True Count Research Articles

True Mitotic Count Prediction in Gastrointestinal Stromal Tumors: Bayesian Network Model and PROMETheus (Preoperative Mitosis Estimator Tool) Application Development.

Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy. The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs. Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed. Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model's accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model. The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

Performance measurements of Total Body PET scanner simulations using the Geant4 toolkit

Total-Body PET (TB PET) is a novel imaging modality that can image the whole body in a single scan. It has increased sensitivity, improved spatial resolution, reduced imaging times compared to conventional clinical PET scanners, and has the potential to detect smaller lesions and multiorgan diseases. Commercially available TB PET scanners include the Siemens Biograph Vision Quadra [1] and the United Imaging uEXPLORER [2]. This study uses the Geant4 toolkit [3] to create a simulation model of two different TB PET detector geometries; Siemens Biograph Vision Quadra and uEXPLORER. Analysis of simulated data and performance measurements were carried out in Python. The study follows the NEMA NU-2 2018 standard [4]. The simulation was used to explore the effect of different scintillator crystals and evaluates the count rate performance and the sensitivity of the scanner. The simulated data was compared to published experimental data from Siemens and Explorer. Good agreement between the count rates of simulated and experimental data was observed for both geometries. The simulated Siemens Quadra measured a system sensitivity value of 178.6 cps/kBq showing a 2% difference compared to the experimental data of 175.3 cps/kBq for LSO crystals. Axial sensitivity profiles plotted also match well. The comparison of different scintillators showed that BGO performs best as it has the highest peak NECR, thus good signal-to-noise ratio. LSO and LYSO also perform well and their physical properties are similar. NaI and CsI exhibit the worst performance; with low true count rates and the random count rates increased almost linearly with activity concentration. Our simulations compared well against experimental data, allowing us to test further scintillator materials and radioisotopes. After implementing image reconstruction and relevant corrections, this simulation can be used for validation of various research ideas in the future. Please click on the 'PDF' for the full abstract!

Consensus of algorithms for lesion segmentation in brain MRI studies of multiple sclerosis

Segmentation of multiple sclerosis (MS) lesions on brain MRI scans is crucial for diagnosis, disease and treatment monitoring but is a time-consuming task. Despite several automated algorithms have been proposed, there is still no consensus on the most effective method. Here, we applied a consensus-based framework to improve lesion segmentation on T1-weighted and FLAIR scans. The framework is designed to combine publicly available state-of-the-art deep learning models, by running multiple segmentation tasks before merging the outputs of each algorithm. To assess the effectiveness of the approach, we applied it to MRI datasets from two different centers, including a private and a public dataset, with 131 and 30 MS patients respectively, with manually segmented lesion masks available. No further training was performed for any of the included algorithms. Overlap and detection scores were improved, with Dice increasing by 4-8% and precision by 3-4% respectively for the private and public dataset. High agreement was obtained between estimated and true lesion load (ρ = 0.92 and ρ = 0.97) and count (ρ = 0.83 and ρ = 0.94). Overall, this framework ensures accurate and reliable results, exploiting complementary features and overcoming some of the limitations of individual algorithms.

Assessment of moth diversity in the Zealandia ecosanctuary through a community science initiative

ABSTRACT A collaborative project involving researchers and citizen scientists was conducted in the Zealandia ecosanctuary (Wellington, Aotearoa New Zealand) during 2019–2022 to investigate Lepidoptera biodiversity. Here we present the first results of that project, reporting on the success of the citizen science approach and presenting a checklist of moth species in Zealandia. Sampling efforts took place roughly monthly during the night, and relied heavily on the participation of keen volunteers. Moths were primarily attracted using a mercury vapour light trap, but sweep-netting was also applied. On each trip, a selection of representative specimens was collected, humanely euthanised, prepared, photographed, and deposited as voucher specimens in the entomological collection of the Museum of New Zealand Te Papa Tongarewa. Photos were uploaded to the community science online platform iNaturalist, where experts could help in identifying the species. We report a total of 262 species in Zealandia, belonging to 29 families; of those species, 22 are exotic. Notably, 217 species were found via our sampling, and the remaining 45 were recorded by citizen scientists and posted on iNaturalist. While this is likely an underestimate of the true species count in Zealandia, we consider the project a successful citizen science initiative that has generated valuable data, a fine collection of voucher specimens for the future, considerable public engagement, and positive media attention.

Selective microbial attachment to LDPE plastic beads during passage through the wastewater network

Urban wastewater treatment plants (WWTP) represent key point-source discharges of microplastics (MP) into the environment, however, little is known about the microbial carrying capacity of plastics travelling through them. The purpose of this study was to quantify the number of cells that become associated with MP at different locations within a WWTP, and to assess differences in microbiome communities. We conducted a field experiment incubating low density polyethylene (LDPE) MP beads in WWTP influent and effluent, as well as tracking free floating beads during passage in wastewater from a large municipal hospital to an urban WWTP, where they were subsequently recovered. Using two cell counting methods - automated flow cytometric true absolute cell counts and indirect cell quantification via protein content based on a model E. coli cell - we quantified cell attachment to LDPE beads. LDPE associated counts ranged from 350 × 103 cells cm−2 after incubation in wastewater effluent, and 990 × 103 cells cm−2 after incubation in wastewater influent. 16S rRNA gene amplicon sequencing was used to determine the bacterial community structure of the plastic-associated microbiomes. Our results showed that distinct bacterial communities developed on the LDPE MP following exposure to each wastewater type. Influent (untreated) wastewater LDPE-associated microbiomes were dominated by Bacillota whereas the microbes that attached in wastewater effluent (tertiary treated) were dominated by Pseudomonadota. In conclusion, this study provides clear evidence that microplastics migrating through the sewer network and WWTP rapidly accumulate microbiomes with unique microbial community structures varying from sewage influent to effluent. These findings demonstrate the differential microbiological risk from MP associated with routine wastewater discharges to those released from intermittent combined sewer overflows (CSOs) during storm events.

Malaria Slide Bank to Strengthen and Improve the Quality of Malaria Diagnosis: A National Slide Repository in India.

Malaria elimination is one of the top health care priorities in India, necessitating accessible and accurate diagnosis for effective treatment. A malaria slide bank in India is a collection of quality-controlled malaria-positive and -negative slides and is considered a vital asset for quality diagnosis. The collection of blood samples, preparation of blood smears, staining, quality control, molecular characterizations, and slide validation were carried out according to standard operating procedures in accordance with the WHO reference laboratory. The true count and parasite density per microliter were computed in accordance with WHO guidelines. Over 27 months, 48 batches (8,196 slides) were prepared. Overall, the majority of slide batches were Plasmodium vivax (45.9%; 22/48), followed by Plasmodium falciparum (25%; 12/48), malaria-negative infections (25%; 12/48), and mixed infections (4.1%; 2/48). All 48 batches passed internal validation by WHO-certified level-1 microscopists. For a batch, the true count was the median of the validators' counts (range, 111-280,795 parasites/µL). Except for mixed infections, the PCR results agreed with the verified microscopy results. Malaria slide bank slides would be a valuable tool for quality control, assurance, and microscopist training.

Study of dead time estimation method based on pulse interval distribution

In nuclear radiation detection, some measured radiation counts are lost due to dead time. Estimation of dead time is necessary to restore the counting information in quantitative analysis. This study aims to propose a method for estimating dead time at high counting rate. First of all, a measurement system of pulse interval distribution was used to estimate the true input counting rate of the detection system and implemented in FPGA. Then, a digital pole-zero cancellation technique was introduced in the measurement system to decrease the decay time constant of pulse. This was aimed at mitigating the impact of pile-up on the pulse interval distribution spectrum, especially at high counting rate. The feasibility of this measurement system at high counting rate was verified by the experimental platform of EDXRF. The dead time was calculated using the measured counting rate and the estimated true input counting rate. The accuracy of the dead time relies on the precision of the estimated true count rate. When the counting rate reaches 600 kCPS, the relative error between the theoretical counting rate and the estimated true input counting rate is less than 5%.

Dollo Parsimony Overestimates Ancestral Gene Content Reconstructions.

Ancestral reconstruction is a widely used technique that has been applied to understand the evolutionary history of gain and loss of gene families. Ancestral gene content can be reconstructed via different phylogenetic methods, but many current and previous studies employ Dollo parsimony. We hypothesize that Dollo parsimony is not appropriate for ancestral gene content reconstruction inferences based on sequence homology, as Dollo parsimony is derived from the assumption that a complex character cannot be regained. This premise does not accurately model molecular sequence evolution, in which false orthology can result from sequence convergence or lateral gene transfer. The aim of this study is to test Dollo parsimony's suitability for ancestral gene content reconstruction and to compare its inferences with a maximum likelihood-based approach that allows a gene family to be gained more than once within a tree. We first compared the performance of the two approaches on a series of artificial data sets each of 5,000 genes that were simulated according to a spectrum of evolutionary rates without gene gain or loss, so that inferred deviations from the true gene count would arise only from errors in orthology inference and ancestral reconstruction. Next, we reconstructed protein domain evolution on a phylogeny representing known eukaryotic diversity. We observed that Dollo parsimony produced numerous ancestral gene content overestimations, especially at nodes closer to the root of the tree. These observations led us to the conclusion that, confirming our hypothesis, Dollo parsimony is not an appropriate method for ancestral reconstruction studies based on sequence homology.

Quantifying partial volume effect in SPECT and planar imaging: optimizing region of interest for activity concentration estimation in different sphere sizes.

To quantify the partial volume effect in single photon emission tomography (SPECT) and planar images of Carlson phantom as well as providing an optimum region of interest (ROI) required to more accurately estimate the activity concentration for different sphere sizes. 131 I solution with the 161.16 kBq/ml concentration was uniformly filled into the different spheres of Carlson phantom (cold background condition) with the diameters of 7.3, 9.2, 11.4, 14.3, 17.9, 22.4 and 29.9 mm, and there was no background activity. In the hot background condition, the spheres were filled with the solution of 131 I with the 1276.5 kBq/ml addition to the background activity concentration of 161.16 kBq/ml in all the phantoms. The spheres were mounted inside the phantom and underwent SPECT and planar images. ROI was drawn closely on the boundary of each sphere image and it was extended to extract the true count. In the cold background condition, the recovery coefficient (RC) value for SPECT images ranged between 0.8 and 1.03. However, in planar imaging, the RC value was 0.72 for the smallest sphere size and it increased for larger spheres until 0.98 for 29.9 mm. In the hot background condition, the RC value for sphere diameters larger than 20 mm was overestimated more than in the cold background condition. The ROI/size required to more accurately determine activity concentration for the cold background ranged from 1.18 to 2.7. However, in the hot background condition, this ratio varied from 1.34 to 4.05. In the quantification of partial volume effects, the spill-out effect seems to play a crucial role in the distribution of the image counts beyond the boundaries of the image pixels. However, more investigations are needed to accurately characterize limitations regarding the object size, background levels, and other factors.

True volumetric counting of CD34+ cells using flow cytometry

While the single-platform flow cytometric CD34+ cell counting method is the preferred choice to predict the yield of mobilized peripheral blood stem cells, most flow cytometers lack the ability of hematology counter analyzers to perform volumetric counting. However, one of the problems using reference microbeads is the vanishing counting bead phenomenon. This phenomenon results in a drop in microbeads concentration and reduces the total and relative number of beads in calibration procedures. In the last years, flow cytometers including a volumetric system to quantify cells have been developed and may represent a promising alternative to enumerate CD34+ cells avoiding the use of beads.In this study we have used a direct true volumetric counting of CD34+ cells under continuous flow pump to overcome potential drawbacks with impact in rare cell analysis. To confirm this hypothesis, we have compared the results of CD34+ cell enumeration using non-volumetric vs. volumetric systems with FC500 (Beckman Coulter) and Attune NxT (ThermoFisher) flow cytometers, respectively, in mobilized peripheral blood samples.No statistically significant differences were observed between measurements of CD34+ cells using beads, when the FC500 and Attune NxT absolute counting values were compared, or when CD34+ counts were compared on the Attune NxT, either using or not using beads. Linear regressions to study the relationship between volumetric and non-volumetric CD34+ counts confirmed the accuracy of each method. Bland-Altman test showed agreement between both methods.Our data showed that CD34+ cell enumeration using a volumetric system is comparable with current counting systems. This method represents an alternative with the advantage of the simplification of sample preparation and the reduction of the analysis subjectivity.

Estimating the effect of latent time-varying count exposures using multiple lists.

A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008.

Probiotic and postbiotic analytical methods: a perspective of available enumeration techniques.

Probiotics are the largest non-herbal/traditional dietary supplements category worldwide. To be effective, a probiotic strain must be delivered viable at an adequate dose proven to deliver a health benefit. The objective of this article is to provide an overview of the various technologies available for probiotic enumeration, including a general description of each technology, their advantages and limitations, and their potential for the future of the probiotics industry. The current "gold standard" for analytical quantification of probiotics in the probiotic industry is the Plate Count method (PC). PC measures the bacterial cell's ability to proliferate into detectable colonies, thus PC relies on cultivability as a measure of viability. Although viability has widely been measured by cultivability, there has been agreement that the definition of viability is not limited to cultivability. For example, bacterial cells may exist in a state known as viable but not culturable (VBNC) where the cells lose cultivability but can maintain some of the characteristics of viable cells as well as probiotic properties. This led to questioning the association between viability and cultivability and the accuracy of PC in enumerating all the viable cells in probiotic products. PC has always been an estimate of the number of viable cells and not a true cell count. Additionally, newer probiotic categories such as Next Generation Probiotics (NGPs) are difficult to culture in routine laboratories as NGPs are often strict anaerobes with extreme sensitivity to atmospheric oxygen. Thus, accurate quantification using culture-based techniques will be complicated. Another emerging category of biotics is postbiotics, which are inanimate microorganisms, also often referred to as tyndallized or heat-killed bacteria. Obviously, culture dependent methods are not suitable for these products, and alternative methods are needed for their quantification. Different methodologies provide a more complete picture of a heterogeneous bacterial population versus PC focusing exclusively on the eventual multiplication of the cells. Alternative culture-independent techniques including real-time PCR, digital PCR and flow cytometry are discussed. These methods can measure viability beyond cultivability (i.e., by measuring cellular enzymatic activity, membrane integrity or membrane potential), and depending on how they are designed they can achieve strain-specific enumeration.

Study on the PET image quality according to various scintillation detectors based on the Monte Carlo simulation

Purpose: Positron emisson tomography (PET) is a crucial medical imaging scanner for the detection of cancer lesions. In order to maintain the improved image quality, it is crucial to apply detectors of superior performance. Therefore, the purpose of this study was to compare PET image quality using Monte Carlo simulation based on the detector materials of BGO, LSO, and LuAP. Materials and Methods: The Geant4 Application for Tomographic Emission (GATE) was used to design the PET detector. Scintillations with BGO, LSO and LuAP were modelled, with a size of 3.95 × 5.3 mm2 (width × height) and 25.0 mm (thickness). The PET detector consisted of 34 blocks per ring and a total of 4 rings. A line source of 1 MBq was modelled and acquired with a radius of 1 mm and length of 20 mm for 20 seconds. The acquired image was reconstructed maximum likelihood expectation maximization with 2 iteration and 10 subsets. The count comparison was carried out. Results and Discussion: The highest true, random, and scatter counts were obtained from the BGO scintillation detector compared to LSO and LuAP. Conclusion: The BGO scintillation detector material indicated excellent performance in terms of detection of gamma rays from emitted PET phantom.

Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis

ObjectiveTo compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. DesignCross-sectional study. SettingGeneral community setting (ie, institutional research laboratory and community free-living). ParticipantsForty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living. InterventionsNot applicable. Main Outcome MeasuresIntraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. ResultsAll algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). ConclusionsBecause of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.

Evaluation of Fecal Egg Count Tests for Effective Control of Equine Intestinal Strongyles.

The American Association of Equine Practitioners strongly advocates evidence-based intestinal strongyle control in horses. It recommends targeted treatment of all heavy egg shedders (>500 eggs per gram (EPG) of feces), while the low shedders (0-200 EPG) are left untreated. As 50-75% of adult horses in a herd are low shedders, preventing them from unnecessary anthelmintic exposure is critical for tackling resistance. There are various fecal egg count (FEC) techniques with many modifications and variations in use, but none is identified as a gold standard. The hypothesis of the study was that the diagnostic performance of 12 commonly used quantitation methodologies (three techniques with four variants) differs. In this regard, method comparison studies were performed using polystyrene beads as proxy for intestinal strongyle eggs. Mini-FLOTAC-based variants had the lowest coefficient of variation (CV%) in bead recovery, whereas McMaster variants had the highest. All four variants of Mini-FLOTAC and the NaNO3 1.33 specific gravity variant of modified Wisconsin followed a linear fit with R2 > 0.95. In contrast, the bead standard replicates for modified McMaster variants dispersed from the regression curve, causing a lower R2. The Mini-FLOTAC method seems less influenced by the choice of floatation solution and has better repeatability parameters and linearity for bead standard recovery. For FEC tests with high R2 (>0.95) but that underestimated the true bead count, a correction factor (CF) was determined to estimate the true count. Finally, the validity of CF was analyzed for 5 tests with R2 > 0.95 to accurately quantify intestinal strongyle eggs from 40 different horses. Overall, this study identified FEC methodologies with the highest diagnostic performance. The limitations in standardizing routine FEC tests are highlighted, and the importance of equalization of FEC results is emphasized for promoting uniformity in the implementation of parasite control guidelines.

A CNN-based approach for joint segmentation and quantification of nuclei and NORs in AgNOR-stained images

Background and ObjectiveOral cancer is the sixth most common kind of human cancer. Brush cytology for counting Argyrophilic Nucleolar Organizer Regions (AgNORs) can help early mouth cancer detection, lowering patient mortality. However, the manual counting of AgNORs still in use today is time-consuming, labor-intensive, and error-prone. The goal of our work is to address these shortcomings by proposing a convolutional neural network (CNN) based method to automatically segment individual nuclei and AgNORs in microscope slide images and count the number of AgNORs within each nucleus. MethodsWe systematically defined, trained and tested 102 CNNs in the search for a high-performing solution. This included the evaluation of 51 network architectures combining 17 encoders with 3 decoders and 2 loss functions. These CNNs were trained and evaluated on a new AgNOR-stained image dataset of epithelial cells from oral mucosa containing 1,171 images from 48 patients, with ground truth annotated by specialists. The annotations were greatly facilitated by a semi-automatic procedure developed in our project. Overlapping nuclei, which tend to hide AgNORs, thus affecting their true count, were discarded using an automatic solution also developed in our project. Besides the evaluation on the test dataset, the robustness of the best performing model was evaluated against the results produced by a group of human experts on a second dataset. ResultsThe best performing CNN model on the test dataset consisted of a DenseNet-169 + LinkNet with Focal Loss (DenseNet-169 as encoder and LinkNet as decoder). It obtained a Dice score of 0.90 and intersection over union (IoU) of 0.84. The counting of nuclei and AgNORs achieved precision and recall of 0.94 and 0.90 for nuclei, and 0.82 and 0.74 for AgNORs, respectively. Our solution achieved a performance similar to human experts on a set of 291 images from 6 new patients, obtaining Intraclass Correlation Coefficient (ICC) of 0.91 for nuclei and 0.81 for AgNORs with 95% confidence intervals of [0.89, 0.93] and [0.77, 0.84], respectively, and p-values < 0.001, confirming its statistical significance. Our AgNOR-stained image dataset is the most diverse publicly available AgNOR-stained image dataset in terms of number of patients and the first for oral cells. ConclusionsCNN-based joint segmentation and quantification of nuclei and NORs in AgNOR-stained images achieves expert-like performance levels, while being orders of magnitude faster than the later. Our solution demonstrated this by showing strong agreement with the results produced by a group of specialists, highlighting its potential to accelerate diagnostic workflows. Our trained model, code, and dataset are available and can stimulate new research in early oral cancer detection.

Seroprevalence as an Indicator of Undercounting of COVID-19 Cases in a Large Well-Described Cohort

Reported confirmed cases represent a small portion of overall true cases for many infectious diseases. The undercounting of true cases can be considerable when a significant portion of infected individuals are asymptomatic or minimally symptomatic, as is the case with COVID-19. Seroprevalence studies are an efficient way to assess the extent to which true cases are undercounted during a large-scale outbreak and can inform efforts to improve case identification and reporting. A longitudinal seroprevalence study of active duty U.S. military members was conducted from May 2020 through June 2021. A random selection of service member serum samples submitted to the Department of Defense Serum Repository was analyzed for the presence of antibodies reactive to SARS-CoV-2. The monthly seroprevalence rates were compared with those of cumulative confirmed cases reported during the study period. Seroprevalence was 2.3% in May 2020 and increased to 74.0% by June 2021. The estimated true case count based on seroprevalence was 9.3 times greater than monthly reported cases at the beginning of the study period and fell to 1.7 by the end of the study. In our sample, confirmed case counts significantly underestimated true cases of COVID-19. The increased availability of testing over the study period and enhanced efforts to detect asymptomatic and minimally symptomatic cases likely contributed to the fall in the seroprevalence to reported case ratio.

Development of a four-layer DOI PET detector using long crystals segmented by subsurface laser engraving

Since the spatial resolution and sensitivity of positron emission tomography (PET) scanners can be improved by using scintillation detectors with depth-of-interaction (DOI) capability, an original four-layer DOI detector using a pixelated crystal array has been developed in our group. For DOI identification, reflectors have been inserted between the crystals in each layer with a specific arrangement in order to cause light sharing between the layers of the crystal array. However, the precise fabrication of pixelated crystal arrays was challenging and expensive compared to monolithic arrays. In the current study, a four-layer DOI detector composed of long crystals segmented into four segments by applying subsurface laser engraving (SSLE) was proposed. LuYSiO5 (LYSO) crystals of 1 × 1× 20 mm3 were segmented into four segments in the largest dimension using SSLE. A 6 × 6 crystal array using the segmented crystals was prepared and mounted on a 4 × 4 channel multi-pixel photon counter (MPPC, 2 × 2 mm active area). The performance of the proposed detector was investigated and compared with that of original four-layer DOI detector composed of discrete crystals of 1 × 1× 5 mm3 in terms of the crystal identification, layer separation and energy resolution. The 2D position maps of the detectors were obtained by the Anger-logic calculation, and the crystal responses were clearly separated for both detectors. Average energy resolutions of 10.0% ± 1.0%, and 10.5% ± 0.9% at 511 keV were obtained for the proposed detector, and the original detector composed of the discrete crystals, respectively. The layer separation performance was evaluated using separation ratio, which was the ratio of true counts in a specific irradiated crystal in a layer to the total counts in all four crystals stacked on top of each other. The responses of all four layers were clearly separated with a separation ratio of over 0.89 for the proposed and original detectors, respectively. The performance of the proposed detector was well comparable to the original detector composed of the discrete crystals, while the fabrication of the segmented detector was precise and simple.

Simultaneous photon counting and charge integrating for pulse pile-up correction in paralyzable photon counting detectors

Objective. In photon counting detectors (PCDs), electric pulses induced by two or more x-ray photons can pile up and result in count losses when their temporal separation is less than the detector dead time. The correction of pulse pile-up-induced count loss is particularly difficult for paralyzable PCDs since a given value of recorded counts can correspond to two different values of true photon interactions. In contrast, charge (energy) integrating detectors work by integrating collected electric charge induced by x-rays over time and do not suffer from pile-up losses. This work introduces an inexpensive readout circuit element to the circuits of PCDs to simultaneously collect time-integrated charge to correct pile-up-induced count losses. Approach. Prototype electronics were constructed to collect time-integrated charges simultaneously with photon counts. A splitter was used to feed the electric signal in parallel to both a digital counter and a charge integrator. After recording PCD counts and integrating collected charge, a lookup table can be generated to map raw counts in the total- and high-energy bins and total charge to estimate pile-up-free true counts. Proof-of-concept imaging experiments were performed with a CdTe-based PCD array to test this method. Main results. The proposed electronics successfully recorded photon counts and time-integrated charge simultaneously, and whereas photon counts exhibited paralyzable pulse pile-up, time-integrated charge using the same electric signal as the counts measurement was linear with x-ray flux. With the proposed correction, paralyzable PCD counts became linear with input flux for both total- and high-energy bins. At high flux levels, uncorrected post-log measurements of PMMA objects severely overestimated radiological path lengths for both energy bins. After the proposed correction, the non-monotonic measurements again became linear with flux and accurately represented the true radiological path lengths. No impact on the spatial resolution was observed after the proposed correction in images of a line-pair test pattern. Significance. Time-integrated charge can be used to correct for pulse pile-up in paralyzable PCDs where analytical solutions may be difficult to use, and integrated charge can be collected simultaneously with counts using inexpensive electronics.

Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals.

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.