Telomeres, potential biomarkers of aging, are known to shorten with continued cigarette smoke exposure. In order to further investigate this process and its impact on cellular stress and inflammation, we used an in vitro model with cigarette smoke extract (CSE) and observed the downregulation of telomere stabilizing TRF2 and POT1 genes after CSE treatment. hTERT is a subunit of telomerase and a well-known oncogenic marker, which is overexpressed in over 85% of cancers and may contribute to lung cancer development in smokers. We also observed an increase in hTERT and ISG15 expression levels after CSE treatment, as well as increased protein levels revealed by immunohistochemical staining in smokers’ lung tissue samples compared to non-smokers. The effects of ISG15 overexpression were further studied by quantifying IFN-γ, an inflammatory protein induced by ISG15, which showed greater upregulation in smokers compared to non-smokers. Similar changes in gene expression patterns for TRF2, POT1, hTERT, and ISG15 were observed in blood and buccal swab samples from smokers compared to non-smokers. The results from this study provide insight into the mechanisms behind smoking causing telomere shortening and how this may contribute to the induction of inflammation and/or tumorigenesis, which may lead to comorbidities in smokers. Full article

Accompanying climate change and sea level rise, tidal marsh mortality in coastal wetlands has been globally observed that urges the documentation of high-resolution, 3D marsh inventory to assist resilience planning. Drone Lidar has proven useful in extracting the fine-scale bare earth terrain and canopy height. Beyond that, this study performed marsh biomass mapping from drone Lidar point cloud in a S. alterniflora-dominated estuary on the Southeast U.S. coast. Three point classes (ground, low-veg, and high-veg) were classified via point cloud deep learning. Considering only vegetation points in the vertical profile, a profile area-weighted height (H_PA) was extracted at a grid size of 50 cm × 50 cm. Vegetation point densities were also extracted at each grid. Adopting the plant-level allometric equations of stem biomass from long-term S. alterniflora surveys, a Lidar biomass index (Lidar_BI) was built to represent the relative quantity of marsh biomass in a range of [0, 1] across the estuary. Compared with the clipped dry biomass samples, it achieved a comparable and slightly better performance (R² = 0.5) than the commonly applied spectral index approaches (R² = 0.4) in the same marsh field. This study indicates the feasibility of the drone Lidar point cloud for marsh biomass mapping. More advantageously, the drone Lidar approach yields information on plant community architecture, such as canopy height and plant density distributions, which are key factors in evaluating marsh habitat and its ecological services. Full article

The rapid acquisition of flow field characterization information is crucial for closed-loop active flow control. The proper orthogonal decomposition (POD) method is a widely used flow field downscaling modeling method to obtain flow characteristics effectively. Based on the POD method, a flow field reduced-order model (ROM) is constructed in this paper for the flow field control of a hydrofoil of a blended-wing-body underwater glider (BWB-UG) with stabilized suction and blowing forces. Compared with the computational fluid dynamics (CFD) simulation, the computational time required to predict the target flow field using the established POD-ROM is only about 0.1 s, which is significantly less than the CFD simulation time. The average relative error of the predicted surface pressure is not more than 6.9%. These results confirm the accuracy and efficiency of the POD-ROM in reconstructing flow characteristics. The timeliness problem of fast flow field prediction in BWB-UG active flow control is solved by establishing a fast prediction model in an innovative way. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including conversation, in-context learning, reasoning, and code generation. This paper explores the potential application of LLMs in radiological information systems (RIS) and assesses the impact of integrating LLMs on RIS development and human–computer interaction. We present ChatUI-RIS, a prototype chat-based user interface that leverages LLM capabilities to enhance RIS functionality and user experience. Through an exploratory study involving 26 medical students, we investigate the efficacy of natural language dialogue for learning and operating RIS. Our findings suggest that LLM integration via a chat interface can significantly improve operational efficiency, reduce learning time, and facilitate rapid expansion of RIS capabilities. By interacting with ChatUI-RIS using natural language instructions, medical students can access and retrieve radiology information in a conversational manner. The LLM-powered chat interface not only streamlines user interactions, but also enables more intuitive and efficient navigation of complex RIS functionalities. Furthermore, the natural language processing capabilities of LLMs can be harnessed to automatically generate code snippets and database queries, accelerating RIS development and customization. Preliminary observations indicate that integrating LLMs in RIS has the potential to revolutionize user interface design, enhance system capabilities, and ultimately improve the overall user experience for radiologists and medical professionals. Full article

To solve the sand problem during the depressurization of methane hydrate (MH), we proposed a method to build a porous grout body with sufficient permeability and strength around the wellbore through inhibitor pre-injection and grouting, and verified its effectiveness and potential in our previous research using artificial cores created with silica sand and alternative hydrates such as TBAB- hydrate and iso-butane hydrate. However, all of the artificial cores mentioned above were created with high homogeneity, injected, cured, and had their physical properties measured in the vertical direction, which differs from real reservoir conditions. To investigate the effects of grouting in a more realistic fluid flow, we conducted further experiments using horizontal 1D cores, 1D cubic models, and a 2D cross-sectional model mimicking the near wellbore. These experiments revealed that (1) the generated gas somewhat suppressed the effects of grouting as in the case of previous experiments, and (2) grouted reservoirs would be heterogenous and anisotropic due to the fluid densities and the distribution of grout particles and turbidite sediments, but sufficient permeability and satisfactory strength could still be attained. The above series of experiments demonstrated that our method has the potential to effectively produce actual MH, preventing sand problems even in heterogeneous and anisotropic grouted reservoirs. Full article

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics. Full article

Acanthamoeba keratitis (AK) is a severe infection of the cornea. Prevention and treatment are difficult due to the inefficacy of currently available compounds. The impact of many commonly used compounds for routine examinations of Acanthamoeba is unexplored but might offer insight useful in combatting AK. In this study, we demonstrate that sodium metabisulfite, a common preservation constituent of eye care solutions, was found to be active against Acanthamoeba trophozoites at concentrations lower than that commonly found in eye drops (IC₅₀ 0.03 mg/mL). We demonstrate that sodium metabisulfite depletes thiamine from growth medium and that Acanthamoeba is a thiamine auxotroph, requiring thiamine salvage for growth. The inhibitory effects of sodium metabisulfite can be overcome by thiamine supplementation. These results are consistent with the lack of key enzymes for thiamine biosynthesis in the genome of Acanthamoeba, an area which might prove exploitable using new or existing compounds. Indeed, this study highlights sodium metabisulfite as a useful inhibitor of Acanthamoeba castellanii trophozoites in vitro and that it acts, at least in part, by limiting available thiamine. Full article

Most infectious diseases of the gastrointestinal tract can easily be treated by exploiting the already available antibiotics with the change in administration approach and delivery system. Ciprofloxacin (CIP) is used as a drug of choice for many bacterial infections; however, long-term therapy and off-site drug accumulation lead to an increased risk of tendinitis and peripheral neuropathy. To overcome this issue, nanotechnology is being exploited to encapsulate antibiotics within polymeric structures, which not only facilitates dose maintenance at the infection site but also limits off-site side effects. Here, sodium alginate (SA) and thiol-anchored chitosan (TC) were used to encapsulate CIP via a calcium chloride (CaCl₂) cross-linker. For this purpose, the B-390 encapsulator was employed in the preparation of nanobeads using a simple technique. The hydrogel-like sample was then freeze-dried, using trehalose or mannitol as a lyoprotectant, to obtain a fine dry powder. Design of Experiment (DoE) was utilized to optimize the nanobead production, in which the influence of different independent variables was studied for their outcome on the polydispersity index (PDI), particle size, zeta potential, and percentage encapsulation efficiency (% EE). In vitro dissolution studies were performed in simulated saliva fluid, simulated gastric fluid, and simulated intestinal fluid. Antibacterial and anti-inflammatory studies were also performed along with cytotoxicity profiling. By and large, the study presented positive outcomes, proving the advantage of using nanotechnology in fabricating new delivery approaches using already available antibiotics. Full article

In this study, a broadband linearizer design method based on an extended design space is proposed to optimize the design complexity and linearization performance of conventional linearizers for broadband operation. The gain characteristics of the power amplifier (PA) and linearizer are fitted to simplify the analyses and to quickly derive the ideal objective function of the linearizer design. Then, the 1 dB compression point of a nonlinear system is redefined and used to further extend the design space of the linearizer. To verify the proposed design method, a millimeter-wave linearizer prototype based on the extended design space was designed and fabricated. The linearizer was tested with continuous-wave and 100 MHz two-tone signals from 40 GHz to 43 GHz. The measurement results of the linearized PA showed that the output 1 dB power point ($O{P}_{1dB}$) was improved by more than 1.7 dB, the phase error was reduced by more than 15$°$, and the third-order intermodulation distortion (IMD3) was suppressed by 8.6 dB–13.1 dB over the working frequencies. The proposed linearizer achieved good linearization performance, low power consumption, and simple design implementation, and it was not necessary to tune the bias during broadband operation, making it applicable in complex communication scenarios. Full article

The functional characteristics of starch can be altered by shear force, which makes the impact on its microstructure of great importance to the food industry. This study investigated the effects of freeze-drying on the gel texture, pasting capabilities, and swelling power of starches made from sweet potatoes (SP), chickpeas (CP), and wheat (WS) combined with Cordia (CG) and Ziziphus gum (ZG). The samples were annealed in water without shearing and in a rapid visco-analyzer (RVA) for 30 min at 60 °C while being spun at 690 rpm. Both native and freeze-dried samples were mixed with 1% or 3% ZG and CG. After annealing, the starches were examined using a texture analyzer and RVA. The results showed that freeze-drying had a substantial (p > 0.05) impact on the starch granule, in addition to the effect of annealing. The peak viscosity of freeze-dried native CP and SP starches increased, but the peak viscosity of freeze-dried wheat starch decreased. The setbacks for CP and WS increased, whereas the setbacks for SP varied slightly. Furthermore, it was demonstrated that annealing in an RVA exhibited a substantially lower peak viscosity than annealing in a water bath; the RVA’s shearing effect may have been the cause of this difference. Cordia gum fared better than ZG in terms of peak viscosity, although ZG significantly reduced setback in comparison to CG. Among the various blends, the native WB sample had the lowest hardness (100 ± 4.9 g), while the freeze-dried WB SP sample had the greatest (175.5 ± 4.8 g). Shearing of starches broke up the granules into smaller pieces, which made them gel at lower temperatures. This could be a good thing when they are needed for food uses that require little cooking. Full article

Systemic sclerosis (SSc) represents a rare and intricate autoimmune connective tissue disease, the pathophysiology of which has not been fully understood. Its key features include progressive fibrosis of the skin and internal organs, vasculopathy and aberrant immune activation. While various anti-nuclear antibodies can serve as biomarkers for the classification and prognosis of SSc, their direct role in organ dysfunction remains unclear. Anti-Th/To antibodies are present in approximately 5% of SSc patients, and are particularly prevalent among those with the limited subtype of the disease. Although the presence of these autoantibodies is associated with a mild course of the disease, there is a strong connection between them and severe clinical manifestations of SSc, including interstitial lung disease, pulmonary arterial hypertension and gastrointestinal involvement. Also, the additional clinical correlations, particularly with malignancies, need further research. Moreover, the disease’s course seems to be influenced by antibodies, specific serum cytokines and TLR signaling pathways. Understanding the relationships between presence of anti-Th/To, its molecular aspects and response to treatment options is crucial for the development of novel, personalized therapeutic techniques and should undergo profound analysis in future studies. Full article

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R²) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R² = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R² = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data. Full article

In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power, green energy source, and mixed energy sources. Differing from mainstream algorithms that require consistency among groups, the proposed method enables knowledge transfer even across varying state and/or action spaces. With the advantage of multiple layers of knowledge extraction, a lightweight knowledge transfer is achieved without the need for neural networks. This facilitates broader applications in self-sustainable wireless networks. Simulation results reveal a notable improvement in the ’warm start’ policy for each equipment, manifesting as a $51.32\%$ increase in initial reward compared to a random policy approach. Full article

With the development of the electronics industry, the requirements for chips are getting higher and higher, and thinner and thinner wafers are needed to meet the processing of chips. In this study, a model of the adhesion state of semiconductor wafers in the stacking–clamping process based on microarray adsorption was established, the composition adhesion was discussed, the microarrays of different materials and pressures were experimentally studied, and a molecular dynamics model was established. The molecular dynamics analysis showed that the adhesion force was only related to the type of atom, and the applied pressure did not change the adhesion force. According to the simulation results, the tangential adhesion between the metal and the wafer is greater than that between the ceramic and the wafer, the adsorption force between the aluminum–magnesium alloy and the silicon wafer is shown in the normal direction, and the repulsion force between other materials and the silicon wafer is shown in the normal direction. During the pressure process, the metal is in the elastic deformation stage between the metal and the wafer, the wafer is plastically deformed in the silicon carbide ceramic and wafer, and the wafer is elastically deformed in the alumina ceramic and wafer. In this paper, the adhesion between the substrate and the wafer is studied, a method of constructing microarrays to enhance adhesion is proposed, and the tangential deformation of the array unit under pressure is studied, which provides theoretical support for increasing the adhesion by constructing microarrays. Full article

To address surface morphological defects that have a destructive effect on the epitaxial wafer from the aspect of 4H-SiC epitaxial growth, this study thoroughly examined many key factors that affect the density of defects in 4H-SiC epitaxial wafer, including the ratio of carbon to silicon, growth time, application of a buffer layer, hydrogen etching and other process parameters. Through systematic experimental verification and data analysis, it was verified that when the carbon–silicon ratio was accurately controlled at 0.72, the density of defects in the epitaxial wafer was the lowest, and its surface flatness showed the best state. In addition, it was found that the growth of the buffer layer under specific conditions could effectively reduce defects, especially surface morphology defects. This provides a new idea and method for improving the surface quality of epitaxial wafers. At the same time, we also studied the influence of hydrogen etching on the quality of epitaxial wafers. The experimental results show that proper hydrogen etching can optimize surface quality, but excessive etching may lead to the exposure of substrate defects. Therefore, it is necessary to carefully control the conditions of hydrogen etching in practical applications to avoid adverse effects. These findings have important guiding significance for optimizing the quality of epitaxial wafers. Full article

Apartment buildings are significantly popular among South Korean construction companies. However, design changes present a common yet challenging aspect, often leading to cost overruns. Traditional cost prediction methods, which primarily rely on numerical data, have a gap in fully capitalizing on the rich insights that textual descriptions of design changes offer. Addressing this gap, this research employs machine learning (ML) and natural language processing (NLP) techniques, analyzing a dataset of 35,194 instances of design changes from 517 projects by a major public real estate developer. The proposed models demonstrate acceptable performance, with R-square values ranging from 0.930 to 0.985, underscoring the potential of integrating structured and unstructured data for enhanced predictive analytics in construction project management. The predictor using Extreme Gradient Boosting (XGB) shows better predictive ability (R² = 0.930; MAE = 16.05; RMSE = 75.09) compared to the traditional Multilinear Regression (MLR) model (R² = 0.585; MAE = 43.85; RMSE = 101.41). For whole project cost changes predictions, the proposed models exhibit good predictive ability, both including price fluctuations (R² = 0.985; MAE = 605.1; RMSE = 1009.5) and excluding price fluctuations (R² = 0.982; MAE = 302.1; RMSE = 548.5). Additionally, a stacked model combining CatBoost and Support Vector Machine (SVM) algorithms was developed, showcasing the effective prediction of cost changes, with or without price fluctuations. Full article

The quality of sintered ore, which serves as the primary raw material for blast furnace ironmaking, is directly influenced by the moisture in the sintering mixture. In order to improve the precision of water addition in the sintering process, this paper proposes an intelligent model for predicting water-filling volume based on Temporal Fusion Transformer (TFT), whose symmetry enables it to effectively capture long-term dependencies in time series data. Utilizing historical sintering data to develop a prediction model for the amount of mixing and water addition, the results indicate that the TFT model can achieve the R squared of 0.9881, and the root mean square error (RMSE) of 3.5951. When compared to the transformer, long short-term memory (LSTM), and particle swarm optimization–long short-term memory (PSO-LSTM), it is evident that the TFT model outperforms the other models, improving the RMSE by 8.5403, 6.9852, and 0.453, respectively. As an application, the TFT model provides an effective interval reference for moisture control in normal sintering processes, which ensures that the error is within 1 t. Full article

In order to investigate the diffusion mechanism of slurry in post-pile grouting, this study develops a formula for calculating the diameter and the climb height of the cement core of jet grouting (CCJG). This research conducts field orthogonal tests using a self-developed grouting nozzle to analyze the effects of disturbance pressure (DP), disturbance time (DT), grouting pressure (GP), and the water–cement ratio (W/C) on the dimensions and strength of the CCJG. The findings revealed that the theoretical formula for calculating the diameter of the CCJG deviates by approximately 8% from the field test results, confirming the formula’s validity. In addition, the DP and DT significantly influence the volume of the CCJG, displaying a linear increase with their augmentation. Conversely, the W/C and DP predominantly affect the CCJG’s strength. Notably, an increase in the W/C results in diminished strength, whereas an increase in the DP enhances it. In addition, soil reinforcement is achieved through replacement, mixing, and compaction as the grout flows outward from the center of the grouting hole. These insights offer a theoretical foundation and technical support for effective grouting construction practices. Full article

Glioblastoma (GBM) is a fatal astrocytic glioma with poor prognosis and treatment resistance. Repurposing potential FDA-approved drugs like anti-psychotics can address the concerns in a timely and cost-effective manner. Epidemiological studies have shown that patients with schizophrenic using anti-psychotics have a low incidence of GBM. Therefore, we aimed to investigate the therapeutic potential of atypical anti-psychotic Iloperidone (ILO) alone and in combination with Temozolomide (TMZ) against GBM. The study assessed the growth inhibitory effect of ILO, TMZ, and their combination (ILO + TMZ) on U-87MG and T-98G cell lines using an MTT assay. The drug interaction coefficient (CDI) was determined, and doses with synergistic effects were used for subsequent experiments, including migratory, invasion, and TUNEL assays. The expressions of DRD2, β-catenin, Dvl2, Twist, and Slug were assessed by RTq-PCR, whereas the β-catenin protein expression was also determined by immunocytochemistry. ILO (p < 0.05) and TMZ (p < 0.01) significantly inhibited the growth of U-87MG cells at all tested doses. The combination of 60 µM of both drugs showed synergistic activity with CDI < 1. The inhibition of migration and apoptosis was more pronounced in the case of combination treatment (p < 0.001). Inhibition of the invading cells was also found to be significant in ILO- and combination-treated groups (p < 0.001). ILO and combination treatment also significantly downregulated the expression of DRD2, while TMZ upregulated the expression (p < 0.001). The expressions of β-catenin (p < 0.001), Dvl2 (p < 0.001), Twist (p < 0.001), and Slug (p < 0.001) were also significantly downregulated in all treatment groups as compared to the vehicle control. The data suggest that ILO possesses strong growth inhibitory activity, possibly due to its effect on DRD2 and β-catenin expression and has the potential to be repurposed against GBM. Full article

Cities are ubiquitous and, though a novel phenomenon by evolutionary standards, provide a home for many species and exert particularly strong and novel selection pressures on them. They thus offer a unique opportunity to study rapid evolutionary processes. We conducted a scoping review of published studies documenting evolutionary processes in urban environments, focusing primarily on more recent work. Unfortunately, cities have not been attractive environments for biological research and thus remain poorly studied, despite slowly growing interest in recent years. Nonetheless, we found studies documenting the effects of mutation, genetic drift, and selection in cities. However, studies show some geographic bias and were not always as conclusive as might be desired. There is even support for incipient urban speciation. Evidence across the board is less abundant and less conclusive than desirable, suggesting the need for more data collection. The urban setting, with its stronger selection, more common intermixing, and abundance of both human and widespread potential non-human zoonosis hosts and human-associated species offers great opportunities to further document evolution in action and explore its conservation implications. Full article

The cyberphysical systems of smart cities are facing increasingly severe attack situations, and traditional separate protection methods are difficult to effectively respond to. It is urgent to coordinate public safety and cybersecurity protection. However, the integration of the two faces many challenges and is a very promising research field. The aim of this study is to investigate technical approaches for the synergy between public safety and cybersecurity. This paper proposes a smart city safety protection model inspired by the human immune mechanism. It was found that through a three-line defense architecture similar to the human immune mechanism, and with the help of certain algorithms and functional middleware modules, public safety and cybersecurity protection components can be dynamically combined to achieve collaboration. This work has verified through experiments a valuable path to effectively resist complicated attack threats intertwined with public safety and cybersecurity factors. Full article

The essence of drug delivery is to use an appropriate carrier that delivers the active substance to the appropriate pathogenic site at a specific time. This study aims to develop a novel drug carrier characterized by the controlled and targeted release of risedronate (RSD). The search for new routes to deliver RSD is important because oral delivery has many disadvantages. The carrier proposed in this work is composed of gelatin, polyphosphates, and zinc. The zinc contained in the carrier is responsible for coordinating the drug. The resulting material releases RSD in a controlled manner. The rate of delivery of the substance to the body depends on the pH of the environment. This study investigated the delivery of RSD in a neutral environment, where the process exhibited a prolonged and consistent release rate. This process has also been studied in an acidic environment, which accelerates the release of the drug. Mixed-environment studies were also conducted. Initially, the drug was released in a neutral environment, and then the conditions rapidly changed to acidic. In this case, the carrier demonstrated high stability and controlled release, adapting the rate of drug release to the prevailing environmental conditions. The presented results indicate the great potential of the new gelatin-based carrier in the delivery of risedronate. Full article