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- Hashim, Ahmed, et al.
(författare)
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Pattern of novel psychoactive substance use among patients presented to the poison control centre of Ain Shams University Hospitals, Egypt : A cross-sectional study
- 2022
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 8:8
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Tidskriftsartikel (refereegranskat)abstract
- Background: Novel psychoactive substances (NPSs) are relatively new substances in the illicit drug market, notpreviously listed in the United Nations Office on Drugs and Crime (UNDOC). Strox and Voodoo are consideredsome of the most popular blends of NPS in the Egyptian drug market.Objectives: The current study was conducted to assess NPS's use pattern: Voodoo and Strox among acutelyintoxicated patients presented to the poison control center of Ain Shams University Hospitals (PCC- ASUH).Methods: A single center based cross-sectional study was carried out in the PCC-ASUH among acutely intoxicatedpatients presenting to the emergency department (ED) over four months (from January–April 2019. using apreviously adopted and validated Fahmy and El-Sherbini socioeconomic scale (SES). Data were presented asmean, median and range as appropriate. Both smoking and crowding indexes were calculated and presented aspreviously reported.Results: Fifty-one patients were presented to the ED of PCC-ASUH during the study period. A total of 96.1% (n ¼49) were males. The mean age was 25 7.5 years. The most common NPS used was Strox: 54.9% (n ¼ 28),followed by Voodoo: 27.4% (n ¼ 14). Neurological and gastrointestinal (GI) symptoms were the most frequentpresentations. The most common motive behind NPS use was the desire to give a trial of new psychoactivesubstances. The mean SES score was 35.1 13.17. Most patients have the preparatory as the highest education36.0% (n ¼ 18).Conclusions: NPS use is common among young males in preparatory education from different social classes,starting it most commonly as a means to experiencing a new high. Neurological and GI manifestations are themost common presenting symptoms of NPS intoxication.
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| 2. |
- Alsafadi, Karam, et al.
(författare)
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An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East
- 2022
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Ingår i: Environmental Research Letters. - UK : Institute of Physics Publishing (IOPP). - 1748-9326. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO2) by plant photosynthesis, which currently accounts for about one-quarter of anthropogenic CO2 emissions per year. This study aimed to detect the variability of carbon productivity using the standardized evapotranspiration deficit index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34%, 30.5%, and 29.6% of cropland area respectively, and 25%, 31.5%, and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.
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| 3. |
- Ellabban, Mohamed A., et al.
(författare)
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Virtual planning of the anterolateral thigh free flap for heel reconstruction
- 2022
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Ingår i: Microsurgery. - : WILEY. - 0738-1085 .- 1098-2752. ; 42:5, s. 460-469
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Tidskriftsartikel (refereegranskat)abstract
- Purpose three dimensional (3-D) virtual planning is an example of computer assisted surgery that improved management of composite tissue defects. However, converting the 3-D construct into two dimensional format is challenging. The purpose of this study was to assess 3-D virtual planning of complex heel defects for better optimized reconstruction. Patients and methods a prospective analysis of 10 patients [9 male and 1 female; mean age = 27.9 years] with post-traumatic heel defects was performed. Heel defects comprised types II (three patients) or III (seven patients) according to Hidalgo and Shaw and were managed using anterolateral thigh (ALT) free flap adopting 3-D virtual planning of the actual defect which was converted into a silicone two dimensional mold. The mean definitive size of the defects was 63.4 cm(3). Functional, aesthetic, and sensory evaluations of both donor and recipient sites were performed 1 year after surgery. Results Six patients received thinned ALT (mean size = 139 cm(3)) while four patients received musculofasciocutaneous ALT flap (mean size = 199 cm(3)). One flap exhibited partial skin flap necrosis. Another flap was salvaged after re-exploration secondary to venous congestion. The mean follow-up was 20.2 months. The Maryland foot score showed 4 excellent, 5 good, and 1 fair cases. The mean American Orthopedic Foot and Ankle hind foot scoring was 76.3 (range: 69-86). All patients regained their walking capability. Conclusions 3-D virtual planning of complex heel defects facilitates covering non-elliptical defects while harvesting a conventional elliptical flap with providing satisfactory functional outcomes and near-normal contour, volume, and sensibility.
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| 4. |
- Mokhtar, Ali, et al.
(författare)
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Prediction of irrigation water quality indices based on machine learning and regression models
- 2022
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Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve this goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI; Kelly’s ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3−) were used as input exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21% and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.
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