Optimization of synchrotron radiation parameters using swarm intelligence and evolutionary algorithms.

Alignment of each optical element at a synchrotron beamline takes days, even weeks, for each experiment costing valuable beam time. Evolutionary algorithms (EAs), efficient heuristic search methods based on Darwinian evolution, can be utilized for multi-objective optimization problems in different application areas. In this study, the flux and spot size of a synchrotron beam are optimized for two different experimental setups including optical elements such as lenses and mirrors. Calculations were carried out with the X-ray Tracer beamline simulator using swarm intelligence (SI) algorithms and for comparison the same setups were optimized with EAs. The EAs and SI algorithms used in this study for two different experimental setups are the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). While one of the algorithms optimizes the lens position, the other focuses on optimizing the focal distances of Kirkpatrick-Baez mirrors. First, mono-objective evolutionary algorithms were used and the spot size or flux values checked separately. After comparison of mono-objective algorithms, the multi-objective evolutionary algorithm NSGA-II was run for both objectives - minimum spot size and maximum flux. Every algorithm configuration was run several times for Monte Carlo simulations since these processes generate random solutions and the simulator also produces solutions that are stochastic. The results show that the PSO algorithm gives the best values over all setups.

Region-Based Semi-Two-Stream Convolutional Neural Networks for Pressure Ulcer Recognition.

Pressure ulcers are a common, painful, costly, and often preventable complication associated with prolonged immobility in bedridden patients. It is a significant health problem worldwide because it is frequently seen in inpatients and has high treatment costs. For the treatment to be effective and to ensure an international standardization for all patients, it is essential that the diagnosis of pressure ulcers is made in the early stages and correctly. Since invasive methods of obtaining information can be painful for patients, different methods are used to make a correct diagnosis. Image-based diagnosis method is one of them. By using images obtained from patients, it will be possible to obtain successful results by keeping patients away from such painful situations. At this stage, disposable wound rulers are used in clinical practice to measure the length, width, and depth of patients' wounds. The information obtained is then entered into tools such as the Braden Scale, the Norton Scale, and the Waterlow Scale to provide a formal assessment of risk for pressure ulcers. This paper presents a novel benchmark dataset containing pressure ulcer images and a semi-two-stream approach that uses the original images and the cropped wound areas together for diagnosing the stage of pressure ulcers. Various state-of-the-art convolutional neural network (CNN) architectures are evaluated on this dataset. Our experimental results (test accuracy of 93%, the precision of 93%, the recall of 92%, and the F1-score of 93%) show that the proposed semi-two-stream method improves recognition results compared to the base CNN architectures.

Comparison of ultrasonography-guided lateral versus medial costoclavicular brachial plexus block in pediatric patients : A randomized clinical trial.

BACKGROUND AND AIMS: Costoclavicular brachial plexus block is gaining popularity due to its ease of application. Lateral and medial costoclavicular approaches have recently been defined. In the current study, we aimed to investigate the procedural execution of these approaches in the pediatric population. METHODS: In this study 55 children aged between 2 and 10 years were randomized to receive lateral (LC group) or medial (MC group) costoclavicular brachial plexus block after induction of general anesthesia for postoperative analgesia. All patients received bupivacaine (1 mg/kg, 0.25%) within the center of the cord cluster. The number of needle maneuvers was recorded as primary outcome. Block performing features (ideal ultrasound-guided brachial plexus cords visualization, needle pathway planning time, needle tip and shaft visualization difficulty, requirement of extra needle maneuver due to insufficient local anesthetic distribution, block performance time, total procedure difficulty) and postoperative pain-related data (block intensities, pain scores and analgesic requirements) were all compared as secondary outcomes. RESULTS: The LC group patients required less ultrasound visualization time (median 14 s, range 11-23 s vs. median 42 s, range 15-67 s, p < 0.001) and fewer needle maneuvers (median 1, range 1-2 vs. median 3, range 2-4, p < 0.001) compared to the MC group. Similarly, the median block performance duration was shorter (median 67 s, range 47-94 s vs. median 140s, 90-204 s, p < 0.01) and procedures were perceived as easier (median 4, range 4-5 vs. median 3, range 2-5, p = 0.04) in the LC group. All other parameters were comparable (p > 0.05). CONCLUSION: The lateral approach required less needle maneuvers than the medial approach. Both techniques represented a good safety profile with favorable analgesic features.

Cloud type classification using deep learning with cloud images.

Clouds play a pivotal role in determining the weather, impacting the daily lives of everyone. The cloud type can offer insights into whether the weather will be sunny or rainy and even serve as a warning for severe and stormy conditions. Classified into ten distinct classes, clouds provide valuable information about both typical and exceptional weather patterns, whether they are short or long-term in nature. This study aims to anticipate cloud formations and classify them based on their shapes and colors, allowing for preemptive measures against potentially hazardous situations. To address this challenge, a solution is proposed using image processing and deep learning technologies to classify cloud images. Several models, including MobileNet V2, Inception V3, EfficientNetV2L, VGG-16, Xception, ConvNeXtSmall, and ResNet-152 V2, were employed for the classification computations. Among them, Xception yielded the best outcome with an impressive accuracy of 97.66%. By integrating artificial intelligence technologies that can accurately detect and classify cloud types into weather forecasting systems, significant improvements in forecast accuracy can be achieved. This research presents an innovative approach to studying clouds, harnessing the power of image processing and deep learning. The ability to classify clouds based on their visual characteristics opens new avenues for enhanced weather prediction and preparedness, ultimately contributing to the overall accuracy and reliability of weather forecasts.

Integration of functional capacity to medically necessary, time-sensitive scoring system: A prospective observational study.

OBJECTIVES: To evaluate 2 new modifications to medically necessary, time-sensitive (MeNTS) scoring systems integrating functional capacity assessment in estimating intensive care unit (ICU) requirements. METHODS: This prospective observational study included patients undergoing elective surgeries between July 2021 and January 2022. The MeNTS scores and our 2 modified scores: MeNTS-METs (integrated Duke activity status index [DASI] as metabolic equivalents [METs]) and MeNTS-DASI-5Q (integrated modified DASI [M-DASI] as 5 questions) were calculated. The patients' ICU requirements (group ICU+ and group ICU-), DASIs, patient-surgery-anesthesia characteristics, hospital stay lengths, rehospitalizations, postoperative complications, and mortality were recorded. RESULTS: This study analyzed 718 patients. The MeNTS, MeNTS-METs, and MeNTS-DASI-5Q scores were higher in group ICU+ than in group ICU- (p<0.001). Group ICU+ had longer operation durations and hospital stay lengths (p<0.001), lower DASI scores (p<0.001), and greater hospital readmissions, postoperative complications, and mortality (p<0.001). The MeNTS-METs and MeNTS-DASI-5Q scores better predicted ICU requirement with areas under the receiver operating characteristic curve (AUC) of 0.806 and 0.804, than the original MeNTS (AUC=0.782). CONCLUSION: The 5-questionnaire M-DASI is easy to calculate and, when added to a triage score, is as reliable as the original DASI for predicting postoperative ICU requirements.

Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome.

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar's test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar's test results found statistically significant differences between different Machine Learning approaches.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

A new model for prediction of bowel gangrene in sigmoid volvulus.

BACKGROUND: Sigmoid volvulus is a pathology that can be mortal because it is frequently encountered in elderly patients. In case of bowel gangrene, mortality and morbidity increase further. We planned a retrospective study, in which the effectiveness of the model was evaluated by creating a model that aims to predict whether intestinal gangrene is present in patients with sigmoid volvulus only by blood tests and thus to quickly guide treatment methods. METHODS: In addition to demographic data such as age and gender, laboratory values such as white blood cell, C-reactive protein (CRP), lactate dehydrogenase (LDH), potassium, and colonoscopic findings and whether there was gangrene in the colon during the operation were evaluated retrospectively. In the analysis of the data, independent risk factors were determined by univariate and multivariate logistic regression analyzes as well as Mann-Whitney U and Chi-square tests. Receiver operating characteristic (ROC) analysis was performed for statistically significant continuous numerical data, and cutoff values were determined and Malatya Volvulus Gangrene Model (MVGM) was created. The effectiveness of the created model was again evaluated by ROC analysis. RESULTS: Of the 74 patients included in the study, 59 (79.7%) were male. The median age of the population was 74 (19-88), and gangrene was detected in 21 (28.37%) patients at surgery. In univariate analyzes, leukocytes <4000/mm3 and >12000/mm3 (OR: 10.737; CI 95%: 2.797-41.211, p=0.001), CRP ≥0.71 mg/dl (OR: 8.107 CI 95%: 2.520-26.082, p<0.0001), potassium ≥3.85 mmoL/L (OR: 3.889; 95% CI): 1.333-11.345, p=0.013), and LDH ≥288 U/L (OR: 3.889; CI 95%: 1.333-11.345, p=0.013), whereas, in multivariate analyzes, only CRP ≥0.71 mg/dL (OR: 3.965; CI 95%: 1.071-15.462, p=0.047) was found to be an independent risk factor for bowel gangrene. The strength of MVGM was AUC 0.836 (0.737-0.936). In addition, it was observed that the probability of bowel gangrene increased approximately 10 times if MVGM was ≥7 (OR: 9.846; 95% CI: 3.016-32.145, p<0.0001). CONCLUSION: Besides being non-invasive compared to the colonoscopic procedure, MVGM is a useful method for detecting bowel gangrene. In addition, it will guide the clinician in taking the patients with intestinal loop gangrene to emergency surgery without wasting time in the treatment steps, as well as avoiding complications that may occur during colonoscopy. In this way, we think that morbidity and mortality rates can be reduced.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: An experimental research study.

BACKGROUND: Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consistently make treatment decisions for such cases. OBJECTIVE: This study aimed to use machine learning algorithms to facilitate the process of deciding whether to choose serial extraction or expansion of maxillary and mandibular dental arches for early treatment of borderline patients suffering from moderate to severe crowding. METHODS: The dataset of 116 patients who were previously treated by senior orthodontists and divided into two groups according to their treatment modalities were examined. Machine Learning algorithms including Multilayer Perceptron, Linear Logistic Regression, k-nearest Neighbors, Naïve Bayes, and Random Forest were trained on this dataset. Several metrics were used for the evaluation of accuracy, precision, recall, and kappa statistic. RESULTS: The most important 12 features were determined with the feature selection algorithm. While all algorithms achieved over 90% accuracy, Random Forest yielded 95% accuracy, with high reliability values (kappa = 0.90). CONCLUSION: The employment of machine learning methods for the treatment decision with or without extraction in the early treatment of patients in the mixed dentition can be particularly useful for pedodontists and general practitioners.

Comparison of Performance Characteristics and Efficacy of Bilateral Thoracic Paravertebral Blocks in Obese and Non-Obese Patients Undergoing Reduction Mammaplasty Surgery: A Historical Cohort Study.

BACKGROUND: Although ultrasound (US)-guided regional anesthesia techniques are advantageous in the management of obese patients; the procedures can still be associated with technical difficulties and greater failure rates. The aim of this study is to compare the performance properties and analgesic efficacy of US-guided bilateral thoracic paravertebral blocks (TPVBs) in obese and non-obese patients. METHODS: Data of 82 patients, who underwent bilateral reduction mammaplasty under general anesthesia with adjunctive TPVB analgesia between December 2016 and February 2020, were reviewed. Patients were allocated into two groups with respect to their BMI scores (Group NO: BMI < 30 and Group O: BMI ≥ 30). Demographics, ideal US visualization time, total bilateral TPVB procedure time, needle tip visualization and performance difficulties, number of needle maneuvers, surgical, anesthetic and analgesic follow-up parameters, incidence of postoperative nausea and vomiting (PONV), sleep duration, length of postanesthesia care unit (PACU) and hospital stay, and patient/surgeon satisfaction scores were investigated. RESULTS: Seventy-nine patients' data were complete. Ideal US visualization and total TPVB performance times were shorter, number of needle maneuvers were fewer and length of PACU stay was shorter in Group NO (p < 0.05). Postoperative pain scores were generally similar within first 24 h (p > 0.05). Time to postoperative pain, total analgesic requirements, incidence of PONV, sleep duration, length of hospital stay were comparable (p > 0.05). Satisfaction was slightly higher in Group NO (p < 0.05). CONCLUSIONS: US-guided TPVB performances in obese patients might be more challenging and take longer time. However, it is still successful providing good acute pain control in patients undergoing reduction mammaplasty surgeries. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . TRIAL REGISTRATION: NCT04596787.

A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models.

Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis. In the first level of the proposed bi-level stacking ensemble approach, predictions are obtained by applying 5-fold cross-validation to three new CNN models. A machine learning classifier selected at the second level is trained according to the obtained predictions, and the final classification result is reached. The performances of the stacking models were compared with the performances of the deep learning models, and McNemar's statistical test was applied to support the results. According to the experimental results, stacking ensemble models performed with a significant difference with 98.42% ACC and 98.19% MCC in the KvasirV2 dataset and 98.53% ACC and 98.39% MCC in the HyperKvasir dataset. This study is the first to offer a new learning-oriented approach that efficiently evaluates CNN features and provides objective and reliable results with statistical testing compared to state-of-the-art studies on the subject. The proposed approach improves the performance of deep learning models and outperforms the state-of-the-art studies in the literature.

Comparison of ultrasound-guided supraclavicular and costoclavicular brachial plexus blocks in pediatric patients: a randomized clinical trial.

PURPOSE: Costoclavicular brachial plexus block has been described recently as a new technique in adults and pediatric patients. In this study, we aimed to compare the supraclavicular and costoclavicular approaches, which are claimed to be effective and practical in pediatric patients. METHODS: Sixty children were randomized to receive supraclavicular (SC group) or costoclavicular (CC group) brachial plexus blocks prior to surgical incision. Block performance times were recorded as the primary outcome. Procedural features (ideal brachial plexus cord visualization/needle pathway planning time, needle tip/shaft visualization difficulty, number of needle maneuvers, requirement of extra needle maneuvers due to insufficient local anesthetic distribution) and postoperative pain-related data (sensorimotor block intensities, Wong-Baker and FLACC pain scores and analgesic requirements) were also evaluated. To observe the tendency toward respiratory complications, ultrasonographic diaphragm movement amplitude (with M-mode) and diaphragm thickness (with B-mode) were measured postoperatively. RESULTS: A total of 56 patients were included. Block performance times [70(7-97) vs. 115(75-180) s] were significantly lower in the CC group (p < 0.01). The block success rates did not differ (p > 0.05). The incidence of hemidiaphragm paralysis was 44% in the SC group (p < 0.001), and inspiratory diaphragm thickness was significantly lower (p < 0.01). None of CC group patients experienced hemidiaphragm paralysis. All other parameters were comparable (p > 0.05). CONCLUSIONS: Although costoclavicular block did not show superiority in pain management, the block performance was perceived as more practical than supraclavicular block. We believe that costoclavicular brachial plexus block stands as a good option in upper extremity surgeries with the advantages of shorter block performance time and reduced ipsilateral hemidiaphragm paralysis risk in pediatric patients.

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing.

Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to ∼87% with test data from datasets of both datasets and trained models, but also up to ∼84% with small "Sample Test Data" generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers.

It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient's previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.

Association of CDH1 -160 C → A and -347 G→ GA polymorphisms and expression of E-cadherin and gastric cancer: A case-control study.

OBJECTIVES: The loss of function of the E-cadherin (CDH1) gene with -160 C→A and -347 G→GA polymorphisms is regarded as a critical step for gastric cancer. It was aimed to investigate possible association of these polymorphisms and immunoexpression of E-cadherin with gastric cancer. MATERIAL AND METHODS: Gastric adenocarcinoma patients and individuals with benign gastric pathologies were included in this case-control study. Demographic data and pathological findings were recorded. Immunohistochemical staining of E-cadherin expression and analysis of -160 C→A and -347 G→GA polymorphisms were done. Differences between allele frequencies of -160 C→A and -347 G→GA polymorphisms and expression of E-cadherin were the primary outcomes. RESULTS: There were 78 gastric cancer patients (Group A) and 113 individuals with benign gastric pathologies (Group B). The number of male patients and mean age were higher in Group A (p <0.001). -160 C→A and 347 G→GA polymorphisms and their allelic distributions showed no difference between the groups (p> 0.05 for all). There was a significant association between -160 C→A polymorphism and grade of E-cadherin expression (p= 0.013). There were no significant differences between survival rates with -160 C→A, 347 G→GA and intensity of E-cadherin expression (p> 0.05 for all). There was no significant association between -160 C→A and -347 G→GA polymorphisms and gastric cancer. CONCLUSION: There was no impact of E-cadherin expression on tumoral features and survival in gastric cancer. -160 C→A polymorphism may influence the expression of E-cadherin in gastric cancer.

Proton Therapy for Mandibula Plate Phantom.

PURPOSE: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature. The results were found to be within acceptable deviation values. METHODS: In this study, mandibular tooth plate phantoms were modelled for the molar and premolar areas and then a Monte Carlo simulation was used to calculate the Bragg curve, lateral straggle/range and recoil values of protons remaining in the therapeutic energy ranges. The mass and atomic densities of all the jawbone layers were selected and the effect of layer type and thickness on the Bragg curve, lateral straggle/range and the recoil were investigated. As protons move through different layers of density, lateral straggle and increases in the range were observed. A range of energies was used for the treatment of tumours at different depths in the mandible phantom. RESULTS: Simulations revealed that as the cortical bone thickness increased, Bragg peak position decreased between 0.47-3.3%. An increase in the number of layers results in a decrease in the Bragg peak position. Finally, as the proton energy increased, the amplitude of the second peak and its effect on Bragg peak position decreased. CONCLUSION: These findings should guide the selection of appropriate energy levels in the treatment of tumour structures without damaging surrounding tissues.

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification.

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.