Analysis of Resin-Based Dental Materials' Composition Depending on Their Clinical Applications.

The objective of this study was to detail the monomer composition of resin-based dental materials sold in the market in 2023 and to evaluate the proportion of bisphenol A (BPA)-derivatives in relation to their applications. A search on manufacturers' websites was performed to reference resin-based dental materials currently on the European market (including the European Union (EU) and United Kingdom (UK). Their monomer composition was determined using material-safety data sheets and was completed by a search on the PubMed database. Among the 543 material compositions exploitable, 382 (70.3%) contained BPA derivatives. Among them, 56.2% contained BisGMA and 28% BisEMA, the most frequently reported. A total of 59 monomers, of which six were BPA derivatives, were found. In total, 309 materials (56.9%) contained UDMA and 292 (53.8%) TEGDMA. Less than one third of materials identified contained no BPA derivatives. These proportions vary a lot depending on their applications, with materials dedicated to the dental care of young populations containing the highest proportions of BPA-derivative monomers. The long-term effects on human health of the different monomers identified including BPA-derivative monomers is a source of concern. For children and pregnant or lactating women arises the question of whether to take a precautionary principle and avoid the use of resin-based dental materials likely to release BPA by opting for alternative materials.

Software defect prediction using learning to rank approach.

Software defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modules, but also rank them in a specific order to optimize the resource allocation and minimize testing costs, especially for projects with limited budgets. This vital task can be accomplished using Learning to Rank (LTR) algorithm. This algorithm is a type of machine learning methodology that pursues two important tasks: prediction and learning. Although this algorithm is commonly used in information retrieval, it also presents high efficiency for other problems, like SDP. The LTR approach is mainly used in defect prediction to predict and rank the most likely buggy modules based on their bug count or bug density. This research paper conducts a comprehensive comparison study on the behavior of eight selected LTR models using two target variables: bug count and bug density. It also studies the effect of using imbalance learning and feature selection on the employed LTR models. The models are empirically evaluated using Fault Percentile Average. Our results show that using bug count as ranking criteria produces higher scores and more stable results across multiple experiment settings. Moreover, using imbalance learning has a positive impact for bug density, but on the other hand it leads to a negative impact for bug count. Lastly, using the feature selection does not show significant improvement for bug density, while there is no impact when bug count is used. Therefore, we conclude that using feature selection and imbalance learning with LTR does not come up with superior or significant results.

Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring.

Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.

A machine learning framework for discovering high entropy alloys phase formation drivers.

In the past years, high entropy alloys (HEAs) witnessed great interest because of their superior properties. Phase prediction using machine learning (ML) methods was one of the main research themes in HEAs in the past three years. Although various ML-based phase prediction works exhibited high accuracy, only a few studied the variables that drive the phase formation in HEAs. Those (the previously mentioned work) did that by incorporating domain knowledge in the feature engineering part of the ML framework. In this work, we tackle this problem from a different direction by predicting the phase of HEAs, based only on the concentration of the alloy constituent elements. Then, pruned tree models and linear correlation are used to develop simple primitive prediction rules that are used with self-organizing maps (SOMs) and constructed Euclidean spaces to formulate the problem of discovering the phase formation drivers as an optimization problem. In addition, genetic algorithm (GA) optimization results reveal that the phase formation is affected by the electron affinity, molar volume, and resistivity of the constituent elements. Moreover, one of the primitive prediction rules reveals that the FCC phase formation in the AlCoCrFeNiTiCu family of high entropy alloys can be predicted with 87% accuracy by only knowing the concentration of Al and Cu.

Arabic fake news detection based on deep contextualized embedding models.

Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Arabic language is scarce. Our contribution is twofold: first, we have constructed a large and diverse Arabic fake news dataset. Second, we have developed and evaluated transformer-based classifiers to identify fake news while utilizing eight state-of-the-art Arabic contextualized embedding models. The majority of these models had not been previously used for Arabic fake news detection. We conduct a thorough analysis of the state-of-the-art Arabic contextualized embedding models as well as comparison with similar fake news detection systems. Experimental results confirm that these state-of-the-art models are robust, with accuracy exceeding 98%.

COVID-19 vaccine uptake and its associated factors among Palestinian healthcare workers: Expectations beaten by reality.

BACKGROUND: In response to this extraordinary outbreak, many countries and companies rush to develop an effective vaccine, authorize, and deliver it to all people across the world. Despite these extensive efforts, curbing this pandemic relies highly upon vaccination coverage. This study aimed to determine SARS-COV-2 vaccine uptake among Palestinian healthcare workers, the factors that influence vaccination uptake, and the motivators and barriers to vaccination. METHODS: A cross-sectional study was conducted using an online anonymous self-administered questionnaire during April and May 2021, after the Palestinian Ministry of Health launched the COVID-19 vaccination campaign. The questionnaire collected socio-demographic characteristics, vaccination attitude and vaccination uptake status, and motivators and barriers towards vaccination. In addition, multivariate logistic regression was performed to identify the influencing factors of vaccination uptake. RESULTS: The study included 1018 participants from different professions, including 560 (55.0%) females. Of the participants, 677 (66.5%; 95% CI: 63.5-69.4%) received the vaccine. Higher uptake was observed among males (aOR = 1.5; 95 %CI: 1.1-2.1), single HCWs (aOR = 1.3; 95 %CI: 1.1-1.8), HCWs working in the non-governmental sector (aOR = 1.6; 95 %CI: 1.2-2.4), higher monthly income (aOR = 1.9; 95 %CI: 1.4-2.8) and smoking (aOR = 1.5; 95 %CI: 1.1-3.5). The lower level of negative vaccination attitudes predicted higher intake; mistrust of vaccine belief (aOR = 1.6; 95 %CI: 1.4-1.7) and worries over unforeseen future effects (aOR = 1.2; 95 %CI: 1.1-1.3). CONCLUSION: In conclusion, the COVID-19 vaccination uptake was comparable to other studies worldwide but still needs to be improved, especially in the context of this ongoing global pandemic. It is imperative to invest resources to promote vaccination uptake and target all the vaccine misconceptions and fears.

Breast cancer detection using artificial intelligence techniques: A systematic literature review.

Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer Foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of several dangerous diseases, helping in early diagnosis and treatment, and thus increasing the patient's chance of survival. Deep learning has been designed to analyze the most important features affecting detection and treatment of serious diseases. For example, breast cancer can be detected using genes or histopathological imaging. Analysis at the genetic level is very expensive, so histopathological imaging is the most common approach used to detect breast cancer. In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning. We also provide recommendations to researchers who will work in this field.

Improved Optical Flow Estimation Method for Deepfake Videos.

Creating deepfake multimedia, and especially deepfake videos, has become much easier these days due to the availability of deepfake tools and the virtually unlimited numbers of face images found online. Research and industry communities have dedicated time and resources to develop detection methods to expose these fake videos. Although these detection methods have been developed over the past few years, synthesis methods have also made progress, allowing for the production of deepfake videos that are harder and harder to differentiate from real videos. This research paper proposes an improved optical flow estimation-based method to detect and expose the discrepancies between video frames. Augmentation and modification are experimented upon to try to improve the system's overall accuracy. Furthermore, the system is trained on graphics processing units (GPUs) and tensor processing units (TPUs) to explore the effects and benefits of each type of hardware in deepfake detection. TPUs were found to have shorter training times compared to GPUs. VGG-16 is the best performing model when used as a backbone for the system, as it achieved around 82.0% detection accuracy when trained on GPUs and 71.34% accuracy on TPUs.

Efficient isolation of human gingival stem cells in a new serum-free medium supplemented with platelet lysate and growth hormone for osteogenic differentiation enhancement.

BACKGROUND: The use of distant autografts to restore maxillary bone defects is clinically challenging and has unpredictable outcomes. This variation may be explained by the embryonic origin of long bone donor sites, which are derived from mesoderm, whereas maxillary bones derive from neural crest. Gingival stem cells share the same embryonic origin as maxillary bones. Their stemness potential and ease of access have been repeatedly shown. One limitation in human cell therapy is the use of foetal calf serum during cell isolation and culture. To overcome this problem, a new serum-free medium enriched with an alternative to foetal calf serum, i.e., platelet lysate, needs to be adapted to clinical grade protocols. METHODS: Different serum-free media enriched with platelet lysate at various concentrations and supplemented with different growth factors were developed and compared to media containing foetal calf serum. Phenotypic markers, spontaneous DNA damage, and stem cell properties of gingival stem cells isolated in platelet lysate or in foetal calf serum were also compared, as were the immunomodulatory properties of the cells by co-culturing them with activated peripheral blood monocellular cells. T-cell proliferation and phenotype were also assessed by flow cytometry using cell proliferation dye and specific surface markers. Data were analysed with t-test for two-group comparisons, one-way ANOVA for multigroup comparisons and two-way ANOVA for repeated measures and multigroup comparisons. RESULTS: Serum-free medium enriched with 10% platelet lysate and growth hormone yielded the highest expansion rate. Gingival stem cell isolation and thawing under these conditions were successful, and no significant DNA lesions were detected. Phenotypic markers of mesenchymal stem cells and differentiation capacities were conserved. Gingival stem cells isolated in this new serum-free medium showed higher osteogenic differentiation potential compared to cells isolated in foetal calf serum. The proportion of regulatory T cells obtained by co-culturing gingival stem cells with activated peripheral blood monocellular cells was similar between the two types of media. CONCLUSIONS: This new serum-free medium is well suited for gingival stem cell isolation and proliferation, enhances osteogenic capacity and maintains immunomodulatory properties. It may allow the use of gingival stem cells in human cell therapy for bone regeneration in accordance with good manufacturing practice guidelines.

Esthetic and functional rehabilitation of peg-shaped maxillary lateral incisors: Practical recommendations.

Peg-shaped maxillary lateral incisors cause many functional and esthetic major consequences in affected patients. Their esthetic and functional rehabilitations are often multidisciplinary, involving different clinical procedures like periodontal, orthodontic, or prosthodontic procedures. No exhaustive protocol has been established to improve their comprehensive management by general dentists or specialists. The aim of this article is to elaborate a simplified clinical protocol of complete management of peg-shaped maxillary lateral incisors by a multidisciplinary team (general practitioners, orthodontists, and prosthodontists). A clinical case of two peg-shaped maxillary lateral incisors completely rehabilitated with multidisciplinary approaches including orthodontic treatment and restoration by veneers and direct composite resin, according to the established protocol. Extraoral, intraoral, and smile clinical analysis are crucial to ensure optimal rehabilitation. Treatment results previsualization via wax-up and/or mock-up play a key role in the communication between practitioner and patient to help the latter make decision. These options also facilitate the achievement of a multidisciplinary approach by accurately estimating the number of dental movements and the type of restorations that are most suitable to the presenting clinical situation.

Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19.

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users' daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either "potentially COVID-19 infected" or "no evident signs of infection". We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).

A Novel Technique for the Treatment of a Case of Verneuil's Disease of Perineum and Axillary Regions.

Hidradenitis suppurativa (HS) is a chronic inflammatory disease involving apocrine glands of the skin. It carries out an important burden on the daily life of the patient. Unfortunately, it presents a major concern for medical care management in the absence of clear guidelines for proper medical and surgical treatment. Hence, we report a case of concomitant axillary and perianal HS. We opted for surgical management using a novel technique, which proved efficacy for a year of follow-up recurrence free.

The Role of GH/IGF Axis in Dento-Alveolar Complex from Development to Aging and Therapeutics: A Narrative Review.

The GH/IGF axis is a major regulator of bone formation and resorption and is essential to the achievement of normal skeleton growth and homeostasis. Beyond its key role in bone physiology, the GH/IGF axis has also major pleiotropic endocrine and autocrine/paracrine effects on mineralized tissues throughout life. This article aims to review the literature on GH, IGFs, IGF binding proteins, and their respective receptors in dental tissues, both epithelium (enamel) and mesenchyme (dentin, pulp, and tooth-supporting periodontium). The present review re-examines and refines the expression of the elements of the GH/IGF axis in oral tissues and their in vivo and in vitro mechanisms of action in different mineralizing cell types of the dento-alveolar complex including ameloblasts, odontoblasts, pulp cells, cementoblasts, periodontal ligament cells, and jaw osteoblasts focusing on cell-specific activities. Together, these data emphasize the determinant role of the GH/IGF axis in physiological and pathological development, morphometry, and aging of the teeth, the periodontium, and oral bones in humans, rodents, and other vertebrates. These advancements in oral biology have elicited an enormous interest among investigators to translate the fundamental discoveries on the GH/IGF axis into innovative strategies for targeted oral tissue therapies with local treatments, associated or not with materials, for orthodontics and the repair and regeneration of the dento-alveolar complex and oral bones.

COVID-19 and Dentistry in 72 Questions: An Overview of the Literature.

The outbreak of Coronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has significantly affected the dental care sector. Dental professionals are at high risk of being infected, and therefore transmitting SARS-CoV-2, due to the nature of their profession, with close proximity to the patient's oropharyngeal and nasal regions and the use of aerosol-generating procedures. The aim of this article is to provide an update on different issues regarding SARS-CoV-2 and COVID-19 that may be relevant for dentists. Members of the French National College of Oral Biology Lecturers ("Collège National des EnseignantS en Biologie Orale"; CNESBO-COVID19 Task Force) answered seventy-two questions related to various topics, including epidemiology, virology, immunology, diagnosis and testing, SARS-CoV-2 transmission and oral cavity, COVID-19 clinical presentation, current treatment options, vaccine strategies, as well as infection prevention and control in dental practice. The questions were selected based on their relevance for dental practitioners. Authors independently extracted and gathered scientific data related to COVID-19, SARS-CoV-2 and the specific topics using scientific databases. With this review, the dental practitioners will have a general overview of the COVID-19 pandemic and its impact on their practice.

Adrenal hemangioma: A rare presentation of bleeding incidentaloma: Case report.

INTRODUCTION: Adrenal incidentalomas are tumors found accidentally by imaging. Among the incidentalomas, hemangiomas are quite rare and in certain cases their surgical intervention should never be overlooked. PRESENTATION OF CASE: We present a case of 70 years old Lebanese female with an adrenal tumor presented as syncope found to have anemia on presentation and a bleeding 17 cm adrenal tumor on imaging. Patient had workup to rule out functioning adrenal tumors and decision to excise the tumor was taken after failure of embolization. Pathology report denied malignancy despite of the huge size and was in favor of hemangioma. CONCLUSION: Adrenal hemangiomas are rare and they rarely present as syncope. Attention to such a life-threatening condition should be sustained. Embolization is primarily implied but one should never neglect the failure rate and the need for surgical intervention.

Software Development Effort Estimation Using Regression Fuzzy Models.

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output, and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call "regression fuzzy logic." State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size, and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

Head to Knee: Cranial Neural Crest-Derived Cells as Promising Candidates for Human Cartilage Repair.

A large array of therapeutic procedures is available to treat cartilage disorders caused by trauma or inflammatory disease. Most are invasive and may result in treatment failure or development of osteoarthritis due to extensive cartilage damage from repeated surgery. Despite encouraging results of early cell therapy trials that used chondrocytes collected during arthroscopic surgery, these approaches have serious disadvantages, including morbidity associated with cell harvesting and low predictive clinical outcomes. To overcome these limitations, adult stem cells derived from bone marrow and subsequently from other tissues are now considered as preferred sources of cells for cartilage regeneration. Moreover, with new evidence showing that the choice of cell source is one of the most important factors for successful cell therapy, there is growing interest in neural crest-derived cells in both the research and clinical communities. Neural crest-derived cells such as nasal chondrocytes and oral stem cells that exhibit chondrocyte-like properties seem particularly promising in cartilage repair. Here, we review the types of cells currently available for cartilage cell therapy, including articular chondrocytes and various mesenchymal stem cells, and then highlight recent developments in the use of neural crest-derived chondrocytes and oral stem cells for repair of cartilage lesions.

Involvement of neural crest and paraxial mesoderm in oral mucosal development and healing.

Tissue engineering therapies using adult stem cells derived from neural crest have sought accessible tissue sources of these cells because of their potential pluripotency. In this study, the gingiva and oral mucosa and their associated stem cells were investigated. Biopsies of these tissues produce neither scarring nor functional problems and are relatively painless, and fresh tissue can be obtained readily during different chairside dental procedures. However, the embryonic origin of these cells needs to be clarified, as does their evolution from the perinatal period to adulthood. In this study, the embryonic origin of gingival fibroblasts were determined, including gingival stem cells. To do this, transgenic mouse models were used to track neural crest derivatives as well as cells derived from paraxial mesoderm, spanning from embryogenesis to adulthood. These cells were compared with ones derived from abdominal dermis and facial dermis. Our results showed that gingival fibroblasts are derived from neural crest, and that paraxial mesoderm is involved in the vasculogenesis of oral tissues during development. Our in vitro studies revealed that the neuroectodermal origin of gingival fibroblasts (or gingival stem cells) endows them with multipotential properties as well as a specific migratory and contractile phenotype which may participate to the scar-free properties of the oral mucosa. Together, these results illustrate the high regenerative potential of neural crest-derived stem cells of the oral mucosa, including the gingiva, and strongly support their use in cell therapy to regenerate tissues with impaired healing.

Effects of High-Temperature-Pressure Polymerized Resin-Infiltrated Ceramic Networks on Oral Stem Cells.

OBJECTIVES: The development of CAD-CAM techniques called for new materials suited to this technique and offering a safe and sustainable clinical implementation. The infiltration of resin in a ceramic network under high pressure and high temperature defines a new class of hybrid materials, namely polymer infiltrated ceramics network (PICN), for this purpose which requires to be evaluated biologically. We used oral stem cells (gingival and pulpal) as an in vitro experimental model. METHODS: Four biomaterials were grinded, immersed in a culture medium and deposed on stem cells from dental pulp (DPSC) and gingiva (GSC): Enamic (VITA®), Experimental Hybrid Material (EHM), EHM with initiator (EHMi) and polymerized Z100™ composite material (3M®). After 7 days of incubation; viability, apoptosis, proliferation, cytoskeleton, inflammatory response and morphology were evaluated in vitro. RESULTS: Proliferation was insignificantly delayed by all the tested materials. Significant cytotoxicity was observed in presence of resin based composites (MTT assay), however no detectable apoptosis and some dead cells were detected like in PICN materials. Cell morphology, major cytoskeleton and extracellular matrix components were not altered. An intimate contact appeared between the materials and cells. CLINICAL SIGNIFICANCE: The three new tested biomaterials did not exhibit adverse effects on oral stem cells in our experimental conditions and may be an interesting alternative to ceramics or composite based CAD-CAM blocks.

Validation of Housekeeping Genes to Study Human Gingival Stem Cells and Their In Vitro Osteogenic Differentiation Using Real-Time RT-qPCR.

Gingival stem cells (GSCs) are recently isolated multipotent cells. Their osteogenic capacity has been validated in vitro and may be transferred to human cell therapy for maxillary large bone defects, as they share a neural crest cell origin with jaw bone cells. RT-qPCR is a widely used technique to study gene expression and may help us to follow osteoblast differentiation of GSCs. For accurate results, the choice of reliable housekeeping genes (HKGs) is crucial. The aim of this study was to select the most reliable HKGs for GSCs study and their osteogenic differentiation (dGSCs). The analysis was performed with ten selected HKGs using four algorithms: ΔCt comparative method, GeNorm, BestKeeper, and NormFinder. This study demonstrated that three HKGs, SDHA, ACTB, and B2M, were the most stable to study GSC, whereas TBP, SDHA, and ALAS1 were the most reliable to study dGSCs. The comparison to stem cells of mesenchymal origin (ASCs) showed that SDHA/HPRT1 were the most appropriate for ASCs study. The choice of suitable HKGs for GSCs is important as it gave access to an accurate analysis of osteogenic differentiation. It will allow further study of this interesting stem cells source for future human therapy.