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| http://dx.doi.org/10.4103/jnrp.jnrp_289_16 | DOI Listing |
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Therapeutic effects of erythropoietin-alginate/chitosan hydrogel on impaired male rat fertility after spinal cord injury.
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Department of Clinical Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Background: Infertility is a significant issue in spinal cord injury (SCI) patients. Men with SCI often experience erectile and ejaculatory dysfunctions, and low sperm quality leading to impaired fertility. In this study, we investigated the effectiveness of Erythropoietin (EPO)alginate/chitosan (CH-AL) hydrogel on SCI-induced male rat infertility.
View Article and Find Full Text PDFEvaluation of preventive medicine amongst people living with human immunodeficiency virus attending a hospital-based care setting.
Intern Med J
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Department of Infectious Diseases, Westmead Hospital, Sydney, New South Wales, Australia.
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Parthenolide improves sepsis-induced coagulopathy by inhibiting mitochondrial-mediated apoptosis in vascular endothelial cells through BRD4/BCL-xL pathway.
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Department of Anesthesiology, Daping Hospital, Army Medical University, No.10, Changjiang Road, Yuzhong District, Chongqing, 400042, China.
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View Article and Find Full Text PDFPalliative care needs and quality of life among adults with advanced chronic illnesses in low-income communities of Bangladesh.
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College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates.
Objectives: Palliative care (PC) is an interdisciplinary approach aimed at improving the physical, psychological, and spiritual well-being of patients and families affected by life-threatening diseases. This study aimed to investigate the need for PC among critically ill patients and their quality of life (QOL) in low-income groups in Bangladesh.
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Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.
BMC Med Inform Decis Mak
January 2025
Department of Clinical Pharmacy and Translational Science, The University of Tennessee Health Science Center, Memphis, TN, USA.
Background: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.
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