Comparing the diagnostic efficacy of optical coherence tomography and frozen section for margin assessment in breast-conserving surgery: a meta-analysis.

AIMS: This meta-analysis assessed the relative diagnostic accuracy of optical coherence tomography (OCT) versus frozen section (FS) in evaluating surgical margins during breast-conserving procedures. METHODS: PubMed and Embase were searched for relevant studies published up to October 2023. The inclusion criteria encompassed studies evaluating the diagnostic accuracy of OCT or FS in patients undergoing breast-conserving surgery. Sensitivity and specificity were analysed using the DerSimonian and Laird method and subsequently transformed through the Freeman-Tukey double inverse sine method. RESULTS: The meta-analysis encompassed 36 articles, comprising 16 studies on OCT and 20 on FS, involving 10 289 specimens from 8058 patients. The overall sensitivity of OCT was 0.93 (95% CI: 0.90 to 0.96), surpassing that of FS, which was 0.82 (95% CI: 0.71 to 0.92), indicating a significantly higher sensitivity for OCT (p=0.04). Conversely, the overall specificity of OCT was 0.89 (95% CI: 0.83 to 0.94), while FS exhibited a higher specificity at 0.97 (95% CI: 0.95 to 0.99), suggesting a superior specificity for FS (p＜0.01). CONCLUSIONS: Our meta-analysis reveals that OCT offers superior sensitivity but inferior specificity compared with FS in assessing surgical margins in breast-conserving surgery patients. Further larger well-designed prospective studies are needed, especially those employing a head-to-head comparison design. PROSPERO REGISTRATION NUMBER: CRD42023483751.

Application of Artificial Intelligence in Oncology Nursing: A Scoping Review.

BACKGROUND: Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing. OBJECTIVE: The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing. METHODS: A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022. RESULTS: A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed. CONCLUSIONS: The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed. IMPLICATIONS FOR PRACTICE: This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.

Fluorogenic Pt complexes distinguish the quantity and folding behavior of RNA G-quadruplexes between live cancerous and healthy cells.

Two Pt complexes with high quantum yields and photostability, and low cytotoxicity, were developed to track RNA G-quadruplexes (GQs) in live cells. Higher number and intensity, and longer lifetime of fluorescent foci in cancer cells than those in healthy cells suggest that the quantity and folding dynamics of RNA GQs could not only correlate to their biological functions, but be two novel biomarkers to characterize cancerous cells.

Incorporating uracil and 5-halouracils into short peptide nucleic acids for enhanced recognition of A-U pairs in dsRNAs.

Double-stranded RNA (dsRNA) structures form triplexes and RNA-protein complexes through binding to single-stranded RNA (ssRNA) regions and proteins, respectively, for diverse biological functions. Hence, targeting dsRNAs through major-groove triplex formation is a promising strategy for the development of chemical probes and potential therapeutics. Short (e.g., 6-10 mer) chemically-modified Peptide Nucleic Acids (PNAs) have been developed that bind to dsRNAs sequence specifically at physiological conditions. For example, a PNA incorporating a modified base thio-pseudoisocytosine (L) has an enhanced recognition of a G-C pair in an RNA duplex through major-groove L·G-C base triple formation at physiological pH, with reduced pH dependence as observed for C+·G-C base triple formation. Currently, an unmodified T base is often incorporated into PNAs to recognize a Watson-Crick A-U pair through major-groove T·A-U base triple formation. A substitution of the 5-methyl group in T by hydrogen and halogen atoms (F, Cl, Br, and I) causes a decrease of the pKa of N3 nitrogen atom, which may result in improved hydrogen bonding in addition to enhanced base stacking interactions. Here, we synthesized a series of PNAs incorporating uracil and halouracils, followed by binding studies by non-denaturing polyacrylamide gel electrophoresis, circular dichroism, and thermal melting. Our results suggest that replacing T with uracil and halouracils may enhance the recognition of an A-U pair by PNA·RNA2 triplex formation in a sequence-dependent manner, underscoring the importance of local stacking interactions. Incorporating bromouracils and chlorouracils into a PNA results in a significantly reduced pH dependence of triplex formation even for PNAs containing C bases, likely due to an upshift of the apparent pKa of N3 atoms of C bases. Thus, halogenation and other chemical modifications may be utilized to enhance hydrogen bonding of the adjacent base triples and thus triplex formation. Furthermore, our experimental and computational modelling data suggest that PNA·RNA2 triplexes may be stabilized by incorporating a BrUL step but not an LBrU step, in dsRNA-binding PNAs.

Dual functional dinuclear platinum complex with selective reactivity towards c-myc G-quadruplex.

G-quadruplexes (GQ) folded by the oncogenic G-rich sequences are the promising targets for developing anticancer therapeutic molecules. However, the current drug development mainly focused on non-covalent dynamic binders to stabilize GQ structures, while the covalent targeting from inorganic complexes via chelating principles, as a potent therapeutic strategy was surprisingly lack of exploration. Herein, a series of dinuclear platinum complexes, [(Pt(Dip)Cl)2(µ-diamine)](NO3)2 (Dip: 4,7-diphenyl-1,10-phenanthroline), were designed to contain two dual-functional Pt cores connected by an alkyl linkage. Pt3 with nonanediamine linkage optimized the specific binding towards c-myc G-quadruplex via dual functional clamp on GQ as 1) non-covalently π-stacking of aromatic ligands, and 2) two Pt(II) cores covalently chelated to guanines at both 3'- and 5'-ends.

Aza-bridged bisphenanthrolinyl Pt(II) complexes: Efficient stabilization and topological selectivity on telomeric G-quadruplexes.

Two platinum complexes with an aza-bridged bis(1,10-phenanthrolin-2-yl)amine (bpa) were synthesized. The two phenanthrolines in bpa entered a flat plane prior to binding of nucleic acids, which bestowed on the two Pt complexes a significantly high stabilizing ability on both DNA and RNA G-quadruplexes. Further extending alkyl tail from aromatic coordination core enabled the complexes to distinguish GQ sequence based upon the topological folding structures and enhanced the selectivity of the complex against duplex DNA. This study paved the way to develop Pt complexes as GQ stabilizers for specific folding topology and the applications to disease and/or personalized anticancer medicine/therapy.