Apatitic calcium phosphate/montmorillonite nano-biocomposite: in-situ synthesis, characterization and dissolution properties.

Recently, calcium phosphate/montmorillonite composites have received attention as a synthetic bone substitutes. In this study, apatitic calcium phosphate/Montmorillonite nano-biocomposites were in-situ synthesized at 22 °C by reaction between calcium hydroxide and orthophosphoric acid in the presence of different contents of montmorillonite (MNa). Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM) and Brunauer-Emmett-Teller (BET) surface areas were used to characterize the prepared powders. The XRD results show that the composites prepared with 2 and 5 wt% MNa and sintered at 900 °C, show the formation of hydroxyapatite (HAP) structure, whereas that prepared with 10 wt% MNa leads to the formation of ß-tricalcium phosphate (ß-TCP) structure. The HAP structure decomposes at 1000 °C and leads to the formation of biocomposite containing HAP, ß and α-TCP. However, ß-TCP composites show thermal stability. FTIR and structural refinement results show the incorporation of clay ions into the apatitic structure causing changes in the crystal structure of the formed calcium phosphate phases. The changes in the composition and structure lead to an increase in the dissolution rate of HAP and a decrease in that of ß-TCP.

Automatic computation of bone defective volume from tomographic images.

One of the most difficult aims of modern biomaterial science is predicting the shape and volume of a bone defect and adjusting the implementation of a bone substitute. Prior to implantation, practitioners must carefully identify the architecture and volume of the defective bone to be filled. This information is often accessed via imaging techniques. The defective bone is frequently confused with its surroundings and the image background. The use of conventional segmentation for the selection and isolation of the cavity to be filled proves to be difficult. In this work, a defect in a dead bone is created and then imaged with the microtomography technique (343 cuts generated). The goal is to separate the defect's shape and volume from both the bone and the background image. An adaptive morphological operation technique was employed to complete these tasks. The proposed method allows for exact segmentation and calculation of the volume of the cavity to be filled. Using several calculated phantoms, the approach is subjectively and quantitatively evaluated: Compared to the high error value of the conventional method, the error value of the proposed one has no bearing on the overall data. The method's accuracy was also confirmed by comparing the calculated volume of the bone defect (0.91 cm3) and the volume of prepared calcium phosphate cement paste necessary for its filling (0.87 cm3). To challenge the method even further, another direct application on a mandibular bone is realized with an advanced number of cuts (1236 cuts). The result of this application proved that the proposed algorithm overcomes the performance of the classical approaches of segmentation with a gain of 2 min on average. A comparison study between the proposed method and other classical segmentation approaches is also presented. The effectiveness of the method is proved by the various reports and metrics generated. The automated procedure can be beneficial in implantology for realizing and guiding surgical acts, as well as in computer-aided scaffolding techniques.

Analyzing pesticides and metal(loid)s in imported tobacco to Saudi Arabia and risk assessment of inhalation exposure to certain metals.

Exposure to toxic chemicals through smoked tobacco is a significant global public health issue due to their genotoxic and carcinogenic properties. The study aims to (1) measure Arsenic (As), Cadmium (Cd), Cobalt (Co), Chromium (Cr), Mercury (Hg), Nickle (Ni), lead (Pb), and 407 pesticides in tobacco commercialized in Saudi Arabia; and (2) evaluate human health risks associated with smoking tobacco. Thus, we analyzed 60 tobacco samples per brand from eight of the most popular tobacco brands in the Saudi market. The analyzed tobacco contained significant concentrations of toxic metal (loid)s and banned pesticides. Twenty-three out of 407 pesticide residues were found, with an average concentration of 0.004-1.155 µg/g. Average concentrations of As, Cd, Co, Cr, Hg, Ni, and Pb for all brands were 0.16, 0.57, 0.75, 1.36, 1.94, 0.01, and 0.37 µg/g, respectively. The risk assessment indicated that high cancer risks are associated with exposure to As, Cd, Cr, and Ni, while lower risks are associated with Pb. Additionally, the potential cancer risk estimated for Cr was higher than other toxic elements. The cumulative cancer risks (95%) under three different cases for all brands were greater than the permissible limits (=10-4). The chances of getting cancer through inhalation of particulate As, Cd, Cr, Ni, and Pb was 4 in 10 000 cases in the best case scenario (1 cigarette per day). Therefore, metal content in cigarette tobacco should be reduced to improve public health.HIGHLIGHTSAll tested brands contained banned pesticide residues except for brand C.Tobacco samples contained significant concentrations of toxic metal(loid)s.A high risk of developing cancer is associated with exposure to As, Cd, Cr, and Ni, while a lower risk is associated with exposure to Pb.

Surgical Parapharyngeal Space Tumor Analysis with Case Series Study.

BACKGROUND: The parapharyngeal space is a hypothetical region in the neck that stretches from the base of the skull to the bigger corner of the hyoid bone. The fascia that connects the styloid process to the tensor veli palatini separates the compartment into prestyloid and poststyloid compartments, with the prestyloid compartment being larger. In the general population, tumors of the parapharyngeal area are very uncommon, accounting for less than 1% of all head and neck neoplasms in the population. In this location, CT scanning and magnetic resonance imaging (MRI) exams are complimentary, and both tests should be performed to examine any lesions found. The most critical component of treatment is the total surgical removal of all the cancerous tissue. Identifying and treating primary parapharyngeal space (PPS) tumors are among the most challenging tasks in the treatment of head and neck cancer. They are also among the most aggressive ones. The primary goal of this study is to review our current knowledge at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, which serves as an academic tertiary referral center and a major teaching center. We will focus on clinical findings, tumor structure, tumor histological distribution, and surgical approaches. MATERIALS AND METHODS: The processing starts with two modules. The first module starts with the input images obtained from various patients and collected as a database. The second module starts with the collection of case series of nine patients undergoing excision via multiple different approaches: transoral, transcervical, transparotid, transmandibular, or infratemporal approach. All cases were conducted at King Abdulaziz University Hospital in Jeddah, Saudi Arabia, between 2014 and 2018. All operative interventions were performed by an otolaryngology-head and neck surgeon. RESULTS: Our study comprised nine patients, of which two underwent transparotid and seven transcervical and combined transcervical/transparotid approach. Complications faced included a hematoma in one of our cases. CONCLUSION: The transcervical approach appeared to be the superior surgical approach when facing a pleomorphic adenoma within the parapharyngeal space, arising from the deep lobe of the parotid gland or parapharyngeal space-occupying paraganglioma.

Hypopharyngeal Reconstruction: Possibilities, Outcomes, and Updates for Improving the Human Health for Quality of Life.

Hypopharyngeal carcinoma is usually present at late stages, necessitating an aggressive line of management consisting of surgical procedures, chemotherapy, and radiation therapy, depending on the case. Practitioners tend to support total laryngectomies or total esophagostomies for most cases of hypopharyngeal carcinoma. The extensive procedures needed will most probably require, depending on the residual defect, a follow-up reconstructive procedure that might require utilizing flaps. Types of reconstructive methods and types of grafts or flaps used could be divided into a multitude of categories depending on the magnitude, shape, extension, and whether the underlying defect that is being reconstructed is circumferential or not. These reconstructive procedures are aimed at improving the quality of life, improving the aesthetic outcome, and restoring the functionality of the pharyngoesophageal segment. When it comes to hypopharyngeal cancer, the most common kind is squamous cell carcinoma (SCC), which has the worst prognosis of all the head and neck malignancies. Overall, the 5-year survival rate remains low, despite recent advancements in diagnostic imaging, radiation, and chemotherapy, as well as enhanced surgical methods and techniques. Hypopharyngeal malignancies are more probable than other tumors to present with advanced primary illness, with nodal metastasis a distinct possibility. The size and amount of local dissemination of the original carcinoma, as well as the extent of involvement of regional lymph nodes, are the most critical factors in predicting prognosis. Hypopharyngeal cancers are more likely than other head and neck cancers to manifest with distant metastases at the time of diagnosis. The appearance of second primary tumors, as well as the development of distant metastases, is a contributing factor to poor survival rate. Imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) with contrast remain the gold standard for evaluating hypopharyngeal carcinoma in the early stages. In most cases, imaging leads to an increase in the tumor stage at the time of presentation. Objectives. The main objectives are to review the research published about flaps, outline the optimum situations that will dictate the usage of a few of the most often used flaps for the rebuilding of the hypopharyngeal segment defects, and outline some of the complications associated with reconstruction. Methods. The processing was carried out with the title-specific search of the PubMed database using the query terms "hypopharyngeal carcinoma" and "reconstruction" to identify the most relevant articles without restricting publication dates. Information about the types of defects and methods of reconstruction was extracted from the reviewed articles. Two books were also reviewed, which were Regional and Free Flaps for Head and Neck Reconstruction (second edition) and Head and Neck Reconstruction: A Defect-Oriented Approach. Conclusion. Deciding the appropriate approach to a case should be individualized and should depend on the capabilities of the center, the defect's size and status, and lastly, the surgeon's training. The use of interpretation in the diagnosis of flaps can offer the best results in restoring functionality and vascularity and might also offer improved cosmesis.

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method's main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure's training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques.

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors.

In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3-10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3-10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.

Micro-computed tomographic and SEM study of porous bioceramics using an adaptive method based on the mathematical morphological operations.

Numerous clinical studies have demonstrated the influence of the size, number and shape of pores into calcium phosphate ceramics on the process of bone regeneration. The main objective of this study is to determine the microstructure, the morphological characteristics and classes of pores of the prepared hydroxyapatite bioceramic using an adaptive method based on the mathematical morphological operations. The study was carried out using X-ray microtomography and Scanning Electron Microscopy images. The conventional method of openings alone presents limitation of calculation and not sufficient to achieve our objective. The proposed method allowed us to extract local characteristics and calculate precisely the morphological parameters while preserving the original volume of pores. The number and classes of pores with their size, surface of contact of the component and the number of connected pores to each pore were calculated. The method is subjectively and quantitatively evaluated using different computed phantoms and its efficiency is clearly demonstrated through the different reports and measurements generated. The proposed method can have interesting applications in the characterization of porous materials used in the medical field or in other sectors.

Effects of various adjuvants (lactic acid, glycerol, and chitosan) on the injectability of a calcium phosphate cement.

Calcium phosphate cements are well-known orthopedic materials for filling bone. Various formulations are proposed. The current challenge is to place the material in the surgical site by methods as least invasive as possible. One approach consists of making the cement injectable by incorporation of various adjuvants. However, the requirement properties of the cement must be preserved: setting times suited to a convenient delay with surgical intervention, limited disintegration in aqueous medium, and sufficient mechanical resistance. Various additives were studied: in particular, lactic acid, glycerol, chitosan, and sodium glycerophosphate. Injectability, setting time, disintegration, and toughness after 10 days were followed in vitro. Glycerol greatly improved injectability and increased setting time, but decreased mechanical properties. Lactic acid reduced setting time, increased toughness of the material, but limited the dissolution rate. After injection, the cement did not present any disintegration. The effects lactic acid were correlated with the formation of calcium complex. Its association with sodium glycerophosphate is particularly interesting. Chitosan alone improved injectability, increased setting time, and limited the evolution of the cement by maintaining the OCP phase. Only slight disintegration was observed. These first results show that is possible to transform the cement into an injectable paste by addition of adjuvants without fundamentally modifying the chemical reactions occurring during setting and hardening.