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Injection of autologous mesenchymal stem cells (AMSCs) as stromal vascular fraction, culture expanded adipose derived stem cells, minimally manipulated fat graft, bone marrow aspirate or cultured bone marrow MSCs, for osteo- and inflammatory arthritis have shown good clinical efficacy in many studies. Questions have been raised as to their safety despite no evidence known to us that they are unsafe when used this way. We hypothesized that AMSC injections are completely safe for the treatment of arthritis.

A PubMed literature search was performed to identify adverse events specifically related to the injection of autologous mesenchymal or hematopoietic stem cells into arthritic joints or intravenously.

2,011 reported injections were found. No stem cell specific adverse events were identified. Specifically no infections, tumorigenesis, or chondrolysis from collagenase were found.

Intra-articular injection of autologous mesenchymal stem cells for the treatment of arthritis is completely safe with no stem cell specific adverse events yet documented, and no increased risk compared with other traditional treatments for arthritis.

Intra-articular injection of autologous mesenchymal stem cells for the treatment of arthritis is completely safe with no stem cell specific adverse events yet documented, and no increased risk compared with other traditional treatments for arthritis.The outbreak of COVID-19 has led to a global health emergency. Emerging from China, it has now been declared as a pandemic. Owing to the fast pace at which it spreads, its control and prevention have now become the greatest challenge. The inner structural analysis of the virus is an important area of research for the invention of the potential drug. The countries are following different strategies and policies to fight against COVID-19; various schemes have also been employed to cope up with the economic crisis. While the government is struggling to balance between the public health sector and the economic collapse, the researchers and medicine practitioners are inclined towards obtaining treatment and early detection of the deadly disease. Further, the impact of COVID-19 on Dentistry is alarming and posing severe threats to the professionals as well. Now, the technology is helping the countries fight against the disease. ML and AI based applications are substantially aiding the process of detection and diagnosis of novel coronavirus. Science of Robotics is another approach followed with an aim to improve patient care.

Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy.

The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015.

The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values.

The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models.

Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

Differentiation of brain lesions by conventional MRI alone is not enough. The introduction of sophisticated imaging methods, such as MR Spectroscopy (MRS), will contribute to accurate differentiation.

To determine the diagnostic accuracy of MRS in differentiating neoplasm and non-neoplastic brain lesion.

This is a cross-sectional descriptive study conducted at Khartoum State from the period of 2015 to 2017. Thirty cases with brain lesions were included in the study investigated with MRS (Single-voxel spectroscopy) and conventional MRI. A comparison of MRS findings and histopathologic analysis was performed. The ratios of Cho/Cr and Cho/NAA were analyzed and compared between neoplastic and non-neoplastic brain masses. Data were analyzed using SPSS version 23.

Out of the 30 patients affected with brain lesions, there were 16 females and 14 males with a mean age of 44 +- 18 years. The ratios of Cho/Cr and Cho/NAA were higher in gliomas, astrocytoma, and meningioma than non-neoplastic lesions. Kappa statistical value (K) showed a good agreement between MRS and histopathological analysis (K= 0.60). The diagnostic accuracy of MRS was 100%, with 82.60% sensitivity, 85.71% specificity, 95% PPV, and 60% NPV.

MRS has high diagnostic accuracy in differentiating neoplasm from non-neoplastic brain tumors. The elevation ratios of Choline-to- N-acetyl aspartate and choline-to- creatine can help neurosurgeons and clinicians differentiate benign from malignant masses.

MRS has high diagnostic accuracy in differentiating neoplasm from non-neoplastic brain tumors. The elevation ratios of Choline-to- N-acetyl aspartate and choline-to- creatine can help neurosurgeons and clinicians differentiate benign from malignant masses.

In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence.

An alternative method for psoriatic lesion segmentation with objective analysis is sought in the present work. selleck products The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique that possesses a higher probability of global convergence for psoriasis lesion segmentation.

A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms, namely Artificial Bee Colony and Seeker Optimization algorithm, has been proposed.