Vascular calcification (VC) is a common comorbidity among patients with chronic kidney disease (CKD), indicating major cardiovascular events. This study aimed to evaluate the effects and safety of intravenous sodium thiosulphate (STS) for VC in CKD patients.Electronic databases were searched for clinical trials that provided data comparing outcomes among patients treated with and without STS. The PRISMA guidelines were followed. Efficacy was assessed using calcification scores and arterial stiffness. Safety was examined by analyzing adverse symptoms, electrolytes and bone mineral density (BMD). Random-effects models were performed. Meta-regression and sensitivity analysis were done. The risk of bias was assessed using the Cochrane tools.Among the 5601 publications, 6 studies involving 305 participants (mean age: 56 years, male: 56.6%) with all participants on maintenance hemodialysis met eligibility criteria. For efficacy, the progression in Agatston scores in the coronary arteries [107 patients, mean difference (MD): -241.27, 95% confidence interval (95% CI): -421.50 to -61.03] and iliac arteries (55 patients, MD: -382.00, 95% CI: -751.07 to -12.93) was lower in the STS treated group compared with controls. The increase in pulse wave velocity was lower in the STS group (104 patients, MD: -1.29 m/s, 95% CI: -2.24 to -0.34 m/s). No association was found between the change in calcification scores and STS regimen. For safety, gastrointestinal symptoms (e.g. nausea) and increased anion gap acidosis were noted. No reduction in BMD by STS was observed.Intravenous STS may attenuate the progression of VC and arterial stiffness in hemodialysis patients. Large and well-designed randomized controlled trials are warranted.
ABSTRACT Melanoma is a lethal form of skin cancer that impacts one out of every five Americans and ranks as the fifth most prevalent cancer among men and women in the United States. Immunoglobulin (Ig) and Proline-rich Receptor-1 (IGPR-1, also called TMIGD2/CD28H) is closely related to immune checkpoint, CD28/PDL1 family receptors. It controls important cellular processes including, immune cell regulation, cell-cell adhesion, mechanosensing, autophagy, and angiogenesis, and its activity is associated with multiple human malignancies. However, the role and signaling mechanism of IGPR-1 in melanoma remains largely undefined. Here, we report that IGPR-1 is mutated or upregulated in nearly 13% of melanoma and its pro-tumor signaling in melanoma cells is mediated by phosphorylation of immunoreceptor tyrosine-based activation motif (ITAM) tyrosine (Y222). IGPR-1 is phosphorylated at Y222 in human melanoma and cell culture. Phosphorylation of Y222 is context-dependent and is catalyzed by EGFR and Src kinase. Inhibition of EGFR by pharmacological and shRNA strategies inhibited phosphorylation of Y222, whereas stimulation with EGF promoted phosphorylation of Y222 in vivo and recombinant active EGFR catalyzed its phosphorylation in an in vitro kinase assay. In vivo co-immunoprecipitation and in vitro GST-pull-down assays demonstrated that phospho-Y222 facilitates the binding of IGPR-1 with the SH2 domain-containing proteins, SHC1 and SHP2. IGPR-1 stimulates multiple key signal transduction pathways relevant to tumorigenesis, including AKT, mTOR, and MAPK. Mutation of Y222 blocked IGPR-1-mediated activation of AKT and MAPK leading to inhibition of 3D-spheroid tumor growth. By investigating the immunoreceptor tyrosine-based motif signaling of IGPR-1, this study uncovers new findings that could have significant diagnostic and therapeutic implications for melanoma.
Significance Statement Patients with CKD are at a markedly higher risk of thrombosis after vascular procedures. Uremic solutes, such as indoxyl sulfate and kynurenine, are important contributors to this complication through tissue factor (TF), a trigger of the extrinsic coagulation cascade. This study examines the role of indoleamine 2,3-dioxygenase-1 (IDO-1), a key enzyme in kynurenine biogenesis, in thrombotic complications in CKD. Using genomic and pharmacological approaches, this study demonstrates that IDO-1 is a critical regulator of TF and thrombosis after vascular injury in CKD mice. Indoxyl sulfate upregulates IDO-1, creating a feedback-forward loop. IDO-1 activity was higher in patients with CKD, who developed thrombosis after vascular interventions. This study identifies IDO-1 as a therapeutic target and uncovers crosstalk between uremic solutes, perpetuating their toxic effect. Background CKD, characterized by retained uremic solutes, is a strong and independent risk factor for thrombosis after vascular procedures . Urem ic solutes such as indoxyl sulfate (IS) and kynurenine (Kyn) mediate prothrombotic effect through tissue factor (TF). IS and Kyn biogenesis depends on multiple enzymes, with therapeutic implications unexplored. We examined the role of indoleamine 2,3-dioxygenase-1 (IDO-1), a rate-limiting enzyme of kynurenine biogenesis, in CKD-associated thrombosis after vascular injury. Methods IDO-1 expression in mice and human vessels was examined. IDO-1 −/− mice, IDO-1 inhibitors, an adenine-induced CKD, and carotid artery injury models were used. Results Both global IDO-1 −/− CKD mice and IDO-1 inhibitor in wild-type CKD mice showed reduced blood Kyn levels, TF expression in their arteries, and thrombogenicity compared with respective controls. Several advanced IDO-1 inhibitors downregulated TF expression in primary human aortic vascular smooth muscle cells specifically in response to uremic serum. Further mechanistic probing of arteries from an IS-specific mouse model, and CKD mice, showed upregulation of IDO-1 protein, which was due to inhibition of its polyubiquitination and degradation by IS in vascular smooth muscle cells. In two cohorts of patients with advanced CKD, blood IDO-1 activity was significantly higher in sera of study participants who subsequently developed thrombosis after endovascular interventions or vascular surgery. Conclusion Leveraging genetic and pharmacologic manipulation in experimental models and data from human studies implicate IS as an inducer of IDO-1 and a perpetuator of the thrombotic milieu and supports IDO-1 as an antithrombotic target in CKD.
Autosomal-dominant polycystic kidney disease (ADPKD) and von Hippel-Lindau (VHL) disease lead to large kidney cysts that share pathogenetic features. The polycystin-1 (PC1) and pVHL proteins may therefore participate in the same key signaling pathways. Jade-1 is a pro-apoptotic and growth suppressive ubiquitin ligase for beta-catenin and transcriptional coactivator associated with histone acetyltransferase activity that is stabilized by pVHL in a manner that correlates with risk of VHL renal disease. Thus, a relationship between Jade-1 and PC1 was sought. Full-length PC1 bound, stabilized and colocalized with Jade-1 and inhibited Jade-1 ubiquitination. In contrast, the cytoplasmic tail or the naturally occurring C-terminal fragment of PC1 (PC1-CTF) promoted Jade-1 ubiquitination and degradation, suggesting a dominant-negative mechanism. ADPKD-associated PC1 mutants failed to regulate Jade-1, indicating a potential disease link. Jade-1 ubiquitination was mediated by Siah-1, an E3 ligase that binds PC1. By controlling Jade-1 abundance, PC1 and the PC1-CTF differentially regulate Jade-1-mediated transcriptional activity. A key target of PC1, the cyclin-dependent kinase inhibitor p21, is also up-regulated by Jade-1. Through Jade-1, PC1 and PC1 cleaved forms may exert fine control of beta-catenin and canonical Wnt signaling, a critical pathway in cystic renal disease. Thus, Jade-1 is a transcription factor and ubiquitin ligase whose activity is regulated by PC1 in a manner that is physiologic and may correlate with disease. Jade-1 may be an important therapeutic target in renal cystogenesis.
Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared low-dimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (10 patients), and radiologist-driven annotations of lumen cross-sections of human arteriovenous fistulae obtained from intravascular ultrasound images (10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image segmentation.
Rationale & ObjectivesArtificial intelligence driven by machine learning algorithms is being increasingly employed for early detection, disease diagnosis, and clinical management. We explored the use of machine learning–driven advancements in kidney research compared with other organ-specific fields.Study DesignCross-sectional bibliometric analysis.Setting & ParticipantsISI Web of Science database was queried using specific Medical Subject Headings (MeSH) terms about the organ system, journal International Standard Serial Number, and research methodology. In parallel, we screened the National Institutes of Health (NIH) RePORTER website to explore funded grants that proposed the use of machine learning as a methodology.PredictorsNumber of publications using machine learning as a research method.OutcomeArticles were characterized by research methodology among 5 organ systems (brain, heart, kidney, liver, and lung). Grants funded by NIH for machine learning were characterized by study sections.Analytical ApproachPercentages of articles using machine learning and other research methodologies were compared among 5 organ systems.ResultsMachine learning-based articles that are focused on the kidney accounted for 3.2% of the total relevant articles from the 5 organ systems. Specifically, brain research published over 19-fold higher number of articles than kidney research. As compared with machine learning, conventional statistical approaches such as the Cox proportional hazard model were used 9-fold higher in articles related to kidney research. In general, a lower utilization of machine learning–based approaches was observed in organ-specific specialty journals than the broad interdisciplinary journals. The digestive disease, kidney, and urology study sections funded 122 applications proposing machine learning–based approaches compared to 265 applications from the neurology, neuropsychology, and neuropathology study sections.LimitationsObservational study.ConclusionsOur analysis suggests lowest use of machine learning as a research tool among kidney researchers compared with other organ-specific researchers, underscoring a need to better inform the kidney research community about this emerging data analytic tool. Artificial intelligence driven by machine learning algorithms is being increasingly employed for early detection, disease diagnosis, and clinical management. We explored the use of machine learning–driven advancements in kidney research compared with other organ-specific fields. Cross-sectional bibliometric analysis. ISI Web of Science database was queried using specific Medical Subject Headings (MeSH) terms about the organ system, journal International Standard Serial Number, and research methodology. In parallel, we screened the National Institutes of Health (NIH) RePORTER website to explore funded grants that proposed the use of machine learning as a methodology. Number of publications using machine learning as a research method. Articles were characterized by research methodology among 5 organ systems (brain, heart, kidney, liver, and lung). Grants funded by NIH for machine learning were characterized by study sections. Percentages of articles using machine learning and other research methodologies were compared among 5 organ systems. Machine learning-based articles that are focused on the kidney accounted for 3.2% of the total relevant articles from the 5 organ systems. Specifically, brain research published over 19-fold higher number of articles than kidney research. As compared with machine learning, conventional statistical approaches such as the Cox proportional hazard model were used 9-fold higher in articles related to kidney research. In general, a lower utilization of machine learning–based approaches was observed in organ-specific specialty journals than the broad interdisciplinary journals. The digestive disease, kidney, and urology study sections funded 122 applications proposing machine learning–based approaches compared to 265 applications from the neurology, neuropsychology, and neuropathology study sections. Observational study. Our analysis suggests lowest use of machine learning as a research tool among kidney researchers compared with other organ-specific researchers, underscoring a need to better inform the kidney research community about this emerging data analytic tool.
