Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Emerging studies show that long noncoding RNAs (lncRNAs) have important roles in carcinogenesis. lncRNA ZEB1 antisense 1 (ZEB1-AS1) is a novel lncRNA, whose clinical significance, biological function, and underlying mechanism remains unclear in glioma. Here, we found that ZEB1-AS1 was highly expressed in glioma tissues, being closely related to clinical stage of glioma. Moreover, patients with high ZEB1-AS1 levels had poor prognoses, with the evidence provided by multivariate Cox regression analysis indicating that ZEB1-AS1 expression could serve as an independent prognostic factor in glioma patients. Functionally, silencing of ZEB1-AS1 could significantly inhibit cell proliferation, migration, and invasion, as well as promote apoptosis. Knockdown of ZEB1-AS1 significantly induced the G0/G1 phase arrest and correspondingly decreased the percentage of S phase cells. Further analysis indicated that ZEB1-AS1 could regulate the cell cycle by inhibiting the expression of G1/S transition key regulators, such as Cyclin D1 and CDK2. Furthermore, ZEB1-AS1 functioned as an important regulator of migration and invasion via activating epithelial to mesenchymal transition (EMT) through up-regulating the expression of ZEB1, MMP2, MMP9, N-cadherin, and Integrin-β1 as well as decreasing E-cadherin levels in the metastatic progression of glioma. Additionally, forced down-regulation of ZEB1-AS1 could dramatically promote apoptosis by increasing the expression level of Bax and reducing Bcl-2 expression in glioma. Taken together, our data suggest that ZEB1-AS1 may serve as a new prognostic biomarker and therapeutic target of glioma.

Long non-coding RNA (lncRNA) CASC8 rs10505477 polymorphism has been identified to be related to risk of many kinds of cancers, such as colorectal cancer, gastric cancer, and invasive ovarian cancer, and it may be involved in the prognosis of gastric cancer patients who have received platinum-based chemotherapy after surgical treatment. So far, there is no study investigating the clinical significance of lncRNA CASC8 rs10505477 in lung cancer susceptibility and treatment. In this study, we genotyped 498 lung cancer patients and 213 healthy control subjects to explore the correlation between the rs10505477 polymorphism and lung cancer risk in a Chinese population. Among the 498 patients, 467 were selected for the chemotherapy response and toxicity study. We found that the single nucleotide polymorphisms (SNP) rs10505477 was greatly related to lung cancer risk in male and adenocarcinoma subgroups in recessive model (adjusted OR = 0.51, 95%CI = 0.29–0.90, p = 0.02; adjusted OR = 0.52, 95%CI = 0.30–0.89, p = 0.02, respectively). It was also closely correlated with platinum-based chemotherapy response in dominant model (adjusted OR = 1.58, 95%CI = 1.05–2.39, p = 0.03). Additionally, we observed that CASC8 rs10505477 polymorphism was significantly relevant to severe hematologic toxicity in non-small-cell lung cancer (NSCLC) subgroup in dominant model (adjusted OR = 0.59, 95%CI = 0.35–0.98, p = 0.04) and in additive model (adjusted OR = 0.62, 95%CI = 0.43–0.90, p = 0.01). Furthermore, it was found that rs10505477 polymorphism was greatly associated with gastrointestinal toxicity in SCLC and cisplatin subgroups in dominant model (adjusted OR = 7.82, 95%CI = 1.36–45.07, p = 0.02; adjusted OR = 1.94, 95%CI = 1.07–3.53, p = 0.03, respectively). Thus, lncRNA CASC8 rs10505477 could serve as a possible risk marker for diagnosing lung cancer, and could be used to forecast the response and toxicity of platinum-based treatment in lung cancer patients.

Glycyrrhetinic acid (GA) has been used clinically in the treatment of patients with chronic hepatitis. This study evaluated the effect of GA on the activity of five P450(CYP450) cytochrome enzymes: CYP2A6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, in human liver microsomes (HLMs) and recombinant cDNA-expressed enzyme systems using a HPLC-MS/MS CYP-specific probe substrate assay. With midazolam as the probe substrate, GA greatly decreased CYP3A4 activity with IC₅₀ values of 8.195 μM in HLMs and 7.498 μM in the recombinant cDNA-expressed CYP3A4 enzyme system, respectively. It significantly decreased CYP3A4 activity in a dose- but not time-dependent manner. Results from Lineweaver–Burk plots showed that GA could inhibit CYP3A4 activity competitively, with a Ki value of 1.57 μM in HLMs. Moreover, CYP2C9 and CYP2C19 could also be inhibited significantly by GA with IC₅₀ of 42.89 and 40.26 μM in HLMs, respectively. Other CYP450 isoforms were not markedly affected by GA. The inhibition was also confirmed by an in vivo study of mice. In addition, it was observed that mRNA expressions of the Cyps2c and 3a family decreased significantly in the livers of mice treated with GA. In conclusion, this study indicates that GA may exert herb-drug interactions by competitively inhibiting CYP3A4.

Tumor immunotherapy is a new therapeutic approach that has been evolving in the last decade and has dramatically changed the treatment options for cancer. Circular RNAs (circRNAs) are non-coding RNAs (ncRNAs) with high stability, tissue-specific and cell-specific expression. There is growing evidence that circRNAs are involved in the regulation of both adaptive and innate immunity. They play important roles in tumor immunotherapy by affecting macrophage, NK and T cell function. The high stability and tissue specificity make them ideal candidate biomarkers for therapeutic effects. CircRNAs also represent one of promising targets or adjuvant for immunotherapy. Investigations in this field progress rapidly and provide essential support for the diagnosis, prognosis and treatment guidance of cancers in the future. In this review, we summarize the role of circRNAs on tumor immunity from the viewpoint of innate and adaptive immunity, and explore the role of circRNAs in tumor immunotherapy.

Dysregulated long noncoding RNAs (lncRNAs) have been found in human diseases, especially in cancer. Emerging evidence indicates that dysregulated lncRNAs are implicated in tumorigenesis and cancer progression. LncRNA AB073614 characterized as a new candidate lncRNA promotes the development of ovarian cancer. However, the role of lncRNA AB073614 in human gliomas remains unknown. The expression of AB073614 was detected in 65 glioma tissues and 13 normal brain tissues by qRT-PCR, showing that lncRNA AB073614 expression was significantly up-regulated in cancerous tissues compared with normal brain tissues (p < 0.001), and it was positively correlated with tumor grade (I–II grades vs. III–IV grades, p = 0.013) in glioma patients. Kaplan-Meier analysis demonstrated that increased AB073614 expression contributed to poor overall survival (HR (hazard ratio) = 1.952, 95%CI: 1.202–3.940, p = 0.0129). Further, univariate Cox regression analysis indicated that lncRNA AB073614 overexpression was an unfavorable prognostic factor in gliomas (HR = 1.997, 95%CI: 1.135–3.514, p = 0.016), regardless of the tumor grade (I–II grades vs. III–IV grades, HR = 1.902, 95%CI: 1.066–3.391, p = 0.029). Finally, after adjustment with age, sex, tumor grade and tumor location, multivariate Cox regression analysis suggested that both highly expressed lncRNA AB073614 (HR = 2.606, 95%CI: 1.408–4.824, p = 0.002) and high tumor grade (III–IV grades, HR = 2.720, 95%CI: 1.401–5.282, p = 0.003) could be considered independent poor prognostic indicators for glioma patients. In conclusion, our study suggested that increased lncRNA AB073614 expression may be identified as a poor prognostic biomarker in gliomas.