Microenvironment-Responsive Targeted Nanomedicine for a Collaborative Integration of Tumor Theranostics and Bone Defect Repair.

Despite advancements in breast cancer treatment, bone metastases remain a significant concern for patients with advanced breast cancer. Current theranostics strategies face challenges in integrating tumor theranostics and bone formation. Herein, we developed an activatable targeted nanomedicine AuMnCO@BSA-N3 (AMCBN) to enable a novel collaborative integration of second near-infrared (NIR-II) fluorescence imaging guided precise theranostics for breast cancer bone metastases and osteogenic microenvironment remolding. This strategy employed a chemical coordination between noble metal complex and metal carbonyl (MnCO). The surface modification of azide groups for enhanced tumor affinity through passive and active targeting. The initiated respondent behavior of AMCBN by tumor microenvironment (H2O2/acidity) accelerate the degradation of coordinated MnCO, resulting in a rapid release of multifunctional agents for efficient chemodynamic therapy (CDT)/gas synergistic therapy. Meanwhile, the exceptional bone-binding properties enable the efficient and controlled release of Mn2+ ions and CO in the bone microenvironment, thereby facilitating the expression of osteogenesis-related proteins and establishing a novel synchronous theranostics process for tumor-bone repair. This article is protected by copyright. All rights reserved.

Secure optical communication system based on polarization regulation of the data fragmentation multipath transmission technology.

We propose and demonstrate a data fragment multipath transmission scheme to achieve a secure optical communication based on polarization regulation. A dual-polarization Mach-Zehnder modulator (DPMZM) is driven by digital signals which are scattered by field-programmable gate array (FPGA) and transmitted in multiple paths. By utilizing two orthogonal polarization states, we have achieved a signal transmission under different optical parameters, and the transmission rate of the two paths can reach over 10 Gbps through a 20 km fiber with 2.5 Gbps hopping rate. In addition, we establish a theoretical model to analyze the security of the system and simulate brute force cracking; the probability of cracking the minimum information unit is 1.53 × 10-53. This proves that it is difficult to obtain a user data even using the fastest computers. Our scheme has provided, to our knowledge, a new approach for physical layer security.

The critical roles of the Zn2Cys6 transcription factor Fp487 in the development and virulence of Fusarium pseudograminearum: A potential target for Fusarium crown rot control.

Fusarium crown rot (FCR) caused by Fusarium pseudograminearum poses a significant threat to wheat production in the Huang-Huai-Hai region of China. However, the pathogenic mechanism of F. pseudograminearum is still poorly understood. Zn2Cys6 transcription factors, which are exclusive to fungi, play pivotal roles in regulating fungal development, drug resistance, pathogenicity, and secondary metabolism. In this study, we present the functional characterization of a Zn2Cys6 transcription factor F. pseudograminearum, designated Fp487. In F. pseudograminearum, Fp487 is shown to be required for mycelial growth through gene knockout and phenotypic analyses. Compared with wild-type CF14047, the ∆Fp487 mutant displayed a slight reduction in growth rate but a significant decrease in conidiogenesis, pathogenicity and 3-acetyl-deoxynivalenol (3AcDON) production. Moreover, the mutant exhibited heightened sensitivity to oxidative and cytomembrane stress. Furthermore, we synthesized dsRNA from the Fp487 gene in vitro, resulting in a reduction in the growth rate of F. pseudograminearum and its virulence on barley leaves through spray-induced gene silencing (SIGS). Notably, this study makes the first instance of inducing the expression of abundant dsRNA from F. pseudograminearum by engineering the Escherichia coli strain HT115 (DE3) and utilizing the SIGS technique to evaluate the virulence effect of dsRNA on F. pseudograminearum. In conclusion, our findings revealed the crucial role of Fp487 in regulating pathogenicity, stress responses, DON production, and conidiogenesis in F. pseudograminearum. Furthermore, Fp487 is a potential RNAi-based target for FCR control.

Aspirin and Celecoxib Regulate Notch1/Hes1 Pathway to Prevent Pressure Overload-Induced Myocardial Hypertrophy.

This study aimed to investigate the molecular mechanisms underlying the protective effects of cyclooxygenase (cox) inhibitors against myocardial hypertrophy.Rat H9c2 cardiomyocytes were induced by mechanical stretching. SD rats underwent transverse aortic constriction to induce pressure overload myocardial hypertrophy. Rats were subjected to echocardiography and tail arterial pressure in 12W. qPCR and western blot were used to detect the expression of Notch-related signaling. The inflammatory factors were tested by ELISA in serum, heart tissue, and cell culture supernatant.Compared with control, levels of pro-inflammatory cytokines IL-6, TNF-α, and IL-1ß were increased and anti-inflammatory cytokine IL-10 was reduced in myocardial tissues and serum of rat models. Levels of Notch1 and Hes1 were reduced in myocardial tissues. However, cox inhibitor treatment (aspirin and celecoxib), the improvement of exacerbated myocardial hypertrophy, fibrosis, dysfunction, and inflammation was parallel to the activation of Notch1/Hes1 pathway. Moreover, in vitro experiments showed that, in cardiomyocyte H9c2 cells, application of ~20% mechanical stretching activated inflammatory mediators (IL-6, TNF-α, and IL-1ß) and hypertrophic markers (ANP and BNP). Moreover, expression levels of Notch1 and Hes1 were decreased. These changes were effectively alleviated by aspirin and celecoxib.Cox inhibitors may protect heart from hypertrophy and inflammation possibly via the Notch1/Hes1 signaling pathway.

Construction of Two-Dimensional Organometallic Coordination Networks with Both Organic Kagome and Semiregular Metal Lattices on Au(111).

Two-dimensional metal-organic networks (2D MONs) having heterogeneous coordination nodes (HCNs) could exhibit excellent performance in catalysis and optoelectronics because of the unbalanced electron distribution of the coordinating metals. Therefore, the design and construction of 2D MONs with HCNs are highly desirable but remain challenging. Here, we report the construction of 2D organometallic coordination networks with an organic Kagome lattice and a semiregular metal lattice on Au(111) via the in situ formation of HCNs. Using a bifunctional precursor 1,4-dibromo-2,5-diisocyanobenzene, the coordination of isocyano with Au adatom on a room-temperature Au(111) yielded metal-organic coordination chains with isocyano-Au-isocyano nodes. In contrast, on a high-temperature Au(111), a selective debromination/coordination cascade reaction occurred, affording 2D organometallic coordination networks with phenyl-Au-isocyano nodes. By combining scanning tunneling microscopy and density functional theory calculations, we determined the structures of coordination products and the nature of coordination nodes, demonstrating a thermodynamically favorable pathway for forming the phenyl-Au-isocyano nodes.

Circulating tumor cells are a good predictor of tumor recurrence in clinical patients with gastric cancer.

Circulating tumor cells (CTCs) as a liquid biopsy have great potential in clinical applications and basic cancer research, but their clinical use in gastric cancer remains unclear. This study investigated whether CTCs could be used as a potential prognosis predictor in patients with gastric cancer. A total of 120 patients with pathologically confirmed gastric cancer were enrolled from January 1, 2015, to December 1, 2019. All patients were initially diagnosed without previous treatment, and then the number of CTCs was detected using the NEimFISH method before radical surgical resection. Regular follow-up was performed in all patients, and the correlations between the number of CTCs and clinical endpoints, such as disease-free survival (DFS) and overall survival (OS), were evaluated. The univariate and multivariate hazard ratios were calculated using the Cox proportional hazard model. Based on the number of CTCs, we defined CTCs ≥ 2 per 7.5 mL of whole blood as the positive group and CTCs < 2 as the negative group. Among the 120 patients who underwent CTC detection before surgery, the rate of CTC-positive patients was 64.17% (77/120) of which stage I and II patients accounted for 22.50% and stage III patients accounted for 41.67% (P = 0.014). By detecting CTCs before surgery and at the time of recurrence, the number of CTCs tends to increase concomitantly with disease progression (median: 2 VS 5 per 7.5 mL). Multivariate analysis showed that age (HR, 0.259; 95% CI, 0.101-0.662; P = 0.005), D-dimer (HR, 3.146; 95% CI, 1.169-8.461; P = 0.023), and lymph node metastasis (HR, 0.207; 95% CI, 0.0071-0.603; P = 0.004) were factors correlated with CTCs. In addition, the median follow-up of all the patients was 38.0 months (range of 28-80 months); the DFS in CTC-positive patients was significantly shorter than that of the CTC-negative patients, and a significant difference was found based on the Cox proportional hazard regression model analysis (44.52 ± 2.83 m vs. 74.99 ± 2.78 m, HR = 4.550, P = 0.018). The OS was shorter in the CTC-positive group than in the CTC-negative group before the operation, but the result was not significant based on the Cox proportional hazard regression model analysis (47.58 ± 2.46 m vs. 70.68 ± 3.53 m, HR = 2.261, P = 0.083). The number of CTCs tends to increase concomitantly with disease progression. In addition, the detection of CTCs was an independent predictor of shorter DFS in gastric cancer. However, the relationship between CTCs and OS needs to be determined in future studies.

The influence of psychosocial work environment, personal perceived health and job crafting on nurses' well-being: a cross-sectional survey study.

BACKGROUND: The World Health Organization urged governments to prioritize the health and work well-being of nursing staff by promoting a positive working environment. A safe and healthy physical and psychosocial work environment is a basic human right for nurses. Job crafting is a necessary skill when facing challenging working conditions. OBJECTIVES: This cross-sectional correlational research based on the Job Demands-Resources Model aimed to explore the correlation between psychosocial work environment and work well-being among nurses working in the intensive care unit (ICU) and determine whether personal perceived health could mediate the relationship and whether job crafting can moderate the mediating effect. The study hypothesized that: 1. The psychosocial work environment would impact nurses' work well-being; 2. Personal perceived health would play a role as a mediator in the relationship between psychosocial work environment and work well-being; 3. Job crafting would moderate the relationship between personal perceived health and work well-being. METHODS: A total of 655 registered nurses (RNs) from 7 ICUs in a teaching hospital in Beijing participated in this study. The RNs completed a battery questionnaire measuring their health, psychosocial work environment, well-being, and job crafting. PROCESS macros analysis was used to test mediating and moderating effects. RESULTS: Personal perceived health mediated the relationship between psychosocial work environment and work well-being (b = 0.012, 95% CI [0.008, 0.016]). The moderated mediated analysis revealed that job crafting moderated perceived health's impact on work well-being (b = -0.007, 95% CI [- 0.010, - 0.003]). CONCLUSION: A better psychosocial work environment with well-designed work organization and job content through job crafting could positively impact nurses' health and work well-being.

Application of individual brain connectome in chronic ischemia: mapping symptoms before and after reperfusion.

How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno-occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes. A total of 499 bypasses in 455 CSOV patients were collected and followed up for 47.8 ± 20.5 months. Using multimodal parcellation with connectivity-based and pathological distortion-independent approach, areal MR features of brain connectome were generated with three measurements of functional connectivity (FC), structural connectivity, and PageRank centrality at the single-subject level. Thirty-three machine-learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes, among which, 11 were deemed acceptable (AUC > 0.7). Notably, the FC feature-based model for long-term neurological outcomes performed very well (AUC > 0.8). Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate "fingerprints" of brain connectome. This study not only establishes brain connectomic fingerprint databases for brain ischemia with distortion, but also provides informative insights for how brain functions before and after reperfusion.

New sesquiterpenoids from the flowers of pentanema britannicum (L.) D.Gut.Larr.

Five new sesquiterpenoids, (4S, 5S, 6S, 7S, 8 R)-5,6-dihydroxy-1-acetoxy-10(14)-en-britannilactone (1), (4S, 5 R, 6S, 7S, 8 R)-5,6-dihydroxy-1-acetoxy-10(14)-en-britannilactone (2), 6-O-propionyl-britannilactone (3), 1ß-hydroxy-3α-acetoxyeudesma-11(13)-en-12,8ß-olide (4) and 1ß,5ß-dihydroxyeudesma-11(13)-en-12,8ß-olide (5), along with twelve known ones were isolated from the flowers of Pentanema britannicum (L.) D.Gut.Larr. Among them, compounds 1 and 2 were stereoisomers which belong to 1,10-seco-eudesmane sesquiterpenoid with rare double bond between C-10 and C-14. The structures of the isolated compounds were elucidated by various spectroscopic methods, including 1D and 2D NMR experiments.

Orexin Neurons to Sublaterodorsal Tegmental Nucleus Pathway Prevents Sleep Onset REM Sleep-Like Behavior by Relieving the REM Sleep Pressure.

Proper timing of vigilance states serves fundamental brain functions. Although disturbance of sleep onset rapid eye movement (SOREM) sleep is frequently reported after orexin deficiency, their causal relationship still remains elusive. Here, we further study a specific subgroup of orexin neurons with convergent projection to the REM sleep promoting sublaterodorsal tegmental nucleus (OXSLD neurons). Intriguingly, although OXSLD and other projection-labeled orexin neurons exhibit similar activity dynamics during REM sleep, only the activation level of OXSLD neurons exhibits a significant positive correlation with the post-inter-REM sleep interval duration, revealing an essential role for the orexin-sublaterodorsal tegmental nucleus (SLD) neural pathway in relieving REM sleep pressure. Monosynaptic tracing reveals that multiple inputs may help shape this REM sleep-related dynamics of OXSLD neurons. Genetic ablation further shows that the homeostatic architecture of sleep/wakefulness cycles, especially avoidance of SOREM sleep-like transition, is dependent on this activity. A positive correlation between the SOREM sleep occurrence probability and depression states of narcoleptic patients further demonstrates the possible significance of the orexin-SLD pathway on REM sleep homeostasis.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Ultralight Three-Layer Gradient-Structured MXene/ Reduced Graphene Oxide Composite Aerogels with Broadband Microwave Absorption and Dynamic Infrared Camouflage.

Infrared and radar detectors posed substantial challenges to weapon equipment and personnel due to their continuous surveillance and reconnaissance capabilities. Traditional single-band stealth devices are insufficient for dual-band detection in both infrared and microwave bands. To overcome this limitation, a gradient-structured MXene/reduced graphene oxide (rGO) composite aerogel (GMXrGA) is fabricated through a two-step bidirectional freeze casting process, followed by freeze-drying and thermal annealing. GMXrGA exhibits a distinct three-layered structure, with each layer playing a crucial role in microwave absorption. This deliberate design amplifies both the efficiency of microwave absorption and the material's effectiveness in dynamic infrared camouflage. GMXrGA displays an ultralow density of 5.2 mg∙cm-3 and demonstrates exceptional resistance to compression, enduring 200 cycles at a maximum strain of 80%. Moreover, it shows superior microwave absorption performance, with a minimum reflection loss (RLmin) of -60.1 dB at a broad effective absorption bandwidth (EAB) of 14.1 GHz (3.9-18.0 GHz). Additionally, the aerogel exhibits low thermal conductivity (≈26 mW∙m-1∙K-1) and displays dynamic infrared camouflage capabilities within the temperature range of 50-120 °C, achieving rapid concealment within 30 s. Consequently, they hold great potential for diverse applications, including intelligent buildings, wearable electronics, and weapon equipment.

Mendelian randomization analyses reveal causal relationship between liver volume and stroke.

BACKGROUND: Observational studies have suggested a potential association between abdominal viscera volume and increased risk of stroke. However, the causal relationship remains unclear. This study aims to utilize Mendelian randomization (MR) to explore the genetic causal relationship between them. METHODS: We conducted MR analysis to study the causal effects of five abdominal viscera volumes on stroke. The genetic variations of abdominal viscera volume were obtained from the UK Biobank, and the summary data for stroke and ischemic stroke were acquired from the MEGASTROKE consortium. This study employed inverse variance weighting (IVW), MR Egger, and weighted median methods. IVW served as the primary MR analysis method, supplemented by other sensitivity analyses to validate the robustness of the results. RESULTS: We found that liver volume can causally increase the risk of stroke [odds ratio (OR): 1.13, 95% confidence interval (CI): 1.03-1.25, P = 0.013] and ischemic stroke (OR: 1.14, 95% CI: 1.03-1.26, P = 0.012). No causal relationships between other abdominal viscera volumes and stroke and ischemic stroke appeared to be present (P>0.05). Sensitivity analyses confirmed the robustness of the results. CONCLUSION: Our research findings indicate a causal relationship between liver volume and stroke, highlighting the potential role of liver volume in the onset of stroke. However, further basic and clinical research is needed to delve into the specific mechanisms underlying the relationship between liver volume and stroke, and to implement interventions aimed at reducing the impact of liver volume on stroke risk.

scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference.

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.

Genotypic-phenotypic landscape computation based on first principle and deep learning.

The relationship between genotype and fitness is fundamental to evolution, but quantitatively mapping genotypes to fitness has remained challenging. We propose the Phenotypic-Embedding theorem (P-E theorem) that bridges genotype-phenotype through an encoder-decoder deep learning framework. Inspired by this, we proposed a more general first principle for correlating genotype-phenotype, and the P-E theorem provides a computable basis for the application of first principle. As an application example of the P-E theorem, we developed the Co-attention based Transformer model to bridge Genotype and Fitness model, a Transformer-based pre-train foundation model with downstream supervised fine-tuning that can accurately simulate the neutral evolution of viruses and predict immune escape mutations. Accordingly, following the calculation path of the P-E theorem, we accurately obtained the basic reproduction number (${R}_0$) of SARS-CoV-2 from first principles, quantitatively linked immune escape to viral fitness and plotted the genotype-fitness landscape. The theoretical system we established provides a general and interpretable method to construct genotype-phenotype landscapes, providing a new paradigm for studying theoretical and computational biology.

Prognostic value of postoperative anti-thyroglobulin antibody in patients with differentiated thyroid cancer.

Purpose: Postoperative thyroglobulin (Tg) generally serves as a biomarker to monitor the recurrence or persistence of differentiated thyroid cancer (DTC), whereas it constrains to interference from anti-thyroglobulin antibody (TgAb). This study aimed to determine the value of postoperative TgAb as a surrogate for monitoring tumor status in DTCs with positive TgAb after successful radioactive iodine (RAI) remnant ablation. Methods: We retrospectively enrolled DTC patients with positive (≥40 IU/mL, Roche) postoperative TgAb measurements. An index of TgAb change (ΔTgAb) was defined to describe the TgAb decrease rate. DTC status was defined as either no evidence of disease (NED) or persistent/recurrent disease (PRD). Univariate and multivariate binary logistic analyses were used to identify the independent risk factors of PRD. Receiver operating characteristic (ROC) curves were performed to determine the optimal cutoff values of each risk factor, and DeLong's test was conducted to compare their predictive powers. Kaplan-Meier curves were used to assess the impact of different TgAb trends in the first year on progression-free survival. Results: Of the 232 patients enrolled, the median diagnosis age was 34 years (range, 18-62 years), with a male-to-female ratio of 1:4.66 (41/191). Among them, after a median follow-up of 44 months (range, 4-128 months),183 (78.87%) patients were evaluated as NED, while the other 49 (21.12%) had either persistent (n = 25) or recurrent disease (n = 24). Multivariate regression showed that ΔTgAb (P < 0.001) and lymph node metastasis (LNM) rate (P = 0.009) were independently relevant to the presence of PRD, with optimal cutoff values of 47.0% and 35.1%, respectively. It is important to note that there is a high negative predictive value (96.93%) of ΔTgAb with the cutoff of 47.0%. DeLong's test showed that ΔTgAb alone and the combination of ΔTgAb and LNM rate were significantly greater than the isolated LNM rate (both P < 0.001) in predicting NED, while there was no statistical difference of the predictive power between ΔTgAb and the combination (P = 0.203). Additionally, patients with ΔTgAb >47.0% had longer progression-free survival than those with ΔTgAb ≤47.0% (not reached vs. 50 months, P < 0.001), and those with ΔTgAb >47.0% or negative conversion within the first year after RAI ablation had longer progression-free survival. Conclusion: Our study suggested that ΔTgAb could serve as a valuable indicator of disease status in DTC patients with positive TgAb. A ΔTgAb of >47.0% is conducive to identify those with NED and may help to obviate their overtreatment. The decrease rate and negative conversion of TgAb in the first year were good predictors of disease-free survival in patients.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

BACKGROUND AND AIM: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. METHODS: In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. RESULTS: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. CONCLUSIONS: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Revealing Hydrogen Bond Effect in Rechargeable Aqueous Zinc-Organic Batteries.

The surrounding hydrogen bond (H-bond) interaction around the active sites plays indispensable functions in enabling the organic electrode materials (OEMs) to fulfill their roles as ion reservoirs in aqueous zinc-organic batteries (ZOBs). Despite important, there are still no works could fully shed its real effects light on. Herein, quinone-based small molecules with a H-bond evolution model has been rationally selected to disclose the regulation and equilibration of H-bond interaction between OEMs, and OEM and the electrolyte. It has been found that only a suitable H-bond interaction could make the OEMs fully liberate their potential performance. Accordingly, the 2,5-diaminocyclohexa-2,5-diene-1,4-dione (DABQ) with elaborately designed H-bond structure exhibits a capacity of 193.3 mA h g-1 at a record-high mass loading of 66.2 mg cm-2 and 100% capacity retention after 1500 cycles at 5 A g-1. In addition, the DABQ//Zn battery also possesses air-rechargeable ability by utilizing the chemistry redox of proton. Our results put forward a specific pathway to precise utilization of H-bond to liberate the performance of OEMs.

Grain boundary defect engineering in rutile iridium oxide boosts efficient and stable acidic water oxidation.

PEMWE is considered a promising technology for coupling with renewable energy sources to achieve clean hydrogen production. However, constrained by the sluggish kinetics of the anodic OER and the acidic abominable environment render the grand challenges in developing the active and stable OER electrocatalyst, leading to low efficiency of PEMWE. Herein, we develop the rutile-type IrO2 nanoparticles with abundant grain boundaries and the continuous nanostructure through the joule heating and sacrificial template method. DFT calculations verified that grain boundaries can modulate the electronic structure of Ir sites and optimize the adsorption of oxygen intermediates, resulting in the accelerated kinetics. The 350-IrO2 affords a rapid OER process with 20 times higher mass activity (0.61 A mgIr-1) than the commercial IrO2 at 1.50 V vs. RHE. Benefiting from the reduced overpotential and the preservation of the stable rutile structure, 350-IrO2 exhibits the stability of 200 h test at 10 mA cm-2 with only trace decay of 11.8 mV. Moreover, the assembled PEMWE with anode 350-IrO2 catalyst outputs the current density up to 2 A cm-2 with only 1.84 V applied voltage, long-term operation for 100 h without obvious performance degradation at 1 A cm-2.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma.

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.