Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.

The efficiency of polymer synthesis is increased by reducing the number of monomer addition cycles needed to create a set of polymer strands. The number of cycles depends on the sequences of the polymer strands and the order in which each type of monomer is made available for addition to the growing strands. Efficiencies are created by grouping the polymer strands into batches such that all the strands in a batch require a similar number of cycles to synthesize. Efficiencies are also created by selecting an order in which the monomers are made available for addition to the growing polymer strands in a batch. Both techniques can be used together. With these techniques, the number of cycles of monomer addition and commensurate reagent use may be reduced by over 10% as compared to naïve synthesis techniques.

Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

The present disclosure provides methods of treating a human having a disease or disorder that would benefit from increasing platelet counts. The method involves inhibiting the enzyme activity of biliverdin IXβ reductase (BLVRB) activity or inhibiting the expression of BLVRB gene.

The present disclosure provides methods of treating a human having a disease or disorder that would benefit from increasing platelet counts. The method involves inhibiting the enzyme activity of biliverdin IXβ reductase (BLVRB) activity or inhibiting the expression of BLVRB gene.

Methods and systems for determining copy number variants are disclosed. An example method can comprise applying a sample grouping technique to select reference coverage data, normalizing sample coverage data comprising a plurality of genomic regions, and fitting a mixture model to the normalized sample coverage data based on the selected reference coverage data. An example method can comprise identifying one or more copy number variants (CNVs) according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model. An example method can comprise outputting the one or more copy number variants.

Methods for identifying compounds that are inhibitors or are likely to be inhibitors of amyloid protein aggregation, as well as three-dimensional, non-crystallographic models (i.e. “pseudo-crystal structures”) of amyloid aggregation utilized in the methods, are described. Means for creating the three-dimensional, non-crystallographic models (i.e. “pseudo-crystal structures”) of amyloid aggregation are also described.

The invention relates to an aqueous solution of AuCl.sub.3×2H.sub.2O or HAuCl.sub.4×4H.sub.2O, ZnCl.sub.2 or ZnSO.sub.4, and AgNO.sub.3 having an Au.sup.3+ concentration of 0.8 mM-1.6 mM, a Zn.sup.2+ concentration of 15 μM-50 μM, and an Ag.sup.+ concentration of 5 μM-50 μM. The invention extends to a kit comprising a solution of the invention, a method of preparing said solution, and the use of a solution of the invention, a solution prepared by a method of the invention, or a kit of the invention in predicting and/or diagnosing and/or monitoring a neurocognitive disorder of the Alzheimer's disease spectrum. The invention extends to a method for predicting and/or diagnosing and/or monitoring a neurocognitive disorder of the Alzheimer's disease spectrum in a patient comprising contacting a tear sample from the patient normalised for protein concentration with a solution of the invention; applying an amount of the tear sample thus obtained to a surface; allowing the tear sample to dry; and drawing a conclusion based on the pattern of the thus obtained dried tear sample, whether the patient is at risk of or suffers from a neurocognitive disorder of the Alzheimer's disease spectrum.

Disclosed is a disease differentiation support method for supporting disease differentiation, the disease differentiation support method including: obtaining a first parameter obtained by analyzing an image including a cell contained in a sample collected from a subject; obtaining a second parameter regarding a number of cells contained in the sample; and generating, by using a computer algorithm, differentiation support information for supporting disease differentiation, on the basis of the first parameter and the second parameter.