Frontiers in Medicine is delighted to present the ‘Reviews in’ series of Research Topics. 'Reviews in' is a series being launched across Frontiers in Medicine’s sections which aims to publish high-quality review articles on key topics within the field. The goal of this series is to highlight recent advances within the field, whilst emphasizing important directions and new possibilities for future inquiries. This Research Topic welcomes Review, Mini-Review and Systematic Review articles on topics within Nephrology. New articles will be added to this collection as they are published. Please note that manuscripts consisting solely of bioinformatics or computational analyses of public genomic or transcriptomic databases and/or in silico analysis of bibliometric trends will not be considered for this collection.

Medical University of South Carolina

First Hospital, Peking University

Monash University

Frontiers in Medicine

Nephrology

Reviews in Nephrology 2022

Preterm birth, defined as birth before the gestational age of 37 weeks, affects 11% of the newborns worldwide. While extensive research has focused on the immediate complications associated with prematurity, emerging evidence suggests a link between prematurity and the development of kidney disease later in life. It has been demonstrated that the normal course of kidney development is interrupted in infants born prematurely, causing an overall decrease in functional nephrons. Yet, the pathogenesis leading to the alterations in kidney development and the subsequent pathophysiological consequences causing kidney disease on the long-term are incompletely understood. In the present review, we discuss the current knowledge on nephrogenesis and how this process is affected in prematurity. We further discuss the epidemiological evidence and experimental data demonstrating the increased risk of kidney disease in these individuals and highlight important knowledge gaps. Importantly, understanding the intricate interplay between prematurity, abnormal kidney development, and the long-term risk of kidney disease is crucial for implementing effective preventive and therapeutic strategies.

Preterm birth, defined as birth before the gestational age of 37&#x2009;weeks, affects 11% of the newborns worldwide. While extensive research has focused on the immediate complications associated with prematurity, emerging evidence suggests a link between prematurity and the development of kidney disease later in life. It has been demonstrated that the normal course of kidney development is interrupted in infants born prematurely, causing an overall decrease in functional nephrons. Yet, the pathogenesis leading to the alterations in kidney development and the subsequent pathophysiological consequences causing kidney disease on the long-term are incompletely understood. In the present review, we discuss the current knowledge on nephrogenesis and how this process is affected in prematurity. We further discuss the epidemiological evidence and experimental data demonstrating the increased risk of kidney disease in these individuals and highlight important knowledge gaps. Importantly, understanding the intricate interplay between prematurity, abnormal kidney development, and the long-term risk of kidney disease is crucial for implementing effective preventive and therapeutic strategies.

Impact of preterm birth on kidney health and development

ObjectiveTo systematically evaluate the clinical efficacy and safety of Shenkang injection (SKI) combined with alprostadil in the treatment of chronic renal failure (CRF).</sec>MethodRandomized controlled trials (RCTs) of Shenkang injection combined with alprostadil in CRF treatment were investigated by retrieving a total of 7 databases including CNKI, Wanfang database, VIP, CBM, PubMed, Embase and Cochrane Library, with the search time ranging from 2012 to now. Revman 5.2 software was used for data analysis, and Cochrane bias risk tool was used to evaluate the quality of the included literature. The final results were represented by relative risk (RR), mean difference (MD) and 95% confidence interval (95% CI).</sec>ResultsA total of 20 RCTs and 1,573 patients were included in this study. Meta-analysis showed that the overall response rate (ORR) of the treatment group was superior to the control group [RR = 0.20, 95% CI (0.16, 0.25), P < 0.00001]. Compared with the control group, the treatment group achieved favorable improvement in terms of the creatinine clearance rate (Ccr) [MD = 9.48, 95% CI (8.73, 10.24), P < 0.00001], serum creatinine (Scr) [MD = −55.12, 95% CI (−63.42, −46.82), P < 0.00001], quantitative urine protein (Upro) [MD = −0.48, 95% CI (−0.53, −0.43), P < 0.00001], and blood urea nitrogen (BUN) [MD=-3.73, 95% CI (−4.08, −3.3) 7, P < 0.00001]. There was no statistical difference in the incidence of adverse reactions in each group.</sec>ConclusionCurrently, Shenkang injection combined with alprostadil has been widely used in clinical treatment of CRF due to the certain effect superior to other methods. However, its specific efficacy and safety need to be further verified through numerous large-scale clinical trials.</sec>

Objective: To systematically evaluate the clinical efficacy and safety of Shenkang injection (SKI) combined with alprostadil in the treatment of chronic renal failure (CRF).Method: Randomized controlled trials (RCTs) of Shenkang injection combined with alprostadil in CRF treatment were investigated by retrieving a total of 7 databases including CNKI, Wanfang database, VIP, CBM, PubMed, Embase and Cochrane Library, with the search time ranging from 2012 to now. Revman 5.2 software was used for data analysis, and Cochrane bias risk tool was used to evaluate the quality of the included literature. The final results were represented by relative risk (RR), mean difference (MD) and 95% confidence interval (95% CI).Results: A total of 20 RCTs and 1,573 patients were included in this study. Meta-analysis showed that the overall response rate (ORR) of the treatment group was superior to the control group [RR = 0.20, 95% CI (0.16, 0.25), P &lt; 0.00001]. Compared with the control group, the treatment group achieved favorable improvement in terms of the creatinine clearance rate (Ccr) [MD = 9.48, 95% CI (8.73, 10.24), P &lt; 0.00001], serum creatinine (Scr) [MD = −55.12, 95% CI (−63.42, −46.82), P &lt; 0.00001], quantitative urine protein (Upro) [MD = −0.48, 95% CI (−0.53, −0.43), P &lt; 0.00001], and blood urea nitrogen (BUN) [MD=-3.73, 95% CI (−4.08, −3.3) 7, P &lt; 0.00001]. There was no statistical difference in the incidence of adverse reactions in each group.Conclusion: Currently, Shenkang injection combined with alprostadil has been widely used in clinical treatment of CRF due to the certain effect superior to other methods. However, its specific efficacy and safety need to be further verified through numerous large-scale clinical trials.

Shenkang injection combined with alprostadil for chronic renal failure: A systematic review and meta-analysis

Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

Since 1969, an herbal medicine extracted from Quercus salicina Blume/Quercus stenophylla Makino (QS) has been clinically used for the management of urolithiasis in Japan. Historically, the decoction of leaves and shoots of QS trees was popularly utilized as a folk prescription to remove urinary calculi. This study was designed to perform a brief review of the updated progress of QS extract for urinary stones based on previous studies. A comprehensive literature search was conducted in multiple electronic databases, including Web of Science, PubMed, and EMBASE, and relevant data on QS extract were extracted. As a result, the major mechanism of QS extract for urolithiasis is observed to be closely related to the anti-oxidative activities according to recent studies, leading to inhibition of the accumulation of renal calcium and prevention of stone formation and recurrence of stones. As for the effect of discharging stones, loosening the upper urinary tract has also been noticed recently. More extensive studies are still necessary to systemically evaluate the individual dosage, drug safety, and targeted stone types.

Since 1969, an herbal medicine extracted from Quercus salicina Blume/Quercus stenophylla Makino (QS) has been clinically used for the management of urolithiasis in Japan. Historically, the decoction of leaves and shoots of QS trees was popularly utilized as a folk prescription to remove urinary calculi. This study was designed to perform a brief review of the updated progress of QS extract for urinary stones based on previous studies. A comprehensive literature search was conducted in multiple electronic databases, including Web of Science, PubMed, and EMBASE, and relevant data on QS extract were extracted. As a result, the major mechanism of QS extract for urolithiasis is observed to be closely related to the anti-oxidative activities according to recent studies, leading to inhibition of the accumulation of renal calcium and prevention of stone formation and recurrence of stones. As for the effect of discharging stones, loosening the upper urinary tract has also been noticed recently. More extensive studies are still necessary to systemically evaluate the individual dosage, drug safety, and targeted stone types.