Chemotherapy‐induced intestinal mucositis (CIM) is a significant dose‐limiting adverse reaction brought on by the cancer treatment. Multiple studies reported that reactive oxygen species (ROS) is rapidly produced during the initial stages of chemotherapy, when the drugs elicit direct damage to intestinal mucosal cells, which, in turn, results in necrosis, mitochondrial dysfunction, and ROS production. However, the mechanism behind the intestinal redox system‐based induction of intestinal mucosal injury and necrosis of CIM is still undetermined. In this article, we summarized relevant information regarding the intestinal redox system, including the composition and regulation of redox enzymes, ROS generation, and its regulation in the intestine. We innovatively proposed the intestinal redox “Tai Chi” theory and revealed its significance in the pathogenesis of CIM. We also conducted an extensive review of the English language‐based literatures involving oxidative stress (OS) and its involvement in the pathological mechanisms of CIM. From the date of inception till July 31, 2021, 51 related articles were selected. Based on our analysis of these articles, only five chemotherapeutic drugs, namely, MTX, 5‐FU, cisplatin, CPT‐11, and oxaliplatin were shown to trigger the ROS‐based pathological mechanisms of CIM. We also discussed the redox system‐mediated modulation of CIM pathogenesis via elaboration of the relationship between chemotherapeutic drugs and the redox system. It is our belief that this overview of the intestinal redox system and its role in CIM pathogenesis will greatly enhance research direction and improve CIM management in the future.

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

Hepatocellular carcinoma (HCC) is the most frequent and deadly type of liver cancer. While the underlying molecular mechanisms are poorly understood, it is documented that lncRNAs may play key roles. Many HCC-associated lncRNAs have been linked to HBV and HCV infection, mediating gene expression, cell growth, development, and death. Studying the regulatory mechanisms and biological functions of HCC-related lncRNAs will assist our understanding of HCC pathogenesis as well as its diagnosis and management. Here, we address the potential of dysregulated lncRNAs in HCC as diagnostic and therapeutic biomarkers, and we evaluate the oncogenic or tumor-suppressive properties of these lncRNAs.

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

Objects: Caloric restriction (CR) is known to extend lifespan and exert a protective effect on organs, and is thus a low-cost and easily implemented approach to the health maintenance. However, there have been no studies that have systematically evaluated the metabolic changes that occur in the main tissues affected by CR. This study aimed to explore the target tissues metabolomic profile in CR mice. Methods: Male C57BL/6J mice were randomly allocated to the CR group (n = 7) and control group (n = 7). A non-targeted gas chromatography–mass spectrometry approach and multivariate analysis were used to identify metabolites in the main tissues (serum, heart, liver, kidney, cortex, hippocampus, lung, muscle, and white adipose) in model of CR. Results: We identified 10 metabolites in the heart that showed differential abundance between the 2 groups, along with 9 in kidney, 6 in liver, 6 in lung, 6 in white adipose, 4 in hippocampus, 4 in serum, 3 in cortex, and 2 in muscle. The most significantly altered metabolites were amino acids (AAs) (glycine, aspartic acid, l-isoleucine, l-proline, l-aspartic acid, l-serine, l-hydroxyproline, l-alanine, l-valine, l-threonine, l-glutamic acid, and l-phenylalanine) and fatty acids (FAs) (palmitic acid, 1-monopalmitin, glycerol monostearate, docosahexaenoic acid, 16-octadecenoic acid, oleic acid, stearic acid, and hexanoic acid). These metabolites were associated with 7 different functional pathways related to the metabolism of AAs, lipids, and energy. Conclusion: Our results provide insight into the specific metabolic changes that are induced by CR and can serve as a reference for physiologic studies on how CR improves health and extends lifespan.

Fluoride is widely present in the environment. Excessive fluoride exposure leads to fluorosis, which has become a global public health problem and will cause damage to various organs and tissues. Only a few studies focus on serum metabolomics, and there is still a lack of systematic metabolomics associated with fluorosis within the main organs. Therefore, in the current study, a non-targeted metabolomics method using gas chromatography-mass spectrometry (GC-MS) was used to research the effects of fluoride exposure on metabolites in different organs, to uncover potential biomarkers and study whether the affected metabolic pathways are related to the mechanism of fluorosis. Male Sprague-Dawley rats were randomly divided into two groups: a control group and a fluoride exposure group. GC-MS technology was used to identify metabolites. Multivariate statistical analysis identified 16, 24, 20, 20, 24, 13, 7, and 13 differential metabolites in the serum, liver, kidney, heart, hippocampus, cortex, kidney fat, and brown fat, respectively, in the two groups of rats. Fifteen metabolic pathways were affected, involving toxic mechanisms such as oxidative stress, mitochondrial damage, inflammation, and fatty acid, amino acid and energy metabolism disorders. This study provides a new perspective on the understanding of the mechanism of toxicity associated with sodium fluoride, contributing to the prevention and treatment of fluorosis.