Poor Reproducibility Research Articles

LGG-06. THREE PRACTICAL CHALLENGES IN IMPLEMENTING RAPNO RESPONSE ASSESSMENT FOR PEDIATRIC LOW-GRADE GLIOMAS

Abstract The RAPNO criteria for pediatric low-grade gliomas (pLGGs), designed to improve upon the RANO criteria, are increasingly being employed to assess treatment responses. However, the adoption of the RAPNO guidelines in our clinical trials has revealed some notable challenges that stymie their practical implementation. These challenges should be addressed to render the guidelines more comprehensive and facilitate their widespread adoption. Definition of tumor burden should be modified: The current guidelines define tumor burden as the sum of the products of the longest tumor diameter and its longest perpendicular diameter. While this approach generally works, implementing it in optic pathway gliomas involving the nerve and subtotally resected pLGGs where the longest diameter is often disproportionally larger than the perpendicular diameter creates a challenge. The result is an inaccurate measure of tumor burden as well as poor reproducibility. Progressive disease should be confirmed with follow-up imaging. Unlike response confirmation, guidelines do not recommend confirmation of progression. Since pLGGs grow slowly, the change in diameters between two consecutive scans is often so small that it can be indistinguishable from measurement error. This is particularly problematic in smaller tumors and in diffusely infiltrating tumors, in which minor measurement change can erroneously shift response to progressive disease. Definition of target lesion should be more specific: pLGGs can recur focally without involving the entire previously treated residual tumor. Based on RAPNO guidelines, the target lesion includes both the growing and the stable residual components. This poses a challenge and can result in a serious underestimate of efficacy, particularly with biologic therapy, when the response of the recurrent focal tumor is major or complete but, by the current criteria, only registers as stable disease or minor response. Our discussion will show real-world examples of these challenges and propose recommendations to enhance the practical application of RAPNO criteria.

II-VI Semiconductor-Based Conductometric Gas Sensors: Is There a Future for These Sensors?

A review of the state of research in the development of conductometric gas sensors based on II-VI semiconductors is given. It was shown that II-VI compounds indeed have properties that are necessary for the development of highly efficient gas sensors. In this case, to achieve the required parameters, all approaches developed for metal oxides can be used. At the same time, during a detailed review, it was concluded that sensors based on II-VI compounds have no prospects for appearing on the gas sensor market. The main obstacle is the instability of the surface state, which leads to poor reproducibility of parameters and drift of sensor characteristics during operation.

Reproducible and high sample throughput isomiR next-generation sequencing for cancer diagnosis.

e15013 Background: Next-generation sequencing (NGS) can produce up to 6 Tb of data per run with single-nucleotide accuracy, making it ideal for quantifying isomiRs, which encompass both canonical miRNAs and their variants, for clinical applications. However, NGS has poor reproducibility and low sample throughput in quantifying circulating isomiRs due to significant technical variations and the limitations of the multiplex strategy, as evidenced by the fact that no isomiR NGS technique has been successfully used to diagnose cancer. Methods: To address these challenges, a library construction method including a dual unique-dual-index (DUDI) technology was developed. DUDI uses a pair of Inner UDI (IUDI) and outer UDI (OUDI) to label a sample. Twelve independent batches of isomiR NGS were carried out, including three repeated batches. Each batch included 100 gastric cancer and 100 control plasma samples. Batch effect, correlation coefficient (R), and principal component (PCA) analyses were used to evaluate technical reproducibility. Machine learning binary classification was used to assess biological reproducibility, with each pair of batch data serving interchangeably as both training and testing data. Results: In this multicenter study, over 700G of isomiR data were generated from 402 gastric cancer and 498 control samples, with a maximum error rate of 1 in 7 million isomiRs being assigned to wrong samples. The PCA plot indicates high technical reproducibility across the three repeated batches, shown by the extensive intermingling of data points from each batch and the lack of distinct batch-wise clustering. This observation is reinforced by that the R value for each of 239 isomiRs between the repeated batches are close to 1. While the mutual machine learning validations between the repeated batches yielded ~95% accuracy, indicating high biological reproducibility. The accuracies of the validations between the different batches of different samples range from 70% to 82%. The lower accuracy is as expected, given the high genetic heterogeneity of cancer and the small sample size. Furthermore, the IsomiR differentiated expression profiles from the current NGS study closely match those from prior qPCR studies. Conclusions: The DUDI library construction method can produce reproducible high sample throughput NGS data, yet it is cost-effective and straightforward. The maximum number of samples that can be multiplexed in an NGS project is almost one million, i.e., 976 * 976, as IUDI and OUDI can be any of the 976 designed DUDIs. This number far exceeds the high sample throughput requirements of any NGS application. While the capability to distinguish true biological variations of IsomiRs from technical noise, demonstrated by the high technical and biological reproducibility and concordance with the qPCR data, enables the development of robust machine learning algorithms for cancer diagnostics.

Progress of highly reproducible capillary electrophoresis

Following rapid developments in capillary electrophoresis (CE), this technology has become an established analytical technique owing to its microscale characteristics, high speed, high efficiency, and versatility. However, the challenges of poor peak stability and/or reproducibility have consistently hindered its wider applications. CE has long been used as a measurement tool for plotting signal intensities versus the migration time; however, the migration time is not an independent variable in CE, but is affected by many direct and indirect parameters, including capillary (length, diameter, and inner surface properties), electric field (or voltage, current, and/or power), temperature, and running buffer (electrolytes, additives, solvents, and their concentration, buffering pH, etc.). These intricacies render the acquisition of reproducible electropherograms difficult. Various studies ranging from those on the early stages of CE development to those on the exploration of three important strategies have been conducted to address this issue. In the first strategy, the CE conditions, especially those parameters that can maintain a stable electro-osmotic flow, are strictly controlled and stabilized to significantly improve peak repeatability. In the second strategy, either the peak position is corrected using internal standards or the peak time is converted into other variables, such as electrophoretic mobility, to offset or eliminate some unstable factors, thereby improving the repeatability and even reproducibility of the peaks; this strategy is useful when plotting signals versus the migration time ratio, correlated migration time, effective mobility, or temperature-correlated mobility. In the third strategy, a new methodology called highly reproducible CE (HRCE) is established using theoretical studies to explore better principles for real-time CE with the aim of the complete removal of the challenge from the root. This strategy includes the development of novel methods that plot electropherograms based on weighted mobility, migrated charge, charge density, or partial differential molar charge density. Similar to ordinary CE approaches, this strategy can also draw electropherograms based on the ratios of these properties. As theoretically predicted, these novel methods can offset or resist changes in critical CE conditions (mainly electric field strength, capillary length and diameter, and/or some buffer parameters such as concentration). Our experimental results demonstrate that given certain prerequisites, a new set of methods can produce highly reproducible electropherograms. This review focuses on the theoretical basis and advancements of HRCE, and elucidates the link between electrophoretic migration/peak expression theories and their impact on reproducibility. Studies on the transformation of time-scale electropherograms in the CE literature are summarized and analyzed in general. However, this review does not directly discuss research on and progress in improving CE repeatability or reproducibility through instrument upgrades, parameter optimization, or practical method refinements.

Pulmonary 4D-flow MRI imaging in landrace pigs under rest and stress

4D-flow MRI is a promising technique for assessing vessel hemodynamics. However, its utilization is currently limited by the lack of reference values, particularly for pulmonary vessels. In this work, we have analysed flow and velocity in the pulmonary trunk (PT), left and right pulmonary arteries (LPA and RPA, respectively) in Landrace pigs at both rest and stress through the software MEVISFlow. Nine healthy Landrace pigs were acutely instrumented closed-chest and transported to the CMR facility for evaluation. After rest measurements, dobutamine was administered to achieve a 25% increase in heart rate compared to rest. 4D-flow MRI images have been analysed through MEVISFlow by two independent observers. Inter- and intra-observer reproducibility was quantified using intraclass correlation coefficient. A significant difference between rest and stress regarding flow and velocity in all the pulmonary vessels was observed. Mean flow increased 55% in PT, 75% in LPA and 40% in RPA. Mean peak velocity increased 55% in PT, 75% in LPA and 66% in RPA. A good-to-excellent reproducibility was observed in rest and stress for flow measurements in all three arteries. An excellent reproducibility for velocity was found in PT at rest and stress, a good one for LPA and RPA at rest, while poor reproducibility was found at stress. The current study showed that pulmonary flow and velocity assessed through 4D-flow MRI follow the physiological alterations during cardiac cycle and after stress induced by dobutamine. A clinical translation to assess pulmonary diseases with 4D-flow MRI under stress conditions needs investigation.

Banff-based histologic chronicity index is associated with graft failure but has poor interobserver reproducibility.

Antibody-mediated rejection (AMR) often leads to chronic kidney allograft damage and is a critical cause of allograft failure. The Banff classification, used to diagnose AMR, has become complex and challenging for clinicians. A Banff-based histologic chronicity index (CI) was recently proposed as a simplified prognostic indicator. Its reliability and reproducibility have not been externally validated. This study investigated 71 kidney allograft biopsies diagnosed with AMR. Interobserver reproducibility of the recently proposed CI and its components (cg, cv, ct, and ci) were assessed. The association between CI and allograft failure was analyzed, and CI cut-off values were evaluated by Cox proportional hazards regression and Kaplan-Meier estimator with log-rank test. The study confirmed the association of CI with allograft failure, but also revealed that the assessment of CI varied between pathologists, impacting its reproducibility as a prognostic tool. Only 49 (69.0%) of the biopsies showed complete agreement on the proposed cut-off value of CI<4 or CI ≥ 4. Furthermore, this cut-off did not reliably stratify allograft failure. Notably, the cg score, which carries significant weight in the CI calculation, had the lowest agreement between observers (kappa=.281). While a simplified prognostic indicator for AMR is needed, this study highlights the limitations of CI, particularly its poor interobserver reproducibility. Our findings suggest that clinicians should interpret CI cautiously and consider establishing their own cut-off values. This study underscores the need to address interobserver reproducibility before CI can be widely adopted for AMR management.

Simultaneous assessment of blood flow and myelin content in the brain white matter with dynamic [11C]PiB PET: a test-retest study in healthy controls.

Exploring the relationship between oxygen supply and myelin damage would benefit from a simultaneous quantification of myelin and cerebral blood flow (CBF) in the brain's white matter (WM). To validate an analytical method for quantifying both CBF and myelin content in the WM using dynamic [11C]PiB positron emission tomography (PET). A test-retest study was performed on eight healthy subjects who underwent two consecutive dynamic [11C]PiB-PET scans. Three quantitative approaches were compared: simplified reference tissue model 2 (SRTM2), LOGAN graphical model, and standardized uptake value ratio (SUVR). The sensitivity of methods to the size of the region of interest was explored by simulating lesion masks obtained from 36 subjects with multiple sclerosis. Reproducibility was assessed using the relative difference and interclass correlation coefficient. Repeated measures correlations were used to test for cross-correlations between metrics. Among the CBF measures, the relative delivery (R1) of the simplified reference tissue model 2 (SRTM2) displayed the best reproducibility in the white matter, with a strong influence of the size of regions analyzed, the test-retest variability being below 10% for regions above 68 mm3 in the supratentorial white matter. [11C]PiB PET-derived proxies of CBF demonstrated lower perfusion of white matter compared to grey matter with an overall ratio equal to 1.71 ± 0.09 when the SRTM2-R1 was employed. Tissue binding in the white matter was well estimated by the Logan graphical model through estimation of the distribution volume ratio (LOGAN-DVR) and SRTM2 distribution volume ratio (SRTM2-DVR), with test-retest variability being below 10% for regions exceeding 106 mm3 for LOGAN-DVR and 300 mm3 for SRTM2-DVR. SRTM2-DVR provided a better contrast between white matter and grey matter. The interhemispheric variability was also dependent on the size of the region analyzed, being below 10% for regions above 103 mm3 for SRTM2-R1 and above 110 mm3 for LOGAN-DVR. Whereas the 1 to 8-minute standardized uptake value ratio (SUVR1-8) showed an intermediary reproducibility for CBF assessment, SUVR0-2 for perfusion or SUVR50-70 for tissue binding showed poor reproducibility and correlated only mildly with SRTM2-R1 and LOGAN-DVR estimations respectively. [11C]PiB PET imaging can simultaneously quantify perfusion and myelin content in WM diseases associated with focal lesions. For longitudinal studies, SRTM2-R1 and DVR should be preferred over SUVR for the assessment of regional CBF and myelin content, respectively. European Union Clinical Trials Register EUDRACT; EudraCT Number: 2008-004174-40; Date: 2009-03-06; https//www.clinicaltrialsregister.eu ; number 2008-004174-40.

Experience Sharing in Pathological Diagnosis of Early Adenocarcinoma of the Lung.

Background: In daily work, there are still many pathologists who have difficulty handling the diagnosis of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic adenocarcinoma, and the boundaries are not clear enough. Sometimes, the diagnosis is difficult, and there is sometimes poor reproducibility between different pathologists. Accurate diagnosis and differential diagnosis require a certain amount of experience. Methods: During the COVID-19 pandemic, we collected a large number (n = 381) of specimens of early lung adenocarcinoma, most of which (n = 356) were solitary lesions and 25 were multifocal lesions. There were 78 nodules in multifocal lesions, total 434 nodules. We summarized very careful microscopic observation and comparative analysis on all frozen and paraffin sections collected from many early lung adenocarcinoma specimens, continuously summarizing our experience. Results: Based on the World Health Organization's 2021 classification and diagnostic criteria for lung adenocarcinoma, new perspectives have been proposed on how to distinguish between atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic adenocarcinoma. In particular, new perspectives have been proposed on how to identify invasive aspects, and there are also some new perspectives on early lung mucinous lesions. Conclusion: Atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic adenocarcinoma all have corresponding morphological diagnostic criteria, but the morphological boundaries are sometimes not easy to determine and require some experience accumulation. The intraoperative frozen pathological diagnosis of early adenocarcinoma of the lung needs to be closely combined with imaging examination, and has very rich morphological experience.

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches.

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

A Study on the Frequency-Domain Black-Box Modeling Method for the Nonlinear Behavioral Level Conduction Immunity of Integrated Circuits Based on X-Parameter Theory.

During circuit conduction immunity simulation assessments, the existing black-box modeling methods for chips generally involve the use of time-domain-based modeling methods or ICIM-CI binary decision models, which can provide approximate immunity assessments but require a high number of tests to be performed when carrying out broadband immunity assessments, as well as having a long modeling time and demonstrating poor reproducibility and insufficient accuracy in capturing the complex electromagnetic response in the frequency domain. To address these issues, in this paper, we propose a novel frequency-domain broadband model (Sensi-Freq-Model) of IC conduction susceptibility that accurately quantifies the conduction immunity of components in the frequency domain and builds a model of the IC based on the quantized data. The method provides high fitting accuracy in the frequency domain, which significantly improves the accuracy of circuit broadband design. The generated model retains as much information within the frequency-domain broadband as possible and reduces the need to rebuild the model under changing electromagnetic environments, thereby enhancing the portability and repeatability of the model. The ability to reduce the modeling time of the chip greatly improves modeling efficiency and circuit design. The results of this study show that the "Sensi-Freq-Model" reduces the broadband modeling time by about 90% compared to the traditional ICIM-CI method and improves the normalized mean square error (NMSE) by 18.5 dB.

A Simple In Situ Marker Guiding Shape‐Controlled Synthesis of Iron Oxide Nanoparticles

AbstractThermal decomposition of iron oleate is a simple and widespread method for synthesizing monodispersed iron oxide nanoparticles (IONPs) with well‐defined morphology. However, the complexity of the underlying mechanism makes this method rather sensitive to variations in experimental conditions, and the lack of simple techniques to monitor the reaction progress in situ usually results in poor reproducibility and time‐consuming optimizations. Here, a simple, robust, and versatile in situ marker to monitor particle formation based on a sudden change in the temperature during reflux is reported. A linear relationship between the onset of particle formation and the concentration of surfactants is unveiled, corroborating a ‘chemically activated’ burst nucleation mechanism. Using this linear relationship as a guide, highly uniform spherical, cubic, and star‐shaped particles between 12 and 30 nm can be obtained. This temperature marker and the derived linear relationship not only deepen the understanding of the reaction process, but also provide a powerful tool for the straightforward optimization of IONPs.

Process Optimization of Scaled-Up Production and Biosafety Evaluation of the Dimethyl-Dioctadecyl-Ammonium Bromide/Poly(lactic acid) Nano-Vaccine.

Nano-adjuvant vaccines could induce immune responses and enhance immunogenicity. However, the application and manufacturing of nano-adjuvant is hampered by its challenging scale-up, poor reproducibility, and low security. Therefore, the present study aimed to optimize the preparation nanoparticles (NPs) using FDA-approved biopolymer materials poly(lactic acid) (PLA) and cationic lipid didodecyl-dimethyl-ammonium bromide (DDAB), develop the scale-up process, and evaluate the stability and biosafety of it. The optimum preparation conditions of DDAB/PLA NPs on a small scale were as follows: DDAB amount of 30 mg, aqueous phase volume of 90 mL, stirring rate at 550 rpm, and solidifying time of 12 h. Under the optimum conditions, the size of the NPs was about 170 nm. In scale-up preparation experiments, the vacuum rotary evaporation of 6 h and the Tangential flow ultrafiltration (TFU) method were the optimum conditions. The results suggested that DDAB/PLA NPs exhibited a uniform particle size distribution, with an average size of 150.3 ± 10.4 nm and a narrow polydispersity index (PDI) of 0.090 ± 0.13, coupled with a high antigen loading capacity of 85.4 ± 4.0%. In addition, the DDAB/PLA NPs can be stored stably for 30 days and do not have side effects caused by residual solvents. For biosafety, the acute toxicity experiments showed good tolerance of the vaccine formulation even at a high adjuvant dose. The local irritation experiment demonstrated the reversibility of muscular irritation, and the repeated toxicity experiment revealed no significant necrosis or severe lesions in mice injected with the high-dose vaccine formulation. Overall, the DDAB/PLA NPs exhibit potential for clinical translation as a safe candidate vaccine adjuvant.

Identifying high-order plasmon modes in silver nanoparticle-over-mirror configuration

Metallic nanoparticle-over-mirror (NPOM) represents as a versatile plasmonic configuration for surface enhanced spectroscopy, sensing and light-emitting metasurfaces. However, experimentally identifying the high-order localized surface plasmon modes in NPOM, especially for the best plasmonic material silver, is often hindered by the small scattering cross-section of high-order plasmon modes and the poor reproducibility of the spectra across different NPOMs, resulted from the polyhedral morphology of the colloidal nanoparticles or the rough surface of deposited polycrystalline metals. In this study, we identify the high-order localized surface plasmon modes in silver NPOM by using differential reflection spectroscopy. We achieved reproducible single-particle absorption spectra by constructing uniform NPOM consisting of silver nanospheres, single-crystallized silver microplates, and a self-assembled monolayer of 1,10-decanedithiol. For comparison, silver NPOM created from typical polycrystalline films exhibits significant spectral fluctuations, even when employing template stripping methods to minimize the film roughness. Identifying high-order plasmon modes in the NPOM configuration offers a pathway to construct high-quality plasmonic substrates for applications such as colloidal metasurface, surface-enhanced Raman spectroscopy, fluorescence, or infrared absorption.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Micro-PINGUIN: microtiter-plate-based instrument for ice nucleation detection in gallium with an infrared camera

Abstract. Ice nucleation particles play a crucial role in atmospheric processes; for example, they can trigger ice formation in clouds and thus influence their lifetime and optical properties. The quantification and characterization of these particles require reliable and precise measurement techniques. In this publication, we present a novel droplet freezing instrument to measure the immersion freezing of biotic and abiotic ice-nucleating particles within the temperature range of 0 to −25 °C. Immersion freezing of the samples is investigated using 384-well PCR plates with a sample volume of 30 µL. Nucleation events are detected with high precision using a thermal camera that records the increase in infrared emission due to the latent heat release. To maximize the thermal contact between the PCR plate and the surrounding cooling unit, we use a gallium bath as a mount for the PCR plate. The instrument was validated relative to a calibrated temperature standard and through reproducibility measurements employing the same suspension. We find that the combination of good thermal connectivity and precise temperature recording enables accurate (±0.81 °C at −10 °C) and reproducible (±0.20 °C) detection of the nucleation temperatures. Consequently, the results that are produced using the MICROtiter-Plate-based instrument for Ice Nucleation detection in GalliUm with an INfrared camera (micro-PINGUIN) are of good quality and the instrument can be used to study the immersion freezing of various ice-nucleating particles. For comparison with already existing instruments, Snomax® (hereafter Snomax) and illite NX suspensions are measured with the new ice nucleation instrument, micro-PINGUIN. Further, we investigated the reproducibility of experiments using Snomax suspensions and found poor reproducibility when suspensions were prepared freshly even if the same batch of Snomax is used. This could be attributed to substrate heterogeneity, aging effects, and dilution errors. The reproducibility of the measurements is greatly improved for Snomax suspensions that are prepared in advance and stored frozen in aliquots. Thus, we suggest the use of suspensions frozen in aliquots for further reproducibility measurements and intercomparison studies.

Abstract PO5-14-06: Transcriptomics-based drug screening in 3D ex vivo patient-derived breast cancer model and patient biopsy for personalized therapy

Abstract Breast cancer is a leading cause of cancer-related mortality for women worldwide. Hormone receptor-positive (HR+) breast tumors, which represent 70% of all breast cancer cases, are treated with endocrine therapy. However, not all patients benefit from this treatment regimen because of patient-to-patient variation. Therefore, high throughput drug screening system is warranted to enable personalized medicine. Patient-derived organoids, which serve as a screening platform, lose their hormone receptors and response upon ex vivo culturing, and may not be adequate models for HR+ breast tumors. Moreover, unidentified biological components in the widely-used basement membrane matrix, Matrigel, result in high batch-to-batch variations and poor reproducibility in organoid cultures. Here, we propose a hydrogel-based 3D ex vivo model with defined structural and chemical properties to test hormone and drug sensitivity of HR+ breast tumors from patient-derived xenografts (PDXs) and patient tumor biopsies using microfluidics. Our data demonstrate the feasibility of this model to preserve cell proliferation and hormone receptor expression over 7 days. We also demonstrate that responses to hormones and FDA-approved drugs are faithfully maintained in this model. Finally, to establish a high throughput hormone and drug testing workflow with transcriptomic readout, we multiplexed barcoded- and drug-treated tumor samples in a single experiment with bulk RNA-sequencing. Our preliminary data demonstrate patient-specific responses to hormones and drugs that correspond to patient genetic mutation profiles, treatment history, disease stages and subtypes. This platform also enables testing of drugs in clinical trials that shows promising therapeutic outcomes for breast cancer, such as CDK4/6i, AKTi, PARPi, mTORi and their combination with endocrine therapy, thanks to the high throughput of the screening system and the low consumption of patient-derived or patient tissue. Given the capability of combining this physiologically-relevant 3D ex vivo model with RNA-seq for HR+ breast tumors, this platform holds potential for high throughput compound testing and transcriptomic profiling of patient biopsies for personalized medicine. Citation Format: Yueyun Zhang, Carlos Henrique Venturi Ronchi, Giovanna Ambrosini, Yuanlong Liu, Patrick Aouad, Daria Matvienko, Christoph Merten, Cathrin Brisken. Transcriptomics-based drug screening in 3D ex vivo patient-derived breast cancer model and patient biopsy for personalized therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-14-06.

Automatic height measurement of central serous chorioretinopathy lesion using a deep learning and adaptive gradient threshold based cascading strategy

Accurately quantifying the height of central serous chorioretinopathy (CSCR) lesion is of great significance for assisting ophthalmologists in diagnosing CSCR and evaluating treatment efficacy. The manual measurement results dominated by single optical coherence tomography (OCT) B-scan image in clinical practice face the dilemma of weak reference, poor reproducibility, and experience dependence. In this context, this paper constructs two schemes: Scheme Ⅰ draws on the idea of ensemble learning, namely, integrating multiple models for locating starting key point in the height direction of lesion in the inference stage, which appropriately improves the performance of a single model. Scheme Ⅱ designs an adaptive gradient threshold (AGT) technique, followed by the construction of cascading strategy, which involves preliminary location of starting key point through deep learning, and then employs AGT for precise adjustment. This strategy not only achieves effective location for starting key point, but also significantly reduces the large appetite of deep learning model for training samples. Subsequently, AGT continues to play a crucial role in locating the terminal key point in the height direction of lesion, further demonstrating its feasibility and effectiveness. Quantitative and qualitative key point location experiments in the height direction of lesion on 1152 samples, as well as the final height measurement display, consistently conveys the superiority of the constructed schemes, especially the cascading strategy, expanding another potential tool for the comprehensive analysis of CSCR.

The Reproducibility and Reliability of Insulin Sensitivity and Secretion Indices in Children and Adolescents

Context. Insulin sensitivity and secretion indices can be useful tools in understanding insulin homeostasis in children at risk for diabetes. There have been few studies examining the reproducibility of these measures in pediatrics. Objective. To determine whether fasting or oral glucose tolerance test (OGTT)-derived insulin measures would be more reproducible and whether there would be differences based on weight, sex, race, and pubertal status. Design. Observational study. Setting. Clinical research unit. Patients or Other Participants. Two hundred fifty-seven overweight/obese (BMI ≥ 85th%, n = 186) and normal weight (BMI &lt; 85th%, n = 71) children without diabetes between ages of 8 and 17 were included in the study. Methods. OGTT tests performed in study participants at two separate visits within a 3-week period. We performed two formal oral glucose tolerance tests within a 3-week period. The reproducibility of fasting measures was compared with OGTT-derived measures by weight categories and compared by weight, sex, race, and pubertal status. Comparisons were made between the correlation coefficients of fasting vs. OGTT-derived measures and between normal weight vs. obese/overweight participants, male vs. female, White vs. Black, and pre- vs. post-midpubertal. Intraclass correlation coefficients were calculated for each comparison as well. Results. For insulin sensitivity, the OGTT-derived measure was more reproducible than the fasting measures. There were no significant differences in reproducibility in the overweight/obese population compared to the normal weight population nor by sex, race, or pubertal status. Conclusions. Nonfasting insulin sensitivity measures are more reproducible than fasting insulin sensitivity measures, regardless of weight category. Insulin secretion measures have poor reproducibility overall. Weight status, sex, race, and midpubertal stage do not impact the reproducibility of insulin sensitivity and secretion measures.

PhuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.