ABSTRACT
Chloroplast is a crucial site for photosynthesis in plants. Determining the location and distribution of proteins in subchloroplasts is significant for studying the energy conversion of chloroplasts and regulating the utilization of light energy in crop production. However, the prediction accuracy of the currently developed protein subcellular site predictors is still limited due to the complex protein sequence features and the scarcity of labeled samples. We propose DaDL-SChlo, a multi-location protein subchloroplast localization predictor, which addresses the above problems by fusing pre-trained protein language model deep learning features with traditional handcrafted features and using generative adversarial networks for data augmentation. The experimental results of cross-validation and independent testing show that DaDL-SChlo has greatly improved the prediction performance of protein subchloroplast compared with the state-of-the-art predictors. Specifically, the overall actual accuracy outperforms the state-of-the-art predictors by 10.7% on 10-fold cross-validation and 12.6% on independent testing. DaDL-SChlo is a promising and efficient predictor for protein subchloroplast localization. The datasets and codes of DaDL-SChlo are available at https://github.com/xwanggroup/DaDL-SChlo.
Subject(s)
Chloroplasts , Language , Protein Transport , Chloroplasts/metabolism , Research DesignABSTRACT
Triple-negative breast cancer (TNBC) is known for its aggressive nature, and TNBC management is currently challenging due to the lack of effective targets. Despite the importance of histone post-translational modifications (hPTMs) in breast cancer, their associations with molecular subtypes of breast cancer, especially TNBC, are poorly understood. In this study, a combination of untargeted and targeted proteomics approaches, supplemented by a derivatization method, was applied to breast cancer cells and tissue samples. Untargeted proteomics of eight breast cancer cell lines belonging to different molecular subtypes revealed 36 modified peptides with 12 lysine modification sites in histone H3, and the most frequently reported top 5 histone H3 methylation and acetylation sites were covered. Then, targeted proteomics was carried out to quantify the total 20 target hPTMs at the covered modification sites (i.e., mono-, di-, trimethylation, and acetylation for each site), indicating the difficulty in distinguishing TNBC cells from normal cells. Subsequently, the analysis in TNBC patients revealed significant expression differences in 4 specific hPTMs (H3K14ac, H3K27me1, H3K36me2, and H3K36me3) between TNBC and adjacent normal tissue samples. These unique hPTM patterns allowed for the differentiation of TNBC from normal cases. This finding provides promising implications for advancing targeted treatment strategies for TNBC in the future.
Subject(s)
Histones , Triple Negative Breast Neoplasms , Humans , Histones/metabolism , Triple Negative Breast Neoplasms/metabolism , Proteomics/methods , Cell Line, Tumor , Mass Spectrometry , Protein Processing, Post-TranslationalABSTRACT
Adenine base editors (ABEs) are genome-editing tools that have been harnessed to introduce precise Aâ¢T to Gâ¢C conversion. The discovery of split genes revealed that all introns contain two highly conserved dinucleotides, canonical "AG" (acceptor) and "GT" (donor) splice sites. ABE can directly edit splice acceptor sites of the adenine (A) base, leading to aberrant gene splicing, which may be further adopted to remodel splicing. However, spliced isoforms triggered with ABE have not been well explored. To address it, we initially generated a cell line harboring C-terminal enhanced GFP (eGFP)-tagged ß-actin (ACTB), in which the eGFP signal can track endogenous ß-actin expression. Expectedly, after the editing of splice acceptor sites, we observed a dramatical decrease in the percentage of eGFP-positive cells and generation of splicing products with the noncanonical splice site. Furthermore, we manipulated Peroxidasin in mouse embryos with ABE, in which a noncanonical acceptor was activated to remodel splicing, successfully generating a mouse disease model of anophthalmia and severely malformed microphthalmia. Collectively, we demonstrate that ABE-mediated splicing remodeling can activate a noncanonical acceptor to manipulate human and mouse genomes, which will facilitate the investigation of basic and translational medicine studies.
Subject(s)
Adenine , RNA Splice Sites , Animals , Humans , Mice , Actins/genetics , Base Sequence , Gene Editing , Introns , RNA Splicing , HEK293 CellsABSTRACT
OBJECTIVES: Obesity and non-alcoholic fatty liver disease (NAFLD) are major health concerns. The circadian rhythm is an autonomous and intrinsic timekeeping system closely associated with energy metabolism and obesity. Thus, this study explored the role of brain and muscle aryl hydrocarbon receptor nuclear translocator-like1 (BMAL1), a circadian clock regulator, in the development of obesity and NAFLD. METHODS: We generated BMAL1 knockout (BMAL1 KO) mice to imitate circadian rhythm disruption. The study comprised three groups from the same litter: BMAL1 KO mice fed a high-fat diet (to establish obesity and NAFLD phenotypes), wild-type mice fed normal chow, and wild-type mice fed a high-fat diet. The metabolic and NAFLD phenotypes were assessed via physiological measurements and histological examinations. Quantitative polymerase chain reaction and western blotting were used to identify and validate changes in the signaling pathways responsible for the altered NAFLD phenotypes in the wild-type and BMAL1 KO mice. RESULTS: BMAL1 depletion protected against obesity and metabolic disorders induced by a high-fat diet. BMAL1 depletion also prevented hepatic steatosis and inhibited cluster of differentiation 36 and peroxisome proliferator-activated receptor gamma (i.e., PPARγ) expression. CONCLUSIONS: BMAL1 plays an important role in the development of obesity and NAFLD and, thus, is a potential therapeutic target for these conditions.
Subject(s)
Non-alcoholic Fatty Liver Disease , Animals , Mice , ARNTL Transcription Factors/genetics , ARNTL Transcription Factors/metabolism , Diet, High-Fat , Liver/metabolism , Mice, Inbred C57BL , Mice, Knockout , Non-alcoholic Fatty Liver Disease/genetics , Non-alcoholic Fatty Liver Disease/prevention & control , Obesity/complications , PPAR gamma/metabolismABSTRACT
MOTIVATION: The registration of serial section electron microscope images is a critical step in reconstructing biological tissue volumes, and it aims to eliminate complex nonlinear deformations from sectioning and replicate the correct neurite structure. However, due to the inherent properties of biological structures and the challenges posed by section preparation of biological tissues, achieving an accurate registration of serial sections remains a significant challenge. Conventional nonlinear registration techniques, which are effective in eliminating nonlinear deformation, can also eliminate the natural morphological variation of neurites across sections. Additionally, accumulation of registration errors alters the neurite structure. RESULTS: This article proposes a novel method for serial section registration that utilizes an unsupervised optical flow network to measure feature similarity rather than pixel similarity to eliminate nonlinear deformation and achieve pairwise registration between sections. The optical flow network is then employed to estimate and compensate for cumulative registration error, thereby allowing for the reconstruction of the structure of biological tissues. Based on the novel serial section registration method, a serial split technique is proposed for long-serial sections. Experimental results demonstrate that the state-of-the-art method proposed here effectively improves the spatial continuity of serial sections, leading to more accurate registration and improved reconstruction of the structure of biological tissues. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/TongXin-CASIA/EFSR.
Subject(s)
Optic Flow , Microscopy/methods , Software , Image Processing, Computer-Assisted/methodsABSTRACT
Natural killer (NK) cells are unique from other immune cells in that they can rapidly kill multiple neighboring cells without the need for antigenic pre-sensitization once the cells display surface markers associated with oncogenic transformation. Given the dynamic role of NK cells in tumor surveillance, NK cell-based immunotherapy is rapidly becoming a "new force" in tumor immunotherapy. However, challenges remain in the use of NK cell immunotherapy in the treatment of solid tumors. Many metabolic features of the tumor microenvironment (TME) of solid tumors, including oxygen and nutrient (e.g., glucose, amino acids) deprivation, accumulation of specific metabolites (e.g., lactate, adenosine), and limited availability of signaling molecules that allow for metabolic reorganization, multifactorial shaping of the immune-suppressing TME impairs tumor-infiltrating NK cell function. This becomes a key barrier limiting the success of NK cell immunotherapy in solid tumors. Restoration of endogenous NK cells in the TME or overt transfer of functionally improved NK cells holds great promise in cancer therapy. In this paper, we summarize the metabolic biology of NK cells, discuss the effects of TME on NK cell metabolism and effector functions, and review emerging strategies for targeting metabolism-improved NK cell immunotherapy in the TME to circumvent these barriers to achieve superior efficacy of NK cell immunotherapy.
Subject(s)
Metabolic Reprogramming , Neoplasms , Humans , Tumor Microenvironment , Killer Cells, Natural , Lactic Acid , Neoplasms/therapyABSTRACT
The limited optical penetration depth and hypoxic tumor microenvironment (TME) are key factors that hinder the practical applications of conventional photodynamic therapy (PDT). To fundamentally address these issues, self-luminescent photosensitizers (PSs) can achieve efficient PDT. Herein, a self-chemiluminescence (CL)-triggered Ir complex PS, namely, IrL2, with low-O2-dependence type I photochemical processes is reported for efficient PDT. The rational design achieves efficient chemiluminescence resonance energy transfer (CRET) from covalently bonded luminol units to the Ir complex in IrL2 under the catalysis of H2O2 and hemoglobin (Hb) to generate O2â¢- and 1O2. Liposome IrL2H nanoparticles (NPs) are constructed by loading IrL2 and Hb. The intracellular H2O2 and loaded Hb catalyze the luminol part of IrL2H, and the Ir2 part is then excited to produce types I and II reactive oxygen species (ROS) through CRET, inducing cell death, even under hypoxic conditions, and promoting cell apoptosis. IrL2H is used for tumor imaging and inhibits tumor growth in 4T1-bearing mouse models through intratumoral injection without external light sources. This work provides new designs for transition metal complex PSs that conquer the limitations of external light sources and the hypoxic TME in PDT.
ABSTRACT
Heteronuclear Fe(µ-H)Zn hydride Cp*Fe(1,2-Cy2PC6H4)HZnEt (3) undergoes reversible intramolecular Caryl-H reductive elimination through coupling of the cyclometalated phosphinoaryl ligand and the hydride, giving rise to a formal Fe(0)-Zn(II) species. Addition of CO intercepts this equilibrium, affording Cp*(Cy2PPh)(CO)Fe-ZnEt that features a dative Fe-Zn bond. Significantly, this system achieves bimetallic H2 addition, as demonstrated by the transformation of the monohydride Fe(µ-H)Zn to a deuterated dihydride Fe-(µ-D)2-Zn upon reaction with D2.
ABSTRACT
The combustion processes and catalytic after-treatment of ammonia/hydrogen-fueled engines, including NOx storage and reduction (NSR) and noble-metal selective catalytic reduction (SCR), can produce the byproduct N2O, a potent greenhouse gas that weakens the zero-carbon attribute of these fuels. Currently, the mechanism of N2O formation on DeNOx catalysts remains unclear due to limited research on catalytic after-treatment for such engines and the complexity of surface catalytic reactions. To elucidate the formation of N2O on the DeNOx catalysts of ammonia/hydrogen fuel engines, the impact factors on N2O formation on platinum catalysts (typical catalysts in NSR and noble-metal SCR) were investigated using first-principles molecular dynamics (FPMD). By employing the blue-moon ensemble enhanced sampling method and the slow-growth approach for free energy surface exploration, together with density functional theory (DFT) for electronic structure analysis, a linear relationship between the spin splitting of the d states of Pt clusters and N2O formation energy barriers was revealed, along with the increased structural sensitivity of Pt clusters with fewer atoms. It is highlighted that the energy barrier for N2O formation is determined by the matching degree of energy levels between molecules and surfaces. These findings provide atomic-scale insights into N2O formation on DeNOx catalysts for ammonia/hydrogen-fueled engines, facilitating N2O emission control for carbon-free engines.
ABSTRACT
Bacterial cystitis, a commonly occurring urinary tract infection (UTI), is renowned for its extensive prevalence and tendency to recur. Despite the extensive utilization of levofloxacin as a conventional therapeutic approach for bacterial cystitis, its effectiveness is impeded by adverse toxic effects, drug resistance concerns, and its influence on the gut microbiota. This study introduces Lev@PADM, a hydrogel with antibacterial properties that demonstrates efficacy in the treatment of bacterial cystitis. Lev@PADM is produced by combining levofloxacin with decellularized porcine acellular dermal matrix hydrogel and exhibits remarkable biocompatibility. Lev@PADM demonstrates excellent stability as a hydrogel at body temperature, enabling direct administration to the site of infection through intravesical injection. This localized delivery route circumvents the systemic circulation of levofloxacin, resulting in a swift and substantial elevation of the antimicrobial agent's concentration specifically at the site of infection. The in vivo experimental findings provide evidence that Lev@PADM effectively prolongs the duration of levofloxacin's action, impedes the retention and invasion of E.coli in the urinary tract, diminishes the infiltration of innate immune cells into infected tissues, and simultaneously preserves the composition of the intestinal microbiota. These results indicate that, in comparison to the exclusive administration of levofloxacin, Lev@PADM offers notable benefits in terms of preserving the integrity of the bladder epithelial barrier and suppressing the recurrence of urinary tract infections.
Subject(s)
Acellular Dermis , Cystitis , Urinary Tract Infections , Swine , Animals , Levofloxacin , HydrogelsABSTRACT
INTRODUCTION: The aim of this study was to investigate the causal relationship between Parkinson's disease (PD) and myocardial infarction (MI), atrial fibrillation and flutter (AF), and venous thromboembolism (VTE) by Mendelian randomization (MR) analysis. METHODS: By using data from publicly available genome-wide association studies from databases, single nucleotide polymorphisms were screened as instrumental variables, and the MR analysis was finished by inverse-variance weighted (IVW), MR-egger, weighted median methods. RESULTS: The primary IVW method showed a negative association between genetically predicted PD and risk of MI (OR = 0.9989; 95% CI: 0.9980-0.9998; p = 0.02). However, PD was not significantly associated with AF or VTE. CONCLUSION: This study suggests a negative association between PD with MI, which implies that PD has a protective effect on MI.
Subject(s)
Parkinson Disease , Vascular Diseases , Venous Thromboembolism , Humans , Genome-Wide Association Study , Mendelian Randomization Analysis , Parkinson Disease/complications , Parkinson Disease/genetics , Polymorphism, Single Nucleotide/geneticsABSTRACT
BACKGROUND: Machine learning models have promising applications in capturing the complex relationship between mixtures of exposures and outcomes. OBJECTIVE: Our study aimed at introducing an explainable machine learning (EML) model to assess the association between metal mixtures with potentially opposing renal effects and renal function in middle-aged and older adults. METHODS: This study extracted data from two cycle years of the National Health and Nutrition Examination Survey (NHANES). Participants aged 45 years or older with complete data on six metals (lead, cadmium, manganese, mercury, and selenium) and related covariates were enrolled. The EML model was developed by the optimized machine learning model together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney disease (CKD) risk with metal mixtures. The results from EML were further compared in detail with multiple logistic regression (MLR) and Bayesian kernel machine regression (BKMR). RESULTS: After adjusting for included covariates, MLR pointed out the lead and arsenic were generally positively associated with CKD, but manganese had a negative association. In the BKMR analysis, each metal was found to have a non-linear association with the risk of CKD, and interactions can exist between metals, especially for arsenic and lead. The EML ranked the feature importance: lead, manganese, arsenic and selenium were close behind in importance after gender, age or BMI for participants with CKD. Strong interactions between mercury and lead, manganese and cadmium and arsenic and manganese were identified by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect plots of BKMR. The EML model determined the "trigger point" at which the risk of CKD abruptly changed. CONCLUSION: Co-exposure to metals with different nephrotoxicity could have different joint association with renal function, and EML can be a powerful method for studying complex exposure mixtures.
Subject(s)
Arsenic , Mercury , Metals, Heavy , Renal Insufficiency, Chronic , Selenium , Middle Aged , Humans , Aged , Arsenic/analysis , Nutrition Surveys , Cadmium/toxicity , Cadmium/analysis , Manganese/toxicity , Manganese/analysis , Selenium/analysis , Environmental Exposure/analysis , Bayes Theorem , Metals , Kidney/chemistry , Machine Learning , Mercury/toxicity , Mercury/analysis , Renal Insufficiency, Chronic/chemically induced , Renal Insufficiency, Chronic/epidemiology , Metals, Heavy/toxicity , Metals, Heavy/analysisABSTRACT
BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission. METHODS: Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension. RESULTS: A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients. CONCLUSIONS: The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.
Subject(s)
Intensive Care Units , Machine Learning , Nomograms , Stroke , Humans , Male , Female , Aged , Middle Aged , Stroke/mortality , Risk Assessment , Aged, 80 and over , PrognosisABSTRACT
Highly doped lanthanide luminescent nanoparticles exhibit unique optical properties, providing exciting opportunities for many ground-breaking applications, such as super-resolution microscopy, deep-tissue bioimaging, confidentiality, and anticounterfeiting. However, the concentration-quenching effect compromises their luminescence efficiency/brightness, hindering their wide range of applications. Herein, we developed a low-temperature suppression cross-relaxation strategy, which drastically enhanced upconversion luminescence (up to 2150-fold of green emission) in Er3+-rich nanosystems. The cryogenic field opens the energy transport channel of Er3+ multiphoton upconversion by further suppressing phonon-assisted cross-relaxation. Our results provide direct evidence for understanding the energy loss mechanism of photon upconversion, deepening a fundamental understanding of the upconversion process in highly doped nanosystems. Furthermore, it also suggests the potential applications of upconversion nanoparticles for extreme ambient-temperature detection and anticounterfeiting.
ABSTRACT
BACKGROUND: Root caries is preventable and can be arrested at any stage of disease development. The aim of this study was to investigate the potential mineral exchange and fluorapatite formation within artificial root carious lesions (ARCLs) using different toothpastes containing 5,000 ppm F, 1,450 ppm F or bioactive glass (BG) with 540 ppm F. MATERIALS AND METHODS: The crowns of each extracted sound tooth were removed. The remaining roots were divided into four parts (n = 12). Each sample was randomly allocated into one of four groups: Group 1 (Deionised water); Group 2 (BG with 540 ppm F); Group 3 (1,450 ppm F) and Group 4 (5,000 ppm F). ARCLs were developed using demineralisation solution (pH 4.8). The samples were then pH-cycled in 13 days using demineralisation solution (6 h) and remineralisation solution (pH 7) (16 h). Standard tooth brushing was carried out twice a day with the assigned toothpaste. X-ray Microtomography (XMT) was performed for each sample at baseline, following ARCL formation and after 13-day pH-cycling. Scanning Electron Microscope (SEM) and 19F Magic angle spinning nuclear magnetic resonance (19F-MAS-NMR) were also performed. RESULTS: XMT results showed that the highest mineral content increase (mean ± SD) was Group 4 (0.09 ± 0.05), whilst the mineral content decreased in Group 1 (-0.08 ± 0.06) after 13-day pH-cycling, however there was evidence of mineral loss within the subsurface for Groups 1, 3 and 4 (p < 0.05). SEM scans showed that mineral contents within the surface of dentine tubules were high in comparison to the subsurface in all toothpaste groups. There was evidence of dentine tubules being either partially or completely occluded in toothpaste groups. 19F-MAS-NMR showed peaks between - 103 and - 104ppm corresponding to fluorapatite formation in Groups 3 and 4. CONCLUSION: Within the limitation of this laboratory-based study, all toothpastes were potentially effective to increase the mineral density of artificial root caries on the surface, however there was evidence of mineral loss within the subsurface for Groups 1, 3 and 4.
Subject(s)
Root Caries , Toothpastes , X-Ray Microtomography , Pilot Projects , Toothpastes/therapeutic use , Humans , Apatites/therapeutic use , Apatites/analysis , Hydrogen-Ion Concentration , Fluorides/therapeutic use , Tooth Remineralization/methods , Cariostatic Agents/therapeutic use , In Vitro Techniques , Microscopy, Electron, ScanningABSTRACT
Nanozymes possess multi-enzyme activities over the natural enzymes, which produce multi-pathway synergistic effects for varies of biomedical applications. Unfortunately, their multi-enzyme activities are in fighting, significantly reducing the synergistic effects. Dynamic regulation of their multi-enzyme activities is the bottleneck for intelligent therapies. Herein, we construct a novel oxygen-nitrogen functionalized carbon quantum dots (O/N-CQDs) with peroxidase-like (Reactive oxygen species (ROS) producer) activity. Interestingly, the peroxidase-like activity can be reversibly converted to catalase-like (ROS scavenger) activity under visible light irradiation. It is found that both the peroxidase/catalase-like activity of O/N-CQDs can be precisely manipulated by the light intensity. The mechanism of switchable enzyme activities is attributed to the polarization of quinoid nitrogen in polyaniline (PANI) precursor retained on O/N-CQDs under visible light, which consumes the ROS to produce O2 and H2O. As a proof-of-concept demonstration, we are able to non-intrusively up and down regulate the ROS level in cells successfully by simply switching off and on the light respectively, potentially facilitating the precise medicine based on the development of the disease. Indeed, the photo-switchable peroxidase/catalase-like activity of O/N-CQDs opens a non-invasive strategy for better manipulations of the multi-activity of nanozymes, promising their wider and more intelligent biomedical applications.
Subject(s)
Carbon , Catalase , Light , Quantum Dots , Reactive Oxygen Species , Quantum Dots/chemistry , Carbon/chemistry , Catalase/metabolism , Catalase/chemistry , Humans , Reactive Oxygen Species/metabolism , Peroxidase/metabolism , Peroxidase/chemistry , Photochemical ProcessesABSTRACT
In perovskite solar cells, passivating the surface or interface that contains a high concentration of defects, specifically deep-level defects, is one of the most important topics to substantially enhance the power conversion efficiency and stability of the devices. Long-chain alkylammonium bromides have been widely and commonly adapted for passivation treatment. However, the mechanism behind is still not well explored as the formation route and the exact structure of these alkylammonium bromide-based low-dimensional perovskites are unclear. Herein, we investigate the physical and chemical properties of an n-hexylammonium bromide (HABr)-based low-dimensional perovskite including both thin films and single crystals. First of all, the HA2PbBr4 perovskite film and aged single crystal demonstrate different X-ray diffraction patterns from those of the fresh as-prepared single crystal. We found that the fresh HA2PbBr4 single crystal exhibits a metastable phase as its structure changes with aging due to the relaxation of crystal lattice strains, whereas the HA2PbBr4 perovskite film is pretty stable as the aged single crystal. Upon reacting with FAPbI3, HABr can be intercalated into the FAPbI3 lattice to form a mixed-cation perovskite of HAFAPbI3Br, which is in a dynamic equilibrium of decomposition and formation. In contrast, the reaction of HABr with excess PbI2 forms a stable HA2PbI2Br2 perovskite. Based on such findings, we rationally develop a HA2PbI2Br2-passivated FACs-based perovskite by reacting HABr with excess PbI2, the photovoltaics based on which are more stable and efficient than those passivated by the HAFAPbI3Br perovskite. Our discovery paves way for a more in-depth study of bromide-containing low-dimensional perovskites and their optoelectronic applications.
ABSTRACT
Life molecules' distributions in live systems construct the complex dynamic reaction networks, whereas it is still challenging to demonstrate the dynamic distributions of biomolecules in live systems. Herein, we proposed a dynamic analysis strategy via sequence-structure bispecific RNA with state-adjustable molecules to monitor the dynamic concentration and spatiotemporal localization of these biomolecules in live cells based on the new insight of fluorescent RNA (FLRNA) interactions and their mechanism of fluorescence enhancement. Typically, computer-based nucleic acid-molecular docking simulation and molecular theoretical calculation have been proposed to provide a simple and straightforward method for guiding the custom-design of FLRNA. Impressively, a novel FLRNA with sequence and structure bispecific RNA named as a structure-switching aptamer (SSA) was introduced to monitor the real-time concentration and spatiotemporal localization of biomolecules, contributing to a deeper insight of the dynamic monitoring and visualization of biomolecules in live systems.
Subject(s)
Fluorescent Dyes , RNA , RNA/chemistry , Molecular Docking Simulation , Fluorescent Dyes/chemistryABSTRACT
The rational design of efficient and multifunctional electrocatalysts for energy conversion devices is one of the major challenges for clean and renewable energy transition. Herein, the local electronic structure of cobalt-platinum nanoclusters is regulated by adjacent platinum atomic site encapsulated in N-doped hollow carbon nanotubes (PtSA -PtCo NCs/N-CNTs) by pyrolysis of melamine-orientation-induced zeolite imidazole metal-organic frameworks (ZIF-67) with thimbleful platinum doping. The introduction of melamine can reactivate adjacent carbon atoms and initiate the oriented growth of nitrogen-doped carbon nanotubes. The systematic analysis suggests the significant role of thimbleful neighboring low-coordinated PtâN2 in altering the localized electronic structure of PtCo nanoclusters. The optimized PtSA -PtCo NCs/N-CNTs-900 exhibit excellent hydrogen evolution reaction (HER)/oxygen evolution reaction (OER)/oxygen reduction reaction (ORR)/ catalytic performance reaching the current density of 10 mA cm-2 in 1 m KOH under the low 47 (HER) and 252 mV (OER) overpotentials, and a high half-wave potential of 0.86 and 0.89 V (ORR) in 0.1 m KOH and 0.1 m HClO4 , respectively. Remarkably, the PtSA -PtCo NC/N-CNT-900 also presents outstanding catalytic performances toward water splitting and rechargeable Zn-air batteries. The theoretical calculations reveal that optimal regulation of the electronic structure of PtCo nanoclusters by thimbleful neighboring Pt atomic reduces the reaction energy barrier in electrochemical process, facilitating the ORR/OER/HER performance.
ABSTRACT
The visual function of patients with infantile nystagmus (IN) can be significantly decreased owing to constant eye movement. While, reaching a definitive diagnosis becomes a challenge due to genetic heterozygous of this disease. To address it, we investigated whether best-corrected visual acuity (BCVA) results can facilitate the molecular diagnosis of IN patients harboring FRMD7 mutations. 200 patients with IN from 55 families and 133 sporadic cases were enrolled. Mutations were comprehensively screened by direct sequencing using gene-specific primers for FRMD7. We also retrieved related literature to verify the results based on our data. We found that the BCVA of patients with IN harboring FRMD7 mutations was between 0.5 and 0.7, which was confirmed by data retrieved from the literature. Our results showed that BCVA results facilitate the molecular diagnosis of patients with IN harboring FRMD7 mutations. In addition, we identified 31 FRMD7 mutations from the patients, including six novel mutations, namely, frameshift mutation c.1492_1493insT (p.Y498LfsTer14), splice-site mutation c.353C > G, three missense mutations [c.208C > G (p.P70A), c.234G > A (p.M78I), and c.1109G > A (p.H370R)], and nonsense mutation c.1195G > T (p.E399Ter). This study demonstrates that BCVA results may facilitate the molecular diagnosis of IN patients harboring FRMD7 mutations.