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公开(公告)号：EP4450650A1
公开(公告)日：2024-10-23
申请号：EP24161453.6
申请日：2024-03-05
摘要： This disclosure relates generally to and, more particularly, to assessment of PCOS. Polycystic ovarian syndrome (PCOS) is a hormonal disorder common among women of reproductive age that causes infertility and affects overall health of the woman. As PCOS is common and curable cause of infertility, an efficient early screening to assess a potential risk of PCOS can ensure early treatment. The current state-of-the-art techniques include diagnostic, screening solutions, imaging techniques which are invasive, complex, expensive. The disclosure is a supervised machine learning algorithm on the samples of individuals to arrive at a panel of biological features/ indicators/ markers/ signatures that can accurately stratify/ classify/ group individuals into 'PCOS' and 'healthy' based upon the differences in the composition of the gut/oral microbial communities.
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2.发明公开
公开(公告)号：EP4451285A1
公开(公告)日：2024-10-23
申请号：EP24153673.9
申请日：2024-01-24
发明人： HAQUE, MOHAMMED MONZOORUL , SINGH, RASHMI , MERCHANT, MITALI , MANDE, SHARMILA SHEKHAR , CHENNAREDDY, VENKATA SIVA KUMAR REDDY , NAGPAL, SUNIL , DUTTA, ANIRBAN
摘要： The present disclosure is related to method and system for identifying and utilizing frugal markers for classification of biological sample. Discovering an optimal and/ or frugal set of features/ biomarkers form a large set of features measured through high-throughput screening techniques, which can characterize a disease/ anomaly with sufficient accuracy, still remains a challenge. According to the present disclosure, given a set of measurements of multiple features characterizing biological samples obtained from disease cases and healthy controls, a classification model combining the measured values of a small subset of the features is computed. The classification model is then used for classifying between disease cases and healthy controls.
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3.发明公开
公开(公告)号：EP4451276A1
公开(公告)日：2024-10-23
申请号：EP24164328.7
申请日：2024-03-19
发明人： BOSE, TUNGADRI , KAUR, HARRISHAM , SINGH, RASHMI , DUTTA, ANIRBAN , HAQUE, MOHAMMED MONZOORUL , MANDE, SHARMILA SHEKHAR
摘要： The present disclosure is related to a method and system for stratification of subjects as one of responders or non-responders to a therapy. It is imperative to critically evaluate the baseline/ initial microbiome structure and composition of individuals and stratifying them before prescribing any microbiome-based drug/ dietary interventions. The method identifies a panel of biological features/ indicators/ markers/ signatures that can accurately stratify/ classify/ group individuals into responders and non-responders (for a given microbiome-based drug/ therapy) based upon the differences in the metabolic functions of the gut microbial communities between the baseline gut microbiome profile (i.e. before the administration of an intervention) and after treatment gut microbiome profile (i.e. after the administration of the intervention). Individuals with samples showing an improvement in gut-health status after the administration of the pre-biotic intervention were tagged as responders and the rest were tagged as non-responders.
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4.发明公开
公开(公告)号：EP4451275A1
公开(公告)日：2024-10-23
申请号：EP24154637.3
申请日：2024-01-30
发明人： BOSE, CHANDRANI , HAQUE, MOHAMMED MONZOORUL , SINGH, RASHMI , MANDE, SHARMILA SHEKHAR , CHENNAREDDY, VENKATA SIVA KUMAR REDDY
摘要： The present disclosure is related to methods and systems for predicting a category of mammographic breast density (MBD) for a subject using a microbial profile obtained from biological sample of the subject. The state-of-art diagnostic/ screening strategies for breast cancer are limited by one or more of factors like technical shortcomings, radiation exposure, and physical discomfort. In the present disclosure, a biological sample is collected from a subject. Then a quantitative abundance of each of a plurality of predetermined microbes associated with the biological sample is determined using a set of probes through a multiplex quantitative Polymerase Chain Reaction (qPCR) technique. Further the quantitative abundance is collated to obtain a microbial abundance matrix. Next a model score is determined based on the microbial abundance matrix, using a pre-determined machine learning (ML) model. Lastly the risk category of breast cancer of the subject is assessed based on the model score.
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