The disclosure relates to a method of operating a secondary-electron multiplier in the ion detector of a mass spectrometer so as to prolong the service life, wherein the secondary-electron multiplier is supplied with an operating voltage in such a way that an amplification of less than 10.sup.6 secondary electrons per impinging ion results, while the output current of the secondary-electron multiplier is amplified using an electronic preamplifier mounted close to the secondary-electron multiplier with such a low noise level that the current pulses of individual ions impinging on the ion detector are detected above the noise at the input of a digitizing unit. Further disclosed are the use of the methods for imaging mass spectrometric analysis of a thin tissue section or mass spectrometric high-throughput analysis/massive-parallel analysis, and a time-of-flight mass spectrometer whose control unit is programmed to execute such methods.

The disclosure relates to a method of operating a secondary-electron multiplier in the ion detector of a mass spectrometer so as to prolong the service life, wherein the secondary-electron multiplier is supplied with an operating voltage in such a way that an amplification of less than 10.sup.6 secondary electrons per impinging ion results, while the output current of the secondary-electron multiplier is amplified using an electronic preamplifier mounted close to the secondary-electron multiplier with such a low noise level that the current pulses of individual ions impinging on the ion detector are detected above the noise at the input of a digitizing unit. Further disclosed are the use of the methods for imaging mass spectrometric analysis of a thin tissue section or mass spectrometric high-throughput analysis/massive-parallel analysis, and a time-of-flight mass spectrometer whose control unit is programmed to execute such methods.

A method for determining the integrity of at least one complex based on at least one biological sample and at least one matrix, including at least the following steps:—acquiring at least one image,—analyzing the image sent by extracting light intensity values representative of at least one spectral band,—relating the light intensity values to one another to obtain representative spectral data,—determining a state of integrity of the complex by comparing each of the representative spectral data by similarity grouping with a determined similarity threshold,—triggering at least one first alert, by the analysis unit, when the representative data are similar to the first state of integrity or to the second state of integrity.

A method for determining the integrity of at least one complex based on at least one biological sample and at least one matrix, including at least the following steps:—acquiring at least one image,—analyzing the image sent by extracting light intensity values representative of at least one spectral band,—relating the light intensity values to one another to obtain representative spectral data,—determining a state of integrity of the complex by comparing each of the representative spectral data by similarity grouping with a determined similarity threshold,—triggering at least one first alert, by the analysis unit, when the representative data are similar to the first state of integrity or to the second state of integrity.

Disclosed are systems and methods to provide rapid and autonomous detection of analyte particles in gas and liquid samples. Disclosed are methods and devices for identifying biological aerosol analytes using MALDI-MS and chemical aerosol analytes using LDI and MALDI-MS using time-of-flight mass spectrometry (TOFMS).

Disclosed are systems and methods to provide rapid and autonomous detection of analyte particles in gas and liquid samples. Disclosed are methods and devices for identifying biological aerosol analytes using MALDI-MS and chemical aerosol analytes using LDI and MALDI-MS using time-of-flight mass spectrometry (TOFMS).

A mass spectrometry device includes a sample stage, an irradiator, a MCP, a fluorescent body, an imager, and a controller. The irradiator irradiates a sample with an energy beam to ionize a plurality of components of the sample while maintaining position information of the plurality of components. The MCP emits electrons in accordance with an ionized sample. The fluorescent body emits fluorescent light in accordance with the electrons. The imager has a shutter mechanism configured to be capable of switching an open state and a close state. The controller controls an opening and closing operation of the shutter mechanism. The controller allows the imager to image the fluorescent light corresponding to each of the plurality of components by performing the opening and closing of the shutter mechanism at a timing for each of the components.

A mass spectrometry device includes a sample stage, an irradiator, a MCP, a fluorescent body, an imager, and a controller. The irradiator irradiates a sample with an energy beam to ionize a plurality of components of the sample while maintaining position information of the plurality of components. The MCP emits electrons in accordance with an ionized sample. The fluorescent body emits fluorescent light in accordance with the electrons. The imager has a shutter mechanism configured to be capable of switching an open state and a close state. The controller controls an opening and closing operation of the shutter mechanism. The controller allows the imager to image the fluorescent light corresponding to each of the plurality of components by performing the opening and closing of the shutter mechanism at a timing for each of the components.

A method of predicting whether an MDS patient has a good or poor prognosis uses a general purpose computer configured as a classifier and mass-spectrometry data obtained from a blood-based sample. The classifier assigns a classification label of either Early or Late (or the equivalent) to the patient's sample. Patients classified as Early are predicted to have a poor prognosis or worse survival whereas those patients classified as Late are predicted to have a relatively better prognosis and longer survival time. The groupings demonstrated a large effect size between groups in Kaplan-Meier analysis of survival. Most importantly, while the classifications generated were correlated with other prognostic factors, such as IPSS score and genetic category, multivariate and subgroup analysis showed that they had significant independent prognostic power complementary to the existing prognostic factors.

A method of predicting whether an MDS patient has a good or poor prognosis uses a general purpose computer configured as a classifier and mass-spectrometry data obtained from a blood-based sample. The classifier assigns a classification label of either Early or Late (or the equivalent) to the patient's sample. Patients classified as Early are predicted to have a poor prognosis or worse survival whereas those patients classified as Late are predicted to have a relatively better prognosis and longer survival time. The groupings demonstrated a large effect size between groups in Kaplan-Meier analysis of survival. Most importantly, while the classifications generated were correlated with other prognostic factors, such as IPSS score and genetic category, multivariate and subgroup analysis showed that they had significant independent prognostic power complementary to the existing prognostic factors.