Separation of Amino Acids, Dyes, and Pigments Using Novel Pressurized Circular TLC Assembly for Secure Medical Imaging Applications.

A novel pressurized flow system for circular thin-layer chromatography (PC-TLC) has been successfully established and employed for the separation of amino acids, dyes, and pigments for safe medical imaging applications. In this system, the mobile phase is applied to a regular TLC plate through the tube and needle of an intravenous infusion set. The needle was fused in a hole underneath the center of the plate, while the second side end of the tube was connected to a microburette containing the solvent. This new assembly proved itself better in terms of separation time (within 5 minutes) and controlled flow of the solvent and horizontal movement of analyte components over chromatograms with better separation and R f values (glutamine: 0.26, valine: 0.44, phenylalanine: 0.60, chlorophyll a: 0.52, chlorophyll b: 0.43, xanthophyll: 0.18, carotenoid: 0.97, and pheophytin: 0.60) when a number of samples of amino acids, dyes, and pigments were separated by the developed apparatus and the conventional TLC procedure. The developed method was found distinctly rapid, precise, and eco-friendly (less solvent consuming) as compared to traditional ascending TLC.

Analysis of modern circulation industry development level using industrial structure mechanism.

This study focuses on China's industrial transformation and urban income inequality. It is shown that between 2011 and 2020, improvements in China's industrial structure have a significant positive influence on lowering income gaps between urban and rural areas when used in conjunction with the empirical research approach. The mechanical study shows that the urban population impacts this causation. Rural-to-urban economic gaps have been reduced through modernisation in different parts of the country. The result remains the same even if the urban-rural consumption gap is used as a proxy for income discrepancy. The mechanism for the industrial structure upgrading model (MISUM) is proposed in this article for the modern circulation industry. Key contributions include: (1) environmental rules in these components have no impact on each other, but the updating of industrial buildings indicates a substantial location-specific dependence; (2) environmental standards have impacts on industrial structures throughout provinces; and (3) environmental standards have a long-term qualifying impact on the industrial structures. This essay focuses on combining environmental regulation with industrial expansion in different regions. In this study, government environmental requirements for industrial structural improvements are shown to be in operation. The test results show the MISUM has been described with high accuracy of 94.2%, carbon emission level of 18%, soil emission level of 11% and efficiency ratio of 97.8% compared to other methods.

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks.

The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R 2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R 2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks.

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases.

Fuzzy parameterized fuzzy hypersoft set (Δ-set) is more flexible and reliable model as it is capable of tackling features such as the assortment of attributes into their relevant subattributes and the determination of vague nature of parameters and their subparametric-valued tuples by employing the concept of fuzzy parameterization and multiargument approximations, respectively. The existing literature on medical diagnosis paid no attention to such features. Riesz Summability (a classical concept of mathematical analysis) is meant to cope with the sequential nature of data. This study aims to integrate these features collectively by using the concepts of fuzzy parameterized fuzzy hypersoft set (Δ-set) and Riesz Summability. After investigating some properties and aggregations of Δ-set, two novel decision-support algorithms are proposed for medical diagnostic decision-making by using the aggregations of Δ-set and Riesz mean technique. These algorithms are then validated using a case study based on real attributes and subattributes of the Cleveland dataset for heart-ailments-based diagnosis. The real values of attributes and subattributes are transformed into fuzzy values by using appropriate transformation criteria. It is proved that both algorithms yield the same and reliable results while considering hypersoft settings. In order to judge flexibility and reliability, the preferential aspects of the proposed study are assessed by its structural comparison with some related pre-developed structures. The proposed approach ensures that reliable results can be obtained by taking a smaller number of evaluating traits and their related subvalues-based tuples for the diagnosis of heart-related ailments.

Effects of short-term exposure to the heavy metal, nickel chloride (Nicl2) on gill histology and osmoregulation components of the gray mullet, Mugil cephalus.

The gray mullet, Mugil cephalus is an inshore and bottom-feeding fish species of Oman sea. Therefore, the gray mullet may be more exposed to heavy metal contamination, as the toxic impacts of heavy metals mullet has been reported in various studies. This study was conducted to evaluate the toxic effects of the heavy metal, nickel (as NiCl2) on osmoregulation of the gray mullet by measuring blood biochemicals, hormones, minerals and gill histology. Fish (10 fish/tank) were experimentally exposed to NiCl2 at three environmentally relevant concentrations of 5, 10 and 15 µg/l for 96 h. Then, fish were challenged with seawater (35 mg/l) for a period of 120 min. The samples (blood and gill tissue) were collected After 96 exposure to NiCl2 and during salinity challenge (30, 60 and 120 min post challenge). The plasma levels of cortisol and glucose significantly increased in NiCl2-exposed fish. In addition, cortisol increased in all experimental groups 30 min after salinity challenge and then returned gradually to the same levels as the control at 120 min post salinity challenge (PSC). The triiodothyronine (T3) and thyroxine (T4) levels significantly decreased in response to 10 and 15 µg/l NiCl2. In all groups, the thyroid hormones significantly elevated at 30 min PSC. After 30 min PSC, T3 levels in all NiCl2-exposed fish and T4 in the treatment, 10 µg/l NiCl2 remained unchanged throughout the salinity challenge. In the treatment, 5 µg/l NiCl2, T4 levels were recovered at 120 min PSC and reached the same levels as the control. Exposure of fish to high concentrations of NiCl2 and salinity stress increased the lactate levels. However, lactate levels in 5 and 10 µg/l NiCl2 groups were recovered at 120 min PSC and reached the same levels as the control. Furthermore, plasma protein increased in response to 10 and 15 µg/l NiCl2. At 30 PSC, the protein levels decreased in control and 5 µg/l NiCl2 group, while it remained unchanged in fish exposed to 10 and 15 µg/l NiCl2 throughout the salinity challenge. Exposure of fish to NiCl2 disrupted the electrolyte (Na+, Cl-) balance both before and after salinity challenge, which may be due to gill lesions induced by the heavy metal and following alternations in gill permeability. However, fish in 5 µg/l NiCl2 re-established the ionic balance in the blood at the end of salinity challenge period. The malondialdehyde (MDA) levels significantly increased in response to 10 and 15 µg/l NiCl2. The MDA levels returned to the same levels as the control group at 120 min PSC. The results of the present study showed that nickel-induced toxicity (especially at high concentrations) can reduce the osmoregulation capabilities of mullet. However, fish are able to recover from the toxic effects over time, if contamination be eliminated.

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data.

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient's health condition remotely. Every layer has specific functionality in the COVID-19 symptoms' monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management.

Water resources management is one of the most important issues nowadays. The necessity of sustainable management of water resources, as well as finding a solution to the water shortage crisis, is a question of our survival on our planet. One of the most important ways to solve this problem is to use water purification systems for wastewater resources, and one of the most necessary reasons for the research of water desalination systems and their development is the problem related to water scarcity and the crisis in the world that has arisen because of it. The present study employs a carbon nanotube-containing nanocomposite to enhance membrane performance. Additionally, the rise in flow brought on by a reduction in the membrane's clogging surface was investigated. The filtration of brackish water using synthetic polyamide reverse osmosis nanocomposite membrane, which has an electroconductivity of 4000 Ds/cm, helped the study achieve its goal. In order to improve porosity and hydrophilicity, the modified raw, multi-walled carbon nanotube membrane was implanted using the polymerization process. Every 30 min, the rates of water flow and rejection were evaluated. The study's findings demonstrated that the membranes have soft hydrophilic surfaces, and by varying concentrations of nanocomposite materials in a prescribed way, the water flux increased up to 30.8 L/m2h, which was notable when compared to the water flux of the straightforward polyamide membranes. Our findings revealed that nanocomposite membranes significantly decreased fouling and clogging, and that the rejection rate was greater than 97 percent for all pyrrole-based membranes. Finally, an artificial neural network is utilized to propose a predictive model for predicting flux through membranes. The model benefits hyperparameter tuning, so it has the best performance among all the studied models. The model has a mean absolute error of 1.36% and an R2 of 0.98.

Cloud based framework for diagnosis of diabetes mellitus using K-means clustering.

Diabetes mellitus is a serious health problem affecting the entire population all over the world for many decades. It is a group of metabolic disorder characterized by chronic disease which occurs due to high blood sugar, unhealthy foods, lack of physical activity and also hereditary. The sorts of diabetes mellitus are type1, type2 and gestational diabetes. The type1 appears during childhood and type2 diabetes develop at any age, mostly affects older than 40. The gestational diabetes occurs for pregnant women. According to the statistical report of WHO 79% of deaths occurred in people under the age of 60, due to diabetes. With a specific end goal to deal with the vast volume, speed, assortment, veracity and estimation of information a scalable environment is needed. Cloud computing is an interesting computing model suitable for accommodating huge volume of dynamic data. To overcome the data handling problems this work focused on Hadoop framework along with clustering technique. This work also predicts the occurrence of diabetes under various circumstances which is more useful for the human. This paper also compares the efficiency of two different clustering techniques suitable for the environment. The predicted result is used to diagnose which age group and gender are mostly affected by diabetes. Further some of the attributes such as hyper tension and work nature are also taken into consideration for analysis.

Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks.

In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions.

Willingness to Adopt Telemedicine in Major Iraqi Hospitals: A Pilot Study.

The Iraqi healthcare services are struggling to regain their lost momentum. Many physicians and nurses left Iraq because of the current situation in the country. Despite plans of calling back the skilled health workforce, they are still worried by the disadvantages of their return. Hence, technology plays a central role in taking advantage of their profession through the use of telemedicine. Studying the factors that affect the implementation of telemedicine is necessary. Telemedicine covers network services, policy makers, and patient understanding. A framework that includes the influencing factors in adopting telemedicine in Iraq was developed in this study. A questionnaire was distributed among physicians in Baghdad Medical City to examine the hypothesis on each factor. The Statistical Package for the Social Sciences was utilized to verify the reliability of the questionnaire and Cronbach's alpha test shows that the factors have values more than 0.7, which are standard.