Accuracy Of Prediction Model Research Articles

GWAS-Assisted and multi-trait genomic prediction for improvement of seed yield and canning quality traits in a black bean breeding panel.

In recent years, black beans (Phaseolus vulgaris L.) have gained popularity in the U.S., with improved seed yield and canning quality being critical traits for new cultivars. Achieving genetic gains in these traits is often challenging due to negative trait associations and the need for specialized equipment and trained sensory panels for evaluation. This study investigates the integration of genomics and phenomics to enhance selection accuracy for these complex traits. We evaluated the prediction accuracy of single and multi-trait genomic prediction (GP) models, incorporating near-infrared spectroscopy (NIRS) data and markers identified through genome-wide association studies (GWAS). The models demonstrated moderate prediction accuracies for yield and canning appearance, and high accuracies for color retention. No significant differences were found between single-trait and multi-trait models within the same breeding cycle. However, across breeding cycles, multi-trait models outperformed single-trait models by up to 45% and 63% for canning appearance and seed yield, respectively. Interestingly, incorporating significant SNP markers identified by GWAS and NIRS data into the models tended to decrease prediction accuracy both within and between breeding cycles. As genotypes from the new breeding cycle were included, the models' prediction accuracy generally increased. Our findings underscore the potential of multi-trait models to enhance the prediction of complex traits such as seed yield and canning quality in dry beans and highlight the importance of continually updating the training dataset for effective GP implementation in dry bean breeding.

Predictive risk model of mild cognitive impairment in patients with malignant haematological diseases after haematopoietic stem cell transplantation

ObjectiveThis study is to develop and validate a robust risk prediction model for mild cognitive impairment (MCI) in patients with malignant haematological diseases after haematopoietic stem cell transplantation (HSCT).MethodsIn this study, we analysed the clinical data of the included patients. Logistic regression analysis was used to identify independent risk factors for cognitive impairment after HSCT in patients with malignant haematological diseases, and a risk prediction model was constructed. Multiple cohorts of patients with haematological malignancies after HSCT (282 cases) from the Affiliated Hospital of Xuzhou Medical University and the First People’s Hospital of Yancheng City between April 2019 and February 2022, and patients from the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University between March 2022 and July 2023 were used for external validation. Logistic regression analysis was performed to develop the predictive model. The predictive value and consistency of the model were evaluated using the area under the curve (AUC) and calibration method, respectively. Decision curve analysis (DCA) was performed to access the utility of the model.ResultsApproximately half (52.26%) of the patients in the study developed mild cognitive impairment (MCI). Older age, allogeneic HSCT, anxiety, graft-versus-host disease, and longer hospital stay were associated with a higher risk of developing MCI. ROC curve analysis confirmed the sound performance of the predictive model and external validation, with AUC of 0.897 and 0.789 respectively. The direction of the calibration curves of the training and validation sets is closer to the diagonal (ideal curve), indicating good model consistency; the DCA curves also show that the model has good predictive ability and stability.ConclusionsWe conclude that it is possible to predict mild cognitive impairment with readily available, mostly pretransplant predictors. The accuracy of the risk prediction models can be improved for use in clinical practice, possibly by adding pretransplant patient-reported functioning and comorbidities.

Unveiling Silent Atherosclerosis in Type 1 Diabetes: The Role of Glycoprotein and Lipoprotein Lipidomics, and Cardiac Autonomic Neuropathy

Introduction: This study aimed to evaluate whether glycoprotein and lipoprotein lipidomics profiles could enhance a clinical predictive model for carotid subclinical atherosclerosis in patients with type 1 diabetes (T1D). Additionally, we assessed the influence of cardiac autonomic neuropathy (CAN) on these predictive models. Methods: We conducted a cross-sectional study including 256 patients with T1D. Serum glycoprotein and lipoprotein lipidomics profiles were determined using 1H-NMR spectroscopy. Subclinical atherosclerosis was defined as carotid intima-media thickness (cIMT) ≥ 1.5 mm. CAN was identified using the Clarke score. Predictive models were built and their performance evaluated using receiver operating characteristic curves and cross-validation. Results: Subclinical atherosclerosis was detected in 32% of participants. Patients with both CAN and atherosclerosis were older, had a longer duration of diabetes, and were more likely to present with bilateral carotid disease. Clinical predictors such as age, duration of diabetes, and smoking status remained the strongest determinants of subclinical atherosclerosis [AUC = 0.88 (95%CI: 0.84–0.93)]. While glycoprotein and lipoprotein lipidomics profiles were associated with atherosclerosis, their inclusion in the clinical model did not significantly improve its diagnostic performance. Stratification by the presence of CAN revealed no impact on the model’s ability to predict subclinical atherosclerosis, underscoring its robustness across different risk subgroups. Conclusions: In a cohort of patients with T1D, subclinical atherosclerosis was strongly associated with traditional clinical risk factors. Advanced glycoprotein and lipoprotein lipidomics profiling, although associated with atherosclerosis, did not enhance the diagnostic accuracy of predictive models beyond clinical variables. The predictive model remained effective even in the presence of CAN, highlighting its reliability as a screening tool for identifying patients at risk of subclinical atherosclerosis.

Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model

Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze the various characteristic factors of traffic accidents and capture from them the inherent complex relationship between accident characteristics and the severity of traffic accidents. However, most accident prediction studies lack analytical predictions of injury severity, and predictive models rely on the content and quality of accident datasets. To increase the robustness and accuracy of prediction models, this paper leverages a Transformer-based architecture for the severity prediction of traffic collisions from human injury severity. This framework learns both text and sequence data from accident datasets. After comparative analysis, the framework can achieve the prediction of human injury severity under different data categories and show good prediction performance at low injury severity levels using only textual data or sequence data.

Development of a nomogram for overall survival in patients with esophageal carcinoma: A prospective cohort study in China.

Esophageal carcinoma (EC) presents a significant public health issue in China, with its prognosis impacted by myriad factors. The creation of a reliable prognostic model for the overall survival (OS) of EC patients promises to greatly advance the customization of treatment approaches. To create a more systematic and practical model that incorporates clinically significant indicators to support decision-making in clinical settings. This study utilized data from a prospective longitudinal cohort of 3127 EC patients treated at Chongqing University Cancer Hospital between January 1, 2018, and December 12, 2020. Utilizing the least absolute shrinkage and selection operator regression alongside multivariate Cox regression analyses helped pinpoint pertinent variables for constructing the model. Its efficacy was assessed by concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Nine variables were determined to be significant predictors of OS in EC patients: Body mass index (BMI), Karnofsky performance status, TNM stage, surgery, radiotherapy, chemotherapy, immunotherapy, platelet-to-lymphocyte ratio, and albumin-to-globulin ratio (ALB/GLB). The model demonstrated a C-index of 0.715 (95%CI: 0.701-0.729) in the training cohort and 0.711 (95%CI: 0.689-0.732) in the validation cohort. In the training cohort, AUCs for 1-year, 3-year, and 5-year OS predictions were 0.773, 0.787, and 0.750, respectively; in the validation cohort, they were 0.772, 0.768, and 0.723, respectively, illustrating the model's precision. Calibration curves and DCA verified the model's predictive accuracy and net benefit. A novel prognostic model for determining the OS of EC patients was successfully developed and validated to help clinicians in devising individualized treatment schemes for EC patients.

Inference of Behavioral Decision-Making Using AI and Statistical Data Analysis in the Big Data Environment

Big data are of essence to enhance the accuracy of making decisions and in improving the ability to forecast. However, working with large and complicated datasets, the traditional methods of analyzing data tend to be grossly inadequate. It is at this point, amongst others, that artificial intelligence has now presented a feasible solution to such limitations, especially with the model called machine learning. Based on that, this research will look into the aspect of integration between artificial intelligence and statistical methods of analysis in inferring behavioral decisions from big data. This work considers a number of datasets related to marketing, health care, and finance, comparing the efficiency of the application of a range of artificial intelligence (AI) models, especially random forests, Long Short Term Memory (LSTM), and convolutional neural network (CNN) algorithms, against the classical statistical ones. Standard performance evaluation indicators apply: accuracy, precision, recall rate, F1 score are applied in the model. Results have shown that, though model interpretability and overfitting are challenges, the predictive accuracy of artificial intelligence models is somewhat better in comparison with conventional statistical methods. The present study underlines the transformative potential of AI in changing decision-making across many industries but also highlights key areas for further improvement on behalf of real-time processing abilities and ethical deployment consideration.

Enhancing Consumer Agent Modeling Through Openness-Based Consumer Traits and Inverse Clustering

This study investigates the relationship between consumer personality traits, specifically openness, and responses to product designs. Consumers are categorized based on their levels of openness, and their affective responses to nine vase designs, varying in curvature and line quantity, are evaluated. The study then introduces the inverse clustering approach, which prioritizes maximizing predictive model accuracy over within-cluster similarity. This method iteratively refines cluster assignments to optimize prediction performance, minimizing errors in forecasting consumer design preferences. The results demonstrate that the inverse clustering approach yields more effective clusters than personality-based clustering. Moreover, while there is some overlap between personality-based and accuracy-based clustering, the inverse clustering method captures additional individual characteristics, extending beyond personality traits and improving the understanding of consumer product design response. The practical implications of this study are significant for product designers, as it enables the development of more personalized designs and optimization of product features to enhance specific consumer perceptions, such as robustness or esthetic appeal.

Exploring Metaheuristic Optimization Algorithms in the Context of Textual Cyberharassment: A Systematic Review

ABSTRACTThe digital landscape and rapid advancement of Information and Communication Technology have significantly increased social interactions, but it has also led to a rise in harmful behaviours such as offensive language, cyberbullying, and HS. Addressing online harassment is critical due to its severe consequences. This study offers a comprehensive evaluation of existing studies that employed metaheuristic optimization algorithms for detecting textual harassment content across social media platforms, highlighting their strengths and limitations. Using the PRISMA methodology, we reviewed and analysed 271 research papers, ultimately narrowing down the selection to 36 papers based on specific inclusion and exclusion criteria. By analysing key factors such as optimization techniques, feature engineering strategies, and dataset characteristics, we identify crucial trends and challenges in the field. Finally, we offer practical recommendations to improve the accuracy of predictive models, including adopting hybrid approaches, enhancing multilingual capabilities, and expanding models to operate effectively across various social media platforms.

Multiple Environmental Variables as Covariates to Improve the Accuracy of Spatial Prediction Models for SOM on Karst Aera

ABSTRACTAims accurately predicting the spatial distribution of soil organic matter (SOM) is essential for environmental management and carbon storage estimation. However, the diversity of sources of variables poses a challenge in studying the spatial distribution of SOM. Methods in order to address this issue, we propose leveraging multiple environmental variables and employing machine learning models, specifically Lightweight gradient boosting machine learning (LightGBM) and random forest (RF), for predicting SOM spatial distribution. 128 soil samples were collected from the Caohai National Nature Reserve, and their SOM content was measured. Results the study found that the average SOM content was 36.75 g/kg. Compared to traditional linear regression models such as ordinary kriging (OK), ordinary least squares (OLS), and geographically weighted regression (GWR), the machine learning models based on nonlinear regression, LightGBM and RF, demonstrated higher cross‐validated coefficients of determination (R2) of 0.62 and 0.60, respectively, outperforming the other models. Additionally, RF exhibited lower mean absolute error (MAE) and root mean square error (RMSE), indicating higher stability and generalization capability. The spatial distribution of SOM among the models showed consistency, with higher SOM content observed in southern and near‐Caohai Lake regions and lower SOM content in northern and farther regions from Caohai Lake. Results from the Shapley additive explanations (SHAP) model highlighted agricultural land (AL), pH, and Elevation (ELV) as primary covariates influencing SOM spatial distribution. Conclusions this study provides valuable insights and support for environmental management and carbon storage estimation in the karst plateau region.

The Clinical Value of Novel Inflammatory Biomarkers for Predicting Mycoplasma pneumoniae Infection in Children.

Mycoplasma pneumoniae (MP) is a major cause of community-acquired pneumonia (CAP), posing diagnostic challenges. This study evaluates novel inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII) and system inflammation response index (SIRI) for MP diagnosis in children. Complete blood count (CBC) results of 424 children with MP infection and 150 health children were collected. NLR, MLR, PLR, SII and SIRI, were respectively calculated. Shapiro-Wilk test, Student's t-test, Mann-Whitney U-test and Pearson chi-squared test were used to analyze the clinical data of the patients and participants. Multiple logistic regression analysis was conducted based on the results of single factor analysis. Receiver operating characteristic (ROC) curve was drawn to evaluate the potential of the above biomarkers for MP infection. Compared with the control group, white blood cell (WBC) count, neutrophil (NEU) count, monocyte (MON) count, NLR, MLR, PLR, SII and SIRI were significantly higher and lymphocyte count (LYM) and platelet (PLT) were significantly lower than those in MP group. The results of multivariate logistic regression analysis indicate that MLR and SIRI can serve as major risk factors for MP infection in children. The predictive accuracy of logistic regression model based on MLR and SIRI is 83.28%. The area under the curve (AUC) results showed that SIRI has better predicting value of MP infection (AUC = 0.892, Sensitivity = 75.7%, Specificity = 92.0%). This study described the significance of novel inflammatory biomarkers in children with MP infection and may provide new auxiliary diagnostic indicators for MP infection.

Beyond traditional prognostics: integrating RAG-enhanced AtlasGPT and ChatGPT 4.0 into aneurysmal subarachnoid hemorrhage outcome prediction

To assess the predictive accuracy of advanced AI language models and established clinical scales in prognosticating outcomes for patients with aneurysmal subarachnoid hemorrhage (aSAH). This retrospective cohort study included 82 patients suffering from aSAH. We evaluated the predictive efficacy of AtlasGPT and ChatGPT 4.0 by examining the area under the curve (AUC), sensitivity, specificity, and Youden’s Index, in comparison to established clinical grading scales such as the World Federation of Neurological Surgeons (WFNS) scale, Simplified Endovascular Brain Edema Score (SEBES), and Fisher scale. This assessment focused on four endpoints: in-hospital mortality, need for decompressive hemicraniectomy, and functional outcomes at discharge and after 6-month follow-up. In-hospital mortality occurred in 22% of the cohort, and 34.1% required decompressive hemicraniectomy during treatment. At hospital discharge, 28% of patients exhibited a favorable outcome (mRS ≤ 2), which improved to 46.9% at the 6-month follow-up. Prognostication utilizing the WFNS grading scale for 30-day in-hospital survival revealed an AUC of 0.72 with 59.4% sensitivity and 83.3% specificity. AtlasGPT provided the highest diagnostic accuracy (AUC 0.80, 95% CI: 0.70–0.91) for predicting the need for decompressive hemicraniectomy, with 82.1% sensitivity and 77.8% specificity. Similarly, for discharge outcomes, the WFNS score and AtlasGPT demonstrated high prognostic values with AUCs of 0.74 and 0.75, respectively. Long-term functional outcome predictions were best indicated by the WFNS scale, with an AUC of 0.76. The study demonstrates the potential of integrating AI models such as AtlasGPT with clinical scales to enhance outcome prediction in aSAH patients. While established scales like WFNS remain reliable, AI language models show promise, particularly in predicting the necessity for surgical intervention and short-term functional outcomes. The study explored the use of advanced AI language models, AtlasGPT and ChatGPT 4.0, to predict outcomes for patients with aneurysmal subarachnoid hemorrhage (aSAH). It found that AtlasGPT provided the highest diagnostic accuracy for predicting the need for decompressive hemicraniectomy, outperforming traditional clinical scales, while both AI models showed promise in enhancing outcome predictions when integrated with established clinical assessment tools.

Establishment of a Prognostic Necroptosis-Related lncRNA Signature in Ovarian Cancer.

Ovarian Cancer (OC) was known for its high mortality rate among gynecological malignancies, often resulting in a poor prognosis. This study sought to identify prognostic necroptosis-related long non-coding RNAs (lncRNAs) (NRlncRNAs) with prognostic potential and to construct a reliable risk prediction model for OC patients. The transcriptome and clinic data were sourced from TCGA and GTEx databases. Initially, NRlncRNAs were discovered by assessing gene correlations and evaluating differences in gene expression. Subsequently, Cox regression and LASSO methods were employed to develop the NRlncRNAs risk model, which was further validated through survival analysis, ROC curves, Cox regression, and nomograms across both the test and entire datasets. Multivariate Cox analysis revealed that the risk score based on 14 NRlncRNAs can independently predict the prognosis of OC. The low-risk group demonstrated significantly higher immune cell infiltration scores and lower tumor immune dysfunction, exclusion, and TIDE scores, as well as an increased number of neoantigens and higher TMB. Notably, the low-risk group also exhibited an elevated HRD score. The model's predictive accuracy was further substantiated through ROC analysis, showing superior performance compared to many existing models.Finally, the expression levels of 14 NRlncRNAs were confirmed using the qRT-PCR in two OC cell lines. These findings suggested that the NRlncRNAs risk model could serve as a more precise indicator for forecasting immune response and outcomes of targeted treatments in OC.

Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study.

Determining the values of various properties for new bio-inks for 3D printing is a very important task in the design of new materials. For this purpose, a large number of experimental works have been consulted, and a database with more than 1200 bioprinting tests has been created. These tests cover different combinations of conditions in terms of print pressure, temperature, and needle values, for example. These data are difficult to deal with in terms of determining combinations of conditions to optimize the tests and analyze new options. The best model demonstrated a specificity (Sp) of 88.4% and a sensitivity (Sn) of 86.2% in the training series while achieving an Sp of 85.9% and an Sn of 80.3% in the external validation series. This model utilizes operators based on perturbation theory to analyze the complexity of the data. For comparative purposes, neural networks have been used, and very similar results have been obtained. The developed tool could easily be applied to predict the properties of bioprinting assays in silico. These findings could significantly improve the efficiency and accuracy of predictive models in bioprinting without resorting to trial-and-error tests, thereby saving time and funds. Ultimately, this tool may help pave the way for advances in personalized medicine and tissue engineering.

Prioritizing Context-Dependent Cancer Gene Signatures in Networks.

There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer.

Adversarial Machine Learning Challenges in Modern Network Security Systems

Hostile machine learning has network security issues that reduce prediction model accuracy. A full defence against these assaults entails establishing hostile scenarios, strengthening models via strategy training, and applying powerful defences. Small adjustments introduce antagonistic inputs into the research. These teach the model to recognize and withstand deception attempts. The proposed solution competed with Trust Shield, Secure Guard, Defend, and Adversary Block in rigorous performance testing. The recommended strategy has a 95.0% success rate for discovering assaults and a much lower 5.0% false positive rate. This is much superior to conventional approaches. Due to its modest accuracy loss and rapid response, it's effective at fighting assaults. This comprehensive overview demonstrates the wide-scale application of the strategy with minimal resources. Finally, this research emphasizes the need for robust and adaptable AI security. This will assist in creating secure and trustworthy AI solutions to protect sensitive data and ensure prediction model accuracy in an increasingly hostile future.

Risk factors and predictive models in the progression from MCI to Alzheimer's disease.

The conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is related to various factors. The causal relationships among these factors remain unclear. This study aims to investigate pathways of the progression by using causal analysis and build a predictive model with high accuracy. 162 MCI patients were recruited from the Alzheimer's Disease Neuroimaging Initiative database. 68 patients progressed to AD. 94 patients did not convert to AD. We captured standard T1-weighted images, processed them for feature extraction, and selected relevant features using mRMR and LASSO to calculate cortical and nuclear scores. The computational causal structure discovery and regression analyses were adopted to analyze the intricate relationships among APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores. The individualized prediction nomogram was constructed. Our results indicated that APOE ε4 alleles was the promoter that caused MCI to transform into AD. Three independent pathways were identified, including P-tau, Aβ1-42, and cortical atrophy. P-tau was the cause of nuclear atrophy. The APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores all had good predictive value for the MCI conversion. The predictive accuracy of the combined model was the highest, with an AUC of 0.918 in the training cohort and 0.908 in the testing cohort. A multi-predictor nomogram was established. Our study elucidated the initiating factors and three independent pathways involved in the conversion of MCI to AD. The predictive value of each factor was clarified and a multi-predictor nomogram was established with high accuracy.

Association of Albumin-To-Creatinine Ratio With Diabetic Retinopathy Among US Adults (NHANES 2009-2016).

This study investigates the relationship between the albumin-to-creatinine ratio and diabetic retinopathy (DR) in US adults using NHANES data from 2009 to 2016. This study assesses the predictive efficacy of the urinary serum albumin-to-creatinine ratio (UACR/SACR Ratio) against traditional biomarkers such as the serum albumin-to-creatinine ratio (SACR) and urinary albumin-to-creatinine ratio (UACR) for evaluating DR risk. Additionally, the study explores the potential of these biomarkers, both individually and in combination with HbA1c, for early detection and risk stratification of DR. This cross-sectional study analysed data from 2594 diabetic adults in the National Health and Nutrition Examination Survey (NHANES 2009-2016). Multivariate logistic regression models, adjusted for demographic (sex, age, race and education) and clinical factors (WBC, PLT, RDW, HbA1c, HBP and CHD), examined the associations between biomarkers and DR. Biomarkers were analysed both continuously and in quartiles to assess dose-response relationships. Receiver operating characteristic (ROC) curve analysis evaluated the predictive accuracy of individual biomarkers and combined models. Elevated SACR levels were inversely related to DR risk, while UACR showed a positive correlation. The UACR/SACR ratio demonstrated superior predictive capability for DR compared to SACR and UACR alone. The most accurate predictive model combined SACR, UACR, UACR/SACR ratio and HbA1c (AUC = 0.614), highlighting DR development complexity. Subgroup analyses revealed stronger associations in participants aged < 60 years and those with hypertension (both p < 0.05), with more pronounced effects observed in males and Mexican Americans, while lifestyle factors showed no significant modifying effect. The UACR/SACR Ratio emerged as a potentially superior DR predictor, particularly in younger patients and those with hypertension, suggesting its utility in enhancing early detection and risk stratification. Comprehensive evaluation of renal function and glycaemic control biomarkers, considering age- and comorbidity-specific patterns, could improve DR risk prediction and management. Future longitudinal studies should validate these findings, particularly in identified high-risk subgroups, and investigate underlying mechanisms, potentially advancing personalised DR prediction and management strategies.

Performance evaluation and comparative analysis of different machine learning algorithms in predicting postnatal care utilization: Evidence from the ethiopian demographic and health survey 2016.

Postnatal care refers to the support provided to mothers and their newborns immediately after childbirth and during the first six weeks of life, a period when most maternal and neonatal deaths occur. In the 30 countries studied, nearly 40 percent of women did not receive a postpartum care check-up. This research aims to evaluate and compare the effectiveness of machine learning algorithms in predicting postnatal care utilization in Ethiopia and to identify the key factors involved. The study employs machine learning techniques to analyse secondary data from the 2016 Ethiopian Demographic and Health Survey. It aims to predict postnatal care utilization and identify key predictors via Python software, applying fifteen machine-learning algorithms to a sample of 7,193 women. Feature importance techniques were used to select the top predictors. The models' effectiveness was evaluated using sensitivity, specificity, F1 score, precision, accuracy, and area under the curve. Among the four experiments, tenfold cross-validation with balancing using Synthetic Minority Over-sampling Technique was outperformed. From fifteen models, the MLP Classifier (f1 score = 0.9548, AUC = 0.99), Random Forest Classifier (f1 score = 0.9543, AUC = 0.98), and Bagging Classifier (f1 score = 0.9498, AUC = 0.98) performed excellently, with a strong ability to differentiate between classes. The Region, residence, maternal education, religion, wealth index, health insurance status, and place of delivery are identified as contributing factors that predict postnatal care utilization. This study assessed machine learning models for forecasting postnatal care usage. Ten-fold cross-validation with Synthetic Minority Oversampling Technique produced the best results, emphasizing the significance of addressing class imbalance in healthcare datasets. This approach enhances the accuracy and dependability of predictive models. Key findings reveal regional and socioeconomic factors influencing PNC utilization, which can guide targeted initiatives to improve postnatal care utilization and ultimately enhance maternal and child health.

Prognostic serum biomarkers of synaptic, neuronal and glial injury in patients with acute ischemic stroke of the anterior circulation.

We aimed to investigate the prognostic role of β-synuclein in comparison to that of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) for predicting functional outcome after acute ischemic stroke (AIS). We measured serum concentrations of β-synuclein, NfL and GFAP 24 h after hospital admission in 213 consecutive patients with moderate-to-severe AIS. We investigated the association between serum biomarkers and radiological/clinical characteristics, 3-months mortality and functional outcome on the modified Rankin Scale (mRS). In 213 patients with AIS [mean age: 76.1 (±12.5) years, 53.1% males, median NIHSS score on admission: 13 (IQR: 9-17)], higher levels of β-synuclein, NfL and GFAP were associated with higher NIHSS scores and with lower Alberta Stroke Program CT Score (ASPECTS) points on admission. Serum β-synuclein levels was significantly correlated with NfL (rho = 0.715, p < 0.001) and GFAP concentrations (rho = 0.684, p < 0.001). The inclusion of serum β-synuclein significantly improved the accuracy of prediction models without biomarkers for overall mortality (AUC: 0.836 vs. 0.752, p < 0.001) and mRS 3-6 vs. 0-2 (AUC: 0.812 vs. 0.624, p < 0.001). Combination models with NfL and/or GFAP showed a similar accuracy. Serum β-synuclein may be used to assess synaptic damage/dysfunction and to predict 3-months clinical outcomes in patients with AIS.

EMSPLA for accurate feature molecular extraction from protein-ligand interactions

Protein-ligand interactions are fundamental in various biological and medical fields, influencing drug discovery and therapeutic development. In recent years, deep learning (DL) has revolutionized the study of these interactions, but significant challenges remain in accurately representing molecular structures for DL models. Traditional featurization techniques often depend on handcrafted features, requiring expert knowledge and potentially missing crucial molecular aspects. This work addresses these challenges by developing and evaluating a novel protein-ligand feature extraction system using an enhanced molecular similarity protein-ligand aligner (EMSPLA). The primary objective is to leverage EMSPLA for similarity matching in protein-ligand interactions, improving predictive model accuracy. The methodology combines convolutional neural networks (CNN) for local feature extraction with an attention module to capture long-distance dependencies, enhancing binding site predictions. Using the PDBbind v.2020 dataset, the EMSPLA model demonstrated superior performance with a root mean square error (RMSE) of 0.67, surpassing current state-of-the-art models. These findings highlight the system’s potential for efficient deployment and scalability, positioning it as a powerful tool in computational biology and drug discovery, ultimately advancing our understanding of protein-ligand interactions.