Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E-App

Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations

Background

diabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development.

Objective

The aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques.

Methods

The input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent dataset comprising 248 cases was employed for external validation. This validation process evaluated the model's applicability and reliability across diverse populations and clinical settings. Importantly, the external dataset required no additional tuning or adjustment of parameters, enabling an unbiased assessment of the model's generalizability and its capacity to predict the risk of DFU.

Results

The ensemble learning method outperformed individual classifiers in various performance evaluation metrics. Based on the ROC analysis, the AUC of the AutoML model for assessing diabetic foot risk was 88.48 % (74.44-97.83 %). Other results were found to be as follows: 87.23 % (63.33 %-100.00 %) for sensitivity, 87.43 % (70.00 %-100.00 %) for specificity, 87.33 % (76.66 %-95.00 %) for accuracy, 87.69 % (75.00 %-100.00 %) for positive predictive value, and 87.70 % (71.79 %-100.00 %) for negative predictive value. In addition to traditional DFU risk factors such as cardiovascular disorders, peripheral artery disease, and neurological damage, we identified new risk factors such as lower limb varicose veins, history of cerebral infarction, blood urea nitrogen, GFR (Glomerular Filtration Rate), and type of diabetes that may be related to the development of DFU. In the external validation set of 158 samples, originating from an initial 248 with exclusions due to missing labels or features, the model still exhibited strong predictive accuracy. The AUC score of 0.762 indicated a strong discriminatory capability of the model. Furthermore, the Sensitivity and Specificity values provided insights into the model's ability to correctly identify both DFU cases and non-cases, respectively.

Conclusion

The predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.

Bone marrow mesenchymal stem cells suppress activated CD4+ T cells proliferation through TGF-beta and IL10 dependent of autophagy in pathological hypoxic microenvironment

BACKGROUND

Bone marrow mesenchymal stem cells (BMSCs) mediated immunomodulation by secreting certain bioactive cytokines has been recognized as a promising approach for disease treatment. However, microenvironmental oxygen tension affect immunomodulatory functions and activate autophagy in BMSCs. The mechanism governing BMSCs immunomodulation in hypoxia hasn't been expounded clearly. The aim of this study is to investigate the function of pathological hypoxia on immunomodulatory properties of bone marrow mesenchymal stem cells and its possible mechanism.

METHODS

BMSCs were cultured in either normoxia (21 % oxygen) or hypoxia (0.1 % oxygen) for 24 h, then electron microscopy (EM) and immunofluorescence staining were used to detect the activation of autophagy. Besides autophagy-related markers were monitored by Western blotting. Atg5 siRNA induced autophagic inhibition. Additional, gene expression levels of Real-time fluorescence quantitative PCR and Western blot were used to detect BMSCs related cytokines. Both the proliferation and apoptosis of CD4+ T cell in co-culture were detected by flow cytometry. Exogenous anti-IL-10 antibody and anti-TGF-β1 antibody were used in co-cultured BMSCs-CM and CD4+ T cells, which enabled us to assess how autophagy affected BMSCs-mediated CD4+ T cell proliferation in low oxygen tension.

RESULT

Compared with normal BMSCs, Hypo-BMSCs enhanced the immunosuppressive effect of BMSCs on CD4+ T cell proliferation, while si-atg5 weakened the inhibition of Hypo-BMSCs. Furthermore, exogenous anti-TGF-β1 antibody and the addition of anti-TGF-β1 antibody reversed the immunosuppressive ability of Hypo-BMSCs.

CONCLUSIONS

Our findings reveal that BMSCs possess significant immunosuppression on CD4+T cell through IL-10 and TGF-β1 dependent of autophagy in hypoxic microenvironment.

Retracted: Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E-App

[This retracts the article DOI: 10.1155/2021/5759184.].

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

BACKGROUND

Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare.

MAIN PROBLEM

Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy.

METHODOLOGY

This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO).

RESULTS

Compared to other techniques, the simulation's outcomes demonstrate that the suggested approach offers greater accuracy.

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model's development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted.

Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

Leukemia cancer is the most common type of cancer that occurs in childhood. The most common types are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) which affect children and adults, respectively. Several health issues occur due to these cancers. Leukemia affects the bone marrow or the lymph nodes. Leukemia produces abnormal white blood cells via the bone marrow system. The affected white blood cells are unable to perform their tasks properly. Detecting leukemia usually requires taking a blood smear from a patient and working with expert hematologists who analyze the smear with a microscope. In this paper, a method to detect ALL and AML using a deep learner classifier is developed and proposed. The method detects both types, determines their severity, and creates a message that recommends next steps to patients. This approach works based on image segmentation and a convolutional neural network (CNN) tool called AlexNet. The obtained results from the proposed approach and using MATLAB reached more than 98% accuracy. The margin exists because several operations are needed to fully detect the blood cancer. A dataset of leukemia from the Kaggle site is used to test the developed method and illustrate its effectiveness. This dataset is C-NMC_Leukemia, and it consists of nearly 10 GB worth of 15,000 images. A confusion matrix of testing images is provided to prove the correctness of the presented approach. Furthermore, a comparative analysis between the proposed algorithm and some works from the literature is presented. This analysis compares the method used to extract features, the classifier that is utilized, the accuracy, the precision, and the recall. The obtained results indicate that the proposed method outperforms other works and produces better results.

A Plain Bayesian Algorithm-Based Method for Predicting the Mental Health Status and Biomedical Diagnosis of University Students

The purpose of this study was to assess e-learning during Corona epidemic regarding advantages, limitations, and their recommendations for managing learning during the epidemic. Based on a case study, this study used qualitative research. Sixteen students from King Saud University's College of Education were invited to take part. These students receive their online lectures via the "Zoom" application. A 20-minute WhatsApp one-on-one semiorganized interview was likewise utilized. To guarantee the reliability, iCloud was utilized to record gatherings and meetings for direct record (adaptability, constancy, confirmability, and validity). Results were presented in three themes: advantages of employing distance education, limitations of usages, and recommendations for improvements. Analyzing the feedbacks collected from students by the four interviewers, important characteristics of distance education emerged. They were student-centered learning, which included: comfortable, self-directed learning, asynchronous learning, and flexibility. The most common limitations associated with distance education, in general, included inefficiency, that is, lack of student feedback, and lack of attentiveness. As for recommendations for improvements the most obvious characteristics that became evident in students' responses were teaching and assessment and quality enhancement.

Epstein–Barr Virus (EBV) and Multiple Sclerosis Disease: A Biomedical Diagnosis

Multiple sclerosis (MS) is a degenerative disease that affects 2.8 million people worldwide. It is a central nervous system disease (CNS), in which the myelin sheath covering the brain and spinal cord neurons is attacked. If the myelin sheath is damaged, a person can suffer permanent damage to the nerves. There are a number of factors that can increase a person's risk of developing MS, such as obesity, smoking, vitamin D deficiency, certain tissue types (HLADRB1∗15 : 01) and infection with the Epstein–Barr virus (EBV). The latter virus can cause infectious mononucleosis, which can, in turn, result in lifelong infection in the host. To establish the relationship between MS and EBV, the author conducted a study on 1176 MS patients admitted to Saudi Arabia King Abdulaziz City centers. The researcher determined that MS occurred twice as much in females as it did in males, and also that EBV was much more widespread in MS female patients than MS male patients (27 : 1). Age was not a factor in the occurrence of EBV. There were limitations on data completeness and availability. Other trials using larger cohorts of patients are needed.

Awareness of Medical Students toward Circadian Rhythm and Sleep Disorder Based on Biomedical Diagnosis

Background

Sleep disorders affect an individual's mental and physical health and vice versa. Sleep medicine is underrecognized as a specialty; therefore, many sleep disorders go undiagnosed. This study is aimed at assessing the knowledge of medical students toward circadian neuroscience and sleep disorder based on biomedical diagnosis.

Methods

This cross-sectional study was conducted in both male and female medical colleges from the third to the sixth year. A self-administered structured questionnaire consisting of sociodemographic data and the Assessment of Sleep Knowledge in Medical Education (ASKME) survey assessed the students' general knowledge and attitude towards sleep disorder and sleep medicine. Chi-square/Fisher exact tests were used to analyse the participants' knowledge level toward specific sociodemographic data. Also, for two-level continuous variables, the Wilcoxon two-sample test was used.

Results

The total number of participants was 296, with 154 female and 142 male participants. The prevalence of inadequate knowledge was considerable with 96.62% of students, compared to adequate knowledge with only 3.38%. The students' attitude to sleep medicine was negative 14.53% and positive among 85.47%. We found that gender was significantly associated with attitude with a p value = 0.0057. The specific interest in sleep medicine had a significant association with knowledge and attitude, p value of 0.0522 and 0.0059, respectively.

Conclusion

This study concluded that medical students possess inadequate knowledge regarding sleep medicine, yet they have a positive attitude towards it.

The Impact of Education Based on New Internet Media Technology on College Students' Mental Health and Biomedical Diagnosis

There has been an upsurge in signs of gloom, tension, dietary problems, and other dysfunctional behaviors in undergrad populaces lately. At the same time, the need for advisory services is constantly increasing. Some have interpreted these patterns as mental health emergencies that require immediate investigation and the development of possible treatments to meet the needs of students. Later, other studies have linked the observed increase in side effects to shape individual shape enhancement, especially the widespread use of web-based entertainment, and the time spent on such development is clearly a decrease in psychological well-being. Showed to be related while the use of personalized computing innovations has drastically changed the scene in which undergrads interact with one another and appears to have a significant impact on emotional wellness. Similar advances also offer various opportunities for psychological well-being improvement and dysfunctional behaviour treatment. In this segment, we examine the hardships and open doors for undergrad psychological wellness that PC gadgets give. We accentuate potential for extra examination in this field, as well as ways for people and associations to draw in more benefits with these advances in valuable and health-advancing ways.

The Predictive Value of Neutrophil-Lymphocyte Ratio in Patients with Polycythemia Vera at the Time of Initial Diagnosis for Thrombotic Events

Objective

To investigate and discuss the predictive value of the neutrophil-to-lymphocyte ratio (NLR) in patients with polycythemia vera (PV) at the time of initial diagnosis, as well as its clinical significance in predicting the occurrence of thrombotic events and the progression of future thrombotic events during follow-ups, with the goal of providing a reference for the early identification of high-risk PV patients and the early intervention necessary to improve the prognosis of PV patients.

Method

A total of 170 patients diagnosed with PV for the first time were enrolled in this study. The risk factors affecting the occurrence and development of thrombotic events in these patients were statistically analyzed.

Results

NLR (P = 0.030), WBC count (P = 0.045), and history of previous thrombosis (P < 0.001) were independent risk factors for thrombotic events at the time of initial diagnosis. Age ≥ 60 years (P = 0.004), NLR (P = 0.025), history of previous thrombosis (P < 0.001), and fibrinogen (P = 0.042) were independent risk factors for the progression of future thrombotic events during follow-ups. The receiver operating characteristic curve (ROC curves) showed that NLR was more effective in predicting the progression of future thrombotic events than age ≥ 60 years, history of previous thrombosis, and fibrinogen. Kaplan-Meier survival analysis showed progression-free survival time of thrombotic events in the high NLR value group (NLR ≥ 4.713) (median survival time 22.033 months, 95% CI: 4.226-35.840), which was significantly lower compared to the low NLR value group (NLR < 4.713) (median overall survival time 66.000 months, 95% CI: 50.670-81.330); the observed difference was statistically significant (P < 0.001). The 60-month progression-free survival in the low NLR value group was 58.8%, while it was 32.8% in the high NLR value group.

Conclusion

Peripheral blood NLR levels in patients with PV resulted as an independent risk factor for the occurrence of thrombotic events at the time of initial diagnosis and for the progression of future thrombotic events during follow-ups. Peripheral blood NLR levels at the time of initial diagnosis and treatment had better diagnostic and predictive value for the progression of future thrombotic events in patients with PV than age ≥ 60 years, history of previous thrombosis, and fibrinogen.

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

PVL (proliferative verrucous leukoplakia) has distinct clinical characteristics. They have a proclivity for multifocality, a high recurrence rate after treatment, and malignant transformation, and they can progress to verrucous or squamous cell carcinoma. AI can aid in the diagnosis and prognosis of cancers and other diseases. Computational algorithms can spot tissue changes that a pathologist might overlook. This method is only used in a few studies to diagnose LB and PVL. To see if their cellular nuclei differed and if this cellular compartment could classify them, researchers used a computational system and a polynomial classifier to compare OLs and PVLs. 161 OL and 3 PVL specimens in the lab were grown, photographed, and used for training and computation. Exam orders revealed patients' sociodemographics and clinical pathologies. The nucleus was segmented using Mask R-CNN, and LB and PVL were classified using a polynomial classifier based on nucleus area, perimeter, eccentricity, orientation, solidity, entropies, and Moran Index (a measure of disorderliness). The majority of OL patients were male smokers; most PVL patients were female, with a third having malignant transformation. The neural network correctly identified cell nuclei 92.95% of the time. Except for solidity, 11 of the 13 nuclear characteristics compared between the PVL and the LB showed significant differences. The 97.6% under the curve of the polynomial classifier was used to classify the two lesions. These results demonstrate that computational methods can aid in diagnosing these two lesions.

Innovate a Standard for the Future Model of Nursing Care at Medical-Surgical Units in Najran University

Aim

Innovate a standard for the future model of nursing care at medical-surgical units in Najran University through a training program for the standard of the future model evaluation on studied nurses' knowledge, attitude about innovation standards, and innovative behavior among nurses.

Methods

A quasi-experimental research was used to achieve the study's goal; the research was carried out at Najran University Hospital at Najran, in the medical and surgical units, as well as outpatient clinics. The sample is a convenience type; 100 nurses were used. Tool. A structured questionnaire sheet was used for data collection that includes nurses' knowledge, attitude, and individual innovative scale.

Results

This reveals the studied nurses related to their individual innovative scale pre- and postintervention. Concerning resistance to change, the mean of them preintervention is x ® SD 9.08 ± 2.60. Concerning opinion leadership, the mean of them postintervention is x ® SD 14.32 ± 3.16. There is a highly significant difference (p < 0.01∗∗) preintervention as regards all domains listed.

Conclusion

The educational program significantly enhances nurses' knowledge and attitude, according to our present study. Nurses' innovative skills are also improved by enhancing their knowledge and attitude. Before and after the educational program was implemented, there was a highly positive linear association between the nurses' knowledge, attitude, and innovative skills at p < 0.01.

Prevalence and Correlation of Metabolic Syndrome in Patients with Bipolar Disorder in NGHA, Riyadh

Background

Metabolic syndrome is considered dangerous, especially to patients that are diagnosed with a mental condition such as bipolar disorder, since these types of patients can be difficult to deal with. Metabolic syndrome can lead to multiple cardiovascular diseases, strokes, and diabetes. A careful approach is important when it comes to facing a complex condition such as this. This research will contribute to giving more information about the prevalence and statistics of metabolic syndrome in bipolar disorder patients at NGHA, Riyadh. No published study in literature has investigated the prevalence of metabolic syndrome in patients with bipolar disorder in NGHA, Riyadh.

Methods

The study was conducted among 191 adult male (66) and female (125) patients at NGHA, Riyadh. The medical records were used for the assessment of metabolic syndrome and referrals by using a chart review for individuals. The main variables are metabolic syndrome and bipolar disorder. It was conducted on both males and females. Data was collected on data collection form and further analysis on relations was made by using SAS (Version 9.4). Chi-squared test and the Wilcoxon Two-sample test for two-level continuous variables. P ≤ 0.05 was determined to be the significance level.

Results

Out of 191 patients, 130 were obese, 85 had diabetes, and 89 were hypertensive. Additionally, 50 (40%) females and 29 (43.9%) males had metabolic syndrome, a total of 79 (41.4%) out of 191.

Conclusion

The findings of this study indicate that there is an elevated prevalence of metabolic syndrome in bipolar disorder patients in NGHA, Riyadh. Highlighting the potential danger that people may not be aware of.

Measuring the Awareness of Chronic Kidney Disease (CKD) with Environmental Evaluation among Adult Diabetic Patients in Hail Region, Saudi Arabia

Introduction

Chronic kidney disease (CKD) is one of the main chronic complications of T2DM that happens among T2DM patients who have uncontrolled glucose. Because CKD is considered a silent disease, the diagnosis is usually made at late stages when there will be few chances to prevent the adverse outcome.

Aim

The goal of this study was to assess adult diabetic patients' awareness of developing chronic kidney disease at the community level in Hail region, Saudi Arabia, in 2022. Patients and Methods. This is a cross-sectional study conducted among diabetic patients in the Hail region, Saudi Arabia. A self-administered questionnaire translated into Arabic was distributed among patients with DM. The questionnaire covers social and demographic variables (such as age, gender, relationship status, and so on) as well as a 7-item questionnaire to assess the DM population's knowledge of CKD.

Results

400 DM patients responded to our survey (51% females vs 49% males). Patients who were diagnosed with type 2 diabetes were 23.8% and 40.5% had a diabetes duration of 5–15 years. Nearly half (46.8%) were considered as a poor level of awareness, 29.3% had a moderate, and 24% had a good awareness level. Factors associated with an increased level of awareness were being a bachelor's degree, being unmarried, being a student, and having a doctor as a source of CKD information.

Conclusion

There was a deficiency in the level of awareness among the diabetic patients in our region. Patients who were single with better education and who were well informed by the doctors about CKD information tend to be more aware of CKD as compared to other DM patients. Further research is warranted in order to establish the awareness level of DM patients regarding CKD and its complications.

Automation of Cephalometrics Using Machine Learning Methods

Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient's lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study's computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.

Functional Localizers for Motor Areas of the Brain Using fMRI

Neuroimaging researchers increasingly take advantage of the known functional properties of brain regions to localize motor regions in the brain and investigate changes in their activity under various conditions. Using this noninvasive functional MRI (fMRI) method makes it possible to identify and localize brain activation. There are many localizers that can be used to identify brain areas, namely, motor areas such as functional localizer, anatomical localizer, or Atlas mask. Eighteen right-handed participants were recruited for this research to test the reliability of five localizers for primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), motor cerebellum, and motor thalamus. Motor execution task, namely, hand clenching was used to activate M1, SMA, and motor cerebellum. A combined action observation and motor imagery (AOMI) task was used to functionally activate PMC. Finally, a mask based on Talairach coordinates Atlas was created and used to identify the motor thalamus. Our results show that all localizers were successfully activated in the desired regions of interest. Motor execution successfully activated M1, SMA, and motor cerebellum. A novel localizer based on AOMI was successfully activated in PMC, and the motor thalamus mask obtained from the thalamus mask was successfully implemented on each participant. In conclusion, all five localizers tested in this research were reliable and can be used for rt-fMRI neurofeedback research to define the regions of interest.

Diagnosing Cancer Using IOT and Machine Learning Methods

Breast cancer affects one in every eight women and is the most common cancer. Aim. To diagnose breast cancer, a potentially fatal condition, using microarray technology, large datasets can now be used. Methods. This study used machine learning algorithms and IOT to classify microarray data. They were created from two sets of data: one with 1919 protein types and one with 24481 protein types for 97 people, 46 of whom had a recurring disease and 51 of whom did not. The apps were written in Python. Each classification algorithm was applied to the data separately, without any feature elimination or size reduction. Second, two alternative feature reduction approaches were compared to the first case. In this case, machine learning techniques like Adaboost and Gradient Boosting Machine are used. Results. Before applying any feature reduction techniques, the logistic regression method produced the best results (90.23%), while the Random Forest method produced good results (67.22%). In the first data, SVM had the highest accuracy rate of 99.23% in both approaches, while in the second data, SVM had the highest rate of 87.87% in RLR and 88.82% in LTE. Deep learning was also done with MLP. The relationship between depth and classification accuracy was studied using it at various depths. After a while, the accuracy rate declined as the number of layers increased. The maximum accuracy rate in the first data was 97.69%, while it was 68.72% in the second. As a result, adding layers to deep learning does not improve classification accuracy.

Development and Characterization of Multigrain Pan Bread Prepared Using Quinoa, Lupin, and Fenugreek Seeds with Yellow Maize as a Gluten-Free Diet

Celiac disease causes serious health problems for humans. Therefore, the consumption of gluten-free diets (GFDs) is the only therapy to prevent patients from developing the disease. The objective of the current study was to investigate the proximate analysis, mineral compositions, and antioxidant activities of the quinoa, germinated sweet lupin, fenugreek, and yellow maize, and they were used to develop gluten-free multigrain pan breads. A total of four different grain blend formulations were used to develop the pan bread. The textural properties, color, and sensory evaluation of the developed multigrain pan bread were also determined. The results of the present study showed a significantly higher fat content was found in germinated lupin (13.56%) and quinoa (12.76%), followed by germinated fenugreek and yellow maize (9.68% and 4.67%, respectively). The results indicated that the development of multigrain pan bread with fortification of quinoa, germinated lupin, germinated fenugreek, and yellow maize imparted significant improvement in the nutritional content. Therefore, it could be recommended that the addition of up to 15% of germinated lupin and fenugreek, 60% quinoa, and 10% yellow maize does not negatively affect the sensory characteristics and quality attributes of pan bread.

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

The Application of Molecular Techniques for Assessment of SOX2 and miR126 Expression as Prognostic Markers in Esophageal Carcinoma

Objective

To study the problem in esophageal cancer, the function of SOX2 and miR-126 has not been completely explored. The objective of this study was to find out how SOX2 and miR-126 act in esophageal cancer and their relation to the clinical and prognostic features.

Methods

The expression of SOX2 and miR-126 was properly assessed in the carcinoma of the esophagus, and the nearby healthy tissues surgically excised from 35 included patients.

Results

SOX2 was elevated in esophageal cancer relative to normal tissues contrary to the miR-126 levels. This inverse relationship was linked to adverse clinical features.

Background

SOX2 has been involved as an oncogene in various types of malignant tumors; microRNA-126 (miR-126) is extensively expressed in vascular endothelial cells, which control angiogenesis. Furthermore, many published reports reasonably concluded that based on the prime characteristic of malignant cells, miR-126 may act appropriately as a promotor or a suppressor for the malignant growth.

Conclusion

In esophageal cancer, SOX2 works as an oncogene, whereas miR-126 acts as a tumor suppressor gene. SOX2 overexpression and miR-126 downregulation were shown to be linked to a poor prognosis.

The Application of Magnetic Resonance Imaging in Skeletal Age Assessment

METHOD

The study includes 80 patients identified from an endocrine clinic, two males and two females from each of 5 age groups (<5, 5 to 7, 8 to 10, 11 to 13, and 14 to 16 years). Skeletal age as determined from an open MRI scanner and radiographs performed on the same day was compared for each child. Two observers assess the skeletal age from radiographs and MRI images independently. After a period of at least three weeks, observers determined the skeletal age of all patients independently. All of the images were in different and random orders, on both of the assessment occasions. The agreement was assessed using the interclass correlation coefficient and Bland Altman plots. Problem Statement. The recurrent use of left-hand radiography in children with chronic conditions might result in the patient being exposed to the same image several times throughout the course of their lives. Use of radiation-free methods such as magnetic resonance imaging (MRI) may be able to assist in reducing the risks associated with radiation exposure, if done properly.

RESULTS

Patients' age ranged from 3 to 16 years, in which the mean of the chronological age was 9.3 years (±2.9) and 9.8 years (±2.7) in girls and boys, respectively. The interrater agreement for skeletal age determination was 0.984 for radiographs and 0.976 for MRI scans. Using the G&P technique, for Observer 1, intraobserver agreement for radiographs and DXA was 0.993 and 0.983, respectively, and 0.995 and 0.994, respectively, for Observer 2. Plotting the rater readings against the line of equality shows no significant differences between readings acquired from radiographs and MRI scans.

CONCLUSION

For the study contribution, it is possible to employ open compact MRI to determine the skeletal age of a person. Our results showed that left-hand MRI scans were of better quality than the radiographs.