Neural Differentiation of Wisdom Tooth Follicle Stem Cells on a Nano-Hydrogel Scaffold Containing Salvia Chloroleucat to Treat Nerve injury in the Cancer of Nervous System.

The human third molar's follicle is one of the sources of stem cells with high differentiation capacities which can be used in nervous system cancer treatment particularly in nerve damge. The purpose of this research was to identify the effects of the aqueous extract of Salvia chloroleuca on the differentiation of the human dental follicle-derived mesenchymal stem cells to neural cells for treti. In this experimental study, the method of culture of digested tissue fragments was used to isolate stem cells from three samples of the extracted wisdom teeth follicles. The nano-hyaluronic acid scaffold has been synthesized by the sol-gel method as a porous composite and the S. chloroleuca extract has been loaded into it. The scaffold was analyzed in terms of mechanical properties, drug release and toxicity. Afterwards, the cells were seeded onto the scaffold using the immersion method. After 21 days, cell differentiation was investigated by morphological confirmation methods and confirming the expression of ß-tubulin and MAP2 genes at mRNA and protein levels. Morphological assessment revealed neural differentiation in the cells of the groups of nano-hyaluronic acid scaffold with S. chloroleuca extract and nano-hyaluronic acid scaffold with S. chloroleuca extract + 10% retinoic acid. Furthermore, the expression of MAP2 and ß-tubulin in these groups was confirmed by RT-PCR, real time PCR and western blot assays. The results of this research showed that the follicle of the third molar contains stem cells with a high capacity for differentiation. Moreover, the extract of S. chloroleuca, could lead to induction of neural differentiation in stem cells.

Evaluating the results of eyebrow lift by combining methods of subcutaneous flap and thread support in patients with droopy eyebrows.

BACKGROUND: Brow lift also known as eyebrow lift was first described in 1919, and since then, many changes have been made in the methods of doing it, although there is still no agreed method of absolute superiority for eyebrow lift. Most previous studies have reported the results generally qualitatively and based on patient or surgeon satisfaction. In this study, by combining two less complicated methods of eyebrow lift, we have evaluated the quantitative results. METHOD: Before the surgery, a standard photograph of the face was taken. The vertical distance between the tail of the eyebrow and interpupillary line was determined. RESULTS: This study was performed on 15 females with a mean age of 38.27 ± 6.82 years. The mean distance between the eyebrow and interpupillary line by photographic measurement before surgery, 3 weeks, and 6 months after surgery was, respectively, 10.45 ± 1.74, 15.72 ± 1.77, and 13.53 ± 1.69 mm using the tail of the eyebrow and 18.47 ± 1.67, 23.33 ± 1.57, and 21.55 ± 1.66 mm using the crown of the eyebrow. In the clinical measurement, the eyebrow tail was 11.98 ± 1.75, 19.22 ± 1.73, 17.35 ± 1.68 and 15.13 ± 1.76 mm away from the pupil line, and the crown of eyebrow was 20.45 ± 1.90, 27.12 ± 1.58, 25.00 ± 1.80, and 23.35±1.78 mm. There is a significant difference between the distance of the tail of the eyebrow and the crown of the eyebrow in both measurement methods (photographic and clinical) at different times (p-value <0.001). CONCLUSION: Performing eyebrow lift with the Pretrichial method has many comparative advantages to other methods. Additionally, eyebrow lift with the thread support is a less invasive method.

Parental anxiety/incompliance and patients' complications during COVID-19 pandemic regarding nasoalveolar molding treatment of infants with cleft lip/palate.

OBJECTIVES: The ideal time for nasoalveolar molding (NAM) of infants with cleft lip and/or palate (CLP) is the first weeks after birth. The burden and responsibility that this method of treatment imposes on parents may result in incompliance. The coronavirus (COVID-19) pandemic and the redirection of health resources can make the situation worse. Therefore, this study evaluated the anxiety, complications, and incompliance of parents undergoing NAM during the COVID-19 pandemic. MATERIALS AND METHODS: Demographic data of 35 infants with CLP treated during the COVID-19 pandemic, compliance and level of anxiety of both parents in addition to their complications were reported. The association between different variables and incompliance was evaluated by simple and multiple logistic regressions. The level of significance was considered at P value less than 0.05. RESULTS: The highest level of parental anxiety related to the NAM process was the delay in ending the treatment. Meanwhile, the reason for the highest level of anxiety related to attending the treatment sessions was the probability of the infant's COVID-19 infection. Fathers expressed lower levels of anxiety than mothers, significantly. The most prevalent NAM complication was skin irritation. Parents of younger infants (≤28 days) and those with a history of COVID-19 infection were more compliant. CONCLUSIONS: COVID-19 pandemic caused a significant increase in the level of anxiety in both parents, mainly due to the delay in treatment ending and the possibility of infant's infection. Moreover, considering the importance of treatment time, parents of younger infants were more compliant with the NAM process.

Aggressive Multiple Central Giant Cell Granulomas of the Jaws.

Central giant cell granuloma (CGCG) is considered a benign intraosseous lesion with a varied range of clinical features in two subtypes, including aggressive and non-aggressive lesions. This study presents a 9-year-old boy with multiple bilateral CGCG in the mandible without any systemic disease or specific syndrome. Clinical, radiographic, and histopathological findings demonstrated the aggressive lesions. It is discussed how the differential diagnosis and treatment can be determined based on the patient's age as well as the size and manner of the lesion.

Effect of Photobiomodulation Therapy on Differentiation of Mesenchymal Stem Cells Derived from Impacted Third Molar Tooth into Neuron-like Cells.

Peripheral nerve damages are among the most important consequences of dental and maxillofacial procedures. Tissue engineering using mesenchymal stem cells (MSCs) is a promising method to manage such injuries. Moreover, photobiomodulation therapy (PBMT) can enhance this treatment. The present study aimed to investigate the effect of PBMT on differentiation of MSCs derived from dental follicle (DF) into neurons. MSCs were isolated from an impacted tooth follicle by digestion method. The stem cells were cultured, and differentiated into neurons. The cells received two sessions of PBMT with 810 or 980 nm diode laser (100 mW, 4 J cm-2 ) in either DMEM or neural inductive medium. Phenotypic characterization of the cells was determined using flow cytometry. In addition, ß-tubulin and MAP2 genes expression level changes were analyzed using RT-PCR and western blot technique. After 14 days, flow cytometry analysis confirmed the mesenchymal nature of cells. RT-PCR and western blot affirmed the expression of ß-tubulin and MAP2 genes and proteins respectively. PBMT with both wavelengths significantly increased ß-tubulin and MAP2 expression in neural inductive medium with highest expression mean in 980 nm group. PBMT with 810 and 980 nm lasers could be a promising adjunctive method in differentiation of DF-originated MSCs into neural cells.

Reconstruction of the Occipital and Parietal Congenital Defect with 3D Custom-Made Titanium Prosthesis: A Case Report with Four and a Half Years of Follow-Up and a Brief Review of Literature.

Management of patients with congenital skull defects requires a multidisciplinary approach. Considering the defect's location and size, brain protection, and the cosmetic outcome makes such reconstructions challenging. Due to limited resemblance to skull contour and donor site morbidity of autogenous bone grafts, alloplastic materials are widely used for skull reconstructions. Titanium alloys have proper strength values, low infection rates, favorable osseointegration property, and excellent marginal adaptability when manufactured by computer-aided design (CAD) and computer-aided manufacturing (CAM). A 13-year-old female patient presented with congenital defects at the superior third of occipital bone and posterior thirds of the bilateral parietal bones. On CT scan, the exact size and shape of the defect were determined. Using CAD/CAM, a 3D virtual model of the prosthesis was designed and then printed with titanium alloy (TiAl6V4) via additive manufacturing method. The prosthesis was placed on the defect in a total surgery time of only 90 minutes. On 4.5 years of follow-up, the contour of the skull was ideal and the skin over the defect and neurologic status was intact. Due to their biocompatibility and rigidity, custom-made titanium prostheses are promising options for reconstructing complex skull defects.

miR-155, miR-191, and miR-494 as diagnostic biomarkers for oral squamous cell carcinoma and the effects of Avastin on these biomarkers.

OBJECTIVES: : Oral squamous cell carcinoma (OSCC) is one of the most common types of head and neck cancer. MicroRNAs, as new biomarkers, are recommended for diagnosis and treatment of different types of cancers. Bevacizumab, sold under the trade name Avastin, is a humanized whole monoclonal antibody that targets and blocks VEGF-A (vascular endothelial growth factor A; angiogenesis) and oncogenic signaling pathways. MATERIALS AND METHODS: This study comprised 50 cases suffering from OSCC and 50 healthy participants. Peripheral blood samples were collected in glass test tubes, and RNA extraction was started immediately. Expression levels of miR-155, miR-191, and miR-494 biomarkers in the peripheral blood of OSCC-affected individuals and healthy volunteers in vivo were evaluated using real-time PCR. The influence of Avastin on the expression levels of the aforementioned biomarkers in vitro and in the HN5 cell line was also investigated. RESULTS: Expression levels of miR-155, miR-191, and miR-494 in the peripheral blood of individuals affected by OSCC were higher than in those who were healthy. Moreover, Avastin at a concentration of 400 µM caused a decrease in the expression levels of the three biomarkers and a 1.5-fold, 3.5-fold, and 4-fold increase in apoptosis in the test samples compared to the controls in the HN5 cell line after 24, 48, and 72 hours, respectively. CONCLUSION: The findings of this study demonstrate that overexpression of miR-155, miR-191, and miR-494 is associated with OSCC, and Avastin is able to regulate and downregulate the expression of those biomarkers and increase apoptosis in cancerous cells in the HN5 cell line.

Development of novel hybridized models for urban flood susceptibility mapping.

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

Identifying sources of dust aerosol using a new framework based on remote sensing and modelling.

Dust particles are transported globally. Dust storms can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method using state-of-the-art machine-learning algorithms - random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) - was evaluated for its ability to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source locations were determined with the ozone monitoring instrument aerosol index and the Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol optical thickness methods. The identified areas were divided into training (70%) and validation (30%) sets. Measurements of the conditioning factors (lithology, wind speed, maximum air temperature, land use, slope angle, soil, rainfall, and land cover) were compiled for the study area and predictive models were developed. The area-under-the-receiver operating characteristics curve (AUC) and true-skill statistics (TSS) were used to validate the maps of the models' predictions. The results show that the RF algorithm performed best (AUC = 89.4% and TSS = 0.751), followed by the SVM (AUC = 87.5%, TSS = 0.73) and the MARS algorithm (AUC = 81%, TSS = 0.69). The results of the RF indicated that wind speed and land cover are the most important factors affecting dust generation. The region of highest dust-source potential that was identified by the RF is in the eastern parts of the study region. This model can be applied to other arid and semi-arid environments that experience dust storms to promote management that prevents desertification and reduces dust production.

Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia.

A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994-2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year; the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability.

Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia.

Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans.

Bacterial Leakage at Implant-Abutment Interface With Different Intermediate Materials.

A gap exists at the implant-abutment interface in two-piece implants and can serve as a reservoir of bacteria and compromise the health of peri-implant tissue. This study aimed to compare the effect of different intermediate materials on bacterial leakage at the implant-abutment interface. A total of 75 implants were divided into 5 groups (n = 15) based on the material applied at the implant-abutment connection: (1) Atridox, (2) chlorhexidine, (3) Gapseal silicone, (4) saliva, and (5) no material. All the implants were inoculated with 0.1 µL of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) suspension, and then the respective material was applied. The abutments were connected to the implants, and appropriate torque was applied as recommended by the manufacturer (Implantium, Dentium, Korea, Seoul). Bacterial leakage was determined by evaluating the turbidity of the broth. Bacterial contamination was found in all samples at different times; in groups 1, 2 and 3, contamination was noted after 7, 5, and 6 days, respectively, on average. Contamination occurred averagely after 4 days in groups 4 and 5. The present study showed that Atridox applied at the implant-abutment interface significantly delayed bacterial leakage.

Land subsidence modelling using tree-based machine learning algorithms.

Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.

Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities.

Land subsidence caused by land use change and overexploitation of groundwater is an example of mismanagement of natural resources, yet subsidence remains difficult to predict. In this study, the relationship between land subsidence features and geo-environmental factors is investigated by comparing two machine learning algorithms (MLA): maximum entropy (MaxEnt) and genetic algorithm rule-set production (GARP) algorithms in the Kashmar Region, Iran. Land subsidence features (N = 79) were mapped using field surveys. Land use, lithology, the distance from traditional groundwater abstraction systems (Qanats), from afforestation projects, from neighboring faults, and the drawdown of groundwater level (DGL) (1991-2016) were used as predictive variables. Jackknife resampling showed that DGL, distance from afforestation projects, and distance from Qanat systems are major factors influencing land subsidence, with geology and faults being less important. The GARP algorithm outperformed the MaxEnt algorithm for all performance metrics. The performance of both models, as measured by the area under the receiver-operator characteristic curve (AUROC), decreased from 88.9-94.4% to 82.5-90.3% when DGL was excluded as a predictor, though the performance of GARP was still good to excellent even without DGL. MLAs produced maps of subsidence risk with acceptable accuracy, both with and without data on groundwater drawdown, suggesting that MLAs can usefully inform efforts to manage subsidence in data-scarce regions, though the highest accuracy requires data on changes in groundwater level.

Applications of Propolis in Dentistry: A Review.

BACKGROUND: Propolis is a resinous substance obtained from the beehives that has antioxidant, anti-bacteria, anti-virus, anti-fungal, anti-tumor and anti-inflammatory activity. The aim of this study was to review the studies about the role of propolis in improving dental and oral health. METHODS: This study reviewed the published articles regarding the applications of propolis in dentistry. An electronic search of the literature was carried out in Farsi electronic databases including Google, Medlib.ir, SID, Iranmedex and Magiran as well as English electronic databases such as PubMed and ISI Web of Knowledge. These databases were searched for articles published between 1997 and October 20, 2017. Non-dental books and journals were also manually searched. RESULTS: This study reviewed published articles on the efficacy of propolis for surgical wound healing, caries prevention, treatment of dentin hypersensitivity, treatment of aphthous ulcers and propolis as a storage medium for avulsed teeth, root canal irrigating solution and mouthwash. CONCLUSION: The result of the reviewed article showed that propolis is effective an agent that is used for multiple purpose in oral health.

Benign paroxysmal positional vertigo as a complication of orthognathic surgery.

Benign paroxysmal positional vertigo (BPPV) is a clinical disorder that may develop following surgical trauma to the maxilla, for example as a result of closed sinus lifting with the use of mallet and osteotome during implant surgery. We proposed that BPPV may also occur following maxillary Le Fort osteotomy during orthognathic surgery. In a prospective study of 50 consecutive cases of orthognathic surgery, we observed that one patient developed BPPV in the postoperative period following bimaxillary orthognathic surgery. The patient was a 23-year-old woman who met the strict criteria for a diagnosis of BPPV, including a positive Dix-Hallpike test. We have described BPPV in more detail and have discussed the necessity of increasing awareness and knowledge of surgeons about BPPV as a possible complication of craniomaxillofacial surgery.

Immediate reconstruction of a large mandibular defect of locally invasive benign lesions (a new method).

Locally invasive benign tumor and large lesions such as ameloblastoma, giant cell granuloma, odontogenic keratocyst, and odontogenic myxoma are a benign, invasive, lesions of the jaws that predominantly affects the mandible. Despite the benign nature of these lesions, there is a high rate of local recurrence after curettage, which usually requires resection. The traditional surgical approach for resection of these lesions, via either mandibulotomy or mandibulectomy is extraoral approach which is associated with significant functional and esthetic sequelae. A case series is presented here in which less invasive and intraoral approach. An intraoral approach provides wide and fast access to the mandible. This approach represents a less invasive alternative that provides access to the mandible for curative resection of benign tumors with minimal postoperative sequelae. At 5 years follow-up, there were minimal functional and esthetic defects. We explored the use of the less invasive and more esthetic incision as an approach to resection and reconstruction of the mandible. It is our belief that these concerns have been best addressed by the minimally invasive procedure used in this report. This approach resulted in a minimal esthetic and functional defect even though a massive mandibular resection was performed.