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Strategies to achieve gender equity in nephrology
Lisa M. CurtisÂÂ
doi : 10.1038/s41581-023-00703-2
Volume 19 Issue 7, July 2023
Blockchain in nephrology
Tamas Szili-Torok, Daan Kremer, Stephan J. L. Bakker, Uwe J. F. Tietge & Martin H. de BorstÂ
Identification of erythropoietin-producing cells
Susan J. AllisonÂÂ
Complosome � the intracellular complement system
Erin E. West & Claudia KemperÂ
doi : 10.1038/s41581-023-00704-1
The complement system is a recognized pillar of host defence against infection and noxious self-derived antigens. Complement is traditionally known as a serum-effective system, whereby the liver expresses and secretes most complement components, which participate in the detection of bloodborne pathogens and drive an inflammatory reaction to safely remove the microbial or antigenic threat.
Immunogenicity in renal cell carcinoma: shifting focus to alternative sources of tumour-specific antigens
Melissa M. Wolf, W. Kimryn Rathmell & Aguirre A. de CubasÂ
doi : 10.1038/s41581-023-00700-5
Renal cell carcinoma (RCC) comprises a group of malignancies arising from the kidney with unique tumour-specific antigen (TSA) signatures that can trigger cytotoxic immunity. Two classes of TSAs are now considered potential drivers of immunogenicity in RCC: small-scale insertions and deletions (INDELs) that result in coding frameshift mutations, and activation of human endogenous retroviruses.
Cell and gene therapy for kidney disease
Jennifer L. Peek & Matthew H. WilsonÂ
doi : 10.1038/s41581-023-00702-3
Kidney disease is a leading cause of morbidity and mortality across the globe. Current interventions for kidney disease include dialysis and renal transplantation, which have limited efficacy or availability and are often associated with complications such as cardiovascular disease and immunosuppression. There is therefore a pressing need for novel therapies for kidney disease.
Network medicine: an approach to complex kidney disease phenotypes
Arvind K. Pandey & Joseph LoscalzoÂ
doi : 10.1038/s41581-023-00705-0
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping.
