International Journal of Experimental Research and Review

African Swine Fever Disease Risk Assessment Using Multi-Criteria Decision Analysis: An Input for GIS-Based Risk Mapping

Grace Dipiao Bulawit, Thelma Domingo Palaoag, Benjamin Enriquez Bulawit Jr — Sun, 30 Jun 2024 00:00:00 +0000

African Swine Fever (ASF) has severely impacted the Philippines' pig industry, caused socioeconomic losses, and affected income, supply, demand, and prices. There needs to be more understanding regarding the risk factors associated with ASF introduction into pig farms and the level of risk each farm faces. Accordingly, using a quantitative research method, this study used the Analytical Hierarchy Process (AHP) to categorize ASF risk factors into biosecurity and spatial risk factors. Twenty-five (25) respondents were selected using a purposive participatory approach to rank the importance level of each risk factor as per the two risk factor categories. The AHP analysis revealed that the highest-risk biosecurity factors are the "absence of protocols for changing clothes, separate entry and exit, disinfection of objects, restriction on food introduction, and external individuals accessing the farms." In the spatial risk factor ranking, the analysis showed that the “distance to pig farms utilizing swill feeding" was the highest risk factor, indicating its significant contribution to the overall risk of farms. A farm risk assessment was also performed based on the AHP results and the level of compliance of each farm on the different risk factors. The study was conducted on selected pig farms in the municipality of Echague by evaluating their compliance with the identified risk factors and determining the level of risk they posed. The risk assessment results for African swine fever on farms reveal a concerning scenario. With 70% of farms assessed as "high risk" in terms of biosecurity and 74% classified as "medium risk" in spatial vulnerability, the overall assessment indicates that 84% of farms are at a moderate risk level. This suggests a widespread need for improved biosecurity practices and disease monitoring to prevent the introduction and spread of African swine fever. The 16% of farms deemed "high risk" pose a significant threat, requiring immediate action to prevent disease outbreaks. These findings emphasize the importance of implementing stringent biosecurity measures, enhancing surveillance efforts, and raising awareness to safeguard the pig industry from the devastating impacts of the ASF disease, like the rising cost of meat and pork-based commodities. Furthermore, these show the importance of considering the biosecurity and spatial risk factors for a more comprehensive risk assessment. The AHP ranking and risk assessment process is crucial in developing a GIS-based risk mapping and surveillance system. This offers government authorities a valuable decision-making tool to proactively prevent the introduction of African swine fever (ASF) and mitigate the necessity for widespread culling of pigs. By implementing targeted interventions informed by the study results, the government can work towards safeguarding the pig industry.

A FTIR Evident-Based Exploration of the Antioxidant Activity of Five Threatened Cactus Species

Sun, 30 Jun 2024 00:00:00 +0000

Cacti, members of the botanical family Cactaceae, comprise approximately 127 genera and approximately 1,850 known species within the Caryophyllales order. Presently, various anthropogenic activities are causing the endangerment of several cactus species. Among the reasons cited for this threat, the aesthetic and medicinal values of cacti have garnered notable attention. This study aims to explore the medicinal potential, particularly in terms of antimicrobial, antioxidant, and phytochemical properties, of five threatened cactus species listed by the International Union for Conservation of Nature (IUCN) and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES): Micranthocereus estevesii, Euphorbia lactea, Haageocereus crestata, Ferocactus acanthodes and Mammillaria huitzilopochtli. A Fourier-transform infrared (FTIR) analysis was conducted to substantiate and corroborate the findings. Notably, no prior studies have investigated the medicinal properties of these five species, underscoring the novelty of our research. Initially, specimens of the five cacti were collected from the Regional Plant Resource Center, Bhubaneswar, air-dried, and milled into powder. Phytoconstituents were then extracted individually using polar (water) and non-polar (methanol) solvents. The antimicrobial properties were assessed using agar well diffusion assays against Candida albicans, Escherichia coli and Staphylococcus aureus. Results indicated that methanol extracts of Micranthocereus estevesii and Euphorbia lactea inhibited Candida albicans, while aqueous extracts of Micranthocereus estevesii and Ferocactus acanthodes inhibited Escherichia coli and Staphylococcus aureus. Methanol extracts exhibited superior antioxidant activity compared to aqueous extracts. FTIR spectroscopy revealed distinctive peak values representing various functional groups in the extract components, including alcohols, carboxylic acids, phenols, aldehydes, alkanes, alkenes, ketones, aromatics, aliphatic amines, primary amines, ethers, alkyl halides, and esters. Both aqueous and methanolic extracts demonstrated promising antibacterial efficacy among the five cactus species studied, suggesting their potential application in pharmaceuticals and medication development. However, habitat degradation and illegal commerce pose significant threats to these species, emphasizing the urgent need for conservation efforts.

E-commerce Adoption and Sustainability with SMEs – An Advanced Bibliometric Analysis

Suraj Kumar, K Francis Sudhakar — Sun, 30 Jun 2024 00:00:00 +0000

Integrating technology, innovation, and entrepreneurship has been pivotal in reducing business costs and fostering global connections between producers and consumers. E-commerce has become a fundamental part of the modern business landscape, particularly for small and medium-sized enterprises (SMEs). With its unique and innovative approach of conducting a bibliometric analysis of the existing research on e-commerce adoption and sustainability within small and medium-scale industries, this study aims to fill a gap in the current literature. The analysis focuses on identifying critical success factors, assessing the relationship between SME characteristics and e-commerce adoption, and examining the organisational implications of e-commerce in SMEs. The findings of this study, presented with all the scientific data, offer insights that can shape the future of SMEs in e-commerce adoption. Bibliographic data was collected through the Web of Science and Scopus databases between 1999 and May 2024. Both databases were combined using the R-studio application, and Biblioshiny was used for the bibliometric analysis. The bibliometric study of data from 1187 related scientific publications was performed using a structured search algorithm in Scopus and Web of Science databases. Research on adoption factors in specific industries has not been extensively explored, making it a promising area for further research. The implications of this study present a bibliometric analysis of the dimensions of SMEs' e-commerce adoption strategy and propose future research directions. Findings highlight the main authors, publications, and productive countries that can be used for research collaborations. This analysis of e-commerce adoption concludes with some limitations, suggestions, and a research agenda for future research.

Pneumonia Detection through Deep Learning: A Comparative Exploration of Classification and Segmentation Strategies

Sun, 30 Jun 2024 00:00:00 +0000

The Convolution Neural Network (CNN) algorithm is one of the most widely used methods for identifying and categorizing lung cancer. This paper covers the most suitable architecture and CNN algorithms for lung cancer and pneumonia deduction and classification. The main contributions to the diagnosis and classification of lung cancer with four steps are Nonlinear transfer learning framework (NLTF), Hierarchical Feature Mapping (HFM), Lifelong Partial Dissection (LPD), and Deep Lifelong Convolutional Neural Network (DLCNN). The application of non-local total fuzzy (NLTF) filtering removes various categories of noise after lung CT imageries and enhances cancer areas. The application of Hybrid Fuzzy Morphology (HFM) constructed segmentation to minimize the region of interest (ROI) for cancer using morphology opening and closing processes. Extraction of traits unique to each disease employing Lung Parenchyma Division (LPD) and extraction of deep seismic features using the Geometric Optimal Algorithm (GOA). Training and testing the proposed Deep Learning Convolutional Neural Network (DLCNN) model using the extracted features to classify benign, malignant lung cancers and Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia.

Predicting Student Academic Performance Using Neural Networks: Analyzing the Impact of Transfer Functions, Momentum and Learning Rate

Mini Agarwal, Bharat Bhushan Agarwal — Sun, 30 Jun 2024 00:00:00 +0000

Artificial Neural Networks (ANN) demonstrate a compelling application of AI in predicting student performance, a critical aspect for both students and educators. Accurate forecasting of student achievements enables educators to monitor progress effectively, allowing educational institutions to optimize outcomes and improve student results. This study focuses on leveraging ANN for predictive analytics in student performance. Through a detailed evaluation of transfer functions, optimizers, learning rates, and momentum values, the model achieves an impressive 98% accuracy with specific configurations: a learning rate of 0.005, momentum of 0.7, Sigmoid transfer function, and SGD optimizer. Additionally, the study performs a comparative analysis of various Machine Learning Algorithms, including ANN, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), and Logistic Regression (LR). Using data from 689 B.Tech students at IP University, the analysis reveals that ANN outperforms other algorithms with an accuracy of 97%. This high accuracy demonstrates the potential of ANN in educational settings, providing a valuable tool for educators to enhance student performance and outcomes.

An Approach for Efficient and Accurate Phishing Website Prediction Using Improved ML Classifier Performance for Feature Selection

Anjaneya Awasthi, Noopur Goel — Sun, 30 Jun 2024 00:00:00 +0000

The article discusses the use of machine learning (ML) to combat phishing websites, which are deceptive sites that mimic trusted entities to steal sensitive information. This is why the continued invention of methods of identifying and counteracting phishing threats is beneficial. Such attacks pose significant risks to the integrity of online security. To enhance the success rate and specificity of predicting phishing websites, this study proposes a new approach that utilizes machine learning algorithms. To enhance the methods mentioned above and achieve better results in classification and better prediction of customer behaviour, the main points exposed to further transformations are increasing classifier accuracy and selecting an optimal feature space. Traditional anti-phishing strategies like blacklisting and heuristic searches often have slow detection times and high false positive rates. The article introduces a novel feature selection method to extract highly correlated features from datasets, thereby enhancing classifier accuracy. Using six feature selection techniques on a phishing dataset, it evaluates eight classifiers, including SVM, Logistic Regression, Random Forest, and others. The study finds that the Random Forest classifier combined with the Chi-2 feature selection method significantly improves model accuracy, achieving up to 96.99%.

Impact of Agronomic Zinc Biofortification on Yield Attributes, Yield and Micronutrient Uptake of Rice (Oryza sativa L.) in Southern Odisha

Swarnajit Pal, Tanmoy Shankar, Sitabhra Majumder, Rahul Adhikary, Subhajit Pal — Sun, 30 Jun 2024 00:00:00 +0000

Cereal crops are low in micronutrients primarily due to Iron and Zinc deficiency in soil. Iron, being the cofactor of various enzymes, performs basic functions in the human body, while its absence causes anaemia. Symptoms of Zn-deficiency appearing in the human body includes retarded growth, hypogonadism, immune dysfunction and cognitive impairment. In rice plants, their deficiency results in stunted growth and poor plant development, leading to yield reduction. Consumption of milled rice containing very low levels of iron and zinc, is one of the principal reasons for widespread malnutrition among rice consumers. Health of millions of people around the world, including India, is directly or indirectly affected due to ‘Hidden Hunger’ or ‘Malnutrition’ of iron and zinc. The current study was conducted in the summer season of 2022 at the Post Graduate Research Farm, M.S. Swaminathan School of Agriculture, comprising 8 treatments of zinc (foliar and basal) applications on rice. Influence of these treatments on grain and straw yield of rice was ascertained by measuring Pearson correlation coefficient and different multivariate tests viz., Multiple Regression, Multilayer Perceptron Neural Analysis (MPN) and Principal Component Analysis (PCA), which indicated that grain zinc and iron content, was highly influenced by the zinc application. Analysis of generated data indicated that basal application of 5 kg Zn ha⁻¹ along with foliar application of 0.25% Zn at maximum tillering and at booting stage produced the highest grain yield (6.80 t ha^-1) and superior outcomes on different yield attributes, nutrient uptake and straw yield of hybrid rice as compared to other treatments, (MARVEL 1011) in the soil of Southern Odisha.

Security and Privacy for Smart Transportation Management using Big Data Analytics

Govindasamy R., Shanmugapriya N., Gopi R. — Sun, 30 Jun 2024 00:00:00 +0000

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

Deep Learning Models for Accurate Diagnosis and Detection of Bone Pathologies: A Comprehensive Analysis and Research Challenges

Harshit Vora, Seema Mahajan, Yogesh Kumar — Sun, 30 Jun 2024 00:00:00 +0000

The presence of bone disease has been observed to have a substantial influence on an individual's overall health. There are conventional techniques to detect as well as diagnose them but they often suffer limitations in the form of misdiagnosis because of manual error as well as maximum time consumption. Therefore, it is of utmost importance to accurately and proficiently identify it by integrating conventional methods with advanced artificial intelligence techniques. The objective of this study is to conduct a comprehensive analysis of the present state of research concerning the identification and diagnosis of bone disease using machine learning and deep learning. A review is conducted in accordance with the PRISMA guidelines which focus on the examination of scholarly articles published within the timeframe of 2019 to 2024. This review analyzes peer-reviewed literature and research findings to show how machine and deep learning can improve bone disease diagnosis accuracy. It has been found that in the case of osteoporosis, the highest recall, precision, and F1 score is computed by random forest with 93%, 94%, and 93%, respectively while as advanced CNN technique computed 98% accuracy for osteoporosis and 98.4% accuracy, 95% sensitivity as well as 97% specificity for osteonecrosis. Likewise, for bone tumor and osteoarthritis, AlexNet achieved 98% and 98.90% accuracy, respectively. The study introduces a novel approach to the diagnosis of bone diseases by emphasizing the usage of advanced learning techniques over conventional methods. Additionally, the paper highlights the significance of analyzing the clinical or imaging data and extracting features to improve image quality and provide a pathway toward more accurate and efficient diagnosis of bone diseases. By delving into these techniques, the paper offers valuable insights into enhancing diagnostic capabilities for bone diseases, which ultimately leads to improved patient care and treatment outcomes.

Mitigating SAD States and Maladaptive Coping in Law Enforcement: Enhance Emotional Competence

Abhishek Sharma, Ekta Yaduvanshi, Ankita Sharma, Proshanto Kumar Saha — Sun, 30 Jun 2024 00:00:00 +0000

Mental health is as essential as physical health for law enforcement officers to protect our country and communities. Law enforcement professionals frequently encounter stressors that can lead to maladaptive coping strategies and contribute to elevated levels of stress, anxiety, and depression (SAD). This study aimed to evaluate the emotional competency and coping mechanisms utilized by police officers and their impact on psychosocial challenges within the force. Additionally, the research sought to determine the prevalence and influence of stress, anxiety, and depression (SAD) among Rajasthani police officers. A total of 689 officers participated in the survey. After data cleaning, 681 responses were considered complete and suitable for analysis. The findings reveal a concerning prevalence of anxiety, depression, and stress among police officers, exacerbated by their use of maladaptive coping mechanisms to navigate these challenges. The study emphasizes the importance of prioritizing the psychological health of law enforcement professionals. It advocates for interventions designed to reduce symptoms of stress, anxiety, and depression (SAD) and encourages police officers to adopt healthier coping mechanisms. The use of emotional coping techniques has been shown to significantly reduce SAD among police officers. These findings underscore the need to address emotional competence as a fundamental component of police officer training and support systems. By equipping law enforcement professionals with the necessary skills to navigate stressors and build resilience, organizations can promote the psychological well-being of officers and enhance overall job performance and satisfaction. This study emphasizes the imperative of investing in the mental health of law enforcement officers and calls for concerted efforts to bolster their overall well-being and optimize their performance in safeguarding communities.

Ethno Medicinal, Phyto-Chemical and Physico-chemical Characterization of Selected Endangered Medicinal Plants of Indravati National Park, Bijapur, Chhattisgarh, India

Sharda Darro, Naureen Shaba Khan — Sun, 30 Jun 2024 00:00:00 +0000

Medicinal plants are beneficial for curing several ailments among traditional healers, indigenous people, local practitioners and forest dwellers. If harnessed, traditional knowledge of the ethnomedicinal plants can play a pivotal role in drug discovery and development. In many cases a single medicinal plant can be of multiple uses also, different plant parts of the same plant can be utilized for more than one disease condition. The Ethno-botanical, Phytochemical and physico-chemical characterization of five selected medicinal plants viz., Nyctanthes arbor – tristis, Andrographis paniculata, Cissus quadragularis, Plumbago zeylania and Costus specious is an essence and has been carried out to assess bioactive potential and to establish traditional belief in the light of scientific interpretation. Fresh leaves were collected from Indravati National Park, Bijapur, Chhattisgarh, India, dried and powdered for phytochemical and Proximate, ultimate, and compositional analysis. Study revealed that the highest concentration of Ash Content (18.513%), Moisture Content (8.56%), Carbon content (48.77%), Hydrogen Content (24.490%), Nitrogen Content (23.860%) was observed to occur in Plumbago zeylanica leaf than other experimental plants. In Cissus quadrangularis, the percentage composition of fat content (0.230%), extractive content (1.05%), Lignin Content (5.53%) was higher than others. Fat content (0.230%), Moisture Content (8.56%), Vit. C content (64.63%) and Oxygen contents (36.655 %) were observed to be higher in Nyctanthes arbor-tristis leaf than others. The concentrations of Crude Fibre (14.49 %), Moisture Content (8.56%), Protein content (12.16%), Carbon content (75.66 %), Cellulose content (47.63 %) were observed to be highest in Costus speciosus species than others. Carbon content (48.77%) and Hydrogen Content (24.490%) were higher in Andrographis paniculata than in others.

Detection of Pleuro Pulmonary Blastoma using Machine Learning Models

Raswitha Bandi, T. Santhisri — Sun, 30 Jun 2024 00:00:00 +0000

Pleura Pulmonary Blastoma (PPB) is a type of lung cancer seen in children. PPB needs to be detected earlier when treating children. The mortality rate of PPB is higher if left untreated. It can be detected from CT images through various machine learning and classification algorithms. The earlier detection of PPB can save children's lives, for which several research works have proposed several machine learning models. Several researchers adopt traditional classification algorithms like random forest and decision tree algorithms for detecting PPB. However, these techniques provided lesser accuracy and were difficult for earlier detections. This paper considered several machine learning algorithms like SVM, LR and MP and experimented with CT images and DICER-1 data to understand their betterness and overcome such issues. The architecture of the following algorithms is discussed in detail, and the results are compared. Through this, the ideal machine learning algorithm for detecting PPB is found. All the algorithms are implemented with the Python software, and the performance metrics of the respective algorithms are recorded. The results show that the SVM algorithm provides better accuracy (96%) for the DICER-1 dataset, which is higher than CT images (95.60%).

Systemic Regulatory Abnormalities in Glycemic Control and its Relation with Depression - A Cross-Sectional Study

Aravindh M, Shabeeba Z Kailash, Kailash Sureshkumar — Sun, 30 Jun 2024 00:00:00 +0000

Globally, depression is the 3rd leading cause of disability-adjusted life years. The presence of depression and its symptoms has been associated with improper control of glucose and poor glycemic index and a bidirectional relationship was seen between depressive disorders and diabetes mellitus. Depression, even at subclinical levels, has the tendency to increase the risk of incident type 2 diabetes by 25–60%. This study aims to find the prevalence of Type 2 diabetes mellitus(T2DM) in depression patients and to find the relationship between the severity of depression and the severity of T2DM in a Cross-sectional study. A total of 178 patients who attended psychiatry OPD of a tertiary healthcare hospital for a study period of 6 months were recruited into the study after considering inclusion and exclusion criteria evaluated by an experienced psychiatrist. Details like sociodemographic profiles and clinical data related to Major depression and T2DM were collected through self-structured proforma. To assess the severity of depression, HAM-D scale was used and the glycemic severity measure was used to assess the severity of T2DM by a well-trained psychiatrist. Statistical analysis was done and results were framed. The prevalence of Type 2 Diabetes mellitus in depressive disorder patients was found to be 25.2%. A significant association was seen between the severity of T2DM and the severity of depression, with a p-value of 0.013 and a chi-square value of 12.699. Significance with certain clinical factors like insulin usage (F=5.635; p = 0.019) and drug compliance (F=16.841; p <0.001) was seen in T2DM patients with depression. Nearly ¼^th of the patients with depression had T2DM and the severity of hyperglycaemia was also found to be high as those patients were on insulin administration, other medical comorbidity disorders were present and drug compliance was found to be poor. Triglyceride glucose index was also found to be high in patients with severe depression, which contributes to improper blood glucose balance. Treating and reducing both the severity of T2DM and depression simultaneously is needed to improve the functioning of the patients and improve the retention in treatment and drug compliance, thereby preventing morbidity and mortality.

Effectiveness of an Information Booklet on the Awareness of Antimicrobial Resistance (AMR) among Health Care Professionals in a Selected Primary Care Hospital

Sun, 30 Jun 2024 00:00:00 +0000

The study aimed to assess the level of knowledge on Antibiotics and its resistance among healthcare professionals and the effectiveness of an information booklet, which includes the Centers for Disease Control and Prevention (CDC) guidelines for to use of Antibiotics to prevent Antimicrobial resistance (AMR). AMR occurs when bacteria change over time and no longer respond to medicines. Antimicrobial resistance leads to higher medical costs, prolonged hospital stays and increased mortality. Knowledge of Antibiotics is essential in preventing from ill effects of AMR. According to the CDC, in 2018, inappropriate prescribing of antibiotics contributed to antibiotics-resistant conditions, which led to 2 million infections and killed about 23,000 Americans annually. In India, 4.95 million people died in 2019 suffered from drug-resistant infections and antibiotic resistance was the direct cause for 1.27 million of those deaths. An experimental research design employed non probability convenient sampling technique to select the 60 samples in a selected primary care hospital. The majority, 44 (73%), of health professionals were between the ages of 21 and 30 years, 40(67%) were nurses, 13(22%) were medical practitioners and 7(11%) were pharmacists. While considering their area of work majority 20(33%) of them, were from the ward and 14(23%) from the ICU setting. Level of knowledge gain score measured using Extended McNemar`s chi-square test revealed health professionals gained 20.27% with 95% CI and it was found statically significant (c2=43.81 P=0.001***). The study results recommend such an interdisciplinary approach of training doctors, nurses and pharmacists to become sensitive and update themselves to reduce the incidence of Antimicrobial resistance among the common public.

A Cross-Sectional Study to Analyze the Physical and Cognitive Fatigue Due to Sleep Disruption Among Shift Workers in Tamilnadu

Vimala Rani Swaminathan, Archana Rajasundaram, Sasikumar Santhoshkumar — Sun, 30 Jun 2024 00:00:00 +0000

The objective of this research is to analyse the extent and manner of the kind of fatigue among shift workers in Tamil Nadu, India. As for shift workers, they often have disturbed night’s sleep. Shift work is distinguished by its irregular and unique work hours, unlike the regular day-time Schedule. It is combined with circadian alteration, which affects most of the bodily activities. They have diverse sorts of health problems, such as physical and mental fatigue. A Cross-sectional Analysis was done among airport workers. 200 participants were randomly selected for the study aged 35-45 years and conducted for a duration of 6 months in the Airport, Tamilnadu. Sleep quality and cognition were assessed using a validated questionnaire. The muscle fatigue was evaluated using Mosso’s ergograph. R-statistical Software (version 4.0.2) was used for Data Analysis. The Primary analyses of data set’s normality were done by Kolmogorov - Smirnov Z test. Significant negative correlation exists between global PSQI score and both cognition score (r= -0.315, P<0.001) and Work done by Mosso (r= -0.405, P<0.001) There is also negative correlation exists between work done and the cognition score (r= - 0.565, P<0.001) which is statistically significant. Decreased Sleep Quality, more fatigue and lowering of cognition levels not only affects physical health but also the quality of life. It has been noted that sleep disturbance leads to physical and mental exhaustion among Tamil Nadu shift workers. The outcomes underscore the need for targeted interventions to improve sleep-related behaviours as well as the treatment of fatigue in this population. Reducing the disturbances of shift work effects on produce, growth as well as health may be authorized by way of implementing organized work shifts and also promoting sleep promotion.

Impact of Pulmonary Rehabilitation on Fatigue and Quality of Life in Patients of Interstitial Lung Disease

Sun, 30 Jun 2024 00:00:00 +0000

Individuals with interstitial lung diseases (ILDs) often experience debilitating fatigue, impacting their quality of life. Pulmonary rehabilitation (PR), comprising tailored exercise, education, and behaviour change, aims to improve physical and psychological conditions in ILD patients. Evaluating fatigue and quality of life pre- and post-pulmonary rehabilitation provides insights into its effectiveness in enhancing patient outcomes. Following ethical approval, an exploratory cross-sectional study was conducted at the Department of Respiratory Medicine, Chettinad Hospital and Research Institute. The study enrolled 73 ILD patients admitted between August 2022 and August 2023. Comprehensive pulmonary function assessments and medical histories were obtained from reliable sources and were assessed with questionnaires like the Fatigue Assessment Scale (FAS) for fatigue and St. George Respiratory Questionnaire (SGRQ) for quality of life before and after a 6-month individualized home-based pulmonary rehabilitation program. Seventy-three (73) patients were enrolled in the study. The age group with the highest number of patients was between 50 – 60 years (40%), with a male predominance of 60 % (44). Independent samples of different tools in the research and their mean values were compared using the T-test. This study illustrates the considerable link between the ILD cases for FAS and SGRQ with statistically significant differences in FAS and SGRQ between time durations, at 3 and 6 months duration, indicating improvement in fatigue and quality of life in those patients at a 3 and 6-month duration (p <0.001). The assessment showed a significant decline in fatigue levels (p<0.001) and a marked betterment in the overall quality of life (p<0.0066) among patients after participating in Pulmonary Rehabilitation when compared with FVC severity. These findings underscore the importance of integrating rehabilitation programs into the comprehensive care of these patients to improve their overall health and well-being.

Apoptosis: From Oncogenesis to Oncotherapy

Kalpataru Halder — Sun, 30 Jun 2024 00:00:00 +0000

Cell death is critical in maintaining the balance between cell proliferation and elimination in all living organisms. Among the different modalities of regulated cell death, apoptosis remains the most extensively studied and interesting pathway for targeted carcinogenesis therapy. Dysfunctions in apoptotic pathways contribute to the development and progression of cancer, and targeting these pathways is essential for effective cancer therapy. This review provides an overview of different types of apoptotic pathways and their significance in the development and progression of cancer. We also discuss the present oncotherapy strategies targeting different cell death pathways and mechanisms and the challenges associated with apoptosis-based therapies. This review highlights the need for the development of 3-D cellular models to study the interaction between tumor cells and their microenvironment, reduced in-vivo toxicity, and increased specificity for certain drugs targeting p53 or inhibitor of apoptosis proteins (IAPs). Overall, this review provides a comprehensive understanding of the significance of apoptosis in oncogenesis and oncotherapy and the potential of targeting apoptotic pathways for effective cancer treatment.Cell death is critical in maintaining the balance between cell proliferation and elimination in all living organisms. Among the different modalities of regulated cell death, apoptosis remains the most extensively studied and interesting pathway for targeted carcinogenesis therapy. Dysfunctions in apoptotic pathways contribute to the development and progression of cancer, and targeting these pathways is essential for effective cancer therapy. This review provides an overview of different types of apoptotic pathways and their significance in the development and progression of cancer. We also discuss the present oncotherapy strategies targeting different cell death pathways and mechanisms and the challenges associated with apoptosis-based therapies. This review highlights the need for the development of 3-D cellular models to study the interaction between tumor cells and their microenvironment, reduced in-vivo toxicity, and increased specificity for certain drugs targeting p53 or inhibitor of apoptosis proteins (IAPs). Overall, this review provides a comprehensive understanding of the significance of apoptosis in oncogenesis and oncotherapy and the potential of targeting apoptotic pathways for effective cancer treatment.

Securing the Data Using an Efficient Machine Learning Technique

Pinkal Jain, Vikas Thada — Sun, 30 Jun 2024 00:00:00 +0000

More accessible data and the rise of advanced data analysis contribute to using complex models in decision-making across various fields. Nevertheless, protecting people’s privacy is vital. Medical predictions often employ decision trees due to their simplicity; however, they may also be a source of privacy violations. We will apply differential privacy to this end, a mathematical framework that adds random values to the data to provide secure confidentiality while maintaining accuracy. Our novel method Dual Noise Integrated Privacy Preservation (DNIPP) focuses on building decision forests to achieve privacy. DNIPP provides more protection against breaches in deep sections of the tree, thereby reducing noise in final predictions. We combine multiple trees into one forest using a method that considers each tree’s accuracy. Furthermore, we expedite this procedure by employing an iterative approach. Experiments demonstrate that DNIPP outperforms other approaches on real datasets. This means that DNIPP offers a promising approach to reconciling accuracy and privacy during sensitive tasks. In DNIPP, the strategic allocation of privacy budgets provides a beneficial compromise between privacy and utility. DNIPP protects privacy by prioritizing privacy concerns at lower, more vulnerable nodes, resulting in accurate and private decision forests. Furthermore, the selective aggregation technique guarantees the privacy of a forest by combining multiple data points. DNIPP provides a robust structure for decision-making in delicate situations, ensuring the model's effectiveness while safeguarding personal privacy.

Evaluation of Pulmonary Status of Post-Tuberculosis Patients with Spirometry and Chest X-Ray

Akhil Paritala, Muthukumaran Lakshmanan, Meenakshi Narasimhan, Sridhar Rathinam — Sun, 30 Jun 2024 00:00:00 +0000

In 2022, 7.5 million new cases of tuberculosis were reported worldwide. Mycobacterium tuberculosis results in tuberculosis, an infectious disease mostly affecting the lungs. However, many completely treated post-tubercular patients experience persistent changes in lung anatomy (bronchial and parenchymal structure), Increasing their risk of lung complications and early death. These changes affect the airway's size, leading to higher resistance and decreased airflow. Our study aimed to assess the overall clinical status and lung function of treated post tuberculosis patients using spirometry. The study constituted patients over the age of 18 who presented to the Outpatient department of the Pulmonary Medicine Department, Chettinad Hospital and research institute, Kelambakkam, after receiving complete treatment and being certified cured. Convenience sampling technique was used, 87 patients participated in this trial. The Institutional Ethical Committee approved the study, which lasted 18 months. A proforma was used to collect a complete socio-demographic history and clinical history, particularly in terms of pulmonary symptomatology, and information about previous anti-tb treatment. Each of these patients had chest radiography, smear microscopy, and lung function testing. Mean age was 44.1± 15.2 years, mean BMI was 22.27 ± 3.66 kg/m2. There were 66.2% men and 33.8% females. 42% employed and 52% literate. In the current study, 41(53.25%) of the individuals reported dyspnea and 22(28.57%) had dry cough, cough with expectoration 12(15.58%), Fever 8(10.4%), Haemoptysis 7(9%), chest pain 2(2.6%). Following Post tuberculosis treatment, 38(44%) had normal chest radiographs, 31(35.6%) of the patients had fibrosis/Fibrotic strand, 8(9.1%) had consolidation, 5(5.7%) had ectatic changes, 3(3.4%) had fibrocavity, 1(1%) had calcification and cicatricial collapse. In the current study, we found that 31(40.25%) had normal spirometry followed by 23(29.87%) had mixed pattern and 12(15.6%) had restrictive pattern findings, 11(14.28%) had obstructive pattern findings. In spite of appropriate suggestions, the majority of post-tb pulmonary impairment individuals suffer in quiet or undergo poor medical care. As a result, comprehensive recommendations for patient follow-up following tuberculosis treatment are required in order to monitor lung function and provide appropriate care to improve quality of life.

Antidiabetic Potency of Flavonoids Using a Systematic Computer-Aided Drug Design Platform

Sun, 30 Jun 2024 00:00:00 +0000

Diabetic mellitus (DM) is a chronic metabolic disorder, with type 2 diabetes (T2DM) being the most prevalent type globally. Despite the availability of several target-specific drugs, the prevalence rate has remained uncontrollable, prompting a systematic exploration of plant secondary metabolites or phytochemicals for mainstream use. Among all natural resources, citrus fruits like oranges, lemons, grapefruits and limes are rich sources of flavonoids and get more attention due to their higher antioxidant, anti-inflammatory and immunomodulatory effects. Additionally, researchers have employed various strategies to locate the most bioactive and drug-able flavonoids from these herbal extracts for use in managing diabetes. Therefore, the present study selected nine citrus-fruit-derived flavonoids and tested their antidiabetic potency using four target enzymes: α-amylase, AKT Serine/Threonine Kinase 1 (AKT1), dipeptidyl peptidase-4 (DPP-IV), and glucose transporter 1 (GLU1) through molecular docking studies. In addition, we have predicted the physiochemical profile, toxicity, bioavailability, lead-likeness, drug-likeness, and lethal dose of flavonoids, along with five standard antidiabetic drugs, to select the most potential candidates. We used AutoDock 4.2 for the docking study, BIOVID-Discovery Studio for the protein-ligand interaction study, SwissADME, ProTox 3.0 and Molsot tools to predict the drug-likeness profile. Individual and average docking scores indicated that naringin (-11.2 and -10.40 kcal/mol) was the most potent flavonoid, and glimepiride (-11.1 and -10.1 kcal/mol) against AKT1 had the most potential among the five antidiabetic drugs. Naringin had non-toxic profiles, a positive drug-likeness score, and ideal physicochemical profiles, which suggested that it might be the best candidate for further testing. To sum up, the computer-aided drug design platform is an important part of the current drug discovery module to accelerate phyto-based drug discovery within limited time and resources.

Secondary Metabolite Fingerprinting and Antioxidant Potency of Eleusine indica: An Integrated Chromatography and In silico Investigation

Sun, 30 Jun 2024 13:52:06 +0000

Natural regimens and constituents serve as the primary sources for drug conservation, and each herbal regimen possesses unique ethnomedicinal and biological properties. Therefore, researchers are currently more interested in exploring the biological activity of existing plant species with the most systematic approach to be used in the specific therapeutics proposed, either crude extracts or bioactive metabolites. The present study also explores the antioxidant potency of ethanolic extracts of Eleusine indica (a weed in an agricultural field) through chemical fingerprinting analyses through high-performance thin-layer chromatography (HPTLC), followed by gas chromatography-mass spectrometry (GCMS) and in silico investigation in a systematic way. Briefly, this investigation aims to observe the presence of secondary metabolites associated with antioxidant activity. After extraction, a fingerprint study was carried out with the mobile phase consisting of ethyl acetate, acetic acid, formic acid, and water in the ratio 10:1.1:1.1:2.3 by volume, where more than eight fluorescent and non-fluorescent bands were found, with the highest density peak area at Rf 0.87 and the lowest at Rf 0.18. As per the GCMS report ethanolic extracts of E. indica are composed of several metabolites. Further, based on the higher Rf value, we have selected seven (EI_1 to EI_7) that constitute molecular docking against three target enzymes: cyclooxygenase-2 (PDB ID: 5F1A), peroxiredoxin-5 (PDB ID: 1HD2), and haemoxygenase 1 (PDB ID: 3CZY) to assess the antioxidant potency. Overall, the results revealed that EI_7 (2,7-dioxatricyclo [4.4. 0.0(3,8)] decane-4,5-diol) showed similar potency with ideal drug-ability profiles similar to ascorbic acid and was considered a lead for further therapeutic use as a complementary agent.