The predictive model for COVID-19 pandemic plastic pollution by using deep learning method

National trends in dyslipidemia prevalence, awareness, treatment, and control in South Korea from 2005 to 2022

Dyslipidemia has steadily increased in South Korea over the past two decades, emerging as a major public health concern and key risk factor for cardiovascular disease. Thus, our study aimed to investigate long-term trends in the prevalence, awareness, treatment, and control of dyslipidemia in South Korea, including the COVID-19 pandemic. This study utilized data from nationally representative cross-sectional surveys conducted as part of the Korea National Health and Nutrition Examination Survey from 2005 to 2022, analyzing long-term trends of dyslipidemia among 98,396 individuals aged over 30. Weighted linear and binary logistic regression were performed to calculate the β coefficients, βdiff, and weighted odds ratios with 95% confidence intervals (CIs). Weighted odds ratios were computed for various socioeconomic groups using aggregated data from 2005 to 2022. The prevalence of dyslipidemia increased from 41.30% (95% CI 40.40-42.21) in 2005-2009 to 48.41% (47.36-49.47) in 2020-2022. Awareness increased from 17.87% (16.75-18.99) to 48.90% (47.34-50.47), treatment from 7.10% (6.39-7.80) to 38.19% (36.61-39.76), and control among prevalence from 6.49% (5.79-7.19) to 31.82% (30.33-33.32). Treatment (βdiff, 3.94 [1.97-5.92]) and control among prevalence (βdiff, 3.52 [1.67-5.38]) increased more rapidly during the pandemic. Higher odds of dyslipidemia were associated with male sex, older population, rural residence, high BMI, central adiposity, low education and income levels, smoking, and high-risk alcohol consumption. Lower odds of awareness, treatment, and control among individuals with dyslipidemia were associated with male sex, younger population, rural residence, higher education and income levels, smoking, and high-risk alcohol consumption. Over the past 18 years, the prevalence, awareness, treatment, and control of dyslipidemia have steadily increased, with persistent disparities among socioeconomic groups.

Responses of natural plastisphere community and zooplankton to microplastic pollution: a review on novel remediation strategies

The ubiquitous presence of microplastics (MP) in different environments has been well documented. Microplastic contamination has rapidly become a serious environmental issue, threatening marine ecosystems and human health. MP has been reported to accumulate organic pollutants associated with various microbial communities. The MP hazard is specifically serious in urban lakes, near-shore beaches, and benthic sediments. To prevent the further spread of MP and mitigate the increasing level of MP contamination, along with its associated environmental and economic concerns, it is essential to address mitigation strategies and their negative impacts. Contributed by low degradability, hydrophobicity, and sorption potential, the plastic surface acts as an important substrate colonized by several microorganisms known as the plastisphere community. Adaptive responses of the plastisphere community, MP ingestion, and surface modifications by the zooplankton provide insight into novel remediation strategies based on integrated natural community-level approaches. Zooplankton studies are extensive and encompass assessments of their abundance, biomass, distribution, and DNA meta-barcoding. Additionally, zooplankton has been utilized as an indicator in various freshwater environmental policies. Overall, employing zooplankton as an indicator in environmental policies is a vital tool for assessing the health of aquatic ecosystems and can assist in guiding management and conservation efforts. This review summarizes (i) the current literature on the estimation of MP distribution in aquatic environments, (ii) the effects of MP accumulation on the environment and its inhabitants, i.e., the interactions with marine microbiota,, (iii) addresses the bioremediation strategies with an emphasis on microbial degradation, ecological functioning and adaptive responses of marine microbes and finally, (iv) the directions of further research aiming to in situ mitigation of MP pollution. Recent advancements have focused on innovative methods such as membrane bioreactors, synthetic biology, organosilane-based techniques, biofilm-mediated remediation, and nanomaterial-enabled strategies. Nano-enabled technologies show substantial potential to enhance microplastic removal efficiency. Further investigation is necessary to develop advanced treatment technologies that can enhance the removal efficiency of microplastics (MPs) in drinking water. Additionally, more research is needed to understand the toxic impacts of MPs on marine ecosystems, including coral reefs, seagrass beds, mangroves, and other important habitats.

Critical care nurses' experiences on environmental sustainability: A qualitative content analysis

BACKGROUND

Intensive care units (ICUs) are the primary producers of greenhouse gas emissions within hospitals, due to the use of several invasive materials. Nurses represent a large portion of the healthcare workforce and can be pivotal in promoting sustainability practices. Several international reports have suggested that nursing can help achieve the sustainable development objectives set by the United Nations.

AIMS

The purpose is to explore behaviour related to environmental sustainability in intensive care nurses.

STUDY DESIGN

A qualitative content analysis comprised of in-depth interviews involving 27 ICU nurses, who were each asked the same open-ended question. The transcripts collected were then analyzed and organized by a team of independently-working researchers. The analysis of the extrapolated concepts was carried out following the Neem M. (2022) method. The study is supported by a grant from the Centre of Excellence for Nursing Scholarship, Rome, July 2024.

FINDINGS

The main recurring themes are as follows: (1) concepts of environmental sustainability in ICUs, (2) critical issues related to sustainable intervention in the ICUs (3) proactive environmental sustainability attitudes in ICUs. Time to know, define criticality, and improve is the conceptualization of sustainable behaviors experienced by ICU nurses.

CONCLUSIONS

Taking the time to know and define the critical issues for implementing sustainable behaviours in the ICU, turned out to be the key to enforce the mindset of green nursing thinking.

IMPLICATIONS TO CLINICAL PRACTICE

Sustainability behaviours need to be proposed and verified by ICU managers by creating sustainability teams and promoting a good working environment, founding the progression to green ICUs by focusing on health impact education and mindfulness.

The relationship between error experience and patient safety culture with safe activities of emergency nurses

Emergency nurses are central to reporting patient safety errors. By encouraging nurses to voluntarily report patient safety errors, hospitals can detect these errors and implement measures to prevent future occurrence. The purpose of this study was to survey the relationships between error experience and patient safety culture (PSC) with safe activities of emergency nurses in Iran. This descriptive-correlational study used a convenience sampling method based on predefined inclusion criteria to select 226 emergency nurses in Ardabil Province, northwest of Iran, from April to July 2023. The data collection tools were demographic and the Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire, a 4-item questionnaire for patient safety error and the patient safety care activities scale. The data were analyzed using the SPSS-26, descriptive statistics, Pearson correlation, and multiple linear regression. The mean of PSC and safety nursing activities were 3.24 (± 0.46), 3.41 (± 0.43), respectively. Multiple linear regression analysis revealed that the potential factors associated with safety nursing activities were communication and providing feedback about errors (β=0.229, P=0.007) and an error reporting system (β = 0.221, P = 005). Changing the attitude of managers and organizational leaders toward a systemic approach is critical to improving PSC and increasing error reporting. Hospitals should review their policies to ensure an educational and supportive environment and provide opportunities for nurses to better participate in hospital safety.

Association between body roundness index and weight-adjusted waist index with asthma prevalence among US adults: the NHANES cross-sectional study, 2005-2018

This study investigated the connection between asthma in US individuals and their body roundness index (BRI) and weight-adjusted waist index (WWI). According to data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES), 3609 of the 25,578 persons in the survey who were 18 years of age or older reported having asthma. After adjusting for all confounders, the probability of asthma prevalence increased by 8% for every unit rise in BRI (OR = 1.08, 95% CI 1.06,1.11). The probability of asthma prevalence increased by 16% for every unit rise in WWI (OR = 1.16, 95% CI 1.08,1.25). The BRI and WWI indices were associated with prevalence and were nonlinearly correlated. The inflection points for threshold saturation effects were 4.36 and 10.69, respectively (log-likelihood ratio test, P < 0.05). Relationship subgroup analyses showed that the positive associations between BRI and WWI and asthma were generalized across populations and there was no significant interaction in most subgroups. In addition, sensitivity analyses verified the robustness of these results, further confirming the conclusion of BRI and WWI as independent risk factors for asthma. Finally, receiver operating characteristic (ROC) analysis showed that BRI outperformed WWI in predicting asthma, suggesting the potential of BRI in early asthma screening. Overall, BRI and WWI are independent risk factors for asthma with important clinical applications.

Generative AI, molecular docking and molecular dynamics simulations assisted identification of novel transcriptional repressor EthR inhibitors to target Mycobacterium tuberculosis

Tuberculosis (TB) remains a persistent global health threat, with Mycobacterium tuberculosis (Mtb) continuing to be a leading cause of mortality worldwide. Despite efforts to control the disease, the emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) TB strains presents a significant challenge to conventional treatment approaches. Addressing this challenge requires the development of novel anti-TB drug molecules. This study employed de novo drug design approaches to explore new EthR ligands and ethionamide boosters targeting the crucial enzyme InhA involved in mycolic acid synthesis in Mtb. Leveraging REINVENT4, a modern open-source generative AI framework, the study utilized various optimization algorithms such as transfer learning, reinforcement learning, and curriculum learning to design small molecules with desired properties. Specifically, focus was placed on molecule optimization using the Mol2Mol option, which offers multinomial sampling with beam search. The study's findings highlight the identification of six promising compounds exhibiting enhanced activity and improved physicochemical properties through structure-based drug design and optimization efforts. These compounds offer potential candidates for further preclinical and clinical development as novel therapeutics for TB treatment, providing new avenues for combating drug-resistant TB strains and improving patient outcomes.

Quality control of hospitals and its effect on hospitalized fatality rate of COVID-19

The reported fatality rate of COVID-19 was significantly differ between different countries, provinces in a country, and hospitals in a province. It is important to find and analysed the source of variation in fatality rates to control future epidemics of infection disease. This study propose an approach to investigates the hospital quality of care and its impact on the in-hospital fatality rates of COVID-19 among over 30 years old patients in Iran. The study included 78,217 COVID-19 hospitalized patients over 30 years old from 34 hospitals in Razavi Khorasan province between January 20, 2020, and June 20, 2021. Attribute control charts were employed to evaluate the quality of care in hospitals during four peaks of COVID-19. To account for the impact of hospital quality on in-hospital fatality rates by adjusting on patient characteristics, two predictive models; Poisson and Binomial regression were utilized. The adjusted odds ratio of COVID-19 fatality in no quality confirmed (NQC) hospitals to the quality confirmed (QC) hospitals is 3.48 C.I.95% (2.38, 5.10) at the first peak among patients without other risk factors. The significant negative interactions with QC and peaks indicates that the provision of quality care serves to mitigate the risk of fatality in NQC hospitals during these peaks. The significant negative interaction between diabetes and QC demonstrated that the relative risk in NQC hospitals for no diabetes patient is higher than diabetic patients. There is no significant interaction between age, SaO2, and ARDS with QC; it means that the odds of fatality in NQC hospitals is not depends to these factors. The use of attribute control charts facilitated timely identification of trends and outliers, enabling proactive interventions to enhance patient care. This research underscores the critical role of hospital quality in managing patient outcomes during the COVID-19 pandemic and emphasizes the necessity for continuous monitoring and quality improvement initiatives in healthcare settings. The findings advocate for collaborative efforts among healthcare providers to implement best practices, particularly during surges in cases, to optimize resource allocation and improve overall quality of care.

Assessment of potential ecological risk for microplastics in freshwater ecosystems

Microplastics (MPs) are one of the most widespread environmental pollutants, but their risk assessment to freshwater ecosystems has not been clearly investigated. Risk assessment has been constrained by the absence of MP concentration in some environment, the diverse types and shapes of MPs, and limitations of polystyrene (PS)-biased toxicity studies. This study examined exposure to MPs in rivers and lakes worldwide, including China (the Three Gorges Dam & Yangtze River (TGD & YR) and the lakes of Wuhan city (WL)), Vietnam (seven lakes of Da Nang city (7UL)), Europe (the Rhine River (RR)), Finland (Kallavesi Lake (KL)), Argentina (nine lakes in the Patagonia region (9LP)), Brazil (Guaiba Lake (GL)), and South Korea (Nakdong River (NR), Han River (HR), and Anyang Stream (AS)), and assessed the risks to aquatic ecosystems based on the toxicity information and morphology of MPs. We also examine the limitations of the traditional risk quotient (RQ)-based risk assessment method for PS-biased toxicity studies. Potential ecological risks were assessed using pollution load index (PLI) and potential ecological risk index (PERI) considering the hazard scores of MP types. RQ was approximately 10-6 to 10-4, indicating negligible risk to aquatic organisms. In contrast, the calculated PLI (>30: extreme danger) and PERI (>1200: extreme danger) values suggest that MPs represent serious ecological threats at all the study locations. Furthermore, principal component analysis (PCA) indicated that MP fibers and fragments have a significant impact on the risks for freshwater systems. These MP morphologies derive from surrounding fishing and agricultural activities, and household and clothing industries. The areas surrounding these rivers and lakes are expected to become more densely populated, potentially leading to increased MP emissions and higher risks, suggesting a need to expand wastewater treatment facilities, reduce consumption of single-use plastics, and raise societal awareness of waste plastics.

Construction of prediction model of early glottic cancer based on machine learning

BACKGROUND

The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.

OBJECTIVE

To explore the clinical applicability of the diagnosis of early glottic cancer based on ML combined with NBI.

MATERIAL AND METHODS

A retrospective study was conducted on 200 patients diagnosed with laryngeal mass, and the general clinical characteristics and pathological results of the patients were collected. Chi-square test and multivariate logistic regression analysis were used to explore clinical and laryngoscopic features that could potentially predict early glottic cancer. Afterward, three classical ML methods, namely random forest (RF), support vector machine (SVM), and decision tree (DT), were combined with NBI endoscopic images to identify risk factors related to glottic cancer and to construct and compare the predictive models.

RESULTS

The RF‑based model was found to predict more accurately than other methods and have a significant predominance over others. The accuracy, precision, recall and F1 index, and AUC value of the RF model were 0.96, 0.90, 1.00, 0.95, and 0.97.

CONCLUSIONS AND SIGNIFICANCE

We developed a prediction model for early glottic cancer using RF, which outperformed other models.

Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions

Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integration of machine learning (ML) techniques with catalytic degradation processes to improve plastic waste management. Catalytic degradation is emphasized for its efficiency and selectivity, while several machine learning techniques are assessed for their capacity to enhance these processes. The review goes into ML applications for forecasting catalyst performance, determining appropriate reaction conditions, and refining catalyst design to improve overall process performance. Briefing about the reinforcement, supervised, and unsupervised learning algorithms that handle all complex data and parameters is explained. A techno-economic study is provided, evaluating these ML-driven system's performance, affordability, and environmental sustainability. The paper reviews how the novel method integrating ML with catalytic degradation for plastic cleanup might alter the process, providing new insights into scalable and sustainable solutions. This review emphasizes the usefulness of these modern strategies in tackling the urgent problem of plastic pollution by offering a comprehensive examination.

The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review

The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection.

COVID-19's influence on Karachi stock exchange: A comparative machine learning algorithms study for forecasting

The COVID-19 pandemic has great effects for economies internationally. This study studies the interconnection between COVID-19 metrics and Pakistan's premier stock exchange, the Karachi Stock Exchange (KSE) with the object of identifying the most effective machine learning (ML) model for predicting KSE developments in the pandemic. Our investigation periods the peak COVID-19 period from March 1, 2020, to November 26, 2021, applying data from both the KSE 100 index and COVID-19 associated variables. Five various ML methods were applied involving Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Regression Tree (Rtree), and Support Vector Machine (SVM) and measured their performance employing critical accuracy metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The outcomes discover that the RF model outperformed its equivalents realizing an R2 of 0.91 with k = 5. These results conflict with a previous study that supported a negative impact of COVID-19 on improved stock markets. The visions from this study can assist investors in managing strategic investment decisions and assist policymakers in making measures to reduce the pandemic's effects on the stock market.

Polyethylene Terephthalate (PET) Recycled by Catalytic Glycolysis: A Bridge toward Circular Economy Principles

Plastic pollution has escalated into a critical global issue, with production soaring from 2 million metric tons in 1950 to 400.3 million metric tons in 2022. The packaging industry alone accounts for nearly 44% of this production, predominantly utilizing polyethylene terephthalate (PET). Alarmingly, over 90% of the approximately 1 million PET bottles sold every minute end up in landfills or oceans, where they can persist for centuries. This highlights the urgent need for sustainable management and recycling solutions to mitigate the environmental impact of PET waste. To better understand PET's behavior and promote its management within a circular economy, we examined its chemical and physical properties, current strategies in the circular economy, and the most effective recycling methods available today. Advancing PET management within a circular economy framework by closing industrial loops has demonstrated benefits such as reduced landfill waste, minimized energy consumption, and conserved raw resources. To this end, we identified and examined various strategies based on R-imperatives (ranging from 3R to 10R), focusing on the latest approaches aimed at significantly reducing PET waste by 2040. Additionally, a comparison of PET recycling methods (including primary, secondary, tertiary, and quaternary recycling, along with the concepts of "zero-order" and biological recycling techniques) was envisaged. Particular attention was paid to the heterogeneous catalytic glycolysis, which stands out for its rapid reaction time (20-60 min), high monomer yields (>90%), ease of catalyst recovery and reuse, lower costs, and enhanced durability. Accordingly, the use of highly efficient oxide-based catalysts for PET glycolytic degradation is underscored as a promising solution for large-scale industrial applications.

A supervised machine learning approach for the prediction of antioxidant activities of Amaranthus viridis seed

This research aimed at modelling and predicting the antioxidant activities of Amaranthus viridis seed extract using four (4) data-driven models. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest Neighbour (k-NN), and Decision Tree (DT) were used as modelling algorithms for the construction of a non-linear empirical model to predict the antioxidant properties of Amaranthus viridis seed extract. Datasets for the modelling operation were obtained from a Box Behnken design while the hyperparameters of the ANN, SVM, k-NN and DT were determined using a 10-fold cross-validation technique. Among the Machine Learning algorithms, DT was observed to exhibit excellent performance and outperformed other Machine Learning algorithms in predicting the antioxidant activities of the seed extract, with a sensitivity of 0.867, precision of 0.928, area under the curve of 0.979, root mean square error of 0.184 and correlation coefficient of 0.9878. It was closely followed by ANN which was used to analyze and explain in detail the effect of the independent variables on the antioxidant activities of the seed extracts. This result affirmed the suitability of DT in predicting the antioxidant activities of Amaranthus viridis.

The Impact of the COVID-19 Pandemic on the Amount of Plastic Waste and Alternative Materials in the Context of the Circular Economy

The COVID-19 pandemic was first reported on 31 December 2019, in Wuhan. Since then, the rapid spread of the virus has directly impacted various aspects of people’s lives, including culture, society, education, and the economy. The environment has also been affected, as the disposal of thousands of tons of single-use personal protective equipment has resulted in a significant increase in waste. The challenge was to create environmentally friendly materials for personal protective equipment. One of the alternatives to polypropylene materials is a biodegradable nonwoven produced using spun-bonded technology. The article discusses various physical and mechanical parameters, the biodegradation process, and the distribution of molar masses during the weeks of nonwoven biodegradation. Additionally, the paper presents the results of in vitro cytotoxicity tests conducted on the material. Biodegradable materials are a viable solution to the challenges posed by a circular economy.

Pandemic COVID-19 ends but soil pollution increases: Impacts and a new approach for risk assessment

For three years, a large amount of manufactured pollutants such as plastics, antibiotics and disinfectants has been released into the environment due to COVID-19. The accumulation of these pollutants in the environment has exacerbated the damage to the soil system. However, since the epidemic outbreak, the focus of researchers and public attention has consistently been on human health. It is noteworthy that studies conducted in conjunction with soil pollution and COVID-19 represent only 4 % of all COVID-19 studies. In order to enhance researchers' and the public awareness of the seriousness on the COVID-19 derived soil pollution, we propose the viewpoint that "pandemic COVID-19 ends but soil pollution increases" and recommend a whole-cell biosensor based new method to assess the environmental risk of COVID-19 derived pollutants. This approach is expected to provide a new way for environmental risk assessment of soils affected by contaminants produced from the pandemic.