Cytokine-mediated crosstalk between cancer stem cells and their inflammatory niche from the colorectal precancerous adenoma stage to the cancerous stage: Mechanisms and clinical implications

publishedVersio

Exploring links between industrialization, urbanization, and Chinese inflammatory bowel disease

publishedVersio

Nanoparticles as drug delivery systems in the treatment of oral squamous cell carcinoma: current status and recent progression

Oral squamous cell carcinoma (OSCC) is a common human malignancy with an estimated incidence of around 377,713 new cases worldwide in 2020. Despite the advance in clinical management, some of OSCC patients still miss the opportunity of completable resection of tumor, and have to accept medical therapies, e.g., chemotherapy, radiotherapy, or immunotherapy when the disease develops into the advanced stage. However, these therapies have been reported to be far from ideal due to the low efficiency of conventional delivery approaches. To obtain a better therapeutic effect, considerable attempts have been made toward to develop an effective drug delivery system (DDS). Nanoparticles (NPs) including inorganic NPs, polymer NPs, lipid NP, extracellular vesicles and cell membrane-based NPs have been evaluated as the better DDS candidates that can specifically accumulate in the tumor microenvironment along with a large amount of blood vessels. Emerging evidence suggested that NPs formulated with anticancer drugs including chemotherapeutic drugs, radiotherapy and immunotarget antibodies could remarkably improve the release and increase concentration of these drugs at the tumor site and show a better therapeutic efficacy, suggesting that NPs might serve as promising DDSs in the treatment of OSCC. Therefore, we have conducted this review to summarize recent progression and current status of diverse NPs as DDSs in this research field

Stabilization of NaZn(BH4)3 via nanoconfinement in SBA-15 towards enhanced hydrogen release

In the present work, the decomposition behaviour of NaZn(BH4)3 nanoconfined in mesoporous SBA-15 has been investigated in detail and compared to bulk NaZn(BH4)3 that was ball milled with SBA-15, but not nanoconfined. The successful incorporation of nanoconfined NaZn(BH4)3 into mesopores of SBA-15 was confirmed by scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray spectroscopy, 11B nuclear magnetic resonance, nitrogen absorption/desorption isotherms, and Fourier transform infrared spectroscopy measurements. It is demonstrated that the dehydrogenation of the space-confined NaZn(BH4)3 is free of emission of boric by-products, and significantly improved hydrogen release kinetics is also achieved, with pure hydrogen release at temperatures ranging from 50 to 150 °C. By the Arrhenius method, the activation energy for the modified NaZn(BH4)3 was calculated to be only 38.9 kJ mol−1, a reduction of 5.3 kJ mol−1 compared to that of bulk NaZn(BH4)3. This work indicates that nanoconfinement within a mesoporous scaffold is a promising approach towards stabilizing unstable metal borohydrides to achieve hydrogen release with high purity

Stabilization of NaZn(BH4)3 via nanoconfinement in SBA-15 towards enhanced hydrogen release

In the present work, the decomposition behaviour of NaZn(BH4)3 nanoconfined in mesoporous SBA-15 has been investigated in detail and compared to bulk NaZn(BH4)3 that was ball milled with SBA-15, but not nanoconfined. The successful incorporation of nanoconfined NaZn(BH4)3 into mesopores of SBA-15 was confirmed by scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray spectroscopy, 11B nuclear magnetic resonance, nitrogen absorption/desorption isotherms, and Fourier transform infrared spectroscopy measurements. It is demonstrated that the dehydrogenation of the space-confined NaZn(BH4)3 is free of emission of boric by-products, and significantly improved hydrogen release kinetics is also achieved, with pure hydrogen release at temperatures ranging from 50 to 150 °C. By the Arrhenius method, the activation energy for the modified NaZn(BH4)3 was calculated to be only 38.9 kJ mol−1, a reduction of 5.3 kJ mol−1 compared to that of bulk NaZn(BH4)3. This work indicates that nanoconfinement within a mesoporous scaffold is a promising approach towards stabilizing unstable metal borohydrides to achieve hydrogen release with high purity

Rule-Guided Compositional Representation Learning on Knowledge Graphs

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Besides, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.Comment: The full version of a paper accepted to AAAI 202

General synthesis of transition metal oxide ultrafine nanoparticles embedded in hierarchically porous carbon nanofibers as advanced electrodes for lithium storage

A unique general, large-scale, simple, and cost-effective strategy, i.e., foaming-assisted electrospinning, for fabricating various transition metal oxides into ultrafine nanoparticles (TMOs UNPs) that are uniformly embedded in hierarchically porous carbon nanofibers (HPCNFs) has been developed. Taking advantage of the strong repulsive forces of metal azides as the pore generator during carbonization, the formation of uniform TMOs UNPs with homogeneous distribution and HPCNFs is simultaneously implemented. The combination of uniform ultrasmall TMOs UNPs with homogeneous distribution and hierarchically porous carbon nanofibers with interconnected nanostructure can effectively avoid the aggregation, dissolution, and pulverization of TMOs, promote the rapid 3D transport of both Li ions and electrons throughout the whole electrode, and enhance the electrical conductivity and structural integrity of the electrode. As a result, when evaluated as binder-free anode materials in Li-ion batteries, they displayed extraordinary electrochemical properties with outstanding reversible capacity, excellent capacity retention, high Coulombic efficiency, good rate capability, and superior cycling performance at high rates. More importantly, the present work opens up a wide horizon for the fabrication of a wide range of ultrasmall metal/metal oxides distributed in 1D porous carbon structures, leading to advanced performance and enabling their great potential for promising large-scale applications

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel \textbf{H}ierarchical \textbf{C}ontrastive \textbf{V}isual \textbf{P}rompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climate modeling, biomedicine, and autonomous driving. The emergence of foundation models, distinguished by their extensive pretraining and task versatility, has led to an increased interest in their adaptability to distribution shifts. GPT-4V(ision) acts as the most advanced publicly accessible multimodal foundation model, with extensive applications across various domains, including anomaly detection, video understanding, image generation, and medical diagnosis. However, its robustness against data distributions remains largely underexplored. Addressing this gap, this study rigorously evaluates GPT-4V's adaptability and generalization capabilities in dynamic environments, benchmarking against prominent models like CLIP, LLaVA, and Gemini. We delve into GPT-4V's zero-shot generalization across 13 diverse datasets spanning natural, medical, and molecular domains. We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation. Our findings delineate GPT-4V's capability boundaries in distribution shifts, shedding light on its strengths and limitations across various scenarios. Importantly, this investigation contributes to our understanding of how AI foundation models generalize to distribution shifts, offering pivotal insights into their adaptability and robustness. The code is publicly available at https://github.com/jameszhou-gl/gpt-4v-distribution-shift.Comment: added the investigation of Gemini. 66 pages, 41 figure