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Water resources are declining at an alarming rate in the world. The use of water resources for agricultural production has contributed to the rapid decline in quantity and degradation of water quality. Though sustainable agriculture must be economically viable, ecologically sound and socially responsible, water scarcity has challenged the sustainability of agriculture, especially in arid and semi-arid regions. There is a relative consensus among professionals that the increasing water scarcity through excessive use of water and mismanagement of the available water resources are major concerns for agricultural sustainability. Agricultural sustainability is assessed using various indicators, but the contribution of the water factor in those indicators is limited. Therefore, we review the role of sustainable water management in achieving agricultural sustainability. We propose an agricultural water poverty index (AWPI) as an instrument to provide a holistic picture of vital issues for sustainable water management. We also distill key components of the agricultural water poverty index and discuss its applications. The agricultural water poverty index can be used to assess the agricultural water poverty among farmers and regions and to provide guidelines for sustainable water management. This article uses the case of Iran to illustrate the application of the agricultural water poverty index in analyzing agricultural water poverty and providing recommendations for sustainable water management. sustainable agriculture / water management sustainability / agricultural water poverty / Iran

This equivalence, randomized, clinical trial aimed to compare the postoperative pain of root canal therapy (RCT) with pulpotomy with mineral trioxide aggregate (PMTA) or calcium-enriched mixture (PCEM) in permanent mature teeth. In seven academic centers, 550 cariously exposed pulps were included and randomly allocated into PMTA (n = 188), PCEM (n = 194), or RCT (n = 168) arms. Preoperative “Pain Intensity” (PI) on Numerical Rating Scale and postoperative PIs until day 7 were recorded. Patients’ demographic and pre-/intra-/postoperative factors/conditions were recorded/analysed. The arms were homogeneous in terms of demographics. The mean preoperative PIs were similar (P=0.998), the mean sum PIs recorded during 10 postoperative intervals were comparable (P=0.939), and the trend/changes in pain relief were parallel (P=0.821) in all study arms. The incidences of preoperative moderate-severe pain in RCT, PMTA, and PCEM arms were 56.5%, 55.7%, and 56.7%, which after 24 hours considerably decreased to 13.1%, 10.6%, and 12.9%, respectively (P=0.578). The time span of endodontic procedures was statistically different; RCT = 69.73, PMTA = 35.37, and PCEM = 33.62 minutes (P<0.001). Patients with greater preoperative pain, symptomatic apical periodontitis, or presence of PDL widening suffered more pain (P=0.002, 0.035, and 0.023, resp.); however, other pre-/intra-/postoperative factors/conditions were comparable. Pulpotomy with MTA/CEM and RCT demonstrate comparable and effective postoperative pain relief.

About 20–40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.