Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging

Enhancement of renal fibrosis in PHF20 transgenic mice

Plant homeodomain finger protein 20 (PHF20) plays a crucial role in various biological processes, but its involvement in renal fibrosis remains unclear. This study investigated the role of PHF20 in renal fibrosis using a unilateral ureteral obstruction (UUO) mouse model, a widely accepted model for chronic kidney disease. PHF20 transgenic (PHF20-TG) and wild-type (WT) mice were utilized to explore how PHF20 influences renal inflammation and fibrosis. After UUO surgery, serum analysis revealed elevated creatinine levels and increased inflammatory markers, indicating worsened renal function in PHF20-TG mice. Histological analyses, including H&E, PAS, and Sirius Red staining, confirmed significant tissue damage and fibrosis in the PHF20-TG group. Molecular investigations demonstrated enhanced activation of the TGF-β/SMAD2/3 and NF-κB signaling pathways, both of which are crucial in the progression of renal fibrosis. Our findings suggest that PHF20 overexpression accelerates early-stage renal fibrosis by amplifying inflammatory responses and promoting collagen deposition. This indicates that PHF20 expression could serve as an early marker for renal fibrosis progression.

Recent trends in AI applications for pelvic MRI: a comprehensive review

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models

OBJECTIVES

To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD).

METHODS

A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models.

RESULTS

All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features.

CONCLUSIONS

Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD.

ADVANCES IN KNOWLEDGE

Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

Kangxianling formula attenuates renal fibrosis by regulating gut microbiota

BACKGROUND

Renal fibrosis (RF) produced adverse effect on kidney function. Recently, intestinal dysbiosis is a key regulator that promotes the formation of renal fibrosis. This study will focus on exploring the protective mechanism of Kangxianling Formula (KXL) on renal fibrosis from the perspective of intestinal flora.

METHODS

Unilateral Ureteral Obstruction (UUO) was used to construct rats' model with RF, and receive KXL formula intervention for 1 week. The renal function indicators were measured. Hematoxylin-eosin (HE), Masson and Sirus red staining were employed to detect the pathological changes of renal tissue in each group. The expression of α-SMA, Col-III, TGF-β, FN, ZO-1, and Occuludin was detected by immunofluorescence and immunohistochemistry. Rat feces samples were collected and analyzed for species' diversity using high-throughput sequencing 16S rRNA.

RESULTS

Rats in UUO groups displayed poor renal function as well as severe RF. The pro-fibrotic protein expression in renal tissues including α-SMA, Col-III, TGF-β and FN was increased in UUO rats, while ZO-1 and Occuludin -1 expression was downregulated in colon tissues. The above changes were attenuated by KXL treatment. 16S rRNA sequencing results revealed that compared with the sham group, the increased abundance of pathogenic bacteria including Acinetobacter, Enterobacter and Proteobacteria and the decreased abundance of beneficial bacteria including Actinobacteriota, Bifidobacteriales, Prevotellaceae, and Lactobacillus were found in UUO group. After the administration of KXL, the growth of potential pathogenic bacteria was reduced and the abundance of beneficial bacteria was enhanced.

CONCLUSION

KXL displays a therapeutical potential in protecting renal function and inhibiting RF, and its mechanism of action may be associated with regulating intestinal microbiota.

Clinical applications of artificial intelligence in liver imaging

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.