Microplastic pollution is becoming a major issue for human health due to the recent discovery of microplastics in most ecosystems. Here, we review the sources, formation, occurrence, toxicity and remediation methods of microplastics. We distinguish ocean-based and land-based sources of microplastics. Microplastics have been found in biological samples such as faeces, sputum, saliva, blood and placenta. Cancer, intestinal, pulmonary, cardiovascular, infectious and inflammatory diseases are induced or mediated by microplastics. Microplastic exposure during pregnancy and maternal period is also discussed. Remediation methods include coagulation, membrane bioreactors, sand filtration, adsorption, photocatalytic degradation, electrocoagulation and magnetic separation. Control strategies comprise reducing plastic usage, behavioural change, and using biodegradable plastics. Global plastic production has risen dramatically over the past 70 years to reach 359 million tonnes. China is the world's top producer, contributing 17.5% to global production, while Turkey generates the most plastic waste in the Mediterranean region, at 144 tonnes per day. Microplastics comprise 75% of marine waste, with land-based sources responsible for 80-90% of pollution, while ocean-based sources account for only 10-20%. Microplastics induce toxic effects on humans and animals, such as cytotoxicity, immune response, oxidative stress, barrier attributes, and genotoxicity, even at minimal dosages of 10 μg/mL. Ingestion of microplastics by marine animals results in alterations in gastrointestinal tract physiology, immune system depression, oxidative stress, cytotoxicity, differential gene expression, and growth inhibition. Furthermore, bioaccumulation of microplastics in the tissues of aquatic organisms can have adverse effects on the aquatic ecosystem, with potential transmission of microplastics to humans and birds. Changing individual behaviours and governmental actions, such as implementing bans, taxes, or pricing on plastic carrier bags, has significantly reduced plastic consumption to 8-85% in various countries worldwide. The microplastic minimisation approach follows an upside-down pyramid, starting with prevention, followed by reducing, reusing, recycling, recovering, and ending with disposal as the least preferable option.
AIMS: This study aims to determine the trend of allergic rhinitis (AR) prevalence in a tertiary hospital between 2017 and 2022, and to compare its prevalence pre- and post-COVID-19 pandemic.
METHODS: This was a cross-sectional study involving the extraction of all Malaysian patients' medical records who were diagnosed with AR and attended the otorhinolaryngology outpatient clinic of a government-funded tertiary hospital in Malaysia between 2017 and 2022.
RESULTS: 3,744 cases out of the 57,968 first-encounter outpatient visits to the otorhinolaryngology clinic were extracted for analysis. Overall, the prevalence of AR cases ranged from 1.83 to 9.23% between 2017 and 2022. There was a significant drop of 21.38 to 70.22% between the pre- and post-COVID-19 pandemic (p
Working from home (WFH) has had both positive and negative impacts on the work conduct. To maximise the benefits of homeworking, previous literature mainly focuses on creating self-help strategies for homeworkers to reduce work stress and maintain work engagement. However, fewer studies take on the policymaker perspective and evaluate optimal working conditions in the homeworking context. Using the Job Demands-Resources (JD-R) model, this study evaluates the effects of various work characteristics (job demands and resources) on the stress and engagement of infrequent and frequent homeworkers. Using the sixth European Working Conditions Survey 2015 which contains 5090 participants from 34 European countries, we studied 6 job demands and 5 job resources via Exploratory Factor Analysis. After testing the model's fitness using Confirmatory Factor Analysis, multiple mixed-effects models were used to test the job demands and resource effects on worker stress and engagement. Dominance Analysis was then used to identify the relative importance of each job demand and resource when explaining employee stress and engagement. We found emotional demands, time pressure, and workload to be the top three demand factors that cause work stress across the groups. Other than daily homeworkers, a positive and fair social climate is the most prominent resource able to boost job engagement across all of the other groups. By identifying the homeworkers' most influential demands and resources, this study will help managers better understand the steps to take to provide healthy job conditions for homeworkers.
Due to rapidly growing demand, the production of vegetables is increasing along the Akaki Rivers. The objective of this study was to examine the degree of fecal contamination and levels of fecal contamination and dissemination throughout the wastewater irrigation system. Irrigation water, irrigated soil, and leafy vegetables were collected twice during 2 vegetable growing seasons, at the maturity period of the growing season, from 19 sampling points along the 2 Akaki Rivers. Composite samples were taken from all sampling points and E.coli was enumerated. The mean E.coli load in wastewater and non-wastewater sources were 1.16±5.53 CFU/100 ml and 2.232±1.292 CFU/100 ml respectively. All counts of E. coli in the wastewater exceeded the WHO's standards indicating that the irrigation water quality was unacceptable. In the wastewater-irrigated and non-wastewater-irrigated soil, the mean E.coli were 3.62 ±1.582 CFU/g and 1.322±87.1 CFU/g respectively. Meanwhile, the mean E.coli counts on the lettuce and Swiss chard were 78 ± 2 CFU/g and 44 ±3CFU/g respectively. The E.coli count on the leafy vegetables was found to be associated with the E.coli in the wastewater and soil. The production of leafy vegetables using wastewater with unacceptably high levels of E.coli and high occupational exposure introduces high levels of risk to the farming communities and to the consumers. Leafy, low-growing raw edible vegetables need careful treatment during food production and harvesting procedures or activities.
This study represents a green synthesis method for fabricating an oxygen evolution reaction (OER) electrode by depositing two-dimensional CuFeOx on nickel foam (NF). Two-dimensional CuFeOx was deposited on NF using in situ hydrothermal synthesis in the presence of Aloe vera extract. This phytochemical-assisted synthesis of CuFeOx resulted in a unique nano-rose-like morphology (petal diameter 30-70 nm), which significantly improved the electrochemical surface area of the electrode. The synthesized electrode was analyzed for its OER electrocatalytic activity and it was observed that using 75% Aloe vera extract in the phytochemical-assisted synthesis of CuFeOx resulted in improved OER electrocatalytic performance by attaining an overpotential of 310 mV for 50 mA cm-2 and 410 mV for 100 mA cm-2. The electrode also sustained robust stability throughout the 50 h of chronopotentiometry studies under alkaline electrolyte conditions, demonstrating its potential as an efficient OER electrode material. This study highlights the promising use of Aloe vera extract as a green and cost-effective way to synthesize efficient OER electrode materials.
This clinical case describes using a milled crown on a cracked tooth as the abutment for a removable partial denture. A 63-year-old male patient diagnosed with lipoma undergoing chemotherapy and radiotherapy presented with symptomatic crack tooth syndrome on tooth 36 and partial edentulism. Conservative treatment using a molar band to extracoronally splint the tooth was conducted to determine the prognosis of the crack line. A lower partial cobalt-chromium denture was constructed by incorporating the milled crown of tooth 36 as the abutment. After six months of follow-up, there were no crack tooth symptoms, and regular review was adopted to monitor the tooth. The construction of a milled crown of a cracked tooth presented good and promising clinical outcomes in preserving tooth vitality and preventing crack propagation in partially dentate dentition for the long term.
A 27-year-old male with thymoma presented with recurrent severe anemia which required multiple transfusions. Bone marrow biopsy showed pure red cell aplasia with normal other cell lineages. He was diagnosed with pure red cell aplasia associated with thymoma and treatment with cyclosporine A was begun. After 12 weeks of treatment, his hemoglobin improved to 11.3 g/dL and the patient remained transfusion independent.
Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary (i.e., autocorrelation coefficient ≈1). The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.
In recent years, limited research has been conducted on enhancing DNA hybridization-based biosensor approaches using statistical models. This study explores the application of response surface methodology (RSM) to improve the performance of a DNA hybridization biosensor for dengue virus (DENV) detection. The biosensor is based on silicon nanowires decorated with gold nanoparticles (SiNWs/AuNPs) and utilizes methylene blue as a redox indicator. The DNA hybridization process between the immobilized DNA probe and the target DENV gene was monitored using differential pulse voltammetry (DPV) based on the reduction of methylene blue. Fourier-transform infrared spectroscopy (FTIR) and electrochemical impedance spectroscopy (EIS) were employed to confirm successful DNA hybridization events on the modified screen-printed gold electrode (SPGE) surface. Several parameters, including pH buffer, NaCl concentration, temperature, and hybridization time, were simultaneously optimized, with NaCl concentration having the most significant impact on DNA hybridization events. This study enhances the understanding of the role of each parameter in influencing DNA hybridization detection in electrochemical biosensors. The optimized biosensor demonstrated the ability to detect complementary oligonucleotide and amplified DENV gene concentrations as low as 0.0891 ng µL-1 (10 pM) and 2.8 ng µL-1, respectively. The developed biosensor shows promise for rapid clinical diagnosis of dengue virus infection.
The method is providing and overview of the organization in the management perspective, within the health big data analysis, especially for the elderly employees, the organizations could sign the elderly employees within the right tasks, it reducing the costs by increasing the employees' job performance and organization performance. By addressing the importance role of big health data analytics (BDHA) in the healthcare system .moreover BDHA enables a patient's medical records to be searched in a dynamic, interactive manner. One billion records were made in two hours. Current clinical reporting compares large health data profiles and meta-big health data, giving health apps basic interfaces. A combination of Hadoop/MapReduce and HBase was used to generate the necessary hospital-specific large heath data. One billion (10TB) and three billion (30TB) HBase large health data files might be created in a week or a month using the concept. Apache Hadoop technologies tested simulated medical records. Inconsistencies reduced big health data. An encounter-centered big health database was difficult to set up due to the complicated medical system connections between big health data profiles. Associated with job performance such as the gender, current/past job positions and the health conditions are important. For genders the 66.36% of respondents in the experiments are females, 33.64 are males, majority of are healthy which are 66.97%, 30.58% are common geriatric disease, rest 2.45% are suffering from occupational disease; In terms of the current/past job positions, 20% of the respondents are working as accountant, followed by sales and management level. The Diagnostic and Statistical Manual, lists 157 distinct illnesses. Individuals may be diagnosed with one or more illnesses as a consequence of medical health professionals watching and analyzing their symptoms. It has been discovered that mental health issues have a negative impact on employees' job performance. For example, research on individuals with anxiety and depression has a direct impact on concentrations, decision-making process, and risk-taking behavior, which can be determined for job performance. Machine learning focuses on approaches that can be used to create accurate predictions about future characteristics based on previous training and post training. Principles such as job task and computational learning are crucial for machine learning algorithms that use a large amount of big health data.
The Coronavirus disease 2019, or COVID-19, has shifted the medical paradigm from face-to-face to telehealth. Telehealth has become a vital resource to contain the virus spread and ensure the continued care of patients. In terms of preventing cardiovascular diseases, automating electrocardiogram (ECG) classification is a promising telehealth intervention. The healthcare service ensures that patient care is appropriate, comfortable, and accessible. Convolutional neural networks (CNNs) have demonstrated promising results in ECG categorization, which require high accuracy and short training time to ensure healthcare quality. This study proposes a one-dimensional-CNN (1D-CNN) arrhythmia classification based on the differential evolution (DE) algorithm to optimize the accuracy of ECG classification and training time. The performance of 1D-CNNs of different activation functions are optimized based on the standard DE algorithm. Finally, based on MIT-BIH and SCDH arrhythmia databases, the performances of optimized and unoptimized 1D-CNN are compared and analysed. Results show that the 1D-CNN optimized by the DE has higher accuracy in heartbeats classification. The optimized 1D-CNN improves from 97.6% to 99.5% on MIT-BIH and from 80.2% to 88.5% on SCDH. Therefore, the optimized 1D-CNN shows improvements of 1.9% and 8.3% in the two datasets, respectively. In addition, compared with the unoptimized 1D-CNN based on the same parameter settings, the optimized 1D-CNN has less training time. Under the conditions of ReLU function and 10 epochs, the training takes 9.22 s on MIT-BIH and 10.35 s on SCDH, reducing training time by 67.2% and 64.2%, respectively.
In general, making evaluations requires a lot of time, especially in thinking about the questions and answers. Therefore, research on automatic question generation is carried out in the hope that it can be used as a tool to generate question and answer sentences, so as to save time in thinking about questions and answers. This research focuses on automatically generating short answer questions in the reading comprehension section using Natural Language Processing (NLP) and K-Nearest Neighborhood (KNN). The questions generated use article sources from news with reliable grammar. To maintain the quality of the questions produced, machine learning methods are also used, namely by conducting training on existing questions. The stages of this research in outline are simple sentence extraction, problem classification, generating question sentences, and finally comparing candidate questions with training data to determine eligibility. The results of the experiment carried out were for the Grammatical Correctness parameter to produce a percentage of 59.52%, for the Answer Existence parameter it yielded 95.24%, while for the Difficulty Index parameter it produced a percentage of 34.92%. So that the resulting average is 63.23%. So, this software deserves to be used as an alternative to automatically create reading comprehension questions.
The spherical fuzzy set (SFS) model is one of the newly developed extensions of fuzzy sets (FS) for the purpose of dealing with uncertainty or vagueness in decision making. The aim of this paper is to define new exponential and Einstein exponential operational laws for spherical fuzzy sets and their corresponding aggregation operators. We introduce the operational laws for exponential and Einstein exponential SFSs in which the base values are crisp numbers and the exponents (weights) are spherical fuzzy numbers. Some of the properties and characteristics of the proposed operations are then discussed. Based on these operational laws, some new aggregation operators for the SFS model, namely Spherical Fuzzy Weighted Exponential Averaging (SFWEA) and Spherical Fuzzy Einstein Weighted Exponential Averaging (SFEWEA) operators are introduced. Finally, a decision-making algorithm based on these newly introduced aggregation operators is proposed and applied to a multi-criteria decision making (MCDM) problem related to ranking different types of psychotherapy.
This paper presents an online system for recording attendance based on facial recognition incorporating facial mask detection. The main objective of this project is to develop an effective attendance system based on face recognition and face mask detection, and to provide this service online through a browser interface. This would allow any user to use this system without the need to install special software. They simply need to open the interface of this system in a browser through any terminal. Recording attendance information online allows data to be easily recorded in a centralized online database. Since faces are used as biometric signatures in this project, all users registered in the system will have their profiles loaded with their face-images samples. Initially, before face recognition can be done, the model training phase based on SVM will be carried out, mainly to develop a trained model that can perform face recognition. A set of synthetic data will also be used to train the same model so that it can perform identification for users wearing face masks. The server application is coded in Python and uses the Open-Source Computer Vision (OpenCV) library for image processing. For web interfaces and the database, PHP and MySQL are used. With the integration of Python and PHP scripting programs, the developed system will be able to perform processing on online servers, while being accessible to users through a browser from any terminal. According to the results and analysis, an accuracy of about 81.8% can be achieved based on a pre-trained model for face recognition and 80% for face mask detection.
After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.
International student mobility has traditionally witnessed a global South-North pattern. In recent years, a shift has occurred as the appeal of alternative geographies waxes, with Malaysia being an exemplar of inbound student mobility destination. To facilitate a deep probe of the under-researched global South-South student mobility, this study utilized a qualitative method to delve into 10 Chinese doctoral students' emic perceptions of their sojourn in Malaysia. Guided by a theoretical framework incorporating decolonization and recolonization, this study unpacks how these sociohistorical forces penetrate into and shape the students' preparation and navigation of a doctoral sojourn. Findings of the study reveal that while taking advantage of the Southern niche to yield commensurate benefits, thereby delegitimizing the Western supremacy, the students' make-do mentality and self-subjugating resistance inadvertently reinforce the Western dominance. Besides, these macro effects generate interlocking and conflicting affective consequences, instilling simultaneously positivity and inclusivity, inferiority, and anxiety. Altogether, decolonization and recolonization are concretely registered at the emotional level and bear a broader social significance. This article concludes with an alert and a call to address covert yet compelling inequalities in international student mobility.
The outbreak of COVID-19 has caused great havoc and affected many parts of the world. It has imposed a great challenge to the medical and health fraternity with its ability to continue mutating and increasing the transmission rate. Some challenges include the availability of current knowledge of active drugs against the virus, mode of delivery of the medicaments, its diagnosis, which are relatively limited and do not suffice for further prognosis. One recently developed drug delivery system called nanoparticles is currently being utilized in combating COVID-19. This article highlights the existing methods for diagnosis of COVID-19 such as computed tomography scan, reverse transcription-polymerase chain reaction, nucleic acid sequencing, immunoassay, point-of-care test, detection from breath, nanotechnology-based bio-sensors, viral antigen detection, microfluidic device, magnetic nanosensor, magnetic resonance platform and internet-of-things biosensors. The latest detection strategy based on nanotechnology, biosensor, is said to produce satisfactory results in recognizing SARS-CoV-2 virus. It also highlights the successes in the research and development of COVID-19 treatments and vaccines that are already in use. In addition, there are a number of nanovaccines and nanomedicines currently in clinical trials that have the potential to target COVID-19.
Vegetables are industrial crops endowed with both nutritional and medicinal values. The overwhelming contributions of vegetables to human living in the form of nutrients and medicine cannot be under emphasised. Thus, this study examined the recoveries of phenolic compounds and antioxidants from Corchorus olitorius leaves using a microwave-enhanced extraction technique. Furthermore, the phenolic compounds in the leaf extract of C. olitorius were comprehensively identified using liquid chromatography-mass spectrometry quadrupole of flight (LC-QToF-MS). At the optimized conditions of microwave-enhanced extraction (extraction time of 131 s, microwave power 305 W, solvent/sample ratio of 12 mL/g, and ethanol concentration of 50%), total phenolic content (TPC) of 343.098 ± 3.05 mg GAE/10 g d.b., IC50 values of 68.89 ± 1.08 and 29.76 ± 1.00 µg/mL for 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6'-sulfonic acid) (ABTS) assays, respectively, were achieved. Furthermore, an aggregate of fourteen phenolic compounds that include 1-galloyl-glucose, 1,3,5-O-tricaffeoylquinic acid, procyanidin C-1, 4,4',5,6-tetrahydroxystilbene, 3,4,5-O-tricaffeoylquinic acid, 5-desgalloylstachyurin, sanguiin H-4, corilagin_1, 1-O-galloylpedunculagin, laevigatin A, pedunculagin, 2,4,6-tri-O-galloyl-β-D-glucose, 1,3,6-trigalloyl-β-D-glucose, and 1,2,3,6-tetra-O-galloyl-β-D-glucose was tentatively identified in the leaf extract of C. olitorius. In general, this study has established C. olitorius leaves as sources of phenolic compounds and natural antioxidants. Thus, the intake can continue to be promoted as a way forward in solving the problem of food insecurity.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11696-023-02771-x.