T3 C18 column (2.1 × 30 mm, 2.5 μm particle size). Mobile phase A was 0.1% formic acid in water and mobile phase B was 0.1% formic acid in methanol. The elution gradient started at 8% B (92% A) and increased to 100% B over 10 min before equilibration in 92% A for 3 min. Leptosperin was quantified against a synthetic standard.

For gross honey fluorescence analysis, 250 μL of the 20‐fold diluted extract used for the Leptosperin analysis was added to the well of a fluorescence‐grade 96‐well plate and the gross fluorescence of each sample measured at MM1 using a SpectraMax i3 (Molecular Devices LLC, Sunnyvale CA, USA).

## **5.4. DHA and MGO**

Concurrent analysis of DHA and MGO was carried out on a Dionex UltimateTM 3000 reverse‐ phase UPLC‐DAD system (Thermo Fisher Scientific, New Zealand) following derivatisation with *O*‐(pentafluorobenzyl) hydroxylamine hydrochloride (PFBHA). Honey samples were prepared in distilled water at a 1:20 w/v ratio. The samples were thoroughly mixed and incu‐ bated at 50°C for 1 hour to allow complete dissolution of any sugar crystals.

The derivatisation procedure was carried out according to methods developed by Kato et al. [41] with some modifications. A 2% stock solution of PFBHA was prepared in 0.1 M citrate buffer adjusted to pH 4 with NaOH (1 M). A working solution of PFBHA derivatising reagent was prepared consisting of 7:2:1 LC‐MS grade acetonitrile:distilled water:PFBHA stock solu‐ tion, and added to the honey samples at a 5:1 v/v ratio. The PFBHA:honey mixture was incu‐ bated at 50°C for 1 hour and cooled to room temperature.

A 5 μL aliquot of the derivatised sample or standard was injected into the UPLC‐DAD system. Separation was carried out by gradient elution on a Hypersil GOLD column (100 × 2.1 mm, 1.9μμm particle size) at a constant flow rate of 0.700 mL/min. The mobile phase consisted of 0.1% v/v aqueous formic acid (Solvent A) and LC‐MS grade acetonitrile (Solvent B), and the gradient elution programme was as follows: initial (B 20%, held 0.6 min), 1.3 min (B 70%), 3 min (B 100%, held 0.5 min) and 4 min (B 20%). The column was thermostatically controlled at 50°C. Dihydroxyacetone was monitored at 214 nm and MGO at 246 nm.

Data acquisition and peak integration were performed with Thermo Fisher ScientificTM Dionex™ Chromeleon™ 7.2 CDS software. Honey DHA and MGO were quantified using external calibration curves generated from the DHA and MGO working standards by linear regression of peak area against concentration.