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Hui Xia

Publications and source records attributed to Hui Xia.

2 recordsLinked to original sources
PubMed2026

Molecular diagnostic tests for isoniazid-resistant tuberculosis: a scoping review.

The paucity of diagnostic tests for isoniazid-resistant tuberculosis is concerning, given its status as the most common form of drug-resistant tuberculosis and a gateway to multidrug-resistant diseases. Molecular drug-susceptibility testing has improved access to timely diagnosis of rifampicin-resistant tuberculosis, but testing for isoniazid-resistant tuberculosis still remains rare. In this Review, we assessed the characteristics of molecular drug-susceptibility testing for detection of isoniazid-resistant tuberculosis, referencing the WHO target product profiles. 9243 citations were screened to select 238 studies published between 2000 and 2024. The diagnostics options have expanded rapidly since 2020, with 27 nucleic acid amplification tests, eight line probe assays, five DNA microarrays, two targeted next-generation sequencing platforms, and two whole-genome sequencing platforms. Most of the evaluated molecular drug-susceptibility tests met diagnostic performance targets but were often complex and costly. Although a few low-complexity nucleic acid amplification tests met key target product profile criteria, additional field validation and greater efforts are needed to ensure optimal feasibility and affordability for low-resource settings.

Isoniazid↗
PubMed2026

Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.

Sweet osmanthus (Osmanthus fragrans), a traditional ornamental plant in China, exhibits substantial variation in autumn flowering time, which significantly affects landscape application and cultivation efficiency. Here, we performed a genome-wide association study on 127 resequenced accessions classified into early, intermediate, and late flowering types, using a set of 2,325,410 single-nucleotide polymorphisms (SNPs) and 246,824 structural variants (SVs). By integrating SNP/insertion and deletion (Indel) and SV data with weighted gene co-expression network analysis, machine learning, and genomic prediction, we dissected the genetic architecture of flowering time. We identified 24 associated SNP/Indels and six SVs, mapping to 30 candidate genes, including known flowering regulators FLK, LOS1, Y14, MIF2, and GID1B. These genes showed tissue-specific expression, with some responding to low temperature. The two hub genes, GUX1 and LYG027904, were located within modules of the co-expression network associated with low-temperature treatment. Haplotype analysis revealed a specific three-SNP haplotype associated with late flowering and linked to LOS1, and epistatic interactions among combined genotypes contributed to phenotypic variation. Notably, integrating SVs with SNP/Indels improved genomic prediction accuracy; the gradient boosting decision tree model outperformed other machine learning algorithms, achieving a mean accuracy of 0.859 and an AUC > 0.8 (where AUC is area under receiver operating characteristic curve) for all flowering types. These findings provide insights into the genetic mechanisms underlying flowering time variation in O. fragrans, offer candidate genes and haplotypes for molecular breeding, and highlight the value of integrating SVs with machine learning for genomic prediction in woody ornamentals.

Machine Learning↗