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Background: A 69-year-old retired businessman, born in 1954, with 12 years of education, had been participating in cognitive enhancement sessions for the past 5 years. His medical history included two ischemic strokes, left hemiplegia, as well as disturbances in the left visual field. This study aimed to examine the individual's cognitive performance over the course of these 5 years, including the COVID-19 pandemic period.

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In this study, stems and leaves of the papaya plant were employed to prepare a high-quality porous adsorbent carbonization and chemical activation using phosphoric acid. This adsorbent demonstrates superior adsorption capabilities for the efficient removal of hazardous alizarin red s (ARS) and methylene blue (MB) dyes. Thus, it contributes to waste reduction and promotes sustainable practices in environmental remediation, aligning with global efforts to develop sustainable materials that address water pollution while supporting circular economy principles.

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Background: Clinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties.

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Biodegradation of azo dyes by Aspergillus flavus and its bioremediation potential using seed germination efficiency.

BMC Microbiol

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Department of Biological and Geological Sciences, Faculty of Education, Ain Shams University, Cairo, 11341, Egypt.

The worldwide textile industry extensively uses azo dyes, which pose serious health and environmental risks. Effective cleanup is necessary but challenging. Developing bioremediation methods for textile effluents will improve color removal efficiency.

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The q-RASPR approach for predicting the property and fate of persistent organic pollutants.

Sci Rep

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Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, Milano, 20156, Italy.

This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data.

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