Reliable 3D Research Articles

A contribution to 3D tracking of deformable bubbles in swarms using temporal information

Reliable Lagrangian 3D tracking of individual bubble swarm members allows a deeper understanding of hydrodynamic bubble–bubble interactions and their collective rise. For multi-view measurements, we have recently developed such a tracking method (Hessenkemper in Int J Multiph Flow 179:104932, 2024), which is able to track deformable bubbles with low to moderate view obstruction through the bubbles to each other. In the present work, we aim to further enhance the 3D tracking performance by additionally incorporating 2D temporal information in the form of previously established 2D tracks in each camera view. The new 3D tracking method is able to disambiguate cross-view object associations at each time step by using the 2D track information accumulated over time. In addition, the temporal information from multiple 2D domains is used in two post-processing steps to improve the completeness of established 3D trajectories. Compared to the previous 3D tracking method, the extended 3D tracking framework shows noticeable improvements in tracking ability, accuracy, and completeness of trajectories.Graphical abstract

Super-Resolution Goes Viral: T4 Virus Particles as Versatile 3D-Bio-NanoRulers.

In the burgeoning field of super-resolution fluorescence microscopy, significant efforts are being dedicated to expanding its applications into the 3D domain. Various methodologies have been developed that enable isotropic resolution at the nanometer scale, facilitating the visualization of 3D subcellular structures with unprecedented clarity. Central to this progress is the need for reliable 3D structures that are biologically compatible for validating resolution capabilities. Choosing the optimal standard poses a considerable challenge, necessitating, among other attributes, precisely defined geometry and the capability for specific labeling at sub-diffraction-limit distances. In this context, the use of the non-human-infecting virus, bacteriophage T4 is introduced as an effective and straightforward bio-ruler for 3D super-resolution imaging. Employing DNA point accumulation for imaging in nanoscale topography (DNA-PAINT) along with the technique of astigmatic imaging, the icosahedral capsid of the bacteriophage T4, measuring 120nm in length and 86nm in width, and its hollow viral tail is uncovered. This level of detail in light microscopy represents a significant advancement in T4 imaging. A simple protocol for the production and preparation of samples is further outlined. Moreover, the extensive potential of bacteriophage T4 as a multifaceted 3D bio-ruler, proposing its application as a novel benchmark for 3D super-resolution imaging in biological studies is explored.

Generation of bovine decellularized testicular bio-scaffolds as a 3D platform for testis bioengineering.

Accelerating the genetic selection to obtain animals more resilient to climate changes, and with a lower environmental impact, would greatly benefit by a substantial shortening of the generation interval. One way to achieve this goal is to generate male gametes directly from embryos. However, spermatogenesis is a complex biological process that, at present, can be partially reproduced in vitro only in the mouse. The development of reliable 3D in vitro models able to mimic the architecture and the physiological microenvironment of the testis, represents a possible strategy to facilitate ex vivo haploid male gamete generation in domestic species. Here we describe the creation of bovine testicular bio-scaffolds and their successful repopulation in vitro with bovine testicular cells. In particular, bovine testes are subjected to three different decellularization protocols. Cellular compartment removal and extracellular matrix preservation are evaluated. The generated bio-scaffolds are then repopulated with bovine testicular fibroblasts. The results obtained demonstrate that the decellularization protocol involving the use of 0.3% sodium dodecyl sulfate (SDS) for 12h efficiently eliminates native cells, while preserving intact ECM composition and microstructure. Its subsequent repopulation with bovine fibroblasts demonstrates successful cell homing, colonization and growth, consistent with the scaffold ability to sustain cell adherence and proliferation. Overall, the generated 3D bio-scaffolds may constitute a suitable artificial niche for ex vivo culture of testicular cells and may represent a possible strategy to reproduce spermatogenesis in vitro.

Enhancing 3D-Printed Chocolate Drug Delivery: The Role of Soy Lecithin in Improving Formulation Printability and Consistency

Abstract Objectives This study explores the potential of extrusion-based 3D printing to improve medication adherence by developing chocolate-based oral dosage forms incorporating active pharmaceutical ingredients such as paracetamol and ibuprofen, while using soy lecithin as an emulsifier to enhance the flow properties of the chocolate formulation, enabling consistent and reliable 3D printing of the dosage forms. Methods Extrusion-based 3D printing was used to obtain drug-loaded solid oral dosage forms. The formulation components as well as the printing design and conditions were optimised, to enhance the appearance and patient acceptability of the final products. Techniques such as spectroscopy, thermal analysis, rheology, mechanical analysis and in-vitro dissolution were employed to assess the physicochemical properties of these formulations. Key Findings: Soy lecithin was essential in obtaining robust and consistent dosage forms. The formulations showed good physicochemical stability of the components and demonstrated consistent drug release. Conclusions The findings suggest that extrusion-based 3D printing, with the aid of emulsifiers such as soy lecithin, is a viable method for producing personalised, chocolate-based drug delivery systems, potentially improving adherence through customizable dosage forms. Further research could enhance understanding of factors such as particle size, crystallinity, and the impact of chewing on drug release, to optimise therapeutic outcomes.

Integration of the Velodyne LiDAR Sensor and Pixhawk for Indoor 3D Mapping

Abstract The importance of indoor mapping has surged across various applications, encompassing human navigation, indoor exploration, and hazardous site mapping. With satellite positioning systems ineffective indoors, alternative navigational sensors and methods, such as inertial measurement units (IMU) and simultaneous localization and mapping algorithms (SLAM), have been harnessed to furnish precise indoor positioning for mapping endeavors. Leveraging LiDAR-based SLAM, which is sensitive to environmental textures and various pose configurations, addresses these challenges. In order to accomplish reliable and precise indoor 3D mapping, this study combines the Velodyne VLP-16 3D-LiDAR sensor with the Pixhawk Cube Orange, which has a 9 Degrees of Freedom (DOF) IMU sensor. The study uses the LIO-SAM (LiDAR Inertial Odometry and Simultaneous Localization and Mapping) technique, which integrates data from LiDAR and IMU to increase mapping precision. It is carried out at the BAAK Dr. Angka Building, Institut Teknologi Sepuluh Nopember, Surabaya. The experimental results reveal that the 3D indoor mapping exhibits commendable visualization and high geometric accuracy, meeting the LoD 3 criteria. Validation using a roll meter yielded a root mean square error (RMSE) of 0.3552 meters, indicating considerable accuracy. The accuracy was further validated by showing LiDAR point cloud errors ranging from 7.92% to 15.75%. Linear regression analysis confirmed a high correlation (R2 0.9954) between LiDAR point cloud measurements and manual measurements, substantiating the method’s reliability. In order to accomplish reliable and precise indoor 3D mapping, this study combines the Velodyne VLP-16 3D-LiDAR sensor with the Pixhawk Cube Orange, which has a 9 Degrees of Freedom (DOF) IMU sensor. The study uses the LIO-SAM (LiDAR Inertial Odometry and Simultaneous Localization and Mapping) technique, which integrates data from LiDAR and IMU to increase mapping precision. It is carried out at the BAAK Dr. Angka Building, Institut Teknologi Sepuluh Nopember, Surabaya.

Design of a Pharmaceutical 3D Printer Using Quality-by-Design Approach

PurposePharmaceutical three-dimensional (3D) printing is an innovative production technique which enables the manufacturing of personalized medicine at the point-of-care. A reliable 3D printer is paramount for the successful implementation in clinical practice. In this paper, the design strategy of a pharmaceutical semi-solid extrusion 3D printer is described, where the concept of quality-by-design is applied.MethodsThe technical design stages are divided in the conceptual design and detailed design stage. The minimal viable product, critical process parameters and implemented control strategies were defined.ResultsThe critical process parameter with the highest impact is the temperature of the cartridge during preheating, i.e. prior to the production process. The temperature is controlled with an accurate thermistor, closed feedback loop and thermal isolation. The temperature can be monitored at all times using the graphical user interface and there is an audit trail using the logging system. Software was developed conforming to GAMP5.ConclusionsBuild-in control strategies in the design of the pharmaceutical 3D printer can mitigate risks during the production process of personalized medicine. The regulatory landscape surrounding 3D-printed drug products remains challenging. By using this design approach, relevant guidelines were taken into account during the design of a pharmaceutical 3D printer. Future development of the 3D printer should include the incorporation of process analytical technology tools and upscaling of feedstock production to further support the implementation of personalized medicine 3D-printed at the point-of-care.

Transformer-based multiview spatiotemporal feature interactive fusion for human action recognition in depth videos

Spatiotemporal feature modeling is the key to human action recognition task. Multiview data is helpful in acquiring numerous clues to improve the robustness and accuracy of feature description. However, multiview action recognition has not been well explored yet. Most existing methods perform action recognition only from a single view, which leads to the limited performance. Depth data is insensitive to illumination and color variations and offers significant advantages by providing reliable 3D geometric information of the human body. In this study, we concentrate on action recognition from depth videos and introduce a transformer-based framework for the interactive fusion of multiview spatiotemporal features, facilitating effective action recognition through deep integration of multiview information. Specifically, the proposed framework consists of intra-view spatiotemporal feature modeling (ISTFM) and cross-view feature interactive fusion (CFIF). Firstly, we project a depth video into three orthogonal views to construct multiview depth dynamic volumes that describe the 3D spatiotemporal evolution of human actions. ISTFM takes multiview depth dynamic volumes as input to extract spatiotemporal features of three views with 3D CNN, then applies self-attention mechanism in transformer to model global context dependency within each view. CFIF subsequently extends self-attention into cross-attention to conduct deep interaction between different views, and further integrates cross-view features together to generate a multiview joint feature representation. Our proposed method is tested on two large-scale RGBD datasets by extensive experiments to demonstrate the remarkable improvement for enhancing the recognition performance.

Initial Virgin Erase State Engineering for Highly Reliable 2D & 3D NAND Flash Memory

Over the past 30-40 years, to enhance the integration density of NAND flash, the size of the Cell Transistor(TR) has been reduced in 2-dimensional (2D) structures, and stacking has been employed in 3D structures. The improvement in integration density has led to data loss due to interference between Cell TRs, and to address this, materials, processes, and logic have been improved, but there are limitations. Interference phenomena caused by Cell TR interference are a major factor reducing the reliability of NAND flash. This study confirms the importance of the initial Virgin Cell TR Vth in the reliability of NAND flash. In 2D structures, reducing the size of Cell TR enhances the coupling effect of adjacent Cell TRs, making it appear that they have different electrons originally stored. In 3D structures, the increase in stacked Cell TRs induces a decrease in Cell current, leading to increased interference between adjacent Cell TRs in the vertical direction. Incorrect changes in Cell TR Vth not only result in data loss but also worsen the reliability under Cycle and Retention conditions. The degree of Cell interference confirmed through simulation can be used to assess the extent of data loss in various patterns of Cell TR states. Quantifying data loss through various patterns reveals that erased Cell TRs are weak. However, for Cell TRs with a high initial Virgin Vth, interference between programmed Cell TRs worsens. In a well-designed 3D structure, interference from erased Cell TRs is relatively good, but reliability is equally degraded. The importance of the initial Virgin Cell TR Vth has been confirmed through verification of Cell TR size differences. To reduce interference between Cell TRs, an optimized initial Virgin Cell TR Vth is essential. A low Virgin Cell Vth induces a relatively high Program environment, increasing the interference effect between programmed Cell TRs. A high Virgin Cell Vth causes erased Cell TRs to receive strong interference from adjacent programmed Cell TRs, leading to defects. Therefore, an optimized Virgin Cell TR Vth can minimize degradation even under Cycle and worst-case Retention conditions. The initial erase state of Cell TRs has been identified as the most critical factor in interference phenomena, providing an important benchmark for future NAND flash research. we suggest a design standard for the next generation of 3D NAND flash cell transistors from cell disturb and retention. Figure 1

A Specialized Pipeline for Efficient and Reliable 3D Semantic Model Reconstruction of Buildings from Indoor Point Clouds.

Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline's efficiency and accuracy offer valuable contributions to future advancements in point cloud processing.

Printed Interconnects for Heterogeneous Systems Integration on Flexible Substrates

AbstractHeterogeneous integration on flexible substrates has been explored in recent years to meet the high‐performance and flexible form factors requirements of several emerging applications. Reliable interconnects, in both 2D and 3D layouts, are critical for the effective use of these systems. But it is challenging to employ conventional bonding and interconnect technology due to considerable mismatches in mechanical and thermal properties. For example, the ultra‐thin chips (UTCs) are too fragile to withstand the static or oscillating forces applied during conventional bonding. In this regard, printing of metal tracks on flexible substrates is a promising alternative to access the contact pads on integrated circuits (ICs), as the interconnects on diverse substrates can be realized at room temperature processing using as much materials as needed. Further, it is easier to attain step coverage. Considering the above attractive features, and the challenges associated with conventional bonding techniques, this article focuses on the development of high‐resolution printing interconnects in both 2D and 3D layouts and explores various printing routes available for high density integration. The article also discusses major conventional interposers/interconnects forming methods and their limitations for flexible hybrid electronics along with few examples of printed interconnects to highlight the opportunities for future.

SparseDet: Towards efficient multi-view 3D object detection via sparse scene representation

Efficient and reliable 3D object detection via multi-view cameras is pivotal for improving the safety and facilitating the cost-effective deployment of autonomous driving systems. However, owing to the learning of dense scene representations, existing methods still suffer from high computational costs and excessive noise, affecting the efficiency and accuracy of the inference process. To overcome this challenge, we propose SparseDet, a model that exploits sparse scene representations. Specifically, a sparse sampling module with category-aware and geometry-aware supervision is first introduced to adaptively sample foreground features at both semantic and instance levels. Additionally, to conserve computational resources while retaining context information, we propose a background aggregation module designed to compress extensive background features into a compact set. These strategies can markedly diminish feature volume while preserving essential information to boost computational efficiency without compromising accuracy. Due to the efficient sparse scene representation, our SparseDet achieves leading performance on the widely used nuScenes benchmark. Comprehensive experiments validate that SparseDet surpasses the PETR while reducing the decoder computational complexity by 47% in terms of FLOPs, facilitating a leading inference speed of 35.6 FPS on a single RTX3090 GPU.

Reproducible 3D culture of multicellular tumor spheroids in supramolecular hydrogel from cancer stem cells sorted by sedimentation field-flow fractionation

Three-dimensional (3D) cancer models, such as multicellular tumor spheroids (MCTS), are biological supports used for research in oncology, drug development and nanotoxicity assays. However, due to various analytical and biological challenges, the main recurring problem faced when developing this type of 3D model is the lack of reproducibility. When using a 3D support to assess the effect of biologics, small molecules or nanoparticles, it is essential that the support remains constant over time and multiples productions. This constancy ensures that any effect observed following molecule exposure can be attributed to the molecule itself and not to the heterogeneous properties of the 3D support. In this study, we address these analytical challenges by evaluating for the first time the 3D culture of a sub-population of cancer stem cells (CSCs) from a glioblastoma cancer cell line (U87-MG), produced by a SdFFF (sedimentation field-flow fractionation) cell sorting, in a supramolecular hydrogel composed of single, well-defined molecule (bis-amide bola amphiphile 0.25% w/v) with a stiffness of 0.4 kPa. CSCs were chosen for their ability of self-renewal and multipotency that allow them to generate fully-grown tumors from a small number of cells.The results demonstrate that CSCs cultured in the hydrogel formed spheroids with a mean diameter of 336.67 ± 38.70 µm by Day 35, indicating reproducible growth kinetics. This uniformity is in contrast with spheroids derived from unsorted cells, which displayed a more heterogeneous growth pattern, with a mean diameter of 203.20 ± 102.93 µm by Day 35. Statistical analysis using an unpaired t-test with unequal variances confirmed that this difference in spheroid size is significant, with a p-value of 0.0417 (p < 0.05).These findings demonstrate that CSC-derived spheroids, when cultured in a well-defined hydrogel, exhibit highly reproducible growth patterns compared to spheroids derived from unsorted cells, making them a more reliable 3D model for biological research and drug testing applications.

ESMformer: Error-aware self-supervised transformer for multi-view 3D human pose estimation

Multi-view 3D human pose estimation (HPE) currently faces several key challenges. Information from different viewpoints exhibits high variability due to complex environmental factors, posing difficulties in cross-view feature extraction and fusion. Additionally, multi-view 3D labeled pose data is rather scarce, and the impact of input 2D poses on 3D HPE accuracy has received little attention. To address these issues, we propose an Error-aware Self-supervised transformer framework for Multi-view 3D HPE (ESMformer). Firstly, we introduce a single-view multi-level feature extraction module to enhance pose features in individual viewpoints, which incorporates a novel relative attention mechanism for representative feature extraction at different levels. Subsequently, we develop multi-view intra-level and cross-level fusion modules to exploit spatio-temporal feature dependencies among human joints, and progressively fuse pose information from all views and levels. Furthermore, we explore an error-aware self-supervised learning strategy to reduce the model’s reliance on 3D pose annotations and mitigate the impact of incorrect 2D poses. This strategy adaptively selects reliable input 2D poses based on 3D pose prediction errors. Experiments on three popular benchmarks show that ESMformer achieves state-of-the-art results and maintains cost-effective computational complexity. Notably, ESMformer does not rely on any 3D pose annotations or prior human body knowledge, making it highly versatile and adaptable in practical applications.11The code and models are available at https://github.com/z0911k/ESMformer.

Deep Learning-Based Workflow for Bone Segmentation and 3D Modeling in Cone-Beam CT Orthopedic Imaging

In this study, a deep learning-based workflow designed for the segmentation and 3D modeling of bones in cone beam computed tomography (CBCT) orthopedic imaging is presented. This workflow uses a convolutional neural network (CNN), specifically a U-Net architecture, to perform precise bone segmentation even in challenging anatomical regions such as limbs, joints, and extremities, where bone boundaries are less distinct and densities are highly variable. The effectiveness of the proposed workflow was evaluated by comparing the generated 3D models against those obtained through other segmentation methods, including SegNet, binary thresholding, and graph cut algorithms. The accuracy of these models was quantitatively assessed using the Jaccard index, the Dice coefficient, and the Hausdorff distance metrics. The results indicate that the U-Net-based segmentation consistently outperforms other techniques, producing more accurate and reliable 3D bone models. The user interface developed for this workflow facilitates intuitive visualization and manipulation of the 3D models, enhancing the usability and effectiveness of the segmentation process in both clinical and research settings. The findings suggest that the proposed deep learning-based workflow holds significant potential for improving the accuracy of bone segmentation and the quality of 3D models derived from CBCT scans, contributing to better diagnostic and pre-surgical planning outcomes in orthopedic practice.

Impact of enniatins and beauvericin on lipid metabolism: Insights from a 3D HepaRG spheroid model

Emerging mycotoxins enniatins (ENNs) and beauvericin (BEA) pose potential health risks to humans through dietary exposure. However, research into their mechanisms of toxicity is limited, with a lack of comprehensive toxicological data. This study investigates from a hepatic lipid metabolism perspective, establishing a more precise and reliable 3D HepaRG hepatocyte spheroid model as an alternative for toxicity assessment. Utilizing physiological indices, histopathological analyses, lipidomics, and molecular docking techniques, it comprehensively elucidates the effects of ENNs and BEA on hepatic lipid homeostasis and their molecular toxicological mechanisms. Our findings indicate that ENNs and BEA impact cellular viability and biochemical functions, significantly altering lipid metabolism pathways, particularly those involving glycerophospholipids and sphingolipids. Molecular docking has demonstrated strong binding affinity of ENNs and BEA with key enzymes in lipid metabolism such as Peroxisome Proliferator-Activated Receptor α (PPARα) and Cytosolic Phospholipase A2 (cPLA2), revealing the mechanistic basis for their hepatotoxic effects and potential to impair liver function and human health. These insights enhance our understanding of the potential hepatotoxicity of such fungal toxins and lay a foundation for the assessment of their health risks.

Evaluation of Water Displacement Method in Estimating Mandibular Ramus Autograft Volume

Objective: This study aims to identify the most reliable method for measuring graft volumes comparable to those harvested from the ramus region using 3D-printed models. Methods: Using a cross-sectional design in an in vitro setting, CBCT images from 20 individuals who met the inclusion criteria for ramus grafting were examined. Volumetric evaluations were conducted on these images, and 3D-printed graft models were created. Two blinded raters assessed the graft volumes using the displacement method (with beakers of 10 cc, 25 cc, 50 cc capacity and a 100 cc biopsy cup) and the overflow liquid method (with beakers of 10 cc, 25 cc, and 50 cc capacity). The intraclass correlation coefficient and t tests were applied for statistical validation of intra- and inter-rater reliability. Results: High levels of both intra- and interrater reliability were observed, particularly for the 10 cc rise and overflow methods. These methods exhibited not only exceptionally high ICC values but also statistically meaningful p values. Furthermore, most of these methods strongly correlated and agreed with the CBCT measurements, except for the 50 cc overflow method, which showed significant divergence. Conclusion: The findings of this study validate the 10 cc beaker methods for reliable 3D printed ramus graft volume measurement and recommend a narrow-diameter syringe for optimal accuracy. These findings have crucial implications for both clinical practice and future research.

Terahertz nano-tomography imaging technique based on a 3D simulation model

THz scattering-type scanning near-field optical microscopy (THz s-SNOM) enables high-resolution nanoscale 2D imaging, crucial for various disciplines including biology, physics, and materials science. This study establishes a reliable 3D model to determine the maximum thickness detectable by the probe. The influences of the demodulation order, tip radius, tip vibration amplitude, and incident frequency on the maximum detectable thickness are analyzed. Using bilayer samples as examples, we successfully inverted the thickness of the first layer within the detectable maximum depth range. However, we found that inversion fails when the thickness of the first layer is too small. This underscores the distinct difference between vertical and lateral resolutions, where vertical resolution represents the minimum calculable thickness. This research unveils complex internal structures, laying the groundwork for future nanolayer imaging.

Impact of discontinuity data acquisition methods on rockfall susceptibility assessment using high-resolution 3D point cloud

This research evaluates the influence of various discontinuity data acquisition methods on three-dimensional rockfall susceptibility assessments, employing a high-resolution 3D point cloud of the Špičunak rock slope in Croatia. Discontinuity mapping was conducted utilising manual and semi-automated methods. Manual mapping was done in open-source software CloudCompare, while semi-automated mapping was done using Discontinuity Set Extractor and two CloudCompare plug-ins, qFacet Fast Marching and qFacet Kd-Tree algorithm. Significant differences were observed between the methods in terms of the number of identified discontinuity planes, sets, mean orientation values, and weighted densities associated with specific sets. Based on the mapping results, three-dimensional rockfall susceptibility maps were generated. The 3D rockfall susceptibility assessment employed a Kinematic Hazard Index (KHI) for planar, wedge, and flexural toppling failure mechanisms, which determines the percentage of discontinuity planes that meet the geometrical conditions specific to each type of failure. The findings underscore that the mapping results greatly affect the 3D rockfall susceptibility assessment, impacting the identification of potential rockfall source areas. The study emphasizes the importance of integrating manual and semi-automated mapping for reliable 3D rockfall susceptibility assessment. Additionally, this study evaluated the effect of varying the number of discontinuity orientation input data on rockfall susceptibility by comparing nine scenarios, each involving a reduction in the number of discontinuity orientation data. The results indicate that accurate discontinuity mapping is more critical than the sheer number of mapped discontinuities for reliable rockfall susceptibility assessment. Therefore, prioritizing the precise determination of discontinuity set numbers and their associated weighted densities is essential for reliable susceptibility evaluations.

A 3D Pancreatic Cancer Model with Integrated Optical Sensors for Noninvasive Metabolism Monitoring and Drug Screening.

A distinct feature of pancreatic ductal adenocarcinoma (PDAC) is a prominent tumor microenvironment (TME) with remarkable cellular and spatial heterogeneity that meaningfully impacts disease biology and treatment resistance. The dynamic crosstalk between cancer cells and the dense stromal compartment leads to spatially and temporally heterogeneous metabolic alterations, such as acidic pH that contributes to drug resistance in PDAC. Thus, monitoring the extracellular pH metabolic fluctuations within the TME is crucial to predict and to quantify anticancer drug efficacy. Here,a simple and reliable alginate-based 3D PDAC model embedding ratiometric optical pH sensors and cocultures of tumor (AsPC-1) and stromal cells for simultaneously monitoring metabolic pH variations and quantify drug response is presented. By means of time-lapse confocal laser scanning microscopy (CLSM) coupled with a fully automated computational analysis, the extracellular pH metabolic variations are monitored and quantified over time during drug testing with gemcitabine, folfirinox, and paclitaxel, commonly used in PDAC therapy. In particular, the extracellular acidification is more pronounced after drugs treatment, resulting in increased antitumor effect correlated with apoptotic cell death. These findings highlight the importance of studying the influence of cellular metabolic mechanisms on tumor response to therapy in 3D tumor models, this being crucial for the development of personalized medicine approaches.

DESIGN AND FABRICATION OF CIVIL 3D PRINTER

The advent of 3D printing technology has revolution- ized the construction industry, offering innovative so- lutions for rapid, cost-effective, and sustainable build- ing practices. This research paper presents a compre- hensive study on the manufacturing and fabrication of a 3D concrete printer tailored for large-scale construc- tion applications. The paper outlines the design princi- ples, mechanical and electrical systems, material con- siderations, and software integration necessary for the development of an efficient and reliable 3D concrete printer. Key components of the printer, including the frame, extrusion system, and control mechanisms, are meticulously designed to ensure precision, stability, and durability. Advanced materials such as high- performance concrete mixes are formulated to meet the unique requirements of 3D printing, balancing workability, strength, and setting time. The integration of sensors and automation technology facilitates real- time monitoring and control, enhancing the accuracy and consistency of the printed structures. The implementation phase involved extensive testing and validation, highlighting the printer’s capability to construct complex geometries and large-scale ele- ments with minimal human intervention. Results indi- cate significant improvements in construction speed, cost savings, and material efficiency compared to tra- ditional methods. The paper concludes with a discus- sion on potential applications, challenges, and future directions for 3D concrete printing technology in the construction industry.