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In modern agriculture, fertilizers are the most significant prerequisite to ensure sustainable crop production, and the intervention of chemical fertilizers has markedly increased crop production and quality. Unfortunately, plants cannot uptake a significant amount of nutrients (>50%) from the applied fertilizers, resulting in low fertilizer use efficiency. The nutrient losses due to leaching, volatilization, denitrification, fixation, erosion, and runoff could result in low fertilizer use efficiency and create environmental pollution as well as a rise in the cost of fertilizer application. To minimize such losses, researchers have suggested various strategies, one of which is the synthesis and application of slow-release fertilizers to extend the bioavailability of nutrients by the sustained release throughout the crop growth period. However, the high cost of current slow-release fertilizers is a major challenge for their widespread use. Carbon-based materials, especially biochar and lignite, have been shown to be effective as soil amendment in recent times. Additionally, these materials have an excellent ability to adsorb nutrients due to their high porosity, surface area, and abundance of functional groups. The cost-effective and abundant supply of these materials across the world can serve as an excellent nutrient-carrier in order to formulate climate-smart and cost-effective slow-release fertilizers. In this review paper, the potential of these materials as nutrient carriers, nutrient adsorption and desorption mechanisms, synthesis methods, nutrient release behavior, and agronomic and environmental implications are discussed in detail for future research priorities as the literature in this direction is very limited and scattered.

BackgroundIntracranial Atherosclerotic Stenosis (ICAS) represents a noteworthy cerebrovascular pathology linked to ischemic stroke, contributing to a considerable burden of morbidity and mortality on a global scale. The present study was undertaken with the primary objective of investigating the frequency, risk factors, and outcomes of ICAS in stroke patients within the Southern Region of Saudi Arabia.MethodsThis was a descriptive cross-sectional study conducted at a tertiary care hospital located in the southern region of Saudi Arabia, from June 2022 to December 2022. The study population consisted of patients aged 18 years and above who were diagnosed with acute ischemic stroke during the designated research period. Patients with hemorrhagic stroke, transient ischemic attack (TIA), or incomplete medical records were excluded from the analysis. Data pertaining to the patients were retrieved from their respective medical records.ResultsOut of 201 patients admitted with stroke, 92 (45.77%) were found to have intracranial stenosis. The majority of patients were female (52.2%) and aged over 55 years (60.9%). The presence of hypertension exhibited a statistically significant correlation with varying degrees of stenosis (p=0.02), as did ischemic heart disease and obesity (p=0.04) and active smoking (p=0.01). Hypertension displayed a marginal association with intracranial stenosis, with an odds ratio of 1.01 (95% CI: 0.25, 4.11) and a p-value of 0.02. Similarly, dyslipidemia showed a potential correlation, with an odds ratio of 1.16 (95% CI: 0.44, 3.03) and a p-value of 0.014. On the other hand, obesity showed a stronger association, with an odds ratio of 4.53 (95% CI: 1.05, 19.51) and a p-value of 0.04. Among the patients, 25 (27.17%) underwent revascularization procedures, while 44 (47.83%) were not eligible for such intervention. During the three-month follow-up, 4 (16%) experienced an ipsilateral stroke, and 3 (12%) suffered from a contralateral transient ischemic attack (TIA). Encouragingly, 18 (72%) of the treated patients showed no recurrence during the follow-up period.ConclusionThis study concludes that approximately half (45.77%) of stroke patients had intracranial stenosis, and significant associations were found between varying degrees of stenosis and hypertension, ischemic heart disease, obesity, and active smoking. Hypertension demonstrated a marginal correlation, while obesity exhibited a stronger association with intracranial stenosis.

Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light-skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker-skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark-skin tone test set with ground truth, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation.

Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light-skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker-skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark-skin tone test set with ground truth, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation.