A process for determining the sensitivity or resistance of at least one identified bacterium to at least one antibiotic, wherein the process includes the following steps: a) bringing a sample including bacterium into contact with the at least one antibiotic, the antibiotic inducing the rupture of the bacterial wall and/or of the cytoplasmic membrane and causing the release of the intracellular compounds of the bacterium when the bacterium is termed “sensitive” to the at least one antibiotic, b) incubating the sample with the at least one antibiotic, c) purifying the sample by removing the intact bacteria and the cell debris, d) analyzing the purified sample by mass spectrometry, e) detecting the presence or absence of at least one peak of at least one protein characteristic of the bacterium, and f) determining the sensitivity or the resistance of the bacterial population to the antibiotic.

The present invention generally pertains to methods of quantifying free fatty acids in a pharmaceutical formulation. In particular, the present invention pertains to a method of quantifying free fatty acids released from polysorbate hydrolysis in a pharmaceutical formulation comprising a pharmaceutical product, polysorbate and free fatty acids, using liquid chromatography-mass spectrometry, without the use of an extraction step.

The disclosure provides methods for measuring M-proteins in a biological sample obtained from a subject, comprising applying purified immunoglobulins to a liquid chromatography (LC) mass spectrometry (MS). In some aspects, the immunoglobulins are purified using an immunocapture (IC). In certain aspects, the subject has a plasma cell disorder, e.g., multiple myeloma.

A method for rapid detection of dry eye syndrome includes collecting a first tear fluid from healthy participant and a second tear fluid from patient with eye dryness; isolating EV samples from the first tear fluid; acquiring a first fingerprint diagram of proteomes of the EV samples from the first tear fluid, the first fingerprint diagram comprises a plurality of first discriminant peaks; isolating EV samples from the second tear fluid; acquiring a second fingerprint diagram of proteomes of the EV samples from the second tear fluid, the second fingerprint diagram comprises a plurality of second discriminant peaks; and comparing the first discriminant peaks and the second discriminant peaks to determine whether the patient has the DES. This is a fast and precise method for detecting the DES of the participant.

The invention proposes preparing biological samples for spectrometry which contain cell structures and/or whole cells of human or animal origin (e.g. thin human and animal tissue sections) or prokaryotes (e.g. microorganisms), and which require constant relative humidity, in a temperature-controlled gas volume whose humidity is determined by a saturated substance solution, for example a suitable salt solution. The invention exploits a physico-chemical phenomenon called “deliquescence”, which manifests itself by keeping the relative humidity above the saturated substance solution constant with a high degree of precision when a specified temperature is maintained. Pure succinic acid exhibits deliquescence at approx. 99% relative humidity, for example. Since an enormous variety of deliquescent salts and other suitable substances are available, it is possible to find the suitable substance for almost any desired relative humidity, with adjustment of the temperature, where necessary.

A method of spectrometric analysis comprises obtaining one or more sample spectra for an aerosol, smoke or vapour sample. The one or more sample spectra are subjected to pre-processing and then multivariate and/or library based analysis so as to classify the aerosol, smoke or vapour sample. The results of the analysis are used for various surgical or non-surgical applications.

The present disclosure provides methods and compositions for the determining the abundance and/or concentration of protein biomarkers in a biological sample.

The present invention relates to a method for detecting a pathogenic strain having resistance to carbapenem antibiotics in a biological sample. According to the present invention, it is possible to directly identify carbapenemases, specifically KPC, OXA, NDM, IMP, VIM and/or GES protein, by mass spectrometry, thereby making it possible to quickly determine not only whether a pathogenic strain has resistance to antibiotics, but also the type of protein involved in the resistance. According to the present invention, the physical and chemical properties of each carbapenemase in vivo, such as the unique N-terminal truncation length, methionine residue oxidation and disulfide bond formation in each type of carbapenemase, are identified and are reflected on reference mass values. Accordingly, it is possible to more closely detect the presence of an antibiotic-resistant strain with high reliability, and thus the present invention may be advantageously used to establish an appropriate strategy for antibiotic administration at an early stage of infection.

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed. The method comprises: using a first device to generate smoke, aerosol or vapour from a target comprising or consisting of a microbial population; mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and analysing said spectrometric data in order to analyse said microbial population.

The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. The present systems and methods are re-trainable to adapt to new sources of data and provides a complete end-to-end training and prediction solution, which is advantageous given the growing massive amount of data. The systems and methods combine deep learning and dynamic programming to solve optimization problems.