Displaying publications 1 - 20 of 35 in total

Abstract:
Sort:
  1. Hossain MS, Nik Ab Rahman NN, Balakrishnan V, Alkarkhi AF, Ahmad Rajion Z, Ab Kadir MO
    Waste Manag, 2015 Apr;38:462-73.
    PMID: 25636860 DOI: 10.1016/j.wasman.2015.01.003
    Clinical solid waste (CSW) poses a challenge to health care facilities because of the presence of pathogenic microorganisms, leading to concerns in the effective sterilization of the CSW for safe handling and elimination of infectious disease transmission. In the present study, supercritical carbon dioxide (SC-CO2) was applied to inactivate gram-positive Staphylococcus aureus, Enterococcus faecalis, Bacillus subtilis, and gram-negative Escherichia coli in CSW. The effects of SC-CO2 sterilization parameters such as pressure, temperature, and time were investigated and optimized by response surface methodology (RSM). Results showed that the data were adequately fitted into the second-order polynomial model. The linear quadratic terms and interaction between pressure and temperature had significant effects on the inactivation of S. aureus, E. coli, E. faecalis, and B. subtilis in CSW. Optimum conditions for the complete inactivation of bacteria within the experimental range of the studied variables were 20 MPa, 60 °C, and 60 min. The SC-CO2-treated bacterial cells, observed under a scanning electron microscope, showed morphological changes, including cell breakage and dislodged cell walls, which could have caused the inactivation. This espouses the inference that SC-CO2 exerts strong inactivating effects on the bacteria present in CSW, and has the potential to be used in CSW management for the safe handling and recycling-reuse of CSW materials.
  2. Shamim A, Balakrishnan V, Tahir M, Shiraz M
    ScientificWorldJournal, 2014;2014:340583.
    PMID: 25506612 DOI: 10.1155/2014/340583
    The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.
  3. 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.
  4. 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 
  5. Wang KW, Balakrishnan V, Liauw PC, Chua EK, Vengadasalam D, Tan YT
    Singapore Med J, 1988 Feb;29(1):53-5.
    PMID: 3406769
    Diabetes mellitus is a common chronic disease in Singapore. Its occurrence in pregnant women was 1.3% in a previous report. In a survey of 145 consecutive pregnant women registered at Alexandra Hospital the incidence of gestational diabetes was 13.1% when a total screen with 75 gm oral glucose challenge was used. The mean age of this sample was 27 years and the mean gestation at screening 33 weeks. There was an excess of Malay and Indian patients. Fifty percent had traditional risk factors tor gestational diabetes. Whether this higher incidence is a result of more stringent screening and/or increased occurrence remains to be confirmed.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Abdul Khalil HPS, Adnan AS, Yahya EB, Olaiya NG, Safrida S, Hossain MS, et al.
    Polymers (Basel), 2020 Aug 06;12(8).
    PMID: 32781602 DOI: 10.3390/polym12081759
    Cellulose nanomaterials from plant fibre provide various potential applications (i.e., biomedical, automotive, packaging, etc.). The biomedical application of nanocellulose isolated from plant fibre, which is a carbohydrate-based source, is very viable in the 21st century. The essential characteristics of plant fibre-based nanocellulose, which include its molecular, tensile and mechanical properties, as well as its biodegradability potential, have been widely explored for functional materials in the preparation of aerogel. Plant cellulose nano fibre (CNF)-based aerogels are novel functional materials that have attracted remarkable interest. In recent years, CNF aerogel has been extensively used in the biomedical field due to its biocompatibility, renewability and biodegradability. The effective surface area of CNFs influences broad applications in biological and medical studies such as sustainable antibiotic delivery for wound healing, the preparation of scaffolds for tissue cultures, the development of drug delivery systems, biosensing and an antimicrobial film for wound healing. Many researchers have a growing interest in using CNF-based aerogels in the mentioned applications. The application of cellulose-based materials is widely reported in the literature. However, only a few studies discuss the potential of cellulose nanofibre aerogel in detail. The potential applications of CNF aerogel include composites, organic-inorganic hybrids, gels, foams, aerogels/xerogels, coatings and nano-paper, bioactive and wound dressing materials and bioconversion. The potential applications of CNF have rarely been a subject of extensive review. Thus, extensive studies to develop materials with cheaper and better properties, high prospects and effectiveness for many applications are the focus of the present work. The present review focuses on the evolution of aerogels via characterisation studies on the isolation of CNF-based aerogels. The study concludes with a description of the potential and challenges of developing sustainable materials for biomedical applications.
  11. Puvanesuaran VR, Noordin R, Balakrishnan V
    PLoS One, 2013;8(4):e61730.
    PMID: 23613920 DOI: 10.1371/journal.pone.0061730
    Toxoplasma gondii is a parasitic protozoan that infects nearly one-third of the world population. The present study was done to isolate and genotype T. gondii from wild boar from forests of Pahang, Malaysia. A total of 30 wild boars' blood, heads and hearts were obtained for this study and 30 (100.0%) were found to be seropositive when assayed with modified agglutination test (MAT ≥ 6). The positive samples were inoculated into mice and T. gondii was only isolated from samples that had strong seropositivity (MAT ≥ 1:24).The isolates were subjected to PCR-RFLP analysis and all the Peninsular Malaysia isolates of T. gondii are of clonal type I.
  12. Saadi Y, Yanto IT, Herawan T, Balakrishnan V, Chiroma H, Risnumawan A
    PLoS One, 2016;11(1):e0144371.
    PMID: 26790131 DOI: 10.1371/journal.pone.0144371
    The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems.
  13. 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.
  14. 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.
  15. Balasubramaniam SD, Balakrishnan V, Oon CE, Kaur G
    Medicina (Kaunas), 2019 Jul 17;55(7).
    PMID: 31319555 DOI: 10.3390/medicina55070384
    Cervical cancer is the fourth most common cancer among women. Infection by high-risk human papillomavirus (HPV) is the main aetiology for the development of cervical cancer. Infection by high-risk human papillomavirus (HPV) and the integration of the HPV genome into the host chromosome of cervical epithelial cells are key early events in the neoplastic progression of cervical lesions. The viral oncoproteins, mainly E6 and E7, are responsible for the initial changes in epithelial cells. The viral proteins inactivate two main tumour suppressor proteins, p53, and retinoblastoma (pRb). Inactivation of these host proteins disrupts both the DNA repair mechanisms and apoptosis, leading to rapid cell proliferation. Multiple genes involved in DNA repair, cell proliferation, growth factor activity, angiogenesis, as well as mitogenesis genes become highly expressed in cervical intraepithelial neoplasia (CIN) and cancer. This genomic instability encourages HPV-infected cells to progress towards invasive carcinoma. The key molecular events involved in cervical carcinogenesis will be discussed in this review.
  16. Balasubramaniam SD, Wong KK, Oon CE, Balakrishnan V, Kaur G
    Life Sci, 2020 Sep 01;256:118026.
    PMID: 32615187 DOI: 10.1016/j.lfs.2020.118026
    AIM: We aimed to determine the biological processes and pathways involved in cervical carcinogenesis associated with high-risk human papillomavirus (HPV) infection.

    MATERIALS AND METHODS: Total RNA was extracted from three formalin-fixed paraffin-embedded (FFPE) samples each of normal cervix, HPV-infected low-grade squamous intraepithelial lesion (LSIL), high-grade SIL (HSIL) and squamous cell carcinoma (SCC). Transcriptomic profiling by microarrays was conducted followed by downstream Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.

    RESULTS: We examined the difference in GOs enriched for each transition stage from normal cervix to LSIL, HSIL, and SCC, and found 307 genes to be differentially expressed. In the transition from normal cervix to LSIL, the extracellular matrix (ECM) genes were significantly downregulated. The MHC class II genes were significantly upregulated in the LSIL to HSIL transition. In the final transition from HSIL to SCC, the immunoglobulin heavy locus genes were significantly upregulated and the ECM pathway was implicated.

    CONCLUSION: Deregulation of the immune-related genes including MHC II and immunoglobulin heavy chain genes were involved in the transitions from LSIL to HSIL and SCC, suggesting immune escape from host anti-tumour response. The extracellular matrix plays an important role during the early and late stages of cervical carcinogenesis.

  17. 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.

  18. 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.

  19. 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.

  20. Hossain MS, Balakrishnan V, Rahman NN, Sarker MZ, Kadir MO
    Int J Environ Res Public Health, 2012 Mar;9(3):855-67.
    PMID: 22690168 DOI: 10.3390/ijerph9030855
    A steam autoclave was used to sterilize bacteria in clinical solid waste in order to determine an alternative to incineration technology in clinical solid waste management. The influence of contact time (0, 5, 15, 30 and 60 min) and temperature (111 °C, 121 °C and 131 °C) at automated saturated steam pressure was investigated. Results showed that with increasing contact time and temperature, the number of surviving bacteria decreased. The optimum experimental conditions as measured by degree of inactivation of bacteria were 121 °C for 15 minutes (min) for Gram negative bacteria, 121 °C and 131 °C for 60 and 30 min for Gram positive bacteria, respectively. The re-growth of bacteria in sterilized waste was also evaluated in the present study. It was found that bacterial re-growth started two days after the inactivation. The present study recommends that the steam autoclave cannot be considered as an alternative technology to incineration in clinical solid waste management.
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links