Predictive Criteria Research Articles

An improved pull-out model for the composites with curved reinforcement

Due to the low bending stiffness and large aspect ratio, nanofillers with curved morphology are ubiquitous in composites, providing a potential avenue to achieve superior mechanical properties and functionalities. However, the theoretical understanding of the morphology-properties relationship in such composites remains constrained due to the difficulties in precisely defining the boundary conditions required for establishing a fully analytical model. In this study, we propose an improved shear-lag model that overcomes these challenges to investigate the influence of reinforcement's curved morphology on the pull-out mechanical behaviors of composites, which enables accurate prediction of stress distributions and interfacial debonding. Our investigation reveals that among the two morphology-related parameters, namely waviness and curvature, the pull-out behaviors and properties of the composites are more sensitive to the former parameter. A higher degree of waviness actually contributes to enhanced load transfer efficiency and larger pull-out loads. Meanwhile, we demonstrate that the load transfer from the curved reinforcement to the matrix, as well as the maximum pull-out load, can be improved by increasing the length and volume fraction of graphene, as well as the frictional stress. Additionally, we identify a critical Young's modulus ratio of approximately 16 between the reinforcement and matrix, serving as a predictive criterion for determining debonding locations. Overall, this study provides deep insights into the pull-out behavior of composites with curved reinforcement and presents theoretical inspirations for designing and optimizing advanced composites to achieve high strength and toughness simultaneously.

Laminar flame properties correlations for H2/C3H8 mixtures at high temperature and pressure conditions

Laminar burning velocity (SL) and flame thickness correlations for H2, C3H8 and their mixtures at autoignition conditions for the study of ignition regime were developed. The thermodynamic conditions covered by the correlations are 750–1100 K for the unburned temperature and 5–60 atm for the pressure, typical of weak/mixed ignition regime occurrence. The data for fitting the correlation were generated using a kinetic model with a detailed reaction mechanism. A new definition of energy mixture fraction as a weighting factor in Le Chatelier's mixing rule for H2/C3H8 was proposed to SL calculations. The expression for the flame thickness calculation was developed considering the correction by the radical diffusion from the reactive zone to the preheating zone. Experimental tests and Schlieren image recording inside the combustion chamber from a Rapid Compression Machine were carried out to evaluate the Sankaran Prediction criteria (Sap) capability to predict the ignition regime. Average absolute relative residual values of 4.36% and 0.91% and the total points with an accuracy of 10% were 92.57% and 99.43% evaluating the test data group in the proposed correlations were obtained for H2 and C3H8, respectively. The new version of energy mixture fraction allows an increase in the amount of data with an accuracy of 10% from 36.4%, 34.3% and 19.2% to 96.4%, 96.6% and 75.6 % for a content of 75%, 80% and 95 % of H2 in volume, respectively. The Sap values are in agreement with observations in the RCM, where deflagrative flame fronts were identified prior to volumetric autoignition, confirming the weak/mixed ignition regime. The proposed formulations are suitable to be used in the Sap calculations and ignition regime prediction as well as to determinate the SL and flame thickness at autoignition conditions.

Sparse Bayesian Learning With Weakly Informative Hyperprior and Extended Predictive Information Criterion.

This article considers the regression problem with sparse Bayesian learning (SBL) when the number of weights P is larger than the data size N, i.e., P》 N. The situation induces overfitting and makes regression tasks, such as prediction and basis selection, challenging. We show a strategy to address this problem. Our strategy consists of two steps. The first is to apply an inverse gamma hyperprior with a shape parameter close to zero over the noise precision of automatic relevance determination (ARD) prior. This hyperprior is associated with the concept of a weakly informative prior in terms of enhancing sparsity. The model sparsity can be controlled by adjusting a scale parameter of inverse gamma hyperprior, leading to the prevention of overfitting. The second is to select an optimal scale parameter. We develop an extended predictive information criterion (EPIC) for optimal selection. We investigate the strategy through relevance vector machine (RVM) with a multiple-kernel scheme dealing with highly nonlinear data, including smooth and less smooth regions. This setting is one form of the regression task with SBL in the P》 N situation. As an empirical evaluation, regression analyses on four artificial datasets and eight real datasets are performed. We see that the overfitting is prevented, while predictive performance may be not drastically superior to comparative methods. Our methods allow us to select a small number of nonzero weights while keeping the model sparse. Thus, the methods are expected to be useful for basis and variable selection.

MINERAL POTENTIAL OF STRATEGICAL AND CRITICAL MINERAL RAW MATERIALS FOR DEVELOPMENT OF HIGH TECHNOLOGICAL INDUSTRY OF THE RUSSIAN FEDERATION

The paper briefly describes the state system of regional geological study of the subsoil, implemented by Rosnedra and VSEGEI, which provides an assessment of the country's mineral resource potential for strategic types of minerals. In 2022, VSEGEI completed the compilation of a set of digital monometal maps of distribution patterns and prediction of strategic and critical types of minerals in the Russian Federation at a scale of 1:2.5 M in GIS format. Reserves of these types of minerals were assessed based on the state accounting system, and an expert reassessment of inferred resources was carried out. As a result, regional predictive criteria concerning the localization of areas promising for the discovery of mineral deposits were developed. Recommendations were given on the organization of additional site appraisal-200/2 and (or) geological and mineragenic mapping-200 on specific nomenclature sheets, promising for exploration for strategic types of minerals

PULL-DOWN INSTABILITY OF THE QUADRATIC NONLINEAR OSCILLATORS

A nonlinear vibration system, over a span of convincing periodic motion, might break out abruptly a catastrophic instability, but the lack of a theoretical tool has obscured the prediction of the outbreak. This paper deploys the amplitude-frequency formulation for nonlinear oscillators to reveal the critically important mechanism of the pseudo-periodic motion, and finds the quadratic nonlinear force contributes to the pull-down phenomenon in each cycle of the periodic motion, when the force reaches a threshold value, the pull-down instability occurs. A criterion for prediction of the pull-down instability is proposed and verified numerically.

GEOCHEMISTRY FEATURES OF ALUMINUM IN OILS AND CLASSIFICATION OF THE DEPOSITS OF THE DNIPRO-DONETSK DEPTH ACCORDING TO ITS CONTENT

Problem Statement and Purpose. The purpose of this publication is to establish, on the basis of geochemical studies of oils, the specifics of the relationship between aluminum concentrations and the content of other impurity metals, the values of the main mining, industrial and technological parameters and indicators of oils and their deposits, and to develop a natural classification of oil deposits on the example of Dnipro-Donetsk depth by content aluminum. Solving the task of the article will contribute to the development of a set of predictive criteria for hydrocarbon accumulations and the scientific substantiation of the geological-economic, technological and ecological assessment of their use, which in turn determines the relevance and practical value of the conducted research. Data & Methods. The factual basis of the work was the results of analyzes of metal content in oils from 36 deposits. At least 30 oil samples from each deposit were tested for metal content using X‑ray fluorescence analysis on an energy-dispersive spectrometer. All values of metals concentrations and their technological parameters were normalized to bring the information to the same scale, regardless of the units of measurement and the scale of the samples. Calculated normalized values of indicators of oil samples from each deposits were processed using the STATISTICA 11.6 program, which performed the calculation of descriptive statistics, correlation, regression, cluster analyzes and graphical visualization of the results of the performed studies. Results. It has been proven that the aluminum content in the oils of the considered deposits is inversely correlated with the following factors: concentrations of Ni, Fe, asphaltenes, the average capacity of the productive horizon, the current temperature of the productive horizons, the values of the current pressure in the productive horizons, the current depth of the productive horizons, the mineralization of the reservoir water from productive horizons, the density of formation water from productive horizons. And direct correlation with paraffin content, oil viscosity values, resin content, boiling point, V / Ni concentration ratio, total metal content, oil density values, Zn, Mn, S, Hg, Cr, V, Co concentrations. A natural classification of Dnipro-Donetsk depth deposits based on aluminum content in oils has been developed. The results of correlation and cluster analyzes were interpreted in genetic terms, which made it possible to establish a list of deposits formed by oil of abiogenic origin.

Raman Optical Activity of Retinal Chromophore in Sensory Rhodopsin II.

Raman optical activity (ROA) spectroscopy was used to study the conformation of the retinal chromophore in sensory rhodopsin II (SRII), which is a blue-green light sensor of microbes. The ROA spectrum consisted of the negative vibrational bands of the chromophore, whose relative intensities are similar to those of the parent Raman spectrum. This spectral feature was explained by the left-handed helical twist of the retinal chromophore on the basis of quantum chemical calculations. On the other hand, we found that the chromophore conformation based on the crystal structures of SRII has a right-handed helical twist, which does not agree with the observation. This specific result suggests that the consistency with chiro-optical properties can be a key criterion for the accurate prediction and/or evaluation of chromophore conformation in retinal-binding proteins.

Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data

Composite development indices show an exponential movement of major economic indicators to identify and predict the overall trend of the national economy. However, the existing method of writing composite development indices is based on simple statistical methods using macroscopic data. Therefore, it presents limitations when grasping regional economic trends late. It is because the time of announcement of composite development indices is concentrated at the end of each month, quarter, and year. This study used the floating population estimated from smartphone data that can be collected in real-time to analyze how floating population patterns affect regional economic situations to compensate for these limitations. The primary purpose was to present a prompt development prediction methodology that reflects this meaningful relationship. A correlation and cross-correlation analysis was performed to exhibit a clear relationship between composite development indices and floating population value. In addition, a time series model and a multiple regression model analyses were applied to predict regional development indices. The results obtained facilitated the prompt selection of regional composite indices after choosing a model that exhibits high prediction accuracy and efficiency of the application. The selected regional development composite indices are expected to be used as a faster and more reliable prediction criterion than the existing development composite indices used to predict a specific city’s economic situation.

Prediction of the survival of patients with advanced-stage ovarian cancer patients undergoing interval cytoreduction with the use of computed tomography reevaluation after neoadjuvant chemotherapy.

To predict ovarian cancer patients' survival by computed tomography (CT) reevaluation after neoadjuvant chemotherapy. In this retrospective single-center cohort study, all patients with advanced epithelial ovarian cancer underwent platinum-based chemotherapy followed by interval cytoreductive surgery. Assessment of abdominal and pelvic lesions before and after chemotherapy using CT scoring criteria. Meanwhile, the progression-free survival and overall survival times were obtained. The Kaplan-Meier method was used to estimate survival curves. Univariate analysis of continuous and categorical variables was performed for prognostic significance using the Cox proportional hazards model. Variables with p < 0.10 on univariate analysis were then included in a multivariate forward stepwise Cox regression analysis. A total of 162 patients were included, with a median age of 52 years (range, 20-72 years). One hundred seven patients (66.0%) underwent suboptimal cytoreduction, and there was no statistically significant difference in patient survival between surgical procedures (log-rank p = 0.092). Six radiographic features were hazard factors for suboptimal cytoreduction. Four features in the postchemotherapy CT images were assigned as predictive criteria by the stepwise regression model (area under the curve [AUC] = 0.689). As compared with a higher AUC (0.713) in the model involving two clinical variables (age and postsurgery CA-125) and two postchemotherapy CT features, the model considering the CT score changes before and after chemotherapy had the highest diagnostic accuracy (AUC = 0.843). CT reevaluation after neoadjuvant chemotherapy is essential for ovarian cancer, the changes of CT feature and score are potential great tools to predict patient survival.

The dynamics of the lipopolysaccharide-binding protein (LBP) level in assessing the risk of adverse outcomes in operated colorectal cancer patients

BackgroundThe main aim of this study is to analyze changes in the lipopolysaccharide-binding protein (LBP) level in blood serum over time and assess it as a potential risk factor for the development of SIRS, infectious and inflammatory complications, organ dysfunction and mortality in patients operated on colorectal cancer (CRC). Methods90 CRC patients were divided into 2 groups: Group 1–50 patients operated on for CRC without acute bowel obstruction (ABO); Group 2–40 patients operated on for CRC with ABO. To determine LBP by ELISA method venous blood was taken 1 h before surgery and 72 h after it (3rd day). ResultsLBP level on the 3rd day after surgery was lower in CRC patients with SIRS, postoperative complications, organ dysfunction and in dead patients. With an LBP value on the 3rd day after surgery being at ≤821.95 ng/mL, the risk of SIRS occurrence is 3.5 times higher, that of the postoperative complications is 5.2 times higher and death is 12.9 times higher than with its higher level (OR 3.5, CI 1.46–8.4; OR 5.2, CI 1.80–15.12; OR 12.9, CI 1.54–108.21, respectively). If the LBP value on the 3rd day after surgery is ≤ 700.15 ng/mL, the risk of organ dysfunctions is 13.5 times higher than with its higher level (OR 13.5, CI 3.536–51.54). ConclusionsThis study demonstrated that in the patients with CRC, the LBP can be used as a predictive criterion for the development of SIRS, postoperative infectious and inflammatory complications, organ dysfunction, and mortality.

Dynamic pull-down theory for the Toda oscillator

The pull-down instability is a newly found phenomenon for oscillators with even nonlinearities. It is a seemingly periodic motion, which will suddenly pull down after some cycles. The phenomenon is similar to the pull-in instability in the micro-electromechanical systems. This paper restudies the well-known Toda oscillator, which is considered to be in periodic motion. Its pseudo-periodic property is studied and the pull-down instability is found for the first time ever. To this end, this paper establishes a variational principle for the Toda oscillator, and the criterion for prediction of the pull-down motion is obtained. A simple formulation to calculate the pull-down instability is proved and verified through examples. Furthermore, an approximate frequency formula is obtained by Li–He modified homotopy perturbation method.

Isogenic lines of rainbow trout (Oncorhynchus mykiss) as a tool to assess how growth and feeding behaviour are correlated to feed efficiency in fish

Accurately measuring individual feed intake is required to include feed efficiency (FE) as an objective in commercial breeding programs. Phenotyping individual feed intake through direct measurements remains complex in fish reared in groups. One way to overcome this challenge is to find proxies for estimating FE. This study aimed to investigate the correlations between fish FE and potential predictive criteria in rainbow trout Oncorhynchus mykiss. As predictive criteria, we considered the variations of body weight assessed as thermal growth coefficients (TGC) and the feeding behaviour assessed as the number of feed demands over a period and the proportion of the demands made before noon. Feed intake was assessed over three different periods in ten isogenic lines allowing a recording for each of the ten genotypes while keeping fish in groups. The protocol consisted of two successive feed deprivation and refeeding phases after initial basal growth periods. Correlations were then calculated between FE, measured either as feed conversion ratio (FCR) or residual feed intake (RFI), and the different indirect criteria. We found positive phenotypic correlations between FCR and RFI over the feed intake measurement periods (r ⩾ 0.79, P < 0.001). Assessment of the relationship between FE traits (FCR and RFI) calculated over the three measurement periods and TGC revealed no significant association. Regarding feeding behaviour, significant differences in the daily profile of feed demands and their numbers were found among the ten isogenic lines. RFI calculated from the first feed intake measurement period and feeding behaviour, assessed as the number of feed demands were positively correlated (r = 0.42–0.49, P ⩽ 0.022). Nevertheless, such correlations were not established for the two other measurement periods following feed deprivation. While we demonstrated that the weight variations during feed deprivation are not a good proxy for FE (FCR or RFI) in rainbow trout, we also highlighted the interest in exploring more the relationships between FE and feeding behaviour in fish.

Discussion on three forms of coupled criterion for notch fatigue limit prediction: Comparison between point-wise and averaged stress (energy) criteria

In the framework of finite fracture mechanics, two coupled criteria were proposed by Leguillon and Carpinteri, respectively. Leguillon’s criterion uses the point-wise stress criterion, whereas Carpinteri’s criterion uses the averaged stress criterion. Both criteria employ the same incremental energy release rate criterion which can be considered as an averaged energy criterion. Mathematically, the averaged energy criterion can be transformed into an equivalent point-wise criterion. In this paper, a new coupled criterion based on the point-wise stress and energy criteria was proposed. The proposed criterion is equivalent to Carpinteri’s criterion when predicting notch fatigue limits. All the three forms of coupled criteria were checked and compared by a large number of experimental results including V-shaped and central hole specimens. When performing the coupled criteria, power functions derived from finite element analysis were used to express the energy release rate and notch stress distributions. The effect of notch size on the crack extension lengths were investigated.

Experimental investigation on the anisotropy of mode-I fracture and tensile failure of layered shale

Mode-I fracture toughness (KIC) and tensile strength (σt) are significant parameters in hydraulic fracturing. Owing to the unique mineral composition and sedimentary environment of shale, the mechanical properties of shale exhibit anisotropy. In this study, the notched semi-circular bend (NSCB) and Brazilian disc (BD) tests were combined with acoustic emission (AE) and digital image correlation (DIC) technology to investigate the anisotropic fracture and tensile behaviors of layered shale. The critical plane approach (CPA) criterion is extended and validated, and the universality of the extended CPA criterion is confirmed for both KIC and σt prediction. In addition, the variation of KIC and σt versus loading angle, the relationship between KIC and σt, the typical failure modes of both NSCB and BD specimens, and the fracture initiation angle of layered shales are discussed comprehensively. The following conclusions were drawn: (1) KIC and σt increased with increasing in the loading angle (β), which indicated significant anisotropy of layered shale, and the AE responses for different loading angles also differed significantly. (2) The variation of anisotropic KIC and σt of different shales could be predicted by adjusting the spatial distribution parameter (Ω0), so that the extended CPA criterion could predict KIC and σt of different shales accurately, which confirmed the universality of the extended CPA criterion for both KIC and σt prediction. (3) There exists an approximately linear or power relationship between KIC and σt, and the power relationship can match both experimental and theoretical results much better. (4) The data from DIC and failure modes confirmed that when β was low, the failure modes were primarily affected by the bedding plane. In contrast, when β was great, the failure modes were primarily affected by the matrix. The NSCB specimens exhibited tensile failure, whereas the BD specimens exhibited more complex failure modes, which were a combination of tensile and shear failure. The results of this study can be used for guiding the design and construction of hydraulic fracturing and rock engineering.

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural ordinary differential equation, which is named the neural ordinary differential gray mode. This neural ordinary differential gray mode permits the forecasting model to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of neural ordinary differential gray mode can provide comprehensive applications as well as learning the properties of distinct data specimens. To acquire a better model which has highly predictive efficiency, afterward, this study trains the model by neural ordinary differential gray mode using the Runge–Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensively spreads the utilize extent of mechanical elastic vehicle wheel.

Prediction and elimination of pollution in ecosystems formed by decommissioned sludge reservoirs

Introduction. The principles of forecasting and eliminating the consequences of pollution formed by decommissioned accumulators of heterophase municipal and industrial waste by means of high-pressure washing are proposed. The main tasks to be solved are the development of sludge accumulator assessment parameters and modelling pollution flows in geo-environments with heterogeneous filtration and rheological characteristics using the proposed prediction criteria.&#x0D; &#x0D; Materials and methods. The prediction is based on the model of mass transfer of pollution in the geo-environment from the standpoint of the laws of the theory of filtration and electrostatics. The regularities of the theory of filtration (Darcy’s Law) form the basis for the transport of “pure” liquids (true solutions) through “ideal” porous media. The regularities of electrostatics form the basis for the mass transfer of viscous pollution flows in fragments of the geo-environment with non-uniform resistance. An industrial experiment was carried out on high-pressure flushing of the geo-environment with flocculant solutions to eliminate pollution formed by a decommissioned hazardous waste storage facility.&#x0D; &#x0D; Results. Filtration and rheological properties of sludge and polluted geo-environment have been studied. Mathematical models of contaminated liquid filtration into the geo-environment from sludge bodies have been developed. The main dependences of the change in the filtration-rheological characteristics of the polluted geo-environment on the values of the solution injection pressure and the trajectory of the predicted radius of pollution are presented.&#x0D; &#x0D; Conclusions. According to the results of industrial research the method of high-pressure reagent washing of rocks of aeration zone from pollution of organic and mineral nature contained in infiltrates based on liquids of various viscosities has been developed. The hydrodynamic connection between absorbing and producing wells was revealed in the conditions of experimental areas of the cleaned geo-environment. The proposed methods for forecasting and eliminating pollution can be applied in projects for the elimination of objects of accumulated environmental damage formed by inactive sludge waste storage tanks.

Evaluation of Diagnostic Accuracy of HACOR Score in Predicting the Success Rate of CPAP in Patients with COVID-19-Induced Hypoxemia

Background: In coronavirus disease 2019 (COVID-19)-associated hypoxemic acute respiratory failure (ARF) may require continuous positive airway pressure (CPAP), and having an appropriate criterion for early diagnosis and prediction of CPAP failure or success can significantly reduce the mortality of these patients. Objectives: This study was conducted to evaluate the diagnostic accuracy of heart rate, acidosis, consciousness, oxygenation, and respiratory rate (HACOR) score in predicting the success rate of CPAP in patients with COVID-19 induced hypoxemia. Methods: This retrospective cross-sectional study was conducted on 200 adult patients aged 63.23 ± 16.23 years with mild to moderate acute respiratory failure (COVID-19), partial pressure of oxygen in arterial blood (PaO2) &lt; 300 mmHg, and partial pressure of carbon dioxide in arterial blood (PaCO2) &lt; 45 mmHg from March 21 to November 21, 2021. The patients were admitted to the respiratory intensive care unit (RICU) of Khatam Al-Anbia (PBUH) Hospital in Shoushtar (southwestern Iran) and were subjected to CPAP for respiratory support. The HACOR score was calculated one hour after the CPAP onset. Data were analyzed by SPSS software (version 22) using Kolmogorov-Smirnov, t-test, pair t-tests, receiver operating characteristic (ROC) curve, sensitivity, specificity, and predictive value indices were used to determine the diagnostic performance of the method. The significance level was set at P ≤ 0.05. Results: The mean HACOR score one hour after the onset of CPAP was 3.50 ± 1.57 (P &lt; 0.001), with a mean of 153.84 ± 27.42 mmHg PaO2/fraction of inspired oxygen (FiO2) ratio (P &lt; 0.001). The cut-off point for the HACOR score one hour after CPAP was 5, which had a sensitivity of 98.06% (94.45% - 99.60%) and a specificity of 83.33% (68.64% - 93.03%). Out of 200 patients, 157 patients (78.5%) had CPAP failure, and 43 patients (21.5%) underwent treatment. The diagnostic accuracy values of the HACOR score and PaO2/FiO2 ratio for predicting CPAP failure were 94.92% and 95.96%, respectively. Conclusions: Our findings support that although the HACOR score had a good diagnostic performance in predicting the success rate of CPAP in patients with COVID-19-induced hypoxemia, PaO2/FiO2 ratio was also shown to be a good predictor of success.

Application and validation of an algorithmic classification of early impairment in cognitive performance

Objective Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging. Method Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non-memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia. Results Among 1,851 participants (mean age = 65.2 ± 11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR = 3.53, 95%CI: 1.59–7.84) over 12.3 follow-up years. Conclusions Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.

A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data

BackgroundIrreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients.MethodsPatients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method.ResultsWe enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA.ConclusionWe constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI.

Novel use of alternate (Alt) response (Rp) criteria (Cr) for early prediction of outcomes in pancreatic (P) neuroendocrine tumors (NETs): Utilizing banked imaging data from the ECOG-ACRIN E2211 study.

4133 Background: The evaluation of treatment (Tx) Rp in NETs using CT/MRI scans can be difficult. Previous studies, including the E2211, have shown improved progression-free survival (PFS) but no significant difference in Rp as measured by RECIST 1.1 (R1.1) Cr. Thus, making it difficult to determine Tx effectiveness with short-term imaging, further complicated by differences in imaging protocols and inter-reader variability. Incorporating tumor density, using smaller threshold changes in tumor size, and novel quantitative features may be more sensitive and precise than R1.1 (e.g. CHOI in GIST) but they have not been studied for NETs. Methods: Banked CT images from the E2211 trial were repurposed to study novel Cr. Three radiologists provided a R1.1 assessment. Density and radiomic features were calculated from a 2D region of interest in the portal venous phase (as per CHOI Cr). Patients were classified as responders (PR), stable disease (SD), or progressive disease (PD) based on R1.1 and CHOI and compared with PFS to determine the best predictor. Radiomic features were analyzed using pyradiomics. Wilcoxon tests were used to compare scan quality by center type, agreement was measured using Cohen’s Kappa, and predictive value was quantified using the c-statistic and AUC for time-varying outcomes. Results: 67 patients had their scans repurposed for the study. Inter-reader agreement for overall imaging studies was fair, with only 5 of 9 PD cases noted by reviewer 1 being agreed upon by reviewer 2 (proportional agreement for PD = 0.56) and a Kappa of 0.6 (95% CI 0.41 – 0.79). The CHOI Cr showed improved prediction of PFS compared to R1.1 (12-month AUC 0.75 for CHOI vs 0.69 for R1.1, c-statistic 0.66 vs 0.64, p=0.22), and was able to predict improved PFS for PR versus SD at the first interval scan. This was not seen with R1.1 (table). Radiomics and texture analysis as a potential adjunct to CHOI is being studied and results will be presented. Conclusions: This study using repurposed banked imaging from an NCTN trial, found that there was significant variability in the interpretation of images, and that the R1.1 Cr had a poor correlation with Rp in PNETs. The CHOI Cr emerged as a better alternative as it may have a better correlation with PFS and the ability to predict outcomes at the point of first disease assessment. Importantly, CHOI Cr can be easily applied in clinical practice to inform treatment decisions. Further research is needed to confirm these findings and define the therapeutic importance. [Table: see text]