Displaying publications 21 - 40 of 65 in total

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  1. Fulltext Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
    Nhu VH, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, et al.
    Int J Environ Res Public Health, 2020 07 08;17(14).
    PMID: 32650595 DOI: 10.3390/ijerph17144933
    We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.
  2. Fulltext Author Correction: Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods
    Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, et al.
    Sci Rep, 2021 Jul 20;11(1):15152.
    PMID: 34285263 DOI: 10.1038/s41598-021-93957-4
  3. Effects of different types of biochar on methane and ammonia mitigation during layer manure composting
    Chen W, Liao X, Wu Y, Liang JB, Mi J, Huang J, et al.
    Waste Manag, 2017 Mar;61:506-515.
    PMID: 28117129 DOI: 10.1016/j.wasman.2017.01.014
    Biochar, because of its unique physiochemical properties and sorption capacity, may be an ideal amendment in reducing gaseous emissions during composting process but there has been little information on the potential effects of different types of biochar on undesired gaseous emissions. The objective of this study was to examine the ability and mechanism of different types of biochar, as co-substrate, in mitigating gaseous emission from composting of layer hen manure. The study was conducted in small-scale laboratory composters with the addition of 10% of one of the following biochars: cornstalk biochar, bamboo biochar, woody biochar, layer manure biochar and coir biochar. The results showed that the cumulative NH3 production was significantly reduced by 24.8±2.9, 9.2±1.3, 20.1±2.6, 14.2±1.6, 11.8±1.7% (corrected for initial total N) in the cornstalk biochar, bamboo biochar, woody biochar, layer manure biochar and coir biochar treatments, respectively, compared to the control. Total CH4 emissions was significantly reduced by 26.1±2.3, 15.5±2.1, 22.4±3.1, 17.1±2.1% (corrected for the initial total carbon) for cornstalk biochar, bamboo biochar, woody biochar and coir biochar treatments than the control. Moreover, addition of cornstalk biochar increased the temperature and NO3(-)-N concentration and decreased the pH, NH4(+)-N and organic matter content throughout the composting process. The results suggested that total volatilization of NH3 and CH4 in cornstalk biochar treatment was lower than the other treatments; which could be due to (i) decrease of pH and higher nitrification, (ii) high sorption capacity for gases and their precursors, such as ammonium nitrogen from composting mixtures, because of the higher surface area, pore volumes, total acidic functional groups and CEC of cornstalk biochar.
  4. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods
    Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, et al.
    Sci Total Environ, 2020 Jan 20;701:134979.
    PMID: 31733400 DOI: 10.1016/j.scitotenv.2019.134979
    Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.
  5. Tetracycline removal in granulation: Influence of extracellular polymers substances, structure, and metabolic function of microbial community
    Yan L, Chen W, Wang C, Liu S, Liu C, Yu L, et al.
    Chemosphere, 2022 Feb;288(Pt 2):132510.
    PMID: 34627823 DOI: 10.1016/j.chemosphere.2021.132510
    Tetracycline is a potentially hazardous residual antibiotic detected in various sewages. High concentration (mg/L) of tetracycline is found in pharmaceutical/hospital wastewater and wastewater derived from livestock and poultry. So far, only antibiotics in μg/L level have been reported in granulation of aerobic sludge during wastewater treatment, but its effects in high concentration are rarely reported. In this study, the influence of tetracycline in high concentration (∼2 mg/L) on the formation of granular sludge, structure, and metabolic function of the microbial community during the granulation of aerobic sludge was investigated to improve the understanding of the aerobic granular sludge formation under high-level of tetracycline. The role of extracellular polymers substances (EPSs) derived from granular sludge in the granulation and tetracycline removal process was also investigated, showing that tetracycline improved the relative hydrophobicity, flocculability and protein/polysaccharide ratio of EPSs, accelerating the granulation of sludge. Succession of microbial communities occurred during the domestication of functional bacteria present in the sludge and was accompanied with regulation of metabolic function. The addition of tetracycline lead to an increase of tetracycline-degrading bacteria or antibiotic resistance genus. Those findings provide new perspectives of the influence of tetracycline on aerobic sludge granulation and the removal mechanism of tetracycline.
  6. Fulltext Computer-assisted quantification of CD3+ T cells in follicular lymphoma
    Abas FS, Shana'ah A, Christian B, Hasserjian R, Louissaint A, Pennell M, et al.
    Cytometry A, 2017 06;91(6):609-621.
    PMID: 28110507 DOI: 10.1002/cyto.a.23049
    The advance of high resolution digital scans of pathology slides allowed development of computer based image analysis algorithms that may help pathologists in IHC stains quantification. While very promising, these methods require further refinement before they are implemented in routine clinical setting. Particularly critical is to evaluate algorithm performance in a setting similar to current clinical practice. In this article, we present a pilot study that evaluates the use of a computerized cell quantification method in the clinical estimation of CD3 positive (CD3+) T cells in follicular lymphoma (FL). Our goal is to demonstrate the degree to which computerized quantification is comparable to the practice of estimation by a panel of expert pathologists. The computerized quantification method uses entropy based histogram thresholding to separate brown (CD3+) and blue (CD3-) regions after a color space transformation. A panel of four board-certified hematopathologists evaluated a database of 20 FL images using two different reading methods: visual estimation and manual marking of each CD3+ cell in the images. These image data and the readings provided a reference standard and the range of variability among readers. Sensitivity and specificity measures of the computer's segmentation of CD3+ and CD- T cell are recorded. For all four pathologists, mean sensitivity and specificity measures are 90.97 and 88.38%, respectively. The computerized quantification method agrees more with the manual cell marking as compared to the visual estimations. Statistical comparison between the computerized quantification method and the pathologist readings demonstrated good agreement with correlation coefficient values of 0.81 and 0.96 in terms of Lin's concordance correlation and Spearman's correlation coefficient, respectively. These values are higher than most of those calculated among the pathologists. In the future, the computerized quantification method may be used to investigate the relationship between the overall architectural pattern (i.e., interfollicular vs. follicular) and outcome measures (e.g., overall survival, and time to treatment). © 2017 International Society for Advancement of Cytometry.
  7. Choosing the best way for urinary decompression and developing a novel predictive model for septic shock using SOFA in these patients
    Law YXT, Shen L, Khor VWS, Chen W, Chen WJK, Durai P, et al.
    Int J Urol, 2022 Dec;29(12):1488-1496.
    PMID: 36070249 DOI: 10.1111/iju.15023
    OBJECTIVES: To identify predictive factors for the development of sepsis/septic shock postdecompression of calculi-related ureteric obstruction using the Sequential Organ Failure Assessment (SOFA) score and to compare clinical outcomes and odd risk ratios of patients developing sepsis/septic shock following the insertion of percutaneous nephrostomy (PCN) versus insertion of retrograde ureteral stenting (RUS).

    METHODS: Clinico-epidemiological data of patients who underwent PCN and/or RUS in two institutions for calculi-related ureteric obstruction were retrospectively collected from January 2014 to December 2020.

    RESULTS: 537 patients (244 patients in PCN group, 293 patients in RUS group) from both institutions were eligible for analysis based on inclusion and exclusion criteria. Patients with PCN were generally older, had poorer Eastern Cooperative Oncology Group status, and larger obstructive ureteral calculi compared to patients with RUS. Patients with PCN had longer durations of fever, the persistence of elevated total white cell and creatinine, and longer hospitalization stays compared with patients who had undergone RUS. RUS up-front has more unsuccessful interventions compared with PCN. There were no significant differences in the change in SOFA score postintervention between the two interventions. In multivariate analysis, the higher temperature just prior to the intervention (adjusted odds ratio [OR]: 2.039, p = 0.003) and Cardiovascular SOFA score of 1 (adjusted OR:4.037, p = 0.012) were significant independent prognostic factors for the development of septic shock postdecompression of ureteral obstruction.

    CONCLUSIONS: Our study reveals that both interventions have similar overall risk of urosepsis, septic shock and mortality rate. Despite a marginally higher risk of failure, RUS should be considered in patients with lower procedural risk. Patients going for PCN should be counseled for a longer stay. Post-HDU/-ICU monitoring, inotrope support postdecompression should be considered for patients with elevated temperature within 1 h preintervention and cardiovascular SOFA score of 1.

  8. Fulltext Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods
    Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, et al.
    Sci Rep, 2018 Oct 18;8(1):15364.
    PMID: 30337603 DOI: 10.1038/s41598-018-33755-7
    Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
  9. Fulltext Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms
    Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, et al.
    Sensors (Basel), 2018 Jul 31;18(8).
    PMID: 30065216 DOI: 10.3390/s18082464
    In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
  10. Fulltext Anemia Management in Peritoneal Dialysis: Perspectives From the Asia Pacific Region
    Li PKT, Choy ASM, Bavanandan S, Chen W, Foo M, Kanjanabuch T, et al.
    Kidney Med, 2021;3(3):405-411.
    PMID: 34136787 DOI: 10.1016/j.xkme.2021.01.011
    Anemia is an important complication in patients with chronic kidney disease. Peritoneal dialysis (PD) is one of the most common modalities of kidney replacement therapy for patients with end-stage kidney disease. PD is particularly prevalent in the Asian Pacific region. Among the different countries and regions, including mainland China, Hong Kong, Japan, Malaysia, Singapore, South Korea, and Thailand, PD accounts for 2.8% to 74.6% of the dialysis population. In addition, 82% to 96% of the PD populations from these countries and regions are receiving erythropoiesis-stimulating agents (ESAs). Asian Pacific countries and regions follow the latest KDIGO (Kidney Disease: Improving Global Outcomes) guidelines for the initiation of treatment of anemia in PD patients. The types of ESAs commonly used include shorter-acting (epoetin alfa and beta) and longer-acting agents, including darbepoetin alfa or methoxy polyethylene glycol-epoetin beta. The most commonly used ESAs in Mainland China, Malaysia, Singapore, and Thailand are the shorter-acting agents, whereas in Hong Kong, Japan, and South Korea, longer-acting ESAs are most common. Oral iron therapy is still the most commonly used iron supplement. The route and dosage of iron administration in PD patients requires more research studies. With the introduction of oral hypoxia-inducible factor prolyl hydroxylase inhibitors into clinical use, the landscape of treatment of anemia in the PD population in the Asia Pacific region may change in the coming years.
  11. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
    He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.
    Sci Total Environ, 2019 May 01;663:1-15.
    PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329
    Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.
  12. Fulltext Simulation training using WhatsApp (Sim-thru-WhatsApp) improves doctors' confidence in endocrine and diabetes case management
    Chen W, Kempegowda P, Melson E, Davitadze M, Aftab M, Ooi E, et al.
    Clin Med (Lond), 2020 03;20(Suppl 2):s62-s63.
    PMID: 32409381 DOI: 10.7861/clinmed.20-2-s62
  13. Fulltext Reliability and technical error of calf circumference and mid-half arm span measurements for nutritional status assessment of elderly Persons in Malaysia
    Jamaiyah H, Geeta A, Safiza MN, Wong NF, Kee CC, Ahmad AZ, et al.
    Malays J Nutr, 2008 Sep;14(2):137-50.
    PMID: 22691771 MyJurnal
    This study sought to examine the reliability of two measurements; Calf Circumference (CC) and Mid-half Arm Span (MHAS). A sample of 130 elderly persons aged 60 years and above seen consecutively in the Kuala Lumpur Hospital outpatient clinic during the period of December 2005 to January 2006, upon consent, were recruited to the study. There was a high degree of reliability for both inter- and intra-examiner (r close to 1). For inter-examiner, on average the CC measurements taken by the first examiner were 0.3 cm lower than that of the second examiner. The upper and lower limit of the differences were +0.4 to -0.9 cm respectively. Inter-examiner MHAS measurements on average by the first examiner were 0.2 cm lower than that of the second examiner. The limits were +1.7 to -2.1 cm. By comparison, the inter-examiner CC measurements were more reliable than the MHAS measurements. For intra-examiner, on average the CC measurements at Time 1 were consistent with Time 2 (mean difference=0) with limits of the difference at + 0.5 cm. MHAS measurements at Time 1 were on average 0.1 cm less than at Time 2 with limits at +1.7 and -1.8 cm. The technical error of measurement (TEM) and coefficient of variation of CC and MHAS for both interexaminer and intra-examiner measurements were within acceptable limits with the exception of MHAS TEM. This study suggests that CC and MHAS measured in elderly persons 60 years and above, using Seca Circumference Tape ® 206, Germany (0.05 cm) are reliable and can be used in a community survey.

    Study site: Outpatient clinic, Hospital Kuala Lumpur
  14. Fulltext Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology
    Niazi MKK, Abas FS, Senaras C, Pennell M, Sahiner B, Chen W, et al.
    PLoS One, 2018;13(5):e0196547.
    PMID: 29746503 DOI: 10.1371/journal.pone.0196547
    Automatic and accurate detection of positive and negative nuclei from images of immunostained tissue biopsies is critical to the success of digital pathology. The evaluation of most nuclei detection algorithms relies on manually generated ground truth prepared by pathologists, which is unfortunately time-consuming and suffers from inter-pathologist variability. In this work, we developed a digital immunohistochemistry (IHC) phantom that can be used for evaluating computer algorithms for enumeration of IHC positive cells. Our phantom development consists of two main steps, 1) extraction of the individual as well as nuclei clumps of both positive and negative nuclei from real WSI images, and 2) systematic placement of the extracted nuclei clumps on an image canvas. The resulting images are visually similar to the original tissue images. We created a set of 42 images with different concentrations of positive and negative nuclei. These images were evaluated by four board certified pathologists in the task of estimating the ratio of positive to total number of nuclei. The resulting concordance correlation coefficients (CCC) between the pathologist and the true ratio range from 0.86 to 0.95 (point estimates). The same ratio was also computed by an automated computer algorithm, which yielded a CCC value of 0.99. Reading the phantom data with known ground truth, the human readers show substantial variability and lower average performance than the computer algorithm in terms of CCC. This shows the limitation of using a human reader panel to establish a reference standard for the evaluation of computer algorithms, thereby highlighting the usefulness of the phantom developed in this work. Using our phantom images, we further developed a function that can approximate the true ratio from the area of the positive and negative nuclei, hence avoiding the need to detect individual nuclei. The predicted ratios of 10 held-out images using the function (trained on 32 images) are within ±2.68% of the true ratio. Moreover, we also report the evaluation of a computerized image analysis method on the synthetic tissue dataset.
  15. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models
    Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, et al.
    Sci Total Environ, 2018 Sep 01;634:853-867.
    PMID: 29653429 DOI: 10.1016/j.scitotenv.2018.04.055
    The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
  16. Fulltext The microbiota structure in the cecum of laying hens contributes to dissimilar H2S production
    Huang CB, Xiao L, Xing SC, Chen JY, Yang YW, Zhou Y, et al.
    BMC Genomics, 2019 Oct 23;20(1):770.
    PMID: 31646963 DOI: 10.1186/s12864-019-6115-1
    BACKGROUND: Host genotype plays a crucial role in microbial composition of laying hens, which may lead to dissimilar odor gas production. The objective of this study was to investigate the relationship among layer breed, microbial structure and odor production.

    RESULTS: Thirty Hy-Line Gray and thirty Lohmann Pink laying hens were used in this study to determine the impact of cecal microbial structure on odor production of laying hens. The hens were managed under the same husbandry and dietary regimes. Results of in vivo experiments showed a lower hydrogen sulfide (H2S) production from Hy-Line hens and a lower concentration of soluble sulfide (S2-) but a higher concentration of butyrate in the cecal content of the Hy-Line hens compared to Lohmann Pink hens (P  0.05). Significant microbial structural differences existed between the two breed groups. The relative abundance of some butyrate producers (including Butyricicoccus, Butyricimonas and Roseburia) and sulfate-reducing bacteria (including Mailhella and Lawsonia) were found to be significantly correlated with odor production and were shown to be different in the 16S rRNA and PCR data between two breed groups. Furthermore, some bacterial metabolism pathways associated with energy extraction and carbohydrate utilization (oxidative phosphorylation, pyruvate metabolism, energy metabolism, two component system and secretion system) were overrepresented in the Hy-Line hens, while several amino acid metabolism-associated pathways (amino acid related enzymes, arginine and proline metabolism, and alanine-aspartate and glutamate metabolism) were more prevalent in the Lohmann hens.

    CONCLUSION: The results of this study suggest that genotype of laying hens influence cecal microbiota, which in turn modulates their odor production. Our study provides references for breeding and enteric manipulation for defined microbiota to reduce odor gas emission.

  17. Fulltext Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images
    Nhu VH, Mohammadi A, Shahabi H, Shirzadi A, Al-Ansari N, Ahmad BB, et al.
    Int J Environ Res Public Health, 2020 06 12;17(12).
    PMID: 32545634 DOI: 10.3390/ijerph17124210
    The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, multispectral Landsat 7 ETM+ images for the years 2000, 2010, and 2017 were acquired. In addition, precipitation and temperature data for 31 years between 1986 and 2017 were collected for further analysis. Results indicate that the increased temperature (by 19%), decreased rainfall of about 62%, and excessive damming in the Urmia Basin along with mismanagement of water resources are the key factors in the declining water level of Lake Urmia. Furthermore, the current research predicts the potential environmental crisis as the result of the lake shrinking and suggests a few possible alternatives. The insights provided by this study can be beneficial for environmentalists and related organizations working on this and similar topics.
  18. Fulltext Efficacy of early initiation of ivabradine treatment in patients with acute heart failure: rationale and design of SHIFT-AHF trial
    Su Y, Ma T, Wang Z, Dong B, Tai C, Wang H, et al.
    ESC Heart Fail, 2020 Dec;7(6):4465-4471.
    PMID: 32945150 DOI: 10.1002/ehf2.12997
    AIMS: Elevated heart rate (HR) in heart failure (HF) is associated with worse outcomes, particularly in acute HF (AHF). HR reduction with ivabradine reduces cardiovascular events in HF patients with reduced ejection fraction. The present trial aimed to test the hypothesis that the early HR reduction using ivabradine improves clinical outcomes in patients with AHF.

    METHODS AND RESULTS: SHIFT-AHF is a prospective, multi-centre, double-blind, randomized, placebo-controlled trial to evaluate the efficacy and safety of ivabradine when adding to standard therapy in AHF patients (SHIFT-AHF). The trial will include 674 AHF patients with left ventricular ejection fraction 

  19. Sustained clinical knowledge improvements from simulation experiences with Simulation via Instant Messaging-Birmingham Advance
    Zhou D, Davitadze M, Ooi E, Ng CY, Allison I, Thomas L, et al.
    Postgrad Med J, 2023 Mar 22;99(1167):25-31.
    PMID: 36947426 DOI: 10.1093/postmj/qgac008
    BACKGROUND: Simulation via Instant Messaging-Birmingham Advance (SIMBA) delivers simulation-based learning through WhatsApp and Zoom, helping to sustain continuing medical education (CME) for postgraduate healthcare professionals otherwise disrupted by the coronavirus (COVID-19) pandemic. This study aimed to assess whether SIMBA helped to improve clinical knowledge and if this improvement in knowledge was sustained over time.

    METHODS: Two SIMBA sessions-thyroid and pituitary-were conducted in July-August 2020. Each session included simulation of various real-life cases and interactive discussion. Participants' self-reported confidence, acceptance, and knowledge were measured using surveys and multiple-choice questions pre- and post-simulation and in a 6- to 12-week follow-up period. The evaluation surveys were designed using Moore's 7 Levels of CME Outcomes Framework.

    RESULTS: A total of 116 participants were included in the analysis. Significant improvement was observed in participants' self-reported confidence in approach to simulated cases (thyroid, n = 37, P 

  20. Fulltext Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
    Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, et al.
    Sensors (Basel), 2018 Nov 05;18(11).
    PMID: 30400627 DOI: 10.3390/s18113777
    The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
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