RESUMEN
(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.
RESUMEN
There is a global health concern associated with the emergence of the multidrug-resistant (MDR) fungus Candida auris, which has significant mortality rates. Finding innovative and distinctive anti-Candida compounds is essential for treating infections caused by MDR C. auris. A bacterial strain with anti-Candida activity was isolated and identified using 16 S rRNA gene sequencing. The whole genome was sequenced to identify biosynthesis-related gene clusters. The pathogenicity and cytotoxicity of the isolate were analyzed in Candida and HFF-1 cell lines, respectively. This study set out to show that whole-genome sequencing, cytotoxicity testing, and pathogenicity analysis combined with genome mining and comparative genomics can successfully identify biosynthesis-related gene clusters in native bacterial isolates that encode antifungal natural compounds active against Candida albicans and C. auris. The native isolate MR14M3 has the ability to inhibit C. auris (zone of inhibition 25 mm) and C. albicans (zone of inhibition 25 mm). The 16 S rRNA gene sequence of MR14M3 aligned with Bacillus amyloliquefaciens with similarity (100%). Bacillus amyloliquefaciens MR14M3 establishes bridges of intercellular nanotubes (L 258.56 ± 35.83 nm; W 25.32 ± 6.09 nm) connecting neighboring cells. Candida cell size was reduced significantly, and crushed phenotypes were observed upon treatment with the defused metabolites of B. amyloliquefaciens MR14M3. Furthermore, the pathogenicity of B. amyloliquefaciens MR14M3 on Candida cells was observed through cell membrane disruption and lysed yeast cells. The whole-genome alignment of the MR14M3 genome (3981,643 bp) using 100 genes confirmed its affiliation with Bacillus amyloliquefaciens. Genome mining analysis revealed that MR14M3-coded secondary metabolites are involved in the biosynthesis of polyketides (PKs) and nonribosomal peptide synthases (NRPSs), including 11 biosynthesis-related gene clusters with one hundred percent similarity. Highly conserved biosynthesis-related gene clusters with anti-C. albicans and anti-C. auris potentials and cytotoxic-free activity of B. amyloliquefaciens MR14M3 proposes the utilization of Bacillus amyloliquefaciens MR14M3 as a biofactory for an anti-Candida auris and anti-C. albicans compound synthesizer.