Cancer stem cells (CSCs) are reportedly responsible for the initiation and propagation of cancer. Since CSCs are highly resistant to conventional chemo- and radiotherapy, they are considered the main cause of cancer relapse and metastasis. Salinomycin (Sali), an anticoccidial polyether antibiotic, has emerged as a promising new candidate for cancer therapy, with selective cytotoxicity against CSCs in various malignancies. Nanotechnology provides an efficient means of delivering Sali to tumors in view of reducing collateral damage to healthy tissues and enhancing the therapeutic outcome. This review offers an insight into the most recent advances in cancer therapy using Sali-based nanocarriers.

Background: MASLD is a prevalent chronic liver condition with substantial clinical implications. This study aimed to assess the effectiveness of three new, elastography-based, scoring systems for advanced fibrosis ≥F3 (Agile 3+), cirrhosis F4 (Agile 4), and fibrotic NASH: NASH + NAS ≥4 + F≥2 (FAST score), in a cohort of biopsy-proven NAFLD meeting MASLD criteria. Our secondary aim was to compare their diagnostic performances with those of other fibrosis prediction tools: LSM-VCTE alone, and common, easily available scores (FIB-4 or APRI). Methods: Single-center, retrospective study, on consecutive patients with baseline laboratory tests, liver biopsy, and reliable LSM-VCTE measurements. The discrimination between tests was evaluated by analyzing the AUROCs. Dual cut-off approaches were applied to rule-out and rule-in ≥F3, F4 and fibrotic NASH. We tested previously reported cut-off values and provided our best thresholds to achieve Se ≥85%, Se ≥90%, and Sp ≥90%, Sp ≥95%. Results: Among 246 patients, 113 (45.9%) were women, and 75 (30.5%) presented diabetes. Agile 3+ and Agile 4 demonstrated excellent performance in identifying ≥F3 and F4, achieving AUROCs of 0.909 and 0.968, while the FAST score yielded acceptable results in distinguishing fibrotic NASH. When compared to FIB-4 and LSM-VCTE, both Agile 3+ and Agile 4 performed better than FIB-4 and had a similar performance to LSM-VCTE, but with higher diagnostic accuracy, hence reducing the grey zone. Conclusion: Agile 3+ and Agile 4 are reliable, non-invasive tests for identifying advanced fibrosis or cirrhosis in MASLD patients, while FAST score demonstrates moderate performance in identifying fibrotic NASH.

Inflammatory bowel diseases (IBDs) are conditions that still pose significant problems. A third of the patients are either misdiagnosed or a proper diagnosis of Crohn’s disease (CD) or ulcerative colitis (UC) cannot be made. We need new biomarkers, so that we can offer patients the best treatment and keep the disease in an inactive state for as long as possible. Alterations in metabolic profiles have been incriminated in the pathophysiology of IBD. The aim of the present study was to identify molecules that could serve as biomarkers for a positive diagnosis of IBD as well as to discriminate UC from colonic CD. Twenty-two patients with active colonic IBD (UC = 17, CD = 5) and 24 age- and gender-matched healthy controls were enrolled. Plasma lipid and metabolic profiles were quantified using ultra-high performance liquid chromatography combined with mass spectrometry. Univariate and multivariate statistical tests were employed. Six lipid species and seven metabolites were significantly altered in IBD compared to healthy controls, with the majority belonging to glycerophospholipid, linoleic acid, and sphingolipid metabolisms. Five lipid species and only one metabolite were significantly increased in UC compared to CD. This preliminary study suggests that lipid and metabolic profiling of serum can become diagnostic tools for IBD. In addition, they can be used to differentiate between CD and UC.

Some previous studies reported that probiotics might decrease the severity of chemotherapy‑induced mucositis. This study assessed the potential protective effect of Lactobacillus plantarum ATCC 8014 on 5‑fluorouracil (5‑FU) induced intestinal mucositis in the Wistar rats. The Crl:WI rats were divided into two groups of six animals (F, L) and one group of 5 animals (M). Group L received for 9 days 3.32x10⁹ CFU/ml of Lactobacillus plantarum orally. In the 7th day of the experiment 400 mg of 5‑FU was administered intraperitoneally in groups L and F. Group M received only the vehicles. All animals were sacrificed in the 9th day. Eleven histological characteristics of mucositis were quantified from 0 (normal) to 3 (severe) for duodenum, jejunum and colon. Semiquantitative grades measured Toll‑like receptor 4 (TLR4) immunopositive cells. The independent groups were analyzed using the Kruskal‑Wallis test, Mann‑Whitney U test, with a Bonferroni correction for alpha (P≤0.016). In the group F, treated with 5‑FU, the most affected areas were the jejunum and the duodenum. The medium score of histological lesions was 27 for jejunum (minimum 25, maximum 32) and 21 for duodenum (minimum 18, maximum 29). Graded microscopic mucosal changes of the jejunum were significantly lower in group L compared with group F (U=0, P=0.009, Mann‑Whitney test). The histological changes depicted on the duodenal and colonic mucosa were less severe in group L than in group F, but without reaching the statistical significance (duodenum: U=6, P=0.172, Mann‑Whitney test; colon: U=12, P=0.916, Mann‑Whitney test). Although the TLR4 immunoexpression was more intense in group L, no significant statistical difference was revealed at duodenum, jejunum or colonic mucosa. Significantly fewer microscopic changes were depicted in L group on the jejunum, suggesting a potential beneficial effect of Lactobacillus plantarum at this level in 5‑FU induced mucositis.

Artificial intelligence (AI) is rapidly transforming imaging modalities in inflammatory bowel disease (IBD), particularly in endoscopy and ultrasound. Despite their critical roles, both modalities are challenged by interobserver variability, subjectivity, and accessibility issues. AI offers significant potential to address these limitations by enhancing lesion detection, standardizing disease activity scoring, and supporting clinical decision-making. In endoscopy, deep convolutional neural networks have achieved high accuracy in detecting mucosal abnormalities and grading disease severity, reducing observer dependency and improving diagnostic consistency. AI-assisted colonoscopy systems have also demonstrated improvements in procedural quality metrics, including adenoma detection rates and withdrawal times. Similarly, AI applications in intestinal ultrasound show promise in automating measurements of bowel wall thickness, assessing vascularity, and distinguishing between inflammatory and fibrotic strictures, which are critical for tailored therapy decisions. Video capsule endoscopy has likewise benefited from AI, reducing interpretation times and enhancing the detection of subtle lesions. Despite these advancements, implementation challenges, including dataset quality, standardization, AI interpretability, clinician acceptance, and regulatory and ethical considerations, must be carefully addressed. The current review focuses on the most recent developments in the integration of AI into experimental designs, medical devices, and clinical workflows for optimizing diagnostic accuracy, treatment strategies, and patient outcomes in IBD management.