Multi-parameter Model Research Articles

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

The Relationship Between Vocational Training Program Characteristics for Prison Inmates, Organizational and Family Support, Transfer Intention: Focused on Moderating Effect of Hairdressing Vocational Training

The purpose of this study is to examine the effects of characteristics of vocational training program for prison inmates including job relevance, educational method, educational environment, instructor qualities on transfer intention. And it is confirmed whether there are mediating effects of organizational and family support and moderating effects of vocational training program between characteristics of training program and transfer intention. For the purpose of this study, 756 samples were collected from prison inmates who participated in vocational training program. The analytical method of this study conducted frequency analysis, reliability and correlation analysis, using SPSS WIN 23.0 program, as well as confirmatory factor analysis, multi-parametric model analysis, multi-group confirmatory factor analysis and multi-group analysis using AMOS 21.0 program. The results of this study are as follows. First, job relevance, a sub-factor of vocational training program characteristics, was found to have a positive significant effect on transfer intention. Second, job relevance, education method, and education environment were found to have a significant positive effect on organizational support. Lastly, in the path from family support to transfer intention, the hairdressing group was found to have a stronger positive influence than other groups. Accordingly, this study compared mediating effects of organizational support and family support in the relationship between the characteristics of correctional institution vocational training programs and transfer intention and analyzed whether there were differences between the hairdressing group and other groups, which were control variables. We aim to provide basic data so that the training program can be effectively applied in practice.

Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

ObjectivesTo construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients.MethodsA total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist’s assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models’ discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC).ResultsThe AUCs of the radiologist’s assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05).ConclusionsA multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC.Critical relevance statementThis study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients.Key PointsThe biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer.Radiomics features and clinical data before and after nCRT provide complementary tumor information.Preoperative prediction of LN status after nCRT contributes to clinical decision-making.Graphical

Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging.

To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images. Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models weretrained with 397 patients and validated with 170 internal and 31 external patients. For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively. Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.

Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study.

The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer. The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism. The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% ( P = 0.027); stage II, 72% ( P = 0.014); stage III, 50% ( P = 0.032); and stage IV, 45% ( P = 0.041). The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.

Multi-parameter similarity model-based lightweight design of battery electric vehicle body-in-white

The bending stiffness, torsional stiffness, and low-order modal frequencies of the body-in-white (BIW) are crucial factors in structural lightweight design. This paper introduces a BIW optimization approach for electric vehicles (BEVs) that integrates comprehensive sensitivity analysis and multi-parameter similarity models. The aim is to efficiently balance the BIW’s stiffness performance with minimal weight, while also managing potential manufacturing cost increases from design modifications. Firstly, the validity of the computational model is demonstrated by the BIW bending and torsion stiffness test and modal test. On this basis, the integrated sensitivity evaluation indexes of the structural member thickness parameters to the stiffness, weight and low-order modal frequency response of the BIW were calculated. The sheet metal thickness parameters of 17 structural members were preferred as design variables, and a multi-parameter similarity prediction model for BIW weight reduction design was constructed. Minimum weight, maximum stiffness and higher modal frequency of the BIW are defined as the optimization objectives. The optimal solution was finally solved using a multi-objective optimization algorithm. The results indicate a 1.582% reduction in weight of the BIW compared to the pre-optimization BIW. This paper delves into the intricate interplay between the parameters of the BIW’s structural components using simulation optimization techniques alongside experimental validation. This study not only proposes a new idea for the problem of how to efficiently and accurately determine the best optimization object from a large number of structural components in the development of vehicle structural safety, but also provides a research basis for automobile manufacturers to implement further weight reduction and optimization work on the BIW at the later stage of BEV development.

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.

Multi-parameter modeling for prediction of gas–water production in tight sandstone reservoirs

Tight sandstone reservoirs are significant sources of natural gas reserves. As traditional reserves become increasingly scarce and costly, optimizing the development of these reservoirs becomes crucial. This study introduces a novel two-phase gas–water flow model for single wells, incorporating both Darcy and non-Darcy flow equations. These equations are derived from mass conservation and momentum principles for both gas and water phases. Using data from a real tight gas well, our model, which includes stress-sensitive phases for gas and water, outperforms traditional Darcy flow models. Specifically, the average relative deviations in daily production rates were 0.1815% for gas and − 0.2677% for water, which are significantly smaller compared to traditional Darcy flow models. Further application of the non-Darcy flow model reveals strategies to enhance well performance. For example, mitigating liquid lock damage within a 2 m radius near the well could restore the permeability from 0.045 to 0.143 mD, thereby tripling the daily gas production. This non-Darcy flow model is easy to implement and shows significant potential in forecasting production yields in tight sandstone reservoirs, highlighting its importance in the petroleum and natural gas industry.

Predictive value of primary tumor volume change during concurrent chemoradiotherapy in patients with unresectable stage III non-small cell lung cancer

Background and purposeNo established early biomarkers currently exist to predict responses during concurrent chemoradiotherapy (CCRT) in patients with unresectable non-small cell lung cancer (NSCLC). This study investigated the potential of gross tumor volume (GTV) and its changes during CCRT as predictors of survival outcomes. Materials and methodsWe identified 227 patients with unresectable stage III NSCLC who underwent definitive CCRT followed by durvalumab between November 2018 and December 2022. GTV was defined as the volume of the primary tumor, assessed at two time points: before starting CCRT for initial planning (GTV1), and at the fourth week of CCRT for adaptive planning (GTV2). Both relative and absolute regressions between GTV1 and GTV2 were calculated. ResultsThe median GTV1 volume was 90 mL (range, 5–840 mL), and the median GTV2 volume was 64 mL (range, 1–520 mL), resulting in median absolute and relative regressions of 18.6 mL and 25.0 %, respectively. Among the GTV parameters, relative GTV regression exhibited the strongest predictive value, with an area under the curve (AUC) of 0.804 for in-field progression and 0.711 for overall progression. The 1-year progression-free survival rates for the high (>30 %), intermediate (0–30 %), and low (≤0%) relative regression groups were 88.0 %, 62.6 %, and 14.3 %, respectively (p = 0.006 for high vs. intermediate; p < 0.001 for intermediate vs. low). Additionally, GTV2 volume demonstrated stronger associations with survival outcomes than GTV1 volume. ConclusionRelative GTV regression was identified as a promising early predictor for patients with unresectable stage III NSCLC. Further development of a multi-parametric predictive model is warranted to guide patient-tailored therapeutic approaches.

Robustness of Turing models and gene regulatory networks with a sweet spot.

Traditional linear stability analysis based on matrix diagonalization is a computationally intensive process for high-dimensional systems of differential equations, posing substantial limitations for the exploration of Turing systems of pattern formation where an additional wave-number parameter needs to be investigated. In this paper, we introduce an efficient and intuitive technique that leverages Gershgorin's theorem to determine upper limits on regions of parameter space and the wave number beyond which Turing instabilities cannot occur. This method offers a streamlined avenue for exploring the phase diagrams of other complex multi-parametric models, such as those found in gene regulatory networks in systems biology. Due to its suitability for the asymptotic limit of infinitely large systems, it predicts the existence of a sweet spot in network size for maximal Jacobian stability.

Prediction of the axillary lymph-node metastatic burden of breast cancer by 18F-FDG PET/CT-based radiomics

BackgroundThe axillary lymph-node metastatic burden is closely associated with treatment decisions and prognosis in breast cancer patients. This study aimed to explore the value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT)–based radiomics in combination with ultrasound and clinical pathological features for predicting axillary lymph-node metastatic burden in breast cancer.MethodsA retrospective analysis was conducted and involved 124 patients with pathologically confirmed early-stage breast cancer who had undergone 18F-FDG PET/CT examination. The ultrasound, PET/CT, and clinical pathological features of all patients were analysed, and radiomic features from PET images were extracted to establish a multi-parameter predictive model.ResultsThe ultrasound lymph-node positivity rate and PET lymph-node positivity rate in the high nodal burden group were significantly higher than those in the low nodal burden group (χ2 = 19.867, p < 0.001; χ2 = 33.025, p < 0.001). There was a statistically significant difference in the PET-based radiomics score (RS) for predicting axillary lymph-node burden between the high and low lymph-node burden groups. (-1.04 ± 0.41 vs. -1.47 ± 0.41, t = -4.775, p < 0.001). The ultrasound lymph-node positivity (US_LNM) (odds ratio [OR] = 3.264, 95% confidence interval [CI] = 1.022–10.423), PET lymph-node positivity (PET_LNM) (OR = 14.242, 95% CI = 2.960–68.524), and RS (OR = 5.244, 95% CI = 3.16–20.896) are all independent factors associated with high lymph-node burden (p < 0.05). The area under the curve (AUC) of the multi-parameter (MultiP) model was 0.895, which was superior to those of US_LNM, PET_LNM, and RS models (AUC = 0.703, 0.814, 0.773, respectively), with statistically significant differences (Z = 2.888, 3.208, 3.804, respectively; p = 0.004, 0.002, < 0.001, respectively). Decision curve analysis indicated that the MultiP model provided a higher net benefit for all patients.ConclusionA MultiP model based on PET-based radiomics was able to effectively predict axillary lymph-node metastatic burden in breast cancer.Trial registrationThis study was registered with ClinicalTrials.gov (registration number: NCT05826197) on May 7, 2023.

ANN-RSM based multi-parametric optimisation and modelling of H2 and syngas from co-gasification of residues from oil palm plants

Despite their abundance, lignocellulose biomass co-gasification studies especially that of oil palm biomass are scarcely reported. In this study, the frond and trunk of the oil palm tree are co-gasified under different conditions. Experimental results were validated, modelled, and optimised with the aid of RSM and ANN for the syngas and H2 results. The optimum yield of syngas and H2 were found to be 49.01 %, and 23.26 % respectively when operated at 900°C with particle size of 2.6 mm and blending ratio of 1:1. ANOVA yielded satisfactory P-values in the case of RSM with 95 % confidence level. The Bayesian regularisation-based ANN with a 3–10–2 topology (3 inputs, 10 hidden neurons, and 2 outputs) has shown to be a very successful and resilient model, as indicated by its significant coefficient of determination R2 of more than 0.95. The selected ANN structure demonstrates an efficient framework for capturing complicated interactions among the data. The model's relevance is shown by its ability to provide statistically relevant predictions. Furthermore, its endurance under varying situations demonstrates its reliable effectiveness, implying a capacity to generalize effectively to new, existing data. Based on the findings of the suggested BP-based ANN, the proposed model may be used in co-gasification processing industries to make critical evaluations of process operating conditions.

Optimal estimation of pure states with displaced-null measurements

We revisit the problem of estimating an unknown parameter of a pure quantum state, and investigate ‘null-measurement’ strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state. Such strategies are known to approach the quantum Fisher information for models where the quantum Cramér-Rao bound (QCRB) is achievable but a detailed adaptive strategy for achieving the bound in the multi-copy setting has been lacking. We first show that the following naive null-measurement implementation fails to attain even the standard estimation scaling: estimate the parameter on a small sub-sample, and apply the null-measurement corresponding to the estimated value on the rest of the systems. This is due to non-identifiability issues specific to null-measurements, which arise when the true and reference parameters are close to each other. To avoid this, we propose the alternative displaced-null measurement strategy in which the reference parameter is altered by a small amount which is sufficient to ensure parameter identifiability. We use this strategy to devise asymptotically optimal measurements for models where the QCRB is achievable. More generally, we extend the method to arbitrary multi-parameter models and prove the asymptotic achievability of the the Holevo bound. An important tool in our analysis is the theory of quantum local asymptotic normality which provides a clear intuition about the design of the proposed estimators, and shows that they have asymptotically normal distributions.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BackgroundThe detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.MethodsWe recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis.ResultsOverall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.ConclusionsThe proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Analysis and Modeling Study on the Effect of Working Parameters of Portable Neutron Tubes on Target Current

Abstract As an important parameter index of sealed neutron tubes, their high-pressure side target current directly affects neutron yield. The working parameters of a neutron tube affect the target current. Using self-developed neutron tubes, the influence of operating parameters of neutron tubes on target current was studied. The effect curves of gas pressure, anode high pressure, and target high pressure on neutron yield were obtained, and the secondary electron suppression characteristics of the target current were obtained. Further, we analyze and fit a multi-parameter coupling model for the target current, to provide a theoretical basis for establishing the working state and improving the performance of sealed neutron tubes.

Incremental analysis of load handling device deflection considering lubrication degradation for predictive maintenance

In the Industry 4.0 era, unmanned automated factories heavily rely on load handling devices (LHDs), which are indispensable but susceptible to failures. Deflection is a crucial health indicator, with excessive deflection causing issues such as tilting and reduced positioning accuracy. This underscores the necessity for reliable deflection prediction methods in LHDs, a currently under-researched area, essential for informing predictive maintenance strategies. This study introduces an incremental analysis method that addresses the impact of internal cumulative wear and lubrication degradation on deflection increase. It involves iteratively updating a multiparametric model to represent the cumulative wear process and utilizes an empirical model based on the Stribeck curve to describe lubrication degradation. To enhance accuracy, particle filtering (PF) is employed to revise friction coefficients derived from the empirical model. Experimental data from an LHD operating in a load-carrying mission validates the accurate and robust nature of this method, particularly when compared with non-friction coefficient revising counterparts. As a typical application case study for preventive maintenance, this method is utilized to simulate various scenarios of lubrication replenishment and quantify the effect of timing on LHD lifespan enhancement through lubricant replenishment. The results facilitate the optimization of maintenance schedules and offer a new approach for updating lubrication-related parameters in digital twin models of equipment-level products, in line with preventative maintenance strategies.

Electrocardiographic predictors of haemodynamic benefit from biventricular pacing: the role of atrioventricular timing measures

Abstract Background Patients with heart failure (HF) and left bundle branch block (LBBB) are selected for biventricular pacing (BVP) cardiac resynchronisation therapy (CRT) based on their 12-lead ECG QRS complex duration (QRSd). This is due to the long-held belief that BVP delivers its beneficial effects almost exclusively by reducing ventricular dyssynchrony. Recent work (1) suggests that atrioventricular delay (AVD) shortening is another key mechanism of benefit and, therefore, that atrioventricular timing may be an important CRT substrate. The ideal 12-lead ECG measure to facilitate patient selection that yields benefits from this mechanism is yet to be elucidated. Purpose We set out to establish whether incorporating a measure of AV delay provides additional benefit over and above using QRSd when using the 12-lead ECG to select patients who are likely to obtain haemodynamic benefit from BVP. Methods We identified patients in sinus rhythm with HF and LBBB meeting clinical criteria for CRT who had undergone implantation of a BVP device and had also undergone high-precision haemodynamic assessment of BVP. This is where pacing is alternated between atrium-only pacing (AAI) and DDD pacing using BVP multiple times for each of a full range of AVDs during continuous beat-by-beat blood pressure recording to determine the peak haemodynamic benefit of BVP in each patient. We extracted potentially predictive, pre-specified intrinsic (unpaced) ECG features that incorporated left or right AV timing to varying extents: PR interval (PR), end-of-p-wave-to-R-wave-peak-time-V6 (Pend-RWPTV6), start-of-p-wave-to-end-of-QRS (P-QRSend) as well as conventional QRSd. Pearson's test assessed the unadjusted correlation between intrinsic ECG intervals and BVP haemodynamic benefit(∆SBP). A multiparametric regression analysis model was constructed to account for confounders and interactions between different ECG features. Results 53 eligible patients were identified. 39 were male (73.6%), 31 had non-ischaemic aetiology (58.5%), mean LVEF was 27.6% ± SD 4.79 and mean LVEDD was 57.9± SD 9.4mm. Unadjusted correlations between intrinsic ECG features were not strong but favoured P-QRSend as a potentially predictive feature; R2 values: PR 0.28 (p=0.04), Pend-RWPTV6 0.19 (p=0.18), P-QRSend 0.27 (p=0.054), QRSd 0.045 (p=0.75). However, the multiparametric model revealed that utilising both PR interval and Pend-RWPTV6 in a predictive model showed better-adjusted correlation with BVP haemodynamic benefit (R2 0.54, p=0.04). Conclusion ECG features that incorporate either right (PR interval) or left (Pend-RWPTV6) AVD or both (P-QRSend) are superior to QRSd for predicting haemodynamic benefit from BVP in patients with HF and LBBB. This further reinforces the role of AVD shortening as a mechanism of benefit of BVP and suggests that selecting patients on the basis of AV timing may yield more overall benefit from CRT delivered by BVP or potentially conduction system pacing.ECG interval measurementCorrelation Graphs

Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

The rapid, accurate, and non-destructive estimation of rubber plantation aboveground biomass (AGB) is essential for producers to forecast rubber yield and carbon storage. To enhance the estimation accuracy, an increasing number of remote sensing variables are incorporated into the development of multi-parameter models, which makes its practical application and the potential impact on predictive precision challenging due to the inclusion of non-essential or redundant variables. Therefore, this study systematically evaluated the performance of different parameter combinations derived from Sentinel-2 imagery, using variable optimization approaches with four machine learning algorithms (Random Forest Regression, RF; XGBoost Regression, XGBR; K Nearest Neighbor Regression, KNNR; and Support Vector Regression, SVR) for the estimation of the AGB of rubber plantations. The results indicate that RF achieved the best estimation accuracy (R2 = 0.86, RMSE = 15.77 Mg/ha) for predicting rubber plantation AGB when combined with Boruta-selected variables, outperforming other combinations (variable combinations obtained based on importance ranking, univariate combinations, and multivariate combinations). Our research findings suggest that the consideration of parameter-optimized remote sensing variables is advantageous for improving the estimation accuracy of forest biophysical parameters, when utilizing a large number of parameters for estimation.

An experimental study on enhancing performance and reducing emissions of CRDI engine operated on Co-pyrolysis oil using particle swarm optimization

This study explores the use of Artificial Neural Network and Particle Swarm Optimization to improve the performance and emissions of a CRDI engine fuelled by a mixture of co-pyrolyzed oil and diesel. Experimental results show that the mixed fuels perform better than pure diesel in various engine conditions, achieving higher brake thermal efficiency and lower smoke emissions. The blend D90P10 has the highest BTE of 37.4% working at FIT 30°bTDC and FIP 500 bar. However, NO and CO2 emissions were increased for the same condition. A multi-parametric ANN model is created to build a predictive model based on this experimental data, relating inputs (fuel injection pressure, fuel injection timing, and blend percentages) to outputs (Brake Specific Fuel Consumption, NO, smoke, CO2 emissions). The ANN model attains high accuracy with R2 values of 0.99, 0.98, 0.99, and 0.99 for BSFC, NO, Smoke, and CO2 emission predictions, respectively. The results indicate that for optimal performance, the best conditions are a FIT of 29.71ºbTDC, a FIP of 500 bar, and a co-pyrolysis oil blend percentage of 19.61%. On the other hand, to minimize engine emissions, the best conditions are FIT of 20.28ºbTDC, FIP of 300 bars, and a blend percentage of 25.6%. A balanced approach results in an optimal condition of FIT at 29.5ºbTDC, FIP of 500 bars, and a blend percentage of 6.11%. Experimental validation of these optimal predictions shows a maximum error of only 3.18%, highlighting the reliability and accuracy of the model.

Elevated pretreatment lactate dehydrogenase and albumin-to-alkaline phosphatase ratio predict poor prognosis and early treatment discontinuation in head and neck cancer patients with preexistent diabetes mellitus.

Increased serum lactate dehydrogenase (LDH) activity is considered as amarker of cellular necrosis and serves as ametabolomic diagnostic marker in several types of cancer including head and neck squamous cell carcinoma (HNSCC). LDH, an enzyme involved in the glycolytic cycle, is correlated not only with the activation of oncogenes such as HIF-α and Myc, but also with effects such as tumor proliferation and metastasis. Serum alkaline phosphatase (ALP) is amarker of cell differentiation and tumor induction. Albumin-to-alkaline phosphatase ratio (AAPR) could be an advantageous biomarker due to its easily accessible dynamics and cost-effectiveness. Elevated values of AAPR could be associated with longer overall survival (OS) in cases with solid tumors. Diabetes mellitus (DM) could influence the outcome of patients with HNSCC by contributing to insulin resistance and chronic inflammation, and by being involved in various aspects of carcinogenesis, disease progression and metastasis. However, the use of antihyperglycemic medications (metformin) can have beneficial effects by inhibiting tumor metabolic pathways. The biomarker role of LDH and AAPR in HNSCC patients with DM has been less evaluated. The purpose of the study was to assess the prognostic value of pretreatment serum lactate dehydrogenase (LDH) and albumin-to-alkaline phosphatase ratio (AAPR) in predicting the duration of non-surgical oncological treatment and glycemic control in cases of head and neck cancers patients with DM, including cases selected from the database of the oncology clinic and oncology outpatient clinic of the Craiova County Hospital. Both LDH and AAPR can be used as pre-treatment biomarkers predictive of treatment response, or prognostic tools included in complex multi-parametric models in HNC associated with DM. However, given the impact of short-term glycemic control on the LDH level, it is necessary to evaluate these biomarkers after assessing and controlling for DM, and with the recommended cut-off value set around 0.5. Due to the limited number of cases, it is necessary to validate the results in multicentric trials with alarger number of patients (Tab. 5, Ref. 50). Keywords: diabetes mellitus, HNC, LDH, AAPR, biomarkers, predictive, head and neck cancers, lactate dehydrogenase, albumin-to-alkaline phosphatase ratio.