Displaying publications 3841 - 3860 of 55742 in total

Abstract:
Sort:
  1. Choudhury BP, Roychoudhury S, Sengupta P, Toman R, Dutta S, Kesari KK
    Adv Exp Med Biol, 2022;1391:83-95.
    PMID: 36472818 DOI: 10.1007/978-3-031-12966-7_6
    Arsenic (As) is one of the most potent natural as well as anthropogenic metalloid toxicants that have various implications in the everyday life of humans. It is found in several chemical forms such as inorganic salt, organic salt, and arsine (gaseous form). Although it is mostly released via natural causes, there are many ways through which humans come in contact with As. Drinking water contamination by As is one of the major health concerns in various parts of the world. Arsenic exposure has the ability to induce adverse health effects including reproductive problems. Globally, around 15% of the couples are affected with infertility, of which about 20-30% are attributed to the male factor. Arsenic affects the normal development and function of sperm cells, tissue organization of the gonads, and also the sex hormone parameters. Stress induction is one of the implications of As exposure. Excessive stress leads to the release of glucocorticoids, which impact the oxidative balance in the body leading to overproduction of reactive oxygen species (ROS). This may in turn result in oxidative stress (OS) ultimately interfering with normal sperm and hormonal parameters. This study deals with As-induced OS and its association with sex hormone disruption as well as its effect on sperm and semen quality.
    Matched MeSH terms: Humans
  2. Gaurav A, Agrawal N, Al-Nema M, Gautam V
    Curr Top Med Chem, 2022;22(26):2190-2206.
    PMID: 36278463 DOI: 10.2174/1568026623666221019110334
    Over the last two decades, computational technologies have played a crucial role in antiviral drug development. Whenever a virus spreads and becomes a threat to global health, it brings along the challenge of developing new therapeutics and prophylactics. Computational drug and vaccine discovery has evolved quickly over the years. Some interesting examples of computational drug discovery are anti-AIDS drugs, where HIV protease and reverse transcriptase have been targeted by agents developed using computational methods. Various computational methods that have been applied to anti-viral research include ligand-based methods that rely on known active compounds, i.e., pharmacophore modeling, machine learning or classical QSAR; structure-based methods that rely on an experimentally determined 3D structure of the targets, i.e., molecular docking and molecular dynamics and methods for the development of vaccines such as reverse vaccinology; structural vaccinology and vaccine epitope prediction. This review summarizes these approaches to battle viral diseases and underscores their importance for anti-viral research. We discuss the role of computational methods in developing small molecules and vaccines against human immunodeficiency virus, yellow fever, human papilloma virus, SARS-CoV-2, and other viruses. Various computational tools available for the abovementioned purposes have been listed and described. A discussion on applying artificial intelligence-based methods for antiviral drug discovery has also been included.
    Matched MeSH terms: Humans
  3. Safaei M, A Sundararajan E, Asadi S, Nilashi M, Ab Aziz MJ, Saravanan MS, et al.
    Int J Environ Res Public Health, 2022 Nov 22;19(23).
    PMID: 36497509 DOI: 10.3390/ijerph192315432
    Obesity and its complications is one of the main issues in today's world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.
    Matched MeSH terms: Humans
  4. Shukar S, Zahoor F, Omer S, Awan SE, Yang C, Fang Y
    Int J Environ Res Public Health, 2022 Dec 06;19(23).
    PMID: 36498446 DOI: 10.3390/ijerph192316373
    This study aimed to examine the current situation of anti-cancer drug shortages in Pakistan, namely its determinants, impacts, adopted mitigation strategies, and proposed solutions. Qualitative semi-structured, in-depth interviews were conducted with 25 pharmacists in oncology hospitals in Pakistan from August to October 2021. Data were collected in person and online, recorded, and subjected to inductive thematic analysis after being transcribed verbatim. Most participants experienced anti-cancer drug shortages that increased during the pandemic. Etoposide, paclitaxel, vincristine, dacarbazine, and methotrexate were frequently short. Important causes included the compromised role of regulatory authorities, lack of local production, and inventory mismanagement. The impacts were delayed/suboptimal treatment and out-of-pocket costs for patients, patients' prioritization, increased workload, negative work environment, and patients' trust issues for pharmacists. The participants proposed that a cautious regulator's role is needed to revise policies for all stakeholders and support all stakeholders financially at their level to increase access to these medicines. Based on the outcomes, it is clear that anti-cancer medicine shortages are a current issue in Pakistan. Governmental authorities need to play a role in revising policies for all levels of the drug supply chain and promoting local production of these drugs. Stakeholders should also collaborate and manage inventory.
    Matched MeSH terms: Humans
  5. Wong MYZ, Yap JJL, Chih HJ, Yan BPY, Fong AYY, Beltrame JF, et al.
    Int J Cardiol, 2023 Jan 15;371:84-91.
    PMID: 36220505 DOI: 10.1016/j.ijcard.2022.10.001
    BACKGROUND: Diabetes is associated with poorer outcomes and increased complication rates in STEMI patients undergoing percutaneous coronary intervention (PCI). Data are notably lacking in the Asia-Pacific region. We report the overall association of Diabetes with clinical characteristics and outcomes in STEMI patients undergoing PCI across the Asia-Pacific, with a particular focus on regional differences.

    METHODOLOGY: The Asia Pacific Evaluation of Cardiovascular Therapies (ASPECT) collaboration consists of data from various PCI registries across Australia, Hong Kong, Singapore, Malaysia, Indonesia and Vietnam. Clinical characteristics, lesion characteristics, and outcomes were provided for STEMI patients. Key outcomes included 30-day overall mortality and major adverse cardiovascular events (MACE).

    RESULTS: A total of 12,144 STEMI patients (mean(SD) age 59.3(12.3)) were included, of which 3912 (32.2%) had diabetes. Patients with diabetes were likely to have a higher baseline risk profile, poorer clinical presentation, and more complex lesion patterns (all p 

    Matched MeSH terms: Humans
  6. W Adnan WF, Nik Mahmood NMZ, Ismail MP, Mohamad Zon E, Othman MS, Kamaludin Z
    Cancer Treat Res Commun, 2022;33:100660.
    PMID: 36455511 DOI: 10.1016/j.ctarc.2022.100660
    BACKGROUND: Endometrial cancer in young women (less than 40-year-old) is associated with anovulatory menses, polycystic ovarian syndrome (PCOS) and subfertility. Endometrial cancer occurring in a miscarriage is rare. We highlight a case of endometrial cancer occurring during miscarriage of a non-viable pregnancy, its management and the outcome.

    CASE: A 32-year-old woman, Gravida 1 Para 0, was referred to our center at 7 weeks gestation in 2018 for uncontrolled diabetes mellitus diagnosed during investigation for subfertility. Her poor compliance with the treatment is consistent with an HbA1c of 8%. During the assessment, she was already complaining of lower abdominal pain. Ultrasound showed irregular IUGS with no fetal echo. She had a miscarriage soon; however, due to ultrasound evidence of thickened and irregular endometrium (17 mm) with mixed echogenicity, dilatation and curettage (D + C) were commenced. The first and second tissues were reported as the product of conception (POC) and well differentiated endometrioid adenocarcinoma, respectively. The first hysteroscopy showed foci area of polypoidal growth at the right posterior endometrium, obscuring the right ostium, with similar histology report. She was commenced on high-dose progestogen with hysteroscopy surveillance 6 months later, which showed disease regression. After two normal hysteroscopies and endometrial biopsies with continuous progestogen therapy for 12 months, cyclical progestogen for 12 months and follow-up for another 6 months, she had spontaneous conception and is currently pregnant at 16 weeks gestation.

    CONCLUSION: Endometrial cancer should be suspected in high-risk patients with first-trimester miscarriage. Individualized treatment with high dose progestogen and follow-up with the proper patient and partner counselling and education has high successful regressionand later on, pregnancy rate.

    Matched MeSH terms: Humans
  7. Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB, et al.
    Comput Biol Med, 2021 May;132:104319.
    PMID: 33799220 DOI: 10.1016/j.compbiomed.2021.104319
    Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
    Matched MeSH terms: Humans
  8. Chowdhury MA, Shuvho MBA, Shahid MA, Haque AKMM, Kashem MA, Lam SS, et al.
    Environ Res, 2021 Jan;192:110294.
    PMID: 33022215 DOI: 10.1016/j.envres.2020.110294
    The rapid spread of COVID-19 has led to nationwide lockdowns in many countries. The COVID-19 pandemic has played serious havoc on economic activities throughout the world. Researchers are immensely curious about how to give the best protection to people before a vaccine becomes available. The coronavirus spreads principally through saliva droplets. Thus, it would be a great opportunity if the virus spread could be controlled at an early stage. The face mask can limit virus spread from both inside and outside the mask. This is the first study that has endeavoured to explore the design and fabrication of an antiviral face mask using licorice root extract, which has antimicrobial properties due to glycyrrhetinic acid (GA) and glycyrrhizin (GL). An electrospinning process was utilized to fabricate nanofibrous membrane and virus deactivation mechanisms discussed. The nanofiber mask material was characterized by SEM and airflow rate testing. SEM results indicated that the nanofibers from electrospinning are about 15-30 μm in diameter with random porosity and orientation which have the potential to capture and kill the virus. Theoretical estimation signifies that an 85 L/min rate of airflow through the face mask is possible which ensures good breathability over an extensive range of pressure drops and pore sizes. Finally, it can be concluded that licorice root membrane may be used to produce a biobased face mask to control COVID-19 spread.
    Matched MeSH terms: Humans
  9. Ong TK, Lim D, Singh M, Fial AV
    J Evid Based Dent Pract, 2022 Dec;22(4):101722.
    PMID: 36494117 DOI: 10.1016/j.jebdp.2022.101722
    OBJECTIVES: The purpose of this review was to appraise the quality of evidence of the existing publications on IR, and to perform a meta-analysis on the treatment outcomes of IR.

    METHODS: The specific PIO questions were as follows: Population: Patients with periapical periodontitis either before or after non-surgical endodontic therapy.

    INTERVENTION: IR performed with retrograde preparation and retrograde filling.

    OUTCOMES: the healing, treatment complications, and the factors influencing these outcomes after IR. Electronic and hand searches were performed in the Web of Science, PubMed, CINAHL, and Cochrane Library databases. Two authors independently screened the titles and abstracts for eligibility. The risk of bias was performed using the NIH Quality Assessment Tool, and each study was rated as "Good", "Fair" or "Poor". The analyses were performed on the treatment outcome (healing and complications), and the factors influencing the outcome of the procedure.

    RESULTS: Fourteen articles were included in the qualitative and quantitative syntheses. One was a prospective cohort study, and the other 13 were retrospective cohort studies. Overall, the evidence of this review was of poor-to-fair quality. The pooled healing rate was 80.2%, and there was a 21.7% of complication rate. Longer follow-up period, the presence of perio-endo disease, the use of non-bioceramic material as retrograde filling, longer extraoral time, and maxillary molar were found to be associated with lower healing rates. However, the differences between the subgroups were not statistically significant.

    CONCLUSIONS: The present review showed IR yielded a good overall healing rate with a low complication rate. Taking the quality of evidence into account, more high-quality studies are required to evaluate the validity of the factors that may influence the treatment outcome of IR.

    Matched MeSH terms: Humans
  10. Mohd Radzi SF, Hassan MS, Mohd Radzi MAH
    BMC Med Inform Decis Mak, 2022 Nov 24;22(1):306.
    PMID: 36434656 DOI: 10.1186/s12911-022-02050-x
    BACKGROUND: In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.

    RESULT: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers' based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.

    CONCLUSION: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.

    Matched MeSH terms: Humans
  11. Tan KL, Sim AKS, Hii ISH, Pidani R, Donohue T
    J Psychol, 2023;157(1):48-70.
    PMID: 36328776 DOI: 10.1080/00223980.2022.2134278
    The COVID-19 pandemic has changed our lives. As many industries face a complete stand-still, it also highlights the need to maintain family satisfaction (FS) during this challenging time, empirical research on achieving this remains scant. This study elucidates how marital status influences employees' religiosity, work-family enrichment (WFE) and FS. Data from 295 employees was examined using the analyzed using the partial least squares method structural equation modeling (PLS-SEM) multigroup analysis. Results suggest that religiosity has a positive significant relationship on the bidirectionality of WFE. The multigroup analysis indicates a significant difference in how single and married employees interpret work-family experience. We extend family-work interfaces by incorporating both the construct of marital status and religiosity. It advances the body of knowledge in understanding work-family interfaces, especially in times of the pandemic.
    Matched MeSH terms: Humans
  12. Zahmatkesh S, Klemeš JJ, Bokhari A, Wang C, Sillanpaa M, Hasan M, et al.
    Chemosphere, 2022 Oct;305:135247.
    PMID: 35688196 DOI: 10.1016/j.chemosphere.2022.135247
    The significant issue affecting wastewater treatment is human faeces containing SARS-CoV-2. SARS-CoV-2, as a novel coronavirus, has expanded globally. While the current focus on the COVID-19 epidemic is rightly on preventing direct transmission, the risk of secondary transmission via wastewater should not be overlooked. Many researchers have demonstrated various methods and tools for preventing and declining this virus in wastewater treatment, especially for SARS-CoV-2 in human faeces. This research reports two people tested for 30 d, with written consent, at Mosa-Ebne-Jafar Hospital of Quchan, Iran, from September 1st to October 9th, 2021. The two people's conditions are the same. The Hyssop plant was used, which boosts the immune system's effectiveness and limonene, rosemary, caffeic acids and flavonoids, all biologically active compounds in this plant, cause improved breathing problems, colds, and especially for SARS-CoV-2. As a result, utilising the Hyssop plant can help in reducing SARS-CoV-2 in faeces. This plant's antioxidant properties effectively reduce SARS-CoV-2 in faeces by 30%; nevertheless, depending on the patient's condition. This plant is also beneficial for respiratory and digestive health.
    Matched MeSH terms: Humans
  13. Nassiri-Ansari T, Atuhebwe P, Ayisi AS, Goulding S, Johri M, Allotey P, et al.
    Lancet, 2022 Jul 02;400(10345):24.
    PMID: 35780789 DOI: 10.1016/S0140-6736(22)01189-8
    Matched MeSH terms: Humans
  14. Harussani MM, Sapuan SM, Rashid U, Khalina A, Ilyas RA
    Sci Total Environ, 2022 Jan 10;803:149911.
    PMID: 34525745 DOI: 10.1016/j.scitotenv.2021.149911
    COVID-19 global pandemic, originated from Wuhan, resulted in a massive increase in the output of polypropylene (PP)-based personal protective equipment (PPE) for healthcare workers. The continuous demand of PPE across the world caused the PP based plastic wastes accumulation. Some alternative approaches that have been practiced apart from collecting the plastic waste in the landfills are incineration approach and open burning. However, there were many drawbacks of these practices, which promote the release of chemical additives and greenhouse gases into the environment. Therefore, a proper approach in treating the plastic wastes, which introduces conversion of plastic wastes into renewable energy is paramount. Along the way of extensive research and studies, the recovery of PP plastic to fuel-like liquid oil and solid char through thermal decomposition of pyrolysis process, helps in reducing the number of PP plastic wastes and produces good quality pyrolysis liquid oil and solid char to be used in fuel applications. This paper summarizes the pyrolysis process for massively produced PP plastic wastes, type of pyrolysis used and the main pyrolysis parameters affecting the product yields. Literature studies of pyrolysis of PP plastic and several key points to optimize solid char production for PP were thoroughly elaborated in this review paper.
    Matched MeSH terms: Humans
  15. Paul A, K S V, Sood A, Bhaumik S, Singh KA, Sethupathi S, et al.
    Bull Environ Contam Toxicol, 2022 Dec 13;110(1):7.
    PMID: 36512073 DOI: 10.1007/s00128-022-03638-9
    Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30 m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.
    Matched MeSH terms: Humans
  16. Alharbi KS, Singh Y, Hassan Almalki W, Rawat S, Afzal O, Alfawaz Altamimi AS, et al.
    Chem Biol Interact, 2022 May 01;358:109898.
    PMID: 35331679 DOI: 10.1016/j.cbi.2022.109898
    Coronavirus disease (COVID-19), a coronavirus-induced illness attributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, is thought to have first emerged on November 17, 2019. According to World Health Organization (WHO). COVID-19 has been linked to 379,223,560 documented occurrences and 5,693,245 fatalities globally as of 1st Feb 2022. Influenza A virus that has also been discovered diarrhea and gastrointestinal discomfort was found in the infected person, highlighting the need of monitoring them for gastro intestinal tract (GIT) symptoms regardless of whether the sickness is respiration related. The majority of the microbiome in the intestines is Firmicutes and Bacteroidetes, while Bacteroidetes, Proteobacteria, and Firmicutes are found in the lungs. Although most people overcome SARS-CoV-2 infections, many people continue to have symptoms months after the original sickness, called Long-COVID or Post COVID. The term "post-COVID-19 symptoms" refers to those that occur with or after COVID-19 and last for more than 12 weeks (long-COVID-19). The possible understanding of biological components such as inflammatory, immunological, metabolic activity biomarkers in peripheral blood is needed to evaluate the study. Therefore, this article aims to review the informative data that supports the idea underlying the disruption mechanisms of the microbiome of the gastrointestinal tract in the acute COVID-19 or post-COVID-mediated elevation of severity biomarkers.
    Matched MeSH terms: Humans
  17. Fong FC, Smith DR
    Environ Res, 2022 Sep;212(Pt A):113099.
    PMID: 35305982 DOI: 10.1016/j.envres.2022.113099
    The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RRcum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RRcum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.
    Matched MeSH terms: Humans
  18. Malik YA
    Malays J Pathol, 2022 Dec;44(3):387-396.
    PMID: 36591708
    The genetic evolution of SARS-CoV-2 began in February 2020, with G614 spike protein strains superseding D614 strains globally. Since then with each subsequent mutations, the SARS-CoV-2 variants of concern, namely Alpha, Beta, Gamma, Delta and Omicron, superseded the previous one to become the dominant strain during the pandemic. By the end of November 2022, the Omicron variant and its descendent lineages account for 99.9% of sequences reported globally. All five VOCs have mutations located in the RBD of the spike protein, resulting in increased affinity of the spike protein to the ACE2 receptors resulting in enhanced viral attachment and its subsequent entry into the host cells. In vitro studies showed the mutations in spike protein help increase the viral fitness, enhancing both transmissibility and replication. In general, Alpha, Beta, Gamma, and Delta variants, were reported with higher transmissibility of 43-90%, around 50%, 170-240%, or 130-170% than their co-circulating VOCs, respectively. The Omicron however was found to be 2.38 times and 3.20 times more transmissible than Delta among the fully-vaccinated and boostervaccinated households. Even the SARS-Cov-2 Omicron subvariants appear to be inherently more transmissible than the ones before. With the broader distribution, enhanced evasion, and improved transmissibility, SARS-CoV-2 variants infection cause severe diseases due to immune escape from host immunity and faster replication. Reports have shown that each subsequent VOC, except Omicron, cause increased disease severity compared with those infected with other circulating variants. The Omicron variant infection however, appears to be largely associated with a lower risk of hospitalisation, ICU admission, mechanical ventilation, and even a shorter length of hospital stay. It has been shown that the relatively much slower replication of the Omicron variants in the lung, resulted in a less severe disease.
    Matched MeSH terms: Humans
  19. Zaini N, Ean LW, Ahmed AN, Abdul Malek M, Chow MF
    Sci Rep, 2022 Oct 20;12(1):17565.
    PMID: 36266317 DOI: 10.1038/s41598-022-21769-1
    Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.
    Matched MeSH terms: Humans
  20. Abdul Rahim AA, Jeffree MS, Ag Daud DM, Pang N, Sazali MF
    Int J Environ Res Public Health, 2022 Sep 16;19(18).
    PMID: 36141974 DOI: 10.3390/ijerph191811704
    Musculoskeletal disorder (MSD) is a major health problem, which can lead to an enormous burden to the institution as well as chronic disability to the individual. Teachers are at risk of developing MSD due to the exposure to various ergonomic risk factors. Teachers of special education, for example, are expected to perform extra duty such as lifting and moving students, feeding food, changing diapers, and helping them in ambulation. Although there is an adequate amount of scientific research on MSD's prevalence and its risk factors among regular teachers, only few studies have focused on special education teachers. This review aimed to address these gaps by describing the evidence from various papers on the prevalence of MSD among regular and special education teachers and the related risk factors. The papers have been gathered using electronic databases, including PubMed, Science Direct, Google Scholar, and Springer. The prevalence of MSD among regular teachers ranges from 48.7% to 73.7%, while the prevalence ranges from 38.7% to 94% in special education teachers. Risk factors, such as individual (age, duration of teaching, working hours, and work burden), physical (teaching activities, affected body areas), and psychological factors (stress, anxiety, fear), were identified. From the review, it is recommended to implement ergonomically designed workplaces, comprehensive ergonomic training, psychological approaches, and functional training among teachers at risk.
    Matched MeSH terms: Humans
Filters
Contact Us

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

External Links