High Predictive Ability Research Articles

Hydrodynamic force and torque models for cylindrical particles in a wide range of aspect ratios

During the pneumatic conveyance of biomass in a coal-fired power station boiler, biomass particles have cylindrical shapes with different aspect ratios. They move through the fluid at any angle and rotate strongly. However, highly accurate and general models of the drag, lift, and torque coefficients (CD, CL, and CT) for biomass particles in a wide range of aspect ratios, especially the CT model and the high aspect ratios, are currently lacking. This paper presents detailed direct numerical simulations of the flow around cylindrical cylinders with varying aspect ratios (6 ≤ AR ≤ 22), Reynolds numbers (100 ≤ Re ≤ 2000), and angles of incidence (0° ≤ θ ≤ 90°). The simulation was conducted using the OpenFOAM solver with the body-fitted mesh method. The flow characteristics and force coefficients of cylindrical particles with different AR were systematically analyzed. New functional correlations between CD, CL, and CT and AR, Re, and θ values were established. The mean squared errors for CD, CL, and CT were 8.8 × 10–2, 2.4 × 10–2, and 4.7 × 10–2, with average relative errors of 5.8%, 3.5%, and 8.17%, respectively. A comparison of the results with other experimental and simulation data in previous literatures showed that the new CD and CL models have considerable higher predictive ability. The generality of the new CD model expanding to low ARs of 1.5 and 3 is verified finally. The new force and torque models are expected to improve the accuracy of Eulerian–Lagrangian simulations of various cylindrical particle-laden flows in the utility of biomass energy.

Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram.

Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. GCN based on MRI could predict MVI on HCC.

The new biomarker that predicts in-hospital mortality in myocardial infarction: glucose/potassium ratio

Aims: Acute myocardial infarction (AMI) remains one of the leading causes of cardiovascular mortality. However, the parameters used to predict short-term mortality are limited. Glucose-potassium ratio (GPR) is a new biomarker that has been recently used. This study aimed to investigate the usefulness of GPR in predicting short-term mortality in patients diagnosed with AMI in the emergency department. Methods: This retrospective cohort study was conducted between January 1, 2022, and June 30, 2022, and included patients registered in the hospital emergency health system with IRB (Institutional Review Board) approval. Patients were identified based on inclusion criteria, and their demographic data, laboratory findings, and outcomes were recorded in the hospital registration system. Data comparisons between deceased and non-deceased patients were performed using the Student's t-test. Correlation analysis was performed using Spearman's correlation coefficients. A binary logistic regression model was created to determine the predictive parameters, and odds ratios and 95% confidence intervals (CI) were presented. Results: A total of 665 patients diagnosed with AMI were included in the study, with 75.9% of all patients being male and an average age of 62.14±11.62. A total of 6.6% of the patients included in the study died due to various causes during hospitalization. When laboratory values of deceased AMI patients were examined, statistically significant increases in glucose, urea, creatinine, WBC, neutrophil, lymphocyte, troponin, ckmb, and lactate levels were observed. The effects of GPR, NLR, PLR, lactate, and troponin levels on in-hospital mortality were analyzed using univariate logistic regression. GPR (95% CI 1.015-1.036, p=0.000), troponin (95% CI 1.001-1.001, p=0.000), and lactate (95% CI 1.437-1.908, p=0.000) were found to be the most important factors associated with mortality in patients with AMI. The ability to predict in-hospital mortality in AMI was compared, and GPR was found to have a higher predictive ability than troponin, a biomarker of myocardial damage (AUC: 0.729; P?=?0.000; Sensitivity:70%; Specificity:70%; Cut-off: 38.21). Conclusion: GPR, as a new and inexpensive biomarker, can be used to determine in-hospital mortality in patients with AMI. This index has a better predictive ability than troponin, NLR, and PLR, but is weaker than lactate.

Correlation between Left Ventricular Pressure–Strain Loop and Severity in Patients with Non-ST-Segment Elevation Acute Coronary Syndrome and Its Application Value in Short-Term Prognosis Evaluation

Purpose To investigate the diagnostic value of nonintrusive left ventricular pressure–strain loop (LV-PSL) for assessing overall myocardial function in sufferers with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) with or without coronary stenosis. The results of this research might provide insights into the diagnosis and management of NSTE-ACS. Methods All 268 sufferers with NSTE-ACS who were received by the First Affiliated Hospital of Nanchang University between June 2019 and June 2021 were enrolled. Sufferers with single or multiple extramural coronary diameter stenosis ≥70% on coronary angiography were defined as the stenosis group. All sufferers underwent noninvasive LV-PSL construction by using cuff blood pressure as the left ventricular pressure before coronary angiography, and the resulting images were imported and analysed with offline analysis software to obtain global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE). The correlation between severity Gensini score and myocardial work (MW) parameters was identified through Spearman analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off values for predicting coronary stenosis, and logistic regression analysis was used to identify independent factors affecting left ventricular myocardial function in sufferers of NSTE-ACS. The occurrence of adverse cardiac events during the follow-up period was recorded. Results Through the comparative analysis of general clinical data, significant differences were found between the stenosis and nonstenosis groups in terms of gender, hyperlipidaemia, hypertension and smoking. However, statistical difference was observed only for hypertension (stenosis group 54.2%; p < 0.05) and hypercholesterolaemia (stenosis group 53.5%; p < 0.05). GLS (tz value 3.063), GCW (tz value 11.494), GWI (tz value 9.627) and GWE (tz value 12.780) reduced and GWW (tz value 11.504) increased in the stenosis group compared with those in the nonstenosis group. All differences were statistically significant (all p < 0.05). Severity Gensini scores were negatively correlated with GLS, GCW, GWI and GWE but positively correlated with GWW (p < 0.001). The ROC curve and univariate and multivariate logistic regression analyses revealed that GWE (odds ratio (OR) 2.881; 95% confidence internal (95% CI) 2.176–3.816; p < 0.001) had the largest area under the curve and greatest sensitivity for coronary stenosis diagnosis. GWE was (OR 2.875; 95% CI 2.217–3.727; p < 0.001) and (OR 2.881; 95% CI 2.176–3.816; p < 0.001). During an average follow-up period of 26.7 months, 19 sufferers experienced adverse cardiac events. GWE exhibited high predictive ability for identifying such events. Conclusions Noninvasive LV-PSL can identify whether sufferers of NSTE-ACS have acute coronary stenosis regardless of the location or size of the stenosis and can detect varying degrees of left ventricular dysfunction in such sufferers. Amongst indices, GWE had the highest diagnostic efficiency for diagnosing sufferers of NSTE-ACS with coronary stenosis and highest predictive ability for adverse cardiac events.

The effect of marker types and density on genomic prediction and GWAS of key performance traits in tetraploid potato.

Genomic prediction and genome-wide association studies are becoming widely employed in potato key performance trait QTL identifications and to support potato breeding using genomic selection. Elite cultivars are tetraploid and highly heterozygous but also share many common ancestors and generation-spanning inbreeding events, resulting from the clonal propagation of potatoes through seed potatoes. Consequentially, many SNP markers are not in a 1:1 relationship with a single allele variant but shared over several alleles that might exert varying effects on a given trait. The impact of such redundant "diluted" predictors on the statistical models underpinning genome-wide association studies (GWAS) and genomic prediction has scarcely been evaluated despite the potential impact on model accuracy and performance. We evaluated the impact of marker location, marker type, and marker density on the genomic prediction and GWAS of five key performance traits in tetraploid potato (chipping quality, dry matter content, length/width ratio, senescence, and yield). A 762-offspring panel of a diallel cross of 18 elite cultivars was genotyped by sequencing, and markers were annotated according to a reference genome. Genomic prediction models (GBLUP) were trained on four marker subsets [non-synonymous (29,553 SNPs), synonymous (31,229), non-coding (32,388), and a combination], and robustness to marker reduction was investigated. Single-marker regression GWAS was performed for each trait and marker subset. The best cross-validated prediction correlation coefficients of 0.54, 0.75, 0.49, 0.35, and 0.28 were obtained for chipping quality, dry matter content, length/width ratio, senescence, and yield, respectively. The trait prediction abilities were similar across all marker types, with only non-synonymous variants improving yield predictive ability by 16%. Marker reduction response did not depend on marker type but rather on trait. Traits with high predictive abilities, e.g., dry matter content, reached a plateau using fewer markers than traits with intermediate-low correlations, such as yield. The predictions were unbiased across all traits, marker types, and all marker densities >100 SNPs. Our results suggest that using non-synonymous variants does not enhance the performance of genomic prediction of most traits. The major known QTLs were identified by GWAS and were reproducible across exonic and whole-genome variant sets for dry matter content, length/width ratio, and senescence. In contrast, minor QTL detection was marker type dependent.

Photodegradation of polychlorinated biphenyls in water/nitrogen-doped silica and air/nitrogen-doped silica systems: Kinetics, mechanism and quantitative structure activity relationship (QSAR) analysis

Developing efficient and low-cost photocatalytic materials is essential for removing polychlorinated biphenyls (PCBs). In this work, the photodegradation process of fourteen representative polychlorinated biphenyls (PCBs) in both water/nitrogen-doped SiO2 (N-SiO2) and air/N-SiO2 systems was studied. The photodegradation kinetics of PCBs is consistent with the pseudo-first-order kinetic equation. The variation in the degradation effects of different PCBs in the two systems is primarily related to the position of the Cl substituent and the effective absorption wavelength range of PCBs. A total of fourteen intermediates for 4′-Dichlorobiphenyl (PCB-15), 2,2′,4,4′,6,6′-Hexachlorobiphenyl (PCB-155), and 2,2′,3,3′,4,4′,5,5′,6,6′-Decachlorobiphenyl (PCB-209) generated from four reaction pathways were identified based on both mass spectrometry analysis and theoretical calculations. Using the values of lnk (k denotes pseudo-first-order kinetic constants) for the 11 PCBs in the training set and the calculated molecular and structural parameters, quantitative structure-activity relationship (QSAR) models for the two systems were constructed by using multiple linear regression (MLR) method to better understand the factors affecting the photodegradation rate of PCBs. The QSAR equations were obtained with Cl atom substitution at position 3 (N3) as the main parameter, which were lnk = −1.98 - 0.19 N3 for the water/N-SiO2 system and lnk = −1.56 - 0.34 N3 for the air/N-SiO2 system, with the correlation coefficient (R2) of 0.66 and 0.73, leave-one-out cross-validation (Q2LOO) of 0.51 and 0.59, respectively, and bootstrapping validation coefficients (Q2BOOT) values of both 0.74, confirming that the models were well fitted and showed high robustness and prediction ability. This study provides valuable insights into photocatalytic degradation studies of PCBs.

Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Models predicting mortality risk of patients with burns to ≥ 50% of the total body surface

BackgroundSeveral models predicting mortality risk of burn patients have been proposed. However, models that consider all such patients may not well predict the mortality of patients with extensive burns. MethodThis retrospective multicentre study recruited patients with extensive burns (≥ 50% of the total body surface area [TBSA]) treated in three hospitals of Eastern China from 1 January 2016 to 30 June 2022. The performances of six predictive models were assessed by drawing receiver operating characteristic (ROC) and calibration curves. Potential predictors were sought via “least absolute shrinkage and selection operator” regression. Multivariate logistic regression was employed to construct a predictive model for patients with burns to ≥ 50% of the TBSA. A nomogram was prepared and the performance thereof assessed by reference to the ROC, calibration, and decision curves. ResultA total of 465 eligible patients with burns to ≥ 50% TBSA were included, of whom 139 (29.9%) died. The FLAMES model exhibited the largest area under the ROC curve (AUC) (0.875), followed by the models of Zhou et al. (0.853) and the ABSI model (0.802). The calibration curve of the Zhou et al. model fitted well; those of the other models significantly overestimated the mortality risk. The new nomogram includes four variables: age, the %TBSA burned, the area of full-thickness burns, and blood lactate. The AUCs (training set 0.889; internal validation set 0.934; external validation set 0.890) and calibration curves showed that the nomogram exhibited an excellent discriminative capacity and that the predictions were very accurate. ConclusionFor patients with burns to ≥ 50％of the TBSA, the Zhou et al. and FLAMES models demonstrate relatively high predictive ability for mortality. The new nomogram is sensitive, specific, and accurate, and will aid rapid clinical decision-making.

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel.

Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)-an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

Using drone-retrieved multispectral data for phenomic selection in potato breeding

Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of − 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.

Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia.

The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF's epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R2 = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R2 = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia.

Multicomponent comprehensive confirms that erythroferrone is a molecular biomarker of pan-cancer

All vertebrates organisms produce erythroferrone, a secretory hormone with structure-related functions during iron homeostasis. However, limited knowledge exists regarding the effect of this hormone on the occurrence and progression of cancer. To systematically and comprehensively identify the diverse implications of Erythroferrone (ERFE) in various malignant tumors, we conducted an in-depth analysis of multiple datasets, including the expression levels of oncogenes and target proteins, biological functions, and molecular characteristics. This analysis aimed to assess the diagnostic and prognostic value of ERFE in pan-cancer. Our findings revealed a significant elevation in ERFE expression across 20 distinct cancer types, with notable increases in gastrointestinal cancers. Utilizing the Cytoscape and STRING databases, we identified 35 ERFE-targeted binding proteins. Survival prognosis studies, particularly gastrointestinal cancers indicated by Colon adenocarcinoma (COAD), demonstrated a poor prognosis in patients with high ERFE expression (p < 0.001), consistently observed across various clinical subgroups. Furthermore, the ROC curve underscored the high predictive ability of EFRE for gastrointestinal cancer (AUC >0.9). Understanding the roles and interactions of ERFE in biological processes can also be aided by examining the genes co-expressed with ERFE in the coat and ranking the top 50 positive and negative genes. In the correlation analysis between the ERFE gene and different immune cells in COAD, we discovered that the expression of ERFE was positively correlated with Th1 cells, cytotoxic cells, and activated DC (aDC) abundance, and negatively correlated with Tcm (T central memory) abundance (P < 0.001). in summary, ERFE emerges as strongly associated with various malignant cancers, positioning it as a prospective biological target for cancer treatment. It stands out as a key molecular biomarker for diagnosing and prognosticating pancreatic cancer, also serves as an independent prognostic risk factor for COAD.

Time and frequency analysis of daily-based nexus between global CO2 emissions and electricity generation nexus by novel WLMC approach

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.

Clustering method for the construction of machine learning model with high predictive ability

In the design of molecules, materials, and processes, a mathematical model y = f(x) is constructed to establish a relationship between the explanatory variable x and the objective variable y using a dataset to design x such that y achieves target values. While it is preferable to develop a model with high predictive ability, achieving this becomes unattainable when the relationship between x and y is inconsistent. In such instances, it becomes necessary to incorporate additional factors to elucidate the relationship between x and y. However, when this proves challenging, this study proposes a clustering method wherein the relationship between x and y remains consistent within each cluster. Utilizing a genetic algorithm, this method selects samples for which the relationship between x and y remains consistent, and establishes clusters of samples sharing a consistent relationship between x and y using these selected samples as the core. Through comparison with other clustering methods across six datasets, including molecules, materials, and spectra, it was confirmed that the proposed method can effectively form clusters demonstrating a high predictive ability for each cluster. Specifically, the method successfully identifies groups of samples exhibiting a consistent relationship between x and y.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models, Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), landslide models were constructed. Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic (ROC) curves. The results showed that LR, RF, SVM, and XGB models have good predictive performance for landslide susceptibility, with the area under curve (AUC) values of 0.752, 0.965, 0.996, and 0.998, respectively. XGB model had the highest predictive ability, followed by RF model, SVM model, and LR model. The frequency ratio (FR) accuracy of LR, RF, SVM, and XGB models was 0.775, 0.842, 0.759, and 0.822, respectively. RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.©2024 China Geology Editorial Office.

A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma.

Specific methylation sites have shown promise in the early diagnosis of lung adenocarcinoma (LUAD). However, their utility in predicting LUAD prognosis remains unclear. This study aimed to construct a reliable methylation-based predictor for accurately predicting the prognosis of LUAD patients. DNA methylation data and survival data from LUAD patients were obtained from the TCGA and a GEO series. A DNA methylation-based signature was developed using univariate least absolute shrinkage and selection operators and multivariate Cox regression models. Eight CpG sites were identified and validated as optimal prognostic signatures for the overall survival of LUAD patients. Receiver operating characteristic analysis demonstrated the high predictive ability of the eight-site methylation signature combined with clinical factors for overall survival. This research successfully identified a novel eight-site methylation signature for predicting the overall survival of LUAD patients through bioinformatic integrated analysis of gene methylation markers used in the early diagnosis of lung cancer.

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

AbstractTopological indices (TIs) can be used to forecast molecules' biological activity, physicochemical features, and toxicity. The use of topological indices and regression analysis can help us better understand drug behavior and contribute to the development of tailored drugs. This study investigates the predictive potential and efficacy of K‐Banhatti and Zagreb type degree‐based topological indices in quantitative structure–property relationship (QSPR) analysis of a comprehensive set of medications used for diabetes type‐I and type‐II disease. The K‐Banhatti and Zagreb type degree‐based topological indices are computed for 14 anti‐diabetes drug molecules using edge/vertex partitioning techniques. By leveraging these topological indices, QSPR regression models are developed to predict the physicochemical properties of the understudy drugs. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the anti‐diabetes drugs. Furthermore, the comparative analysis revealed that, for all the considered properties except enthalpy of vaporization, Zagreb type indices outperform K‐Banhatti indices with high predictive ability. Hence, it can be concluded that the Zagreb type indices are the best alternatives to theoretically predict the properties of anti‐diabetes drugs. This theoretical analysis can help chemists in their right choice of the topological indices to theoretically predict the properties of anti‐diabetes drugs without going into laborious experimentation.

Prediction and Detection of Glaucomatous Visual Field Progression Using Deep Learning on Macular Optical Coherence Tomography.

A deep learning model trained on macular OCT imaging studies detected clinically significant functional glaucoma progression and was also able to predict future progression. To use macular optical coherence tomography (OCT) imaging to predict the future and detect concurrent visual field progression, respectively, using deep learning. A retrospective cohort study. A pretraining data set was comprised of 7,702,201 B-scan images from 151,389 macular OCT studies. The progression detection task included 3902 macular OCT imaging studies from 1534 eyes of 828 patients with glaucoma, and the progression prediction task included 1346 macular OCT studies from 1205 eyes of 784. A novel deep learning method was developed to detect glaucoma progression and predict future progression using macular OCT, based on self-supervised pretraining of a vision transformer (ViT) model on a large, unlabeled data set of OCT images. Glaucoma progression was defined as a mean deviation (MD) rate of change of ≤ -0.5 dB/year over 5 consecutive Humphrey visual field tests, and rapid progression was defined as MD change ≤ -1 dB/year. Diagnostic performance of the ViT model for prediction of future visual field progression and detection of concurrent visual field progression using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The model distinguished stable eyes from progressing eyes, achieving an AUC of 0.90 (95% CI, 0.88-0.91). Rapid progression was detected with an AUC of 0.92 (95% CI, 0.91-0.93). The model also demonstrated high predictive ability for forecasting future glaucoma progression, with an AUC of 0.85 (95% CI 0.83-0.87). Rapid progression was predicted with an AUC of 0.84 (95% CI 0.81-0.86). A deep learning model detected clinically significant functional glaucoma progression using macular OCT imaging studies and was also able to predict future progression. Early identification of patients undergoing glaucoma progression or at high risk for future progression may aid in clinical decision-making.

Virtual screening and pharmacokinetics analysis of inhibitors against tuberculosis: Structure and ligand-based approach

Life-threatening diseases like tuberculosis have raised concerns in the medical and scientific communities. The damage-causing disease makes the scientific community employ the in-silico approach for design of new inhibitors that can inhibit or retard the havoc caused by this deadly disease. The insilico approach was used in this study to create a mathematical model with promising molecular properties, and receptors from the library were used to screen compounds and estimate the kinetic ability of the screened inhibitors that can cure this disease. 2D molecular properties evolved in the built model with high predictive ability. Three inhibitors x, y, and z emerged with better and higher molecular properties, the lowest binding energy (and higher binding affinity), and a better pharmacokinetic assessment compared to the template used in designing the effective compounds, with binding affinities of -15.56 kcal/mol, -18.51 kcal/mol, and -18.58 kcal/mol, respectively. Virtual screening of these compounds showed that they have good binding energy and excellent docking positions with the inhibiting potential of the receptor. Also, pharmacokinetic predictions and ADMET, depict orally active ability of the inhibitors, possess good human intestinal absorption, and violate none of the RO5 as potential drug candidates to cure this disease. Hence, further laboratory tests are recommended for these to determine their toxicities and biological assays.

Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO variations

Accurately estimating the concentration of carbon monoxide (CO) with high spatiotemporal resolution is crucial for assessing its meteorological-environmental-health impacts. Although machine learning models have high predictive ability in environmental research, there are relatively few explanations for model outputs. Utilizing the top-of-atmosphere radiation data of China’s new generation geostationary satellites (FY-4A and FY-4B) and interpretable machine learning models, the 24-hour near-surface CO concentrations in China was conducted (resolution: 1 hour, 0.04°). The model improved by 6.6% when using the all-sky dataset (cloud-contained model, R2 = 0.759) compared to the clear-sky dataset (cloud-removed model). The interpretability analysis of the CO estimation model used two methods, namely ante-hoc (model feature importance) and post-hoc (SHapley Additive exPlanations). The importance of daytime meteorological factors increased by 51% compared to nighttime. Combining partial dependency plots, the impact of key meteorological factors on CO was elucidated to gain a deeper understanding of the spatiotemporal variations of CO.