The Efficiency of AuNPs in Cancer Cell Targeting Compared to Other Nanomedicine Technologies Using Fuzzy PROMETHEE.

Cancer is a disease with rare, diverse symptoms, causing abnormal cell growth in an uncontrolled way, leading to cell damage, apoptosis, and eventually death of the patient. This study uses the Fuzzy PROMETHEE technique to develop a new path for cancer treatment based on nanoparticles (NPs) applications, used in controlled anticancer drug delivery (drug release, toxicity, and unspecific site targeting) to enhance patient safety. The different nanoparticles employed in the drug delivery analysis are gold nanoparticles (AuNPs), liposomes, dendrimers, polymeric micelles (PMs), and quantum dots (QDs). Fuzzy predictable preference organization mode and evaluation multicriteria choice were used as tactics in making the best decision using the data from the factors of cost, size, shape, surface charge, ligand type, pH and temperature stimuli, biocompatibility, accumulation ratio, toxicity, specificity, stability, efficacy, adverse effect, and safety factor of the NPs. The results obtained from the total net flow of the visual PROMETHEE scenario for anticancer drug delivery, based on NPs data analysis, show that AuNPs are ranked the highest among the other NPs. The Phi values obtained for the NPs are as follows: AuNPs (0.1428), PMs (0.0280), QDs (-0.0467), dendrimers (-0.0593), and liposomes (-0.0649). This study highlights the optimal choice of NPs as an intelligent drug delivery system that facilitates therapeutic efficiency, where cancer cells are accurately targeted to enhance treatment quality and patient safety.

Optimization of Dental Devices and Tools Used on Teeth.

Taking decisions is important in every aspect of life. Decision-making has become a difficult problem in any situation where there are multiple criteria. The application of multicriteria decision-making methods that can bring mathematical and logical solutions to the problem from an analytical perspective has experienced considerable growth recently. It provides great benefits in solution and subsequent stages. Medical equipment selection is also a challenging, complex, and difficult problem for the decision-maker, due to the requirements of conflicting criteria, which must be taken into account simultaneously. In this context, the aim of this study implicates the principle of multicriteria decision-making theories on various types of instruments used in dentistry. Since the data used in this study are not numeric but linguistic, the Fuzzy PROMETHEE decision-making method is used. In this research, six dental tools most commonly used by professionals to perform operations on patients are compared and evaluated. Fuzzy PROMETHEE decision-making method investigations show that the dental mirror is the most effective tool among all compared tools, followed by dental suction, dental air abrasion, dental handpiece, dental laser, and dental X-ray, consequently, basing on the selected criteria and the importance weight given to each criterion. Using this technique, one can obtain more specific ranking results based on a specific preference level.

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.

A comparison between one- and two-stage revisional gastric bypass.

BACKGROUND: The safety of performing a one-stage revision from laparoscopic adjustable gastric banding (LAGB) to laparoscopic Roux-en-Y gastric bypass (LRYGB) has been questioned. The objective of this study was to compare safety and outcomes of one-stage versus two-stage revisional LRYGB performed after failed LAGB. METHODS: A retrospective analysis of all patients undergoing revisional LRYGB after failed LAGB between January 2007 and March 2017 was performed. Patients undergoing one- and two-stage revisions were compared. The primary outcome assessed was the early complication rate, while secondary outcomes included late complications, weight loss, and improvement of comorbidities. RESULTS: During the study period, 161 revisional LRYGB's were performed, including 121 one-stage and 40 two-stage procedures. Baseline demographic data, BMI and presence of comorbidities were similar between the groups. In patients undergoing a two-stage procedure, band slippage, port infection, and erosion were more commonly cited as indications for revision. Similar early complication rates were demonstrated between the groups. However, late complications were more common in the two-stage group (20.0% vs. 7.4%, P = 0.03), including higher rates of gastro-gastric fistula (5.0% vs. 0%, P = 0.01) and anemia (10.0% vs. 1.1%, P = 0.02). Three-fourths of the cohort had a follow-up of more than 6 months, and the two groups demonstrated similar weight loss results and improvement/resolution of comorbidities. CONCLUSION: The performance of one-stage revisional LRYGB after failed LAGB seems to be a safe procedure, with noninferior outcomes when compared to a two-stage revisional procedure. It is a valid option, except in cases of mechanical and infectious band complications.

Effect of clear aligner therapy on the buccolingual inclination of mandibular canines and the intercanine distance.

OBJECTIVE: To compare the changes in buccolinugal inclination of mandibular canines and intercanine distance in patients treated with clear aligners to those treated with preadjusted edgewise appliances. MATERIALS AND METHODS: The buccolingual inclination of mandibular canines and the intercanine distance were measured on pre- and posttreatment cone-beam computed tomograms of 30 patients who had been treated with clear aligners and 30 patients who had been treated with fixed preadjusted edgewise appliances. Differences between the aligner and fixed appliance groups and between pre- and posttreatment measurements were tested for statistical significance. RESULTS: In both groups, most of the mandibular canines had positive buccolingual inclinations (ie, their crowns were positioned lateral to their roots) both before and after treatment. While there was no difference between the groups pretreatment, the posttreatment buccolingual inclination was significantly greater in the aligner group. In the fixed appliance group, the canines became more upright with treatment, while the buccolingual inclination did not change significantly in the clear aligner group. The intercanine distance did not differ between the groups either before or after treatment. However, it increased significantly over the course of treatment in the aligner group, whereas it did not change significantly in the fixed appliance group. CONCLUSIONS: Orthodontic treatment with clear aligners tends to increase the mandibular intercanine distance with little change in inclination in contrast to treatment with fixed appliances, which leaves the intercanine distance unchanged but leads to more upright mandibular canines.

Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances.

The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed using artificial and real interval data sets.