[Fragments Woven into a Whole: A New Approach to the Diagnosis of Cancer through Mass Spectrometry and Machine-Learning].

Conventionally, a definitive diagnosis of cancer is derived from histopathological diagnostics based on morphological criteria that are difficult to standardize on a quantifiable basis. On the other hand, while molecular tumor markers and blood biochemical profiles give quantitative values evaluated by objective criteria, these parameters are usually generated by deductive methods such as peak extraction. Therefore, some of the data that may contain useful information on specimens are discarded. To overcome the disadvantages of these methods, we have developed a new approach by employing both mass spectrometry and machine-learning for cancer diagnosis. Probe electrospray ionization (PESI) is a derivative of electrospray ionization that uses a fine acupuncture needle as a sample picker as well as an ion emitter for mass spectrometry. This method enables us to ionize very small tissue samples up to a few pico liters in the presence of physiological concentrations of inorganic salts, without the need for any sample pretreatment. Moreover, as this technique makes it possible to ionize all components with minimal suppression effects, we can retrieve much more molecular information from specimens. To make the most of data enriched with lipid compounds and substances with lower molecular weights such as carbohydrates, we employed machine-learning named the dual penalized logistic regression machine (dPLRM). This method is completely different from pattern-matching in that it discriminates categories by projecting the spectral data into a mathematical space with very high dimensions, where final judgment is made. We are now approaching the final clinical trial to validate the usefulness of our system.

Real-time diagnosis of chemically induced hepatocellular carcinoma using a novel mass spectrometry-based technique.

Real-time analyses of hepatocellular carcinoma were performed in living mice to assess the applicability of probe electrospray ionization-mass spectrometry (PESI-MS) in medical diagnosis. The number of peaks and the abundance of ions corresponding to triacylglycerols (TAGs) were higher in cancerous tissues than in noncancerous tissues. Multiple sequential scans of the specimens were performed along a predetermined line extending over the noncancerous region to detect the boundary of the cancerous region. Our system successfully discriminated the noncancerous and cancerous tissues based on the intensities of the TAG ions. These results highlight the potential application of PESI-MS for clinical diagnosis in cancer.

Analysis of renal cell carcinoma as a first step for developing mass spectrometry-based diagnostics.

Immediate diagnosis of human specimen is an essential prerequisites in medical routines. This study aimed to establish a novel cancer diagnostics system based on probe electrospray ionization-mass spectrometry (PESI-MS) combined with statistical data processing. PESI-MS uses a very fine acupuncture needle as a probe for sampling as well as for ionization. To demonstrate the applicability of PESI-MS for cancer diagnosis, we analyzed nine cases of clear cell renal cell carcinoma (ccRCC) by PESI-MS and processed the data by principal components analysis (PCA). Our system successfully delineated the differences in lipid composition between non-cancerous and cancerous regions. In this case, triacylglycerol (TAG) was reproducibly detected in the cancerous tissue of nine different individuals, the result being consistent with well-known profiles of ccRCC. Moreover, this system enabled us to detect the boundaries of cancerous regions based on the expression of TAG. These results strongly suggest that PESI-MS will be applicable to cancer diagnosis, especially when the number of data is augmented.

A theoretical formulation of the electrophysiological inverse problem on the sphere.

The construction of three-dimensional images of the primary current density (PCD) produced by neuronal activity is a problem of great current interest in the neuroimaging community, though being initially formulated in the 1970s. There exist even now enthusiastic debates about the authenticity of most of the inverse solutions proposed in the literature, in which low resolution electrical tomography (LORETA) is a focus of attention. However, in our opinion, the capabilities and limitations of the electro and magneto encephalographic techniques to determine PCD configurations have not been extensively explored from a theoretical framework, even for simple volume conductor models of the head. In this paper, the electrophysiological inverse problem for the spherical head model is cast in terms of reproducing kernel Hilbert spaces (RKHS) formalism, which allows us to identify the null spaces of the implicated linear integral operators and also to define their representers. The PCD are described in terms of a continuous basis for the RKHS, which explicitly separates the harmonic and non-harmonic components. The RKHS concept permits us to bring LORETA into the scope of the general smoothing splines theory. A particular way of calculating the general smoothing splines is illustrated, avoiding a brute force discretization prematurely. The Bayes information criterion is used to handle dissimilarities in the signal/noise ratios and physical dimensions of the measurement modalities, which could affect the estimation of the amount of smoothness required for that class of inverse solution to be well specified. In order to validate the proposed method, we have estimated the 3D spherical smoothing splines from two data sets: electric potentials obtained from a skull phantom and magnetic fields recorded from subjects performing an experiment of human faces recognition.