Project description:immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.
Project description:The present data suggest that the infiltration of inactive CD8+ T cells with low clonality is induced by chemotaxis in the HIGH group, possibly leading to poor prognosis of GC patients.
Project description:Hepatocellular carcinoma (HCC) remains one of the most lethal malignant tumors. Multi-modal therapeutics for HCC have attracted increasing attention. However, designing a nanotherapeutic platform with effective chem-/sono- double-modal HCC therapies is still challenging. Here, we first designed a novel porphyrin-based nanomase (Porzyme) P-Por-Os with nano-sonosensitivity for HCC therapy. Endowed with a large hollow spherical structure of poly porphyrins, which efficiently improved the electron transport capacity of polyporphyrins and exposed the active site of superficial metal atoms, P-Por-Os effectively catalyzed the production of ROS from H2O2. This so-called Chemodynamic Therapy (CDT) was further significantly reinforced by Sonodynamic Therapy (SDT) and achieving this therapeutic ultrasound-controlled anti-tumor effects in HCC. More importantly, we developed an metallothionein-2A (MT2A)-targeted enhancement to reverse the resistance to ROS and suppress cancer progression, which could theoretically be termed Precision Gene Therapy (PGT). Overall, the new therapeutic schedule with the triple-combination of P-Por-Os-based CDT, SDT, and PGT achieved remarkable anti-HCC effectiveness. This study reported an innovative artificial enzyme P-Por-Os with high tumor-killing activity and proposed a new triple-combination with CDT, SDT and PGT treatment strategy for HCC.
Project description:In this study, we investigated miRNA expression profiles in ileal mucosa from CD patients in different settings (post-operative recurrent (POR) CD, newly diagnosed CD and late stage CD)) and controls.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
