Cyclic catalysis of intratumor Fe3+/2+ initiated by a hollow mesoporous iron sesquioxide nanoparticle for ferroptosis therapy of large tumors.

Numerous nanoparticles have been utilized to deliver Fe2+ for tumor ferroptosis therapy, which can be readily converted to Fe3+via Fenton reactions to generate hydroxyl radical (•OH). However, the ferroptosis therapeutic efficacy of large tumors is limited due to the slow conversion of Fe3+ to Fe2+via Fenton reactions. Herein, a strategy of intratumor Fe3+/2+ cyclic catalysis is proposed for ferroptosis therapy of large tumors, which was realized based on our newly developed hollow mesoporous iron sesquioxide nanoparticle (HMISN). Cisplatin (CDDP) and Gd-poly(acrylic acid) macrochelates (GP) were loaded into the hollow core of HMISN, whose surface was modified by laccase (LAC). Fe3+, CDDP, GP, and LAC can be gradually released from CDDP@GP@HMISN@LAC in the acidic tumor microenvironment. The intratumor O2 can be catalyzed into superoxide anion (O2•-) by LAC, and the intratumor NADPH oxidases can be activated by CDDP to generate O2•-. The O2•- can react with Fe3+ to generate Fe2+, and raise H2O2 level via the superoxide dismutase. The generated Fe2+ and H2O2 can be fast converted into Fe3+ and •OH via Fenton reactions. The cyclic catalysis of intratumor Fe3+/2+ initiated by CDDP@GP@HMISN@LAC can be used for ferroptosis therapy of large tumors.

Nanoscale chemical characterization of materials and interfaces by tip-enhanced Raman spectroscopy.

Materials and their interfaces are the core for the development of a large variety of fields, including catalysis, energy storage and conversion. In this case, tip-enhanced Raman spectroscopy (TERS), which combines scanning probe microscopy with plasmon-enhanced Raman spectroscopy, is a powerful technique that can simultaneously obtain the morphological information and chemical fingerprint of target samples at nanometer spatial resolution. It is an ideal tool for the nanoscale chemical characterization of materials and interfaces, correlating their structures with chemical performances. In this review, we begin with a brief introduction to the nanoscale characterization of materials and interfaces, followed by a detailed discussion on the recent theoretical understanding and technical improvements of TERS, including the origin of enhancement, TERS instruments, TERS tips and the application of algorithms in TERS. Subsequently, we list the key experimental issues that need to be addressed to conduct successful TERS measurements. Next, we focus on the recent progress of TERS in the study of various materials, especially the novel low-dimensional materials, and the progresses of TERS in studying different interfaces, including both solid-gas and solid-liquid interfaces. Finally, we provide an outlook on the future developments of TERS in the study of materials and interfaces.

Unveiling Electrostatic Interaction Modulated Sensitive and Selective SERS Detection for Negatively Charged Molecules.

Strengthening the interaction between the target and SERS substrate is crucial for sensitive SERS detection; we thereby explored the molecular structure-dependent SERS sensitivity for negatively charged targets on the positively charged SERS substrate. Both experimental and theoretical studies confirm that the SERS sensitivity is determined by the electrostatic interaction between the target and linker. This interaction is not only manipulated by the protonation capacity of the linker and its surface adsorption configuration and geometry but also significantly determined by the target's structure, encompassing electronegativity and the number of interaction sites. The optimized interaction leads to a marked improvement in detection sensitivity of up to 1-3 orders of magnitude. The interaction mechanism revealed in this work not only provides theoretical guidance and technical support for electrostatically driven SERS detection but also offers a conceptual framework that can be extended to various SERS detections based on diverse surface forces.

Label-Free Detection of DNA Base Mutation and Hybridized DNA by an Electrostatically Modified 3D Plasmonic Array.

Surface-enhanced Raman scattering (SERS) represents a promising avenue for DNA detection as it offers intrinsic chemical insights with high sensitivity compared to conventional methods. However, label-free and quantitative detection of unmodified DNA by SERS remains a major challenge in DNA analysis. To overcome this challenge, we propose a positively charged plasmonic nanosurface for DNA capture and quantitative analysis. Highly sensitive and uniform SERS enhancement was realized by a three-dimensional plasmonic array supporting well-designed hybrid plasmonic modes. Subsequently, the plasmonic array was modified with an electrostatically functionalized PDDA (poly(diene-dimethylammonium-chloride)) self-assembled monolayer in a single step. The effectiveness of the resulting PDDA-SERS substrate was demonstrated by the label-free and quantitative detection of base content and base mutation in hybridized DNA. The PDDA-SERS substrate provides a robust platform for SERS analysis not only of DNA but also of other electronegative analytes.

Radially distributed charging time constants at an electrode-solution interface.

An electrochemically homogeneous electrode-solution interface should be understood as spatially invariant in both terms of intrinsic reactivity for the electrode side and electrical resistance mainly for the solution side. The latter remains presumably assumed in almost all cases. However, by using optical microscopy to spatially resolve the classic redox electrochemistry occurring at the whole surface of a gold macroelectrode, we discover that the electron transfer occurs always significantly sooner (by milliseconds), rather than faster in essence, at the radial coordinates closer to the electrode periphery than the very center. So is the charging process when there is no electron transfer. Based on optical measurements of the interfacial impedance, this spatially unsynchronized electron transfer is attributed to a radially non-uniform distribution of solution resistance. We accordingly manage to eliminate the heterogeneity by engineering the solution resistance distribution. The revealed spatially-dependent charging time 'constant' (to be questioned) would help paint our overall fundamental picture of electrode kinetics.

Encephaloduroarteriosynangiosis for Symptomatic Intracranial Atherosclerotic Arterial Steno-Occlusive Disease: Clinical and Radiological Outcomes.

BACKGROUND: This study investigated the long-term clinical and angiographic outcomes of encephaloduroarteriosynangiosis treatment for symptomatic intracranial atherosclerotic arterial steno-occlusive disease to further evaluate the potential therapeutic role of encephaloduroarteriosynangiosis in this population. METHODS AND RESULTS: A total of 152 adult patients with symptomatic intracranial atherosclerotic arterial steno-occlusive disease who were treated with encephaloduroarteriosynangiosis and intensive medical management across 3 tertiary centers in China between January 2011 and September 2019 were retrospectively included. The primary outcomes were defined as postoperative cerebrovascular events, including ischemic and hemorrhagic stroke. The postoperative neovascularization was analyzed qualitatively and quantitatively by using angiography. Clinical, radiological, and long-term follow-up data were analyzed using Cox regression, logistic regression, and linear regression analyses. Primary outcome rates were 3.2% (5/152) within 30 days, 6.6% (10/152) within 2 years, 9.2% (14/152) within 5 years, and 11.1% (17/152) during a median 9.13 years follow-up. Initial infarction symptoms were positively associated with recurrent ischemic stroke. Additionally, posterior circulation involvement and coexisting cardiac disease indicated poorer neurological status, whereas encephaloduroarteriosynangiosis neovascularization efficacy was negatively associated with older age and vascular risk factors but positively associated with posterior circulation involvement. CONCLUSIONS: Encephaloduroarteriosynangiosis plus intensive medical management appears efficacious and safe for symptomatic intracranial atherosclerotic arterial steno-occlusive disease, with low perioperative risk and favorable long-term results. Further prospective trials are needed to verify its efficacy and determine the optimal patient selection criteria.

A strategy of "adding fuel to the flames" enables a self-accelerating cycle of ferroptosis-cuproptosis for potent antitumor therapy.

Cuproptosis in antitumor therapy faces challenges from copper homeostasis efflux mechanisms and high glutathione (GSH) levels in tumor cells, hindering copper accumulation and treatment efficacy. Herein, we propose a strategy of "adding fuel to the flames" for potent antitumor therapy through a self-accelerating cycle of ferroptosis-cuproptosis. Disulfiram (DSF) loaded hollow mesoporous copper-iron sulfide (HMCIS) nanoparticle with conjugation of polyethylene glycol (PEG) and folic acid (FA) (i.e., DSF@HMCIS-PEG-FA) was developed to swiftly release DSF, H2S, Cu2+, and Fe2+ in the acidic tumor microenvironment (TME). The hydrogen peroxide (H2O2) levels and acidity within tumor cells enhanced by the released H2S induce acceleration of Fenton (Fe2+) and Fenton-like (Cu2+) reactions, enabling the powerful tumor ferroptosis efficacy. The released DSF acts as a role of "fuel", intensifying catalytic effect ("flame") in tumor cells through the sustainable Fenton chemistry (i.e., "add fuel to the flames"). Robust ferroptosis in tumor cells is characterized by serious mitochondrial damage and GSH depletion, leading to excess intracellular copper that triggers cuproptosis. Cuproptosis disrupts mitochondria, compromises iron-sulfur (Fe-S) proteins, and elevates intracellular oxidative stress by releasing free Fe3+. These interconnected processes form a self-accelerating cycle of ferroptosis-cuproptosis with potent antitumor capabilities, as validated in both cancer cells and tumor-bearing mice.

Ultralow-Frequency Tip-Enhanced Raman Scattering Discovers Nanoscale Radial Breathing Mode on Strained 2D Semiconductors.

Collective excitations including plasmons, magnons, and layer-breathing vibration modes emerge at an ultralow frequency (<1 THz) and are crucial for understanding van der Waals materials. Strain at the nanoscale can drastically change the property of van der Waals materials and create localized states like quantum emitters. However, it remains unclear how nanoscale strain changes collective excitations. Herein, ultralow-frequency tip-enhanced Raman spectroscopy (TERS) with sub-10 nm resolution under ambient conditions is developed to explore the localized collective excitation on monolayer semiconductors with nanoscale strains. A new vibrational mode is discovered at around 12 cm-1 (0.36 THz) on monolayer MoSe2 nanobubbles and it is identified as the radial breathing mode (RBM) of the curved monolayer. The correlation is determined between the RBM frequency and the strain by simultaneously performing deterministic nanoindentation and TERS measurement on monolayer MoSe2. The generality of the RBM in nanoscale curved monolayer WSe2 and bilayer MoSe2 is demonstrated. Using the RBM frequency, the strain of the monolayer MoSe2 on the nanoscale can be mapped. Such an ultralow-frequency vibration from curved van der Waals materials provides a new approach to study nanoscale strains and points to more localized collective excitations to be discovered at the nanoscale.

A Survey on 3D Skeleton-Based Action Recognition Using Learning Method.

Three-dimensional skeleton-based action recognition (3D SAR) has gained important attention within the computer vision community, owing to the inherent advantages offered by skeleton data. As a result, a plethora of impressive works, including those based on conventional handcrafted features and learned feature extraction methods, have been conducted over the years. However, prior surveys on action recognition have primarily focused on video or red-green-blue (RGB) data-dominated approaches, with limited coverage of reviews related to skeleton data. Furthermore, despite the extensive application of deep learning methods in this field, there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures. To address these limitations, this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional (3D) skeleton data as a valuable modality. Subsequently, we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures, i.e., recurrent neural networks, convolutional neural networks, graph convolutional network, and Transformers. All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion. Finally, we offer insights into the current largest 3D skeleton dataset, NTU-RGB+D, and its new edition, NTU-RGB+D 120, along with an overview of several top-performing algorithms on these datasets. To the best of our knowledge, this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.

Development and validation of a novel nomogram for predicting good neoangiogenesis after encephaloduroarteriosynangiosis in patients with moyamoya disease and type 2 diabetes mellitus: a case-control study.

OBJECTIVE: Diabetes is often linked to poorer outcomes in patients with moyamoya disease (MMD). However, experience has shown that certain individuals with diabetes have favorable outcomes after encephaloduroarteriosynangiosis (EDAS). The authors aimed to develop a nomogram to predict good neoangiogenesis in patients with MMD and type 2 diabetes mellitus (T2DM) to aid neurosurgeons in the identification of suitable candidates for EDAS. METHODS: Adults with MMD and T2DM who underwent EDAS between June 2004 and December 2018 were included in the analysis. In total, 126 patients (213 hemispheres) with MMD and T2DM from the Fifth Medical Centre of the Chinese PLA General Hospital were included and randomly divided into training (152 hemispheres) and internal validation (61 hemispheres) cohorts at a ratio of 7:3. Univariate logistic and least absolute shrinkage and selection operator regression analyses were used to identify the significant factors associated with good neoangiogenesis, which were used to develop a nomogram. The discrimination, calibration, and clinical utility were assessed. RESULTS: A total of 213 hemispheres in 126 patients were reviewed, including 152 (71.36%) hemispheres with good postoperative collateral formation and 61 (28.64%) with poor postoperative collateral formation. The authors selected 4 predictors (FGD5 rs11128722, VEGFA rs9472135, Suzuki stage, and internal carotid artery [ICA] moyamoya vessels) for nomogram development. The C-indices of the nomogram in the training and internal validation cohorts were 0.873 and 0.841, respectively. The nomogram exhibited a sensitivity of 84.5% and specificity of 81.0%. The positive and negative predictive values were 92.1% and 66.7%, respectively. The calibration curves indicated high predictive accuracy, and receiver operating characteristic curve analysis showed the superiority of the nomogram. The decision-making analysis validated the fitness and clinical application value of this nomogram. Then a web-based calculator to facilitate clinical application was generated. CONCLUSIONS: The nomogram developed in this study accurately predicted neoangiogenesis in patients with MMD and T2DM after EDAS and may assist neurosurgeons in identifying suitable candidates for indirect revascularization surgery.

Challenges and support needs in psychological and physical health among pilots: a qualitative study.

Introduction: Physical and mental health problems among pilots affect their working state and impact flight safety. Although pilots' physical and mental health problems have become increasingly prominent, their health has not been taken seriously. This study aimed to clarify challenges and support needs related to psychological and physical health among pilots to inform development of a more scientific and comprehensive physical and mental health system for civil aviation pilots. Methods: This qualitative study recruited pilots from nine civil aviation companies. Focus group interviews via an online conference platform were conducted in August 2022. Colaizzi analysis was used to derive themes from the data and explore pilots' experiences, challenges, and support needs. Results: The main sub-themes capturing pilots' psychological and physical health challenges were: (1) imbalance between family life and work; (2) pressure from assessment and physical examination eligibility requirements; (3) pressure from worries about being infected with COVID-19; (4) nutrition deficiency during working hours; (5) changes in eating habits because of the COVID-19 pandemic; (6) sleep deprivation; (7) occupational diseases; (8) lack of support from the company in coping with stress; (9) pilots' yearly examination standards; (10) support with sports equipment; (11) respecting planned rest time; and (12) isolation periods. Discussion: The interviewed pilots experienced major psychological pressure from various sources, and their physical health condition was concerning. We offer several suggestions that could be addressed to improve pilots' physical and mental health. However, more research is needed to compare standard health measures for pilots around the world in order to improve their physical and mental health and contribute to overall aviation safety.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (µg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.

Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities.

While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.

Diverse nucleotide substitutions in rice base editing mediated by novel TadA variants.

CRISPR-mediated base editors have been widely used to correct defective alleles and create novel alleles by artificial evolution for the rapid genetic improvement of crops. The editing capabilities of base editors strictly rely on the performance of various nucleotide modification enzymes. Compared with the well-developed adenine base editors (ABEs), cytosine base editors (CBEs) and dual base editors suffer from unstable editing efficiency and patterns at different genomic loci in rice, significantly limiting their application. Here, we comprehensively examined the base editing activities of multiple evolved TadA8e variants in rice. We found that both TadA-CDd and TadA-E27R/N46L achieved more robust C-to-T editing than previously reported hyperactive hAID∗Δ, and TadA-CDd outperformed TadA-E27R/N46L. A C-to-G base editor (CGBE) engineered with TadA-CDd and OsUNG performed highly efficient C-to-G editing in rice compared with that of TadA-N46P. In addition, a dual base editor constructed with a single protein, TadDE, enabled simultaneous, highly efficient C-to-T and A-to-G editing in rice. Collectively, our results demonstrate that TadA8e derivatives improve both CBEs and dual base editors in rice, providing a powerful way to induce diverse nucleotide substitutions for plant genome editing.

Pyruvate kinase M2 sustains cardiac mitochondrial integrity in septic cardiomyopathy by regulating PHB2-dependent mitochondrial biogenesis.

Previous studies have highlighted the protective effects of pyruvate kinase M2 (PKM2) overexpression in septic cardiomyopathy. In our study, we utilized cardiomyocyte-specific PKM2 knockout mice to further investigate the role of PKM2 in attenuating LPS-induced myocardial dysfunction, focusing on mitochondrial biogenesis and prohibitin 2 (PHB2). Our findings confirmed that the deletion of PKM2 in cardiomyocytes significantly exacerbated LPS-induced myocardial dysfunction, as evidenced by impaired contractile function and relaxation. Additionally, the deletion of PKM2 intensified LPS-induced myocardial inflammation. At the molecular level, LPS triggered mitochondrial dysfunction, characterized by reduced ATP production, compromised mitochondrial respiratory complex I/III activities, and increased ROS production. Intriguingly, the absence of PKM2 further worsened LPS-induced mitochondrial damage. Our molecular investigations revealed that LPS disrupted mitochondrial biogenesis in cardiomyocytes, a disruption that was exacerbated by the absence of PKM2. Given that PHB2 is known as a downstream effector of PKM2, we employed PHB2 adenovirus to restore PHB2 levels. The overexpression of PHB2 normalized mitochondrial biogenesis, restored mitochondrial integrity, and promoted mitochondrial function. Overall, our results underscore the critical role of PKM2 in regulating the progression of septic cardiomyopathy. PKM2 deficiency impeded mitochondrial biogenesis, leading to compromised mitochondrial integrity, increased myocardial inflammation, and impaired cardiac function. The overexpression of PHB2 mitigated the deleterious effects of PKM2 deletion. This discovery offers a novel insight into the molecular mechanisms underlying septic cardiomyopathy and suggests potential therapeutic targets for intervention.

SYN1 variant causes X-linked neurodevelopmental disorders: a case report of variable clinical phenotypes in siblings.

The SYN1 gene encodes synapsin I, variants within the SYN1 gene are linked to X-linked neurodevelopmental disorders with high clinical heterogeneity, with reflex epilepsies (REs) being a representative clinical manifestation. This report analyzes a Chinese pedigree affected by seizures associated with SYN1 variants and explores the genotype-phenotype correlation. The proband, a 9-year-old boy, experienced seizures triggered by bathing at the age of 3, followed by recurrent absence seizures, behavioral issues, and learning difficulties. His elder brother exhibited a distinct clinical phenotype, experiencing sudden seizures during sleep at the age of 16, accompanied by hippocampal sclerosis. Whole exome sequencing (WES) confirmed a pathogenic SYN1 variant, c.1647_1650dup (p. Ser551Argfs*134), inherited in an X-linked manner from their mother. Notably, this variant displayed diverse clinical phenotypes in the two brothers and one previously reported case in the literature. Retrospective examination of SYN1 variants revealed an association between truncating variants and the pathogenicity of REs, and non-truncating variants are more related to developmental delay/intellectual disability (DD/ID). In summary, this study contributes to understanding complex neurodevelopmental disorders associated with SYN1, highlighting the clinical heterogeneity of gene variants and emphasizing the necessity for comprehensive genetic analysis in elucidating the pathogenic mechanisms of such diseases.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.