Biochemical histology analysis of tissue samples by Desorption Electrospray Ionization (DESI) mass spectrometry imaging

For over 100 years, the histopathological analysis of cytology, biopsy or resection specimens has been the final step in the process of diagnosing multiple diseases, including cancer. In recent years, standard clinical care is continuously becoming more complex, and as a result, diagnostic pathology workup is also more complex and extensive. Moreover, despite being considered a gold standard in making a diagnosis, histopathological investigations can be timeconsuming. Additionally, an examination of the stained slides is subject to intra-observer error. Therefore, it is evident that some additional techniques are required to complement making a diagnosis. Desorption electrospray ionisation mass spectrometric imaging (DESI-MSI) is an emerging mass spectrometry technique with great potential in tissue analysis, especially in histological settings. DESI-MSI enables visualising the spatial distribution of lipid species across tissue sections allowing a direct correlation of the metabolomic information with the morphological features. However, this technique has always relied on frozen sections, which are not required in routine histopathology settings very often. Moreover, some embedding media, e.g. OCT, a common choice in diagnostic laboratories, have been proven not to be very well suited for MSI. The main aim of this study was to make DESI-MSI more compatible with the standard pathology procedures. Therefore, the first step was to assess OCT's impact on the quality of DESI-MSI data. The acquired data suggested that this embedding medium could be used for histopathological and mass spectrometric analyses. There were no clear polymeric signals causing differences in the negative mode data, but some reduction in intensities might be attributable to polymer-induced ion suppression. In positive mode data, the interferences due to OCT were more overt but could be negated by removing the regular peaks of the various polymeric distributions. As formalin-fixed, paraffin-embedded (FFPE) samples are the gold standard in histopathology laboratories worldwide, the next step was to optimise the pre-DESI-MSI protocol to allow the analysis of specimens that have been processed that way. A new protocol has been adapted and successfully tested on FFPE mouse and human tissue samples for tissue classification. Additionally, DESI-MSI has been used to analyse fresh-frozen and FFPE colorectal samples. 88.5% accuracy for normal samples and 91.7% for tumours was achieved when a batch of 38 fresh-frozen samples was analysed. Tissue microarray (TMA) consisting of 54 cores was used further to test the application of DESI-MSI to FFPE samples. A 10μm thick sections were subjected to analysis in negative and positive modes, and accuracy of over 80% and 92% for tissue prediction was achieved, respectively. Equally good results were obtained for TMA sections which were 5μm thick. This last observation was crucial in the light of making DESIMSI as histology-friendly as possible, as 10μm tissue sections are not routinely prepared in histopathology laboratories. Lastly, a new statistical approach based on ion colocalisation features has been applied to DESI-MSI data acquired for cirrhotic liver diseases. It allowed to identify top correlations of ions, and their distribution within analysed tissue sections was visualised. It is possible that using this approach, some biochemical interactions that are distinguishing the three classes of cirrhotic liver diseases (metabolic, hepatitis and cholangiopathy) could be captured. The colocalisation patterns can potentially be used for data-driven hypothesis generation, suggesting possible local molecular mechanisms characterising the samples of interest.Open Acces

Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier

Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations