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Resorbable synthetic and natural polymer-based membranes have been extensively studied for guided tissue regeneration. Alloplastic biomaterials are often used for tissue regeneration due to their lower immunoreactivity when compared with allogeneic and xenogeneic materials. Plenum® Guide is a synthetic membrane material based on polydioxanone (PDO), whose surface morphology closely mimics the extracellular matrix. In this study, Plenum® Guide was compared with collagen membranes as a barrier material for bone-tissue regeneration in terms of acute and subchronic systemic toxicity. Moreover, characterizations such as morphology, thermal analysis (Tm = 107.35 °C and crystallinity degree = 52.86 ± 2.97 %, final product), swelling (thickness: 0.25 mm ≅ 436% and 0.5 mm ≅ 425% within 24 h), and mechanical tests (E = 30.1 ± 6.25 MPa; σ = 3.92 ± 0.28 MPa; ε = 287.96 ± 34.68%, final product) were performed. The in vivo results revealed that the PDO membranes induced a slightly higher quantity of newly formed bone tissue than the control group (score: treated group = 15, control group = 13) without detectable systemic toxicity (clinical signs and evaluation of the membranes after necropsy did not result in differences between groups, i.e., non-reaction -> tissue-reaction index = 1.3), showing that these synthetic membranes have the essential characteristics for an effective tissue regeneration. Human adipose-derived stem cells (hASCs) were seeded on PDO membranes; results demonstrated efficient cell migration, adhesion, spread, and proliferation, such that there was a slightly better hASC osteogenic differentiation on PDO than on collagen membranes. Hence, Plenum® Guide membranes are a safe and efficient alternative for resorbable membranes for tissue regeneration.

Although ongoing debates persist over the scope of phenomena classified as regenerative processes, the most up-to-date definition of regeneration is the replacement or restoration of damaged or missing cells, tissues, organs, or body parts to full functionality. Despite extensive research on this topic, new methods in regenerative medicine are continually sought, and existing ones are being improved. Small extracellular vesicles (sEVs) have gained attention for their regenerative potential, as evidenced by existing studies conducted by independent research groups. Of particular interest are sEVs derived from the oral mucosa, a tissue renowned for its rapid regeneration and minimal scarring. While the individual regenerative potential of both sEVs and the oral mucosa is somewhat understood, the combined potential of sEVs derived from the oral mucosa has not been sufficiently explored and highlighted in the existing literature. Serving as a broad compendium, it aims to provide scientists with essential and detailed information on this subject, including the nature of the materials employed, isolation and analysis methodologies, and clinical applications. The content of this survey aims to facilitate the comparison of diverse methods for working with sEVs derived from the oral mucosa, aiding in the planning of research endeavors and identifying potential research gaps.

The COVID-19 pandemic is a global challenge, demanding researchers address different approaches in relation to prevention, diagnostics and therapeutics. Amongst the many tactics of tackling these therapeutic challenges, small extracellular vesicles (sEVs) or exosomes are emerging as a new frontier in the field of ameliorating viral infections. Exosomes are part of extracellular vesicles (EVs)—spherical biological structures with a lipid bilayer of a diameter of up to 5000 nm, which are released into the intercellular space by most types of eukaryotic cells, both in physiological and pathological states. EVs share structural similarities to viruses, such as small size, common mechanisms of biogenesis and mechanisms for cell entry. The role of EVs in promoting the viral spread by evading the immune response of the host, which is exhibited by retroviruses, indicates the potential for further investigation and possible manipulation of these processes when tackling the spread and treatment of COVID-19. The following paper introduces the topic of the use of exosomes in the treatment of viral infections, and presents the future prospects for the use of these EVs.

Progesterone (P4) and estradiol (E2) play a significant role in mammalian reproduction. Our study demonstrated that separated porcine cumulus cells (CCs) and/or granulosa cells (GCs) might proliferate in vitro during short-term, real-time primary culture. The GCs were analyzed according to gene expression of the progesterone receptor (nuclear form) (pgr), progesterone receptor membrane component 1 (pgrmc1), and estrogen-related receptor beta 3 (esrrb3) in relation to two housekeeping genes: actb and pbgd. GCs were cultivated in medium with the E2. Both pgr/actb and pgr/pbgd revealed higher expression between 24 and 168 h of IVC of prolonged E2 treatment and at 48 h of IVC after acute E2 administration. The pgrmc1/actb and pgrmc1/pbgd displayed increased expression after prolonged E2 treatment between 24 and 120 h of IVC. The highest level of esrrb3/actb at 120 and 144 h, as well as esrrb3/pbgd at 120 h, in untreated controls as compared to the hormone-stimulated group, was observed. We suggest that E2 significantly influences the upregulation of pgr, pgrmc1, and esrrb3 expression in porcine GCs during real-time cell proliferation. Since esrrb3 expression is stimulated by E2 in both an acute and prolonged manner, estradiol may be recognized as a potential estrogen receptor agonist in GCs.

The purpose of this study is to explore the possibilities for the application of laser therapy in medicine and dentistry by analyzing lasers’ underlying mechanism of action on different cells, with a special focus on stem cells and mechanisms of repair. The interest in the application of laser therapy in medicine and dentistry has remarkably increased in the last decade. There are different types of lasers available and their usage is well defined by different parameters, such as: wavelength, energy density, power output, and duration of radiation. Laser irradiation can induce a photobiomodulatory (PBM) effect on cells and tissues, contributing to a directed modulation of cell behaviors, enhancing the processes of tissue repair. Photobiomodulation (PBM), also known as low-level laser therapy (LLLT), can induce cell proliferation and enhance stem cell differentiation. Laser therapy is a non-invasive method that contributes to pain relief and reduces inflammation, parallel to the enhanced healing and tissue repair processes. The application of these properties was employed and observed in the treatment of various diseases and conditions, such as diabetes, brain injury, spinal cord damage, dermatological conditions, oral irritation, and in different areas of dentistry.

This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medical Sciences. These radiographs displayed a sequence of 60 visible teeth. The evaluation of the radiographs was conducted using two methods (manual and automatic), and the results obtained from each technique were afterward compared. For the ground-truth method, one oral and maxillofacial radiology expert with more than ten years of experience and one trainee in oral and maxillofacial radiology evaluated the radiographs by classifying teeth as healthy and unhealthy. A tooth was considered unhealthy when periapical periodontitis related to this tooth had been detected on the radiograph. At the same time, a tooth was classified as healthy when no periapical radiolucency was detected on the periapical radiographs. Then, the same radiographs were evaluated by artificial intelligence, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA). Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) correctly identified periapical lesions on periapical radiographs with a sensitivity of 92.30% and identified healthy teeth with a specificity of 97.87%. The recorded accuracy and F1 score were 96.66% and 0.92, respectively. The artificial intelligence algorithm misdiagnosed one unhealthy tooth (false negative) and over-diagnosed one healthy tooth (false positive) compared to the ground-truth results. Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) showed an optimum accuracy for detecting periapical periodontitis on periapical radiographs. However, more research is needed to assess the diagnostic accuracy of artificial intelligence-based algorithms in dentistry.

Exosomes are a heterogenous subpopulation of extracellular vesicles 30–150 nm in range and of endosome-derived origin. We explored the exosome formation through different systems, including the endosomal sorting complex required for transport (ESCRT) and ESCRT-independent system, looking at the mechanisms of release. Different isolation techniques and specificities of exosomes from different tissues and cells are also discussed. Despite more than 30 years of research that followed their definition and indicated their important role in cellular physiology, the exosome biology is still in its infancy with rapidly growing interest. The reasons for the rapid increase in interest with respect to exosome biology is because they provide means of intercellular communication and transmission of macromolecules between cells, with a potential role in the development of diseases. Moreover, they have been investigated as prognostic biomarkers, with a potential for further development as diagnostic tools for neurodegenerative diseases and cancer. The interest grows further with the fact that exosomes were reported as useful vectors for drugs.

: This study aimed to evaluate the influence of panoramic radiograph quality on the performance of an artificial intelligence (AI)-based tooth detection system and to identify specific image quality criteria associated with detection accuracy. : A total of 424 panoramic radiographs were retrospectively selected from a clinical database. Radiographic quality was assessed using a modified Clinical Image Evaluation Chart, including criteria related to bite block presence, anteroposterior positioning, occlusal plane curvature, patient movement, anatomical coverage, overlapping contact points, air gap, contrast, cervical spine overlap, symmetry of the ascending mandibular ramus, and the number of visible teeth. Automated tooth detection was performed using a convolutional neural network based on the Mask R-CNN architecture (SynbrAIn, Italy). AI detection outputs were validated against expert human evaluation. Spearman's rank correlation analyses were conducted to assess associations between individual image quality criteria and the number of AI detection errors per radiograph. : Significant negative associations were observed between AI detection errors and the number of visible teeth (ρ = -0.311, < 0.001), presence of a bite block (ρ = -0.248, < 0.001), reduced patient movement (ρ = -0.204, < 0.001), correct anteroposterior positioning (ρ = -0.165, < 0.001), and overall image quality score (ρ = -0.120, = 0.010). In contrast, the presence of an air gap above the anterior teeth (ρ = 0.099, = 0.042) and overlapping contact points (ρ = 0.122, = 0.012) were positively associated with increased detection errors. No significant associations were identified for occlusal plane curvature, contrast, cervical spine overlap, anatomical coverage, or mandibular ramus symmetry. Overall, the AI system was more sensitive to indicators of anatomical completeness and patient positioning than to minor radiographic imperfections. : Panoramic radiograph quality, particularly indicators of anatomical completeness and patient positioning, is associated with the performance of AI-based tooth detection. While the AI system demonstrated robustness to common image quality variations, adherence to standardized acquisition protocols remains important to minimize detection errors.

Exosomes are biological nanoscale spherical lipid bilayer vesicles, 40–160 nm in diameter, produced by most mammalian cells in both physiological and pathological conditions. Exosomes are formed via the endosomal sorting complex required for transport (ESCRT). The primary function of exosomes is mediating cell-to-cell communication. In terms of cancer, exosomes play important roles as mediators of intercellular communication, leading to tumor progression. Moreover, they can serve as biomarkers for cancer detection and progression. Therefore, their utilization in cancer therapies has been suggested, either as drug delivery carriers or as a diagnostic tool. However, exosomes were also reported to be involved in cancer drug resistance via transferring information of drug resistance to sensitive cells. It is important to consider the current knowledge regarding the role of exosomes in cancer, drug resistance, cancer therapies, and their clinical application in cancer therapies.

This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.