Artificial 3D Research Articles

Applications and challenges of artificial intelligence-driven 3D vision in biomedical engineering: A biomechanics perspective

This paper explores the applications and challenges of artificial intelligence (AI)-driven 3D vision technology in biomedical engineering, with a specific focus on its integration with biomechanics. 3D vision technology offers richer spatial information compared to traditional 2D imaging and is increasingly applied in fields like medical image analysis, surgical navigation, lesion detection, and biomechanics. In biomechanics, AI-driven 3D vision is used for analyzing human movement, modeling musculoskeletal systems, and assessing joint biomechanics. However, challenges persist, including image quality, computational resource demands, data privacy, and algorithmic bias. This paper reviews the development of 3D vision technology and AI, discusses its applications in biomedicine and biomechanics, and addresses the key technical obstacles, offering insights into the future development of these technologies in the context of biomedical and biomechanical research.

Chitosan Approaches in Colon Cancer Therapy: Understanding its Macromolecular Interactions and Structure-Property Relationships

Colon cancer is still a significant cause of cancer-related deaths and, therefore, new treatment approaches are required. As a naturally derived polysaccharide, Chitosan has become one of the most promising materials in polymer-based drug delivery systems because of its outstanding biocompatibility, biodegradability, and pH-responsive drug release. This review explores the expanding roles of Chitosan and its chemical modifications in colon cancer therapy, emphasizing the relationship between macromolecular interactions and structure-property dynamics. Recent progress in Chitosan-based nanocarriers, including nanoparticles, nanogels and hybrid systems, highlights their significant potential to overcome multidrug resistance and minimize off-target toxicity. These advancements are closely aligned with developments in precision medicine, incorporating stimuli-responsive components and targeted ligands for enhanced efficacy. While preclinical and clinical studies yield promising results, critical challenges such as scalability, reproducibility and regulatory compliance remain obstacles to clinical translation. This review emphasizes the transformative potential of Chitosan in addressing therapeutic barriers in colon cancer treatment. Looking ahead, future advancements should focus on utilizing cutting-edge technologies, such as artificial intelligence and 3D bioprinting, to enhance drug formulations and develop personalized therapeutic solutions that are more effective and tailored to individual needs.

Advancements in Minimally Invasive Techniques in Pediatric Dentistry: A Review.

Minimally invasive dentistry (MID) has revolutionized pediatric dental care by emphasizing the preservation of healthy tooth structures, reducing treatment-related trauma, and improving patient compliance. This narrative review explores advancements in MID techniques, including silver diamine fluoride (SDF), resin infiltration, atraumatic restorative treatment (ART), bioactive materials, laser-assisted therapies, and three-dimensional (3D) printing technologies. These approaches prioritize early diagnosis, prevention, and conservative management, aligning with patient-centered and sustainable practices. SDF demonstrates high efficacy in arresting caries progression but presents esthetic challenges due to discoloration. Resin infiltration provides esthetic and noninvasive treatment for white spot lesions, while ART offers cost-effective and child-friendly caries management in resource-limited settings. Bioactive materials support tissue regeneration, and laser technologies enable precise and painless procedures, although their adoption is limited by high costs and training requirements. Emerging tools, such as artificial intelligence and 3D printing, enhance diagnostic accuracy and treatment precision. Despite challenges related to cost, operator training, and infrastructure, MID techniques continue to evolve, offering promising solutions for pediatric dental care. Future research should focus on optimizing materials, improving accessibility, and integrating digital technologies to broaden the impact of minimally invasive approaches. This review highlights MID's transformative role in improving oral health outcomes and ensuring sustainable, patient-focused care for children.

Development of nebulized inhalation delivery for fusion-inhibitory lipopeptides to protect non-human primates against Nipah-Bangladesh infection.

Nipah virus (NiV) is a lethal zoonotic paramyxovirus that can be transmitted from person to person through the respiratory route. There are currently no licensed vaccines or therapeutics. A lipopeptide-based fusion inhibitor was developed and previously evaluated for efficacy against the NiV-Malaysia strain. Intraperitoneal administration in hamsters showed superb prophylactic activity and promising efficacy, however the intratracheal delivery mode in non-human primates proved intractable and spurred the development of an aerosolized delivery route that could be clinically applicable. We developed an aerosol delivery system in an artificial respiratory 3D model and optimized the combinations of flow rate and particle size for lung deposition. We characterized the nebulizer device and assessed the safety of lipopeptide nebulization in an African green monkey model that mimics human NiV infection. Three nebulized doses of fusion-inhibitory lipopeptide were administered every 24 hours, resulting in peptide deposition across multiple regions of both lungs without causing toxicity or adverse hematological and biochemical effects. In peptide-treated monkeys challenged with a lethal dose of NiV-Bangladesh, animals retained robust levels of T and B-lymphocytes in the blood, infection-induced lethality was significantly delayed, and 2 out of 5 monkeys were protected from NiV infection. The present study establishes the safety and feasibility of the nebulizer delivery method for AGM studies. Future studies will compare delivery methods using next-generation fusion-inhibitory anti-NiV lipopeptides to evaluate the potential role of this aerosol delivery approach in achieving a rapid antiviral response.

Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation.

The accuracy of intraoperative rapid frozen pathology is suboptimal, and the assessment of invasiveness in malignant pulmonary nodules significantly influences surgical resection strategies. Predicting the invasiveness of lung adenocarcinoma based on preoperative imaging is a clinical challenge, and there are no established standards for the optimal threshold value using the threshold segmentation method to predict the invasiveness of stage T1 lung adenocarcinoma. This study aimed to explore the efficacy of three-dimensional solid component volume (3D SCV) [calculated by artificial intelligence (AI) threshold segmentation method] in predicting the aggressiveness of T1 lung adenocarcinoma and to determine its optimal threshold and cut-off point. A retrospective case-control analysis was conducted on 1,179 confirmed T1 lung adenocarcinoma nodules from two centers. AI-based threshold segmentation was used to calculate seven sets of solid component volume data (V-550, V-450, V-350, V-250, V-150, V-50, V0) at seven computed tomography (CT) threshold values (-550, -450, -350, -250, -150, -50, 0 HU). The receiver operating characteristic (ROC) curve was employed to explore the optimal threshold value for predicting the invasiveness of T1 lung adenocarcinoma based on solid component volume. Subgroup analysis was performed according to the diameter of the pulmonary nodule, divided into T1a (≤10 mm), T1b (10 mm < T1b ≤20 mm), and T1c (20 mm < T1b ≤30 mm), to investigate the optimal threshold for each subgroup. The solid component volume was a stable predictive factor for the invasiveness of T1 lung adenocarcinoma. The optimal threshold value for predicting the invasiveness of T1 lung adenocarcinoma using AI-based 3D SCV segmentation was -350 HU, with an area under the curve (AUC) and 95% confidence interval (CI) of 0.930 (0.915-0.945), a cut-off value of 106.5 mm3, a sensitivity of 88.462%, and a specificity of 83.853%. For the T1a subgroup, the optimal threshold was -450 HU, with an AUC of 0.926, a cut-off value of 204.5 mm3, a sensitivity of 85.269%, and a specificity of 87.013%. For the T1b subgroup, the optimal threshold was -250 HU, with an AUC of 0.922, a cut-off value of 106 mm3, a sensitivity of 89.726%, and a specificity of 82.266%. For the T1c subgroup, the optimal threshold was -350 HU, with an AUC of 0.961, a cut-off value of 472 mm3, a sensitivity of 95.652%, and a specificity of 83.529%. The -350 HU threshold is reasonable for predicting the invasiveness of T1 lung adenocarcinoma based on solid component volume using the threshold segmentation method. However, there are certain differences in the threshold values depending on the diameter of the pulmonary nodule.

IoT technology as a support tool for the calculation of Crop Water Stress Index in a Vitis vinifera L. cv. Chardonnay vineyard in Northern Italy

Nowadays agriculture is one of the main sectors affected by climate change. The continuous increase of temperature and drought periods are posing serious problems in terms of shift of plants’ phenological phases and a reduction of crop yield quantity and quality. Among the indexes used to assess plant water status, the Crop Water Stress Index (CWSI) is one of the most studied due to its ease of calculation. We performed a study in a vineyard in Trentino (San Michele all’Adige, Northern Italy) where we took advantage of IoT Technology to build a device to measure leaf temperature and automatically calculate the CWSI. Parameters necessary to determine the CWSI were the temperature of a non-transpiring leaf, (artificial 3D printed black leaf), and the temperature of a fully-transpiring leaf (wet bulb temperature of the air). We compared various types of thermometers to measure temperatures of the real leaves, and with repeated measuring campaigns performed during the summer of 2022 we could obtain spatial maps of CWSI that could highlight the stress levels of the vineyard and therefore address the irrigation management in a context of precision agriculture.

Trends analysis and future study of the pharmaceutical industry field: a scoping review.

The main aim of this study was to explore and understand the emerging developments, changes, and patterns shaping the future of the pharmaceutical industry. The pharmaceutical industry is evolving rapidly due to scientific breakthroughs, societal changes, and technological advancements. Companies can adapt, and innovate to stay competitive and advance healthcare objectives by understanding the changes and patterns shaping the future. We conducted a thorough Scoping Review across PubMed, Embase, Web of Science, and Scopus, focusing on future trends in the pharmaceutical industry. Studies were carefully selected based on predefined criteria, ensuring a comprehensive and relevant collection. We identified and selected 24 studies that met our predefined criteria from a pool of 3617 studies. These studies served as the foundation for a detailed exploration of trends within the pharmaceutical industry. This compilation offers a profound understanding of the diverse forces propelling change, including technological advancements, regulatory shifts, evolving market dynamics, and regional nuances. This research highlights transformative trends such as artificial intelligence, 3D printing, blockchain technology, and digital integration revolutionizing drug discovery, development, manufacturing, and delivery. However, successful implementation hinges on addressing challenges through practical strategies, interdisciplinary collaborations, and region-specific considerations. Future research should explore implementation approaches, facilitating the effective translation of these innovations into tangible healthcare benefits. This forward-looking analysis empowers the pharmaceutical industry to strategically position itself for long-term success in addressing evolving global needs.

Pseudo Multi-Modal Approach to LiDAR Semantic Segmentation.

To improve the accuracy and reliability of LiDAR semantic segmentation, previous studies have introduced multi-modal approaches that utilize additional modalities, such as 2D RGB images, to provide complementary information. However, these methods increase the cost of data collection, sensor hardware requirements, power consumption, and computational complexity. We observed that multi-modal approaches improve the semantic alignment of 3D representations. Motivated by this observation, we propose a pseudo multi-modal approach. To this end, we introduce a novel class-label-driven artificial 2D image construction method. By leveraging the close semantic alignment between image and text features of vision-language models, artificial 2D images are synthesized by arranging LiDAR class label text features. During training, the semantic information encoded in the artificial 2D images enriches the 3D features through knowledge distillation. The proposed method significantly reduces the burden of training data collection and facilitates more effective learning of semantic relationships in the 3D backbone network. Extensive experiments on two benchmark datasets demonstrate that the proposed method improves performance by 2.2-3.5 mIoU over the baseline using only LiDAR data, achieving performance comparable to that of real multi-modal approaches.

3D Crystal Construction by Single-Crystal 2D Material Supercell Multiplying.

2D stacking presents a promising avenue for creating periodic superstructures that unveil novel physical phenomena. While extensive research has focused on lateral 2D material superstructures formed through composition modulation and twisted moiré structures, the exploration of vertical periodicity in 2D material superstructures remains limited. Although weak van der Waals interfaces enable layer-by-layer vertical stacking, traditional methods struggle to control in-plane crystal orientation over large areas, and the vertical dimension is constrained by unscalable, low-throughput processes, preventing the achievement of global order structures. In this study, a supercell multiplying approach is introduced that enables high-throughput construction of 3D superstructures on a macroscopic scale, utilizing artificially stacked single-crystalline 2D multilayers as foundational repeating units. By employing wafer-scale single-crystalline 2D materials and referencing the crystal orientation of substrates, the method ensures precise alignment of crystal orientation within and across each supercell, thereby achieving controllable periodicity along all three axes. A centimeter-scale 3R-MoS₂ crystal is successfully constructed, comprising over 200 monolayers of single-crystalline MoS₂, through a bottom-up stacking process. Additionally, the approach accommodates the integration of amorphous oxide, enabling the assembly of 3D non-linear optical crystals with quasi-phase matching. This method paves the way for the bottom-up construction of macroscopic artificial 3D crystals with atomic plane precision, enabling tailored optical, electrical, and thermal properties and advancing the development of novel artificial materials and high-performance applications.

DEVELOPMENT OF INNOVATIVE MEDICAL DEVICES IN NEONATOLOGY: LITERATURE REVIEW

The aim of this study is to review the development of medical devices in neonatology, exploring technological advances, identifying innovations and challenges, and proposing recommendations for future research. The methodology involved a narrative review of the literature, searching databases such as PubMed, EBSCO, Cochrane, and IEEE Xplore, focusing on studies from the last ten years on medical devices for neonatology. The results highlight that technologies such as artificial intelligence and machine learning have improved the monitoring and prognosis of neonatal conditions, allowing for more precise and personalized interventions. 3D printing emerges as a promising tool for creating personalized medical devices, meeting the specific needs of each patient. Wearable devices for continuous monitoring of vital signs and advanced enteral and parenteral feeding devices have demonstrated a positive impact on neonatal health. Mechanical ventilators with gentler and smarter modes have reduced serious complications, and catheters with antimicrobial coatings have helped to reduce the incidence of neonatal sepsis. The conclusions indicate that technological innovations are fundamental to improving neonatal care. The integration of artificial intelligence, 3D printing and continuous monitoring has the potential to transform neonatology, improving clinical outcomes and the quality of life of newborns. However, challenges such as the variability of neonatal pathologies, cost and accessibility of devices and the need for adequate training of health professionals need to be addressed. Investment in continuous research, clinical validation and interdisciplinary training is recommended to ensure the effective adoption of these innovative technologies in daily clinical practices.

Validation of a dynamic 4D microsurgical bypass simulator for training and teaching microvascular anastomosis techniques with blood flow and fluorescence imaging

ObjectiveMicrovascular anastomosis is challenging, and training surgeons to develop and maintain skills is imperative. Current training models either miss the simulation of the surgical workflow, lack 3D key-hole space, need ethical approval, require special preparation, or lack realism. To circumvent these issues, this study describes the use of a mixed reality 3D printed model with integrated blood flow for training cerebral anastomosis and assesses its validity. MethodsDifferent-sized 3D-printed artificial micro artery models in a 3D brain space with a keyhole opening were used. The model was connected to a 4D simulator to induce pulsatile blood flow. Neurosurgical microscopes and exoscopes were used for visualization. Nine participants (n = 6 board-certified cerebrovascular neurosurgeons; n = 3 in-training) participated in the study and practiced anastomosis techniques with the simulator. Two senior, experienced vascular neurosurgeons mentored live teaching activity on the simulator. Participants completed a survey to score the face and content validity of the simulation on a 5-point Likert scale. Simulation time and anastomosis score differences between in-training and board-certified participants were compared for construct validity. ResultsParticipants scored the simulation difficulty similar to actual surgery, proving face validity. All participants agreed that practice on the provided simulator models would improve bypass techniques (μ = 4.67/5 ± 0.47) and instrument handling (μ = 4.56/5 ± 0.68). Board-certified participants had better anastomosis scores than in-training participants (non-significant difference). ConclusionsThe 4D simulator and the high-fidelity artificial 3D printed model effectively simulated actual bypass surgery in a key-hole fashion with blood flow abilities. Limited resources and preparation time are needed for the training setup. The model provides benefits in learning and maintaining anastomosis skills and allows for easy adaptation to different microanatomical scenarios.

Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.

Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1-weighted MRI scan. We quantified iNPH, Parkinson disease, Alzheimer disease, and healthy controls on T1-weighted 3D brain MRI scans using 452 scans for training and 110 scans for testing. Automatic component measurement algorithms were developed for the Evans index, Sylvian fissure enlargement, high-convexity tightness, callosal angle, and normalized lateral ventricle volume. XGBoost models were trained for both automated measurements and manual labels for iNPH prediction. A total of 452 patients (200 men; mean age, 73.2 [SD, 6.5] years) were included in the training set. Of the 452 patients, 111 (24.6%) had iNPH. We obtained area under the curve (AUC) values of 0.956 for automatically measured high-convexity tightness and 0.830 for Sylvian fissure enlargement. Intraclass correlation values of 0.824 for the callosal angle and 0.924 for the Evans index were measured. By means of the decision tree of the XGBoost model, the model trained on manual labels obtained an average cross-validation AUC of 0.988 on the training set and 0.938 on the unseen test set, while the fully automated model obtained a cross-validation AUC of 0.983 and an unseen test AUC of 0.936. We demonstrated a machine learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRIs with minimal human intervention, making possible large-scale iNPH screening.

Conventional and digital technologies for measuring and monitoring food waste in the healthcare foodservice

ABSTRACT Monitoring and measurement are crucial activities in managing and reducing the impact of food waste. In Europe, the average food waste per person was estimated at 131 kg, with approximately 10% originating from restaurants and foodservice. Unfortunately, there is a lack of detailed information regarding food waste in hospital settings. Despite this, effective hospital food management plays a significant role in the environmental, social, and economic well-being of patients, and is closely linked to their nutrition and recovery prospects. This research examines key studies on hospital food waste management, focusing on practical implications and identifying strategies for improving sustainability in hospital foodservice through artificial intelligence technologies. It explores emerging trends, opportunities, and challenges in measuring and monitoring food waste while considering patient satisfaction and nutrition as critical factors for sustainable healthcare systems, through a semi-integrative literature review. Precisely, the amount of food waste per patient per day varies depending on the meal, with breakfast waste at about 0.08 kg, lunch waste ranging from 0.15 to 0.47 kg, and dinner waste between 0.11 and 0.48 kg, with the highest values recorded for lunch. Furthermore, considering the imperative to improve data precision, this study introduces current solutions that integrate artificial intelligence, 3D scanners, and digital scales to classify food categories, measure waste quantities, and assess the corresponding economic and environmental impacts. This research is intended for both academic and industry professionals in the foodservice sector, providing valuable insights into developing environmentally friendly strategies and achieving sustainability goals.

Deriving physical parameters of unresolved star clusters

Context.Recently, it has been noticed that the discrepancies in the integrated colour indices (CIs) between star clusters and models are mostly due to the projection of bright stars in the apertures. In order to reduce this problem, the method of adaptive aperture photometry has been proposed. This method has been applied to star clusters from theM 31PanchromaticHubbleAndromeda Treasury (PHAT) survey, and studies show that the adaptive aperture photometry performs better than the conventional approach.Aims.The aim of this study is to determine the best achievable limits on the accuracy and applicability of the aperture photometry method for studying star clusters in the local Universe.Methods.We computed a large network of artificial 3D star clusters spanning the parameter space of theM 31clusters. We then simulated images of these clusters by projecting each onto a 2D plane from 100 directions. Star cluster images were generated in six passbands to match the PHAT survey. To investigate the limiting accuracy of aperture photometry and the limits of its applicability to star cluster studies, we measured the simulated images and performed parameter determination tests.Results.We demonstrate that star clusters with and without post-main-sequence stars have significant photometric differences. We show that in order to obtain reliable physical parameters of star clusters, the CIs must be measured using an aperture with a radius larger than the cluster’s half-light radius. Furthermore, we demonstrate that the parameter determination of young clusters (∼10 Myr) is problematic regardless of the aperture size used. Therefore, it is advisable to determine the parameters of these clusters using colour-magnitude diagram fitting methods, when possible. We also show that the randomness of the viewing angle can lead to a CI uncertainty of up to 0.1 mag, depending on cluster parameters and aperture size.

3D printing and CBCT anatomical reproducibility assessment in forensic scenarios

IntroductionThe scientific community highlighted the relevance of 3D physical models since the beginning of the XXI century, complementary to three-dimensional(3D) digital volume by computer tomography, to support court discussions on medico-legal issues. The recreation of 3D evidence can be an important tool for investigators and experts, providing a better understanding of the causes and circumstances of the events involved in a crime. ObjectiveThe present study aims to assess the reproducibility of 3D printed and 3D tomographic volumes generated from mandibles following simulated forensic injuries, highlighting the recreation of crime tools. Material and methodsConcerning the study design presented, data collection was performed in three phases. Nine simulated injuries of forensic interest were selected (phase1) and all the mandibles were scanned tomographically, individually, by Cone Beam Computed Tomography CBCT (phase 2). Then, in phase 3, the DICOM images were used for 3D printing with the Ender 3® printer by the Fused Deposition Modeling (FDM) technique. The data analysis followed two procedures: the comparison between the artificial mandible and 3D tomographic volume (AT) and the comparison between the artificial mandible and 3D printed volume, or the copy (AC). Data were analyzed using T-Student and ICC tests and presented in Bland-Altman plots. ConclusionThe analogic technique applied in 3D printed volume, when compared with computerized technique, using 3D digital images and measurement, showed to be accurate and reproducible. Further studies are needed in search of standardization for three-dimensional measurements in digitized and printed volumes.

Operando Colorations from Real-Time Growth of 3D-Printed Nanoarchitectures.

While artificial 3D nanostructures can generate precise and flexible coloration, their real-time color changes during 3D nanoprinting remain unexplored owingto the inherent challenges of in situ transient measurements and observations. In this study, a 3D-printing system which supports the operando observation/measurement of the color dynamics of subwavelength metallic nanoarchitectures fabricated in real time is developed and evaluated. During 3D printing, the dimensions and geometries of the 3D nanostructures grow over time, producing a large library of optical spectra associated with real-time color changes. Only a timer is needed to define the expected colors from a single 3D print run. Fin-like nanostructures are used to toggle colors based on the polarization effect and produce color gradients. Based on structural coloration, nanoarchitectures are designed and printed to animate desired color patterns. Moreover, the resulting color dynamics can also serve as an operando identifier for real-time structural information during 3D nanoprinting. A single print run enables the efficient creation of a comprehensive library of desired colorations owing to the flexibility in time-dependent controllability and 3D geometries at the subwavelength scale. 3D nanoprinted plasmonic structures exhibiting time-varying colorations (4D printing of colors) uniquely redefines the coloring stategy,offering considerable potential for numerousapplications.

Fabrication of pristine 2D heterostructures for scanning probe microscopy

Material-by-design has been a long-standing aspiration that has recently become a reality. Such designer materials have been repeatedly demonstrated using the top-down approach of mechanical exfoliation and stacking, leading to a variety of artificial 2D heterostructures with new properties that are otherwise unattainable. Consequently, tremendous research frontiers in physics, chemistry, engineering, and life science have been created. While thousands of layered crystals exist in nature, only a few dozen of them with manageable chemical-stability have been made into heterostructures using this method. Moreover, experimental investigations of materials that have received limited exploration in the 2D realm, such as cuprates, halides, and perovskites, along with their heterostructures, have been fundamentally hindered by their rapid chemical degradation. Another critical challenge imposed by exfoliating and stacking 2D layers in ambient environment is the absorption of itinerant gas molecules that further contaminate sensitive 2D interfaces in the heterostructures. Such contamination and compromised material properties significantly hinder surface-sensitive local probes—scanning probe microscopy (SPM)—that often require nanometer to atomic scale surface cleanliness. In this article, we aim to provide a technical review of recent development toward 2D materials and heterostructure fabrication in more controlled environments that are suitable for SPM characterizations. These include the development of more efficient mechanical exfoliation and dry-transfer techniques, as well as the incorporation of 2D material exfoliation and transfer in inert gas, low vacuum, and, eventually, ultra-high vacuum environments. Finally, we provide an outlook on the remaining challenges and opportunities in ultra-clean 2D material fabrication techniques.

Dental pulp mesenchymal stem cells-response to fibrin hydrogel reveals ITGA2 and MMPs expression

Regenerative endodontic procedures (REP) aim at reestablishing tooth vitality by replacing the irreversibly damaged dental pulp removed by the dental practitioner with a new functional one. The current treatment of advanced caries relies on the replacement of the inflamed or necrosed dental pulp with an inert filling material. This leads to a functional but non-vital tooth, which lacks the ability to sense dental tissue damage, and to protect from further bacterial attack. Therapeutic strategies inspired by tissue engineering called REP propose to regenerate a fully functional dental pulp directly in the canal space. Promising results were obtained using dental pulp mesenchymal stem cells (DP-MSCs) in combination with bio-inspired artificial and temporary 3D hydrogels made of extracellular matrix molecules such as collagen and fibrin biomacromolecules. However, the uncontrolled mechanisms of DP regeneration from DP-MSCs in 3D biomacromolecules fail to regenerate a fully functional DP and can induce fibrotic scarring or mineralized tissue formation to a non-negligible extent. The lack of knowledge regarding the early molecular mechanisms initiated by DP-MSCs seeded in ECM-made hydrogels is a scientific lock for REP. In this study, we investigated the early DP-MSC–response in a 3D fibrin hydrogel. DP-MSCs isolated from human third molars were cultured for 24 h in the fibrin hydrogel. The differential transcript levels of extracellular and cell surface genes were screened with 84-gene PCR array. Out of the 84 genes screened, 9 were found to be overexpressed, including those coding for the integrin alpha 2 subunit, the collagenase MMP1 and stromelysins MMP3, MMP10 and MMP12. Over-expression of ITGA2 was confirmed by RT-qPCR. The expression of alpha 2 integrin subunit protein was assessed over time by immunoblot and immunofluorescence staining. The increase in the transcript level of MMP1, MMP3, MM10 and MMP12 was confirmed by RT-qPCR. The overexpression of MMP1 and 3 at the protein level was assessed by immunoblot. MMP3 expression by DP-MSCs was observed by immunofluorescence staining. This work demonstrates overexpression of ITGA2 and of MMP1, 3, 10 and 12 by DP-MSCs cultured in a fibrin hydrogel. The main preliminary extracellular and cell surface response of the DP-MSCs to fibrin hydrogel seems to rely on a ITGA2/MMP3 axis. Further investigations are needed to precisely decipher the role of this axis in dental pulp tissue building. Nevertheless, this work identifies extracellular and cell surface molecules that could be potential checkpoints to be targeted to guide proper dental pulp tissue regeneration.

AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study

Background & AimsBody composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Here we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE). MethodsThis retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010–2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival (OS) and performed multivariate analysis including established estimates of survival. ResultsUnivariate survival analysis revealed that impaired median OS was predicted by low SM volume (p < 0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. ConclusionsSkeletal muscle volume is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine. Impact and implicationsBody composition assessment parameters, especially skeletal muscle mass, have been identified as relevant prognostic factors for many diseases and treatments. In this study skeletal muscle volume has been identified as an independent prognostic factor for patients with HCC undergoing TACE. Therefore, skeletal muscle mass as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with AI evaluation is essential for automated body composition assessment quantification output enabling broad availability in multidisciplinary case discussions.

Using Artificial Intelligence-Based 3D Smile Design: REBEL in Prosthetic Rehabilitation with Patient's Personal Preferences. Case-1

Each smile is unique, and smiles reflect the different shapes, textures, and colors of the teeth and their combinations. When creating a new smile design dentists need to consider the biology, structure, function, and esthetics to create the fifth dimension of the smile. This approach is completely different from the conventional one. A smile reflects the personality and emotional state of the patient, and the translation of this into a new smile design occurs either by analog methods or digitally. A smile is a person's ability to express a wide range of emotions with the movement and structure of the teeth, dentofacial, esthetic lips, social and psychological (Rambabu et al, 2018) it also determines how well a person functions in society. When a person has a harmonious smile. it is always assumed that it increases self-esteem and their ability to interact properly and more confidently in their surroundings.