CircPVT1 promotes ER-positive breast tumorigenesis and drug resistance by targeting ESR1 and MAVS.

The molecular mechanisms underlying estrogen receptor (ER)-positive breast carcinogenesis and endocrine therapy resistance remain incompletely understood. Here, we report that circPVT1, a circular RNA generated from the lncRNA PVT1, is highly expressed in ERα-positive breast cancer cell lines and tumor samples and is functionally important in promoting ERα-positive breast tumorigenesis and endocrine therapy resistance. CircPVT1 acts as a competing endogenous RNA (ceRNA) to sponge miR-181a-2-3p, promoting the expression of ESR1 and downstream ERα-target genes and breast cancer cell growth. Furthermore, circPVT1 directly interacts with MAVS protein to disrupt the RIGI-MAVS complex formation, inhibiting type I interferon (IFN) signaling pathway and anti-tumor immunity. Anti-sense oligonucleotide (ASO)-targeting circPVT1 inhibits ERα-positive breast cancer cell and tumor growth, re-sensitizing tamoxifen-resistant ERα-positive breast cancer cells to tamoxifen treatment. Taken together, our data demonstrated that circPVT1 can work through both ceRNA and protein scaffolding mechanisms to promote cancer. Thus, circPVT1 may serve as a diagnostic biomarker and therapeutic target for ERα-positive breast cancer in the clinic.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

CARM1 hypermethylates the NuRD chromatin remodeling complex to promote cell cycle gene expression and breast cancer development.

Protein arginine methyltransferase CARM1 has been shown to methylate a large number of non-histone proteins, and play important roles in gene transcriptional activation, cell cycle progress, and tumorigenesis. However, the critical substrates through which CARM1 exerts its functions remain to be fully characterized. Here, we reported that CARM1 directly interacts with the GATAD2A/2B subunit in the nucleosome remodeling and deacetylase (NuRD) complex, expanding the activities of NuRD to include protein arginine methylation. CARM1 and NuRD bind and activate a large cohort of genes with implications in cell cycle control to facilitate the G1 to S phase transition. This gene activation process requires CARM1 to hypermethylate GATAD2A/2B at a cluster of arginines, which is critical for the recruitment of the NuRD complex. The clinical significance of this gene activation mechanism is underscored by the high expression of CARM1 and NuRD in breast cancers, and the fact that knockdown CARM1 and NuRD inhibits cancer cell growth in vitro and tumorigenesis in vivo. Targeting CARM1-mediated GATAD2A/2B methylation with CARM1 specific inhibitors potently inhibit breast cancer cell growth in vitro and tumorigenesis in vivo. These findings reveal a gene activation program that requires arginine methylation established by CARM1 on a key chromatin remodeler, and targeting such methylation might represent a promising therapeutic avenue in the clinic.

A specific JMJD6 inhibitor potently suppresses multiple types of cancers both in vitro and in vivo.

Jumonji C-domain-containing protein 6 (JMJD6), an iron (Fe2+) and α-ketoglutarate (α-KG)-dependent oxygenase, is expressed at high levels, correlated with poor prognosis, and considered as a therapeutic target in multiple cancer types. However, specific JMJD6 inhibitors that are potent in suppressing tumorigenesis have not been reported so far. We herein report that iJMJD6, a specific small-molecule inhibitor of JMJD6 with favorable physiochemical properties, inhibits the enzymatic activity of JMJD6 protein both in vitro and in cultured cells. iJMJD6 is effective in suppressing cell proliferation, migration, and invasion in multiple types of cancer cells in a JMJD6-dependent manner, while it exhibits minimal toxicity in normal cells. Mechanistically, iJMJD6 represses the expression of oncogenes, including Myc and CCND1, in accordance with JMJD6 function in promoting the transcription of these genes. iJMJD6 exhibits suitable pharmacokinetic properties and suppresses tumor growth in multiple cancer cell line- and patient-derived xenograft models safely. Furthermore, combination therapy with iJMJD6 and BET protein inhibitor (BETi) JQ1 or estrogen receptor antagonist fulvestrant exhibits synergistic effects in suppressing tumor growth. Taken together, we demonstrate that inhibition of JMJD6 enzymatic activity by using iJMJD6 is effective in suppressing oncogene expression and cancer development, providing a therapeutic avenue for treating cancers that are dependent on JMJD6 in the clinic.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.

Prevalence and Association of Sarcopenia with Mortality in Patients with Head and Neck Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: The objective of this meta-analysis was to assess the association of sarcopenia defined on computed tomography (CT) head and neck with survival in head and neck cancer patients. METHODS: Following a PROSPERO-registered protocol, two blinded reviewers extracted data and evaluated the quality of the included studies using the Quality In Prognostic Studies (QUIPS) tool, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework. A meta-analysis was conducted using maximally adjusted hazard ratios (HRs) with the random-effects model. Heterogeneity was measured using the I2 statistic and was investigated using meta-regression and subgroup analyses where appropriate. RESULTS: From 37 studies (11,181 participants), sarcopenia was associated with poorer overall survival (HR 2.11, 95% confidence interval [CI] 1.81-2.45; p < 0.01), disease-free survival (HR 1.76, 95% CI 1.38-2.24; p < 0.01), disease-specific survival (HR 2.65, 95% CI 1.80-3.90; p < 0.01), progression-free survival (HR 2.24, 95% CI 1.21-4.13; p < 0.01) and increased chemotherapy or radiotherapy toxicity (risk ratio 2.28, 95% CI 1.31-3.95; p < 0.01). The observed association between sarcopenia and overall survival remained significant across different locations of cancer, treatment modality, tumor stages and geographical region, and did not differ between univariate and multivariate HRs. Statistically significant correlations were observed between the C3 and L3 cross-sectional area, skeletal muscle mass, and skeletal muscle index. CONCLUSIONS: Among patients with head and neck cancers, CT-defined sarcopenia was consistently associated with poorer survival and greater toxicity.

Methionine redox regulation of actin-interacting proteins primarily governs antioxidative signaling and response to the salvianolic acid B treatment in EA.hy926 cells.

Actin-interacting proteins are important molecules for filament assembly and cytoskeletal signaling within vascular endothelium. Disruption in their interactions causes endothelial pathogenesis through redox imbalance. Actin filament redox regulation remains largely unexplored, in the context of pharmacological treatment. This work focused on the peptidyl methionine (M) redox regulation of actin-interacting proteins, aiming at elucidating its role on governing antioxidative signaling and response. Endothelial EA.hy926 cells were subjected to treatment with salvianolic acid B (Sal B) and tert-butyl-hydroperoxide (tBHP) stimulation. Mass spectrometry was employed to characterize redox status of proteins, including actin, myosin-9, kelch-like erythroid-derived cap-n-collar homology-associated protein 1 (Keap1), plastin-3, prelamin-A/C and vimentin. The protein redox landscape revealed distinct stoichiometric ratios or reaction site transitions mediated by M sulfoxide reductase and reactive oxygen species. In comparison with effects of tBHP stimulation, Sal B treatment prevented oxidation at actin M325, myosin-9 M1489/1565, Keap1 M120, plastin-3 M592, prelamin-A/C M187/371/540 and vimentin M344. For Keap1, reaction site was transitioned within its scaffolding region to the actin ring. These protein M oxidation regulations contributed to the Sal B cytoprotective effects on actin filament. Additionally, regarding the Keap1 homo-dimerization region, Sal B preventive roles against M120 oxidation acted as a primary signal driver to activate nuclear factor erythroid 2-related factor 2 (Nrf2). Transcriptional splicing of non-POU domain-containing octamer-binding protein was validated during the Sal B-mediated overexpression of NAD(P)H dehydrogenase [quinone] 1. This molecular redox regulation of actin-interacting proteins provided valuable insights into the phenolic structures of Sal B analogs, showing potential antioxidative effects on vascular endothelium.

Photosynthetic System Based on a Polyoxometalate-Based Dehydrated Metal-Organic Framework for Nitrogen Fixation.

In nature, biological nitrogen fixation is accomplished through the π-back-bonding mechanism of nitrogenase, which poses significant challenges for mimic artificial systems, thanks to the activation barrier associated with the N≡N bond. Consequently, this motivates us to develop efficient and reusable photocatalysts for artificial nitrogen fixation under mild conditions. We employ a charge-assisted self-assembly process toward encapsulating one polyoxometalate (POM) within a dehydrated Zr-based metal-organic framework (d-UiO-66) exhibiting nitrogen photofixation activities, thereby constructing an enzyme-mimicking photocatalyst. The dehydration of d-UiO-66 is favorable for facilitating nitrogen chemisorption and activation via the unpaired d-orbital electron at the [Zr6O6] cluster. The incorporation of POM guests enhanced the charge separation in the composites, thereby facilitating the transfer of photoexcited electrons into the π* antibonding orbital of chemisorbed N2 for efficient nitrogen fixation. Simultaneously, the catalytic efficiency of SiW9Fe3@d-UiO-66 is enhanced by 9.0 times compared to that of d-UiO-66. Moreover, SiW9Fe3@d-UiO-66 exhibits an apparent quantum efficiency (AQE) of 0.254% at 550 nm. The tactics of "working-in-tandem" achieved by POMs and d-UiO-66 are extremely vital for enhancing artificial ammonia synthesis. This study presents a paradigm for the development of an efficient artificial catalyst for nitrogen photofixation, aiming to mimic the process of biological nitrogen fixation.

Synthesis and photodynamic activity of novel thieno[3,2-b]thiophene fused BODIPYs with good bio-solubility and anti-aggregation effect.

To discover new photosensitizers with long wavelength UV-visible absorption, high efficiency, and low side effects for photodynamic therapy, here, a series of novel thieno[3,2-b]thiophene-fused BODIPY derivatives were designed, synthesized and characterized. These compounds had a distinct absorption band at 640-680 nm, fluorescence emission at 650-760 nm, and good solubility with anti-aggregation effects. These new compounds possessed obvious singlet oxygen generation ability and photodynamic anti-Eca-109 cancer cells activities in vitro. Among them, compound II4 could be well uptaked by Eca-109 cells, and result in the apoptosis after laser irradiation, and have outstanding photodynamic efficiency both in vitro and in vivo. Therefore, II4 could be considered as a potential photosensitizer drug candidate for PDT and photo-imaging.

Toward a Medication Information Literacy Indicator System for Older Adults: A Delphi Study.

BACKGROUND: The safety of medication use among older adults is a growing concern, given the aging population. Despite widespread attention, the exploration of medication literacy in older adults, particularly from the perspective of information literacy, is in its nascent stages. METHODS: This study utilized the existing literature to define medication information literacy (MIL) as a theoretical framework. A two-round Delphi survey was conducted to identify the essential components of a MIL indicator system for older adults. The analytic hierarchy process (AHP) was then used to assign weights to each indicator. RESULTS: The study observed relatively high response rates in both rounds of the questionnaire, which, along with expert authority coefficients (Cr) of 0.86 and 0.89, underscores the credibility and expertise of the panellists. Additionally, Kendall's coefficient of concordance (Kendall's W) ranging from 0.157 to 0.33 (p < 0.05) indicates a consensus among experts on the identified indicators. Utilizing the Delphi process, a MIL indicator system for older adults was developed, comprising five primary and 23 secondary indicators. These indicators were weighted, with medication information cognition and acquisition emerging as pivotal factors in enhancing medication literacy among older adults. CONCLUSIONS: This study developed a MIL indicator system tailored for older adults using the Delphi approach. The findings can inform healthcare professionals in providing customized medication guidance and assist policymakers in crafting policies to enhance medication safety among older adults. PATIENT OR PUBLIC CONTRIBUTION: Patient and public engagement played a pivotal role in the development of our medication information literacy indicator system for older adults. Their involvement contributed to shaping research questions, facilitating study participation, and enriching evidence interpretation. Collaborations with experts in geriatric nursing, medicine, and public health, along with discussions with caregivers and individuals with lived experience, provided invaluable insights into medication management among older adults. Their input guided our research direction and ensured the relevance and comprehensiveness of our findings.

Fragment screening and structural analyses highlight the ATP-assisted ligand binding for inhibitor discovery against type 1 methionyl-tRNA synthetase.

Methionyl-tRNA synthetase (MetRS) charges tRNAMet with l-methionine (L-Met) to decode the ATG codon for protein translation, making it indispensable for all cellular lives. Many gram-positive bacteria use a type 1 MetRS (MetRS1), which is considered a promising antimicrobial drug target due to its low sequence identity with human cytosolic MetRS (HcMetRS, which belongs to MetRS2). Here, we report crystal structures of a representative MetRS1 from Staphylococcus aureus (SaMetRS) in its apo and substrate-binding forms. The connecting peptide (CP) domain of SaMetRS differs from HcMetRS in structural organization and dynamic movement. We screened 1049 chemical fragments against SaMetRS preincubated with or without substrate ATP, and ten hits were identified. Four cocrystal structures revealed that the fragments bound to either the L-Met binding site or an auxiliary pocket near the tRNA CCA end binding site of SaMetRS. Interestingly, fragment binding was enhanced by ATP in most cases, suggesting a potential ATP-assisted ligand binding mechanism in MetRS1. Moreover, co-binding with ATP was also observed in our cocrystal structure of SaMetRS with a class of newly reported inhibitors that simultaneously occupied the auxiliary pocket, tRNA site and L-Met site. Our findings will inspire the development of new MetRS1 inhibitors for fighting microbial infections.

Association between the levels of urinary cell cycle biomarkers and non-recovery of renal function among critically ill geriatric patients with acute kidney injury.

The lack of early renal function recovery among geriatric patients with acute kidney injury (AKI) in the intensive care unit (ICU) is a commonly observed and acknowledged poor prognostic factor, especially for older adults. However, no reliable prognostic biomarker is available for identifying individuals at risk of renal non-recovery or mortality in older adults. In this prospective observational cohort study, we enrolled critically ill older adults (aged ≥ 60 years) with AKI from the ICU and followed their disease progression. The primary endpoint was renal non-recovery within seven days of follow-up, while the secondary endpoint was the determinants of 30-day mortality after AKI. We assessed the predictive accuracy using receiver operating characteristic curves and performed between-group comparisons using the log-rank test. Among 209 older adults, 117 (56.0%) experienced renal recovery. Multiple regression analysis revealed that urine levels of tissue inhibitor of metalloproteinase-2 (TIMP-2) multiplied by insulin-like growth factor-binding protein 7 (IGFBP7) ([TIMP-2]*[IGFBP7]), AKI stages 2-3, and the Acute Physiology and Chronic Health Evaluation (APACHE II) score were independently associated with renal non-recovery. The regression model incorporating [TIMP-2]*[IGFBP7] demonstrated a fair predictive value (AUC 0.774, p < 0.001), with the optimal threshold set at 0.81 (ng/mL)2/1000. When [TIMP-2]*[IGFBP7] was combined with AKI severity and the APACHE score, the AUC increased to 0.851. In conclusion, urine [TIMP-2]*[IGFBP7] is a reliable biomarker associated with renal non-recovery in critically ill older adults, and its predictive efficacy can be further enhanced when combined with AKI severity and the APACHE score.

Anomalous Nernst Effect Induced Terahertz Emission in a Single Ferromagnetic Film.

Despite its important role in understanding ultrafast spin dynamics and revealing novel spin/orbit effects, the mechanism of the terahertz (THz) emission from a single ferromagnetic nanofilm upon a femtosecond laser pump still remains elusive. Recent experiments have shown exotic symmetry, which is not expected from the routinely adopted mechanism of ultrafast demagnetization. Here, by developing a bidirectional pump-THz emission spectroscopy and associated symmetry analysis method, we set a benchmark for the experimental distinction of the THz emission induced by various mechanisms. Our results unambiguously unveil a new mechanism─anomalous Nernst effect (ANE) induced THz emission due to the ultrafast temperature gradient created by a femtosecond laser. Quantitative analysis shows that the THz emission exhibits interesting thickness dependence where different mechanisms dominate at different thickness ranges. Our work not only clarifies the origin of the ferromagnetic-based THz emission but also offers a fertile platform for investigating the ultrafast optomagnetism and THz spintronics.

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OBJECTIVES: To investigate the risk factors for bronchopulmonary dysplasia (BPD) in twin preterm infants with a gestational age of <34 weeks, and to provide a basis for early identification of BPD in twin preterm infants in clinical practice. METHODS: A retrospective analysis was performed for the twin preterm infants with a gestational age of <34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020. According to their conditions, they were divided into group A (both twins had BPD), group B (only one twin had BPD), and group C (neither twin had BPD). The risk factors for BPD in twin preterm infants were analyzed. Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins. RESULTS: A total of 904 pairs of twins with a gestational age of <34 weeks were included in this study. The multivariate logistic regression analysis showed that compared with group C, birth weight discordance of >25% between the twins was an independent risk factor for BPD in one of the twins (OR=3.370, 95%CI: 1.500-7.568, P<0.05), and high gestational age at birth was a protective factor against BPD (P<0.05). The conditional logistic regression analysis of group B showed that small-for-gestational-age (SGA) birth was an independent risk factor for BPD in individual twins (OR=5.017, 95%CI: 1.040-24.190, P<0.05). CONCLUSIONS: The development of BPD in twin preterm infants is associated with gestational age, birth weight discordance between the twins, and SGA birth.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

High-Efficiency 3D DNA Walker Immobilized by a DNA Tetrahedral Nanostructure for Fast and Ultrasensitive Electrochemical Detection of MiRNA.

Herein, by directly limiting the reaction space, an ingenious three-dimensional (3D) DNA walker (IDW) with high walking efficiency is developed for rapid and sensitive detection of miRNA. Compared with the traditional DNA walker, the IDW immobilized by the DNA tetrahedral nanostructure (DTN) brings stronger kinetic and thermodynamic favorability resulting from its improved local concentration and space confinement effect, accompanied by a quite faster reaction speed and much better walking efficiency. Once traces of target miRNA-21 react with the prelocked IDW, the IDW could be largely activated and walk on the interface of the electrode to trigger the cleavage of H2 with the assistance of Mg2+, resulting in the release of amounts of methylene blue (MB) labeled on H2 from the electrode surface and the obvious decrease of the electrode signal. Impressively, the IDW reveals a conversion efficiency as high as 9.33 × 108 in 30 min with a much fast reaction speed, which is at least five times beyond that of typical DNA walkers. Therefore, the IDW could address the inherent challenges of the traditional DNA walker easily: slow walking speed and low efficiency. Notably, the IDW as a DNA nanomachine was utilized to construct a sensitive sensing platform for rapid miRNA-21 detection with a limit of detection (LOD) of 19.8 aM and realize the highly sensitive assay of biomarker miRNA-21 in the total RNA lysates of cancer cell. The strategy thus helps in the design of a versatile nucleic acid conversion and signal amplification approach for practical applications in the areas of biosensing assay, DNA nanotechnology, and clinical diagnosis.

Host-Guest Self-Assembled Interfacial Nanoarrays for Precise Metabolic Profiling.

Accurate and rapid metabolic profiling of cerebrospinal fluid (CSF) is urgently needed but remains challenging for clinical diagnosis of central nervous system diseases and biomarker discovery. Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) holds promise for metabolic analysis. Its low signal reproducibility, however, severely restricts acquisition of quantitative MS data in clinical practice. Herein, a multifunctional self-assembled AuNPs array (MSANA)-based LDI-MS platform for direct amino acids analysis and metabolic profiling in patient CSF samples is developed. MSANA featuring a highly ordered and closely packed two-dimensional nanostructure permits capture and direct analysis of aromatic amino acids by LDI-MS with high selectivity and micromolar sensitivity. Meanwhile, the MSANA-based LDI-MS platform exhibits excellent reproducibility (RSD < 10%), largely outperforming the direct matrix spotting approach widely used now (RSD < 44%). The platform is successfully used in metabolic profiling of CSF (1 µL) within minutes for discrimination of medulloblastoma patients from non-tumor controls. Taken together, the MSANA-based LDI-MS platform shows potential clinical values toward large-scale metabolic diagnostics and pathogenic mechanism study.

Monodispersed Pt Sites Supported on NiFe-LDH from Synchronous Anchoring and Reduction for High Efficiency Overall Water Splitting.

Precise design of low-cost, efficient and definite electrocatalysts is the key to sustainable renewable energy. Herein, this work develops a targeted-anchored and subsequent spontaneous-redox strategy to synthesize nickel-iron layered double hydroxide (LDH) nanosheets anchored with monodispersed platinum (Pt) sites (Pt@LDH). Intermediate metal-organic frameworks (MOF)/LDH heterostructure not only provides numerous confine points to guarantee the stability of Pt sites, but also excites the spontaneous reduction for PtII . Electronic structure, charge transfer ability and reaction kinetics of Pt@LDH can be effectively facilitated by the monodispersed Pt moieties. As a result, the optimized Pt@LDH that with the 5% ultra-low content Pt exhibits the significant increment in electrochemical water splitting performance in alkaline media, which only afford low overpotentials of 58 mV at 10 mA cm-2 for hydrogen evolution reaction (HER) and 239 mV at 10 mA cm-2 for oxygen evolution reaction (OER), respectively. In a real device, Pt@LDH can drive an overall water-splitting at low cell voltage of 1.49 V at 10 mA cm-2 , which can be superior to most reported similar LDH-based catalysts. Moreover, the versatility of the method is extended to other MOF precursors and noble metals for the design of ultrathin LDH supported monodispersed noble metal electrocatalysts promoting research interest in material design.

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

MOTIVATION: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability. RESULTS: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.