Biomedical subjects

Harsimran Kaur

Publications and source records attributed to Harsimran Kaur.

4 recordsLinked to original sources

PubMed2026

Triazole resistance in clinical Aspergillus fumigatus isolates in India, a multicenter surveillance study.

BACKGROUND: Triazole resistance in Aspergillus fumigatus is a global public health concern associated with treatment failure, notably in invasive aspergillosis. However, population-level data on triazole resistance from India remain limited, with most reports originating from single-center studies. METHODS: We conducted a multicenter surveillance study to assess the prevalence of triazole resistance among clinical A. fumigatus isolates across India. Antifungal susceptibility testing was performed using the CLSI broth microdilution method (M38-Ed3), and molecular characterization was conducted on resistant isolates. A total of 518 isolates were analyzed: 115 prospectively collected from 13 tertiary-care hospitals from 2015-2020, and 403 archived isolates obtained from the National Culture Collection of Pathogenic Fungi (1994-2020). RESULTS: The overall pooled prevalence of non-wildtype isolates was 4.1% for itraconazole (95% CI: 2.54-6.17%), 3.9% for posaconazole (95% CI: 2.39-5.94%), while 1.4% were resistant to voriconazole (95% CI: 0.55-2.77%). One multi-azole-resistant isolate from an immunocompromised, mold-active triazole-naïve patient carried the TR34/L98H mutation, suggesting environmental acquisition. Prevalence of resistance did not differ significantly across geographic regions or between public and private sector hospitals. Linear regression analysis revealed a significant temporal increase in median MICs of all three licensed triazoles between 1994 and 2020. Approximately 29% of isolates exhibited amphotericin B MICs exceeding the epidemiological cutoff value; however, the clinical significance of this finding remains uncertain. CONCLUSIONS: Azole resistance among clinical A. fumigatus isolates in India remains uncommon (<5%), supporting the continued use of triazoles as first-line therapy. However, the observed temporal increase in triazole MICs underscores the need for sustained national surveillance to detect emerging resistance trends.

Aspergillus fumigatus↗

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2025

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗

PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗