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.

General Recognition of U-G, U-A, and C-G Pairs by Double-Stranded RNA-Binding PNAs Incorporated with an Artificial Nucleobase.

Chemically modified peptide nucleic acids (PNAs) show great promise in the recognition of RNA duplexes by major-groove PNA·RNA-RNA triplex formation. Triplex formation is favored for RNA duplexes with a purine tract within one of the RNA duplex strands, and is severely destabilized if the purine tract is interrupted by pyrimidine residues. Here, we report the synthesis of a PNA monomer incorporated with an artificial nucleobase S, followed by the binding studies of a series of S-modified PNAs. Our data suggest that an S residue incorporated into short 8-mer dsRNA-binding PNAs (dbPNAs) can recognize internal Watson-Crick C-G and U-A, and wobble U-G base pairs (but not G-C, A-U, and G-U pairs) in RNA duplexes. The short S-modified PNAs show no appreciable binding to DNA duplexes or single-stranded RNAs. Interestingly, replacement of the C residue in an S·C-G triple with a 5-methyl C results in the disruption of the triplex, probably due to a steric clash between S and 5-methyl C. Previously reported PNA E base shows recognition of U-A and A-U pairs, but not a U-G pair. Thus, S-modified dbPNAs may be uniquely useful for the general recognition of RNA U-G, U-A, and C-G pairs. Shortening the succinyl linker of our PNA S monomer by one carbon atom to have a malonyl linker causes a severe destabilization of triplex formation. Our experimental and modeling data indicate that part of the succinyl moiety in a PNA S monomer may serve to expand the S base forming stacking interactions with adjacent PNA bases.

Sequence- And Structure-Specific Probing of RNAs by Short Nucleobase-Modified dsRNA-Binding PNAs Incorporating a Fluorescent Light-up Uracil Analog.

RNAs are emerging as important biomarkers and therapeutic targets. The strategy of directly targeting double-stranded RNA (dsRNA) by triplex-formation is relatively underexplored mainly due to the weak binding at physiological conditions for the traditional triplex-forming oligonucleotides (TFOs). Compared to DNA and RNA, peptide nucleic acids (PNAs) are chemically stable and have a neutral peptide-like backbone, and thus, they show significantly enhanced binding to natural nucleic acids. We have successfully developed nucleobase-modified dsRNA-binding PNAs (dbPNAs) to facilitate structure-specific and selective recognition of dsRNA over single-stranded RNA (ssRNA) and dsDNA regions at near-physiological conditions. The triplex formation strategy facilitates the targeting of not only the sequence but also the secondary structure of RNA. Here, we report the development of novel dbPNA-based fluorescent light-up probes through the incorporation of A-U pair-recognizing 5-benzothiophene uracil (btU). The incorporation of btU into dbPNAs does not affect the binding affinity toward dsRNAs significantly, in most cases, as evidenced by our nondenaturing gel shift assay data. The blue fluorescence emission intensity of btU-modified dbPNAs is sequence- and structure-specifically enhanced by dsRNAs, including the influenza viral RNA panhandle duplex and HIV-1-1 ribosomal frameshift-inducing RNA hairpin, but not ssRNAs or DNAs, at 200 mM NaCl, pH 7.5. Thus, dbPNAs incorporating btU-modified and other further modified fluorescent nucleobases will be useful biochemical tools for probing and detecting RNA structures, interactions, and functions.

A complex multiscale virtual particle model based elastic network model (CMVP-ENM) for the normal mode analysis of biomolecular complexes.

Understanding the molecular flexibility and dynamics is central to the analysis of biomolecular functions. In this work, complex multiscale virtual particle model based elastic network models (CMVP-ENMs) have been proposed for the normal mode analysis of biomolecular complexes or biomolecular assemblies. The term complex used in our CMVP-ENMs refers to the multi-material or multi-constituent. Different "materials" or constituents contribute differently to the general flexibility and dynamics of complex biomolecular structures. In our CMVP-ENMs, the key idea is to incorporate relative density or weight information of different components into the spring parameter of elastic network models. Two different models, including the CMVP based Gaussian network model (CMVP-GNM) and the CMVP based anisotropic network model (CMVP-ANM), have been proposed. With the consideration of complex component information, our CMVP-GNM, compared with the traditional GNM, can deliver a better accuracy in the B-factor prediction of protein-nucleic acid complexes. Moreover, our CMVP-ANM can be used to remove the "tip effect" by systematically suppressing the extremely-large vectors, in the highly flexible regions, of the normal modes generated by the ANM. In this way, our CMVP-ANM can be used to handle biomolecular structures with large hanging loops or extruding ends, which usually cause an irrationally-large-vector problem in ANM predictions. Finally, we explore the potential applications of our method by the cryo-EM data analysis. We find that by tuning the relative density ratio, we can systematically enhance or suppress the modes in different components, so that it can reveal the dynamics of the special regions that we are interested in.

Fabrication of circular assemblies with DNA tetrahedrons: from static structures to a dynamic rotary motor.

DNA tetrahedron as the simplest 3D DNA nanostructure has been applied widely in biomedicine and biosensing. Herein, we design and fabricate a series of circular assemblies of DNA tetrahedron with high purity and decent yields. These circular nanostructures are confirmed by endonuclease digestion, gel electrophoresis and atomic force microscopy. Inspired by rotary protein motor, we demonstrate these circular architectures can serve as a stator for a rotary DNA motor to achieve the circular rotation. The DNA motor can rotate on the stators for several cycles, and the locomotion of the motor is monitored by the real-time fluorescent measurements.

Efficient Long-Range Hole Transport Through G-Quadruplexes.

DNA offers a means of long-range charge transport for biology and electric nanodevices. Here, a series of tetra-stranded G-quadruplexes were assembled within a dendritic DNA architecture to explore oxidative charge transport (hole transport) through the G-quadruplex. Efficient charge transport was achieved over 28 Šupon UV irradiation. Over a longer G-quadruplex bridge, hole transport was escalated to a higher efficiency, which resulted in a higher yield than that of the optimal duplex DNA for charge transport, that is, the adenine tract. Efficient long-range hole transport suggests tetra-stranded G-quadruplexes, instead of an oxidation hotspot, hold better potential as an electron conduit than duplex DNA.

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.

Problem-Solving Skills Training for Parents of Children With Chronic Health Conditions: A Systematic Review and Meta-Analysis.

Importance: Problem-solving skills training (PSST) has a demonstrated potential to improve psychosocial well-being for parents of children with chronic health conditions (CHCs), but such evidence has not been fully systematically synthesized. Objective: To evaluate the associations of PSST with parental, pediatric, and family psychosocial outcomes. Data Sources: Six English-language databases (PubMed, Embase, CINAHL, PsycINFO, Web of Science, and Cochrane Library), 3 Chinese-language databases (China National Knowledge Infrastructure, China Science and Technology Journal Database, and Wanfang), gray literature, and references were searched from inception to April 30, 2023. Study Selection: Randomized clinical trials (RCTs) that performed PSST for parents of children with CHCs and reported at least 1 parental, pediatric, or family psychosocial outcome were included. Data Extraction and Synthesis: Study selection, data extraction, and quality assessment were conducted independently by 2 reviewers. Data were pooled for meta-analysis using the standardized mean difference (SMD) by the inverse variance method or a random-effects model. Subgroup analyses of children- and intervention-level characteristics were conducted. Main Outcomes and Measures: The psychosocial outcomes of the parents, their children, and their families, such as problem-solving skills, negative affectivity, quality of life (QOL), and family adaptation. Results: The systematic review included 23 RCTs involving 3141 parents, and 21 of these trials were eligible for meta-analysis. There was a significant association between PSST and improvements in parental outcomes, including problem-solving skills (SMD, 0.43; 95% CI, 0.27-0.58), depression (SMD, -0.45; 95% CI, -0.66 to -0.23), distress (SMD, -0.61; 95% CI, -0.81 to -0.40), posttraumatic stress (SMD -0.39; 95% CI, -0.48 to -0.31), parenting stress (SMD, -0.62; 95% CI, -1.05 to -0.19), and QOL (SMD, 0.45; 95% CI, 0.15-0.74). For children, PSST was associated with better QOL (SMD, 0.76; 95% CI, 0.04-1.47) and fewer mental problems (SMD, -0.51; 95% CI, -0.68 to -0.34), as well as with less parent-child conflict (SMD, -0.38; 95% CI, -0.60 to -0.16). Subgroup analysis showed that PSST was more efficient for parents of children aged 10 years or younger or who were newly diagnosed with a CHC. Significant improvements in most outcomes were associated with PSST delivered online. Conclusions and Relevance: These findings suggest that PSST for parents of children with CHCs may improve the psychosocial well-being of the parents, their children, and their families. Further high-quality RCTs with longer follow-up times and that explore physical and clinical outcomes are encouraged to generate adequate evidence.

Most suitable plant communities for the slope reclamation of the Zhengzhou-Xinxiang section of the Beijing-Hong Kong-Macao expressway.

The construction of expressways in China has produced diverse habitats along slopes characterized by steep gradients, uneven water distribution, poor soil conditions, and no routine maintenance. Manually planting beneficial species is an essential method of effectively improving slope soils to prevent soil erosion. However, few studies have evaluated the reclamation effects and plant community composition and structure used to restore slopes along expressways. This study focused on the Zhengzhou-Xinxiang section of the Beijing-Hong Kong-Macao Expressway. A total of 10 representative plant communities were evaluated using the analytic hierarchy process (AHP)-fuzzy integrated evaluation method. The sites were divided into four layers, namely, plant communities, soil nutrients, soil physical properties, and other ecological factors, and 14 indicators were assessed. The evaluation results showed that four of these plant communities (PCs) were excellent, three PCs were good, one PC was normal, two PCs were poor. The four excellent PCs had high Shannon-Wiener index, pielou index, richness index or community productivity. It is worth noting that most excellent plant community structures were tree + shrub + herb. Based on these results, we recommend that fill slopes should be restored using a combination of trees, herbs, and shrubs; also, the vegetation should include native plants, such as B. papyrifera, U. pumila, A. fruticosa, and Cynodon dactylon (L.). This study could provide ideas for plant community composition and structure of new highway slopes in similar climate environment, and provide theoretical support for plant community composition and structure and soil improvement for the existing slope.

FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control.

Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.

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.

Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction.

Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning-based material, chemical, and biological data analysis. Here, we propose persistent spectral-based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.

Simultaneous RNA-DNA FISH.

A highly useful tool for studying lncRNAs is simultaneous RNA-DNA FISH, which reveals the localization and quantitative information of RNA and DNA in cellular contexts. However, a simple combination of RNA FISH and DNA FISH often generates disappointing results because the fragile RNA signals are often damaged by the harsh conditions used in DNA FISH for denaturing the DNA. Here, we describe a robust and simple RNA-DNA FISH protocol, in which amino-labeled nucleic acid probes are used for RNA FISH . The method is suitable to detect single RNA molecules simultaneously with DNA.

Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis.

It has long been observed that trimethylamine N-oxide (TMAO) and urea demonstrate dramatically different properties in a protein folding process. Even with the enormous theoretical and experimental research work on these two osmolytes, various aspects of their underlying mechanisms still remain largely elusive. In this paper, we propose to use the weighted persistent homology to systematically study the osmolytes molecular aggregation and their hydrogen-bonding network from a local topological perspective. We consider two weighted models, i.e., localized persistent homology (LPH) and interactive persistent homology (IPH). Boltzmann persistent entropy (BPE) is proposed to quantitatively characterize the topological features from LPH and IPH, together with persistent Betti number (PBN). More specifically, from the localized persistent homology models, we have found that TMAO and urea have very different local topology. TMAO is found to exhibit a local network structure. With the concentration increase, the circle elements in these networks show a clear increase in their total numbers and a decrease in their relative sizes. In contrast, urea shows two types of local topological patterns, i.e., local clusters around 6 Å and a few global circle elements at around 12 Å. From the interactive persistent homology models, it has been found that our persistent radial distribution function (PRDF) from the global-scale IPH has same physical properties as the traditional radial distribution function. Moreover, PRDFs from the local-scale IPH can also be generated and used to characterize the local interaction information. Other than the clear difference of the first peak value of PRDFs at filtration size 4 Å, TMAO and urea also shows very different behaviors at the second peak region from filtration size 5 Å to 10 Å. These differences are also reflected in the PBNs and BPEs of the local-scale IPH. These localized topological information has never been revealed before. Since graphs can be transferred into simplicial complexes by the clique complex, our weighted persistent homology models can be used in the analysis of various networks and graphs from any molecular structures and aggregation systems.

Weighted persistent homology for biomolecular data analysis.

In this paper, we systematically review weighted persistent homology (WPH) models and their applications in biomolecular data analysis. Essentially, the weight value, which reflects physical, chemical and biological properties, can be assigned to vertices (atom centers), edges (bonds), or higher order simplexes (cluster of atoms), depending on the biomolecular structure, function, and dynamics properties. Further, we propose the first localized weighted persistent homology (LWPH). Inspired by the great success of element specific persistent homology (ESPH), we do not treat biomolecules as an inseparable system like all previous weighted models, instead we decompose them into a series of local domains, which may be overlapped with each other. The general persistent homology or weighted persistent homology analysis is then applied on each of these local domains. In this way, functional properties, that are embedded in local structures, can be revealed. Our model has been applied to systematically study DNA structures. It has been found that our LWPH based features can be used to successfully discriminate the A-, B-, and Z-types of DNA. More importantly, our LWPH based principal component analysis (PCA) model can identify two configurational states of DNA structures in ion liquid environment, which can be revealed only by the complicated helical coordinate system. The great consistence with the helical-coordinate model demonstrates that our model captures local structure variations so well that it is comparable with geometric models. Moreover, geometric measurements are usually defined in local regions. For instance, the helical-coordinate system is limited to one or two basepairs. However, our LWPH can quantitatively characterize structure information in regions or domains with arbitrary sizes and shapes, where traditional geometrical measurements fail.

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.

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.

A Molecular Dynamics-Quantum Mechanics Theoretical Study of DNA-Mediated Charge Transport in Hydrated Ionic Liquids.

Charge transport (CT) through biomolecules is of high significance in the research fields of biology, nanotechnology, and molecular devices. Inspired by our previous work that showed the binding of ionic liquid (IL) facilitated charge transport in duplex DNA, in silico simulation is a useful means to understand the microscopic mechanism of the facilitation phenomenon. Here molecular dynamics simulations (MD) of duplex DNA in water and hydrated ionic liquids were employed to explore the helical parameters. Principal component analysis was further applied to capture the subtle conformational changes of helical DNA upon different environmental impacts. Sequentially, CT rates were calculated by a QM/MM simulation of the flickering resonance model based upon MD trajectories. Herein, MD simulation illustrated that the binding of ionic liquids can restrain dynamic conformation and lower the on-site energy of the DNA base. Confined movement among the adjacent base pairs was highly related to the increase of electronic coupling among base pairs, which may lead DNA to a CT facilitated state. Sequentially combining MD and QM/MM analysis, the rational correlations among the binding modes, the conformational changes, and CT rates illustrated the facilitation effects from hydrated IL on DNA CT and supported a conformational-gating mechanism.

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.

Simultaneous RNA-DNA FISH.

A highly useful tool for studying lncRNAs is simultaneous RNA-DNA FISH, which reveals the localization and quantitative information of RNA and DNA in cellular contexts. However, a simple combination of RNA FISH and DNA FISH often generates disappointing results because the fragile RNA signals are often damaged by the harsh conditions used in DNA FISH for denaturing the DNA. Here, we describe a robust and simple RNA-DNA FISH protocol, in which amino-labeled nucleic acid probes are used for RNA FISH. The method is suitable to detect single-RNA molecules simultaneously with DNA.