Raw Data Research Articles

Implementasi Intelejen Bisnis dengan Visualisasi Data Gaji dan Algoritma Linear Regresion

To understand these employment dynamics, it is important to explore how long Data Scientists typically work in this field and what sectors they are more likely to be involved in. This information is very important for companies looking to recruit Data Scientists or determine the average salary of their employees. With this activity, managers can more clearly know the employee's salary range based on their work experience and area of specialization. This gives managers a more precise tool for assessing compensation appropriate to the level of experience and expertise possessed by employees. Nowadays, the concept of Business Intelligence can be applied by all industrial sectors, as long as the industry has a database system to develop its business. Business Intelligence processes and analyzes large amounts of raw data and then displays it in a business report with interactive visuals, namely a dashboard. This research aims to contribute to the understanding of average salary predictions and provide valuable information for business owners to make informed decisions. The results show that linear regression is accurate and forms a linear regression model with an R-square result of 0.263429. The salary prediction calculation process is carried out by considering influencing factors, and the results are then presented visually using Power BI to provide business owners with more interactive information.

Modelling science return from the lunar crater radio telescope on the far side of the moon.

The era following the separation of CMB photons from matter, until the emergence of the first stars and galaxies, is known as the Cosmic Dark Ages. Studying the electromagnetic radiation emitted by neutral hydrogen having the 21 cm rest wavelength is the only way to explore this significant phase in the Universe's history, offering opportunities to investigate essential questions about dark matter physics, the standard cosmological model andinflation. Due to cosmological redshift, this signal is now only observable at frequencies inaccessible from the Earth's surface due to ionospheric absorption and reflection. With the Lunar Crater Radio Telescope (LCRT), we aim to conduct unprecedented measurements of the sky-averaged redshifted signal spectrum in the 4.7-47 MHz band, by deploying a 350 m diameter parabolic reflector mesh inside a lunar crater on the far side of the Moon and suspending a receiver at its focus. This work discusses the feasibility of the LCRT science goals through the development of a science model, with emphasis on post-processing techniques to extract the Dark Ages signal from the galactic foreground dominating the expected raw data. This model can be used to vary critical instrument and mission parameters to understand their effect on the quality of the retrieved signal. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

Adaptation and validation of the EPIC-Norfolk food frequency questionnaire for assessing dietary intake in Ukrainian adults

IntroductionCurrently, there are no validated food frequency questionnaires (FFQs) for evaluating nutrient intake in Ukrainian adults. This study aimed to adapt and validate the European Prospective Investigation into Cancer Food...

SLIM-Based High-Resolution Ion Mobility Reveals New Structural Insights into Isomeric Vitamin D Metabolites and their Isotopologues.

Testing for vitamin D deficiency remains a high-volume clinical assay, much of which is done using mass spectrometry-based methods to alleviate challenges in selectivity associated with immunoassays. Ion mobility-mass spectrometry (IM-MS) has been proposed as a rapid alternative to traditional LC-MS/MS methods, but understanding the structural ensemble that contributes to the ion mobility behavior of this molecular class is critical. Herein we demonstrate the first application of high-resolution Structures for Lossless Ion Manipulations (SLIM) IM separations of several groups of isomeric vitamin D metabolites. Despite previous IM studies of these molecules, the high resolving power of SLIM (Rp ∼ 200) has revealed additional conformations for several of the compounds. The highly similar collision cross sections (CCS), some differing by as little as 0.7%, precluded adequate characterization with low-resolution IM techniques where, in some cases, wider than expected peak widths and/or subtle shoulders may have hinted at their presence. Importantly, these newly resolved peaks often provided a unique mobility that could be used to separate isomers and provides potential for their use in quantification. Lastly, the contribution of isotopic labeling to arrival time distribution for commonly used 13C- and deuterium-labeled internal standards was explored. Minor shifts of ∼0.2-0.3% were observed, and in some instances these shifts were specific to the conformer being measured (i.e., "closed" vs "open"). Accounting for these shifts is important during raw data extraction to ensure reproducible peak area integration, which will be a critical consideration in future quantitative applications.

Towards global insect biomonitoring with frugal methods.

None of the global targets for protecting nature are currently met, although humanity is critically dependent on biodiversity. A significant issue is the lack of data for most biodiverse regions of the planet where the use of frugal methods for biomonitoring would be particularly important because the available funding for monitoring is insufficient, especially in low-income countries. We here discuss how three approaches to insect biomonitoring (computer vision, lidar, DNA sequences) could be made more frugal and urge that all biomonitoring techniques should be evaluated for global suitability before becoming the default in high-income countries. This requires that techniques popular in high-income countries should undergo a phase of 'innovation through simplification' before they are implemented more broadly. We predict that techniques that acquire raw data at low cost and are suitable for analysis with AI (e.g. images, lidar-signals) will be particularly suitable for global biomonitoring, while techniques that rely heavily on patented technologies may be less promising (e.g. DNA sequences). We conclude the opinion piece by pointing out that the widespread use of AI for data analysis will require a global strategy for providing the necessary computational resources and training. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Delivering on a promise: futureproofing automated insect monitoring methods.

Due to rapid technological innovations, the automated monitoring of insect assemblages comes within reach. However, this continuous innovation endangers the methodological continuity needed for calculating reliable biodiversity trends in the future. Maintaining methodological continuity over prolonged periods of time is not trivial, since technology improves, reference libraries grow and both the hard- and software used now may no longer be available in the future. Moreover, because data on many species are collected at the same time, there will be no simple way of calibrating the outputs of old and new devices. To ensure that reliable long-term biodiversity trends can be calculated using the collected data, I make four recommendations: (1) Construct devices to last for decades, and have a five-year overlap period when devices are replaced. (2) Construct new devices to resemble the old ones, especially when some kind of attractant (e.g. light) is used. Keep extremely detailed metadata on collection, detection and identification methods, including attractants, to enable this. (3) Store the raw data (sounds, images, DNA extracts, radar/lidar detections) for future reprocessing with updated classification systems. (4) Enable forward and backward compatibility of the processed data, for example by in-silico data 'degradation' to match the older data quality. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Comparison of quantitative whole body PET parameters on [68Ga]Ga-PSMA-11 PET/CT using ordered Subset Expectation Maximization (OSEM) vs. bayesian penalized likelihood (BPL) reconstruction algorithms in men with metastatic castration-resistant prostate cancer

BackgroundPSMA PET/CT is a predictive and prognostic biomarker for determining response to [177Lu]Lu-PSMA-617 in patients with metastatic castration resistant prostate cancer (mCRPC). Thresholds defined to date may not be generalizable to newer image reconstruction algorithms. Bayesian penalized likelihood (BPL) reconstruction algorithm is a novel reconstruction algorithm that may improve contrast whilst preventing introduction of image noise. The aim of this study is to compare the quantitative parameters obtained using BPL and the Ordered Subset Expectation Maximization (OSEM) reconstruction algorithms.MethodsFifty consecutive patients with mCRPC who underwent [68Ga]Ga-PSMA-11 PET/CT using OSEM reconstruction to assess suitability for [177Lu]Lu-PSMA-617 therapy were selected. BPL algorithm was then used retrospectively to reconstruct the same PET raw data. Quantitative and volumetric measurements such as tumour standardised uptake value (SUV)max, SUVmean and Molecular Tumour Volume (MTV-PSMA) were calculated on both reconstruction methods. Results were compared (Bland-Altman, Pearson correlation coefficient) including subgroups with low and high-volume disease burdens (MTV-PSMA cut-off 40 mL).ResultsThe SUVmax and SUVmean were higher, and MTV-PSMA was lower in the BPL reconstructed images compared to the OSEM group, with a mean difference of 8.4 (17.5%), 0.7 (8.2%) and − 21.5 mL (-3.4%), respectively. There was a strong correlation between the calculated SUVmax, SUVmean, and MTV-PSMA values in the OSEM and BPL reconstructed images (Pearson r values of 0.98, 0.99, and 1.0, respectively). No patients were reclassified from low to high volume disease or vice versa when switching from OSEM to BPL reconstruction.Conclusions[68Ga]Ga-PSMA-11 PET/CT quantitative and volumetric parameters produced by BPL and OSEM reconstruction methods are strongly correlated. Differences are proportional and small for SUVmean, which is used as a predictive biomarker. Our study suggests that both reconstruction methods are acceptable without clinical impact on quantitative or volumetric findings. For longitudinal comparison, committing to the same reconstruction method would be preferred to ensure consistency.

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT)acquisition. A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance between any two image pixel/voxel locations and . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensembleaveraging. The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given localROI. A new method was developed to enable local NPS from a single PCD-CTacquisition.

Improving the identification of bone‐specific physical activity using wrist‐worn accelerometry: A cross‐sectional study in 11–12‐year‐old Australian children

AbstractPhysical activity (PA) during childhood and adolescence is important for the accrual of maximal peak bone mass. The precise dose that benefits bone remains unclear as methods commonly used to analyze PA data are unsuitable for measuring bone‐relevant PA. Using improved accelerometry methods, this study identified the amount and intensity of PA most strongly associated with bone outcomes in 11–12‐year‐olds. Participants (n = 770; 382 boys) underwent tibial peripheral quantitative computed tomography to assess trabecular and cortical density, endosteal and periosteal circumference and polar stress‐strain index. Seven‐day wrist‐worn raw acceleration data averaged over 1‐s epochs was used to estimate time accumulated above incremental PA intensities (50 milli‐gravitational unit (mg) increments from 200 to 3000 mg). Associations between time spent above each 50 mg increment and bone outcomes were assessed using multiple linear regression, adjusted for age, sex, height, weight, maturity, socioeconomic position, muscle cross‐sectional area and PA below the intensity of interest. There was a gradual increase in mean R2 change across all bone‐related outcomes as the intensity increased in 50 mg increments from >200 to >700 mg. All outcomes became significant at >700 mg (R2 change = 0.6%–1.3% and p = 0.001–0.02). Any further increases in intensity led to a reduction in mean R2 change and associations became non‐significant for all outcomes >1500 mg. Using more appropriate accelerometry methods (1‐s epochs; no a priori application of traditional cut‐points) enabled us to identify that ∼10 min/day of PA >700 mg (equivalent to running ∼10 km/h) was positively associated with pQCT‐derived measures of bone density, geometry and strength in 11–12‐year‐olds.

PSII-10 Development of an interdisciplinary undergraduate and graduate course in precision livestock farming systems

Abstract The modern livestock industry is increasingly adopting data, technologies, and automation into daily husbandry and management routines and decisions. These advances in precision livestock farming (PLF) enable improved financial margins, sustainability, efficiency, and animal productivity and welfare. However, there is currently a gap between the engineers and data scientists designing the technologies and the animal scientists and caretakers using them. At North Carolina State University a novel course on PLF Systems for undergraduate and graduate students was developed in the Department of Animal Science. Through lecture delivery, livestock production scenarios, scientific literature, and data-based homework assignments students explored data collection, management, analysis, and data-based decision making. Students gained hands-on experience with a variety of sensor types and signal processing through lab-based activities. Basic hardware and software development concepts were explored during an Arduino-based microcontroller activity. The course was developed and refined using student feedback to include additional sensor and lab-based days and additional in-class supported activities utilizing Excel for data manipulation, processing, and presentation. Self-reported student learning results were collected across two semesters (19 students; 18 Animal Science, 1 Biological Engineering) with84% of students indicated the course was effective in increasing their ability to evaluate and implement technology in livestock production systems and 74% of students reported increased knowledge of coding and software, as well as their ability to clean, analyze, and make sense of raw data. The topics and delivery methods implemented in this course could inform the basis for development of similar interdisciplinary PLF courses at other academic institutions.

Publishing important work that lacks validity or reproducibility – pushing frontiers or corrupting science?

ABSTRACT Scientific research requires objectivity, impartiality and stringency. However, scholarly literature is littered with preliminary and explorative findings that lack reproducibility or validity. Some low-quality papers with perceived high impact have become publicly notable. The collective effort of fellow researchers who follow these false leads down blind alleys and impasses is a waste of time and resources, and this is particularly damaging for early career researchers. Furthermore, the lay public might also be affected by socioeconomic repercussions associated with the findings. It is arguable that the nature of scientific research is such that its frontiers are moved and shaped by cycles of published claims inducing in turn rounds of validation by others. Using recent example cases of room-temperature superconducting materials research, I argue instead that publication of perceptibly important or spectacular claims that lack reproducibility or validity is epistemically and socially irresponsible. This is even more so if authors refuse to share research materials and raw data for verification by others. Such acts do not advance, but would instead corrupt science, and should be prohibited by consensual governing rules on material and data sharing within the research community, with malpractices appropriately sanctioned.

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.

Environmental data and scores: Lost in translation

AbstractThis paper investigates methodological issues and limited coverage of providers' environmental scores, which are increasingly employed by investors, financial institutions and policymakers for corporate environmental assessment. The contribution of the paper is twofold. First, regression analysis shows a substantial heterogeneity among the environmental scores of seven providers in the reliance on raw data. However, as some variables are found meaningful across providers, the request to enhance disclosure should focus on such variables. The heterogeneity of the unexplained component of the regression across providers can be arguably referred to as judgemental factors and underlines the providers' different focus on financial risk or environmental impact. Second, we propose a classification system based on corporate disclosure data that aims to enable investors to extend the environmental assessment of companies not rated by providers. This system has been calibrated to implement two common investment strategies, that is, best‐in‐class and exclusion and allows to build portfolios with both environmental and financial profiles similar to portfolios based on providers' scores. The work aims to contribute to the intersection between the analysis of methodologies of E‐scores and their practical use for investment purposes. Rather than asking for a mirage of full comparability of E‐scores, the paper substantiates that is of utmost importance to improve the disclosure of corporate data to enhance the environmental assessment as well as the transparency on providers' methodologies to enable investors to select E‐scores consistent with their risk‐impact preferences. Such transparency will foster the development of sustainable finance.

Learning optimal biomarker-guided treatment policy for chronic disorders.

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

Enhanced gate-valve failure detection in water distribution networks using ML and pressure data

ABSTRACT This study introduces an innovative diagnostic approach for identifying gate-valve failures in water distribution systems. By implementing high-frequency pressure sensors upstream and downstream of the gate valves, we obtained detailed pressure data that are pivotal for fault diagnosis. We explored three distinct machine-learning algorithms and two data-handling techniques to ensure optimal performance in real-world applications. In our methodology, supervised learning algorithms are used to analyze pressure differentials and predict valve behavior. We rigorously tested these algorithms using both raw and feature-engineered data, and the results indicated the effectiveness of the Gaussian-naïve Bayes model with six extracted features. This approach enhances the precision and reliability of diagnostics in water distribution networks.

Impact of root canal disinfection on the bacteriome present in primary endodontic infection: A next generation sequencing study.

To investigate the bacteriome present in teeth with primary endodontic infection (PEI) and apical periodontitis (AP) and to determine quantitatively and qualitatively the impact of chemomechanical preparation (CMP) using 2.5% sodium hypochlorite NAOCl on the bacteriome found in PEI with AP using the Illumina MiSeq platform. Thirty-six paired samples from 18 patients were successfully sequenced and analysed. Samples were collected at two sampling times: before (s1) and after (s2) CMP using 2.5% NaOCl. The DNA was extracted from s1 and s2 samples and quantified using quantitative PCR (qPCR). All 36 samples were sequenced using the Illumina MiSeq platform. Raw V3-V4 amplicon sequencing data were processed with the DADA2 pipeline to generate amplicon sequence variants (ASVs). Alpha diversity metrics representing abundance (Chao1) and diversity and evenness (Shannon, Simpson) were computed. The paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The PERMANOVA method (with 999 permutations) was applied to compare community composition between sample types (s1 versus s2) and between patient IDs. ALDEx2 (ANOVA-like differential expression tool for high-throughput sequencing data) to investigate differentially abundant taxa between s1 and s2. A paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The qPCR counts were significantly higher in s1 compared to s2 (p = .0007). The Chao1 index indicated no difference in alpha diversity (p < .7019); whereas Shannon (p = .0056) and Simpson (p = .02685) indexes showed higher values in s2. The PERMANOVA test using Adonis2 showed a significant effect of sample time on community composition (R2 = .0630, p = .012). Patient ID also showed a significant effect on community composition (R2 = .6961, p = .001). At the genus level, Dialister, Mogibacterium, Prevotella, and Olsenella were differentially enriched at s1, while Actinomyces, Stenotrophomonas_unclassified, Enterococcus_unclassified, and Actinomyces_unclassified were differentially enriched in s2. The bacteriome present in teeth with PEI with AP is complex and diverse. CMP using 2.5% NaOCl showed a high quantitatively and qualitatively disinfectant impact on the bacteriome present in PEI with AP.

Online dashboards for SARS-CoV-2 wastewater-based epidemiology.

Aim: Wastewater-based epidemiology (WBE) is increasingly used to monitor pandemics. In this manuscript, we review methods and limitations of WBE, as well as their online dashboards. Materials & methods: Online dashboards were retrieved using PubMed and search engines, and annotated for timeliness, availability of English version, details on SARS-CoV-2 sublineages, normalization by population and PPMoV load, availability of case/hospitalization count charts and of raw data for export. Results: We retrieved 51 web portals, half of them from Europe. Africa is represented from South Africa only, and only seven portals are available from Asia. Conclusion: WBS provides near-real-time cost-effective monitoring of analytes across space and time in populations. However, tremendous heterogeneity still persists in the SARS-CoV-2 WBE literature.

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this group, online monitoring of the concentration of different constituents of VOCs in different production enterprises of the petroleum and chemical industries in Shandong Province, with the concentration of the laboratory test, to build a relevant model. The correlation coefficient about the PID test concentration and the actual concentration correlation coefficient was obtained through the collection of a large number of data trainings. Based on the application of PID in VOC monitoring, the establishment of a PID high-precision calibration model is important for the precise monitoring of VOCs. In this paper, multiple quantitative analyses were conducted, based on SVM regression of PID response to VOC signals, to study the high-precision VOC monitoring method. To select the response signals of PID under different concentrations of environmental VOCs measured by the research group, first, the PID response to VOC signals was modeled using the support vector machine principle to verify the effect of traditional SVM regression. For the problem of raw data redundancy, calculate the time-domain and frequency-domain characteristics of the PID signal, and conduct the principal component analysis of the time-domain of the PID signal. In order to make the SVM regression more generalized and robust, the selection of kernel function parameters and penalty factor of SVM is optimized by genetic algorithm. By comparing the accuracy of PID calibration models such as PID signal feature extraction, SVM regression, and principal component analysis SVM regression, the superiority of photoionization detector using the signal feature extraction PCA-GA-SVM method to monitor VOCs is verified.

Abstract PO5-13-07: Prediction of recurrence in triple negative breast cancer patients after receiving neoadjuvanttreatment using plasma metabolomics

Abstract Background Early-stage triple-negative breast cancer (TNBC) is usually treated with neoadjuvant chemotherapy (NADC), but prognosis remains poor compared with other BC subtypes. Unfortunately, less than 30% of BC patients achieve pathological complete response (pCR) to NADC and the overall survival of TNBC when cancer spreads is estimated at 10-13 months. Complete response is associated with improved progression-free and overall survival rates, but more accurate prognostic markers are needed. In this study, we assessed the prognostic value of blood circulating metabolites by using targeted-based metabolomics. Methods Patients received standard neoadjuvant chemotherapy with epirubicin plus cyclophosphamide followed by paclitaxel. Blood samples were obtained upon completion of chemotherapy and before surgery. Pathological response to treatment was recorded according to the Miller &amp; Payne system, and categorized as partial (MP 1, 2, &amp; 3) or complete response (MP 4 &amp; 5). Blood samples were collected in EDTA tubes, centrifuged and plasma was stored at -80°C. After QC standard addition and methanol extraction of proteins, four fractions of the resulting extract were analysed: two by reverse-phase/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one by reverse phase/UPLC-MS/MS with negative ion mode ESI, and one by hydrophilic interaction liquid chromatography/UPLC-MS/MS) by Metabolon using a Thermo Scientific Q-Exactive mass spectrometer with a heated electrospray ionization source and an Orbitrap mass analyser. Using Metabolon's hardware and software, raw data were extracted, identified, QC processed and quantified using the area under the curve (AUC). Identified compounds were compared with library entries of purified standards or recurrent unknown entities. The prognostic capacity of each metabolite was assessed using BRB ArrayTools. Selected metabolites were validated using a targeted approach using compound standards as follows. Plasma samples were re-analysed by UPLC/MS using a Vanquish LC coupled to an Orbitrap Exploris 240 MS (Thermo Fisher Scientific) with positive and negative ionization mode ESI under polarity switching. Identification of compounds were performed by comparison of retention times (against in-house authentic standards), accurate mass (with an accepted deviation of 3ppm), and MS/MS spectra. These analyses were carried out by MS-Omics (Denmark). Results Twenty patients treated in the Medical Oncology Unit of the University Hospital of Jaén were recruited. Median age was 50 (31-76). Twelve patients obtained a pathological complete response. Median follow-up after surgery was 25 months. Seven patients eventually had distant metastases (2 in the lungs, 1 in the brain, 1 in the bones and 3 multi-site). Metabolon untargeted analysis identified and quantified 985 metabolites, 789 after xenobiotics exclusion. Survival analysis identified 16 metabolites related with distant relapse (p&amp;lt; 0.05). The ten metabolites with lower p-value were selected for targeted validation. Ms-Omics pipeline allowed absolute quantification of 5 metabolites, 3 of them showing high correlation (R &amp;gt;0.7) between both measurements (targeted and untargeted). Validation of the prognostic value of the candidate metabolites is ongoing in a larger TNBC cohort. Conclusions Metabolomics is a useful tool for the detection of simply assessable and cost-effective prognostic biomarkers in TNBC. Easily monitoring the presence of minimal residual disease is worth to be settled up in the clinical practice for the most aggressive molecular subtype of breast cancer. In this work we have defined a set of metabolites that could predict distant relapse events in TNBC patients treated with neoadjuvant chemotherapy. Further analyses are being carried out to validate the prognostic value of the proposed candidate metabolites. Citation Format: Pedro Sánchez-Rovira, Rocio Urbano-Cubero, Angelo Gamez Pozo, Carmen González-Olmedo, Alicia Cano-Jimenez, Andrea Zapater- Moros, Leticia Díaz-Beltrán, Lucia Trilla-Fuertes, Mariana Díaz-Almirón, Enrique Espinosa, Pilar Zamora, Juan Angel Fresno Vara. Prediction of recurrence in triple negative breast cancer patients after receiving neoadjuvanttreatment using plasma metabolomics [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 PO5-13-07.