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IntroductionCoronary artery disease (CAD) management in the setting of immune thrombocytopenia (ITP) remains very challenging to clinicians as a reasonable balance between bleeding and thrombosis risks needs to be achieved, and the evidence guiding such management is scarce.MethodsWe conducted a systematic review following the PRISMA guidelines to summarize the available literature on the management and outcomes of CAD coexisting with ITP. We searched PubMed and Embase for studies published in English exploring CAD and ITP management until 05 October 2022. Two independent reviewers screened and assessed the articles for inclusion. Patients' characteristics, CAD treatment modalities, ITP treatment, and complications were reported.ResultsWe identified 32 CAD cases, among which 18 cases were revascularized with percutaneous coronary intervention (PCI), 12 cases underwent coronary artery bypass graft surgery (CABG), and two cases were managed conservatively. More than 50% were men, with a mean age of 61 ± 13 years and a mean baseline platelet count of 52 ± 59 × 109/L. Irrespective of the revascularization modality, most patients were treated with either corticosteroids alone, intravenous immunoglobulins (IVIG) alone, or in combination. Among those who underwent PCI, two patients had bleeding events, and one patient died. Similarly, among those with CABG, one patient developed bleeding, and one patient died.ConclusionWe found that revascularization with either PCI or CABG with the concurrent use of corticosteroids and/or IVIG for ITP was feasible, with an existing non-negligible risk of bleeding and mortality.

Abstract In many countries like Pakistan, where crops are irrigated by wastewater, the accumulation of heavy metals is a serious problem, especially when such an irrigation is a widespread practice. The focus of this study was to know the highly toxic metals like cadmium (Cd), chromium (Cr), and lead (Pb) in water, agricultural soil, and crops, besides their probable risk to human health in the area of Vehari district. The physicochemical parameters were determined for the samples, including organic matter, organic carbon, pH, and electrical conductivity. Water used for irrigation, samples of vegetables for Cd, Cr, and Pb concentration, as well as transfer factor from soil to plants (TF) were analyzed for calculating the daily intake of metals (DIM) and their health risk index (HRI). The results show that the wastewater used for irrigation was contaminated with Cr (0.07mg/kg), Cd (0.054mg/kg), and Pb (0.38mg/kg). In the tube well, the concentrations of heavy metals were: Cd (0.053mg/kg), Pb (0.01mg/kg), and Cd (0.03mg/kg). Application of wastewater increased heavy metals concentration in soil and vegetables. Heavy metals concentrations in wastewater irrigated soil before sowing vegetables in mg/kg were: Pb (0.91), Cd (0.12), and Cr (0.48). After the application of wastewater, significant enrichment of wastewater was observed in Pb (1.93mg/kg), Cd (0.07mg/kg), and Cr (0.34mg/kg). Our study showed a high-risk index of food crops polluted with heavy metals and resultantly greater health risk to humans and animals. That is why preventive measures should be adopted to reduce heavy metals pollution to irrigation water and soils to protect both humans and animals in the Vehari district.

This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management.