Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

Reactive Oxygen Species (ROS)-Tyrosinase Cascade-Activated Near-Infrared Fluorescent Probe for the Precise Imaging of Melanoma

As a highly aggressive malignancy, the issue of curing melanoma at an advanced stage could suffer from severe metastasis and a lower 5-year survival rate. Therefore, the early diagnosis of melanoma with high accuracy is vital and contributes to a significantly improved 5-year survival rate. This work reports a dual-locked receptor, m-BA-Hcy, which releases the near-infrared (NIR) fluorophore Hcy-OH upon the dual activation of reactive oxygen species (ROS) and tyrosinase (TYR). The substitution of boric acid on the phenyl ring was studied, which influences the feasibility of the performance of the envisaged cascade reaction. The sensing behavior was discussed in terms of optical spectroscopy and reaction mechanism, and imaging was fully performed at the cellular and organism levels. Receptor m-BA-Hcy was hence clarified to possess supreme sensitivity and accuracy for melanoma detection.

Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction

The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health records (EHRs) frequently suffer from class imbalances, leading to prediction biases and differences between specificity and sensitivity, which hinder reliable model development despite the valuable insights contained in these datasets. To address this, we have introduced a novel approach to enhance MI risk prediction using self-reported attributes from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) dataset. Our approach incorporates three innovative techniques: the Dual-Path Artificial Neural Network (DP-ANN) to mitigate biased decision making across imbalanced datasets, the Triple Criteria Selection (TCS) for unbiased feature selection, and Minority Weighted Sampling (MWS) to tackle challenges of uncontrolled minority class sampling. These methods collectively enhance MI prediction and feature relevance. The DP-ANN model has achieved balanced performance, with an average specificity of 80%, sensitivity of 82%, and AUC-ROC of 89.5%, improving imbalance variance by approximately 14.96% compared to prior studies. By outperforming other models across four heavily imbalanced datasets, our approach demonstrates robustness and generalizability. Additionally, SHapley Additive exPlanations (SHAP) analysis has revealed key predictors and risk factors for MI, such as coronary heart disease and bronchitis in females, and stroke among individuals aged 35-54. In conclusion, our study provides a robust model for healthcare professionals to assess MI risk through targeted factors, promoting early detection and potentially improving patient outcomes.

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.