A data science-based analysis of seasonal patterns in outpatient presentations due to olfactory dysfunction.

BACKGROUND: Changes in human olfactory function throughout the year appear to be a common perception due to the seasonal oscillations in some etiologies associated with olfactory loss. However, longitudinal data from large cohorts were rarely analysed for temporal patterns of human olfaction apart from oscillations in specific etiologies of olfactory loss. METHODS: Temporal patterns in the presentation of patients with olfactory disorders to a single centre were investigated as part of a cohort study. The time series analysis performed utilized a power spectrum analysis and an autoregressive integrated moving average (ARIMA) model in order to demonstrate repetitive fluctuations or trends in the monthly number of patients reporting from January 1999 to December 2017. The analyses additionally addressed temporal changes in the causes to which the olfactory disorder was attributed and in the degree of olfactory loss. RESULTS: A cohort of 7,014 patients was included. Descriptive analysis showed that the presentation of olfactory disorders had seasonal variation, high in March, without a trend. Power spectrum analysis showed a general seasonality of the numbers of patients, without further pattern in the causes or the degree of olfactory dysfunction. CONCLUSIONS: The yearly periodicity in patient presentations at a specialized smell and taste clinic, was not readily attributable to seasonally changing medical causes of olfactory loss such as viral infections. This suggests that in addition to exploring the seasonality of olfactory etiologies, the changes in human olfactory acuity merit further assessments in longitudinal studies.

Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery.

BACKGROUND: Prevention of persistent pain after breast cancer surgery, via early identification of patients at high risk, is a clinical need. Psychological factors are among the most consistently proposed predictive parameters for the development of persistent pain. However, repeated use of long psychological questionnaires in this context may be exhaustive for a patient and inconvenient in everyday clinical practice. METHODS: Supervised machine learning was used to create a short form of questionnaires that would provide the same predictive performance of pain persistence as the full questionnaires in a cohort of 1000 women followed up for 3 yr after breast cancer surgery. Machine-learned predictors were first trained with the full-item set of Beck's Depression Inventory (BDI), Spielberger's State-Trait Anxiety Inventory (STAI), and the State-Trait Anger Expression Inventory (STAXI-2). Subsequently, features were selected from the questionnaires to create predictors having a reduced set of items. RESULTS: A combined seven-item set of 10% of the original psychological questions from STAI and BDI, provided the same predictive performance parameters as the full questionnaires for the development of persistent postsurgical pain. The seven-item version offers a shorter and at least as accurate identification of women in whom pain persistence is unlikely (almost 95% negative predictive value). CONCLUSIONS: Using a data-driven machine-learning approach, a short list of seven items from BDI and STAI is proposed as a basis for a predictive tool for the persistence of pain after breast cancer surgery.

A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes.

BACKGROUND: Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine-learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain. METHODS: Based on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine-learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase. RESULTS: Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research. CONCLUSIONS: The present computational functional genomics-based approach provided a computational systems-biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine-learned methods provide innovative approaches to knowledge discovery from previous evidence. SIGNIFICANCE: We show that knowledge discovery in genetic databases and contemporary machine-learned techniques can identify relevant biological processes involved in Persitent pain.

Machine-learned analysis of quantitative sensory testing responses to noxious cold stimulation in healthy subjects.

BACKGROUND: Pain in response to noxious cold has a complex molecular background probably involving several types of sensors. A recent observation has been the multimodal distribution of human cold pain thresholds. This study aimed at analysing reproducibility and stability of this observation and further exploration of data patterns supporting a complex background. METHOD: Pain thresholds to noxious cold stimuli (range 32-0 °C, tonic: temperature decrease -1 °C/s, phasic: temperature decrease -8 °C/s) were acquired in 148 healthy volunteers. The probability density distribution was analysed using machine-learning derived methods implemented as Gaussian mixture modeling (GMM), emergent self-organizing maps and self-organizing swarms of data agents. RESULTS: The probability density function of pain responses was trimodal (mean thresholds at 25.9, 18.4 and 8.0 °C for tonic and 24.5, 18.1 and 7.5 °C for phasic stimuli). Subjects' association with Gaussian modes was consistent between both types of stimuli (weighted Cohen's κ = 0.91). Patterns emerging in self-organizing neuronal maps and swarms could be associated with different trends towards decreasing cold pain sensitivity in different Gaussian modes. On self-organizing maps, the third Gaussian mode emerged as particularly distinct. CONCLUSION: Thresholds at, roughly, 25 and 18 °C agree with known working temperatures of TRPM8 and TRPA1 ion channels, respectively, and hint at relative local dominance of either channel in respective subjects. Data patterns suggest involvement of further distinct mechanisms in cold pain perception at lower temperatures. Findings support data science approaches to identify biologically plausible hints at complex molecular mechanisms underlying human pain phenotypes. SIGNIFICANCE: Sensitivity to pain is heterogeneous. Data-driven computational research approaches allow the identification of subgroups of subjects with a distinct pattern of sensitivity to cold stimuli. The subgroups are reproducible with different types of noxious cold stimuli. Subgroups show pattern that hints at distinct and inter-individually different types of the underlying molecular background.

Current evidence of cannabinoid-based analgesia obtained in preclinical and human experimental settings.

Cannabinoids have a long record of recreational and medical use and become increasingly approved for pain therapy. This development is based on preclinical and human experimental research summarized in this review. Cannabinoid CB1 receptors are widely expressed throughout the nociceptive system. Their activation by endogenous or exogenous cannabinoids modulates the release of neurotransmitters. This is reflected in antinociceptive effects of cannabinoids in preclinical models of inflammatory, cancer and neuropathic pain, and by nociceptive hypersensitivity of cannabinoid receptor-deficient mice. Cannabis-based medications available for humans mainly comprise Δ9 -tetrahydrocannabinol (THC), cannabidiol (CBD) and nabilone. During the last 10 years, six controlled studies assessing analgesic effects of cannabinoid-based drugs in human experimental settings were reported. An effect on nociceptive processing could be translated to the human setting in functional magnetic resonance imaging studies that pointed at a reduced connectivity within the pain matrix of the brain. However, cannabinoid-based drugs heterogeneously influenced the perception of experimentally induced pain including a reduction in only the affective but not the sensory perception of pain, only moderate analgesic effects, or occasional hyperalgesic effects. This extends to the clinical setting. While controlled studies showed a lack of robust analgesic effects, cannabis was nearly always associated with analgesia in open-label or retrospective reports, possibly indicating an effect on well-being or mood, rather than on sensory pain. Thus, while preclinical evidence supports cannabinoid-based analgesics, human evidence presently provides only reluctant support for a broad clinical use of cannabinoid-based medications in pain therapy. SIGNIFICANCE: Cannabinoids consistently produced antinociceptive effects in preclinical models, whereas they heterogeneously influenced the perception of experimentally induced pain in humans and did not provide robust clinical analgesia, which jeopardizes the translation of preclinical research on cannabinoid-mediated antinociception into the human setting.

Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis.

BACKGROUND: To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain. METHODS: We analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up. Preoperatively, all patients underwent a cold pain test (immersion of the hand into a water bath at 2-4 °C). The patients rated the pain intensity using a numerical ratings scale (NRS) from 0 to 10. Supervised machine-learning techniques were used to construct a classifier that could predict patients at risk of persistent pain. RESULTS: Whether or not a patient rated the pain intensity at NRS=10 within less than 45 s during the cold water immersion test provided a negative predictive value of 94.4% to assign a patient to the "persistent pain" group. If NRS=10 was never reached during the cold test, the predictive value for not developing persistent pain was almost 97%. However, a low negative predictive value of 10% implied a high false positive rate. CONCLUSIONS: Results provide a robust exclusion of persistent pain in women with an accuracy of 94.4%. Moreover, results provide further support for the hypothesis that the endogenous pain inhibitory system may play an important role in the process of pain becoming persistent.

Dysregulation of lysophosphatidic acids in multiple sclerosis and autoimmune encephalomyelitis.

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. Graphical summary of lysophosphatidic signaling in multiple sclerosis.

Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses.

Next-generation sequencing (NGS) provides unrestricted access to the genome, but it produces 'big data' exceeding in amount and complexity the classical analytical approaches. We introduce a bioinformatics-based classifying biomarker that uses emergent properties in genetics to separate pain patients requiring extremely high opioid doses from controls. Following precisely calculated selection of the 34 most informative markers in the OPRM1, OPRK1, OPRD1 and SIGMAR1 genes, pattern of genotypes belonging to either patient group could be derived using a k-nearest neighbor (kNN) classifier that provided a diagnostic accuracy of 80.6±4%. This outperformed alternative classifiers such as reportedly functional opioid receptor gene variants or complex biomarkers obtained via multiple regression or decision tree analysis. The accumulation of several genetic variants with only minor functional influences may result in a qualitative consequence affecting complex phenotypes, pointing at emergent properties in genetics.

A small yet comprehensive subset of human experimental pain models emerging from correlation analysis with a clinical quantitative sensory testing protocol in healthy subjects.

BACKGROUND: Picturing the complexity of pain in human experimental settings has increased the predictivity for clinical pain but requires increasingly complex test batteries. This raises problems in studies in which time is objectively limited, for example by the course of action of an analgesic drug. We addressed the selection of a small yet comprehensive set of pain tests for the use in such a situation. METHOD: Nineteen different pain measures from 'classical' pain models (n = 9) and a clinically established QST-pain test battery (n = 10), were obtained from 72 healthy volunteers (34 men). The nonparametric correlation structure among the various pain measures was analysed using Ward clustering. RESULTS: Four clusters emerged, each consisting of highly correlated pain measures. The pain model groups emerged comprised (I) pain thresholds and tolerances to blunt pressure or electrical pain; (II) pain thresholds to thermal stimuli; (III) pain measures obtained following application of punctate mechanical, intranasal CO2 chemical or cutaneous laser heat stimuli; and (IV) detection thresholds to thermal stimuli. The first three clusters agreed with an immediate mechanistic interpretation as reflecting C-fibre mediated pain, thermal pain and Aδ-fibre mediated pain, respectively, whereas the last cluster contained non-painful measures and was disregarded. CONCLUSIONS: When basing a selection of a small comprehensive set of pain models on the assumption that highly correlated pain measures account for redundant results and therefore, one member of each group suffices an economic yet comprehensive pain study, results suggest inclusion of established C-fibre, Aδ-fibre mediated and thermal pain measures.

A brain-lesion pattern based algorithm for the diagnosis of posttraumatic olfactory loss.

BACKGROUND: Brain areas processing olfactory information exhibit functionally relevant morphological dynamics. This suggests the exploitation of anatomical information in the diagnosis of an olfactory dysfunction. Following previous identifications of olfactory eloquent areas such as the olfactory bulbs and tracts, we focused at a brain-morphology based algorithm for establishing the diagnosis of olfactory loss following brain injury. METHODOLOGY: Forty-one patients with a history of head trauma dated back 40 ± 39 months, and additional 23 patients without head trauma, were assessed for damages in 11 olfaction-relevant brain areas using magnetic resonance imaging (MRI). Olfactory function was derived from the use of a standardized, reliable and validated olfactory test. An olfactory diagnostic algorithm was derived following classification and regression tree analysis of the brain lesion pattern. RESULTS: Subjects were assigned to olfactory diagnoses of anosmia, hyposmia or normosmia. These diagnoses were predictable at an accuracy of 62.3 % from the degree of damage in the olfactory bulb and in the left temporal lobe pole. The main diagnosis algorithm addressed the presence of anosmia, which could be predicted from the degree of damage in these brain areas at an accuracy of 81.3 %. CONCLUSIONS: We independently reproduced previously identified brain regions in which morphological damage is associated with olfactory loss. Based on this reproduction, an algorithm was developed for the diagnosis of anosmia from central-nervous damage. Thus, we introduce a morphological component to the olfactory diagnosis that specifically addresses clinical cases of olfactory loss following head trauma.

Cytochrome P450 epoxygenase dependence of opioid analgesia: fluconazole does not interfere with remifentanil-mediated analgesia in human subjects.

Cytochrome P450 (CYP) inhibitors may reduce opioid analgesia by inhibiting CYP activity-dependent post-opioid receptor signaling pathways in the brain. This suggestion was predicated on observations of highly attenuated morphine antinociception in rodents after intracerebroventricular injection of fluconazole or carrying a neuron-specific deletion of the cytochrome P450 reductase. However, based on assessments of thermal and electrical pain tolerance, respiratory function, and side effects in 21 healthy volunteers, before and during steady-state concentrations of 1.5 and 3.0 ng/ml of remifentanil at the effect site (viz., the central nervous system), administration of 400 mg/day fluconazole for 8 days in a double-blind, placebo-controlled manner failed to attenuate opioid effects. Although CYP inhibitors such as fluconazole are unlikely to attenuate remifentanil analgesia in humans, extrapolation of the findings to other opioids is premature because differences among opioid effects, such as ligand-selective biased signaling at opioid receptors, leave the possibility that CYP-dependent opioid signaling in the brain might be limited to morphine and may not extend to remifentanil.

Congenital taurine deficiency in mice is associated with reduced sensitivity to nociceptive chemical stimulation.

The amino acid taurine is required for development and functioning of the central and peripheral nervous system where it exerts osmoregulatory, neuromodulatory and anti-apoptotic actions. It is subject to cellular import by the taurine transporter slc6a6. Absence of the transporter and consequently, absence of taurine leads to several neurologic deficits and sensory losses. In a slc6a6 knock-out mouse model, consequences of congenital taurine deficiency were assessed in nociceptive sensory processes. The formalin assay, hot plate assay, and summated generator potentials in response to local nociceptive stimulation with gaseous CO2 were applied. Reduced responsiveness of slc6a6(-/-) mice to nociceptive stimulation was observed in particular to chemical nociceptive stimuli. Scl6a6 knock-out mice spent significantly less time licking the formalin injected paw and displayed smaller amplitudes of the nociceptive nasal mucosa potentials than wild-type mice (p=0.002 and 0.01 respectively). In contrast, withdrawal latencies on a hot plate did not significantly differ, suggesting that intracellular taurine deficits lead in particular to a hyposensitivity of nociceptive sensory neurons sensitive to noxious chemical stimulation. As hereditary absence of taurine affects biological processes of anatomical structure development, the altered nociceptive responses likely reflect consequences of compromised peripheral nervous system development.

Quantitation of ribavirin in human plasma and red blood cells using LC-MS/MS.

LC-MS/MS has been applied for the rapid determination of the nucleoside analogue ribavirin in human plasma and red blood cells. The incorporation of ribavirin to the erythrocytes has been assayed after in vitro incubation of the cells at different concentrations of the antiviral drug. After protein precipitation, samples were injected into a C8 column, achieving a complete separation of ribavirin from the endogenous isobaric compound uridine. Calibration ranges varied from 10 to 10,000 ng/mL in plasma and from 0.2 to 200 ng/cell pellet in red blood cells. Precision and accuracy values were always below 10 and 13%, respectively, in all assayed matrices. Ribavirin was demonstrated to remain unchanged after short and long time storage. No matrix effects could be assessed for the analyzed matrices. The developed method has been fully validated. Monitoring of ribavirin concentration in red blood cells in addition to the classic plasma monitoring of the drug could help to explain its efficacy and safety profiles in patients.

Simultaneous and sensitive LC-MS/MS determination of tetrahydrocannabinol and metabolites in human plasma.

Cannabis is not only a widely used illicit drug but also a substance which can be used in pharmacological therapy because of its analgesic, antiemetic, and antispasmodic properties. A very rapid and sensitive method for determination of ∆(9)-tetrahydrocannabinol (THC), the principal active component of cannabis, and two of its phase I metabolites in plasma has been developed and validated. After solid-phase extraction of plasma (0.2 mL), the clean extracts were analyzed by tandem mass spectrometry after a 5-min liquid chromatographic separation. The linear calibration ranges were from 0.05 to 30 ng mL(-1) for THC and 11-nor-∆(9)-carboxy-tetrahydrocannabinol (THC-COOH) and from 0.2 to 30 ng mL(-1) for ∆(9)-(11-OH)-tetrahydrocannabinol (11-OH-THC). Imprecision and inaccuracy were always below 7 and 12 % (expressed as relative standard deviation and relative error), respectively. The method has been successfully applied to determination of the three analytes in plasma obtained from healthy volunteers after oral administration of 20 mg dronabinol.

Comparative clinical effects of hydromorphone and morphine: a meta-analysis.

We have conducted a meta-analysis of the clinical effects of morphine and hydromorphone to compare their benefit in analgesia. Embase and Medline were searched with an end-date of June 2009 for randomized, controlled trials or observational studies that addressed comparative analgesic and side-effects or particular side-effects. Two researchers independently identified included studies and extracted the data. Estimates of opioid effects were combined by using a random-effects model. Meta-analysis of eight studies suggested that hydromorphone (494 patients) provides slightly better (P=0.012) clinical analgesia than morphine (510 patients). The effect-size was small (Cohen's d=0.266) and disappeared when one study was removed, although the advantage of hydromorphone was more evident in studies of better quality (Jadad's rating). Side-effects were similar, for example, nausea (P=0.383, nine studies, 456 patients receiving hydromorphone and 460 morphine); vomiting (P=0.306, six studies, 246 patients receiving hydromorphone and 239 morphine); or itching (P=0.249, eight studies, 405 patients receiving hydromorphone, 410 morphine). This suggests some advantage of hydromorphone over morphine for analgesia. Additional potential clinical pharmacological advantages with regard to side-effects, such as safety in renal failure or during acute analgesia titration, are based on limited evidence and require substantiation by further studies.

Genetically polymorphic OCT1: another piece in the puzzle of the variable pharmacokinetics and pharmacodynamics of the opioidergic drug tramadol.

We investigated whether tramadol or its active metabolite, O-desmethyltramadol, are substrates of the organic cation transporter OCT1 and whether polymorphisms in OCT1 affect tramadol and O-desmethyltramadol pharmacokinetics. Tramadol showed high permeability through parallel artificial membrane permeability assays (PAMPAs). Tramadol uptake in HEK293 cells did not change after OCT1 overexpression, and the concentrations of tramadol in the plasma of healthy volunteers were independent of their OCT1 genotypes. In contrast, O-desmethyltramadol showed low membrane permeability, and OCT1 overexpression increased O-desmethyltramadol uptake 2.4-fold. This increase in uptake was reversed by OCT1 inhibitors and absent when loss-of-function OCT1 variants were overexpressed. Volunteers carrying loss-of-function OCT1 polymorphisms had significantly higher plasma concentrations of O-desmethyltramadol (P = 0.002, n = 41) and significantly prolonged miosis, a surrogate marker of opioidergic effects (P = 0.005, n = 24). In conclusion, polymorphisms in OCT1 influence the pharmacokinetics of O-desmethyltramadol, presumably by affecting its uptake into liver cells, and thus may modulate the efficacy of tramadol treatment.

Bedside-to-bench pharmacology: a complementary concept to translational pharmacology.

Translational pharmacology is contemporary; it efficiently covers the progress of research from cellular and animal experiments to the patient. Its logical reasoning is deductive by definition. Abductive reasoning in pharmacological research is often placed second. Although straightforward processes might suit some organizational models, a reasoning process preferring only one direction may sacrifice opportunities. Both directions of reasoning may lead to independent yet comparable or even identical successes, and it is only by using both together that one gains the full benefit of human intellectual resources.

Selective antagonism of opioid-induced ventilatory depression by an ampakine molecule in humans without loss of opioid analgesia.

Ventilatory depression is a significant risk associated with the use of opioids. We assessed whether opioid-induced ventilatory depression can be selectively antagonized by an ampakine without reduction of analgesia. In 16 healthy men, after a single oral dose of 1,500 mg of the ampakine CX717, a target concentration of 100 ng/ml alfentanil decreased the respiratory frequency by only 2.9 +/- 33.4% as compared with 25.6 +/- 27.9% during placebo coadministration (P < 0.01).Blood oxygenation and the ventilatory response to hypercapnic challenge also showed significantly smaller decreases with CX717 than with placebo. In contrast, CX717 did not affect alfentanil-induced analgesia in either electrical or heat-based experimental models of pain. Both ventilatory depression and analgesia were reversed with 1.6 mg of naloxone. These results support the use of ampakines as selective antidotes in humans to counter opioid-induced ventilatory depression without affecting opioid-mediated analgesia.