Predictive Power Research Articles

The Role of Spiritual Well-Being in Spanish Cancer Patients: Exploring the Relationship Between Health, Religiosity and Spirituality, and the Underlying Psychosocial and Behavioral Pathways

ABSTRACT This study aims to examine the interactions of spiritual well-being with different psychosocial and behavioral aspects that have been proposed in the literature as pathways underlying the relationship of religiosity and spirituality (R/S) with physical health, specifically in cancer patients (N = 351). Using a cross-sectional research design, a series of hierarchical regression analyses show the predictive power of different elements of R/S (attachment to God, religious practice, commitment and coping, and spiritual experiences) on spiritual well-being. In turn, spiritual well-being (meaning/peace and faith) predicts gratitude and compassion (as psychological pathways). Thus, finally, spiritual well-being, together with gratitude and compassion, correlates to the rest of the psychological (positive and negative emotions), social (social support) and behavioral (healthy behaviors) pathways to different extents. Spiritual well-being may play an important role in understanding the relationships between R/S and health, so considering it along with its associated factors may be key to improving the health and quality of life of people affected by cancer in Spain.

Albumin-bilirubin score in non-malignant liver and other diseases

The albumin-bilirubin (ALBI) score is derived from albumin and bilirubin levels. Currently, the ALBI score is widely used in various clinical settings. A recent article in the World Journal of Gastroenterology summarized the application of the ALBI score in various non-malignant liver diseases. The ALBI score has a predictive power that is superior or non-inferior to established numerous measures. This may be related to its contiguity, sensitivity, and inclusion of albumin. While we recognize the good results of the ALBI score in a number of diseases, the ALBI score also has limitations. Variation studies for population characteristics and other factors should be performed to validate the performance of ALBI. Further modifications or optimization of ALBI scores should be taken into account.

Albumin-bilirubin score in non-malignant liver and other diseases

The albumin-bilirubin (ALBI) score is derived from albumin and bilirubin levels. Currently, the ALBI score is widely used in various clinical settings. A recent article in the World Journal of Gastroenterology summarized the application of the ALBI score in various non-malignant liver diseases. The ALBI score has a predictive power that is superior or non-inferior to established numerous measures. This may be related to its contiguity, sensitivity, and inclusion of albumin. While we recognize the good results of the ALBI score in a number of diseases, the ALBI score also has limitations. Variation studies for population characteristics and other factors should be performed to validate the performance of ALBI. Further modifications or optimization of ALBI scores should be taken into account.

Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach

Abstract. This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.

American Foregut Classification is Superior to Hill Classification in Evaluation of the Gastroesophageal Junction

Background: The American Foregut Society (AFS) classification is intended to be more comprehensive and accurate than the Hill grade in assessing the gastroesophageal junction. However, real-world data regarding predictive ability and interobserver variability are limited. Methods: We performed a multi-reader validation study on images collected from patients who underwent endoscopic evaluation of reflux with hernia provocation using both AFS and Hill classification. The primary outcome was inter-reader agreement, assessed using Fleiss K. Secondary outcomes were percentage of patients with pathologic reflux at each Hill grade or AFS grade, correlation between acid exposure time (AET) and individual components of the AFS grade, and difference in predictive ability based on expert versus non-expert graders. Results: Sixty-four eligible patients were identified. Inter-rater reliability was substantial for AFS classification ( K = 0.65) and fair for Hill classification ( K= 0.28). The AFS grade demonstrated moderate overall correlation with AET (ρ = .36) and the Hill grade demonstrated overall weak correlation (ρ = .28). No single component of the AFS score performed significantly better in predicting pathologic reflux than the AFS grade (AFS F = 9.93, P = .0025) and there was no significant difference in overall grading between expert- and non-expert readers (ρ = .38 vs ρ = .29). Conclusion: The AFS score can classify the GEJ with moderate interobserver agreement and is superior to the Hill grade in predicting acid exposure time. AFS grade has better predictive power than the individual components used in scoring and is robust to differences in reader experience.

Patient-specific multifactorial mortality risk assessment using classification and regression tree analysis in the context of ambulatory blood pressure monitoring.

Ambulatory blood pressure monitoring is essential for understanding blood pressure patterns beyond clinical visits, aiding in risk assessment, treatment evaluation, and managing hypertension. This retrospective cohort study aimed to identify risk factors for all-cause mortality and major cardiovascular events in patients who underwent ambulatory blood pressure monitoring. Eligible participants aged 18 or older, with an estimated glomerular filtration rate (eGFR) > 60ml/min/1.73m2, who underwent ambulatory blood pressure monitoring for various reasons, were included in the study. Data were gathered through telephone interviews, electronic health records, and the national health record system. Descriptive analysis and classification and regression tree modeling were used to uncover significant risk factors related to all-cause mortality and cardiovascular events, and to assess the model's performance compared to traditional Cox survival analysis. The study included 1291 patients, primarily male (51.8%) with a mean age of 61.1 ± 15.2years. During a mean follow-up of 46.9months, 76 (5.9%) patients died of any cause, and 195 (15.1%) had a cardiovascular event. The highest survival rates were observed in patients with a diastolic blood pressure (BP) dipping percentage between -2% and 29%, nighttime systolic BP variability below 32mmHg, and age below 72. Conversely, smokers with a diastolic BP dipping percentage below -10% showed the lowest survival rates. The best cardiovascular outcomes were observed in patients with diastolic BP dipping above -11%, nighttime mean systolic BP < 144mmHg, no statin use, normotensive status, and daytime mean heart rate ≥ 60bpm. Conversely, the worst outcomes were seen in patients with diastolic BP dipping below -11% and a morning surge ≥ 14mmHg. In all-cause mortality and cardiovascular event analysis, the combined model demonstrated excellent calibration and predictive power, like the classification and regression tree model and traditional analysis. These findings highlight the potential of a combined model for assessing mortality and cardiovascular event risk in patients who have undergone ambulatory blood pressure monitoring.

Advanced Disease Detection and Personalized Medicine: Integrated Data Approaches for Enhanced Parkinson's Disease and Breast Cancer Detection and Treatment in the USA

This present study focuses on integrating data-driven approaches into personalized medicine for better detection and treatment of Parkinson's disease and breast cancer through the US healthcare system. The deeper integration of genomics, clinical records, and patient self-reported data with machine learning algorithms will enhance early disease detection and optimization of treatment pathways. The results show that, more precisely, Random Forest and XGBoost machine learning models hold great promise for considerably improving diagnostic precision and predictive power. This realization opens a door for precision medicine-tailored health services according to the peculiarities of individual patients, which would improve treatment outcomes and encourage preventive healthcare. In addition, this approach aligns with the latest US efforts in precision medicine and contributes to evidence-based transformation in healthcare practice.

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions

Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye. In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-training on 153,306 scan pairs, we show that such a pre-training framework can provide both a retrieval system and encoders that produce comprehensive OCT and fundus image representations that generalize well for various downstream tasks on three independent external datasets, explicitly focusing on clinically pertinent prediction tasks. In addition, we show that interchanging OCT with lower-cost fundus imaging can preserve the predictive power of the trained models.

Integrating contact tracing data to enhance outbreak phylodynamic inference: a deep learning approach.

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision making and outbreak control.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms.

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Dependence and information flow among U.S technology industries

ABSTRACT The technology sector serves as a fundamental pillar for all industry sectors in the economy, playing a central role in driving worldwide economic growth. It particularly distinguishes itself in the U.S., representing approximately one-third of the global technology market. However, the technology sector is not a homogenous group of similarly focused companies, its industries vary significantly in their characteristics. This paper analyzes the relationships among U.S. technology industries defined by the Global Industry Classification Standard using Vine copulas and transfer entropy. It spans 1 January 2010–31 January 2024, to clarify intra-sectoral heterogeneity and identify key industries predicting the performance of others. Our findings highlight the predictive power of the semiconductor industry, with significant information transmission to all other industries. Contrarily, the flow in the opposite direction is significant in only 3 of 5 cases. The identification of semiconductors’ central role is particularly valuable for both retail and institutional investors concentrating on technology stocks, as it may indicate future developments in the entire sector. Moreover, due to the industry’s global importance, shocks in the semiconductor industry could propagate across the entire technology sector, leading to broader economic instability. Therefore, the semiconductor industry should be closely monitored by the policymakers too.

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.

The Genetic Basis of Non-Contact Soft Tissue Injuries-Are There Practical Applications of Genetic Knowledge?

Physical activity increases the risk of non-contact injuries, mainly affecting muscles, tendons, and ligaments. Genetic factors are recognized as contributing to susceptibility to different types of soft tissue injuries, making this broad condition a complicated multifactorial entity. Understanding genetic predisposition seems to offer the potential for personalized injury prevention and improved recovery strategies. The candidate gene analysis approach used so far, has often yielded inconclusive results. This manuscript reviews the most commonly studied genetic variants in genes involved in the musculoskeletal system’s structure and recovery processes (ACTN3, ACE, CKM, MLCK, AMPD1, IGF2, IL6, TNFα, CCL2, COL1A1, COL5A1, MMP3, and TNC). Referring to the literature, it was highlighted that single-gene analyses provide limited insight. On the other hand, novel genetic testing methods identify numerous variants of uncertain physiological relevance. Distinguishing between functionally important variants, modifying variants, and the thousands of irrelevant variants requires advanced bioinformatics methods and basic multiomics research to identify the key biological pathways contributing to injury susceptibility. Tools like the Total Genotype Score (TGS) and Polygenic Risk Score (PRS) offer a more holistic view by assessing the combined effect of multiple variants. However, these methods, while useful in research, lack clinical applicability. In conclusion, it is too early to determine the clinical implications of genetic variability as a tool for improving well-established training and injury prevention methods, as the predictive power of genetic testing for injury predisposition is currently low.

Predictive accuracy of the logit model to determine factors affecting delivery and postnatal care services utilization in Tanzania

PurposeThis paper examines factors affecting the delivery care (DC) and postnatal care (PNC) services utilization in Tanzania using the predictive accuracy of the binary logit model by including DC as among the predictors of PNC utilization.Design/methodology/approachThe study used secondary data from the Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022) based on a sample of 13,266 women aged 15–49. Binary logit was used to analyze the association between independent variables and each binary dependent variable, followed by a stepwise likelihood ratio test for binary logistic regression analysis to select the most important predictors associated with DC and PNC. Odds ratios were used to predict the likelihood of the occurrence of significant explanatory variables in explaining the outcomes.FindingsThe results showed that the type of place of residence, wealth index, region, highest educational level, husband/partner’s level of education and respondent’s occupation were significant predictors of DC utilization. In contrast, the woman’s age, region, highest educational level, wealth index, DC and husband/partner’s educational level were significant predictors of PNC utilization. Public health interventions are needed to promote and increase the utilization of delivery and postal care services. The targets should be poor women and those residing in rural or remote areas.Originality/valueIncluding DC as a predictor variable (which was not used before) in PNC utilization improved the predictive power of the model and the robustness of the results.

Liquid plugs in narrow tubes: application to airway occlusion

We study liquid plugs in the pulmonary airways based on the two-phase axisymmetric weighted residual integral boundary-layer model of Dietze et al. (J. Fluid Mech., vol. 894, 2020, A17), which was originally developed to study liquid films coating the inner surface of a cylindrical tube in interaction with a core gas flow. The augmented form of this model, which was never applied beyond a proof of concept, allows for the representation of liquid pseudo-plugs. Here, we demonstrate its predictive power vs experiments and direct numerical simulations, in terms of the dynamics of plug formation and the characteristics of developed liquid plugs, such as their shape, flow field, speed and length, as well as the associated wall stresses and their spatial derivatives. In particular, we show that the augmented model allows us to establish a direct continuation path from travelling-wave solutions (TWS) to travelling-plug solutions (TPS). We then apply the model to predict mucus plugs in the conducting zone of the tracheobronchial tree, based on the lung architecture model of Weibel. We proceed by numerical continuation of travelling-state solutions in terms of the airway generation, whereby we impose the wavelength of the linearly most-amplified convective instability (CI) mode or that of the absolute instability (AI) mode. We identify the critical airway generation for liquid-plug formation (TWS/TPS transition), maximum potential for wall-stress-induced epithelial cell damage and CI/AI transition, and investigate how these phenomena are affected by the main control parameters, i.e. airway orientation vs gravity, air flow rate, mucus properties and airway size.

Complete response to fifth-line anti-PD-1 rechallenge in fumarate hydratase-mutated papillary renal cell carcinoma

Fumarate hydratase (FH) mutated papillary renal cell carcinoma is an aggressive variant of kidney cancer that poorly responds to conventional targeted therapies and immunotherapy. Here, we present the 10-year follow-up of a heavily pre-treated patient with several lines of therapy, achieving a remarkable complete response to anti-PD-1 rechallenge. In addition, we highlight a common immune-related adverse event of anti-PD-1, eosinophilia, as a possible biomarker of response and using TCGA data analysis, provide proof-of-concept for tumor expression of the eosinophil-related gene SIGLEC8, as a promising powerful predictor of prognosis for papillary renal cell carcinoma patients.

Navigating the immune landscape with plasma cells: A pan-cancer signature for precision immunotherapy.

Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified a novel plasma cell signature, Plasma cell.Sig, through a pan-cancer single-cell RNA sequencing analysis, which predicts patient outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power with an area under the curve (AUC) exceeding 0.7. Notably, the low-risk group, as classified by Plasma cell.Sig, exhibited enriched immune cell infiltration and heightened tumor immunogenicity, indicating an enhanced responsiveness to immunotherapy. Conversely, the high-risk group showed reduced immune activity and potential mechanisms of immune evasion. These findings not only enhance understanding of the intrinsic and extrinsic immune landscapes within the tumor microenvironment but also pave the way for more precise, biomarker-guided immunotherapy approaches in oncology.

A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer

PurposeTo investigate the independent risk factors associated with the development of lymph node metastasis (LNM) in patients with colorectal cancer (CRC), focusing on preoperative systemic inflammatory indicators, and to construct a corresponding risk predictive model.Materials and MethodsThe clinical data of 241 patients with CRC who underwent surgery after the first diagnosis between January 2012 and December 2017 at our hospital were reviewed. A best logistic regression model was constructed by Lasso regression for multivariate analysis, from which a Nomogram was derived. Using bootstrap to conduct internal validation. The model’s predictive performance and clinical practicability were evaluated using the receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis (DCA). External validation was conducted using retrospective data from 170 patients who underwent surgery between January 2020 and May 2022 at another hospital.ResultsCross-validation indicated smoking history, neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), fibrinogen-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. These factors were included in the logistic regression, and multivariate analysis confirmed that smoking history, NLR, LMR, FAR, and FOB were independent risk factors (P < 0.05). The ROC and calibration curve of the original model and external validation indicated strong predictive power of the model. DCA suggested the model’s favorable clinical utility.ConclusionsThe model constructed in this study has robust predictive performance and clinical utility for the preoperative determination of CRC LMN, offering significant for clinical decision-making in patients with CRC.

Forecasting exchange rate volatility: An amalgamation approach

The importance of exchange rate volatility forecasting has both practical and academic merit. Our aim is to provide a comprehensive analysis of the forecasting ability of financial and macroeconomics variables for future exchange rate volatility. We employ seven widely traded currencies against the US dollar and examine linear models and a variety of machine learning, dimensionality reduction and forecast combination approaches, along with creating a grand forecast (amalgamation approach) from these approaches. Our findings highlight the predictive power of the amalgamation approach, as well as the positive contribution of macroeconomic and financial variables in the forecasting experiment. Furthermore, we generate forecasts on the separate frequencies of volatility using wavelet analysis, in order to extract frequency-related information and examine timing effects in the performance of the methods.

Exploring the predictive power of artificial neural networks in linking global Islamic indices with a local Islamic index

This study aims to analyze the correlations and information flow between a local Sharia-compliant index and global Sharia-compliant indices using Artificial Neural Network (ANN) models, shedding light on the integration of international and national Sharia-compliant markets. Given recent market conditions and uncertainties at both local and global levels, the research investigates the predictive power of ANNs and examines the relationship between the Dubai Financial Market Sharia Index (DFMSI) and three well-known global Islamic indices: the Dow Jones Islamic Market World Index, the Standard and Poor’s Global Sharia Index, and the Financial Times Stock Exchange Sharia All World Index. By incorporating data from multiple global Sharia indices, the study expands data sources beyond the local market, providing a comprehensive analysis of factors influencing the DFMSI’s price. This approach reduces reliance on a single dataset and strengthens the robustness of the research findings. Additionally, the inclusion of the three global Sharia indices offers benefits such as access to global market insights, risk management, and practical applications for investors. The study demonstrates that ANNs, particularly the Nonlinear Autoregressive with External Input Neural Network (NARX), outperform classical models in accurately predicting the DFMSI. This research contributes significantly to the understanding of Sharia-compliant investments by providing a comprehensive analysis of how multiple global indices affect a local market index. It also offers valuable insights for investors and portfolio managers, suggesting that the integration of advanced machine learning models can enhance decision-making processes and improve risk management strategies.