Mass spectrometry imaging （MSI） is an emerging molecular imaging technique that combines high spatial resolution and the ability to acquire molecular information. This technology can directly obtain spatial distribution maps of metabolites, proteins, lipids and other molecules in tumor tissues, reveal the abnormal molecular changes during the occurrence and development of liver cancer, and make up for the deficiency of information at the molecular level in traditional imaging （such as CT, MRI）. In liver cancer research, MSI has demonstrated unique advantages and application potential in early diagnosis and screening, precise classification, tumor heterogeneity analysis, and exploration of molecular mechanisms. The principle characteristics of MSI technology and its latest progress in the early diagnosis, classification and molecular mechanism research of liver cancer were reviewed, and the current challenges and future development directions were discussed, which aimed to provide a new perspective and theoretical basis for an in-depth understanding of the pathological mechanism of liver cancer and promoted its precise diagnosis and treatment.

Tacrolimus is a commonly used medication for the treatment of nephrotic syndrome.Due to its narrow therapeutic window and significant pharmacokinetic differences among individuals,therapeutic drug monitoring is required during its clinical use.In the process of therapeutic drug monitoring,machine learning-based personalized dosing prediction models for tacrolimus can excavate medication patterns from a large amount of clinical data,assist in clinical decision-making,and achieve individualized precise medication.Machine learning models,the application progress of machine learning in personalized administration of tacrolimus for patients with nephrotic syndrome,modeling points of machine learning prediction models,and the limitations of current prediction models were reviewed in this paper,which could provide references for future research in this field.