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Neurotransmitters carrying specific chemical information play a key role in the nervous system, which inspires ion‐based devices to mimic biological functions of nervous system, including sensing, transduction, and computing. In this work, the interaction mechanism of zwitterionic amino acids, glycine (Gly), and gamma‐aminobutyric acid (GABA) (two common inhibitory neurotransmitters) are analyzed with porous carbon electrodes. The charge balance of these zwitterionic neurotransmitters relies on zwitterion protonation in the vicinity of the negative electrodes (produced cations adsorbed by the negative electrode) and deprotonation near the positive electrode (produced anions to balance the positive charges) as demonstrated based on the theoretical dissociation analyses and local pH measurements. Additionally, electrosorption‐induced concentration changes of Gly and GABA in aqueous solutions and phosphate‐buffered saline (PBS) solutions are realized by electrosorption via nanoporous carbon electrodes. Based on this mechanistic understanding, the ionologic functions in printed ionic electrolyte double‐layer transistors are demonstrated. These neurotransmitter‐based iontronic devices hold potential for further organism‐machine interfacing and neuromorphic computing applications. [ABSTRACT FROM AUTHOR]

The revolution of automotive vehicles (from petrol vehicles to electric vehicles) has set high demands for the performance of batteries. Lithium-metal batteries (LMBs) show great potential owing to their high energy density but encounter poor cycle life and safety issues. It is of great significance to reveal LMB failure mechanisms and understand their relationship with battery performance. This review presents an overview of the state-of-the-art Li-metal anodes, with an emphasis on two typical failure modes: capacity degradation and dendritic growth of Li metal. The critical correlations between the composition, structure and failure are explained point by point. The chemical and electrochemical stabilities of the lithium anode are discussed. Particularly, for the first time, five types of lithium-metal anodes are classified to develop a comprehensive understanding of LMBs. Furthermore, strategies are suggested to improve the practical performance of LMBs, including material innovation, electrolyte modification and advanced characterization. [ABSTRACT FROM AUTHOR]

This study sought to identify a reference tissue‐based quantification approach for improving the statistical power in detecting changes in brain glucose metabolism, amyloid, and tau deposition in Alzheimer's disease studies. A total of 794, 906, and 903 scans were included for 18F‐FDG, 18F‐florbetapir, and 18F‐flortaucipir, respectively. Positron emission tomography (PET) and T1‐weighted images of participants were collected from the Alzheimer's disease Neuroimaging Initiative database, followed by partial volume correction. The standardized uptake value ratios (SUVRs) calculated from the cerebellum gray matter, centrum semiovale, and pons were evaluated at both region of interest (ROI) and voxelwise levels. The statistical power of reference tissues in detecting longitudinal SUVR changes was assessed via paired t‐test. In cross‐sectional analysis, the impact of reference tissue‐based SUVR differences between cognitively normal and cognitively impaired groups was evaluated by effect sizes Cohen's d and two sample t‐test adjusted by age, sex, and education levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of centrum semiovale and cerebellum gray matter in detecting glucose metabolism decreases, while the centrum semiovale reference tissue‐based SUVR provided higher t values for the detection of amyloid and tau deposition increases. The three reference tissues generated comparable d images for 18F‐FDG, 18F‐florbetapir, and 18F‐flortaucipir and comparable t maps for 18F‐florbetapir and 18F‐flortaucipir, but pons‐based t map showed superior performance in 18F‐FDG. In conclusion, the tracer‐specific reference tissue improved the detection of 18F‐FDG, 18F‐florbetapir, and 18F‐flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring of disease progression, and therapeutic response in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The field of bioinspired iontronics, bridging electronic devices and ionic systems, has multiple biological applications. Carbon-based ultracapacitive devices hold promise for controlling bioactive ions via electric double layers due to their high-surface-area and biocompatible porous carbon electrodes. However, the interplay between complex bioactive ions and porous carbons remains unclear due to the variety of structures of bioactive ions present in biological systems. Herein, we investigate the adsorption behavior of a series of bioactive ammonium-based cations with varying alkyl chain lengths in nanoporous carbons. We find that strong physisorption results from the synergistic hydrophobic interaction and electrostatic attraction between porous carbons (with a negative zeta potential) and bioactive cations. Bioactive cations with varying alkyl chain lengths can be irreversibly physically adsorbed and confined within nanoporous carbons resulting in anion enrichment and depletion during electric polarization. This situation, in turn, results in a characteristic memristive behavior in all-carbon capacitive ionic memristor devices. Our findings highlight the relationship between the resistance state of the memristor and ion adsorption mechanisms in all-carbon capacitive devices, which hold potential for future transmitter delivery, biointerfacing, and neuromorphic devices., (© 2024 Wiley‐VCH GmbH.)

DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited. Here, a molecular learning vector quantization neural network (LVQNN) model based on DNA strand displacement (DSD) technology for breast tumor diagnosis is developed. Compared to previous work, the molecular LVQNN boasts powerful computing abilities, handling high-dimensional data for intelligent cancer diagnosis. To verify the feasibility and versatility of the network, two distinct typical datasets are selected: one from a single source with cell morphology data from 569 cases, and a more extensive one spanning different populations and ages, with miRNA gene expression data from 1881 cases. By using the molecular LVQNN, diagnostic experiments are conducted on 50 and 120 public individuals from these two datasets, respectively, achieving accuracy rates of 94% and 97.5%. This study demonstrates that the LVQNN model exhibits significant advantages in breast cancer diagnosis and enhances diagnostic accuracy while introducing new approaches for intelligent cancer diagnosis, anticipated to bring significant breakthroughs and application prospects to precise cancer medicine., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Background: The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD ( n  = 218,792,101,866 cases), end-stage renal disease (ESRD, n  = 16,405, 326 cases), diabetes ( n  = 202,046, 9,889 cases), creatinine ( n  = 7,810), and uric acid (UA, n  = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects., Results: The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant ( p  < 0.05) discrimination between the Kaplan-Meier curves of each age, diabetes, ipth, and creatinine subclassification. MR analysis indicated that diabetes had a causal role in risk of CVD ( β  =   0.088, p  < 0.0001) and ESRD ( β  =   0.26, p =  0.007). In turn, ESRD was found to have a causal role in risk of diabetes ( β  =   0.027, p  = 0.013). Additionally, creatinine exhibited a causal role in the risk of ESRD ( β  =   4.42, p  = 0.01)., Conclusions: The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Tian, Xu, Wang, Zhu, Huang, Liu, Li and Li.)

This study sought to identify a reference tissue-based quantification approach for improving the statistical power in detecting changes in brain glucose metabolism, amyloid, and tau deposition in Alzheimer's disease studies. A total of 794, 906, and 903 scans were included for 18 F-FDG, 18 F-florbetapir, and 18 F-flortaucipir, respectively. Positron emission tomography (PET) and T1-weighted images of participants were collected from the Alzheimer's disease Neuroimaging Initiative database, followed by partial volume correction. The standardized uptake value ratios (SUVRs) calculated from the cerebellum gray matter, centrum semiovale, and pons were evaluated at both region of interest (ROI) and voxelwise levels. The statistical power of reference tissues in detecting longitudinal SUVR changes was assessed via paired t-test. In cross-sectional analysis, the impact of reference tissue-based SUVR differences between cognitively normal and cognitively impaired groups was evaluated by effect sizes Cohen's d and two sample t-test adjusted by age, sex, and education levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of centrum semiovale and cerebellum gray matter in detecting glucose metabolism decreases, while the centrum semiovale reference tissue-based SUVR provided higher t values for the detection of amyloid and tau deposition increases. The three reference tissues generated comparable d images for 18 F-FDG, 18 F-florbetapir, and 18 F-flortaucipir and comparable t maps for 18 F-florbetapir and 18 F-flortaucipir, but pons-based t map showed superior performance in 18 F-FDG. In conclusion, the tracer-specific reference tissue improved the detection of 18 F-FDG, 18 F-florbetapir, and 18 F-flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring of disease progression, and therapeutic response in Alzheimer's disease., (© 2022 United Imaging Healthcare. Human Brain Mapping published by Wiley Periodicals LLC.)

Aberrant expression of long non-coding RNAs (lncRNAs) has been reported in multiple cancers. However, the underlying mechanisms mediated by super-enhancers remain elusive. Here we sought to define the role of a novel lncRNA termed lncRNA-DAW in tumorigenesis. Our results revealed that lncRNA-DAW was driven by a liver-specific super-enhancer and transcriptionally activated by HNF4G, leading to frequent elevation in hepatocellular carcinoma (HCC) specimens. Ectopic expression of lncRNA-DAW promoted both in vivo and in vitro tumor growth. By using RNA sequencing, Wnt2 was screened out as a downstream effector of lncRNA-DAW. We next found that lncRNA-DAW physically interacted with EZH2, a negative regulator of Wnt2. This interplay subsequently potentiated CDK1-EZH2 interaction, leading to the phosphorylation and ubiquitination of EZH2. The lncRNA-DAW-mediated EZH2 degradation facilitated the de-repression of Wnt2 transcription, which eventually activated the Wnt/β-catenin pathway. Furthermore, we verified that Wnt2 potentiated in vitro and in vivo cancer cell growth by activating the Wnt/β-catenin pathway. Finally, Wnt2 amplification was confirmed as a common event in liver cancer, and the expression of lncRNA-DAW was positively correlated with Wnt2 in HCC specimens. Collectively, we are the first to identify lncRNA-DAW as a novel candidate oncogene in liver cancer, and this lncRNA may serve as a novel clinical diagnosis biomarker for liver cancer., Competing Interests: The authors declare no competing interests., (© 2021.)