Utilizing in vitro cell lines and mCRPC PDX tumor models, we discovered a synergistic effect of enzalutamide and the pan-HDAC inhibitor vorinostat, offering a therapeutic proof-of-concept. New therapeutic strategies, incorporating both AR and HDAC inhibitors, are supported by these findings, potentially leading to better patient outcomes in advanced mCRPC.
The pervasive oropharyngeal cancer (OPC) is often addressed with radiotherapy as a crucial therapeutic element. Radiotherapy planning for OPC cases currently relies on manually segmenting the primary gross tumor volume (GTVp), a procedure prone to substantial discrepancies between different clinicians. Although deep learning (DL) has shown potential in automating GTVp segmentation, there has been limited exploration of comparative (auto)confidence metrics for the models' predictive outputs. Determining the uncertainty of instance-specific deep learning models is essential for building clinician confidence and widespread clinical use. This study developed probabilistic deep learning models for GTVp automatic segmentation, using extensive PET/CT datasets, and meticulously examined and compared different uncertainty estimation methods.
For our development dataset, the 2021 HECKTOR Challenge training dataset was utilized, containing 224 co-registered PET/CT scans of OPC patients, and their respective GTVp segmentations. A separate cohort of 67 co-registered PET/CT scans from OPC patients, including their respective GTVp segmentations, provided the basis for external validation. The performance of GTVp segmentation and uncertainty estimation was investigated using two approximate Bayesian deep learning methods, MC Dropout Ensemble and Deep Ensemble, both comprised of five submodels each. Segmentation effectiveness was gauged using the volumetric Dice similarity coefficient (DSC), mean surface distance (MSD), and the 95th percentile Hausdorff distance (95HD). A novel measure, along with the coefficient of variation (CV), structure expected entropy, structure predictive entropy, and structure mutual information, was employed to gauge the uncertainty.
Gauge the size of this measurement. To assess the utility of uncertainty information, the accuracy of uncertainty-based segmentation performance prediction was evaluated using the Accuracy vs Uncertainty (AvU) metric, complemented by an examination of the linear correlation between uncertainty estimates and the Dice Similarity Coefficient (DSC). The examination additionally included referral approaches categorized as batch-based and instance-based, resulting in the exclusion of patients exhibiting high uncertainty levels. The batch referral process employed the area under the referral curve, using DSC (R-DSC AUC), for evaluation, whereas the instance referral process involved scrutinizing the DSC metric at various uncertainty threshold values.
The models' performance in terms of segmentation and their uncertainty estimates were quite similar. The MC Dropout Ensemble's metrics are composed of a DSC of 0776, MSD of 1703 mm, and a 95HD of 5385 mm. The Deep Ensemble exhibited DSC 0767, MSD 1717 mm, and 95HD 5477 mm. Structure predictive entropy, the uncertainty measure exhibiting the highest correlation with DSC, demonstrated correlation coefficients of 0.699 for the MC Dropout Ensemble and 0.692 for the Deep Ensemble, respectively. Selleckchem Abraxane The highest AvU value, 0866, was a consistent result for both models. Based on the results, the coefficient of variation (CV) yielded the best uncertainty estimations for both models, achieving an R-DSC AUC of 0.783 for the MC Dropout Ensemble and 0.782 for the Deep Ensemble. Improvements in average DSC of 47% and 50% were achieved when referring patients based on uncertainty thresholds from the 0.85 validation DSC for all uncertainty measures, resulting in 218% and 22% patient referrals for MC Dropout Ensemble and Deep Ensemble models, respectively, compared to the complete dataset.
The investigated techniques demonstrated a consistent, yet differentiated, capability in estimating the quality of segmentation and referral performance. A crucial initial step toward broader uncertainty quantification deployment in OPC GTVp segmentation is represented by these findings.
The examined methods exhibited a similar, yet distinct, impact on predicting segmentation quality and referral effectiveness. Uncertainty quantification in OPC GTVp segmentation finds its initial, crucial application in these findings, paving the way for broader implementation.
Footprints, or ribosome-protected fragments, are sequenced in ribosome profiling to quantify translation activity across the entire genome. Its single-codon accuracy enables the identification of translational regulatory events, such as ribosome arrest or halting, on specific genes. However, the enzymes' preferences in the library's construction yield pervasive sequence anomalies, thereby obscuring translation dynamics. Ribosome footprint over- and under-representation frequently overwhelms local footprint densities, leading to potentially five-fold skewed elongation rate estimations. To ascertain the genuine translation patterns, uninfluenced by inherent biases, we present choros, a computational methodology that models ribosome footprint distributions to yield footprint counts corrected for bias. Choros's application of negative binomial regression allows for the precise estimation of two parameter sets: (i) the biological contributions from codon-specific translation elongation rates; and (ii) the technical contributions from nuclease digestion and ligation efficiencies. These parameter estimations yield bias correction factors, designed to eliminate sequence-related artifacts. Through the application of choros to multiple ribosome profiling datasets, we achieve accurate quantification and attenuation of ligation biases, thus yielding more faithful representations of ribosome distribution. We contend that the observed pattern of ribosome pausing near the start of coding sequences is a likely consequence of inherent technical biases. Standard analysis pipelines for translational measurements can be made more effective by incorporating choros, which will consequently lead to improved biological discovery.
Sex hormones are posited to be the causative factor in sex-based health disparities. The study addresses the association between sex steroid hormones and DNA methylation-based (DNAm) age and mortality risk markers, incorporating Pheno Age Acceleration (AA), Grim AA, DNA methylation-based estimates of Plasminogen Activator Inhibitor 1 (PAI1), and the measurement of leptin levels.
We integrated data across three population-based cohorts, namely the Framingham Heart Study Offspring Cohort, the Baltimore Longitudinal Study of Aging, and the InCHIANTI Study. These combined data include 1062 postmenopausal women without hormone therapy and 1612 men of European descent. Separately for each study and sex, the sex hormone concentrations were standardized, with a mean of 0 and a standard deviation of 1. For sex-stratified analysis, linear mixed regression models were employed, accompanied by a Benjamini-Hochberg correction for multiple testing. A sensitivity analysis was undertaken, isolating the effect of the training dataset previously used to establish Pheno and Grim age.
A decrease in DNAm PAI1 is linked to Sex Hormone Binding Globulin (SHBG) levels in men (per 1 standard deviation (SD) -478 pg/mL; 95%CI -614 to -343; P1e-11; BH-P 1e-10), and also in women (-434 pg/mL; 95%CI -589 to -279; P1e-7; BH-P2e-6). The testosterone/estradiol (TE) ratio exhibited an association with a lower Pheno AA (-041 years; 95%CI -070 to -012; P001; BH-P 004), and a reduced DNAm PAI1 (-351 pg/mL; 95%CI -486 to -217; P4e-7; BH-P3e-6), in men. Selleckchem Abraxane In males, a one standard deviation rise in serum total testosterone was statistically significantly correlated with a lower DNA methylation level at the PAI1 gene, by an amount of -481 pg/mL (95% confidence interval: -613 to -349; P2e-12; BH-P6e-11).
SHBG levels displayed an inverse association with DNAm PAI1, both in men and women. Higher testosterone and a greater ratio of testosterone to estradiol in men were observed in conjunction with lower DNAm PAI and a younger epigenetic age. A decrease in DNAm PAI1 levels is linked to diminished mortality and morbidity, implying a potentially protective impact of testosterone on lifespan and likely cardiovascular health through the DNAm PAI1 pathway.
A connection was established between SHBG and lower DNA methylation of PAI1 in both the male and female populations. A correlation was observed between higher testosterone and a greater testosterone-to-estradiol ratio, and a lower DNAm PAI-1 value, along with a younger epigenetic age, specifically in men. A decrease in DNA methylation of PAI1 is correlated with reduced mortality and morbidity, implying a possible protective effect of testosterone on lifespan and cardiovascular health, specifically through DNAm PAI1.
The lung's extracellular matrix (ECM) acts to uphold tissue structural integrity, thereby influencing the characteristics and functions of resident fibroblasts. Fibroblast activation is a consequence of altered cell-extracellular matrix interactions due to lung-metastatic breast cancer. To study cell-matrix interactions in the lung in vitro, there is a demand for bio-instructive ECM models that reflect the lung's ECM composition and biomechanical properties. Our work details the creation of a synthetic, bioactive hydrogel that replicates the elasticity of the lung, incorporating a representative proportion of the most abundant ECM peptide motifs, crucial for integrin binding and matrix metalloproteinase (MMP)-driven degradation, prevalent in the lung, fostering quiescence of human lung fibroblasts (HLFs). Transforming growth factor 1 (TGF-1), metastatic breast cancer conditioned media (CM), and tenascin-C each stimulated hydrogel-encapsulated HLFs, mimicking their natural in vivo responses. Selleckchem Abraxane To study the independent and combinatorial effects of the ECM on fibroblast quiescence and activation, we propose this tunable synthetic lung hydrogel platform.