Assessment of preoperative pressure pain tolerance is significantly correlated with the level of postoperative pain. Pain tolerance assessment after fentanyl was administered and fentanyl sensitivity predicted the dose of analgesics used in the first 24 h after surgery. The algometer is thus a simple, useful tool for predicting postoperative pain and analgesic consumption.

ChIA-PET2 is a versatile and flexible pipeline for analyzing different types of ChIA-PET data from raw sequencing reads to chromatin loops. ChIA-PET2 integrates all steps required for ChIA-PET data analysis, including linker trimming, read alignment, duplicate removal, peak calling and chromatin loop calling. It supports different kinds of ChIA-PET data generated from different ChIA-PET protocols and also provides quality controls for different steps of ChIA-PET analysis. In addition, ChIA-PET2 can use phased genotype data to call allele-specific chromatin interactions. We applied ChIA-PET2 to different ChIA-PET datasets, demonstrating its significantly improved performance as well as its ability to easily process ChIA-PET raw data. ChIA-PET2 is available at https://github.com/GuipengLi/ChIA-PET2.

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Background Eosinophilic inflammation is a major phenotype associated with poorly controlled disease in nasal polyp patients. The difference between systemic and local eosinophilia in relation to disease control is poorly understood. Objective To explore whether blood and polyp tissue eosinophil numbers are independent risk factors for poor disease control in patients with nasal polyp. Methods By using the electronic medical records database and manual evaluation, 183 nasal polyp patients who had undergone endoscopic sinus surgery at least one year prior to the study with complete data of tissue specimens, baseline blood routine test, nasal endoscopy and sinus computed tomography, were identified and recruited to assess disease control based on the criteria of a European position paper on rhinosinusitis and nasal polyps 2012 (EPOS 2012). Multiple logistic regression model was used to determine the association between blood and tissue eosinophil numbers and risk of poor disease control by adjusting for demographics and comorbidities. Results We broke down the cohort into 4 groups according to blood (0.3 ​× ​10 9 /L) and tissue (10%) eosinophils. The patients without eosinophilic inflammation represented the largest group (41.5%). The group with concordant blood and tissue eosinophilia represented the second largest (31.2%), and the patients with isolated tissue (15.3%) or blood (12.0%) eosinophilia were relatively rare. Multiple logistic regression models found blood eosinophil count and tissue eosinophil percentage were independently associated with increased risk for poor disease control after adjustments for covariates related to poor treatment outcome. Furthermore, subjects with concordant blood and tissue eosinophilia had a higher risk for poor disease control than those with isolated blood or tissue eosinophilia. Conclusion Concordant blood and tissue eosinophilia relates to a higher likelihood of poor disease control than isolated blood or tissue eosinophilia after adjustment of potential confounders in nasal polyp patients.

BackgroundNon-alcoholic fatty liver disease (NAFLD) given its association with obesity and diabetes may perhaps exert distinct free fatty acids (FFA) pattern, but the understanding of this phenomenon is limited. To this effect, we evaluated FFA profiles among healthy subjects and NAFLD patients stratified by body weight, to identify FFA valuable for early diagnosis of NAFLD.MethodsSerum FFA profiles of healthy and NAFLD (lean, overweight and obese) subjects was determined using gas chromatography–mass spectrometry (GC–MS) and distinctions in FFA patterns were evaluated using one-way ANOVA while Receiver operating characteristics (ROC) and logistic regression models were used to explore FFA significant for diagnosing NAFLD.ResultsNAFLD patients presented significantly higher (P < 0.05) serum FFA profiles compared to healthy controls (HC). While total FFA profiles were insignificantly different between lean (2093.33 ± 558.11 μg/ml) and overweight (2420.81 ± 555.18 μg/ml) NAFLD patients, obese NAFLD (2739.01 ± 810.35 μg/ml) presented most significantly elevated (P < 0.05) total FFA profiles compared with HC. Of the four FFA; myristic acid (14:0), palmitoleic acid (16:1), γ-linolenic acid (γ-18:3) and cis-7,10,13,16,19-docosapentaenoic acid (22:5), selected in ROC analysis given their high Youden’s index and AUC, only 14:0; 5.58(1.37, 22.76) and 16:1; 4.36(1.34, 14.13) had statistical significant odd ratios.ConclusionOur findings suggest 14:0 and 16:1 are promising for early diagnosis of NAFLD.Electronic supplementary materialThe online version of this article (doi:10.1186/s12944-017-0551-1) contains supplementary material, which is available to authorized users.

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Dietary habits are crucial in the progression of hepatic lipid accumulation and nonalcoholic fatty liver disease (NAFLD). However, there are limited studies using 1H-magnetic resonance spectroscopy (1H-MRS) and dual-echo in-phase and out-phase magnetic resonance spectroscopy imaging (dual-echo MRI) to assess the effects of dietary nutrient intakes on hepatic lipid contents. In the present study, we recruited 36 female adults (NAFLD:control = 19:17) to receive questionnaires and medical examinations, including dietary intakes, anthropometric and biochemical measurements, and 1H-MRS and dual-echo MRI examinations. NAFLD patients were found to consume diets higher in energy, protein, fat, saturated fatty acid (SFA), and polyunsaturated fatty acid (PUFA). Total energy intake was positively associated with hepatic fat fraction (HFF) and intrahepatic lipid (IHL) after adjustment for age and body-mass index (BMI) (HFF: β = 0.24, p = 0.02; IHL: β = 0.38, p = 0.02). Total fat intake was positively associated with HFF and IHL after adjustment for age, BMI and total energy intake (HFF: β = 0.36, p = 0.03; IHL: β = 0.42, p = 0.01). SFA intake was positively associated with HFF and IHL after adjustments (HFF: β = 0.45, p = 0.003; IHL: β = 1.16, p = 0.03). In conclusion, hepatic fat content was associated with high energy, high fat and high SFA intakes, quantified by 1H-MRS and dual-echo MRI in our population. Our findings are useful to provide dietary targets to prevent the hepatic lipid accumulation and NAFLD.