MehNet: a vigesimal-based model by amino acid melting points generates unique ID numbers for protein sequences.

The amino acid encoding plays a pivotal role in machine learning-based methods for predicting protein structure and function, as well as in protein mapping techniques. Additionally, the classification of protein sequences presents its own challenges. The current study aims to assign a constant value to each amino acid, thereby creating distinctions among protein sequences. The datasets used in this study were obtained from the UniProt Knowledgebase. Subsequently, these datasets underwent preprocessing steps, and identical sequences were categorized under the same headings. Each amino acid was ranked based on its respective melting point and was assigned a vigesimal digit. These generated vigesimal digits were subsequently converted to decimal values. The centerpiece of this methodology was the melting point hashing table, which was given the name 'MehNet'. Ultimately, each protein sequence was assigned a unique identification number. This approach successfully digitized protein sequences. Notably, experiments involving randomly distributed vigesimal digits for amino acids did not yield results as promising as those achieved with MehNet. The model's classification phase, which utilizes a k-nearest neighbors (kNN) classifier, demonstrates exceptional performance in miscellaneous viral sequences. It achieves high accuracy rates, with an overall accuracy of 99.75%. Notably, it achieves an outstanding accuracy of 99.92% for the Influenza C class, highlighting its ability to distinguish closely related viral sequences.Communicated by Ramaswamy H. Sarma.

Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction.

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.

Growing Taller without Hormones? Dr. Consult Google-An Evaluation of Online Information Related to Limb Lengthening.

PURPOSE: The aim of this study was to investigate the reliability, content and readability of the information available on the Internet related to limb lengthening surgeries, which have recently been progressively in fashion. METHODS: The three most commonly used browsers on the Internet were determined and a search term for "Limb Lengthening Surgery" was typed for each browser. The websites were categorized by their type, and the content and the quality of them was evaluated using the DISCERN score, the Journal of American Medical Association (JAMA) benchmark and the Global Quality Score (GQS). The Flesch Kincaid Grade Level (FKGL) and the Flesch Reading Ease Score (FKRS) were used to evaluate the readability. Each website also assessed the presence (or absence) of the Health on Net (HON) code. RESULTS: The academic category was found to be significantly higher than the medical and commercial categories. Mean FKGL and FCRS scores, DISCERN score values, JAMA, GQS and LLCS score values of Websites with HON code were significantly higher than those without. CONCLUSIONS: The quality of online information related to limb lengthening was of low quality. Although some websites, especially academic resources, were of higher quality, the readability of their content is just about 2.5 degrees higher than the sixth-grade reading level.

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels.

Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences.

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

A biomechanical study: Comparison of three different implant options in high Tibial osteotomy.

BACKGROUND: Many implant options could be preferable for fixation after osteotomy in varus knee medial compartment arthrosis. Due to usage characteristics, it is important to compare the biomechanical properties of them. For this purpose, we aimed to examine three different implant types biomechanically in our study. METHODS: Ovine tibiae undergoing medial open-wedge high tibial osteotomy were fixed in vitro with three different implants using an angular wedge plate, a metal block plate and an external fixator system. The fixed ovine tibiae were subjected to axial tensile, axial loading and three-point bending tests in a test machine. All biomechanical tests were repeated five times, the maximum and minimum values were ignored, and the average values of the remaining three test results were taken into account. The test results were interpreted after converted into force-elongation curves in Trapezium-X software. FINDINGS: Biomechanical test results revealed some differences between implant types. While the metal block plate had the highest axial tensile strength value, it was the fixation group showing the lowest strength in axial load tests. The used fixator system was the highest strength in axial load tests and the lowest strength in axial tensile tests. INTERPRETATION: Considering the clinically significant forces related to the biomechanical stability of the three different implants used for high tibial osteotomy, the fixator system would appear to be slightly superior, although it should be noted that torsional forces, as well as parameters that could change in living tissue, might affect the results.

Automated differential diagnosis method for iron deficiency anemia and beta thalassemia trait based on iterative Chi2 feature selector.

INTRODUCTION: The differential diagnosis of anemia is an important issue for hematology laboratories. We aimed at investigating the performance of a powerful computer-based model to aid diagnosis. MATERIALS AND METHODS: Our work presents a new feature selection-based automated disease diagnosis model. To create a testbed, a new corpus is collected retrospectively. Our data sets contain beta thalassemia trait, iron deficiency anemia, and healthy groups. Our presented automated ailment classification model consists iterative chi2 (IChi2) feature selection and classification phases. The used data set includes 25 features, and IChi2 selects the 20 most valuable of them. These are forwarded to 24 traditional classifiers. RESULTS: In this work, two data sets have been used to test our proposal. In the classification phase of this model, 24 shallow classifiers have been used and the best accurate classifiers are Medium Gaussian Support Vector Machine (MGSVM) and Coarse Tree (CT) for the first and second data sets, respectively. These classifiers have been attained 97.48% and 99.73% classification accuracies using the first and second data sets, consecutively. These results are calculated using 10-fold cross-validation. Moreover, hold-out validation has been used in this work, and the results are given in the experiments. CONCLUSION: Our results denoted the success of IChi2-based classification model for diagnosis on the laboratory data set. We have found a new and robust model to differentiate iron deficiency anemia and beta thalassemia trait. This model may be beneficial for rational laboratory use.

Visfatin as a Promising Marker of Cardiometabolic Risk.

Adipose tissue is an endocrine organ that produces molecules with important functions in the human body called adipokines. Visfatin can be secreted from various sources, such as macrophages, chondrocytes and amniotic epithelial cells other than adipose tissue. The main effect of visfatin is to promote inflammatory processes. In addition, visfatin has pivotal effects on the entire cardiovascular system, such as endothelial dysfunction, atherosclerosis, plaque rupture and mobilization, myocardial damage, fibrosis and new vessel formation. Vascular pathologies in other tissues also mediate its effects. Visfatin changes in a similar manner to cardiac markers in acute myocardial infarction, and the most cited feature in research studies is that it may be a cardiovascular risk marker. Visfatin is therefore expected to be widely used in cardiovascular pathology in the near future. Visfatin has many target tissues and various effects that occur in relatively complex biological pathways, making it difficult to understand visfatin adequately. In this review, we provide comprehensive information about this promising molecule.

Vitamin D improves oxidative stress and histopathological damage in rat ovaries caused by hyperthyroidism.

OBJECTIVE: The aim of this study is to investigate the histopathological and biochemical efficacy of vitamin D on oxidative damage and fibrosis in rat ovaries induced by experimental hyperthyroidism. METHODS: This study is a comparative, prospective experimental rat study. Sprague-Dawley female rats were divided into four groups. Only distilled water was given to the rats in group 1 for 25 days. In group 2, 100 µg/day L-thyroxine was given to rats for 25 days. In Group 3, 100 µg/day L-thyroxine and 200 IU/day vitamin D were given to rats for 25 days. In group 4, only 200 IU/day vitamin D was administered for 25 days. RESULTS: This study is the first to demonstrate the protective effect of vitamin D against ovarian damage caused by experimental hyperthyroidism. Hyperthyroidism caused fibrotic degenerative changes in the ovaries and an increase in the fibrillin 1 score. It caused serum follicle-stimulating hormone (FSH) levels to increase and serum E2 levels to decrease. In addition, malondialdehyde (MDA) and total oxidant status (TOS) levels increased in rats with hyperthyroidism. Vitamin D decreased MDA and TOS values and increased total antioxidant status (TAS) values in rats with hyperthyroidism. It also increased TSH values by causing a decrease in TT3 and TT4 values. It decreased fibrosis, follicle degeneration, stromal degeneration, and fibrillin 1 score in ovarian tissue. CONCLUSION: Vitamin D has positive histopathological and biochemical effects on the oxidative stress and follicle damage caused by hyperthyroidism in ovarian tissue. Human studies with larger case populations should be conducted to evaluate the effects and clinical applications of vitamin D.

The relationship between visfatin and cardiac markers on induced myocardial infarction in rats.

Myocardial infarction (MI) is one of the most important reason of mortality into worldwide. Visfatin is a novel adipokine which was reported increased in metabolic syndrome and obesity. Moreover, it is known that visfatin increases in aterosclerotic endotelial dysfunction. In our study we want to demonstrate how visfatin changes in isoproterenol (ISO) induced MI. Rats were allocated into 4 groups in which each group included 6 rats in this study. 200 mg/kg ISO was administered into rats except control group to induce MI. I. and II. Group rats in 6th hour, III. Group rats in 24th hour and IV. Group rats in 7th day were decapitated. Visfatin was searched in cardiac tissues of all groups by immunohistochemistry stainning. Visfatin and cardiac markers' levels were measured in serum samples. Serum visfatin levels gradually increased in 6th and 24th hour in MI rats compared to controls. In 7th day visfatin levels decreased to control levels. These changes correlated with serum troponin T levels. These findings were supported by immunohistochemistry stainning of visfatin in cardiac tissues. It has been shown that visfatin could be useful in diagnosing MI and may be a biomarker for cardiac ischemia because of increasing in systemic circulation and cardiac tissues in MI like troponins.