Multiomics

The goal of this study is to uncover the molecular mechanisms responsible for secondary brain injury in patients with post-cardiac arrest syndrome by analyzing cerebrospinal fluid (CSF) using multi-omics techniques. The main question this study aims to answer is: Which genome-, transcriptome-, proteome-, and metabolome-level changes in CSF are associated with secondary brain injury after cardiac arrest? To address this question, CSF samples collected from post-cardiac arrest patients will undergo multi-omics analyses. Identified molecular pathways will be used to screen existing drug databases and generate new therapeutic candidates through computational modeling and compound synthesis. These findings will provide the scientific foundation needed to design and implement future preclinical experiments using cardiac arrest animal models.

This study is a pre-post, open-label cohort study designed to investigate how a food-grade probiotic supplement affects biological measurements and wellbeing in healthy adults. Participants will take one capsule daily for either 1 month or 6 months. During the study, participants will complete online cognitive tasks and provide blood and stool samples collected during home visits by trained staff. The samples will be analysed to explore changes in gut bacteria and other biological markers. This study aims to understand whether the supplement is well tolerated and whether measurable biological changes occur. The study does not involve any experimental drugs or invasive procedures beyond blood sampling and stool collection, and participants will not be asked to change any current prescribed medications or treatments; with eligibility exclusions applying for recent antibiotics or immunosuppressants. The supplement is being studied for research purposes only and is not intended to diagnose, treat, or prevent disease. Participants will be invited to participate in a follow-up visit to assess long-term effects.

NCT/DKFZ/DKTK MASTER is a prospective, continuously recruiting, multicenter observational study for biology-guided stratification of adults with rare cancers, including rare subtypes of common entities, using comprehensive molecular profiling, and clinical decision-making in a multidisciplinary molecular tumor board.

Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates. In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine. The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading. The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.