Pullulan-Based Spray-Dried Mucoadhesive Microparticles for Sustained Oromucosal Drug Delivery.

Mucoadhesive microparticles for oromucosal drug delivery offer several advantages, including intimate contact with the mucosa, delivery to less accessible regions, extended residence time, sustained drug release, reduced irritation, and improved patient compliance. In this study, pullulan was used to prepare mucoadhesive spray-dried microparticles for delivering benzydamine hydrochloride (BZH) to oral mucosa. The BZH-pullulan spray-dried microparticles had a mean size of <25 µm with an angle of repose values between 25.8-36.6°. Pullulan markedly extended drug-release time to >180 min, ~9 times greater than the duration (i.e., 20 min) reportedly achieved by chitosan. Kinetic analysis showed the drug-release rate was concentration dependent and jointly controlled by drug diffusion and polymer chain relaxation. Further, pullulan was mucoadhesive and was able to retain up to 78.8% w/w of microencapsulated gold nanoparticle probes at the mucosal membrane. These data strongly suggest that BZH-pullulan microparticles have great potential for oromucosal drug delivery, by providing elongated residence time in situ and sustained drug release for the treatment of local diseases.

Deep Learning Promotes Profiling of Multiple miRNAs in Single Extracellular Vesicles for Cancer Diagnosis.

Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profiling single-EV multi-miRNA signatures by combining total internal reflection fluorescence (TIRF) imaging with a deep learning (DL) algorithm for the first time. This innovative technique allows for the precise characterization of EV miRNAs at the single-vesicle level, overcoming the challenges posed by EV heterogeneity. TIRF with high resolution and a signal-to-noise ratio can simultaneously detect multi-miRNAs in situ in individual EVs. DL algorithm avoids complicated and inaccurate artificial feature extraction, achieving automated high-resolution image analysis. Using this approach, we reveal that the main variation of EVs from 5 cancer cells and normal plasma is the triple-positive EV subpopulation, and the classification accuracy of single triple-positive EVs from 6 sources can reach above 95%. In the clinical cohort, 20 patients (5 lung cancer, 5 breast cancer, 5 cervical cancer, and 5 colon cancer) and 5 healthy controls are predicted with an overall accuracy of 100%. This single-EV strategy provides new opportunities for exploring more specific EV biomarkers to achieve cancer diagnosis and classification.

Preparation of nitrogen doped hyper-crosslinked polymer-based hard carbon for high performance Li+/Na+ battery anode.

Hyper cross-linked polymers (HCPs), as a key precursor of hard carbon (HC) anode materials, stand out because of their capacity for molecular-scale structural design and comparatively straightforward preparation techniques, which are not seen in other porous materials synthesized procedure. A novel synthesis method of HCPs is developed in this paper, which is through the incorporation of functional macromolecules, the structural control and heteroatom doping of the product has been achieved, thus augmenting its electrochemical performance in batteries. In this work, carbonized tetraphenylporphyrin zinc (TPP-Zn) doped HCP-based hard carbon (CTHCP) with stable structure was prepared by Friedel-Crafts reaction and carbonization by using naphthalene and trace TPP-Zn as monomers, dimethoxybenzene (DMB) as crosslinking agent and FeCl3 as catalyst. The introduction of TPP-Zn, a functional macromolecule with unique two-dimensional structure, realized the pore structure regulation and N doping of the raw carbonized HCP-based hard carbon (CHCP). The results showed that CTHCP had higher mesoporous volume, N content and wider layer spacing than CHCP. In addition, CTHCP anode exhibited excellent Li+/Na+ storage performance, initial reversible capacity, rate performance and long cycle life. More amount of N-containing (N-5) active sites and mesoporous content in CTHCP anode was the main reason for the improvement of Na+ storage effect. While the increased interlayer spacing had a greater effect on the lithium storage capacity. This study uncovered the design rules of HC anode materials suitable for Li+/Na+ batteries and provided a new idea for the preparation of high-performance HC anode materials.

Adenosine-modulating synthetic high-density lipoprotein for chemoimmunotherapy of triple-negative breast cancer.

Adenosine (ADO) is a common chemotherapy-associated immune checkpoint that hinders anti-tumor immunity-mediated efficacy of chemotherapy. Herein, we created a synthetic high-density lipoprotein (sHDL) by co-assembly of a doxorubicin (DOX)-apolipoprotein A1 mimetic peptide conjugate, PSB-603 (an A2BR inhibitor), phospholipid, and cholesterol oleate with a microfluidic-based method. The obtained DP-sHDL showed a self-promoted drug delivery to cancer cells via remodeling tumor microenvironment. DP-sHDL could trigger the release of ATP from cancer cells and inhibit its conversion into ADO. Consequently, DP-sHDL, while increasing immunogenic cell death, reduced intratumoral ADO levels by 58%. This treatment improved both the density and activity of CD8+ T cells as well as NK cells and relieved the immunosuppressive microenvironment, and led to a substantial inhibition of 4T1 tumor growth, thereby extending the survival of mice. The efficacy of DP-sHDL could be further improved when used in combination with immune checkpoint blockade therapy. We envision that this platform provides a simple yet promising strategy to enhance anti-tumor response of chemotherapy by relieving treatment-associated immunosuppression.

Is state-owned enterprise merging private enterprise "market choice" or "space crowding" ? -Based on the motives of equity transfer of mixed-ownership enterprises.

Many state-owned enterprises have mergers and acquisitions (M&A) with private enterprises, which has caused private enterprises to worry about their living space being squeezed. Based on 572 data records about equity transfers of Chinese listed companies extracted from CSMAR4.0 from 2013 to 2020, this paper categorized ownership structures into three categories: privatization of state-owned enterprises (Category 1), state-owned enterprises merging private enterprises (Category 2), and state-owned enterprises merging state-owned enterprises (Category 3). The categorical regression of ex-ante equity transfer motivation revealed that the motives for Category 1 conformed to the phenomenon of the "pretty girl gets married first" and "embezzlement view." Category In contrast, the motives for Category 2 conformed to the "fiscal revenue view." The categorical regression of ex-post equity transfer motivation showed that all three types significantly improved various efficiencies and represented an optimal allocation of resources. Moreover, it was revealed that the transfer of equity to state-owned enterprises by inefficient private firms in Category 2 also significantly improved enterprise efficiency. Thus, it can be considered as a rational behavior of market selection and never squeezed the space crowding. Further analysis showed that the efficiency improvement is due to the symbiotic development relationship rather than the antagonistic relationship between heterogeneous shareholders. Therefore, it is suggested to initiate market-oriented reform by actively developing ownership mixed-ownership economy and adhering to the "two unwavering" basic economic system.

Retrolaminar migration as a complication of intraocular silicone oil injection detected on unenhanced CT.

AIM: To describe the clinical and radiologic features of retrolaminar migration silicone oil (SiO) and observe the dynamic position of ventricular oil accumulation in supine and prone. METHODS: For this retrospective study, 29 patients who had a history of SiO injection treatment and underwent unenhanced head computed tomography (CT) were included from January 2019 to October 2022. The patients were divided into migration-positive and negative groups. Clinical history and CT features were compared using Whitney U and Fisher's exact tests. The dynamic position of SiO was observed within the ventricular system in supine and prone. CT images were visually assessed for SiO migration along the retrolaminar involving pathways for vision (optic nerve, chiasm, and tract) and ventricular system. RESULTS: Intraocular SiO migration was found in 5 of the 29 patients (17.24%), with SiO at the optic nerve head (n=1), optic nerve (n=4), optic chiasm (n=1), optic tract (n=1), and within lateral ventricles (n=1). The time interval between SiO injection and CT examination of migration-positive cases was significantly higher than that of migration-negative patients (22.8±16.5mo vs 13.1±2.6mo, P<0.001). The hyperdense lesion located in the frontal horns of the right lateral ventricle migrated to the fourth ventricle when changing the position from supine to prone. CONCLUSION: Although SiO retrolaminar migration is unusual, the clinician and radiologist should be aware of migration routes. The supine combined with prone examination is the first-choice method to confirm the presence of SiO in the ventricular system.

Tunable non-volatile memories based on 2D InSe/h-BN/GaSe heterostructures towards potential multifunctionality.

Floating-gate memories based on two-dimensional van der Waal (2D vdW) heterostructures play an important role in the development of next-generation information technology. The diversity of 2D vdW materials and their heterostructures provides flexibility in the design of novel storage architectures. However, 2D InSe/h-BN/GaSe heterostructures are rarely reported in the field of tunable non-volatile memories, probably due to the quality limitation of materials and complex interfaces from stackings. Herein, a floating-gate 2D InSe/h-BN/GaSe memory with high performance and atmosphere stability is demonstrated. It exhibits both a large ON/OFF current ratio of ∼105 and a good extinction ratio of ∼103, with an estimated maximum storage capacity of 5.1 × 1012 cm-2. Moreover, the storage performance can be regulated by optimizing the thickness of the insulating h-BN layer. Different device configurations have been explored to validate the working mechanism. Furthermore, a simulation of biological synaptic behavior is achieved on the same prototype device. The enhanced non-volatile characteristics enable the exploration of the integrated 2D memory and potential multifunctionality.

Hounsfield units on abdominal computed tomography: a new tool for predicting osteoporosis.

BACKGROUND: Osteoporosis can cause bone fractures and disability, but early diagnosis faces challenges. Our proposed diagnostic indicators offer a new approach for early detection, which benefits early identification. PURPOSE: To determine the most appropriate threshold for predicting osteoporosis in patients with each section of vertebral body. MATERIAL AND METHODS: A retrospective analysis of 210 patients, including 646 vertebrae, who had both abdominal computed tomography (CT) and dual-energy X-ray absorptiometry (DXA) within six months. The correlation between DXA T-score and CT Hounsfield units (HU) values was tested by Pearson. The area under the curve (AUC) was calculated using the threshold obtained from the regression equation. RESULTS: The thresholds matching the T-score of -2.5 were 85, 95, 85, and 90 HU for the upper axial plane of the vertebral body (Lau), the middle axial plane of the vertebral body (Lam), the lower axial plane of the vertebral body (Lad), and the mid-sagittal plane of the vertebral body (Lsm), respectively. Defining osteoporosis using CT as Lau ≤ 85, Lam ≤ 95, Lad ≤ 85, or Lsm ≤ 90 HU had a specificity of 88.1% (116/134) and sensitivity of 90.8% (69/76) for distinguishing DXA osteoporosis of the lumbar spine in 210 patients. T-score ≤-2.5 defined as Lau ≤85 or Lam ≤95 or Lad ≤85 or Lsm ≤90 HU had a specificity of 85.9% (275/320) and sensitivity of 82.8% (270/326) for DXA T-score ≤-2.5 in 646 lumbar vertebrae. CONCLUSION: CT HU values obtained based on different sections of the vertebral body in abdominal CT can be used as a supplementary measure to assess osteoporosis.

Study of the Microstructure of Coal at Different Temperatures and Quantitative Fractal Characterization.

In order to understand the influence of underground coal fires on coal fractures and pores, mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) are combined to study the development of coal pore and fracture under high-temperature treatment and calculate the fractal dimension to analyze the relationship between the development of coal pore and fracture and the fractal dimension. The results show that the volume of pores and fractures of the coal sample (C200) treated at 200 °C (0.1715 mL/g) is greater than that of the coal sample (C400) treated at 400 °C (0.1209 mL/g), and both are greater than the original coal sample (RC) (0.1135 mL/g). The volume increase is mainly due to mesopores and macropores, and the proportions of mesopores and macropores in C200 were 70.15 and 59.97% in C400. The MIP fractal dimension shows a decreasing trend with the increase of temperature, and the connectivity of coal samples improved with the increase of temperature. The changes in volume and three-dimensional fractal dimension of C200 and C400 showed the opposite trend and are related to the different stress of coal matrix at different temperatures. The experimental SEM images confirm that the connectivity of coal fractures and pores improves with the increase of temperature. Based on the SEM experiment, the larger the fractal dimension, the more complex the surface is. The SEM surface fractal dimensions indicate that the surface fractal dimension of C200 is the smallest and that of C400 is the largest, which is consistent with the observations made by SEM. The combination of the two fractal dimensions is used to characterize the self-similarity of coal using the fractal dimension difference. When the temperature increased to 200 °C, the unordered expansion of the coal sample resulted in the largest fractal dimension difference and the lowest self-similarity. When heated to 400 °C, the fractal dimension difference of the coal sample is the smallest, and the microstructure of coal shows a regular groove-like development.

Design of public cultural sign based on Faster-R-CNN and its application in urban visual communication.

People are increasingly enthusiastic about pursuing spiritual life as economic and social development continues. Consequently, public cultural content has emerged as a pivotal instrument for promoting international soft power across diverse nations and regions. In today's era of advanced artificial intelligence, cultural sign design optimization has become achievable through its deployment. This article establishes an automatic layout optimization framework, specifically tailored to meet the visual communication requirements of public cultural signage. Our framework employs Faster-R-CNN for detecting and extracting key elements of the poster, yielding an impressive average detection accuracy of 94.6%. Subsequently, we use the three-division method in design to optimize the layout, ensuring that cultural logo design conforms to visual communication principles. Our framework produced an average cultural logo satisfaction rating exceeding 70% in actual tests, providing novel insights for cultural sign design within the artificial intelligence context and significantly enhancing the efficacy of visual communication conveyed through such signage.

Application of visual communication in digital animation advertising design using convolutional neural networks and big data.

In the age of big data, visual communication has emerged as a critical means of engaging with customers. Among multiple modes of visual communication, digital animation advertising is an exceptionally potent tool. Advertisers can create lively and compelling ads by harnessing the power of digital animation technology. This article proposes a multimodal visual communication system (MVCS) model based on multimodal video emotion analysis. This model automatically adjusts video content and playback mode according to users' emotions and interests, achieving more personalized video communication. The MVCS model analyses videos from multiple dimensions, such as vision, sound, and text, by training on a large-scale video dataset. We employ convolutional neural networks to extract the visual features of videos, while the audio and text features are extracted and analyzed for emotions using recurrent neural networks. By integrating feature information, the MVCS model can dynamically adjust the video's playback mode based on users' emotions and interaction behaviours, which increases its playback volume. We conducted a satisfaction survey on 106 digitally corrected ads created using the MVCS method to evaluate our approach's effectiveness. Results showed that 92.6% of users expressed satisfaction with the adjusted ads, indicating the MVCS model's efficacy in enhancing digital ad design effectiveness.

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning.

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

A machine learning-based colorimetric sensor array for high-precision pathogen identification in household refrigerators.

A precise and convenient sensor was constructed to identify pathogens in household refrigerators (4 °C, RH = 55%) by integrating a volatile organic compound fingerprint-responsive gel-based colorimetric sensor array and a neural network. The platform is expected to be extended to the intelligent food packaging field and has promise for point-of-need monitoring of pathogens.

Gemcitabine-loaded synthetic high-density lipoprotein preferentially eradicates hepatic monocyte-derived macrophages in mouse liver with colorectal cancer metastases.

Liver metastasis of colorectal cancer (CRC) is the critical cause of CRC-related death due to its unique immunosuppressive microenvironment. In this study we generated a gemcitabine-loaded synthetic high-density lipoprotein (G-sHDL) to reverse immunosuppression in livers with CRC metastases. After intravenous injection, sHDL targeted hepatic monocyte-derived alternatively activated macrophages (Mono-M2) in the livers of mice bearing both subcutaneous tumors and liver metastases. The G-sHDL preferentially eradicated Mono-M2 in the livers with CRC metastases, which consequently prevented Mono-M2-mediated killing of tumor antigen-specific CD8+ T cells in the livers and thus improved the densities of tumor antigen-specific CD8+ T cells in the blood, tumor-draining lymph nodes and subcutaneous tumors of the treated mice. While reversing the immunosuppressive microenvironment, G-sHDL also induced immunogenic cell death of cancer cells, promoted maturation of dendritic cells, and increased tumor infiltration and activity of CD8+ T cells. Collectively, G-sHDL inhibited the growth of both subcutaneous tumors and liver metastases, and prolonged the survival of animals, which could be further improved when used in conjunction with anti-PD-L1 antibody. This platform can be a generalizable platform to modulate immune microenvironment of diseased livers.

Ultrafast Microfluidic PCR Thermocycler for Nucleic Acid Amplification.

The polymerase chain reaction (PCR) is essential in nucleic acid amplification tests and is widely used in many applications such as infectious disease detection, tumor screening, and food safety testing; however, most PCR devices have inefficient heating and cooling ramp rates for the solution, which significantly limit their application in special scenarios such as hospital emergencies, airports, and customs. Here, we propose a temperature control strategy to significantly increase the ramp rates for the solution temperature by switching microfluidic chips between multiple temperature zones and excessively increasing the temperature difference between temperature zones and the solution; accordingly, we have designed an ultrafast thermocycler. The results showed that the ramp rates of the solution temperature are a linear function of temperature differences within a range, and a larger temperature difference would result in faster ramp rates. The maximum heating and cooling ramp rates of the 25 µL solution reached 24.12 °C/s and 25.28 °C/s, respectively, and the average ramp rate was 13.33 °C/s, 6-8 times higher than that of conventional commercial PCR devices. The thermocycler achieved 9 min (1 min pre-denaturation + 45 PCR cycles) ultrafast nucleic acid amplification, shortening the time by 92% compared to the conventional 120 min nucleic acid amplification, and has the potential to be used for rapid nucleic acid detection.

Efficient Strategy for U(VI) Photoreduction: Simultaneous Construction of U(VI) Confinement Sites and Water Oxidation Sites.

Reduction of hexavalent uranium [U(VI)] by the photocatalytic method opens up a novel way to promote the selectivity, kinetics, and capacity during uranium removal, where organic molecules act as the sacrificial agents. However, the addition of sacrificial agents can cause a secondary environmental pollution and increase the cost. Here, a UiO-66-based photocatalyst (denoted as MnOx/NH2-UiO-66) simultaneously with efficient U(VI) confinement sites and water oxidation sites was successfully developed, achieving excellent U(VI) removal without sacrificial agents. In MnOx/NH2-UiO-66, the amino groups served as efficient U(VI) confinement sites and further decreased the U(VI) reduction potential. Besides, MnOx nanoparticles separated the photogenerated electron-hole pairs and provided water oxidation sites. The U(VI) confinement sites and water oxidation sites jointly promoted the U(VI) photoreduction performance of MnOx/NH2-UiO-66, resulting in the removal ratio of MnOx/NH2-UiO-66 for U(VI) achieving 97.8% in 2 h without hole sacrifice agents. This work not only provides an effective UiO-66-based photocatalyst but also offers a strategy for effective U(VI) photoreduction.

Lightweight Anonymous Authentication and Key Agreement Protocol Based on CoAP of Internet of Things.

To solve the problem regarding the lack of a lightweight and secure authentication and key agreement protocol in the Constrained Application Protocol of the Internet of Things environment, we explore the security flaws and applicability problems in the current related research. Then, we propose a new lightweight authentication and key agreement protocol based on the CoAP framework. The scheme adopts shared secret and elliptic curve public key technology, which ensures the anonymity of the communicators and provides strong security and anti-attack capacity. In terms of security analysis, the Dolev-Yao Adversary model and a security model checking analysis method based on CPN Tools are improved, in order to verify the correctness and security of the proposed scheme. Compared with other schemes, regarding communication overhead, computational cost, and security, the proposed scheme provides a robust and comprehensive security guarantee, although it is not the lightest.

Material removal and surface damage in high-speed grinding of enamel.

Although high-speed grinding of the enamel surface is often required in restorative dentistry, the knowledge of grinding mechanics, material removal, and fracture damage mechanism related to this process is still relatively limited; therefore, it is important to perform relevant scientific and theoretical research. As per the occlusal surface and the buccal/lingual surface of the teeth, the experimental scheme of high-speed grinding of the enamel surface using a diamond grinding bur was designed, and the grinding force, force ratio, grinding temperature, chips, surface morphology, surface damage, and other important characteristics were tested and analyzed. Furthermore, the grinding geometry model, grinding mechanics, material fracture, and removal mechanism associated with the high-speed grinding of an enamel surface were considered. The results show that the grinding force, friction coefficient, grinding temperature, and surface damage achieved through buccal/lingual surface grinding are considerably greater, and the grinding quality is worse than that obtained via occlusal surface grinding under the same grinding conditions. With the increase in the feed rate, grinding force, friction coefficient, grinding temperature, and surface damage obviously increase, and the surface quality decreases. The embrittlement effect and the ironing mechanism are present during the process of high-speed grinding of enamel. Regardless of the feed rate, the three types of material fracture modes of the buccal/lingual surface are more serious than those of the occlusal surface (making it more likely to produce unstable large chips or tearing chips); moreover, the brittle fracture and damage of the final machined surface are more obvious. The cutting mechanics and cutting mechanism identified in this study will provide scientific guidance for dental grinding operations.

Microvesicle-inspired oxygen-delivering nanosystem potentiates radiotherapy-mediated modulation of tumor stroma and antitumor immunity.

The efficacy of radiotherapy is greatly challenged by intense hypoxia, intricate stroma and suppressive immune microenvironments in tumors. Herein, we rationally designed a microvesicle-inspired oxygen-delivering polyfluorocarbon nanosystem loading DiIC18(5) and halofuginone (M-FDH) with prominent capacity of improving tumor oxygenation and intratumor distribution, synergizing radiation to disrupt tumor stroma and boost antitumor immunity for combinational cancer therapy. M-FDH produced a 10.98-fold enhancement of tumor oxygenation and caused efficient production of reactive oxygen species (ROS) upon radiation. M-FDH + X ray treatment resulted in notable DNA damages, over 90% elimination of cancer-associated fibroblasts (CAFs) and major components of extracellular matrix, significant enhancement of tumoricidal CD3+CD8+ T cells, and profound elimination of suppressive immune cells in 4T1 tumors. The therapeutic benefits of M-FDH + X ray on suppressing tumor growth were confirmed in two murine tumor models. Therefore, this study provides an encouraging microvesicle-inspired strategy to target cancer cells and CAFs in tumors and synergize radiotherapy for effective cancer treatment.

DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing.

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein-ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein-ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein-ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein-ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.