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Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71-0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.

Introduction: Non-alcoholic fatty liver disease (NAFLD) represents a major global health challenge, often undiagnosed because of suboptimal screening tools. Advances in machine learning (ML) offer potential improvements in predictive diagnostics, leveraging complex clinical datasets. Methods: We utilized a comprehensive dataset from the Dryad database for model development and training and performed external validation using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020 cycles. Seven distinct ML models were developed and rigorously evaluated. Additionally, we employed the SHapley Additive exPlanations (SHAP) method to enhance the interpretability of the models, allowing for a detailed understanding of how each variable contributes to predictive outcomes. Results: A total of 14,913 participants were eligible for this study. Among the seven constructed models, the light gradient boosting machine achieved the highest performance, with an area under the receiver operating characteristic curve of 0.90 in the internal validation set and 0.81 in the external NHANES validation cohort. In detailed performance metrics, it maintained an accuracy of 87%, a sensitivity of 92.9%, and an F1 score of 0.92. Key predictive variables identified included alanine aminotransferase, gammaglutamyl transpeptidase, triglyceride glucose-waist circumference, metabolic score for insulin resistance, and HbA1c, which are strongly associated with metabolic dysfunctions integral to NAFLD progression. Conclusions: The integration of ML with SHAP interpretability provides a robust predictive tool for NAFLD, enhancing the early identification and potential management of the disease. The model's high accuracy and generalizability across diverse populations highlight its clinical utility, though future enhancements should include longitudinal data and lifestyle factors to refine risk assessments further. [ABSTRACT FROM AUTHOR]

Objective: Rathke's cleft cysts (RCCs) exhibit variable growth patterns, thus posing a challenge in predicting progression. While some RCCs may not cause symptoms, others can insidiously cause pituitary dysfunction, which is often irreversible, even following surgery. Hence, it is crucial to identify asymptomatic RCCs that grow rapidly and pose a higher risk of causing endocrinologic dysfunction. This enables timely surgical intervention to prevent permanent damage. Our study examines the growth rate of RCCs, identifies factors that accelerate growth, and discusses the clinical implications of these findings. Methods: A retrospective analysis of a prospectively maintained electronic database revealed 45 patients aged 18–80 years who underwent endoscopic endonasal surgery (EEA) for RCCs between 2010 and 2022 at our center. Of these, 20 required early operative intervention. The remaining 25 patients were followed closely clinically and radiologically before requiring surgery (initial conservative management group). We conducted an analysis of the factors predicting growth over time in this group. Using a regression model, we constructed a risk score system to predict RCC growth over time. Results: Patients in the initial conservative group had smaller cysts and were generally older than those in the early surgery group. Patients with preoperative pituitary dysfunction showed a higher median growth of 1.0 mm in the longest diameter compared to those with normal pituitary function, with an increase of 0.5 mm. A sum of annual cyst growth of all (z, y, x) diameters, at a rate of 3 mm or greater, was associated with a clinically significant increase in the risk of pituitary dysfunction, exceeding 50%.The most significant factors predicting rapid growth in RCCs were smoking status, age, and T1-weighted magnetic resonance imaging (MRI) intensity of cysts. Smoking was the most critical risk factor for rapid cyst growth (p = <.001). Our risk score system accurately predicted RCC growth with a 74% accuracy rate, 73% sensitivity, and 75% specificity. Conclusion: Our analysis showed a strong link between active smoking and the rapid growth of RCC. This novel finding has significant preventive implications but needs validation by a large population database. Surgical intervention for RCC currently is often reserved for symptomatic cases. However, utilizing our risk-based scoring system to predict rapidly growing cysts may indicate early surgery in minimally symptomatic patients, thereby potentially preserving pituitary function. [ABSTRACT FROM AUTHOR]

Background: The best antiviral treatment for chronic hepatitis B (CHB) poses a complex challenge. The treatment effect of the combination of nucleoside analogues (NAs) and pegylated interferon alpha (PegIFN) was still in debate. Methods: We studied patients treated with NAs and PegIFN-2b at our institution from November 2019 to January 2022. Logistic regression identified independent factors influencing clinical cure. The predictive accuracy of the formula was assessed using the Receiver operating characteristic (ROC) curve at different time points (before therapy, 12 weeks, and 24 weeks into treatment). Results: A total of 120 patients were enrolled in the final analysis. Among the cohort of patients under study, 71 (59.1%) patients had clinical cure while 49 (40.9%) patients did not. Hepatitis B surface antigen (HBsAg) at baseline and age were the powerful variables predicting the clearance of HBsAg. The area under the ROC (AUC) was 0.907 for pre-treatment predictive model, 0.958 for 12-week predictive model and 0.747 for 24-week predictive model. Conclusion: This study provided predictive formulas for clinical cure, offering valuable insights for CHB treatment. PegIFN and NAs exhibited efficacy. Future research that explores additional factors, such as HBV genotype, in a larger cohort study is needed. [ABSTRACT FROM AUTHOR]

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation‐specific drugs. Since composite mutations have been described to occur in sub‐clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub‐clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co‐occurring mutations in a non‐composite context. We also found that co‐mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co‐mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance‐causing mutations. [ABSTRACT FROM AUTHOR]

Background: A high number of stillborn piglets has a negative impact on production and animal welfare. It is an important contributor to piglet mortality around farrowing and continues to rise with the increase of prolificacy. The objective of this study was to build a predictive model of the stillborn rate. Results: This study was performed on two farrow-to-finish farms and one farrow-to-wean farm located in Brittany, France. At each farm, the number of total born (TB), born alive (BA), stillborn piglets (S), the same data at the previous farrowing (TBn− 1, BAn− 1 and Sn− 1), backfat thickness just before farrowing and at previous weaning and parity rank were recorded in our dataset of 3686 farrowings. Bayesian networks were used as an integrated modelling approach to investigate risk factors associated with stillbirth using BayesiaLab® software. Our results suggest the validity of a hybrid model to predict the percentage of stillborn piglets. Three significant risk factors were identified by the model: parity rank (percentage of total mutual information: MI = 64%), Sn− 1 (MI = 25%) and TBn− 1 (MI = 11%). Additionally, backfat thickness just before farrowing was also identified for sows of parity five or more (MI = 0.4%). In practice, under optimal conditions (i.e., low parity rank, less than 8% of stillborn piglets, and a prolificacy lower than 14 piglets at the previous farrowing), our model predicted a stillborn rate almost halved, from 6.5% (mean risk of our dataset) to 3.5% for a sow at the next farrowing. In contrast, in older sows with a backfat thickness less than 15 mm, more than 15% of stillborn and a prolificacy greater than 18 piglets at the previous farrowing, the risk is multiplied by 2.5 from 6.5 to 15.7%. Conclusion: Our results highlight the impact of parity, previous prolificacy and stillborn rate on the probability of stillborn. Moreover, the importance of backfat thickness, especially in old sows, must be considered. This information can help farmers classify and manage sows according to their risk of giving birth to stillborn piglets. [ABSTRACT FROM AUTHOR]

Backgrounds: During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future. Methods: In this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portion of the data as an independent testing cohort to determine the most accurate and stable model and compared it with other scoring systems. Finally, patients were categorized according to risk scores and then the correlation between their clinical data and risk scores was studied. Results: The elderly accounted for the majority of hospitalized patients with COVID-19. The C-index of the model constructed by combining the stepcox [both] and survivalSVM algorithms was 0.840 in the training cohort and 0.815 in the validation cohort, which was calculated to have the highest C-index in the testing cohort compared to the other 119 ML model combinations. Compared with current scoring systems, including the CURB-65 and several reported prognosis models previously, our model had the highest AUC value of 0.778, representing an even higher predictive performance. In addition, the model's AUC values for specific time intervals, including days 7,14 and 28, demonstrate excellent predictive performance. Most importantly, we stratified patients according to the model's risk score and demonstrated a difference in survival status between the high-risk, median-risk, and low-risk groups, which means a new and stable risk assessment system was built. Finally, we found that COVID-19 patients with a history of cerebral infarction had a significantly higher risk of death. Conclusion: This novel risk assessment system is highly accurate in predicting the prognosis of patients with COVID-19, especially elderly patients with COVID-19, and can be well applied within the PPPM framework. Our ML model facilitates stratified patient management, meanwhile promoting the optimal use of healthcare resources. [ABSTRACT FROM AUTHOR]

Background: Extensively drug-resistant Acinetobacter baumannii (XDRAB) has become a significant pathogen in hospital environments, particularly in intensive care units (ICUs). XDRAB's resistance to conventional antimicrobial treatments and ability to survive on various surfaces pose a substantial threat to patient health, often resulting in severe infections such as ventilator-associated pneumonia (VAP) and bloodstream infections (BSI). Methods: We retrospectively analyzed clinical data from 559 patients with XDRAB infections admitted to Jinhua Central Hospital between January 2021 and December 2023. Patients were randomly divided into a training set (391 cases) and a testing set (168 cases). Variables were selected using Lasso regression and logistic regression analysis, and a predictive model was constructed and validated internally and externally. Model performance and clinical utility were evaluated using the Hosmer-Lemeshow test, C-index, ROC curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: Lasso regression analysis was used to screen 35 variables, selecting features through 10-fold cross-validation. We chose lambda.1se=0.03450 (log (lambda.1se)=-3.367), including 10 non-zero coefficient features. These features were then included in a multivariate logistic regression analysis, identifying 8 independent risk factors for XDRAB infection: ICU stay of 1-7 days (OR=3.970, 95%CI=1.586-9.937), ICU stay >7 days (OR=12.316, 95%CI=5.661-26.793), hypoproteinemia (OR=3.249, 95%CI=1.679-6.291), glucocorticoid use (OR=2.371, 95%CI=1.231-4.564), urinary catheterization (OR=2.148, 95% CI=1.120-4.120), mechanical ventilation (OR=2.737, 95%CI=1.367-5.482), diabetes mellitus (OR=2.435, 95%CI=1.050-5.646), carbapenem use (OR=6.649, 95%CI=2.321-19.048), and β-lactamase inhibitor use (OR=4.146, 95%CI=2.145-8.014). These 8 factors were used to construct a predictive model visualized through a nomogram. The model validation showed a Cindex of 0.932 for the training set and 0.929 for the testing set, with a Hosmer-Lemeshow test p-value of 0.47, indicating good calibration. Furthermore, the DCA curve demonstrated good clinical decision-making performance, and the CIC curve confirmed the model's reliable clinical impact. Conclusion: Regression analysis identified ICU stay duration, hypoproteinemia, glucocorticoid use, urinary catheterization, mechanical ventilation, diabetes mellitus, carbapenem use, and β-lactamase inhibitor use as independent risk factors for XDRAB infection. The corresponding predictive model demonstrated high accuracy and stability. [ABSTRACT FROM AUTHOR]

Background: Apnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups. Methods: A total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set. Results: Results of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ2=5.192, df=8, P=0.737), with Nagelkerke R2 of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility. Conclusions: Our risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility. [ABSTRACT FROM AUTHOR]

Purpose: Surgical site infection (SSI) is common after laparoscopic appendectomy, resulting in prolonged hospital stay and increased costs. This study examined the relationship between body composition parameters and risk of incisional SSI in patients with complicated appendicitis. Methods: We included 411 patients who underwent laparoscopic surgery for complicated appendicitis at a single institution between March 2015 and October 2023. Body composition parameters were derived from preoperative computed tomography (CT). A nomogram was constructed based on the independent predictors of incisional SSI. Results: Overall, 45 (10.9%) patients developed incisional SSI. Visceral fat area (VFA) was independently associated with risk of incisional SSI (hazard ratio 1.015, 95% confidence interval 1.010–1.020, P < 0.001). A nomogram integrating VFA and two other independent predictors (diabetes and conversion) demonstrated high discriminative (area under the curve = 0.793) and calibration abilities. Conclusions: CT-derived VFA could be a valuable predictor of incisional SSI in patients with complicated appendicitis undergoing laparoscopic surgery. A VFA-based nomogram may help in identifying patients at high risk of SSI. [ABSTRACT FROM AUTHOR]

Background: The issue of patients returning to work is increasingly garnering attention from countries worldwide. This study aims to investigate the risk factors associated with patients returning to work after undergoing permanent enterostomies. Additionally, it seeks to establish and validate a nomogram prediction model, thereby providing a more effective reference for patients aiming to return to work. Methods: This study was a cross-sectional investigation conducted between September 2022 and September 2023. We conveniently selected 293 postoperative patients with permanent colorectal stomas due to colorectal cancer from three tertiary hospitals in Liaoning Province. Participants were categorized into Returned and Non-Returned groups based on their return to work status. Data were collected using a general information questionnaire, a Stoma Acceptance Questionnaire, and the Ostomy Adjustment Inventory. Binary logistic regression analysis was performed using SPSS 25.0 software to identify independent influencing factors. A predictive model was constructed using R Studio 4.3.0 software. Internal validation was conducted through 1,000 rounds of Bootstrap resampling, and model performance was assessed using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow (H-L) test, and calibration curves. Results: After surgery, the return-to-work rate for patients with permanent colorectal stomas was 29.69%. Age, education level, postoperative time, stoma complication, adjuvant therapy, stoma acceptance score, and ostomy adjustment inventory score were identified as independent factors influencing the return-to-work status of these patients (P < 0.05). These factors were incorporated into a logistic regression model generated by R software, resulting in a ROC curve with an area under the curve (AUC) of 0.916 (95% CI: 0.884–0.947). The Youden index was 0.731, and the cutoff value was 0.228. Sensitivity and specificity were 0.920 and 0.811, respectively. The H-L test demonstrated good model fit (χ2 = 12.858, P = 0.117, P > 0.05). Calibration curves indicated a close alignment between predicted and actual probabilities. Conclusions: The postoperative return-to-work rate is low in patients with permanent enterostomies. The prediction model developed in this study demonstrates strong performance and offers predictive value, providing a scientific foundation for assessing patients' return to work. Caregivers should prioritize the early identification of various patient types for proactive intervention to enhance the rate of postoperative return to work. [ABSTRACT FROM AUTHOR]

Background: Colon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge. Method: Pre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA). Result: The total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively. Conclusion: The study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: This study evaluates the association of novel inflammatory markers and Doppler parameters in late‐onset FGR (fetal growth restriction), utilizing a machine‐learning approach to enhance predictive accuracy. Materials and Methods: A retrospective case–control study was conducted at the Department of Perinatology, Ministry of Health Etlik City Hospital, Ankara, from 2023 to 2024. The study included 240 patients between 32 and 37 weeks of gestation, divided equally between patients diagnosed with late‐onset FGR and a control group. We focused on novel inflammatory markers—systemic immune‐inflammation index (SII), systemic inflammatory response index (SIRI), and neutrophil‐percentage‐to‐albumin ratio (NPAR)—and their correlation with Doppler parameters of umbilical and uterine arteries. Machine‐learning algorithms were employed to analyze the data collected, including demographic, neonatal, and clinical parameters, to develop a predictive model for FGR. Results: The machine‐learning model, specifically the Random Forest algorithm, effectively integrated the inflammatory markers with Doppler parameters to predict FGR. NPAR showed a significant correlation with FGR presence, providing a robust tool in the predictive model (Accuracy 77%, area under the curve [AUC] 0.851). In contrast, SII and SIRI, while useful, did not achieve the same level of predictive accuracy (Accuracy 75% AUC 0.818 and Accuracy 73% AUC 0.793, respectively). The model highlighted the potential of combining ultrasound measurements with inflammatory markers to improve diagnostic accuracy for late‐onset FGR. Conclusions: This study illustrates the efficacy of integrating machines with traditional diagnostic methods to enhance the prediction of late‐onset FGR. Further research with a larger cohort is recommended to validate these findings and refine the predictive model, which could lead to improved clinical outcomes for affected pregnancies. Trial Registration: ClinicalTrials.gov identifier: NCT06372938 [ABSTRACT FROM AUTHOR]

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature. [ABSTRACT FROM AUTHOR]

Background: The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short‐, intermediate‐, and long‐term overall survival (OS) for patients in this clinical setting. Materials and Methods: This study constitutes a sub‐analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer‐induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). Results: Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non‐bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL‐8], chloride levels, C‐reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL‐8 included both in the PMs for short‐ and long‐term OS. The AUC values for ROC curves for 3‐week, 24‐week, and 52‐week OS were 0.901, 0.767, and 0.806, respectively. Conclusions: We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases. [ABSTRACT FROM AUTHOR]

Background: Intensity modulation with dynamic multi‐leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient‐specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP‐specific information upon QA failures. Purpose: By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP‐specific regions associated with plan deliverability. Methods: The patient cohort consisted of 259 and 190 cases for left‐ and right‐breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three‐channel 3D images generated from DICOM RT plans were fed into a DenseNet‐based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1‐score, and area under the curve (AUC). The gradient‐weighted class activation map (Grad‐CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. Results: The DenseNet‐based predictive model yielded AUCs of 92.2% and 93.8%, F1‐scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left‐ and right‐breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing‐labeled from passing‐labeled classes for the non‐deliverable cases. The PCM with the highest correlation to the Grad‐CAM varied from patient cases, implying that plan deliverability would be considered patient‐specific. Conclusion: This work demonstrated that the deep learning‐based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control‐point specific planning parameter information associated with plan deliverability in a patient‐specific manner. [ABSTRACT FROM AUTHOR]

Objective To explore risk factors of early lung infection after liver transplantation and to construct a prediction model of early lung infection after liver transplantation. Methods The clinical data of 269 patients who underwent orthotopic liver transplantation for the first time were retrospectively analyzed. Patients were divided into the infected group (n=97) and the non-infected group (n=172) according to whether pulmonary infection occurred within 30 days after operation. The preoperative general data, preoperative laboratory examination results, intraoperative and postoperative data of the patients were collected. Multivariate Logistic regression analysis were used to screen risk factors of pulmonary infection. Based on the results of multivariate analysis, the prediction model was constructed and the prediction efficiency of the model was evaluated. Results Univariate and multivariate Logistic regression analysis showed that preoperative PNI ≤ 41.70 (OR=1.972, 95%CI: 1.047-3.714, P=0.036), LMR≤1.52 (OR=2.020, 95%CI: 1.102-3.705, P=0.023), MELD score＞ 10.72（OR=1.985, 95%CI: 1.103-3.573，P=0.022), operative time > 448.00 min (OR=2.676, 95%CI: 1.515-4.727, P=0.001) and intensive care unit (ICU) hospitalization time > 4.0 days (OR=2.623, 95%CI: 1.335-5.154, P=0.005) were independent risk factors for early pulmonary infection after liver transplantation. The ROC area under the curve (AUC) of the prediction model based on the results of multivariate Logistic regression analysis was 0.768, the sensitivity was 80.41% and the specificity was 60.47%. Conclusion The prediction model based on PNI, LMR, MELD score, operation time and ICU hospitalization time can effectively predict the occurrence of early pulmonary infection after liver transplantation. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop and validate a simple scoring system to predict in‐hospital mortality after endoscopic variceal ligation (EVL) for esophageal variceal bleeding. Methods: Data from a 13‐year study involving 46 Japanese institutions were split into development (initial 7 years) and validation (last 6 years) cohorts. The study subjects were patients hospitalized for esophageal variceal bleeding and treated with EVL. Variable selection was performed using least absolute shrinkage and selection operator regression, targeting in‐hospital all‐cause mortality as the outcome. We developed the Hospital Outcome Prediction following Endoscopic Variceal Ligation (HOPE‐EVL) score from β coefficients of multivariate logistic regression and assessed its discrimination and calibration. Results: The study included 980 patients: 536 in the development cohort and 444 in the validation cohort. In‐hospital mortality was 13.6% and 10.1% for the respective cohorts. The scoring system used five variables: systolic blood pressure (<80 mmHg: 2 points), Glasgow Coma Scale (≤12: 1 point), total bilirubin (≥5 mg/dL: 1 point), creatinine (≥1.5 mg/dL: 1 point), and albumin (<2.8 g/dL: 1 point). The risk groups (low: 0–1, middle: 2–3, high: ≥4) in the validation cohort corresponded to observed and predicted mortality probabilities of 2.0% and 2.5%, 19.0% and 22.9%, and 57.6% and 71.9%, respectively. In this cohort, the HOPE‐EVL score demonstrated excellent discrimination ability (area under the curve [AUC] 0.890; 95% confidence interval [CI] 0.850–0.930) compared with the Model for End‐stage Liver Disease score (AUC 0.853; 95% CI 0.794–0.912) and the Child–Pugh score (AUC 0.798; 95% CI 0.727–0.869). Conclusions: The HOPE‐EVL score practically and effectively predicts in‐hospital mortality. This score could facilitate the appropriate allocation of resources and effective communication with patients and their families. [ABSTRACT FROM AUTHOR]

Background: Gallbladder cancer (GBC) is a highly aggressive malignancy, with limited survival profiles after curative surgeries. This study aimed to develop a practical model for predicting the postoperative overall survival (OS) in GBC patients. Methods: Patients from three hospitals were included. Two centers (N = 102 and 100) were adopted for model development and internal validation, and the third center (N = 85) was used for external testing. Univariate and stepwise multivariate Cox regression were used for feature selection. A nomogram for 1-, 3-, and 5-year postoperative survival rates was constructed accordingly. Performance assessment included Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier curves were utilized to evaluate the risk stratification results of the nomogram. Decision curves were used to reflect the net benefit. Results: Eight factors, TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), body mass index (BMI), R0 resection, blood platelet count, and serum levels of albumin, CA125, CA199 were incorporated in the nomogram. The time-dependent C-index consistently exceeded 0.70 from 6 months to 5 years, and time-dependent ROC revealed an area under the curve (AUC) of over 75% for 1-, 3-, and 5-year survival. The calibration curves, Kaplan-Meier curves and decision curves also indicated good prognostic performance and clinical benefit, surpassing traditional indicators TNM staging and CA199 levels. The reliability of results was further proved in the independent external testing set. Conclusions: The novel nomogram exhibited good prognostic efficacy and robust generalizability in GBC patients, which might be a promising tool for aiding clinical decision-making. Highlights: • A representative surgical cohort of GBC was retrospectively established. • Clinical variables related to surgical management were comprehensively collected and analyzed. • The study introduced the independent prognostic significance of aCCI and CA125 in GBC. • A novel multi-factor nomogram was developed with satisfactory prognostic performance, as confirmed by internal and external validations. [ABSTRACT FROM AUTHOR]

Background: A short cervix in mid-trimester pregnancy is a risk factor for spontaneous preterm birth. However, there is currently a lack of predictive models and classification systems for predicting spontaneous preterm birth in these patients, especially those without additional risk factors for spontaneous preterm birth. Methods: A retrospective observational cohort study of low-risk singleton pregnant women with a short cervix (≤ 25 mm) measured by transvaginal ultrasonography between 22 and 24 weeks was conducted. A multivariate logistic regression model for spontaneous preterm birth < 32 weeks in low-risk pregnant women with a short cervix was constructed. Moreover, we developed a nomogram to visualize the prediction model and stratified patients into three risk groups (low-, intermediate-, and high-risk groups) based on the total score obtained from the nomogram model. Results: Between 2020 and 2022, 213 low-risk women with a short cervix in mid-trimester pregnancy were enrolled in the study. Univariate logistic analysis revealed that a high body mass index, a history of three or more miscarriages, multiparity, a short cervical length, leukocytosis, and an elevated C-reactive protein level were associated with spontaneous preterm birth < 32 weeks, but multivariate analysis revealed that multiparity (OR, 3.31; 95% CI, 1.13–9.68), leukocytosis (OR, 3.96; 95% CI, 1.24–12.61) and a short cervical length (OR, 0.88; 95% CI, 0.82–0.94) were independent predictors of sPTB < 32 weeks. The model incorporating these three predictors displayed good discrimination and calibration, and the area under the ROC curve of this model was as high as 0.815 (95% CI, 0.700-0.931). Patients were stratified into low- (195 patients), intermediate- (14 patients) and high-risk (4 patients) groups according to the model, corresponding to patients with scores ≤ 120, 121–146, and > 146, respectively. The predicted probabilities of spontaneous preterm birth < 32 weeks for these groups were 6.38, 40.62, and 71.88%, respectively. Conclusions: A noninvasive and efficient model to predict the occurrence of spontaneous preterm birth < 32 weeks in low-risk singleton pregnant women with a short cervix and a classification system were constructed in this study and can provide insight into the optimal management strategy for patients with different risk stratifications according to the score chart. [ABSTRACT FROM AUTHOR]

Objective: Obesity and maternal age are increasing among pregnant patients. The understood effect of body mass index (BMI), advanced maternal age (AMA), and second stage of labor on obstetric anal sphincter injury (OASIS) at delivery is varied. The objective of this study was to assess whether incorporating BMI, second stage of labor length, and AMA into a model for predicting OASIS among forceps‐assisted vaginal deliveries (FAVD) had a higher predictivity value compared to models without these additions. Method: This was an IRB‐approved retrospective cohort study of singleton gestations who underwent a FAVD between 2017 and 2021. The primary outcome was prediction of OASIS via established models versus models including the addition of new predictive factors. Results: A total of 979 patients met inclusionary criteria and were included in the final analysis. 20.4% of patients had an OASIS laceration, 11.3% of neonates had NICU admissions, 23.7% had a composite all neonatal outcome, and 8% had a composite subgaleal/cephalohematoma outcome. Comparisons of known factors that predict OASIS (nulliparity, race, episiotomy status) to known factors with additional predictors (BMI, AMA, and length of second stage in labor) were explored. After comparing each model's AUC to one another (a total of 3 comparisons made), there was no statistically significant difference between the models (all P > 0.62). Conclusion: Including BMI, AMA, and second stage of labor length does not improve the predictivity of OASIS in patients with successful FAVD. These factors should not impact a provider's decision to perform a FAVD when solely considering increased odds of OASIS. Synopsis: We attempted to improve established models predicting OASIS in FAVDs. However, we did not find a significant difference in predictive ability when including additional factors. [ABSTRACT FROM AUTHOR]

Objective: Our objective was to evaluate the predictive factors and metastatic time for liver and lung metastasis in locally advanced rectal cancer (RC) patients. Methods: Univariate and multivariate analysis were performed to identify risk factors and prognostic factors for liver metastasis and lung metastasis in RC. Survival probabilities were calculated using the Kaplan–Meier model and compared using the log-rank test between groups. The probability of time-to-event occurrence was calculated using the random survival forest model. Finally, the SEER database was used to verify our findings. Results: Our results indicated that pathological T stage and pathological N stage were independent predictive factors for liver metastasis. Furthermore, CEA level, pathological T stage, and tumor deposit were independent predictive factors for lung metastasis. Based on the results of a multivariate Cox analysis, we categorized patients with liver and lung metastasis into three groups based on their scores. The results revealed that patients with higher scores had a higher probability of experiencing metastasis. For liver metastasis, Groups 1, 2, and 3 all exhibited higher occurrence rates within the first 24 months. However, for lung metastasis, Group 4 showed the highest occurrence rate at the 12th month, while Groups 5 and 6 exhibited the highest occurrence rates at the 15th month. Conclusions: In summary, we developed predictive models to determine the likelihood of liver and lung metastasis in RC patients. It is crucial to implement a more intensive surveillance program for patients with unfavorable risk profiles in order to facilitate early detection of metastasis. [ABSTRACT FROM AUTHOR]

This study aims to utilize Genome-Wide Association Analysis (GWAS) to identify genetic markers associated with enhanced power resulting from resistance training. Additionally, we analyze the potential biological effects of these markers and establish a predictive model for training outcomes. 193 Han Chinese adults (age: 20 ± 1 years) underwent resistance training involving squats and bench presses at 70% 1RM, twice weekly, 5 sets x 10 repetitions, for 12 weeks. Whole-genome genotyping was conducted, and participants' countermovement jump (CMJ) height, lower limb muscle strength, and body muscle mass were assessed. CMJ height change was used to assess changes in power and subjected to Genome-Wide Association Analysis (GWAS) against genotypes. Employing Polygenic Score (PGS) calculations and stepwise linear regression, a predictive model for training effects was constructed. The results revealed a significant increase in CMJ height among participants following the resistance training intervention (Δ% = 16.53%, p < 0.01), with individual differences ranging from -35.90% to 125.71%. 38 lead SNPs, including PCTP rs9907859 (p < 1 x 10-8), showed significant associations with the percentage change in CMJ height after training (p < 1 x 10-5). The explanatory power of the predictive model for training outcomes, established using PGS and phenotypic indicators, was 62.6%, comprising 13.0% from PGS and 49.6% from phenotypic indicators. SNPs associated with power resistance training were found to participate in the biological processes of musculoskeletal movement and the Striated muscle contraction pathway. These findings indicate that individual differences in the training effect of CMJ exist after resistance training, partially explained by genetic markers and phenotypic indicators (62.6%). [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR) is a leading cause of blindness in patients with type 2 diabetes mellitus (T2DM). Cardiovascular risk factors, such as hypertension, obesity, and dyslipidemia, may play a crucial role in the development and progression of DR, though the evidence remains mixed. This study aimed to assess cardiovascular risk factors as independent predictors of DR and to develop a predictive model for DR progression in T2DM patients. Methods: A retrospective cross-sectional study was conducted on 377 patients with T2DM who underwent a comprehensive eye exam. Clinical data, including blood pressure, lipid profile, BMI, and smoking status, were collected. DR staging was determined through fundus photography and classified as No DR, Non-Proliferative DR (NPDR), and Mild, Moderate, Severe, or Proliferative DR (PDR). A Multivariate Logistic Regression was used to evaluate the association between cardiovascular risk factors and DR presence. Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC. Results: The prevalence of DR in the cohort was 41.6%, with 34.5% having NPDR and 7.1% having PDR. A multivariate analysis identified systolic blood pressure (SBP), LDL cholesterol, and body mass index (BMI) as independent predictors of DR progression (p < 0.05). The Random Forest model showed a moderate predictive ability, with an AUC of 0.62 for distinguishing between the presence and absence of DR XGBoost showing a better performance, featuring a ROC-AUC of 0.68, while SBP, HDL cholesterol, and BMI were consistently identified as the most important predictors across models. After tuning, the XGBoost model showed a notable improvement, with an ROC-AUC of 0.72. Conclusions: Cardiovascular risk factors, particularly BP and BMI, play a significant role in the progression of DR in patients with T2DM. The predictive models, especially XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR. [ABSTRACT FROM AUTHOR]

Background: Despite patients with successful revascularization as evidenced by angiographic findings, inadequate clinical management of coronary microcirculatory dysfunction (CMD) may result in preventable adverse outcomes. Therefore, it is imperative to use a multimodal data‑driven predictive model for the occurrence of CMD in patients with acute myocardial infarction (AMI) following emergency percutaneous coronary intervention (PCI). Methods: A prospective case−control analysis was conducted on a cohort of 77 patients with AMI who underwent PCI. The most informative predictors were selected for the predictive model through the application of LASSO analysis and multi‐factor logistic regression. The diagnosis of CMD is based on findings from cardiac magnetic resonance (CMR). Results: Based on the findings from LASSO analysis and multi‐factor logistic regression, variables including sex, neutrophil‐to‐lymphocyte ratio (NLR), Gensini score, and diabetes mellitus were identified as independent predictors for the development of CMD in AMI patients who underwent emergency PCI. The predictive model was evaluated using bootstrap self‐sampling 500 times. The resulting predictive model demonstrated an AUC value of 0.897 (95% CI: 0.827−0.958). The calibration curves exhibited good concordance between the predictions generated by the model and the CMR analysis. Furthermore, decision curve analysis revealed that the predictive model provided valuable clinical benefit in predicting CMD. Conclusions: The multivariate predictive model, constructed using readily available clinical variables in patients with AMI who underwent PCI, demonstrates satisfactory predictability for identifying comorbid CMD, thereby facilitating the identification of high‐risk patients. [ABSTRACT FROM AUTHOR]

The seedcorn maggot, Delia platura (Meigen), is a pest affecting many crops, including corn. The early spring emergence of adults and belowground seed damage by maggots leave no room for rescue treatments during the short growing season in New York State. Degree-day (DD) models play a crucial role in predicting insect emergence and adult peak activity and are essential for effective pest management. The current D. platura DD model was launched on the Network for Environment and Weather Applications (NEWA) in 2022, using existing scientific literature from other North American regions. The NEWA model predicted adult D. platura first emergence at an average of 471 (39°F) DD in 2022. To gain an accurate and precise understanding of D. platura adult spring emergence and activity, we used interpolated temperature data to calculate the DD for each specific location where adults were captured in the field. DD calculations were performed using the average method, setting a biofix on January 1st and a base temperature of 39°F. In 2023, overwintering adults emerged at an average of 68 DD, and in 2022, adult activity was registered at an average of 282 DD. Accurately predicting the emergence of D. platura could contribute to informing integrated pest management strategies that incorporate timing and cultural practices over chemical solutions to protect crops and the environment. [ABSTRACT FROM AUTHOR]

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5) and 10 μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist of the main cause for many diseases. Based on the existence of statutory limits from the World Health Organization (WHO) for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict air pollutant concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural network (ANN) models. In the frame of a specific work, a significant number of ANNs are developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. The results showed that ANNs present a remarkable prognostic ability in order to forecast air pollution levels in an urban environment. [ABSTRACT FROM AUTHOR]

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Summary: Objective: To construct a predictive model for Psoriatic Arthritis (PsA) based on clinical and ultrasonic characteristics in patients with plaque psoriasis (PsP). Patients and Methods: Demographic, clinical, and ultrasound data were collected from patients with PsP and PsA between May 2019 and December 2022. Results: A total of 212 patients with PsP and 123 with PsA in the training cohort, whereas the validation cohort comprised 91 patients with PsP and 49 with PsA. The multivariate logistic regression identified nail psoriasis (odds ratio [OR] 1.88, 95% CI: 1.07–3.29), synovitis (OR 18.23, 95% CI: 4.04–82.33), enthesitis (OR 3.71, 95% CI: 1.05–13.14), and bone erosion (OR 11.39, 95% CI: 3.05–42.63) as effective predictors for PsA. The area under the curve was 0.750 (95% CI, 0.691–0.806) and 0.804 (95% CI, 0.723–0.886) for the training and validation cohorts, respectively. The Hosmer‐Lemeshow goodness‐of‐fit test showed good consistency for both the training cohort (p = 0.970) and the validation cohort (p = 0.967). Calibration curves also indicated good calibration for both cohorts. The DCA revealed that the predictive model had good clinical utility. Conclusions: We have developed a quantitative, intuitive, and convenient predictive model based on nail psoriasis, synovitis, enthesitis, and bone erosion to assess the risk of PsA in patients with plaque psoriasis. [ABSTRACT FROM AUTHOR]

Hydrogen is crucial for achieving SDGs by driving energy transition and combating climate change. Proton exchange membrane fuel cell technology, leveraging hydrogen, faces challenges in meeting high‐power demands. The multistack fuel cell system (MFCS) tackles this by integrating multiple substacks, yet its air supply needs meticulous control. Proportional integral derivative (PID) decoupling from single‐stack falls short of MFCS. This article proposes nonlinear model predictive control (NMPC) for optimized air flow and pressure decoupling. Modeling MFCS's air system and designing a predictive model, it is aimed to ensuring precise control of air flow and pressure in each substack. The decoupling experiments show that NMPC outperforms PID, accurately managing air flow and pressure and reducing load fluctuations. For air mass flow, NMPC cuts mean‐absolute error (MAE) by 64.56% and root‐mean‐square error (RMSE) by 81.36%. For pressure, MAE drops 81.23% and RMSE 83.59%. Comprehensive step load tests confirm NMPC's precise, dynamic regulation too, compared to PID, NMPC lowers average MAE for air mass by 20.67%, pressure by 32.22%. RMSE improvements of 31.08% and 33.23% highlight NMPC's strength. NMPC's quick response mitigates coupling issues, enhancing vehicle load adaptability. [ABSTRACT FROM AUTHOR]

Objective: To investigate the clinical and endocrine risk factors for pregnancy loss in women with abnormal glucose/lipid metabolism and a history of pregnancy loss, and to develop a predictive model to assess the risk of pregnancy loss in these women's subsequent pregnancies. Methods: Patients with a history of pregnancy loss who had abnormal glucose/lipid metabolism were retrospectively included in this study, and their pre-pregnancy baseline and clinical characteristics were collected. A predictive nomogram was constructed based on the results of the multivariable logistic regression model analysis, and its calibration and discriminatory capabilities were evaluated. The internal validation was then performed and the net benefits were assessed by the clinical decision curve. Results: The predictive model was eventually incorporated eight variables, including maternal age, previous pregnancy losses, anticardiolipin antibody (aCL) IgG, aCL IgM, thyroid peroxidase antibody, complement 4, free thyroxine and total cholesterol. The area under the curve (AUC) of the nomogram was 0.709, and Chi-square value and P value of the Hosmer–Lemeshow test were 12.786 and 0.119, respectively, indicating that the nomogram had a satisfactory calibration and discriminatory performance. The validation cohort showed a similar result for the discrimination of the nomogram (AUC = 0.715). The clinical decision curve demonstrated the nomogram had good positive net benefits. Conclusions: This is the first study to predict the risks of subsequent pregnancy loss in women with abnormal glucose/lipid metabolism and history of pregnancy loss using pre-pregnancy clinical and endocrine parameters. This predictive nomogram may provide clinicians assistance to personalize the management of subsequent pregnancies in these patients. [ABSTRACT FROM AUTHOR]

Purpose: Persistent hyperparathyroidism (PTHPT) in kidney transplant recipients is associated with bone loss, graft dysfunction and cardiovascular mortality. There is no clear consensus on the management of PTHPT. Accurate risk prediction of the disease is needed to support individualized treatment decisions. We aim to develop a useful predictive model to provide early intervention for hyperparathyroidism in these patients. Methods: We retrospectively analyzed 263 kidney transplantations in the urology department of China-Japan Friendship Hospital from January 2018 to December 2022. The overall cohort was randomly assigned 70% of the patients to the training cohort and 30% to the validation cohort. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for PTHPT and to construct the predictive model. This model was assessed regarding discrimination, consistency, and clinical benefit. Results: The occurrence of PTHPT was 25.9% (68 out of 263 patients) in this study. Dialysis duration, postoperative 3-month intact parathyroid hormone (iPTH), 3-month corrected calcium (cCa), and 3-month phosphorus (P) are independent risk factors for the development of PTHPT. The nomogram showed good discrimination with the area under the curve (AUC) value of 0.926 in the training cohort and 0.903 in the validation cohort. The calibration curve and decision curve also showed that the model was well-evaluated. Conclusion: We developed a validated nomogram model to predict PTHPT after kidney transplantation. This can help the clinic prevent and control PTHPT early and improve patients' prognosis. [ABSTRACT FROM AUTHOR]

This work predicts, hot flow curves of 2205 DSS using strain-compensated Arrhenius rate-type constitutive model. Twenty-five (25) × Ø10 diameter × 15 mm height cylindrical samples were hot compressed at a temperature between 850 and 1050 °C at an interval of 50 °C and strain rates between 0.001 and 5 s−1, using Gleeble 1500D. After the tests, corrected flow curves were plotted followed by computation of deformations constants at various deformation conditions using steady state stress. The values of the constants were (α = 0.009708, Q = 445 kJ/mol and n = 3.7) and seemed comparable to the previous studies of DSS. Steady state predictive model was then constructed using the calculated constants and showed a reasonably good accuracy with low value of MARE = 7.78%. Furthermore, calculated strain compensated Arrhenius rate type model was used to predict flow curves at various deformation. The model had a good estimation of flow curves of flow curves at 900–1050 °C across all strain rates as reflected by MARE = 5.47%. A notable discrepancy between predicted and experimental flow stress was observed at 850 °C and across all the strain rates. A model refinement using generalised reduced gradient improved the accuracy of the model by 34.7% despite deformation conditions at 850 °C and low strain rates (0.01/ 0.1) s−1 showing minimum improvement. Further modification of Z-parameter by compensating for the strain rate improved the accuracy of the model at 850 °C/0.01 s−1/0.1 s−1. Lastly, a comparison of the current model with the other non-linear model showed that the latter was more accurate in estimation of flow curves since it relied on characteristics flow stress points controlled by underlying active deformation mechanisms. [ABSTRACT FROM AUTHOR]

Purpose: The duration of response to treatment is a significant prognostic indicator, with early recurrence (ER) often predicting poorer survival outcomes in nasopharyngeal carcinoma (NPC) survivors. This study seeks to elucidate the factors contributing to the onset of ER following radiotherapy in NPC survivors. Methods: This investigation encompassed 2,789 newly diagnosed NPC patients who underwent radical intensity-modulated radiotherapy. Ordinal logistic regression analysis was employed to evaluate the independent predictors of earlier recurrence. A machine learning-based prediction model of NPC recurrence patterns was developed. Tumorous RNA-sequencing (in-house cohort: N = 192) and biological tipping point analysis were utilized to infer potential molecular mechanisms associated with ER. Results: Our results demonstrated that ER within 24 months post-initial treatment was the optimal time frame for identifying early malignant progression in NPC survivors. The ER cohort (150 of 2,789, 5.38%) exhibited a notably short median overall survival of 48.6 months. Multivariate analyses revealed that male gender, T4 stage, local or regional residual disease, detectable pre- and post-radiotherapy EBV DNA, and the absence of induction chemotherapy were significant predictors of earlier recurrence. The machine learning-based predictive model further underscored the importance of tumor-related factors in NPC recurrence. Moreover, ER emerged as a pivotal stage in NPC progression, with 15 critical transition signals identified potentially associated with the negative modulation of the immune response. Conclusions: Our comprehensive analysis of NPC recurrence patterns has unveiled insights into the key factors driving ER and provided novel insights into potential early warning biomarkers and the mechanisms underlying NPC progression. [ABSTRACT FROM AUTHOR]

Background: Older adults are vulnerable to social isolation due to declining physical and cognitive function, decreased interpersonal interactions, and reduced outdoor activities after retirement. This study aimed to develop and validate a predictive model to assess the risk of social isolation among older adults in China. Methods: Using data from the 2011 China Health and Retirement Longitudinal Study (CHARLS). The study cohort was randomly divided into training and validation groups in a 70:30 ratio. We used least absolute shrinkage and selection operator (LASSO) regression analysis with tenfold cross-validation to identify optimal predictive factors and examined the correlates of social isolation using logistic regression. A nomogram was constructed for the predictive model, and its accuracy was assessed using calibration curves. The predictive performance of the model was assessed using area under the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: From the 2011 CHARLS database, 4,747 older adults were included in the final analysis, of whom 1,654 (34.8%) experienced social isolation. Multifactorial logistic regression identified educational level, marital status, gender, physical activity, physical self -maintenance ability, and number of children as predictive factors for social isolation. The predictive model achieved an AUC of 0.739 (95%CI = 0.722–0.756) in the training set and 0.708 (95%CI = 0.681–0.735) in the validation set. The Hosmer-Lemeshow test yielded P values of 0.111 and 0.324, respectively (both P > 0.05), indicating significant agreement between the nomogram and observed outcomes. The nomogram showed excellent predictive ability according to ROC and DCA. Conclusions: The predictive model developed to assess the risk of social isolation in the Chinese older adults shows promising utility for early screening and intervention by clinical healthcare professionals. [ABSTRACT FROM AUTHOR]

Background: Although malnutrition is common in cancer patients, its molecular mechanisms has not been fully clarified. This study aims to identify significantly differential metabolites, match the corresponding metabolic pathways, and develop a predictive model of malnutrition in patients with gastric cancer. Methods: In this cross-sectional study, we applied non-targeted metabolomics using liquid chromatography-mass spectrometry to explore the serum fingerprinting of malnutrition in patients with gastric cancer. Malnutrition-specific differential metabolites were identified by orthogonal partial least-squares discriminant analysis and t-test and matched with the Human Metabolome Database and the LIPID Metabolites and Pathways Strategy. We matched the corresponding metabolic pathways of malnutrition using pathway analysis at the MetaboAnalyst 5.0. We used random forest analyses to establish the predictive model. Results: We recruited 220 malnourished and 198 non-malnourished patients with gastric cancer. The intensities of 25 annotated significantly differential metabolites were lower in patients with malnutrition than those without, while two others were higher in patients with malnutrition than those without, including newly identified significantly differential metabolites such as indoleacrylic acid and lysophosphatidylcholine(18:3/0:0). We matched eight metabolic pathways associated with malnutrition, including aminoacyl-tRNA biosynthesis, tryptophan metabolism, and glycerophospholipid metabolism. We established a predictive model with an area under the curve of 0.702 (95% CI: 0.651–0.768) based on four annotated significantly differential metabolites, namely indoleacrylic acid, lysophosphatidylcholine(18:3/0:0), L-tryptophan, and lysophosphatidylcholine(20:3/0:0). Conclusions: We identified 27 specific differential metabolites of malnutrition in malnourished compared to non-malnourished patients with gastric cancer. We also matched eight corresponding metabolic pathways and developed a predictive model. These findings provide supportive data to better understand molecular mechanisms of malnutrition in patients with gastric cancer and new strategies for the prediction, diagnosis, prevention, and treatment for those malnourished. [ABSTRACT FROM AUTHOR]

Background: Severe fever with thrombocytopenia syndrome (SFTS) has attracted attention due to the rising incidence and high severity and mortality rates. This study aims to construct a machine learning (ML) model to identify SFTS patients at high risk of death early in hospital admission, and to provide early intensive intervention with a view to reducing the risk of death. Methods: Data of patients hospitalized for SFTS in two hospitals were collected as training and validation sets, respectively, and six ML methods were used to construct the models using the screened variables as features. The performance of the models was comprehensively evaluated and the best model was selected for interpretation and development of an online web calculator for application. Results: A total of 483 participants were enrolled in the study and 96 (19.88%) patients died due to SFTS. After a comprehensive evaluation, the XGBoostbased model performs best: the AUC scores for the training and validation sets are 0.962 and 0.997. Conclusion: Using ML can be a good way to identify high risk individuals in SFTS patients. We can use this model to identify patients at high risk of death early in their admission and manage them intensively at an early stage. [ABSTRACT FROM AUTHOR]

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis and increased mortality. The aim of this study was to develop a predictive model for postoperative pneumonia (POP) in patients with aSAH. A retrospective analysis was conducted on 308 patients with aSAH who underwent surgery at the Neurosurgery Department of the First Affiliated Hospital of Soochow University. Univariate and multivariate logistic regression and lasso regression analysis were used to analyze the risk factors for POP. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the constructed model. Finally, the effectiveness of modeling these six variables in different machine learning methods was investigated. In our patient cohort, 23.4% (n = 72/308) of patients experienced POP. Univariate, multivariate logistic regression analysis and lasso regression analysis revealed age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count as independent risk factors for POP. Subsequently, these six factors were used to build the final model. We found that age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count were independent risk factors for POP in patients with aSAH. Through validation and comparison with other studies and machine learning models, our novel predictive model has demonstrated high efficacy in effectively predicting the likelihood of pneumonia during the hospitalization of aSAH patients. [ABSTRACT FROM AUTHOR]

In order to explore a high-efficiency lyoprotectant formula for Lactobacillus casei, this study took Lactobacillus casei as the research subject and employed freeze-drying technology as the research method. Initially, cryoprotective agent, antioxidant, filler and acid base regulator were screened through single-factor experiments. Subsequently, an optimized composite formula was developed using trehalose, sodium thiosulfate, mannitol, and sorbitol as lyoprotectant materials through orthogonal optimization design. Finally, a predictive model for the viable cell count after freeze-drying was established. The results indicated that the optimal combination of the composite lyoprotectant was 15% trehalose, 2% sodium thiosulfate, 15% mannitol, and 3% sorbitol, achieving a survival rate of 90.20% after freeze-drying. After 4 weeks of long-term efficacy validation, the survival rate of the strain was 88.04%. The established linear and exponential models demonstrated high predictive accuracy for the viable cell count after freeze-drying, with both R² values being 0.80. This suggests that the composite lyoprotectant developed in this study provides effective and long-term protection for Lactobacillus casei. [ABSTRACT FROM AUTHOR]

Objective: To explore the influencing factors of poor intestinal function recovery after operation in children with congenital intestinal atresia, and to construct a predictive model. Methods: 126 children with congenital intestinal atresia who were admitted to Hebei Children's Hospital from March 2020 to March 2023 were selected, intestinal function recovery after operation was counted, children were divided into poor recovery group and good recovery group according to intestinal function recovery after operation. The influencing factors of poor intestinal function recovery after operation in children with congenital intestinal atresia were analyzed by multivariate Logistic regression, and the predictive model was constructed based on Logistic regression. The value of predictive model in distinguishing postoperative poor intestinal function recovery after operation in children with congenital intestinal atresia were analyzed by receiver operating characteristic (ROC) curve. Results: 37 cases had poor intestinal function recovery after operation among 126 children with congenital intestinal atresia, and 89 cases had good recovery. The birth weight and the proportion of enteral nutrition support after operation in poor recovery group were lower than those in good recovery group (P < 0.05) the proportion of Grosfeld pathological type IV, jejunal atresia and premature infants in poor recovery group were higher than those in good recovery group (P < 0.05), the anastomotic diameter in poor recovery group was larger than that in good recovery group (P < 0.05) Multivariate Logistic regression analysis showed that premature infants, jejunal atresia and Grosfeld pathological type IV were risk factors for poor intestinal function recovery after operation (P < 0.05) and enteral nutrition support after operation was a protective factor (P < 0.05) The area under the curve of the predictive model based on Logistic regression to predict poor intestinal function recovery after operation in children with congenital intestinal atresia was 0.852, and the sensitivity and specificity were 83.78% and 85.39% respectively. Hosmer-Lemeshow test P > 0.05 the model fitting effect was good. Conclusion: Premature infants, jejunal atresia and Grosfeld pathological type IV are risk factors for poor intestinal function recovery after operation, and enteral nutrition support after operation is a protective factor. The predictive model base on Logistic regression has a good predictive value for poor intestinal function recovery after operation in children with congenital intestinal atresia. [ABSTRACT FROM AUTHOR]

The secondary mining movement in non-pillar coal extraction causes significant overrun damage to flexible formwork concrete walls, leading to extensive deformation of roadway roof and bottom plates. This adversely affects working face efficiency and safety. The engineering context focuses on the non-pillar gob-side retaining walls in the 1315 working face of Zhaozhuang Coal Mine and the 23107 working face of Xiegou Coal Mine. Through on-site investigation, numerical simulation, theoretical analysis, and testing, we explore the stress migration law and destabilizing mechanism of the flexible formwork concrete wall influenced by the secondary mining movement of the coal-free pillar along the hollow wall. The research results showed that: (1) During the mining back process, the concrete wall formed with flexible formwork may experience stress concentration, leading to excessive damage and compromising mining safety. (2) Developing a predictive stress model for the concrete wall with flexible formwork is essential. If the stress surpasses the ultimate compressive strength during mining back, reinforcement becomes necessary.3) The length of damage overrun in the flexible formwork concrete wall exhibits two distinct stages as the distance back to mining increases. The first stage shows nearly linear growth, while the second stage indicates a decreasing growth rate, ultimately stabilizing. The application of Z6 concrete reinforcing agent effectively strengthens the flexible formwork concrete wall. [ABSTRACT FROM AUTHOR]

This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts. [ABSTRACT FROM AUTHOR]

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO2/NOx emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO2/NOx emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed. [ABSTRACT FROM AUTHOR]

The construction industry plays a substantial role in shaping the economies of many countries. Construction management faces various challenges that can lead to project failures, particularly in infrastructure projects struggling to meet cost and time requirements. Inadequate project planning and the intricate nature of construction projects can cause participants' project goals to not align. It is crucial to address these challenges early in the planning stages to ensure project success. This research involved investigating previous studies to understand current practices for improving infrastructure project planning and selecting the best pre-project planning tool. Infrastructure projects in the Saudi construction industry are used as a case study. A questionnaire was prepared based on essential alignment issues affecting team alignment during pre-project planning. Participants rated the level of agreement with alignment issues and the overall success of a project they worked on. The study utilized descriptive and inferential analysis techniques to assess infrastructure project success rates and develop a predictive model driven by the alignment tool. Multiple linear regression techniques were used during the model's development, and validation and reliability outputs were obtained. By evaluating all relevant stakeholders, the model generates a score to facilitate the pre-project planning process, increasing the likelihood of project success. The study found that the model's predictive accuracy was 94%. This research is significant in creating a predictive model applicable to infrastructure projects, enhancing project management practices by enabling project teams to evaluate project progress, identify projects in need of corrective action, and ultimately improve project performance, leading to cost and time savings. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment. This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer. Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients’ prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive. Four genes – FABP5, C5AR1, CTSL, and LTA4H – were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen. Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients. [ABSTRACT FROM AUTHOR]

Introduction Material and Methods Results Conclusions The main goal of placenta accreta spectrum (PAS) screening is to enable delivery in an expert center in the presence of an experienced team at an appropriate time. Our study aimed to identify independent risk factors for emergency deliveries within the IS‐PAS 2.0 database cohort and establish a multivariate predictive model.A retrospective analysis of prospectively collected PAS cases from the IS‐PAS database between January 2020 and June 2022 by 23 international expert centers was performed. All PAS cases (singleton and multiple pregnancies) managed according to local protocols were included. Individuals with emergent delivery were identified and compared to those with scheduled delivery. A multivariate analysis was conducted to identify the possible risk factors for emergency delivery and was used to establish a predictive model. Maternal outcomes were compared.Overall, 315 women were included in the study. Of these, 182 participants (89 with emergent and 93 with scheduled delivery) were included in the final analysis after exclusion of those with unsuspected PAS antenatally or who lacked information about the urgency of delivery. Gestational age at delivery was higher in the scheduled group (34.7 vs. 32.9, p < 0.001). Antenatal bleeding (OR 2.9, p = 0.02) and a placenta located over a uterine scar (OR 0.38, p = 0.001) were the independent predictive factors for emergent delivery (AUC 0.68). Ultrasound (US) markers: loss of clear zone (p = 0.001), placental lacunae (p = 0.01), placental bulge (p = 0.02), and presence of bridging vessels (p = 0.02) were more frequently documented in the scheduled group. None of these markers improved the predictive values of the model. Higher PAS grades were identified in the scheduled group (p = 0.01). There were no significant differences in maternal outcomes.Antenatal bleeding and the placental location away from the uterine scar remained the most significant predictors for emergent delivery among patients with PAS, even when combining more predictive risk factors, including US markers. Based on these results, patients who bleed antenatally may benefit from transfer to an expert center, as we found no differences in maternal outcomes between groups delivered in expert centers. Earlier‐scheduled delivery is not supported due to the low predictive value of our model. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Methods Results Conclusion Impact Patient or Public Contribution The aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injuries (PIs) in children undergoing living donor liver transplantation (LDLT).A retrospective cohort study.The research was carried out at China's largest paediatric liver transplantation centre. A total of 438 children who underwent LDLT between June 2021 and December 2022 constituted the study cohort. The dataset was partitioned randomly into 70% for training datasets (306 cases) and 30% for testing datasets (132 cases). Utilising four machine learning algorithms—Decision Tree, Random Forest, Gradient Boosting Decision Tree and eXtreme Gradient Boosting—we identified risk factors and constructed predictive models.Out of 438 children, 42 developed PIs, yielding an incidence rate of 9.6%. Notably, 94% of these cases were categorised as Stage 1, and 54% were localised on the occiput. Upon evaluating the four prediction models, the Decision Tree model emerged as the most effective. The primary contributors to pressure injury in the Decision Tree model were identified as operation time, intraoperative corticosteroid administration, preoperative skin protection measures and preoperative skin conditions. A visualisation elucidating the logical inference process for the 10 variables within the Decision Tree model was presented. Ultimately, based on the Decision Tree model, a predictive system was developed.Machine learning algorithms facilitate the identification of crucial factors, enabling the creation of an effective Decision Tree model to forecast pressure injury development in children undergoing LDLT.With this predictive model at their disposal, nurses can assess the pressure injury risk level in children more intuitively. Subsequently, they can implement tailored preventive strategies to mitigate the occurrence of PIs.Paediatric patients contributed electronic health records datasets. [ABSTRACT FROM AUTHOR]

Introduction: Osimertinib, a third-generation EGFR-TKI, is known for its high efficacy against brain metastases (BM) in non-small cell lung cancer (NSCLC) due to its ability to penetrate the blood-brain barrier. This study aims to evaluate the use of brain MRI radiomics in predicting the intracranial efficacy to osimertinib in NSCLC patients with BM. Materials and Methods: This study analyzed 115 brain metastases from NSCLC patients with the EGFR-T790M mutation treated with second-line osimertinib. The primary endpoint was intracranial response, and the secondary endpoint was intracranial progression-free survival (iPFS). We performed tumor delineation, image preprocessing, and radiomics feature extraction. Using a 5-fold crossvalidation strategy, we built radiomic models with eight feature selectors and eight machine learning classifiers. The models' performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Results: The dataset of 115 brain metastases was divided into training and validation sets in a 7:3 ratio. The radiomic model utilizing the mRMR feature selector and stepwise logistic regression classifier showed the highest predictive accuracy, with AUCs of 0.879 for the training cohort and 0.786 for the validation cohort. This model outperformed a clinical-MRI morphological model, which included age, ring enhancement, and peritumoral edema (AUC: 0.794 for the training cohort and 0.697 for the validation cohort). The radiomic model also showed strong performance in calibration and decision curve analyses. Using a radiomic-score threshold of 199, patients were classified into two groups with significantly different median iPFS (3.0 months vs. 15.4 months, p < 0.001). Conclusion: This study demonstrates that MRI radiomics can effectively predict the intracranial efficacy of osimertinib in NSCLC patients with brain metastases. This approach holds promise for assisting clinicians in personalizing treatment strategies. [ABSTRACT FROM AUTHOR]