Biology subjects

Salminen, L.

Publications and source records attributed to Salminen, L..

3 recordsLinked to original sources

bioRxiv2025

Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA produced stronger effect sizes and revealed findings in brain regions that traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

neuroscience↗

bioRxiv2023

Field cancerization impacts tumor development, T-cell exhaustion and clinical outcomes in bladder cancer

Bladder field cancerization may be associated with disease outcome in patients with bladder cancer. To investigate this, we analyzed biopsies from bladder urothelium and urine samples by genomics and proteomics analyses. Samples were procured from multiple timepoints from 134 patients with early stage bladder cancer and detailed long term follow-up. We measured the field cancerization in normal-appearing bladder biopsies and found that high levels were associated with high tumor mutational burden, high neoantigen load, and high tumor-associated CD8 T-cell exhaustion. Non-synonymous mutations in known bladder cancer driver genes such as KDM6A and TP53 were identified as early disease drivers in normal urothelium. High field cancerization was associated with worse outcome but not with response to BCG. The level of urinary tumor DNA (utDNA) reflected the bladder tumor burden and originated from both tumors and field cancerization. High utDNA levels after BCG were associated with worse clinical outcomes for the patients. Our results indicate that the level of field cancerization may affect clinical outcome, tumor development and immune responses. utDNA measurements have significant prognostic value and reflect the disease status of the bladder.

cancer biology↗

bioRxiv2019

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

BackgroundA subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-to-middle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV.\n\nMethodsAnalyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity.\n\nResultsThe analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors.\n\nConclusionMachine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.

neuroscience↗