BackgroundRe-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.MethodsElectrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.ResultsA classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).ConclusionsThe REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0845-2) contains supplementary material, which is available to authorized users.

Abstract Currently, the main targets of drug therapy for ulcerative colitis [UC] are endoscopic and clinical remission. However, there is active discussion about the additional advantages of including histological remission as a target. Accumulating evidence indicates that microscopic activity persists in endoscopically quiescent UC, that histological changes may lag behind clinical remission after treatment, and that absence of histological activity predicts lower rates of relapse, hospitalization, surgery and subsequent neoplasia. Obtaining useful information from mucosal biopsies in this setting depends on accurate and consistent evaluation of histological features. However, there is no standardization of biopsy procedures, histological sample processing technique or histological scoring systems, and there is no agreement on the definitions of histological remission, response or activity. Accordingly, a consensus expert panel convened by the European Crohn’s and Colitis Organisation [ECCO] reviewed the literature and agreed a number of position statements regarding harmonization of UC histopathology. The objective was to provide evidence-based guidance for the standardization and harmonization of procedures, definitions and scoring systems for histology in UC, and to reach expert consensus where possible. We propose the absence of intraepithelial neutrophils, erosion and ulceration as a minimum requirement for the definition of histological remission. For randomized control trials we recommend the use of the Robarts histopathology index [RHI] or the Nancy index [NI]. For observational studies or in clinical practice we recommend the use of the NI. To predict the risk of future neoplasia in UC, cumulative histological scores over time are more useful than single scores.

BackgroundSurvival from ovarian cancer (OC) is improved with surgery, but surgery can be complex and tumour identification, especially for borderline ovarian tumours (BOT), is challenging. The Rapid Evaporative Ionisation Mass Spectrometric (REIMS) technique reports tissue histology in real-time by analysing aerosolised tissue during electrosurgical dissection.MethodsAerosol produced during diathermy of tissues was sampled with the REIMS interface. Histological diagnosis and mass spectra featuring complex lipid species populated a reference database on which principal component, linear discriminant and leave-one-patient-out cross-validation analyses were performed.ResultsA total of 198 patients provided 335 tissue samples, yielding 3384 spectra. Cross-validated OC classification vs separate normal tissues was high (97·4% sensitivity, 100% specificity). BOT were readily distinguishable from OC (sensitivity 90.5%, specificity 89.7%). Validation with fresh tissue lead to excellent OC detection (100% accuracy). Histological agreement between iKnife and histopathologist was very good (kappa 0.84, P < 0.001, z = 3.3). Five predominantly phosphatidic acid (PA(36:2)) and phosphatidyl-ethanolamine (PE(34:2)) lipid species were identified as being significantly more abundant in OC compared to normal tissue or BOT (P < 0.001, q < 0.001).ConclusionsThe REIMS iKnife distinguishes gynaecological tissues by analysing mass-spectrometry-derived lipidomes from tissue diathermy aerosols. Rapid intra-operative gynaecological tissue diagnosis may improve surgical care when histology is unknown, leading to personalised operations tailored to the individual.

No abstract

BackgroundAlzheimer's Disease (AD) is the most common neurodegenerative disease and the leading cause of dementia among senile subjects. It has been proposed that AD can be caused by defects in mitochondrial oxidative phosphorylation. Given the fundamental contribution of the mitochondrial genome (mtDNA) for the respiratory chain, there have been a number of studies investigating the association between mtDNA inherited variants and multifactorial diseases, however no general consensus has been reached yet on the correlation between mtDNA haplogroups and AD.Methodology/Principal FindingsWe applied for the first time a high resolution analysis (sequencing of displacement loop and restriction analysis of specific markers in the coding region of mtDNA) to investigate the possible association between mtDNA-inherited sequence variation and AD in 936 AD patients and 776 cognitively assessed normal controls from central and northern Italy. Among over 40 mtDNA sub-haplogroups analysed, we found that sub-haplogroup H5 is a risk factor for AD (OR = 1.85, 95% CI:1.04–3.23) in particular for females (OR = 2.19, 95% CI:1.06–4.51) and independently from the APOE genotype. Multivariate logistic regression revealed an interaction between H5 and age. When the whole sample is considered, the H5a subgroup of molecules, harboring the 4336 transition in the tRNAGln gene, already associated to AD in early studies, was about threefold more represented in AD patients than in controls (2.0% vs 0.8%; p = 0.031), and it might account for the increased frequency of H5 in AD patients (4.2% vs 2.3%). The complete re-sequencing of the 56 mtDNAs belonging to H5 revealed that AD patients showed a trend towards a higher number (p = 0.052) of sporadic mutations in tRNA and rRNA genes when compared with controls.ConclusionsOur results indicate that high resolution analysis of inherited mtDNA sequence variation can help in identifying both ancient polymorphisms defining sub-haplogroups and the accumulation of sporadic mutations associated with complex traits such as AD.

Phytosterols, besides hypocholesterolemic effect, present anti-inflammatory properties. Little information is available about their efficacy in Inflammatory Bowel Disease (IBD). Therefore, we have evaluated the effect of a mixture of phytosterols on prevention/induction/remission in a murine experimental model of colitis. Phytosterols were administered x os before, during and after colitis induction with Dextran Sodium Sulfate (DSS) in mice. Disease Activity Index (DAI), colon length, histopathology score, 18F-FDG microPET, oxidative stress in the intestinal tissue (ileum and colon) and gallbladder ileum and colon spontaneous and carbachol (CCh) induced motility, plasma lipids and plasma, liver and biliary bile acids (BA) were evaluated. A similar longitudinal study was performed in a DSS colitis control group. Mice treated with DSS developed severe colitis as shown by DAI, colon length, histopathology score, 18F-FDG microPET, oxidative stress. Both spontaneous and induced ileal and colonic motility were severely disturbed. The same was observed with gallbladder. DSS colitis resulted in an increase in plasma cholesterol, and a modification of the BA pattern. Phytosterols feeding did not prevent colitis onset but significantly reduced the severity of the disease and improved clinical and histological remission. It had strong antioxidant effects, almost restored colon, ileal and gallbladder motility. Plasmatic levels of cholesterol were also reduced. DSS induced a modification in the BA pattern consistent with an increase in the intestinal BA deconjugating bacteria, prevented by phytosterols. Phytosterols seem a potential nutraceutical tool for gastrointestinal inflammatory diseases, combining metabolic systematic and local anti-inflammatory effects.

Clearance of surgical margins in cervical cancer prevents the need for adjuvant chemoradiation and allows fertility preservation. In this study, we determined the capacity of the rapid evaporative ionization mass spectrometry (REIMS), also known as intelligent knife (iKnife), to discriminate between healthy, preinvasive, and invasive cervical tissue. Cervical tissue samples were collected from women with healthy, human papilloma virus (HPV) ± cervical intraepithelial neoplasia (CIN), or cervical cancer. A handheld diathermy device generated surgical aerosol, which was transferred into a mass spectrometer for subsequent chemical analysis. Combination of principal component and linear discriminant analysis and least absolute shrinkage and selection operator was employed to study the spectral differences between groups. Significance of discriminatory m/z features was tested using univariate statistics and tandem MS performed to elucidate the structure of the significant peaks allowing separation of the two classes. We analyzed 87 samples (normal = 16, HPV ± CIN = 50, cancer = 21 patients). The iKnife discriminated with 100% accuracy normal (100%) vs. HPV ± CIN (100%) vs. cancer (100%) when compared to histology as the gold standard. When comparing normal vs. cancer samples, the accuracy was 100% with a sensitivity of 100% (95% CI 83.9 to 100) and specificity 100% (79.4 to 100). Univariate analysis revealed significant MS peaks in the cancer-to-normal separation belonging to various classes of complex lipids. The iKnife discriminates healthy from premalignant and invasive cervical lesions with high accuracy and can improve oncological outcomes and fertility preservation of women treated surgically for cervical cancer. Larger in vivo research cohorts are required to validate these findings.REIMS | cervical cancer | mass spectrometry | iKnife | fertility preservation

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.