Recharacterization of RSL3 reveals that the selenoproteome is a druggable target in colorectal cancer.

Ferroptosis is an iron-dependent, non-apoptotic form of cell death resulting from the accumulation of lipid peroxides. Colorectal cancer (CRC) accumulates high levels of intracellular iron and reactive oxygen species (ROS), thereby sensitizing cells to ferroptosis. The selenoprotein glutathione peroxidase (GPx4) is a key enzyme in the detoxification of lipid peroxides and can be inhibited by the compound (S)-RSL3 ([1S,3R]-RSL3). However, the stereoisomer (R)-RSL3 ([1R,3R]-RSL3), which does not inhibit GPx4, exhibits equipotent activity to (S)-RSL3 across a panel of CRC cell lines. Utilizing CRC cell lines with an inducible knockdown of GPx4, we demonstrate that (S)-RSL3 sensitivity does not align with GPx4 dependency. Subsequently, a biotinylated (S)-RSL3 was then synthesized to perform affinity purification-mass spectrometry (AP-MS), revealing that (S)-RSL3 acts as a pan-inhibitor of the selenoproteome, targeting both the glutathione and thioredoxin peroxidase systems as well as multiple additional selenoproteins. To investigate the therapeutic potential of broadly disrupting the selenoproteome as a therapeutic strategy in CRC, we employed further chemical and genetic approaches to disrupt selenoprotein function. The findings demonstrate that the selenoprotein inhibitor Auranofin can induce ferroptosis and/or oxidative cell death both in-vitro and in-vivo. Consistent with this data we observe that AlkBH8, a tRNA-selenocysteine methyltransferase required for the translational incorporation of selenocysteine, is essential for CRC growth. In summary, our research elucidates the complex mechanisms underlying ferroptosis in CRC and reveals that modulation of the selenoproteome provides multiple new therapeutic targets and opportunities in CRC.

Ketogenic Diet Alters the Epigenetic and Immune Landscape of Prostate Cancer to Overcome Resistance to Immune Checkpoint Blockade Therapy.

Resistance to immune checkpoint blockade (ICB) therapy represents a formidable clinical challenge limiting the efficacy of immunotherapy. In particular, prostate cancer poses a challenge for ICB therapy due to its immunosuppressive features. A ketogenic diet (KD) has been reported to enhance response to ICB therapy in some other cancer models. However, adverse effects associated with continuous KD were also observed, demanding better mechanistic understanding and optimized regimens for using KD as an immunotherapy sensitizer. In this study, we established a series of ICB-resistant prostate cancer cell lines and developed a highly effective strategy of combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic KD (CKD), or dietary supplementation of the ketone body ß-hydroxybutyrate (BHB), which is an endogenous HDACi. CKD and BHB supplementation each delayed prostate cancer tumor growth as monotherapy, and both BHB and adaptive immunity were required for the antitumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that HDACi and ketogenesis enhanced ICB efficacy through both cancer cell-intrinsic mechanisms, including upregulation of MHC class I molecules, and -extrinsic mechanisms, such as CD8+ T-cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen-presenting cells, and diminished neutrophil infiltration. Overall, these findings illuminate a potential clinical path of using HDACi and optimized KD regimens to enhance ICB therapy for prostate cancer. SIGNIFICANCE: Optimized cyclic ketogenic diet and 1,3-butanediol supplementation regimens enhance the efficacy of immune checkpoint blockade in prostate cancer through epigenetic and immune modulations, providing dietary interventions to sensitize tumors to immunotherapy.

Prompt Tuning in Biomedical Relation Extraction.

Biomedical relation extraction (RE) is critical in constructing high-quality knowledge graphs and databases as well as supporting many downstream text mining applications. This paper explores prompt tuning on biomedical RE and its few-shot scenarios, aiming to propose a simple yet effective model for this specific task. Prompt tuning reformulates natural language processing (NLP) downstream tasks into masked language problems by embedding specific text prompts into the original input, facilitating the adaption of pre-trained language models (PLMs) to better address these tasks. This study presents a customized prompt tuning model designed explicitly for biomedical RE, including its applicability in few-shot learning contexts. The model's performance was rigorously assessed using the chemical-protein relation (CHEMPROT) dataset from BioCreative VI and the drug-drug interaction (DDI) dataset from SemEval-2013, showcasing its superior performance over conventional fine-tuned PLMs across both datasets, encompassing few-shot scenarios. This observation underscores the effectiveness of prompt tuning in enhancing the capabilities of conventional PLMs, though the extent of enhancement may vary by specific model. Additionally, the model demonstrated a harmonious balance between simplicity and efficiency, matching state-of-the-art performance without needing external knowledge or extra computational resources. The pivotal contribution of our study is the development of a suitably designed prompt tuning model, highlighting prompt tuning's effectiveness in biomedical RE. It offers a robust, efficient approach to the field's challenges and represents a significant advancement in extracting complex relations from biomedical texts. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00162-9.

Knockdown of TOP2A suppresses IL-17 signaling pathway and alleviates the progression of ulcerative colitis.

BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa, with a gradually increasing incidence. Therefore, it is necessary to actively seek targets for the treatment of UC. METHODS: Common differentially expressed genes (DEGs) were screened from two microarray data sets related to UC. Protein-protein interaction network was constructed to find the hub genes. The UC mouse model and cell model were induced by dextran sulfate sodium (DSS). The pathological changes of colon tissue were observed by hematoxylin-eosin staining. Immunohistochemistry and immunofluorescence were performed to detect the expressions of Ki67 and Claudin-1. The performance of mice was observed by disease activity index (DAI). The effect of TOP2A on proliferation, inflammation, oxidative stress, and interleukin-17 (IL-17) signaling pathway in UC model was measured by cell counting kit-8, enzyme-linked immunosorbent assay, and western blot. RESULTS: Through bioinformatics analysis, 295 common DEGs were screened, and the hub gene TOP2A was selected. In UC model, there was obvious inflammatory cell infiltration in the colon and less goblet cells, while si-TOP2A lessened it. More Ki67 positive cells and less Claudin-1 positive cells were observed in UC model mice. Furthermore, knockdown of TOP2A increased the body weight and colon length of UC mice, while the DAI was decreased. Through in vivo and in vitro experiments, knockdown of TOP2A also inhibited inflammation and IL-17 signaling pathway, and promoted proliferation in DSS-induced NCM460 cells. CONCLUSION: Knockdown of TOP2A alleviated the progression of UC by suppressing inflammation and inhibited IL-17 signaling pathway.

Ensemble pretrained language models to extract biomedical knowledge from literature.

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events.

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs' capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs' potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Anchoring Carbon Spheres on Titanium Dioxide Modified Commercial Polyethylene (PE) Separator to Suppress Lithium Dendrites for Lithium Metal Batteries.

Lithium dendrites are easily generated for excessively-solved lithium ions (Li+ ) inside the lithium metal batteries, which will lead serious safety issues. In this experiment, carbon spheres (CS) are successfully anchored on TiO2  (CS@TiO2 ) in the hydrothermal polymerization, which is filtrated on the commercial PE separator (CS@TiO2 @PE). The negative charge in CS can suppress random diffusion of anions through electrostatic interactions. Density functional theory (DFT) calculations show that CS contributes to the desolvation of Li+ , thereby increasing the migration rate of Li+ . Furthermore, TiO2  exhibits high affinity to liquid electrolytes and acts as a physical barrier to lithium dendrite formation. CS@TiO2 is a combination of the advantages of CS and TiO2 . As results, the Li+  transference number of the CS@TiO2 @PE separator can be promoted to 0.63. The Li||Li cell with the CS@TiO2 @PE separator exhibits a stable cycle performance for more than 600 h and lower polarization voltage (17 mV) at 1 mA cm-2 . The coulombic efficiency (CE) of the Li||Cu cells employe the CS@TiO2 @PE separator is 81.63% over 130 cycles. The discharge capacity of LiFePO4 ||Li cells based on the CS@TiO2 @PE separator is 1.73 mAh (capacity retention = 91.53% after 260 cycles). Thus, the CS@TiO2 layer inhibits lithium dendrite formation.

Study on the radiotherapy effect and serum neutral granulocyte lymphocyte ratio and inflammatory factor expression of nasopharyngeal carcinoma.

Purpose: To compare target area delineation schemes in intensity-modulated radiotherapy (IMRT) effect on patients with locally advanced nasopharyngeal carcinoma (NPC). Methods: A total of 88 NPC patients received IMRT and were assigned into control group (n = 44) and observation group (n = 44) based on MRI and CT imaging. In the control group, the treatment range was determined as the clinical target volume (CTV) as the gross tumor volume (GTV) + 5 mm. In the observation group, high-risk target areas CTVp1 was GTVp + 5 mm, lymphatic drainage area CTVn1 was GTVn + 5 mm, medium-risk CTVp2 was CTVp1 + 5 mm margin + the whole nasopharyngeal area, CTVn2 was CTVn1 + 5 mm. Radiotherapy treatment course was 6-8 weeks, four times a week. Results: The observation group had higher total effective rate, with fewer complications. Neutrophil lymphocyte ratio (NLR), interleukin (IL)-6, and tumor necrosis factor (TNF)-α levels were lower after radiotherapy in both groups compared to before radiotherapy, with the observation group demonstrating lower levels than the control group. The effective group exhibited lower serum NLR, IL-6, and TNF-α compared to the non-effective group. T stage, target location, serum NLR, IL-6, and TNF-α were risk factors for the effect of radiotherapy. Conclusions: Serum NLR, IL-6, and TNF-α have predictive significance for radiotherapy effect.

Preparation of Surface Dispersed WO3/BiVO4 Heterojunction Arrays and Their Photoelectrochemical Performance for Water Splitting.

In this work, a surface dispersed heterojunction of BiVO4-nanoparticle@WO3-nanoflake was successfully prepared by hydrothermal combined with solvothermal method. We optimized the morphology of the WO3 nanoflakes and BiVO4 nanoparticles by controlling the synthesis conditions to get the uniform BiVO4 loaded on the surface of WO3 arrays. The phase composition and morphology evolution with different reaction precursors were investigated in detail. When used as photoanodes, the WO3/BiVO4 composite exhibits superior activity with photocurrent at 3.53 mA cm-2 for photoelectrochemical (PEC) water oxidation, which is twice that of pure WO3 photoanode. The superior surface dispersion structure of the BiVO4-nanoparticle@WO3-nanoflake heterojunction ensures a large effective heterojunction area and relieves the interfacial hole accumulation at the same time, which contributes to the improved photocurrents together with the stability of the WO3/BiVO4 photoanodes.

Construction and validation of a novel tumor necrosis factor-related apoptosis-inducing ligand mutant MuR5S4-TR.

AIM: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) can selectively kill tumor cells but has no significant effect on normal cells. However, the use of TRAIL is limited for resistance by more than 50% of the tumor cell lines. It's very important to develop a more efficient form of TRAIL for cancer treatment. METHODS: The N-terminal in soluble fragments (114-281aa) of TRAIL was redesigned to construct a novel TRAIL mutant-MuR5S4-TR. The Cell Counting Kit-8 method to explore the antitumor effects. The potential mechanisms were also explored. RESULTS: Novel TRAIL mutant with cell-penetrating peptides (CPP) like and Second mitochondria-derived activator of caspases (Smac) like structure-MuR5S4-TR was successfully constructed. The prokaryotic expression system was successfully built, and the MuR5S4-TR was purified and reconfirmed by western blot. MuR5S4-TR could enhance the antitumor effects of TRAIL in most of the cancer cell lines significantly, NCI-H460 lung cancer cell line, for instance. After MuR5S4-TR treatment, the expressions of death receptor 4 (DR4), DR5, Caspase-8, and cleaved Caspase-3 were remarkably increased, however, there was no significant difference in X-linked inhibitor of apoptosis expression. CONCLUSION: We constructed a novel TRAIL mutant with CPP-like and Smac-like structure-MuR5S4-TR. The MuR5S4-TR showed significantly stronger antitumor effects than TRAIL in many tumor cell lines. The MuR5S4-TR showed strong antitumor effects both in vitro and in vivo. This preliminary study implies that MuR5S4-TR may be a more efficient form of TRAIL for cancer therapy.

Sensitization of cancer cells to ferroptosis coincident with cell cycle arrest.

Ferroptosis is a non-apoptotic form of cell death that can be triggered by inhibiting the system xc- cystine/glutamate antiporter or the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). We have investigated how cell cycle arrest caused by stabilization of p53 or inhibition of cyclin-dependent kinase 4/6 (CDK4/6) impacts ferroptosis sensitivity. Here, we show that cell cycle arrest can enhance sensitivity to ferroptosis induced by covalent GPX4 inhibitors (GPX4i) but not system xc- inhibitors. Greater sensitivity to GPX4i is associated with increased levels of oxidizable polyunsaturated fatty acid-containing phospholipids (PUFA-PLs). Higher PUFA-PL abundance upon cell cycle arrest involves reduced expression of membrane-bound O-acyltransferase domain-containing 1 (MBOAT1) and epithelial membrane protein 2 (EMP2). A candidate orally bioavailable GPX4 inhibitor increases lipid peroxidation and shrinks tumor volumes when combined with a CDK4/6 inhibitor. Thus, cell cycle arrest may make certain cancer cells more susceptible to ferroptosis in vivo.

Power and Sensitivity Management of Carbon Nanotube Transistor Glucose Biosensors.

Continuous glucose monitoring (CGM), which is significant for the daily management of diabetes, requires a low-power-consumption sensor system that can track low nanomolar levels of glucose in physiological fluids, such as sweat and tears. However, traditional electrochemical methods are limited to analytes in micromolar to millimolar ranges and entail high power consumption. Carbon nanotube (CNT) film field-effect transistors (FETs) are promising for constructing extremely sensitive biosensors, but their wide applications in CGM are limited by the strong screening effect of physiological fluids and the zero charge of glucose molecules. In this study, we demonstrate a glucose aptamer-modified CNT FET biosensor to realize a highly sensitive CGM system with sub-nW power consumption by applying a suitable gate voltage. A positive gate voltage can enlarge the effective Debye screening length at the double layer to reduce the local ion population nearby and then improve the sensitivity of the FET-based biosensors by 5 times. We construct CNT FET sensors for CGM with a limit of detection of 0.5 fM, a record dynamic range up to 109, and a power consumption down to ∼100 pW. The proposed field-modulated sensing performance scheme is applicable to other aptamer-based FET biosensors for detecting neutral or less charged molecules and opens opportunities to develop facilely modulated, highly sensitive, low-power, and noninvasive CGM systems.

Selective Etching of Metal-Organic Frameworks for Open Porous Structures: Mass-Efficient Catalysts with Enhanced Oxygen Reduction Reaction for Fuel Cells.

Fe-Nx-C-based single-atom (SA-Fe-N-C) catalysts have shown favorable oxygen reduction reaction (ORR) activity. However, their application in proton exchange membrane fuel cells is hindered by reduced performance owing to the thick catalyst layer, restricting mass transfer and the O2 supply. Metal-organic frameworks (MOFs) are a promising class of crystal materials, but their narrow pores exacerbate the sluggish mass-transport properties within the catalyst layer. This study developed an approach for constructing an open-pore structure in MOFs via chelation-assisted selective etching, resulting in atomically dispersed Fe atoms anchored on an N, S co-doped carbon framework. The open-pore structure reduces oxygen transport resistance in the membrane electrode assembly (MEA) with unprecedented ORR activity and stability, as evidenced by finite element simulations. In an acidic electrolyte, the OP-Fe-NC catalyst shows a half-wave potential of 0.89 V vs RHE, surpassing Pt/C by 20 mV, and a current density of 29 mA cm-2 at 0.9 ViR-free in the MEA. This study provides an effective structural strategy for fabricating electrocatalysts with high mass efficiency and atomic precision for energy storage and conversion devices.

Cannabis Use Is Associated With Depression Severity and Suicidality in the National Comorbidity Survey-Adolescent Supplement.

Objective: To investigate the association of cannabis use with major depression and suicidal behavior in adolescence. Method: Data are from the National Comorbidity Survey-Adolescent Supplement N=10,123, a nationally representative survey of adolescents aged 13 to 18 years. Weighted logistic regression and ordinal regression analyses of major depression and suicidal behavior outcomes were conducted on cannabis variables, incorporating sociodemographic characteristics. Results: Adolescents with lifetime cannabis use have 2.07 times higher odds of mild/moderate (adjusted odds ratio [aOR]; 95% CI=1.69, 2.53) and 3.32 times higher odds of severe major depressive disorder (MDD; aOR; 95% CI=2.31, 4.75). Cannabis use (aOR 6.90, 95% CI=4.67,10.19), mild/moderate MDD (aOR 4.10, 95% CI=2.82, 5.98), and severe MDD (aOR 13.97, 95% CI = 7.59, 25.70) were associated with higher odds of suicide attempt. Past 12-month cannabis use (aOR 3.70, 95% CI = 2.16, 6.32), mild/moderate major depressive episodes (MDE) (aOR 7.85, 95% CI=3.59, 17.17), and severe MDE (aOR 36.36, 95% CI=13.68,96.64) were associated with higher odds of suicide attempt. The frequency of past 12-month cannabis use was associated with higher odds of suicide attempt and with MDE severity, with higher odds among individuals who use cannabis 3 or more days than among individuals who use cannabis less frequently, suggesting a dose effect. Among cannabis users, older age of onset of cannabis use was associated with lower odds of suicidal behaviors. Conclusion: Cannabis use is associated with higher odds of depression and depression severity in adolescence. Furthermore, depression and cannabis use are independently associated with higher odds of suicide attempt. Diversity & Inclusion Statement: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

Adjacent Copper Single Atoms Promote C-C Coupling in Electrochemical CO2 Reduction for the Efficient Conversion of Ethanol.

The electrochemical CO2 reduction reaction (CO2RR) using renewable electricity is one of the most promising strategies for reaching the goal of carbon neutrality. Multicarbonous (C2+) products have broad applications, and ethanol is a valuable chemical and fuel. Many Cu-based catalysts have been reported to be efficient for the electrocatalytic CO2RR to C2+ products, but they generally offer limited selectivity and current density toward ethanol. Herein, we proposed a silica-mediated hydrogen-bonded organic framework (HOF)-templated approach to preparing ultrahigh-density Cu single-atom catalysts (SACs) on thin-walled N-doped carbon nanotubes (TWN). The content of Cu in the catalysts prepared by this method could be up to 13.35 wt %. It was found that the catalysts showed outstanding performance for the electrochemical CO2RR to ethanol, and the Faradaic efficiency (FE) of ethanol increased with the increase in Cu-N3 site density. The FE of ethanol over the catalysts with 13.35 wt % Cu could reach ∼81.9% with a partial current density of 35.6 mA cm-2 using an H-type cell, which is the best result for electrochemical CO2RR to ethanol to date. In addition, the catalyst could be stably used for more than 25 h. Experimental and density functional theory (DFT) studies revealed that the adjacent Cu-N3 active sites (one Cu atom coordinates with three N) were the active sites for the reaction, and their high density was crucial for the high FE of ethanol because the adjacent Cu-N3 sites with a short distance could promote the C-C coupling synergistically.

A Cell Cycle-Dependent Ferroptosis Sensitivity Switch Governed by EMP2.

Ferroptosis is a non-apoptotic form of cell death characterized by iron-dependent lipid peroxidation. Ferroptosis can be induced by system xc- cystine/glutamate antiporter inhibition or by direct inhibition of the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). The regulation of ferroptosis in response to system xc- inhibition versus direct GPX4 inhibition may be distinct. Here, we show that cell cycle arrest enhances sensitivity to ferroptosis triggered by GPX4 inhibition but not system xc- inhibition. Arrested cells have increased levels of oxidizable polyunsaturated fatty acid-containing phospholipids, which drives sensitivity to GPX4 inhibition. Epithelial membrane protein 2 (EMP2) expression is reduced upon cell cycle arrest and is sufficient to enhance ferroptosis in response to direct GPX4 inhibition. An orally bioavailable GPX4 inhibitor increased markers of ferroptotic lipid peroxidation in vivo in combination with a cell cycle arresting agent. Thus, responses to different ferroptosis-inducing stimuli can be regulated by cell cycle state.

Superhydrophilic and Superaerophobic Ru-Loaded NiCo Bimetallic Hydroxide Achieves Efficient Hydrogen Evolution over All pH Ranges.

Realizing an effective, binder-free, and super-wetting electrocatalyst for the hydrogen evolution reaction (HER) at full pH is essential for the creation of clean hydrogen. In this study, the Ru-loaded NiCo bimetallic hydroxide (Ru@NiCo-BH) catalyst was prepared by spontaneous redox reaction. The chemical interaction between Ru NPs and NiCo-BH by the Ru-O-M (M=Ni, Co) interface bond, the electron-rich Ru active site, and the multi-channel nickel foam carrier make the superhydrophilic and superaerophobic surface advantageous for mass transfer in the HER process. Therefore, Ru@NiCo-BH has remarkable HER activity, with low overpotential of 29, 68 and 80 mV, a 10 mA cm-2 current density can be obtained in alkaline, neutral and acidic electrolytes respectively. This work provides a reference for the rational development of universal electrocatalysts for hydrogen evolution in the all pH ranges through simple design strategies.

Reprogramming of iron metabolism confers ferroptosis resistance in ECM-detached cells.

Cancer cells often acquire resistance to cell death programs induced by loss of integrin-mediated attachment to extracellular matrix (ECM). Given that adaptation to ECM-detached conditions can facilitate tumor progression and metastasis, there is significant interest in effective elimination of ECM-detached cancer cells. Here, we find that ECM-detached cells are remarkably resistant to the induction of ferroptosis. Although alterations in membrane lipid content are observed during ECM detachment, it is instead fundamental changes in iron metabolism that underlie resistance of ECM-detached cells to ferroptosis. More specifically, our data demonstrate that levels of free iron are low during ECM detachment because of changes in both iron uptake and iron storage. In addition, we establish that lowering the levels of ferritin sensitizes ECM-detached cells to death by ferroptosis. Taken together, our data suggest that therapeutics designed to kill cancer cells by ferroptosis may be hindered by lack of efficacy toward ECM-detached cells.

Lithiophilic Interface Layer Induced Uniform Deposition for Dendrite-free Lithium Metal Anodes in a 3D Polyethersulfone Frame.

Lithium metal anodes possess ultrahigh theoretical specific capacity for next-generation lithium metal batteries, but the infinite volume expansion and the growth of lithium dendrites remain a huge obstacle to their commercialization. Therefore, here, we construct a CuO-loaded 3D polyethersulfone (PES) nanofiber frame onto a lithiophilic Cu2O/Cu substrate to promote the lithium storage performance of the composite anode, and the 3D frame can effectively alleviate the volume expansion of lithium (Li) metal anodes. Meanwhile, lithium reacts with CuO in the composite nanofiber and Cu2O of the substrate to generate Li2O, which can strengthen the solid electrolyte interface (SEI) layer and achieve the uniform deposition of lithium. In addition, the combination of the heat treatment method and electrospinning technology solves the problem of poor adhesion between the fiber film and the substrate. As a result, the PES/CuO-Cu2O (PCC) composite current collector still maintains a smooth and flat lithium-depositing layer at 5 mA cm-2. The PCC-assembled Li||Cu half-cell can operate stably for 320 cycles at 0.5 mA cm-2, which is about 4 times that of bare Cu. Furthermore, symmetrical batteries with PCC@Li can maintain excellent cycle stability for 1770 h. Accordingly, this work provides a low-cost and highly effective strategy for stabilizing the lithium metal anode.