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Correlations between personality traits and a wide range of sensory thresholds were examined. Participants (N = 124) completed a personality inventory (NEO-FFI) and underwent assessment of olfactory, trigeminal, tactile and gustatory detection thresholds, as well as examination of trigeminal and tactile pain thresholds. Significantly enhanced odor sensitivity in socially agreeable people, significantly enhanced trigeminal sensitivity in neurotic subjects, and a tendency for enhanced pain tolerance in highly conscientious participants was revealed. It is postulated that varied sensory processing may influence an individual's perception of the environment; particularly their perception of socially relevant or potentially dangerous stimuli and thus, varied with personality.
Background: A delta and C fibers are the major pain-conducting nerve fibers, activate only partly the same brain areas, and are differently involved in pain syndromes. Whether a stimulus excites predominantly A delta or C fibers is a commonly asked question in basic pain research but a quick test was lacking so far. Methodology/Principal Findings: Of 77 verbal descriptors of pain sensations, "pricking", "dull" and "pressing" distinguished best (95% cases correctly) between A delta fiber mediated (punctate pressure produced by means of von Frey hairs) and C fiber mediated (blunt pressure) pain, applied to healthy volunteers in experiment 1. The sensation was assigned to A delta fibers when "pricking" but neither "dull" nor "pressing" were chosen, and to C fibers when the sum of the selections of "dull" or "pressing" was greater than that of the selection of "pricking". In experiment 2, with an independent cohort, the three-descriptor questionnaire achieved sensitivity and specificity above 0.95 for distinguishing fiber preferential non-mechanical induced pain (laser heat, exciting A delta fibers, and 5-Hz electric stimulation, exciting C fibers). Conclusion: A three-item verbal rating test using the words "pricking", "dull", and "pressing" may provide sufficient information to characterize a pain sensation evoked by a physical stimulus as transmitted via A delta or via C fibers. It meets the criteria of a screening test by being easy to administer, taking little time, being comfortable in handling, and inexpensive while providing high specificity for relevant information.
Effect sizes in experimental pain produced by gender, genetic variants and sensitization procedures
(2011)
Background: Various effects on pain have been reported with respect to their statistical significance, but a standardized measure of effect size has been rarely added. Such a measure would ease comparison of the magnitude of the effects across studies, for example the effect of gender on heat pain with the effect of a genetic variant on pressure pain. Methodology/Principal Findings: Effect sizes on pain thresholds to stimuli consisting of heat, cold, blunt pressure, punctuate pressure and electrical current, administered to 125 subjects, were analyzed for 29 common variants in eight human genes reportedly modulating pain, gender and sensitization procedures using capsaicin or menthol. The genotype explained 0–5.9% of the total interindividual variance in pain thresholds to various stimuli and produced mainly small effects (Cohen's d 0–1.8). The largest effect had the TRPA1 rs13255063T/rs11988795G haplotype explaining >5% of the variance in electrical pain thresholds and conferring lower pain sensitivity to homozygous carriers. Gender produced larger effect sizes than most variant alleles (1–14.8% explained variance, Cohen's d 0.2–0.8), with higher pain sensitivity in women than in men. Sensitization by capsaicin or menthol explained up to 63% of the total variance (4.7–62.8%) and produced largest effects according to Cohen's d (0.4–2.6), especially heat sensitization by capsaicin (Cohen's d = 2.6). Conclusions: Sensitization, gender and genetic variants produce effects on pain in the mentioned order of effect sizes. The present report may provide a basis for comparative discussions of factors influencing pain.
Background and Aims: Chronic infection with the hepatitis B virus (HBV) is a major health issue worldwide. Recently, single nucleotide polymorphisms (SNPs) within the human leukocyte antigen (HLA)-DP locus were identified to be associated with HBV infection in Asian populations. Most significant associations were observed for the A alleles of HLA-DPA1 rs3077 and HLA-DPB1 rs9277535, which conferred a decreased risk for HBV infection. We assessed the implications of these variants for HBV infection in Caucasians.
Methods: Two HLA-DP gene variants (rs3077 and rs9277535) were analyzed for associations with persistent HBV infection and with different clinical outcomes, i.e., inactive HBsAg carrier status versus progressive chronic HBV (CHB) infection in Caucasian patients (n = 201) and HBsAg negative controls (n = 235).
Results: The HLA-DPA1 rs3077 C allele was significantly associated with HBV infection (odds ratio, OR = 5.1, 95% confidence interval, CI: 1.9–13.7; p = 0.00093). However, no significant association was seen for rs3077 with progressive CHB infection versus inactive HBsAg carrier status (OR = 2.7, 95% CI: 0.6–11.1; p = 0.31). In contrast, HLA-DPB1 rs9277535 was not associated with HBV infection in Caucasians (OR = 0.8, 95% CI: 0.4–1.9; p = 1).
Conclusions: A highly significant association of HLA-DPA1 rs3077 with HBV infection was observed in Caucasians. However, as a differentiation between different clinical courses of HBV infection was not possible, knowledge of the HLA-DPA1 genotype cannot be translated into personalized anti-HBV therapy approaches.
Increasing evidence about the central nervous representation of pain in the brain suggests that the operculo-insular cortex is a crucial part of the pain matrix. The pain-specificity of a brain region may be tested by administering nociceptive stimuli while controlling for unspecific activations by administering non-nociceptive stimuli. We applied this paradigm to nasal chemosensation, delivering trigeminal or olfactory stimuli, to verify the pain-specificity of the operculo-insular cortex. In detail, brain activations due to intranasal stimulation induced by non-nociceptive olfactory stimuli of hydrogen sulfide (5 ppm) or vanillin (0.8 ppm) were used to mask brain activations due to somatosensory, clearly nociceptive trigeminal stimulations with gaseous carbon dioxide (75% v/v). Functional magnetic resonance (fMRI) images were recorded from 12 healthy volunteers in a 3T head scanner during stimulus administration using an event-related design. We found that significantly more activations following nociceptive than non-nociceptive stimuli were localized bilaterally in two restricted clusters in the brain containing the primary and secondary somatosensory areas and the insular cortices consistent with the operculo-insular cortex. However, these activations completely disappeared when eliminating activations associated with the administration of olfactory stimuli, which were small but measurable. While the present experiments verify that the operculo-insular cortex plays a role in the processing of nociceptive input, they also show that it is not a pain-exclusive brain region and allow, in the experimental context, for the interpretation that the operculo-insular cortex splay a major role in the detection of and responding to salient events, whether or not these events are nociceptive or painful.
The manifestation of chronic back pain depends on structural, psychosocial, occupational and genetic influences. Heritability estimates for back pain range from 30% to 45%. Genetic influences are caused by genes affecting intervertebral disc degeneration or the immune response and genes involved in pain perception, signalling and psychological processing. This inter-individual variability which is partly due to genetic differences would require an individualized pain management to prevent the transition from acute to chronic back pain or improve the outcome. The genetic profile may help to define patients at high risk for chronic pain. We summarize genetic factors that (i) impact on intervertebral disc stability, namely Collagen IX, COL9A3, COL11A1, COL11A2, COL1A1, aggrecan (AGAN), cartilage intermediate layer protein, vitamin D receptor, metalloproteinsase-3 (MMP3), MMP9, and thrombospondin-2, (ii) modify inflammation, namely interleukin-1 (IL-1) locus genes and IL-6 and (iii) and pain signalling namely guanine triphosphate (GTP) cyclohydrolase 1, catechol-O-methyltransferase, μ opioid receptor (OPMR1), melanocortin 1 receptor (MC1R), transient receptor potential channel A1 and fatty acid amide hydrolase and analgesic drug metabolism (cytochrome P450 [CYP]2D6, CYP2C9).
The single nucleotide polymorphism 118A>G of the human micro-opioid receptor gene OPRM1, which leads to an exchange of the amino acid asparagine (N) to aspartic acid (D) at position 40 of the extracellular receptor region, alters the in vivo effects of opioids to different degrees in pain-processing brain regions. The most pronounced N40D effects were found in brain regions involved in the sensory processing of pain intensity. Using the mu-opioid receptor-specific agonist DAMGO, we analyzed the micro-opioid receptor signaling, expression, and binding affinity in human brain tissue sampled postmortem from the secondary somatosensory area (SII) and from the ventral posterior part of the lateral thalamus, two regions involved in the sensory processing and transmission of nociceptive information. We show that the main effect of the N40D micro-opioid receptor variant is a reduction of the agonist-induced receptor signaling efficacy. In the SII region of homo- and heterozygous carriers of the variant 118G allele (n=18), DAMGO was only 62% as efficient (p=0.002) as in homozygous carriers of the wild-type 118A allele (n=15). In contrast, the number of [3H]DAMGO binding sites was unaffected. Hence, the micro-opioid receptor G-protein coupling efficacy in SII of carriers of the 118G variant was only 58% as efficient as in homozygous carriers of the 118A allele (p<0.001). The thalamus was unaffected by the OPRM1 118A>G SNP. In conclusion, we provide a molecular basis for the reduced clinical effects of opioid analgesics in carriers of mu-opioid receptor variant N40D.
High glucosylceramides and low anandamide contribute to sensory loss and pain in Parkinson's disease
(2020)
Background: Parkinson's disease (PD) causes chronic pain in two‐thirds of patients, in part originating from sensory neuropathies. The aim of the present study was to describe the phenotype of PD‐associated sensory neuropathy and to evaluate its associations with lipid allostasis, the latter motivated by recent genetic studies associating mutations of glucocerebrosidase with PD onset and severity. Glucocerebrosidase catalyzes the metabolism of glucosylceramides.
Methods: We used quantitative sensory tests, pain ratings, and questionnaires and analyzed plasma levels of multiple bioactive lipid species using targeted lipidomic analyses. The study comprised 2 sets of patients and healthy controls: the first 128 Israeli PD patients and 224 young German healthy controls for exploration, the second 50/50 German PD patients and matched healthy controls for deeper analyses.
Results: The data showed a 70% prevalence of PD pain and sensory neuropathies with a predominant phenotype of thermal sensory loss plus mechanical hypersensitivity. Multivariate analyses of lipids revealed major differences between PD patients and healthy controls, mainly originating from glucosylceramides and endocannabinoids. Glucosylceramides were increased, whereas anandamide and lysophosphatidic acid 20:4 were reduced, stronger in patients with ongoing pain and with a linear relationship with pain intensity and sensory losses, particularly for glucosylceramide 18:1 and glucosylceramide 24:1.
Conclusions: Our data suggest that PD‐associated sensory neuropathies and PD pain are in part caused by accumulations of glucosylceramides, raising the intriguing possibility of reducing PD pain and sensory loss by glucocerebrosidase substituting or refolding approaches. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Dysregulation of lysophosphatidic acids in multiple sclerosis and autoimmune encephalomyelitis
(2017)
Bioactive lipids contribute to the pathophysiology of multiple sclerosis. Here, we show that lysophosphatidic acids (LPAs) are dysregulated in multiple sclerosis (MS) and are functionally relevant in this disease. LPAs and autotaxin, the major enzyme producing extracellular LPAs, were analyzed in serum and cerebrospinal fluid in a cross-sectional population of MS patients and were compared with respective data from mice in the experimental autoimmune encephalomyelitis (EAE) model, spontaneous EAE in TCR1640 mice, and EAE in Lpar2 -/- mice. Serum LPAs were reduced in MS and EAE whereas spinal cord LPAs in TCR1640 mice increased during the ‘symptom-free’ intervals, i.e. on resolution of inflammation during recovery hence possibly pointing to positive effects of brain LPAs during remyelination as suggested in previous studies. Peripheral LPAs mildly re-raised during relapses but further dropped in refractory relapses. The peripheral loss led to a redistribution of immune cells from the spleen to the spinal cord, suggesting defects of lymphocyte homing. In support, LPAR2 positive T-cells were reduced in EAE and the disease was intensified in Lpar2 deficient mice. Further, treatment with an LPAR2 agonist reduced clinical signs of relapsing-remitting EAE suggesting that the LPAR2 agonist partially compensated the endogenous loss of LPAs and implicating LPA signaling as a novel treatment approach.
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.
Background: Cannabis proofed to be effective in pain relief, but one major side effect is its influence on memory in humans. Therefore, the role of memory on central processing of nociceptive information was investigated in healthy volunteers.
Methods: In a placebo-controlled cross-over study including 22 healthy subjects, the effect of 20 mg oral Δ9-tetrahydrocannabinol (THC) on memory involving nociceptive sensations was studied, using a delayed stimulus discrimination task (DSDT). To control for nociceptive specificity, a similar DSDT-based study was performed in a subgroup of thirteen subjects, using visual stimuli.
Results: For each nociceptive stimulus pair, the second stimulus was associated with stronger and more extended brain activations than the first stimulus. These differences disappeared after THC administration. The THC effects were mainly located in two clusters comprising the insula and inferior frontal cortex in the right hemisphere, and the caudate nucleus and putamen bilaterally. These cerebral effects were accompanied in the DSDT by a significant reduction of correct ratings from 41.61% to 37.05% after THC administration (rm-ANOVA interaction "drug" by "measurement": F (1,21) = 4.685, p = 0.042). Rating performance was also reduced for the visual DSDT (69.87% to 54.35%; rm-ANOVA interaction of "drug" by "measurement": F (1,12) = 13.478, p = 0.003) and reflected in a reduction of stimulus-related brain deactivations in the bilateral angular gyrus.
Conclusions: Results suggest that part of the effect of THC on pain may be related to memory effects. THC reduced the performance in DSDT of nociceptive and visual stimuli, which was accompanied by significant effects on brain activations. However, a pain specificity of these effects cannot be deduced from the data presented.
An important measure in pain research is the intensity of nociceptive stimuli and their cortical representation. However, there is evidence of different cerebral representations of nociceptive stimuli, including the fact that cortical areas recruited during processing of intranasal nociceptive chemical stimuli included those outside the traditional trigeminal areas. Therefore, the aim of this study was to investigate the major cerebral representations of stimulus intensity associated with intranasal chemical trigeminal stimulation. Trigeminal stimulation was achieved with carbon dioxide presented to the nasal mucosa. Using a single‐blinded, randomized crossover design, 24 subjects received nociceptive stimuli with two different stimulation paradigms, depending on the just noticeable differences in the stimulus strengths applied. Stimulus‐related brain activations were recorded using functional magnetic resonance imaging with event‐related design. Brain activations increased significantly with increasing stimulus intensity, with the largest cluster at the right Rolandic operculum and a global maximum in a smaller cluster at the left lower frontal orbital lobe. Region of interest analyses additionally supported an activation pattern correlated with the stimulus intensity at the piriform cortex as an area of special interest with the trigeminal input. The results support the piriform cortex, in addition to the secondary somatosensory cortex, as a major area of interest for stimulus strength‐related brain activation in pain models using trigeminal stimuli. This makes both areas a primary objective to be observed in human experimental pain settings where trigeminal input is used to study effects of analgesics.
Background: The categorization of individuals as normosmic, hyposmic, or anosmic from test results of odor threshold, discrimination, and identification may provide a limited view of the sense of smell. The purpose of this study was to expand the clinical diagnostic repertoire by including additional tests. Methods: A random cohort of n = 135 individuals (83 women and 52 men, aged 21 to 94 years) was tested for odor threshold, discrimination, and identification, plus a distance test, in which the odor of peanut butter is perceived, a sorting task of odor dilutions for phenylethyl alcohol and eugenol, a discrimination test for odorant enantiomers, a lateralization test with eucalyptol, a threshold assessment after 10 min of exposure to phenylethyl alcohol, and a questionnaire on the importance of olfaction. Unsupervised methods were used to detect structure in the olfaction-related data, followed by supervised feature selection methods from statistics and machine learning to identify relevant variables. Results: The structure in the olfaction-related data divided the cohort into two distinct clusters with n = 80 and 55 subjects. Odor threshold, discrimination, and identification did not play a relevant role for cluster assignment, which, on the other hand, depended on performance in the two odor dilution sorting tasks, from which cluster assignment was possible with a median 100-fold cross-validated balanced accuracy of 77–88%. Conclusions: The addition of an odor sorting task with the two proposed odor dilutions to the odor test battery expands the phenotype of olfaction and fits seamlessly into the sensory focus of standard test batteries.
Internalin B–mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. Here, we propose a method based on hidden Markov modeling and explainable artificial intelligence that machine-learns the key differences in MET mobility between internalin B–treated and –untreated cells from single-particle tracking data. Our method assigns receptor mobility to three diffusion modes (immobile, slow, and fast). It discriminates between internalin B–treated and –untreated cells with a balanced accuracy of >99% and identifies three parameters that are most affected by internalin B treatment: a decrease in the mobility of slow molecules (1) and a depopulation of the fast mode (2) caused by an increased transition of fast molecules to the slow mode (3). Our approach is based entirely on free software and is readily applicable to the analysis of other membrane receptors.
Diminished sense of smell impairs the quality of life but olfactorily disabled people are hardly considered in measures of disability inclusion. We aimed to stratify perceptual characteristics and odors according to the extent to which they are perceived differently with reduced sense of smell, as a possible basis for creating olfactory experiences that are enjoyed in a similar way by subjects with normal or impaired olfactory function. In 146 subjects with normal or reduced olfactory function, perceptual characteristics (edibility, intensity, irritation, temperature, familiarity, hedonics, painfulness) were tested for four sets of 10 different odors each. Data were analyzed with (i) a projection based on principal component analysis and (ii) the training of a machine-learning algorithm in a 1000-fold cross-validated setting to distinguish between olfactory diagnosis based on odor property ratings. Both analytical approaches identified perceived intensity and familiarity with the odor as discriminating characteristics between olfactory diagnoses, while evoked pain sensation and perceived temperature were not discriminating, followed by edibility. Two disjoint sets of odors were identified, i.e., d = 4 “discriminating odors” with respect to olfactory diagnosis, including cis-3-hexenol, methyl salicylate, 1-butanol and cineole, and d = 7 “non-discriminating odors”, including benzyl acetate, heptanal, 4-ethyl-octanoic acid, methional, isobutyric acid, 4-decanolide and p-cresol. Different weightings of the perceptual properties of odors with normal or reduced sense of smell indicate possibilities to create sensory experiences such as food, meals or scents that by emphasizing trigeminal perceptions can be enjoyed by both normosmic and hyposmic individuals.
Selecting the k best features is a common task in machine learning. Typically, a few features have high importance, but many have low importance (right-skewed distribution). This report proposes a numerically precise method to address this skewed feature importance distribution in order to reduce a feature set to the informative minimum of items. Computed ABC analysis (cABC) is an item categorization method that aims to identify the most important items by partitioning a set of non-negative numerical items into subsets "A", "B", and "C" such that subset "A" contains the "few important" items based on specific properties of ABC curves defined by their relationship to Lorenz curves. In its recursive form, the cABC analysis can be applied again to subset "A". A generic image dataset and three biomedical datasets (lipidomics and two genomics datasets) with a large number of variables were used to perform the experiments. The experimental results show that the recursive cABC analysis limits the dimensions of the data projection to a minimum where the relevant information is still preserved and directs the feature selection in machine learning to the most important class-relevant information, including filtering feature sets for nonsense variables. Feature sets were reduced to 10% or less of the original variables and still provided accurate classification in data not used for feature selection. cABC analysis, in its recursive variant, provides a computationally precise means of reducing information to a minimum. The minimum is the result of a computation of the number of k most relevant items, rather than a decision to select the k best items from a list. In addition, there are precise criteria for stopping the reduction process. The reduction to the most important features can improve the human understanding of the properties of the data set. The cABC method is implemented in the Python package "cABCanalysis" available at https://pypi.org/project/cABCanalysis/.