Synthesis, monomer conversion, and mechanical properties of polylysine based dental composites.

OBJECTIVE: The aim of this study was to synthesize a new bioactive and antibacterial composite by incorporating reactive calcium phosphate and antibacterial polylysine into a resin matrix and evaluate the effect of these fillers on structural analysis, degree of monomer conversion, mechanical properties, and bioactivity of these newly developed polypropylene based dental composites. METHODOLOGY: Stock monomers were prepared by mixing urethane dimethacrylate and polypropylene glycol dimethacrylate and combined with 40 wt% silica to make experimental control (E-C). The other three experimental groups contained a fixed percentage of silica (40 wt%), monocalcium phosphate monohydrate, and ß-tri calcium phosphate (5 wt% each) with varying amounts of polylysine (PL). These groups include E-CCP0 (0 wt% PL), E-CCP5 (5 wt% PL) and E-CCP10 (10 wt% PL). The commercial control used was Filtek™ Z250 3M ESPE. The degree of conversion was assessed by using Fourier transform infrared spectroscopy (FTIR). Compressive strength and Vicker's micro hardness testing were evaluated after 24 h of curing the samples. For bioactivity, prepared samples were placed in simulated body fluid for 0, 1, 7, and 28 days and were analyzed using a scanning electron microscope (SEM). SPSS 23 was used to analyze the data and one-way ANOVA and post hoc tukey's test were done, where the significant level was set ≤0.05. RESULTS: Group E-C showed better mechanical properties than other experimental and commercial control groups. Group E-C showed the highest degree of conversion (72.72 ± 1.69%) followed by E-CCP0 (72.43 ± 1.47%), Z250 (72.26 ± 1.75%), E-CCP10 (71.07 ± 0.19%), and lowest value was shown by E-CCP5 (68.85 ± 7.23%). In shear bond testing the maximum value was obtained by E-C. The order in decreasing value of bond strength is E-C (8.13 ± 3.5 MPa) > Z250 (2.15 ± 1.1 MPa) > E-CCP10 (2.08 ± 2.1 MPa) > E-CCP5 (0.94 ± 0.8 MPa) > E-CCP0 (0.66 ± 0.2 MPa). In compressive testing, the maximum strength was observed by commercial control i.e., Z250 (210.36 ± 18 MPa) and E-C (206.55 ± 23 MPa), followed by E-CCP0 (108.06 ± 19 MPa), E-CCP5 (94.16 ± 9 MPa), and E-CCP10 (80.80 ± 13 MPa). The maximum number of hardness was shown by E-C (93.04 ± 8.23) followed by E-CCP0 (38.93 ± 9.21) > E-CCP10 (35.21 ± 12.31) > E-CCP5 (34.34 ± 12.49) > Z250 (25 ± 2.61). SEM images showed that the maximum apatite layer as shown by E-CCP10 and the order followed as E-CCP10 > E-CCP5 > E-CCP0 >Z250> E-C. CONCLUSION: The experimental formulation showed an optimal degree of conversion with compromised mechanical properties when the polylysine percentage was increased. Apatite layer formation and polylysine at the interface may result in remineralization and ultimately lead to the prevention of secondary caries formation.

Modifying dental composites to formulate novel methacrylate-based bone cements with improved polymerisation kinetics, and mechanical properties.

OBJECTIVES: The aim was to develop bone composites with similar working times, faster polymerisation and higher final conversion in comparison to Cortoss™. Additionally, low shrinkage/heat generation and improved short and longer-term mechanical properties are desirable. METHODS: Four urethane dimethacrylate based composites were prepared using tri-ethylene-glycol dimethacrylate (TEGDMA) or polypropylene dimethacrylate (PPGDMA) diluent and 0 or 20 wt% fibres in the glass filler particles. FTIR was used to determine reaction kinetics, final degrees of conversions, and polymerisation shrinkage/heat generation at 37 °C. Biaxial flexural strength, Young's modulus and compressive strength were evaluated after 1 or 30 days in water. RESULTS: Experimental materials all had similar inhibition times to Cortoss™ (140 s) but subsequent maximum polymerisation rate was more than doubled. Average experimental composite final conversion (76%) was higher than that of Cortoss™ (58%) but with less heat generation and shrinkage. Replacement of TEGDMA by PPGDMA gave higher polymerisation rates and conversions while reducing shrinkage. Early and aged flexural strengths of Cortoss™ were 93 and 45 MPa respectively. Corresponding compressive strengths were 164 and 99 MPa. Early and lagged experimental composite flexural strengths were 164-186 and 240-274 MPa whilst compressive strengths were 240-274 MPa and 226-261 MPa. Young's modulus for Cortoss™ was 3.3 and 2.2 GPa at 1 day and 1 month. Experimental material values were 3.4-4.8 and 3.0-4.1 GPa, respectively. PPGDMA and fibres marginally reduced strength but caused greater reduction in modulus. Fibres also made the composites quasi-ductile instead of brittle. SIGNIFICANCE: The improved setting and higher strengths of the experimental materials compared to Cortoss™, could reduce monomer leakage from the injection site and material fracture, respectively. Lowering modulus may reduce stress shielding whilst quasi-ductile properties may improve fracture tolerance. The modified dental composites could therefore be a promising approach for future bone cements.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization.

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

Breast Cancer Detection and Classification Empowered With Transfer Learning.

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16.

The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

Breast Cancer Prediction Empowered with Fine-Tuning.

In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women's and making it the most widespread cancer, and it is the second major reason for women's death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning.

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.

To assess the role of cataract extraction in glaucoma management by its intraocular pressure lowering effect.

OBJECTIVE: To measure the mean change of intraocular pressure in glaucoma patients with cataract after uncomplicated phacoemulsification surgery with intraocular lens implanted in capsular bag. METHODS: The quasi-experimental study was conducted at the Ophthalmology Department of Pakistan Institute of Medical Sciences Shaheed Zulfiqar Ali Bhutto Medical University, Islamabad, Pakistan, from June 11 to December 10, 2018, and comprised patients who had uncomplicated cataract extraction by phacoemulsification with intraocular lens implant in the capsular bag in glaucomatous eyes of age 30-80 years. Visual acuity, intraocular pressure, slit lamp examination, fundoscopy, visual fields, details about topical medication and relevant history were recorded not more than 5 days before cataract extraction. Intraocular pressure was recorded using Goldman's applanation tonometer one day before surgery, and post-surgery 1 month and 3 months. Data was analysed using SPSS 20. RESULTS: Of the 40 patients, 19(47.50%) were males and 21(52.50%) were females. The overall mean age was 52.23±9.44 years. Mean pre-operation intraocular pressure was 20.42±1.69mmHg, while at 1 month post-surgery it was 18.55±0.90mmHg and at 3 months it was 17.03±1.19mmHg (p=0.0001). CONCLUSIONS: There was a significant change in intraocular pressure readings in glaucoma patients with cataract after uncomplicated phacoemulsification surgery with intraocular lens implanted in capsular bag.

Diagnosing the Stage of Hepatitis C Using Machine Learning.

Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.

Thymosin alpha 1: A comprehensive review of the literature.

Thymosin alpha 1 is a peptide naturally occurring in the thymus that has long been recognized for modifying, enhancing, and restoring immune function. Thymosin alpha 1 has been utilized in the treatment of immunocompromised states and malignancies, as an enhancer of vaccine response, and as a means of curbing morbidity and mortality in sepsis and numerous infections. Studies have postulated that thymosin alpha 1 could help improve the outcome in severely ill corona virus disease 2019 patients by repairing damage caused by overactivation of lymphocytic immunity and how thymosin alpha 1 could prevent the excessive activation of T cells. In this review, we discuss key literature on the background knowledge and current clinical uses of thymosin alpha 1. Considering the known biochemical properties including antibacterial and antiviral properties, time-honored applications, and the new promising findings regarding the use of thymosin, we believe that thymosin alpha 1 deserves further investigation into its antiviral properties and possible repurposing as a treatment against severe acute respiratory syndrome coronavirus-2.

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions.

Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches.

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

Polymerization kinetics stability, volumetric changes, apatite precipitation, strontium release and fatigue of novel bone composites for vertebroplasty.

PURPOSE: The aim was to determine effects of diluent monomer and monocalcium phosphate monohydrate (MCPM) on polymerization kinetics and volumetric stability, apatite precipitation, strontium release and fatigue of novel dual-paste composites for vertebroplasty. MATERIALS AND METHODS: Polypropylene (PPGDMA) or triethylene (TEGDMA) glycol dimethacrylates (25 wt%) diluents were combined with urethane dimethacrylate (70 wt%) and hydroxyethyl methacrylate (5 wt%). 70 wt% filler containing glass particles, glass fibers (20 wt%) and polylysine (5 wt%) was added. Benzoyl peroxide and MCPM (10 or 20 wt%) or N-tolyglycine glycidyl methacrylate and tristrontium phosphate (15 wt%) were included to give initiator or activator pastes. Commercial PMMA (Simplex) and bone composite (Cortoss) were used for comparison. ATR-FTIR was used to determine thermal activated polymerization kinetics of initiator pastes at 50-80°C. Paste stability, following storage at 4-37°C, was assessed visually or through mixed paste polymerization kinetics at 25°C. Polymerization shrinkage and heat generation were calculated from final monomer conversions. Subsequent expansion and surface apatite precipitation in simulated body fluid (SBF) were assessed gravimetrically and via SEM. Strontium release into water was assessed using ICP-MS. Biaxial flexural strength (BFS) and fatigue properties were determined at 37°C after 4 weeks in SBF. RESULTS: Polymerization profiles all exhibited an inhibition time before polymerization as predicted by free radical polymerization mechanisms. Initiator paste inhibition times and maximum reaction rates were described well by Arrhenius plots. Plot extrapolation, however, underestimated lower temperature paste stability. Replacement of TEGDMA by PPGDMA, enhanced paste stability, final monomer conversion, water-sorption induced expansion and strontium release but reduced polymerization shrinkage and heat generation. Increasing MCPM level enhanced volume expansion, surface apatite precipitation and strontium release. Although the experimental composite flexural strengths were lower compared to those of commercially available Simplex, the extrapolated low load fatigue lives of all materials were comparable. CONCLUSIONS: Increased inhibition times at high temperature give longer predicted shelf-life whilst stability of mixed paste inhibition times is important for consistent clinical application. Increased volumetric stability, strontium release and apatite formation should encourage bone integration. Replacing TEGDMA by PPGDMA and increasing MCPM could therefore increase suitability of the above novel bone composites for vertebroplasty. Long fatigue lives of the composites may also ensure long-term durability of the materials.

A Comprehensive Review of Skull Base Osteomyelitis: Diagnostic and Therapeutic Challenges among Various Presentations.

Skull base osteomyelitis (SBO) is a complex and fatal clinical entity that is often misdiagnosed for malignancy. SBO is commonly a direct complication of otogenic, sinogenic, odontogenic, and rhinogenic infections and can present as central, atypical, or pediatric clival SBO. This review describes the clinical profile, investigational approach, and management techniques for these variants. A comprehensive literature review was performed in PubMed, MEDLINE, Research Gate, EMBASE, Wiley Online Library, and various Neurosurgical and Neurology journals with the keywords including: SBO, central or atypical SBO, fungal osteomyelitis, malignant otitis externa, temporal bone osteomyelitis, and clival osteomyelitis. Each manuscript's reference list was reviewed for potentially relevant articles. The search yielded a total of 153 articles. It was found that with early and aggressive culture guided long-term intravenous broad-spectrum antibiotic therapy decreases post-infection complications. In cases of widespread soft tissue involvement, an early aggressive surgical removal of infectious sequestra with preferentially Hyperbaric Oxygen (HBO) therapy is associated with better prognosis of disease, less neurologic sequelae and mortality rate. Complete resolution of the SBO cases may take several months. Since early treatment can improve mortality rates, it is paramount that the reporting radiologists and treating clinicians are aware of the cardinal diagnostic signs to improve clinical outcomes of the disease. It will decrease delayed diagnosis and under treatment of the condition. However, due to rarity of the condition, complete prognostic factors have not fully been analyzed and discussed in the literature.

High-intensity focused ultrasound: past, present, and future in neurosurgery.

Since Lynn and colleagues first described the use of focused ultrasound (FUS) waves for intracranial ablation in 1942, many strides have been made toward the treatment of several brain pathologies using this novel technology. In the modern era of minimal invasiveness, high-intensity focused ultrasound (HIFU) promises therapeutic utility for multiple neurosurgical applications, including treatment of tumors, stroke, epilepsy, and functional disorders. Although the use of HIFU as a potential therapeutic modality in the brain has been under study for several decades, relatively few neuroscientists, neurologists, or even neurosurgeons are familiar with it. In this extensive review, the authors intend to shed light on the current use of HIFU in different neurosurgical avenues and its mechanism of action, as well as provide an update on the outcome of various trials and advances expected from various preclinical studies in the near future. Although the initial technical challenges have been overcome and the technology has been improved, only very few clinical trials have thus far been carried out. The number of clinical trials related to neurological disorders is expected to increase in the coming years, as this novel therapeutic device appears to have a substantial expansive potential. There is great opportunity to expand the use of HIFU across various medical and surgical disciplines for the treatment of different pathologies. As this technology gains recognition, it will open the door for further research opportunities and innovation.