Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines

Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminay study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data

Raman spectroscopy in head and neck cancer

In recent years there has been much interest in the use of optical diagnostics in cancer detection. Early diagnosis of cancer affords early intervention and greatest chance of cure. Raman spectroscopy is based on the interaction of photons with the target material producing a highly detailed biochemical 'fingerprint' of the sample. It can be appreciated that such a sensitive biochemical detection system could confer diagnostic benefit in a clinical setting. Raman has been used successfully in key health areas such as cardiovascular diseases, and dental care but there is a paucity of literature on Raman spectroscopy in Head and Neck cancer. Following the introduction of health care targets for cancer, and with an ever-aging population the need for rapid cancer detection has never been greater. Raman spectroscopy could confer great patient benefit with early, rapid and accurate diagnosis. This technique is almost labour free without the need for sample preparation. It could reduce the need for whole pathological specimen examination, in theatre it could help to determine margin status, and finally peripheral blood diagnosis may be an achievable target

Potential for Raman spectroscopy to provide cancer screening using a peripheral blood sample

Cancer poses a massive health burden with incidence rates expected to double globally over the next decade. In the United Kingdom screening programmes exists for cervical, breast, and colorectal cancer. The ability to screen individuals for solid malignant tumours using only a peripheral blood sample would revolutionise cancer services and permit early diagnosis and intervention. Raman spectroscopy interrogates native biochemistry through the interaction of light with matter, producing a high definition biochemical 'fingerprint' of the target material. This paper explores the possibility of using Raman spectroscopy to discriminate between cancer and non-cancer patients through a peripheral blood sample. Forty blood samples were obtained from patients with Head and Neck cancer and patients with respiratory illnesses to act as a positive control. Raman spectroscopy was carried out on all samples with the resulting spectra being used to build a classifier in order to distinguish between the cancer and respiratory patients' spectra; firstly using principal component analysis (PCA)/linear discriminant analysis (LDA), and secondly with a genetic evolutionary algorithm. The PCA/LDA classifier gave a 65% sensitivity and specificity for discrimination between the cancer and respiratory groups. A sensitivity score of 75% with a specificity of 75% was achieved with a 'trained' evolutionary algorithm. In conclusion this preliminary study has demonstrated the feasibility of using Raman spectroscopy in cancer screening and diagnostics of solid tumours through a peripheral blood sample. Further work needs to be carried out for this technique to be implemented in the clinical setting

A core outcome set for future endometriosis research : an international consensus development study

Objective: To develop a core outcome set for endometriosis. Design: Consensus development study. Setting: International. Population: One hundred and sixteen healthcare professionals, 31 researchers and 206 patient representatives. Methods: Modified Delphi method and modified nominal group technique. Results: The final core outcome set includes three core outcomes for trials evaluating potential treatments for pain and other symptoms associated with endometriosis: overall pain; improvement in the most troublesome symptom; and quality of life. In addition, eight core outcomes for trials evaluating potential treatments for infertility associated with endometriosis were identified: viable intrauterine pregnancy confirmed by ultrasound; pregnancy loss, including ectopic pregnancy, miscarriage, stillbirth and termination of pregnancy; live birth; time to pregnancy leading to live birth; gestational age at delivery; birthweight; neonatal mortality; and major congenital abnormalities. Two core outcomes applicable to all trials were also identified: adverse events and patient satisfaction with treatment. Conclusions: Using robust consensus science methods, healthcare professionals, researchers and women with endometriosis have developed a core outcome set to standardise outcome selection, collection and reporting across future randomised controlled trials and systematic reviews evaluating potential treatments for endometriosis. Tweetable abstract: @coreoutcomes for future #endometriosis research have been developed @jamesmnduffy

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation