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Summary: Autophagy is crucial for degrading and recycling cellular components. Fusion between autophagosomes and lysosomes is pivotal, directing autophagic cargo to degradation. This process is driven by STX17-SNAP29-VAMP8 and STX7-SNAP29-YKT6 in mammalian cells. However, the interaction between STX17 and YKT6 and its significance remain to be revealed. In this study, we challenge the notion that STX17 and YKT6 function independently in autophagosome-lysosome fusion. YKT6, through its SNARE domain, forms a complex with STX17 and SNAP29 on autophagosomes, enhancing autophagy flux. VAMP8 displaces YKT6 from this complex, leading to the formation of the fusogenic complex STX17-SNAP29-VAMP8. We demonstrated that the YKT6-SNAP29-STX17 complex facilitates both lipid and content mixing driven by STX17-SNAP29-VAMP8, suggesting a priming role of YKT6 for efficient membrane fusion. Our results provide a potential regulation mechanism of autophagosome-lysosome fusion, highlighting the importance of YKT6 and its interactions with STX17 and SNAP29 in promoting autophagy flux.

International trade is an important part of the world economy, and the rapid development of big data and complex network theory provides the possibility of an in-depth study of international trade networks. This study constructs the international trade flow gravity model and international trade flow index system on the basis of the clustered dynamic panel gravity model. It explores the influencing factors of national trade flow and the fitting effect of the international trade gravity model through factor analysis and regression analysis of variables in the system. The sample data’s KMO value is 0.834, according to the results, and there is a significant correlation between the variables. The degree of explanation of the public factors to the original variables is more than 0.5, and the variance contribution rate of trade structure, marketized structure, population and macroeconomics, international capital, and exchange rate reaches 71.832%, which makes the factor analysis more effective. The R² value of the trade gravity model reaches 0.837, with a remarkable effect. Among the sample countries, the highest trade flow potential is the United States (125,467.0), while the lowest is Portugal (512.4). The national GDP and population have a significant effect on international trade flows. The volume of each trading country should be used as a reference basis for conducting trade transactions when conducting international trade.

This study aims to investigate the influence of punctum image in destinations on tourists’ behavioral intentions. The study proposes the concepts of punctum and studium image based on semiology and uses an interpretable moderated mediation model to examine the effects of punctum image, studium image, flow experience, and human interaction on behavioral intention. The proposed research collected data from 440 tourists who visited a destination and used a structural equation model to analyze the relationships among the variables. The study employed bootstrapping to test the significance of the mediating effects and conducted a moderated mediation analysis to examine the moderating role of human interaction. The findings of the study indicate that punctum image has a positive effect on studium image and behavioral intention. Studium image is found to mediate the relationship between punctum image and behavioral intention. Additionally, the study shows that there are multiple mediations of studium image and flow experience between punctum image and behavioral intention. Finally, the study finds that human interaction plays a moderating role in two of the mediating effects. The results of this research have important theoretical and managerial implications. The study highlights the importance of punctum image in destination branding and suggests that destinations should focus on creating memorable and distinctive images to attract tourists. The study also emphasizes the role of studium image and flow experience in shaping tourists’ behavioral intentions, which could help destinations to design more effective marketing strategies. Finally, the study underscores the importance of human interaction in shaping tourists’ experiences and suggests that destinations should focus on providing high-quality human interactions to enhance tourists’ experiences and satisfaction. The use of an interpretable moderated mediation model in this study enhances the interpretability and practical applicability of the results for decision-makers.

Abstract Atopic dermatitis (AD) is a common inflammatory skin disease. Matrine is the main component of the traditional Chinese medicine Sophora flavescens, and it poses good therapeutic effects on inflammatory diseases. This study aimed to explore the pharmacological effects of matrine on AD and its underlying mechanism. An AD mouse model and inflamed human epidermal keratinocyte cells (HaCaT) cells were established. Histopathological aspects were examined using hematoxylin and eosin staining, toluidine blue staining, and immunohistochemistry. The mRNA and protein expressions were assessed using quantitative real‐time polymerase chain reaction and Western blot, respectively. The secretions of cytokines and chemokines were examined by enzyme‐linked immunosorbent assay. Flow cytometry was carried out to analyze the proportions of T‐helper (Th) 1 and Th2 cells. Herein, our results displayed that matrine diminished AD symptoms and decreased heat shock protein 90 (Hsp90) expression. Matrine decreased the Th2 cytokine levels in the ear tissues and serum, and it also significantly repressed inflammatory cytokines (thymus activation regulated chemokine and interleukin‐6) secretions by repressing the Hsp90/NF‐κB signaling axis in inflamed HaCaT cells. Furthermore, matrine inhibited Th2 differentiation of CD4+ T cells when co‐cultured with inflamed HaCaT cells. Matrine can regulate the Th1/Th2 inflammatory response by inhibiting the Hsp90/NF‐κB signaling axis to alleviate AD. Therefore, it may be a candidate for AD treatment.

Casein and α-lactalbumin (α-LA) are the main allergens in cow's milk, which can affect the skin, respiratory system, and gastrointestinal tract, even cause anaphylactic shock. Developing a rapid and sensitive detection method of casein and α-LA is still pursued. Herein, a surface-enhanced Raman scattering based lateral flow assay (SERS-LFA) method for rapid and highly sensitive detection of milk allergens in food was established for effectively preventing allergic symptoms. Gold@silver nanoparticles (Au@Ag NPs) were synthesized as SERS active substrate to prepare the antibody-modified SERS Probe and SERS-LFA strips toward casein and α-LA were assembled according to the sandwich mode. The detection results were calculated according to colorimetric and Raman signal. The introduction of SRES signal significantly increased the sensitivity of detection with the limit of detection (LOD) of 0.19 ng/mL and 1.74 pg/mL, and exhibited an excellent linear relationship within the range of 0.55–791.50 ng/mL and 0.1 pg/mL–100.0 ng/mL for casein and α-LA, respectively. Furthermore, SERS-LFA strips was highly specific with the recovery rates for corresponding 80.36 %–105.12 % and 85.73 %–118.22 % for casein and α-LA, respectively. Therefore, the SERS-LFA could be in great potential to develop a unique allergen detection method.

The metaverse has been discussed in fields such as medicine, manufacturing, finance, education, and public services, but the application scenarios based on virtual reality have not truly achieved the ＆quot;real-virtual-real＆quot; loop interaction method.Its interaction mode has also not truly given the digital world the same consciousness and perception as the physical world.Taking medicine as a case study, the prospective applications and challenges of generative artificial intelligence models in metaverse organisms were explored, including digitizing biological cells, and building connections between digitized cells and digital neurons, in order to promote metaverse life forms to have perception and biochemical reactions consistent with the physical world, thereby empowering the development of the medical field.In response to the current advantages and disadvantages of the metaverse and generative artificial intelligence models, the clever design of human-machine collaboration mechanisms was discussed to promote conscious interaction between humans and metaverse organisms in medicine.

Abstract Knowledge of shale pore structure characteristics is crucial to understand gas storage and seepage mechanisms. Organic matter (OM) pores are considered the most important pore type in shale, and one of the currently significant research questions focuses on the spatial distribution and connectivity of OM pores. To answer this question, typical OM‐rich siliceous shale samples from the Lower Silurian Longmaxi Formation were comprehensively investigated using focused ion beam scanning electron microscopy. A three‐dimensional model of the OM‐rich region of interest was segmented and reconstructed based on numerous two‐dimensional slices. The types of OM were found to control the development of organic pores, and OM pores including honeycomb‐shaped pores, spongy‐shaped pores, and slit‐like irregular pores are mainly formed in the pyrobitumen. The pore structure parameters of the OM‐rich ROI revealed that the pore size distribution of honeycomb‐shaped OM pores formed in the pyrobitumen was mainly distributed in the range of 10–50 and 80–100 nm, while the throat equivalent diameter distribution demonstrated a unimodal curve with the main peak located at approximately 30 nm. Pore connectivity analysis further indicated that pyrobitumen also contained several isolated nano‐pores, and pores with diameters smaller than 40 nm were poorly connected. Furthermore, permeability simulation revealed clear discrepancies in different directions owing to the heterogeneity of the OM pores. These findings provide experimental evidence for the assessment of shale gas resources and their development potential.

Abstract Research on biomarker-driven therapy and immune check-point blockade in non-small cell lung cancer (NSCLC) is rapidly evolving. The width and depth of clinical trials have also dramatically improved in an unprecedented speed. The personalized treatment paradigm evolved every year. In this review, we summarize the promising agents that have shifted the treatment paradigm for NSCLC patients across all stages, including targeted therapy and immunotherapy using checkpoint inhibitors. Based on recent evidence, we propose treatment algorithms for NSCLC and propose several unsolved clinical issues, which are being explored in ongoing clinical trials. The results of these trials are likely to impact future clinical practice.

Abstract Stage III non‐small cell lung cancer (NSCLC) encompasses a group of diseases with high heterogeneity. Such patients should actively receive comprehensive treatments. It is imperative for all stage III NSCLC patients to receive consultation with a multiple disciplinary team, which allows the development of a proposal for clinical diagnosis and treatment. In this consensus, stage III NSCLC is divided into two types (operable and inoperable) according to different clinical conditions. Resectable NSCLC is further subdivided into two conditions (with or without driver genes). For each clinical scenario, this consensus emphasizes that the foundation of any medical decisions regarding the optimal diagnostic or therapy procedure is scientific evidence from clinical research. Finally, based on the level of evidence and strength of recommendations, this consensus provides recommendations for the management of stage III NSCLC from six perspectives. The objective of this consensus is to help clinicians choose the best treatment and promote the standardization of stage III NSCLC diagnosis and treatment in China.

Pancreatic β-cell compensation is a major mechanism in delaying T2DM progression. Here, the authors show that circGlis3 levels are affected by QKI and FUS expression, and that increasing circGlis3 lengthens β-cell compensation via enhancing insulin secretion and reducing apoptosis.

Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the complex brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.

Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.

The purpose of drug discovery is to identify new drugs, and the solubility of drug molecules is an important physicochemical property in medicinal chemistry, that plays a crucial role in drug discovery. In solubility prediction, high-precision computational methods can significantly reduce the experimental costs and time associated with drug development. Therefore, artificial intelligence technologies have been widely used for solubility prediction. This study utilized the attention layer in mechanism in the deep learning model to consider the atomic-level features of the molecules, and used gated recurrent neural networks to aggregate vectors between layers. It also utilized molecular fragment technology to divide the complete molecule into pairs of fragments, extracted characteristics from each fragment pair, and finally fused the characteristics to predict the solubility of drug molecules. We compared and evaluated our method with five existing models using two performance evaluation indicators, demonstrating that our method has better performance and greater robustness.

Knowledge of the interaction mechanisms between shale and CH4/CO2 is crucial for the implementation of CO2 sequestration with enhanced CH4 recovery (CS-EGR) in shale reservoir. As one of the main constituents of shale, clay minerals can profoundly affect the storage capacity of gases in nanopores. In this paper, the adsorption behaviors of both CO2 and CH4 on montmorillonite, illite as well as kaolinite under dry condition are investigated by Grand Canonical Monte Carlo (GCMC) simulation. The results exhibit that the maximum adsorption capacity of single-component CH4 and CO2 is associated with the types of clay crystals. Specifically, the montmorillonite has the strongest adsorption capacity for CO2, followed by illite and kaolinite, while the sequence in maximum adsorption capacity of CH4 is predicted in the order of kaolinite > montmorillonite > illite. These discrepancies are closely related to the characteristics of adsorbate molecules as well as the different structures of clay crystals. Meanwhile, the maximum adsorption capacity of CH4 in studied clay minerals gradually decreases as pore size increases, while nanopores with 2-nm basal spacing demonstrate the highest adsorption capacity for CO2. In addition, it is observed that the studied clay minerals tend to preferentially adsorb CO2 rather than CH4 during binary gas mixtures simulation. The selectivity of CH4/CO2 mixtures in montmorillonite and kaolinite exhibits various performances as the adsorption pressure increases, with the selectivity in montmorillonite being the largest, especially at low pressure. The cation exchange significantly enhances the electrostatic interaction with CO2 molecules, leading to a higher loading of CO2 as well as larger value of selectivity. These findings can provide basis and guidance for the CS-EGR project in shale reservoirs.

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3−.

Currently, the alteration of external factors during crude oil extraction easily disrupts the thermodynamic equilibrium of asphaltene, resulting in the continuous flocculation and deposition of asphaltene molecules in crude oil. This accumulation within the pores of reservoir rocks obstructs the pore throat, hindering the efficient extraction of oil and gas, and consequently, affecting the recovery of oil and gas resources. Therefore, it is crucial to investigate the principles of asphaltene deposition inhibition and the synthesis of asphaltene inhibitors. In recent years, the development of nanotechnology has garnered significant attention due to its unique surface and volume effects. Nanoparticles possess a large specific surface area, high adsorption capacity, and excellent suspension and catalytic abilities, exhibiting unparalleled advantages compared with traditional organic asphaltene inhibitors, such as sodium dodecyl benzene sulfonate and salicylic acid. At present, there are three primary types of nanoparticle inhibitors: metal oxide nanoparticles, organic nanoparticles, and inorganic nonmetal nanoparticles. This paper reviews the recent advancements and application challenges of nanoparticle asphaltene deposition inhibition technology based on the mechanism of asphaltene deposition and nano-inhibitors. The aim was to provide insights for ongoing research in this field and to identify potential future research directions.

Cryogenic scanning tunneling microscopy was employed in combination with density-functional theory calculations to explore quantum dots made of In adatoms on the InAs(110) surface. Each adatom adsorbs at a surface site coordinated by one cation and two anions, and transfers one electron to the substrate, creating an attractive quantum well for electrons at the surface. We used the scanning-probe tip to assemble the positively charged adatoms into precisely defined quantum dots exhibiting a bound state roughly 0.1 eV below the Fermi level at an intrinsic linewidth of only ∼4 meV, as revealed by scanning tunneling spectroscopy. For quantum-dot dimers, we observed the emergence of a bonding and an antibonding state with even and odd wave-function character, respectively, demonstrating the capability to engineer quasimolecular electronic states. InAs(110) constitutes a promising platform in this respect because highly perfect surfaces can be readily prepared by cleavage and charged adatoms can be generated in situ by the scanning-probe tip.

In this work, the authors describe a three-dimensional Marangoni transport process in an aqueous two-phase system. Marangoni-driven spreading initiated with salt leads to the formation of Fermat’s spiral patterns, that are of relevance for materials fabrication and microfluidics.

Abstract Leukemic cutaneous T‐cell lymphomas (L‐CTCL) are lymphoproliferative disorders of skin‐homing mature T‐cells causing severe symptoms and high mortality through chronic inflammation, tissue destruction, and serious infections. Despite numerous genomic sequencing efforts, recurrent driver mutations have not been identified, but chromosomal losses and gains are frequent and dominant. We integrated genomic landscape analyses with innovative pharmacologic interference studies to identify key vulnerable nodes in L‐CTCL. We detected copy number gains of loci containing the STAT3/5 oncogenes in 74% (n = 17/23) of L‐CTCL, which correlated with the increased clonal T‐cell count in the blood. Dual inhibition of STAT3/5 using small‐molecule degraders and multi‐kinase blockers abolished L‐CTCL cell growth in vitro and ex vivo, whereby PAK kinase inhibition was specifically selective for L‐CTCL patient cells carrying STAT3/5 gains. Importantly, the PAK inhibitor FRAx597 demonstrated encouraging anti‐leukemic activity in vivo by inhibiting tumor growth and disease dissemination in intradermally xenografted mice. We conclude that STAT3/5 and PAK kinase interaction represents a new therapeutic node to be further explored in L‐CTCL.

Abstract Purpose Gamma knife radiosurgery (GK) is a commonly used approach for the treatment of intracranial lesions. Its radiation response is typically not immediate, but delayed. In this study, we analyzed cases from a prospectively collected database to assess the influence of COVID-19 pandemic on the decision making in patients treated by gamma knife radiosurgery. Methods From January 2019 to August 2021, 540 cases of intracranial lesions were treated by GK with 207 cases before COVID-19 pandemic as a control. During the COVID-19 pandemic, 333 cases were similarly treated on patients with or without the COVID-19 vaccination. All the GK treated parameters as well as time profile in the decision making were analyzed. The parameters included age, sex, characteristic of lesion, targeted volume, peripheral radiation dose, neurological status, Karnofsky Performance Status (KPS), time interval from MRI diagnosis to consultation, time interval from the approval to treatment, frequency of outpatient department (OPD) visit, and frequency of imaging follow-up. Results Longer time intervals from diagnosis to GK consultation and treatment were found in the pandemic group (36.8 ± 25.5/54.5 ± 27.6 days) compared with the pre-COVID control (17.1 ± 22.4/45.0 ± 28.0 days) or vaccination group (12.2 ± 7.1/29.6 ± 10.9 days) (p

Introduction This study aims to estimate the impact of smoking-attributable mortality of selected cancers, in the period 2010–2019 in Tianjin, China. Methods A case-control study was conducted to assess the smoking-attributed major causes of cancer deaths. Unmatched multiple logistic regression was used to calculate mortality risk ratios. Results Smoking-attributable cancer deaths were 23709 (28.87%) among adult males and 8648 (13.37%) among adult females in the period 2010–2019 in Tianjin, China. Lung cancer remains the largest cause of smoking-attributable deaths; among men, the death rates were 49.06% of lung cancers, 27.55% of mouth, pharynx, larynx, or esophagus cancers, 13.56% of kidney and other urinary cancers, and 10.11% of liver cancers; among women the corresponding death rates were 31.56% of lung cancers and 10.59% of the mouth, pharynx, larynx, or esophagus cancer, 10.56% of bladder cancers. Smoking-attributed cancer deaths in men increased from 1817 in 2010 to 2695 in 2019; for women, the number remained stable at just over 800 per year during the past decade. Conclusions At least one in three cancer deaths in men and one in six in women would be potentially preventable through appropriate control of tobacco smoking in Tianjin, China. Effective control programs against tobacco smoking should be further implemented.