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Biomedical subjects

Hongzhe Li

Publications and source records attributed to Hongzhe Li.

2 recordsLinked to original sources

Persistent antimicrobial resistance during soil remediation driven by residual heavy metal co-selection.

Remediation of heavy metal-contaminated soil is a global priority, particularly as reclaimed land increasingly intersects with urban development and human exposure. However, the ecological consequences of soil remediation, especially its impact on antimicrobial resistance (AMR) as a global health threat, have remained poorly understood. Here, we combined single-cell Raman-D₂O probing with genome-resolved metagenomics to monitor the dynamics of phenotypic and genotypic resistance to metals and antibiotics during a 120-day remediation of soils with three contamination levels from a lead-zinc smelting site. Although chemical remediation substantially reduced bioavailable metals (by 42%-65%), AMR was not diminished. Instead, both phenotypic activity and gene abundance of metal- and antibiotic-resistant microorganisms increased, resulting in a two- to three-fold increase in AMR-associated health risks. Among 76 metagenome assembled genomes (MAGs) from phenotypic resistance communities, all Cd resistance-associated MAGs harbored multidrug resistance genes, half of which were colocalized with metal resistance determinants, and their prevalence continued to rise with remediation. These findings reveal that although remediation alleviates acute metal toxicity, residual low-concentration bioavailable metals sustain evolutionary selection for resistance, highlighting a disconnect between chemical recovery and biological safety. Moreover, the improved soil nutrient and physiochemical properties of remediated soils further promoted the proliferation of antibiotic-resistant bacteria. This study offers new ecological insights into the unintended consequences of anthropogenic interventions, underscoring the need to integrate biological safety into soil health and safety assessments.

Soil Microbiology

dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.

MOTIVATION: There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), which can model non-linear growth trajectories under complex distributions that address the heterogeneity in human populations.Fitting GAMLSS models requires large, generalizable sample sizes, especially for accurate estimation of extreme quantiles, but obtaining such multi-site data can be challenging due to privacy concerns and practical considerations. In settings where patient data cannot be shared, privacy-preserving distributed algorithms for federated learning can be used, but no such algorithm exists for GAMLSS. RESULTS: We propose distributed GAMLSS (dGAMLSS), a distributed algorithm that can fit GAMLSS models across multiple sites without sharing patient-level data. This includes specific considerations for the fitting of smooth functions at varying levels of communication efficiency. We demonstrate the effectiveness of dGAMLSS in constructing population reference charts across clinical, genomics, and neuroimaging settings and show that dGAMLSS is able to reproduce pooled reference charts and inference down to numerical differences. AVAILABILITY AND IMPLEMENTATION: An R package providing examples of the dGAMLSS algorithm, as well as functions for sharing and aggregating site-specific parameters, is available at https://github.com/hufengling/dGAMLSS.

Algorithms