Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with prognosis critically dependent on the stage at diagnosis. Traditional tissue biopsy presents well-known limitations, including tumor heterogeneity and invasiveness. Liquid biopsy, encompassing the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and other cell-free biomarkers, has emerged as a transformative approach for non-invasive tumor profiling. This comprehensive narrative review outlines the recent evidence published on the current state and future perspectives of liquid biopsy in CRC, with a focused emphasis on the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in data analysis and clinical translation. Methods: A narrative review of the literature was conducted by searching PubMed/MEDLINE, EMBASE, and ClinicalTrials.gov for articles published between January 2020 and January 2026, using a predefined Boolean search string combining terms related to liquid biopsy biomarkers, colorectal cancer, and artificial intelligence methodologies. Filters were applied to include only English-language human studies. Additional relevant sources were consulted to ensure comprehensive coverage of the available literature. Liquid biopsy platforms, particularly ctDNA sequencing and methylation profiling, demonstrate increasing clinical utility across the CRC care continuum from population screening to post-surgical minimal residual disease (MRD) detection and real-time therapy monitoring. AI-driven analytical frameworks, including Random Forest, Convolutional Neural Networks, LSTM models, and more recently Large Language Models (LLMs), substantially augment the sensitivity and specificity of liquid biopsy interpretation, enabling multimodal data integration. The convergence of liquid biopsy technology and AI-driven analytics represents a paradigm shift toward precision oncology in CRC. Remaining challenges include analytical standardization, model explainability, regulatory harmonization, and equitable access. Future integration of federated learning frameworks and LLM-based clinical decision support tools will be essential for responsible clinical translation.

Urinary tract infections (UTIs) sit at a leading edge of the antimicrobial-resistance (AMR) crisis, and Romania ranks among the most affected European countries, yet large single-center series of urinary isolates with complete antibiograms remain scarce. We describe the full resistance landscape of urinary pathogens at a Romanian tertiary hospital, with particular attention to multidrug-resistant Klebsiella and to the often-overlooked urease-positive organisms. We analyzed all positive urine cultures processed over 13 consecutive months (February 2025–February 2026). The first isolate per patient was the primary unit of analysis (n = 2331); the full isolate-level dataset (n = 3348) was analyzed secondarily. Resistance phenotypes (putative ESBL, carbapenem resistance, MDR, VRE) were derived from per-agent susceptibility data following Magiorakos et al., and resistance rates were calculated only on tested isolates. Associations were tested with chi-square/Fisher tests and Benjamini–Hochberg correction. A total of 3348 isolates from 2331 unique patients were analyzed (median age 68 years; 55% male). Escherichia coli predominated (40.5%), followed by Enterococcus spp. (22.2%) and Klebsiella spp. (18.9%). Among the first isolates, 15.3% were MDR; 16.7% of E. coli, Klebsiella and Proteus showed a putative ESBL phenotype; and 3.9% of Enterobacterales were carbapenem-resistant. Resistance was concentrated in Klebsiella (36.2% MDR, 22.8% putative ESBL, 14.7% carbapenem-resistant; all higher than E. coli, p < 0.001) and in Pseudomonas spp. (48.1% carbapenem-resistant). E. coli retained excellent activity to nitrofurantoin (1.2%), fosfomycin (1.2%; tested in a subset) and ertapenem (0.1%) despite high trimethoprim–sulfamethoxazole (31.5%) and fluoroquinolone (24.3%) resistance. Urease-positive organisms formed a distinct subgroup with high trimethoprim–sulfamethoxazole resistance but preserved carbapenem activity. The urinary resistance burden was substantial but uneven, concentrated in Klebsiella and Pseudomonas. These findings support a stratified, locally guided empirical strategy and combined stewardship–infection control efforts.

Background and Objectives: Systemic inflammatory biomarkers have emerged as potential prognostic indicators in metastatic colorectal cancer (mCRC). However, the prognostic robustness of inflammatory indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP), C-reactive protein-to-albumin ratio (CAR), and Glasgow Prognostic Score (GPS) remains incompletely characterized. In this study, we aimed to evaluate the prognostic significance of NLR, PLR, CRP, CAR, and GPS for progression-free survival in metastatic colorectal cancer in a cohort of patients from Romania. Materials and Methods: This retrospective observational study included 148 patients diagnosed with mCRC. Inflammatory biomarkers were determined from baseline laboratory parameters. Progression-free survival (PFS) was the primary endpoint. Statistical analyses included correlation testing, Kaplan–Meier survival analysis, Cox proportional hazards regression, Firth penalized Cox regression, restricted cubic spline modeling, time-dependent receiver operating characteristic (ROC) analysis, LASSO penalized regression, multiple imputation, and parsimonious multivariable Cox models adjusted for major clinicopathologic confounders. Results: Median PFS was 21 months (95% CI 19–24). In univariable Cox analyses, elevated NLR (HR 1.98, 95% CI 1.11–3.51, p = 0.020), PLR (HR 1.89, 95% CI 1.25–2.85, p = 0.002), CRP (HR 1.45, 95% CI 1.15–1.83, p = 0.002), and CAR (HR 1.44, 95% CI 1.05–1.98, p = 0.022) were associated with shorter PFS. Restricted cubic spline analysis demonstrated a significant nonlinear association between NLR and PFS (p = 0.0025). After multiple imputation, NLR remained associated with shorter PFS (HR 2.04, 95% CI 1.13–3.68, p = 0.018). However, in a multivariable model adjusted for major clinicopathologic confounders, this association was not retained (HR 1.41, 95% CI 0.81–2.43, p = 0.221) and time-dependent ROC analyses demonstrated its limited discriminatory performance. Conclusions: Although some inflammatory markers were associated with shorter PFS in univariable analyses, the prognostic effect of NLR was attenuated after adjustment and was not consistently confirmed across all analyses.