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Objective. The number of cancer survivors is increasing, and the high prevalence of frailty not only reduces quality of life but also affects the treatment of cancer patients. This study aimed to identify the prevalence and risk factors of frailty in cancer patients and to construct a nomogram to predict the probability of frailty. Methods. Nine hundred fifty‐eight cancer patients were included in this retrospective study, randomly divided into a development set (n = 680) and a validation set (n = 278). Frailty was assessed using the Tilburg frailty indicator (TFI). Social support, medical coping styles, and psychological distress were assessed by the Social Support Self‐Rating Scale (SSRS), Medical Coping Modes Questionnaire (MCMQ), and distress thermometer (DT), respectively. Results. The prevalence of frailty in cancer patients was 45.93%. Cancer patients who exercised regularly, ate a balanced diet, and actively coped with diseases were less likely to become frail. The risk factors for frailty identified by a multivariate analysis were parenteral nutrition, advanced TNM staging, vegetarian diet, unemployment, psychological distress ≥4, low physical activity, and negative coping styles. These risk factors were used to construct a nomogram, and the C‐index, calibration curve, and decision curve analysis (DCA) were used to assess the performance of the nomogram. The C‐index was 0.762, and the calibration curve showed satisfactory coherence. The net benefit of the nomogram was better between threshold probabilities of 17%–96% in DCA. Conclusion. Special focus needs to be placed on frail cancer patients due to their high prevalence and severe outcomes. Clinical medical workers could use this nomogram to identify high‐risk patients and intervene early to prevent frailty. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) is a common type of malignant tumor with therapeutic challenges. Cancer-associated fibroblasts (CAFs) promote LUAD growth and metastasis, regulate the tumor immune response, and influence tumor treatment responses and drug resistance. However, the molecular mechanisms through which CAFs control LUAD progression are largely unknown. In this study, we aimed to determine the correlations between CAF-related genes and overall survival (OS) in patients with LUAD. Methods: We acquired the gene expression data and clinical information of 522 patients with LUAD patients from The Cancer Genome Atlas (TCGA) and 442 patients with LUAD from the Gene Expression Omnibus (GEO) databases. CAF infiltration levels were assessed using the Microenvironment Cell Population (MCP) counter, the Estimating the Proportions of Immune and Cancer cells (EPIC) algorithm, and Tumor Immune Dysfunction and Exclusion (TIDE) scores. A CAF-related gene network was constructed using the Weighted gene co-expression network analysis (WGCNA). Based on the CAF-related genes, univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to identify prognostic genes. Gene expression levels within the prognostic model were validated using the Cancer Cell Line Encyclopedia (CCLE) databases and Western blotting. Results: Our results demonstrated that high CAF scores were associated with lower survival rates in patients with LUAD. Gene modules that were highly correlated with high CAF scores were closely associated with tissue characteristics and extracellular matrix structures in LUAD. In addition, correlations between CAF scores and responses to immunotherapy and chemotherapy were observed. Finally, we found that SNAI2 expression was higher in lung cancer tissues than in normal tissues. Conclusion: Deepening our understanding of the influence of CAFs on tumor progression and treatment response at the molecular level can aid the development of more effective therapeutic strategies. This study provides important insights into the functional mechanisms of action of CAFs in LUAD and highlights their clinical implications. [ABSTRACT FROM AUTHOR]

Portland cement concrete (PCC) is a major contributor to human-made CO2 emissions. To address this environmental impact, fly ash geopolymer concrete (FAGC) has emerged as a promising low-carbon alternative. This study establishes a robust compressive strength prediction model for FAGC and develops an optimal mixture design method to achieve target compressive strength with minimal CO2 emissions. To develop robust prediction models, comprehensive factors, including fly ash characteristics, mixture proportions, curing parameters, and specimen types, are considered, a large dataset comprising 1136 observations is created, and polynomial regression, genetic programming, and ensemble learning are employed. The ensemble learning model shows superior accuracy and generalization ability with an RMSE value of 1.81 MPa and an R2 value of 0.93 in the experimental validation set. Then, the study integrates the developed strength model with a life cycle assessment-based CO2 emissions model, formulating an optimal FAGC mixture design program. A case study validates the effectiveness of this program, demonstrating a 16.7% reduction in CO2 emissions for FAGC with a compressive strength of 50 MPa compared to traditional trial-and-error design. Moreover, compared to PCC, the developed FAGC achieves a substantial 60.3% reduction in CO2 emissions. This work provides engineers with tools for compressive strength prediction and low carbon optimization of FAGC, enabling rapid and highly accurate design of concrete with lower CO2 emissions and greater sustainability. [ABSTRACT FROM AUTHOR]

Background: Labor pain is the nastiest conceivable pain women are involved in during labor and delivery. In this way, the end of labor pain is frequently outlined by myths and equivocalness. Hence, giving a compelling absence of pain in labor remained a challenge specifically in developing countries including Ethiopia. Objectives: This systematic review and meta-analysis aimed to explore the pooled prevalence of labor analgesia and associated factors to pregnant women in Ethiopia. Design: A systematic review and meta-analysis were utilized in agreement with the Preferred Reporting Items for Systematic Reviews. Data Sources: PubMed/Medline, SCOPUS, EMBASE, Web of Science, Google Scholars, and the Cochrane Library and supplemented it with manual were deliberately looked at until January 1–30, 2024. Methods: Two authors independently extricated all principal information utilizing standardized data extraction designs, and the analysis was done utilizing STATA version 17. Heterogeneity over the studies was evaluated utilizing I 2 measurement. The funnel plot and Egger's weighted regression tests were utilized to assess subjective and objective publication biases respectively. Also, the pooled effect of labor pain management and the associations were evaluated utilizing a random-effects model. Results: The general pooled prevalence of labor analgesia in the present study was 23.3% with a (95% confidence interval (CI): 13.5, 33.1). Maternal age (odds ratio (OR): 1.91; 95% CI: 1.11, 2.77), parity of the mother (OR: 0.28; 95% CI: 0.06, 0.63), history of pregnancy misfortune (OR: 0.12; 95% CI: 0.11, 0.36), length of labor (OR: 2.09; 95% CI: 1.06, 3.13), and awareness about labor analgesia (OR: 1.91; 95% CI: 0.34, 3.49) were significantly related with labor analgesia among pregnant women in Ethiopia. Conclusions: The generally pooled prevalence of labor analgesia among pregnant women in Ethiopia was low. Maternal age, parity of the mother, history of pregnancy loss, length of labor, and awareness of labor analgesia were factors influencing labor analgesia among pregnant women in Ethiopia. This finding proposes exceptional consideration to make laboring mothers free of pain by scaling up the strategies and utilizing labor pain administration in a way that universally recognized standards are met. Registration number: PROSPERO CRD: 42024525636 [ABSTRACT FROM AUTHOR]

Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation. [ABSTRACT FROM AUTHOR]

Background: Chromosomal 1q gains and amplifications (+1q21) are frequently observed in patients with newly diagnosed multiple myeloma (NDMM). However, the interpretation of the high‐risk (HR) prognostic implications stemming from 1q21 abnormalities remain challenging to implement effectively. Methods: In a comprehensive analysis of 367 consecutive patients with symptomatic MM, we assessed the prognostic significance of +1q21 using FISH with a threshold of 7.4%. The patient cohort was randomly divided into a training set (66.5%, n = 244) and a validation set (33.5%, n = 133). A multivariate Cox regression analysis was conducted to identify significant prognostic factors associated with PFS. Weight scores were assigned to each risk factor based on the β‐value of the corresponding regression coefficient. A predictive risk‐scoring model involving +1q21 was then developed, utilizing the total score derived from these weight scores. The model's discriminative ability was evaluated using the AUC in both the training and validation sets. Finally, we compared the performance of the +1q21‐involved risk with the established R‐ISS and R2‐ISS models. Results: Upon initial diagnosis, 159 patients (43.32%) exhibited +1q21, with 94 (59.11%) having three copies, referred to as Gain(1q21), and 65 (40.89%) possessing four or more copies, referred to as Amp (1q21). Both were significantly linked to a reduced PFS in myeloma (p < 0.05), which could be effectively mitigated by ASCT. The +1q21‐involved risk model, with an AUC of 0.697 in the training set and 0.725 in the validation set, was constructed including Gain(1q21), Amp(1q21), no‐ASCT, and TP53 deletion. This model, termed the ultra‐high‐risk (UHR) model, demonstrated superior performance in predicting shorter PFS compared to the R‐ISS stage 3 and R2‐ISS stage 4. Conclusion: The UHR model, which integrates the presence of +1q21 with no‐ASCT and TP53 deletion, is designed to identify the early relapse subgroup among patients with +1q21 in NDMM. [ABSTRACT FROM AUTHOR]

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky–Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R2 values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system. [ABSTRACT FROM AUTHOR]

Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model prediction and is still an unsolved problem. In this regard, some robust estimators of the biased parameter are proposed based on the least trimmed squares (LTS) technique and its extensions using a semidefinite programming approach. Based on a set of observations with a sample size of n, and the integer trimming parameter h ≤ n, the LTS estimator computes the hyperplane that minimizes the sum of the lowest h squared residuals. Even though the LTS estimator is statistically more effective than the widely used least median squares (LMS) estimate, it is less complicated computationally than LMS. It is shown that the proposed robust extended Liu estimators perform better than classical estimators. As part of our proposal, using Monte Carlo simulation schemes and a real data example, the performance of robust Liu estimators is compared with that of classical ones in restricted partially linear models. [ABSTRACT FROM AUTHOR]

The six‐minute walking test (6MWT) is commonly used to measure functional capacity in field settings, primarily through the distance covered. This study aims to establish reference curves for the six‐minute walking distance (6MWD) and peak heart rate (PHR) and develop a predictive equation for cardiovascular capacity in Tunisian children and adolescents. A total of 1501 participants (706 boys and 795 girls), aged 10–18 years, were recruited from schools in Tunisia. The Lambda (L), Mu (M), and Sigma (S) methods (LMS method) were employed to develop smoothed percentile curves for 6MWD and PHR. Multivariate linear regression was utilized to formulate a prediction equation for 6MWD. Smoothed percentiles (3rd, 10th, 25th, 50th, 75th, 90th, and 97th) for 6MWD and PHR were presented with age. All variables showed a strong positive correlation (p < 0.001) with a six‐minute walking distance (r ranged from 0.227 to 0.558 for girls and from 0.309 to 0.610 for boys), except resting heart rate, which showed a strong negative correlation (girls: r = −0.136; boys: r = −0.201; p < 0.001). Additionally, PHR showed a weak correlation (p > 0.05). The prediction equations, based on age as the primary variable, were established for both genders. For boys: 6MWD = 66.181 + 38.142 × Age (years) (R2 = 0.372; Standard Error of Estimate (SEE) = 122.13), and for girls: 6MWD = 105.535 + 28.390 × Age (years) (R2 = 0.312; SEE = 103.66). The study provides normative values and predictive equations for 6MWD and PHR in Tunisian children and adolescents. These findings offer essential tools for identifying, monitoring, and interpreting cardiovascular functional deficits in clinical and research settings. Highlights: Established reference curves for six‐minute walking distance (6MWD) and peak heart rate (PHR) from the data of 1501 children and adolescents aged 10–18 years were recruited from multicenter schools in Tunisia.Developed predictive equations for 6MWD based on age using multivariate linear regression.Provides normative values and predictive equations for 6MWD and PHR, offering valuable tools for identifying and monitoring cardiovascular functional deficits in clinical and research settings. [ABSTRACT FROM AUTHOR]

Background: In sepsis, the relationship between lymphocyte counts and patient outcomes is complex. Lymphocytopenia and lymphocytosis significantly influence survival, illustrating the dual functionality of lymphocytes in responding to infections. This study investigates this complex interaction, focusing on how variations in lymphocyte counts correlate with all-cause hospital mortality among sepsis patients. Methods: This retrospective cohort study analyzed data from two extensive critical care databases: the Medical Information Mart for Intensive Care IV 2.0 (MIMIC-IV 2.0) from Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the eICU Collaborative Research Database (eICU-CRD), which was Multi-center database from over 200 hospitals across the United States conducted by Philips eICU Research Institute. We included adult patients aged 18 years and older who met the Sepsis-3 criteria, characterized by documented or suspected infection and a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. Sepsis patients were categorized into quartiles based on lymphocyte counts. The primary outcome was all-cause mortality in the hospital, with 90 and 60-day all-cause mortality as the secondary outcomes. Univariable and multivariable Cox proportional hazard regressions were utilized to assess lymphocyte counts' impact on hospital mortality. An adjusted restricted cubic spline (RCS) analysis was performed to elucidate this relationship further. Subgroup analyses were also conducted to explore the association across various comorbidity groups among sepsis and septic shock patients. Results: Our study included 37,054 patients, with an observed in-hospital mortality rate of 16.6%. Univariable and multivariable Cox proportional hazard regression models showed that lymphocyte counts were independently associated with in-hospital mortality (HR = 1.04, P < 0.01; HR = 1.06, P < 0.01). RCS regression analysis revealed a U-shaped relationship between lymphocyte levels and hospital mortality risk in sepsis and septic shock patients (P for overall < 0.001, P for nonliner < 0.01; P for overall = 0.002, P for nonliner = 0.014). Subgroup analyses revealed that elevated lymphocyte counts correlated with increased hospital mortality among sepsis patients with liver disease and requiring renal replacement therapy (P for overall = 0.021, P for nonliner = 0.158; P for overall = 0.025, P for nonliner = 0.759). These findings suggest that lymphocytes may have enhanced prognostic value in specific subsets of critically ill sepsis patients. Conclusion: Our findings demonstrate that lymphocyte counts are a significant independent predictor of hospital mortality in sepsis and septic shock patients. We observed a U-shaped association between lymphocyte levels and mortality risk, indicating that high and low counts are linked to increased mortality. This result highlights the complex role of lymphocytes in sepsis outcomes and suggests the need for further investigation into the underlying mechanisms and potential therapeutic approaches. Integrating lymphocyte count assessment into risk stratification algorithms and clinical decision support tools could enhance the early identification of high-risk sepsis patients. [ABSTRACT FROM AUTHOR]

Addressing the difficult problem of predicting the height of water-conducting fracture zones in shallow and thin coal seams, a prediction model of water-conduction fracture zones based on a backpropagation (BP) neural network was developed by integrating theoretical analysis, field measurements, and algorithmic advancements. Firstly, through overburden migration analysis and correlation tests, the height index system of the water-conducting fracture zone was determined. This system includes mining height, buried depth, dip angle, working face width, and overburden rock lithology, with five groups of characteristic parameters. Then, 35 pairs of minefield-measured data were collected to establish the measured height data set of the water-conducting fracture zone. Secondly, a BP neural network prediction model and a traditional support vector regression (SVR) prediction model were constructed based on a Pytorch framework, and the models were trained and tested by selecting data sets. Thirdly, the optimal prediction model was determined by comparing the model with the empirical model and multiple regression model of mining regulations for coal pillar maintenance and pressure in buildings, water bodies, railways, and main shafts. Finally, a typical mine was selected for application to verify the suitability of the optimal model. The results show that: (1) the predicted value of the neural network model is consistent with the change trend of the measured value, which accords with the theoretical law; (2) compared with traditional forecasting methods, the error of the BP neural network prediction model is stable and the prediction effect is the best; (3) dropout can effectively mitigate mitigation training overfitting, achieve regularization, and improve prediction accuracy; (4) the field application further verified that the BP neural network model is the best for predicting the height of water-conducting fracture zones of extremely thin coal seams, and the research results can provide technical guidance for similar fragile coal seams. [ABSTRACT FROM AUTHOR]

Background: Oral aripiprazole exhibits favorable clinical efficacy and safety in the suppression of tics in children and adolescents with tic disorders. This study aims to evaluate and compare the cost-effectiveness of high-dose and low-dose aripiprazole in children and adolescents with tic disorders from the perspective of the Chinese healthcare system. Methods: A questionnaire survey was conducted on 146 patients with tic disorders, of whom 144 completed EQ-5D-Y and YGTSS. Four models were built to convert YGTSS onto EQ-5D-Y utility using two mapping algorithms. We constructed a decision tree model containing efficacy and safety to compare the cost-effectiveness of high-dose and low-dose aripiprazole based on our mapping function. Results: The GLM with model 1 (YGTSS total tic scores) was selected as the preferred function in our decision tree model. The base case cost-effectiveness analysis showed that compared to low-dose aripiprazole, high-dose aripiprazole improves effectiveness by 0.001QALYs and increases the overall cost by $197.99, resulting in an ICER of $174339.22 per QALY, which exceeds three times the gross domestic product per capita. Hence, high-dose aripiprazole is not likely to be a cost-effective option for child patients with tic disorders. One-way sensitivity analysis and probabilistic sensitivity analysis showed that these results is robust. Conclusion: On the basis of currently available data, low-dose aripiprazole may be a safe, effective, and economical dosage for children and adolescents with tic disorders. Limitations: The main limitation of our study is the lack of utility directly used for cost-effectiveness analysis. We obtained the utility of patients with tic disorders indirectly by the mapping function. This may introduce some bias and uncertainty. And it is a limitation to use the direct medical costs of Germany in our model. Although we converted it to the equivalent value of China using purchasing power parities, caution should be exercised when interpreting the results of this study. [ABSTRACT FROM AUTHOR]

Objectives: Parkinson disease (PD) is the third leading cause of mortality among middle‐aged and older individuals in China. This study aimed to explore the trends and distribution features of PD mortality in China from 2013 to 2021 and make predictions for the next few decades. Methods: Relevant data were obtained from the Chinese Center for Disease Control and Prevention Disease Surveillance Point system. The joinpoint regression model was used to evaluate trends. The R software was used to predict future trends. Results: Age‐standardized mortality rate (ASMR) of PD increased from 0.59 per 100,000 individuals to 1.22 per 100,000 individuals from 2013 to 2021, with an average annual percent change (AAPC) of 9.50 (95% CI: 8.24–10.78). The all‐age ASMR of PD were higher in male individuals than in female individuals, and ASMR increased with age. The number of deaths and ASMR increased gradually from west to east and from rural to urban areas. Furthermore, ASMR is expected to increase to 2.66 per 100,000 individuals by 2040. Conclusions: The heightened focus on the ASMR of PD among male individuals, urban areas, eastern China, and individuals aged ≥85 years has become a key determinant in further decreasing mortality, thereby exhibiting novel challenges to effective strategies for disease prevention and control. [ABSTRACT FROM AUTHOR]

This study aims to establish an effective approach for evaluating the safety performance of road infrastructure. Road safety levels are typically quantified using safety performance indicators. However, due to the stochastic nature of accidents, many safety performance indicators cannot adequately and completely describe reality. Therefore, predictive methods based on regression models are widely used. This approach also allows for the identification of latent risk conditions in the infrastructure, even in the absence of accidents. Among available approaches, the Highway Safety Manual (HSM) methodology is chosen for its synthesis of validated highway research and best practices for incorporating safety into both new design and rehabilitation. For this study, a preliminary new version of HSM is used. The application of this method, which combines a predictive model with observed accidents through an empirical Bayesian approach, requires a calibration process that is crucial to tailoring this method to the specific study context. In this research, the predictive model is calibrated for single carriageway roads with one lane per direction across the Italian national network. Following calibration, the safety indicators are evaluated. The results obtained according to different indicators are compared to show the importance of adopting this method to counteract the regression to the mean of observed crashes. In fact, the method, supported by empirical Bayesian analysis, enables the identification of high-risk sections of the road network, selecting more sections that would be neglected by traditional indicators based solely on observed crashes. Finally, a possible approach to prioritizing sites for inspection based both on the excess of crashes and the Safety Potential (SAPO) is proposed. In addition, SAPO is adjusted to local conditions to account for the specific context and the decreasing trend of accidents over the years. [ABSTRACT FROM AUTHOR]

Background: For the past 50 years, standard guidelines have recommended the use of sex-adjusted mid-parental height to predict a child's final height. Here, we studied the accuracy of this procedure. Methods: We used height data in a cohort of 23 very large nuclear families (mean = 11 adult children per family). We compared the actual final height of the children to their height predicted by the standard procedure, as well as to alternative height predictions that incorporate corrections of mid-parental height for age, sex, and regression to the mean. Results: Standard mid-parental height explained 36% of the variance in children's heights, with a heritability of 74%, and children were on average 2.7 cm taller than predicted by their target heights. When we introduced a nonlinear correction for the age of the parents, employed a multiplicative (rather than additive) correction for sex, and accounted for regression to the mean, the variance explained increased to 40%, heritability increased to 80%, and prediction bias was reduced from 2.7 cm to 0.14 cm (representing an improvement in prediction by half a standard deviation of the height distribution). We further measured the empirical distribution of the heights of adult children around their predicted height. We describe how this distribution can be used to estimate the probability that a child's height is within the normal expected range. Conclusions and Relevance: Based on these observations, we propose an improved method for predicting children's target heights. Our procedure for determining whether the deviation of a child's projected height from the target height is in the normal range can be used to assess whether the child should be tested further for potential medical abnormalities. [ABSTRACT FROM AUTHOR]

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area. [ABSTRACT FROM AUTHOR]

Background and objectives: To develop a nomogram for mild cognitive impairment (MCI) in patients with subjective cognitive decline (SCD) undergoing physical examinations in China. Methods: We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell's concordance index, calibration curves, and decision curve analysis (DCA). Results: Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit. Discussion: We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Methods: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. Results: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aß42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aß42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Conclusions: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions. [ABSTRACT FROM AUTHOR]

Background: Neuroblastoma, a prevalent solid tumor in children, often manifests with hidden onset sites, rapid growth, and high metastatic potential. The prognosis for children with high-risk neuroblastoma remains poor, highlighting the urgent need for novel prognostic models and therapeutic avenues. In recent years, puerarin, as a kind of small molecule drug extracted from Chinese medicine Pueraria lobata, has demonstrated significant anticancer effects on various cancer cell types. In this study, through bioinformatics analysis and in vitro experiments, the potential and mechanism of puerarin in the treatment of neuroblastoma were investigated, and a prognostic model was established. Methods: A total of 9 drug-disease related targets were observed by constructing a database of drug targets and disease genes. Besides, GO and KEGG enrichment analysis was performed to explore the potential mechanism of its therapeutic effect. To construct the prognostic model, risk regression analysis and LASSO analysis were carried out for validation. Finally, the prognostic genes were identified. Parachute test and immunofluorescence staining were performed to verify the potential mechanism of puerarin in neuroblastoma treatment. Results: Three prognostic genes, i.e., BIRC5, TIMP2 and CASP9, were identified. In vitro studies verified puerarin's impact on BIRC5, TIMP2, and CASP9 expression, inhibiting proliferation in neuroblastoma SH-SY5Y cells. Puerarin disrupts the cytoskeleton, boosts gap junctional communication, curtailing invasion and migration, and induces mitochondrial damage in SH-SY5Y cells. Conclusions: Based on network pharmacology and bioinformatics analysis, combined with in vitro experimental verification, puerarin was hereby observed to enhance GJIC in neuroblastoma, destroy cytoskeleton and thus inhibit cell invasion and migration, cause mitochondrial damage of tumor cells, and inhibit cell proliferation. Overall, puerarin, as a natural medicinal compound, does hold potential as a novel therapy for neuroblastoma. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with diabetes are at greater risk of developing liver cancer than the general population. However, risk prediction models or scoring systems for liver cancer mostly focus on patients with preexisting liver diseases, and models for patients with diabetes or those in the absence of liver diseases remain rare. This study aims to develop a risk scoring system among diabetes patients and a sub-model for diabetes patients without cirrhosis or chronic viral hepatitis. In the final model, presence of chronic hepatitis B/C, cirrhosis, alanine aminotransferase, age, and sex were included as predictors. In the sub-model, alanine aminotransferase, triglycerides, age, and sex were included as predictors. Findings of the study may potentially provide individualized risk prediction for diabetes patients. Background: Most liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis. Methods: A retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment. Results: The liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (n = 1995) and those who remained free of cancer (n = 1969) during follow-up (median: 6.2 years) were selected for model building. In the final time-to-event scoring system, presence of chronic hepatitis B/C, alanine aminotransferase, age, presence of cirrhosis, and sex were included as predictors. The concordance index was 0.706 (95%CI: 0.676–0.741). In the sub-model for patients without cirrhosis/chronic viral hepatitis, alanine aminotransferase, age, triglycerides, and sex were selected as predictors. Conclusions: The proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients. [ABSTRACT FROM AUTHOR]

Background Resilience of national health systems in Europe remains a major concern in times of multiple crises and as more evidence is emerging relating to the indirect effects of the COVID-19 pandemic on health care utilization (HCU), resulting from de-prioritization of regular, non-pandemic healthcare services. Most extant studies focus on regional, disease specific or early pandemic HCU creating difficulties in comparing across multiple countries. We provide a comparatively broad definition of HCU across multiple countries, with potential to expand across regions and timeframes. Methods Using a cross-country federated research infrastructure (FRI), we examined HCU for acute cardiovascular events, elective surgeries and serious trauma. Aggregated data were used in forecast modelling to identify changes from predicted European age-standardized counts via fitted regressions (2017–19), compared against post-pandemic data. Results We found that elective surgeries were most affected, universally falling below predicted levels in 2020. For cardiovascular HCU, we found lower-than-expected cases in every region for heart attacks and displayed large sex differences. Serious trauma was the least impacted by the COVID-19 pandemic. Conclusion The strength of this study comes from the use of the European Population Health Information Research Infrastructure's (PHIRI) FRI, allowing for rapid analysis of regional differences to assess indirect impacts of events such as pandemics. There are marked differences in the capacity of services to return to normal in terms of elective surgery; additionally, we found considerable differences between men and women which requires further research on potential sex or gender patterns of HCU during crises. [ABSTRACT FROM AUTHOR]

Disruptions can start at one supplier in the supply network and ripple through, impacting other suppliers and firms, known as cascading disruptions. This research analyzes the effect of supply network modularity on cascading disruptions. Modularity measures the degree to which a supply network can be divided into self‐contained sub‐networks and has different effects on supply network resilience. A highly modular supply network prevents cascading disruptions from spreading through the whole network because of the lack of bridges between modules (lack of inter‐module connectivity). Hence, the size of cascading disruptions—measured by the number of suppliers impacted by a cascading disruption—in highly modular supply networks tends to be smaller than the size of cascading disruptions in less modular supply networks. However, the high level of internal connectivity within a module (excessive intra‐module connectivity) acts as an incubator for cascading disruptions. This means a small disruption in a modular network may impact fewer suppliers (i.e., smaller cascading size) but with higher severity measured by service level. Finally, building upon the theoretical concept of nexus suppliers, this research proposes a new predictive model to identify the operational nexus suppliers whose disruptions would considerably impact focal firms' operations. The model's accuracy is empirically tested on real‐world global supply networks involving 2598 unique firms and suppliers across 51 countries and 111 industries. The model identifies nexus suppliers with 95% accuracy, allowing managers and policymakers to plan for mitigation strategies proactively. [ABSTRACT FROM AUTHOR]

The proportion of reclaimed asphalt pavement (RAP) bitumen that gets activated and coats the virgin aggregate during the initial stage of plant production of RAP mixes, in which the RAP material is initially mixed with superheated virgin aggregate, designated as the degree of RAP bitumen activity (DoA), is an important parameter influencing the quality and performance of the RAP mix. DoA is influenced by a variety of mix and process parameters. This paper presents an empirical model, developed using the results from a previous experimental investigation and those obtained from the additional experiments conducted in the present investigation, for prediction of the DoA of RAP binder under varying combinations of mix and process variables. The experimental investigation involved mixing separately identifiable size fractions of RAP material (1.18 to 4.75 mm) and virgin aggregate (9.5 to 26.5 mm) and measuring the quantity of RAP bitumen transferred to virgin aggregate to estimate the DoA. The 41 sets of experimental data considered for the development of the empirical model covered different RAP material proportions (15% to 75%), RAP binder quality (softening point of 69°C to 78°C), RAP binder content (4.0% to 6.0%), mixing duration (0.5 to 3 min), mixing temperature (70°C to 180°C), and superheating temperature of virgin aggregate (155°C to 197.5°C). The gradation and shape of the RAP material used in different coating experiments were also different. Additional experiments were carried out in the present investigation to study the effect of superheating temperature of virgin aggregates. DoA decreased exponentially with RAP bitumen stiffness and RAP content, whereas it increased with mixing temperature, mixing time, RAP bitumen content, and the proportion of coarser RAP material. The superheating temperature of virgin aggregate did not affect DoA significantly. A regression model was developed for estimation of DoA with reasonable accuracy without having to carry out the coating experiments. Such a predictive model is useful for obtaining inputs for the estimation of the quality of final binder blend in the RAP mix. [ABSTRACT FROM AUTHOR]

This research introduces a methodology for data-driven regression modeling of components exhibiting nonlinear characteristics, utilizing the sparse identification of nonlinear dynamics (SINDy) method. The SINDy method is extended to formulate regression models for interconnecting components with nonlinear traits, yielding governing equations with physically interpretable solutions. The proposed methodology focuses on extracting a model that balances accuracy and sparsity among various regression models. In this process, a comprehensive model was generated using linear term weights and an error histogram. The applicability of the proposed approach is demonstrated through a case study involving a sponge gasket with nonlinear characteristics. By contrasting the predictive model with experimental responses, the reliability of the methodology is verified. The results highlight that the regression model, based on the proposed technique, can effectively establish an accurate dynamical system model, accounting for realistic conditions. [ABSTRACT FROM AUTHOR]

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection. [ABSTRACT FROM AUTHOR]

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment. Methods: Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine. Results: One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders. Conclusions: Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy. [ABSTRACT FROM AUTHOR]

Thermal spray coating is a critical process in many industries, involving the application of coatings to surfaces to enhance their functionality. This paper proposes a framework for modelling and predicting critical target variables in thermal spray coating processes, based on the application of statistical design of experiments (DoE) and the modelling of the data using generalized linear models (GLMs) with a particular emphasis on gamma regression. Experimental data obtained from thermal spray coating trials are used to validate the presented approach, demonstrating that it is able to accurately model and predict critical target variables. As such, the framework has significant potential for the optimization of thermal spray coating processes, and can contribute to the development of more efficient and effective coating technologies in various industries. [ABSTRACT FROM AUTHOR]

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F] Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice. [ABSTRACT FROM AUTHOR]

Simple Summary: We conducted a study at the Melanoma Unit of the Hospital Clinic of Barcelona to investigate brain metastases in patients with cutaneous melanoma. We collected data from patients diagnosed between January 1998 and September 2023. Patients with melanoma in situ or those with prior lung or breast cancer were excluded. Our aim was to identify factors associated with the development and survival outcomes of brain metastases. We analyzed patient demographics, tumor characteristics, and survival data. The diagnosis of brain metastases was confirmed using imaging techniques, and biopsies were performed when feasible. Our study followed strict guidelines for reporting observational studies. We found that younger age and larger primary tumor thickness increased the risk of developing brain metastases. Additionally, the presence of ulceration and microscopic satellitosis in the primary tumor were associated with a higher risk. Melanomas located on the trunk had a higher risk compared to those on the extremities. Patients with brain metastases had a median survival of around six months. Neurological symptoms and leptomeningeal involvement were associated with poorer survival outcomes. Higher number of brain lesions and elevated levels of lactate dehydrogenase (LDH) also predicted worse survival. Our findings highlight the importance of early detection and monitoring of melanoma patients, especially those at higher risk of brain metastases. Understanding these factors can aid in personalized treatment approaches and improving patient outcomes. Background: Melanoma is the cancer with the highest risk of dissemination to the central nervous system (CNS), one of the leading causes of mortality from this cancer. Objective: To identify patients at higher risk of developing CNS metastases and to evaluate associated prognostic factors. Methods: A cohort study (1998–2023) assessed patients who developed CNS melanoma metastases. Multivariate logistic regression was used to identify predictive factors at melanoma diagnosis for CNS metastasis. Cox regression analysis evaluated the CNS-independent metastasis-related variables impacting survival. Results: Out of 4718 patients, 380 (8.05%) developed CNS metastases. Multivariate logistic regression showed that a higher Breslow index, mitotic rate ≥ 1 mm2, ulceration, and microscopic satellitosis were significant risk factors for CNS metastasis development. Higher patient age and the location of the primary tumor in the upper or lower extremities were protective factors. In survival analysis, post-CNS metastasis, symptomatic disease, prior non-CNS metastases, CNS debut with multiple metastases, elevated LDH levels, and leptomeningeal involvement correlated with poorer survival. Conclusion: Predictive factors in the primary tumor independently associated with brain metastases include microscopic satellitosis, ulceration, higher Breslow index, and trunk location. Prognostic factors for lower survival in CNS disease include symptomatic disease, multiple CNS metastases, and previous metastases from different sites. [ABSTRACT FROM AUTHOR]

Simple Summary: Mutation of the SMARCB1 gene can cause one of the most aggressive and lethal cancers of early childhood and infancy, malignant rhabdoid tumor (MRT). Despite the standard multimodal therapy (resection, conventional chemotherapy, and radiotherapy), the outlook for young children with MRT is poor. For infants, the disease can also preclude the use of radiotherapy. Numerous experimental treatments explore epigenetic mechanisms, but the DNA damage response has not yet been extensively evaluated as a therapeutic approach for MRT. We report a new therapeutic strategy for SMARCB1-deficient MRTs, combining PARP1 inhibition and DNA damage induction. The observed synergy between the PEGylated PARP1 inhibitor talazoparib (PEG~TLZ) and the DNA alkylating agent temozolomide (TMZ) may lead to improved therapeutic strategies for patients with this challenging cancer. We identified a new potential biomarker of response to PEG~TLZ+TMZ, O6-methylguanine methyltransferase (MGMT), and uncovered dysregulated signaling pathways involved in the response. Additionally, we elucidated the pro-survival role of SMARCB1 loss in MRT cells. Malignant rhabdoid tumors (MRTs) are among the most aggressive and treatment-resistant malignancies affecting infants, originating in the kidney, brain, liver, and soft tissues. The 5-year event-free survival rate for these cancers is a mere 20%. In nearly all cases of MRT, the SMARCB1 gene (occasionally SMARCA4)—a pivotal component of the SWI/SNF chromatin remodeling complex—is homozygously deleted, although the precise etiology of these tumors remains unknown. While young patients with localized MRT generally show improved outcomes, especially those who are older and have early-stage disease, the overall prognosis remains poor despite optimal standard treatments. This highlights the urgent need for more effective treatment strategies. We investigated the antitumor activity of a PARP1 inhibitor (talazoparib, TLZ) combined with a DNA alkylating agent (temozolomide, TMZ) in MRT xenograft models. PARP1 is a widely targeted molecule in cancer treatment and, beyond its role in DNA repair, it participates in transcriptional regulation by recruiting chromatin remodeling complexes to modulate DNA accessibility for RNA polymerases. To widen the therapeutic window of the drug combination, we employed PEGylated TLZ (PEG~TLZ), which has been reported to reduce systemic toxicity through slow drug release. Remarkably, our findings indicate that five out of six MRT xenografts exhibited an objective response to PEG~TLZ+TMZ therapy. Significantly, the loss of SMARCB1 was found to confer a protective effect, correlating with higher expression levels of DNA damage and repair proteins in SMARCB1-deficient MRT cells. Additionally, we identified MGMT as a potential biomarker indicative of in vivo MRT response to PEG~TLZ+TMZ therapy. Moreover, our analysis revealed alterations in signaling pathways associated with the observed antitumor efficacy. This study presents a novel and efficacious therapeutic approach for MRT, along with a promising candidate biomarker for predicting tumor response. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate pre-operative surgical risk predictions form the foundation of pre-operative counseling and informed consent. There are currently no validated risk calculators that are able to accurately predict post-operative complications for women undergoing gynecological cancer surgery in both high- and low-middle-income healthcare settings. Using the dataset from the international GO SOAR database, we present a novel artificial intelligence surgical risk calculator capable of accurately predicting the risk of complications associated with gynecological cancer surgery. The GO SOAR surgical risk calculator uses readily available pre-operative data available across all-income healthcare settings, ensuring benefits to women globally. The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien–Dindo I-II), major morbidity (Clavien–Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities. [ABSTRACT FROM AUTHOR]

Crop yield prediction plays a key role in modern agriculture, it enables farmers to make decisions about resource distribution, crop production management, and marketing business strategies. Regression models are extensively used for crop yield prediction. The performance of different regression techniques may vary depending on various factors such as the dataset, features, and modeling assumptions. In this paper, a comparative study was conducted to evaluate and compare the performance of different regression models for agriculture crop yield prediction. Collected a comprehensive dataset encompassing historical crop yield data, weather parameters and pesticides data features from various agricultural regions, then applied and compared various regression models, including Linear Regression (LR), K Nearest neighbor Regression (KNR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR),Linear Model Lasso Regression, Elasticnet Regression, Ridge Regression to predict crop yields for various crops. This study involved evaluating the performance of these regression models based on several performance metrics, including R² score, Root Mean Squared Error(RMSE), Mean Squared Error(MSE), Mean Absolute Error(MAE), Median Absolute Error(Median AE), Explain variance score and computing time. The results of our study provide insights into the comparative performance of different regression models for crop yield prediction in agriculture. Determined that the performance of the regression models varies crop type, area, and dataset used. Overall, the random forest regression model demonstrated the best performance in terms of R2, followed by K Nearest neighbor with hyper parameter tuning and decision tree regression. However, the choice of the most suitable regression model may also depend on other factors such as the interpretability and computational efficiency requirements of the application. Our research findings contribute to the existing literature on crop yield prediction in agriculture and afford treasured information for farmers, policymakers, and researchers to make conversant conclusions about the selection of appropriate regression models for crop yield prediction in their specific contexts. Further research could explore the combination of different regression models or the integration of other ML techniques to better the R2and robustness of crop yield prediction models in agriculture. [ABSTRACT FROM AUTHOR]

BACKGROUND: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688–141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5-km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to e300,000. The directions of associations were less consistent for walkability and share of single residents. DISCUSSION: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. [ABSTRACT FROM AUTHOR]

Effective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of resources. Survival models can be used to predict survival probabilities in such trials. However, survival data from clinical trials are sparse, and DeepSurv cannot accurately capture their effective features, making the models weak in generalization and decreasing their prediction accuracy. In this paper, we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder (DAE) and DeepSurv models. The DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training, and then the robust features are provided to DeepSurv as input for training. The clinical trial dataset for training the model was obtained from the ClinicalTrials.gov dataset. A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant women. The experimental results showed that the denoising autoencoder and deep survival regression (DAE‐DSR) model was able to extract meaningful and robust features for survival analysis; the C‐index of the training and test datasets were 0.74 and 0.75 respectively. Compared with the Cox proportional hazards model and DeepSurv model, the survival analysis curves obtained by using DAE‐DSR model had more prominent features, and the model was more robust and performed better in actual prediction. [ABSTRACT FROM AUTHOR]

Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate staffing, and facilitating informed discharge planning. While conventional methods rely on statistical models and clinical expertise, recent advances in machine learning (ML) present promising avenues for enhancing LOS prediction. This research focuses on developing an ML-based LOS prediction model trained on a comprehensive real-world dataset and discussing the important factors towards practical deployment of trained ML models in clinical settings. This research involves the development of a comprehensive adult cardiac patient dataset (SaudiCardioStay (SCS)) from the King Faisal Specialist Hospital & Research Centre (KFSH&RC) hospital in Saudi Arabia, comprising 4930 patient encounters for 3611 unique patients collected from 2019 to 2022 (excluding 2020). A diverse range of classical ML models (i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), artificial neural networks (ANNs), Average Voting Regression (AvgVotReg)) are implemented for the SCS dataset to explore the potential of existing ML models in LOS prediction. In addition, this study introduces a novel approach for LOS prediction by incorporating a dedicated LOS classifier within a sophisticated ensemble methodology (i.e., Two-Level Sequential Cascade Generalization (2LSCG), Three-Level Sequential Cascade Generalization (3LSCG), Parallel Cascade Generalization (PCG)), aiming to enhance prediction accuracy and capture nuanced patterns in healthcare data. The experimental results indicate the best mean absolute error (MAE) of 0.1700 for the 3LSCG model. Relatively comparable performance was observed for the AvgVotReg model, with a MAE of 0.1703. In the end, a detailed analysis of the practical implications, limitations, and recommendations concerning the deployment of ML approaches in actual clinical settings is presented. [ABSTRACT FROM AUTHOR]

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model. [ABSTRACT FROM AUTHOR]

The purpose of this study is to establishment and validation of an early predictive model for severe acute pancreatitis (SAP). Methods: From January 2015 to August 2022, 2986 AP patients admitted to Changsha Central Hospital were enrolled in this study. They were randomly divided into a modeling group (n = 2112) and a validation group (n = 874). In the modeling group, identify risk factors through logistic regression models and draw column charts. Use internal validation method to verify the accuracy of column chart prediction. Apply calibration curves to evaluate the consistency between nomograms and ideal observations. Draw a DCA curve to evaluate the net benefits of the prediction model. Results: Nine variables including respiratory rate, heart rate, WBC, PDW, PT, SCR, AMY, CK, and TG are the risk factors for SAP. The column chart risk prediction model which was constructed based on these 9 independent factors has high prediction accuracy (modeling group AUC = 0.788, validation group AUC = 7.789). The calibration curve analysis shows that the prediction probabilities of the modeling and validation groups are consistent with the observation probabilities. By drawing a DCA curve, it shows that the model has a wide threshold range (0.01– 0.88). Conclusion: The study developed an intuitive nomogram containing readily available laboratory parameters to predict the incidence rate of SAP. [ABSTRACT FROM AUTHOR]

The aims of this study were to predict carcass and meat traits, as well as the chemical composition of the 9th to 11th rib sections of beef cattle from portable NIR spectra. The 9th to 11th rib section was obtained from 60 Nellore bulls and cull cows. NIR spectra were acquired at: P1 –center of Longissimus muscle; and P2 –subcutaneous fat cap. The models accurately estimated (P ≥ 0.083) all carcass and meat quality traits, except those for predicting red (a*) and yellow (b*) intensity from P1, and 12th-rib fat from P2. However, precision was highly variable among the models; those for the prediction of carcass pHu, 12th rib fat, toughness from P1, and those for 12th rib fat, a* and b* from P2 presented high precision (R2 ≥ 0.65 or CCC ≥ 0.63), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.39). Models built from P1 and P2 accurately estimated (P ≥ 0.066) the chemical composition of the meat plus fat, bones and, meat plus fat plus bones, except those for predicting the ether extract (EE) and crude protein (CP) of bones and the EE of Meat plus bones fraction from P2. However, precision was highly variable among the models (–0.08 ≤ R2 ≤ 0.86) of the 9th and 11th rib section. Those models for the prediction of dry matter (DM) and EE of the bones from P1; of EE from P1; and of EE, mineral matter (MM), CP from P2 of meat plus fat plus bones presented high precision (R2 ≥ 0.76 or CCC ≥ 0.62), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.45). Thus, models built from portable NIR spectra acquired at different points of the 9th to 11th rib section were recommended for predicting carcass and muscle quality traits as well as for predicting the chemical composition of this section of beef cattle. However, it is noteworthy, that the small sample size was one of the limitations of this study. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [ABSTRACT FROM AUTHOR]

To construct and internally and externally validate a nomogram model for predicting the severity of acute pancreatitis (AP) based on the CT severity index (CTSI).A retrospective analysis of clinical data from 200 AP patients diagnosed at the Hefei Third Clinical College of Anhui Medical University from June 2019 to June 2022 was conducted. Patients were classified into non-severe acute pancreatitis (NSAP, n = 135) and severe acute pancreatitis (SAP, n = 65) based on final clinical diagnosis. Differences in CTSI, general clinical features, and laboratory indicators between the two groups were compared. The LASSO regression model was used to select variables that might affect the severity of AP, and these variables were analyzed using multivariate logistic regression. A nomogram model was constructed using R software, and its AUC value was calculated. The accuracy and practicality of the model were evaluated using calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed using the bootstrap method. Finally, 60 AP patients treated in the same hospital from July 2022 to December 2023 were selected for external validation.LASSO regression identified CTSI, BUN, D-D, NLR, and Ascites as five predictive factors. Unconditional binary logistic regression analysis showed that CTSI (OR = 2.141, 95%CI:1.369–3.504), BUN (OR = 1.378, 95%CI:1.026–1.959), NLR (OR = 1.370, 95%CI:1.016–1.906), D-D (OR = 1.500, 95%CI:1.112–2.110), and Ascites (OR = 5.517, 95%CI:1.217–2.993) were independent factors influencing SAP. The established prediction model had a C-index of 0.962, indicating high accuracy. Calibration curves demonstrated good consistency between predicted survival rates and actual survival rates. The C-indexes for internal and external validation were 0.935 and 0.901, respectively, with calibration curves close to the ideal line.The model based on CTSI and clinical indicators can effectively predict the severity of AP, providing a scientific basis for clinical decision-making by physicians. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose to incorporate a preliminary location estimate into a localization chain, such that location-based features can be calculated as inputs to a range-error prediction model. This way, we can add information to range-only measurements without relying on additional hardware such as an inertial measurement unit (IMU). This improves performance and reduces overfitting behavior. We demonstrate our LARC method using an open-access measurement dataset with distances up to 20 m, using a simple regression model that can run purely on the CPU in real-time. The inclusion of the proposed features for range-error mitigation decreases the ranging error 90th percentile (P90) by 58% to 15 cm (compared to the uncorrected range error), for an unseen trajectory. The 2D localization P90 error is improved by 21% to 18 cm. We show the robustness of our approach by comparing results to a changed environment, where metallic objects have been moved around the room. In this modified environment, we obtain a 56% better P90 ranging performance of 16 cm. The 2D localization P90 error improves as much as for the unchanged environment, by 17% to 18 cm, showing the robustness of our method. This method evolved from the first-ranking solution of the 2021 and 2022 International Conference on Indoor Position and Indoor Navigation (IPIN) Competition. [ABSTRACT FROM AUTHOR]

Background: Globus pharyngeus is a clinical condition, wherein, a patient senses a lump or a foreign body in the throat with a tightening or choking feeling. A strong association between globus pharyngeus and gastroesophageal reflux disease (GERD) was reported. Therefore, we sought to investigate the predictive factors of globus pharyngeus in patients with established GERD and fit a predictive scoring model for globus pharyngeus. Methods: In this case-control study, 143 patients having globus pharyngeus along with GERD (case) and 109 patients having globus pharyngeus without GERD (control) were enrolled. Data comprising demographics, comorbidities, and psychosocial stress levels were recorded. The predictive factors of globus pharyngeus in patients with GERD were unraveled, and a predictive scoring model was fit for globus pharyngeus. Results: Proton pump inhibitor usage in the case group was significantly higher compared to controls (63.63% vs 24.78%, P < 0.001), and differences in Hiatus Hernia and Stress levels were highly significant between the two groups (P < 0.001). Multivariate logistic regression revealed that variates, Hiatus Hernia, psychosocial stress, and age were highly significant (P < 0.001) independent predictors of globus pharyngeus. Using the regression coefficients of all the independent predictor variables, a predictive scoring model was fitted, which yielded an area under receiver operating characteristic (AUROC) curve of 78.9. Conclusion: Hiatus hernia, psychosocial stress, and age are significant independent predictors of globus pharyngeus in GERD, and our predictive scoring model may help in identifying patients at higher odds of experiencing globus pharyngeus and modulate treatment accordingly. [ABSTRACT FROM AUTHOR]

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians' ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow. [ABSTRACT FROM AUTHOR]

Based on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a novel unsupervised SVM‐KNN model, integrated by support vector machine (SVM) and k nearest neighbour (KNN), is the optimum detector for PC refinements. Based on the refined data by the SVM‐KNN detector, several common nonparametric regressors have largely improved their prediction accuracies on pitch angle and rotor speed curves from roughly 86% and 90.6%, respectively, (raw data) to both 99% (refined data). Noticeably, under the SVM‐KNN refinement, the errors have been reduced by roughly five times and 10 times for pitch angle and rotor speed predictions, respectively. Ultimately, bootstrapped prediction interval is applied to conduct the uncertainty analysis of the optimal predictive regression model, reinforcing the performance monitoring and anomaly detection. [ABSTRACT FROM AUTHOR]

Participation is important for children's quality of life (QOL). This study aimed to identify participation factors that influence QOL among Japanese children with neurodevelopmental disorders. Ninety-two Japanese parents of children with neurodevelopmental disorders participated in this study. The parents completed the parent version of the Kid- and Kiddo-KINDL health-related QOL questionnaire and the Participation and Environment Measure for Children and Youth. The data were examined using the random forest algorithm to analyze the participation factors that affected the children's QOL. The analyses revealed that school and community environmental factors that affected participation were the most important predictors of QOL among children. As school and community environments can significantly impact the QOL of children with neurodevelopmental disorders, greater focus should be placed on participation in environmental contexts. [ABSTRACT FROM AUTHOR]

(1) Background: HBV-DNA is an essential clinical indicator of primary hepatocellular carcinoma (HCC) prognosis. Our study aimed to investigate the prognostic implication of a low load of HBV-DNA in HCC patients who underwent local treatment. Additionally, we developed and validated a nomogram to predict the recurrence of patients with low (20–100 IU/mL) viral loads (L-VL). (2) Methods: A total of 475 HBV-HCC patients were enrolled, including 403 L-VL patients and 72 patients with very low (<20 IU/mL) viral loads (VL-VL). L-VL HCC patients were randomly divided into a training set (N = 282) and a validation set (N = 121) at a ratio of 7:3. Utilizing the Lasso–Cox regression analysis, we identified independent risk factors for constructing a nomogram. (3) Results: L-VL patients had significantly shorter RFS than VL-VL patients (38.2 m vs. 23.4 m, p = 0.024). The content of the nomogram included gender, BCLC stage, Glob, and MLR. The C-index (0.682 vs. 0.609); 1-, 3-, and 5-year AUCs (0.729, 0.784, and 0.783, vs. 0.631, 0.634, the 0.665); calibration curves; and decision curve analysis (DCA) curves of the training and validation cohorts proved the excellent predictive performance of the nomogram. There was a statistically significant difference in RFS between the low-, immediate-, and high-risk groups both in the training and validation cohorts (p < 0.001); (4) Conclusions: Patients with L-VL had a worse prognosis. The nomogram developed and validated in this study has the advantage of predicting patients with L-VL. [ABSTRACT FROM AUTHOR]

Background: Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention. Methods: We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary twosample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings. Results: After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data. Conclusion: By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD. [ABSTRACT FROM AUTHOR]

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep learning based prediction model (DLIF), that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F] Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice. [ABSTRACT FROM AUTHOR]

Background: Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. Methods: In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). Results: The ICV values were proved to be consistent with the anesthetists' ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511–0.0513), RMSE (0.0792, 95% CI: 0.0790–0.0794), and MAPE (0.2086, 95% CI: 0.2077–0.2095) and the largest R2 (0.9047, 95% CI: 0.9043–0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. Conclusions: Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions. [ABSTRACT FROM AUTHOR]

Background. Although neonatal sepsis is a major public health problem contributing to 30-50% of neonatal deaths in low- and middle-income countries, data on predictors of time to death are limited in Eastern Ethiopia. This study is aimed at determining predictors of time to death among neonates with sepsis admitted in public hospitals in Eastern Ethiopia. Methods. An institutional-based retrospective cohort study was conducted among 415 neonates admitted to referral hospitals in Eastern Ethiopia with sepsis from January 1, 2021, to December 31, 2021. Data were collected from medical records by using structured checklist and entered using EpiData 3.1 and analyzed using Stata 17. The Kaplan-Meier curves and log-rank tests were used to describe survival experience among different categories. The proportional hazard assumption and goodness of fit for the Cox regression model were checked. The Cox regression model was used to identify the significant predictors. Hazard ratios (HRs) with 95% confidence intervals (CI) were calculated. Finally, statistical significance was set at a p value < 0.05 in the Cox regression analysis. Results. Of the 415 neonates with neonatal sepsis, 71 (17.1%) (95% CI: 13.60-21.08) died at discharge, with a median time to death of 14 days. The overall incidence rate of mortality was 36.5 per 1000 neonate days. Low birthweight (AHR = 2.50 ; 95% CI: 1.15-5.44), maternal age ≥ 35 years (AHR = 3.17 ; 95% CI: 1.11, 9.04), low fifth-minute Apgar score (AHR: 2.32; 95% CI: 1.30-4.14), and late initiation of breastfeeding (AHR = 4.82 ; 95% CI: 1.40-16.65) were independent predictors of mortality among neonates with sepsis. Conclusions. Almost one in five neonates with sepsis died at discharge. Low birthweight, maternal age ≥ 35 years, low fifth-minute Apgar score, and late initiation of breastfeeding were predictors of mortality. [ABSTRACT FROM AUTHOR]