Displaying publications 81 - 100 of 2377 in total

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  1. Yang L, Meng H, Wang J, Wu Y, Zhao Z
    PLoS One, 2024;19(4):e0299729.
    PMID: 38578727 DOI: 10.1371/journal.pone.0299729
    Urban agglomerations are sophisticated territorial systems at the mature stage of city development that are concentrated areas of production and economic activity. Therefore, the study of vulnerability from the perspective of production-living-ecological space is crucial for the sustainable development of the Yellow River Basin and global urban agglomerations. The relationship between productivity, living conditions, and ecological spatial quality is fully considered in this research. By constructing a vulnerability evaluation index system based on the perspectives of production, ecology, and living space, and adopting the entropy value method, comprehensive vulnerability index model, and obstacle factor diagnostic model, the study comprehensively assesses the vulnerability of the urban agglomerations along the Yellow River from 2001 to 2020. The results reveal that the spatial differentiation characteristics of urban agglomeration vulnerability are significant. A clear three-level gradient distribution of high, medium, and low degrees is seen in the overall vulnerability; these correspond to the lower, middle, and upper reaches of the Yellow River Basin, respectively. The percentage of cities with higher and moderate levels of vulnerability did not vary from 2001 to 2020, while the percentage of cities with high levels of vulnerability did. The four dimensions of economic development, leisure and tourism, resource availability, and ecological pressure are the primary determinants of the urban agglomeration's vulnerability along the Yellow River. And the vulnerability factors of various urban agglomerations showed a significant evolutionary trend; the obstacle degree values have declined, and the importance of tourism and leisure functions has gradually increased. Based on the above conclusions, we propose several suggestions to enhance the quality of urban development along the Yellow River urban agglomeration. Including formulating a three-level development strategy, paying attention to ecological and environmental protection, developing domestic and foreign trade, and properly planning and managing the tourism industry.
  2. Tan MHP, Ong SC, Tahir NAM, Ali AM, Mustafa N
    PLoS One, 2024;19(4):e0297589.
    PMID: 38574169 DOI: 10.1371/journal.pone.0297589
    INTRODUCTION: Health state utility values (HSUV) for Type 2 diabetes mellitus (T2DM) complications are useful in economic evaluations to determine cost effectiveness of an intervention. However, there is a lack of reference ranges for different severity and stages of individual complications. This study aimed to provide an overview of HSUV decrement ranges for common T2DM complications focusing on different severity and stages of complications.

    METHOD: A systematic search was conducted in MEDLINE, SCOPUS, WEB OF SCIENCE. (Jan 2000 to April 2022). Included studies for HSUV estimates were from outpatient setting, regardless of treatment types, complication stages, regions and HRQoL instruments. Health Related Quality of Life (HRQoL) outcomes was to be presented as HSUV decrement values, adjusted according to social demographics and comorbidities. Adjusted HSUV decrements were extracted and compiled according to individual complications. After which, subsequently grouped into mild or severe category for comparison.

    RESULTS: Searches identified 35 studies. The size of the study population ranged from 160 to 14,826. The HSUV decrement range was widest for cerebrovascular disease (stroke): -0.0060 to -0.0780 for mild stroke and -0.035 to -0.266 for severe stroke; retinopathy: mild (-0.005 to -0.0862), moderate (-0.0030 to -0.1845) and severe retinopathy (-0.023 to -0.2434); amputation: (-0.1050 to -0.2880). Different nature of complication severity defined in studies could be categorized into: those with acute nature, chronic with lasting effects, those with symptoms at early stage or those with repetitive frequency or episodes.

    DISCUSSION: Overview of HSUV decrement ranges across different stages of each T2DM diabetes-related complications shows that chronic complications with lasting impact such as amputation, severe stroke with sequelae and severe retinopathy with blindness were generally associated with larger HSUV decrement range. Considerable heterogeneities exist across the studies. Promoting standardized complication definitions and identifying the most influential health state stages on HSUV decrements may assist researchers for future cost-effectiveness studies.

  3. Ewe ELR, Lee CP, Lim KM, Kwek LC, Alqahtani A
    PLoS One, 2024;19(4):e0298699.
    PMID: 38574042 DOI: 10.1371/journal.pone.0298699
    Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed-Lightweight Attentive VGG16 with Random Forest (LAVRF) model. LAVRF introduces a refined adaptation of the VGG16 model integrated with attention modules, complemented by a Random Forest classifier. By streamlining the VGG16 architecture, the Lightweight Attentive VGG16 effectively manages complexity while incorporating attention mechanisms that dynamically concentrate on pertinent regions within input images, resulting in enhanced representation learning. Leveraging the Random Forest classifier provides notable benefits, including proficient handling of high-dimensional feature representations, reduction of variance and overfitting concerns, and resilience against noisy and incomplete data. Additionally, the model performance is further optimized through hyperparameter optimization, utilizing the Optuna in conjunction with hill climbing, which efficiently explores the hyperparameter space to discover optimal configurations. The proposed LAVRF model demonstrates outstanding accuracy on three datasets, achieving remarkable results of 99.98%, 99.90%, and 100% on the American Sign Language, American Sign Language with Digits, and NUS Hand Posture datasets, respectively.
  4. Gao X
    PLoS One, 2024;19(4):e0301286.
    PMID: 38578793 DOI: 10.1371/journal.pone.0301286
    Enhancing green innovation for business sustainability represents a pressing global challenge. In the context of the manufacturing industry, the relationship between proactive green innovation (PGI) and structural social capital (SSC) remains a profoundly under-researched area. Drawing upon the theories of social capital and dynamic capability (DC), this study investigated the relationship between SSC and PGI within manufacturing enterprises via three individual and sequential mediating factors, namely cognitive social capital (CSC), relational social capital (RSC), and DC. Adopting a cross-sectional quantitative design, this study collected survey data from 485 manufacturing sector employees in China using purposive sampling. Structural equation modeling analysis of the data revealed no significant direct impact of SSC on PGI, but a strong indirect impact through the sequential mediating influences of CSC, RSC, and DC. The findings suggests that PGI within manufacturing enterprises is not wholly shaped by SSC; rather, firm-level dynamic capabilities, characterized by a sequential mechanism, plays a crucial role in achieving PGI within these enterprises. This paper offers both theoretical and practical contributions and provides recommendations for future research based on its limitations.
  5. Noorhidayah M, Azrizal-Wahid N, Low VL, Yusoff NR
    PLoS One, 2024;19(4):e0301392.
    PMID: 38578719 DOI: 10.1371/journal.pone.0301392
    Despite is known to have widespread distribution and the most active species of the family Chlorocyphidae, the molecular data of Rhinocypha fenestrella (Rambur, 1842) are relatively scarce. The present study is the first that examined the genetic diversity and phylogeographic pattern of the peacock jewel-damselfly R. fenestrella by sequencing the cytochrome C oxidase I (cox1) and 16S rRNA gene regions from 147 individuals representing eight populations in Malaysia. A total of 26 and 10 unique haplotypes were revealed by the cox1 and 16S rRNA genes, respectively, and 32 haplotypes were recovered by the concatenated sequences of cox1+16S. Analyses indicated that haplotype AB2 was the most frequent and the most widespread haplotype in Malaysia while haplotype AB1 was suggested as the common ancestor haplotype of the R. fenestrella that may arose from the Negeri Sembilan as discovered from cox1+16S haplotype network analysis. Overall haplotype and nucleotide diversities of the concatenated sequences were Hd = 0.8937 and Pi = 0.0028, respectively, with great genetic differentiation (FST = 0.6387) and low gene flow (Nm = 0.14). Population from Pahang presented the highest genetic diversity (Hd = 0.8889, Pi = 0.0022, Nh = 9), whereas Kedah population demonstrated the lowest diversity (Hd = 0.2842, Pi = 0.0003, Nh = 4). The concatenated sequences of cox1+16S showed genetic divergence ranging from 0.09% to 0.97%, whereas the genetic divergence for cox1 and 16S rRNA genes were 0.16% to 1.63% and 0.01% to 0.75% respectively. This study provides for the first-time insights on the intraspecific genetic diversity, phylogeographic pattern and ancestral haplotype of Rhinocypha fenestrella. The understanding of molecular data especially phylogeographic pattern can enhance the knowledge about insect origin, their diversity, and capability to disperse in particular environments.
  6. Naaem R, Hashmi FK, Yaqub S, Mohamed Noor DA
    PLoS One, 2024;19(4):e0299010.
    PMID: 38578776 DOI: 10.1371/journal.pone.0299010
    BACKGROUND: Precision medicine (PM) is in great progressive stages in the West and allows healthcare practitioners (HCPs) to give treatment according to the patient's genetic findings, physiological and environmental characteristics. PM is a relatively new treatment approach in Pakistan Therefore, it is important to investigate the level of awareness, attitude, and challenges faced by oncology physicians while practicing PM for various therapies, especially cancer treatment.

    OBJECTIVES: The present study aims to explore the level of awareness, attitude, and practice of PM in Pakistan along with the challenges faced by the oncologists for the treatment of cancer using the PM approach.

    METHODS: Phenomenology-based qualitative approach was used. Face-to-face in-depth interviews were conducted using the purposive sampling approach among oncologists in Lahore, Pakistan. The data were analyzed using thematic content analysis to identify themes and sub-themes.

    RESULTS: Out of 14 physicians interviewed 11 were aware of PM. They were keen on training to hone their skills and agreed on providing PM. Oncologists believed PM was expensive and given to affluent patients only. Other impeding factors include cost, lack of knowledge, and drug unavailability.

    CONCLUSIONS: Despite basic knowledge and will to practice, resource and cost constraints were marked as significant barriers. Additional training programs and inclusion into the curriculum may help to pave the way to PM implementation in the future. In addition, health authorities and policymakers need to ensure a cheaper PM treatment can be made available for all cancer patients.

  7. Ahmad Basri MAF, Wan Ismail WS, Kamal Nor N, Mohd Tohit N, Ahmad MN, Mohamad Aun NS, et al.
    PLoS One, 2024;19(4):e0301517.
    PMID: 38574084 DOI: 10.1371/journal.pone.0301517
    The use of virtual reality in social skills training for high functioning autism spectrum disorder (HFASD) youth has been found to be engaging and enjoyable. Despite the promising results, previous literature indicates that there has been no consensus on the social skills target in the training content. There is also limited research on how evidence-based strategies like cognitive and behaviour techniques are instantiated into the VR environment to teach social skills. The aim of this study is to determine the key components to design a social skills training content using virtual reality for youths with HFASD. The Fuzzy Delphi method (FDM) was used to obtain expert consensus on social skills difficulties and cognitive behavioral techniques included in the content in three phases. In phase 1, a questionnaire was developed from in-depth interviews and scientific literature review. The in-depth interviews were conducted with 13 HFASD youth, 7 parents and 6 experts. In phase 2, 3 experts rated the relevance of the items in the questionnaire using an item-level content validity index (I-CVI) assessment. In phase 3, the questionnaire was distributed to 10 experts to rate their level of agreement on each component using a 7-point Likert scale. Components that received a value above 75%, threshold value (d) ≤ 0.2, fuzzy score (A) ≥ α - cut value = 0.5 and higher rank based on defuzzification score were prioritized to be included in the content. Items that received higher expert consensus on social skills difficulties included assessing non-verbal responses, initiating, maintaining, and leaving conversations, emotional difficulties and difficulties in perspective taking. Cognitive and behavioral techniques that received higher expert consensus were psychoeducation, modelling, relaxation techniques, reinforcements, and perspective-taking questions. These key components can be used as a framework for the development of virtual learning content for social skills training in future studies.
  8. Ramachandran K, Dahlui M, Nik Farid ND
    PLoS One, 2024;19(3):e0299308.
    PMID: 38437241 DOI: 10.1371/journal.pone.0299308
    The World Health Organisation (WHO) recommends that all babies be exclusively breastfed, stating that donor milk is the next best alternative in the absence of the mother's own milk. Milk sharing takes many forms, namely wet nursing, co-feeding, cross-feeding, and a human milk bank (HMB). However, the establishment of a human milk bank is still not widely accepted and is a debatable topic because of religious concerns in Malaysia. The aim of this study is to determine the facilitators and barriers among Malaysians towards the acceptance of an HMB. A cross-sectional study with 367 participants was conducted; the participants answered an online-validated, self-administered questionnaire. Data on sociodemographic, knowledge on breastfeeding benefits, knowledge and attitude on HMB-specific issues were analysed in terms of frequency before proceeded with multiple logistic regression. The majority of the respondents were Muslim (73.3%), had completed their tertiary education (82.8%), and were employed (70.8%). Only 55.9% of respondents had heard of HMB, stating the internet as their main source of information, but many respondents were agreeable to its establishment (67.8%). Most respondents had a good score on knowledge of breastfeeding benefits and on HMB-specific issues (70% and 54.2%, respectively), while 63.8% had a positive attitude towards HMB. In the multivariate analysis, mothers with a good score on general knowledge of breastfeeding (AOR: 1.715; 95% CI 1.047-2.808) were more likely to accept the establishment of HMB, while being a Muslim was negatively associated with its establishment (AOR = 0.113, 95% CI 0.050-0.253). The study found a high prevalence of mothers who were willing to accept the establishment of HMB. By educating mothers on the benefits of breastfeeding, as well as addressing their religious concerns, the establishment of a religiously abiding HMB in Malaysia may be accepted without compromising their beliefs or the health benefit of donor milk.
  9. Chong DW, Jayaraj VJ, Ab Rahim FI, Syed Soffian SS, Azmi MF, Mohd Yusri MY, et al.
    PLoS One, 2024;19(4):e0299659.
    PMID: 38593177 DOI: 10.1371/journal.pone.0299659
    INTRODUCTION: Colorectal cancer is a growing global health concern and the number of reported cases has increased over the years. Early detection through screening is critical to improve outcomes for patients with colorectal cancer. In Malaysia, there is an urgent need to optimize the colorectal cancer screening program as uptake is limited by multiple challenges. This study aims to systematically identify and address gaps in screening service delivery to optimize the Malaysian colorectal cancer screening program.

    METHODS: This study uses a mixed methods design. It focuses primarily on qualitative data to understand processes and strategies and to identify specific areas that can be improved through stakeholder engagement in the screening program. Quantitative data play a dual role in supporting the selection of participants for the qualitative study based on program monitoring data and assessing inequalities in screening and program implementation in healthcare facilities in Malaysia. Meanwhile, literature review identifies existing strategies to improve colorectal cancer screening. Additionally, the knowledge-to-action framework is integrated to ensure that the research findings lead to practical improvements to the colorectal cancer screening program.

    DISCUSSION: Through this complex mix of qualitative and quantitative methods, this study will explore the complex interplay of population- and systems-level factors that influence screening rates. It involves identifying barriers to effective colorectal cancer screening in Malaysia, comparing current strategies with international best practices, and providing evidence-based recommendations to improve the local screening program.

  10. Lim XY, Lau MS, Zolkifli NA, Sastu Zakaria UR, Mohd Rahim NS, Lai NM, et al.
    PLoS One, 2024;19(4):e0297839.
    PMID: 38603736 DOI: 10.1371/journal.pone.0297839
    Herbal medicine is popularly used among patients who suffer from allergic rhinitis. This systematic review and meta-analysis was conducted to evaluate the efficacy and safety of single medicinal plants in the management of allergic rhinitis. We searched MEDLINE, CENTRAL, and Web of Science for randomised controlled trials which evaluated the use of single medicinal plant for allergic rhinitis among adults and children. Twenty-nine randomised controlled trials (n = 1879) were eligible while 27 (n = 1769) contributed data for meta-analyses. Most studies (studies = 20) compared medicinal plants against placebo and Petasites hybridus was most frequently investigated (studies = 5). Very-low-to-low-certainty evidence suggests that compared to placebo, single medicinal plants may improve overall total nasal symptoms (SMD -0.31, 95% CI -0.59 to -0.02; participants = 249; studies = 5; I2 = 21%) especially nasal congestion and sneezing; and rhinoconjunctivitis quality of life (RQLQ) scores (MD -0.46, 95% CI -0.84 to -0.07; participants = 148; studies = 3; I2 = 0%). Moderate-certainty evidence show no clear differences between single medicinal plants and antihistamine in overall symptoms (Total nasal symptoms: SMD -0.14, 95% CI -0.46 to 0.18; participants = 149; studies = 2; I2 = 0%). As adjunctive therapy, moderate-certainty evidence shows that medicinal plants improved SNOT-22 scores when given as intranasal treatment (MD -7.47, 95% CI -10.75 to -4.18; participants = 124; studies = 2; I2 = 21%). Risk of bias domains were low or not clearly reported in most studies while heterogeneity was substantial in most pooled outcomes. Route of administration and age were identified to be plausible source of heterogeneity for certain outcomes. Medicinal plants appear to be well tolerated up to 8 weeks of use. Clear beneficial evidence of medicinal plants for allergic rhinitis is still lacking. There is a need for improved reporting of herbal trials to allow for critical assessment of the effects of each individual medicinal plant preparation in well-designed future clinical studies.
  11. Purificacion M, Shah RBM, De Meeûs T, Bakar SB, Savantil AB, Yusof MM, et al.
    PLoS One, 2024;19(4):e0297662.
    PMID: 38603675 DOI: 10.1371/journal.pone.0297662
    The cocoa pod borer (CPB) Conopomorpha cramerella (Snellen) (Lepidoptera: Gracillaridae) is one of the major constraints for cocoa production in South East Asia. In addition to cultural and chemical control methods, autocidal control tactics such as the Sterile Insect Technique (SIT) could be an efficient addition to the currently control strategy, however SIT implementation will depend on the population genetics of the targeted pest. The aim of the present work was to search for suitable microsatellite loci in the genome of CPB that is partially sequenced. Twelve microsatellites were initially selected and used to analyze moths collected from Indonesia, Malaysia, and the Philippines. A quality control verification process was carried out and seven microsatellites found to be suitable and efficient to distinguish differences between CPB populations from different locations. The selected microsatellites were also tested against a closely related species, i.e. the lychee fruit borer Conopomorpha sinensis (LFB) from Vietnam and eight loci were found to be suitable. The availability of these novel microsatellite loci will provide useful tools for the analysis of the population genetics and gene flow of these pests, to select suitable CPB strains to implement the SIT.
  12. Ismail AM, Ab Hamid SH, Abdul Sani A, Mohd Daud NN
    PLoS One, 2024;19(4):e0299585.
    PMID: 38603718 DOI: 10.1371/journal.pone.0299585
    The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results.
  13. Zhao Y, Hamat B, Wang T, Wang S, Pang LLL
    PLoS One, 2024;19(4):e0302005.
    PMID: 38603676 DOI: 10.1371/journal.pone.0302005
    AIMS: In order to explore new consumer demands for Chinese tea set products, propose an innovative tea set product design and evaluation method to improve the user experience and satisfaction of the produced tea sets, thereby promoting the development of the tea set market and the promotion of tea culture.

    METHODS: Firstly, grounded theory (GT) was used to analyze interview data to extract consumer demand indicators and construct a design evaluation hierarchical model. Secondly, the Analytical Hierarchy Process (AHP) was used to calculate the weights of the indicators, determine their priority of importance, and obtain several indicators that have a greater impact on the tea set design to guide innovative design practice. Lastly, the tea set design schemes were evaluated using the fuzzy comprehensive evaluation method to select the optimal design scheme and also to act as a guideline for further design optimization.

    CONCLUSION: This study explores the innovative design and evaluation method for tea set products based on GT-AHP-FCE and validates the feasibility of this approach through a practical example of tea set design inspired by "The Classic of Mountains and Seas.". It provides innovative theoretical and practical guidance for designers of subsequent tea set products and also provides a new path for the inheritance and innovation of traditional culture.

  14. Su C, Wei J, Lei Y, Xuan H, Li J
    PLoS One, 2024;19(4):e0298261.
    PMID: 38598458 DOI: 10.1371/journal.pone.0298261
    In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC's exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.
  15. Zaid SNA, Abdul Kadir A, Mohd Noor N, Ahmad B, Yusoff MSB, Ramli AS, et al.
    PLoS One, 2024;19(4):e0302237.
    PMID: 38630657 DOI: 10.1371/journal.pone.0302237
    INTRODUCTION: Healthcare workers play a crucial role in supporting COVID-19 vaccination as they are the most trusted source of information to the public population. Assessing the healthcare workers' hesitancy towards COVID-19 vaccination is pertinent, however, there are limited validated tools to measure their hesitancy on COVID-19 vaccines. This study aims to adapt and validate the first COVID-19 hesitancy scale among healthcare workers in Malaysia.

    MATERIALS AND METHODS: This study adapted and translated the Vaccine Hesitancy Scale (VHS) developed by the WHO SAGE Working Group. The scale underwent a sequential validation process, including back-back translation, content, face, and construct validity for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The reliability was tested using internal consistency (Cronbach's alpha composite reliability (CR) and average variance extracted (AVE)).

    RESULTS: The data for EFA and CFA were completed by a separate sample of 125 and 300 HCWs, respectively. The EFA analysis of the C19-VHS-M scale was unidimensional with 10 items. A further CFA analysis revealed a uniform set of nine items with acceptable goodness fit indices (comparative fit index = 0.997, Tucker-Lewis index = 0.995, incremental fit index = 0.997, chi-squared/degree of freedom = 1.352, and root mean square error of approximation = 0.034). The Cronbach's alpha, CR and AVE results were 0.953, 0.95 and 0.70, respectively.

    CONCLUSIONS: The questionnaire was valid and reliable for use in the Malay language.

  16. Lim SH, Lim YC, Zaki RA, Johari BM, Chang CY, Omar SFS, et al.
    PLoS One, 2024;19(4):e0298376.
    PMID: 38626017 DOI: 10.1371/journal.pone.0298376
    BACKGROUND: Post Acute COVID Syndrome (PACS), a complex and poorly understood condition characterised by persistent symptoms following the acute phase of COVID-19 infection, has emerged as a significant global health concern. Healthcare workers who had been at the forefront of the pandemic response are at heightened risk of contracting the virus and subsequently developing PACS. Therefore, we aim to determine the prevalence and risk factors for PACS among healthcare workers infected with COVID-19.

    METHODS: A cross-sectional study was conducted between October 2022 and August 2023 using an online REDCap electronic data capture tool questionnaire. PACS was defined as new or persistent symptoms lasting more than 28 days after a positive SARS-CoV-2 polymerase chain reaction or rapid test kit antigen test. Multivariable logistic regression was performed to determine predictors associated with PACS.

    RESULTS: Among 609 infected healthcare workers, they were predominantly female (71.8%), Malays (84.6%), and aged 18-39 years (70.1%). 50.7% of infected healthcare workers experienced PACS. The most common PACS symptoms experienced were fatigue (27.9%), cough (25.1%), decreased physical strength (20.5%), and musculoskeletal pain (19.2%). Those who are more likely to develop PACS were females, underlying asthma, and COVID-19 severity category 3. On the other hand, those who received booster vaccinations were less likely to develop PACS.

    CONCLUSION: PACS is prevalent among healthcare workers with COVID-19 at the University Malaya Medical Centre. These findings emphasise the critical need for those with higher risk to receive regular health monitoring and checkups to detect any early signs of PACS. It underscores the need for continuous support and healthcare interventions to mitigate the impacts of PACS and ensure the physical and mental well-being of healthcare workers.

  17. Wang B, Waris M, Adamiak K, Adnan M, Hamad HA, Bhatti SM
    PLoS One, 2024;19(4):e0295853.
    PMID: 38625885 DOI: 10.1371/journal.pone.0295853
    The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country's economic development.
  18. Tian X, Tian Z, Khatib SFA, Wang Y
    PLoS One, 2024;19(4):e0300195.
    PMID: 38625972 DOI: 10.1371/journal.pone.0300195
    Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.
  19. Ke GN, Gow A, Wong RMM, Raman S, Mohammad Z, De-Lima N, et al.
    PLoS One, 2024;19(4):e0301009.
    PMID: 38630742 DOI: 10.1371/journal.pone.0301009
    The world's health, economic, and social systems have been adversely impacted by the COVID-19 pandemic. With lockdown measures being a common response strategy in most countries, many individuals were faced with financial and mental health challenges. The current study explored the effect of the COVID-19 pandemic on the psychological well-being, perception of risk factors and coping strategies of two vulnerable groups in Malaysia, namely women and older adults from low-income households (USD592). A purposive sample of 30 women and 30 older adults was interviewed via telephone during Malaysia's Movement Control Order (MCO) regarding the challenges they faced throughout the pandemic. Thematic analysis was subsequently conducted to identify key themes. The themes identified from the thematic analysis indicated a degree of overlap between both groups. For women, seven themes emerged: 1) Psychological challenges due to COVID-19 pandemic, 2) Family violence, 3) Finance and employment related stress and anxiety, 4) Women's inequality and prejudice, 5) Coping strategies, 6) Professional support, and 7) Women's empowerment. Similarly, there were six themes for the older adults: 1) Adverse emotional experiences from COVID-19, 2) Threats to health security, 3) Loss of social connections, 4) Government aid to improve older adults' psychological well-being, 5) Psychological support from family members and pets, and 6) Self-reliance, religion, and spirituality. The findings provide valuable information on the specific burdens faced by these groups, and support psychological interventions and mitigations that would be appropriate to improve well-being during the recovery phase.
  20. Butt UM, Letchmunan S, Hassan FH, Koh TW
    PLoS One, 2024;19(4):e0296486.
    PMID: 38630687 DOI: 10.1371/journal.pone.0296486
    Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.
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