**Meet the editor**

Dr. P. Venkatesan is currently working as a senior scientist in the Division of Extension Systems Management of ICAR-National Academy of Agricultural Research Management (NAARM). He specialized in agricultural extension and has 15 years of experience as a developmental scientist and 3 years as a core scientist. He joined NAARM in May 2014. He specialized in the area of

indigenous technical knowledge, participatory technology development, participatory impact monitoring and assessment, tribal development, technology assessment, and refinement of socioeconomic and environmental impact assessment. He has published several research papers, books, manuals, and policy papers. To his credit, he has guided a number of research scholars in rural development and management. He is a recipient of four national and two international awards.


**Nina-Katharina Krahe \*, Ralf G. Berger and Franziska Ersoy**

Institut für Lebensmittelchemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167 Hannover, Germany<EMAIL_ADDRESS>(R.G.B<EMAIL_ADDRESS>(F.E.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +49-511-762-17257

Received: 20 February 2020; Accepted: 23 March 2020; Published: 27 March 2020

**Abstract:** Alkene cleavage is a possibility to generate aldehydes with olfactory properties for the fragrance and flavor industry. A dye-decolorizing peroxidase (DyP) of the basidiomycete *Pleurotus sapidus* (PsaPOX) cleaved the aryl alkene *trans*-anethole. The PsaPOX was semi-purified from the mycelium via FPLC, and the corresponding gene was identified. The amino acid sequence as well as the predicted tertiary structure showed typical characteristics of DyPs as well as a non-canonical Mn2+-oxidation site on its surface. The gene was expressed in *Komagataella pfa*ffi*i* GS115 yielding activities up to 142 U/L using 2,2 -azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) as substrate. PsaPOX exhibited optima at pH 3.5 and 40 ◦C and showed highest peroxidase activity in the presence of 100 μM H2O2 and 25 mM Mn2+. PsaPOX lacked the typical activity of DyPs towards anthraquinone dyes, but oxidized Mn2+ to Mn3+. In addition, bleaching of β-carotene and annatto was observed. Biotransformation experiments verified the alkene cleavage activity towards the aryl alkenes (*E*)-methyl isoeugenol, α-methylstyrene, and *trans*-anethole, which was increased almost twofold in the presence of Mn2+. The resultant aldehydes are olfactants used in the fragrance and flavor industry. PsaPOX is the first described DyP with alkene cleavage activity towards aryl alkenes and showed potential as biocatalyst for flavor production.

**Keywords:** alkene cleavage; aryl alkenes; basidiomycota; biocatalysis; carotene degradation; dye-decolorizing peroxidase (DyP); manganese; *Komagataella pfa*ffi*i*; *Pleurotus sapidus*


**3. Polymer properties**

There are some important physical and chemical properties needed to be considered before selecting polymers for synthesis followed by the design and development of nanoparticles such as (1) structural integrity, (2) low immunogenic nature, (3) biocompatibility (for biomedical applications), (4) biologically active nature, and (5) structural versatility (which can easily allow topographic changes internally as well externally). Polymers with physicochemical properties such as hydrophilic (solvent loving), hydrophobic (solvent repelling), and amphiphilic (affinity to absorb and repel the solvent) nature, surface energy, viscosity, lubricity, density, surface tension, pH sensitivity, smoothness, swelling nature, solubility, amorphous and crystalline nature, polymer dissolution, bioadhesive and sticky nature, polymer-solvent interaction, polymer erosion, flexibility, chain formatting nature, chemical composition, microstructural design, and other surface properties need to be examined carefully for the design of nanoparticles and capsules. By considering these properties, nanoparticle preparation methods can also be modified for the polymerization reactions (**Figure 3**). For example, (1) natural polymers like (a) chitin, (b) cellulose, and (c) starch can be widely used for nanoparticle development through the modified chemical synthesis methods [29, 30]. Further, (2) semisynthetic polymers like (a) cellulose nitrate, (b) rubber, (c) rayon and (d) silk and fiber-based materials, and (e) guncotton are synthesized based on their flexibility and other stresses and strain rate for various applications, and these are very much suitable to design the ribbon type of fibrils for textile industry-based usages. However, (3) synthetic polymers such as (a) polymethyl methacrylate amide (PMMA); (b) nylon, nylon-6, and nylon-6,6; and (c) polystyrene (PS) are very much useful for plastic industries to manufacture polyvinyl chloride (PVC) based pipes, tubes, cables, and other plasticwares. The polymer tetrafluoroethylene (Teflon or PTFE) with monomer chemical formula (–C2F4–) is under wide range utilization in the name of Teflon tape as a sealant for pipe linkages and insulation tape. The natural rubbers isoprene and neoprene can be used to develop various products industrially [31, 32].

The natural rubber, vulcanized rubber, or sulfur has been used in tire industries. Similarly, formaldehyde and benzophenol combination is used to develop combs and other plasticwares, and its trade name is Bakelite. The polymer polyethylene (PE) is used for polyethylene covers/carriage bags and much more houseware and industrial manufacturing. The polymer PE is again categorized into two types based on its density for the manufacturing of plastic materials such as low-density polyethylene (LDPE) and high-density polyethylene (HDPE) based on applications [33]. The following properties of polymers are essential to develop potential nanoparticles for drug delivery systems (DDSs), gene delivery systems (GDSs), cell delivery systems (CDSs), tissue engineering (TE), and other nanobiotechnology applications such as molecular weight, self-assembly nature, density, network

**145**

**Figure 3.**

*development of nanoparticles.*

biocompatibility for human beings [36].

**4. Classification of nanoparticles**

*Polymer Properties: Functionalization and Surface Modified Nanoparticles*

forming capacity, and interlocking nature of functional groups followed by the selfassembly. Based on applications (biomedical or industrial), polymeric nanoparticles should possess the following characteristics such as biocompatibility and biodegradability with good cell survival rate, and they should obey the properties as mentioned in earlier sections [34]. However, every nanoparticle system has their limitations for their use in medical biotechnology and nanotechnology applications. By changing reaction methods, solvent concentration, temperature conditions, pH levels, its surface profile and another size, shape, and morphology can be modified [35]. Design and development of surfactant-free nanoparticles without using any harmful solvents and catalyst is a novel approach for green synthesis and technology. The term green synthesis, green technology, green nanoparticles, and green reactions define the methods and approaches followed by engineer nanoparticles and materials without any side effects, toxicity, and harm to mankind applications. The nanoparticles and capsules developed based on amino acid-based block copolymers, and plant extracts are termed as green nanoparticles as they show good

*Schematic shows the characteristic properties of polymers needed to be considered for the design and*

Based on core synthesis materials, nanoparticles are categorized into various types such as (a) protein nanoparticles (PrNPs), (b) polymeric nanoparticles (PNPs), (c) organic nanoparticles (ONPs), (d) inorganic nanoparticles (INPs),

*DOI: http://dx.doi.org/10.5772/intechopen.84424*

*Polymer Properties: Functionalization and Surface Modified Nanoparticles DOI: http://dx.doi.org/10.5772/intechopen.84424*

#### **Figure 3.**

*Role of Novel Drug Delivery Vehicles in Nanobiomedicine*

(**Figure 2**) [20, 21, 26–29].

**3. Polymer properties**

products industrially [31, 32].

combination of a large number of high molecular mass polymers give micronsized nanoparticles, and they can be used for bulk phenomenal approaches such as absorption, chemisorption, etc. There is a class of polymers named as block copolymers, and these are again categorized into several such as (a) random block copolymers (BCPs), (b) star block copolymers, (c) grafted block copolymers, and (d) alternating block copolymers, and these are classified based on their synthesis approaches and final structural confirmations, and these are low-density polymers

There are some important physical and chemical properties needed to be considered before selecting polymers for synthesis followed by the design and development of nanoparticles such as (1) structural integrity, (2) low immunogenic nature, (3) biocompatibility (for biomedical applications), (4) biologically active nature, and (5) structural versatility (which can easily allow topographic changes internally as well externally). Polymers with physicochemical properties such as hydrophilic (solvent loving), hydrophobic (solvent repelling), and amphiphilic (affinity to absorb and repel the solvent) nature, surface energy, viscosity, lubricity, density, surface tension, pH sensitivity, smoothness, swelling nature, solubility, amorphous and crystalline nature, polymer dissolution, bioadhesive and sticky nature, polymer-solvent interaction, polymer erosion, flexibility, chain formatting nature, chemical composition, microstructural design, and other surface properties need to be examined carefully for the design of nanoparticles and capsules. By considering these properties, nanoparticle preparation methods can also be modified for the polymerization reactions (**Figure 3**). For example, (1) natural polymers like (a) chitin, (b) cellulose, and (c) starch can be widely used for nanoparticle development through the modified chemical synthesis methods [29, 30]. Further, (2) semisynthetic polymers like (a) cellulose nitrate, (b) rubber, (c) rayon and (d) silk and fiber-based materials, and (e) guncotton are synthesized based on their flexibility and other stresses and strain rate for various applications, and these are very much suitable to design the ribbon type of fibrils for textile industry-based usages. However, (3) synthetic polymers such as (a) polymethyl methacrylate amide (PMMA); (b) nylon, nylon-6, and nylon-6,6; and (c) polystyrene (PS) are very much useful for plastic industries to manufacture polyvinyl chloride (PVC) based pipes, tubes, cables, and other plasticwares. The polymer tetrafluoroethylene (Teflon or PTFE) with monomer chemical formula (–C2F4–) is under wide range utilization in the name of Teflon tape as a sealant for pipe linkages and insulation tape. The natural rubbers isoprene and neoprene can be used to develop various

The natural rubber, vulcanized rubber, or sulfur has been used in tire industries. Similarly, formaldehyde and benzophenol combination is used to develop combs and other plasticwares, and its trade name is Bakelite. The polymer polyethylene (PE) is used for polyethylene covers/carriage bags and much more houseware and industrial manufacturing. The polymer PE is again categorized into two types based on its density for the manufacturing of plastic materials such as low-density polyethylene (LDPE) and high-density polyethylene (HDPE) based on applications [33]. The following properties of polymers are essential to develop potential nanoparticles for drug delivery systems (DDSs), gene delivery systems (GDSs), cell delivery systems (CDSs), tissue engineering (TE), and other nanobiotechnology applications such as molecular weight, self-assembly nature, density, network

**144**

*Schematic shows the characteristic properties of polymers needed to be considered for the design and development of nanoparticles.*

forming capacity, and interlocking nature of functional groups followed by the selfassembly. Based on applications (biomedical or industrial), polymeric nanoparticles should possess the following characteristics such as biocompatibility and biodegradability with good cell survival rate, and they should obey the properties as mentioned in earlier sections [34]. However, every nanoparticle system has their limitations for their use in medical biotechnology and nanotechnology applications. By changing reaction methods, solvent concentration, temperature conditions, pH levels, its surface profile and another size, shape, and morphology can be modified [35]. Design and development of surfactant-free nanoparticles without using any harmful solvents and catalyst is a novel approach for green synthesis and technology. The term green synthesis, green technology, green nanoparticles, and green reactions define the methods and approaches followed by engineer nanoparticles and materials without any side effects, toxicity, and harm to mankind applications. The nanoparticles and capsules developed based on amino acid-based block copolymers, and plant extracts are termed as green nanoparticles as they show good biocompatibility for human beings [36].


*3.14.11 The resurrection or transformation*

A profound change in the values of the character. Each journey of storytelling mindfulness allows us a transformation without expectations that makes us not the same as we started the trip. Each meditation process allows us to become aware of transpersonal micro transformations in each meditation, becoming aware of the fact that we change every moment.


*4.3. Human iPSCs*

The contribution of animal models to our overall understanding of DCM has been indispensable. However, many important differences exist between animal models and humans. Additionally, cardiac tissues from DCM patients are difficult to obtain and exhibit a low survival rate in long-term culture. The emergence of induced pluripotent stem cells (iPSCs) [130], and the rapidly advancing technology associated with them have made it possible to obtain functional cardiac myocytes through the differentiation of human iPSCs derived from DCM patients [113,114]. Stem cell-derived cardiac myocytes from cells isolated directly from patients with cardiomyopathies recapitulate certain aspects of human cardiovascular disease and represent a powerful new model system to study the basic mechanisms of inherited cardiomyopathies. Thus, human induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CMs) are an important complement to experimental animal models to study the cellular, molecular, and physiological mechanisms associated with the pathogenesis of DCM, as well as to establish high-throughput platforms for drug screening in human cells.

DCM was first modeled by Sun and coworkers using iPSCs-CMs derived from a member of a family with DCM carrying a heterozygous R173W mutation in cardiac troponin T (TNNT2) [111]. iPSC-derived cardiac myocytes from this patient recapitulated some of the morphological and functional phenotypes of familial DCM with inherited mutations in troponin T. This study describes the first successful modeling of dilated cardiomyopathy in hiPSC-CMs. Another patient-specific DCM iPSC line was generated from a single member of a family with an autosomal dominant nonsense mutation (p.R225X) in exon 4 of the lamin A/C (LMNA) gene. hiPSC-CMs from this patient showed morphologic changes, including a higher prevalence of nuclear bleb formation, micronucleation, as well as nuclear senescence and cellular apoptosis [112]. Additionally, Tse and colleagues generated hiPSC-CMs derived from a DCM patient with a novel heterozygous mutation of p.A285V codon conversion on exon 4 of the desmin (DES) gene [113]. In this study, hiPSC-CMs were able to provide histologic and functional confirmation that the candidate gene variant detected by whole exome sequencing was responsible for the disease.

To study the molecular mechanisms underlying DCM in DMD, Lin and co-workers generated cardiac myocytes (CMs) from DMD patients and healthy control induced pluripotent stem cells (iPSCs). Using DMD patient-derived iPSC-CMs, they have established an *in vitro* model that manifests the major phenotypes of DCM in DMD patients, and uncovered a potential new disease mechanism [114]. In this regard, Lin and co-workers examined a collection of muscular dystrophies (including DMD and Becker Muscular Dystrophy) and healthy hiPSC-derived cardiac myocytes. This included demonstration that loading of the treated DMD hiPSC-derived cardiac myocytes with the calcium sensitive dye, Rhod-2AM, revealed increased cytosolic calcium concentration. The use of calcium assays in hiPSC derived cardiac myocytes is becoming commonplace due to the ease and availability of high speed/resolution optical imaging techniques. Typically, this uses voltage-sensitive dyes or genetically-encoded voltage indicators to measure action potentials and calcium wave propagation. Indeed, Guan and coworkers showed a two-fold increase in T50 (duration of recovery) of calcium transients in hiPSC-derived cardiac myocytes from DMD patients compared to healthy hiPSC derived cardiac myocytes [131]. In addition, Tsurumi et al. reported that the measurement of the fluorescent ratio (410/490 nm) of indo-1 demonstrated that the intracellular calcium concentration was much higher in cardiac myocytes differentiated from DMD-hiPSCs than in those differentiated from control-hiPSCs [132].

HiPSC-CMs represent a unique platform to study basic mechanisms of cardiomyopathies using a human cell-based system. The literature published in the past decade demonstrates the utility of patient-specific iPSCs in disease modeling of cardiomyopathy, and has provided unique insights into disease mechanisms. However, the hiPSC-CMs that are available today still largely represent an immature version of the adult cardiac myocyte, and thus have inherent limitations in the study of cardiovascular disease. Systems to induce greater maturation in hiPSC-CMs are being developed, but considerable work remains to be done to further advance this model system.


**Xinmin Li 1, Guokai Jiang 2, Wei Chen 3, Tingna Shi 4, Guozheng Zhang 1 and Qiang Geng 1,\***

Received: 24 April 2019; Accepted: 17 May 2019; Published: 20 May 2019

**Abstract:** This paper presents a commutation torque ripple suppression strategy for brushless DC motor (BLDCM) in the high-speed region, which considers the back electromotive force (back-EMF) variation during the commutation process. In the paper, the influence of actual back-EMF variation on the torque and outgoing phase current during the commutation process is analyzed. A modified smooth torque mechanism is then reconstructed considering the back-EMF variation, based on which a novel torque ripple suppression strategy is further designed. Compared with the traditional strategy which controls the chopping duty cycle relatively smoothly in the commutation process, the proposed strategy dynamically regulates the chopping duty cycle, which makes it show a gradual decrease. This strategy can suppress the commutation torque ripple even in a long commutation process, and broaden the speed range of the commutation torque ripple reduction. Under the experimental conditions of this paper, the proposed strategy can effectively reduce the commutation torque ripple in the high-speed region, and avoid the outgoing phase current cannot be reduced to zero. The experimental results verify the correctness of the theoretical analysis and the feasibility of the proposed strategy.

**Keywords:** brushless DC motor; commutation torque ripple; back electromotive force


*Article* **Pain Catastrophising Predicts Alcohol Hangover Severity and Symptoms**

#### **Sam Royle \*, Lauren Owen, David Roberts and Lynne Marrow**

Department of Psychology, School of Health and Society, University of Salford, Frederick Road, Salford M6 6PU, UK<EMAIL_ADDRESS>(L.O<EMAIL_ADDRESS>(D.R<EMAIL_ADDRESS>(L.M.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +44-161-2950-278

Received: 8 January 2020; Accepted: 15 January 2020; Published: 20 January 2020

**Abstract:** Alcohol hangover is a cause of considerable social and economic burden. Identification of predictors of alcohol hangover severity have the potential to contribute to reductions in costs associated with both absenteeism/presenteeism and health care. Pain catastrophising (PC) is the tendency to ruminate and describe a pain experience in more exaggerated terms. The current study examines the possibility that this cognitive coping strategy may influence experience of alcohol hangover. The aims of the current study were to (1) examine the relationship between hangover severity and PC, (2) explore and identify discreet factors within the Acute Hangover Scale (AHS) and (3) explore whether independent factors/dimensions of acute hangover are differentially predicted by PC. A retrospective survey (*n* = 86) was conducted in which participants completed the Acute Hangover Scale (AHS); the Pain Catastrophising Scale (PCS); a questionnaire pertaining to the amount of alcohol consumed; and a demographic information questionnaire. Regression analyses showed a significant relationship between PC and hangover severity scores and demonstrated that PC was, in fact, a stronger predictor of perceived hangover severity than estimated peak blood alcohol concentrations (eBACs). Factor analysis of the AHS scale, resulted in the identification of two distinct symptom dimensions; 'Headache and thirst', and 'Gastric and cardiovascular' symptoms. Regression analyses showed that both eBAC and PCS score were significantly associated with 'Headache and thirst'. However, only PCS score was associated with 'Gastric and cardiovascular' symptoms. These novel findings implicate a role for cognitive coping strategies in self-reports of alcohol hangover severity, and may have implications for understanding behavioural response to hangover, as well as suggesting that hangover and PC may be important factors mediating the motivation to drink and/or abuse alcohol, with potential implications in addiction research. Furthermore, these findings suggest that distinct alcohol hangover symptoms may be associated with different mechanisms underlying the experience of alcohol hangover.

**Keywords:** hangover; catastrophising; alcohol; veisalgia; acute hangover scale

#### **1. Introduction**

#### *1.1. Alcohol Hangover, Symptoms and Economic Burden*

Alcohol hangover is a phenomenon that occurs the day after the ingestion of alcohol, once the blood alcohol concentration (BAC) is approaching nil [1], and it is associated with a wide variety of symptoms, such as headache, nausea, and concentration problems [2,3]. Hangover is thought to be a considerable cause of economic loss through workplace absenteeism and lost productivity [4]. Researchers have also speculated that the severity of alcohol hangover is linked to the development of alcohol use disorders (AUDs) [5,6], indicating that a better understanding of the individual hangover experience and its mediators may offset the associated financial and social burden of AUD.

A number of explanations for the variance seen in alcohol hangover presentation have been suggested, including gene associations of alcohol metabolism [7,8], gender differences [9] inflammatory response to alcohol consumption [10,11], immunological functioning [12], and congener content of alcoholic drinks [13], as well as individual differences in psychosocial factors such as anxiety and mood [14], or guilt related to the actions carried out whilst drinking [3]. There is, however, little consensus regarding the biological mechanisms that underpin the experience of alcohol hangover [4], and this is particularly true for psycho-social variables. Identification of mediating factors of alcohol hangover severity may thus inform mechanistic investigations of hangover, as well as having the potential to reduce costs associated with absenteeism/presenteeism and improve health care outcomes.

#### *1.2. Hangover and Risk of Alcohol Abuse*

Despite the lack of mechanistic explanations for the influence of predictor variables on the experience of hangover, and mixed findings regarding relationships between familial risk for addiction and experience of hangover [15,16], there is some evidence that alcohol hangover experience may be a potential risk factor for alcohol use disorder (AUD) [15]. In this regard, hangover has been conceptualised as affecting cognitive control processes that influence local drinking behaviour. Evidence suggests that people who experience a more severe hangover will drink less, when they engage in drinking the day of a hangover [17], and that hangover can increase the time before the next alcoholic drink is consumed in frequent drinkers [18], with hangover occurrence predicting a 6 hour delay to next drink when used as sole predictor in a survival model. It is notable, however, that hangover occurrence was only associated with a delayed time to next drink in multivariate models when interacting with the onset of financial stressors, or the presence of high levels of craving at the end of the drinking episode (pre-hangover). This may implicate a role for the hangover in the delay of further engagement with drinking, when experienced alongside a continued desire/motivation to drink. The investigation of differences in factors related to motivational and inhibitive processes, such as cognitive coping strategies, during hangover, therefore has the potential to contribute to understanding of possible relationships between the hangover experience and propensity for development of AUDs.

#### *1.3. Alcohol Hangover and Pain Catastrophising*

Pain catastrophising (PC) has been broadly defined as an exaggerated negative orientation towards actual or anticipated pain experiences [19] and has been described as the tendency to recall pain experiences in more exaggerated terms, to feel helpless and ruminate over painful events. PC appears to be moderated, to some degree, by gender [20], psychosocial and dispositional factors [19,20]. However, despite these moderating factors, PC contributes unique and significant variance to the prediction of self-reported pain intensity, as well as to neural processing of pain [21]. Evidence has shown that the relationship between PC and pain ratings is partially mediated by diminished diffuse noxious inhibitory controls (a measure of endogenous pain inhibition), indicating a disruption in pain inhibition and suggesting a relationship between PC and pain inhibition [22]. Neurological evidence (utilising functional magnetic resonance imaging) has demonstrated that PC predicts the experience of pain, in that, during exposure to a painful stimulus, pain specific response activation in the dorsolateral prefrontal cortex (dlPFC) and medial prefrontal cortex (mPFC) correlate negatively with PC [23]. The effect of PC on brain activity in the mPFC and dlPFC also seems to be mediated by the severity of pain experienced, with reduced activity during more intense pain [24]. Additionally, the dlPFC shows greater bilateral activation during response inhibition, in comparison to interference monitoring and suppression [25], indicating some anatomical overlap between inhibitive processes and PC. It has been argued that PC may heighten pain experiences by reducing the efficiency of inhibitory pathways, though evidence for this position is indirect [26].

Alcohol hangover is characterised by pain symptoms. Indeed, the medical term for alcohol hangover "veisalgia" comes from the Norwegian kveis, which refers to the uneasiness following debauchery, and algia, the Greek term for pain. Cytokines, proteins produced during immune response that are involved in both the initiation and persistence of pathologic pain [27], are altered during hangover. Interleukin (IL-2; IL-10) and interferon (IFN-γ) cytokines have been shown to be elevated in blood during hangover [10]. In saliva, elevations of IL-2, IL-4, IL-5, IL-6, IL-10, IFN-γ, and TNF-α have been observed during hangover, and in urine, elevations of IL-4 and IL-6, as well as decreases in IL-8 have been observed in comparison to non-hangover-days [28]. These differences in cytokine levels between hangover and non-hangover days do not, however, appear to explain variance in the experience of hangover, with similar changes observed in both those reporting hangover, and those reporting hangover resistance [29]. Headache, a continuous pain in the head which has also been associated with changes in cytokine levels [29,30], represents the 3rd most common symptom of alcohol hangover [31] and symptomatic ratings of headache severity have large statistical effects in measures of hangover severity [32]. PC therefore presents a good candidate for potentially explaining some of the variance in self-reported hangover severity scores. Consequently, the current study hypothesises that greater PC will be associated with elevated hangover severity scores.

It has also been argued that hangover lacks in mechanistic explanations [4], despite the wide variety of symptoms associated with the hangover experience [2]. Certainly, dehydration is thought to represent one potential mechanism, with thirst being one of the most commonly reported hangover symptoms [2,31], due to the diuretic effects of alcohol [33]. Vasopressin levels, a biological marker of dehydration, do not, however, necessarily correlate with overall hangover severity [12]. It is possible that this is due to dehydration representing a mechanism of hangover that explains only a particular symptom cluster. Factor analysis of measures of hangover severity may therefore provide some direction for investigations of the mechanisms that give rise to symptom clusters. Furthermore, certain symptom clusters may be independently moderated by PC and may represent better predictors of alcohol abuse risk.

The current retrospective survey was therefore designed with three main aims: (i) to examine whether increased PC scores are associated with elevated hangover severity scores, (ii) to explore the factor structure of the acute hangover scale (AHS), and (iii) to explore whether different dimensions of the AHS are independently associated with PC.

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

#### *2.1. Participants*

Participants were recruited through opportunity sampling—both at a university in the north-west United Kingdom, and online via social media. Ninety-one participants completed the survey, with 3 participants excluded from analysis because they reported a non-binary gender, which presents issues for calculating blood alcohol concentrations. A further 2 participants were excluded from analyses based on age, with one reporting an age of 5 years, violating exclusion criteria, and one reporting an age of 80 years, representing an extreme outlier in the current sample (+6.24 SDs from the mean). The remaining sample of 86 had an overall mean age of 25.93 years (SD: 6.03, range: 18–46), with 51 female respondents (59%). No incentive was provided for participation. Exclusion criteria for the study were: below 18 years of age (not of legal drinking age for the area in which they reside); having not experienced hangover in the last 6 months. Exclusion was based on self-report: independent verification of these criteria was not possible with an online survey. Ethnicity was not analysed as a variable as the majority of participants (88%) self-identified as white.

#### *2.2. Materials*

Participants were required to complete an online survey that included the Acute Aangover Scale (AHS) [32], to measure hangover severity retrospectively, and the Pain Catastrophising Scale (PCS) [19], to measure PC associated with the experience. The PCS consists of 13 items addressing the experience of catastrophic thoughts related to the experience of pain, rated on a scale from 0 (not at all) to 4 (all the time), with total score calculated as the sum of all items. The PCS has shown a high level of reliability, with Cronbach's α scores typically exceeding 0.8 [19,30,31], and the PCS has been validated in a variety of samples, including those 'seeking treatment' (vs. not seeking treatment) [34], pain outpatients (vs. community participants) [35], those with back pain (Norwegian version) [36], and those with chronic pain (Korean and Brazilian-Portuguese versions) [37,38]. The AHS consists of 9 items addressing the severity of 8 hangover symptoms, plus an overall hangover severity rating, all given on a scale from 0 (None) to 7 (Incapacitating), with AHS total score calculated as the mean of all items. The AHS has been validated for concurrent hangover severity measurement, but also shows very high correlations with scales designed for measurement of the most recent (retrospective) hangover experience [39], as well as having been utilised for recent hangover severity measurement in other research [40]. Participants reported the drinking that led to their most recent hangover using the items from McKinney and Coyle's investigation, which asks about the number of a variety of standardised drinks consumed (e.g., pints of beer, bottles of beer, alcopops, etc.), and allows for the number of units of ethanol consumed to be calculated [41]. Demographics were also collected, including age (which was recorded for use as a covariable since previous evidence has suggested a role in the experience of hangover [42]), height and weight, (for the calculation of estimated blood alcohol concentrations), and ethnicity. Estimated blood alcohol concentrations were calculated using the method from Siedl et al. [9], resulting in the estimated peak blood alcohol concentration assuming no elimination (eBAC), and assuming a 15% elimination rate (eBAC15%). Both the AHS and PCS were adapted to reference how the participants felt during their most recent hangover.

#### *2.3. Procedure*

Participants, recruited via social media (Twitter, Reddit) and posters located around a university in the north-west, UK, completed an online survey hosted on Online Surveys by Jisc (https://www. onlinesurveys.ac.uk/). Participants completed the survey during their own time. The total time to complete the study was approximately 15 min. No incentives were offered for participation.

#### *2.4. Ethics*

The materials and methods utilised in this procedure were approved by the University of Salford Health Sciences Research ethics board (HSR1617-15), and all participants provided informed consent. The use of Online Surveys for data collection allowed for participant anonymity, and the system adheres to high ethical standards (e.g., no use of 'cookies' which store files to the local PC used to complete the survey).

#### **3. Results**

All analyses were carried out in IBM SPSS <IP_ADDRESS> (IBM Corp., Armonk, NY, USA).

#### *3.1. Factor Analysis*

A dimension reduction procedure was carried out on responses to the items of the AHS to establish whether symptom clusters existed. The item 'hangover' was excluded from this analysis, as this is non-symptomatic and thought to capture a broad rating of hangover severity [32]. Descriptive statistics for the remaining items are presented in Table 1. Inter-item correlations are presented in Table 2.

As all items failed to meet at least parametric assumption of normality in univariate analyses, principal axis factor analysis was utilised for dimension reduction [43]. Reduction was carried out using a direct obliminal rotation with a delta of 0, given the likelihood of correlations between dimensions of hangover, and in line with the recommendations of Costello and Osborne [43]. Factors with an eigenvalue above 1 were retained, with results checked visually using the Scree test. Results indicated a solution with 2 dimensions, one factor consisting of symptoms linked to dehydration, and one to stress responses. Kaiser–Meyer–Olkin statistics indicated good sampling adequacy (KMO = 0.722), though KMO values for individual variables indicated a potential issue with the item 'nausea' (KMO = 0.495; removal of this item did not result in changes to the factor structure). Bartlett's test of sphericity indicated acceptable deviance from an identity matrix (× 2(28) = 138.762, *p* < 0.001), and 35% of residuals between observed and reproduced correlations had absolute values above 0.05, indicating moderate model fit. The factor model is summarized in Table 3.

**Table 1.** Descriptive statistics for ratings of hangover symptom severity on the acute hangover scale (AHS).

SD—standard deviation; normality—*p*-Value for Shapiro–Wilk analysis of normality. The *n* for all items was 86. Significant results indicated by \*\*\* *p* < 0.01, \*\*\*\* *p* < 0.001.

Significant correlations indicated by \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.005, \*\*\*\* *p* < 0.001.

**Table 3.** Factor loadings of AHS items based on principal axis factoring.

Composite scores were calculated for each factor based on the mean of the items which had their primary loadings on each factor (Headache and thirst: mean = 5.32, SD = 1.17; Gastric and cardiovascular symptoms: mean = 3.20, SD = 1.53) with higher scores indicating greater severity of symptoms within the cluster. The bold indicates most relevance.

#### *3.2. Regression Models*

Three initial regression models of the AHS score were formed, with PCS score and age used as predictor variables in all three models, and the contribution of 'measures of drinking' assessed across separate models. A further two regression models were formed to assess dimensions of the AHS identified in factor analysis. Descriptive statistics for the variables included across these regression models are presented in Table 4. To minimize overfitting of the data, rather than utilize an automated variable selection method, for each model, each variable was entered into the regression concurrently [44], with a model formed for each of the three 'measures of drinking' obtained; units consumed, eBAC, and eBAC15%. Gender was also entered into the model with the number of units consumed, since this is controlled for in the calculations used to derive eBAC and eBAC15% scores and approximates differences in the body fat composition of different genders which influences alcohol distribution through body water during consumption.

**Table 4.** Descriptive statistics for variables included across the five regression models constructed.

Variables: AHS—acute hangover scale; PCS—pain catastrophising scale; total units—units of alcohol consumed, calculated from self-report; eBAC—the estimated blood alcohol concentration assuming no elimination; eBAC15%—the estimated blood alcohol concentration assuming 15% elimination rate; Headache and thirst—mean score for items identified within dimension 1 of the factor analysis; Gastric and cardiovascular symptoms—mean score for items identified within dimension 2 of the factor analysis; SD—standard deviation; normality—significance of Shapiro–Wilk analysis. The *n* for all measures was 86, \*\*\* *p* < 0.005, \*\*\*\* *p* < 0.001.

Results indicated that eBAC represented the drinking measure that explained the most variance in AHS scores (power = 0.50, calculated post-hoc), and as such the model containing this variable was carried forward for regression analyses of factor scores calculated during factor axis analysis. A regression model containing eBAC, PCS score, and age, as predictor variables, was therefore formed for each of the two factor scores derived. Summaries of regression models are presented in Table 5, with a summary of individual variable contributions presented in Table 6.

Tolerance, variance inflation factors (VIFs), and collinearity diagnostics indicated no issues with multicollinearity. Manual examination of standardized residuals plotted against standardized predicted values suggested no issues with heteroscedasticity in the data, and multivariate normality was present in all models. Durbin–Watson statistics indicated independence of errors. Some issues were identified in casewise diagnostics, with a small number of cases indicating issues with either problematic covariance ratios or high leverage values in regression models.

Given that a number of cases presented potential issues with covariance and leverage, and in line with recommendations made by Babyak [44], validation of the final models (those containing the eBAC drinking measure) was carried out using bootstrap methods with 2000 random resamples drawn. Bootstrapped models are summarised in Table 7.

**Table5.**Summaryofmodelstatisticsforregressionanalyses. DV (dependent variable): AHS—Acute hangover scale; Headache and thirst—mean score for items identified within dimension 1 of the factor analysis; Gastric and cardiovascular symptoms—mean score for items identified within dimension 2 of the factor analysis. R—value of r for model; R2—value of r squared for model; Adj R2—adjusted r squared for model; F—F value for model; F Sig.—Significance of the F value for the model; Durbin-Watson—Durbin-Watson statistic for the model. Significant results indicated by \*, \* *p* < 0.05, \*\*\* *p* < 0.005.

5

Gastric and cardiovascular

symptoms

Age

eBAC

PCS score

Age

0.398

 0.158

 0.128

 5.141

 0.003 \*\*\*

 1.906

**Table 6.** Summary of statistics determining independent variable contributions to regression effects.

\* *p* < 0.05, \*\*\* *p* < 0.005, \*\*\*\* *p* < 0.001.

*J. Clin. Med.* **2020** , *9*, 280

**Table 7.** Summary of bootstrapped regression model coefficients.

DV—dependent variable; IV—independent variable; B—beta weight; SE—standard error; BCa 95% CI—bias-corrected accelerated 95% confidence interval. Bootstrap results based on 2000 bootstrap samples. Significant results indicated by \*, \* *p* < 0.05 = \*, \*\*\* *p* < 0.005, \*\*\*\* *p* < 0.001.

Results of bootstrap analyses have a fairly high level of agreement with original regression models, with significant predictor variables remaining constant. Both eBAC and total PCS score demonstrated significant relationships with total AHS score, and a composite score based on symptoms of the AHS related to 'Headache and thirst'. Only total PCS score demonstrated a relationship with a composite score based on 'Gastric and cardiovascular' symptoms. eBAC approached significance in this model. However, bootstrapping did indicate some bias toward significance of this variable with this sample.

#### **4. Discussion**

#### *4.1. Summary of the Main Findings*

Hangover, the mental and physical symptoms experienced the day after drinking and once BAC is approaching 0, has previously been associated with a variety of other factors, including genetic influences on alcohol metabolism [7,8], gender [9], inflammatory responses [10,11], immunological function [12] and congeners [13]. However, few psychosocial predictors of hangover have been identified so far.

The three main aims of the current study were: (i) to examine whether increased PC scores are associated with elevated hangover severity scores, (ii) to explore the factor structure of the acute hangover scale (AHS), and (iii) to explore whether different dimensions of the AHS were independently associated with PC. The current study demonstrated that PC was a predictor of perceived hangover severity and was, in fact, a stronger predictor than the estimated peak blood alcohol concentration (eBAC). Exploration of the dimensions of the AHS revealed two distinct symptom dimensions; 'Headache and thirst'; and 'Gastric and cardiovascular' symptoms. While both eBAC and PC were significantly associated with 'Headache and thirst', only PC was associated with 'Gastric and cardiovascular' symptoms.

#### *4.2. Relationships between Pain Catastrophising and Hangover Severity*

Relationships between PC and AHS scores were investigated using multiple linear regression. Initial models indicated that, of the drinking measures, a calculated blood alcohol concentration that did not account for elimination (eBAC) was the best predictor of the AHS score. One potential explanation is that the preferential metabolism of ethanol limits downstream action to eliminate ethanol metabolites leading to a build-up of biologically active compounds [45]. This would be consistent

with the time course of alcohol hangover, with symptomology extending beyond the period of acute ethanol intoxication.

Regression models (and calculated product measures [46]) indicated PC was a better predictor of perceived hangover severity than eBAC. Given relationships between PC and other psychosocial variables such as depression and anxiety [20,21], PC could provide a mechanism through which other psychosocial variables influence self-report hangover severity scores. Furthermore, given links between PC and inhibitive processes [26], this cognitive strategy may influence motivational responses to hangover, providing a potential link between hangover experience and local behaviour, such as engagement with further drinking. Such effects could have implications in addiction research [15].

The results of the current investigation may also have implications for the measurement of hangover, given its reliance on self-report measures, such as the AHS [32]. This issue has been largely ignored in hangover research for purposes of practicality, with a lack of other approaches available. Results from the regression models developed as part of this investigation indicate a moderate effect of PC on AHS score, comparable to the effect observed for measures of alcohol consumption, and support the view that self-report hangover questionnaires contain a significant subjective element. This may reinforce the need for an objective measure of hangover. However, research into biomarkers of hangover severity has yet to find a reliable indicator [1,28]. An alternative approach to measuring hangover severity in a more objective manner may be to examine the cognitive effects of hangover. A meta-analytic examination of the next-day cognitive effects of hangover published in 2018 suggested that effects can be seen during hangover on short- and long-term memory, sustained attention, and psychomotor speed [47]. Differences in performance on tasks examining these functions between hangover and non-hangover days could therefore present a measure of functional hangover severity.

Questions can be raised regarding the value of any of these measurement approaches. Arguably, the subjective experience of hangover is likely to influence the behavioural response to the experience, and may provide value over 'objective' measurements of hangover, such as cognitive performance measures or biomarkers, in particular contexts (e.g., the investigation of absenteeism/presenteeism and other acute behaviours). In comparison, objective measures may be more useful in investigations examining the biological correlates of alcohol hangover. Further research will need to examine the comparative value of different measurement approaches in relation to different outcomes. However, controlling for PC in future analyses may also aid in understanding the hangover experience, particularly with regard to the investigation of biomarkers.

#### Dimensions of the AHS

A recent review of the physiology of hangover identified alcohol metabolites, neurotransmitter alterations, inflammatory factors, and mitochondrial (metabolic) dysfunction as the most likely factors involved in hangover symptomology [48]. PC has also been associated with alterations to immune responses, with heightened reactivity of cytokine IL-6 related to increased levels of PC as measured immediately after painful stimulation [49]. This relationship between PC and IL-6 also appeared to be independent of pain ratings given during stimulation. Likewise, immune responses during hangover have been shown to include increases in IL-6 levels [28], with IL-6 thought to have particular importance as a messenger molecule that connects peripheral regulatory processes with the central nervous system during responses to both physiological and psychological stress [49].

In this study, factor analysis of AHS responses resulted in two symptom dimensions; (1) Headache and thirst ('headache', 'tired', 'thirsty'), and (2) Gastric and cardiovascular symptoms ('nausea', 'dizziness/faintness', 'heart racing', 'loss of appetite', and 'stomach ache'). The 'Headache and thirst' symptom cluster could be related to the diuretic properties of alcohol [29], which can lead to dehydration. Dehydration has been linked to headache [50], with tiredness and thirst being considered common symptoms. Headache may also be the result of cytokine release prompted by physiological stress associated with alcohol consumption [29,30], or indeed physiological stress may be caused by dehydration. However, there is potential for overlap in the causes of symptom clusters. Speculation regarding the biological mechanisms underlying symptom cluster experience is, however, not possible based on the current investigation. Future work will be needed to identify specific biological associations with the experience of hangover symptom clusters. Penning et al.'s factor analysis also identified dehydration ('disturbed water balance') as a dimension of the hangover experience [2]. However, in their investigation, the item 'headache' was not loaded on this dimension. Dehydration causes physiological changes, e.g., to electrolytic balance, which have proposed associations with hangover. However, evidence for relationships between physiological changes and hangover severity is lacking [12], though they have not been investigated in relation to specific symptom clusters. One potential explanation for the 'Gastric and cardiovascular' symptom cluster emerging is that they can all be linked to physiological stress responses. Effects of stress response on the autonomic nervous system are well established [51], and acute physiological stress can also induce various responses in the gastrointestinal system [52].

An alternative explanation of the factor structure of the AHS identified in this investigation relates to the prevalence of symptoms. Tiredness, thirst and headache, the items loaded within the 'Headache and thirst' dimension, represent the three most commonly reported hangover symptoms [31]. It is possible that these symptom clusters are thus representative of different groups that either experience one or both of the symptom clusters. An extension of this reasoning, given the prevalence of headache and thirst symptoms in hangover, could be that less severe hangovers consist of symptoms included within the 'Headache and thirst' symptom cluster, with more severe hangovers including 'Gastric and cardiovascular' symptoms.

#### *4.3. Dimensions of the AHS Independently Associated with PC*

Composite scores based on 'Headache and thirst' symptoms, and 'Gastric and cardiovascular' symptoms, identified during factor analysis of AHS responses, were also assessed using regression. Both eBAC and PCS score significantly predicted 'Headache and thirst' symptom scores with approximately equal contributions. The observation of PC score as a significant predictor in this model is possibly due to the inclusion of headache severity ratings in the construction of this score, with PC having previously been linked with both the presence of weekly headache [53], and the severity of migraine symptoms, a phenomenon associated with headache [54]. Given the diuretic effects of alcohol [33], it follows that measures of alcohol consumption would be related to symptoms associated with dehydration.

Finally, only PCS score significantly predicted composite scores based on 'Gastric and cardiovascular' symptoms, though eBAC was only marginally non-significant. Product measures supported the interpretation that PC was more strongly related with 'Gastric and cardiovascular' symptoms than eBAC, and robust regression provided some validation of this model. PC has been related to activity in the mPFC [24], an anatomical area that has also been shown to mediate stress response [55]. This may provide a link through which this cognitive strategy can influence stress responses occurring as a result of hangover. The exclusion of eBAC from this model may indicate that these symptoms are not direct products of alcohol consumption, or that this symptom set is not associated linearly with the volume of alcohol consumed (e.g., threshold effects). It has, however, been previously suggested that increased levels of fatty acids seen during hangover are products of a stress response concurrent with hangover [12], which could indicate that 'Gastric and cardiovascular' symptoms in hangover are somewhat independent of the amount of alcohol consumed.

#### *4.4. Conclusions and Directions for Future Research*

Hangover represents a considerable economic toll due to its influence on local behaviour, such as lost productivity and workplace absenteeism [4]. Furthermore, the experience of hangover may be related to downstream health consequences by promoting deviant drinking practices [12]. The current investigation revealed, for the first time, that PC predicts alcohol hangover severity and that this effect occurs in a symptom specific manner. PC may also provide a cognitive strategy through which other psychosocial variables can influence hangover.

Exploratory factor analysis provided evidence of two distinct sub-structures of the AHS, 'Headache and thirst', and 'Gastric and cardiovascular' symptoms. Results of this investigation could be interpreted as suggesting that dehydration and physiological stress responses represent areas that warrant further examination, with differences in regression models based on composite hangover scores for symptom clusters providing some evidence that symptom clusters are somewhat independent. This may provide an explanation for why markers of dehydration have not always correlated with overall hangover severity [12], as well as why thirst had the lowest item-total correlation during development of the AHS [32]. Further research will be required to establish whether particular covariables correlate with symptom clusters either derived from dimension reduction procedures or theoretical mechanistic relationships. The AHS also measures a somewhat limited sample of hangover symptoms, and recent research has adopted the approach of combining the symptoms identified in a number of validated hangover measures, in order to capture the diversity of the hangover experience [56]. These measures consist largely of different symptoms, but show high correlations, and further research will be needed to examine whether the dimensions of the hangover experience suggested here are evident within this broader context, as well as their relationships with PC.

As noted previously, hangover has also been associated with effects on local drinking behaviour [17, 18], with an ecological momentary assessment conducted by Epler et al. in 2014 indicating that the presence of hangover delayed the onset of the next drinking episode when interacting with either the onset of financial stressors, or the presence of craving at the end of the drinking episode [18]. Epler et al.'s (2014) sample consisted of participants with a reasonably low risk of alcohol problems (average AUDIT score = 12.21), but no research has addressed this relationship in high-risk or clinical groups. Evidence has suggested relationships between craving and AUD symptomology in a sample containing a high proportion of participants meeting criteria for diagnosis of AUD. However, no relationship was found between craving and drinking habits in this sample [57]. This may suggest that interactions between craving, hangover, and local drinking behaviour do not exist in those at a high risk for AUD. Greater craving in the high-risk sample also showed a relationship with increased impulsive discounting (a devaluation of future reward) [57], which may provide a mechanism for observed losses of inhibitory response control in alcohol disorders, as well as other disorders, such as depression [58]. Weaker inhibition processes have also been noted in those with a family history of AUD [59], and in young-adult binge drinkers [60], a form of drinking associated with an increased incidence of hangover. Inhibition is also inherently linked with impulsivity [61], which has itself been strongly associated with AUD [58]. Given the links between PC and motivational/inhibitive processes [26,62], future research should consider PC and hangover alongside factors related to motivation/inhibition, such as performance on inhibition dependent tasks, and craving. Vatsalya et al.'s (2018) investigation found no relationship between hangover severity (as measured by the AHS) and a single item measure of craving [40]. However, this craving measurement is unlikely to capture the theoretical complexity of the phenomenon and future research would benefit from the use of context appropriate, validated craving measures [63].

Future research should therefore seek to elucidate the potential interaction between PC and cognitive processing systems mediating inhibitory control and the craving response during alcohol hangover.

**Author Contributions:** Conceptualisation, S.R., D.R. and L.M.; methodology, S.R., D.R. and L.M.; formal analysis, S.R.; investigation, S.R.; data curation, S.R.; writing—original draft preparation, S.R.; writing—review and editing, S.R., D.R., L.O. and L.M.; visualisation, S.R., L.O. and L.M.; supervision, D.R., L.O. and L.M.; project administration, S.R.; funding acquisition, D.R., L.O. and L.M. All authors have read and agreed to the published version of the manuscript.

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

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

#### **References**

© 2020 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/).


**3.1. Domain-level research patterns**

Citation paths at a disciplinary level are depicted in the visual representation called a dual-map overlay [6] (see **Figure 4**). The left regions represent where the collected literature publishes while the right regions render where it cites from. Citing literature and cited literature are also called research frontier and knowledge base respectively. The base map consists of the journal/ conference-level citation relationships among over 10,000 venues. Major clusters are labeled by terms chosen from the titles of venues in corresponding clusters. First, all of the terms' loglikelihood ratios are calculated based on their frequency in clusters. The use of LLR achieves to represent those terms' uniqueness in clusters. Then, top three terms are selected to tag clusters, based on their LLR values in descending order. Citation trajectories are colored based on the citing regions. The width of the paths is proportional to the z-score-scaled citation frequency.

**Table 3** describes these trajectories in descending order of the third column, namely Z-score. The color of each row is corresponding to the path. Findings indicate that scientometrics has been largely driven by social sciences and medicine as represented by "psychology, education, health" and "medicine, medical, clinical" respectively at the first column. Literature from social sciences heavily cites from "psychology, education, social", "systems, computing, computer", "health, nursing, medicine", "economics, economic, political", and "molecular, biology, genetics", yielding five citation paths. Research frontiers from medicine are based on "health, nursing, medicine" and "molecular, biology, genetics", having two additional trajectories. These observations

show scientometrics is multidisciplinary and partially interdisciplinary; Multidisciplinary since scientometrics research has been published in multiple disciplines; Partially interdisciplinary for literature published in "psychology, education, health" has a variety of intellectual bases

**Research frontier Knowledge base Z-score** Psychology, education, health Psychology, education, social 8.841 Psychology, education, health Systems, computing, computer 4.766 Medicine, medical, clinical Health, nursing, medicine 4.052 Psychology, education, health Health, nursing, medicine 3.313 Psychology, education, health Economics, economic, political 2.724 Psychology, education, health Molecular, biology, genetics 2.461 Medicine, medical, clinical Molecular, biology, genetics 1.984

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**WoS category Year Frequency Density** Information science & library science 1990 3880 138.571 Computer science 1990 3260 116.429 Computer science, interdisciplinary applications 1990 2284 81.571 Computer science, information systems 1990 925 33.036 Business & economics 1992 653 25.115 Management 1992 374 14.385 Engineering 1992 292 11.231 Public administration 1992 199 7.654 Planning & development 1992 179 6.885 Education & educational research 1992 165 6.346 Social sciences – other topics 1992 160 6.154 Science & technology – other topics 1993 462 18.480 Multidisciplinary sciences 1993 348 13.920 Business 1994 242 10.083 Neurosciences & neurology 1996 159 7.227 Environmental sciences & ecology 1997 261 12.429 General & internal medicine 1999 145 7.632 Surgery 2000 162 9.000 Public, environmental & occupational health 2003 201 13.400 Environmental sciences 2006 189 15.750

**Table 3.** Domain-level citation trends.

while "medicine, medical, clinical" largely cites from neighboring domains.

**Table 4.** Top 20 frequently assigned WoS categories.

**Figure 4.** Citation paths at a disciplinary level.

Scientometrics of Scientometrics: Mapping Historical Footprint and Emerging Technologies… http://dx.doi.org/10.5772/intechopen.77951 15

**Table 3.** Domain-level citation trends.

(DTM) which is a generative technique extended from Latent Dirichlet Allocation (LDA). DTM captures the evolution of latent topics in a collection of documents whereas it was

Citation paths at a disciplinary level are depicted in the visual representation called a dual-map overlay [6] (see **Figure 4**). The left regions represent where the collected literature publishes while the right regions render where it cites from. Citing literature and cited literature are also called research frontier and knowledge base respectively. The base map consists of the journal/ conference-level citation relationships among over 10,000 venues. Major clusters are labeled by terms chosen from the titles of venues in corresponding clusters. First, all of the terms' loglikelihood ratios are calculated based on their frequency in clusters. The use of LLR achieves to represent those terms' uniqueness in clusters. Then, top three terms are selected to tag clusters, based on their LLR values in descending order. Citation trajectories are colored based on the citing regions. The width of the paths is proportional to the z-score-scaled citation frequency. **Table 3** describes these trajectories in descending order of the third column, namely Z-score. The color of each row is corresponding to the path. Findings indicate that scientometrics has been largely driven by social sciences and medicine as represented by "psychology, education, health" and "medicine, medical, clinical" respectively at the first column. Literature from social sciences heavily cites from "psychology, education, social", "systems, computing, computer", "health, nursing, medicine", "economics, economic, political", and "molecular, biology, genetics", yielding five citation paths. Research frontiers from medicine are based on "health, nursing, medicine" and "molecular, biology, genetics", having two additional trajectories. These observations

oblivious to the preceding model [12].

**3.1. Domain-level research patterns**

**Figure 4.** Citation paths at a disciplinary level.

**3. Results**

14 Scientometrics

**Table 4.** Top 20 frequently assigned WoS categories.

show scientometrics is multidisciplinary and partially interdisciplinary; Multidisciplinary since scientometrics research has been published in multiple disciplines; Partially interdisciplinary for literature published in "psychology, education, health" has a variety of intellectual bases while "medicine, medical, clinical" largely cites from neighboring domains.

We considered WoS category assignment to literature as another important indicator representing domain-level thematic concentration. The top 20 frequently assigned WoS categories to the records are described in **Table 4**. It shows the year it was first assigned, and the density of how many times per year a specific category has been given, from its first year. The table is sorted in ascending order of the year. Results show that three categories have been assigned more than 2000 times – "information science & library science" (n = 3880), "computer science" (n = 3260), and "computer science, interdisciplinary applications" (n = 2284). These categories were first assigned from the beginning in the data set, demonstrating the greatest densities. The most frequently assigned category to be added to the top four list is "computer science, information systems." This category also demonstrates a relatively high density (33.036), given its first year of assignment was 1990. This finding suggests that literature under these four categories has had the largest influence on the emergence and development of scientific knowledge in scientometrics. In turn, research with scientific foci in social sciences, engineering, medical & health sciences, and environmental sciences brought along a multidisciplinary grasp to the domain.

### **3.2. Trending keywords**

Given by authors and indexers, keywords reflect representative concepts underlying published literature. The top 20 frequently occurring keywords in the data set are described in **Table 5**. It shows the year it first appeared, and the density of how many times on average a specific keyword has appeared, from its first year. Findings indicate that in the beginning, "bibliometrics" and "scientometrics" focused on employing "citation analysis" to examine the "impact" of a "science". We assume that "journal" and "publication" were considered as units of analysis. Another effort focused on evaluating research "performance" and "productivity" and examining the "pattern" of scientific "collaboration." The other stream of research had interest in devising a "bibliometric indicator" such as journal "impact factor", which led to the recent development of the widely accepted author-level metric "h-index."

**Figure 5** displays the keyword co-occurrence in the data set. We used a technique called a density visualization guided by VOSviewer. The font size of a keyword is proportional to its occurrence frequency. The more frequently a pair of keywords co-occurs, the closer the pair is located to the red spots. The visualization resulted in 484 keywords which occurred more than or equal to 18 times. As depicted, "bibliometrics" frequently co-occurred with "impact" which is consistent with the finding above. It also determined that devising an "impact fac-

**Table 6** lists 20 keywords which have surged during a specific duration of time. The investigation of keyword bursts adds temporal contexts in understanding historic footprint and emerging technologies in scientometrics which were oblivious to the snapshot metrics. The keywords were sorted in ascending order of the beginning years of bursts. "physics" is one of the keywords with the longest bursts, ending in 2010. It also has the second strongest bursts when not including "science." It indicates applications of scientometrics to physics and/or knowledge transfer from physics to scientometrics had intensively been conducted from the early years. The widely accepted author-level metric, namely h-index, was also derived from physics. The second longest bursts from 1992 is led by "law", also demonstrating a relatively high value of bursts. It shows the identification of laws existing in scientometrics phenomena was among the important initiatives. "publication output" is the keyword with the third longest and strongest bursts. It is argued that the evaluation of research performance and productivity was one of the key themes in the domain. The strongest burst episode from 1992 is associated with "indicator." In consideration with other keywords such as "stationary distribution", "model", and "informetric distribution", we argue modeling an indicator of impact measure was of greatest interest in scientometrics.

We analyzed another text fields, namely titles and abstracts since more informational points of content can be examined than only exploring keywords. We aimed to uncover the evolution of latent topics in the records over time. Toward that end, we removed stop words from

tor" for "journal ranking" was among the important themes in scientometrics.

**Keyword Year Frequency Density** Productivity 1992 270 10.385 Collaboration 1993 353 14.120 Bibliometric indicator 1993 290 11.600 Pattern 1993 273 10.920 Network 1994 357 14.875 Impact factor 1996 527 23.955 Index 2002 324 20.250 h-index 2007 386 35.091 Scopus 2008 280 28.000

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**3.3. Temporal topic models**

**Table 5.** Top 20 frequently occurring keywords.

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**Table 5.** Top 20 frequently occurring keywords.

**Figure 5** displays the keyword co-occurrence in the data set. We used a technique called a density visualization guided by VOSviewer. The font size of a keyword is proportional to its occurrence frequency. The more frequently a pair of keywords co-occurs, the closer the pair is located to the red spots. The visualization resulted in 484 keywords which occurred more than or equal to 18 times. As depicted, "bibliometrics" frequently co-occurred with "impact" which is consistent with the finding above. It also determined that devising an "impact factor" for "journal ranking" was among the important themes in scientometrics.

**Table 6** lists 20 keywords which have surged during a specific duration of time. The investigation of keyword bursts adds temporal contexts in understanding historic footprint and emerging technologies in scientometrics which were oblivious to the snapshot metrics. The keywords were sorted in ascending order of the beginning years of bursts. "physics" is one of the keywords with the longest bursts, ending in 2010. It also has the second strongest bursts when not including "science." It indicates applications of scientometrics to physics and/or knowledge transfer from physics to scientometrics had intensively been conducted from the early years. The widely accepted author-level metric, namely h-index, was also derived from physics. The second longest bursts from 1992 is led by "law", also demonstrating a relatively high value of bursts. It shows the identification of laws existing in scientometrics phenomena was among the important initiatives. "publication output" is the keyword with the third longest and strongest bursts. It is argued that the evaluation of research performance and productivity was one of the key themes in the domain. The strongest burst episode from 1992 is associated with "indicator." In consideration with other keywords such as "stationary distribution", "model", and "informetric distribution", we argue modeling an indicator of impact measure was of greatest interest in scientometrics.

#### **3.3. Temporal topic models**

**Keyword Year Frequency Density** Science 1991 1613 59.741 Bibliometric analysis 1991 871 32.259 Journal 1991 815 30.185 Citation 1991 803 29.741 Bibliometrics 1992 1914 73.615 Impact 1992 969 37.269 Citation analysis 1992 814 31.308 Publication 1992 700 26.923 Scientometrics 1992 646 24.846 Indicator 1992 596 22.923 Performance 1992 348 13.385

to the recent development of the widely accepted author-level metric "h-index."

grasp to the domain.

16 Scientometrics

**3.2. Trending keywords**

We considered WoS category assignment to literature as another important indicator representing domain-level thematic concentration. The top 20 frequently assigned WoS categories to the records are described in **Table 4**. It shows the year it was first assigned, and the density of how many times per year a specific category has been given, from its first year. The table is sorted in ascending order of the year. Results show that three categories have been assigned more than 2000 times – "information science & library science" (n = 3880), "computer science" (n = 3260), and "computer science, interdisciplinary applications" (n = 2284). These categories were first assigned from the beginning in the data set, demonstrating the greatest densities. The most frequently assigned category to be added to the top four list is "computer science, information systems." This category also demonstrates a relatively high density (33.036), given its first year of assignment was 1990. This finding suggests that literature under these four categories has had the largest influence on the emergence and development of scientific knowledge in scientometrics. In turn, research with scientific foci in social sciences, engineering, medical & health sciences, and environmental sciences brought along a multidisciplinary

Given by authors and indexers, keywords reflect representative concepts underlying published literature. The top 20 frequently occurring keywords in the data set are described in **Table 5**. It shows the year it first appeared, and the density of how many times on average a specific keyword has appeared, from its first year. Findings indicate that in the beginning, "bibliometrics" and "scientometrics" focused on employing "citation analysis" to examine the "impact" of a "science". We assume that "journal" and "publication" were considered as units of analysis. Another effort focused on evaluating research "performance" and "productivity" and examining the "pattern" of scientific "collaboration." The other stream of research had interest in devising a "bibliometric indicator" such as journal "impact factor", which led

> We analyzed another text fields, namely titles and abstracts since more informational points of content can be examined than only exploring keywords. We aimed to uncover the evolution of latent topics in the records over time. Toward that end, we removed stop words from

of scientometrics to biomedicine" and "literature-based research in medicine" respectively. Knowledge discovery in healthcare and biomedical sciences has been among the greatest interest in scientometrics. We assume that this stream of research has ups and downs based

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**3.** Falling topics: Topic 5 has fallen. We labeled it "history and philosophy of scientometrics." It is obvious that a study of theory and practice tends to be prominent in early years of a science. As staging into the maturation, this kind of topic naturally moves way from inter-

**4.** Static topics: Topic 11 has been statically distributed over time. Based on the extracted terms, Topic 11 is interpreted as "mapping intellectual structure using citation and network analysis." This is one of the canonical research themes in scientometrics receiving

on the change of scientific foci.

est. It has also decreased in scientometrics.

**Table 6.** Top 20 keywords with the greatest intensive burstiness.

consistent attention from the beginning of the domain.

**Figure 5.** Keyword co-occurrence network (n = 484).

text, using a list of stop words in Python NLTK. The text was lowercased, tokenized, and deaccented. Then, we lemmatized the tokens and extracted noun phrases by bigram indexing. Text pre-processing and topic modeling were driven by gensim, a robust text mining toolkit in Python. **Table 7** describes 20 topics and 10 corresponding terms per topic. The terms were sorted in descending order of the average probabilities over the 28 years. Results show that most of the terms having high probabilities are unigram-formed.

**Figure 6** illustrates the topical trends from 1990 till 2017 using a visualization technique called a bump chart. The topics are sorted in descending order of normalized probability distributions in the beginning year. We further discuss nine prominent topics, Topics 9, 17, 7, 4, 1, 5, 11, 16, and 0, due to their relatively high probability distributions. We categorized these topics into four trends: (1) rising, (2) rising-falling, (3) falling, and (4) static.

of scientometrics to biomedicine" and "literature-based research in medicine" respectively. Knowledge discovery in healthcare and biomedical sciences has been among the greatest interest in scientometrics. We assume that this stream of research has ups and downs based on the change of scientific foci.

**Table 6.** Top 20 keywords with the greatest intensive burstiness.

text, using a list of stop words in Python NLTK. The text was lowercased, tokenized, and deaccented. Then, we lemmatized the tokens and extracted noun phrases by bigram indexing. Text pre-processing and topic modeling were driven by gensim, a robust text mining toolkit in Python. **Table 7** describes 20 topics and 10 corresponding terms per topic. The terms were sorted in descending order of the average probabilities over the 28 years. Results show that

**Figure 6** illustrates the topical trends from 1990 till 2017 using a visualization technique called a bump chart. The topics are sorted in descending order of normalized probability distributions in the beginning year. We further discuss nine prominent topics, Topics 9, 17, 7, 4, 1, 5, 11, 16, and 0, due to their relatively high probability distributions. We categorized these topics

**1.** Rising topics: Topics 9, 17, 7, and 1 are consistently rising. Topic 9 we labeled "applications of scientometrics to material sciences" has received the greatest attention over time. Topic 17 which has sharply increased is named "publication-based scholarly communication." Topics 7 and 1 have been always in the top topic list and recently received increasing attention. We labeled them "evaluation of funded research" and "applications of scientometrics to medical education" respectively. Findings indicate that applications of scientometrics to domains other than biomedical sciences are of increasing concerns in

**2.** Rising-falling topics: Topics 4, 16, and 0 repeat rising and falling. Topic 4 can be named "literature-based research in healthcare." Topics 16 and 0 can be understood as "applications

most of the terms having high probabilities are unigram-formed.

**Figure 5.** Keyword co-occurrence network (n = 484).

18 Scientometrics

into four trends: (1) rising, (2) rising-falling, (3) falling, and (4) static.

the scientific community.

disease two use country data one online journal research theory library information period result paper search number health number internet sci water function study study

**Topic 16 Topic 17 Topic 18 Topic 19** research communication journal ecology rehabilitation bibliometrics citation species stem cell scholarly communication analysis geography neuroscience dss impact climate change

credit publishing study city

**Table 7.** 20 generated topics.

**Figure 6.** Topical trends.

guideline science impact factor conservation paper library information paper knowledge study media reference biodiversity transplantation theory science tourism article impact author study

capacity system subject bibliometric analysis

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**Table 7.** 20 generated topics.

**Topic 0 Topic 1 Topic 2 Topic 3** article psychology publication productivity journal education cancer faculty author nursing document publication article published Brazilian drug index number research research gender literature study descriptor result study psychiatry Korean study research theses Latin American conclusion medicine school literature woman publication aids drug year **Topic 4 Topic 5 Topic 6 Topic 7** health science research research research history country evaluation publication scientometrics science impact public health book collaboration funding literature reception publication assessment medicine removal output policy method philosophy physics researcher result nature university project disease colleague study scientist health care sport productivity work **Topic 8 Topic 9 Topic 10 Topic 11** performance technology research structure indicator literature field analysis research patent analysis map bibliometric indicator nanotechnology information network quality serial study mapping evaluation indexing science citation group application development data measure development data cluster data material paper database peer review core knowledge method **Topic 12 Topic 13 Topic 14 Topic 15** study distribution information paper population model web research method data library publication country index link literature

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**Figure 6.** Topical trends.

#### **3.4. Document co-citation network**

Previous section utilized titles and abstracts to investigate topical trends without any bound context. This section examined those fields in a context of document-level co-citation relationship. **Figure 7** visualizes the document co-citation network in the data set. Each node is a cited reference extracted from the reference sections of the records and the size of the node is proportional to its cumulative frequency of received citations. Nodes with inner circles in red represent articles with citation bursts. We labeled the most highly cited 20 articles in black following a truncated form of <LAST NAME> < ABBREVIATED FIRST NAME> (<YEAR>) so as to only display first authors' names and published years (see the upward in **Figure 7**). They are cited more than or equal to 95 times locally, meaning in the data set. The color legend at the top of the display indicates links and citations in cooler colors happen more closely to 1990 whereas hotter ones occur in closer years to 2017. Based on the color scheme, we can keep track of the evolution of the document network. Findings show that most of the landmark articles were published relatively recently. Cumulative citations and citation bursts also intensively happened with these articles. Next, we conducted clustering and labeled the clusters in blue, using LLR (see the downward in **Figure 7**). Clusters are numbered in such a way that higher rankings are given to the clusters containing more references. In order to add richer contexts in interpreting the clustering results, we generated another visualization

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In **Figure 8**, we re-grouped all the nodes on multiple lines so that the cluster memberships can be more accessibly identified. As depicted in the figure, emerging trends can further be captured by examining Clusters 1, 6, 10, 16, 17, 18 given cluster sizes, recency, cumulative citations, and citation bursts. **Table 8** summarizes these clusters in terms of cluster size, three types of labels, and mean year of citees, i.e. cluster age. Of the selected clusters, Cluster 1 is the largest and oldest. In consideration with Cluster 6, results show that impact measure is still among the important themes in scientometrics. The third largest and newest group of literature is Cluster 10. It indicates practical applications of social media analytics to scientometrics is receiving the most recent attention. Other emerging topics include international collaboration (Cluster 16) and applications to medicine (Cluster 17) and environmental sciences and

called a timeline visualization (see **Figure 8**).

**Figure 8.** Timeline visualization with LLR cluster labels.

policy (Cluster 18).

**Figure 7.** Document co-citation networks with truncated labels of first authors' names and published years (upward) and cluster labels (downward) (n = 1856, e = 6127).

red represent articles with citation bursts. We labeled the most highly cited 20 articles in black following a truncated form of <LAST NAME> < ABBREVIATED FIRST NAME> (<YEAR>) so as to only display first authors' names and published years (see the upward in **Figure 7**). They are cited more than or equal to 95 times locally, meaning in the data set. The color legend at the top of the display indicates links and citations in cooler colors happen more closely to 1990 whereas hotter ones occur in closer years to 2017. Based on the color scheme, we can keep track of the evolution of the document network. Findings show that most of the landmark articles were published relatively recently. Cumulative citations and citation bursts also intensively happened with these articles. Next, we conducted clustering and labeled the clusters in blue, using LLR (see the downward in **Figure 7**). Clusters are numbered in such a way that higher rankings are given to the clusters containing more references. In order to add richer contexts in interpreting the clustering results, we generated another visualization called a timeline visualization (see **Figure 8**).

In **Figure 8**, we re-grouped all the nodes on multiple lines so that the cluster memberships can be more accessibly identified. As depicted in the figure, emerging trends can further be captured by examining Clusters 1, 6, 10, 16, 17, 18 given cluster sizes, recency, cumulative citations, and citation bursts. **Table 8** summarizes these clusters in terms of cluster size, three types of labels, and mean year of citees, i.e. cluster age. Of the selected clusters, Cluster 1 is the largest and oldest. In consideration with Cluster 6, results show that impact measure is still among the important themes in scientometrics. The third largest and newest group of literature is Cluster 10. It indicates practical applications of social media analytics to scientometrics is receiving the most recent attention. Other emerging topics include international collaboration (Cluster 16) and applications to medicine (Cluster 17) and environmental sciences and policy (Cluster 18).

**Figure 8.** Timeline visualization with LLR cluster labels.

**3.4. Document co-citation network**

22 Scientometrics

Previous section utilized titles and abstracts to investigate topical trends without any bound context. This section examined those fields in a context of document-level co-citation relationship. **Figure 7** visualizes the document co-citation network in the data set. Each node is a cited reference extracted from the reference sections of the records and the size of the node is proportional to its cumulative frequency of received citations. Nodes with inner circles in

**Figure 7.** Document co-citation networks with truncated labels of first authors' names and published years (upward)

and cluster labels (downward) (n = 1856, e = 6127).

approaches to a variety of domains such as material sciences, medicine, and environmental sciences have received increasing attention. In reverse, practical applications of social media analytics to scientometrics is also receiving the most recent interest. Impact measure and science mapping are among the canonical research themes receiving consistent attention from

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The present chapter aimed to explore epistemological characteristics, historic areas of innovation, and emerging trends in scientometrics. We achieved this by investigating domain-level citation paths, WoS category assignment, keyword co-occurrence, temporal topic models, and document clusters. The findings indicate the domain of scientometrics is multidisciplinary and partially interdisciplinary. Social sciences and biomedicine have published to the field, but not yet cited from each other. We argue that the maturation of scientometrics as a scientific field is still ongoing. Next, early studies tried to measure a science's impact and performance and productivity of published research. Successive effort investigated laws and indicators in scientometrics and explored scientific collaboration. Recent literature is paying attention to topics such as applying scientometrics approaches to different domains and bringing social

The approaches of the present study provide advantages in investigating intellectual structure of a science as follows. First, we tried to make our data collection inclusive by investigating closely neighboring domains. Conventional studies of domain analysis often cover only a fraction of published literature. Our method provides a systematic way to explore the broader coverage of a scientific discipline. Second, we investigated the domain from a multi-faceted point of view. Domain-level citation trajectories, subject category assignment, networks of subject categories and keywords, bursting keywords, topic models, and document co-citation networks were identified in this study. Sub-sections in Results triangulated each other, adding richer interpretations from macro units of analysis to micro ones. Finally, the analytical procedure and tools employed in the present work enabled us to explore time-aware research trends in the domain. In addition, one can conduct this kind of domain analysis of his or her concern as frequently as needed without prior knowledge or experience. Thus, the proposed approaches have a relatively higher reproducibility and lower cost for conducting studies at

There are several limitations in our work. First, the topic search we conducted on WoS may have missed relevant records. It is acknowledged that the vocabulary mismatch presents a challenge for keyword-based search. We may be able to overcome this drawback by employing citation indexing or iterative search query development as an alternative strategy in order to capture a much broader context. Second, WoS as our source of data may have underrepresented conference proceedings. It is also recognized as an issue for disciplines such as social sciences and arts and humanities [13]. At the time of data retrieval, the authors' institutes only subscribed to the core collection of WoS. Thus, it was inevitable not to miss some

the beginning of the domain.

media analytics in scientometrics.

a larger scale, especially as in the era of mass publication.

**5. Conclusion**

**Table 8.** Cluster summary.