Superior Predictive Capability Research Articles

Performance Assessment of Individual and Ensemble Learning Models for Gully Erosion Susceptibility Mapping in a Mountainous and Semi-Arid Region

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts.

Vision-Based Prediction of Flashover Using Transformers and Convolutional Long Short-Term Memory Model

The prediction of fire growth is crucial for effective firefighting and rescue operations. Recent advancements in vision-based techniques using RGB vision and infrared (IR) thermal imaging data, coupled with artificial intelligence and deep learning techniques, have shown promising solutions to be applied in the detection of fire and the prediction of its behavior. This study introduces the use of Convolutional Long Short-term Memory (ConvLSTM) network models for predicting room fire growth by analyzing spatiotemporal IR thermal imaging data acquired from full-scale room fire tests. Our findings revealed that SwinLSTM, an enhanced version of ConvLSTM combined with transformers (a deep learning architecture based on a new mechanism called multi-head attention) for computer vision purposes, can be used for the prediction of room fire flashover occurrence. Notably, transformer-based ConvLSTM deep learning models, such as SwinLSTM, demonstrate superior prediction capability, which suggests a new vision-based smart solution for future fire growth prediction tasks. The main focus of this work is to perform a feasibility study on the use of a pure vision-based deep learning model for analysis of future video data to anticipate behavior of fire growth in room fire incidents.

Development and validation of the prediction model based on autophagy-associated genes in bronchopulmonary dysplasia

Background Bronchopulmonary dysplasia (BPD) is the most common chronic respiratory disease among preterm infants. Owing to the limitations in current diagnostic methods, developing a predictive model for BPD is crucial. Methods Using 243 autophagy-associated genes and dataset GSE32472, differential expression of autophagy-associated genes was identified at postnatal days 5, 14, and 28 between BPD patients and controls. LASSO and multivariate logistic regression analyses were performed to screen for diagnostic prediction genes. Receiver Operating Characteristic, Harrell’s concordance index, and decision curve analysis (DCA) were used to evaluate the diagnostic prediction model in GSE32472 and GSE220135. A BPD mouse model was constructed and qRT-PCR and Western blot were used to verify gene expression in lung tissue. Results Based on p < 0.05, we constructed a diagnostic prediction model for BPD using WIPI1, TOMM70A, BAG3, and PRKCQ. For the training database, the model’s C-index and Area under Curve were both 0.941, and a high applicability value was demonstrated by the DCA curve. These outcomes were also confirmed in the validation cohort GSE220135, demonstrating the superior diagnostic prediction capability of our approach. In addition, significant variations in immune cell infiltration were observed between BPD patients and controls. According to the results of qRT-PCR, BPD model mice had significantly lower expression levels of WIPI1, TOMM70A, BAG3, and PRKCQ than controls. Conclusions We constructed and validated a diagnostic prediction model for BPD based on WIPI1, TOMM70A, BAG3, and PRKCQ. These four genes may influence BPD development by regulating immune responses and immune cells.

VIS/NIR Spectroscopy as a Non-Destructive Method for Evaluation of Quality Parameters of Three Bell Pepper Varieties Based on Soft Computing Methods

Spectroscopic analysis was employed to evaluate the quality of three bell pepper varieties within the 350–1150 nm wavelength range. Quality parameters such as firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins were measured. To enhance data reliability, principal component analysis (PCA) was used to identify and remove outliers. Raw spectral data were initially modeled using partial least squares regression (PLSR). To optimize wavelength selection, support vector machines (SVMs) were combined with genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and imperial competitive algorithm (ICA). The most effective wavelength selection method was subsequently used for further analysis. Three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural networks (ANNs)—were applied to the selected wavelengths. PLSR analysis of raw data yielded a maximum R2 value of 0.98 for red pepper pH, while the lowest R2 (0.58) was observed for total phenols in yellow peppers. SVM-PSO was determined to be the optimal wavelength selection algorithm based on ratio of performance to deviation (RPD), root mean square error (RMSE), and correlation values. An average of 15 effective wavelengths were identified using this combined approach. Model performance was evaluated using root mean square error of cross-validation and coefficient of determination (R2). ANN consistently outperformed MLR and PLSR in predicting firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins for all three varieties. R2 values for the ANN model ranged from 0.94 to 1.00, demonstrating its superior predictive capability. Based on these results, ANN is recommended as the most suitable method for evaluating the quality parameters of bell peppers using spectroscopic data.

Expansion limits of meshed split-thickness skin grafts

Split-thickness skin grafts are widely used to treat chronic wounds. Procedure design requires surgeons to predict how much a patch of the patient's own skin expands when it is meshed with rows of slits and stretched over a larger wound area. Accurate prediction of graft expansion remains a challenge, with current models overestimating the actual expansion, leading to suboptimal outcomes. Inspired by the principles of mechanical metamaterials, we developed a model that distinguishes between the kinematic rearrangement of structural elements and their stretching, providing a more accurate prediction of skin graft expansion. Our model was validated against extensive data from skin graft surgeries, demonstrating vastly superior predictive capability compared to existing methods. This metamaterial-inspired approach enables informed decision-making for potentially improving healing outcomes. Statement of SignificanceAccurately predicting the expansion of meshed skin grafts is crucial for minimizing patient trauma and optimizing healing outcomes in reconstructive surgery. However, current quantitative models, which treat grafts as tessellated trusses of rigid bars, fail to accurately estimate graft expansion. We have uncovered the mechanisms underlying skin graft expansion and developed a straightforward method based on these findings. This method, designed for practical use by surgeons, provides accurate predictions of graft expansion, as validated against extensive data from skin graft surgeries.

EV Charging Prediction in Residential Area Based on SE‐GRU‐MA Model Consider Multi‐Source Data Feature Mining

The number of electric vehicle (EV) in residential areas is growing rapidly, resulting in large‐scale charging of EVs connected to the distribution network. This poses a challenge to the safe and stable operation of the distribution network. In order to cope with this challenge, it is crucial to achieve accurate EV charging load prediction. However, current researches on EV charging load prediction suffer from insufficient data feature mining and lower prediction accuracy. To address this issue, this paper proposes a SE‐GRU‐MA residential EV charging load prediction method that incorporates multi‐source data feature mining. The proposed method employs a multi‐source data feature mining approach based on Pearson correlation analysis, which enhances the training efficiency and prediction accuracy of the prediction model. Additionally, this study develops a prediction model based on SE‐GRU‐MA hybrid network to achieve accurate EV charging load prediction. Simulation cases on actual history data validate that the proposed feature mining method can effectively promote prediction accuracy, and proposed SE‐GRU‐MA prediction model exhibits superior prediction capability in comparison to existing models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Glucose-albumin ratio (GAR) as a novel biomarker for predicting postoperative pneumonia (POP) in older adults with hip fractures

Postoperative pneumonia (POP) is a common complication after hip fracture surgery and is associated with increased mortality and other complications in elderly patients. This study aims to evaluate biomarkers, especially the glucose-albumin ratio (GAR), for predicting POP in elderly hip fracture patients. A total of 1279 elderly patients admitted to our hospital with hip fractures were included. We assessed 29 biomarkers and focused on GAR to determine its prognostic and predictive value for POP. Multivariable logistic regression and propensity score-matched analyses were conducted to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for POP, adjusting for potential confounders. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off of GAR for predicting POP. Among the biomarkers and combinations assessed, GAR demonstrated superior predictive capability for POP in elderly hip fracture patients. ROC analyses showed moderate predictive accuracy of GAR for POP, with an area under the curve of 0.750. Using the optimal cut-off of 0.175, the high GAR group was significantly associated with increased odds of POP (adjusted OR 2.14, 95%, CI 1.50–3.05). These associations remained significant after propensity score matching and subgroup analyses. Dose–response relationships between GAR and POP were observed. In conclusion, GAR may be a promising biomarker to predict POP risk in elderly hip fracture patients. Further studies are warranted to validate its clinical utility. However, this study has certain limitations, including its retrospective design, potential for selection bias due to the exclusion criteria, and the single-center nature of the study, which should be addressed in future prospective, multicenter studies.

Physics-informed dynamic mode decomposition for reconstruction and prediction of dense particulate pipe flows

Dynamic mode decomposition (DMD) effectively captures the growth and frequency characteristics of individual modes, enabling the construction of reduced-order models for flow evolution, thereby facilitating the prediction of fluid dynamic behavior. However, DMD's predictive accuracy is inherently constrained by its inability to inherently incorporate physical principles. Therefore, for dense particulate pipe flows with complex flow mechanisms, we introduce a physics-informed dynamic mode decomposition (PIDMD) approach, which augments the purely data-driven DMD framework by incorporating the conservation of mass as a constraint. This ensures that the extracted dynamic modes adhere to known physical principles. Initially, we apply the DMD to reconstruct and predict the velocity field, comparing the results against benchmark computational fluid dynamics-discrete element method (CFD-DEM) simulations. Findings indicate that while DMD can reconstruct the flow field simulated by CFD-DEM and provide predictions of future flow states, its predictive accuracy gradually deteriorates over time. Next, we utilize both PIDMD and DMD to reconstruct and predict particle volume fraction, evaluating both models based on CFD-DEM outcomes. The results indicate that both PIDMD and DMD can predict particle aggregation toward the center, but PIDMD provides more accurate predictions regarding the size of particle aggregations and their distribution near the tube wall. Furthermore, the average prediction error for particle volume fraction using PIDMD is 6.54%, which is lower than the error of 13.49% obtained by DMD. Both qualitative and quantitative comparisons highlight the superior predictive capability of PIDMD. The methodology developed in this study provides valuable insights for high-precision predictions of particulate flows.

Prediction of cardiac deterioration in acute heart failure patients: Evaluation of the efficacy of single laboratory indicator models versus comprehensive models.

This study aims to compare the efficacy of single-indicator models versus comprehensive models in predicting cardiac deterioration events in patients with acute heart failure (AHF), providing a more precise predictive tool for clinical practice. This retrospective cohort study included 484 patients with AHF treated at our hospital between June 2018 and January 2023. Patients were categorized into a deterioration group and a non-deterioration group based on the occurrence of cardiac deterioration events within 1 year, defined as cardiogenic shock, cardiac arrest, or the need for mechanical circulatory support. We collected clinical data, laboratory markers, and imaging indicators for analysis. Both single-indicator models and comprehensive models (clinical data + indicators) were constructed and evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) to assess their predictive performance. Among the 484 AHF patients, 121 were in the deterioration group and 363 were in the non-deterioration group. Among the single indicators, WBC had the highest AUC of 0.683. The indicator model (WBC, NOMO, Cr, BUN, Troponin, NT-proBNP, D-Dimer, LVEF, and RVFAC) achieved an AUC of 0.886 in the training set and 0.876 in the validation set. The comprehensive model (age, time from onset to admission, heart failure type, WBC, NOMO, Cr, BUN, troponin, NT-proBNP, LA, D-dimer, fibrinogen, and RVFAC) had an AUC of 0.940 in the training set and 0.925 in the validation set. In the training set, the comprehensive model had a significantly higher AUC than the indicator model (P < .05), while no significant difference was observed between the 2 in the validation set (P > .05). Furthermore, decision curve analysis (DCA) and calibration curve analysis indicated that the comprehensive model provided greater clinical benefits and better predictive accuracy in clinical applications. The comprehensive model demonstrates superior predictive capability for cardiac deterioration events in AHF patients, significantly outperforming both single-indicator and indicator models. This suggests that a comprehensive assessment can more accurately identify high-risk patients, offering a more reliable basis for clinical decision-making.

Prognostic impact of the tricuspid annular plane systolic excursion/pulmonary arterial systolic pressure ratio in acute pulmonary embolism

Abstract Background Currently available risk stratification models for acute pulmonary embolism (PE) include hemodynamic status, cardiac biomarkers, right ventricle (RV) dysfunction on imaging, and clinical scores. Focusing on length-tension relationship of the ventricle might have a superior predictive capability over RV dysfunction in terms of mortality and classification of patients with acute PE. In this study, our hypothesis suggests that tricuspid annular plane systolic excursion (TAPSE)/systolic pulmonary artery pressure (sPAP) ratio has superior predictive capability for in-hospital mortality in patients with acute PE compared to TAPSE or sPAP as distinct measures. Methods This single-center study comprised retrospectively evaluated 703 patients referred to our tertiary cardiovascular center with acute PE. We divided patients into quartiles by TAPSE/sPAP ratio. Different models were developed to quantify the predictive relationship between in-hospital death and echocardiographic measurements. A base model was created with variables including risk status and RV/LV ratio &amp;gt;1. Then, to evaluate the predictive contribution of each measurement; TAPSE/sPAP, TAPSE, and sPAP were sequentially added to the base model. After that, the performance of each model was evaluated. Results Predictive and discriminative power was the highest in model containing TAPSE/sPAP. There was still a significant inverse association between TAPSE/sPAP and risk of in-hospital death even after adjusting by risk status and RV/LV ratio &amp;gt;1. ROC curve analysis for TAPSE/sPAP revealed the best cut-off value as 0.34. Conclusion The outcomes of our study reveals that the ratio of TAPSE/sPAP serves as a more potent predictor of mortality than either of the two measurements taken separately. The interpretation and utilization of the TAPSE/sPAP cut-off value in acute PE can assist in identifying patients at risk of deterioration and guide the consideration of more intensive treatment options across all risk groups.

Stratification risk computation of primary graft gysfunction in heart transplant recipients on a smartphone application

Abstract Introduction There are no standardized guidelines for helping graft allocation in heart transplant (HTx) and accepting transplants from marginal donors and donors after circulatory arrest make decision making even more complex and time consuming than ever. The organ allocation process should include a risk analysis for primary graft dysfunction (PGD) that represents the most dreadful complication of HTx. There are 3 score systems validated to predict PGD but none has clear superior predictive capability compared to the others and the complexity of score computation potentially discourage their clinical use. A score integrating all the variables presented in a user-friendly format should improve efficacy and safeness of the organ allocation process. Methods Severe PGD defined as need for temporary mechanical circulatory support (ECMO) in the first 24 hours following HTx was considered the end-point for risk stratification computation. As a derivation cohort we used meta-analytic approach for identifying variables that are independently associated to severe PGD. The overall prevalence of each variable was determined to define the absolute risk increase value in presence of each factor. We then created a scoring system applying multivariate logistic regression model. As a validation cohort we studied the performance of the score in a retospective series of HTx performed at our institution from 2013 to 2022. We used C statistic equivalent to the area under a receiver-operating characteristic curve for dichotomous outcomes. Values above 0.7 were considered acceptable. We eventually developed an application for smartphone for risk computation. Results On 64 papers screened, 5 studies were considered eligible for the derivation cohort definition. Cumulative number of patients considered was 10’175 with an incidence of severe PGD of 7.1% (CI 5.3 - 10.4) and a 30-day mortality of 41%. 13 variables were identified (table 1). The ROC curve predicted the use of ECMO with the specificity and the sensitivity of 0,81 and 0,92 respectively. We have included in the validation cohort 128 out of 150 transplanted patients, excluding pediatric and double organs transplants. The mean donor age was 45 +/- 12,4 years, 72 (54%) were men. The mean recipient age was 57 +/- 9,3; 92 were men; 32 recipients (25%) needed ECMO support within 24 hours post-transplant; 75% of the donors in the ECMO group were &amp;gt; 40 y.o.; 30 days mortality was 3.1% and 6,2% in the non-ECMO and ECMO group respectively. We validated the model on our cohort defying a binary outcome (ECMO yes/no). Conclusion Clinicians fill donor, recipient and surgical parameters in user-friendly smartphone application identifying patients that are most likely to need ECMO support post HTx. The decision-making process during organ allocation evolves from what was once little more than personal experience and intuition to a reliable evidence-based method based on personalized risk prediction.Table 1

An Improved Fick Model for Predicting Carbonation Depth of Concrete

Concrete carbonation can weaken its strength, cause the corrosion of steel reinforcement, and shorten its service life. Predicting the concrete carbonation depth is a critical aspect of assessing concrete durability. Currently, mathematical models for the concrete carbonation depth, exemplified by the Fick model, suffer from a low fitting accuracy and limited applicability due to the complexity and variability of concrete materials and service environments. In light of this, this work proposes an improved Fick model that incorporates a correction term to effectively enhance the curve fitting accuracy. The correction term in the improved model provides a reasonable adjustment for deviations in the development pattern of the concrete carbonation depth from the Fick model under different conditions, thereby broadening the applicability of the new model compared to the Fick model. Several sets of experimental data on the concrete carbonation depth are used to validate the universality and superiority of the new model. The results of the case studies indicate that the average prediction error and standard deviation of the new model are significantly smaller than those of the Fick model. For the first two examples, in most situations, the average prediction error and standard deviation of the new model are less than 50% of those of the Fick model, with the lowest average prediction error being only 4% and the lowest standard deviation being only 2% of the Fick model’s respective values. For the third example, the new model demonstrates superior predictive capability for the later-stage concrete carbonation depth compared to the Fick model and the ANN model. Specifically, for the carbonation depth of the concrete on the 56th day, the relative error between the predicted value of the new model and the measured value is only 2%, which is much smaller than the 27% of the Fick model and the 12% of the ANN model. These results demonstrate the unique advantage of the proposed model in predicting the carbonation depth, especially when only a limited amount of experimental data are available.

Fatigue Fracture Mechanism and Life Prediction of TA1 Titanium Alloy Clinched Joints

ABSTRACTThis study investigated the fatigue fracture mechanisms and life prediction of clinched joints made from titanium alloy TA1. The fatigue tests revealed that TA1 titanium alloy clinched joints exhibited failure characterized by fracture of the lower plate at three distinct fatigue load levels. Additionally, finite element analysis indicated that cold work hardening enhanced the fatigue performance of these joints. Observations of fracture surfaces using scanning electron microscopy identified the crack source and its propagation path, which correlated with the location of maximum principal stress from the finite element simulations. Fretting wear was also observed in this critical region. Furthermore, fatigue life predictions for TA1 titanium alloy clinched joints were made using Paris' law and the local strain approach. Both methods closely matched experimental results across different fatigue life intervals. Overall, the local strain approach exhibited superior predictive capability compared to Paris' law, taking into account various influencing factors.

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we propose a multimodal representation learning model, termed multimodal graph isomorphism network (MMGIN), to address this challenge for compound toxicity multitask learning. Based on fingerprints and molecular graphs of compounds, our MMGIN model incorporates a multimodal representation learning model to acquire a comprehensive compound representation. This model adopts a two-channel structure to independently learn fingerprint representation and molecular graph representation. Subsequently, two feedforward neural networks utilize the learned multimodal compound representation to perform multitask learning, encompassing compound toxicity classification and multiple compound category classification simultaneously. To test the effectiveness of our model, we constructed a novel data set, termed the compound toxicity multitask learning (CTMTL) data set, derived from the TOXRIC data set. We compare our MMGIN model with other representative machine learning and deep learning models on the CTMTL and Tox21 data sets. The experimental results demonstrate significant advancements achieved by our MMGIN model. Furthermore, the ablation study underscores the effectiveness of the introduced fingerprints, molecular graphs, the multimodal representation learning model, and the multitask learning model, showcasing the model's superior predictive capability and robustness.

Advanced Lung Cancer Inflammation Index as Predictor of All-Cause Mortality in ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention

Background: The advanced lung cancer inflammation index (ALI) is an independent prognostic biomarker used to assess inflammation and nutritional status in various cancers, heart failure, and acute coronary syndromes. This study investigates the prognostic significance of ALI in patients experiencing ST-elevation myocardial infarction (STEMI) treated with primary percutaneous coronary intervention (pPCI), comparing its predictive abilities with the established Neutrophil-Lymphocyte Ratio (NLR). Methods: We conducted a retrospective analysis of 1171 patients from the Matrix Registry, encompassing demographic and clinical data for STEMI cases treated with pPCI, and ALI was determined using the formula [serum albumin (g/dL) × body mass index (kg/m2)]/NLR at the time of hospital admission. The primary outcome was all-cause mortality. Results: Of the 1171 patients, 86 died during the follow-up period. Univariate analysis identified age, female gender, smoking, hypertension, diabetes, prior myocardial infarction (PMI), lower left ventricular ejection fraction (LVEF), and reduced ALI as factors associated with mortality. Multivariate analysis confirmed age (HR: 1.1, 95% CI: 1.05–1.11, p &lt; 0.001) and PMI (HR: 2.4, 95% CI: 1.4–4.3, p = 0.001) as prominent independent predictors, alongside ALI (HR: 0.95, 95% CI: 0.92–0.97, p &lt; 0.001) and LVEF (HR: 0.98, 95% CI: 0.97–0.99, p = 0.04). An ALI cut-off of ≤10 indicated a higher mortality risk (HR: 2.3, 95% CI: 1.5–3.7, p &lt; 0.001). The area under the curve for ALI (0.732) surpassed that for NLR (0.685), demonstrating ALI’s superior predictive capability. Conclusions: ALI is an independent prognostic factor for all-cause mortality in STEMI patients undergoing pPCI, showing greater discriminatory power than NLR, particularly in patients with ALI values ≤ 10, who face a 2.3-fold higher mortality risk.

Liver Dysfunction and Systemic Inflammation Drive Organ Failures in Acute Decompensation of Cirrhosis: A Multicentric Study.

Hospitalized patients with acute decompensation (AD) of cirrhosis are at risk of progressing to acute-on-chronic liver failure (ACLF), significantly increasing their mortality. The aim of this study was to identify key predictors and patient trajectories predisposing to ACLF. In this multicenter, prospective study spanning 2 years, clinical, biochemical, and 90-day survival data were collected from 625 patients with AD (European Association for the Study of the Liver criteria) across North, South, and East India. We divided the cohort into a Derivation cohort (DC: 318 patients) and a Validation cohort (VC: 307 patients). Predictive models for pre-ACLF were derived, validated, and compared with established scores such as model for end-stage liver disease (MELD) 3.0 and chronic liver failure Consortium acute decompensation. Of 625 patients (mean age 49 years, 83% male, 77.5% with alcohol-related liver disease), 32.2% progressed to ACLF. Patients progressing to ACLF showed significantly higher bilirubin (10.9 vs 8.1 mg/dL), leukocyte counts (9,400 vs 8,000 per mm 3 ), international normalized ratio (1.9 vs 1.8), and MELD 3.0 (28 vs 25) but lower sodium (131 vs 134 mEq/L) and survival (62% vs 86%) compared with those without progression ( P < 0.05) in the DC. Consistent results were noted with alcohol-associated hepatitis, infection and hepatic encephalopathy as additional risk factors in VC. Liver failure at presentation (odds ratio: 2.4 [in DC], 6.9 [in VC]) and the 7-day trajectories of bilirubin, international normalized ratio, and MELD 3.0 significantly predicted ACLF progression ( P < 0.001). A new pre-ACLF model showed superior predictive capability (area under the curve of 0.71 in DC and 0.82 in VC) compared with MELD 3.0 and chronic liver failure Consortium acute decompensation scores ( P < 0.05). Approximately one-third of AD patients in this Indian cohort rapidly progressed to ACLF, resulting in high mortality. Early identification of patients at risk can guide targeted interventions to prevent ACLF.

Performance Modeling of the Fenton Process Used as a Single Unit for Treating Raw Textile Effluent

This study investigates the direct application of the Fenton Oxidation Process (FOP) to untreated textile wastewater, specifically from a woven fabric production facility. Under optimized conditions (pH 3, 0.7 g/L Fe+2, 2 mM H2O2), the process achieved significant removal efficiencies: 81% Chemical Oxygen Demand (COD), 80% Suspended Solids (SS), and 93% color removal. Artificial Neural Networks (ANN) and NARX-ANN were utilized in Matlab R2020a to model FOP performance, employing Levenberg-Marquardt (trainlm) and Scaled Conjugate Gradient (trainscg) algorithms. With a 9-20-3 network topology, the ANN model demonstrated superior predictive capability, achieving an R2 of 0.9843.

Human bias and CNNs’ superior insights in satellite based poverty mapping

Satellite imagery is a potent tool for estimating human wealth and poverty, especially in regions lacking reliable data. This study compares a range of poverty estimation approaches from satellite images, spanning from expert-based to fully machine learning-based methodologies. Human experts ranked clusters from the Tanzania DHS survey using high-resolution satellite images. Then expert-defined features were utilized in a machine learning algorithm to estimate poverty. An explainability method was applied to assess the importance and interaction of these features in poverty prediction. Additionally, a convolutional neural network (CNN) was employed to estimate poverty from medium-resolution satellite images of the same locations. Our analysis indicates that increased human involvement in poverty estimation diminishes accuracy compared to machine learning involvement, exemplified with the case of Tanzania. Expert defined features exhibited significant overlap and poor interaction when used together in a classifier. Conversely, the CNN-based approach outperformed human experts, demonstrating superior predictive capability with medium-resolution images. These findings highlight the importance of leveraging machine learning explainability methods to identify predictive elements that may be overlooked by human experts. This study advocates for the integration of emerging technologies with traditional methodologies to optimize data collection and analysis of poverty and welfare.

B-117 Rapid Classification of Cleanliness Grades of Vaginal Discharge through Computational Analysis of SERS Spectra via Variational Auto-Encoder Model And Long Short-Term Memory Networks

Abstract Background Classification of cleanliness grades of vaginal discharge plays a pivotal role in clinical laboratories, serving as indicators for the presence of various vaginal infections such as bacterial vaginosis and candidal vulvovaginitis, etc. The conventional method for assessing vaginal discharge cleanliness involves manual microscopy, a technique susceptible to variations based on the examiner's expertise and subjective judgment, hindering the attainment of swift and accurate diagnoses. Methods This study introduces a novel approach by integrating Surface-Enhanced Raman Spectroscopy (SERS) with a deep learning algorithm for the rapid classification of vaginal discharge cleanliness grades. The proposed classification method combines the Variational Auto-Encoder (VAE) and Long Short-Term Memory (LSTM) to process SERS spectra. The quality of the training data is assessed using the signal-to-noise ratio (SNR) as a standard indicator. Results The application of the VAE-LSTM algorithm significantly enhances classification performance, providing swift and accurate results while reducing the required time. Comparative analyses with various classical machine learning algorithms and evaluation metrics reveal the superior predictive capability and time efficiency of the VAE-LSTM algorithm, demonstrating 100% accuracy and a fitting time of 2 seconds. Blind testing of SERS spectra collected from unknown patients further validates the reliability of the proposed method. Conclusions This study presents a promising and practical approach for the rapid classification of vaginal discharge cleanliness grades. The integration of the SERS technique with the VAE-LSTM algorithm not only ensures accuracy and efficiency in clinical diagnosis but also lays the foundation for advancements in non-invasive diagnostic technologies in gynecological healthcare.

Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis

BackgroundProstate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.MethodsThis study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.ResultsAn ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.ConclusionsThis study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.