Despite significant treatment advances, breast cancer remains the leading cause of cancer death in women. From the current treatment situation, in addition to developing chemoresistant tumours, distant organ metastasis, and recurrences, patients with breast cancer often have a poor prognosis. Aptamers as "chemical antibodies" may be a way to resolve this dilemma. Aptamers are single-stranded, non-coding oligonucleotides (DNA or RNA), resulting their many advantages, including stability for long-term storage, simplicity of synthesis and function, and low immunogenicity, a high degree of specificity and antidote. Aptamers have gained popularity as a method for diagnosing and treating specific tumors in recent years. This article introduces the application of ten different aptamer delivery systems in the treatment and diagnosis of breast cancer, and systematically reviews their latest research progress in breast cancer treatment and diagnosis. It provides a new direction for the clinical treatment of breast cancer.

Currently, both acute kidney injury (AKI) and chronic kidney disease (CKD) are considered to be the leading public health problems with gradually increasing incidence rates around the world. Rhein is a monomeric component of anthraquinone isolated from rhubarb, a traditional Chinese medicine. It has anti-inflammation, anti-oxidation, anti-apoptosis, anti-bacterial and other pharmacological activities, as well as a renal protective effects. Rhein exerts its nephroprotective effects mainly through decreasing hypoglycemic and hypolipidemic, playing anti-inflammatory, antioxidant and anti-fibrotic effects and regulating drug-transporters. However, the latest studies show that rhein also has potential kidney toxicity in case of large dosages and long use times. The present review highlights rhein’s molecular targets and its different effects on the kidney based on the available literature and clarifies that rhein regulates the function of the kidney in a positive and negative way. It will be helpful to conduct further studies on how to make full use of rhein in the kidney and to avoid kidney damage so as to make it an effective kidney protection drug.

There are three categories of drugs that treat human epidermal growth factor receptor type 2 (HER-2) positive breast cancer: monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), and tyrosine kinase inhibitors (TKIs). The purpose of this study is to analyze and compare the adverse reactions of three classes of anti-HER-2 drugs to various body systems in patients based on the FDA Adverse Event Reporting System (FAERS). All data reports were extracted from the FAERS between 2004 and 2024. Data mining of adverse events associated with anti-HER-2 drugs was carried out using disproportionality analysis. A multivariate logistic regression analysis was conducted to explore the risk factors associated with AEs leading to hospitalization. A total of 47,799 patients were screened for the three classes of drugs, among which ADC drugs caused the largest proportion of deaths. MAb has the strongest ADR signals associated with “cardiac disorders”. Moreover, trastuzumab was associated with a greater risk of cardiotoxicity. Logistic regression analysis revealed that the treatment with mAbs should be wary of serious adverse reactions in “infections and infestations” and “metabolism and nutrition disorders”. Moreover, “endocrine disorders” were the factor associated with the highest risk of prolonged hospitalization due to trastuzumab deruxtecan (T-DXd). The safety of tucatinib among TKI drugs is greater than that of other drugs. In general, from the perspective of the effects of the three classes of drugs on the various body systems of patients, we should focus on mAb-associated “cardiac disorders”, ADC-associated “hepatobiliary disorders”, “respiratory, thoracic and mediastinal disorders”, and TKI-associated “gastrointestinal disorders.

With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters have important roles in influencing drug resistance, drug–drug interactions, and tissue-specific drug targeting. The investigation of drug transporter substrates and inhibitors is a crucial aspect of pharmaceutical development. However, long duration and high expenses pose significant challenges in the investigation of drug transporters. In this review, we discuss the present situation and challenges encountered in applying machine learning techniques to investigate drug transporters. The transporters involved include ABC transporters (P-gp, BCRP, MRPs, and BSEP) and SLC transporters (OAT, OATP, OCT, MATE1,2-K, and NET). The aim is to offer a point of reference for and assistance with the progression of drug transporter research, as well as the advancement of more efficient computer technology. Machine learning methods are valuable and attractive for helping with the study of drug transporter substrates and inhibitors, but continuous efforts are still needed to develop more accurate and reliable predictive models and to apply them in the screening process of drug development to improve efficiency and success rates.

The kidney is critical in the human body’s excretion of drugs and their metabolites. Renal transporters participate in actively secreting substances from the proximal tubular cells and reabsorbing them in the distal renal tubules. They can affect the clearance rates (CLr) of drugs and their metabolites, eventually influence the clinical efficiency and side effects of drugs, and may produce drug–drug interactions (DDIs) of clinical significance. Renal transporters and renal transporter-mediated DDIs have also been studied by many researchers. In this article, the main types of in vitro research models used for the study of renal transporter-mediated DDIs are membrane-based assays, cell-based assays, and the renal slice uptake model. In vivo research models include animal experiments, gene knockout animal models, positron emission tomography (PET) technology, and studies on human beings. In addition, in vitro–in vivo extrapolation (IVIVE), ex vivo kidney perfusion (EVKP) models, and, more recently, biomarker methods and in silico models are included. This article reviews the traditional research methods of renal transporter-mediated DDIs, updates the recent progress in the development of the methods, and then classifies and summarizes the advantages and disadvantages of each method. Through the sorting work conducted in this paper, it will be convenient for researchers at different learning stages to choose the best method for their own research based on their own subject’s situation when they are going to study DDIs mediated by renal transporters.

Pancreatic cancer (PC) remains a high mortality disease worldwide with a 5-year survival rate of less than 10%. Economic and living standard improvements in developing countries have significantly raised behavioral and metabolic risk factors of PC-related burden over the past decades. However, previous studies have not fully clarified how these risk factors contribute to PC over time. By employing the Global Burden of Disease (GBD) Study 2019, we examined PC-associated burden and its related risk factors from 1990 to 2019 in the present paper. During that time frame, the number of PC death cases significantly increased throughout the world; and developing regions have a higher trend compared to developed regions. Smoking, high fasting plasma glucose (FPG), as well as high body mass index (BMI) have become significant drivers of PC burden, which has also contributed to the rise in PC-related deaths in developing countries. Meanwhile, the rapid increase in premature deaths in developing countries should draw the publics attention. It is therefore necessary to intervene on the PC-associated risk factors to significantly reduce death cases and burden. The renewal of PC burden analysis in this paper at multiple levels in GBD database is very beneficial for each country to determine individual policies to control the increasing trend of this disease.