Comprehensive Analysis of PKD1 and PKD2 by Long-Read Sequencing in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is mainly caused by heterogeneous variants in the PKD1 and PKD2 genes. Genetic analysis of PKD1 has been challenging due to homology with 6 PKD1 pseudogenes and high GC content. METHODS: A single-tube multiplex long-range-PCR and long-read sequencing-based assay termed "comprehensive analysis of ADPKD" (CAPKD) was developed and evaluated in 170 unrelated patients by comparing to control methods including next-generation sequencing (NGS) and multiplex ligation-dependent probe amplification. RESULTS: CAPKD achieved highly specific analysis of PKD1 with a residual noise ratio of 0.05% for the 6 pseudogenes combined. CAPKD identified PKD1 and PKD2 variants (ranging from variants of uncertain significance to pathogenic) in 160 out of the 170 patients, including 151 single-nucleotide variants (SNVs) and insertion-deletion variants (indels), 6 large deletions, and one large duplication. Compared to NGS, CAPKD additionally identified 2 PKD1 variants (c.78_96dup and c.10729_10732dup). Overall, CAPKD increased the rate of variant detection from 92.9% (158/170) to 94.1% (160/170), and the rate of diagnosis with pathogenic or likely pathogenic variants from 82.4% (140/170) to 83.5% (142/170). CAPKD also directly determined the cis-/trans-configurations in 11 samples with 2 or 3 SNVs/indels, and the breakpoints of 6 large deletions and one large duplication, including 2 breakpoints in the intron 21 AG-repeat of PKD1, which could only be correctly characterized by aligning to T2T-CHM13. CONCLUSIONS: CAPKD represents a comprehensive and specific assay toward full characterization of PKD1 and PKD2 variants, and improves the genetic diagnosis for ADPKD.

Coexistence of anti-glomerular basement membrane disease and IgA nephropathy: an illustrative case and comprehensive literature review.

Anti-glomerular basement membrane (GBM) disease is a rare autoimmune condition characterized by the presence of positive anti-GBM autoantibodies, linear deposition of immunoglobulin G (IgG) along the GBM and severe kidney injury. In a limited number of cases, the association of anti-GBM disease with other glomerulonephritis has been reported. Herein, we present the case of a 66-year-old female patient with progressive worsen kidney function and decreased urine output. A renal biopsy revealed crescent glomerulonephritis with lineal IgG deposition along the GBM and mesangial IgA deposition, which supported the diagnosis of concurrent anti-GBM disease and IgA nephropathy (IgAN). In an extensive literature review, we identified a total of thirty-nine patients were reported anti-GBM disease combined with IgAN. The clinical characteristics of these patients demonstrate that the anti-GBM disease combined with IgAN tends to be milder with a more indolent course and a better prognosis than the classic anti-GBM disease, and its potential pathogenesis deserves to be further explored.

A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation.

Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.