Atomically dispersed ternary FeCoNb active sites anchored on N-doped honeycomb-like mesoporous carbon for highly catalytic degradation of 4-nitrophenol.

In the last decades, 4-nitrophenol is regarded as one of highly toxic organic pollutants in industrial wastewater, which attracts great concern to earth sustainability. Herein, atomically dispersed ternary FeCoNb active sites were incorporated into nitrogen-doped honeycomb-like mesoporous carbon (termed FeCoNb/NHC) by a two-step pyrolysis strategy, whose morphology, structure and size were characterized by a set of techniques. Further, the catalytic activity and reusability of the as-prepared FeCoNb/NHC were rigorously examined by using 4-NP catalytic hydrogenation as a proof-of-concept model. The influence of the secondary pyrolysis temperature on the catalytic performance was investigated, combined by illuminating the catalytic mechanism. The resultant catalyst exhibited significantly enhanced catalytic features with a normalized rate constant (kapp) of 1.2 × 104 min-1g-1 and superior stability, surpassing the home-made catalysts in the control groups and earlier research. This study provides some constructive insights for preparation of high-efficiency and cost-effectiveness single-atom nanocatalysts in organic pollutants environmental remediation.

Low Rh doping accelerated HER/OER bifunctional catalytic activities of nanoflower-like Ni-Co sulfide for greatly boosting overall water splitting.

Facile synthesis of high-efficiency and stable bifunctional electrocatalyst is essential for producing clean hydrogen in energy storage systems. Herein, low Rh-doped flower-like Ni3S2/Co3S4 heterostructures were facilely prepared on porous nickel foam (labeled Rh-Ni3S2/Co3S4/NF) by a hydrothermal method. The correlation of the precursors types with the morphological structures and catalytic properties were rigorously investigated for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in the control groups. The low Rh doping within the catalyst played important role in boosting the catalytic characteristics. The resulting catalyst showed the smaller overpotentials of 197 and 78 mV to drive a current density of 10 mA cm-2 for the OER and HER in alkaline electrolyte, respectively. And the potential only required 1.71 V to drive a current density of 100 mA cm-2 in a water splitting device. It reflects excellent overall water splitting of the home-made Rh-Ni3S2/Co3S4/NF. This strategy shed some constructive light for preparing transition metal sulfide-based electrocatalysts in water splitting devices.

Melatonin derivative 6a protects Caenorhabditis elegans from formaldehyde neurotoxicity via ADH5.

Formaldehyde (FA) is a carcinogen that is not only widespread in the environment, but is also produced endogenously by metabolic processes. In organisms, FA is converted to formic acid in a glutathione (GSH)-dependent manner by alcohol dehydrogenase 5 (ADH5). The abnormal accumulation of FA in the body can cause a variety of diseases, especially cognitive impairment leading to Alzheimer's disease (AD). In this study, melatonin derivative 6a (MD6a) markedly improved the survival and chemotactic performance of wild-type Caenorhabditis elegans exposed to high concentrations of FA. MD6a lowered FA levels in the nematodes by enhancing the release of covalently-bound GSH from S-hydroxymethyl-GSH in an adh-5-dependent manner. In addition, MD6a protected against mitochondrial dysfunction and cognitive impairment in beta-amyloid protein (Aß) transgenic nematodes by lowering endogenous FA levels and reducing Aß aggregation in an adh-5-dependent manner. Our findings suggest that MD6a detoxifies FA via ADH5 and protects against Aß toxicity by reducing endogenous FA levels in the C. elegans AD models. Thus, ADH5 might be a potential therapeutic target for FA toxicity and AD.

An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Bimetal Oxides Anchored on Carbon Nanotubes/Nanosheets as High-Efficiency and Durable Bifunctional Oxygen Catalyst for Advanced Zn-Air Battery: Experiments and DFT Calculations.

To meet increasing requirement for innovative energy storage and conversion technology, it is urgent to prepare effective, affordable, and long-term stable oxygen electrocatalysts to replace precious metal-based counterparts. Herein, a two-step pyrolysis strategy is developed for controlled synthesis of Fe2O3 and Mn3O4 anchored on carbon nanotubes/nanosheets (Fe2O3-Mn3O4-CNTs/NSs). The typical catalyst has a high half-wave potential (E1/2 = 0.87 V) for oxygen reduction reaction (ORR), accompanied with a smaller overpotential (η10 = 290 mV) for oxygen evolution reaction (OER), showing substantial improvement in the ORR and OER performances. As well, density functional theory calculations are performed to illustrate the catalytic mechanism, where the in situ generated Fe2O3 directly correlates to the reduced energy barrier, rather than Mn3O4. The Fe2O3-Mn3O4-CNTs/NSs-based Zn-air battery exhibits a high-power density (153 mW cm-2) and satisfyingly long durability (1650 charge/discharge cycles/550 h). This work provides a new reference for preparation of highly reversible oxygen conversion catalysts.

Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT.

BACKGROUND AND PURPOSE: Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT. MATERIALS AND METHODS: This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification. RESULTS: The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model (P = .002) and the clinical features model (P = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model (P = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features (P = .028; P = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron (P < .001) and the clinical-radiomics model with logistic regression (P = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration. CONCLUSIONS: The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies.

Cyclo(L-Pro-L-Trp) from Chilobrachys jingzhao alleviates formalin-induced inflammatory pain by suppressing the inflammatory response and inhibiting TRAF6-mediated MAPK and NF-κB signaling pathways.

Chronic pain has emerged as a significant public health issue, seriously affecting patients' quality of life and psychological well-being, with a lack of effective pharmacological treatments. Numerous studies have indicated that macrophages play a crucial role in inflammatory pain, and targeting neuro-immune interactions for drug development may represent a promising direction for pain management. Chilobrachys jingzhao (C. jingzhao) is used as a folk medicine of the Li nationality with the efficacy of eliminating swelling, detoxicating, and relieving pain, and the related products are widely used in the market. However, the chemical constituents of C. jingzhao have not been reported, and the pharmacodynamic substance and the precise functional mechanism are unrevealed. Here we isolated a cyclic dipeptide, cyclo(L-Pro-L-Trp) (CPT) from C. jingzhao for the first time. CPT remarkably alleviated formalin-induced inflammatory pain and significantly inhibited inflammatory responses. In vivo, CPT attenuated neutrophil infiltration and plantar tissue edema and suppressed the mRNA expression of pro-inflammatory molecules. In vitro, CPT suppressed inflammation triggered by lipopolysaccharide (LPS) in both RAW 264.7 and iBMDM cells, reducing expressions of inducible nitric oxide synthase (iNOS), superoxide, and pro-inflammatory molecules. A mechanistic study revealed that CPT exerted an anti-inflammatory activity by blocking the mitogen-activated protein kinases (MAPK) and nuclear factor-kappa B (NF-κB) signaling pathways, as well as alleviating the ubiquitination of tumor necrosis factor receptor-associated factor 6 (TRAF6). Our results elucidated the pharmacodynamic material basis of C. jingzhao, and CPT can be a promising lead for alleviating inflammation and inflammatory pain.

Prognostic value of 18F-FDG PET/CT in patients with relapsed multiple myeloma.

This study aimed to assess the prognostic value of 18F-fluorodeoxyglucose positron emission tomography/computer tomography (18F-FDG PET/CT) in patients with relapsed multiple myeloma (MM). Fifty-one consecutive patients with relapsed MM were enrolled in this retrospective study. 18F-FDG parameters based on the Italian Myeloma Criteria for PET Use (IMPeTUs) and clinical data were analyzed for overall survival (OS) and progression-free survival (PFS). The Cox proportional risk model was used for univariate and multivariate analysis, and Kaplan-Meier survival curves were used for survival analysis. The median length of follow-up was 20 months (IQR, 5-29 months), the median PFS for the entire cohort was 8 months (IQR, 3-17 months) and the median OS was 21 months (IQR, 8-49 months). Multivariate survival analysis demonstrated that the Deauville score of BM > 3 [HR 2.900, 95% CI (1.011, 8.319), P = 0.048] and the presence of EMD [HR 3.134, 95% CI (1.245, 7.891), P = 0.015] were independent predictors of poor PFS. The presence of EMD [HR 12.777, 95% CI (1.825, 89.461), P = 0.010] and the reduced platelets count [HR 7.948, 95% CI (1.236, 51.099), P = 0.029] were adversely associated with OS. 18F-FDG PET/CT parameters based on IMPeTUs have prognostic significance in patients with relapsed MM.

FAM3A plays a key role in protecting against tubular cell pyroptosis and acute kidney injury.

Acute kidney injury (AKI) is in high prevalence worldwide but with no therapeutic strategies. Programmed cell death in tubular epithelial cells has been reported to accelerate a variety of AKI, but the major pathways and underlying mechanisms are not defined. Herein, we identified that pyroptosis was responsible for AKI progression and related to ATP depletion in renal tubular cells. We found that FAM3A, a mitochondrial protein that assists ATP synthesis, was decreased and negatively correlated with tubular cell injury and pyroptosis in both mice and patients with AKI. Knockout of FAM3A worsened kidney function decline, increased macrophage and neutrophil cell infiltration, and facilitated tubular cell pyroptosis in ischemia/reperfusion injury model. Conversely, FAM3A overexpression alleviated tubular cell pyroptosis, and inhibited kidney injury in ischemic AKI. Mechanistically, FAM3A promoted PI3K/AKT/NRF2 signaling, thus blocking mitochondrial reactive oxygen species (mt-ROS) accumulation. NLRP3 inflammasome sensed the overload of mt-ROS and then activated Caspase-1, which cleaved GSDMD, pro-IL-1ß, and pro-IL-18 into their mature forms to mediate pyroptosis. Of interest, NRF2 activator alleviated the pro-pyroptotic effects of FAM3A depletion, whereas the deletion of NRF2 blocked the anti-pyroptotic function of FAM3A. Thus, our study provides new mechanisms for AKI progression and demonstrates that FAM3A is a potential therapeutic target for treating AKI.

Atomically dispersed bimetallic active sites as H2O2 self-supplied nanozyme for effective chemodynamic therapy, chemotherapy and starvation therapy.

Tumor microenvironment (TME)-responsive chemodynamic therapy (CDT) is severely hindered by insufficient intracellular H2O2 level that seriously deteriorates antitumor efficacy, albeit with its extensively experimental and theoretical research. Herein, we designed atomically dispersed FeCo dual active sites anchored in porous carbon polyhedra (termed FeCo/PCP), followed by loading with glucose oxidase (GOx) and anticancer doxorubicin (DOX), named FeCo/PCP-GOx-DOX, which converted glucose into toxic hydroxyl radicals. The loaded GOx can either decompose glucose to self-supply H2O2 or provide fewer nutrients to feed the tumor cells. The as-prepared nanozyme exhibited the enhanced in vitro cytotoxicity at high glucose by contrast with those at less or even free of glucose, suggesting sufficient accumulation of H2O2 and continual transformation to OH for CDT. Besides, the FeCo/PCP-GOx-DOX can subtly integrate starvation therapy, the FeCo/PCP-initiated CDT, and DOX-inducible chemotherapy (CT), greatly enhancing the therapeutic efficacy than each monotherapy.

Self-shedding MOF-nanocarriers modulated CdS/MoSe2 heterojunction activity through in-situ ion exchange: An enhanced split-type photoelectrochemical sensor for deoxynivalenol.

Deoxynivalenol (DON), a mycotoxin produced by Fusarium, poses a significant risk to human health and the environment. Therefore, the development of a highly sensitive and accurate detection method is essential to monitor the pollution situation. In response to this imperative, we have devised an advanced split-type photoelectrochemical (PEC) sensor for DON analysis, which leverages self-shedding MOF-nanocarriers to modulate the photoelectric response ability of PEC substrate. The PEC sensing interface was constructed using CdS/MoSe2 heterostructures, while the self-shedding copper peroxide nanodots@ZIF-8 (CPNs@ZIF-8) served as the Cu2+ source for the in-situ ion exchange reaction, which generated a target-related signal reduction. The constructed PEC sensor exhibited a broad linear range of 0.1 pg mL-1 to 500 ng mL-1 with a low detection limit of 0.038 pg mL-1, demonstrating high stability, selectivity, and proactivity. This work not only introduces innovative ideas for the design of photosensitive materials, but also presents novel sensing strategies for detecting various environmental pollutants.

Modeling and biological evaluation of pegmolesatide, a novel and potent erythropoiesis-stimulating agent.

Pegmolesatide, a synthetic, polyethylene-glycolylated, peptide-based erythropoiesis-stimulating agent (ESA), has been recently approved in China. Pegmolesatide is derived from the structure of endogenous erythropoietin (EPO), a natural product in mammals. This study compared the in vitro effects and selectivity of pegmolesatide to those of recombinant EPO and carbamylated EPO (CEPO) through computer-aided analyses and biological tests. The findings indicate that pegmolesatide exhibited the same stimulating effect on erythropoiesis as EPO with fewer side effects than EPO and CEPO.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis.

Background: The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy. Methods: Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0. Results: A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively. Conclusion: Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Pt─O Bond Accelerated Cu0/Cu+ Activity for Boosting Low-Energy Bipolar Hydrogen Production.

To address the imperative challenge of producing hydrogen in a low-energy consumption electrocatalytic system, this study emphasizes the utilization of thermodynamically favorable biomass oxidation for achieving energy-efficient hydrogen generation. This research integrates ultralow PtO2-loaded flower-like nanosheets (denoted as PtO2@Cu2O/Cu FNs) with Cu0/Cu+ pairs and Pt─O bonds, thereby yielding substantial enhancement in both hydrogen evolution reaction (HER, -0.042 VRHE at 10 mA cm-2) and furfural oxidation reaction (FFOR, 0.09 VRHE at 10 mA cm-2). As validated by DFT calculations, the dual built-in electric field (BIEF) is elucidated as the driving force behind the enhanced activities, in which Pt─O bonds expedite the HER, while Cu+/Cu0 promotes low-potential FFOR. By coupling the FFOR and HER together, the resulting bipolar-hydrogen production system requires a low power input (0.5072 kWh per m3) for producing H2. The system can generate bipolar hydrogen and high value-added furoic acid, significantly enhancing hydrogen production efficiency and concurrently mitigating energy consumption.

Selective Increase in CO2 Electroreduction to Ethanol Activity at Nanograin-Boundary-Rich Mixed Cu(I)/Cu(0) Sites via Enriching Co-Adsorbed CO and Hydroxyl Species.

Selective producing ethanol from CO2 electroreduction is highly demanded, yet the competing ethylene generation route is commonly more thermodynamically preferred. Herein, we reported an efficient CO2-to-ethanol conversion (53.5 % faradaic efficiency at -0.75 V versus reversible hydrogen electrode (vs. RHE)) over an oxide-derived nanocubic catalyst featured with abundant "embossment-like" structured grain-boundaries. The catalyst also attains a 23.2 % energy efficiency to ethanol within a flow cell reactor. In situ spectroscopy and electrochemical analysis identified that these dualphase Cu(I) and Cu(0) sites stabilized by grain-boundaries are very robust over the operating potential window, which maintains a high concentration of co-adsorbed *CO and hydroxyl (*OH) species. Theoretical calculations revealed that the presence of *OHad not only promote the easier dimerization of *CO to form *OCCO (ΔG~0.20 eV) at low overpotentials but also preferentially favor the key *CHCOH intermediate hydrogenation to *CHCHOH (ethanol pathway) while suppressing its dehydration to *CCH (ethylene pathway), which is believed to determine the remarkable ethanol selectivity. Such imperative intermediates associated with the bifurcation pathway were directly distinguished by isotope labelling in situ infrared spectroscopy. Our work promotes the understanding of bifurcating mechanism of CO2ER-to-hydrocarbons more deeply, providing a feasible strategy for the design of efficient ethanol-targeted catalysts.

Photodynamic-Assisted Electrochemiluminescence Enhancement toward Advanced BODIPY for Precision Diagnosis of Parkinson.

Nowadays, signal enhancement is imperative to increase sensitivity of advanced ECL devices for expediting their promising applications in clinic. In this work, photodynamic-assisted electrochemiluminescence (PDECL) device was constructed for precision diagnosis of Parkinson, where an advanced emitter was prepared by electrostatically linking 2,6-dimethyl-8-(3-carboxyphenyl)4,4'-difluoroboradiazene (BET) with 1-butyl-3-methylimidazole tetrafluoroborate ([BMIm][BF4]). Specifically, protoporphyrin IX (PPIX) can trigger the photodynamic reaction under light irradiation with a wavelength of 450 nm to generate lots of singlet oxygen (1O2), showing a 2.43-fold magnification in the ECL responses. Then, the aptamer (Apt) was assembled on the functional BET-[BMIm] for constructing a "signal off" ECL biosensor. Later on, the PPIX was embedded into the G-quadruplex (G4) of the Apt to magnify the ECL signals for bioanalysis of α-synuclein (α-syn) under light excitation. In the optimized surroundings, the resulting PDECL sensor has a broad linear range of 100.0 aM ∼ 10.0 fM and a low limit of detection (LOD) of 63 aM, coupled by differentiating Parkinson patients from normal individuals according to the receiver operating characteristic (ROC) curve analysis of actual blood samples. Such research holds great promise for synthesis of other advanced luminophores, combined with achieving an early clinical diagnosis.

Preparation and epitope analysis of monoclonal antibodies against African swine fever virus DP96R protein.

BACKGROUND: Many proteins of African swine fever virus (ASFV, such as p72, p54, p30, CD2v, K205R) have been successfully expressed and characterized. However, there are few reports on the DP96R protein of ASFV, which is the virulence protein of ASFV and plays an important role in the process of host infection and invasion of ASFV. RESULTS: Firstly, the prokaryotic expression vector of DP96R gene was constructed, the prokaryotic system was used to induce the expression of DP96R protein, and monoclonal antibody was prepared by immunizing mice. Four monoclonal cells of DP96R protein were obtained by three ELISA screening and two sub-cloning; the titer of ascites antibody was up to 1:500,000, and the monoclonal antibody could specifically recognize DP96R protein. Finally, the subtypes of the four strains of monoclonal antibodies were identified and the minimum epitopes recognized by them were determined. CONCLUSION: Monoclonal antibody against ASFV DP96R protein was successfully prepared and identified, which lays a foundation for further exploration of the structure and function of DP96R protein and ASFV diagnostic technology.

Exploring the nexus between FDI inflows and economic growth: A sectoral level analysis.

This study investigates the relationship between Foreign Direct Investment (FDI) inflows and economic growth at sectoral levels in Bangladesh, employing a panel study framework. Utilizing sectoral-level panel data spanning six sectors from 2007-08 to 2018-19, the analysis is conducted using Panel Vector Error Correction Model (Panel VECM). Results from panel unit root tests confirm that all variables are integrated of order one I (1), indicating stationarity. The Pedroni panel co-integration test further supports the presence of co-integration among the variables. Notably, the Panel VECM reveals evidence of a unidirectional causal relationship from Real Gross Domestic Product (RGDP) to Real Foreign Direct Investment (RFDI) across all six sectors of Bangladesh. The findings underscore the significance of formulating pragmatic policies and implementing them effectively to attract FDI across sectors, thereby contributing to the overall economic growth of Bangladesh.