Displaying publications 1 - 20 of 35 in total

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  1. Ni Z, Peng ML, Balakrishnan V, Tee V, Azwa I, Saifi R, et al.
    JMIR Res Protoc, 2024 Feb 15;13:e54349.
    PMID: 38228575 DOI: 10.2196/54349
    BACKGROUND: Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.

    OBJECTIVE: This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.

    METHODS: In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.

    RESULTS: The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.

    CONCLUSIONS: Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.

    INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54349.

  2. Cheah MH, Gan YN, Altice FL, Wickersham JA, Shrestha R, Salleh NAM, et al.
    JMIR Hum Factors, 2024 Jan 26;11:e52055.
    PMID: 38277206 DOI: 10.2196/52055
    BACKGROUND: The HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts. We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability.

    OBJECTIVE: This study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM.

    METHODS: We conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence.

    RESULTS: Most participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM.

    CONCLUSIONS: The current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP. To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV.

  3. Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, et al.
    Phys Rev Lett, 2024 Jan 12;132(2):021803.
    PMID: 38277607 DOI: 10.1103/PhysRevLett.132.021803
    The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140  fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.
  4. Balakrishnan V, Kherabi Y, Ramanathan G, Paul SA, Tiong CK
    Prog Biophys Mol Biol, 2023 May;179:16-25.
    PMID: 36931609 DOI: 10.1016/j.pbiomolbio.2023.03.001
    Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.
  5. Nisaa AA, Oon CE, Sreenivasan S, Balakrishnan V, Rajendran D, Tan JJ, et al.
    Prev Nutr Food Sci, 2023 Mar 31;28(1):1-9.
    PMID: 37066035 DOI: 10.3746/pnf.2023.28.1.1
    We previously reported that breast milk from women with (W) or without (WO) vaginal yeast infection during pregnancy differs in its immunological and antimicrobial properties, especially against pathogenic vaginal Candida sp.. Here, we investigated the differences in microbiota profiles of breast milk from these groups. Seventy-two breast milk samples were collected from lactating mothers (W, n=37; WO, n=35). The DNA of bacteria was extracted from each breast milk sample for microbiota profiling by 16S rRNA gene sequencing. Breast milk from the W-group exhibited higher alpha diversity than that from the WO-group across different taxonomic levels of class (P=0.015), order (P=0.011), family (P=0.020), and genus (P=0.030). Compositional differences between groups as determined via beta diversity showed marginal differences at taxonomic levels of phylum (P=0.087), family (P=0.064), and genus (P=0.067). The W-group showed higher abundances of families Moraxellaceae (P=0.010) and Xanthomonadaceae (P=0.008), and their genera Acinetobacter (P=0.015), Enhydrobacter (P=0.015), and Stenotrophomonas (P=0.007). Meanwhile, the WO-group showed higher abundances of genus Staphylococcus (P=0.046) and species Streptococcus infantis (P=0.025). This study shows that, although breast milk composition is affected by vaginal infection during pregnancy, this may not pose a threat to infant growth and development.
  6. Nisaa AA, Oon CE, Sreenivasan S, Balakrishnan V, Tan JJ, Teh CS, et al.
    Food Sci Biotechnol, 2023 Mar;32(4):471-480.
    PMID: 36911325 DOI: 10.1007/s10068-022-01088-x
    The aim of this study was to investigate the different immunological and antimicrobial properties of breast milk from women with (W) or without (WO) vaginal yeast infections during pregnancy in 85 lactating women (W, n = 43; WO, n = 42). Concentrations of IL-10, IgA, IgM, IgG, EGF, and TGF-α were similar in both groups. However, breast milk of women aged below 31 years old from the W-group showed higher concentration of EGF than the WO-group (p = 0.031). Breast milk from WO-group exhibited higher anti-Candida properties than W-group, both via growth inhibition and aggregation of yeast cells (p 
  7. Wong MTJ, Dhaliwal SS, Balakrishnan V, Nordin F, Norazmi MN, Tye GJ
    PMID: 36674401 DOI: 10.3390/ijerph20021647
    (1) Background: The assessment of vaccine effectiveness against the Omicron variant is vital in the fight against COVID-19, but research on booster vaccine efficacy using nationwide data was lacking at the time of writing. This study investigates the effectiveness of booster doses on the Omicron wave in Malaysia against COVID-19 infections and deaths; (2) Methods: This study uses nationally representative data on COVID-19 from 1 January to 31 March 2022, when the Omicron variant was predominant in Malaysia. Daily new infections, deaths, ICU utilization and Rt values were compared. A screening method was used to predict the vaccine effectiveness against COVID-19 infections, whereas logistic regression was used to estimate vaccine effectiveness against COVID-19-related deaths, with efficacy comparison between AZD1222, BNT162b2 and CoronaVac; (3) Results: Malaysia's Omicron wave started at the end of January 2022, peaking on 5 March 2022. At the time of writing, statistics for daily new deaths, ICU utilization, and effective reproductive values (Rt) were showing a downtrend. Boosted vaccination is 95.4% (95% CI: 95.4, 95.4) effective in curbing COVID-19 infection, compared to non-boosted vaccination, which is 87.2% (95% CI: 87.2, 87.2). For symptomatic infection, boosted vaccination is 97.4% (95% CI: 97.4, 97.4) effective, and a non-boosted vaccination is 90.9% (95% CI: 90.9, 90.9). Against COVID-19-related death, boosted vaccination yields a vaccine effectiveness (VE) of 91.7 (95% CI: 90.6, 92.7) and full vaccination yields a VE of 65.7% (95% CI: 61.9, 69.1). Looking into the different vaccines as boosters, AZD1222 is 95.2% (CI 95%: 92.7, 96.8) effective, BNT162b2 is 91.8% (CI 95%: 90.7, 92.8) effective and CoronaVac is 88.8% (CI 95%: 84.9, 91.7) effective against COVID-19 deaths. (4) Conclusions: Boosters are effective in increasing protection against COVID-19, including the Omicron variant. Given that the VE observed was lower, CoronaVac recipients are encouraged to take boosters due to its lower VE.
  8. Balakrishnan V, Ng KS, Kaur W, Govaichelvan K, Lee ZL
    J Affect Disord, 2022 Feb 01;298(Pt B):47-56.
    PMID: 34801606 DOI: 10.1016/j.jad.2021.11.048
    BACKGROUND: This systematic review and meta-analysis aim to synthesize the extant literature reporting the effects of COVID-19 pandemic based on the pooled prevalence of depression among affected populations in Asia Pacific, as well as its risk factors.

    METHOD: A systematic review and meta-analysis approach was adopted as per the PRISMA guidelines, targeting articles published in PubMed, Google Scholar and Scopus from January 2021 to March 30, 2021. The screening resulted in 82 papers.

    RESULTS: The overall pooled depression prevalence among 201,953 respondents was 34% (95%CI, 29-38, 99.7%), with no significant differences observed between the cohorts, timelines, and regions (p > 0.05). Dominant risk factors found were fear of COVID-19 infection (13%), gender (i.e., females; 12%) and deterioration of underlying medical conditions (8.3%), regardless of the sub-groups. Specifically, fear of COVID-19 infection was the most reported risk factor among general population (k = 14) and healthcare workers (k = 8). Gender (k = 7) and increased workload (k = 7) were reported among healthcare workers whereas education disruption among students (k = 7).

    LIMITATION: The review is limited to articles published in three electronic databases. Conclusion The pandemic has caused depression among the populations across Asia Pacific, specifically among the general population, healthcare workers and students. Immediate attention and interventions from the concerned authorities are needed in addressing this issue.

  9. Balakrishnan V, Ng KS, Kaur W, Lee ZL
    Curr Psychol, 2022 Jan 12.
    PMID: 35035200 DOI: 10.1007/s12144-021-02556-z
    With the record surge of positive cases in Southeast Asia, there is a need to examine the adverse mental effects of COVID-19 among the under-researched countries. This study aims to synthesize the extant literature reporting the effects of COVID-19 pandemic on the psychological outcomes of people in Southeast Asia, and its risk factors. A scoping review was adopted targeting articles published in PubMed, Google Scholar and Scopus from January 2020 to March 30, 2021. Articles were screened using predetermined eligibility criteria, resulting in 26 papers. Elevated prevalence of adverse mental effects was noted in most of the countries as the pandemic progressed over time, with Malaysia and Philippines reporting higher prevalence rates. Mental decline was found to be more profound among the general population compared to healthcare workers and students. Dominant risk factors reported were age (younger), sex (females), education (higher), low coping skill and social/family support, and poor reliability in COVID-19 related information. Adverse mental effects were noted among population, healthcare workers and students in most of the Southeast Asian countries. Intervention and prevention efforts should be based at community-level and prioritize young adults, females, and individuals with dire financial constraints.
  10. Dass SA, Balakrishnan V, Arifin N, Lim CSY, Nordin F, Tye GJ
    Front Immunol, 2022;13:833715.
    PMID: 35242137 DOI: 10.3389/fimmu.2022.833715
    2020 will be marked in history for the dreadful implications of the COVID-19 pandemic that shook the world globally. The pandemic has reshaped the normality of life and affected mankind in the aspects of mental and physical health, financial, economy, growth, and development. The focus shift to COVID-19 has indirectly impacted an existing air-borne disease, Tuberculosis. In addition to the decrease in TB diagnosis, the emergence of the TB/COVID-19 syndemic and its serious implications (possible reactivation of latent TB post-COVID-19, aggravation of an existing active TB condition, or escalation of the severity of a COVID-19 during TB-COVID-19 coinfection), serve as primary reasons to equally prioritize TB. On a different note, the valuable lessons learnt for the COVID-19 pandemic provide useful knowledge for enhancing TB diagnostics and therapeutics. In this review, the crucial need to focus on TB amid the COVID-19 pandemic has been discussed. Besides, a general comparison between COVID-19 and TB in the aspects of pathogenesis, diagnostics, symptoms, and treatment options with importance given to antibody therapy were presented. Lastly, the lessons learnt from the COVID-19 pandemic and how it is applicable to enhance the antibody-based immunotherapy for TB have been presented.
  11. Hassan NSM, Hossain MS, Balakrishnan V, Zuknik MH, Mustaner M, Easa AM, et al.
    Foods, 2021 Nov 17;10(11).
    PMID: 34829117 DOI: 10.3390/foods10112838
    Palm oil is known to be rich in carotenoids and other phytonutrients. However, the carotenoids and phytonutrients degrade due to high heat sterilization of oil palm fruits. The present study was conducted to produce carotenoid-rich virgin palm oil (VPO) using cold-press extraction. Herein, the influence of sterilization of oil palm fresh fruits in the production of cold-pressed VPO was determined with varying sterilization temperatures, times, and amounts of palm fruits in sterilization. The experimental sterilization conditions were optimized using response surface methodology (RSM) based on the maximum VPO yield and minimum FFAs in cold-pressed VPO. The optimal sterilization experimental conditions of oil palm fruits were determined to be a sterilization temperature of 62 °C, a time of 90 min, and an amount of oil palm fruits of 8 kg. Under these experimental conditions, the maximum cold-pressed VPO yield and the minimal content of free fatty acids (FFAs) obtained were 27.94 wt.% and 1.32 wt.%, respectively. Several analytic methods were employed to determine cold-pressed VPO quality and fatty acids compositions and compared with the crude palm oil. It was found that cold-pressed VPO contains higher carotenoids (708 mg/g) and unsaturated fatty acids compared with the carotenoid (343 mg/g) and fatty acid compositions in CPO. The findings of the present study reveal that the sterilization temperature potentially influences the carotenoid and nutrient contents in VPO; therefore, the optimization of the sterilization conditions is crucial to producing carotenoid- and phytonutrient-rich VPO.
  12. Balakrishnan V, Shi Z, Law CL, Lim R, Teh LL, Fan Y
    J Supercomput, 2021 Nov 05.
    PMID: 34754140 DOI: 10.1007/s11227-021-04169-6
    We present a benchmark comparison of several deep learning models including Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short Term Memory, assessed based on various word embedding approaches, including the Bi-directional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was administered using the Easy Data Augmentation approach resulting in two datasets (original versus augmented). All the models were assessed in two setups, namely 5-class versus 3-class (i.e., compressed version). Findings show the best prediction models were Neural Network-based using Word2Vec, with CNN-RNN-Bi-LSTM producing the highest accuracy (96%) and F-score (91.1%). Individually, RNN was the best model with an accuracy of 87.5% and F-score of 83.5%, while RoBERTa had the best F-score of 73.1%. The study shows that deep learning is better for analyzing the sentiments within the text compared to supervised machine learning and provides a direction for future work and research.
  13. Zakaria ND, Omar MH, Ahmad Kamal NN, Abdul Razak K, Sönmez T, Balakrishnan V, et al.
    ACS Omega, 2021 Sep 28;6(38):24419-24431.
    PMID: 34604624 DOI: 10.1021/acsomega.1c02670
    Electrodeposition is an electrochemical method employed to deposit stable and robust gold nanoparticles (AuNPs) on electrode surfaces for creating chemically modified electrodes (CMEs). The use of several electrodeposition techniques with different experimental parameters allow in obtaining various surface morphologies of AuNPs deposited on the electrode surface. By considering the electrodeposition of AuNPs in various background electrolytes could play an important strategy in finding the most suitable formation of the electrodeposited AuNP films on the electrode surface. This is because different electrode roughnesses can have different effects on the electrochemical activities of the modified electrodes. Thus, in this study, the electrodeposition of AuNPs onto the glassy carbon (GC) electrode surfaces in various aqueous neutral and acidic electrolytes was achieved by using the cyclic voltammetry (CV) technique with no adjustable CV parameters. Then, surface morphologies and electrochemical activities of the electrodeposited AuNPs were investigated using scanning electron microscopy (SEM), atomic force microscopy (AFM), CV, and electrochemical impedance spectroscopy (EIS). The obtained SEM and 3D-AFM images show that AuNPs deposited at the GC electrode prepared in NaNO3 solution form a significantly better, uniform, and homogeneous electrodeposited AuNP film on the GC electrode surface with nanoparticle sizes ranging from ∼36 to 60 nm. Meanwhile, from the electrochemical performances of the AuNP-modified GC electrodes, characterized by using a mixture of ferricyanide and ferrocyanide ions [Fe(CN6)3-/4-], there is no significant difference observed in the case of charge-transfer resistances (R ct) and heterogeneous electron-transfer rate constants (k o), although there are differences in the surface morphologies of the electrodeposited AuNP films. Remarkably, the R ct values of the AuNP-modified GC electrodes are lower than those of the bare GC electrode by 18-fold, as the R ct values were found to be ∼6 Ω (p < 0.001, n = 3). This has resulted in obtaining k o values of AuNP-modified GC electrodes between the magnitude of 10-2 and 10-3 cm s-1, giving a faster electron-transfer rate than that of the bare GC electrode (10-4 cm s-1). This study confirms that using an appropriate supporting background electrolyte plays a critical role in preparing electrodeposited AuNP films. This approach could lead to nanostructures with a more densely, uniformly, and homogeneously electrodeposited AuNP film on the electrode surfaces, albeit utilizing an easy and simple preparation method.
  14. Dass SA, Selva Rajan R, Tye GJ, Balakrishnan V
    Hum Vaccin Immunother, 2021 09 02;17(9):2981-2994.
    PMID: 33989511 DOI: 10.1080/21645515.2021.1913960
    Cervical cancer is ranked as the fourth most common cancer in women worldwide. Monoclonal antibody has created a new dimension in the immunotherapy of many diseases, including cervical cancer. The antibody's ability to target various aspects of cervical cancer (oncoviruses, oncoproteins, and signaling pathways) delivers a promising future for efficient immunotherapy. Besides, technologies such as hybridoma and phage display provide a fundamental platform for monoclonal antibody generation and create the opportunity to generate novel antibody classes including, T cell receptor (TCR)-like antibody. In this review, the current immunotherapy strategies for cervical cancer are presented. We have also proposed a novel concept of T cell receptor (TCR)-like antibody and its potential applications for enhancing cervical cancer therapeutics. Finally, the possible challenges in TCR-like antibody application for cervical cancer therapeutics have been addressed, and strategies to overcome the challenges have been highlighted to maximize the therapeutic benefits.
  15. Balakrishnan V, Ng KS, Rahim HA
    Technol Soc, 2021 Aug;66:101676.
    PMID: 36540782 DOI: 10.1016/j.techsoc.2021.101676
    This study investigates the underlying motives for online fake news sharing during the COVID-19 pandemic, an unprecedented time that witnessed a spike in the spread of false content. Motives were identified based on a fake news sharing model developed using the SocioCultural-Psychological-Technology (SCulPT) model, Uses and Gratification (U&G) theory and Self-Determination Theory (SDT), and further extended using fake news predictors/gratifications from past studies. A self-administered survey resulted in 869 online Malaysian respondents aged between 18 and 59 years old (Mean = 22.6, Standard deviation = 6.13). Structured equation modelling revealed the fake news sharing model to collectively account for 49.2 % of the variance, with Altruism (β = 0.333; p 
  16. Khairil Anwar NA, Mohd Nazri MN, Murtadha AH, Mohd Adzemi ER, Balakrishnan V, Mustaffa KMF, et al.
    Acta Biochim Biophys Sin (Shanghai), 2021 Jul 28;53(8):961-978.
    PMID: 34180502 DOI: 10.1093/abbs/gmab077
    Aggressive tissue biopsy is commonly unavoidable in the management of most suspected tumor cases to conclusively verify the presence of cancerous cells through histological assessment. The extracted tissue is also immunostained for detection of antigens (tissue tumor markers) of potential prognostic or therapeutic importance to assist in treatment decision. Although liquid biopsies can be a powerful tool for monitoring treatment response, they are still excluded from standard cancer diagnostics, and their utility is still being debated in the scientific community. With a myriad of soluble tissue tumor markers now being discovered, liquid biopsies could completely change the current paradigms of cancer management. Recently, soluble programmed cell death ligand-1 (sPD-L1), which is found in the peripheral blood, i.e. serum and plasma, has shown potential as a pre-therapeutic predictive marker as well as a prognostic biomarker to monitor treatment efficacy. Thus, this review focuses on the emergence of sPD-L1 and promising technologies for its detection in order to support liquid biopsies for future cancer management.
  17. Morelli M, Urbini F, Bianchi D, Baiocco R, Cattelino E, Laghi F, et al.
    PMID: 33806314 DOI: 10.3390/ijerph18052526
    BACKGROUND: Sexting is an increasingly common phenomenon among adolescents and young adults. Some studies have investigated the role of personality traits in different sexting behaviors within mainstream personality taxonomies like Big Five and HEXACO. However, very few studies have investigated the role of maladaptive personality factors in sexting. Therefore, the present study investigated the relationship between Dark Triad Personality Traits and experimental (i.e., sharing own sexts), risky (i.e., sexting under substance use and with strangers), and aggravated sexting (i.e., non-consensual sexting and sexting under pressure) across 11 countries.

    METHODS: An online survey was completed by 6093 participants (Mage = 20.35; SDage = 3.63) from 11 different countries which covered four continents (Europe, Asia, Africa, and America). Participants completed the Sexting Behaviors Questionnaire and the 12-item Dark Triad Dirty Dozen scale.

    RESULTS: Hierarchical regression analyses showed that sharing own sexts was positively predicted by Machiavellianism and Narcissism. Both risky and aggravated sexting were positively predicted by Machiavellianism and Psychopathy.

    CONCLUSIONS: The present study provided empirical evidence that different sexting behaviors were predicted by Dark Triad Personality Traits, showing a relevant role of Machiavellianism in all kinds of investigated sexting behaviors. Research, clinical, and education implications for prevention programs are discussed.

  18. Mohamed SH, Hossain MS, Mohamad Kassim MH, Ahmad MI, Omar FM, Balakrishnan V, et al.
    Polymers (Basel), 2021 Feb 19;13(4).
    PMID: 33669623 DOI: 10.3390/polym13040626
    There is an interest in the sustainable utilization of waste cotton cloths because of their enormous volume of generation and high cellulose content. Waste cotton cloths generated are disposed of in a landfill, which causes environmental pollution and leads to the waste of useful resources. In the present study, cellulose nanocrystals (CNCs) were isolated from waste cotton cloths collected from a landfill. The waste cotton cloths collected from the landfill were sterilized and cleaned using supercritical CO2 (scCO2) technology. The cellulose was extracted from scCO2-treated waste cotton cloths using alkaline pulping and bleaching processes. Subsequently, the CNCs were isolated using the H2SO4 hydrolysis of cellulose. The isolated CNCs were analyzed to determine the morphological, chemical, thermal, and physical properties with various analytical methods, including attenuated total reflection-Fourier transform-infrared spectroscopy (ATR-FTIR), field-emission scanning electron microscopy (FE-SEM), energy-filtered transmission electron microscopy (EF-TEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The results showed that the isolated CNCs had a needle-like structure with a length and diameter of 10-30 and 2-6 nm, respectively, and an aspect ratio of 5-15, respectively. Additionally, the isolated CNCs had a high crystallinity index with a good thermal stability. The findings of the present study revealed the potential of recycling waste cotton cloths to produce a value-added product.
  19. Dass SA, Tan KL, Selva Rajan R, Mokhtar NF, Mohd Adzmi ER, Wan Abdul Rahman WF, et al.
    Medicina (Kaunas), 2021 Jan 12;57(1).
    PMID: 33445543 DOI: 10.3390/medicina57010062
    Triple-negative breast cancer (TNBC) is an aggressive breast type of cancer with no expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). It is a highly metastasized, heterogeneous disease that accounts for 10-15% of total breast cancer cases with a poor prognosis and high relapse rate within five years after treatment compared to non-TNBC cases. The diagnostic and subtyping of TNBC tumors are essential to determine the treatment alternatives and establish personalized, targeted medications for every TNBC individual. Currently, TNBC is diagnosed via a two-step procedure of imaging and immunohistochemistry (IHC), which are operator-dependent and potentially time-consuming. Therefore, there is a crucial need for the development of rapid and advanced technologies to enhance the diagnostic efficiency of TNBC. This review discusses the overview of breast cancer with emphasis on TNBC subtypes and the current diagnostic approaches of TNBC along with its challenges. Most importantly, we have presented several promising strategies that can be utilized as future TNBC diagnostic modalities and simultaneously enhance the efficacy of TNBC diagnostic.
  20. Khalid AM, Hossain MS, Ismail N, Khalil NA, Balakrishnan V, Zulkifli M, et al.
    Polymers (Basel), 2020 Dec 30;13(1).
    PMID: 33396583 DOI: 10.3390/polym13010112
    In the present study, magnetic oil palm empty fruits bunch cellulose nanofiber (M-OPEFB-CNF) composite was isolated by sol-gel method using cellulose nanofiber (CNF) obtained from oil palm empty fruits bunch (OPEFB) and Fe3O4 as magnetite. Several analytical methods were utilized to characterize the mechanical, chemical, thermal, and morphological properties of the isolated CNF and M-OPEFB-CNF. Subsequently, the isolated M-OPEFB-CNF composite was utilized for the adsorption of Cr(VI) and Cu(II) from aqueous solution with varying parameters, such as pH, adsorbent doses, treatment time, and temperature. Results showed that the M-OPEFB-CNF as an effective bio-sorbent for the removal of Cu(II) and Cr(VI) from aqueous solution. The adsorption isotherm modeling revealed that the Freundlich equation better describes the adsorption of Cu(II) and Cr(VI) on M-OPEFB-CNF composite. The kinetics studies revealed the pseudo-second-order kinetics model was a better-described kinetics model for the removal of Cu(II) and Cr(VI) using M-OPEFB-CNF composite as bio-sorbent. The findings of the present study showed that the M-OPEFB-CNF composite has the potential to be utilized as a bio-sorbent for heavy metals removal.
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