Qualitative Features Research Articles

Single or Blended Application of Non-Microbial Plant-Based Biostimulants and Trichoderma atroviride as a New Strategy to Enhance Greenhouse Cherry Tomato Performance

The need to increase yield and enhance the sustainability of crop production systems has led to the development and employment of natural products, such as plant biostimulants. In recent years, a number of reports have researched the effects of biostimulants on plant performance; however, few studies have focused on the mutual application of microbial and/or non-microbial biostimulants. This research, conducted in the framework of the SO.MI.PR.O.N regional project, aimed to investigate the single or mutual application of three biostimulants, a tropical plant extract (PE), a vegetal protein hydrolysate (PH), and Trichoderma atroviride, on ‘Creativo’ F1 cherry tomato plants cultivated during two growing cycles (2022–2023 and 2023–2024). Our results showed that plants treated with the combination Tricho + PE + PH had statistically significant higher fresh shoot biomass (+64.2%, 1647.0 g plant−1), total fruit production (+37.9%, 1902.5 g plant−1), marketable fruit production (+52.9%, 1778.5 g plant−1), and average weight of marketable fruits (+53.1%, 17.0 g) compared to control plants (untreated plants). Furthermore, biostimulant treatments, especially T. atroviride, variably enhanced cherry tomato fruits’ qualitative traits, such as firmness, total soluble solids, ascorbic acid, lycopene, and total polyphenols compared to control plants. Overall, the best combinations to increase tomato fruit qualitative features were PE + PH, Tricho + PE, and Tricho + PH. From an economic point of view, the best treatment for achieving the highest net return was PE. This study underlines that biostimulant features (yield, qualitative aspects, and economic profitability) can be supported through the application of specific biostimulant combinations.

The Study Of Dermatoglyphics Pattern In Females With Primary Infertility

Background: Dermatoglyphics, the study of fingerprint ridge patterns, has been recognized for its potential in reflecting genetic and developmental variations. Primary infertility, defined as the inability to conceive after a year of regular, unprotected intercourse, is influenced by genetic, hormonal, and environmental factors. This study aims to investigate dermatoglyphic patterns in females with primary infertility to explore possible correlations between these patterns and infertility, providing insights for early diagnosis and management. Objective: The primary objective of this protocol is to systematically analyze dermatoglyphic patterns in females with primary infertility and compare them with those of fertile females. The study seeks to identify specific fingerprint characteristics that may serve as non-invasive markers for primary infertility. Methodology: This cross-sectional study will include two groups: females diagnosed with primary infertility and age-matched fertile controls. Dermatoglyphic data will be collected using the ink method, and the following parameters will be analyzed: total ridge count (TRC), patterns of arches, loops, and whorls, and other key qualitative features. Statistical analysis will be performed to evaluate significant differences between the two groups. Expected outcome: The study is expected to reveal distinct dermatoglyphic patterns in females with primary infertility compared to fertile females. Identifying such patterns may provide valuable markers for assessing genetic predispositions and could potentially assist in early diagnosis and personalized treatment strategies for infertility. Conclusion: This protocol outlines a method for studying the potential role of dermatoglyphic patterns in primary infertility, proposing a non-invasive, cost-effective diagnostic approach. Further research may help in establishing dermatoglyphics as a supplementary tool in infertility assessment and managemen.

8402 The Usefulness Of The Contrast-Enhanced Ultrasound (CEUS) In Preoperative Localization Of Parathyroid Gland Adenomas

Abstract Disclosure: A. Żyłka: None. K. Dobruch-Sobczak: None. H. Piotrzkowska-Wróblewska: None. P. Góralski: None. J. Gałczyński: None. E. Bakuła-Zalewska: None. M. Dedecjus: None. Objectives: Preoperative localization of parathyroid gland adenomas in patients diagnosed with primary hyperparathyroidism (pHPT) is challenging process. The aim of the study was to assessed the usefulness of contrast-enhanced ultrasound (CEUS) as complimentary diagnostic modality to conventional ultrasonography for detection of hyperfunctioning parathyroid gland lesions. Methods: Between the years 2021 and 2022, 36 adult patients (33 women and 3 men, aged 37-76) diagnosed with pHPT, underwent high-resolution ultrasound (US) and CEUS of parathyroid gland adenomas, suspected after fine-needle aspiration biopsy with positive parathormone (PTH) wash-out results and/or avid in 99mTechnetium -sestamibi scintigraphy. During CEUS examination 1.5 ml of SonoVue contrast has been injected and followed by analysing of qualitative CEUS enhancement features. All patients were qualified to surgical treatment according to clinical symptoms, laboratory and imaging findings and underwent parathyroidectomy with histopathologic results confirming parathyroid gland adenomas. Analysis of the B-mode features and visual CEUS enhancement patterns was performed retrospectively. Ten CEUS qualitative parameters of lesions were assessed including: homo- or heterogenous enhancement; hyper- or hypoenhancement; distribution of contrast in wash-in phase as: centripetal, centrifugal or combined and in wash-out phase as: centrally, peripherally or combined. Then the model was extended using the results based on US B-mode imaging which involved size, echogenicity, calcifications, cystic components and vascularity patterns assessed in Color Doppler (CD) and superb microvascular imaging (SMI). Results: Histopathologic results included 36 parathyroid adenomas (in 33 women and 3 men), the medium size 18.8mm (±11.1) and volume 3.7mm3 (±8.2). In B-mode US examination the majority of lesions were assessed as hypoechoic (n=36; 100%) with dominant inhomogenous echostructure (n=20; 55.5%), mixed vascularity (n=24; 66.6%) and polar vessel observed in 14 lesions (38.8%). There were no adenomas with calcifications, cystic components were observed only in 4 nodules (11.1%). In the CEUS study, dominated qualitative features were homogenous (n=22; 61,1%) and hyperenhancement (n=27; 75%), with centripetal distribution of contrast in wash-in phase (n=24; 66,6%) and dominant centrally wash-out of the contrast (n=20; 55,5%). Conclusions: Preliminary results indicate ultrasonographic patterns characteristic for parathyroid gland adenomas in multiparametric ultrasound involved CEUS examination, which seemed to be valuable tool for localization hyperfunctional parathyroid glands. Further multicenter studies should be performed to standardize qualitative CEUS features in order to diagnose primary hyperparathyroidism preoperatively. Presentation: 6/1/2024

8402 The Usefulness of the Contrast-Enhanced Ultrasound (CEUS) in Preoperative Localization of Parathyroid Gland Adenomas

Abstract Disclosure: A. Żyłka: None. K. Dobruch-Sobczak: None. H. Piotrzkowska-Wróblewska: None. P. Góralski: None. J. Gałczyński: None. E. Bakuła-Zalewska: None. M. Dedecjus: None. Objectives: Preoperative localization of parathyroid gland adenomas in patients diagnosed with primary hyperparathyroidism (pHPT) is challenging process. The aim of the study was to assessed the usefulness of contrast-enhanced ultrasound (CEUS) as complimentary diagnostic modality to conventional ultrasonography for detection of hyperfunctioning parathyroid gland lesions. Methods: Between the years 2021 and 2022, 36 adult patients (33 women and 3 men, aged 37-76) diagnosed with pHPT, underwent high-resolution ultrasound (US) and CEUS of parathyroid gland adenomas, suspected after fine-needle aspiration biopsy with positive parathormone (PTH) wash-out results and/or avid in 99mTechnetium -sestamibi scintigraphy. During CEUS examination 1.5 ml of SonoVue contrast has been injected and followed by analysing of qualitative CEUS enhancement features. All patients were qualified to surgical treatment according to clinical symptoms, laboratory and imaging findings and underwent parathyroidectomy with histopathologic results confirming parathyroid gland adenomas. Analysis of the B-mode features and visual CEUS enhancement patterns was performed retrospectively. Ten CEUS qualitative parameters of lesions were assessed including: homo- or heterogenous enhancement; hyper- or hypoenhancement; distribution of contrast in wash-in phase as: centripetal, centrifugal or combined and in wash-out phase as: centrally, peripherally or combined. Then the model was extended using the results based on US B-mode imaging which involved size, echogenicity, calcifications, cystic components and vascularity patterns assessed in Color Doppler (CD) and superb microvascular imaging (SMI). Results: Histopathologic results included 36 parathyroid adenomas (in 33 women and 3 men), the medium size 18.8mm (±11.1) and volume 3.7mm3 (±8.2). In B-mode US examination the majority of lesions were assessed as hypoechoic (n=36; 100%) with dominant inhomogenous echostructure (n=20; 55.5%), mixed vascularity (n=24; 66.6%) and polar vessel observed in 14 lesions (38.8%). There were no adenomas with calcifications, cystic components were observed only in 4 nodules (11.1%). In the CEUS study, dominated qualitative features were homogenous (n=22; 61,1%) and hyperenhancement (n=27; 75%), with centripetal distribution of contrast in wash-in phase (n=24; 66,6%) and dominant centrally wash-out of the contrast (n=20; 55,5%). Conclusions: Preliminary results indicate ultrasonographic patterns characteristic for parathyroid gland adenomas in multiparametric ultrasound involved CEUS examination, which seemed to be valuable tool for localization hyperfunctional parathyroid glands. Further multicenter studies should be performed to standardize qualitative CEUS features in order to diagnose primary hyperparathyroidism preoperatively. Presentation: 6/1/2024

Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman’s correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model’s discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer–Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Agro-morphological genetic diversity assessment of Amaranthus genotypes from Ethiopia based on qualitative traits

Amaranths are a promising plant in the family of Amaranthaceae because of their nutritional and functional properties, such as their high antioxidant content and dietary fiber content. However, it's being disregarded for several reasons, such as ignorance, a lack of in-depth research, and the plant's long-term genetic growth in Ethiopia, among other factors. In the current work, we described the genetic diversity of 120 amaranth genotypes using qualitative criteria. The experiment, which was configured with an alpha lattice design and duplicated twice, was run for two seasons in 2020 and 2021. Twenty qualitative descriptors were looked for in the gathered data. Among the 20 qualitative variables that were assessed, the chi-square test result indicated the presence of prevailing phenotypic variation. The results of the agro-morphological characterization also revealed a significant amount of variance. The overall mean of the Shannon diversity indices (H') was 0.61. The indices for germination rate, leaf margin, prominence of leaf veins, and the existence of auxiliary inflorescence varied from 0.12 to 0.99. The estimated diversity indices showed more intra-regional diversity (0.66) than inter-regional diversity (0.34), demonstrating the existence of gene flow between growing regions. Shannon–Weaver Diversity Index, ranged from 0.00 for auxiliary inflorescence to 1.94 for leaf coloration, with an overall mean of 19 characters (95%) that were found to have high diversity (> 0.76) while auxiliary inflorescence was invariant. Except for auxiliary inflorescence, all qualitative features showed a lot of variation. Additionally, Amaranthus hybridus L. subsp. cruentus (L.) Theil recorded the greatest Shannon diversity index (0.47) while Amaranthus spinosus L. recorded the lowest (0.00). The hierarchical clustering grouped all the genotypes into three clusters. The first cluster included the most genotypes (58), followed by the second (47), and the third cluster contained the fewest (15). Principal component analysis showed that the first six principal components with eigenvalues greater than one contributed 72% of the variability among genotypes. The study unequivocally demonstrated that, even when the genotypes were grouped into a small number of clusters, there was still enough divergence within the clusters to demonstrate the genotypes of amaranth to have a high genetic diversity. These results indicate that there is substantial genetic diversity among Ethiopian amaranth genotypes, which should be safeguarded and may be utilized in breeding in the future.

Automated remote speech-based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy.

We investigated the agreement between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing. We examined 78 cases from the Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD) study, including cognitively normal individuals and individuals with subjective cognitive decline, mild cognitive impairment, and dementia. We used Bayesian Bland-Altman analysis of word count and the qualitative features of semantic cluster size, cluster switches, and word frequencies. We found high levels of agreement for word count, with a 93% probability of a newly observed difference being below the minimally important difference. The qualitative features had fair levels of agreement. Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode. Our results support the use of automated speech recognition particularly for the assessment of quantitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes. High levels of agreement were found between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing, particularly for word count.The qualitative features had fair levels of agreement.Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode.Automated speech recognition for the assessment of quantitative and qualitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes, seems feasible and reliable.

Prioritisation of Specific Wheat Sowing Technologies Based on AnalyticHierarchy Process and Farmers’ Perceptions

Selection of an appropriate wheat-sowing technology in the combine harvested paddy fields is essential for the timely sowing of wheat after harvesting paddy. The importance of parameters that affect farmers' decisions in selection of wheat sowing technology (WST) in the combine harvested paddy fields is assessed in the present work by implementing an analytical hierarchy process (AHP) technique. AHP is the most comprehensive approach for making judgements with many criteria since it supports a hierarchical structure and quantitative and qualitative features. The suitability of four most commonly used wheat sowing technologies, namely, happy seeder (T1 ), super seeder (T2 ), smart seeder (T3 ) and roto seeder (T4 ), was assessed based on the importance given to six criteria, namely, ease of use (C1 ), machine capital cost (C2 ), fuel consumption (C3 ), machine field capacity (C4 ), crop yield (C5 ), and crop germination (C6 ). Based on farmers’ perceptions and using the analytical hierarchy process approach, criteria C5 was assigned a maximum criteria priority weights of 3.21 and 3.87 for selection of T1 and T2 , respectively; while criteria C6 was assigned a maximum criteria priority weights of 3.56 and 3.63 for selection of T3 and T4 , respectively. The results showed that farmers were less concerned with machine field capacity and more focused on crop output (yield) while selecting a wheat sowing technology. Besides crop yield, crop germination and ease of use were other important factors that farmers considered highly while prioritising and selecting a wheat sowing technology for a combine harvested paddy field.

Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors.

Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively. To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively. The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed. The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p=0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891). In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.

Measuring technological development and sovereignty

The article discusses the issue of measuring technological sovereignty, which is essential for developing a scientific and technical strategy to enhance Russia’s technological independence. Index methods do not provide an accurate representation of the level of technological sovereignty. Solving this problem requires assessing the efficiency of economic technologies. The aim of the study is to explain the direct (non-indexed) method of measuring technology sovereignty, taking into account qualitative features of technologies. The study employs the theory of technological change and empirical regression analysis and uses statistical data from the Russian economy as its information base. A general algorithm for measuring technological independence is proposed. It is applied to each technological area separately, considering the qualitative characteristics of the technologies used. An indicator for assessing the level of producibility is calculated based on the ratio of innovative to non-innovative products and services. The impact of investments in new and old technologies on this indicator is assessed, allowing to obtain a structural picture of Russia’s technological development and renewal. The general conclusion is that investments in new technologies have not been a determining factor in increasing the level of producibility for the Russian economy. The measurement of technological sovereignty requires changes to the federal statistical system for tracking technological development. This would not only allow for a more accurate quantification of the superiority of domestically produced technologies over imported ones but would also reflect the qualitative aspects of potential dependence on foreign technologies in a more comprehensive way. It is important to identify the balance between new and obsolete technologies, as well as between domestic and foreign-produced technologie

Suicide publications from nursing perspective: A bibliometric analysis from 1982 to 2023

AimThis study aims to demonstrate the quantitative and qualitative features of suicide publications in the field of nursing from a bibliometric perspective. DesignDescriptive study. MethodsWeb of Science was used during the research. The data were searched with the keyword combination “suicide and nursing,” including studies published until March 2023, the study date, without a time limit. ResultsIn this study, 1608 suicide research/reviews in the field of nursing were identified between 1982 and 2023, and the research was completed with 1274 suicide studies in line with the exclusion criteria. 3374 authors wrote 1173 research and 101 review/systematic review articles, with an average of 13.31 citations for each publication. Most were published in 2021. ConclusionsNurses seem to begin to take a more active part in suicide studies. It may be recommended that nurses conduct more studies to determine suicide processes and biopsychosocially and ethically appropriate approaches to patients/healthy individuals.

Qualitative Traits and Antioxidant Properties of Blood Oranges Are Affected by the Genotype and the Climatic Conditions.

Blood oranges are increasingly cultivated worldwide as consumers become more aware of the health benefits of their nutraceutical properties and natural antioxidants, specifically polyphenols and anthocyanins. The amounts of these compounds in the fruit mostly depend on the cultivar, rootstock, maturity stage, and environmental conditions. This work focused on the study of the qualitative features of numerous blood orange cultivars grown in three different environments in Spain and Italy. The aim of the work was to investigate the accumulation of primary and secondary metabolites, including bioactive compounds, and to characterize fruit qualitative traits at the time of harvest. Simple sugars were identified and quantified by liquid chromatography and organic acids, polyphenols, and flavonoids by spectrophotometric analysis. The antioxidant potential of the juice was assessed by ABTS, DPPH, and FRAP assays. Cultivation area affected juice color, with Moro and T. Ippolito being the varieties with the highest pigmentation. The cultivation area also determined the pattern of primary and secondary metabolite accumulation in the Tarocco lines. Furthermore, the antioxidant potential was influenced by the diverse environments. Principal Component Analysis highlighted three clusters, two overlapping clusters for the varieties grown in the two Spanish plots and a third clearly separated cluster for the genotypes grown in Italy. This study provides novel knowledge on primary and secondary metabolite accumulation in blood oranges, elucidating the role of genotype and environmental conditions on fruit quality.

Soluble markers of viral rebound and post-treatment HIV control.

We focus on the different classes of biological molecules measurable in easily accessible bodily fluids that have the potential to serve as biomarkers for the HIV post-treatment controller (PTC) phenotype and/or the timing of viral rebound after stopping antiretroviral therapy (ART). Various viral components and host factors measurable in body fluids can play crucial roles in understanding and predicting the PTC phenotype. We review recent findings linking viral components, the quantitative and qualitative features of antibodies (including autologous HIV-specific antibodies), markers of inflammation and tissue damage, other host proteins (including hormones such as sex hormones), as well as metabolites, extracellular vesicles, and cell-free DNA to HIV control post-ART interruption. Several of these molecules can or have the potential to predict the time and probability of viral rebound after stopping ART and are biologically active molecules that can directly or indirectly (by modulating immune pressures) impact the size and activity of HIV reservoirs during and post-ART interruption. A comprehensive model combining multiple markers is needed to predict the PTC phenotype. This model can be leveraged to predict and understand the PTC phenotype, which can guide novel curative interventions to replicate this phenotype in post-treatment non-controllers.

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Abstract A008: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry

Abstract Background: Cancer disparities exist within the Hispanic/Latino/a/x (H/L) population by nativity status. Non-US born H/L colorectal cancer (CRC) patients and Prostate Cancer (PCa) patients in California have a statistically significant lower risk of cancer-specific survival when compared to Non-Hispanic White patients after controlling for confounders. However, birthplace information is largely incomplete in population-based cancer registries. For example, more than 40% of H/L patients have missing information on nativity in the California Cancer Registry (CCR) database. With the goal of better imputing missing birthplace, we built a Machine Learning (ML) model to predict nativity among CCR H/L cancer patients. Methods: H/L cases with primary invasive CRC (n = 75,613) and PCa (n = 93,042) at least 18 years old, diagnosed between 1988 and 2021 contained in the CCR research file were analyzed (46.36% had missing birthplace). A binary indicator variable assigning cases with known birthplace to US-born vs. Non-US born was constructed as the target for prediction. A stratified split by levels of the outcome was used to generate training (70%, n = 63,323) and testing (30%, n = 27,139) datasets. We used a pruned classification tree to perform feature selection, with the 10 features with the highest importance (measured by the overall reduction in impurity for any split containing each feature) used to predict nativity. Missing observations of the quantitative and qualitative features were mean, or mode imputed, respectively. The classification methods considered were classification trees, Random Forest (RF), and Boosting. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and the rate of correctly classified observations. Results: The top three features with the highest importance were the NHIA (NAACCR Hispanic/Latino Identification Algorithm) label, the Hispanic/Spanish identity, and the year of social security number issuance (SSN); the latter is part of a current SSN-based algorithm used to impute nativity status in this population. Classification models showed similar prediction performance. The highest AUC was reported by the RF (AUC = 0.9822), followed by Boosting (AUC = 0.9785), and the pruned classification tree (AUC = 0.9647). A similar rate of correctly classified observations was reported for the Boosting and RF models (∼94%). The RF model ranked feature importance in a similar order as the pruned classification tree. Ultimately, the RF model was the best performing classifier with a sensitivity and specificity of 0.61 and 0.90, respectively. This classifier substantially outperforms the SSN-based algorithm, which reported a correct classification rate of 81%, a sensitivity = 0.54, and a specificity = 0.41 in this dataset. Conclusions: This proof-of-concept analysis showcases a potential for developing a ML model to overcome missing nativity status in H/L cancer patients to enable more accurate classification by nativity in future research using CCR data. Citation Format: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry. Joel Sanchez Mendez, Lihua Liu, Juan P. Lewinger, Laura Fejerman, Mariana C. Stern [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A008.

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens’ PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 − 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a “Capsular” morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed “Unipolar” morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and “Capsular” enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

Sensory Evaluation and Consumers' Acceptance of a Low Glycemic and Gluten-Free Carob-Based Bakery Product.

Carob pulp flour has antidiabetic and antioxidant activities, is naturally sweet, and is rich in fibers. It is obtained from carob pod pulp from the evergreen tree Ceratonia siliqua L., which is grown in Mediterranean areas and is known for locust bean gum production. Despite its valuable effects on health, such as the modulation of the glycemic index, this ingredient has a tremendous impact on technological and hedonic features, mainly on color, flavor, and texture. In this paper, the qualitative features and consumers' acceptance of a carob-based gluten-free bakery product where rice flour was substituted at 40% with carob pulp flour were studied. A panel group of experts described the bread as dark, quite dense, sweet, aromatic, and with a limited bubble dispersion. On the other hand, the sensory assessment and the willingness to pay of consumers were assessed in two groups (a fully informed one about heathy attributes of the food and a blind one). The results indicated a moderate appreciation of the overall quality of the product (average score between 4 and 5 points on a 9-point Likert scale). The information about the food's healthy properties and the ability to maintain a low glycemic index did not enhance the consumers' perception of the product, while previous knowledge and involvement in the product consumption were perceived to have primary importance regarding the final consumers' choice. Finally, an accelerated shelf-life test was run on the packaged snack to evaluate the general quality and stability. The protective packaging helped in limiting bread decay and maintaining the textural characteristics.

Neural networks in oncourology

In recent decades, neural networks have been widely applied in many fields of science and medicine. Accurate and early diagnosis of malignancies is a key challenge in oncology. Neural networks can analyse a wide range of medical data and identify relationships between qualitative and quantitative features. This allows for more precise and timely diagnoses. Moreover, they can be used to predict tumour progression, evaluate treatment effectiveness, and optimise treatment plans for each patientIn oncourology, the use of neural networks offers new perspectives for the diagnosis, prognosis, and treatment of various cancer conditions related to the urinary tract and male reproductive system. This review article explores how neural networks are being used in this field and present research into the use of neural networks for diagnosing, predicting the course and treating urological oncological diseases. The advantages and limitations of using neural networks in this field are demonstrated, and possible directions for future research are suggested. The application of neural networks in oncourology opens new horizons for the development of a personalised approach to diagnosing and treating oncological diseases. Artificial intelligence has the potential to become a powerful tool for improving the accuracy of patient outcome predictions and reducing undesirable side effects of therapy. Introducing neural networks into oncourological practice creates new opportunities for enhancing the work of healthcare organisations and improving the quality of care provided to patients. This can lead to better treatment outcomes and improved patient satisfaction.

EFFECTS IN DISCRETE AND CONTINUOUS STRENGTHENING OF MACHINES ELEMENTS

The work describes approaches to problem solving of operational and effective improvement of the technical and tactical characteristics of military and civilian machines: tanks, armored vehicles, diesel locomotives, turbo-expander power plants, autonomous power plants of strategic objects, etc. For this purpose, fundamentally new methods of strengthening of contacting machines elements are being developed. These methods combine the advantages of discrete and continuous strengthening methods, which have largely exhausted their capabilities by now. Qualitative features and advantages of proposed discrete-continuous strengthening methods require effective implementation. We are talking about the justification of progressive technical solutions based on the criteria of strength and durability. For this purpose, the methodology of multi-criteria synthesis in the expanded space of design and technological parameters will be improved. In addition, as a basis for substantiating advanced technological solutions, a set of related problems of analysis of the stress-strain state, friction and wear is formed. Therefore, based on the improved system approach, a much higher level of strength and durability of the reinforced structural parts is achieved than with traditional methods. The results of test problems solving are illustrated. In the result, micro-macromodels are generalized to determine the joint influence of structural and technological factors, microstructure, physical and mechanical properties of materials of reinforced layers on the contact interaction and stress-strain state of representative fragments of the system of discrete-continuous reinforced bodies. The results of analysis of combined influence of structural and technological factors, microstructure, physical and mechanical properties of materials reinforced layers have been established on the contact interaction and stress-strain state of test representative fragments of the system of discrete-continuously reinforced bodies. At the same time, a significant influence of reinforced layers properties was established on strength of contacting bodies.

Comparison between pancreatoblastoma (PB) and solid pseudopapillary neoplasm (SPN) in pediatric patients with enhanced CT

Rationale and objectivesTo investigate whether computed tomography features can differentiate pancreatoblastoma (PB) from solid pseudopapillary tumor (SPN) in children. Materials and methodsClinical and imaging data of 18 cases of PB and 61 cases of SPN confirmed by surgery or biopsy were retrospectively analyzed. All enrolled patients underwent 3 phases (non-contrast, arterial, and portal venous phases) of CT scanning. Qualitative CT analysis (location, margin, solid/cystic component proportion, calcification, hemorrhage, peritumoral vascularity, bile duct dilatation, pancreatic duct dilatation, pancreatic atrophy, vascular invasion, peripancreatic invasion, and distant metastases) and quantitative analysis (maximum tumor diameter, interface between tumor and parenchyma [delta], arterial enhancement ratio [AER], and portal enhancement ratio [PER]) were performed. The general CT morphologic features, age and tumor markers were compared also compared between the groups. Univariate analysis and the F test were conducted to identify features of PB. Then logistic Regression classifier was trained using the top five features with the highest F-value. Moreover, we used 5-fold cross-validation techniques for the validation of our model. ResultsPB exhibited a significantly higher frequency of location in the body/tail, larger tumor size, poorly defined margins, calcification, peritumoral vascularity, pancreatic atrophy, and less hemorrhage. In addition, PB had higher AER, PER and lower delta relative to SPN (p < 0.05). PB presented a younger age and higher levels of AFP. Results of the F test indicated that AFP, AER, Age, calcification and pancreatic atrophy were the top five features included in the model that could differentiate pediatric PB from SPN. The combined model of CT and clinical features performed well in differentiating PB from SPN, with an AUC of 0.981 in the training cohort and 0.953 in the validation cohort. ConclusionsAFP, AER, age, calcification and pancreatic atrophy are robust CT and clinical features for differentiating pediatric PB from SPN. A combination of qualitative and quantitative CT features may provide good diagnostic accuracy in differentiating PB from SPN in children.