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HighlightsPolysaccharide-based microneedles are novel and emerging tools for ocular drug delivery and the research on the diagnosis and treatment of eye diseases is advancing at a fast pace.Microneedle devices constructed from polysaccharide molecules derived from ocular tissue have the potential to significantly enhance the efficiency of clinical treatments and improve patient compliance with therapeutic regimens.Guided by our vast clinical experience, this is the first review collates the cutting-edge scientific findings from the interdisciplinary field combining natural macromolecules and ophthalmology.The eye, a complex organ isolated from the systemic circulation, presents significant drug delivery challenges owing to its protective mechanisms, such as the blood-retinal barrier and corneal impermeability. Conventional drug administration methods often fail to sustain therapeutic levels and may compromise patient safety and compliance. Polysaccharide-based microneedles (PSMNs) have emerged as a transformative solution for ophthalmic drug delivery. However, a comprehensive review of PSMNs in ophthalmology has not been published to date. In this review, we critically examine the synergy between polysaccharide chemistry and microneedle technology for enhancing ocular drug delivery. We provide a thorough analysis of PSMNs, summarizing the design principles, fabrication processes, and challenges addressed during fabrication, including improving patient comfort and compliance. We also describe recent advances and the performance of various PSMNs in both research and clinical scenarios. Finally, we review the current regulatory frameworks and market barriers that are relevant to the clinical and commercial advancement of PSMNs and provide a final perspective on this research area.

Geological complexities along mountain highways frequently trigger landslides, posing significant threats to transportation safety and infrastructure. This study evaluates landslide susceptibility along the Lizha-Jiezi section of China’s G345 national highway using Random Forest (RF) and Support Vector Machine (SVM) models. Eleven conditioning factors including altitude, slope, aspect, plan curvature, profile curvature, lithology, distance to fault, rainfall, distance to river, normalized difference vegetation index (NDVI), and distance to road were analyzed using remote sensing and field surveys. A landslide inventory of 67 events was divided into training (70%) and validation (30%) datasets, with non-landslide samples selected at least 100 m away from landslide locations to minimize spatial overlap. Factor contribution analysis identified distance to road as the most significant predictor, highlighting anthropogenic impacts on slope destabilization. Model validation via receiver operating characteristic (ROC) curves demonstrated RF’s superior performance (AUC = 0.887) over SVM (AUC = 0.735). The RF-derived susceptibility map classified five risk levels, revealing high-risk zones concentrated within 200 m of roads, consistent with field observations. Results emphasize the necessity of integrating anthropogenic factors into landslide risk management for mountainous infrastructure. This study provides actionable insights for mitigation strategies and land-use planning, offering a scalable framework adaptable to similar regions.

The systematic analysis of the synergistic mechanism between microbial fiber-degrading enzymes and short-chain fatty acids under high-fiber diet conditions is limited. In this study, we evaluated the effects of a high-fiber diet on the growth performance, nutrient digestibility, blood and serum metrics, cellulase/hemicellulase activity, and fecal microbial composition of growing Tibetan pigs. Forty Tibetan pigs were allocated to a control group (CON, the diet contains 5% crude fiber) or a high-fiber group (HF, the diet contains 10% crude fiber) based on crude fiber levels as a blocking factor. The pre-trial period was 7 d, and the formal trial lasted 28 d. CON group and HF group showed no effect on growth performance and nutrient apparent digestibility (p > 0.05). The HF group showed significantly higher fecal cellulase and hemicellulase activities than those of the CON group (p < 0.05). Additionally, the HF group showed significantly elevated levels of acetic, propionic, and butyric acids, as well as increased relative abundances of Fibrobacter and p-75-a5 in the feces (p < 0.05). The correlation analysis revealed that Fibrobacter exhibited significant positive correlations with acetic acid, butyric acid, cellulase, and hemicellulase, whereas p-75-a5 was significantly positively correlated with hemicellulase (p < 0.05). In conclusion, this study provides strong evidence that the efficient utilization of dietary fiber by Tibetan pigs results from highly specialized microbial mechanisms in their large intestine, as reflected by their fecal microbiota composition. Fibrobacter and p-75-a5 play a crucial role in enabling these pigs to utilize fiber effectively. Certain specific microbiota secrete a greater quantity of enzymes to facilitate the decomposition of dietary fiber, and this process ultimately leads to the generation of more metabolites.

The Axi gold deposit, a low-sulfidation epithermal deposit in the Western Tianshan, China, hosts over 50 t of gold resources and is widely regarded as the result of coupled processes of rock deformation, heat transfer, pore fluid flow, and chemical reactions. However, research on the ore-forming processes of this gold deposit from a coupled perspective remains limited, resulting in its ore-forming mechanisms being incompletely understood. In this paper, we use the concept of mineralization rate based on computational modeling to indicate the 3D spatial distribution of mineralization. The simulation results reveal the following: (1) temperature gradients play a key role in influencing mineral precipitation, whereas the effect of pore fluid pressure gradients is relatively negligible; (2) gold precipitation, characterized by a negative mineralization rate, predominantly took place along fault zones that exhibit vertical transitions from steep to gentle slopes or lateral bends, which are further distinguished by the accumulation of fluids and the presence of significant temperature gradients. Notably, this particular distribution pattern of gold precipitation closely mirrors the spatial arrangement of known gold orebodies. These findings suggest that the coupling of multiple physical and chemical processes at specific fault sites plays a critical role in ore formation, providing new insights into the mechanisms governing the development of the Axi gold deposit. Furthermore, based on these observations, it can be inferred that the deeper regions of the Axi gold deposit hold considerable mineralization potential.

Deep learning methods have demonstrated remarkable success in recognizing geochemical anomalies for mineral exploration. Typically, these methods identify anomalies by reconstructing the geochemical background, which is marked by long-distance spatial variability, giving rise to long-range spatial dependencies within geochemical signals. However, current deep learning models for geochemical anomaly recognition face limitations in capturing intricate long-range spatial dependencies. Additionally, concerns emerge from the uncertainty associated with preprocessing in existing deep learning models, which involve generating interpolated images and topological graphs to represent the spatial structure of geochemical samples. In this paper, we present a novel end-to-end method for geochemical anomaly extraction based on the Transformer model. Our model utilizes self-attention mechanism to adequately capture both global and local interconnections among geochemical samples from a holistic perspective, enabling the reconstruction of geochemical background. Moreover, the self-attention mechanism allows the Transformer model to directly input free-form geochemical samples, eliminating the uncertainty associated with the employment of prior interpolation or graph generation typically required for geochemical samples. To align geochemical data with Transformer's architecture, we tailor a specialized data organization integrating learnable positional encoding and data masking. This enables the ingestion of entire geochemical data into the Transformer for anomaly recognition. Capitalizing on the flexibility afforded by the attention mechanism, we devise a contrastive loss for training, establishing a self-supervised learning scheme that enhances model generalizability for anomaly recognition. The proposed method is utilized to recognize geochemical anomalies related to Au mineralization in the northwest Jiaodong Peninsula, Eastern China. By comparison with anomalies identified by models of graph attention network and geographically weighted regression, it is demonstrated that the proposed method is more effective and geologically sound in identifying mineralization-associated anomalies. This superior performance in geochemical anomaly recognition is attributed to its ability to capture long-range dependencies within geochemical data.

Finding new, effective predictive variables for 3D mineral prospectivity modeling is both important and challenging. The 3D ore-forming numerical modeling quantitively characterizes the complex coupling-mineralization process of the structure, fluid, heat, and wall rock, which may be potential indicators for mineral exploration. We here conducted 3D mineral prospectivity modeling with the integration of ore-forming computational simulation information in the Xiadian orogenic gold deposit, China, to examine whether the simulation data input can improve the reliability of prospectivity modeling. First, we constructed the 3D models of the orebody and fault to extract the fault geometric features using spatial analysis, as they are always considered to be the crucial controls of gold distribution. Second, we performed 3D numerical modeling of the deformation–fluid–heat-coupling process of the structurally controlled hydrothermal Au system using the FLAC3D platform. Finally, the fault-geometry features (buffer, dip, dip variation, and undulation) and the ore-formation-simulation indices (volume strain, shear strain, temperature variation, and fluid flux) were integrated using Bayesian decomposition modeling, which has a promising nonlinear model ability and a flexible variable-integration ability. The prospectivity modeling results demonstrated that the model generated by combining geometry and simulation variables achieved significantly higher AUC, precision, accuracy, Kappa, and F1 scores compared to other models using a single-predictor-variable dataset. This suggests that the joint use of geometry and simulation variables construct a comprehensive association between gold and its ore-controlling factors, thereby resulting in a highly reliable prospectivity model. Thus, the approach of 3D mineral prospectivity modeling aided by ore-forming numerical simulation proves to be more useful in guiding mineral exploration, especially in the condition of fewer variables. Based on the prospectivity modeling outcomes, we identified four gold targets at depth in the Xiadian district that warrant focused exploration efforts.

Structural deformation is ubiquitous throughout geological history. For a mineral deposit that underwent structural deformation after its formation, its geological architecture may have been severely distorted from its original geometry. Due to lack of concern for this fact, the effectiveness of existing mineral prospectivity methods could be limited in areas that experienced structural deformation. This paper proposes a three-dimensional (3D) mineral prospectivity modeling method with geometric restoration. An energy-based geometric restoration approach is presented to restore the existing geometry of geological architecture to the original one according to a series of prior constraints. To represent the original ore-forming environment, the original mineralization distribution and the predictor variables are estimated from the restored 3D geological models. Then, Random Forest is applied to build the mineral prospectivity model that associates predictor variables with the original mineralization distribution. The proposed method was applied to the world-class Jinchuan Ni–Cu–(PGE) sulfide deposit, which underwent significant off-fault deformation after its formation. It was found that, by restoration of the geometry of geological objects and the mineralization distribution, the predictor variables are more reasonable and significant to indicate spatial associations to the mineralization at Jinchuan. This led to a more accurate prospectivity model with superior evaluation metrics (AUCs, F1 scores, kappa coefficients, and PR curves, etc.) compared with the prospectivity model built without geometric restoration. Therefore, 3D mineral prospectivity modeling with geometric restoration is probably much more effective and reliable in quantifying spatial associations with mineralization and in targeting subsurface orebodies in areas that underwent structural deformation.

The genesis of magmatic Ni–Cu–precious metal sulfide ore deposits in open system magma conduits provides a means to reconcile the very large ratios of sulfide to magma in relatively small mafic–ultramafic intrusions. The Jinchuan Intrusion in Gansu Province, NW China, is a classic example where the relationships between the chonoliths and ores can be investigated with extensive exploration data on Ni and Cu concentrations generated during exploration, development, and mining. Along these lines, in this work, a novel approach for inference and modeling of magma conduits with exploration data is proposed. More specifically, for the reconstruction of three-dimensional models (3D) of magma conduits, a Markov random field (MRF) model, which is solved by utilizing a graph cut algorithm (an algorithm for n -dimensional image segmentation), was developed to find a globally optimal solution, in terms of segmentation of the pattern of magma conduits from the exploration data. In addition, a specialized implicit 3D modeling scheme was devised to generate automatically the underlying 3D geometry of the magma conduits from the solutions of the MRF. The proposed approach was used to establish the 3D architecture of the magma conduit system at Jinchuan, and it illustrated the presence of three different magma conduits. The western intrusion resulted from a bifurcated magma conduit whose entrance was located at the bottom of segment III and its major branch extended sub-horizontally to the southeast of the intrusion. The formation of the eastern intrusion was attributed to two “funnel-shaped” magma conduits whose entrances were located at the bottom of Nos. 1 and 2 orebodies, respectively. The reconstructed magma conduits were supported by evidence from lithological assemblages, grained sizes of olivine, ore-style distributions, and spatial variations of PGE tenors, which reflected the effectiveness and robustness of the proposed approach in 3D inferring and modeling magma conduits. Following the dip direction of the associated magma conduits, the refined locations structurally below orebodies 1, 2, and III-1 were expected to have a high potential for mineral exploration.

This study aimed to investigate the changes in the blood metabolic profiles of grazing yaks during the cold season to reveal their physiological status and seek the nutrients needed to be supplemented. Six castrated yaks (3 years old) with 166.8 kg (standard deviation = 5.3) of liveweight grazed in the Qinghai-Tibetan Plateau were used as experimental animals without supplementary feeding. Blood samples of each animal were collected in October and December 2015, and March 2016 for the analysis of serum biochemicals and metabolome. Results showed serum indices involved in protein metabolism in grazing yaks showed greater differences during the cold season than the metabolisms of energy or minerals. Cold stress in December had minor effects on the serum metabolic profiles of yaks compared with those in October. Yaks in October and December shared seven differential serum metabolites and enrichments of the “arachidonic acid metabolism” and “glycine, serine, and threonine metabolism” pathways compared with those in March caused by the shortage of feeds. Summarily, the nutrient deficiency would be influential on the physiological status of grazing yaks during the cold season, especially on the protein metabolism, which could be improved by supplementary feeds.