Growth Mechanism of Carbon Nanotubes Revealed by in situ Transmission Electron Microscopy.

Elucidating the growth mechanism of carbon nanotubes (CNTs) is critical to obtaining CNTs with desired structures and tailored properties for their practical applications. With atomic resolution imaging, in situ transmission electron microscopy (TEM) has been a key technique to reveal the microstructure and dynamics of CNTs in real time. In this review, recent advances in the development of in situ TEM with different types of environmental reactors will be introduced. The catalytic growth mechanisms of CNTs revealed by in situ TEM under realistic conditions are discussed from fundamental thermodynamics and kinetics to the detailed nucleation, growth, and termination mechanisms, including the state and phase of active catalysts, interfacial connections between catalyst and growing CNTs, and catalyst-related growth kinetics of CNTs. Great progresses have been made on how a CNT nucleates, grows and terminates, focusing on the interface dynamics and kinetic fluctuations. Finally, challenges and future directions for understanding the atomic dynamics under the real growth conditions are proposed. It is expected that breakthroughs in the fundamental growth mechanisms will pave the way to the ultimate goal of designing and controlling the atomic structures of CNTs for their applications in various devices.

Regorafenib induces damage-associated molecular patterns, cancer cell death and immune modulatory effects in a murine triple negative breast cancer model.

Damage associated molecular patterns (DAMPs), including calreticulin (CRT) exposure, high-mobility group box 1 protein (HMGB1) elevation, and ATP release, characterize immunogenic cell death (ICD) and may play a role in cancer immunotherapy. Triple negative breast cancer (TNBC) is an immunogenic subtype of breast cancer with higher lymphocyte infiltration. Here, we found that regorafenib, a multi-target angiokinase inhibitor previously known to suppress STAT3 signaling, induced DAMPs and cell death in TNBC cells. Regorafenib induced the expression of HMGB1 and CRT, and the release of ATP. Regorafenib-induced HMGB1 and CRT were attenuated following STAT3 overexpression. In a 4T1 syngeneic murine model, regorafenib treatment increased HMGB1 and CRT expression in xenografts, and effectively suppressed 4T1 tumor growth. Immunohistochemical staining revealed increased CD4+ and CD8+ tumor-infiltrating T cells in 4T1 xenografts following regorafenib treatment. Regorafenib treatment or programmed death-1 (PD-1) blockade using anti-PD-1 monoclonal antibody reduced lung metastasis of 4T1 cells in immunocompetent mice. While regorafenib increases the proportion of MHC II high expression on dendritic cells in mice with smaller tumors, the combination of regorafenib and PD-1 blockade did not show a synergistic effect on anti-tumor activity. These results suggest that regorafenib induces ICD and suppresses tumor progression in TNBC. It should be carefully evaluated when developing a combination therapy with an anti-PD-1 antibody and a STAT3 inhibitor.

One-dimensional van der Waals heterostructures: Growth mechanism and handedness correlation revealed by nondestructive TEM.

We recently synthesized one-dimensional (1D) van der Waals heterostructures in which different atomic layers (e.g., boron nitride or molybdenum disulfide) seamlessly wrap around a single-walled carbon nanotube (SWCNT) and form a coaxial, crystalized heteronanotube. The growth process of 1D heterostructure is unconventional-different crystals need to nucleate on a highly curved surface and extend nanotubes shell by shell-so understanding the formation mechanism is of fundamental research interest. In this work, we perform a follow-up and comprehensive study on the structural details and formation mechanism of chemical vapor deposition (CVD)-synthesized 1D heterostructures. Edge structures, nucleation sites, and crystal epitaxial relationships are clearly revealed using transmission electron microscopy (TEM). This is achieved by the direct synthesis of heteronanotubes on a CVD-compatible Si/SiO2 TEM grid, which enabled a transfer-free and nondestructive access to many intrinsic structural details. In particular, we have distinguished different-shaped boron nitride nanotube (BNNT) edges, which are confirmed by electron diffraction at the same location to be strictly associated with its own chiral angle and polarity. We also demonstrate the importance of surface cleanness and isolation for the formation of perfect 1D heterostructures. Furthermore, we elucidate the handedness correlation between the SWCNT template and BNNT crystals. This work not only provides an in-depth understanding of this 1D heterostructure material group but also, in a more general perspective, serves as an interesting investigation on crystal growth on highly curved (radius of a couple of nanometers) atomic substrates.

An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

Light-Induced Dynamic Restructuring of Cu Active Sites on TiO2 for Low-Temperature H2 Production from Methanol and Water.

The structure and configuration of reaction centers, which dominantly govern the catalytic behaviors, often undergo dynamic transformations under reaction conditions, yet little is known about how to exploit these features to favor the catalytic functions. Here, we demonstrate a facile light activation strategy over a TiO2-supported Cu catalyst to regulate the dynamic restructuring of Cu active sites during low-temperature methanol steam reforming. Under illumination, the thermally deactivated Cu/TiO2 undergoes structural restoration from inoperative Cu2O to the originally active metallic Cu caused by photoexcited charge carriers from TiO2, thereby leading to substantially enhanced activity and stability. Given the low-intensity solar irradiation, the optimized Cu/TiO2 displays a H2 production rate of 1724.1 µmol g-1 min-1, outperforming most of the conventional photocatalytic and thermocatalytic processes. Taking advantages of the strong light-matter-reactant interaction, we achieve in situ manipulation of the Cu active sites, suggesting the feasibility for real-time functionalization of catalysts.

Plasma cell-free tumor DNA, PIK3CA and TP53 mutations predicted inferior endocrine-based treatment outcome in endocrine receptor-positive metastatic breast cancer.

PURPOSE: How to factor both tumor burden and oncogenic genomic mutations as variables to predict the outcome of endocrine-based therapy (ET) in ER-positive/HER2-negative metastatic breast cancer patients (MBC) remains to be explored. METHOD: Blood samples prospectively collected from 163 ER-positive/HER2-negative female MBC patients, before ET, were used for cell-free tumor DNA (cfDNA) analysis. cfDNA was subjected to next-generation sequencing (NGS) to interrogate oncogenic PIK3CA hotspot and TP53 DNA-binding domain (DBD) mutations, including single nucleotide variants (SNVs) or small insertions and deletions (InDels). The variant calling threshold was set at 0.5%. Progression-free survival (PFS) was measured from the start of the ET treatment to the time of disease progression of the same treatment regimen. RESULTS: Overall, the median PFS was 8.3 months (95% CI 5.7-11.1 months). The median cfDNA was 38.5 ng (range 4.4-1935 ng). The proportion of patients with PIK3CA and TP53 alterations were 25.1 and 15.3%, respectively. Patients with high total cfDNA (HR 1.74, p = 0.003), PIK3CA mutation (HR 1.74, p = 0.007), and TP53 mutation (HR 1.64, p = 0.047) in liquid biopsy conferred worse outcome after ET. Even for patients with low tumor burden, the detrimental effect of PIK3CA or TP53 mutation remained significant (p < 0.001). For patients with either PIK3CA (p < 0.001) or TP53 mutation (p = 0.004), there was significant positive correlation between allele frequency (AF) and total cfDNA. CONCLUSION: After adjustment of cfDNA level, PIK3CA and TP53 mutations observed in liquid biopsy exerted detrimental effects on the outcome of ET-based regimens. The AF of PIK3CA or TP53 may be a surrogate marker for PFS.

An Emerging Family of Piezocatalysts: 2D Piezoelectric Materials.

Piezocatalysis is an emerging technique that holds great promise for the conversion of ubiquitous mechanical energy into electrochemical energy through piezoelectric effect. However, mechanical energies in natural environment (such as wind energy, water flow energy, and noise) are typically tiny, scattered, and featured with low frequency and low power. Therefore, a high response to these tiny mechanical energies is critical to achieving high piezocatalytic performance. In comparison to nanoparticles or 1D piezoelectric materials, 2D piezoelectric materials possess characteristics such as high flexibility, easy deformation, large surface area, and rich active sites, showing more promise in future for practical applications. In this review, state-of-the-art research progresses on 2D piezoelectric materials and their applications in piezocatalysis are provided. First, a detailed description of 2D piezoelectric materials are offered. Then a comprehensive summary of the piezocatalysis technique is presented and examines the piezocatalysis applications of 2D piezoelectric materials in various fields, including environmental remediation, small-molecule catalysis, and biomedicine. Finally, the main challenges and prospects of 2D piezoelectric materials and their applications in piezocatalysis are discussed. It is expected that this review can fuel the practical application of 2D piezoelectric materials in piezocatalysis.

High-valent Cu(III)-CF3 compound-mediated esterification reaction.

Cu(III)-CF3 compounds are reported herein as novel coupling reagents to mediate ester synthesis from carboxyl acids and alcohols/phenols. Carboxylic acids are transformed to trifluoromethyl ester and acyl fluoride activated species that interact with each other. The broad substrate scope and late-stage application of this method are demonstrated. This study opens up new opportunities to develop interesting reactions using Cu(III)-CF3 compounds without transferring a CF3 group to the products.

The relationship between longer leukocyte telomeres and dNCR in non-cardiac surgery patients: a retrospective analysis.

BACKGROUND: Cognitive decline following surgery is a common concern among elderly individuals. Leukocyte telomere length (LTL) can be assessed as a biological clock connected to an individual lifespan. However, the mechanisms causing this inference are still not fully understood. As a result of this, LTL has the potential to be useful as an aging-related biomarker for assessing delayed neurocognitive recovery (dNCR) and related diseases. METHODS: For this study, 196 individuals over 60 who were scheduled due to major non-cardiac surgical operations attended neuropsychological testing before surgery, followed by additional testing one week later. The finding of dNCR was based on a measured Z-score ≤ -1.96 on two or more separate tests. The frequency of dNCR was presented as the primary outcome of the study. Secondly, we evaluated the association between dNCR and preoperative LTL. RESULTS: Overall, 20.4% [40/196; 95% confidence interval (CI), 14.7-26.1%] of patients exhibited dNCR 1-week post-surgery. Longer LTL was identified as a predictor for the onset of early cognitive impairment resulting in postoperative cognitive decline [odds ratio (OR), 14.82; 95% CI, 4.01-54.84; P < 0.001], following adjustment of age (OR, 12.33; 95% CI, 3.29-46.24; P < 0.001). The dNCR incidence based on LTL values of these patients, the area under the receiver operating characteristic (ROC) curve was 0.79 (95% CI, 0.722-0.859; P < 0.001). At an optimal cut-off value of 0.959, LTL values offered respective specificity and sensitivity values of 64.7% and 87.5%. CONCLUSIONS: In summary, the current study revealed that the incidence of dNCR was strongly associated with prolonged LTL. Furthermore, this biomarker could help identify high-risk patients and offer insight into the pathophysiology of dNCR.