Methods and kits for screening, diagnosing, detecting or predicting a patient outcome/risk variable for a lung transplant recipient after transplant or an EVLP outcome by measuring biomarker levels of at least three biomarkers selected from IL-6, IL-8, IL-10 and IL-1β optionally in combination with one or both of sTNFR1 and sTREM1 in EVLP perfusate are described. The methods involve for example, i. obtaining one or more test EVLP perfusate samples of a donor lung; ii. determining in one or more test EVLP perfusate sample of a donor lung, a polypeptide level of the at least three biomarkers selected from IL-8, IL-6, IL-10 and IL-1β and optionally one or both of sTNFR1 and sTREM1 i; and iii. a) comparing the one or more parameter values related to a level of the at least three biomarkers in the perfusate sample with control EVLP data or a cut-off level, wherein the differential level is indicative of outcome/risk of after transplant or of an EVLP outcome; or b) using the one or more parameter values related to a level of the at least three biomarkers in combination, as part of an algebraic calculation or model of outcome/risk.

In some aspects, the present invention provides methods for predicting whether a subject will develop autoantibodies to an anti-TNFα drug during the course of anti-TNFα drug therapy. In other aspects, the present invention provides methods for predicting the level of an anti-TNFα drug in a subject during the course of anti-TNFα drug therapy. Systems for predicting anti-TNFα drug levels and the likelihood of autoantibody formation during the course of anti-TNFα drug therapy are also provided herein. The present invention further provides methods for predicting a clinical outcome (e.g., endoscopic response) of a subject on anti-TNFα drug therapy.

Provided herein are methods for reducing risk of severe symptoms and outcomes associated with Coronavirus Disease 2019 (COVID-19) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection by measuring levels of interleukin 6 (IL-6), IL-8, IL-22, and serum ferritin in a subject. Also provided are methods for treating a subject exposed to or at elevated risk of expose to SARS-CoV-2 based on levels of IL-6, IL-8, IL-22, and ferritin in the serum of the subject.

Methods of identifying an individual as being a slow gingivitis responder or a high gingivitis responder are disclosed. Some methods are based on IL-1β levels in the individual's GCF at the site of inflammation. Some methods are based on MIF and/or CCL-1 levels in the individual's GCF in healthy tissue distant from the site of inflammation. Some disclosed methods are based on temporal differences in IL-8, IL-6 and/or TNFα levels in the individual's GCF in healthy tissue distant from the site of inflammation during the development of plaque induced inflammation. Methods of treating an individual who has gingivitis and methods of preventing gingivitis are also provided. The treatment and prevention methods comprise determining if individual is a slow gingivitis responder or a high gingivitis responder and applying oral care compositions to the individual's oral cavity.

In one aspect, a system for detecting inflammation is disclosed, which includes at least one sensor comprising at least one port for receiving a biological sample, and at least one electrochemical cell in fluid communication with said at least one port for receiving said biological sample, said electrochemical cell comprising at least two electrically conductive electrodes, where at least one of said electrodes is functionalized with at least one probe exhibiting specific binding to at least one inflammation biomarker. The system may further include a circuitry for detecting a redox current flowing through said at least one electrode and/or an electrical impedance across said electrodes in response to interaction of the functionalized electrode with the sample, and generating signals in response to said detection. By way of example, the biological sample can be a subject's blood serum.

This disclosure provides kits and methods for detecting markers in a sample from a subject with unknown status and generating a risk assessment of the presence or absence of cancer, such as colorectal cancer. In embodiments, a kit comprises at least two reagents, each specifically binding to one of at least two polypeptides in a sample from the subject. The polypeptides include IL-8 and ferritin. The kit further includes at least one standard comprising a known amount of at least one of the polypeptides. The kit can also include computer readable media comprising instructions to analyze the detected amounts of the at least two polypeptides using a machine learning algorithm to determine whether a subject has an increased risk of the presence of colorectal cancer.

Disclosed herein is a method for detecting liver disease in a patient. Also, disclosed are methods of isolating EVs derived from hepatocytes. Methods of assessing effectiveness of liver therapies are also disclosed. Methods involve isolating or otherwise obtaining EVs derived from hepatocytes and analyzing the content of the EVs.

The invention provides methods, kits and reagents for diagnosing fibromyalgia (FM) in an individual by determining whether the levels of one or more cytokines in the individual are altered, as compared to control levels. The altered level(s) or patterns of expression of the cytokines measured in the affected individual compared to the level from the control is predictive/indicative of FM in the individual.

A method of classifying a lung graft subjected to normothermic ex vivo lung perfusion (EVLP), during perfusion and/or after perfusion, the method comprising: a) collecting a test sample from the lung graft; b) measuring a polypeptide level of a negative transplant predictor gene product selected from CCG predictor gene products M-CSF, IL-8 SCGF-beta, GRO-alpha, G-CSF, MIP-1 alpha, and/or MIP-1beta, endothelin predictor gene products endothelin 1 (ET-1) and/or big ET-1, and/or apoptosis predictor gene products cytokeratin 18 (CK-18), caspase 3 and/or HMGB-1 in the sample and/or determining a metabolite profile of the sample for lung grafts that are from donors where the death was due to cardiac death (DCD); c) identifying the graft as a good candidate for transplant or a poor candidate for transplant wherein an increased polypeptide level of one or more negative transplant outcome predictor gene products compared to an outcome control or a reference metabolic profile is indicative the graft is a poor candidate for transplant.

In some aspects, the present invention provides methods for predicting whether a subject will develop autoantibodies to an anti-TNFα drug during the course of anti-TNFα drug therapy. In other aspects, the present invention provides methods for predicting the level of an anti-TNFα drug in a subject during the course of anti-TNFα drug therapy. Systems for predicting anti-TNFα drug levels and the likelihood of autoantibody formation during the course of anti-TNFα drug therapy are also provided herein. The present invention further provides methods for predicting a clinical outcome (e.g., endoscopic response) of a subject on anti-TNFα drug therapy.