The role of complementary and alternative medicines in general health and immunity

The immune system is a host protection system that includes numerous biological structures and processes in an organism which protects from diseases. It has been showed that there is significant relationship between immune system function and infectious diseases both in animal models and in humans. The aim of this research is to investigate whether Complementary and Alternative Medicines (CAMs) can be useful in boosting immune system to prevent and/or treat the infectious diseases in the early stage of infection. Accordingly, the previous research on this issue is investigated and the results re provided. This study also performs an analysis on the consumers’ reviews on turmeric to find the effectiveness of turmeric intake in improving general health status of patient through WebMD data. The results of this study demonstrated that the majority of consumers are highly satisfied with the use of turmeric in improving their health conditions. It is also found that the majority of patients have used turmeric as the alternative therapies and got positive results in their treatments. In general, the results of this research provided several recommendations on the use of CAMs for infectious diseases and revealed that immune system may be boosted by CAMs and accordingly help in prevention and/or treatment of infectious diseases. However, further evaluations for the use of CAMs through consumers’ experience analysis are needed to come to robust conclusions regarding the benefits of CAM as an alternative medicine for infectious disease such as COVID-19

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment

Smart methods to deal with COVID-19 at university-level institutions using social network analysis techniques

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus’s spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days

Sustainability performance assessment using self-organizing maps (SOM) and classification and ensembles of regression trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment

Photocatalytic response in water pollutants with addition of biomedical and anti-leishmanial study of iron oxide nanoparticles

Public health is a major concern globally, owing to the presence of industrial dyes in the effluent. Nanoparticles with green synthesis are an enthralling research field with various applications. This study deals with investigating the photocatalytic potential of Fe-oxide nanoparticles (FeO-NPs) for the degradation of methylene blue dye and their potential biomedical investigations. Biosynthesis using Anthemis tomentosa flower extract showed to be an effective method for the synthesis of FeO-NPs. The freshly prepared FeO-NPs were characterized through UV/Vis spectroscopy showing clear peak at 318 nm. The prepared FeO-NPs were of smaller size and spherical shape having large surface area and porosity with no aggregations. The FeO-NPs were characterized using XRD, FTIR, HRTEM, SEM and EDX. The HRTEM results showed that the particle size of FeO-NPs was 60–90 nm. The antimicrobial properties of FeO-NPs were investigated against two bacterial Staphylococcus aureus 13 (±0.8) and Klebsiella pneumoniae 6(±0.6) and three fungal species Aspergillus Niger, Aspergillus flavus, and Aspergillus fumigatus exhibiting a maximum reduction of 57% 47% and 50%, respectively. Moreover, FeO-NPs exhibited high antioxidant properties evaluated against ascorbic acid. Overall, this study showed high photocatalytic, antimicrobial, and antioxidant properties of FeO-NPs owing to their small size and large surface area. However, the ecotoxicity study of methylene blue degradation products showed potential toxicity to aquatic organisms

Machine learning-based anomaly detection in NFV: a comprehensive survey

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learningbased algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems

Evaluation of factors to respond to the COVID-19 pandemic using DEMATEL and fuzzy rule-based techniques

Since social and environmental conditions have changed dramatically in recent years, the spectrum of diseases caused by infections is also evolving rapidly. The outspread of COVID-19 has resulted in an emergency situation across the globe with significant effects on the population’s lives, families, and societies, leading to concerns the World Health Organization. Accordingly, the virus has substantially threatened the Malaysians’ public health and contributed considerably to increased healthcare expenses. Since the novel coronavirus was found in China, Malaysia’s government has started its actions according to the World Health Organization procedures and concentrated on addressing and preventing the spread of the infection. The present paper aims to find and evaluate the factors to respond to the COVID-19 outbreak in Malaysia, limiting the outspread of the disease in this country. This study used the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy Rule-Based techniques to evaluate the factors through a set of questionnaires completed by the health care professionals. According to the data analysis results, movement control order, international travel restrictions, and the mass gathering cancellations were of most importance in the prevention of COVID-19 infections transmission

Applying the ecosystem services - EBM framework to sustainably manage Qatar's coral reefs and seagrass beds

Given the current natural and anthropogenic threats facing Qatar's marine environment and the consequential expected decline in ecosystem services, this paper examines the potential application of the Ecosystem Services-EBM framework developed by Granek et al. (2010) to sustainably manage Qatar's coral reef and seagrass bed ecosystems. Using interviews with stakeholders and field-collected data from sixteen coral reef sites and 6 seagrass meadows as well as secondary data, the paper presents new knowledge regarding the status of these ecosystems and the benefits they provide that are most valued by stakeholders. The research identifies existing and missing ecological and socio-economic data, as well as the processes and management strategies required to implement the five-step framework within a Qatari context. Key goals for implementing EBM identified by stakeholders include: adoption of scientific planning and valuation of marine environment, contextualizing and drafting legislation, regulations and policies in support of EBM; monitoring and enforcement of laws; and, promotion of public awareness and engagement. The article concludes with recommendations for filling remaining data gaps and highlights opportunities available to Qatar to become a leader in implementing EBM. These include maximizing the increasing role that stakeholders can play in mitigating further decline of the country's coastal ecosystems and leveraging mega events planned in Qatar, such as FIFA World Cup 2022

Prevalence of Mistreatment or Belittlement among Medical Students – A Cross Sectional Survey at a Private Medical School in Karachi, Pakistan

Background: Mistreatment or belittlement of medical students either by faculty or fellow students has often been reported. Perception of mistreatment has also been associated with increased degree of psychological morbidity. There is a lack of such studies being conducted amongst the medical students of Pakistan. The aim of this study was to determine the prevalence and forms of perceived mistreatment and presence of mental health morbidity in a private medical school in Pakistan. Also, any association between mental health morbidity and mistreatment was to be identified. Methods: A cross sectional study was carried out on medical students from Aga Khan University Hospital, Karachi, Pakistan during the period of June-September 2007. A self administered questionnaire, adapted from Frank et al and Baldwin et al was distributed to a total of 350 students. The questionnaire consisted of three parts: the first dealing with the demographics of the population, the second concerning the various forms of mistreatment, while the third assessed the mental health of students using the General Health Questionnaire 12(GHQ12). Descriptive statistics were performed. The Chi-square test and Fisher\u27s exact tests were applied. Results: A total of 350 students were approached out of which 232 completed the questionnaire giving a response rate of 66.2%. Mistreatment was reported by 62.5% (145/232) of the respondents. Of these, 69.7% (83/145) were males and 54.9% (62/145) were females. There was a significant relationship between gender, year division, stress at medical school and possible use of drugs/alcohol and reported mistreatment but no statistical relationship was seen with psychiatric morbidity. The overall prevalence of psychological morbidity was 34.8% (77/221). Conclusion: This study suggests high prevalence of perceived mistreatment and psychological morbidity among Pakistani medical students. However, no association was found between these two aspects of medical student education. There is a need to bring about changes to make the medical education environment conducive to learning. Increased student feedback, support systems and guidance about progress throughout the year and the provision of adequate learning resources may provide help with resolving both of these issues

Global injury morbidity and mortality from 1990 to 2017: Results from the global burden of disease study 2017

Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care