Predicted Value Research Articles

The Predictive Validity of the Full Outline of UnResponsiveness Score Compared to the Glasgow Coma Scale in the Intensive Care Unit: A Systematic Review.

The Full Outline of UnResponsiveness (FOUR) score was developed to overcome the limitations of the Glasgow Coma Scale (GCS) when assessing individuals with impaired consciousness. We sought to review the evidence regarding the predictive validity of the GCS and FOUR score in intensive care unit (ICU) settings. This review was prospectively registered in PROSPERO (CRD42023420528). Systematic searches of CINAHL, MEDLINE, and Embase were undertaken. Prospective observational studies were included if both GCS and FOUR score were assessed in adults during ICU admission and if mortality and/or validated functional outcome measure scores were collected. Studies were excluded if they exclusively investigated patients with traumatic brain injury. Screening, data extraction, and quality assessment using the Quality in Prognosis Studies tool were conducted by two reviewers. Twenty studies of poor to moderate quality were included. Many studies only included patients with neurological illness and excluded sedated patients, despite high proportions of intubated patients. The FOUR score achieved higher area under the receiver operating characteristic curve values for mortality prediction compared with the GCS, and the FOUR score achieved significantly higher area under the receiver operating characteristic curve values for predictions of ICU mortality. Both coma scales showed similar accuracy in predicting "unfavorable" functional outcome. The FOUR score appeared to be more responsive than the GCS in the ICU, as most patients with a GCS score of 3 obtained FOUR scores between 1 and 8 due to preserved brainstem function. The FOUR score may be superior to the GCS for predicting mortality in ICU settings. Further adequately powered studies with clear, reliable methods for assessment of index and outcome scores are required to clarify the predictive performance of both coma scales in ICUs. Inclusion of sedated patients may improve generalizability of findings in general ICU populations.

How likely are Eastern European and Central Asian Countries to Achieve Global NCD Targets?

Abstract Background In Europe, premature deaths from noncommunicable diseases (NCDs) have declined significantly, except in twelve countries of Eastern Europe and Central Asia (EECA), namely Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan. Analyses of the long-term trends of NCD premature mortality in EECA are limited making it hard to know what to expect over the next years. Methods We used the 1990-2019 Global Burden of Disease database to analyze NCD trends and identify country-specific change points by using piecewise linear regression. We assessed the likelihood of achieving NCD global targets (GTs) to reduce premature mortality among persons 30-69 years old from four NCDs: cardiovascular diseases (CVD), cancers, diabetes mellitus (DM) and chronic respiratory diseases (CRD) by 25% from 2010 to 2025 (WHO25X25 target) and by 33% from 2015 to 2030 (SDG 3.4.1). Results Only Kazakhstan and Russia are likely to achieve NCD-GTs. For Kazakhstan, WHO25X25 and SDG 3.4.1 GTs mortality rates were 494.3 and 374.8 per 100,000 population respectively; the corresponding predicted values (PVs) were 360.6 [CI 260.1-461.1] and 245.1 [CI 113.4-376.8]. For Russia, WHO25X25 and SDG 3.4.1 GTs were 560.5 and 442.8 per 100,000 population respectively; the corresponding PVs were 427.7 [CI 270.3-585.1] and 311.0 [CI 102.8-519.1]. Achieving NCD-GTs is also possible for Kyrgyzstan. For the rest of EECA countries achieving NCD-GTs is unlikely. All EECA countries have increasing premature mortality from DM, eleven - from CRD and cancers, and six - from CVD. Most EECA countries have higher NCD rates and slower progress among men compared with women. Conclusions In most EECA countries the trend towards achieving NCD-GTs is slow or reversed. Further quantitative and qualitative research is needed to understand the underlying reasons. Separate indicators are needed to monitor countries’ progress for cancers, DM and CRDs. Key messages • Most EECA countries have slow or reversed trend in achieving NCD-GTs. All EECA have increasing premature mortality from DM, eleven – from CRD and cancers, and six – from CVD. • Further research is needed to understand the underlying reasons for slow progress towards NCD-GTs in EECA. Separate indicators are needed to monitor progress for cancers, DM and CRDs.

How likely are Eastern European and central Asian countries to achieve global NCD targets: multi-country analysis

BackgroundIn Europe, mortality rates from noncommunicable diseases (NCDs) among persons 30–69 years of age (“NCD premature mortality rates”) have declined significantly, except in twelve countries of Eastern Europe and Central Asia, namely Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan. Data on long-term trends in NCD mortality in these countries are limited. We analyzed NCD premature mortality rates, identified change points in NCD mortality trends and forecasted how likely countries are to achieve the global NCD targets, stratified by gender and NCD type.MethodsWe used the 1990–2019 Global Burden of Disease database to analyze NCD trends and identified country-specific change points by using piecewise linear regression. We assessed the likelihood of achieving the global targets for reducing NCD premature mortality rates among persons 30–69 years of age from four NCDs: cancers, diabetes, cardiovascular and chronic respiratory diseases. The global NCD targets are 25% reduction in mortality from 2010 to 2025 (WHO 25X25 target) and 33%—from 2015 to 2030 (SDG 3.4.1). We applied the analysis to both genders and four NCDs.ResultsOnly Kazakhstan and Russia are likely to achieve the global NCD targets. For Kazakhstan, WHO 25X25 and SDG 3.4.1 global targets for mortality rates were 494.3 and 374.8 per 100,000 population respectively; the corresponding predicted values (PVs) were 360.6 [CI 260.1–461.1] and 245.1 [CI 113.4–376.8]. For Russia, WHO 25X25 and SDG 3.4.1 global targets were 560.5 and 442.8 per 100,000 population respectively; the corresponding PVs were 427.7 [CI 270.3–585.1] and 311.0 [CI 102.8–519.1]. Achieving NCD global targets is less likely for Kyrgyzstan, while it is unlikely for the rest of countries. Most countries had higher mortality rates and slower progress among men compared with women. The likelihood of achieving overall global NCD targets was mainly explained by reduction in cardiovascular mortality.ConclusionsIn most Eastern Europe and Central Asia countries, progress towards achieving NCD global targets is slow, or there’s a reverse trend. Further quantitative and qualitative research is needed to understand the underlying reasons. Separate indicators are needed to monitor trends for cancers, diabetes and chronic respiratory diseases.

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

Agreement of Medication Information Derived From EHR Data Compared to Medicare Insurance Claims: An Analysis of Biologic Disease-Modifying Antirheumatic Drugs.

Few studies have reported the agreement between medication information derived from ambulatory EHR data compared to administrative claims for high-cost specialty drugs. We used data from a national EHR-enabled registry, the Rheumatology Informatics System for Effectiveness (RISE), with linked Medicare claims in a population of patients with rheumatoid arthritis (RA) to investigate variations in agreement for different biologic disease-modifying agents (bDMARDs) between two data sources (RISE EHR data vs. Medicare claims), categorized by drug, route of administration, and patient insurance factors (dual eligibility). Patients ≥ 65 years old, with ≥ 2 visits in RISE with RA ICD codes ≥ 30 days apart, and continuous enrollment in Medicare Parts B and D in 2017-2018 were included. We classified patients as bDMARD users or nonusers in Medicare claims or EHR data in 2018, and we calculated sensitivity, specificity, positive predicted value (PPV), and negative predicted value (NPV) of EHR data for identifying bDMARD users, using Medicare as the reference standard. We also calculated these metrics after stratifying by clinic-administered (Part B) versus. pharmacy-dispensed (Part D) bDMARDs and by patient dual-eligibility. A total of 26 097 patients were included in the study. Using Medicare claims as the reference standard, EHR data had a sensitivity of 75.0%-90.8% for identifying patients with the same medication and route. PPV for Part B bDMARDs was higher compared with Part D bDMARDs (range 94.3%-97.3% vs. 51.0%-69.6%). We observed higher PPVs for Part D bDMARDs among patients who were dual-eligible (range 82.4%-95.1%). The risk of misclassification of drug exposure based on EHR data sources alone is small for Medicare Part B bDMARDs but could be as high as 50% for Part D bDMARDs, in particular for patients who are not dually eligible for Medicare and Medicaid.

Rapid Real-Time Prediction Techniques for Ammonia and Nitrite in High-Density Shrimp Farming in Recirculating Aquaculture Systems

Water quality early warning is a key aspect in industrial recirculating aquaculture systems for high-density shrimp farming. The concentrations of ammonia nitrogen and nitrite in the water significantly impact the cultured animals and are challenging to measure in real-time, posing a substantial challenge to water quality early warning technology. This study aims to collect data samples using low-cost water quality sensors during the industrial recirculating aquaculture process and to construct predictive values for ammonia nitrogen and nitrite, which are difficult to obtain through sensors in the aquaculture environment, using data prediction techniques. This study employs various machine learning algorithms, including General Regression Neural Network (GRNN), Deep Belief Network (DBN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM), to build predictive models for ammonia nitrogen and nitrite. The accuracy of the models is determined by comparing the predicted values with the actual values, and the performance of the models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. Ultimately, the optimized GRNN-based predictive model for ammonia nitrogen concentration (MAE = 0.5915, MAPE = 28.95%, RMSE = 0.7765) and the nitrite concentration predictive model (MAE = 0.1191, MAPE = 29.65%, RMSE = 0.1904) were selected. The models can be integrated into an Internet of Things system to analyze the changes in ammonia nitrogen and nitrite concentrations over time through aquaculture management and routine water quality conditions, thereby achieving the application of recirculating aquaculture system water environment early warning technology.

Language models outperform cloze predictability in a cognitive model of reading.

Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye-movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT-2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements.

Serum soluble interleukin-2 receptor alpha may predict tubulointerstitial inflammatory cell infiltration and short-term disease progression in immunoglobin A nephropathy.

This study aims to explore the relationship between serum soluble interleukin-2 receptoralpha (sIL-2Rα) levels and histologic features in immunoglobin A nephropathy (IgAN), and evaluate its predicting values on disease progression and remission status. IgAN patients were included retrospectively. Lee classification, Oxford classification and histological scoring were evaluated. Patients' estimated filtration rate (eGFR) and proteinuria remission status were collected during 6-month follow-up. Logistic regression was used to determine the risk factors and predicting value. Receiver operating characteristic (ROC) curve were used to determine the predicting value for outcome. One hundred seventy-two subjects were included in this study. Individuals in moderate-to-severe tubulointerstitial inflammatory cell infiltration group manifested with significantly elevated serum sIL-2Rα levels than those in non-to-mild group. Serum sIL-2Rα levels were positively correlated with infiltration scores. Serum sIL-2Rα was an independent risk factor for moderate-to-severe inflammatory cell infiltration [sIL-2Rα: OR 1.29 (1.015-1.640, p = 0.038)]. ROC curve analysis regarding predictive value for moderate-to-severe inflammatory cell infiltration of sIL-2Rα suggested area under curve was 0.859 (0.801-0.918, p = 0.000) when sIL-2Rα combined with eGFR < 60mL/(min·1.73 m2), 24-h proteinuria excretion > 1.0g, and hemoglobin. It showed good sensitivity (71.6%) and specificity (87.6%). Additionally, sIL-2Rα levels at kidney biopsy were strong predictive factor for kidney function loss 6months after kidney biopsy [OR 4.161 (1.013-17.088, p = 0.048)]. High serum sIL-2Rα was significantly associated with serious inflammatory cell infiltration in IgAN, and it showed strong predictive value for disease prognosis. Serum sIL-2Rα could be a useful noninvasive biomarker to evaluate the extent of histological injury and disease prognosis in IgAN.

External validation of the BE-ALIVE score for predicting 30-day mortality in patients presenting with acute coronary syndromes

IntroductionThe BE-ALIVE score is an additive scoring system for estimating 30-day mortality in patients presenting with an acute coronary syndrome (ACS) [1]. However, it had only previously been tested on an internal validation cohort. The aim was to assess the scoring system on an external validation cohort. MethodsThe scoring system comprises six domains: (1) Base Excess (1 point for < −2 mmols/L), (2) Age (<65 years: 0 points, 65–74: 1 point, 75–84: 2 points, ≥ 85: 3 points), (3) Lactate (<2 mmols/L: 0 points, 2–4.9: 1 point, 5–9.9: 3 points, ≥ 10: 6 points), (4) Intubated & Ventilated (2 points), (5) Left Ventricular function (normal or mildly impaired: -1 point, moderately impaired: 1 point, severely impaired: 3 points) and (6) External / out of hospital cardiac arrest (1 point).We applied the BE-ALIVE score was applied to 205 consecutive patients at a different institution. ResultsCalibration was strong, with an observed to expected ratio of 1.01, a calibration slope of 1.26 and calibration in the large of −0.03. The Spiegelhalter's Z-statistic was −0.95 (p = 0.34). The AUC was 0.95 (0.92–0.98) in the external validation cohort versus 0.90 (0.85–0.95) during internal validation.Overall performance was excellent with a Brier score of 0.07 versus 0.06 during internal validation. The negative predictive value for 30-day mortality of a BE-ALIVE score < 4 was 98 %, with a positive predicted value of a score ≥ 10 of 95 %. ConclusionsThe BE-ALIVE score remains a robust predictor of 30-day mortality in an external validation cohort.

Predicting effective thermal conductivity of HGM composite using ML

Data-driven materials research can be used as an effective tool to supplement conventional approaches in designing new materials. Hollow glass microsphere (HGM) composites are widely used for high-temperature thermal insulation applications, and their synthesis is traditionally done by varying parameters of the constituents on a trial-and-error basis. In this work, a prediction tool based on a supervised machine learning (ML) model is developed to predict the effective thermal conductivity (ETC) of an HGM composite by tailoring the composition and parameters of the constituents to reduce the time and cost involved. A comprehensive database containing the various input features is generated from previous experimental investigations conducted on various HGM composites. ML models, namely random forest regression (RF), K-nearest neighbor (KNN), support vector regression (SVR), and artificial neural network (ANN), are used to predict the ETC of HGM composites. Feature importance analysis showed that the matrix material’s thermal conductivity and the composite’s bulk density have the highest impact on the ETC of the HGM composite, followed by porosity and average microsphere size. ANN emerged as the best model to predict HGM composite ETC with the lowest root mean square error and highest R2 value for predictions. Moreover, a systematic approach for key feature selection using ANN shows that adding or omitting additional features beyond an optimal combination degrades the model’s predictive accuracy.

Comparison of machine learning algorithms for predicting cognitive impairment using neuropsychological tests

Objectives Neuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer’s disease dementia (ADD) among groups of subjects. Patients and methods A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset. Results The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects. Conclusion According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.

Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa

Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.

Advanced attention-based spatial-temporal neural networks for enhanced CO2 water-alternating-gas performance prediction and history matching

This study combines convolutional neural networks, spatial pyramid pooling, and long short-term memory networks (LSTM) with self-attention (SA) mechanisms (abbreviated as CSAL) to address the problem of production dynamics prediction in tight reservoirs during the CO2 water-alternating-gas (CO2-WAG) injection process. By integrating DenseNet and SPP modules, this method effectively captures and processes complex spatial features in tight reservoirs. Concurrently, the LSTM enhanced with SA mechanisms improves the prediction capability of temporal data during the CO2-WAG process. Experimental results demonstrate that the CSAL model performs excellently in both the training and testing phases, achieving a coefficient of determination (R2) exceeding 0.98, significantly enhancing the model's prediction accuracy. Compared to models without attention mechanisms, the CSAL model increases the R2 value in time series prediction by 10%. Furthermore, employing the Ensemble Smoother with Multiple Data Assimilation algorithm, the CSAL model achieves high-precision history matching, significantly reducing the error between predicted values and actual observations. This study validates the application potential and superiority of the CSAL model in the CO2-WAG process in tight reservoirs.

Total Cholesterol Determination Accuracy in Dried Blood Spots.

Background Detecting total cholesterol in dried blood spots could aid in identifying individuals with a high likelihood of familial hypercholesterolemia and could be used as a screening measure. This study aims to assess the diagnostic accuracy of dried blood spots on Whatman 903 paper cards using a manual enzymatic technique. Methods: A total of 394 samples were collected as serum and dried blood spots were compared. Cholesterol was determined in serum using the automated reference method, while cholesterol on paper was measured using a manual enzymatic method. Within- and between-day diagnostic variability were analyzed. The correlation between both methods was assessed using Passing-Bablok regression and Bland-Altman plot. Internal validation of our correlation formula was performed on 149 samples, along with external validation of the formula proposed by Corso et al. Results: The within- and between-day coefficient of variation was found to be lower than 10.14% and 14.09%, respectively. Passing-Bablok regression indicated a precision of 0.803 and an accuracy of 0.96. Internal validation precision was measured at 0.716. The resulting positive and negative predicted values were 0.77 and 0.92, respectively, vs. 0.46 and 0.96 from the external formula. Conclusions: Total cholesterol analysis in dried blood spots demonstrates high precision and reproducibility. This method reliably enables the incorporation of this biological marker into neonatal screening for familial hypercholesterolemia detection.

Design and Characterization of Porous Electrodes for Redox Flow Batteries

The commercialization of redox flow batteries as large-scale energy storage systems depends on lowering the capital and operating costs which depend on achieving maximum power density. Optimizing electrode design parameters and flow field-electrode combinations is critical to minimize losses and maximize active material utilization. Modeling the current drawn from an electrode offers a means to interpret electrode structure based on the electrolyte distribution driven by internal design. However, direct imaging methods (such as in-situ fluorescence microscopy and X-ray tomography) and numerical methods for current to potential prediction (3D Lattice Boltzmann modeling) are specific to individual electrode structures and possess limited predictable value to obtain an ideal electrode design. In this work, we present an electrode impedance spectroscopy (EIS)-based approach for determining electrode design parameters and devise a general method for determination of effective diffusion layer thickness. Using COMSOL modeling we verified the development of slug flow behavior in highly randomized porous electrode systems, indicating that the mass transport properties are dictated by inter-fiber distance. An analytical model to predict current as a function of applied overpotential is presented which allows us to vary structural parameters of the porous electrodes to study the influence of surface area, inter-fiber distances and diffusion layer thickness on the RFB performance. This model is a highly flexible tool applicable to any electrochemical system using a porous electrode and identifies critical variables affecting electrode design to characterize optimal electrode structure to minimize mass transport losses and optimize operational parameters.

Prediction of internal jugular vein catheter length inserted through the posterior approach of the sternocleidomastoid muscle.

This study aimed to determine an equation to estimate the optimal insertion length for catheter placement via the posterior approach of the sternocleidomastoid muscle in cancer patients. This retrospective study included patients with cancer who underwent infusion port implantation surgery in the Oncology Department of the 900th Hospital of Joint Logistic Support Force of the Chinese People Liberation Army from April 2017 to September 2023. Patient height (H), weight (W), chest length (C), and length of the internal jugular vein catheter (L) were collected from medical records. The patients were randomized 7:3 to the training and validation sets. Linear regression analyses were used in the training set to determine formulas to predict catheter length. The formula predictive value was analyzed using the Bland-Altman method in the validation set. This study included 336 patients, with a mean age of 58.27 ± 11.70 years, randomized in the training (n = 235) and validation (n = 101) sets. Linear regression analysis revealed that the equations for catheter length relative to H, body mass index (BMI), and C are L = 0.144 × H - 8.258 (R² = 0.608, P < .001), L = -0.103 × B + 17.384 (R² = 0.055, P < .001), and L = 0.477 × C + 1.769 (R² = 0.342, P < .001), respectively. The multivariable linear regression analysis showed that the equation between the length of the catheter and H and C was L = 0.131 × H + 0.086 × C-8.515 (R² = 0.614, P < .001). The Bland-Altman analysis in the validation set showed that the predicted values of internal jugular vein catheter length and the actual values showed good agreement. The optimal L might be determined by simple formulas based on patients H and C.

Population Pharmacokinetics and Exposure-Response Relationship of Zimberelimab in Chinese Patients with Advanced Tumors.

This study aimed to establish a population pharmacokinetic (PopPK) model using data from 2 clinical trials of zimberelimab, evaluate the pharmacokinetics (PKs) of zimberelimab, explore the feasibility of 360mg once every 3 weeks (Q3W) and 480mg once every 4 weeks (Q4W) as alternative dosage regimens, and analyze the exposure-response relationship of the efficacy and safety of zimberelimab for advanced tumors. The PKs of zimberelimab were described using the 2-compartment model with time-dependent nonlinear elimination. The prediction-corrected visual predictive check was used to evaluate the model's predictive value on blood drug concentrations. In total, 2165 PK observations from 321 participants were included. The PopPK model demonstrated a high level of concordance between the observed data and the predicted values, indicative of a robust fit to the PK data of zimberelimab. The PK variables were similar for the 240mg once every 2 weeks, 360mg Q3W, and 480mg Q4W regimens. No covariates significantly affecting the PK variables in the final model were found. The exposure variables of zimberelimab have no obvious correlations with efficacy and safety, and 360mg Q3W and 480mg Q4W are worthy of further study. This study establishes a PopPK model and analyzes the exposure-response relationship of zimberelimab, which helps to explore the potential for alternative dosing regimens and offers a foundation for optimizing therapeutic strategies for advanced cancer patients through simulation-based methods.

ProsTAV, a clinically useful test in prostate cancer: an extension study

PurposeTo assess the clinical performance of ProsTAV®, a blood-based test based on telomere associate variables (TAV) measurement, to support biopsy decision-making when diagnosing suspicious prostate cancer (PCa).MethodsPreliminary data of a prospective observational pragmatic study of patients with prostate-specific antigen (PSA) levels 3–10 ng/ml and suspicious PCa. Results were combined with other clinical data, and all patients underwent prostate biopsies according to each center’s routine clinical practice, while magnetic resonance imaging (MRI) before the prostate biopsy was optional. Sensitivity, specificity, positive and negative predicted values, and subjects where biopsies could have been avoided using ProsTAV were determined.ResultsThe mean age of the participants (n = 251) was 67.4 years, with a mean PSA of 5.90 ng/ml, a mean free PSA of 18.9%, and a PSA density of 0.14 ng/ml. Digital rectal examination was abnormal in 21.1% of the subjects, and according to biopsy, the prevalence of significant PCa was 47.8%. The area under the ROC curve of ProsTAV was 0.7, with a sensitivity of 0.90 (95% CI, 0.85–0.95) and specificity of 0.27 (95% CI, 0.19–0.34). The positive and negative predictive values were 0.53 (95% CI, 0.46–0.60) and 0.74 (95% CI, 0.62–0.87), respectively. ProsTAV could have reduced the biopsies performed by 27% and showed some initial evidence of a putative benefit in the diagnosis pathway combined with MRI.ConclusionsProsTAV increases the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/ml and could be considered a complementary tool to improve the patient diagnosis pathway.

Comparism of Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in Optimisation of Soybean Soapstock Biodiesel Production

Soybean soapstock (SS), a lipid rich by-product of soybean oil production is a promising feedstock for the production ofbiodiesel due to its availability and affordability. In the esterification and transesterification reactions involving soyabeansoapstock, sodium hydroxide, methanol and n-hexane were used as catalyst, solvent and co-solvent respectively. The physico-chemical properties of the biodiesel obtained were determinedusing the Association of Analytical Chemist (AOAC) and American Society of Testing Materials (ASTM) methods. The esterification and transesterification reactions were optimised using both response surface methodology (RSM) under design expert 7.0 platform and Particle swarm technique in ANFIS (ANFIS-PSO) using the MATLAB software. The optimized acid value from the esterification reaction using RSM and ANFIS-PSO were 4.956 and 1.488 while the yield obtained were 97.29% and 99.91%respectively with ANFIS-PSO proving to be the better optimization technique in both cases. Comparison plots made for both reactions shows the ANFIS-PSO curve mirroring the experimental and thus signifying a closer trend when compared to the RSM curve. The suitability of the ANFIS-PSO prediction was further highlighted by the error analysis carried out on both techniques. The Residual sum of squares (RSS), Mean absolute error (MAE), Root mean square error (RMSE), Correlation coefficient (R), Coefficient of determination (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;), Adjusted R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;, Absolute average deviation (AAD) and Mean absolute percent error (MAPE) values for the ANFIS-PSO predictions in both reactions were better than the RSM predictions. It can thus be concluded that soybean soapstock is a viable feedstock for biodiesel production and ANFIS-PSO is a more efficient optimization technique when compared with RSM in esterification and transesterification of soybean soapstock.

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations.

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.