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  1. Tanimu B, Hamed MM, Bello AD, Abdullahi SA, Ajibike MA, Shahid S
    Environ Sci Pollut Res Int, 2024 Feb;31(10):15986-16010.
    PMID: 38308777 DOI: 10.1007/s11356-024-32128-0
    Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.
    Matched MeSH terms: Time Factors
  2. Rezaiy R, Shabri A
    Water Sci Technol, 2024 Feb;89(3):745-770.
    PMID: 38358500 DOI: 10.2166/wst.2024.028
    This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.
    Matched MeSH terms: Time Factors
  3. Gunawardena SA, Abeyratne P, Jayasena A, Rajapaksha S, Senadhipathi H, Siriwardana D, et al.
    Sci Justice, 2023 Sep;63(5):638-650.
    PMID: 37718011 DOI: 10.1016/j.scijus.2023.08.001
    Estimating the post mortem interval (PMI) in skeletonized cases is an extremely challenging exercise. Sri Lanka lacks adequate taphonomic research which is a serious limitation when assessing PMI in forensic death investigations. Methods that have been proposed to estimate PMI using the total body score (TBS) and accumulated degree days (ADD) are mainly based on data from continental and temperate climates and have shown less reliability in tropical climates. With the intention of developing a region-specific, evidence-based guide which would be applicable to tropical climates like Sri Lanka, we selected thirteen skeletonized remains with known PMIs from forensic case records and analysed their taphonomy in relation to selected weather data. We also compared the ADD values within our dataset with reference ranges calculated using published formula. All except one were found from outdoor locations. The TBS ranged from 24 to 32 and had a weak positive correlation with the PMI. The earliest appearance of skeletonization was 15 days in a body found indoors. The highest rate of skeletonization was seen in a body with a TBS of 32 and a PMI of 23 days. The average daily temperature and relative humidity were similar across all the cases however, the amount of rainfall varied. Bodies exposed to monsoon rains (n = 6) had a lower mean rate of skeletonization compared to those that were not exposed (n = 4) suggesting lower rates of decomposition during periods of heavy rainfall. No correlation was found between ADD and TBS. In 9 (69.2%) cases, the actual ADD was much lower than reference ADD ranges for TBS values, indicating poor applicability of TBS and ADD based formulae in estimating PMI within the Sri Lankan climate. Our study shows a strong need for taphonomic and entomological research in tropical climates to further explore the impact of monsoons on biotic and abiotic factors affecting skeletonization.
    Matched MeSH terms: Time Factors
  4. FinNie O, Aye SA, Krishnappa P, Ravindran R
    Med J Malaysia, 2023 Mar;78(2):202-206.
    PMID: 36988531
    INTRODUCTION: The purpose of tissue processing is to fix the tissue in a solid medium toenable thin sections. Conventional method of tissue processing is the standardized method of tissue processing which has been used for more than 10 decades. However, the conventional method is time-consuming, and the overall turnaround time for the histopathology report is at least two days. The objective of this study is to identify the protocol for tissue processing procedure using domestic microwave oven. To determine the tissue processing time when using domestic microwave oven. To compare the morphological quality of tissue slides made by domestic microwave oven and conventional method using automated tissue processor.

    MATRIALS AND METHODS: The conventional protocol and three microwave protocols of tissue processing were used in this study. A pilot study was done prior to the real run to determine the baseline timing for microwave protocol. The baseline timing was fixed at 2 minutes,30 minutes,5 minutes and 25 minutes. The processing time of the microwave protocol was adjusted from 62 minutes to 70 minutes to 77 minutes by increasing the dehydration and wax impregnation time while the time for tissue fixation and clearing remain the same throughout all the microwave protocols.

    RESULTS: The group 2 microwave protocol produced the sections that is closely comparable to group 1 conventional protocol. The morphological quality of histopathology slides is best observed when the processing time of microwave protocol is 62 minutes.

    CONCLUSION: The most appropriate microwave protocol for tissue processing is group 2 as the morphological quality of histopathology slides are more superior than that of group 1 with an overall percentage of 80% of satisfactory slides in group 2 and 76.68% in group 1.

    Matched MeSH terms: Time Factors
  5. Lim SH, Tan TL, Ngo PW, Lee LY, Ting SY, Tan HJ
    Med J Malaysia, 2023 Mar;78(2):241-249.
    PMID: 36988537
    INTRODUCTION: Time is the greatest challenge in stroke management. This study aimed to examine factors contributing to prehospital delay and decision delay among stroke patients.

    MATERIALS AND METHODS: A cross-sectional study involving acute stroke patients admitted to Seri Manjung Hospital was conducted between August 2019 and October 2020 via faceto- face interview. Prehospital delay was defined as more than 120 minutes taken from recognition of stroke symptoms till arrival in hospital, while decision delay was defined as more than 60 minutes taken from recognition of stroke symptoms till decision was made to seek treatment.

    RESULTS: The median prehospital delay of 102 enrolled patients was 364 minutes (IQR 151.5, 1134.3) while the median for decision delay was 120 minutes (IQR 30.0, 675.0). No history of stroke (adj. OR 4.15; 95% CI 1.21, 14.25; p=0.024) and unaware of thrombolysis service (adj. OR 17.12; 95% CI 1.28, 229.17; p=0.032) were associated with higher odds of prehospital delay, while Indian ethnicity (adj. OR 0.09; 95% CI 0.02, 0.52; p=0.007) was associated with lower odds of prehospital delay as compared to Malay ethnicity. On the other hand, higher National Institutes of Health Stroke Scale (NIHSS) score (adj. OR 0.86; 95% CI 0.78, 0.95; p=0.002) was associated with lower odds of decision delay.

    CONCLUSION: Public awareness is crucial to shorten prehosital delay and decision delay for better patients' outcomes in stroke. Various public health campaigns are needed to improve the awareness for stroke.

    Matched MeSH terms: Time Factors
  6. Vivekanandhan G, Abdolmohammadi HR, Natiq H, Rajagopal K, Jafari S, Namazi H
    Math Biosci Eng, 2023 Jan;20(3):4760-4781.
    PMID: 36896521 DOI: 10.3934/mbe.2023220
    Human evolution is carried out by two genetic systems based on DNA and another based on the transmission of information through the functions of the nervous system. In computational neuroscience, mathematical neural models are used to describe the biological function of the brain. Discrete-time neural models have received particular attention due to their simple analysis and low computational costs. From the concept of neuroscience, discrete fractional order neuron models incorporate the memory in a dynamic model. This paper introduces the fractional order discrete Rulkov neuron map. The presented model is analyzed dynamically and also in terms of synchronization ability. First, the Rulkov neuron map is examined in terms of phase plane, bifurcation diagram, and Lyapunov exponent. The biological behaviors of the Rulkov neuron map, such as silence, bursting, and chaotic firing, also exist in its discrete fractional-order version. The bifurcation diagrams of the proposed model are investigated under the effect of the neuron model's parameters and the fractional order. The stability regions of the system are theoretically and numerically obtained, and it is shown that increasing the order of the fractional order decreases the stable areas. Finally, the synchronization behavior of two fractional-order models is investigated. The results represent that the fractional-order systems cannot reach complete synchronization.
    Matched MeSH terms: Time Factors
  7. Abdul Nasir M, Ahmad TS, Low TH, Devarajooh C, Gunasagaran J
    PLoS One, 2023;18(5):e0286301.
    PMID: 37252923 DOI: 10.1371/journal.pone.0286301
    We aimed to investigate the association between flexor tendon degeneration and outcome of open trigger digit release. We recruited 162 trigger digits (136 patients) who had open trigger digit release from February 2017 to March 2019. Intraoperatively, six features of tendon degenerations were identified: irregular tendon surface, tendon fraying, intertendinous tear, synovial thickening, hyperaemia of sheath and tendon dryness. Longer duration of preoperative symptoms was associated with worsening tendon surface irregularity and fraying; increased number of steroid injections was associated with worsening tendon surface irregularity and dryness; higher DASH score was associated with severe tendon fraying, dryness and intertendinous tear; limited proximal interphalangeal joint (PIPJ) motion was associated with severe tendon dryness. At 1-month post-surgery, DASH score remained high in severe intertendinous tear group while PIPJ motion remained limited in severe tendon dryness group. In conclusion, the severity of various flexor tendon degenerations influenced the outcome of open trigger digit release at 1-month but did not affect the outcome at 3- and 6-months post-surgery.
    Matched MeSH terms: Time Factors
  8. Shaikh AK, Nazir A, Khan I, Shah AS
    Sci Rep, 2022 Dec 29;12(1):22562.
    PMID: 36581655 DOI: 10.1038/s41598-022-26499-y
    Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with "day as covariates" remained better than the 1, 2, 3, and 4-week scenarios.
    Matched MeSH terms: Time Factors
  9. Chughtai JU, Haq IU, Islam SU, Gani A
    Sensors (Basel), 2022 Dec 12;22(24).
    PMID: 36560104 DOI: 10.3390/s22249735
    Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
    Matched MeSH terms: Time Factors
  10. Hasan NA, Hong JGS, Teo IH, Zaidi SN, Hamdan M, Tan PC
    Int J Gynaecol Obstet, 2022 Dec;159(3):951-960.
    PMID: 35726368 DOI: 10.1002/ijgo.14313
    OBJECTIVE: To evaluate immediate oxytocin and early amniotomy compared with delayed amniotomy after Foley catheter cervical ripening in nulliparous women on intervention-to-delivery interval.

    METHODS: A randomized trial was conducted from September 2020 to March 2021. A total of 140 term nulliparas (70 early amniotomy, 70 delayed amniotomy) with Foley catheter-ripened cervices (dilatation ≥3 cm achieved), singleton fetus, cephalic presentation with intact membranes, and reassuring fetal heart rate tracing were recruited. Women were randomized to immediate titrated intravenous oxytocin infusion and early amniotomy or delayed amniotomy (after 4 h of oxytocin). The primary outcome was intervention (oxytocin)-to-delivery interval (h).

    RESULTS: Intervention-to-delivery intervals (h) were mean ± standard deviation 9.0 ± 3.6 versus 10.6 ± 3.5 h (mean difference of 1.4 h) (P = 0.004) for the early versus delayed amniotomy arms, respectively. Birth rates at 6 h after oxytocin infusion were 19 of 70 (27.1%) versus 8 of 70 (11.4%) (relative risk, 2.38 [95% confidence interval (CI), 1.11-5.06]; number needed to treat: 7 [95% CI, 3.5-34.4]) (P = 0.03), cesarean delivery rates were 29 of 70 (41.4%) versus 33 of 70 (47.1%) (relative risk, 0.88; 95% CI, 0.61-1.28) (P = 0.50), and maternal satisfaction on birth process were a median of 7 (interquartile range, 7-8) versus 7 (interquartile range, 7-8) (P = 0.40) for the early versus delayed amniotomy arms, respectively.

    CONCLUSION: In term nulliparas with cervices ripened by Foley catheter, immediate oxytocin and early amniotomy compared with a planned 4-h delay to amniotomy shortened the intervention-to-delivery interval but did not significantly reduce the cesarean delivery rate.

    Matched MeSH terms: Time Factors
  11. Irfan M, Razzaq A, Suksatan W, Sharif A, Madurai Elavarasan R, Yang C, et al.
    J Therm Biol, 2022 Feb;104:103101.
    PMID: 35180949 DOI: 10.1016/j.jtherbio.2021.103101
    The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.
    Matched MeSH terms: Time Factors
  12. Zaini N, Ean LW, Ahmed AN, Malek MA
    Environ Sci Pollut Res Int, 2022 Jan;29(4):4958-4990.
    PMID: 34807385 DOI: 10.1007/s11356-021-17442-1
    Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
    Matched MeSH terms: Time Factors
  13. Hussain S, Mustafa MW, Al-Shqeerat KHA, Saeed F, Al-Rimy BAS
    Sensors (Basel), 2021 Dec 17;21(24).
    PMID: 34960516 DOI: 10.3390/s21248423
    This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.
    Matched MeSH terms: Time Factors
  14. Lee YS, Oh YS, Choi EK, Chern AKC, Jiampo P, Chutinet A, et al.
    Open Heart, 2021 12;8(2).
    PMID: 34857666 DOI: 10.1136/openhrt-2021-001745
    PURPOSE: Dabigatran is a direct thrombin inhibitor approved for stroke prophylaxis in patients with non-valvular atrial fibrillation (NVAF). Real-world data about patient preference, satisfaction and convenience in patients in Asia are not available. The study aimed to explore the perception of patients with newly diagnosed NVAF regarding dabigatran versus vitamin K antagonists (VKAs), when used for stroke prevention.

    PATIENTS AND METHODS: This was a multinational, multicentre, non-interventional study involving 49 sites across 5 countries in South East Asia and South Korea where 934 patients newly diagnosed with NVAF were initiated on either dabigatran (N=591) or VKA (N=343). Data were collected at baseline and over two follow-up visits across 6 months. Treatment satisfaction and patient convenience were evaluated using the Perception on Anticoagulant Treatment Questionnaire-2 (PACT-Q2).

    RESULTS: The mean age of the patients was 65.9±10.4 years, and 64.2% were male. Mean CHA2DS2-VASc score was 2.4±1.5, and mean HAS-BLED score was 1.2±0.9. At baseline, patients initiated on dabigatran had higher stroke risk, bleeding risk, creatinine clearance and proportion of patients with concomitant illnesses compared with patients initiated on VKAs. Treatment convenience was perceived to be significantly better with dabigatran versus VKAs at visits 2 and 3 (p=0.0423 and 0.0287, respectively). Treatment satisfaction was significantly better with dabigatran compared with VKAs at visit 3 (p=0.0300).

    CONCLUSION: In this study, dabigatran is associated with better patient perception in terms of treatment convenience and satisfaction compared with VKAs when used for stroke prevention in newly diagnosed NVAF patients from South East Asia and South Korea.

    TRIAL REGISTRATION NUMBER: NCT02849509.

    PLAIN LANGUAGE SUMMARY: Patient satisfaction with dabigatran versus VKAs in South East Asia. Patients with atrial fibrillation are at high risk of stroke and require anticoagulants for stroke prevention. Two such anticoagulants are dabigatran and VKAs. We wanted to compare the extent of satisfaction and treatment convenience among newly diagnosed patients with atrial fibrillation from the South East Asian region when they were given either dabigatran or VKAs. Consenting patients filled out a standardised questionnaire called the PACT-Q2 over three visits after they were started on either dabigatran (591 patients) or VKAs (343 patients). We found that satisfaction and convenience were significantly higher when patients received dabigatran than when they received VKAs.

    Matched MeSH terms: Time Factors
  15. Chen SP, Lin SR, Chen TH, Ng HS, Yim HS, Leong MK, et al.
    Biomed Pharmacother, 2021 Dec;144:112333.
    PMID: 34678724 DOI: 10.1016/j.biopha.2021.112333
    Diabetes mellitus (DM) is concomitant with significant morbidity and mortality and its prevalence is accumulative in worldwide. The conventional antidiabetic agents are known to mitigate the symptoms of diabetes; however, they may also cause side and adverse effects. There is an imperative necessity to conduct preclinical and clinical trials for the discovery of alternative therapeutic agents that can overcome the drawbacks of current synthetic antidiabetic drugs. This study aimed to investigate the efficacy of lowering blood glucose and underlined mechanism of γ-mangostin, mangosteen (Garcinia mangostana) xanthones. The results showed γ-Mangostin had a antihyperglycemic ability in short (2 h)- and long-term (28 days) administrations to diet-induced diabetic mice. The long-term administration of γ-mangostin attenuated fasting blood glucose of diabetic mice and exhibited no hepatotoxicity and nephrotoxicity. Moreover, AMPK, PPARγ, α-amylase, and α-glucosidase were found to be the potential targets for simulating binds with γ-mangostin after molecular docking. To validate the docking results, the inhibitory potency of γ-mangostin againstα-amylase/α-glucosidase was higher than Acarbose via enzymatic assay. Interestingly, an allosteric relationship between γ-mangostin and insulin was also found in the glucose uptake of VSMC, FL83B, C2C12, and 3T3-L1 cells. Taken together, the results showed that γ-mangostin exerts anti-hyperglycemic activity through promoting glucose uptake and reducing saccharide digestion by inhibition of α-amylase/α-glucosidase with insulin sensitization, suggesting that γ-mangostin could be a new clue for drug discovery and development to treat diabetes.
    Matched MeSH terms: Time Factors
  16. Sharif Nia H, Gorgulu O, Naghavi N, Froelicher ES, Fomani FK, Goudarzian AH, et al.
    BMC Cardiovasc Disord, 2021 11 23;21(1):563.
    PMID: 34814834 DOI: 10.1186/s12872-021-02372-0
    BACKGROUND: Although various studies have been conducted on the effects of seasonal climate changes or emotional variables on the risk of AMI, many of them have limitations to determine the predictable model. The currents study is conducted to assess the effects of meteorological and emotional variables on the incidence and epidemiological occurrence of acute myocardial infarction (AMI) in Sari (capital of Mazandaran, Iran) during 2011-2018.

    METHODS: In this study, a time series analysis was used to determine the variation of variables over time. All series were seasonally adjusted and Poisson regression analysis was performed. In the analysis of meteorological data and emotional distress due to religious mourning events, the best results were obtained by autoregressive moving average (ARMA) (5,5) model.

    RESULTS: It was determined that average temperature, sunshine, and rain variables had a significant effect on death. A total of 2375 AMI's were enrolled. Average temperate (°C) and sunshine hours a day (h/day) had a statistically significant relationship with the number of AMI's (β = 0.011, P = 0.014). For every extra degree of temperature increase, the risk of AMI rose [OR = 1.011 (95%CI 1.00, 1.02)]. For every extra hour of sunshine, a day a statistically significant increase [OR = 1.02 (95% CI 1.01, 1.04)] in AMI risk occurred (β = 0.025, P = 0.001). Religious mourning events increase the risk of AMI 1.05 times more. The other independent variables have no significant effects on AMI's (P > 0.05).

    CONCLUSION: Results demonstrate that sunshine hours and the average temperature had a significant effect on the risk of AMI. Moreover, emotional distress due to religious morning events increases AMI. More specific research on this topic is recommended.

    Matched MeSH terms: Time Factors
  17. Norsa'adah B, Rampal KG, Mohd Amin R
    Asian Pac J Cancer Prev, 2021 Nov 01;22(11):3623-3631.
    PMID: 34837921 DOI: 10.31557/APJCP.2021.22.11.3623
    BACKGROUND: Breast cancer patients in Malaysia often present late, delaying diagnosis and treatment. Decisions on health-seeking behaviour are influenced by a complex interplay of several factors. Early detection and subsequent successful treatment are the main goal in order to reduce breast cancer mortality. The aims of this study were to identify the time taken by women with breast cancer for consultation, diagnosis and first definitive treatment and the factors associated with the initiation of definitive treatment.

    METHODS: In this cohort study, we interviewed 328 women with histologically confirmed breast cancer at five medical centres in Malaysia. Times were measured from recognition of symptoms to first consultation to diagnosis and to the first definitive treatment. The event was initiation of definitive treatment. Data was analysed using multivariable Cox proportional hazards regression.

    RESULTS: The mean age was 47.9 (standard deviation 9.4) years and 79.9% were ethnic Malays. The median follow-up time was 6.9 months. The median times for first doctor consultation, diagnosis and initiation of treatment were 2 months, 5.5 months and 2.4 weeks, respectively. The percentage of consultation delay more than a month was 66.8%, diagnosis delay more than three months was 73.2% and treatment delay more than one month was 11.6%. Factors associated with not initiating the definitive treatment were pregnancy (adjusted hazard ratio (AHR) 1.75; 95% Confidence Interval (CI): 1.07, 2.88), taking complementary alternative medicine (AHR 1.45; 95% CI: 1.15, 1.83), initial refusal of mastectomy (AHR 3.49; 95% CI: 2.38, 5.13) and undergoing lumpectomy prior to definitive treatment (AHR 1.62; 95% CI: 1.16, 2.28).

    CONCLUSIONS: Delays in diagnosis and consultation were more serious than treatment delays. Most respondents would accept treatment immediately after diagnosis. Respondents themselves were responsible for a large proportion of the delays. This study was successful in understanding the process of breast cancer patients' experience, from symptoms recognition to consultation, diagnosis and treatment.

    Matched MeSH terms: Time Factors
  18. Malek NNA, Jawad AH, Ismail K, Razuan R, ALOthman ZA
    Int J Biol Macromol, 2021 Oct 31;189:464-476.
    PMID: 34450144 DOI: 10.1016/j.ijbiomac.2021.08.160
    A magnetic biocomposite blend of chitosan-polyvinyl alcohol/fly ash (m-Cs-PVA/FA) was developed by adding fly ash (FA) microparticles into the polymeric matrix of magnetic chitosan-polyvinyl alcohol (m-Cs-PVA). The effectiveness of m-Cs-PVA/FA as an adsorbent to remove textile dye (reactive orange 16, RO16) from aquatic environment was evaluated. The optimum adsorption key parameters and their significant interactions were determined by Box-Behnken Design (BBD). The analysis of variance (ANOVA) indicates the significant interactions can be observed between m-Cs-PVA/FA dose with solution pH, and m-Cs-PVA/FA dose with working temperature. Considering these significant interactions, the highest removal of RO16 (%) was found 90.3% at m-Cs-PVA/FA dose (0.06 g), solution pH (4), working temperature (30 °C), and contact time (17.5 min). The results of adsorption kinetics revealed that the RO16 adsorption was better described by the pseudo-second-order model. The results of adsorption isotherm indicated a multilayer adsorption process as well described by Freundlich model with maximum adsorption capacity of 123.8 mg/g at 30 °C. An external magnetic field can be easily applied to recover the adsorbent (m-Cs-PVA/FA). The results supported that the synthesized m-Cs-PVA/FA presents itself as an effective and promising adsorbent for textile dye with preferable adsorption capacity and separation ability during and after the adsorption process.
    Matched MeSH terms: Time Factors
  19. Rajagopal R, Leong SH, Jawin V, Foo JC, Ahmad Bahuri NF, Mun KS, et al.
    J Pediatr Hematol Oncol, 2021 Oct 01;43(7):e913-e923.
    PMID: 33633029 DOI: 10.1097/MPH.0000000000002116
    BACKGROUND: A higher incidence of pediatric intracranial germ cell tumors (iGCTs) in Asian countries compared with Western countries has been reported. In Malaysia, the literature regarding pediatric iGCTs have been nonexistent. The aim of this study was to review the management, survival, and long-term outcomes of pediatric iGCTs at a single tertiary center in Malaysia.

    PATIENTS AND METHODS: We retrospectively reviewed data from patients below 18 years of age with iGCTs treated at the University Malaya Medical Center (UMMC) from 1998 to 2017.

    RESULTS: Thirty-four patients were identified, with a median follow-up of 3.54 years. Sixteen (47%) patients had pure germinoma tumors (PGs), and the remaining patients had nongerminomatous germ cell tumors (NGGCTs). The median age was 12 years, with a male:female ratio of 4.7:1. Abnormal vision, headache with vomiting, and diabetes insipidus were the commonest presenting symptoms. Twenty-eight patients received initial surgical interventions, 24 were treated with chemotherapy, and 28 received radiotherapy. Eight patients experienced relapses. The 5- and 10-year event-free survival rates were similar at 61.1%±12.6% and 42.9%±12.1% for PG and NGGCT, respectively. The 5- and 10-year overall survival rates were the same at 75.5%±10.8% and 53.3%±12.3% for PG and NGGCT, respectively. Four patients died of treatment-related toxicity. Most of the survivors experienced good quality of life with satisfactory neurologic status.

    CONCLUSIONS: The survival rate of childhood iGCTs in UMMC was inferior to that reported in developed countries. Late diagnosis, poor adherence to treatment, and treatment-related complications were the contributing factors. Although these results highlight a single institution experience, they most likely reflect similar treatment patterns, outcomes, and challenges in other centers in Malaysia.

    Matched MeSH terms: Time Factors
  20. Erman M, Biswas B, Danchaivijitr P, Chen L, Wong YF, Hashem T, et al.
    BMC Cancer, 2021 Sep 14;21(1):1021.
    PMID: 34521387 DOI: 10.1186/s12885-021-08738-z
    BACKGROUND: Clinical effectiveness and safety data of pazopanib in patients with advanced or mRCC in real-world setting from Asia Pacific, North Africa, and Middle East countries are lacking.

    METHODS: PARACHUTE is a phase IV, prospective, non-interventional, observational study. Primary endpoint was the proportion of patients remaining progression free at 12 months. Secondary endpoints were ORR, PFS, safety and tolerability, and relative dose intensity (RDI).

    RESULTS: Overall, 190 patients with a median age of 61 years (range: 22.0-96.0) were included. Most patients were Asian (70%), clear-cell type RCC was the most common (81%), with a favourable (9%), intermediate (47%), poor (10%), and unknown (34%) MSKCC risk score. At the end of the observational period, 78 patients completed the observational period and 112 discontinued the study; 60% of patients had the starting dose at 800 mg. Median RDI was 82%, with 52% of patients receiving  10%) TEAEs related to pazopanib included diarrhoea (30%), palmar-plantar erythrodysesthesia syndrome (15%), and hypertension (14%).

    CONCLUSIONS: Results of the PARACHUTE study support the use of pazopanib in patients with advanced or mRCC who are naive to VEGF-TKI therapy. The safety profile is consistent with that previously reported by pivotal and real-world evidence studies.

    Matched MeSH terms: Time Factors
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