Trigeminal neuralgia: New classification and diagnostic grading for practice and research

Trigeminal neuralgia (TN) is an exemplary condition of neuropathic facial pain. However, formally classifying TN as neuropathic pain based on the grading system of the International Association for the Study of Pain is complicated by the requirement of objective signs confirming an underlying lesion or disease of the somatosensory system. The latest version of the International Classification of Headache Disorders created similar difficulties by abandoning the term symptomatic TN for manifestations caused by major neurologic disease, such as tumors or multiple sclerosis. These diagnostic challenges hinder the triage of TN patients for therapy and clinical trials, and hamper the design of treatment guidelines. In response to these shortcomings, we have developed a classification of TN that aligns with the nosology of other neurologic disorders and neuropathic pain. We propose 3 diagnostic categories. Classical TN requires demonstration of morphologic changes in the trigeminal nerve root from vascular compression. Secondary TN is due to an identifiable underlying neurologic disease. TN of unknown etiology is labeled idiopathic. Diagnostic certainty is graded possible when pain paroxysms occur in the distribution of the trigeminal nerve branches. Triggered paroxysms permit the designation of clinically established TN and probable neuropathic pain. Imaging and neurophysiologic tests that establish the etiology of classical or secondary TN determine definite neuropathic pain

A classification of chronic pain for ICD-11.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450869

A Novel Tool for the Assessment of Pain: Validation in Low Back Pain

Joachim Scholz and colleagues develop and validate an assessment tool that distinguishes between radicular and axial low back pain

Divergent functional isoforms drive niche specialisation for nutrient acquisition and use in rumen microbiome

Many microbes in complex competitive environments share genes for acquiring and utilising nutrients, questioning whether niche specialisation exists and if so, how it is maintained. We investigated the genomic signatures of niche specialisation in the rumen microbiome, a highly competitive, anaerobic environment, with limited nutrient availability determined by the biomass consumed by the host. We generated individual metagenomic libraries from 14 cows fed an ad libitum diet of grass silage and calculated functional isoform diversity for each microbial gene identified. The animal replicates were used to calculate confidence intervals to test for differences in diversity of functional isoforms between microbes that may drive niche specialisation. We identified 153 genes with significant differences in functional isoform diversity between the two most abundant bacterial genera in the rumen (Prevotella and Clostridium). We found Prevotella possesses a more diverse range of isoforms capable of degrading hemicellulose, whereas Clostridium for cellulose. Furthermore, significant differences were observed in key metabolic processes indicating that isoform diversity plays an important role in maintaining their niche specialisation. The methods presented represent a novel approach for untangling complex interactions between microorganisms in natural environments and have resulted in an expanded catalogue of gene targets central to rumen cellulosic biomass degradation

Lipid Classes and Fatty Acid Patterns are Altered in the Brain of γ-Synuclein Null Mutant Mice

The well-documented link between α-synuclein and the pathology of common human neurodegenerative diseases has increased attention to the synuclein protein family. The involvement of α-synuclein in lipid metabolism in both normal and diseased nervous system has been shown by many research groups. However, the possible involvement of γ-synuclein, a closely-related member of the synuclein family, in these processes has hardly been addressed. In this study, the effect of γ-synuclein deficiency on the lipid composition and fatty acid patterns of individual lipids from two brain regions has been studied using a mouse model. The level of phosphatidylserine (PtdSer) was increased in the midbrain whereas no changes in the relative proportions of membrane polar lipids were observed in the cortex of γ-synuclein-deficient compared to wild-type (WT) mice. In addition, higher levels of docosahexaenoic acid were found in PtdSer and phosphatidylethanolamine (PtdEtn) from the cerebral cortex of γ-synuclein null mutant mice. These findings show that γ-synuclein deficiency leads to alterations in the lipid profile in brain tissues and suggest that this protein, like α-synuclein, might affect neuronal function via modulation of lipid metabolism

A cross-sectional study to estimate the point prevalence of painful diabetic neuropathy in Eastern Libya.

BACKGROUND: Painful Diabetic Neuropathy (PDN) is a complication that affects up to one third of people living with diabetes. There is limited data on the prevalence of PDN from countries in the Middle East and North Africa. The aim of this study was to estimate the point prevalence of PDN in adults in Eastern Libya using the self-report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) pain scale. METHODS: We invited patients attending the Benghazi Diabetes Centre who had diabetes for ≥ 5 years to take part in the study. Patients provided consent and completed the Arabic S-LANSS. Anthropometrics, marital status, socioeconomic and education information was recoded and fasting plasma glucose concentration determined. RESULTS: Four hundred and fifty participants completed the study (age = 19 to 87 years, BMI = 17.6 to 44.2 kg/m2, 224 women). One hundred and ninety five participants (43.3%) reported pain in their lower limbs in the previous 6 months and 190/195 participants (97.4%) reported a S-LANSS score of ≥ 12 suggesting they had neuropathic pain characteristics. Thus, 42.2% (190/450) of participants with diabetes were categorised as experiencing pain with neuropathic characteristics. Mean ± SD duration of diabetes for participants with PDN (20.4 ± 6.5 years) was significantly higher compared with those without PDN (11.1 ± 4.6 years). Participants with PDN smoked tobacco for more years than those without pain (7.9 ± 12.3 years versus 1.1 ± 3.9 years respectively); had significantly higher fasting plasma glucose concentration (143.6 ± 29.3 mg/dl versus 120.0 ± 17.3 mg/dl) and had a significantly higher levels of education and employment status. The most significant predictors of PDN were duration of diabetes (OR = 25.85, 95% CI = 13.56-49.31), followed by smoking for men (OR = 8.28, 95% CI = 3.53-9.42), obesity (OR = 3.96, 95% CI = 2.25-6.96) and high fasting plasma glucose concentration (OR = 3.51, 95% CI = 1.99-6.21). CONCLUSION: The prevalence of PDN in people with diabetes in Eastern Libya was 42.2%. Risk factors for developing PDN were high fasting plasma glucose concentration, long duration of diabetes, and higher level of educational and employment status

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules