Raw Counts Research Articles

Evaluating the feasibility of using whole blood gene expression profiles to identify novel targets in canine septic peritonitis.

To assess gene expression profiles in canine whole blood with and without septic peritonitis to assess workflow feasibility and identify potential blood biomarkers that could be further investigated in future studies. This study enrolled 6 dogs with cytologically confirmed septic peritonitis of any cause and 6 healthy dogs. All dogs had a CBC and biochemistry performed. The dogs with septic peritonitis also had point-of-care lactate and blood oxygen saturation measured for acute patient physiologic and laboratory evaluation score calculation. All dogs then had 2.5 mL of whole blood collected and placed into an RNA stabilization tube, which was processed using a commercial assay based on the hybridization of fluorescent probes for transcript quantification. Quality control, normalization, and data visualization were performed. Raw counts were exported, and differential expression was performed. The evaluation of canine whole blood expression profiles was confirmed to be feasible. Differential expression analysis of septic and nonseptic dogs demonstrated distinct gene expression profile signatures. Five genes of interest were upregulated in septic whole blood including matrix metallopeptidase 9, IL-1 receptor type 2, proliferating cell nuclear antigen, phosphatidylinositol 3-kinase catalytic-γ, and cluster of differentiation 55. The study and associated workflow were feasible and can be scaled in confirmatory studies. Future studies are now proposed to further validate the increased expression of putative biomarkers in a larger cohort of canine septic peritonitis patients with more relevant comparator control cohorts.

Booking Sustainability: Publicly Traded Companies as Catalysts for Public Goods Provision in Brazil

This study assesses the extent of public goods provision by Brazilian firms and how this behavior has changed over time. We use text data of publicly traded companies’ annual standardized financial declarations from 2010 and 2022 and apply natural language processing techniques to extract ESG (environmental, social, and governance) keywords related to the provision of public goods. Context and sentiment analyses were used to supplement the information extracted from the raw keyword counts; these analyses were conducted using diverse regression techniques. We found a pronounced increase in keyword mentions over time; in particular, “responsibility” and “sustainability” appeared more frequently. Virtually all firms became more dedicated to ESG practices, particularly those that had a low frequency of ESG mentions in a positive context. Overall, it seems that large Brazilian corporations have embedded comprehensive ESG policies into their business practices, thus aligning their strategies with those of pioneering multinationals.

Advantage of Log Odds of Metastatic Lymph Nodes After Curative-Intent Resection of Gallbladder Cancer.

Lymph node metastasis (LNM) is among the most important predictors of poor prognosis after surgery for gallbladder cancer (GBC). Traditionally, staging has been based on the raw count of LNM, with a high risk of understaging patients who undergo inadequate lymph node dissection (LND). The log odds of metastatic lymph nodes (LODDS) may represent an alternative staging approach to stratify patients more accurately after resection of GBC. In this cross-sectional study, patients who underwent curative-intent surgery with LND for GBC were identified from an international database. Two predictive models were built and compared, each integrating a different lymph nodes status indicator [i.e., American Joint Committee on Cancer (AJCC) and LODDS]. Among 199 patients, the median number of lymph nodes examined was 5 [interquartile range (IQR): 3.0, 8.0]; most patients had T1 (n = 26, 13.1%) or T2 (n = 97, 48.7%) disease, and a subset had LNM (n = 87, 44.0%). Multivariable Cox analysis demonstrated LODDS was an independent predictor of overall survival [hazard ratio (HR) 1.84, 95% confidence interval (CI) 1.5-2.3; p < 0.001]. The LODDS model demonstrated better performance compared with a traditional model that utilized the AJCC N category [concordance (C) index: 0.814 versus 0.763; p < 0.001]. Patients classified as high- versus low-risk based on LODDS had much worse overall survival (OS) (4.9% versus 83.7%, respectively; p < 0.001). The LODDS model performance remained high even among patients with inadequateLND (< 6 LN) (C index: 0.87). An online calculator was developed ( https://catalano-giovanni.shinyapps.io/LoddsGBC/ ). A novel prognostic model based on LODDS may overcome the inherent limitations of the current AJCC staging system, reducing understaging among patients with fewer than six total lymph nodes evaluated.

Abstract 4137902: Layer-specific Gene Expression Associated with Intima Hyperplasia in Arterio-venous Fistula

Background: The mechanism of hemodialysis arteriovenous fistula (AVF) vein intima hyperplasia (IH) may be specific to its layers (intima, media, and adventitia) but is poorly understood. To address this gap, we studied layer-specific transcriptomics related to IH in veins of two-stage AVF creation. Methods: Vein samples were collected during the initial creation of brachial artery-basilic vein AVF (1st stage) and vein transposition surgery 6 weeks later (2nd stage) and were sectioned in OCT. Tissues from each layer were separately collected by laser microdissection. Following Takara’s SMARTer Stranded Total RNA-Seq Pico Input protocol, libraries were constructed and sequenced in NovaSeq. The sequencing reads were processed by fastp, aligned to genome GENCODE 45 by STAR and gene-level counts were estimated by RSEM. Intima was assessed by histology. Layer-specific differentially expressed genes in pre-access or AVF veins associated with IH in AVF veins were detected by edgeR and further identified the enriched pathways and predicted functions using Qiagen IPA. Results: 44 veins were collected from 22 patients. 13,522 protein-coding genes that had a raw count of at least 10 in more than 70% of samples were included in analysis. 10 AVF veins had severe IH. Among the layers of pre-access vein, intima had the largest number of genes significantly associated with severe AVF IH and most or all these genes were upregulated (35/4, 26/2, 5/0 for the number of up/downregulated genes in intima, media, and adventitia of pre-access vein, respectively); cellular movement and immune cell trafficking were more strongly activated in intima of pre-access vein with severe AVF IH. No genes in any layer of AVF vein were significantly associated with severe AVF IH. From 1 st to 2 nd stage, more genes significantly changed expression in intima with severe AVF IH than intima without severe AVF IH (197/150 vs 144/93 for up/downregulated genes), while media (20/4 vs 149/31) and adventitia (99/27 vs 196/70) were the opposite; TGFB1, angiotensinogen, and TNF were more strongly activated in intima, while cell survival and migration were diminished in media and adventitia with severe AVF IH, respectively. Conclusion: Intima of pre-access vein has more genes associated with severe AVF IH. The layer-specific gene expression changes during AVF development and predicted functions are associated with AVF IH. These data can be used to develop layer-specific therapies to reduce AVF IH.

ADTGP: correcting single-cell antibody sequencing data using Gaussian process regression.

We present ADTGP, an R package that uses Gaussian process regression to correct droplet-specific technical noise in single-cell protein sequencing data. ADTGP improves the interpretability of the data by modeling the distribution of protein expression, conditioned on equal isotype control counts across cells. ADTGP is written in R and needs only the protein raw counts, isotype control raw counts, and a design matrix to run. ADTGP can be installed from https://github.com/northNomad/ADTGP. It depends on Stan and the R package 'cmdstanr'.

Exploring the diagnostic potential of miRNA signatures in the Fabry disease serum: A comparative study of automated and manual sample isolations.

Fabry disease, an X-linked lysosomal storage disorder caused by galactosidase α (GLA) gene mutations, exhibits diverse clinical manifestations, and poses significant diagnostic challenges. Early diagnosis and treatment are crucial for improved patient outcomes, pressing the need for reliable biomarkers. In this study, we aimed to identify miRNA candidates as potential biomarkers for Fabry disease using the KingFisher™ automated isolation method and NanoString nCounter® miRNA detection assay. Clinical serum samples were collected from both healthy subjects and Fabry disease patients. RNA extraction from the samples was performed using the KingFisher™ automated isolation method with the MagMAX mirVanaTM kit or manually using the Qiagen miRNeasy kit. The subsequent NanoString nCounter® miRNA detection assay showed consistent performance and no correlation between RNA input concentration and raw count, ensuring reliable and reproducible results. Interestingly, the detection range and highly differential miRNA between the control and disease groups were found to be distinct depending on the isolation method employed. Nevertheless, enrichment analysis of miRNA-targeting genes consistently revealed significant associations with angiogenesis pathways in both isolation methods. Additionally, our investigation into the impact of enzyme replacement therapy on miRNA expression indicated that some differential miRNAs may be sensitive to treatment. Our study provides valuable insights to identify miRNA biomarkers for Fabry disease. While different isolation methods yielded various detection ranges and highly differential miRNAs, the consistent association with angiogenesis pathways suggests their significance in disease progression. These findings lay the groundwork for further investigations and validation studies, ultimately leading to the development of non-invasive and reliable biomarkers to aid in early diagnosis and treatment monitoring for Fabry disease.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer’s disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating &gt;16 ha of UAS imagery with &gt;700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (0.25 m tall) varied between sites within 0.52 &lt; &lt; 0.82, whereas the detection of smaller plants (&lt;0.25 m) was lower, 0.03 &lt; &lt; 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Microglia specific alternative splicing alterations in multiple sclerosis.

Several aberrant alternative splicing (AS) events and their regulatory mechanisms are widely recognized in multiple sclerosis (MS). Yet the cell-type specific AS events have not been extensively examined. Here we assessed the diversity of AS events using web-based RNA-seq data of sorted CD15-CD11b+ microglia in white matter (WM) region from 10 patients with MS and 11 control subjects. The GSE111972 dataset was downloaded from GEO and ENA databases, aligned to the GRCh38 reference genome from ENSEMBL via STAR. rMATS was used to assess five types of AS events, alternative 3'SS (A3SS), alternative 5'SS (A5SS), skipped exon (SE), retained intron (RI) and mutually exclusive exons (MXE), followed by visualizing with rmats2sashimiplot and maser. Differential genes or transcripts were analyzed using the limma R package. Gene ontology (GO) analysis was performed with the clusterProfiler R package. 42,663 raw counts of AS events were identified and 132 significant AS events were retained based on the filtered criteria: 1) average coverage >10 and 2) delta percent spliced in (ΔPSI) >0.1. SE was the most common AS event (36.36%), followed by MXE events (32.58%), and RI (18.94%). Genes related to telomere maintenance and organization primarily underwent SE splicing, while genes associated with protein folding and mitochondrion organization were predominantly spliced in the MXE pattern. Conversely, genes experiencing RI were enriched in immune response and immunoglobulin production. In conclusion, we identified microglia-specific AS changes in the white matter of MS patients, which may shed light on novel pathological mechanisms underlying MS.

Exploring the gamma counting function in PET for sample activity measurement

With the widespread availability of small animal PET scanners globally, there exists a significant opportunity to enhance their operational utility. Our research introduces a novel utilization pathway by integrating gamma-counting functionalities within PET systems. This approach leverages the shared gamma counting technology between PET systems and traditional gamma counters, enabling direct radioactivity measurement from PET-derived raw count data. We established a correlation between the raw data from all-digital PET and the radioactivity of samples, conducting experiments to assess the system's measurement capabilities. The system demonstrated high accuracy and stability across a detectable range of 25.53–1900.69 kBq in single-photon mode and 19.61–1107.04 kBq in coincidence mode, with the difference between the sample activity and the linear function being no more than 5 %. Due to the multi-channel characteristic of PET detectors, our system exhibits a tenfold higher upper detection limit than that of gamma counters, without dead-time correction. This study demonstrates that all-digital PET can directly measure radioactivity, simplifying nuclear medicine procedures by integrating gamma counting within PET systems. This strategy not only reduces the need for multiple instruments but also has the potential to improve quantitative accuracy by minimizing cross-calibration errors, thereby maximizing the operational utility of existing all-digital PET scanners.

Using mobile acoustic monitoring and false‐positive N‐mixture models to estimate bat abundance and population trends

AbstractEstimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false‐positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white‐nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population‐wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance‐based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false‐negative and false‐positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.

A capturing math language use during block play: Creation of the spatial and quantitative mathematical language coding system

The goals of the current study were: 1) to modify and expand an existing spatial mathematical language coding system to include quantitative mathematical language terms and 2) to examine the extent to which preschool-aged children used spatial and quantitative mathematical language during a block play intervention. Participants included 24 preschool-aged children (Age M = 57.35 months) who were assigned to a block play intervention. Children participated in up to 14 sessions of 15-to-20-minute block play across seven weeks. Results demonstrated that spatial mathematical language terms were used with a higher raw frequency than quantitative mathematical language terms during the intervention sessions. However, once weighted frequencies were calculated to account for the number of codes in each category, spatial language was only used slightly more than quantitative language during block play. Similar patterns emerged between domains within the spatial and quantitative language categories. These findings suggest that both quantitative and spatial mathematical language usage should be evaluated when considering whether child activities can improve mathematical learning and spatial performance. Further, accounting for the number of codes within categories provided a more representative presentation of how mathematical language was used versus solely utilizing raw word counts. Implications for future research are discussed.

Population-adjusted national rankings in the Olympics

Ranking countries in the Olympic Games by medal counts clearly favors large-population countries over small ones, while ranking by medals-per-capita produces national rankings with very small population countries on top. We discuss why this happens, and propose a new national ranking system for the Olympics, also based upon medals won, which is inclusive in the sense that countries of widely-varying population can achieve high rankings. This population-adjusted probability ranking ranks countries by how much evidence they show for high capability at Olympic sports. In particular, it ranks countries according to how improbable their medal counts would be in an idealized reference model of the Games which posits that all medal-winning nations have equal propensity per capita for winning medals. The ranking index U is defined using a simple binomial sum. Here we explain the method, and we present population-adjusted national rankings for the last three summer Olympics (London 2012, Rio 2016 and Tokyo 2020, held in 2021). If the advantages of this ranking method come to be understood by sports media covering the Olympics and by the interested public, it could be widely reported alongside raw medal counts, thus adding excitement and interest to the Olympics.

Fourier analysis in single photon imaging.

Single photon imaging has become an established sensing approach. Compared to intensity imaging, versatile advantages have been demonstrated, such as imaging with high sensitivity, at a high frame rate, and with a high dynamic range. In this paper, we investigate the Fourier analysis of single photon counting at a frame rate of approximately 100 kHz and a high spatial resolution of 512 px × 512 px. We observed signal modulation in raw data as well as in data converted to photon flux, but with the data processing, the signal's frequency response is affected by significant damping. Thus, analysis sensible to signal frequency should work on the raw single photon counting signal. Furthermore, imaging of magnitude and phase in the Fourier domain can visualize areas of certain signal modulation, and the gradient of the phase angle can reveal the direction of movements. Finally, we have applied our method to real world scenarios by analyzing unmanned aerial vehicle's motion in outdoor experiments.

Clearing the Air, Breathe Easy: Intensive Care Unit Remodeling Unveils Insights into Aspergillosis Prevention

Background: Invasive aspergillosis (IA) poses a substantial threat to morbidity and mortality, particularly among immunocompromised individuals. In 2023, a New York City Intensive Care Unit (ICU) experienced an aspergillus outbreak following a structural water leak, resulting in two patients diagnosed with Invasive Aspergillus niger in their bronchial cultures. Immediate interventions, including patient relocation and ICU reconstruction were implemented to mitigate further impacts. This study aims to assess the impact of timely relocation of patients and renovation of the ICU, on the incidence of invasive aspergillosis. Method: A quasi-experimental study design of ICU patients over a nine month period included surveillance by the Infection Prevention department from March 1 to December 1, 2023. Surveillance included review of microbiology reports, environmental cultures, and patient chart reviews. The Pre-intervention spanned March 1 to May 1, and the post-intervention from May 4 to December 1. Indoor mold assessments of pre- and post-intervention involved testing wall surfaces for moisture, air sample collection for fungal spores, and surface swabs for direct fungal analysis. The intervention included relocating all seventeen patients from the impacted ICU and comprehensive reconstruction. Reconstruction involved the removal and replacement of all sheetrock within the unit extending four feet from the floor with moisture-resistant sheetrock. Additionally, moisture resistant single sheet welded vinyl flooring and cove-bases were installed. All heating, ventilation, and air-conditioning (HVAC) systems were inspected and cleaned. Construction activities strictly adhered to Infection Control Risk Assessment (ICRA) guidelines, with emphasis on maintaining negative pressure, to ensure a safe environment. Result: Environmental swab samples from 50% of ICU rooms indicated growth of Aspergillus/Penicillium, Chaetomium, and Stachybotrys/Memnoniella type spores during the pre-intervention phase. Environmental microbiology results strongly suggest the indoor environment as the fungal spore source, with the presence of fruiting structures indicating surface mold growth. Indoor air samples, when compared to outdoor samples collected during pre-intervention, showed rare (2-6 raw count) growth of Aspergillus in 55% of the sampled rooms and subequently no growth post-intervention. Prospective surveillance revealed no further aspergillus growth in the ICU population and environment. Conclusion: Our findings highlight a potential correlation between environmental modifications and reduced IA incidence. Swift mitigation and structural interventions are crucial in averting potentially fatal outcomes, marking a significant advancement of prevention strategies for inner-city hospital settings. Although promising, study limitations include the inability to speciate environmental aspergillus for comparison to patient bronchial cultures and the absence of baseline bronchial cultures for affected patients on admission.

Evaluation of Parasight All-in-One system for the automated enumeration of helminth ova in canine and feline feces

Background Digital imaging combined with deep-learning-based computational image analysis is a growing area in medical diagnostics, including parasitology, where a number of automated analytical devices have been developed and are available for use in clinical practice.MethodsThe performance of Parasight All-in-One (AIO), a second-generation device, was evaluated by comparing it to a well-accepted research method (mini-FLOTAC) and to another commercially available test (Imagyst). Fifty-nine canine and feline infected fecal specimens were quantitatively analyzed by all three methods. Since some samples were positive for more than one parasite, the dataset consisted of 48 specimens positive for Ancylostoma spp., 13 for Toxocara spp. and 23 for Trichuris spp.ResultsThe magnitude of Parasight AIO counts correlated well with those of mini-FLOTAC but not with those of Imagyst. Parasight AIO counted approximately 3.5-fold more ova of Ancylostoma spp. and Trichuris spp. and 4.6-fold more ova of Toxocara spp. than the mini-FLOTAC, and counted 27.9-, 17.1- and 10.2-fold more of these same ova than Imagyst, respectively. These differences translated into differences between the test sensitivities at low egg count levels (< 50 eggs/g), with Parasight AIO > mini-FLOTAC > Imagyst. At higher egg counts Parasight AIO and mini-FLOTAC performed with comparable precision (which was significantly higher that than Imagyst), whereas at lower counts (> 30 eggs/g) Parasight was more precise than both mini-FLOTAC and Imagyst, while the latter two methods did not significantly differ from each other.ConclusionsIn general, Parasight AIO analyses were both more precise and sensitive than mini-FLOTAC and Imagyst and quantitatively correlated well with mini-FLOTAC. While Parasight AIO produced lower raw counts in eggs-per-gram than mini-FLOTAC, these could be corrected using the data generated from these correlations.Graphical

Development and validation of a noise insertion algorithm for photon-counting-detector CT.

Inserting noise into existing patient projection data to simulate lower-radiation-dose exams has been frequently used in traditional energy-integrating-detector (EID)-CT to optimize radiation dose in clinical protocols and to generate paired images for training deep-learning-based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD-CT) also requires such a method to accomplish these tasks. However, clinical PCD-CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log-normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD-CT. To develop and validate a projection domain noise insertion algorithm for PCD-CT that does not require access to the raw count data. A projection-domain noise model developed originally for EID-CT was adapted for PCD-CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD-CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment. The NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges. A projection-domain noise insertion algorithm was developed and validated for PCD-CT to synthesize low-dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep-learning-based methods.

EPEN-06. IDENTIFICATION OF CSF MIRNA BIOMARKERS FOR THE EARLY DETECTION OF MEASURABLE RESIDUAL DISEASE IN PAEDIATRIC EPENDYMOMA: A BIOMECA STUDY

Abstract BACKGROUND Ependymoma (EPN) is the second most common malignant paediatric brain tumour. 50% of children relapse. Detection of Measurable Residual Disease (MRD) via MRI is difficult and lacks sensitivity for very early recurrence, necessitating an urgent need for identifying new, robust biomarkers for the early diagnosis, prognosis, and clinical management of disease progression in EPN. We hypothesise that microRNA (miRNA) quantification in the cerebrospinal fluid (CSF) will have clinical utility in the early detection of MRD in these patients. PATIENTS/METHODS A discovery cohort of EPN CSF samples (n=12) from patients enrolled on the SIOP Ependymoma II clinical trial (trials.gov identifier: NCT02265770) and control leukaemia CSF samples (n=8) underwent RNA-sequencing (QIAGEN) after total RNA extraction using QIAGEN miRNeasy Serum/Plasma kit. The EPN cohort consisted of infratentorial (IT; n=9), supratentorial (ST; n=1) and spinal (SP; n=2) cases taken prior to, or at, resection (primary/relapse). RESULTS Raw sequencing data entered a quality-control pipeline (Tally/Cutadapt), followed by alignment to miRBase-v22 (https://mirbase.org/) using Chimira (PMID:26093149). Two outlying (infratentorial) EPN samples were removed, leaving a 10 (IT+ST+SP) versus 8 comparison. Extracted raw counts were normalised across samples using DESeq2 (PMID:25516281) and 87 differentially-expressed miRNAs identified (adj-p&amp;lt;0.05). EPN samples completely segregated from controls on clustering/heatmap analysis. From these 87 miRNAs, a core set of 53 were overlapping in comparisons removing spinal (IT+ST remaining) and supratentorial (IT alone) samples. These miRNAs underwent combined ranking by adjusted-p and abundance (normalised count) values, and top-ranking candidates taken forward for confirmation using highly-sensitive Taqman PCR, prior to validation in additional samples. CONCLUSION Circulating miRNA quantification offers promise for EPN diagnosis and MRD detection. Further CSF study in larger patient cohorts, and serum/plasma analysis, are now warranted.

Assessment of long-term stored specimens in the Siriraj Hospital colorectal cancer biobank for RNA sequencing and profiling

Abstract Objectives Biobanks play an important role in advancing cancer research, yet concerns persist regarding the molecular integrity of long-term stored samples. This study assessed fresh frozen (FF) tissues and formalin-fixed paraffin-embedded (FFPE) tissues from the Siriraj Hospital colorectal cancer (CRC) biobank collected during two distinct periods (2011–2012 and 2020–2021). Methods In 2022, FF and FFPE primary cancer tissues from 75 CRC patients were evaluated. RNA sequencing (RNA-Seq) analyzed comprehensive gene expression profiles in FF tissues preserved at −80 °C, while nCounter profiling elucidated cancer-specific RNA transcripts in FFPE tissues stored at ambient temperature. Comparative analyses were conducted between specimens from 2011 to 2012 and 2020–2021. Results The FF tissues stored for approximately 10.5 years were well-suited for RNA-Seq compared to the intact tissues preserved for 1.5 years. Despite consistencies in RNA quantity, RNA integrity, amount of sequencing reads, and CRC gene signature, gene enrichment analysis revealed the decreased ribosome biogenesis, spliceosome and antifolate resistance pathways in the 2011–2012 group. Moreover, the FFPE tissues also showed no alteration in RNA quantity between the two periods, and the nCounter profiling demonstrated comparable CRC-specific gene counts in spite of the significant reduction of raw counts in the 2011–2012 group. Conclusions We report that FF tissues from CRC patients, stored for 10 years, are viable for whole transcriptome RNA-Seq, despite altered pathways such as ribosome biogenesis, spliceosome, and antifolate resistance. Moreover, 10-year-stored FFPE CRC tissues remain suitable for specific RNA profiling using the nCounter pan-cancer panel, despite a significant reduction in raw counts. These findings underscore the enduring contribution of biobanks to molecular research, highlighting their value a decade post-collection.

Upregulation of GREB1 in colorectal cancer ovarian metastases may be a potential therapeutic target.

e15596 Background: Classification of ovarian metastases (OM) in colorectal cancer (CRC) as peritoneal metastasis (PM) remains controversial. Moreover, OM demonstrate resistance to systemic therapy, suggesting distinct molecular tumorigenesis. Gene expression profiling and transcriptomic analysis was performed to distinguish OM, PM, and primary CRC (pCRC) and identify potential therapeutic targets. Methods: RNA sequencing was performed on tissue from the Total Cancer Care database. Raw counts were scaled in reads per million to filter out low-expressed genes. Afterward, raw counts of retained genes were normalized in trimmed mean of M-values and then log2-transformed using the Limma R package. Genes with a |Log2FC| &gt; 0.58 and an adjusted p-value &lt; 0.05 were considered dysregulated. In silico motif enrichment analysis of estrogen receptor 1 (ESR1) on growth regulating estrogen receptor binding 1 (GREB1) was conducted via the FIMO program. Results: There were 115 patients with tissue from OM (n = 6), PM (n = 4), and pCRC (n = 105). The average age was 58 years and most patients were male (57.4%), Caucasian (89.5%), and had microsatellite stable disease (69.6%). Most patients underwent cytotoxic chemotherapy (67%), while fewer underwent targeted therapy (38.3%) or radiation therapy (27.8%). The median overall survival for the pCRC, OM, and PM populations was 7.1, 3.4, and 4.0 months, respectively. Among upregulated genes, LAMC3, SCUBE1, and GREB1 had the highest differential expression in OM compared to PM and PEG3, C7, and GREB1 had the highest differential expression in OM compared to pCRC (all p &lt; 0.001). Among downregulated genes, IGLC1, IGHG2, and IGHG1 had the greatest differential expression in OM compared to PM and IGHG1, IGKV4-1, and MMP12 had the greatest differential expression in OM compared to pCRC (all p &lt; 0.001, Figure 1). GREB1 was upregulated in OM compared to both PM and pCRC. There are two estrogen response element (ERE) sites within the promoter region of GREB1. ESR1 transcription factor was shown to bind to these ERE (p &lt; 0.001). Conclusions: Transcriptomic analysis demonstrated clear molecular distinction between OM, PM and pCRC. Differences in gene expression profiling suggest distinct underlying mechanisms of metastasis. We identified a significant upregulation of GREB1 in OM compared to PM and pCRC. GREB1 contains both ERE and ESR1 in the upstream promoter regions implying potential upstream transcription regulation mediated by ESR1. GREB1 is a potential novel, hormonal target in treatment of OM in CRC. Continued analysis is ongoing to identify other potential targets.