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Long coronavirus disease 2019 (COVID-19) is linked to an increased risk of post-acute sequelae affecting the pulmonary and extrapulmonary organ systems. Up to 20% of COVID-19 patients may proceed to a more serious form, such as severe pneumonia, acute respiratory distress syndrome (ARDS), or pulmonary fibrosis. Still, the majority of patients may only have mild, self-limiting sickness. Of particular concern is the possibility of parenchymal fibrosis and lung dysfunction in long-term COVID-19 patients. Furthermore, it has been observed that up to 43% of individuals hospitalized with COVID-19 also had acute renal injury (AKI). Care for kidney, brain, lung, cardiovascular, liver, ocular, and tissue injuries should be included in post-acute COVID-19 treatment. As a powerful immunomodulatory tool in regenerative medicine, dental stem cells (DSCs) have drawn much interest. Numerous immune cells and cytokines are involved in the excessive inflammatory response, which also has a significant effect on tissue regeneration. A unique reservoir of stem cells (SCs) for treating acute lung injury (ALI), liver damage, neurological diseases, cardiovascular issues, and renal damage may be found in tooth tissue, according to much research. Moreover, a growing corpus of in vivo research is connecting DSC-derived extracellular vesicles (DSC-EVs), which are essential paracrine effectors, to the beneficial effects of DSCs. DSC-EVs, which contain bioactive components and therapeutic potential in certain disorders, have been shown as potentially effective therapies for tissue damage after COVID-19. Consequently, we explore the properties of DSCs in this work. Next, we'll look at how SARS-CoV-2 affects tissue damage. Lastly, we have looked at the use of DSCs and DSC-EVs in managing COVID-19 and chronic tissue damage, such as injury to the heart, brain, lung, and other tissues.

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The advancement of nanoscience and material design in recent times has facilitated the creation of point-of-care devices for cancer diagnosis and biomolecule sensing. Exosomes (EXOs) facilitate the transfer of bioactive molecules between cancer cells and diverse cells in the local and distant microenvironments, thereby contributing to cancer progression and metastasis. Specifically, EXOs derived from cancer are likely to function as biomarkers for early cancer detection due to the genetic or signaling alterations they transport as payload within the cancer cells of origin. It has been verified that EXOs circulate steadily in bodily secretions and contain a variety of information that indicates the progression of the tumor. However, acquiring molecular information and interactions regarding EXOs has presented significant technical challenges due to their nanoscale nature and high heterogeneity. Colorimetry, surface plasmon resonance (SPR), fluorescence, and Raman scattering are examples of optical techniques utilized to quantify cancer exosomal biomarkers, including lipids, proteins, RNA, and DNA. Many optically active nanoparticles (NPs), predominantly carbon-based, inorganic, organic, and composite-based nanomaterials, have been employed in biosensing technology. The exceptional physical properties exhibited by nanomaterials, including carbon NPs, noble metal NPs, and magnetic NPs, have facilitated significant progress in the development of optical nanobiosensors intended for the detection of EXOs originating from tumors. Following a summary of the biogenesis, biological functions, and biomarker value of known EXOs, this article provides an update on the detection methodologies currently under investigation. In conclusion, we propose some potential enhancements to optical biosensors utilized in detecting EXO, utilizing various NP materials such as silicon NPs, graphene oxide (GO), metal NPs, and quantum dots (QDs).

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OBJECTIVES: This study evaluated the performance of the YOLOv5 deep learning model in detecting different mandibular fracture types in panoramic images. METHODS: The dataset of panoramic radiographs with mandibular fractures was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control images without fractures were also distributed among the datasets. The YOLOv5 algorithm was trained to detect six mandibular fracture types based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class. RESULTS: A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and 0.812, respectively). CONCLUSION: The trained algorithm achieved promising results in detecting most fracture types, particularly in body and symphysis regions indicating machine learning potential as a diagnostic aid for clinicians.

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Several studies suggest that oral pathogenic biofilms cause persistent oral infections. Among these is periodontitis, a prevalent condition brought on by plaque biofilm. It can even result in tooth loss. Furthermore, the accumulation of germs around a dental implant may lead to peri-implantitis, which damages the surrounding bone and gum tissue. Furthermore, bacterial biofilm contamination on the implant causes soft tissue irritation and adjacent bone resorption, severely compromising dental health. On decontaminated implant surfaces, however, re-osseointegration cannot be induced by standard biofilm removal techniques such as mechanical cleaning and antiseptic treatment. A family of nanoparticles known as nanozymes (NZs) comprise highly catalytically active multivalent metal components. The most often employed NZs with antibacterial activity are those that have peroxidase (POD) activity, among other types of NZs. Since NZs are less expensive, more easily produced, and more stable than natural enzymes, they hold great promise for use in various applications, including treating microbial infections. NZs have significantly contributed to studying implant success rates and periodontal health maintenance in periodontics and implantology. An extensive analysis of the research on various NZs and their applications in managing oral health conditions, including dental caries, dental pulp disorders, oral ulcers, peri-implantitis, and bacterial infections of the mouth. To combat bacteria, this review concentrates on NZs that imitate the activity of enzymes in implantology and periodontology. With a view to the future, there are several ways that NZs might be used to treat dental disorders antibacterially.

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BACKGROUND: Several measures have been implemented to minimize the side effects of impacted third molar (M3) removal including the use of platelet-rich fibrin (PRF). PURPOSE: This study compared the effects of three modifications of PRF (leukocyte-PRF [L-PRF], advanced-PRF [A-PRF], and advanced-PRF plus [A-PRF +]) on the side effects of impacted M3 removal. STUDY DESIGN, SETTING, AND SAMPLE: This double-blinded randomized controlled trial was conducted at the Oral Surgery Department of Kashan University between September 2022 and May 2023 on patients undergoing mandibular impacted M3 removal. Exclusion criteria were age over 30, local inflammation and infection, medication usage, and systemic disease. INDEPENDENT VARIABLE: The independent variable was the PRF product grouped into four categories (control, L-PRF, A-PRF, and A-PRF+). Study subjects were randomly distributed among the four groups. MAIN OUTCOME VARIABLE(S): The main outcome variables were postoperative sequelae including measures of soft tissue healing, pain, analgesic use, alveolar osteitis, trismus, and swelling. Subjects were assessed at baseline and on days 1, 2, 3, and 7 postsurgery. COVARIATES: Age, sex, duration of surgery, and side of surgery were the covariates. ANALYSES: Changes at different time points were analyzed using repeated measures analysis of variance. Pairwise comparisons were performed if significant. P values ≤.05 were considered statistically significant. RESULTS: The sample consisted of 64 subjects (16 per group). All three modifications of PRF yielded significantly better soft tissue healing index than the control group on days 2, 3, 7, and 14 postoperatively (P > .05). A-PRF and A-PRF + had significantly better healing index than L-PRF on the third day (P = .02, P = .01). All the study groups significantly reduced visual analog scale pain score than the control group on days 1, 2, and 3. A-PRF and A-PRF + had significantly lower visual analog scale scores than L-PRF on the second day (P = .003, P = .02). No significant difference was found in maximum mouth opening during follow-up sessions (P = .2). Study groups had less facial swelling on days 2 and 3 than the control group (P < .05). CONCLUSION AND RELEVANCE: L-PRF, A-PRF, and A-PRF + can improve postoperative outcomes after M3 removal but may not impact trismus. A-PRF and A-PRF + may be more effective than L-PRF in promoting soft tissue healing and reducing pain. A-PRF and A-PRF + have comparable results.

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