High Predictive Ability Research Articles

Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India

The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables.

Early vs. delayed QTc prolongation in acute poisoning: A prognostic accuracy study-A case series.

Given the limited capacity of intensive care units in many countries, it is crucial to identify reliable prognostic markers to optimize poisoning patients management and improve outcomes. This study aimed to assess the predictive value of three variables, namely the initial QTc interval (iQTc) measured within two hours of admission, the delayed QTc interval (dQTc) measured between 6 and 12 hours of entry, and the QTc interval trend over time (ΔQTc), for mortality in patients with undifferentiated poisoning. A retrospective case series was conducted on 70 patients with undifferentiated poisoning admitted to the intensive care unit (ICU) of Afzalipour Hospital between March 21, 2021, and March 20, 2023. The results of the multivariate analysis revealed that dQTc, base deficit, and creatinine were independently associated with mortality (P value < 0.001). The dQTc had the highest predictive ability, with an area under the curve (AUC) of 0.84, followed by ΔQTc with an AUC of 0.76, and iQTc with an AUC of 0.67. Additionally, the results of the Generalized Estimating Equation model with repeated measurements revealed a higher odds ratio for dQTc (OR, 6.33; 95% CI, 2.54-15.79) compared to iQTc (OR, 4.92; 95% CI, 1.71-14.17). The study concluded that monitoring the dQTc interval could provide valuable prognostic information in acute poisoning cases.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods

AbstractThe broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and extreme learning machine (ELM) are used to predict the discharge coefficient (Cd) of the triangular broad‐crested weir. The comprehensive analysis shows that the ELM model has high stability, predictive ability, and computational speed. The coefficient of determination (R^2) is 0.99982, the mean absolute percentage error (MAPE) is 0.000261, the Nash‐Sutcliffe coefficient (NSE) is 0.99977, and the root means square error (RMSE) is 4.17E‐05 in the testing phase. The apex angle θ is the most critical parameter affecting the Cd, and the contribution to the Cd is 52.45%. A new computational formula is proposed and compared with the accuracy of empirical formulas, showing that the intelligent method has higher accuracy and efficiency.

Plasma metabolites and inflammatory proteins profiling predict outcome of Fufang Duzhong Jiangu granules treating Kashin-Beck disease.

To investigate predictive biomarkers that could be used to identify patients' response to treatment, plasma metabolomics and proteomics analyses were performed in Kashin-Beck disease (KBD) patients treated with Fufang Duzhong Jiangu Granules (FDJG). Plasma was collected from 12 KBD patients before treatment and 1month after FDJG treatment. LC-MS and olink proteomics were employed for obtaining plasma metabolomics profiling and inflammatory protein profiles. Patients were classified into responders and non-responders based on drug efficacy. Enrichment analyses of differential metabolites and proteins of the responders at baseline and after treatment were conducted to study the mechanism of drug action. Differential metabolites and proteins between the two groups were screened as biomarkers to predict the drug efficacy. The receiver operating characteristic curve was used to evaluate the prediction accuracy of biomarkers. The changes in metabolites and inflammatory proteins in responders after treatment reflected the mechanism of FDJG treatment for KBD, which may act on glycerophospholipid metabolism, d-glutamine and d-glutamate metabolism, nitrogen metabolism and NF-kappa B signaling pathway. Three metabolites were identified as potential predictors: N-undecanoylglycine, β-aminopropionitrile and PC [18:3(6Z,9Z,12Z)/20:4(8Z,11Z,14Z,17Z)]. For inflammatory protein, interleukin-8 was identified as a predictive biomarker to detect responders. Combined use of these four biomarkers had high predictive ability (area under the curve = 0.972).

Multi-factor fuzzy sets decision system forecasting consumer insolvency risk

The objective of this study is to develop a multi-factor decision system predicting insolvency risk for natural persons with the use of fuzzy sets. Considering that the financial situation of households is affected by various endogenous and exogenous factors, the main assumption of this study is that the system for predicting financial difficulties should not be limited to the use of only a few financial variables concerning consumers, but also include variables describing the environment. The author proposes a system consisting of three different forecasting models that connect the macroeconomic and microeconomic environments. It monitors the economic situation of households by also identifying those environmental variables, which may directly, or indirectly, endanger the consumer, such as unemployment rate (job market situation), inflation and interest rates, exchange rates, or economic situation in the country (GDP growth rate, the dynamics of retail sales, etc.). Moreover, the created cause-and-effect tool is in the form of a flexible application that can be easily adapted to changing economic conditions. Another unique feature of the study is the proposed use of newly developed ratios in household finance, similar to that in financial ratio analysis, which is commonly used in corporate finance. The proposed ratios demonstrated high predictive abilities. The paper also identifies the predictive capabilities of selected macroeconomic variables from the perspective of their impact on the risk of consumer insolvency. The research relies on four samples consisting of a total of 2400 consumers from Taiwan and Poland. The author created three forecasting models separately for the South-East Asian and Central European regions, and two multi-factor systems, each consisting of 1260 decision rules. The findings clearly showed that fuzzy logic is a significantly more effective method compared to traditional models based on classical logic.

Predicting gully formation: An approach for assessing susceptibility and future risk

AbstractGully erosion is a significant natural hazard and a form of soil erosion. This research aims to predict gully formation in the Kalshour basin, Sabzevar, Iran. Employing the Information Gain Ratio (IGR) index, we identified 13 key factors out of 22 for modeling, with elevation emerging as the most influential factor in gully formation. The study evaluated the performance of individual machine learning algorithms and ensemble algorithms, including the Functional Tree (FT) as the main classifier, Bagging (Bagg), AdaBoost (Ada), Rotation Forest (RoF), and Random Subspace (RSS). Using a data set of 400 gully and non‐gully points obtained through field investigations (70% for training and 30% for testing), the RoF model achieved an area under the curev (AUC) value of 0.99, indicating its high predictive ability for gully‐susceptible areas. Other algorithms also performed well (Ada: 0.90, FT: 0.92, RSS: 0.94, Bagg: 0.95). However, the RoF algorithm with the functional tree as the main classifier (RoF_FT) demonstrated the highest ability in gully classification and susceptibility mapping, enhancing the functional tree's performance. In addition to AUC, the RoF_FT model achieved an F1 score of 0.89 and an MCC of 0.78 on the validation set, indicating a high balance between precision and recall, and a strong correlation between predicted and actual classes, respectively. Similarly, other models showed robust performance with high F1 scores and MCC values, but the RoF_FT model consistently outperformed them, underscoring its robustness and reliability. The resulting gully erosion‐susceptibility map can be valuable for decision‐makers and local managers in soil conservation and minimizing damages. Implementing proactive measures based on these findings can contribute to sustainable land management practices in the Kalshour basin.Recommendations Gully erosion threat: Gully erosion poses a significant threat to soil, with far‐reaching environmental consequences. Predictive modeling: This research focuses on predicting gully formation in the Kalshour basin, Sabzevar, Iran, using advanced machine learning algorithms. Key findings for decision‐makers: The study evaluates the performance of various machine learning algorithms and ensemble algorithms, with the Functional Tree serving as the main classifier. This not only enhances our ability to predict gully formation but also provides a valuable tool for decision‐makers and local managers in soil conservation. Impact on sustainable land management: By offering a gully erosion‐susceptibility map, the research empowers decision‐makers to implement proactive measures, minimizing damage and contributing to sustainable land management practices. Interdisciplinary approach: The study's combination of geospatial analysis, machine learning, and soil conservation aligns with the journal's mission to advance understanding in environmental modeling.

Genotypic variation in blueberry flower morphology and nectar reward content affects pollinator attraction in a diverse breeding population

BackgroundPollination is crucial to obtaining optimal blueberry yield and fruit quality. Despite substantial investments in seasonal beekeeping services, blueberry producers consistently report suboptimal pollinator visitation and fruit set in some cultivars. Flower morphology and floral rewards are among the key factors that have shown to contribute to pollinator attraction, however little is known about their relative importance for improving yield in the context of plant breeding. Clarifying the relationships between flower morphology, nectar reward content, pollinator recruitment, and pollination outcomes, as well as their genetic components, can inform breeding priorities for enhancing blueberry production. In the present study, we measured ten flower and nectar traits and indices of successful pollination, including fruit set, seed count, and fruit weight in 38 southern highbush blueberry genotypes. Additionally, we assessed pollinator visitation frequency and foraging behavior over two growing seasons. Several statistical models were tested to optimize the prediction of pollinator visitation and pollination success, including partial least squares, BayesB, ridge-regression, and random forest.ResultsRandom forest models obtained high predictive abilities for pollinator visitation frequency, with values of 0.54, 0.52, and 0.66 for honey bee, bumble bee, and total pollinator visits, respectively. The BayesB model provided the most consistent prediction of fruit set, fruit weight, and seed set, with predictive abilities of 0.07, -0.08, and 0.42, respectively. Variable importance analysis revealed that genotypic differences in nectar volume had the greatest impact on honey bee and bumble bee visitation, although preferences for flower morphological traits varied depending on the foraging task. Flower density was a major driving factor attracting nectar-foraging honey bees and bumble bees, while pollen-foraging bumble bees were most influenced by flower accessibility, specifically corolla length and the length-to-width ratio.ConclusionsHoney bees comprised the majority of pollinator visits, and were primarily influenced by nectar volume and flower density. Corolla length and the length-to-width ratio were also identified as the main predictors of fruit set, fruit weight, seed count, as well as pollen-foraging bumble bee visits, suggesting that these bees and their foraging preferences may play a pivotal role in fruit production. Moderate to high narrow-sense heritability values (ranging from 0.30 to 0.77) were obtained for all floral traits, indicating that selective breeding efforts may enhance cultivar attractiveness to pollinators.

Construction and validation of a predictive model of mortality of tuberculosis-destroyed lung patients requiring mechanical ventilation: A single-center retrospective cohort study.

The mortality rate for intensive care unit tuberculosis-destroyed lung (TDL) patients requiring mechanical ventilation (MV) remains high. We conducted a retrospective analysis of adult TDL patients requiring MV who were admitted to the intensive care unit of a tertiary infectious disease hospital in Chengdu, Sichuan Province, China from January 2019 to March 2023. Univariate and multivariate COX regression analyses were conducted to determine independent patient prognostic risk factors that were used to construct a predictive model of patient mortality. A total of 331 patients were included, the median age was 63.0 (50.0-71.0) years, 262 (79.2%) were males and the mortality rate was 48.64% (161/331). Training and validation data sets were obtained from 245 and 86 patients, respectively. Analysis of the training data set revealed that body mass index <18.5 kg/m2, blood urea nitrogen ≥7.14 mmol/L and septic shock were independent risk factors for increased mortality of TDL patients requiring MV. These variables were then used to construct a risk-based model for predicting patient mortality. Area under curve, sensitivity, and specificity values obtained using the model for the training data set were 0.808, 79.17%, and 68.80%, respectively, and corresponding values obtained using the validation data set were 0.876, 95.12%, and 62.22%, respectively. Concurrent correction curve and decision curve analyses confirmed the high predictive ability of the model, indicating its potential to facilitate early identification and classification-based clinical management of high-risk TDL patients requiring MV.

Towards accurate and efficient diagnoses in nephropathology: An AI-based approach for assessing kidney transplant rejection

The Banff classification is useful for diagnosing renal transplant rejection. However, it has limitations due to subjectivity and varying concordance in physicians' assessments. Artificial intelligence (AI) can help standardize research, increase objectivity and accurately quantify morphological characteristics, improving reproducibility in clinical practice. This study aims to develop an AI-based solutions for diagnosing acute kidney transplant rejection by introducing automated evaluation of prognostic morphological patterns. The proposed approach aims to help accurately distinguish borderline changes from rejection. We trained a deep-learning model utilizing a fine-tuned Mask R-CNN architecture which achieved a mean Average Precision value of 0.74 for the segmentation of renal tissue structures. A strong positive nonlinear correlation was found between the measured infiltration areas and fibrosis, indicating the model's potential for assessing these parameters in kidney biopsies. The ROC analysis showed a high predictive ability for distinguishing between ci and i scores based on infiltration area and fibrosis area measurements. The AI model demonstrated high precision in predicting clinical scores which makes it a promising AI assisting tool for pathologists. The application of AI in nephropathology has a potential for advancements, including automated morphometric evaluation, 3D histological models and faster processing to enhance diagnostic accuracy and efficiency.

Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.

Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.

Fuzzy Logic Prediction of Hypertensive Disorders in Pregnancy Using the Takagi–Sugeno and C-Means Algorithms

Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research.

Low-cost multispectral sensor reveals cold chain breaks, meat type, and storage time in chicken meat samples

A portable and low-cost spectrometric system has been proposed to discriminate cold chain (frozen) interruption during the supply chain, as well as, the sample type among four different tested chicken meat and products (nugget, wing, thigh and drumstick) and the storage time elapsed after the cold chain break. To achieve this, an AS7265x sensor chipset was used and controlled by a custom programmed microcontroller through a Qwiic I2C interface. Communication between the user and the sensor setup was made using a custom programmed interface developed using the Python. Frozen chicken samples were thawed under various temperature-time combinations (4, 8, 16, and 24 h at 4 and 24 °C) and then refrozen at -18 °C. The refrozen samples were stored for up to 60 days. Spectral measurements (410–940 nm) were taken before thawing and at 1, 10, 30 and 60 days of storage after refreezing. The spectra obtained were analysed using Principal Component Analysis and outliers were eliminated using Mahalobobis distance. Several classification models (including Decision Tree, Random Forest, AdaBoost, Support Vector Machine, k-Nearest Neighbors, Gaussian-Naïve Bayes, Multilayer Perceptron, Gaussian Process, Linear and Quadratic Discriminant Analysis and Partial Least Squares Discriminant Analysis) were trained where appropriate. The top models with the highest predictive ability were selected and merged to build Soft Voting Classifiers (SVC) for each feature to be predicted. The SVC models correctly classified the samples with 93%, 92% and 83% accuracy for cold chain interruption, chicken meat/product type and storage time after thaw-refreeze cycle, respectively. In addition, a graphical interface was also developed so that it can be used by end users for food safety concerns. The results showed the potential of the developed low-cost sensor to rapidly detect the potential food safety risks due to cold chain breakage and to trace back the problem by predicting the storage time after refreezing.

Prediction of free chloride concentration in fly ash concrete by machine learning methods

Free chloride concentration distribution is important for assessing the corrosion risk of steel bars in reinforced concrete structures under chloride environment. In this study, a group of 3150 free chloride concentration data sets were obtained. Afterwards, three machine learning methods, namely support vector regression (SVR), multilayer perceptron (MLP) and one-dimensional convolutional neural network (1D-CNN) were adopted to construct models to predict chloride concentration distribution. The results show that 1D-CNN and MLP models are better at predicting the chloride concentration in fly ash concrete, whereas the prediction capability of SVR is relatively poor. Moreover, free chloride concentration prediction based on unmeasured parameters was conducted. The results show that the 1D-CNN and MLP models both have high prediction abilities – that is, predicted results are consistent with experimental measurements, performing generally better than the time-varying model constructed based on Fick's second law. When the free chloride concentrations were higher than 0.1%, the SVR model had a better prediction effect, but had unsatisfactory results and differed significantly from the actual chloride concentration when at a lower concentration. Overall, the 1D-CNN model performs the best in predicting free chloride concentration of concrete at different penetration depths, exposure time and with different fly ash content.

Explorative study on the value of hepcidin in predicting iron non-responsiveness in paediatric inflammatory bowel disease.

In a considerable proportion of anaemic children with inflammatory bowel disease (IBD), haemoglobin (Hb) does not normalise after iron therapy. We evaluated the added value of novel iron markers (hepcidin and soluble transferrin receptor [sTfR]) as compared to traditional iron markers (ferritin and transferrin saturation [TSAT]) to determine the best strategy for the prediction of non-responsiveness to iron suppletion. In this secondary analysis of prospectively collected data, we measured iron markers in anaemic children (Hb Z-score < -2.0) with IBD at baseline and one month after the initiation of iron therapy. Non-responsiveness was defined as an increase in Hb Z-score of less than 1 within a month. Logistic regression analysis was used to construct multi-biomarker prognostic models. Of 40 anaemic paediatric IBD patients, sixteen (40%) were non-responsive to iron therapy after one month. Hb Z-score and hepcidin Z-score had the highest predictive ability (area under the ROC curve [AUROC] 0.80) providing sensitivity of 69% and specificity 92%. In a post-hoc analysis we defined hepcidin cut-off values to predict iron non-responsiveness. A diagnostic strategy that involves baseline Hb Z-score and hepcidin Z-score in anaemic children with IBD reliably identifies those who will not respond to iron therapy. Non-response to oral and intravenous iron suppletion therapy is high in paediatric IBD and should be identified early. Prediction models using baseline hepcidin demonstrated higher sensitivity and specificity to predict iron non-response compared to models using baseline traditional iron indicators (ferritin and transferrin saturation). In a post hoc analysis, we defined cut-off values for hepcidin to facilitate the correct timing of iron treatment in young anaemic patients with chronic inflammatory bowel disease.

A Relational Graph Convolution Network-Based Smart Risk Recognition Model for Financial Transactions

The financial transaction relationships between existing entities are complex and diverse. In this situation, traditional risk control methods mainly ignored such complex and implicit relationship characteristics, remaining difficult to cope with complex and ever-changing financial risks. To address this issue, this paper proposes a novel relational graph convolution network (GCN)-based smart risk recognition model for financial transactions. Firstly, the classic GCN is simplified based on spatiotemporal effect. Then, feature extraction is conducted for financial transaction data, and a transformer encoder-based GCN model is proposed for risk recognition. The proposed model in this work is named as graph transformer graph convolutional network (GT-GCN) for short. In addition, fuzzy evaluation method is added into it. Finally, some experiments are conducted on real-world financial transaction data to make validation for the proposed GT-GCN. The research results indicate that the GT-GCN can not only effectively identify risks in financial transactions, but also has high accuracy and predictive ability. The application of GT-GCN to actual datasets also has good scalability and adaptability, and it can be resiliently extended into many other fields.

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study.

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.

O-247 Validation of an AI (Artificial intelligence) model assessing oocyte quality in a single donor program with frozen-thawed oocytes for blastocyst prediction

Abstract Study question Can an AI model predict blastocyst development in a specific subset population of frozen-thawed oocytes in a single donor program? Summary answer An AI oocyte quality score is predictive of blastocyst formation of frozen-thawed oocytes using post-ICSI images. What is known already The ability to score oocytes based on quality is of paramount importance. A methodology to categorize oocyte quality continues to elude IVF professionals. CHLOE OQ™ is an AI oocyte-quality (OQ) score designed to analyse a single oocyte Timelapse image and provide an OQ score. Responsible use of AI needs the validation of models across different clinical scenarios. The increasing use of frozen-thawed oocytes in IVF has necessitated to objectively score the developmental potential of this oocytes. The objective of this study was to determine if the morphological characteristics of frozen-thawed oocyte images are predictive of blastocyst formation. Study design, size, duration Retrospective cohort study using time-lapse videos of 1318 post-ICSI frozen-thawed donated oocytes between January 2021 and August 2023, with known blastulation outcomes from a single private clinic. Participants/materials, setting, methods Oocytes were evaluated using OQ Score and placed into categories according to their OQ score. Group A: &amp;gt;0.6, Group B: 0.3-0.59, Group C: 0.001-0.29 and Group D: 0. The correlation between OQ score, embryo quality (EQ) score and blastocyst development was measured with binary logistic regression (AUC) and a 2-sample t-test. Chi-square was performed to determine if there were significant differences in blastocyst development rates across these distinct oocyte quality groups. Main results and the role of chance Oocytes that underwent blastulation exhibited a significantly higher OQ Score (7.0 ± 2.3) compared to those that did not blastulate (5.3 ± 3.0, p &amp;lt; 0.001). The predictive capacity for blastocyst formation was observed for OQ score (AUC= 0.67) and EQ Score (AUC= 0.93), with a baseline blastulation of 66% (n = 1318, p &amp;lt; 0.001). Of interest, as early as Day 3 a Blastocyst prediction score was highly predictive of blastulation (AUC= 0.92, p &amp;lt; 0.001). When assessing a subset of transferred oocytes, OQ Score had a higher predictive ability (AUC=0.71, n = 163, p &amp;lt; 0.001).Oocyte quality scores within OQ score groups showed a direct association with blastocyst formation rate: [Group A 74% (656/878), Group B 56% (130/231), Group C 43% (70/164), Group D 13% (3/23), p &amp;lt; 0.05]. The highest-scoring group demonstrated a five-fold increase in blastulation rate compared to the lowest-scoring group (p &amp;lt; 0.001). Furthermore, the highest-scoring group resulted in the highest proportion of good-quality embryos compared to the lowest-scoring group [78% (456/582) vs 0.19% (1/582), p &amp;lt; 0.001]. Limitations, reasons for caution Retrospective assessment of a single clinic. Results reflect the use of frozen-thawed donated oocytes. Therefore, the proportion of data within the four oocyte quality groups is not equally distributed and is biased towards high quality oocytes. This may impact the predictive ability within the smaller subgroups. Wider implications of the findings Developing an objective methodology to categorize oocyte quality post-retrieval is crucial. It is important to validate AI models in different clinical scenarios, for example in oocyte donor programs using frozen-thawed oocytes. AI is a tool that can be used to predict blastocyst development rates within a frozen donor oocyte program. Trial registration number not applicable

Prognostic Utility of Ratio of Systolic Blood Pressure to Left Ventricular End- Diastolic Pressure Post Successful Primary Percutaneous Coronary Intervention in Prediction of Major Adverse Cardiac Events in Patients with ST-segment Elevation Myocardial Infarction

Abstract Background Of all causes of Cardiovascular disease (CVD), ischemic heart disease (IHD) remains the major contributor, accounting for half of all CVD-associated morbidity and mortality. IHD as measured by the Global Burden of Disease project is driven predominantly by acute myocardial infarction (MI) and, to a lesser extent, angina. Aim of the Work To evaluate the prognostic utility of the ratio of SBP/LVEDP at the time of PPCI in prediction of MACE both in-hospital and during 3-months follow up in patients presented with STEMI with a single vessel disease. Patients and Methods This was a prospective study that was conducted on 153 patients presented with STEMI and underwent successful PPCI for a single vessel disease in Ain-Shams University Hospitals and Nasr City Health Insurance Hospital in the period from March 2019 to March 2021. Results Our results demonstrated, that SBP/LVEDP ratio can be used to predict MACE occurrence at a cutoff level of ≤ 3.9, and can be used to predict patients who are MACE-free at cutoff level of ≥ 9.2. The lower ratio of SBP/LVEDP (≤3.9) was detected at 27 patients (17.6%) from the whole study population (153 patients) with higher statistical significance difference of older age group. Different risk factors and positive family history showed no statistical significance difference with lower ratio. In this study, lower ratio showed higher statistical significant difference in anterior STEMI group, lower EF% “Simpson”, higher LVESV, DD grade II, higher WMSI, with the culprit lesion vessel when its LAD, with higher LVEDP, lower invasive SBP, and lower invasive DBP. This study found that lower ratio had a better prognostic ability with a higher AUC (0.880) when compared to the widely used GRACE and TIMI risk scores. Lower ratio ≤3.9 showed higher statistical significant difference with higher GRACE RS and higher TIMI RS when compared with higher ratio. Our ratio had a strong prognostic ability in prediction of MACE with lower ratio with higher statistical significant difference of occurrence of MACE in 26 patients (96.3%, P = 0.000) when compared with patients with the same ratio but without occurrence of MACE in 1 patient (3.7%). Conclusion Our study concluded that SBP/LVEDP is an easily rapidly determined ratio at the time of PPCI that can provide important prognostic information regarding risk stratification of STEMI patients, with a high predictive ability for occurrence of MACE at a cutoff point of ≤ 3.9, during hospital stay and short term follow up.

Thermomechanical bending of functionally graded carbon nanotubes reinforced composite plate by meshless method

AbstractFunctionally graded (FG) carbon nanotubes (CNTs) reinforced composite possessing continuously varied material properties have received extensive concern in the area of aviation, automotive, military, and spaceflight. The nonlinear bending of FG CNT composite is performed by developing a novel meshless model on the base of Smoothed Particle Hydrodynamics (SPH) method. The equivalent temperature relative material properties of composite is established in the light of the extended rule of mixture model. Different gradient dispersions of CNTs are taken into account and modeled. The governing equations are explored using the Mindlin theory and discretized though a symmetric SPH method with high prediction ability. The developed model is verified by analyzed several examples and compared to the solution obtained in literature. Influences of CNT dispersion, content, plate aspect ratio, boundary conditions and lamination angle on the bending response are elaborated. The present study provides an alternative potential method to solve the mechanical performances of FG CNT composites based on the meshless SPH formulation.Highlights Thermo‐mechanics of FG‐CNTRC is firstly solved by SPH method. The model is verified by solving the benchmarks in literature. Nonlinear bending behaviors of FG‐CNTRC are demonstrated. The minimum deflection take place in FG‐X type composite. For laminate plate, the smallest deformation occurs in 0° CNTRC.